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Contents lists available at SciVerse ScienceDirect
Landscape and Urban Planning
journal homepage: www.elsevier.com/locate/landurbplan
A systematic approach to relate plant-species diversity to land use diversity
across landscapes
Anke Jentsch a , Manuel Jonas Steinbauer a,b,∗ , Martin Alt c , Vroni Retzer b , Constanze Buhk c ,
Carl Beierkuhnlein b
a
Disturbance Ecology, Bayreuth University, 95440 Bayreuth, Germany
Biogeography, Bayreuth University, 95440 Bayreuth, Germany
c
Geoecology/Physical Geography, Landau University, Fortstrasse 7, D-76829 Landau, Germany
b
h i g h l i g h t s
The presented approach integrates advantages of regularly used sampling methodologies.
It is able to cover a relationship of alpha and beta diversity with land use diversity consistently over multiple scales.
An easy integration of further information is especially possible on the polygon scale.
a r t i c l e
i n f o
Article history:
Received 3 August 2011
Received in revised form 9 June 2012
Accepted 20 June 2012
Available online xxx
Keywords:
Additive partitioning
Biodiversity monitoring
Land use patch
Germany
Systematic grid
Scaling effects
a b s t r a c t
Land use change is a major driver of biodiversity patterns, therefore conservation management in cultivated landscapes should seek to optimize land use diversity. Especially under changing environmental
conditions there is an increasing need of identifying management options for preserving biodiversity.
However, the design of historical data sets is often inappropriate for detecting biodiversity responses to
ongoing rapid changes. Here, we present an approach to quantify plant species diversity and relate it to
land use diversity.
Data mining took place at the landscape scale in two mountainous regions of Central Europe, differing in
natural and cultural history. Within these landscapes a representative systematic rectangular grid (7 × 7
plots of 1 ha) was sampled. At each plot polygons of uniform land use were mapped and presence–absence
data of plant species were recorded. Plant species diversity differed significantly between landscapes:
species richness and within landscape beta diversity in the calcareous mosaic landscape was higher
than in the siliceous mosaic landscape. Land use diversity explained the significant variation in species
richness. The relationship between plant species diversity and land use diversity is consistent in different
cultivated landscapes and on multiple scales.
The chosen sampling approach integrates the advantage of random but grid-based sampling with land
use polygon specific information. This enables not only to investigate also similarity pattern (in land use
and species composition) but also an integration of further information on the patch scale, if needed.
© 2012 Elsevier B.V. All rights reserved.
1. Introduction
The future of biodiversity is strongly linked to changing agricultural land use (Mattison & Norris, 2005; Young, 2009). Most
current approaches focus on mere species richness. Indicators of
∗ Corresponding author at: Biogeography, Bayreuth University, 95440 Bayreuth,
Germany. Tel.: +49 0921 552211.
E-mail addresses: [email protected] (A. Jentsch),
[email protected] (M.J. Steinbauer), [email protected]
(M. Alt), [email protected] (V. Retzer), [email protected] (C. Buhk),
[email protected] (C. Beierkuhnlein).
species richness are mapped increasingly in order to fulfill international obligations and the target to stop biodiversity loss until
2020. However, there is still a lack of more comprehensive methods to assess biodiversity changes at the landscape scale (Green
et al., 2005; Pereira & Cooper, 2006).
In Europe, the evaluation, protection, and management of biodiversity in cultivated landscapes have been identified as one of
the major tasks for the future (Bennett, Radford, & Haslem, 2006).
Cultivated landscapes are important for biodiversity conservation
because they occupy large surfaces. In Germany, about 4% of the
total terrestrial area is attributed primarily to nature conservation areas, 52.3% is used for agriculture and 30.1% for forestry
(Statistical Yearbook, 2011). In contrast to the majority of other
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states, land cover in Germany has been comparably stable in the
last decades (Young, 2009). However, agricultural intensification
has been related to a negative trend in the population density of
many formerly common and widespread species (Scharlemann,
Balmford, & Green, 2005) and to a decline in species richness
(Dorrough & Scroggie, 2008; Reidsma, Tekelenburg, van den Berg,
& Alkemade, 2006).
Biodiversity covers a wide range of biotic properties from the
diversity in genotypes over species to ecosystems. It includes quantitative, qualitative and functional aspects. Besides spatial and
temporal scales, the degree of influence of land use on biodiversity is strongly dependent on the specific part of biodiversity under
focus (Young, 2009). However, as we will never be able to measure or to conserve diversity at all levels of structural, taxonomic
and functional organization (Sarkar, 2006), decisions and prioritizations are important for an efficient assessment.
In Central European landscapes, landscape heterogeneity is
the major driver of species richness and species distribution patterns (e.g. Deutschewitz, Lausch, Kühn, & Klotz, 2003; Morris,
2000; Zechmeister & Moser, 2001). Some countries use established
nationwide systematic sampling grids for biodiversity monitoring. In Norway the 3Q program is based on the 3 km × 3 km grid
of the Norwegian national forestry inventory (Dramstad et al.,
2002), in Switzerland the grid is similar to the National Forest
Inventory (Hintermann, Weber, & Zangger, 2000; Plattner, Birrer,
& Weber, 2004). Such approaches of combining site specific assessment of species diversity with land use diversity play an important
role for the science–policy interface by offering a sound scientific
basis for the design and evaluation of appropriate management
measures and by developing agro-environmental schemes to protect or increase landscape biodiversity (Duelli, 1997; Pinto-Correia,
Gustavsson, & Pirnat, 2006).
Species inventory data have been used for the analysis of alpha
and beta diversity (see e.g. Koleff & Gaston, 2002; Mac Nally,
Fleishman, Bulluck, & Betrus, 2004). However, there are several
reasons why available data are not suited for assessing species
diversity change at regional scales. Spatially high resolved collections of inventory data is time consuming and costly (Dauber et al.,
2003).
There are also methodological restrictions for temporal comparisons. If it takes years to survey a landscape completely, such
an approach can hardly be applied to identify short-term trends.
Ignoring year to year differences in weather, species demography
and phenology may bias assessments that are irregularly carried
out.
Species richness pattern of different species groups are often
not correlated (but see Duelli & Obrist, 1998) and their dependence
to landscape heterogeneity (within a patch and in the surrounding matrix) varies among groups (Atauri & de Lucio, 2001; Dauber
et al., 2003). The possibility and reasonability of a joint assessment
for overall species richness of a landscape is thus often questioned
(Dauber et al., 2003) despite of empirical prove for the suitability of
plant diversity as an indicator for species diversity (Duelli & Obrist,
1998).
Here novel analyses and evaluations were applied on two
datasets gathered by using a systematic grid method in the landscapes of Northern Frankenalb (Retzer, 1999) and Fichtelgebirge
(Neßhöver, 1999). The systematic grid method is designed to
(a) comprehensively assess plant species diversity across landscapes and to (b) relate species diversity to regional land use
patterns.
Under this premise, our paper evaluates the applied sampling
methodology, indicates possible integrations with additional information or approaches and shows that the method is capable to
characterize various aspects of biodiversity within and between
landscapes.
2. Materials and methods
2.1. Landscapes
Two cultivated landscapes, distinguished by bedrock and land
use, were chosen in southern Germany: a mosaic landscape with
a high land use diversity (number and spatial turnover in land use
types) on calcareous bedrock, the Northern Frankenalb (‘calcareous mosaic landscape’), and a landscape on siliceous bedrock with
low land use diversity consisting primarily of production forest, the
high mountainous area of the Fichtelgebirge (‘siliceous forest landscape’). Each randomly selected study area (ca. 2.5 km × 2.5 km)
comprises a typical subset of each landscape (see Table 1 for a land
use classification). Elevation of the Northern Frankenalb ranges
from 430 to 540 m asl, mean annual precipitation is 850 mm. Land
use is characterized by small-scale alternation of forests, fields and
grasslands. Bedrock in the Fichtelgebirge is made up of granite and
phyllite. Elevation ranges from 650 to 800 m asl, mean annual precipitation is 1100 mm.
2.2. Sampling design
In each landscape we projected a systematic rectangular grid
with 7 × 7 plots of 1 ha each on topographical maps (Fig. 1a). The
systematic grid sampling design was chosen to ensure equal spacing between neighboring plots for the comparable calculation of
similarity coefficients. To minimize autocorrelation effects, interplot distance (300 m) was set distinctly larger than the average
diameter of a land use polygon. We aligned the grid east–west
and north–south to facilitate orientation. In the field, one corner of the plot was located and the 1 ha area was marked. Land
use polygons (minimum mapping area of 10 m2 ) within the plot
were mapped (Fig. 1b) and land use on each polygon was classified hierarchically into land use classes and sub-classes (Table 1
and Fig. 1c). Vascular plant species within each polygon were
recorded as presence/absence data during the growing season of
1998. Nomenclature follows Oberdorfer (1994). The time needed
for data collection differed depending on landscape heterogeneity
from about 1–4 plots per day if done by a taxonomic expert.
2.3. Analysis
Data were graphically inspected for normality by using Q–Q plot.
Normally distributed data were assessed with parametric methods (t-test). If normality could not be achieved by transformation,
the non-parametric Wilcoxon test was applied (Quinn & Keough,
2002). To investigate whether the findings identified within the
landscapes can be generalized, the combined data set was analyzed
additionally.
2.3.1. Species diversity
Differences in average species richness per plot between both
landscapes were assessed by a Wilcoxon test. Additive partitioning was used to differentiate the contribution of different scales
to species richness (Veech, Summerville, Crist, & Gering, 2002;
Wagner, Wildi, & Ewald, 2000). To investigate differences in species
richness between landscapes, incidence-based species accumulation curves were calculated with EstimateS 7.50 (Colwell, 2004). To
asses the “variation or rate in species turnover” (Soininen, 2010) distance decay was addressed with Mantel tests (1000 permutations)
of species dissimilarity matrices against the geographic distance
matrix (Nekola & White, 1999; Steinbauer, Dolos, Reineking, &
Beierkuhnlein, in press). To check whether the decay in similarity with distance is independent from land use, a partial mantel
test controlling for land use similarity (see Section 2.3.3) was performed.
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Table 1
Categories of land use classes and sub-classes used for land use classification. On the highest hierarchical level nine distinct land use classes are differentiated which are
subdivided into twenty-nine land use sub-classes. The list contains only categories found in the study areas. The last two columns list the frequency of occurrence of each
land-use sub-class for both landscapes in absolute numbers for better comparison.
Land use class
Land use sub-class
Calcareous mosaic landscape
Field
Fallow
Cereal
Maize
Root crop
Rape
Green fodder
3
30
2
2
1
7
0
0
0
0
0
0
Grassland
Mown grassland
Sown grassland
Dry grassland
Ruderal area
Afforested grassland
Extensive orchard
36
2
1
1
10
4
4
0
0
0
0
0
Forest
Even-aged closed forest (tree cover > 80%)
Even-aged semi-closed forest (tree cover 50-80%)
Even-aged open forest (tree cover < 50%)
Un-even aged forest (selective cutting system)
Young reforestation
37
22
23
1
20
40
44
59
0
0
Road
Field/forest track
Asphalt road
Gravel road
Skidder trail
38
2
5
14
25
3
13
59
Rock
Single freestanding rock
Rock under forest
4
9
0
0
Margin
Forest margin
Field margin
Grassland margin
17
10
7
0
0
0
Hedgerow
Hedgerow with shrubs
11
0
Water
Running water
1
1
Special use
Place for Easter fire
1
0
To analyze beta diversity of the vegetation (here seen as similarity of species composition) the Sörensen similarity index for
presence–absence data was calculated for all pairs of plots in
one landscape. The Sörensen index performs well in different
studies and evaluations (see Baselga, 2010; Steinbauer et al., in
press). Standard deviation of the Sörensen index was calculated
to assess within-landscape heterogeneity (Jurasinski & Kreyling,
2007). Between-landscape similarity was assessed firstly, by comparing each plot of the first landscape to those of the other
and secondly, by treating each landscape as a single relevé of
presence/absence data and computing Sörensen similarity. Mean
similarity between both landscapes was compared using a permutation test implemented in function diffmean (package simba
version 0.3-4; Jurasinski, 2012).
2.3.2. Relating species diversity and land use diversity
From the hierarchical land use classification scheme (see
Table 1) three basic variables were readily available to quantify land use diversity on each plot (“land use alpha diversity”;
see Fig. 1d): (1) the number of different land use classes (luc),
(2) the number of different land use sub-classes (lus), and (3)
the number of land use polygons (lup). As these parameters
are not independent from each other (lup contains all information of lus, lus contains all information of luc), they could
not be used directly for the calculation of linear models due to
multi-colinearity effects (Graham, 2003). To deal with this problem the relative joint (with other parameters) and independent
(specific) variation in species richness explained by the different land use diversity parameters was analyzed by hierarchical
partitioning (Chevan & Sutherland, 1991; Mac Nally, 2002). The
significance of the variables was assessed by 1000 randomizations
Siliceous forest landscape
of the explanatory variables resulting in a p-value of 0.05 (the 95%
confidence limit of the z-scores).
2.3.3. Relating vegetation beta diversity and land use similarity
Land use similarity between plots (“land use beta diversity”)
was derived by calculating Bray–Curtis similarity of land use classes
or sub-classes. The Bray–Curtis similarity index (=1 − Bray–Curtis
dissimilarity) was used, as it is the quantitative counterpart to the
presence/absence based Sörensen index applied to the vegetation
data. The relation between land use similarity and beta diversity
of the vegetation of all pairs of plots was tested using a Procrustes
rotation test for the first two principal component axes of species
and land use data (Jackson, 1995).
Analyses were performed with R statistical package version
2.10.0 (R Development Core Team, 2009) including the package
vegan version 1.17-0 for the calculation of similarity indices and
Procrustes test (Oksanen et al., 2010) and simba version 0.3-4
(Jurasinski, 2012). Hierarchical partitioning (Chevan & Sutherland,
1991) was calculated with the package hier.part version 1.01 (Mac
Nally & Walsh, 2004).
3. Results
3.1. Species richness at different scales
The calcareous mosaic landscape (438 species) and the siliceous
forest landscape (234 species) share 171 species. Mean species
richness per plot was significantly higher in the calcareous
mosaic landscape (120 ± 35 species/ha) than in the siliceous
forest landscape (50 ± 24 species/ha; exact Wilcoxon, p < 0.001,
Fig. 2b). Expected mean species richness derived from the species
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(a) Landscape
(b) Plot (= grid cell)
2
7
1
3
4
5
0
6
500m
N
50m
100m
(c) Classification
1
2
3
4
5
6
7
Land use class
Sub-class
Forest
Forest
Forest
Boundary
Boundary
Grassland
Road
Un-even aged
Young reforestation
Even-aged semi-closed
Forest margin
Forest margin
Mown grassland
Field / forest track
40
300
200
100
Both landscapes
0
Siliceous
forest landscape
0
Calcareous
mosaic landscape
20
400
between landscapes
within landscape
plot scale (1 ha)
land use polygon scale
Both landscapes
60
500
Siliceous
forest landscape
80
(b)
Calcareous
mosaic landscape
100
Total species richness (#)
(a)
Total species richness (%)
Fig. 1. Sampling design: (a) design of systematic plots within each landscape; (b) mapping of land use diversity: land use polygons observed on each 1-ha plot are mapped
according to a hierarchical system (see Table 1 for the full classification system); (c) Classification of the observed land use polygons.
Fig. 2. Relative (a) and total (b) contribution of the different scales of investigation to total species richness separately for the calcareous mosaic landscape, the siliceous
forest landscape and both landscapes. Error bars indicate standard deviation.
accumulation curves per sampled area was consistently higher in
the calcareous mosaic landscape (no overlap of 95% confidence
intervals; Fig. 3).
Additive partitioning (Fig. 2) showed that the relative contribution of the different scales to overall species richness was rather
similar in both landscapes: approximately 8% of the variation in
species richness could be encountered on the polygon scale and
about 25% at the plot scale (1 ha). However, at the plot scale a
higher proportion of the total variation was covered in the calcareous mosaic landscape (28%) than in the siliceous forest landscape
(21%). Generally, the variation on the landscape scale was most
important and accounts for 75% of the species richness. When comparing both landscapes, within-landscape variation was larger than
variation between-landscapes.
3.2. Beta diversity within and between landscapes
Within landscapes, similarity of the vegetation was significantly lower in the calcareous mosaic landscape (0.46 ± 0.14) than
in the siliceous forest landscape (0.50 ± 0.11) (permutation test:
p < 0.001; for average Sörensen all plots against all; see Fig. A1 for an
overview). Between-landscape similarity (0.22) was significantly
lower than within-landscape similarities when comparing each
plot between the landscapes. Treating each landscape as a single
relevé of presence/absence data resulted in a Sörensen similarity of
0.54. The average absolute deviation value was significantly higher
in the calcareous mosaic landscape (Wilcoxon rank test: p < 0.001).
Both landscapes show a significant distance-decay with Pearson’s r = 0.09 (p = 0.034, Mantel test with 1000 permutations) in the
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classes, land use sub-classes and land use polygons; luc, lus, and lup)
were significantly positively related with species richness (p < 0.05;
Fig. 4). In the siliceous forest landscape only the numbers of land
use sub-classes and polygons were significantly related with plot
species richness (Fig. 4). Although these variables covered largely
redundant information – which leads to the high joint contribution
observed for each variable – the number of sub-classes as well as
the number of land use polygons carried independent information
for all three data sets (Fig. 4).
In the calcareous mosaic landscape all three variables luc, lus and
lup were significant and together accounted for 53% (R2 = 0.53; joint
and independent contributions) variability in species richness. In
the siliceous forest landscape, only the variables lus and lup were
significant and together explained 34% variability. For both landscapes all three variables were significant and together accounted
for 57% variability in species richness.
Expected mean species number
500
400
calcareous mosaic
landscape
300
200
siliceous forest
landscape
100
0
0
10
20
30
40
5
50
Number of sampled plots (ha)
3.3.3. Land use similarity and beta diversity of species
composition
To investigate whether spatial land use similarity was reflected
in the beta diversity of the vegetation, the first two principal component axes for species composition data and land use data were
compared using a Procrustes rotation test (Table 2). Similarity of
land use classes and land use sub-classes (both presence/absence)
explained significant amounts of the variation in vegetation composition for the calcareous mosaic landscape and the combined data
set (r = 0.64–0.81), but only marginally amounts for the siliceous
forest landscape. The similarity of land use sub-classes consistently
explained a lower proportion of the variation of the beta diversity.
Fig. 3. Expected mean species number in species accumulation curve of 1-ha plots
for the calcareous mosaic landscape and the siliceous forest landscape. Dotted lines
indicate 95% confidence limits.
calcareous mosaic landscape and r = 0.15 (p = 0.001) in the siliceous
forest landscape. The spatial decay in similarity in species composition remains unaffected when controlling for similarity in land
use.
3.3. Land use diversity as indicator of species diversity
3.3.1. Differences in land use diversity
In total, 29 different land use sub-classes grouped into nine different land use classes were registered (see Table 1). The number
of land use classes (luc) was significantly higher in the calcareous
mosaic landscape (Ø 3.7/plot) than in the siliceous forest landscape
(Ø 2.2/plot; t-test: p < 0.001). Also the number of land use subclasses was significantly higher in the calcareous mosaic landscape
(Ø 6.8/plot versus Ø 3.8/plot; t-test: p < 0.001). In the calcareous
mosaic landscape, all land use classes listed in Table 1 could be
found, while in the siliceous forest landscape forests and roads were
the dominant land use classes.
4. Discussion
4.1. Systematic grid method
A systematic grid method was applied in this biodiversity
assessment because spatial but unbiased information is needed for
the assessment of land use effects on biodiversity. Point records
and indicators for biodiversity are not capable to detect spatial
patterns of biodiversity. Other aspects of biodiversity than species
richness, such as beta-diversity and spatial heterogeneity are of a
more general and functional importance compared to species numbers. Independent of specific species or communities the gained
metrics of beta-diversity can be used as more abstract values for
3.3.2. Species richness and land use diversity
In the calcareous mosaic landscape and the combined data set,
all three land use diversity variables (the numbers of land use
(a)
(b)
*
Explained variance (%)
50
*
(c)
50
*
40
joint
independent
*
50
40
40
*
*
*
30
30
30
20
20
20
10
10
10
0
0
luc
lus
lup
Calcareous mosaic landscape
*
0
luc
lus
lup
Siliceous forest landscape
luc
lus
lup
Both landscapes
Fig. 4. Hierarchical partitioning of the independent (dark gray) and joint (light gray) contribution (given as the percentage of the total explained variance) of the three
predictor parameters of land use diversity (luc = number of different land use classes, lus = number of different land use sub-classes, and lup = number of land use polygons)
to the explanation of species richness for (a) the calcareous mosaic landscape, (b) the siliceous forest landscape and (c) the combined data set. Stars indicate significance
(p < 0.05) assessed with 1000 permutations of a randomization test. Percentage of explained variance derived from measures of fit-R2 in regression analyses.
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Table 2
Correlation between vegetation beta diversity and land use heterogeneity shown as Procrustes correlation coefficient for the first two principal component axes of the species
and land use data. Land use heterogeneity was calculated for: (a) presence/absence data of land use classes and (b) presence/absence data of land use sub-classes. Data are
shown for each study site and the combined data set. High correlations indicate that plots which include shares of similar land use classes or sub-classes are also similar in
their species composition, and vice versa. Significances are based on a symmetric Procrustean rotation test with 1000 permutations.
Procrustes test
Calcareous mosaic landscape
Siliceous forest landscape
Both landscapes
(a) Presence/absence of different land use classes
(b) Presence/absence of different land use sub-classes
r = 0.82; p < 0.001
r = 0.76; p < 0.001
r = 0.30; p = 0.020
r = 0.18; p = 0.361
r = 0.81; p < 0.001
r = 0.64; p < 0.001
spatial and temporal comparisons and thus for the monitoring of
trends of biodiversity.
In order to refer to planning and land use scales, we decided for
a plot size of 1 ha, which is an established grain size in landscape
analyses (e.g. Lososova et al., 2011; McKenzie, Gibson, Keighery,
& Rolfe, 2004). In the studied landscapes 1 ha proved to be large
enough to incorporate at least two different land use polygons. This
is important as the number of different land use polygons within
a specific area is a quantification of small scale spatial turnover in
land use. However, the systematic grid method has the advantage
that the spatial turnover in land use can additionally be estimated
from the change in land use similarity between the equidistant
plots (distance 300 m).
The decision for a regular grid was made due to the distance
decay of similarity in species composition. Stochastic distributions
can be more representative for landscape traits if only quantitative values such as species richness are recorded. In case of spatial
comparisons between neighboring plots as aimed for in this study,
unequal distances require an increased number of comparisons
for different classes of distance and are thus increasing the sampling effort strongly. However, the decision for grain (plot size) and
distance between plots has to respond to landscape specific conditions. Methodological developments are needed here to deliver
a landscape specific procedure, which nevertheless is able to yield
comparable results.
Regarding to regional characteristics, we applied a sampling
design that does not reach or even cross borders of landscapes
but represents a spatial subset of a larger region. For this sample data can be acquired in short time periods. Studies that work
on coarser sampling scale (e.g. Bratli et al., 2006; Steinmann,
Eggenberg, Wohlgemuth, Linder, & Zimmermann, 2011) are often
based on accumulated species inventories (e.g. state census) that
pose large uncertainties regarding sampling bias (e.g. Deutschewitz
et al., 2003; and see especially Mahecha & Schmidtlein, 2007).
However, the Biodiversity Monitoring in Switzerland Programme
(Plattner et al., 2004) poses a positive example applying a regular grid with 1 km2 plots. Smaller plot sizes (like the 0.2 m2 used
by Féoroff, Ponge, Dubs, Fernández-González, & Lavelle, 2005) are
too small for vegetation surveys including large biotic individuals
(such as clones or trees). Additionally, small plots cannot be related
to land use diversity because they are located within specific units
and not integrating them. Assessments that are aiming to cope with
land use changes have to incorporate all types of land use and not
concentrate on agricultural fields or forests separately.
The systematic grid method integrates several advantages of
regularly used methodologies. The random placement of the whole
grid ensures an unbiased selection of the study area within the
investigated landscape. Usually a random placement is associated
with a larger sample size (given equal quality of the results), as
a subjective sampling or stratified random sampling of key habitats or land use types is much more efficient in quantifying overall
species richness of the landscape scale (gamma diversity). However, the subdivision of plots in polygons of uniform land use,
combined with similarity analyses enables a solution that is both
nearly equally efficient to preferential sampling but also capable to
detect previously unknown and in consequence unexpected spatial
traits which are missed in preferential sampling.
The differentiation of uniform land use polygons within the
plots is important as results may change if different land use
polygons are combined in one plot without a differentiation (e.g.
Økland et al., 2006). Different from subjective sampling but like
other systematic sampling approaches, the applied methodology
enables standardized repeated measurements, both in space and
time (Buhk, Jentsch, Retzer, & Beierkuhnlein, 2007; Retzer, 1999;
Simmering, Waldhardt, & Otte, 2006). Once, a standardized protocol for land use/land cover is defined (Turner, Lambien, & Reenberg,
2007), repeated application of this approach in time or in other
landscape will yield data for sound comparisons.
Finally, replicated grids at the landscape scale can be integrated
into studies at larger scales (e.g. Økland et al., 2006) without a loss
of accuracy (Mahecha & Schmidtlein, 2007).
Developments of geospatial data availability such as InVeKos
and the future instruments in remote sensing will make our
approach even more efficient. Land use diversity can also be
assessed by these tools. Nevertheless, biological information on
species occurrence and composition has to be recorded at a field
specific spatial resolution within a landscape matrix. Digitally
available highly resolved information on land use characteristics
can be correlated with biodiversity measures and then be used as
a proxy for biodiversity. But, this cannot be affected at the current
state of knowledge.
Changes in land use will result in a changing number and quality of polygons of a plot (here of 1 ha). The associated information
on plant species per polygon (patch within the plot) enables an
assessment of the effect of land use on the distribution of specific
species.
Note, that the polygons of the same land use identified within
one plot are not necessary covering the entire patch of the specific
land use (that may exceed beyond the borders of the plot). This is
different from other approaches that use land use patches as the
unit of focus (e.g. Dauber et al., 2003; Sitzia & Trentanovi, 2011).
However, as the plots in the systematic grid method are a random
subsample of the landscape (and all polygons thus have an equal
chance of being cut) we argue that results gained in studies using
patches can be compared to those gained via random polygons.
4.2. Species richness at different scales
The systematic grid method was efficient in sampling landscape species richness: the recorded number of 234 plant species
detected in the siliceous forest landscape was higher than the number reported for the whole corresponding quadrant (ca. 30 km2 ) in
a long-term national survey of higher plants based on reports of
various biologists and literature (Schönfelder & Bresinsky, 1990).
In the calcareous mosaic landscape, the study area stretched over
two adjacent quadrants of this survey: 363 species were recorded
in the northern quadrant and 393 species in the southern quadrant, respectively. These numbers equal 109% (southern quadrant)
or 65% (northern quadrant) of the species richness reported by
Schönfelder and Bresinsky (1990). Another survey, which covers
only the calcareous mosaic landscape, estimates 775 species for
the northern quadrant and 567 species for the southern quadrant
(Gatterer, Bauer, & Nezadal, 2003a; Gatterer, Bauer, & Nezadal,
2003b). In comparison to those data, we detected between 47%
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(north) and 69% (south) of the species richness of a whole quadrant, although the sampled area comprises only approximately 1%
of the quadrant area. This illustrates that defined spatial subsamples of highly diverse landscapes are able to record a substantial
portion of biodiversity in a short time period. These subsamples
are easier to be reassessed and results are less likely to diverge due
to methodological bias.
The lower species richness in the siliceous forest landscape can
be explained by two factors. First, due to the lower land use diversity in the siliceous forest landscape fewer “habitats” were sampled,
resulting in a lower total species richness (habitat heterogeneity
hypothesis; e.g. Brose, 2001). However, when accounting for that
bias (by using the residuals of the correlation between land use
diversity and species richness, data not shown), plots in the calcareous mosaic landscape were still richer in species. This is in
accordance with the species pool being generally higher in calcareous areas compared to siliceous regions of Central Europe (Ewald,
2003). Effects of land use diversity on species richness are modified
by bedrock.
Species richness can be linked to specific indicators at different
scales. Patch characteristics have been shown to explain significant
parts of species richness of plants and bees (Dauber et al., 2003), and
the proportion of certain habitats (e.g. grasslands, rocks) is critical
for the persistence of specialists (Cousins, Lavorel, & Davies, 2003).
Furthermore, species groups respond differently to small scale
(habitat, management unit, patch) characteristics and large-scale
(landscape) features (Dauber et al., 2003; Jeanneret, Schupbach, &
Luka, 2003). The results presented here indicated that – independent of landscape characteristics – most variation in plant species
richness is contained on the landscape scale rather than on smaller
scales (patch, plot). This is in accordance with findings from Chandy
and co-workers who concluded that the level of 7–217 ha is most
important for the species richness of forest vegetation (Chandy,
Gibson, & Robertson, 2006). Also Wagner et al. (2000) found a large
proportion (∼55%) of within landscape variability contributing to
the explanation of total species richness. Thus, different scales have
to be integrated into monitoring approaches.
4.3. Beta diversity within and between landscapes
Although beta diversity is not independent from species richness (Baselga, 2010), both measures cover different aspects of
biodiversity and not all records of alpha diversity can be used for the
calculation of beta diversity and species turnover. In addition, correlations between different taxa are higher for beta diversity than
those for species richness and thus may have higher indicator value
for nature conservation and landscape management (Su, Debinski,
Jakubauskas, & Kindscher, 2004). Here, we analyzed spatial patterns of dissimilarity in species composition between neighboring
plots.
The calcareous mosaic landscape of Northern Frankenalb differed from the siliceous forest landscape of Fichtelgebirge in two
respects. Although the percentage of common species that were
found in each plot was higher in the calcareous mosaic landscape,
average similarity of species composition between neighboring
plots was lower. This indicates a more intense species turnover
in space. In addition, spatial heterogeneity (expressed as standard
deviations from Sörensen mean; see Jurasinski & Kreyling, 2007)
was higher in the calcareous landscape.
4.4. Land use diversity as a general indicator of plant species
diversity
We identify suitable landscape indicators of species richness:
all three parameters for land use diversity (luc, lus, and lup) contributed significantly to the explanation of species richness—with
7
the exception of the number of polygons in the siliceous forest
landscape which had too low variability. This illustrates the role
of management for species richness in cultural landscapes.
The importance of land use diversity is supported by the results
of the Procrustes tests: in the data sets with a high variability in
land use diversity (calcareous mosaic landscape and combined data
set) a large proportion of the variation in beta diversity can be
explained by land use diversity. Thus, agri-environmental schemes
that support the heterogeneity of land use are needed (Kleijn et al.,
2006).
The fact that for the combined data set the relation between
biodiversity and land use diversity is almost as good (beta diversity,
Table 2) or even better (species richness, Fig. 4) than the best model
from a single landscape indicates the generality of this correlation
for cultural landscapes.
Landscape heterogeneity (including land use but also abiotic
diversity) has been addressed as a factor for the explanation of
plant species diversity (e.g. Brose, 2001; Deutschewitz et al., 2003;
Wagner et al., 2000). It was found to be important for animal diversity, too (e.g. Dauber et al., 2003; Weibull, Ostman, & Granqvist,
2003; Woodcocka et al., 2010).
Other studies highlight the role of land use intensity (e.g.
Austrheim, Gunilla, Olsson, & Grontvedt, 1999; Féoroff et al., 2005;
Reidsma et al., 2006; Zechmeister & Moser, 2001). In contrast,
studies on land use diversity and the spatial variation of species
composition are less frequent (but see Williams, Marsh, & Winter,
2002). As land use diversity explains a significant amount of spatial variation in species composition it can be used as a suitable
indicator for species diversity on a landscape scale (Duelli, 1997;
Simmering et al., 2006). However, spatial properties of patches
could be as important as management conditions in explaining
species richness (Sitzia & Trentanovi, 2011) and need to be accordingly integrated with the biodiversity aspects.
Generally, the same sampling scheme with minor adaptations
can also be used to investigate other drivers of biodiversity such
as geomorphographic heterogeneity (Burnett, August, Brown, &
Killingbeck, 1998; Jedicke, 2001; Nichols, Killingbeck, & August,
1998), disturbance regime (Jentsch, 2004; Buhk et al., 2007) including land use specific disturbances like land use intensity (Féoroff
et al., 2005; Zechmeister, Schmitzberger, Steurer, Peterseil, &
Wrbka, 2003), or habitat specificity of species (Simmering et al.,
2006; Wagner & Edwards, 2001).
Appendix A. Supplementary data
Supplementary data associated with this article can be
found, in the online version, at http://dx.doi.org/10.1016/
j.landurbplan.2012.06.012.
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