THE SURVIVAL OF GRASSHOPPERS AND
BUSH CRICKETS IN HABITATS
VARIABLE IN SPACE AND TIME
DISSERTATION ZUR ERLANGUNG DES NATURWISSENSCHAFTLICHEN
DOKTORGRADES DER BAYERISCHEN JULIUS-MAXIMILIANS-UNIVERSITÄT
WÜRZBURG
VORGELEGT VON
SILKE HEIN
AUS
SCHWEINFURT
WÜRZBURG 2004
Eingereicht am: ……………………….………………..
Mitglieder der Prüfungskommission:
Vorsitzender: Prof. Dr. U. Scheer
1. Gutachter: Prof. Dr. Hans Joachim Poethke
2. Gutachter: Prof. Dr. Jürgen Tautz
Tag des Promotionskolloquiums: ………………………
Doktorurkunde ausgehändigt am: ...……………………
Table of contents
CHAPTER 1
General Introduction
1.1
INTRODUCTION, SCOPE AND OUTLINE OF THE THESIS
1.1.1
1.1.2
Introduction
Scope and outline of the thesis
1.2
SPECIES AND LANDSCAPES
1.2.1
1.2.2
1.2.3
Why grasshoppers and bush crickets?
The species
The landscapes
1.3
CONCEPTUAL FRAMEWORK
1.3.1
1.3.2
Habitat suitability and patch capacity
Dispersal
Chapter 2
Habitat suitability models for the conservation of
thermophilic grasshoppers and bush crickets
2.1
2.2
INTRODUCTION
MATERIAL AND METHODS
2.2.1
2.2.2
2.2.3
The species
Field work
Statistical analyses
2.3
RESULTS
2.3.1
2.3.2
2.3.3
Influence of plot characteristics on the occurrence probability of species
Conservational/Practical aspects
Additional analyses
2.4
DISCUSSION
2.4.1
2.4.2
2.4.3
2.4.4
Influence of plot characteristics on the occurrence probability of species
Microhabitat preferences and influence of surrounding type of biotope
Model validation
Conservational/Practical aspects
9
11
11
12
13
13
14
16
19
21
23
27
29
30
30
31
33
37
37
39
41
41
41
43
44
44
CHAPTER 3
The generality of habitat suitability models: How well may
grasshoppers be predicted by butterflies?
3.1
3.2
INTRODUCTION
MATERIAL AND METHODS
3.2.1
3.2.2
3.2.3
The species
Field work
Statistical analyses
3.3
RESULTS
3.3.1
3.3.2
Single species models
Test of transferability
3.4
DISCUSSION
3.4.1
3.4.2
Single species models
Test of transferability
CHAPTER 4
Movement patterns of Platycleis albopunctata in different
types of habitat: matrix is not always matrix
4.1
4.2
INTRODUCTION
MATERIAL AND METHODS
4.2.1
4.2.2
4.2.3
4.2.4
4.2.5
4.2.6
The species
Field work
Re-sight rates
Movement behaviour depending on habitat type
Edge-mediated behaviour
Statistical analyses
4.3
RESULTS
4.3.1
4.3.2
4.3.3
Re-sight rates
Movement behaviour depending on habitat type
Edge-mediated behaviour
4.4
DISCUSSION
4.4.1
4.4.2
Re-sight rates
Movement behaviour
45
47
48
48
49
50
52
52
53
55
55
56
59
61
62
62
62
63
63
64
64
65
65
66
67
68
68
68
CHAPTER 5
Computer-generated null-models as an approach to detect
perceptual range in mark - re-sight studies – an example
with grasshoppers
5.1
5.2
INTRODUCTION
MATERIAL AND METHODS
5.2.1
5.2.2
Field experiment
Simulation experiments
5.3
RESULTS
5.3.1
5.3.2
Field experiment
Detection ability
5.4
DISCUSSION
5.4.1
5.4.2
5.4.3
Re-sight rates
Simulation experiments and detection ability
Movement behaviour and perceptual range
CHAPTER 6
Patch density, movement pattern, and realized dispersal
distances in a patch-matrix landscape – a simulation study
6.1
6.2
6.3
INTRODUCTION
METHODS
RESULTS
6.3.1
6.3.2
Single-patch scenario
Multi-patch scenario
6.4
DISCUSSION
SUMMARY
ZUSAMMENFASSUNG
BIBLIOGRAPHY
PUBLICATIONS
CONFERENCES & WORKSHOPS
CURRICULUM VITAE
DANKSAGUNG
ERKLÄRUNG
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Chapter 1
General Introduction
11
Chapter 1 – General Introduction
1.1 INTRODUCTION, SCOPE AND OUTLINE OF THE
THESIS
1.1.1
Introduction
For a long time anthropogenic land use has contributed to a diversification
of the landscape (Settele 1998). Increasing land use has created new habitats for
animal and plant species (Huston 1994, Mühlenberg et al. 1996). In Central
Europe species important to nature conservation as well as high species diversity
in general have been mostly found on extensively managed areas (van der Maarel
& Titlyanova 1989, Kull & Zobel 1991, Bignal & McCracken 1996). The
landscape pattern has remained static, as most areas have been utilised in the same
way over many years and even centuries. However, due to increased economic
pressure, extensively managed areas are nowadays either abandoned and lie
fallow, or are fertilized and intensively used (Mühlenberg et al. 1996). In both
cases, rare and protected plant and animal species go extinct due to natural
succession or increased disturbance (Fuller 1987, Vos & Zonnefeld 1993, Beaufoy
et al. 1994, Poschlod et al. 1996).
Additionally, as a consequence of the above mentioned processes remnants
of extensively managed semi-natural habitats become increasingly fragmented
(Bakker 1989, Bjornstad et al. 1998, Cousins et al. 2003). With accelerating loss and
fragmentation of many natural and semi-natural habitats, an increasing number of
species has been and still is forced to persist in spatially structured or metapopulations (Oostermeijer et al. 1996).
Within Central Europe both abandonment and fragmentation especially
applies to mesoxerophytic grasslands (e.g. in the nature reserve ‘Hohe Wann’ in
Mid Germany) as well as other dry grasslands, which only have a low agricultural
productivity (Van Dijk 1991, Poschlod et al. 1996). On the one hand these
grasslands need some level of disturbance to increase small scale environmental
heterogeneity and thus species diversity (Huston 1979, 1994, McConnaughay &
Bazzaz 1987, Jacquemyn et al. 2003). On the other hand disturbance should not
exceed a certain level as with increased management intensity species diversity
declines (Kruess & Tscharntke 2002).
As means to conserve these areas different management regimes have been
suggested, i.e. goat and/or cattle grazing, rototilling, fire and mowing (e.g.
Schreiber 1977, Bakker 1989, Bobbink & Willems 1993, Kahmen et al. 2002,
Kleyer et al. submitted). Different management regimes in different time intervals
result in a landscape consisting of a mosaic of different habitat patches, with
habitat quality consistently changing in time.
For the protection and conservation of (insect-) populations in such
variable, fragmented landscapes, it is important to know at what successional time
a patch is of ideal or at least acceptable suitability for a specific species or species
Chapter 1 – General Introduction
12
assemblage. Additionally, to ensure long-term survival of populations, the spatial
arrangement of ‘suitable’ habitats, which determines their reachability, must be
adequate to guarantee the exchange of individuals between populations.
1.1.2
Scope and outline of the thesis
In the light of the general considerations above, this thesis investigates the
factors determining habitat suitability for thermophilic grasshoppers and bush
crickets at different spatial scales as well as aspects of their dispersal behaviour.
For this purpose I developed convincing statistical habitat suitability models for
thermophilic grasshopper and bush cricket species. These models should be of
practical use for the prediction of suitable habitats in conservation biology. Thus,
besides the development of robust models, spatial autocorrelation as well as
transferability in time and space were tested (Chapter 2). Additionally, I compare
the generality of models developed for specific species in Chapter 3 by testing
their transferability to other orthoptera species as well as to species from other
insect groups (moths and butterflies).
Results of empirical studies on the dispersal of the bush cricket Platycleis
albopunctata in different ‘matrix habitats’ are presented in Chapter 4. Preliminary
studies on bush crickets’ and grasshoppers’ behaviour at boundaries between
different habitats yielded contrasting results concerning edge permeability and
habitat detection. Thus, in Chapter 5 an experiment on habitat detection of the
grasshopper species Oedipoda caerulescens compares observed arriving rates with
those theoretically derived by simulation models with different underlying
movement behaviours. Spatially explicit, individual based simulation studies
which show the influence of the number of patches in a landscape on the
reachability of a specific patch complete the investigations on inter-patch
dispersal (Chapter 6).
Before presenting the different scientific manuscripts (Chapters 2-6), this
chapter will give an introduction of why to use grasshoppers and bush crickets in
metapopulation studies. It also presents additional information concerning the
studied species and the study sites, which were not included in the publications
(Chapter 2-6), but may be useful to familiarise the reader with the studies’
conservational background. Additionally, the theoretical background of this thesis
is presented in an overview on the key processes determining persistence of
metapopulations.
Chapter 1 – General Introduction
13
1.2 SPECIES AND LANDSCAPES
1.2.1
Why grasshoppers and bush crickets?
In general insects have experienced higher rates of decline than other
popular taxa, especially in the (calcareous) grasslands of Europe (Bourn &
Thomas 2002 and references therein). Thus, many insect species are now
endangered and need protection. Most studies on species decline or impact of
fragmentation on insects have been conducted on butterflies (e.g. New et al. 1995,
Amler et al. 1999), because they are assumed to be good indicator species for
other species, like Orthoptera (Marshall & Haes 1988) or Hymenoptera (Bourn &
Thomas 2002).
In this thesis I have chosen grasshoppers and bush crickets as model
organisms. For this choice I had several reasons. First, like many other taxa in
Central Europe, the populations of many grasshoppers and bush crickets are
declining, and about 50-60 % of all Caelifera and Ensifera species are considered
to be endangered (on the Red Lists in Germany: Bellmann 1993, and Switzerland:
Nadig & Thorens 1994). As far as the xero-thermophilic species are concerned,
13 of 23 species of Caelifera and 10 of 14 species of Ensifera are threatened in
Germany (Köhler 1996). In semi-arid grassland ecosystems, grasshoppers and
bush crickets are two of the dominant consumer groups, weighted by both, their
biomass and their numbers (Köhler 1996). In contrast to butterflies, grasshoppers
and bush crickets are generally assumed to be food generalists (polyphagous to
omnivorous, Detzel 1998) and are therefore usually not limited by food resources
in natural habitats. Thus, habitat capacity is likely determined by other factors, e.g.
egg laying places or temperature. Additionally, most grasshoppers and bush
crickets are found in dry and open habitats (diversity is highest in warm lowland
habitats; Detzel 1998), which are the focus of many nature conservation efforts in
Europe and elsewhere (Poschlod & Schumacher 1998, Pykälä 2003). On open
grasslands grasshoppers and bush crickets utilise different structures during their
life cycle (e.g. long lawn structures for food or shelter, short lawn areas with
increased temperature for egg development) and therefore are good indicators of
structural heterogeneity. Consequently, their habitat requirements cover the
habitats of a variety of different other animal species on open grasslands.
The most important reason why I decided to study grasshoppers and bush
crickets is their intermediate dispersal capability in contrast to butterflies. Markrecapture experiments are more easily conducted with grasshoppers and bush
crickets as only a smaller and thus better controllable area must be searched
(examples: Samietz et al. 1996, Kindvall 1999). Whereas the high mobility of
butterflies often leads to methodological problems (low re-sight rates, large areas
to be searched). Additionally, the autecology of most grasshopper and bush
cricket species is well known and understood.
Chapter 1 – General Introduction
1.2.2
14
The species
A general ‘expert opinion’ about the habitat requirements of the three main
species under investigation (Stenobothrus lineatus, Metrioptera bicolor, Platycleis
albopunctata) can be extracted from the literature. However, these opinions have
not been validated by thorough and quantitative field studies of the kind
presented in Chapter 2 and 3. Concerning dispersal behaviour, there is a small
number of publications available, especially for M. bicolor. However, for most
species only anecdotal evidence exists.
Besides the above mentioned species Oedipoda caerulescens, another
grasshopper species, was studied in one of the dispersal experiments. This species
was chosen as its distribution is restricted to very hot and open hillsides with very
sparse vegetation. Thus, one would expect a good ability to detect habitat and a
clear response to habitat borders for that species. Additionally, their habitats can
be easily distinguished from non-habitats and borders between habitat and nonhabitat are often clearly visible to the investigator.
The current knowledge on species’ habitat requirements as well as on
dispersal behaviour can be summarised as follows:
Stenobothrus lineatus (PANZER 1796; Orthoptera: Acrididae)
Figure 1.1:
Male (above) and female
(below) individual of
S. lineatus.
English name. Stripe-winged grasshopper
German name. Heidegrashüpfer
Biology. Females lay their eggs in the upper ground layer, in root
felts of grasses or glue egg pods to grass leaves close to the ground
(Samietz et al. 1996). The species has 4 to 5 larval instars and feeds on
grasses and herbs. It is most often classified as thermo- and
xerophilic (Samietz et al. 1996, Ingrisch & Köhler 1998, Samietz
1998, Maas et al. 2002). The life cycle is univoltine, with a single
hibernation period for the eggs. Imagines are found from the end of
June till the beginning of November.
Habitats. The species inhabits arid and semi-arid grasslands as well
as broom heath, juniper heath, and short lawn edges of woods.
Sheep-grazed areas and short vegetation structure are preferred
habitat elements (Detzel 1998).
Dispersal behaviour. Individuals can fly but rarely do so, mostly in
cases of escape. Mass et al. (2002) state a low mobility for S. lineatus,
because Samietz (1998) did only find a mean activity radius of 1218 m and Ehrlinger (1991) determined a mean activity radius of 27 m
and 84 m.
Chapter 1 – General Introduction
15
Metrioptera bicolor (PHILIPPI 1796; Orthoptera: Tettigoniidae)
Figure 1.2:
Macroptere male (above)
and microptere female
(below) individual of
M. bicolor.
English name. Two-coloured bush cricket
German name. Zweifarbige Beißschrecke
Biology. The species is thermo- and xerophilic. Eggs are laid in plant
stems, egg development is uni-, sometimes bivoltine. Individuals are
in general herbivores, but are sometimes feeding on small insects
(e.g. aphids). Imagines can be found from July till October (Maas et
al. 2002).
Habitats. Individuals are mainly found on mesoxerophytic
grassland. As the species is orientated towards vertical structures it
prefers biotopes with long lawn. M. bicolor can also be found on
juniper heath, poor, semi-arid and sandy grasslands (Detzel 1998).
Dispersal behaviour. Long-winged individuals have been found but
flight capability was seldom investigated. Kindvall and Ahlen (1992)
describe the species as very resident and unwilling to leave its native
habitat patches. Ingrisch & Köhler (1998) detected a colonisation
distance of around 300 m.
Platycleis albopunctata (GOEZE 1778; Orthoptera: Tettigoniidae)
Figure 1.3:
Female individual of
P. albopunctata.
English name. Grey bush cricket
German name. Westliche Beißschrecke
Biology. The eggs are laid into dry plant stems, in the soil or moss.
Consequently, places with sparse vegetation are preferred (Gottschalk
1996). Individuals feed on grass-seeds and herbs, e.g. Daucus carota,
Taraxacum officinalis, but also small animals like flies and bugs (Walter
1994). The species is classified as a thermo- and xerophilic species
(Harz 1969, Ingrisch & Köhler 1998) and has an obligatory annual
life cycle (Ingrisch 1986).
Habitats. P. albopunctata inhabits dry locations, especially dry
grassland as well as habitats with similar structure (Detzel 1998).
Open soil, sparse vegetation and fringes are preferred habitat
elements (Haupt 1995).
Dispersal behaviour. Individuals can fly well when it is hot (Maas et
al. 2002) and show a good mobility between 50 m and 350 m (Walter
1994).
Oedipoda caerulescens (LINNAEUS 1758; Orthoptera: Acrididae)
Figure 1.4:
Male (above) and female
(below) O. caerulescens.
English name. Blue-winged grasshopper
German name. Blauflügelige Ödlandschrecke
Biology. The first larvae appear in May or June and 4 to 5 instars can
be found. Adults of the blue-winged grasshopper appear in July and
can be found until the end of October (Merkel 1980). Generally
O. caerulescens is classified as xero-thermophilic.
Habitats. The species is mainly found in dry habitats with sparse
vegetation (Appelt 1996). It inhabits stony calcareous meadows,
quarries and sand pits. Imagines are considered to be geophilic,
inhabit open field and live on the ground. Larvae, however, are also
found in dense vegetation.
Dispersal behaviour. Individuals usually move on the ground. If
disturbed O. caerulescens typically flies a few meters (3 to 6 m) in half
circles downhill. It is noticeable that individuals land on an open
place again. Both sexes fly spontaneously to locate mates. Although
adults are able to fly, most grasshoppers in a study of Appelt (1996)
moved within a range of 5 to 20 m. Only 3 % of the studied
population moved more than 70 m (Appelt 1996). The maximal
distance observed in that study was 800 m. Very seldom flights of up
to 100 m have been observed (Detzel 1998).
Chapter 1 – General Introduction
1.2.3
16
The landscapes
The nature reserve ‘Hohe Wann’
Most of the field studies were conducted in the nature reserve ‘Hohe
Wann’ in Northern-Bavaria, Germany (latitude 50° 03′, longitude 10° 35′). The
whole area covers approximately 10 km in NS-direction and 4 km in EWdirection. It is situated in the conurbation of the ‘Fränkisches Keuperland’ (Elsner
1994). Altitudes of the study area range from 238 to 388 m above sea level.
From a geological point of view the region belongs to the ‘Fränkisches
Schichtstufenland’ which is mainly built by the layers of ‘Keuper’ (205-195
millions of years before today), the youngest sediment originated in the time of
Trias (Bayerisches Geologisches Landesamt 1998). The frequent change of
sediment conditions (from marine to terrestrial) at the time of ‘Keuper’ resulted in
heterogeneous rock series. Gypsum, clay stone as well as sandstone can be found.
The changing storage of weak weathered sandstone layers with those of clay as
well as hard and weather resistant layers in combination with the soft inclination
of the layers against south-east results in the typical structure of the
‘Schichtstufenland’ (Rutte 1981). Based on these underlying rocks different types
of soil have been built.
The area is situated in a transition zone between oceanic and continental
climate and characterised by a vegetation period of 150-160 days (Elsner 1994), a
mean annual average temperature of 7-8°C and a mean annual precipitation of
650 to 700 mm (Bayerische Landesanstalt für Bodenkultur & Pflanzenbau 2001).
Both,
geo-morphological
heterogeneity and climate as well as
small-scale microclimatic differences
traceable to different exposition,
inclination and land use cause the
patchwork of vegetation found in the
study area. Abundant small sites of
mesoxerophytic grassland, formerly
used as vine yards or pastures (Figure
1.5, Elsner 1994) are the most
obvious characteristics of the nature
reserve. These sites are of high
conservational value as they harbour a
high diversity of rare and protected
plant and animal species (e.g. Anemone
sylvestris, Decticus vericurivorus). The
patches are separated by areas in
different agricultural use. Flat
Figure 1.5: Exemplary parts of hillsides with
locations are mainly used as crop
mesoxerophytic grassland and intervening
hedges in the nature reserve ‘Hohe Wann’.
fields, whereas the majority of the
Chapter 1 – General Introduction
17
hillsides is utilised as grassland or lays
fallow and is overgrown by bushes.
North exposed hillsides are often
wooded.
Semi-arid
grasslands
like
mesoxerophytic grasslands are most
often found on south- to south-west
faced slopes (Figure 1.6). Their plant
species composition changes with the
management regime. For example,
mowed areas are characterised by
Bromus erectus or Festuca rupicola (Elsner
1994). Generally, mowing occurs late
in the season and is of no economic
value, but financed by conservation
agencies to keep the grasslands open
and to prevent overgrowing by
bushes and shrubs (Figure 1.7). If
Figure 1.6: Short and long lawn mesoxerophytic these areas are only mowed every
grasslands in the nature reserve ‘Hohe Wann’.
second or third year, fringe vegetation
can also be found (Elsner 1994). The
conservation of these areas is a major
goal of local and regional conservation
efforts.
Unfortunately,
the
management by mowing is very
expensive. The steep hillsides must be
mowed by hand and hay must be
taken away to avoid re-fertilisation. In
principal, this management approach
is based on a ‘static’ perception of the
It
prevents
the
Figure 1.7: Abandoned mesoxerophytic grassland landscape.
with ongoing succession by overgrowing bushes.
characteristic landscape processes of
succession and disturbance.
The different studies presented in this thesis were part of the MOSAIKproject funded by the German Federal Ministry of Education and Research. The
MOSAIK-project studied the impact and costs of two alternative management
regimes, i.e. rototilling and grazing by goats, on the plant and animal composition
of these conservation areas. Both management regimes allow for a succession
between management episodes. This should lead to a spatio-temporal change in
habitat quality and thus to a dynamic landscape with habitat suitability changing in
space and time (Schröder et al. 2003).
Chapter 1 – General Introduction
18
The nature reserve ‘Leutratal’
For the development of the habitat suitability models presented in Chapter
2 and 3, spatial transferability of the resulting models needed to be tested (cf.
Leftwich et al. 1997, Dennis & Eales 1999, Schröder & Richter 1999, Schröder
2000, Fleishman et al. 2003). Thus, we additionally sampled an area approximately
200 km away from the ‘Hohe Wann’ in the Thuringian nature reserve ‘Leutratal’
near Jena (latitude 50° 52′, longitude 11° 34′).
This area is also characterised by a high fraction of mesoxerophytic
grasslands with a wide variety of rare plant and animal species (Heinrich et al.
1998). The ‘Leutratal’ is a typical part of the shell limestone landscape of
Thuringia (Figure 1.8, Heinrich et al.
1998). The geology of the region is
dominated by different layers dating
back to the Trias. Soils of clay,
limestone-clay and shell-limestone
determine the biotic inventory. Clay
and limestone soils have an
unbalanced water supply. Together
with a high degree of desiccation in
summer this leads to extreme
conditions for plant and animal
species. Mean annual temperature in
the region is 9.3°C with a mean
annual precipitation of 587 mm
(Heinrich et al. 1998). The vegetation
period lasts from May till September.
The management regime mainly used
in this area is mowing, financed by
Figure 1.8: View on the hillside of the nature
reserve ‘Leutratal’. Mesoxerophytic grasslands with nature conservation agencies or by
intervening bushes and hedges.
the government.
Chapter 1 – General Introduction
19
1.3 CONCEPTUAL FRAMEWORK
Spatial heterogeneity of the environment as well as fragmentation of
habitats generally result in a mosaic-like or patchy distribution of organisms
(Settele 1998). The spatial constellation of habitats with high habitat capacity (in
the following: suitable habitats/patches) determines the spatial distribution of
populations. Individuals inhabit suitable habitats and use the area in between at
best for the exchange between patches (Settele 1998). For the description of such
spatially structured populations, different classifications of populations have been
introduced based on spatial structure of populations and the exchange of
individuals (see Box 1).
Spatially structured populations can only survive in regional connected
assemblages as survival probability of small and/or isolated populations may be
low (Veith & Klein 1996). Therefore, they cannot be described with classical
population models for single populations and cannot account for the mutual
influence on populations in a landscape (Andrewartha & Birch 1954, Poethke et
al. 1996a). A closer examination of spatially structured populations at a landscape
scale became first possible with the development of models of island
biogeography (McArthur & Wilson 1967) and metapopulation theory (Levins
1970, Hanski & Gilpin 1997). In recent years the metapopulation concept (Levins
1969, 1970) has become increasingly popular in theoretical as well as empirical
studies on the spatial and temporal structure of populations (e.g. Hanski 1994a, b,
Hill et al. 1996, Poethke et al. 1996a, b, Reich & Grimm 1996, Settele 1998).
A metapopulation is a regional assemblage of locally connected
populations inhabiting discrete habitat patches, and can be described as a
population of populations (Levins 1970, Halle 1996). Local patches have a substantial
risk of extinction, but can also be (re-) colonised; metapopulations are thus
characterised by (substantial) population turnover (Levins 1970, Hanski & Gilpin
1991). Fundamentally, such a population assemblage can only persist if the
extinction probability of local populations is lower than their colonisation
probability. Additionally, to prevent a random concurrent extinction of all local
populations, a sufficient number of local patches must belong to the
metapopulation (Poethke et al. 1996a, b). It is obvious that the probability that a
certain patch will be occupied at any moment in time depends on its capacity as
well as its isolation.
To predict occupancy patterns of patches in metapopulations (Ij) Hanski
(1994a) presented a simple stochastic model that corresponds to the island-model
of MacArthur & Wilson (1967) for the prediction of species number on islands
differing in size and isolation. However, Hanski (1994a) assumes that extinction
risk (Ej) as well as colonisation probability (Cj) are patch specific:
20
Chapter 1 – General Introduction
Ij =
Cj
Ej + Cj
(1.1)
Additionally, the rescue-effect (Brown & Kodrik-Brown 1977), the
reduction of extinction probability due to immigration can be included in the
model. Empirically, extinction probabilities may either be estimated from longterm observation data (see Hanski & Zhang 1993, Hanski et al. 1994, Hill et al.
1996) or by using values extracted from simulations which are based on measures
that are more readily determined (Poethke et al. 1996b).
Box 1: Models of spatial structure and interactions of populations
A population may be defined as a group of interacting individuals of a species (breeding
and competing with each other) occupying a particular space at a particular time, at least
partially isolated from other populations of the same species (Dempster & McLean 1998,
Krebs 2001). However, what actually constitutes a population will vary from
species to species and from study to study depending on spatial scale and focus of
the study (Begon et al. 1990). For the description of a single population one may
quantify different population properties (e.g. population size). Alternatively,
population structure, e.g. demographic/genetic structure, spatial structure, sex
ratio or the distribution of individuals in time, may be used for characterisation.
Although in reality transitions between different classes are fluent, one may
differentiate the following types of populations based on spatial structure and
exchange of individuals between habitat patches:
Continuous population
Each single population is sufficiently large and able to survive over
large time periods (of course the extinction probability of any
population reaches a value of 1 if time goes to infinity; Veith &
Klein 1996)
Patchy population
Isolation between populations has reached a high degree but
frequent exchange of individuals takes place. In this case a quasi
continuous population is reached (Harrison 1991).
Metapopulation
A regionally connected population of local populations (Levins 1970) on
qualitatively similar patches, with an own, independent population
dynamic and a high local extinction probability. Populations are in
an equilibrium between extinction and colonisation (islandequilibrium model ).
If some populations have a high and others a low probability of
survival and if locally extinct patches are colonised by individuals
core satellite
from
quasi-persistent
populations
the
model/mainland-island model can be applied (Boorman & Levitt
1973). In this model patches vary in patch size only.
If especially the peripheral patches are qualitatively worse than a
central patch one would call the system a source-sink model
(Pulliam 1988). In that situation recolonisation becomes a ‘one-way
street’ from the central source-population to peripheral sinks. Sinkpopulations cannot survive without constant immigration.
Chapter 1 – General Introduction
21
The two main aspects leading to extinction are low patch area and/or low
patch quality, which both contribute to patch capacity. The area of a patch can be
easily obtained from a cartographic map or geographic information system (GIS)
whereas the quality depends on different environmental conditions. Based on the
distribution of a species across different environmental parameters conclusions
about patch quality and thus patch capacity are possible (see paragraph 1.3.1 this
Chapter).
The probability that a patch will be colonised is mainly determined by its
reachability, i.e. the probability that a dispersing individual will reach the patch, as
well as the probability that immigrants can establish a population in the new
patch. The reachability of a patch depends on the number and capacity of
occupied patches in its surrounding. To determine the reachability of patches a
large number of studies have been conducted in recent years. Especially the
dispersal process has been intensively investigated with studies ranging from
empirical mark- and recapture studies (e.g. Baguette & Nève 1994, Hill et al. 1996,
Brommer & Fred 1999, Kindvall 1999, Roland et al. 2000, Ricketts 2001) to
theoretical investigations on evolutionary aspects of the dispersal process
(Hovestadt et al. 2000, Hovestadt et al. 2001, Poethke & Hovestadt 2002, Poethke
et al. 2003; see paragraph 1.3.2 this Chapter).
1.3.1
Habitat suitability and patch capacity
Habitat suitability together with patch area determines patch capacity. To
measure habitat suitability the needs of a species must be known. Morrison et al.
(1998) define a species’ habitat as an area with the combination of resources and
environmental conditions that allows individuals to survive and to reproduce.
From an evolutionary point of view, high habitat quality means high fitness of
individuals in that specific habitat. Thus, different kinds of habitat result in
different levels of fitness or suitability in the landscape (in evolutionary time
suitability is equivalent to fitness, Krebs 2001). Habitat suitability is not constant
but affected by many factors within the habitat, such as food supply, shelter and
predators (Krebs 2001). The resulting occurrence of a species describes its
realised niche (see Box 2). Vice versa one can infer a species’ habitat requirements
from its distribution across environmental variables (Huston 1994, Rosenzweig
1995). The concept of the niche and thus the premise that predictable relations
exist between the occurrence of a species and certain features of its environment
is the underlying principle in habitat suitability modelling (Rosenzweig 1981).
The analyses of species-habitat or species-environment relationships has a
long history and was first institutionalised with the development of habitat
suitability index-models (HSI-models) by the U.S. Fish & Wildlife Service (1981).
Species-environment models may either address single species or more complex
multi-species assemblages when identifying relations between occurrence and
environmental features at a variety of scales (local to biogeographic; Verner et al.
1986, Kuhn 1998, Bonn & Schröder 2001, Heglund 2002, Storch 2002).
Chapter 1 – General Introduction
22
Examples of models include prediction of species occurrence, distribution and
abundance using habitat suitability, pattern recognition, and wildlife-habitat
relation models (Morrison et al. 1998).
Box 2: The concept of the niche – History and Application
Historically, Grinnell (1917) first emphasized the environmental requirements of a
species and considered the niche a fundamental distributional unit of a species.
Elton (1930) later defined the niche as the ‘role’ of the species in the community,
which is a behaviour-based concept. This definition highlighted the role other
species play in shaping the expressed niche of an organism. Both definitions are
considered conceptually vague and years later a quantitative concept of the niche
was proposed by Hutchinson (1957). Based on this concept, the niche is best
described by the coordinates of a species with n-dimensional resource axes and
combines both the behavioural and the distributional concepts of Elton and
Grinnell (Cao 1995, Morrison et al. 1998). Thus, generally data on a multitude of
variables within the environment are collected, and the measures most strongly
related to the occurrence of the species are selected. With this measures one can
devise models that generally describe the location of that species in just a few
dimensions (Krebs 2001).
In reality, the presence of competing species restricts a given species to a narrower
range of conditions – its ‘realized’ niche. The foundation of our current modelling
efforts lies in the characterization of a species’ realized niche rather than simply
determining habitat relations. Theoretically, along an environmental gradient most
species should exhibit maximum density at some point (Gauch & Chase 1974).
In the beginning these models were mostly based on ‘expert knowledge’
and general statements on habitat preferences of specific species. With the
increasing availability of statistical software, an overwhelming array of statistical
methods was employed in the assessment of species’ relations to their
environment. The distribution, or response of an organism in regard to a given
environmental variable is generally considered nonlinear (Gauch & Chase 1974,
Austin 1976, Heglund et al. 1994). Multivariate statistics were established for the
quantitative analyses of empirical data and for model development (Brennan et al.
1986, Morrison et al. 1998). Statistical procedures that were often used are
discriminant analyses as well as general linear models (GLM, Guisan &
Zimmermann 2000).
For a variety of reasons logistic regression analysis has gained importance
as a non-parametric, non-linear alternative to discriminant analysis to model
species-habitat relationships (Guisan & Zimmermann 2000, Guisan et al. 2002).
First, this procedure is the only suitable one that allows an analyses of categorial
variables (Capen et al. 1986, Kleyer et al. 1999/2000). Second, this method is
favoured because better results in classification of results and more robust models
are received. Additionally, coefficients are easy to interpret and a variety of
measures for model calibration and discrimination have been developed
23
Chapter 1 – General Introduction
(Nagelkerke 1991, Buckland et al. 1997, Fielding & Bell 1997, Backhaus 2000,
Hosmer & Lemeshow 2000, Manel et al. 2001, Austin 2002). Logistic regression
examines the relationship between independent variables (habitat parameters) and
a dichotomous dependent variable (incidence of a specific species, Trexler &
Travis 1993). This relationship can be expressed by the following equation:
P( y = 1) =
β
β
1
1+ e
− ( β 0 + β 1 x1 + ...+ β k x k )
(1.2)
P(y = 1) : probability that the dependent variable (i.e. incidence) takes the value 1 (i.e. species present)
: constant
0
xk
: independent predictor variables
: coefficient of independent variable
k
The approach described above provides a fairly static picture of
populations, while in reality species-environment relations are dynamic.
Populations fluctuate in abundance between years in response to a number of
factors, including weather, food, conditions, habitat, predator abundance, and
parasite loads (Wiens 1989 ). As habitats may vary in time and space, models
should be evaluated in both. Ideally, models should be developed and tested using
independent data sets derived from field studies in different years and regions
(Fleishman et al. 2003). As this is time consuming and cannot be done in every
study different methods of re-sampling evaluation have been suggested (for a
review see Verbyla & Litvatitis 1989). For example, bootstrap techniques have
shown good results. Through re-sampling (with replacement), the bootstrap
allows to estimate the optimism (bias) in measures of predictive accuracy and,
then, subtract the estimate of optimism from the initial apparent measure to
obtain a bias-corrected estimate (Efron & Tibshirani 1993).
In this thesis robust habitat suitability models are developed and evaluated
for different grasshopper and bush cricket species with the use of single and
multiple parameter logistic regression analyses (Chapter 2). Additionally, these
models together with models for two butterfly species are tested for their
transferability within one insect group and between the two groups (Chapter 3).
The resulting quantitative predictions of species occurrences in a specific
landscape under different management scenarios may be used as the basis for the
prediction of long term survival of populations in population viability analyses
(Schröder et al. 2003).
1.3.2
Dispersal
Dispersal (see Box 3) is one of the most important, yet least understood,
processes in ecology, population biology, and evolution, as it gives populations,
communities, and ecosystems their characteristic texture in space and time
(Kenward et al. 2001, Macdonald & Johnson 2001). It acts as the ‘glue’ that binds
populations together (Wiens 2001) and has diverse ramifications for population
Chapter 1 – General Introduction
24
dynamics (e.g. avoidance of kin competition, speciation by founder effects). The
most fundamental population dynamic consequences at the level of local
populations and metapopulations are population regulation via density-dependent
emigration and large-scale persistence of classical metapopulations due to
establishment of new populations and rescue of threatened populations (e.g.
Brown & Kodrik-Brown 1977, Hanski 2001).
Box 3: Definitions of dispersal
Movement reveals different forms (Dingle 1996). Dispersal is often distinguished
from migration by the fact that the dispersal process is a ‘one way street’ with no
return of the individual (in contrast to migration, e.g. birds migrating to Africa in
winter and back in summer, Begon et al. 1990). Howard (1960) defined dispersal as
‘the permanent movement by an individual from birth place to place of reproduction’ (Kenward et
al. 2001). This corresponds to Bullock et al. (2001) who use a common definition of
dispersal as ‘intergenerational movement’. They thus exclude so-called ‘dispersal in time’
(e.g. seed banks) as well as foraging movements of animals. Especially in the case of
birds this became complicated as individuals were repeatedly recaptured at breeding
sites. Thus a common separation introduced by Greenwood (1980) and also used by
others (Greenwood & Harvey 1982) separates natal and breeding dispersal. This
definition is also used by Clobert et al. (2001): The term natal dispersal, is the
movement between the natal area or social group and the area or social group where breeding first
takes place, and breeding dispersal, is the movement between two successive breeding areas or
social groups. In practice, e.g. if movements are recorded in detail by radio tracking an
animal along its ‘life path’ (Baker 1978, Bullock et al. 2001), problems arise, resulting
in a further detailed terminology (see Kenward et al. 2001). Thus, generally the
above mentioned definitions are used and specified in more detail depending on
study species and focus of the research (see Bullock et al. 2001, Clobert et al. 2001).
Biologists from the two main fields concerned with dispersal, behavioural
and population ecology, have typically taken two almost entirely different
approaches to study dispersal, both with regard to questions asked and the
methods used (Andreassen et al. 2001). Behavioural ecologists have typically been
concerned with understanding the proximate and ultimate causes of dispersal (e.g.
Bengtsson 1978) and based their conclusions mainly on empirical studies.
Population biologists on the other hand have typically considered dispersal as a
key process for understanding population dynamics, spatial synchrony, population
genetics (e.g. Stenseth 1983) and the evolution of dispersal (Hovestadt et al. 2000,
Hovestadt et al. 2001, Poethke & Hovestadt 2002, Poethke et al. 2003). They have
examined their problems often by using theoretical modelling studies (including
metapopulation dynamics; Hanski & Gilpin 1997). Although, the studies on
dispersal in this thesis focus on the behavioural aspects of dispersal, spatially
explicit individual based modelling is used as a tool to derive adequate hypothesis
(Chapter 5) or to extrapolate individual behaviour to a landscape level (Chapter
6). Nevertheless, the results from Chapter 4-6 will also contribute to a better
25
Chapter 1 – General Introduction
understanding of population dynamic consequences of dispersal, as they adjust
frequently made assumptions in population models which are not supported by
empirical studies of dispersal behaviour.
Generally dispersal can be divided into three phases: emigration, transfer
and immigration (see Andreassen et al. 2001). Most studies on dispersal have,
implicitly or explicitly, adopt the view that individuals leave a given patch or
population, cross a gap, and (somehow) end up later in another patch or
population (Wiens 2001). Both decisions, when to leave and when to stop, vary
among species and individuals. Whether an individual leaves a patch, for example,
may depend on the mode of dispersal, genetic predisposition to disperse, local
population density, habitat change, age, or reproductive status, among other
factors (Wiens 2001). The decision to stop may involve various elements of
habitat selection or patch choice, such as conspecific attraction, habitat quality, or
physiological factors.
The simplest and arguably most efficient way to disperse is to follow a
straight path from the origin to the stopping place. This view of dispersal is
fostered by mark-recapture studies, in which the linear distance between the
marking location and the recapture location provides empirical measure of
dispersal distance (Wiens 2001). Thus, island biogeography (MacArthur & Wilson
1967) or metapopulation and source-sink models of population dynamics
(Pulliam 1988, Hanski & Gilpin 1997) consider the linear distance of an island or
subpopulation from a source to be a key determinant of colonisation
probabilities. Generally, the dispersal process of individuals is described in terms
of a dispersal-distance function (Wiens 2001). A common used form of such a
function has been proposed by Hanski (1994a, b):
P(d ) = c * e −αd
(1.3)
It describes the probability to settle at a specific place (P(d)) as a function
of the Euclidian distance (d) between two individual patches and a species specific
dispersal parameter (α). The influence of spatial arrangement of patches in a
landscape on the exchange of individual between patches is often neglected (but
see Chapter 6). Additionally, in such models the matrix is featureless and
‘distance’ is measured as a linear value, a ‘gap’ to be crossed. Indeed, both
empirical studies (Grubb & Doherty 1999) and models (With & King 1999a, b) of
gap-crossing focus on size of the gaps but not on their composition. All of these
approaches thus assume that individuals either move from one place to another in
a straight line or in a random walk unaffected by any environmental features.
The area between the start- and the end-point of dispersal, however,
usually is a richly textured mosaic of patches of different shape, sizes,
arrangements, and qualities. It thus becomes important to know, how individuals
move within different habitats (Wiens 2001, see Chapter 4). Information about
Chapter 1 – General Introduction
26
such movement rules may then be used to model long distance dispersal in real
landscapes (Kindvall 1999).
Independent of the degree of heterogeneity that exists in a landscape
individuals should benefit if they are able to detect suitable habitat from distance.
This should especially be the case for a habitat specialist. The analyses of such
detection distances or perceptual ranges is often conducted with direct
observations of individuals’ movement behaviour. In Chapter 5 I present an
approach to test for the detection ability of species even though direct
observations are not possible. Individual based simulations are used to develop
adequate null hypothesis with different theoretical assumptions about the
underlying movement rules. Simulation results are then compared exemplarily
with field data from arrival rates of a grasshopper species.
Chapter 2
Habitat suitability models for the
conservation of thermophilic grasshoppers
and bush crickets
with Julia Voss, Boris Schröder and Hans-Joachim Poethke.
SUBMITTED TO BIOLOGICAL CONSERVATION
Abstract. In our study we investigate habitat preferences of the two
bush cricket species Metrioptera bicolor and Platycleis albopunctata and the
grasshopper species Stenobothrus lineatus in the nature reserve ‘Hohe
Wann’ (Northern Bavaria, Germany). To determine species habitat
preferences, we developed statistically derived habitat suitability
models. For validation of the models in time and space, the study was
repeated in a second year and in a second study area. We found that
vegetation structure as well as topographical parameters like the
exposition determine habitat selection. Besides these factors which
are rather costly to determine at the landscape level, the type of
biotope seems to be the most reliable factor that determines the
occurrence of the studied species. Internal validation demonstrates
that habitat suitability models based on this factor allowed to set up
robust models. Inclusion of the surrounding landscape into the
analysis resulted only in the case of S. lineatus in a significant influence
of the plot surrounding in a radius between 25 m and 50 m. None of
the species showed distinct microhabitat preferences.
With the help of this model analyses of habitat suitability can
easily be carried out on the basis of already existing vegetation maps
for the conservation of the three species under study. Thus, our
results can serve as a basis for the estimation of the survival
probability of the species studied.
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
29
2.1 INTRODUCTION
The conservation of the fauna and flora of mesoxerophytic grasslands is a
major topic in (Central European) conservation biology as these areas are
inhabited by a variety of rare and protected thermophilic plant and animal species
(Poschlod & Schumacher 1998). Mesoxerophytic grasslands are found on poor
soil conditions. These areas are often inaccessible at strong inclination and
provide only low nutritioned food. As a consequence of the low economic value
utilization of these areas decreases and more and more areas are abandoned. The
grasslands are thus exposed to successional processes which results in the loss of
valuable species. Thus, the management of such areas has increasingly been
occupied with the conservation of specific successional stages and the prevention
of further succession. To predict which successional stages are suitable for
specific species reliable information on species specific habitat requirements is
needed. Such information is a critical prerequisite for the choice of protected
areas, the design of management strategies, and the assessment of possible effects
of various land-use changes (Fielding & Haworth 1995, Oppel et al. in press) on
the survival of plant and animal species.
In recent years statistically derived habitat suitability models have become a
common tool for the estimation of critical factors that determine habitat
suitability for and habitat selection by a species (Ferrier 1991, Lindenmayer et al.
1991, Pearce et al. 1994, Guisan & Zimmermann 2000, Pearce & Ferrier 2000a, b).
Such models use the presence and absence of a species at a set of survey sites in
relation to environmental or habitat variables to detect functional relationships
between a species and its environment (Guisan & Zimmermann 2000, Austin
2002). Besides other methods logistic regression is a well established method to
perform such habitat modelling (Trexler & Travis 1993, Guisan & Zimmermann
2000), because it is a simple and robust procedure, and yields comparatively high
performance as well as biologically interpretable model parameters (Manel et al.
1999a).
One problem with models based on simple presence/absence data is that
data are only snap-shots from a certain time period and a certain region. Such
models are static (Guisan & Zimmermann 2000) and need to be validated in
space and time before they can be extrapolated to other areas (Morrison et al.
1998). To obtain an unbiased estimate of a model’s predictive performance,
evaluation is best undertaken with independent data collected from sites others
than those used to develop the model (Schröder & Richter 1999, Pearce & Ferrier
2000a, b). Alternatively, internal validation techniques can be applied (Lehmann et
al. 2002, Reineking & Schröder 2003). As habitat selection behaviour is scaledependent different spatial scales should be taken into account when studying
species specific habitat preferences (Orians & Wittenberger 1991).
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
30
In this study we investigate habitat selection of two bush crickets and one
grasshopper species typically found on mesoxerophytic grassland. We ask for
biotic and abiotic site parameters relevant for the habitat preferences of the three
species and how species occurrence can be predicted with simple and easily
available measures. To test for scale effects, we looked for preferred
microhabitats within experimental plots and then analysed the influence of the
surrounding types of biotope on the occurrence probability of the species. In our
analyses we present a straightforward way to determine habitat suitability using
logistic regression analyses and to evaluate and validate such models. Thereby we
combine different methods independently suggested by other authors (Fielding &
Bell 1997, Guisan & Zimmermann 2000, Schröder 2000).
2.2 MATERIAL AND METHODS
2.2.1
The species
From the literature and experts a general ‘expert opinion’ about the habitat
requirements of the three species can be extracted. However, these opinions have
not yet been confirmed by thorough field studies of the kind presented in this
article. Current knowledge on species habitat requirements can be summarized as
follows:
Stenobothrus lineatus
The stripe-winged grasshopper Stenobothrus lineatus (PANZER 1796;
Orthoptera: Acrididae) is a medium-sized to large grasshopper species (body
length: 15 – 26 mm). It is thermo- and xerophilic and inhabits arid and semi-arid
grasslands as well as broom heath, juniper heath, and short lawn edges of woods.
Sheep-grazed areas and short vegetation structure are preferred habitat elements
(Detzel 1998).
Metrioptera bicolor
The two-coloured bush cricket Metrioptera bicolor (PHILIPPI 1796;
Orthoptera: Tettigoniidae) is medium-sized (body length: 15 – 18 mm), thermoand xerophilic, and mainly inhabits mesoxerophytic grassland As it is orientated
towards vertical structures the species prefers long lawn biotopes. Metrioptera
bicolor can also be found on juniper heath, poor grassland, semiarid and sandy
grasslands (Detzel 1998). Kindvall and Ahlen (1992) describe the species as very
resident and unwilling to leave its native habitat patches.
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
31
Platycleis albopunctata
The grey bush cricket Platycleis albopunctata (GOEZE 1778; Orthoptera:
Tettigoniidae) is a medium- to large-sized bush cricket species (body length: 18 –
22 mm). It is classified as a thermo- and xerophilic species (Harz 1969, Ingrisch &
Köhler 1998), which inhabits dry locations, especially dry grassland as well as
habitats with similar structure (Detzel 1998). Open soil, sparse vegetation and
fringes are preferred habitat elements. In the nature reserve ‘Hohe Wann’ it is
therefore found within a more narrow distribution than M. bicolor.
2.2.2
Field work
The study was conducted in August and September in the years 2001 and
2002 in the nature reserve ‘Hohe Wann’ in Northern-Bavaria, Germany (latitude
50° 03′, longitude 10° 35′). The study area is characterised by a patchwork of
vegetation caused by the geological and geomorphological heterogeneity of the
area and small-scale microclimatic differences traceable to different exposition,
inclination and land use. Additionally, agricultural fields are usually very small.
The most obvious characteristic of the nature reserve are abundant sites of
mesoxerophytic grassland, formerly used as vine yards (Elsner 1994). These
patches are separated by agricultural landscape of different use. The whole area
covers approximately 10 km in NS-direction and 4 km in EW-direction.
Incidence of the grasshopper and bush cricket species was recorded on
146 experimental sites selected by stratified random sampling across the ten main
types of biotope occurring in the region. To increase the resolution of our
approach we sampled with high effort in habitats with – based on prior
knowledge – uncertain status regarding the species’ occurrence (Table 2.1).
We used a Geographic information system (GIS, ESRI  ArcView 3.2) to
determine the main types of biotope in the area and sampled each type (Table
2.1). Distance between two experimental sites was at least 30 m. We characterised
each site by the vegetation structure of a randomly chosen 1 m2 plot. For the
analysis of micro structural preference of the grasshoppers and bush crickets
vegetation structure was also recorded at 1 m2 plots immediately surrounding the
point where individuals of the species under study were found. Vegetation
structure analysis included estimates of horizontal plant cover, vertical plant cover
and vegetation height (cf. Sundermeier 1999). Additionally we recorded the type
of biotope, the actual management regime, the inclination and exposition of the
plots.
For the determination of grasshopper and bush cricket incidence we
carried out transect sampling (inter-transect distance = 1.5m) on the experimental
sites (15 m x 15 m). The census was terminated (i) as soon as a specimen was
found or (ii) after a maximum of 20 minutes of sampling time. As the activity of
grasshoppers and bush crickets strongly depends on weather conditions, censuses
were only carried out during ‘good’ weather condition (sunshine, cloud cover
32
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
< 3/8; air temperature > 17 °C; wind speed < 4 m/s; Mühlenberg 1993) to
ensure the same detection probability on all plots.
Table 2.1: Overview of sample plots, their distribution across types of biotope and
frequency of occupancy for the three species under study. Results are shown for two years
at sample site ‘Hohe Wann’ and one year at the sample site ‘Leutratal’.
year
2001
location
Hohe
Wann
type of
biotope
# of
plots
all
146
23
123
60
86
64
82
45
6
39
24
21
25
20
7
1
6
1
6
2
5
24
-
24
5
19
7
17
26
9
17
18
8
21
5
10
8
6
7
7
6
7
-
3
8
6
7
7
6
9
1
1
1
-
1
7
5
6
7
6
9
-
1
8
6
7
7
6
143
28
115
70
73
50
93
45
13
32
25
20
16
29
8
1
7
4
4
1
7
22
-
22
7
15
3
19
26
7
19
22
4
20
6
10
8
6
7
6
5
7
-
3
8
6
7
6
5
8
1
2
1
-
2
7
4
6
6
5
9
1
-
1
8
5
7
6
5
28
18
10
-
-
16
12
5
2
3
-
-
4
1
5
-
5
-
-
2
3
5
2
3
-
-
1
4
6
4
2
-
-
6
0
3
2
3
2
1
1
2
2
-
-
3
1
0
2
2
Extensively
managed meadow
Intensively
managed meadow
Inten. managed
meadow meagre
mesoxerophytic
grassland
fringe vegetation
crop field
fallow land
hedge
forest
thermophilic forest
2002
Hohe
Wann
all
extensively
managed meadow
Intensively
managed meadow
Inten. managed
meadow meagre
mesoxerophytic
grassland
fringe vegetation
crop field
fallow land
hedge
forest
thermophilic forest
2002 Leutratal
all
extensively
managed meadow
Intensively
managed meadow
Inten. managed
meadow meagre
mesoxerophytic
grassland
fringe vegetation
crop field
hedge
P. albopunctata
M. bicolor
S. lineatus
occup. unocc. occup. unocc. occup. unocc.
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
33
To test for the transferability of the resulting habitat suitability models in
space (cf. Leftwich et al. 1997, Dennis & Eales 1999, Schröder & Richter 1999,
Schröder 2000, Fleishman et al. 2003), we additionally sampled an area
approximately 200 km away from our original study site in the Thuringian nature
reserve Leutratal near Jena (latitude 50° 52′, longitude 11° 34′). This area is also
characterised by a high fraction of mesoxerophytic grasslands with a wide variety
of rare plant and animal species (Heinrich et al. 1998). Here we studied 28
experimental sites across 5 types of biotope (Table 2.1) in the same manner as in
our main study area. Only two of our species (P. albopunctata, S. lineatus) could be
found there, thus the spatial validation of the habitat suitability model for
M. bicolor was not possible.
2.2.3
Statistical analyses
Development of habitat suitability models
We used single and multiple parameter logistic regression models to
predict occurrence probability depending on plot parameters (Manel et al. 1999a,
b, Hosmer & Lemeshow 2000). For the selection of adequate models we started
with an univariate analysis to assess individual model variables independently
from each other and to obtain information on each variable’s role (Hosmer &
Lemeshow 2000). To choose uncorrelated parameters for the development of
multiple parameter models we calculated all pairwise Spearman rank correlations
and selected only one variable of those pairs showing correlation (ρs ≥ 0.7
(Fielding & Haworth 1995)). We did not use independent factors from a principal
component analyses (PCA) because these turned out to create difficulties in their
biological interpretation and are consequently difficult to use in conservation
biology. As integrating measures for horizontal and vertical vegetation cover, we
used the ‘total horizontal cover’, which describes the vertical structures at a plot
(Sundermeier 1999), and the ‘percentage open ground’.
Initial single parameter models included all types of biotope investigated.
Due to total separation causing numerical instabilities in some types of habitat we
restricted our analyses to those types of biotope with at least minimal variation in
occupancy. This was done to get a more detailed explanation of the species’
habitat requirements. Eliminated biotopes were included into our models by
formulating rules, like ‘If forest then not suitable habitat’. These can be easily
implemented into the regression equations. Thus, the reduction of our data set on
the one hand increases our error due to exclusion of observations, that can be
predicted without error, on the other hand we receive more detailed information
on the habitat selection of the species.
Model evaluation can be conducted in different ways. First, model
calibration judges the agreement between observed and predicted values
(Schröder 2000). One measure of goodness of fit in this context is Nagelkerkes R2
value (Nagelkerke 1991, Harrell 2001). Values exceeding 0.4 describe a good
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
34
explanatory value of the variable. Model discrimination, the power of one or more
variables to separate presence and absence of the species (Schröder 2000), can be
assessed with threshold dependent or independent measures (Fielding & Bell
1997). A problem with threshold dependent measures is their failure to use all of
the information provided by the classifier (Fielding & Bell 1997). In our analyses
we used a threshold independent value to characterise model discrimination, i.e.
the area under the receiver operating characteristic curve (ROC-curve), the AUCvalue (Hanley & McNeil 1982). The AUC-value provides a single measure of
overall accuracy that is not dependent upon a particular threshold (Hosmer &
Lemeshow 2000, Schröder 2000, Manel et al. 2001). Values above 0.7 describe an
acceptable discrimination, values between 0.8 and 0.9 denote good discrimination
and for a value above 0.9 discrimination is excellent (Hosmer & Lemeshow 2000).
For comparison of different models with the same dependent variable we used
the Akaike Information Criterion (AIC, see also Buckland et al. 1997, Augustin et
al. 2001). Which allows to choose the model with the optimal compromise
between goodness of fit and the lowest number of parameters.
Spatial autocorrelation
One general problem with spatial data is the spatial autocorrelation
of the dependent variable (Legendre 1993), i.e. the tendency of neighbouring
sample units to possess similar characteristics (Fielding & Bell 1997). In the
presence of positive spatial autocorrelation the incidence of a species at one place
always implies that the probability of occurrence in the neighbourhood is
increased (Smith 1994). Spatial autocorrelation has the effect of reducing the
number of independent observations, which is not generally reflected by an
equivalent decrease in the error degrees of freedom (Legendre 1993).
Consequently, error terms are underestimated, leading to over-optimistic
estimates of population parameters (Fielding & Haworth 1995) and abetting
pseudo replication (Guisan & Zimmermann 2000). To test whether our data show
spatial autocorrelation we used standardised deviation residuals to calculate
Moran’s I as an index of covariance between different point locations (Lichstein et
al. 2002, Karagatzides et al. 2003). Models with spatial autocorrelation were
excluded from further analyses to avoid misleading conclusions.
Validation of the models
If a model is only tested on the data on which it was developed,
information about model performance tends to be over-optimistic (Verbyla &
Litvaitis 1989, Reineking & Schröder 2003). Thus, model validation should be
carried out either externally with independent data or internally applying
resampling techniques. We used internal as well as external validation by first
applying a bootstrapping procedure (Verbyla & Litvaitis 1989, Reineking &
Schröder 2003) and then testing the transferability of the model in space and time
35
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
(Schröder 2000). We first calculated the AUC value of the full model with 300
bootstrap samples and then tested for stability of the model with variable
selection by using the backward stepwise approach with α = 0.05 and 300
bootstrap samples (see also Oppel et al. in press). A model with specific variables is
classified as ‘stable’ if the variables are included into most of the models in 300
bootstrap samples (R 1.7.1 available at http://cran.r-project.org using the libraries
Hmsic and Design provided by F. Harrell).
Model validation in space uses one data set from one region to derive the
model (training set) and the other data set from a second region to test the model
(validation set, Verbyla & Litvaitis 1989). Validation in time works with two data
sets from the same region in different time periods (Bonn & Schröder 2001). To
test the transferability of models in time and space we checked if the AUC-value
derived from applying one model to predict a species’ occurrences elsewhere or in
another year significantly exceeds a critical AUC-value (e.g. 0.7) as described in
Schröder (2000) by equation (1) following Beck & Shultz (1986).
z=
AUC − AUCcrit
with z ~ N(0, σ)
SEAUC
(2.1)
AUC: area under ROC-curve
AUCcrit: critical AUC-value, i.e. 0.7
SEAUC: standard error of the area under ROC-curve
Conservational/Practical aspects
The developed multiple parameter logistic regression models investigate
habitat preferences of the species under study in a great detail. Unfortunately,
such detailed information is not usually available in practical conservation biology.
We thus estimated more simple models which only take into account the type of
biotope which often is – as in our case – the only landscape-wide information
available. Conservation biology is often interested in optimal management
strategies, thus, we also studied the influence of management regime on species
occurrence.
Additional analyses
Separate analyses for different types of biotope
Due to the fact that the type of biotope explained most of the variance in
the incidence of all three species and to test whether some variables only or still
have a significant influence within one type of biotope, we compared parameters
from occupied and unoccupied plots for each type of biotope separately in a
Mann-Whitney U test.
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
36
Microhabitat preferences and influence of surrounding type of biotope
Habitat selection of species takes place at different spatial scales (Johnson
1980, Orians & Wittenberger 1991, Mackey & Lindenmauer 2001, Oppel et al. in
press). To account for this we further expanded our analyses and looked for
microhabitat preferences within one experimental plot. To do so, we used only
data from occupied plots and compared the parameters from the random point
with those of the ‘cricket (detection) point’ on the same experimental plot by a
Wilcoxon match paired test with additional sequential Bonferroni correction (Rice
1989).
To test for effects on a larger spatial scale we analysed the influence of
surrounding landscape composition on model performance. To do so, we
calculated the relative area of each type of biotope in a certain ring around the
plot for different radii (r = 10 m, 25 m as well as r = 50 m) and weighted it by the
predicted occurrence probability determined in the univariate logistic regression
analyses with the type of biotope as plot parameter. In each case the inner ring
(either the plot itself (r = 10 m), or the ring with r = 25 m) was subtracted from
the outer ring (r = 25 m and r = 50 m). These calculations were carried out in a
GIS (ESRI  ArcView 3.2). If we received overlapping rings we excluded one by
random selection to avoid pseudo replication. This resulted in a reduction of our
data sets from n = 146 to n = 118. This method produces one single metric
regression parameter for each radius (instead of categorial variables or
percentages) and thus avoids the use of too many degrees of freedom as well as
dependent variables in the analyses. To see whether the immediate surrounding
landscape has significant influence on species occurrence the values for r = 25 m
and r = 50 m are added to the values for r = 10 m (which corresponds to our
experimental plot of 15 m x 15 m) in a multiple regression analyses. Expansion of
this analyses to a scale more relevant for dispersal and metapopulation aspects
(r = 100, 200m), was not possible as our plots were restricted to the nature
reserve and thus, too many overlapping rings would have resulted in a severe
reduction of our data sets. All analyses were carried out with the statistical
package SPSS 11 and in R 1.7.1 (available at http://cran.r-project.org using the
libraries Hmsic and Design provided by F. Harrell), respectively.
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
37
2.3 RESULTS
2.3.1
Influence of plot characteristics on the occurrence probability of species
The prevalences of our species varied between species and years (Table
2.2). M. bicolor showed a relatively constant prevalence in both years (2001:
41.1 %, 2002: 49 %). The prevalence of S. lineatus decreased slightly in the year
2002 (2001: 43.8 %, 2002: 35 %). In both years P. albopunctata was only found on
around 20 % of the plots (2001: 15.8 %, 2002: 19.6 %). S. lineatus as well as
P. albopunctata had a very high prevalence in the second study area (S. lineatus:
57.1 %, P. albopunctata: 34.3 %).
Based on the occupancy pattern across some of the types of biotope and
the total separation in certain habitats (Table 2.1) we deduced the following rules:
•
•
•
Platycleis albopunctata does not occur in: rich meadows, extensively
managed meadows, crop fields, fallow land, hedges, and forests.
Metrioptera bicolor does not occur in: rich meadows, crop fields,
fallow land, hedges, and forests.
Stenobothrus lineatus does not occur in: rich meadows, crop fields,
fallow land, hedges, and forests.
Because habitat suitability may not be determined by a single factor
alone but probably by a combination of different factors we conducted multiple
logistic regression analyses for each species with different combinations of
independent variables from the reduced data sets.
Stenobothrus lineatus
Multiple logistic regression analyses for the data from 2001 resulted in six
significant models free of spatial autocorrelation. The four models with the lowest
AIC are shown in table 2.2. The model with the lowest AIC predicts a high
occurrence of S. lineatus in fringes, mesoxerophytic grassland, extensively managed
meadows and grazed areas with low vegetation height. Only two of the multiple
models were transferable in time, none in space. Internal validation of the models
showed that only those with the variables ‘type of biotope’ and ‘low vegetation
height’ were robust, but they were neither transferable in time nor space.
Table 2.2: Model characteristics for significant models (p < 0.05, AUC-value ≥ 0.7) for the three investigated species. Only models without spatial
autocorrelation are shown. Transferability in space (s) or time (t) is indicated by superior characters.
bootstrapping
species model
S. lineatus
S1t
S2t
S3
S4
S5ts
M1
M. bicolor
M2
M3
M4
M5
M6
P. albopunctata
M7t
P1
P2
P3
P4t
model parameter
type of biotope
management
vegetation height (quadratic term)
type of biotope
vegetation height (quadratic term)
vegetation height
type of biotope
vegetation height (quadratic term)
management
total horizontal cover
type of biotope (all data)
type of biotope
cosine exposition
vegetation height
sheep livestock
type of biotope
cosine exposition
vegetation height
type of biotope
sheep livestock
vegetation height
type of biotope
sheep livestock
type of biotope
vegetation height
management
cosine exposition
vegetation height
total horizontal cover
type of biotope (all data)
type of biotope
management
vegetation height
type of biotope
sine exposition
vegetation height
type of biotope
vegetation height
type of biotope (all data)
AUC ± SE
R 2Nagelkerke
AIC
full model
AUCbootstrapped
stable parameters
AUCbootstrapped, bw
0.848 ± 0.036
0.508
102.35
0.807
type of biotope
0.701
0.809 ± 0.042
0.366
114.85
0.779
type of biotope
vegetation height (quadratic term)
0.728
0.762 ± 0.046
0.367
115.92
0.735
whole model
0.786 ± 0.045
0.335
117.73
0.766
whole model
0.846 ± 0.03
0.505
133.14
0.834
whole model
0.799 ± 0.044
0.347
120.30
0.757
type of biotope
0.670
0.780 ± 0.045
0.301
122.99
0.750
type of biotope
0.673
0.782 ± 0.044
0.310
123.39
0.751
type of biotope
0.692
0.747 ± 0.047
0.277
124.63
0.724
type of biotope
0.699
0.772 ± 0.046
0.267
125.65
0.749
type of biotope
0.7
0.741 ± 0.048
0.268
128.32
0.675
no model estimated
0.806 ± 0.04
0.387
150.3
0.787
whole model
0.864 ± 0.047
0.475
68.58
0.807
type of biotope
vegetation height
0.855 ± 0.047
0.434
71.98
0.806
whole model
0.812 ± 0.054
0.354
76.09
0.780
whole model
0.855 ± 0.04
0.415
88.9
0.838
no model estimated
0.783
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
39
Metrioptera bicolor
Out of the six significant multiple parameter models without spatial
autocorrelation the highest occurrence of M. bicolor was predicted for sheepgrazed fringe vegetation with high vegetation and south-faced exposition.
However, after internal validation with backwards variable selection all multiple
parameter models were identified as unstable and had to be reduced to single
parameter models with the ‘type of biotope’ as the sole variable (Table 2.2). None
of the complex multiple parameter models was transferable in time. Spatial
validation was not possible because M. bicolor could not be found in the nature
reserve Leutratal.
Platycleis albopunctata
The model that included ‘type of biotope’, ‘type of management’ and
‘vegetation height’ had the smallest AIC-value (Table 2.2). P. albopunctata prefers
sites of mowed fringe vegetation and generally low vegetation height. This model
had to be reduced to two parameters (‘type of biotope’ and ‘vegetation height’)
after internal validation with backwards variable selection. None of the multiple
parameter models was transferable in time or space.
2.3.2
Conservational/Practical aspects
The variable ‘type of biotope’ showed a high explanatory power for all
three species in single parameter models (Table 2.2). Internal validation of the
single parameter model with ‘type of biotope’ as variable was successful for all
three species. Transferability in time was possible for that model in all cases and
for S. lineatus transferability in space could also be proven (Table 2.2). For all three
species the ‘fringe vegetation’ has the highest probability of occurrence followed
by mesoxerophytic grassland (Figure 2.1).
The ‘type of management’ alone did not yield high explanatory power for
the spatial distribution of the species, but was included in some of the multiple
models and may be important in terms of conservational aspects. For
P. albopunctata mowing always correlates with a high incidence. The other two
species (M. bicolor and S. lineatus) are most often found on plots under extensive
sheep-grazing management (Figure 2.2). Intensively managed areas as well as
areas with no management at all are avoided by all three species. Generally, the
‘type of management’ can not explain as much variation in incidence as the ‘type
of biotope’.
40
0.6
0.4
0.0
0.2
mean inzidence
mean
incidence
0.8
1.0
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
CF
IMM IMMM EMM MG
FV
FL
H
F
type of habitat
type of biotope
0.6
0.4
0.0
0.2
meanincidence
inzidence
mean
0.8
1.0
Figure 2.1: Mean incidence in the different biotopes, crop field
(CF), intensively managed meadows (IMM), intensively managed
meadows meagre (IMMM), extensively managed meadows
(EMM), mesoxerophytic grasslands (MG), fringe vegetation (FV),
fallow land (FL), hedges (H), forest (F) for S. lineatus (white bars),
M. bicolor (grey bars) and P. albopunctata (black bars).
No info
Fallow
Crop field
Mowing
type of management
Sheep
Grazing
type of management
Figure 2.2: Mean incidence for different types of management for
S. lineatus (white bars), M. bicolor (grey bars) and P. albopunctata (black
bars).
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
2.3.3
41
Additional analyses
Separate analyses for different types of biotope
The comparison of occupied and unoccupied plots carried out separately
for each ‘type of biotope’ allows a closer look at habitat selection, yielding specific
models for specific types of biotope. Low ‘vegetation height’, low ‘total horizontal
cover’ as well as low ‘cover at the heights of 20/30/40 cm’ are attributes
preferred by S. lineatus on mesoxerophytic grasslands (Mann-Whitney U test,
p < 0.05 for all cases). By conducting the same analyses for M. bicolor we could not
detect any significant differences between occupied and unoccupied plots (MannWhitney U test, p > 0.05 for all cases). P. albopunctata prefers extensively managed
meadows with west-faced exposition (Mann-Whitney U test, p < 0.05 for all
cases).
Microhabitat preferences and influence of surrounding type of biotope
In our study all three species did not show any significant preferences of
distinct microhabitat parameters (Wilkoxon match paired test after Bonferroni
correction for multiple comparisons, all comparisons p > 0.05). The surrounding
in 10 m to 25 m and 25 m to 50 m did not provide additional information for the
occurrence probability of M. bicolor and P. albopunctata. For S. lineatus the
surrounding between 25 m and 50 m as well as the calculated values for the plot
itself remained in the model after stepwise backward variable selection
(AUCbootstrapped = 0.823).
2.4 DISCUSSION
2.4.1
Influence of plot characteristics on the occurrence probability of species
In our study the species with the most restricted habitat requirements was
P. albopunctata. It only occurred on fringe vegetation, mesoxerophytic grasslands
and intensively managed meadows with meagre parts. The two other species,
M. bicolor and S. lineatus, were also found on extensively managed meadows. This
result corresponds well with the literature on habitat requirements of the species
(Detzel 1998).
After the reduction of the data set to those types of biotope that yielded at
least some incidence, the variable ‘type of biotope’ significantly contributes to
many models. It obviously has a great influence on the occurrence probability of
the species. Additionally, for S. lineatus low vegetation height as well as low total
horizontal cover strongly influence occurrence. This may be explained by the fact
that egg development depends on temperature (van Wingerden et al. 1991) and
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
42
females lay their eggs in the upper ground layer or on the button upholstery of
grasses (Oschmann 1993).
For M. bicolor a south-faced exposition, high vegetation and sheep grazing
are the factors that – in combination with the type of biotope – explain most of
the variance in the data. For this species fringes, mesoxerophytic grasslands, as
well as extensively managed meadows offer good food resources as the larvae are
feeding on grasses as well as on flowers of grasses and herbs (Ingrisch 1976). This
holds especially if they are managed by sheep-grazing. As males of this species are
calling in higher vegetation (Detzel 1998) and females lay their eggs in grass stems
(Hartley & Warne 1972) a preference for high vegetation may correspond to the
vertical orientation of the species.
The occurrence of P. albopunctata is best described by the combination of
the variables: ‘type of biotope’, ‘type of management’ and ‘vegetation height’ at
the plots. Low vegetation height and thus increased temperature promotes the
larval development of P. albopunctata (Detzel 1998). Apart from the influence of
the type of biotope, management regime (i.e. man-made disturbance) contributes
to habitat suitability for all three species. P. albopunctata is mostly found on mowed
mesoxerophytic grasslands and fringes, which provide an elevated ground
temperature, and low vegetation due to their position on the steeper, upper southfaced hillsides. The other two species preferred areas which are extensively grazed
by sheep. As Detzel (1998) describes a preference for areas managed by sheepgrazing also for P. albopunctata, our result seems to be in contrast to the habitat
requirements of P. albopunctata described in that study. But our result may be
biased by the fact that the mesoxerophytic grasslands as well as the fringes
investigated are all managed by mowing, therefore mowing and fringes cannot be
separated.
Although we cannot precisely predict the best management practice for the
conservation of P. albopunctata, we would like to point out that even mowed
habitats may be suitable for that species. Grazing by sheep is mainly used on
extensively managed meadows, where it produces a heterogeneous mosaic of high
and short vegetation patches within one area (Adler et al. 2001). Such complex
habitats offer a variety of different local conditions for different activities like
feeding, mating or reproduction. Intensively managed, agricultural or silvicultural
areas as well as abandoned areas with no management at all are avoided by all
three species.
Both, the ‘type of biotope’ as well as the ‘type of management’ are proxies
for the real driving factors of the incidence of grasshoppers and bush crickets.
Their influence on occurrence probability is determined by a variety of different
other factors (e.g. predation risk, exposition, temperature regime) that are actually
relevant to the species’ survival or habitat selection. This may explain the
contrasting effects of management regime for P. albopunctata. It is neither the ‘type
of biotope’ nor the exact management regime that determines species occurrence,
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
43
but the resulting level of the actually relevant factors (e.g. increased temperature
for egg development) that determines habitat quality for the species.
The effect of spatial autocorrelation is neglected in most studies on
species-habitat-relationships (but see Hinch et al. 1994, Smith 1994, Collingham et
al. 2000, Osborne et al. 2001, Judas et al. 2002, Keitt et al. 2002). To avoid
misleading interpretations due to inadequate degrees of freedom in significance
tests we excluded models from the analyses if residuals showed spatial
autocorrelation. Especially in the case of P. albopunctata this led to the exclusion of
a number of models. This may be due to the fact that the species shows a
clustered occurrence pattern in our region which is explained by the limited
dispersal ability of the species.
Although the type of stratified random sampling used in our study is an
adequate and commonly used method (see also Wessels et al. 1998, Hirzel &
Guisan 2002), we would recommend to use a two step approach whenever
possible for further studies. This should start with a preliminary study (which was
not possible in our case) with which clear non-habitat structures can be
determined to exclude these by rules from the analyses (Dufrene & Legendre
1991, Aspinall & Lees 1994). The intensive work on the detailed habitat
requirements of a species can then be done in a main study with a high sample
size in habitats with intermediate occupancy (see also Hirzel & Guisan 2002).
Such an approach with higher sample sizes always improves the quality of the
results (Hirzel & Guisan 2002). Especially in the case of the bush cricket P.
albopunctata where we only had a prevalence of around 20% in the nature reserve
‘Hohe Wann’, this would most probably have improved the precision of our
model. In the case of the other two investigated species prevalence was around 40
to 50 %. This is regarded as optimal for the development of logistic regression
models (Hosmer & Lemeshow 2000).
2.4.2
Microhabitat preferences and influence of surrounding type of biotope
In contrast to the findings of Krätzel (1999) and Krätzel et al. (2002) for
M. bicolor we did not find any evidence for the preference for distinct
microstructures. This may be due to the fine-grained scale of our study. Our plots
were rather small (just 225 m2) compared to the 800 – 1320 m2 in the study of
Krätzel (1999) and Krätzel et al. (2002). Thus our plots may have been rather
homogeneous. We would nevertheless expect that our species do actively select
distinct microhabitats at a larger plot size respectively on a larger spatial scale than
investigated here. This argument is supported by our separate analyses for each
type of biotope which showed a clear preference for some structural aspects of
occupied compared to unoccupied plots for P. albopunctata and S. lineatus.
For P. albopunctata and M. bicolor no additional influence of the plot
surrounding on habitat occupancy could be detected. The plot surrounding in a
radius of 25 m is highly correlated with the plots type of biotope because these
area represents roughly the mean, natural patch size in the nature reserve ‘Hohe
Chapter 2 – Habitat suitability for grasshoppers and bush crickets
44
Wann’. For S. lineatus the surrounding between 25 m and 50 m was a significant
variable in the model together with the plots own type of biotope. Whether this
influence is based on metapopulation effects or the spatial heterogeneity of
S. lineatus habitats can not be decided in this study. But an additional study
covering more of the surrounding could yield more information.
2.4.3
Model validation
Validation either internally or externally is still not very common in habitat
suitability model building, though it is the best possibility to really judge how
robust and general a model is. For all three species the simple model including the
‘type of biotope’ as explanatory variable could be validated internally and showed
a good transferability in time. For S. lineatus it also could be transferred in space.
Spatial validation was not possible in all other cases eventually due to the low
sample size in the second study area (28 plots) and the fact that the range of
tested categories for ‘type of biotope’ and ‘management’ in the second study area
was too small (see Table 2.1). In such a case it is more likely that we compared
two different sampling designs rather than performing an external validation
(Lehmann et al. 2002). For future studies we would recommend to use the same
sampling design with similar sampling effort to perform a spatial validation of
habitat suitability models. The failure of transferability in time for the multiple
parameter models of M. bicolor might be caused by their instability. None of these
models were stable with respect to the selected explanatory variables and only the
variable ‘type of biotope’ resulted in stable models after bootstrapping with
stepwise backward variable selection.
2.4.4
Conservational/Practical aspects
The type of biotope is always a powerful predictor of species occurrence if
we used it as single variable or even in the reduced data sets and multiple
regression analyses. Thus, we can on the one hand conclude that the type of
biotope as highly integrating variable accounts for some parameters we did not
estimate. On the other hand, and especially useful for nature conservation, simple
vegetation type maps (type of biotope) might sometimes be sufficient to predict
species occurrence with satisfactory precision. The use of these models would
provide a quick and efficient possibility to determine the distribution of species
under different management regimes and in other regions.
Chapter 3
The generality of habitat suitability
models: How well may grasshoppers be
predicted by butterflies?
with Birgit Binzenhöfer, Hans-Joachim Poethke, Robert Biedermann,
Josef Settele and Boris Schröder
IN PREP. FOR BASIC AND APPLIED ECOLOGY
Abstract. Knowledge of the relationship between habitat properties
and the occurrence of a particular species is an essential prerequisite
for the conservation of species. Habitat suitability models are one
possibility to describe habitat preferences of a species quantitatively
and objectively. In this study we compare the habitat preferences of
different insect species (grasshoppers, bush crickets, butterflies) in the
same area and with the same methods. To identify common
parameters to predict occurrence probability of these species, we first
tested transferability of single species models to species within the
same insect group. The ‘best’ group models were then tested for
transferability between the different groups.
Although in the single species models different key factors have been
shown to be responsible for habitat suitability, some models were
successfully transferred. The habitat preferences of the moth
Z. carniolica were particularly well suited for the prediction of suitable
habitats for all other species. The variable ‘type of biotope’ played a
dominant role in all models. With this predictor suitable habitat may
be predicted for all studied species under different management
regimes.
Chapter 3 – Generality of habitat suitability models
47
3.1 INTRODUCTION
Historically, arid grasslands arose due to anthropogenic influences, e.g.
logging or abandonment of vineyards or fields on steep hill sides. In former times,
they have most often been used as grazing sites or mowing areas. Situated mostly
on nutrient poor soil or inaccessible hill sides they usually are of low agricultural
productivity (Willems 1990, Van Dijk 1991) and only worked with high effort.
Nowadays these semi-natural habitats are increasingly lost by either intensification
(e.g. through fertilisation) or abandonment (e.g. succession by shrubs and hedges;
Mühlenberg 1996, Van Dijk 1991, Poschlod et al. 1996, Kahmen et al. 2002). But
because these areas simultaneously accommodate a variety of threatened and
protected animal and plant species (van der Maarel & Titlyanova, Kull & Zobel
1991, Bignal & McCracken 1996), it is a declared objective of nature conservation
in Europe to protect them (Bobbink & Willems 1993, Söderström et al. 2001).
However, management regimes for these areas are not easily developed since
maintenance depends on regular management, but species diversity is known to
decline with increased management intensity (Kruess & Tscharntke 2002). Thus, a
variety of different management regimes has been proposed and tested (Schreiber
1977, Bakker 1989, Bobbink & Willems 1993, Kahmen et al. 2002) including the
ones from the MOSAIK-project: rototilling and extensive grazing by goats (see
Fritzsch et al., Kleyer et al. this issue).
Independent of the kind of management and the time intervals between
management events a certain vegetation composition or rather successional stage
of the managed area can be found. This successional stage results in different
levels of habitat suitability for different animal species. Which degree of
succession will be advantageous for the desired ‘target species’ can be determined
by describing the habitat requirements of a species with statistical habitat
suitability models (Bonn & Schröder 2001, Hein et al. submitted). Different
species or insect groups vary in their respective habitat needs. Therefore the
resulting favoured successional stage will vary from species to species.
For the conservation of grasslands it is important to protect as many
species as possible. Thus, habitat requirements of a large number of single species
have to be brought together. In this context one could imagine two approaches.
First, the identification of single species which are representatives for others
concerning habitat requirements (New 1995, Simberloff 1998, Bonn & Schröder
2001). Secondly, the combination of single species habitat requirements to find a
common set of variables relevant for the occurrence of a maximum number of
species. Both methods would make it feasible to predict suitable habitats and the
impact of different management types on the occurrence of more than one
species.
Chapter 3 – Generality of habitat suitability models
48
Within the MOSAIK project the habitat requirements of different insect
species have been studied separately (Binzenhöfer et al. in prep., Hein et al.
submitted, Strauß et al. in prep.). In this study we compare results of these
analyses for three different Orthoptera (Ensifera: Metrioptera bicolor, Platycleis
albopunctata, Caelifera: Stenobothrus lineatus ) and two butterfly species (Lepidoptera:
Coenonympha arcania, Zygaena carniolica) typically found on semi-arid grasslands and
tested whether the inclusion of factors delivered by a digital terrain model and a
landscape model (see Schröder et al. this issue) increases their predictive power.
For the resulting single species models we will first test transferability within the
species of one insect group (within group transfer). The resulting ‘group’ models
are then transferred to the other groups (between group transfer; Bonn &
Schröder 2001). Additionally, for the determination of a representative or
‘umbrella’ species we tested whether the incidence of one species can be used to
predicted incidences of others just as well (Hanley & McNeil 1983, Bonn &
Schröder 2001).
3.2 MATERIAL AND METHODS
This study is based on data from different authors (Binzenhöfer et al. in
prep., Hein et al. submitted, Strauß et al. in prep.). All studies used the same
experimental design to achieve comparable data sets for the different species. To
keep this section short, we would like to refer to the original papers for a detailed
description of data collection and characterization of the experimental plots
(Binzenhöfer et al. in prep., Strauß et al. in prep., Hein et al. submitted). In cases
were analyses differed in the original papers we adjusted all analyses to the
methods presented in Binzenhöfer et al. (in prep.) and Strauß et al. (in prep.).
3.2.1 The species
Grasshoppers and bush crickets
The stripe-winged grasshopper Stenobothrus lineatus (PANZER 1796;
Orthoptera: Acrididae), the two-coloured bush cricket Metrioptera bicolor
(PHILIPPI 1796; Orthoptera: Tettigoniidae) and the grey bush cricket Platycleis
albopunctata (GOEZE 1778; Orthoptera: Tettigoniidae) are classified as thermoand xerophilic (Harz 1969, Detzel 1998, Ingrisch & Köhler 1998). They typically
inhabit arid and semi-arid grasslands as well as broom and juniper heath. S. lineatus
and P. albopunctata are also found on fringes and areas with open soil and sparse
vegetation. In contrast M. bicolor is more orientated towards vertical structures and
thus prefers long lawn biotopes. For S. lineatus sheep-grazed areas and short
vegetation structure are preferred habitat elements (Detzel 1998). In general,
Chapter 3 – Generality of habitat suitability models
49
P. albopunctata has a more narrow distribution in Germany than the other two
species.
Butterflies and moths
The flight period of the Pearly Heath Coenonympha arcania (LINNAEUS
1761; Satyridae: Satyrinae) lasts from end of May till beginning of August.
Generally this species is univoltine (Ebert & Rennwald 1991, Hensle 1995).
Imagines are found on mesoxerophytic grasslands with bushes in the near vicinity
of hedges and forest edges (Ebert & Rennwald 1991, Weidemann 1995, Settele et
al. 1999). Caterpillars feed preferentially on Holcus lanatus, Brachypodium pinnatum,
Festuca ovina or Melica spec. (Ebert & Rennwald 1991, Weidemann 1995).
The day active moth Zygaena carniolica (SCOPOLI 1763; Lepidoptera:
Zygaenidae) has a flight period of four to five weeks from the end of June to mid
August (Ebert & Rennwald 1991). This xerothermophilic species (Ebert &
Rennwald 1991) inhabits mainly fallow as well as extensively grazed or mowed
mesoxerophytic grasslands (Ebert & Rennwald 1991, Weidemann 1995).
Preferred caterpillar feeding plants are Onobrychis viciifolia and Lotus corniculatus.
Imagines prefer purple flowering nectar plants like Knautia arvensis, Scabiosa
columbaria and Centaurea spec. (Ebert & Rennwald 1991, Weidemann 1995).
3.2.2
Field work
All studies were conducted in the field seasons of 2001 and 2002 in the
nature reserve ‘Hohe Wann’ in Northern-Bavaria, Germany (latitude 50° 03′,
longitude 10° 35′). The study area is characterised by abundant sites of
mesoxerophytic grasslands, formerly used as vine yards (Elsner 1994). These
patches are separated by agricultural landscape of different use. The whole area
covers approximately 10 km in NS-direction and 4 km in EW-direction.
Incidence of the species under study was recorded on 146, 139 and 106
experimental sites for orthoptera, Z. carniolica and C. arcania respectively.
Experimental plots were selected by stratified random sampling across the ten
main types of biotope occurring in the region. To increase the resolution of the
logistic regression models we sampled with high effort in habitats with - based on
prior knowledge – uncertain status regarding the species’ occurrence (i.e. different
kinds of open grasslands). To assure comparability of results the study plots of
the grasshoppers and bush cricket studies (15 m x 15 m) were always a randomly
chosen corner of the butterfly/moth plots (30 m x 30 m). Different plot sizes
were chosen as butterflies/moths are more mobile than grasshoppers or bush
crickets.
For the determination of grasshopper, bush cricket, butterfly and moth
incidence we carried out transect sampling on the experimental sites. The census
was terminated (i) as soon as a specimen was found or (ii) after a maximum of 15
minutes (butterflies, moths) or 20 minutes (grasshoppers, bush crickets) of
Chapter 3 – Generality of habitat suitability models
50
sampling time. As the activity of grasshoppers, bush crickets, butterflies and day
active moths strongly depends on weather conditions, censuses were only carried
out during ‘good’ weather condition (sunshine, cloud cover < 3/8; air
temperature > 17 °C; wind speed < 4 m/s (Mühlenberg 1993)) to ensure the
same detection probability on all plots.
3.2.3
Statistical analyses
Single species models
We used single and multiple parameter logistic regression to determine the
impact of ‘landscape’ factors delivered by the digital terrain model and landscape
model (like solar radiation, slope, soil type, geology, Schröder et al. this issue) on
the occurrence probability of all studied insect species (Manel et al. 1999a, b,
Hosmer & Lemeshow 2000). For the selection of adequate models we started
with an univariate analysis to assess indiviudal model variables independently
from each other (Hosmer & Lemeshow 2000). To choose uncorrelated
parameters for the development of multiple parameter models we calculated all
pairwise Spearman rank correlations and selected only one variable of those pairs
showing severe correlation (ρs ≥ 0.5; Fielding & Haworth 1995, Schröder 2000).
Only parameters with p-values < 0.2 (Hosmer & Lemeshow 2000) were included
into multiple analyses.
For model evaluation we used Nagelkerkes R2 value (Nagelkerke 1991,
Harrell 2001). Model discrimination was assessed by the threshold independent
AUC-value, i.e. the area under the receiver operating characteristic curve (ROCcurve), the (Hanley & McNeil 1983, Hosmer & Lemeshow 2000, Schröder 2000,
Manel et al. 2001). Variables with an AUC-value > 0.7 were included into the
models from the single species studies and their importance was tested with the
stepwise backwards elimination procedure.
For comparison of the resulting models with the same dependent variable
we used the Akaike Information Criterion (AIC, see also Buckland et al. 1997,
Augustin et al. 2001), which allows to choose the model with the optimal
compromise between goodness of fit and the lowest number of parameters.
Models with the lowest AIC were tested for spatial autocorrelation by calculating
Moran’s I as an index of covariance between different point locations (Lichstein et
al. 2002, Karagatzides et al. 2003). Only models without spatial autocorrelation
were used for further analyses and internally validated by applying a bootstrapping
procedure (Verbyla & Litvaitis 1989, Reineking & Schröder 2003). For internal
validation we first calculated the AUC value of the full model with 300 bootstrap
samples and then tested for stability of the model with variable selection by using
the backward stepwise approach with α = 0.05 and 300 bootstrap samples (see
also Oppel et al. in press).
Chapter 3 – Generality of habitat suitability models
51
Test of transferability
Each of the single species models was tested for transferability in two
ways. First, the model was computed for the species it was developed for. Then
predicted occurrence probabilities for that species were used to predict the
occurrence of the other species (Hanley & McNeil 1983).
Secondly, we tested transferability by using the parameters from the ‘best’
model for species A and calculating a new model with these parameters based on
the incidence of species B. In both cases agreement between predictions and
occurrences are tested by comparing the AUC value with an AUCcrit ≥ 0.5 (ROCtool available at http://brandenburg.geoecology.uni-potsdam.de/users/schroeder
/download.html by Schröder B.). The transfer quality of a model is best when all
possible combinations result in significantly better AUCs than 0.5. For within group
comparisons of transferability this methods result in five possible combination for
the three orthoptera species, respectively in three combinations for the two
butterfly species. The highest possible number of successful transfers of a single
species model for between group comparisons is seven.
The ‘type of biotope’ or derivates of this parameter were included in all
single species models. Additionally, this parameter is landscape wide available.
Thus, we tested the ‘type of biotope’ of the experimental plot of grasshopper and
bush cricket incidences, which corresponds in most cases to the main type of
biotope on all experimental plots, for its prediction ability. Thereby, its
transferability was tested between species (25 possibilities for combination).
Chapter 3 – Generality of habitat suitability models
52
3.3 RESULTS
3.3.1
Single species models
The univariate analyses of the ‘landscape’ factors from the digital terrain
and the landscape model (Schröder et al. this issue) resulted in an influence of
slope, solar radiation, disturbance intensity, management, geology and soil type on
the occurrence probability of grasshopper, bush crickets and butterfly species. For
the studied butterfly species, the grasshopper S. lineatus as well as for the bush
cricket M. bicolor inclusion of these parameters in the so far best single species
models did not result in new models as the parameters were eliminated during
stepwise backward procedure (Table 3.1). Only for the bush cricket P. albopunctata
inclusion of the parameter ‘solar radiation’ resulted in new models with lower AIC
than the one presented by Hein et al. (submitted; Table 3.1).
Table 3.1: Single species models with model characteristics (AUC with SE, R 2Nagelkerke, AIC,
AUCbootstrapped after internal validation with backwards variable selection) for the five investigated
species (p < 0.05).
Species
S. lineatus
M. bicolor
P.
albopunctata
C. arcania
Z. carniolica
model parameter
type of biotope
vegetation height (quadratic
term)
suitable habitat in r = 50 m
(i.e. fringes, mesoxerophytic grassland,
extensively managed meadows)
sinus exposition
vegetation height
solar radiation
proportion fringes in r = 75m
time of first management
incidence tree layer
proportion mesoxerophytic &
extensively managed grassland in
r=100m
proportion hedges on suitable
area in radius=25m
Incidence Centaurea jacea
Incidence Scabiosa columbaria
Suitable types of biotope in
radius = 25m
AUC
R
2
Nagelkerke
AIC
AUCbootstrapped
0.762
0.367
115.92
0.735
0.740
0.227
117.94
0.723
0.949
0.712
40.84
0.941
0.94
0.7
92
0.9
0.92
0.6
94
0.911
(i.e. fringes, mesoxerophytic grassland,
extensively managed grassland)
For all three orthoptera species the ‘fringe vegetation’ has the highest
probability of occurrence followed by mesoxerophytic grassland. In addition, low
vegetation height increases occurrence probability of S. lineatus. For P. albopunctata
Chapter 3 – Generality of habitat suitability models
53
habitat suitability decreases with increasing vegetation height, increasing solar
radiation and west faced exposition (Table 3.1).
The occurrence probability of C. arcania is positively influence by late ‘time
of first management’ and an existing ‘tree layer’ (Table 3.1). Additionally, a high
‘proportion of mesoxerophytic grassland and extensively managed meadows in
the radius of 100 m’ and a high ‘proportion of hedges on suitable habitat in the
radius of 25 m’ increase the occurrence probability of the species. The highest
probability of occurrence for Z. carniolica is predicted for areas with a high
proportion of mesoxerophytic grasslands, fringes and extensively managed
meadows (indicated by the inclusion of the variable ‘proportion of suitable habitat
in a radius of 25 meter’). Additionally, the occurrence of Scabiosa columbaria and
Centaurea jacea contributed to the model (Table 3.1).
3.3.2
Test of transferability
For grasshoppers and bush crickets transferability of a single species model
within group was best conducted with the model of S. lineatus including the
variables ‘type of biotope’ and ‘vegetation height’ (Table 3.2). The occurrence of
C. arcania and Z. carniolica is best predicted by the model of Z. carniolica (Table 3.2).
Hereby, the proportion of suitable habitat (fringes, mesoxerophytic grassland,
extensively managed grassland) in a radius of 25 m as well as the occurrence of
the sucking plants Centaurea jacea and Scabiosa columbaria are explaining variables in
the model.
Table 3.2: Results of within group transfers. For orthoptera the model of S. lineatus, including the
variables ‘type of biotope’ and ‘vegetation height’ showed the best transferability (five successful
out of five possible transfers). For butterflies the model of Z. carniolica, including ‘suitable habitat in
r = 25 m’ and occurrence of C. jacea and S. columbaria was best transferable. AUCs significantly
exceeding an AUCkrit = 0.7 are indicated by **, those significantly exceeding an AUCkrit = 0.5 by *.
Original models are marked in grey.
Independent
variables from the
model of species
Species
incidence used
for model
calibration
S. lineatus
S. lineatus
M. bicolor
P. albopunctata
Z. carniolica
Z. carniolica
C. arcania
Species incidence
to be predicted
S. lineatus
M. bicolor
P. albopunctata
M. bicolor
P. albopunctata
Z. carniolica
C. arcania
C. arcania
Model outcome - transferability
AUC
Lower…upper
confidence bounds
0.762*
0.632*
0.870**
0.771*
0.868**
0.919**
0.613*
0.898**
0.677…0.848
0.525…0.739
0.794…0.946
0.68…0.862
0.788…0.948
0.870…0.969
0.507…0.719
0.837…0.960
In the between group transfer the model of Z. carniolica performs slightly
better than the one from S. lineatus (one more successful transfer; Table 3.3). The
independent variable ‘type of biotope’ showed the highest transferability to all
species, with 100 % (=25/25) successful transfers (Table 3.4).
Chapter 3 – Generality of habitat suitability models
54
Table 3.3: Results of between group transfer with the butterfly ‘group’ model (Z. carniolica). AUCs
significantly exceeding an AUCkrit = 0.7 are indicated by **, those significantly exceeding an AUCkrit
= 0.5 by *. Original models are marked in grey.
Independent
variables from
the model of
species
Species
incidence used
for model
calibration
Z. carniolica
Z. carniolica
S. lineatus
M. bicolor
P. albopunctata
Species incidence
to be predicted
Z. carniolica
S. lineatus
M. bicolor
P. albopunctata
S. lineatus
M. bicolor
P. albopunctata
Model outcome - transferability
AUC
Lower…upper
confidence bounds
0.919**
0.760*
0.766*
0.738*
0.775*
0.747*
0.787*
0.870…0.969
0.681…0.839
0.681…0.839
0.650…0.826
0.688…0.862
0.659…0.835
0.674…0.9
Table 3.4: Results of tests of transferability with the independent variable ‘type of biotope’ from
15m x 15m experimental plots. AUC significantly exceeding an AUCkrit = 0.7 are indicated by **,
those significantly exceeding an AUCkrit = 0.5 by *. Models representing a single species model are
marked in grey.
Independent
variable
Species incidence
used for model
calibration
type of biotope
S. lineatus
type of biotope
M. bicolor
type of biotope
P. albopunctata
type of biotope
Z. carniolica
type of biotope
C. arcania
Species incidence to be
predicted
S. lineatus
M. bicolor
P. albopunctata
Z. carniolica
C. arcania
S. lineatus
M. bicolor
P. albopunctata
Z. carniolica
C. arcania
S. lineatus
M. bicolor
P. albopunctata
Z. carniolica
C. arcania
S. lineatus
M. bicolor
P. albopunctata
Z. carniolica
C. arcania
S. lineatus
M. bicolor
P. albopunctata
Z. carniolica
C. arcania
Model outcome transferability
Lower…upper
confidence
AUC
bounds
0.846**
0.802**
0.846**
0.803**
0.711*
0.842**
0.806**
0.842**
0.807**
0.736*
0.807**
0.766*
0.855**
0.777*
0.678*
0.847**
0.803**
0.840**
0.82**
0.745*
0.773*
0.762*
0.832**
0.770*
0.811**
0.787…0.904
0.734…0.870
0.769…0.922
0.727…0.880
0.624…0.799
0.780…0.904
0.739…0.873
0.762…0.922
0.734…0.879
0.650…0.821
0.740…0.873
0.693…0.839
0.785…0.925
0.696…0.859
0.589…0.766
0.785…0.910
0.73…0.877
0.773…0.906
0.754…0.887
0.659…0.831
0.692…0.854
0.68…0.845
0.756…0.907
0.696…0.844
0.741…0.880
Chapter 3 – Generality of habitat suitability models
55
3.4 DISCUSSION
3.4.1
Single species models
The univariate logistic regression analyses with the ‘landscape’ parameters
derived from the digital terrain and landscape model (Schröder et al. this issue)
resulted in significant models for all species. But none of these parameters is
included in the multiple parameter models for S. lineatus, M. bicolor, Z. carniolica and
C. arcania. Only the occurrence probability of P. albopunctata increased significantly
with the inclusion of the factor solar radiation in the multiple parameter logistic
regression models. This is in agreement with the high temperature requirements
of this species for egg and larval development. Based on this results one may
conclude that disturbance factors like disturbance frequency or intensity have no
influence on habitat suitability for our species. We consider this a very unlikely
situation. Especially, adult butterflies are sensitive to changes in temperature, light
and humidity levels, parameters that are often affected by habitat disturbance
(Wood & Pullin 2002). We assume that this result is better explained by the fact
that other factors already included in the models contain the effects of
disturbance or other ‘landscape’ parameters. For example the type of biotope may
be such a factor, e.g. extensively and intensively managed meadows differ in the
degree of management frequency.
The proportion of suitable habitat (fringes, mesoxerophytic and
extensively managed grasslands) in a radius of 25 m around the experimental plots
influences occurrence probability of butterflies. This circle covers an area twice as
large as the experimental plot size of 30 m x 30 m, but within the natural size of
fringes and mesoxerophytic grasslands in the nature reserve ‘Hohe Wann’. This
might stem from the fact that based on their high mobility habitat selection of
butterflies takes place at a larger scale than the experimental plot size. On the
other hand, the ‘type of biotope’ at our experimental plot should be highly
correlated with the surrounding in 25 m, so that we could trace our result back to
a technical reason. ‘Type of biotope’ represents a categorial variable in contrast to
the metric variable ‘suitable habitat in r = 25 m’, which results in a decrease in
degrees of freedom and thus increased model performance. This fact may also
contribute to the exclusion of the ‘type of biotope’ and inclusion of the
proportion of ‘suitable habitat’ in radii of 50 and 75 m in the model of M. bicolor
respectively P. albopunctata as well. We can not easily differentiate between a
technical failure or a real influence of the surrounding. Nevertheless, the
composition of nearby habitats in the surrounding landscape will also affect the
suitability of local habitat patches and thus population viability. For example,
insects may utilise multiple habitat types during their life cycle (Noss 1990,
Dunning et al. 1992, Villard et al. 1999, Sönderström & Pärt 2000) or they need to
Chapter 3 – Generality of habitat suitability models
56
disperse between habitat patches. This depends on the quality of habitats in the
surrounding landscape as well (Bennett et al. 1994, Roland et al. 2000).
3.4.2
Test of transferability
Models based on the incidence of C. arcania can not be applied to other
species. Additionally, the incidence of C. arcania can only be badly predicted by
other species’ occurrences. This may be due to the comparatively euryoceous
properties of C. arcania. C. arcania is mesophilic, e.g. its habitats cover a broad
range of types of biotopes. Habitats suitable for Z. carniolica and the investigated
grasshopper and bush cricket species only cover a small part of habitat suitable
for C. arcania. Vice versa only a small number of C. arcania habitats are suitable for
the other species.
Occurrence probabilities of all five species studied can be best predicted
with the model of the moth Z. carniolica. This species may thus act as
representative or ‘umbrella species’ for others. However, this needs further
investigations. In the models the variable ‘type of biotope’, vegetation height and
the occurrence of the sucking plants C. jacea and S. columbaria play an important
role. Thereby the two plant species may not directly influence grasshoppers and
bush crickets but maybe they are indicators for extensively managed areas, which
are preferred by the species.
Generally, stenoecious but mobile species may be best suited as ‘umbrella’
species (New 1995). They exactly represent habitat requirements of species
typically found in specific habitats and can reach all potential habitats better than
species with low mobility. Although one would expect that based on their higher
mobility the moth Z. carniolica is much better suited as representative species for
xerothermophilic species living on semi arid grasslands, in our case the
transferability of the model of the moth Z. carniolica did only differ slightly (in one
more successful transfer) from the one of the grasshopper S. lineatus. This may be
due to the fact that mobility is not a decisive factor for the survival of this species
in the nature reserve ‘Hohe Wann’, as connectivity is probably very high for S.
lineatus. To really quantify this argument a landscape wide mapping of S. lineatus
for the determination of connectivity would be necessary. Alternatively, one could
test and compare the predictive quality of both models in other landscapes.
In general, the parameter ‘type of biotope’ showed a high potential for the
prediction of species occurrence. Particularly, the models of the grasshopper
S. lineatus, the bush cricket M. bicolor as well as the moth Z. carniolica can predict
occurrence of the other species very well. This result may be of outstanding
practical value, particularly in conservation biology, as the ‘type of biotope’ can be
relatively easy determined in contrast to costly analyses of disturbance or
vegetation structure and composition. Additionally, information on the type of
biotope already exists for many regions of conservational interest. The type of
biotope seems to reflect an aggregation of different parameters ranging from
geological prerequisites (i.e. soils, slope) to anthropogenic influences
Chapter 3 – Generality of habitat suitability models
57
(disturbance). Most importantly, it obviously also represents the perception of the
environment by insect species. With reference to the MOSAIK project a
prediction of the kind of type of biotope resulting from different management
regime, e.g. by botanical analyses (Fritsch et al. this issue, Kühner & Kleyer this
issue), would allow for a classification of suitable and unsuitable areas for
threatened species according to the type of management.
Chapter 4
Movement patterns of Platycleis
albopunctata in different types of habitat:
matrix is not always matrix
with Julia Gombert, Thomas Hovestadt and Hans-Joachim Poethke
ECOLOGICAL ENTOMOLOGY (2003) 28, 432-438
Abstract. Inter-patch movement is usually assumed to be
homogeneous across a landscape. As the intervening area between
suitable patches is usually richly textured, it cannot be assumed to be
uniform in real landscapes. In an experimental mark-and-re-sight
study, the movement behaviour of the bush cricket Platycleis
albopunctata in four habitat types as well as at the border between two
of these habitat types was observed. Analysis of recapture data
indicated differences in mortality risk (or emigration rates) between
habitat types. When released at the border between suitable habitat
and a crop field, Platycleis albopunctata did not show a consistent
preference for the suitable habitat. This suggests that the crop field is
at least temporarily attractive for Platycleis albopunctata. Movement in
suitable habitat was not always different from movement in the
matrix, and movement between different types of matrix also
differed. The results indicate that the movement behaviour of
Platycleis albopunctata is influenced not only by suitability for breeding
but also by structural resistance as well as other factors (e.g. food
availability or habitat-specific mortality risk).
Chapter 4 – Movement patterns of Platycleis albopunctata
61
4.1 INTRODUCTION
The progressive exploitation of landscapes leaves many (insect)
populations fragmented into metapopulations (i.e. small local populations not
capable of long-term survival without interactions with neighbouring populations;
Ehrlich et al. 1980, Hanski 1994a, b, Thomas & Hanski 1997). In such cases, the
balance between extinction and colonisation of vacant patches determines the
regional survival of species (Levins 1969, Hanski 1994a, b, Hanski & Thomas
1994, Hill et al. 1996). Inter-patch dispersal of individuals is the key factor
determining colonisation probability and has been investigated intensively in
theory (Hanski 1994a, b, Hanski & Thomas 1994, Gustafson & Gardner 1996,
Hill et al. 1996, Hovestadt et al. 2000, 2001, Poethke & Hovestadt 2002) and
practice (butterflies: Baguette & Nève 1994, Hanski et al. 1994, Hill et al. 1996,
Kuussaari et al. 1996, Nève et al. 1996, Brommer & Fred 1999, Conradt et al. 2000,
Roland et al. 2000, Matter & Roland 2002, Menéndez et al. 2002, beetles: Crist et al.
1992, Wiens et al. 1993, 1997, With et al. 1999, Roslin 2000, crickets: Riegert et al.
1954, Baldwin et al. 1958, Kindvall et al. 1998, Kindvall 1999).
Euclidian distance between suitable habitat patches and the species specific
dispersal capacity are usually assumed to determine colonisation probability (Hanski
1994a, b). In general, movement of individuals in the matrix is supposed to be
random or linear, but at least homogeneous across a landscape (Hanski 1994a, b,
With & King 1999, Zollner & Lima 1999b). This is a simplifying but unavoidable
assumption (Franklin 1993) because only few detailed studies of movement
behaviour of insect species in unsuitable habitat are available. A small number of
empirical studies has shown that movement behaviour differs between habitat
and matrix (Wiens et al. 1997, Schultz 1998, Kindvall 1999). In these studies,
movement has been investigated in only two habitat types, however landscapes,
especially those dominated by anthropogenic influences, are often mosaics of a
broad variety of habitat types (Kindvall 1999, Wiens 2001). Ricketts (2001)
investigated the resistance of three habitat types for butterfly dispersal using a
mark-recapture study, however there are very few data on how insects move in
such heterogeneous landscapes. This may be due, in part, to the fact that
individuals are hard to follow over longer distances (Gaines & Bertness 1993,
Zollner & Lima 1999a). Knowledge of movement rules could be used in
simulation models to generate more accurate predictions on the species-specific
dispersal capacity and thus on survival probabilities of species in complex
landscapes (Kindvall 1999).
In the work reported here, the movement behaviour of the bush cricket
species Platycleis albopunctata was investigated in four habitat types. One of these
areas was considered to represent suitable habitat allowing for successful
reproduction for the species, the other three areas were assumed to be unsuitable
Chapter 4 – Movement patterns of Platycleis albopunctata
62
for this purpose (according to, for example, Harz 1969, Detzel 1998). Individually
marked adult P. albopunctata were released in these areas and their movement
behaviour was recorded and analysed to evaluate whether individuals
differentiated between the habitats, whether movement behaviour differed
between suitable and unsuitable (matrix) habitats, and whether movement
behaviour differed between unsuitable (matrix) habitats. Generally, dispersal by
flight cannot be excluded for P. albopunctata, however this is not documented and
methodologically it is nearly impossible to investigate. Thus, this study was
confined to dispersal on foot.
4.2 MATERIAL AND METHODS
4.2.1
The species
The grey bush cricket Platycleis albopunctata (Goeze) (Orthoptera,
Tettigoniidae) is a medium-sized to large bush cricket species (18–22 mm). It is
classified as a thermo- and xero-philic species (Harz 1969, Ingrisch & Köhler
1998), which inhabits dry locations, especially dry grassland as well as habitats
with similar structure (Detzel 1998). Open soil, sparse vegetation, and fringes are
preferred habitat elements.
The grey bush cricket is assumed to be a relatively mobile species (Detzel
1998). A mark-recapture study by Walter (1992) showed that most of the marked
individuals were recaptured in a radius of 50 m, but some individuals moved
> 100 m in a single day. Flight studies in a wind channel demonstrated the
capability of some individuals to fly continuously for ≥ 15 min. This flight time
would correspond to distances between 2 and 3 km (Gottschalk 1997), but
observations of flying individuals are not documented in the literature.
4.2.2
Field work
The study was conducted in July and August 2001 in the Hohe Wann
nature reserve in Northern Bavaria, Germany (50° 03′ N, 10° 35′ E). The study
area is characterised by a patchwork of vegetation caused by the geological and
geomorphological heterogeneity of the area and a variety of small-scale
microclimatic differences due to different exposure and inclination. Agricultural
fields are usually very small. The most obvious characteristic of the nature reserve
is mesoxerophytic grassland (Elsner 1994).
For all experiments, individuals of Platycleis albopunctata were marked
individually with a coloured point code on the pronotum and a numbered
reflective tape was fixed on the femur (cf. Heller & von Helversen 1990, Samietz
& Berger 1997, Kindvall 1999). This technique allows easy detection of
63
Chapter 4 – Movement patterns of Platycleis albopunctata
individuals at night (using a head lamp) and produces very high re-sight rates (>
80%; Heller & von Helversen 1990, Kindvall 1999). Before and after marking,
individuals were kept in a cooling box to reduce potential handling trauma
(Stettmer 1996).
4.2.3
Re-sight rates
Re-sight rates make it possible to estimate mortality risk, detection error,
and quality of the marking method. In addition to the findings about individuals,
findings of lost reflective tape were recorded. Tape could either be lost
accidentally or be left by predators feeding on crickets. Individuals that were not
found on one day, but re-sighted on one of the following days (discontinuously
found individuals), were probably overlooked and allow an estimation of the
detection error during the experiment. Individuals that were lost until the end of
the observation period, were assumed to be either dead or to have emigrated.
4.2.4
Movement behaviour depending on habitat type
In order to test whether the movement behaviour of P. albopunctata varies
with habitat type, a mesoxerophytic grassland (4000 m2) representing a suitable
breeding habitat and three sites considered unsuitable (Detzel 1998) were selected
as experimental sites: (1) a bare, harvested crop field (40900 m2), (2) fallow land (a
former crop field lying fallow for 2 years) (22500 m2), and (3) a rich meadow
(15800 m2). In all three cases, there were no suitable habitats in the near vicinity
(closer than 400 m). Vegetation structure parameters and abiotic factors
characterising the four experimental areas were measured in eight 1 x 1 m census
plots along two transects across each of the experimental areas (Table 4.1).
Vegetation structure estimates were measured according to Sundermeier (1999).
Table 4.1 Characteristic vegetation-structure and abiotic parameters on the four experimental
areas. Mean values ± SD of eight censuses for each experimental site are shown.
Type
Mesoxerophytic
grassland
Harvested crop
field
Rich meadow
Fallow land
Exposure
Inclination
(°)
Vegetation
height (cm)
Total vegetation
cover (vertical)
(%)
Vegetation cover
(horizontal) up to
10 cm (%)
S
33 ± 24
29 ± 13
97 ± 3
58 ± 26
none
0
0
48 ± 19
0.6 ± 0.4
NE
SE
14 ± 4
13 ± 10
11 ± 3
12 ± 6
90 ± 5
81 ± 12
83 ± 8
60 ± 16
In each area, 46 individuals of Platycleis albopunctata (23 males, 23 females)
were released at randomly chosen points within 6 m of the centre of the
experimental area. Individuals were released in the afternoon, singly, next to
numbered wooden sticks. In the following 6 nights, the position of each resighted individual was recorded. A 24-h observation interval was chosen because
Chapter 4 – Movement patterns of Platycleis albopunctata
64
bush crickets and grasshoppers are known to be stationary most of the time and
to move only occasionally between different sites on a small spatial scale (With
1994a).
Comparison of daily movement distances among the 6 observation days
showed that distances covered on the first day in the three unsuitable habitats
were significantly shorter than on the following days (Wilkoxon matched paired
test, p < 0.05). This is probably due to the fact that individuals did not have a
24 h time interval between release and observation on the first day compared with
the following observation days. The data from the first day were therefore
excluded from further analysis.
4.2.5
Edge-mediated behaviour
In order to investigate whether individuals showed a preference for
suitable habitat, 50 individuals (25 males, 25 females) were released directly along
the border between a harvested crop field and a mesoxerophytic grassland
(suitable habitat). The distance between the release points was 1 m. Release points
were marked with numbered wooden sticks. In the following 2 nights, it was
recorded whether individuals moved into the mesoxerophytic grassland, into the
crop field, or stayed at the border (within a 1 m corridor along the edge). From
these values, edge permeability P(out) was calculated as the proportion of observed
individuals that moved into the crop field (Kindvall 1999). To account for the
potential influence of handling on behaviour immediately after release, data were
recorded on the 2 consecutive nights after the release of individuals in late
afternoon.
For all experiments, individuals of Platycleis albopunctata were caught in
habitats ≥ 850 m from the experimental sites, in order to equalise the treatment
effect of removal in the four different habitat types. All individuals involved in
this study were captured in the field on the day of the experiment.
4.2.6
Statistical analyses
Data analysis was complicated by the fact that not all individuals were
found continuously in the 6 observation nights. This resulted in an incomplete
capture history and left three different possibilities for data analysis: (1) The most
conservative decision would be to take only the data from individuals found
(continuously) throughout the whole period. This would have the advantage of
analysing only unambiguously documented data without making assumptions
about the days missing in the record; however this approach reduces the amount
of data available for interpretation severely. Only the data from the first 4 days
would provide sufficiently high sample numbers to merit statistical analysis. (2)
Another way to analyse the data would have been to replace missing data by
calculated mean values. In that case, all measured data could be used for the
analysis, however this may lead to a wrong interpretation und underestimation of
Chapter 4 – Movement patterns of Platycleis albopunctata
65
true values, especially in the case of daily movement distances. (3) The alternative
chosen was to use all data resulting from measurements during 2 consecutive
days, even if the record was previously interrupted. In this case, the sample size
was sufficiently high and the risk of underestimating daily movement distances
could be avoided.
All data were tested for normal distribution and variance homogeneity
using the Kolmogorov-Smirnov and Levene tests. As most of the data were not
distributed normally, Kruskal-Wallis H tests were needed for comparison of more
than two independent samples, and Mann-Whitney U tests for two independent
samples were applied. The Kolmogorov-Smirnov Z test was used for comparison
of frequency distributions of daily movement distances on the four experimental
areas. As the Kolmogorov-Smirnov Z test is a test for two samples, the results for
multiple comparisons were corrected by applying the sequential Bonferroni
method (Rice 1989). Daily movement distances on one experimental area were
tested for variation between days using a Friedmann test and Wilkoxon matched
paired tests as data were not independent of each other. The Rayleigh test was
used to test for a uniform distribution of circular data. Rayleigh's uniformity test
calculates the probability of the null hypothesis that the data are distributed in a
uniform manner.
4.3 RESULTS
4.3.1
Re-sight rates
The fate of marked individuals and markings over time on the four
experimental sites are shown in Figure 4.1. Mean re-sight rates were highest for
the crop field and the mesoxerophytic grassland where day-to-day re-sight rates
were > 80% during the whole experimental period. In the two other experimental
areas, the loss of individuals (mortality and/or emigration) was higher, but day-today re-sight rates were still > 75% on the fallow land and > 60% on the rich
meadow. Re-sight rates during the edge experiment were > 80% on both
observation days. The proportion of individuals that was not re-sighted
continuously remained nearly constant (between 4 and 9%) during the
observation period and for all experimental sites.
66
(a)
(b)
Percentage of released individuals
Percentage of released individuals
Chapter 4 – Movement patterns of Platycleis albopunctata
100
80
60
40
20
0
3
4
5
6
Observation day
(c)
Percentage of released individuals
2
80
60
40
20
0
1
2
3
4
80
60
40
20
0
1
5
6
Observation day
2
3
4
5
6
5
6
Observation day
(d)
100
Percentage of resleased individuals
1
100
100
80
60
40
20
0
1
2
3
4
Observation day
Figure 4.1: Re-sight rates (light grey), findings of separated reflective tape (black),
missing individuals found on following days (dark grey), and permanently lost
individuals (white) on each observation day for (a) harvested crop field, (b) fallow
land, (c) rich meadow, and (d) suitable habitat (mesoxerophytic grassland). The
number of released individuals was n = 46 (= 100%) on all sites.
4.3.2
Movement behaviour depending on habitat type
The experiment was designed to document the movement behaviour of P.
albopunctata in different habitat types. First, the general orientation of individuals
on the four experimental areas was analysed. A preferred direction could not be
detected for any site (Rayleigh test, p > 0.05).
Frequency distributions of daily movement distances on each experimental
area showed that crickets moved only short distances in mesoxerophytic grassland
(suitable habitat). Only once did an individual move > 40 m (n = 137), and 78%
of the observed daily movement distances were < 10 m. For the other three
experimental areas, daily movement distances > 40 m were observed 19 times
(n = 276) and only 67% of the observed daily movement distances were < 10 m.
Thus, the variation in daily movement distances was higher outside the habitat
and long-distance movements were observed more frequently (Fishers Exact test,
p < 0.01). The maximum distance moved on 1 day was 136.7 m by a male on the
crop field.
67
Chapter 4 – Movement patterns of Platycleis albopunctata
Mean daily distance (m)
To compare daily movement distances on the four habitats, a mean value
for each individual was calculated, resulting in independent values for each area
(see also Haddad 1999). These calculated mean values of daily movement
distances in the mesoxerophytic grassland and the rich meadow were significantly
shorter than those in the crop field and the fallow land (Mann Whitney U test, p
< 0.05; Figure 4.2). The distance between release point and last re-sight point, i.e.
the net displacement (Turchin 1998) of individuals, did not differ significantly in
the four experimental areas (Kruskal-Wallis H test, p > 0.05).
60
40
a
a
b
b
20
0
Crop f ield
Fallow land
Rich meadow
Suitable habitat
Experimental area
Figure 4.2: Daily movement distances (median with 25% and
75% quartiles, minimum, and maximum) for the four
experimental areas. Significant differences are indicated by
different letters in the box-and-whisker plots (Kruskal-Wallis H
test, p < 0.05; Mann-Whitney U test, p < 0.05).
4.3.3
Edge-mediated behaviour
In the edge experiment, 20 crickets (48% of the re-sighted individuals)
turned into the mesoxerophytic grassland during the first day. Three crickets
(7% of re-sighted individuals) were found on the crop field (resulting edgepermeability P(out) = 0.143) and 19 crickets (45% of re-sighted individuals) stayed
at the border. Thus crickets showed a significant preference for the
mesoxerophytic grassland on the first observation day (Chi Square test,
p < 0.001).
On the second day, six additional crickets (14% of re-sighted individuals)
were found in the grassland habitat, however of six crickets (14% of re-sighted
individuals) that were found in the grassland on the first day, three went into the
crop field and three returned to the border. Thus, 16 individuals (47% of resighted individuals) were found in mesoxerophytic grassland, eight individuals
(24% of re-sighted individuals) stayed at the border, and 10 crickets (29% of resighted individuals) turned into the crop field at the end of the second day (edge
Chapter 4 – Movement patterns of Platycleis albopunctata
68
permeability P(out) = 0.558). On this day, no significant preference of the crickets
for the grassland could be detected (Chi Square test, p > 0.05).
4.4 DISCUSSION
4.4.1
Re-sight rates
The detailed analysis of re-sight data shows that mortality and emigration
rate on the suitable habitat, i.e. mesoxerophytic grassland, are low (see Heller &
von Helversen 1990, Walter 1992, Gottschalk 1997, Samietz & Berger 1997,
Kindvall et al. 1998, Opitz et al. 1998, Kindvall 1999). Less expected, this also
applies to the data from the harvested crop field. This may be explained by the
combination of good camouflage of individuals, low predation risk, and good
availability of food, resulting in temporary suitability of this area (see below). On
the other two habitat types (the rich meadow and the fallow land), mortality (or
emigration) was higher. This confirms the classification of these habitats as less
suitable areas although movement is short on the rich meadow. As more
separated reflective tapes were found on these sites (often still attached to a leg),
higher mortality seems to be responsible at least in part for reduced re-sight rates.
This may result from the seemingly high abundance of spiders on these sites
(Hein & Gombert pers. obs.) and the fact that the grey bush crickets are probably
not well camouflaged in such green environments.
4.4.2
Movement behaviour
Generally, individuals are expected to stay in favourable habitats (low
movement speed, large turning angles, low straightness of movement), leave
hostile and unprofitable habitats as fast as possible (high movement speed, small
turning angles, straight movement; Baars 1979, Coyne et al. 1982, Zalucki &
Kitching 1982, Turchin 1991, Kindvall 1999) and turn back into their habitat
when they reach a border (low edge permeability).
When released at a border, individuals of Platycleis albopunctata showed a
clear preference for the mesoxerophytic grassland on the first observation day.
On the second day, however no significant preference for the mesoxerophytic
grassland could be detected, and six of the 20 individuals that had moved into the
grassland on the first day, moved into the crop field or returned to the border,
suggesting that the crickets did not differentiate strongly between the two habitat
types. This can have two explanations, either handling induced a shock and
individuals tried to hide in high vegetation (also indicated by the high number of
individuals that stayed at the border) or crickets in fact recognised the
mesoxerophytic grassland as suitable habitat and thus turned into it.
Chapter 4 – Movement patterns of Platycleis albopunctata
69
Movement distances on the crop field were longer than in mesoxerophytic
grassland. However, individuals did not leave the field, even though the
vegetation on the harvested crop field was very sparse and spatial resistance
should consequently be low. One reason for this could be that a lot of scattered
grain, on which crickets were feeding, could be found on the crop field. In
combination with the low mortality risk and the behaviour at the edge, these
observations suggest that for Platycleis albopunctata the crop field might have been a
temporarily suitable habitat and P. albopunctata had no motivation to leave the crop
field.
Individuals of Platycleis albopunctata are not normally found on harvested
crop fields, however. Presumably, harvested crop fields are sinks for
P. albopunctata populations, which become extinct as soon as the fields are
ploughed. If, on the other hand, the handling effect produced this result, all
experimental observations on grasshopper and bush cricket movement should be
judged carefully.
For the other two sites (rich meadow and fallow land), a difference in
frequency distributions of movement distances could be shown. Although the
majority of individuals moved short distances in all types of habitat, the
proportion of individuals that moved long distances was higher in the rich
meadow and the fallow land. Thus, for these two habitats, the theoretical
assumption that movement speed is higher in unsuitable habitat can be
confirmed.
Dispersal rates and distances are the key factors that determine
colonisation rates of suitable patches, influence patch extinction rates, and
ultimately determine the fate of local populations and the survival of
metapopulations (e.g. Opdam 1990, Hansson 1991, Wiens 2001). From the results
of this study, it can be concluded that matrix movement behaviour is not uniform
across different types of matrix and is not necessarily different from movement
behaviour within suitable habitat. Not only habitat suitability but also other
factors like vegetation structure as well as predation risk and food availability are
likely to determine the movement behaviour of P. albopunctata.
The results of this study indicate that the classical patch-matrix view may
be too simple to describe bush cricket behaviour in a heterogeneous landscape.
Species-specific dispersal capacity will depend not only on the structural resistance
of different types of matrix but also on habitat-specific mortality, food availability,
and edge permeability among different types of habitat. As these factors are
distributed heterogeneously in a landscape, species-specific dispersal capacity will
also vary (see also Ricketts 2001). This interpretation of the results of this study is
supported by the findings of Roland et al. (2000) and Ricketts (2001). Their
observations on the exchange of butterflies between meadows demonstrate
clearly that connectivity of patches is influenced mainly by the kind of intervening
matrix (see also Crist et al. 1992, Butterweck 1998, Wiens, 2001). There is also
evidence from empirical studies (Hamazaki 1996) and theoretical investigations
Chapter 4 – Movement patterns of Platycleis albopunctata
70
(Pfenning et al. submitted) that the shape and configuration of habitat patches
influence connectivity. Thus, it can be concluded that distance is not necessarily
sufficient to define the connectivity of patches.
Chapter 5
Computer-generated null-models as an
approach to detect perceptual range in markre-sight studies – an example with
grasshoppers
with Hans-Joachim Poethke and Thomas Hovestadt
SUBMITTED TO ECOLOGICAL ENTOMOLOGY
Abstract. Dispersal and habitat detection are key factors for the
colonization of habitat fragments in heterogeneous landscapes. The
ability to recognize habitat from a certain distance should increase
survival chances of a dispersing individual. However, due to
methodological problems there is little information on the perceptual
range of most species. In a field experiment we released 44
individually marked individuals of the grasshopper species Oedipoda
caerulescens (Orthoptera: Acrididae: Locustinae) into unfamiliar, hostile
environment at various distances from an adjacent patch of preferred
habitat. We measured whether individuals reached the habitat or not,
as well as the daily movement distances. The number of individuals,
that reached the habitat, was tested against computer-generated
predictions with different underlying rules for the movement
behaviour of individuals but without the ability to detect habitat from
distance. On the first day a significantly higher proportion of
grasshoppers arrived in the habitat than predicted by any of the nullmodels. We conclude that individuals of Oedipoda caerulescens are able
to detect their preferred habitat from a distance. Edge permeability
was very low as none of the individuals left the habitat once they had
reached it. Additional analyses showed that individuals changed
movement behaviour from a directed walk with great daily distances
in unsuitable habitat to a walk with significantly shorter daily
distances within preferred habitat. We discuss the problems that arose
in our field experiment and give recommendations for further studies.
Chapter 5 – Detection of perceptual range
73
5.1 INTRODUCTION
Extinction and colonization of patches are the two key factors that
determine survival of metapopulations (Hanski 1994a, b, Ovaskainen & Hanski
2001). Inter-patch dispersal, i.e. movements of individuals between discrete
patches of preferred habitat, is responsible for the (re-) colonization of patches
and is therefore a critical feature in any kind of metapopulation model (Hanski
1994a, b, Hanski & Thomas 1994, Gustafson & Gardner 1996, Hill et al. 1996,
Hovestadt et al. 2000, Hovestadt et al. 2001, Poethke & Hovestadt 2002). Usually,
such models use simplifying approximations to describe the dispersal process e.g.
a negative exponential function for dispersal distances (i.e. Hanski 1994a, b,
Nieminen 1996), constant dispersal rates (Comins et al. 1980, Hanski 1994a, b,
Gandon & Michalakis 1999, 2001, Travis et al. 1999), global dispersal (Comins et
al. 1980, Gandon & Michalakis 1999, 2001, Ronce et al. 2000, Metz & Gyllenberg
2001), because little empirical information on (long-distance) dispersal events is
available (cf. Clark 1998, Clark et al. 1998). To understand how organisms survive
in fragmented landscapes we need to know how the landscape structure and the
behavioural rules of a species interact (Goodwin et al. 1999) to ‘generate’ the
distribution of dispersing individuals across any given landscape (Johnson et al.
1992, Wiens et al. 1993, With 1994, Crist & Wiens 1995, Ims 1995, Goodwin et al.
1999, Kindvall 1999, Zollner & Lima 1997, 1999). In recent years numerous
studies have focused on movement behaviour within and outside preferred
habitat. It has been shown that some species change their movement behaviour
when moving in different types of habitat (Baars 1979, Coyne et al. 1982, Zalucki
& Kitching 1982, Wiens et al. 1997, Schultz 1998, Kindvall 1999, Martin et al.
2001, Hein et al. 2003). Additionally, the edge-permeability, i.e. the probability to
leave a patch, has been investigated, especially in studies examining corridors as a
means to promote habitat connectivity (Schultz 1998, Kindvall 1999, Schultz &
Crone 2001, Hein et al. 2003). All these studies imply that species move differently
inside than outside preferred habitat, i.e. when in the matrix. However, the ability
to locate suitable habitat from a distance has only rarely been investigated, mostly
for vertebrates (Yeomans 1995, Zollner & Lima 1997, Gillis & Nams 1998,
Lidicker 1999, Zollner 2000, Mech & Zollner 2002) but sometimes also for
arthropods (Root & Kareiva 1984, Kareiva 1985, Fahrig & Paloheimo 1987,
Nottingham 1988, Capman et al. 1990, Schooley & Wiens 2003). Zollner & Lima
(1997, 1999) call the distance from which an individual can detect suitable habitat
its ‘perceptual range’. A large perceptual range should improve the dispersal
success of individuals in unfamiliar or hostile landscapes. If so, we would also
need to adjust our measures of landscape connectivity (Fahrig 1988, Turner et al.
1993, Zollner & Lima 1997, 1999, Moilanen & Hanski 2001).
Chapter 5 – Detection of perceptual range
74
To study movement mark and recapture studies as well as the direct
observation by pursuit of individuals have been tested as methods for insects,
mainly for butterflies (Turchin 1998). As our work is based on studies on the
movement behaviour of grasshoppers and bush crickets we (and probably other
scientists investigating other taxa as well) are confronted with some problems if
we try to directly follow individuals on their path. First, grasshoppers and bush
crickets spend most of their time roosting in the sun without moving (Ingrisch &
Köhler 1998, Hein & Gombert pers. obs.). Thus, to collect direct observations of
moving individuals a substantial investment of time would be necessary. But even
if one accepts this disadvantage another problem is the relatively small size of
individuals requiring a short distance between observer and individual. An
observer is thus likely to influence movement direction, speed or other aspects of
an individuals’ behaviour. Nevertheless, information about movement behaviour
can be gained by mark and recapture studies with controls in greater time
intervals. For grasshoppers and bush crickets reflective tape markings make high
nocturnal re-sight probabilities possible (Heller & von Helversen 1990). However,
with this method we loose detailed information about consecutive steps and a
detailed movement analyses suggested by Turchin (1998) is thus not possible. In
this study we present an approach to extract movement parameters from (daily)
mark- and recapture-data to detect perceptual range by comparing observed data
on arrival in habitat with computer generated data based on different hypothesis
about the underlying movement behaviour.
In a field experiment we investigated the behaviour of the blue-winged
grasshopper O. caerulescens near a border between a preferred habitat (inhabited by
large number of individuals of O. caerulescens) and an unsuitable habitat. In order to
simulate the dispersal situation (see Conradt et al. 2000, Conradt et al. 2001,
Harrison 1989, Zollner & Lima 1997, 1999, Schooley & Wiens 2003) we released
individually marked adult O. caerulescens at certain distances from the habitat patch
in the adjacent matrix and recorded their movement behaviour as well as the
number of individuals arriving in the habitat. Data from the field experiment were
used to parameterize the null-models. Following the recommendation of
Goodwin et al. (1999) we implemented these different models into individual
based computer simulations and generated frequency distributions for the
number of individuals arriving in the habitat patch for each of the different nullmodels (all supposing a perceptual range of zero). The observed numbers of
arriving individuals are then compared to the numbers generated by the different
null-models.
Chapter 5 – Detection of perceptual range
75
5.2 MATERIALS AND METHODS
5.2.1
Field experiment
The species
Oedipoda caerulescens (Orthoptera: Acrididae: Locustinae), the blue-winged
grasshopper, is a medium sized to large grasshopper species (15-28 mm, Bellmann
1993, Detzel 1998). Generally, O. caerulescens is classified as xero-thermophil. The
species inhabits stony calcareous meadows with sparse vegetation, quarries and
sand pits. Imagines are considered to be geophilic, inhabit open field and live on
the ground. Larvae, however, are also found in dense vegetation.
Individuals usually move on the ground. If disturbed O. caerulescens typically
fly a few meters (3 to 6 m) in half circles downhill. It is noticeable that individuals
always land on an open place again. Both sexes fly spontaneously to locate mates.
Very seldom flights of up to 100 m have been observed (Detzel 1998).
Field work
The study was conducted in August 2001 on the hillsides of Costermano
(province of Verona, Northern Italy). The experiment took place on a meadow
(in the following called the matrix), with very high vegetation cover (99%) and a
mean vegetation height of 35 cm, close to a habitat patch. In the surrounding of
Lake Garda such meadows are typical intervening habitat structures which
separate patches of habitat from each other. The adjacent, elongated habitat patch
was characterized by its southern exposition, strong inclination, sparse vegetation
(20% vegetation cover) and consequently a lot of bare soil (80%). The suitability
of this patch for O. caerulescens was indicated by the large number of individuals of
O. caerulescens that were living on it. We have chosen explicitly this release situation
with the non-habitat above the habitat to avoid that individuals only react on the
release situation and instinctively walk uphill to compensate eventual escape
flights (before capture) and to exclude visual detection of habitat.
To determine the ability of O. caerulescens to detect habitat we collected 21
males and 23 females from patches of suitable habitat at least 200 m away from
the experimental area. The grasshoppers were individually marked with a coloured
point code on the pronotum and numbered reflective tapes on the femur (Heller
& von Helversen 1990, Samietz & Berger 1997, Kindvall 1999, Hein et al. 2003).
This technique makes it easy to detect individuals at night and has been shown to
lead to very high re-sight rates (Heller & von Helversen 1990, Kindvall 1999).
Before and after marking individuals were kept in a cooling box to reduce
potential handling trauma (Stettmer 1996).
Marked individuals were released in the matrix singly, next to a numbered
wooden stick on a line forming a 90° angle with the border between habitat and
meadow. The distance between consecutive release points was 1 m for the first 30
76
Chapter 5 – Detection of perceptual range
individuals and 2 m for the following 14 individuals (Figure 5.1). The maximum
release distance to the habitat was thus 58 m. All individuals of O. caerulescens
included in this study were captured on the day the experiment started. In the
four nights following the release of individuals the meadow, the habitat, and the
surrounding landscape up to 200 m away from the release points were intensively
searched (7 students) for marked individuals. Daily movement distances and
movement direction were recorded for each individual, first 5 h after release and
then in 24 h intervals.
α
movement of grasshopper
N
release points
2.0 m
meadow with high vegetation
1.0 m
preferred habitat
Figure 5.1: Schematic overview of the experimental setting and an exemplary
movement path. Individuals were released in a perpendicular to the habitat border.
Distance between release points were one meter for the first 30 individuals and two
meter for the last 14 individuals. Turning angles were measured in relation to the
habitat border.
Re-sight rates
Re-sight rates allow an estimation of mortality risk, detection error and the
quality of the marking method. In addition to the re-sights of individuals, we also
recorded findings of lost reflective tape. Tape could either be lost accidentally, e.g.
if it was not properly attached, or be left over by predators feeding on
grasshoppers. Individuals that were not found on one day, but re-sighted on one
of the following days (in the following ‘discontinuously found individuals’) were
probably overlooked and allow an estimation of the detection failure during the
experiment. Individuals that were never re-sighted after a certain day, were
assumed to be either dead or to have left the monitored area (see also Hein et al.
2003).
Statistical analyses
Daily movement distances were analysed separately for individuals that had
reached the habitat they and those still outside the habitat. Inter-group differences
were tested with a Mann-Whitney U test. General directionality of individual
movement paths was tested with the Rayleigh test (Zar 1984).
Chapter 5 – Detection of perceptual range
77
We tested for the detection ability of grasshoppers by comparing the
observed number of individuals in habitat with the predictions generated by
different null-models. Based on 10000 simulation experiments for each nullmodel, we used a one-sided test of significance with a significance level α = 0.05.
The null-model was thus rejected if less than 500 simulation runs predicted a
number of individuals in the habitat as large as or larger than observed in the
field.
5.2.2
Simulation experiments
Landscape and release situation
To generate adequate data for the null hypotheses we simulated the
situation with the same dimensions as in the field (see above). Individuals were
released on a straight line perpendicular to a border with preferred habitat.
Distances between release points were one unit (in our case meter) for the first 30
individuals and two units for the last 14 individuals. The landscape itself was
unlimited inasmuch as animals were neither reflected nor absorbed by an outer
border. Any individual started its dispersal at the release point with an initial
direction randomly drawn from the interval [0° .. 360°].
Model assumptions/development of null-models
The definition of our different null-models is based on the following
considerations. As we strive for a conservative statement about the possibility of
habitat detection, we decided to implement rather extreme assumptions in our
null-models, i.e. assumptions likely to predict large numbers of individuals in the
habitat even though habitat detection is not assumed. If individuals would not
respond to the environment at all, they should exhibit a random movement across
the landscape and arrive and stay in preferred habitat by chance over time.
However, empirical studies have shown that many insect species do react to
structural changes in their environment by a change in movement pattern (Baars
1979, Crist et al. 1992, Kindvall 1999, Hein et al. 2003). This is also true for the
grasshopper species we studied (Hein, unpublished data and data this
experiment). If individuals move randomly but with shorter movement distances
inside than outside the habitat, they would stay with a higher probability in habitat
than outside. However, the most extreme assumption is that individuals always
stay in habitat once they have reached it, i.e. either edge permeability is zero
(Zollner & Lima 1997) or movement ceases inside habitat. As this latter
assumption is likely to generate the largest predicted numbers of individuals to
end up in the habitat, it is implemented in all four null-models tested. In the
simulations individuals always stay in the habitat patch once they have reached it.
The probability to reach habitat is further determined by (i) the number
and length of steps taken, (ii) the mortality risk and (iii) the correlation between
consecutive movement steps. This is taken into account in our four null-models:
78
Chapter 5 – Detection of perceptual range
In null-model 1 each time-step corresponds to one day in the field
experiment. In each time step an individual moves a distance randomly drawn
from the table of net displacement distances observed for the matrix, i.e. step
lengths from individuals in habitat or reaching the habitat were excluded. In this
null-model individuals thus take only one step per day. Initial direction and
directions of all following steps are randomly drawn from the interval [0° .. 360°].
It is obvious that individuals will in fact not just take a single large step
during a day. Daily net displacement will thus certainly be smaller than the actual
movement path. In null-model 2 and null-model 3 we take into account that the
possibility of more than one random step (of shorter step length) within a day
increases the probability that an individual would arrive in habitat by chance due
to the fact that a random walk covers a larger area. From our field observations
we cannot directly estimate the real distance moved by the animals between two
observations. However, we approximated minute-wise step lengths for this
models using equation 1 from Turchin (1998), under the assumption of a
completely random walk.
m=
with
1
* E (d 2 )
z
E (d 2 ) =
(5.1)
1
*∑d2
n
m = mean step length/unit time
z = number of desired steps (in our case: one step/minute, i.e. 1440 steps/ day)
n = number of observations
d = measured daily net displacement in non-habitat
This gives an estimated mean value of 0.57 units (meter)/minute.
Movement distances of 800 m/day implied by this value are certainly very unlikely
but we nevertheless used this conservative value likely to predict a large number
of individuals arriving in the habitat.
So far we have not taken into account that in the field a number of
individuals is lost over time, which automatically reduces the possible number of
arriving individuals. Thus, in contrast to null-model 2 (without mortality), for nullmodel 3 we additionally calculated dispersal mortality based on our recapture data
and included a stepwise dispersal mortality of µ = 0.000138/minute. In both
models initial movement direction and turning angles were randomly drawn from
the uniform interval [0° .. 360°]. The direction of the actual step was thus not
correlated with the previous steps.
Null-model 4 considers the influence of the degree of correlation between
movement steps on the ability to reach habitat. In this null-model individuals
started movement in a random direction and kept this direction until they reached
suitable habitat. We realized this model by randomly drawing the initial direction
79
Chapter 5 – Detection of perceptual range
of an individual from the uniform interval [0° .. 360°] and maintaining this
direction during the following steps. With a completely straight walk there exists
no difference between a single step and a consecutive number of many small
steps, thus in null-model 4 we used the same step length as in null-model 1.
As the field experiment was terminated after 4 days, the simulation time
was T = 4 days (5760 minutes). As mentioned before each scenario was repeated
10 000 times with 44 individuals to generate a distribution for the number of
individuals that arrived in the habitat after each day (T = 1,..,4 days;
T = 440,..,5760 minutes).
5.3 RESULTS
Field experiment
Re-sight rates
fraction of released individuals
5.3.1
1.0
Due to the marking technique it
0.8
was possible to identify individuals
0.6
without re-capturing them. Day to day
re-sight rates always were above 70%.
0.4
The proportion of individuals that were
0.2
not continuously re-sighted was similar
0.0
during the first two days, but decreased
1
2
3
4
day after release
to zero on the third day (Figure 5.2).
Figure 5.2: Fraction of released individuals
This indicates a constant detection error
re-sighted inside (right hatched) and outside
for the first two days. One possible the habitat (left hatched), findings of
reason for the oversight or loss of reflective tape (black), individuals not found
individuals during the experimental time on this but on one of the following days
(cross hatched) and permanently lost
could be that the complex structure of individuals (white) on each observation day.
the meadow reduced the visibility of the
reflective tape. But, if this were true the proportion of individuals that were not
sighted on one day but re-sighted on a later day should have been greater. It
seems more realistic to assume that individuals were either killed by predators or
eventually left the study area (by flight). To determine the risk of disappearance
we pooled the findings of reflective tapes and the number of individuals that were
permanently lost and used this data in a logistic regression analysis with distance
as explanatory variable for each observation day. On the first day (individuals
were just released four hours before patrol) no significant influence of release
distance could be detected (logistic regression p > 0.05). For days 2 and 3 the
80
Chapter 5 – Detection of perceptual range
distance from habitat had a significant influence on the likelihood of
disappearance (p < 0.05, R 2Nagelkerke: 0.171/0.322, AUC-value: 0.675/0.622).
Movement behaviour
daily displacement [m]
To get an idea how individuals
40
move at a border we analysed the
general movement direction as well as
30
daily movement distances of the
grasshoppers. On the first day
20
individuals, which had not reached the
habitat yet, had a significantly preferred
10
direction (Rayleigh-test: p < 0.001,
n = 33) towards the habitat with a mean
0
habitat
non-habitat
angle of 347° (95% confidence interval
327°- 23.63°). Due to the small number
of individuals remaining outside the Figure 5.3: Medians and quartiles of daily
moved in habitat and non-habitat
habitat a preference was not longer distances
(Mann-Whitney U test: p = 0.001, Whiskers
detectable for the following days represent the data range without extreme
(Rayleigh-test for all other days: values).
p > 0.05). Individuals within the habitat
walked significantly shorter distances than individuals in non habitat structures
(Figure 5.3, Mann-Whitney U test, p = 0.001).
To strictly exclude the possibility that individuals collected landscape
information during the experiment or that orientation towards the habitat resulted
from random movements (Goodwin et al. 1999, Zollner & Lima 1997, 1999) one
should ideally only regard the information about initial orientation, i.e. rather
shortly after release. In our case this would be the first day (5 hours after release).
However, this was not possible, because even 5 hours after release too many
individuals had already arrived in the habitat.
For movements within the habitat daily displacement as well as orientation
direction on day 2, 3 and 4 did not differ significantly from each other (Wilkoxon
matched paired test, p > 0.05 for all cases). Day one was excluded from this
analysis because all individuals started movement in the matrix. The frequency
distribution of the sine of the turning angle is random inside the habitat.
Nevertheless, once individuals had reached the habitat, they never left it in the
following days.
5.3.2
Detection ability
In the case of null-model 1 (daily step length) and null-model 4 (directed walk)
a mean of 4 and not more than 14 individuals were predicted to reach the habitat
during the first day. The frequency distributions of individuals that reached the
habitat did not differ significantly between these models (Figure 5.4). With the
81
Chapter 5 – Detection of perceptual range
larger number of steps implemented in null-model 2 and 3 the mean number of
individuals predicted to arrive in the habitat increased to 12 individuals at the first
day (maximum of 18).
# of simulation experiments
a)
null-model 1
2000
2000
1000
1000
0
b)
0
1
3
5
7
9 11 13 15 17 19 21 23 25
1
3
5
7
9 11 13 15 17 19 21 23 25
3
5
7
9 11 13 15 17 19 21 23 25
3
5
7
9 11 13 15 17 19 21 23 25
3
5
7
9 11 13 15 17 19 21 23 25
null-model 2
2000
2000
1000
1000
0
0
1
3
5
7
9 11 13 15 17 19 21 23 25
1
null-model 3
2000
2000
1000
1000
0
0
1
3
5
7
9 11 13 15 17 19 21 23 25
1
null-model 4
2000
2000
1000
1000
0
0
1
3
5
7
9 11 13 15 17 19 21 23 25
1
# of individuals in habitat
Figure 5.4: Frequency distributions of the number of individuals predicted
to arrive in the habitat by the four null-models (1, 2, 3,4) with a simulation
time of a) one and b) four days. The numbers of individuals that were found
in habitat in the field experiment are indicated by the dashed lines. On the
first day, we observed a significantly higher number of individuals in the
habitat than expected by any of our four null-models.
As expected, on the fourth day the predicted number of individuals to
arrive increased for all models, with a clear difference between models (Figure
5.4b). The highest number of individuals predicted to arrive in habitat was
generated by null-model 3.
On the first day in the field experiment 55% (18 individuals) of the resighted individuals reached the habitat and on the fourth day all 20 re-sighted
individuals were found within the habitat (figure 5.4). For the first day the number
of individuals that reached the habitat was significantly higher than the number
predicted by any of the null-models (p < 0.05 for all cases, see figure 5.4). After
the third day all individuals except one had reached the habitat and stayed there.
The comparison between simulations and field data for the fourth day yields
different results depending on assumed movement rules. In the case of nullmodel 1 and null-model 4 significantly more individuals reach the habitat in the field
than predicted by the simulations. In contrast the number of individuals in habitat
does not differ significantly from those predicted by null-models 2 and 3.
Chapter 5 – Detection of perceptual range
82
5.4 DISCUSSION
5.4.1
Re-sight rates
In comparison to other studies day to day re-sight rates above 70 % can be
classified as satisfactory for non-habitat structures (see Ingrisch & Köhler 1998,
Kindvall 1999, Hein et al. 2003). The increase in the probability to disappear with
increasing distance to habitat may result from the fact that individuals further
away from the border stay longer in the matrix than individuals close to the
border and thus are longer exposed to a higher mortality risk. This is likely to be
the case because outside their habitat individuals are not well camouflaged against
predators. Additionally, for the case of O. caerulescens other and more predators
maybe found on the meadow than in the habitat of the species. On the other
hand one cannot exclude that some individuals performed long distance dispersal
and thus were not re-sighted.
5.4.2
Simulation experiments and detection ability
After one day 18 out of the 44 grasshoppers released in the matrix were
already found in the habitat, on the fourth day the number raised to 20
individuals. Clearly, individuals seem to react to the border since all individuals
stayed within the habitat after they had reached it. Consequently our assumption
of an edge permeability of zero seems to be warranted. All in all, on the first day
more individuals than predicted by any of our four null-models reached the
preferred habitat. This is especially noticeable as for the first day simulations run
for 24 h, whereas in the field experiment individuals only had 5 hours for
orientation and movement towards the habitat.
The simulation experiments show that even without the ability to detect
habitat from a distance, altered rules for individuals behaviour can significantly
increase the fraction of individuals predicted to reach the habitat. Obviously, an
increase in the temporal resolution, i.e. number of steps (null-model 2, null-model 3)
increases the fraction of individuals predicted to arrive in the habitat patch.
Nevertheless, for the first day we thus believe that in our field experiment
individuals did not move randomly but perceived their habitat from a certain
distance. However, we cannot completely rule out alternative explanations for the
difference between observed and predicted numbers of grasshoppers arriving in
habitat. For example, a systematic search for habitat following the pathway of an
Archimedian spiral or any other non-random movement might lead to a sooner
detection of habitat than predicted by our models. But we have no indication why
we should assume such a systematic search by our species. Preliminary movement
experiments with O. caerulescens inside different types of habitat and non-habitat
structures did not show significantly preferred movement directions (Hein
unpublished data).
Chapter 5 – Detection of perceptual range
83
In general, one should give most weight to the results for the first day as
with increasing time other mechanisms like the gathering of information about
the surrounding landscape, species condition or an increased mortality risk may
change and influence the results. Principally, model predictions should come
closer to one another over longer periods of time.
5.4.3
Movement behaviour and perceptual range
Given that a detection of habitat from a distance is principally proven, it
becomes interesting to know from which distance individuals are exactly capable
to detect habitat. At the moment an individual recognizes habitat in a distance it
should alter its behaviour (otherwise we would not have cues that it detected the
habitat, Zollner & Lima 1999). For example, individual movement paths should
become more directed and/or faster. Generally, the analyses of such changes in
behaviour are ideally based on direct, small scale observations of individuals.
However, a combination between both direct pursuit and simulation experiments
like those presented here are a ‘second-best’ solution in cases were permanent
pursuit cannot be conducted for technical or financial reasons. Unfortunately, in
our case such an analyses was not possible as even after 5 h of release too many
individuals had reached the habitat already. For further studies we would
recommend a combination of both, direct observations of the initial orientation
behaviour of single individuals once shortly after release and mark-recapture
studies with longer time intervals. This would allow for a thorough analysis of
initial orientation as well as arrival data.
An effect of distance on patch detection was found by other authors, with
detection distances ranging from very short distances of < 0.3 m for skippers
(Capman et al., 1990) and < 2 m for flea beetles (Kareiva 1985) up to 10-22 m
from the habitat boundary for butterflies (Schultz & Crone 2001). It is hard to
know how the individuals actually detected the habitat. We tend to exclude
attraction by conspecific songs because O. caerulescens only produces scratches of
low-intensity. Some insects can orientate very well olfactory (Schooley & Wiens
2003), but little is known about the olfactory abilities of grasshoppers. In any case,
we could not find a habitat-specific plant which might serve as an olfactory cue.
In our view the most likely way of orientation is the visual detection of open
ground. This could be achieved by short orientation flights. Although we did not
observe such flights in the field we cannot exclude them. Another possible way of
orientation would be the recognition of the heat radiated by infra-red radiation of
the open areas, which should be much higher than for the dense vegetation of the
meadow.
If individuals can differentiate between their habitat and non–habitats their
probability to stay inside the habitat at a habitat boundary should increase. In our
study individuals that had reached the habitat never left it again, although the
elongated structure of the habitat should increase boundary encounters. Adding
Chapter 5 – Detection of perceptual range
84
such edge-mediated behaviour to simulation studies leads to an increased
residence time (Schultz & Crone 2001). This has strong influence on population
parameters such as emigration rate and therefore influences long-term survival of
metapopulations (Schultz & Crone 2001).
On the other hand, perception of habitat increases colonization probability
due to reduced search time and (likely) reduced dispersal mortality (Gaines &
McClenaghan 1980, Doak et al. 1992, Hanski & Zhang 1993). Including a
perceptual range into metapopulation models would thus alter immigration rates,
and consequently influence measures of habitat connectivity due to the increase in
the ‘effective patch area’ (Meriam 1991, Schooley & Wiens 2003). This would
result in shorter effective distances between patches (Hanski 1994a, b) but also in
an increased ‘shadow effect’ of patches exerted on other patches laying behind
them (cf. Hein et al. in press). In fact, a very large perceptual range may diminish
the importance of factors such as the spatial arrangement of habitat patches on
population dynamics. Based on our results we support the notion that landscapes
should be described with respect to the behavioural and physiological abilities of
the animals under study (Meriam 1991, Johnson et al. 1992, Wiens et al. 1993,
With 1994, Hein et al. 2003).
Chapter 6
Patch density, movement pattern, and
realised dispersal distances in a patch-matrix
landscape – a simulation study
with Brenda Pfenning, Thomas Hovestadt and Hans-Joachim Poethke
ECOLOGICAL MODELLING (IN PRESS)
Abstract. In metapopulation models it is common practice to use
species specific dispersal distances to predict the exchange of
individuals between habitat patches. The influence of patch
distribution on the reachability of a habitat patch is usually ignored. In
a patch-matrix simulation we investigated the effect of patch number,
movement pattern and dispersal mortality on realized animal dispersal
distances.
In our spatially-explicit, individual-based simulations we
demonstrate that: 1. with increasing number of patches the number of
immigrants into patches further away from the release point decreases
in all scenarios (‘shadow effect’) 2. this effect is strongest for a random
movement 3. for rather uncorrelated walks a proper adjustment of
mean dispersal distance in the negative exponential model can account
for the effect of patch density on realized dispersal distances 4. with a
more directed walk this is not possible as the distribution of moving
animals in the matrix becomes highly heterogeneous. This is due to
the narrow but far reaching shadow patches exert on other patches
further away from the release point.
Chapter 6 – Realised dispersal distances in patch-matrix landscape
87
6.1 INTRODUCTION
Long-term persistence and stability of (meta-) populations within
fragmented landscapes crucially depends on recolonisation between habitat
patches (Gustafson & Gardner 1996). Colonization probability is implicitly limited
by inter-patch distance and dispersal ability of the species in question.
Consequently, dispersal is assumed to be one key process responsible for
metapopulation persistence (Hanski 1994, Hanski & Thomas 1994, Hill et al. 1996;
Moilanen & Hanski 1998, Moilanen & Nieminen 2002). It determines the longterm survival of metapopulations due to demographic as well as genetic effects
(Levins 1969, den Boer 1981, Taylor 1990, Cohen & Levin 1991, McCauley 1995,
Cresswell 1997, With et al. 1999, Poethke et al. 2003). Additionally, dispersal is an
important factor determining local extinctions (Poethke et al. 1996a, b).
In his widely recognised incidence function model, Hanski (1994) assumes
that inter-patch dispersal depends on the distance between patches and the general
dispersal capabilities of the species under study. The model assumes that the
exchange of individuals between patches can readily be described by a negative
exponential distribution. This approach is easy to handle, makes clear predictions,
is theoretically well based (MacArthur & Wilson 1967, Pulliam 1988) and thus
often used in modelling species dispersal across landscapes (Hanski & Thomas
1994, Hanski & Gilpin 1997, With & King 1999a, b).
Recently, this simplifying approach has been criticised for a variety of
reasons. First, dispersal will be affected by the quality of the matrix (Gustafson &
Gardner 1996, Moilanen & Hanski 1998, Thomas et al. 2001, Wiegand et al. 1999,
Wiens 2001) in which dispersal takes place. Second, it also may be affected by the
distribution of suitable habitat patches in the landscape (Gustafson & Gardner
1996, Zollner & Lima 1999b, King & With 2002). If these objections are founded,
we should find dispersal parameters to depend not only on a species traits but also
on the specific characteristics of a given landscape. Therefore it is important to
investigate the effects of landscape structure on the distribution of dispersal
distances.
One possibility to obtain dispersal distance distributions for the prediction
of colonization probabilities at the landscape level is to fit dispersal kernels to the
empirically observed distance distributions (e.g. Paradis et al. 2002). However,
successful patch to patch dispersal, which is usually long-distance dispersal, is hard
to investigate, as recapture rates are generally low and individuals are hard to
follow (Gaines & Bertness 1993, see also Zollner & Lima 1999a). Consequently,
only a few studies on dispersal at landscape level, mostly on butterfly dispersal, are
available (e.g. Harrison 1989, Baguette & Nève 1994, Hanski et al. 1994, Hill et al.
1996, Kuussaari et al. 1996, Nève et al. 1996a,b, Sutcliffe et al. 1997, Brommer &
Fred 1999). On the other hand, if data are available and dispersal kernels can be
Chapter 6 – Realised dispersal distances in patch-matrix landscape
88
fitted, this does not allow to differentiate between the effect of variation in
dispersal behaviour of the species, the influence of the matrix, or the impact of
number of patches on realized dispersal distances.
A second approach to determine colonization probabilities at the landscape
level is based on information about the habitat specific movement patterns of a
species. In simulation studies this pattern can be extrapolated to dispersal at the
landscape scale (Dunning et al. 1995, South et al. 2002). Data are derived from
empirical studies which focus on small scale movement of animals in habitats of
different structure and resistance (Wiens & Milne 1989, Kareiva 1990, Crist et al.
1992, Haefner & Crist 1994, With & Crist 1995, Wiens et al. 1997) or suitability
(Wiens et al. 1997, Schultz 1998, Kindvall 1999, Wiens 2001, Hein et al. 2003) as
well as on the behaviour at borders between different types of habitat (Conradt et
al. 2000, Schultz & Crone 2001).
In this paper we use such a mechanistic simulation study to investigate the
relevance of the number of habitat patches, movement pattern and dispersal
mortality on the distribution of realized dispersal distances. We model dispersing
individuals which leave their home patch to search for new habitat patches in a
heterogeneous landscape consisting of a hostile matrix and a variable number of
suitable habitat patches. Although our simulation is motivated by our fieldwork
with bush crickets in patchy semiarid grasslands, our results should be qualitatively
applicable to other species as well.
6.2 METHODS
We simulated the movement of dispersing animals in a landscape with
patches of suitable habitat distributed in an unsuitable matrix. Because we focus
on reachability of patches and realized dispersal distances of individuals, there is
no need to consider the size or dynamics of local populations or of a
metapopulation at large. Midpoints of circular patches of suitable habitat of equal
size (one hectare (1 * 104 m2), r = 56.4 m) were distributed inside a circular arena
(R = 1000 m; 3.14 * 106 m2) around the central ‘release point’. The edge of this
arena was implemented as a neither reflecting nor absorbing border. Patches were
distributed at random within this arena, but were eventually relocated to fulfil two
conditions. (i) The minimum distance of the centre of a patch from the release
point had to be larger than 2 r. (ii). Patches were not allowed to overlap, i.e. the
distance between the centres of any two patches had to be larger than 2 r. In this
respect, our model is similar to that of Zollner and Lima (1999b).
In the simulations, any individual started its dispersal at the central release
point in the matrix with an initial direction randomly drawn from the uniform
interval [0° .. <360°]. At each time-step an individual moved a certain distance
Chapter 6 – Realised dispersal distances in patch-matrix landscape
89
drawn from a table of daily step lengths (n=159) empirically derived for the
movement of bush crickets Platycleis albopunctata in unsuitable habitat (Hein et al.
2003). These data were approximately negative exponentially distributed with a
mean dispersal distance (D) of 8.62 m, i.e. less than 1/10 of a patch’s diameter. To
cover the range of movement patterns observed for different insect species we
implemented three different movement rules to alter the direction of an individual:
‘Cricket walk’: A movement pattern with turning angles drawn from an
empirically derived distribution for Platycleis albopunctata. This movement pattern
results in a correlated random walk as turning angles (measured against the
direction of the previous step) were distributed nearly uniformly in the interval [172° .. 170°] (for a similar approach see Kindvall 1999).
‘Directed walk’: A directed (linear) walk, i.e. animals exactly maintained the
direction randomly selected at the beginning of their path. Examples of (fairly)
directional movements have been observed for individuals that detect their habitat
patch from a certain distance (Conradt et al. 2000, Schultz & Crone 2001) or that
move in unsuitable habitat (e.g. beetles, Baars 1979, Crist et al. 1992, Roslin 2000;
crickets, Kindvall 1999; hedgehogs, Doncaster et al. 2001).
‘Random walk’: A completely (uncorrelated) random walk, i.e. for each step
the direction was randomly taken from the uniform interval [-180°..180]. The
direction of the actual step was thus not correlated with that of any previous step.
Consequently, this movement pattern should become similar to that of a
Brownian diffusion process. Empirical examples for a (nearly) random walk can be
found for individuals that move in suitable habitat (Crist et al. 1992, Kindvall 1999,
Schultz 1998).
In our simulations, the movement of an animal was terminated as soon as
one of the following events occurred: (i) the animal arrived in a patch of habitat,
(ii) the animal died (with stepwise dispersal mortality m, see below), (iii) the
maximum simulation time was reached. Maximum simulation time T = 300 was
chosen to allow mean net-displacement in a landscape without habitat patches to
surpass R.
To investigate the effect of patch number, movement pattern, and dispersal
mortality on the distance distribution of animals arriving in a habitat patch, we
tested all combinations of the following parameter values. (i) Patch number (P)
with P = 1, 8, 16, 32, or 64. (ii) The three movement patterns described above, i.e.
‘cricket walk’, ‘directed walk’ and ‘random walk’. (iii) Step-wise dispersal mortality
m set to m = 0.01 or 0.02. Implicitly, we thus assume mortality to be a purely
random process independent of the previous travel history. The resulting mean
survival times for these two values, i.e. 100 or 50 time steps (days), fall into a
realistic range as the activity period of bush crickets varies between 60 and 150
days (see Detzel 1998, Kindvall 1999). Additionally, we included simulations with
no dispersal mortality (m = 0.0) – a highly unlikely situation in nature – to study
the pure interaction effect of patch number and movement pattern. For each
parameter combination we ran simulations on 1000 different random landscape
Chapter 6 – Realised dispersal distances in patch-matrix landscape
90
with 1000 individuals each. In total we thus produced movement paths for 1 * 106
simulated individuals.
Over the course of a simulation run we counted the number of individuals
arriving in each patch and – at its end – estimated the realized mean dispersal
distance (D), i.e. the mean travel distance for all individuals which arrived in a
patch. For analysis and presentation of the data, patches were pooled into distance
classes and mean values of arriving individuals were estimated for all patches
within a given distance class.
To visualize the effects of movement pattern and high patch density, we
ran one additional simulation for the random walk as well as the directed walk. In
each scenario we simulated 10 000 individuals over 1000 time steps in a landscape
with 64 patches (r = 2 m). The landscape was divided into a regular grid of 40 000
(200 x 200; R = 100 m) cells of 1 m x 1 m. Step length was distributed uniformly
in the interval [0..1]. We counted how many individuals were sighted in each cell
throughout the whole simulation time. The spatial distribution of these counts
represents the accumulated density distribution for the likelihood to observe a
moving individual that started at the ‘release point’ in that area of the matrix and
gives an intuitive idea about the reachability of different regions in the artificial
landscape. Clearly, a habitat patch is more likely to ‘collect’ many immigrants if
surrounded by matrix with a high density distribution than if surrounded by matrix
with a low density distribution.
91
Chapter 6 – Realised dispersal distances in patch-matrix landscape
6.3 RESULTS
6.3.1
Single-patch scenario.
First, we tested the effect of movement pattern and dispersal mortality on
the distance-dependent reachability of a solitary patch. Expectedly, more
individuals arrive in patches in scenarios without than with mortality (Figure 6.3).
The single-patch scenario corresponds to the isolation-by-distance situation with
inter-patch dispersal independent of the landscape structure (cf. Hanski 1994,
Kitching 1971). Not surprisingly, the probability to reach a patch decreases with
increasing distance between the patch and the release point for all tested
parameters (Figure 6.1).
Though the commonly used negative exponential fit produces high
coefficients of determination for a ‘directed walk’, this distribution does not
represent the data adequately. It overestimates short distance dispersal and
underestimates the tail of the distribution (cf. Dobzhansky & Wright 1943,
Harrison 1989, Hill et al. 1996) which is the most interesting part regarding
dispersal distances and colonization probabilities. For an exactly straight walk, the
expected distribution for the distance dependent probability of arrival (P(d))
should follow an arcsine-function.
P(d) = 1/π * arcsine(2r/d)
(6.1)
with d = distance between release point and centre of patch and
2r = diameter of patch.
For d >> r this relationship can be approximated by a simple power
function, i.e.
P(d) ≈ 1/π * 2r/d
(6.2)
Consistently, for the directed movement pattern the relationship is better
described by a power distribution. For the random walk, the relationship between
dispersal distance and the probability to reach a patch can accurately be described
by a negative exponential distribution (cf. Kitching 1971, Figure 6.1b).
Implementation of the empirical distribution (‘cricket walk’) for turning angles and
step length results in a distribution also consistent with the negative exponential
model.
92
Chapter 6 – Realised dispersal distances in patch-matrix landscape
500.000
a.
a.
individuals/patch
individuals/patch
500.000
5.000
0.050
5.000
0.050
0.001
0.001
0
200
400
600
0
800
200
400
600
800
distance [m]
distance [m]
500.000
500.000
b.
individuals/patch
individuals/patch
b.
5.000
0.050
0.050
0.001
0.001
0
200
400
600
800
distance [m]
Figure 6.1: Number of individuals arriving on
average in a patch in the given distance class
with 106 released individuals for a) a directed
walk and b) a random walk. We repeated
simulations for three different mortalities (●: m
= 0, ○ : m = 0.01, ▲: m = 0.02). For each
mortality we released 1000 individuals in each
of 1000 different patch configurations.
6.3.2
5.000
0
200
400
600
800
distance [m]
Figure 6.2: Number of individuals arriving
on average in a patch in the given distance
class with 106 released individuals for a) a
directed walk and b) a random walk. We
repeated simulations for four different
numbers of patches in the world (○: 1, ●: 8,
□: 32, ▲: 64 patches; dispersal mortality m =
0.02). For each scenario we released 1000
individuals in each of 1000 different patch
configurations.
Multi-patch scenario.
As can be seen in figure 6.2, the relative number of individuals arriving in
patches far away from the release point steadily declines as more patches are
dispersed over the landscape. However, the principal type of the relationship, i.e. a
negative exponential distribution for the random walk and a power distribution for
the directed walk is not altered. The reduction in the relative number of
individuals arriving in patches far away from the release point is in general larger
for the random walk compared with the directed walk, and highest when dispersal
mortality is highest. With a directed movement pattern both, the probability to
arrive in a patch further away as well as the probability to miss a patch in the near
vicinity is increased (c.f. Cain 1985, Zollner & Lima 1999b). Thus, with increasing
directionality of movement pattern not only the distance distribution for
individuals arriving in a patch is altered, but also a smaller fraction of individuals
arrive in a patch at all. Regardless of the model used for the fit of data, increasing
93
Chapter 6 – Realised dispersal distances in patch-matrix landscape
D [m]
dispersal mortality results in
350
fewer individuals arriving in
300
patches at greater distance
250
(Figure 6.3). The general effect
200
of mortality is reduced as the
150
number of patches is increased.
Increasing the number of
100
patches in the landscape opens
50
the opportunity for patches close
0
1
8
16
32
64
to the release point to ‘trap’
patch num ber
dispersing individuals which are
thus not available for arrival in
Figure 6.3: Mean dispersal distance (D) as a
function of the movement pattern (dots: ‘directed
the more distant patches.
walk”, triangles: ‘random walk”), the step wise
Consequently, distant patches
dispersal mortality (full symbols: m = 0.00, open
experience a ‘shadow effect’
symbols: m = 0.02) and the number of patches. For
presentation, patches were grouped into 10 evenly
from those patches closer to the
spaced distance classes. As outlined in the methods,
release point and the mean
minimum distance of a patch’s midpoint to the
dispersal distance (D), i.e. the
release point was larger than the patch’s diameter
(112.8m), maximum distance was 1000-r (943.6m).
exponent of the negative
exponential function, decreases
with increasing patch number and dispersal mortality. This shadow effect is
visualised in figure 6.4. Additionally, these figures show that – in identical multipatch-landscapes – distance (including a correction factor for patch density) is a
reasonable predictor of the extent of the ‘shadow effect’ if individuals exhibit a
random movement. For a directed movement the variation in the number of
immigrants into patches at a given distance is very high and distance is thus not
well correlated with colonization probability.
< 8 counts/cell
8 – 12.5 counts/cell
12.5 – 20 counts/cell
20 – 31.5 counts/cell
31.5 – 50 counts/cell
> 50 counts/cell
Figure 6.4: Examples of a cumulative density distribution of individual counts per grid cell at the
end of a simulation (T = 1000) with 10 000 individuals and 64 patches (m = 0.00) for a) a directed
walk and b) a random walk. Different log-transformed density classes are differently shaded.
a.
Chapter 6 – Realised dispersal distances in patch-matrix landscape
94
6.4 DISCUSSION
In most metapopulation models a negative exponential relationship is
assumed to describe the distribution of dispersal distances (e.g. Hanski 1994,
Nieminen 1996). This adequately represents the movement behaviour of the
majority of species studied empirically. However, in agreement with recent
empirical (Wiens et al. 1997, Baguette et al. 2000) and theoretical studies (Portnoy
& Willson 1993, Clark 1998, Clark et al. 1998, 1999, Higgins & Richardson 1999,
Hovestadt et al. 2001) our own simulations show, that this assumption may not
always be met. It depends critically on the kind of movement pattern and will
almost surely be violated whenever animal movement becomes very directed. In
such cases an arcsine-function (or the power function) will more adequately
describe the distribution of dispersal distances (cf. Portnoy & Willson 1993, Hill et
al. 1996, Kot et al. 1996 and references therein, Turchin 1998).
Principally, the path taken by an individual (without mortality) and the
mortality risk can be seen as two independent random factors influencing the final
fate of a moving individual, i.e. whether it will arrive in a patch at all (in the limited
time of the experiment) and if so, which patch it will be. For the interpretation of
the influence of mortality on realized dispersal distances, we thus can also consider
the time it took an individual to arrive in the target patch (for the scenario without
mortality) and then ask in behind, whether it would have arrived if mortality had
occurred. Clearly, an individual which took longer would have had a lower chance
of arrival than an individual which arrived soon. Evidently, it will on average take
longer to arrive in a distant patch than in a patch close to the release point – and
this especially holds for random walk where net-displacement only increases in
proportion to the square root of travel time (Turchin 1998).
These basic facts allow to explain the two patterns observed with the
introduction of dispersal mortality. (i) The relative reduction in mean dispersal
distance is larger for the random walk. This is due to the fact that the individuals
arriving in distant patches have used longer time spans than in the scenario with a
directed walk and will thus suffer more strongly from dispersal mortality. (ii) The
general effect of mortality is reduced as the number of patches is increased. This is
can be explained by the fact that the average travel time needed to arrive in a patch
declines as patch density is increased. Thus, a larger fraction of individuals will
have the chance to arrive in a patch before they eventually die.
In addition to the influence of movement pattern and dispersal mortality on
the resulting species specific dispersal distance distributions, the results of our
simulation experiments clearly demonstrate, that landscape structure (in our case
the number and distribution of habitat patches) can have a significant effect on the
exchange of individuals between habitat patches. Besides variable matrix structure
(see Wiens et al. 1997) and the changing mortality risk during dispersal (Pulliam et
al. 1992, Wiegand et al. 1999) we can thus add the spatial distribution of patches to
Chapter 6 – Realised dispersal distances in patch-matrix landscape
95
the list of factors that influence the probability of an individual to reach a habitat
patch. This can be traced to the ‘shadow’ a patch exerts on other patches further
away from the source patch.
The effect of the spatial arrangement of patches is less severe for a less
correlated random walk. In this case, the dispersal distance distribution will follow
the negative exponential function regardless of the number of patches, i.e. distance
is a reliable predictor for dispersal between patches. However, the dispersal
parameter (mean dispersal distance D) will not only dependent on the species’
specific movement capabilities, but needs to be adjusted to the specific habitat
configuration.
However, with increasing directionality of movement the influence of patch
configuration on the reachability of patches becomes more complicated. Less
variance in the number of immigrants is accounted for by distance (Figure 6.4).
This is due to the substantial heterogeneity of disperser density resulting from the
extended, but narrow shadows of patches exerted on the landscape further away
from the source patch (release point). The situation will become even more
complicated if we account for the findings of Roland et al. (2000) and Hein et al.
(2003), who demonstrate an effect of the quality of habitat separating suitable
patches on dispersal between patches (see also Wiens 2001).
In our simulations we did not allow for any secondary dispersal. Clearly, in
a real landscape individuals may decide to leave a patch again. This may happen if
actual patch ‘quality’ is correlated in space, e.g. when weather conditions are
similar in neighbouring patches, an infectious disease has spread out in a region, or
nearby patches are inhabited by kin. In all these situations the expected benefits of
dispersal, i.e. arrival in a patch with better conditions may not be fulfilled in a
patch close to the home site and secondary dispersal may thus become an
appropriate strategy. However, secondary dispersal is only likely to occur if costs
of dispersal are rather low, i.e. when distances between patches are fairly small.
Especially for organisms with a limited life-expectancy or short reproductive
period we have not only to consider the (mortality) costs of dispersal as such but
also the time costs associated with travelling. Unfortunately, there is very little
empirical evidence on secondary dispersal and we do not have a clue what a
realistic probability for secondary dispersal might be. However, it is very clear that
a (very) high probability of secondary (or tertiary ...) dispersal would finally break
up the shadow effect.
Different measures to determine landscape connectivity have been used
during the last years (see Moilanen & Nieminen 2002 for a review). Independent
of the fact whether isolation/connectivity is determined as the straight line
distances between patches (e.g. Akςakaya & Altwood 1997), as a buffer-based
measure (Moilanen and Nieminen, 2002) or information about the matrix between
patches is included in the measure (e.g. Lahaye et al. 1994), we would strongly
recommend either of two approaches to derive landscape specific dispersal
parameters. One option is to determine the realized or functional connectivity
Chapter 6 – Realised dispersal distances in patch-matrix landscape
96
(sensu Wiens et al. 1997, With et al. 1999) by modelling dispersal behaviour within
a landscape context to get landscape specific matrices of exchange between
patches (cf. South 1999, Pfenning et al. in revision). This requires proper
knowledge of the effect of all relevant factors, i.e. the intervening habitat as well as
the arrangement of suitable habitat and individual movement behaviour. This
approach may be specifically indicated, if we strive for the design of population
models to develop optimal conservation strategies (see South et al. 2002). In such a
case, a precise prediction of extinction- and survival-probabilities of animal
populations is of essential importance. As an alternative, at least under some
conditions simple correction factors accounting for patch density and inter-patch
distance, may be applied to adjust estimates of colonization probability to any
specific landscape (cf. Heinz et al. submitted).
97
Summary
Summary
The progressive exploitation of landscapes by man increases fragmentation
of valuable areas with high biodiversity (e.g. mesoxerophytic grasslands).
Consequently, many populations nowadays exist as metapopulations, i.e.
ensembles of small local populations not capable of long-term survival without
interactions with neighbouring populations (Ehrlich et al. 1980, Hanski 1994a, b,
Thomas & Hanski 1997). In such cases, the balance between extinction and
colonisation of (vacant) patches determines the regional survival of species
(Levins 1969, Hanski 1994, Hanski & Thomas 1994, Hill et al. 1996). To
determine long term survival of species and to assess the impact of different
management regimes proper knowledge of species habitat requirements as well as
information on their dispersal behaviour is needed. The aim of this thesis was to
develop methods and measures for the identification of suitable areas for
grasshoppers and bush crickets, as well as to quantify the reachability of single
patches by individuals.
The first part of my work (Chapters 2 & 3) focuses on the quantification of
habitat suitability for grasshoppers and bush crickets. A good habitat patch is
characterised by high habitat capacity, i.e. its area as well as quality (Thomas et al.
2001). In my work I concentrated on the determination of habitat quality for two
bush cricket and one grasshopper species on three spatial scales. Based on
presence/absence data, I developed statistical habitat suitability models using
logistic regression analyses. The resulting models are evaluated and validated in
space and time. It turned out that habitat selection of the three species mainly
took place on an intermediate spatial scale, as only parameters measured directly
at the experimental plot or close to it significantly increase the power of the final
models. The relevant scale falls into the same range as the species’ mean dispersal
distances. Besides the rather coarse grained factor ‘type of habitat’ structural
factors like vegetation height, as well as abiotic factors like exposition or solar
radiation are correlated with the occurrence of the species.
The model of S. lineatus, including the parameters ‘type of biotope’ and
‘vegetation height’ was most successful in predicting the occurrences of the bush
cricket species. To further test whether the occurrence of species of different
insect groups can be predicted with a common model, I tested the usefulness of
the orthoptera models for the prediction of butterflies in the same region and vice
versa. While transferability of the orthoptera models was poor, the model of the
moth Z. carniolica performed quite successful. It included the proportion of
suitable habitat (fringes, mesoxerophytic grasslands, extensively managed
meadows) as well as the occurrence of the two sucking plants C. jacea and
S. columbaria as relevant factors. Z. carniolica is classified as stenoecious and thus
represents other species typically found on fringes and mesoxerophytic
grasslands. The high mobility of Z. carniolica simultaneously guarantees the
Summary
98
reachability of regional suitable areas and thus ensures that the influence of the
random effects of colonisation on the model are marginal.
Unfortunately, the factors predicting habitat quality for a single species are
normally not available at the landscape level. Thus, they cannot be used for the
prediction of occurrences, without extensive censuses in the field. Nevertheless,
my results show that the sole use of the variable ‘type of habitat’, which often is
available landscape wide, will be sufficient for the classification of habitat
suitability in a landscape as well. Thus, I conclude that for practical use in
conservation biology the type of biotope can be used to predict occurrence of the
studied species.
Besides quality and quantity of suitable habitat, dispersal of individuals
between patches is a key factor influencing the survival of (meta-) populations.
Thus, the second part of my work concentrates on theoretical as well as empirical
studies on the dispersal behaviour of bush crickets (Chapters 4-6). In field
experiments I could show that the assumption of a dichotomous movement
behaviour (matrix: directed walk, patch: random walk) does not apply for bush
crickets. Instead, movement pattern changes continuously with structural
resistance, temperature, mortality risk and resource availability (egg laying places,
food). This result is confirmed in my experiments on the behaviour of bush
crickets at habitat borders. For different borders I could demonstrate different
edge permabilities. In an additional experiment I observed that grasshoppers
could detect suitable habitat from a certain distance. Because the dispersal
behaviour of individuals plays an important role in theoretical models, my
empirical data can be used to parameterise such models.
In addition to the influence of movement pattern on the reachability of
suitable habitats, I could demonstrate, with spatially explicit simulation models,
that the influence of the landscape context in which dispersal takes place has a
critical impact on the exchange of individuals between patches. This effect is
enhanced if mortality risk during dispersal is accounted for.
The results from my studies on habitat suitability can be used to identify
suitable habitat for thermophilic grasshoppers and bush crickets in a landscape.
Consequently, the potential suitability of an area as habitat, based on predictions
on changes in the type of biotope by management regime, can be predicted. But
this information alone is not sufficient to determine regional survival probability
of a species. My investigations concerning the dispersal behaviour clearly show,
that the reachability of suitable areas is dependent on the spatial configuration of
patches and the structure of areas between habitats. Additionally, factors specific
for individuals, like motivation (influences by resource availability, age, etc.) and
physiological factors, like the ability to detect habitat, play a crucial role for the
reachability of suitable habitats.
99
Zusammenfassung
Zusammenfassung
Die zunehmende anthropogene Nutzung von Landschaften führt zu einer
steigenden Fragmentierung schützenswerter Flächen (wie z.B. Halbtrockenrasen)
mit hoher Artendiversität. Damit verbunden ist gleichzeitig eine Zerschneidung
von
großen,
zusammenhängenden
Populationen
in
sogenannte
Metapopulationen, d.h. in Verbände von kleinen lokalen Populationen, die jeweils
ohne den Austausch mit anderen Populationen langfristig nicht überleben
könnten (Ehrlich et al. 1980, Hanski 1994, Thomas & Hanski 1997). In solchen
Fällen bestimmt das Gleichgewicht zwischen Aussterben und (Wieder-)
Besiedlung von Habitaten die regionale Überlebenswahrscheinlichkeit von Arten
(Levins 1969, Hanski 1994a, b, Hanski & Thomas 1994, Hill et al. 1996). Um diese
bestimmen
und
damit
auch
die
Auswirkungen
verschiedener
Managementmaßnahmen beurteilen zu können, braucht man ein gutes
Verständnis der Habitatansprüche der Arten, sowie Informationen über ihr
Ausbreitungsverhalten. Ziel dieser Arbeit war es, geeignete Flächen für
Heuschrecken in einer Landschaft identifizieren zu können, sowie einen Beitrag
zur Quantifizierung der Erreichbarkeit einzelner Flächen durch Individuen zu
leisten.
Der erste Teil dieser Arbeit (Kapitel 2 & 3) beschäftigt sich mit der
Quantifizierung der Habitateignung von Flächen für Heuschrecken. Ein gutes
Habitat ist durch eine hohe Habitatkapazität charakterisiert. Diese wird zum einen
durch die Größe einer Fläche bestimmt. Zum anderen beeinflusst vor allem die
Qualität einer Fläche die Habitatkapazität. In dieser Arbeit habe ich die
Habitatqualität für drei Heuschreckenarten auf drei räumlichen Ebenen bestimmt.
Dazu habe ich, basierend auf Inzidenzdaten, statistische Habitateignungsmodelle
mittels logistischer Regression erstellt, evaluiert und validiert. Es zeigte sich, dass
die Habitatwahl der Heuschrecken auf einer mittleren räumlichen Skalenebene
erfolgt, da nur Parameter, die unmittelbar auf der Probefläche oder in nahem
Umkreis aufgenommen wurden, zu einer signifikanten Verbesserung der
Endmodelle führen. Dies steht mit der beobachteten Ausbreitungsdistanz dieser
Tiere im Einklang. Neben dem nur grob klassifizierten Landschaftsfaktor
„Biotoptyp“ korrelieren vor allem strukturelle Faktoren wie die Vegetationshöhe,
sowie abiotische Faktoren wie die Exposition oder Sonneneinstrahlung einen
Einfluss mit dem Vorkommen der Heuschreckenarten.
Bei der Bestimmung eines gemeinsamen Models für alle drei
Heuschreckenarten erwies sich das Model der Art S. lineatus mit den Parametern
Biotoptyp und Vegetationshöhe als am besten geeignet zur Vorhersage der
Vorkommen der anderen Heuschreckenarten. Um zu testen, ob auch die
Vorkommen von Arten unterschiedlicher Tiergruppen mittels eines gemeinsamen
Modells vorhergesagt werden können, habe ich sowohl die Heuschreckenmodelle
zur Prognose von Faltervorkommen getestet, als auch Modelle für Falter auf
Heuschrecken übertragen. Dabei erwiesen sich die Heuschreckenmodelle zur
Zusammenfassung
100
Prognose der anderen Arten weniger geeignet als das Modell für das Widderchen
Z. carniolica in das der Anteil an geeignetem Habitat (Saum, Halbtrockenrasen,
Extensivwiese) sowie die Vorkommen der beiden Saugpflanzen C. jacea und
S. columbaria einfließen. Diese Art wird als standorttreu eingestuft und
repräsentiert damit auch die anderen Arten, die typisch für Säume und
Halbtrockenrasen sind. Die erhöhte Mobilität von Z. carniolica im Vergleich zu
den Heuschrecken garantiert gleichzeitig auch die Erreichbarkeit aller geeigneten
Flächen im Gebiet und damit ein Modell, das nur unwesentlich durch
Zufallseffekte bei der Besiedlung beeinflusst wird.
Die Verteilung vieler der Faktoren, die die Habitatqualität für die
Einzelarten bestimmen, ist in der Regel nicht flächendeckend kartiert, so dass sie
ohne aufwendige Erhebungen im Gelände zur Vorkommensprognose nicht
verwendet werden können. Meine Ergebnisse zeigen, dass auch die alleinige
Verwendung des Biotoptyps, der häufig flächendeckend in Biotoptypenkarten
erfasst ist, zur Beurteilung der Eignung einer Fläche ausreichen kann. Deshalb
schlage ich für den praktischen Naturschutz eine Verwendung des Biotoptyps zur
Prognose des Vorkommens der untersuchten Heuschrecken- und Falterarten vor.
Neben der Habitatqualität und –quantität spielt vor allem der Austausch
zwischen Flächen eine entscheidende Rolle für das Überleben der
Metapopulation. Im zweiten Teil meiner Arbeit habe ich mich deshalb, sowohl
theoretisch als auch empirisch, mit dem Ausbreitungsverhalten von Heuschrecken
beschäftigt (Kapitel 4-6). In Freilandexperimenten konnte ich zeigen, dass die
Annahme eines dichotomen Bewegungsverhaltens (ungeeignetes Habitat:
gerichteter Lauf, geeignetes Habitat: zufälliger Lauf) für Heuschrecken in einer
realen Landschaft nicht zutrifft. Vielmehr wird die Bewegung in einer Fläche
besser als Kontinuum beschrieben, das durch strukturelle Resistenz, Temperatur,
Mortalitätsrisiko und Ressourcenverfügbarkeit bestimmt wird. Die jeweilige
Kombination dieser Parameter veranlasst die Heuschrecken dann zu einem
entsprechenden Bewegungsmuster, das sich zwischen den beiden Extremen
gerichteter und zufälliger Lauf bewegt. In Experimenten zum Grenzverhalten von
Heuschrecken bestätigte sich dieses Ergebnis. Für verschiedene Grenzstrukturen
konnte ich unterschiedliche Übertrittswahrscheinlichkeiten nachweisen. In einem
weiteren Versuch konnte ich feststellen, dass Heuschrecken geeignete Habitate
auch aus einer gewissen Entfernung detektieren können. Da das
Ausbreitungsverhalten von Tieren in unterschiedlichen theoretischen Modellen
eine wichtige Rolle spielt (Hanski 1994a, b), können diese empirischen Daten zur
Parametrisierung dieser Modelle verwendet werden.
Zusätzlich zum Einfluss des Laufmusters der Tiere auf die Erreichbarkeit
geeigneter Habitate, zeigte sich in den von mir durchgeführten
individuenbasierten, räumlich expliziten Simulationsstudien deutlich, dass der
landschaftliche Kontext, in dem die Ausbreitung stattfindet, die Erreichbarkeit
einzelner Habitate entscheidend beeinflusst. Dieser Effekt ist zusätzlich abhängig
von der Mortalitätsrate beim Ausbreitungsvorgang.
Zusammenfassung
101
Mit den Ergebnissen aus den Untersuchungen zur Habitateignung lassen
sich die für thermophile Heuschrecken geeigneten Habitate in einer Landschaft
identifizieren. Somit lässt sich die potentielle Eignung einer Fläche als Habitat,
basierend auf Vorhersagen über die Änderung des Biotoptyps durch ein
Managementverfahren, vorhersagen. Diese Information allein reicht aber nicht
aus, um die regionale Überlebenswahrscheinlichkeit einer Art bestimmen zu
können. Meine Untersuchungen zum Ausbreitungsverhalten zeigen deutlich, dass
die Erreichbarkeit geeigneter Flächen von der räumlichen Anordnung der
Habitate und der Struktur der Flächen, die zwischen Habitaten liegen, abhängt.
Zusätzlich spielen individuenspezifische Faktoren wie Motivation (bedingt durch
Ressourcenverfügbarkeit, Alter, etc.) und physiologische Faktoren (Möglichkeit
zur Detektion von geeignetem Habitat) eine ausschlaggebende Rolle für die
Erreichbarkeit von geeigneten Flächen.
103
Bibliography
Bibliography
Adler P.B., Raff D.A. & Lauenroth W.K. (2001) The effect of grazing on the spatial heterogeneity
of vegetation. Oecologia, 128, pp. 465-479.
Amler H.K., Bahl A., Henle K., Kaule G., Poschlod P. & Settele J. (1999) Populationsbiologie in der
Naturschutzpraxis. Ulmer Verlag, Stuttgart.
Andreassen H.P., Stenseth N.C. & Ims R.A. (2001) Dispersal behaviour and population dynamics
of vertebrates. In Dispersal Ecology (ed. by Bullock J.M., Kenward R.E. & Hails R.S.), Blackwell
Science, Oxford.
Andrewartha H.G. & Birch L.C. (1954) The distribution and abundance of animals. University of
Chicago Press, Chicago.
Appelt M. (1996) Elements of population vulnerability of the blue-winged grasshopper, Oedipoda
caerulescens (LINNAEUS, 1758) (Caelifera, Acrididae). In Species survival in fragmented landscapes (ed. by
Settele J., Margules C., Poschlod P. & Henle K.), Kluwer Academic Publishers, Dordrecht.
Aspinall R.J. & Lees B.G. (1994) Sampling and analysis of spatial environmental data. In Advances in
GIS Research (ed. by Waugh T.C. & Healey R.G.), Taylor and Francis, Southampton.
Augustin N.H., Cummins R.P. & French D.D. (2001) Exploring spatial vegetation dynamics using
logistic regression and a multinomial logit model. Journal of Applied Ecology, 38, pp. 991-1006.
Austin M.P. (1976) On non-linear species response models in ordination. Vegetatio, 33, pp. 33-41.
Austin M.P. (2002) Spatial prediction of species distribution: an interface between ecological theory
and statistical modelling. Ecological Modelling, 157, pp. 101-118.
Baars M.A. (1979) Patterns of movement of radioactive Carabid beetles. Oecologia, 44, pp. 125-140.
Backhaus K., Erichson B., Plinke W. & Weiber R. (2000) Multivariate Analysemethoden - Eine
anwendungsorientierte Einführung. Springer Verlag, Berlin.
Baguette M. & Nève G. (1994) Adult movements between populations in the specialist butterfly
Proclossiana eunomia. Ecological Entomology, 19, pp. 1-5.
Baguette M., Petit S. & Queva F. (2000) Population spatial structure and migration of three
butterfly species within the same habitat network: consequences for conservation. Journal of Applied
Ecology, 37, pp. 100-108.
Baker R.R. (1978) The Evolutionary Ecology od Animal Migration. Hodder & Stoughton, London.
Bakker J.P. (1989) Nature management by grazing and cutting. Kluwer Academic Publishers, Dordrecht.
Baldwin W.F., Riordan D.F. & Smith R.W. (1958) Note on dispersal of radio-active grasshoppers.
The Canadian Entomologist, 90, pp. 374-376.
Bayerische
Landesanstalt
für
Bodenkultur
und
http://www.stmelf.bayern.de/lbp/agm/station/w109info.htm.
Pflanzenanbau
(2001)
Bayerisches Geologisches Landesamt (1998) Geologische Karte von Bayern 1 : 500 000 mit
Erläuterungen und farbigen Beilagen. CD-ROM
Beaufoy G., Baldock D. & Clark J. (1994) The nature of farming: low intensity farming systems in nine
European Countries. Institute for European Environment Polica, London.
Beck J.R. & Shultz E.K. (1986) The use of ROC curves in test performance evaluation. Archives of
Pathology and Laboratory Medicine, 110, pp. 13-20.
Begon M.E., Harper J.L. & Townsend C.R. (1990) Ökologie. Spektrum Akademischer Verlag, Berlin.
Bibliography
104
Bellmann H. (1993) Heuschrecken: beobachten - bestimmen. Naturbuch Verlag, Augsburg.
Bengtsson B.O. (1978) Avoiding inbreeding: at what cost? Journal of Theoretical Biology, 73, pp. 439444.
Bignal E.M. & McCracken D.I. (1996) Low-intensity farming systems in the conservation of the
countryside. Journal of Applied Ecology, 33, pp. 413-424.
Bjornstad O.N., Andreassen H.P. & Ims R.A. (1998) Effects of habitat patchiness and connectivity
on the spatial ecology of the root vole Microtus oeconomus. Journal of Animal Ecology, 67, pp. 127-140.
Bobbink R. & Willems J.H. (1993) Restoration management of abandoned chalk grassland in the
Netherlands. Biodiversity and Conservation, 2, pp. 616-626.
Bonn A. & Schröder B. (2001) Habitat models and their transfer for single and multi species
groups: a case study of carabids in an alluvial forest. Ecography, 24, pp. 483-496.
Boorman S.A. & Levitt P.R. (1973) Group selection on the boundary of a stable population.
Theoretical Population Biology, 4, pp. 85-128.
Bourn N.A.D. & Thomas J.A. (2002) The challenge of conserving grassland insects at the margins
of their range in Europe. Biological Conservation, 104, pp. 285-292.
Brennan L.A., Block W.M. & Gutierrez R.J. (1986) The use of multivariate statistics for developing
habitat suitability index models. In Wildlife 2000: Modelling habitat relationships of terrestrial vertebrates
(ed. by Verner J., Morrison M.L. & Ralph C.J.), University of Wisconsin Press, Madison.
Brommer J.E. & Fred M.S. (1999) Movement of the Apollo butterfly Parnassius apollo related to host
plant and nectar plant patches. Ecological Entomology, 24, pp. 125-131.
Brown J.H. & Kodrik-Brown A. (1977) Turnover rates in insular biogeography: effect of
immigration on extinction. Ecology, 58, pp. 445-449.
Buckland S.T., Burnham K.P. & Augustin N.H. (1997) Model selection: An integral part of
inference. Biometrics, 53, pp. 603-618.
Bullock J.M., Kenward R.E. & Hails R.S. (2001) Dispersal Ecology. Blackwell Publishing, Oxford.
Butterweck M. (1998) Metapopulationsstudien an Waldlaufkäfern (Coleoptera: Carabidae) - Einfluß von
Korridoren und Trittsteinbiotopen. Wissenschaft und Technik Verlag, Berlin.
Cain M.L. (1985) Random search by herbivorous insects: a simulation model. Ecology, 66, pp. 876888.
Cao G. (1995) The definition of the niche by fuzzy set theory. Ecological Modelling, 77, pp. 65-71.
Capen D.E., Fenwick J.W., Inkley D.B. & Boynton A.C. (1986) Multivariate models of songbird
habitat in New England forests. In Wildlife 2000: Modelling habitat relationships of terrestrial vertebrates.
(ed. by Verner J., Morrision M.L. & Ralph C.J.), University of Wisconsin Press, Madison.
Capman W.C., Batzli G.O. & Simms L.E. (1990) Responses of the common sooty wing skipper to
patches of host plants. Ecology, 71, pp. 1430-1440.
Clark J.S. (1998) Why trees migrate so fast: confronting theory with dispersal biology and the
paleorecord. American Naturalist, 152, pp. 204-224.
Clark J.S., Fastie C., Hurtt G., Jackson S.T., Johnson C., King G.A., Lewis M., Lynch J., Pacala S.,
Prentice C., Schupp E.W., Webb III T. & Wyckoff P. (1998) Reid's paradox of rapid plant
migration. Dispersal theory and interpretation of paleoecological records. BioScience, 48, pp. 13-24.
Clark J.S., Silman M., Kern R., Macklin E. & HilleRisLambers J. (1999) Seed dispersal near and far:
patterns across temperate and tropical forests. Ecology, 80, pp. 1475-1494.
Bibliography
105
Clobert J., Danchin E., Dhondt A.A. & Nichols D.J. (2001) Dispersal. Oxford University Press,
Oxford.
Cohen D. & Levin S.A. (1991) Dispersal in patchy environments: the effects of temporal and
spatial structure. Theoretical Population Biology, 39, pp. 63-99.
Collingham Y.C., Wadsworth R.A., Huntley B. & Hulme P.E. (2000) Predicting the spatial
distribution of non-indigenous riparian weeds: issues of spatial scale and extent. Journal of Applied
Ecology, 37, pp. 13-27.
Comins H.N., Hamilton W.D. & May R.M. (1980) Evolutionarily stable dispersal strategies. Journal
of Theoretical Biology, 82, pp. 205-230.
Conradt L., Bodsworth E.J., Roper T.J. & Thomas C.D. (2000) Non-random dispersal in the
butterfly Maniola jurtina: implications for metapopulation models. Proceedings of the Royal Society
London, 267, pp. 1505-1510.
Conradt L., Roper T.J. & Thomas C.D. (2001) Dispersal behaviour of individuals in
metapopulations of two British butterflies. Oikos, 95, pp. 416-424.
Cousins S.A.O., Lavorel S. & Davies I. (2003) Modelling the effects of landscape pattern and
grazing regimes on the persistence of plant species with high conservation value in grasslands in
south-eastern Sweden. Landscape Ecology, 18, pp. 315-332.
Coyne J.A., Boussy I.A., Prout T., Bryant S.H., Jones J.S. & Moore J.A. (1982) Long-distance
migration of Drosophila. The American Naturalist, 119, pp. 589-595.
Cresswell J.E. (1997) Spatial heterogeneity, pollinator behaviour and pollinator-mediated gene flow:
bumble bee movements in variously aggregated rows of oil-seed rape. Oikos, 78, pp. 546-556.
Crist T.O., Guertin D.S., Wiens J.A. & Milne B.T. (1992) Animal movement in heterogeneous
landscapes: an experiment with Eleodes beetles in shortgrass prairie. Functional Ecology, 6, pp. 536544.
Crist T.O. & Wiens J.A. (1995) Individual movements and estimation of population size in darkling
beetles (Coleoptera: Tenebrionidae). Journal of Animal Ecology, 64, pp. 733-746.
Dempster J.P. & McLean I.F.G. (1998) Insect populations. In theory and practice. Kluwer Academic
Publishers, Dordrecht.
den Boer P.J. (1981) On the survival of populations in a heterogeneous and variable environment.
Oecologia, 50, pp. 39-53.
Dennis R.L.H. & Eales H.T. (1999) Probability of site occupancy in the large heath butterfly
Coenonympha tullia determined from geographical and ecological data. Biological Conservation, 87, pp.
295-301.
Detzel P. (1998) Die Heuschrecken Baden-Württembergs. Ulmer, Stuttgart.
Dingle H. (1996) Migration: the biology of life on the move. Oxford University Press, Oxford.
Doak D.F., Marino P.C. & Kareiva P.M. (1992) Spatial scale mediates the influence of habitat
fragmentation on dispersal success: implications for conservation. Theoretical Population Biology, 41,
pp. 315-336.
Dobzhansky T. & Wright S. (1943) Genetics of natural populations. X. Dispersion rates in
Drosophila pseudoobscura. Genetics, 28, pp. 304-340.
Doncaster C.P., Rondinini C. & Johnson P.C.D. (2001) Field test for environmental correlates of
dispersal in hedgehogs Erinaceus europaeus. Journal of Animal Ecology, 70, pp. 33-46.
Dufrene M. & Legendre P. (1991) Geographic structure and potential ecological factors in Belgium.
Journal of Biogeography, 18, pp. 257-266.
Bibliography
106
Dunning J.B., Danielson B.J. & Pulliam H.R. (1992) Ecological processes that affect populations in
complex landscapes. Oikos, 65, pp. 169-175.
Dunning J.B., Stewart D.J., Danielson B.J., Noon B.R., Root T.L., Lamberson R.H. & Stevens E.E.
(1995) Spatially explicit population-models - current froms and future uses. Ecological Applications, 5,
pp. 3-11.
Ebert G. & Rennwald E. (1991) Die Schmetterlinge Baden-Württembergd. Ulmer Verlag, Stuttgart.
Efron B. & Tibshirani R.J. (1993) An introduction to the bootstrap. Chapman & Hall, London.
Ehrlich P.R., Murphy D.D., Singer M.C., Sherwood C.B., White R.R. & Brown I.L. (1980)
Extinction, reduction, stability and increase: the responses of checkerspot butterfly (Euphydryas)
populations to the California drought. Oecologia, 46, pp. 101-105.
Ehrlinger M. (1991) Kleinräumige Wanderungen der Feldheuschrecke Stenobothrus lineatus zwischen
unterschiedlich exponierten Halbtrockenrasen. Diplomarbeit, Fakultät für Biologie, Bayerische
Julius-Maximiliansuniversität Würzbrug, Würzburg.
Elsner O. (1994) Geplantes Naturschutzgebiet "Südlicher Hassbergtrauf" im Landkreis Hassberge.
Institut für Vegetationskunde und Landschaftsökologie, Zeckern.
Elton C. (1930) Animal ecology and evolution. Clarendon Press, Oxford.
Fahrig L. & Paloheimo J.E. (1987) Interpatch dispersal of the cabbage butterfly. Canadian Journal of
Zoology, 65, pp. 616-622.
Fahrig L. (1988) A general model of populations in patchy habitats. Applied Mathematical Computing,
27, pp. 53-66.
Ferrier (1991) Computer-based extension of forest fauna survey data: current issues, problems and
directions. In Conservation of Australia's Forest Fauna (ed. by Lunney D.), Royal Zoological Society of
New South Wales, Sydney.
Fielding A.H. & Haworth P.F. (1995) Testing the generality of bird-habitat models. Conservation
Biology, 9, pp. 1466-1481.
Fielding A.H. & Bell J.F. (1997) A review of methods for the assessment of prediction errors in
conservation presence/absence models. Environmental Conservation, 24, pp. 38-49.
Fleishman E., Mac Nally R. & Fay J.P. (2003) Validation tests of predictive models of butterfly
occurrence based on environmental variables. Conservation Biology, 17, pp. 806-817.
Franklin J.F. (1993) Preserving biodiversity: species, ecosystems, or landscapes? Ecological
Applications, 3, pp. 202-205.
Fuller R.M. (1987) The changing extent and conservation interest of lowland grasslands in England
and Wales: a review of grassland surveys 1930-84. Biological Conservation, 40, pp. 281-300.
Gaines M.S. & McClenaghan L.R.J. (1980) Dispersal in small mammals. Annual Review Ecolological
Systematics, 11, pp. 163-196.
Gaines S.D. & Bertness M. (1993) The dynamics of juvenile dispersal: why field ecologists must
integrate. Ecology, 74, pp. 2430-2435.
Gandon S. & Michalakis Y. (1999) Evolutionary stable dispersal rate in a metapopulation with
extinctions and kin competition. Journal of Theoretical Biology, 199, pp. 275-290.
Gandon S. & Michalakis Y. (2001) Multiple causes of the evolution of dispersal. In Dispersal (ed. by
Clobert J., Danchin E., Dhondt A.A. & Nichols D.), Oxford University Press, New York.
Gauch H.G., Jr. & Chase G.B. (1974) Fitting the Gaussian curve to ecological data. Ecology, 55, pp.
1377-1381.
Bibliography
107
Gillis E.A. & Nams V.O. (1998) How red-backed voles find habitat patches. Canadian Journal of
Zoology, 76, pp. 791-794.
Goodwin B.J., Bender D.J., Contreras T.A., Fahrig L. & Wegner J.F. (1999) Testing for habitat
detection distances using orientation data. Oikos, 84, pp. 160-163.
Gottschalk E. (1996) Population vulnerability of the grey bush cricket Platycleis albopunctata
(GOETZE, 1778) (Ensifera: Tettigoniidae). In Species survival in fragmented landscapes (ed. by Settele J.,
Margules C., Poschlod P. & Henle K.), Luwer Academic Publishers, Dordrecht.
Gottschalk E. (1997) Habitatbindung und Populationsökologie der Westlichen Beissschrecke
(Platycleis albopunctata, GOETZE 1778) (Orthoptera:Tettigoniidae) - Eine Grundlage für den Schutz
der Art. PhD Thesis, Lehrstuhl für Tierökologie, Bayerische Julius-Maximilians-Universität
Würzburg, Würzburg, Germany.
Greenwood P.J. (1980) Mating systems, philopatry and dispersal in birds and mammals. Animal
Behaviour, 28, pp. 1140-1162.
Greenwood P.J. & Harvey P.H. (1982) The natal and breeding dispersal of birds. Annual Review of
Ecolology and Systematics, 13, pp. 1-21.
Grinell J. (1917) The niche-relationships of the California trasher. Auk, 34, pp. 427-433.
Grubb T.C., Jr. & Doherty P.F., Jr. (1999) On home-range gap-crossing. Auk, 116, pp. 618-628.
Guisan A. & Zimmermann N.E. (2000) Predictive habitat distribution models in ecology. Ecological
Modelling, 135, pp. 147-186.
Guisan A., Edwards Jr T.C. & Hastie T. (2002) Generalized linear and generalized additive models
in studies of species distributions: setting the scene. Ecological Modelling, 157, pp. 89-100.
Gustafson E.J. & Gardner R.H. (1996) The effect of landscape heterogeneity on the probabilty of
patch colonization. Ecology, 77, pp. 94-107.
Haddad N.M. (1999) Corridor use predicted from behaviors at habitat boundaries. The American
Naturalist, 153, pp. 215-227.
Haefner J.W. & Crist T.O. (1994) Spatial model of movement and foraging in harvester ants
(Pogonomyrex) (I): the role of memory and communication. Journal of Theoretical Biology, 166, pp.
299-313.
Halle S. (1996) Metapopulationen und Naturschutz - eine Übersicht. Zeitschrift für Ökologie und
Naturschutz, 5, pp. 141-150.
Hamazaki T. (1996) Effects of patch shape on the number of organisms. Landscape Ecology, 11, pp.
299-306.
Hanley J.A. & McNeil B.L. (1982) The meaning and use of the area under a ROC curve. Radiology,
143, pp. 29-36.
Hanley J.A. & McNeil B.L. (1983) A method comparing the areas under receiver operating
characteristic curves derived from the same cases. Radiology, 148, pp. 839-843.
Hanski I. & Zhang D.-Y. (1993) Migration, metapopulation dynamics and fugitive co-existence.
Journal of Theoretical Biology, 163, pp. 491-504.
Hanski I., Kuussaari M. & Nieminen M. (1994) Metapopulation structure and migration in the
butterfly Melitaea cinxia. Ecology, 75, pp. 747-762.
Hanski I. & Thomas C.D. (1994) Metapopulation dynamics and conservation: a spatially explicit
model applied to butterflies. Biological Conservation, 68, pp. 167-180.
Hanski I. (1994a) A practical model of metapopulation dynamics. Journal of Animal Ecology, 63, pp.
151-162.
Bibliography
108
Hanski I. (1994b) Patch-occupancy dynamics in fragmented landscapes. Tree, 9, pp. 131-135.
Hanski I. & Gilpin M.E. (1997) Metapopulation biology: ecoloy, genetics and evolution. Academic Press, San
Diego, California.
Hanski I. (2001) Population dynamic consequences of dispersal in local populations and in
metapopulations. In Dispersal (ed. by Oxford University Press, Oxford.
Hansson L. (1991) Dispersal and connectivity in metapopulations. In Metapopulation Dynamics (ed.
by Gilpin M.E. & Hanski I.), Academic Press, London.
Harrell F.E.J. (2001) Regression modeling strategies: with applications to linear models, logistic regression, and
survival analysis. Springer-Verlag,
Harrison S. (1989) Long-distance dispersal and colonization in the Bay checkerspot butterfly,
Euphydryas bayensis. Ecology, 70, pp. 1236-1243.
Harrison S. (1991) Local extinction in a metapopulation context: an empirical evaluation. Biological
Journal of the Linnean Society of London, 42, pp. 73-88.
Hartley J.C. & Warne A.C. (1972) The developmental biology of the egg stage of Western
European Tettigoniidae (Orthoptera). Journal of Zoology, 168, pp. 267-298.
Harz K. (1969) The Orthoptera of Europa. The Hague,
Haupt H. (1995) Faunistische Beobachtungen an Heuschrecken (Orthoptera: Saltatoria) im
Unteren Odertal bei Schwedt (Brandenburg) mit einem Wiederfund von Platycleis montana
KOLLAR, 1833. Articulata, 8, pp. 83-87.
Heglund P.J., Jones J.R., Fredrickson L.H. & Kaise M.S. (1994) Use of boreal forested wetlands by
Pacific loons (Gavia pacifica Lawrence) and horned grebes (Podiceps auritus L.): Relations with
limnological characteristics. Hydrobiologia, 279/280, pp. 171-183.
Heglund P.J. (2002) Foundations of Species-Environment Relations. In Predicting species occurrences
(ed. by Scott J.M., Heglund P.J., Morrision M.L., Haufler J.B., Raphael M.G., Wall W.A. & Samson
F.B.), Island Press, Washington.
Hein S., Gombert J., Hovestadt T. & Poethke H.J. (2003) Movement patterns of Platycleis
albopunctata in different types of habitat: matrix is not always matrix. Ecological Entomology, 28, pp.
432-438.
Hein S., Pfenning B., Hovestadt T. & Poethke H.J. (in press) Patch density, movement pattern, and
realized dispersal distances in a patch-matrix landscape - a simulation study. Ecological Modelling, pp.
Heinrich W., Marstaller R., Bährmann R., Perner J. & Schäller G. (1998) Naturschutzreport - Jena:
Das Naturschutzgebiet "Leutratal" bei Jena - Struktur- und Sukzessionsforschung in GraslandÖkosystemen. Jena.
Heller K.-G. & von Helversen O. (1990) Survival of a Phaneropterid bush-cricket studied by a new
marking technique (Orthoptera: Phaneropteridae). Entomologica Generalis, 15, pp. 203-208.
Hensle J. (1995) Fund einer Herbstgeneration von Coenonympha arcania im Elsaß. Nachrichten des
entomologischen Vereins Apollo, 16, pp. 28.
Higgins S.I. & Richardson D.M. (1999) Predicting plant migration rates in a changing world: the
role of long-distance dispersal. American Naturalist, 153, pp. 464-475.
Hill J.K., Thomas C.D. & Lewis O.T. (1996) Effects of habitat patch size and isolation on dispersal
by Hesperia comma butterflies: implications for metapopulation structure. Journal of Animal Ecology, 65,
pp. 725-735.
Hinch S.G., Somers K.M. & Collins N.C. (1994) Spatial autocorrelation and assessment of habitatabundance relationships in littoral zone fish. Canadian Journal of Fisheries and Aquatic Sciences, 51, pp.
701-712.
Bibliography
109
Hirzel A. & Guisan A. (2002) Which is the optimal sampling strategy for habitat suitability
modelling. Ecological Modelling, 157, pp. 331-341.
Hosmer D.W. & Lemeshow S. (2000) Applied logistic regression. Wiley, New York.
Hovestadt T., Poethke H.J. & Messner S. (2000) Variability in dispersal distances generates typical
successional patterns: a simple simulation model. Oikos, 90, pp. 612-619.
Hovestadt T., Messner S. & Poethke H.J. (2001) Evolution of reduced dispersal mortality and "fattailed" dispersal functions in autocorrelated landscapes. Proceedings of the Royal Society of London (B),
268, pp. 385-391.
Huk T. (1997) Laufkäfer als Zielarten für ein Naturschutzmanagement von Niedermooren.
Huston M.A. (1979) A general hypothesis of species diversity. The American Naturalist, 113, pp. 81101.
Huston M.A. (1994) Biological diversity the coexistence of species on changing landscapes. Cambridge
University press, Cambridge.
Hutchinson G.E. (1957) Concluding remarks. Cold Spring Harbour Symposium on Quantitative Biology
22,
Ims R.A. (1995) Movement patterns related to spatial structures. In Mosaic landscapes and ecological
processes (ed. by L. H., Fahrig L. & Meriam G.), Chapman & Hall, London.
Ingrisch S. (1976) Vergleichende Untersuchung zum Nahrungsspektrum mitteleuropäischer
Laubheuschrecken (Saltatoria: Tettigoniidae). Entomologische Zeitschrift, 20, pp. 217-224.
Ingrisch S. (1986) The plurennial life cycle of the European Tettigoniidae. 1. The effect of
temperature on embryonic development and hatching. Oecologia, 70, pp. 606-616.
Ingrisch S. & Köhler G. (1998) Die Heuschrecken Mitteleuropas. Westarp-Wissenschaften, Magdeburg.
Jacquemyn H., Brys R. & Hermy M. (2003) Short-term effects of different management regimes on
the response of calcareous grassland vegetation to increased nitrogen. Biological Conservation, 111, pp.
137-147.
Johnson D.H. (1980) The comparison of usage and availability measurements for evaluating
resource preference. Ecology, 61, pp. 65-71.
Johnson A.R., Wiens J.A., Milna B.T. & Crist T.O. (1992) Animal movements and population
dynamics in heterogeneous landscapes. Landscape Ecology, 7, pp. 63-75.
Judas M., Dornieden K. & Strothmann U. (2002) Distribution patterns of carabid beetle species at
the landscape-level. Journal of Biogeography, 29, pp. 491-508.
Kahmen S., Poschlod P. & Schreiber K.-F. (2002) Conservation management of calcareous
grasslands. Changes in plant species composition and response of functional traits during 25 years.
Biological Conservation, 104, pp. 319-328.
Karagatzides J.D., Manson H.R. & Tsuji L.J.S. (2003) Spatial distribution and performance of
Scirpus americanus ramets across a temperate intertidal marsh resource gradient. Plant Ecology, 169, pp.
215-226.
Kareiva P. (1985) Finding and losing host plants by Phyllotreta: patch size and surrounding habitat.
Ecology, 66, pp. 1809-1816.
Kareiva P. (1990) Population dynamics in spatially complex environments: Theory and data.
Philosophical Transactions of the Royal Society of London, 330, pp. 175-190.
Keitt T.H., Bjornstad O.N., Dixon P.M. & Citron-Pousty S. (2002) Accounting for spatial pattern
when modeling organism-environment interactions. Ecography, 25, pp. 616-625.
Bibliography
110
Kenward R.E., Rushton S.P., Perrins C.M., Macdonald D.W. & South A.B. (2001) From marking
to modelling: dispersal study techniques for land vertebrates. In Dispersal Ecology (ed. by Bullock
J.M., Kenward R.E. & Hails R.S.), Blackwell Publishing, Oxford.
Kindvall O. & Ahlen I. (1992) Geometrical factors and metapopulation dynamics of the bush
cricket, Metrioptera bicolor Philippi (Orthoptera: Tettigoniidae). Conservation Biology, 6, pp. 520-529.
Kindvall O., Vessby K., Berggren A. & Hartman G. (1998) Individual mobility prevents an allee
effect in sparse populations of the bush cricket Metrioptera roeseli: an experimental study. Oikos, 81,
pp. 449-457.
Kindvall O. (1999) Dispersal in a metapopulation of the bush cricket Metrioptera bicolor (Orthoptera:
Tettigoniidae). Journal of Animal Ecology, 68, pp. 172-185.
King A.W. & With K.A. (2002) Dispersal success on spatially structured landscapes: when do
spatial pattern and dispersal behavior really matter? Ecological Modelling, 147, pp. 23-39.
Kitching R. (1971) A simple simulation model of dispersal of animals among units of discrete
habitats. Oecologia, 7, pp. 95-116.
Kleyer M., Kratz R., Lutze G. & Schröder B. (1999/2000) Habitatmodelle für Tierarten:
Entwicklung, Methoden und Perspektiven für die Anwendung. Z. Ökologie und Naturschutz, 8, pp.
177-194.
Köhler G. (1996) The ecological background of population vulnerability in Central European
grasshoppers and bush crickets: a brief review. In Species survival in fragmented landscapes (ed. by Settele
J., Margules C., Poschlod P. & Henle K.), Kluwer Academic Publishers, Dordrecht.
Kot M., Lewis M.A. & van den Driessche P. (1996) Dispersal data and the spread of invading
organisms. Ecology, 77, pp. 2027-2042.
Krätzel K. (1999) Habitatpräferenzen der beiden wärmeliebenden Heuschreckenarten Metrioptera
bicolor und Platycleis albopunctata. Diploma Thesis, Lehrstuhl für Zoologie, Bayerische-JuliusMaximilians-Universität Würzburg, Würzburg.
Krätzel K., Butterweck M.D. & Hovestadt T. (2002) Habitatwahl von Metrioptera bicolor auf
unterschiedlichen Maßstabsebenen (Ensifera: Tettigoniidae). Articulata, 17, pp. 21-37.
Krebs C.J. (2001) Ecology. Benjamin Cummings, San Francisco.
Kruess A. & Tscharntke T. (2002) Grazing intensity and the diversity of grasshoppers, butterflies,
and trap-nesting bees and wasps. Conservation Biology, 16, pp. 1570-1580.
Kuhn W. (1998) Flächendeckende Analyse ausgewählter ökologischer Parameter.
Hochschulschriften, Peter Lang Europäischer Verlag der Wissenschaften, Frankfurt.
Europäische
Kull K. & Zobel M. (1991) High species richness in an Estonian wooded meadow. Journal of
Vegetation Science, 2, pp. 277-714.
Kuussaari M., Nieminen M. & Hanski I. (1996) An experimental study of migration in the Glanville
fritillary butterfly Melitea cinxia. Journal of Animal Ecology, 65, pp. 791-801.
Lahaye W.S., Gutierrez R.J. & Akcakaya H.R. (1994) Spotted owl metapopulation dynamics in
southern california. Journal of Animal Ecology, 63, pp. 775-785.
Leftwich K.N., Angermeier P.L. & Dolloff C.A. (1997) Factors influencing behavior and
transferability of habitat models for a benthic stream fish. Transactions of the American Fisheries Society,
126, pp. 725-734.
Legendre P. (1993) Spatial autocorrelation: Trouble or new paradigm? Ecology, 74, pp. 1659-1673.
Lehmann A., Overton J.M. & Leathwick J.R. (2002) GRASP: generalized regression analysis and
spatial prediction. Ecological Modelling, 157, pp. 189-207.
Bibliography
111
Levins R. (1969) Some demographic and genetic consequences of environmental heterogeneity for
biological control. Bulletin of the Entomological Society of America, 15, pp. 237-240.
Levins R. (1970) Extinction. In Some mathematical problems in biology (ed. by Gerstenhaber M.),
American Mathematical Society, Providence.
Lichstein J.W., T.R. S., Shriner S.A. & Franzreb K. (2002) Spatial autocorrelation and
autoregressive models in ecology. Ecological Monographs, 72, pp. 445-463.
Lidicker W.Z.J. (1999) Responses of mammals to habitat edges: an overview. Landscape Ecology, 14,
pp. 333-343.
Lindenmayer D.B., Cunningham P.B., Tanton M.T., Nix H.A. & Smith A.P. (1991) The
conservation of arboreal marsupials in the montane ash forests of the central highlands of Victoria,
South East Australia: III. The habitat requirements of leadbeater´s possum Gymnobelideus leadbeateri
and models of the diversity and abundance of arboreal marsupials. Biological Conservation, 56, pp.
295-315.
Maas S., Detzel P. & Staudt A. (2002) Gefährdungsanalyse der Heuschrecken Deutschlands Verbreitungsatlas, Gefährdungseinstufung und Schutzkonzepte. Bundesamt für Naturschutz (BfN), Bonn.
MacArthur R.H. & Wilson E.O. (1967) The theory of island biogeography. Princeton University Press,
Princeton, NJ.
Macdonald D.W. & Johnson D.D.P. (2001) Dipersal in theory and practice: consequences for
conservation biology. In Dispersal (ed. by Clobert J., Danchin E., Dhondt A.A. & Nichols J.D.),
Oxford University Press, Oxford.
Mackey B.G. & Lindenmayer D.B. (2001) Towards a hierarchical framework for modelling the
spatial distribution of animals. Journal of Biogeography, 28, pp. 1147-1166.
Manel S., Dias J.M., Buckton S.T. & Ormerod S.J. (1999a) Alternative methods for predicting
species distribution: an illustration with Himalayan river birds. Journal of Applied Ecology, 36, pp. 734747.
Manel S., Dias J.-M. & Ormerod S.J. (1999b) Comparing discriminant analysis, neural networks and
logistic regression for predicted species distributions: a case study with a Himalayan river bird.
Ecological Modelling, 120, pp. 337-347.
Manel S., Williams H.C. & Ormerod S.J. (2001) Evaluating presence-absence models in ecology:
the need to account for prevalence. Journal of Applied Ecology, 38, pp. 921-931.
Marshall J.A. & Haes E.C.M. (1988) Grasshoppers and allied insects of Great Britain and Irland. Harley
Books, Colchester.
Martin M., Bastardie F., Richard D. & Burel F. (2001) Studying boundary effects on animal
movement in heterogeneous landscapes: the case of Abax ater (Coleoptera: Carabidae) in hedgrow
network landscapes). Life Sciences, 324, pp. 1029-1035.
Matter S.F. & Roland J. (2002) An experimental examination of the effects of habitat quality on the
dispersal and local abundance of the butterfly Parnassius smintheus. Ecological Entomology, 27, pp. 308316.
McCauley D.E. (1995) Effects of population dynamics on genetics in mosaic landscapes. In Mosaic
landscapes and ecological processes (ed. by Hanson L., Fahrig L. & Merriam G.), Chapman & Hall,
London.
McConnaughay K.D.M. & Bazzaz F.A. (1987) The relationship between gap size and performance
of several colonizing annuals. Ecology, 68, pp. 411-416.
Mech S.G. & Zollner P.A. (2002) Using body size to predict perceptual range. Oikos, 98, pp. 47-52.
Menéndez R., Gutiérrez D. & Thomas C.D. (2002) Migration and allee effects in the six-spot
burnet moth Zygaena filipendulae. Ecological Entomology, 27, pp. 317-325.
Bibliography
112
Meriam G. (1991) Corridors and connectivity: animal populations in heterogeneous environments.
In Nature conservation 2: the role of corridors (ed. by Saunders D.A. & Hobbs R.J.), Surrey Beatty &
Sons, Chipping Norton, NSW.
Merkel E. (1980) Sandtrockenstandorte und ihre Bedeutung für zwei "Ödland"-Schrecken der
Roten Liste. Schriftenreihe Naturschutz und Landschaftspflege, 12, pp. 63-69.
Metz J.A.A. & Gyllenberg M. (2001) How should we define fitness in structured metapopulation
models? Including an application to the calculation of evolutionary stable dispersal strategies.
Proceedings of the Royal Society of London (B), 268, pp. 499-508.
Moilanen A. & Hanski I. (1998) Metapopulation dynamics: effects of habitat quality and landscape
structure. Ecology, 79, pp. 2503-2515.
Moilanen A. & Hanski I. (2001) On the use of connectivity measures in spatial ecology. Oikos, 95,
pp. 147-151.
Moilanen A. & Nieminen M. (2002) Simple connectivity measures in spatial ecology. Ecology, 83, pp.
1131-1145.
Morrison M.L., Marcot B.G. & Mannan R.W. (1998) Wildlife-habitat relationsships - concepts and
applications. The University of Wisconsin Press, Madison.
Mortimer S.R., Hollier J.A. & Brown V.K. (1998) Interactions between plant and insect diversity in
the restoration of lowland calcareous grasslands in southern Britain. Applied Vegetation Science, 1, pp.
101-114.
Mühlenberg M. (1993) Freilandökologie. UTB, Heidelberg.
Mühlenberg M., Henle K., Settele J., Poschlod P., Seitz A. & Kaule G. (1996) Studying species
survival in fragmented landscapes: The approach of the FIFB. In Species survival in fragmented
landscapes (ed. by Settele J., Margules C., Poschlod P. & Henle K.), Kluwer Academic Publishers,
Dordrecht.
Nadig A. & Thorens P. (1994) Rote Liste der gefährdeten Heuschrecken der Schweiz. In Rote Listen
der gefährdeten Tierarten der Schweiz (ed. by Duelli P.), Bundesamt für Umwelt, Wald und Landschaft,
Bern.
Nagelkerke N.J.D. (1991) A note on a general definition of the coefficient of determination.
Biometrika, 78, pp. 691-692.
Nève G., Barascud B., Hughes R.M., Aubert J., Descimon H., Lebrun P. & Baguette M. (1996a)
Dispersal, colonisation power and metapopulation structure in the vulnerable butterfly Proclossiana
eunomia (Lepidoptera, Nymphalidae). Journal of Applied Ecology, 33, pp. 14-22.
Nève G., Mousson L. & Baguette M. (1996b) Adult dispersal and genetic structure of butterfly
populations in a fragmented landscape. Acta Ecologia, 17, pp. 621-626.
New T.R. (1995) An introduction to invertebrate conservation biology. Oxford University Press, Oxford.
New T.R., Pyle R.M., Thomas J.A., Thomas C.D. & Hammond P.C. (1995) Butterfly conservation
management. Annual Review of Entomology, 40, pp.
Nieminen M. (1996) Migration of moth species in a network of small islands. Oecologia, 108, pp.
643-651.
Noss R.F. (1990) Indicators for monitoring biodiversity: a hierarchical approach. Conservation Biology,
4, pp. 355-364.
Nottingham S.F. (1988) Host plant finding for oviposition by adult cabbage root fly, Delia radicum.
Journal of Insect Physiology, 34, pp. 227-234.
Oostermeijer J.G.B., Berholz A. & Poschlod P. (1996) Genetical aspects of fragmented plant
populations. In Species survival in fragmented landscapes (ed. by Settele J., Margules C., Poschlod P. &
Henle K.), Kluwer Academic Publishers, Dordrecht.
Bibliography
113
Opdam P. (1990) Dispersal in fragmented populations: the key to survival. In Species dispersal in
agricultural habitats (ed. by Bunce R.G.H. & Howard D.C.), Belhaven Press, New York.
Opitz S., Köhler G. & Mabelis A. (1998) Local movement of the grasshopper Chorthippus parallelus
(Zett.). Proceedings of Experimental and Applied Entomology, 9, pp. 53-57.
Oppel S., Schaefer H.M., Schmidt V. & Schröder B. (in press) Habitat selection by the pale-headed
brush-finch (Atlapetes pallidiceps) in southern Ecuador: implications of conservation. Biological
Conservation, pp.
Orians G.H. & Wittenberger J.F. (1991) Spatial and temporial scales in habitat selection. American
Naturalist, 137, pp. 29-49.
Osborne P.E., Alonso J.C. & Bryant R.G. (2001) Modelling landscape-scale habitat use using GIS
and remote sensing: a case study with great bustards. Journal of Applied Ecology, 38, pp. 458-471.
Oschmann M. (1993) Artunterschiede in der Phänologie der Heuschrecken (Saltatoria). Articulata,
8, pp. 35-43.
Ovaskainen O. & Hanski I. (2001) Spatially structured metapopulation models: global and local
assessment of metapopulation capacity. Theoretical Population Biology, 60, pp. 281-302.
Paradis E., Baillie S.R. & Sutherland W.J. (2002) Modeling large-scale dispersal distances. Ecological
Modelling, 151, pp. 279-292.
Pearce J.L., Burgman M.A. & Franklin D.C. (1994) Habitat selection by helmeted honeyeater.
Wildlife Research, 21, pp. 53-63.
Pearce J. & Ferrier S. (2000a) An evaluation of alternative algorithms for fitting species distribution
models using logistic regression. Ecological modelling, 128, pp. 127-147.
Pearce J. & Ferrier S. (2000b) Evaluating the predictive performance of habitat models developed
using logistic regression. Ecological modelling, 133, pp. 225-245.
Poethke H.J., Gottschalk E. & Seitz A. (1996a) Gefährdungsgradanalyse einer räumlich
strukturierten Population der westlichen Beißschrecke (Platycleis albopunctata): Ein Beispiel für den
Einsatz des Metapopulationskonzeptes im Artenschutz. Zeitschrift für Ökologie und Naturschutz, 5, pp.
229-242.
Poethke H.J., Seitz A. & Wissel C. (1996b) Species survival and metapopulations: conservation
implications from ecological theory. In Species survival in fragmented landscapes (ed. by Settele J.,
Margules C., Poschlod P. & Henle K.), Kluwer Academic Publishers, London.
Poethke H.J. & Hovestadt T. (2002) Evolution of density and patch-size dependent dispersal rates.
Proceedings of the Royal Society of London (B), 269, pp. 637-645.
Poethke A., Hovestadt T. & Mitesser O. (2003) Local extinction and the evolution of dispersal
rates: causes and correlations. American Naturalist, 161, pp. 631-640.
Portnoy S. & Willson M.F. (1993) Seed dispersal curves - behavior of the tail of the distribution.
Evolutionary Ecology, 7, pp. 25-44.
Poschlod P., Bakker J., Bonn S. & Fischer S. (1996) Dispersal of plants in fragmented landscapes.
In Species survival in fragmented landscapes (ed. by Settele J., Margules C., Poschlod P. & Henle K.),
Kluwer Academic Publishers, Dordrecht.
Poschlod P. & Schumacher W. (1998) Rückgang von Pflanzen - und Pflanzengeseellschaften des
Grünlandes.- Gefährdungsursachen und Handlungsbedarf. Schriftenreihe für Vegetationskunde, 29, pp.
83-99.
Pulliam H.R. (1988) Source, sinks, and population regulation. American Naturalist, 132, pp. 652-661.
Pulliam H.R., Dunning J.B. & Liu J.G. (1992) Population-dynamics in complex landscapes - a case
study. Ecological Applications, 2, pp. 165-177.
Bibliography
114
Pykälä J. (2003) Effects of restoration with cattle grazing on plant species composition and richness
of semi-natural grasslands. Biodiversity and Conservation, 12, pp. 2211-2226.
Reich M. & Grimm V. (1996) Das Metapopulationskonzept in Ökologie und Naturschutz: Eine
kritische Bestandsaufnahme. Zeitschrift für Ökologie und Naturschutz, 5, pp. 123-139.
Reineking B. & Schröder B. (2003) Computer-intensive methods in the analysis of species-habitat
relationships. Proceedings "Genes, Bits and Ecosystems" Theory in Ecology, Frankfurt.
Rice W. (1989) Analyzing tables of statistical tests. Evolution, 43, pp. 223-225.
Ricketts T.H. (2001) The matrix matters: Effective Isolation in Fragmented Landscapes. American
Naturalist, 158, pp. 87-99.
Riegert P.W., Fuller R.A. & Putnam L.G. (1954) Studies on dispersal of grasshoppers (Acrididae)
tagged with phosphorus-32. The Canadian Entomologist, 86, pp. 223-232.
Roland J., Keyghobadi N. & Fownes S. (2000) Alpine Parnassius butterfly dispersal: effects of
landscape and population size. Ecology, 81, pp. 1642-1653.
Ronce O., Gandon S. & Rousset F. (2000) Kin selection and natal dispersal in an age-structured
population. Theoretical Population Biology, 58, pp. 143-159.
Root R.B. & Kareiva P.M. (1984) The search for resources by cabbage butterfly (Pieris rapae):
ecological consequences and adaptive significance of Markovian movements in a patchy
environment. Ecology, 65, pp. 147-165.
Rosenzweig M.L. (1981) A theory of habitat selection. Ecology, 62, pp. 327-335.
Rosenzweig M.L. (1995) Species diversity in space and time. Cambridge University Press, Cambridge.
Roslin T. (2000) Dung beetle movements at two spatial scales. Oikos, 91, pp. 323-335.
Rutte E. (1981) Bayerns Erdgeschichte. Der geologische Führer durch Bayern. Ehrenwirth, München.
Samietz J., Berger U. & Köhler G. (1996) A population vulnerability analysis of the stripe-winged
grasshopper, Stenobothrus lineatus (Caelifera: Acrididae). In Species survival in fragmented landscapes (ed. by
Settele J., Margules C., Poschlod P. & Henle K.), Kluwer Academic Publishers, Dordrecht.
Samietz J. & Berger U. (1997) Evaluation of movement parameters in insects - bias and robustness
with regard to resight numbers. Oecologia, 110, pp. 40-49.
Samietz J. (1998) Populationsgefährdungsanalyse an einer Heuschreckenart - Methoden, empirische
Grundlagen und Modellbildung bei Stenobothrus lineatus (Panzer). Göttingen.
Schooley R.L. & Wiens J.A. (2003) Finding habitat patches and directional connectivity. Oikos, 102,
pp. 559-570.
Schreiber K.F. (1977) Zur Sukzession und Flächenfreihaltung auf Brachland in Baden-Württemberg.
Verhandlungen der Gesellschaft für Ökologie, Göttingen.
Schröder B. & Richter O. (1999) Are habitat models transferable in space and time? Zeitschrift für
Ökologie und Naturschutz, 8, pp. 195-205.
Schröder B. (2000) Zwischen Naturschutz und Theoretischer Ökologie: Modelle zur
Habitateignung und räumlichen Populationsdynamik für Heuschrecken im Niedermoor. PhD
thesis, Institut für Geographie und Geoökologie der Technischen Universität Braunschweig,
Technische Universität Braunschweig, Braunschweig.
Schröder B., Biedermann R. & Kleyer M. (2003) Ecological and socio-economic assessment of
management systems for open landscapes - an integrated landscape model approach.
Bibliography
115
Schröder B., Rudner M., Biedermann R. & Kleyer M. (2003) Ökologische & sozio-ökonomische
Bewertung von Managementsystemen für die Offenhaltung von Landschaften - ein integriertes
Landschaftsmodell. UFZ Berichte, in press, pp.
Schultz C.B. (1998) Dispersal behavior and its implications for reserve design in a rare Oregon
butterfly. Conservation biology, 12, pp. 284-292.
Schultz C.B. & Crone E.E. (2001) Edge-mediated dispersal behavior in a prairie butterfly. Ecology,
82, pp. 1879-1892.
Settele J. & Poethke A. (1996) Towards demographic population vulnerability categories of
butterflies: Requirements of and species selection for future population ecological research. In
Species survival in fragmented landscapes (ed. by Settele J., Margules C., Poschlod P. & Henle K.), Kluwer
Academic Publishers, Dodrecht.
Settele J. (1998) Metapopulationsanalyse auf Rasterdatenbasis. B.G. Teubner Verlagsgesellschaft,
Stuttgart.
Settele J., Feldmann R. & Reinhardt R. (1999) Die Tagfalter Deutschlands - Ein Handbuch für
Freilandökologen, Umweltplaner und Naturschützer. Ulmer Verlag, Stuttgart.
Simberloff D. (1998) Flagships, umbrellas, and keystones: is single species management passé in the
landscape era? Biological Conservation, 83, pp. 247-257.
Smith P.A. (1994) Autocorrelation in logistic regression modelling of species´ distributions. Global
Ecology and Biogeography letters, 4, pp. 47-61.
Söderström B. & Pärt T. (2000) Influence of landscape scale on farmland birds in seminatural
pastures. Conservation Biology, 14, pp. 522-533.
Söderström B., Svensson B., Vessby K. & Glimskär A. (2001) Plants, insects and birds in seminatural pastures in relation to local habitat and landscape factors. Biodiversity and Conservation, 10, pp.
1839-1863.
South A. (1999) Dispersal in spatially explicit population models. Conservation Biology, 13, pp. 10391046.
South A.B., Rushton S.P., Kenward R.E. & Macdonald D.W. (2002) Modelling vertebrate dispersal
and demography in real landscapes: how does uncertainty regarding dispersal behaviour influence
predictions of spatial population dynamics? In Dispersal Ecology (ed. by Bullock J.M., Kenward R.E.
& Hails R.S.), Blackwell publishing, Oxford.
Stenseth N.C. (1983) Causes and consequences of dispersal in small mammals. In The Ecology of
Animal Movement (ed. by Swingland I.R. & Greenwood P.J.), Clarendon Press, Oxford.
Stettmer C. (1996) Colonisation and dispersal patterns of banded (Calopteryx splendens) and beautifull
demoiselles (C. virgo) (Odonata: Calopteryidae) in south-east German streams. EuropeanJournal of
Entomology, 93, pp. 579-593.
Storch I. (2002) On spatial resolution in habitat models: Can small-scale forest structure explain
Capercaillie numbers? Conservation Ecology, 6. online., pp.
Sundermeier A. (1999) Zur Vegetationsdichte der Xerothermrasen nordwestlich von Halle/Saale. Cramer, J.,
Stuttgart, Germany.
Sutcliffe O.L., Thomas C.D. & Peggie D. (1997) Area-dependent migration by ringlet butterflies
generates mixture of patchy population and metapopulation attributes. Oecologia, 109, pp. 229-234.
Taylor A.D. (1990) Metapopulations, dispersal, and predator-prey dynamics: an overview. Ecology,
71, pp. 429-433.
Thomas C.D. & Hanski I. (1997) Butterfly Metapopulations. In Metapopulation Biology (ed. by Hanski
I. & Gilpin M.E.), Academic Press, London.
Bibliography
116
Thomas J.A., Bourn N.A.D., Clarke R.T., Stewart K.E., Simcox D.J., Pearman G.S., Curtis R. &
Goodger B. (2001) The quality and isolation of habitat patches both determine where butterflies
persist in fragmented landscapes. Proceedings of the Royal Society London, 268, pp. 1791-1796.
Travis J.M., Murell D.J. & Dythamn C. (1999) The evolution of density-dependent dispersal.
Proceedings of the Royal Society of London (B), 266, pp. 1837-1842.
Trexler J.C. & Travis J. (1993) Nontraditional regression analyses. Ecology, 74, pp. 1629-1637.
Turchin P. (1991) Translating foraging movements in the heterogeneous environments into the
spatial distribution of foragers. Ecology, 72, pp. 1253-1266.
Turchin P. (1998) Quantitative analysis of movement: Measuring and modeling population redistribution in
animals and plants. Sinauer Associates, Massachusetts.
Turner M.G., Wu Y., Romme W.H. & Wallace L.L. (1993) A landscape simulation model of winter
foraging by large ungulates. Ecological Modelling, 69, pp. 163-184.
U.S. Fish & Wildlife Service (1981) Habitat evaluation procedures (HEP). U.S. Department of the
Interior, U.S, Fish and Wildlife Service, Division of Ecological Services, Washington, D.C,.
van der Maarel E. & Titlyanova A. (1989) Above-ground and below-ground biomass related in
steppes under different grazing conditions. Oikos, 56, pp. 364-370.
Van Dijk G. (1991) The status of semi-natural grasslands in Europe. In The conservation of lowland dry
grassland birds in Europe (ed. by Goriup P.D., Batten L.A. & Norton J.A.), Joint Nature Conservation
Committee, Peterborough.
van Wingerden W.K.R.E., Musters J.C.M. & Maascamp F.I.M. (1991) The influence of temperature
on the duration of egg development in West European grasshoppers (Orthoptera: Acrididae).
Oecologia, 87, pp. 417-423.
Veith M. & Klein M. (1996) Zur Anwendung des Metapopulationskonzeptes auf
Amphibienpopulationen. Zeitschrift für Ökologie und Naturschutz, 5, pp. 217-228.
Verbyla D.L. & Litvaitis J.A. (1989) Resampling Methods for evaluating classification accuracy of
wildlife habitat models. Environmental Managment, 13, pp. 783-787.
Verner J., Morrision M.L. & Ralph C.J. (1986) Wildlife 2000: Modeling habitat relationships of terrestrial
vertebrates. University of Wisconsin Press, Madison, Wisconsin.
Villard M.A., Trzcinski M.K. & Merriam G. (1999) Fragmentation effects on forest birds: relative
influence of woodland cover and configuration on landscape occupancy. Conservation Biology, 13, pp.
774-783.
Vos C.C. & Zonnefeld J.I.S. (1993) Patterns and processes in a landscape under stress: the study
area. In Landscape Ecology of a Stressed Environment (ed. by Vos C.C. & Opdam P.), Chapmann & Hall,
London.
Walter R. (1992) Untersuchungen zur Mobilität und zum Habitat von Platycleis albopunctata
(GOETZE 1778) (Orthoptera, Ensifera). Diplomathesis, Fakultät für Biologie der Universität
Hohenheim, Hohenheim, Germany.
Walter M. (1994) Untersuchungen zur Nahrungsökologie der Westlichen Beißschrecke Platycleis
albopunctata (Orthoptera: Tettigoniidae). Diploma Thesis, University of Würzburg, Würzburg.
Weidemann H.J. (1995) Tagfalter: beobachten, bestimmen. Naturbuch Verlag, Augsburg.
Wessels K.J., van Jaarsveld A.S., Grimbeek J.D. & van der Linde M.J. (1998) An evaluation of the
gradsect biological survey method. Biodiversity and Conservation, 7, pp. 1093-1121.
Wiegand T., Moloney K.A., Naves J. & Knauer F. (1999) Finding the missing link between
landscape structure and population dynamics: a spatially explicit perspective. American Naturalist,
154, pp. 605-627.
Bibliography
117
Wiens J.A. (1989) The ecology of bird communities. Cambridge Univrsity Press, Cambrdige.
Wiens J.A. & Milne B.T. (1989) Scaling of "landscapes" in landscape ecology, or landscape ecology
from beetle´s perspective. Landscape ecology, 3, pp. 87-96.
Wiens J.A., Crist T.O. & Milne B.T. (1993) On quantifying insect movements. Environmental
Entomology, 22, pp. 710-715.
Wiens J.A., Stenseth N.C., Van Horne B. & Ims R.A. (1993) Ecological mechanisms and landscape
ecology. Oikos, 66, pp. 369-380.
Wiens J.A., Schooley R.L. & Weeks R.D. (1997) Patchy landscapes and animal movements: Do
beetles percolate ? Oikos, 78, pp. 257-264.
Wiens J.A. (2001) The landscape context of dispersal. In Dispersal (ed. by Clobert J., Danchin E.,
Dhondt A.A. & Nichols J.D.), Oxford University Press, Oxord.
Willems J.H. (1990) Calcareous grasslands in Continental Europe. Calcareous Grasslands - Ecology and
Management, University of Sheffield.
With K.A. (1994a) Ontogenetic shifts in how grasshoppers interact with landscape structure: an
analysis of movement patterns. Functional Ecology, 8, pp. 477-485.
With K.A. (1994b) Using fractal analysis to assess how species perceive landscape structure.
Landscape Ecology, 9, pp. 25-36.
With K.A. & Crist T.O. (1995) Critical thresholds in species´ response to landscape structure.
Ecology, 76, pp. 2446-2459.
With K.A., Cadaret S.J. & Davis C. (1999) Movement responses to patch structure in experimental
fractal landscapes. Ecology, 80, pp. 1340-1353.
With K.A. & King A.W. (1999a) Dispersal success on fractal landscapes: a consequence of
lacunarity thresholds. Landscape Ecology, 14, pp. 73-82.
With K.A. & King A.W. (1999b) Extinction thresholds for species in fractal landscapes. Conservation
biology, 13, pp. 314-326.
Yeomans S.R. (1995) Water finding in adult turtles: random search or oriented behaviour? Animal
Behaviour, 49, pp. 977-987.
Zalucki M.P. & Kitching R.L. (1982) The analysis and description of movement in adult Danaus
plexippus L. (Lepidoptera: Danainae). Behaviour, 80, pp. 174-198.
Zar J.H. (1984) Biostatistical analysis. Prentice Hall, New Jersey.
Zollner A.P. & Lima S.L. (1997) Landscape-level perceptual abilities in white-footed mice:
perceptual range and the detection of forested habitat. Oikos, 80, pp. 51-60.
Zollner P.A. & Lima S.L. (1999a) Illumination and the perception of remote habitat patches by
white-footed mice. Animal Behaviour, 55, pp. 489-500.
Zollner P.A. & Lima S.L. (1999b) Search strategies for landscape-level interpatch movements.
Ecology, 80, pp. 1019-1030.
Zollner P.A. (2000) Comparing the landscape level perceptual abilities of forest sciurids in
fragmented agricultural landscapes. Landscape Ecology, 15, pp. 523-533.
119
Publications
Publications
full papers:
Hein, S., Gombert, J., Hovestadt, T. and Poethke, H.J. (2003) Movement patterns
of Platycleis albopunctata in different types of habitat: matrix is not always
matrix. Ecological Entomology, 28, pp. 432-438.
Hein, S., Pfenning, B., Hovestadt, T. and Poethke, H.J. (2004) Patch density,
movement pattern, and realized dispersal distances in a patch-matrix
landscape - a simulation study. Ecological Modelling, in press.
Hein, S., Poethke, H.J. and Hovestadt, T. (submitted to Ecological Entomology)
Computer-generated null-models as an approach to detect perceptual range
in mark-re-sight studies – an example with grasshoppers.
Hein, S., Voss, J., Schröder, B. and Poethke, H.J. (submitted to Biological
Conservation) Habitat suitability models for the conservation of thermophilic
grasshoppers and bush crickets.
in prep.
Hein, S., Binzenhöfer, B., Poethke, H.J., Biedermann, R., Settele, J. and Schröder,
B. (in prep. for Basic and Applied Ecology) Habitat suitability models for two
different insect groups: What are the important factors?
Biedermann, R., Binzenhöfer, B., Hein, S. and Schröder, B. (in prep. for Basic and
Applied Ecology) Habitat models for insects: 'scales of perception'.
Conferences & Workshops
120
Conferences & Workshops
Organisation of
Workshops
‘Survival of bush crickets in fragmented landscapes’, 2001, Field
Station Fabrikschleichach, University of Würzburg, Germany
‘Small scale movement of bush crickets’, 2002, Westerhever,
University of Kiel, Germany
‘Large scale dispersal of bush crickets’, 2003, Field Station
Fabrikschleichach, University of Würzburg, Germany
Visited
Workshops
‘Population size estimations’, 2000, Jena, Germany
‘Simulations in ecology’, 2000, Pesina, Italy
‘Continuous statistics’, 2001, Oldenburg, Germany
‘Habitat suitability models’, 2003, Leipzig, Germany
Visited
Conferences
Conference of the Gesellschaft für Ökologie (GFÖ) 2001, Basel,
Switzerland – poster presentation
Conference of the British Ecological Society (BES) 2001, Reading,
Great Britain – poster presentation
Annual meeting of the Deutschen Gesellschaft für Orthopterologie,
2002, Münster, Germany
Conference of the Gesellschaft für Ökologie (GFÖ) 2002, Cottbus,
Germany – talk
‘Dispersal in fragmented landscapes’, 2003, Louvain-la-Neuve,
Belgium– poster presentation
‘Insect Evolutionary Ecology’ and ‘Entomology 2003’, Reading, Great
Britain – poster presentation
Conference of the Gesellschaft für Ökologie (GFÖ) 2003,
Halle/Saale, Germany – talk
‘Weidelandschaften und Wildnisgebiete’, 2003, Lüneburg, Germany
Invited talk at Technical University of Braunschweig, 2004,
Department of Zoology, Braunschweig, Germany – talk
121
Curriculum Vitae
Curriculum vitae
Silke Hein
Geburtsdatum
08.03.1974
Geburtsort
Schweinfurt
Anschrift
Forschungsstation Fabrikschleichach
Glashüttenstr. 5
96181 Rauhenebrach
Tel. 0049-9554-92230
AUSBILDUNG
1980 - 1993
Grundschule & Johann-Philipp-von-Schönborn-Gymnasium
Münnerstadt
Nov. 1993
Studium Gymnasiallehramt Biologie und Chemie an der Universität
Würzburg
Sept. 1999
Studiengangwechsel Biologie Diplom
Mai 2000
Diplom im Fach Biologie an der Universität Würzburg
Mai 2000Feb. 2004
Doktorarbeit im „MOSAIK“-Projekt bei Prof. H.-J. Poethke,
Forschungsstation Fabrikschleichach, Lehrstuhl für Tierökologie
und Tropenbiologie, Universität Würzburg
AUSLANDSAUFENTHALTE
1997
6 Monate Auslandsstudium an der Universität Uppsala, Schweden (unterstützt
durch das „SOKRATES“ Programm)
1998
8 Wochen Comoé Nationalpark, Elfenbeinküste („DAAD“-Stipendium)
1999
4 Monate „Molekularbiologisches Praktikum“ an der Universität Zürich,
Schweiz, Kontakt: Dr. Gerald Kerth, Prof. Barbara König
2001
3 Monate Forschungsaufenthalt in der AgResearch Lincoln, Neuseeland,
Kontakt: Dr. Nigel Barlow, Dr. John Kean, Prof. Steve Wratten („DAAD“Stipendium)
2002
1 Woche an der SLU (Swedish University of Agricultural Sciences) und am
Swedish Species Information Centre, Uppsala, Schweden, Kontakt: Dr. Oskar
Kindvall
Würzburg, 1. März 2004
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Danksagung
Viele Menschen haben mich während dieser Arbeit unterstützt und mir
geholfen. Bei Ihnen allen möchte ich mich herzlich bedanken.
Zunächst möchte ich mich bei Achim Poethke für das Vertrauen, das er
mir besonders zu Beginn und auch während der Arbeit entgegen gebracht hat,
bedanken. Er hat mir immer den nötigen Freiraum für eine eigenständige Arbeit
gelassen, war aber immer auch verfügbar, wenn ich Hilfe brauchte. Ich danke ihm
für viele konstruktive Diskussionen und innovative Ideen. Er hat auch mein
Interesse an der Modellierung geweckt und ich hoffe, ich kann dabei auch noch
ein Weilchen bleiben.
Eine wichtige Rolle für mich und diese Arbeit spielte auch Thomas
Hovestadt. Ohne seine Hilfe, sowohl theoretisch als auch bei der Formulierung
unserer Veröffentlichungen, wären selbige wohl kaum zustande gekommen. Seine
Gutmütigkeit, sowie die umfangreichen Statistikkenntnisse, ein ergiebiges
Literaturwissen und eine immer währende Bereitschaft zur Diskussion trugen
zum Gelingen dieser Arbeit und zu meiner Ausbildung bei. Gleichzeitig ist er
dafür verantwortlich, dass ich neben der Doktorarbeit, nun auch ohne weiteres in
die gehobene Küche einsteigen kann.
Jule Gombert, Julia Voss und Claudius Stanke haben während ihrer
Diplomarbeiten dazu beigetragen, dass meine Freilandarbeiten lustig,
unterhaltsam und nie langweilig wurden, dafür danke ich Ihnen herzlich. Sie
haben die Zeit meiner Doktorarbeit an der Station angenehm bunt und heimelig
werden lassen. Besonders glücklich bin ich, dass aus unserem „Dienstverhältnis“
wirklich tolle Freundschaften entstanden sind und ich hoffe das bleibt auch
weiterhin so.
Die Forschungsstation Fabrikschleichach war für mich immer ein
angenehmer Arbeitsplatz. Verantwortlich dafür sind die Menschen, die dort
arbeiten. Ihnen allen danke ich herzlich. Besonders danke ich Brenda Pfenning
für die tolle Zusammenarbeit und für die anstrengenden, aber auch lustigen
Stunden, die wir für unser erstes gemeinsames Seminar miteinander verbracht
haben. Jederzeit wieder!! Ihr und auch Elisabeth Obermeier danke ich weiterhin
für ihre Freundschaft und Unterstützung in vielen Diskussionen.
Anne Böhm ist sicherlich die gute und perfekt organisierte „Seele“ der
Station. Sie hatte immer ein gutes Wort und ein offenes Ohr, wenn es Probleme
gab oder ich einfach nur mal quatschen wollte. Ihre Bonbongaben haben immer
zur Aufheiterung beigetragen. Gleichzeitig war sie immer eine kompetente Hilfe
beim Ausfüllen aller amtlichen Anfragen und Abrechnungen. Viele Deiner Hilfen
gehen sicher weit über das hinaus was Dein „Job“ ist Anne, vielen, lieben Dank
dafür. Roland Bickel lieferte alle praktischen Hilfsmittel, die für die Freilandarbeit
nötig waren, in perfekter handwerklicher Ausführung. Hildegard Schramm war
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immer verfügbar, wenn mir oder anderen an der Station ein Malheur (ob in der
Küche oder auf der Heimfahrt) passiert war. Euch beiden: Vielen Dank!!
Für die Möglichkeit im Naturschutzgebiet „Hohe Wann“ mit
Heuschrecken arbeiten zu können danke ich der Unteren und Oberen
Naturschutzbehörde (Würzburg).
Diese Arbeit ist Teil des Verbundprojektes MOSAIK. Alle Kollegen trugen
dazu bei, dass wir ein wirklich tolles Projektteam waren, so dass die
Statusseminare für mich immer sehr schöne Erlebnisse waren. Ich empfand die
Zusammenarbeit im Projekt als extrem zuträglich für meine Arbeit. Vor allem die
Hilfe von Boris Schröder trug wesentlich zum Gelingen des Habitatmodellteils
dieser Arbeit bei. Er war immer zu Diskussionen bereit und wurde nicht müde
alles immer und immer wieder in „Konzeptvorstellungen“ zu besprechen. Ohne
seine Hilfe wären die Habitatmodelle bei weitem nicht so weit gediehen.
Besonderer Dank gebührt auch Birgit Binzenhöfer. Ihr danke ich für ihr
Durchhaltevermögen beim gemeinsamen Einrichten der Untersuchungsflächen.
Weiterhin haben ihre umfassenden Freilandkenntnisse (sowohl zoologisch also
auch botanisch) sowie auch die gemeinsamen Diskussionen zu den
Habitatmodellen zur Verbesserung dieser Arbeit beigetragen.
Katrin Fritzsch danke ich für die lustige Zeit an der Station und bei
Besuchen in Oldenburg, für unzählige CDs guter Musik und für viele interessante
Gespräche.
Für die gute Zusammenarbeit bei der Freilandarbeit danke ich Helene
Rümer, Caroline Rosenberger, Martina Wagner, Julia Mäschig, Corinna Pohl, Anja
Teicher, Stefanie Döbler, Timo Pöhner wie auch Anna Eichner. Eckhard
Gottschalk danke ich für die Exkursionen bei denen ich immer viel gelernt habe,
und das nicht nur über Heuschrecken.
Die Freundschaft mit Kerstin Fröhle, Jule Gombert, Julia Voss, Claudius
Stanke, Christian Herbert, Andree Thomas und Ralf Lohse (Lehrling) hat mir
geholfen über der Doktorarbeit den „Rest“ nicht zu vergessen. Sie waren
außerdem auch immer für mich da, wenn es mal nicht so lief.
Ich danke Lars Fieseler für die Geduld, wenn wieder einmal ein perfektes
„Kletterwochenende“ zu einem „Freilandwochenende“ wurde und für die Zeit,
die ich zusammen mit ihm verbringen kann, sowie für viele andere Dinge, die sich
hier gar nicht aufzählen lassen.
Ganz besonderer Dank gilt meiner Familie. Meinen Geschwistern für ihre
Freundschaft und Nähe, meinen Eltern sowohl für ihre finanzielle Unterstützung,
als auch dafür, dass Sie mich bei all meinen Entscheidungen unterstützt haben
und für ihre Tochter immer da sind.
Erklärung
gemäß § 4 Abs. 3 Ziff. 3, 5 und 8
der Promotionsordnung der Fakultät für Biologie der
Bayerischen Julius-Maximilians-Universität Würzburg
Hiermit erkläre ich ehrenwörtlich, die vorliegende Arbeit in allen Teilen
selbständig und nur mit den angegebenen Quellen und Hilfsmitteln angefertigt zu
haben.
Diese Dissertation hat weder in gleicher noch in ähnlicher Form in einem anderen
Prüfungsverfahren vorgelegen.
Des weiteren erkläre ich, dass ich früher weder akademische Grade erworben
habe, noch zu erwerben versucht habe.
Würburg, den 01.03.04
(Silke Hein)