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Large-scale biodiversity research in the southern taiga,
Northern Mongolia
Michael Mühlenberg1, Hermann Hondong1, Choimaa Dulamsuren1 and Klaus von Gadow2
1
2
Centre for Nature Conservation, University of Göttingen, von-Siebold-Str. 2, D-37075 Göttingen.
[email protected]
Institute of Forest Management, University of Göttingen, Büsgenweg 5, D-37077 Göttingen.
[email protected]
Abstract
The Khentii Mountains of Northern Mongolia, where the Siberian forest belt borders the steppe,
represent a unique and greatly untouched ecosystem. Altogether 15 000 km2 of primeval forest
and grassland are completely protected by law (Mongolian Ministry for nature and environment
1996). Timber exploitation and use of non-timber forest products are permitted in a buffer zone
around the core area. Field research is being conducted since 1996 on an established field station
in the western Khentii. The aim of the research is to understand the structure of the spatiotemporal heterogeneous forest, its dynamics and the impact of utilisation on the ecology and biodiversity of the forest. To deal with the large map scales, the forest was stratified into different types
using a range of vegetation attributes. The types were mapped using a 4 km2 grid within an area of
about 150 km2. In order to describe the structure, sample plots were distributed at random within
each of the strata. The natural regeneration was studied separately.
Keywords: Mongolia, Siberian taiga, biodiversity, forest structure, forest regeneration, forest management
1
Introduction
This paper describes some results of the Khonin Nuga Large Scale Ecological Research
Project in Northern Mongolia. Various research teams representing a variety of scientific
disciplines from different countries, operate from the Khonin Nuga research station which
has been established in the West-Khentii region by the Centre for Nature Conservation of the
University of Goettingen in co-operation with the Faculty of Biology of the National
University of Mongolia. The general objective of the Khonin Nuga research project is to
understand the spatiotemporal mosaic of the ecosystem, its dynamics and the impact of utilisation on the ecology and biodiversity of the forest. Scientists from many countries, representing a great variety of disciplines, have been conducting research in the Khonin Nuga
project. The project objectives require interdisciplinary and longterm commitment by small
groups including one senior scientist and at least one (usually German or Mongolian) junior
scientist. Several such teams have been conducting surveys of plants, insects, small mammals,
birds and fish. These surveys and the associated scientific work have been continued by
some groups for up to three years. Experts with long-term experience in Siberia have visited
the Khonin Nuga research station over the years. To become independent from short-term
project money and thus ensure long-term survival and continuance the station is run with
relatively low cost.
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Michael Mühlenberg et al.
An important basis for coordinating the different botanical and zoological research activities is a common site classification system. Soil samples were analysed in order to correlate
the vegetation types with soil types. Profiles through river valleys were taken by
DULAMSUREN (2003) at five different places. These profiles were analysed to understand
the natural mosaic of habitats in relation to different forest characteristics. The vegetation
classification provided the foundation for a definition of habitat types for the Khentii
(MÜHLENBERG et al. 2000). The habitat types allow a more comprehensive description and
ecological interpretation than the common plant community types, by creating compatibility
between botanical and zoological studies.
For the first few years of investigation it was not our goal to analyse ecosystem functions
(e.g. KREBS et al. 2001, reporting about the Kluane Project). Instead, since about half of the
flora and fauna of Central Europe is encountered in the region, there is a challenge to
simply learn about the ecology of different species by contrasting and comparing the
environmental conditions in which they occur. Consequently, the main objectives of the
Khonin Nuga research project are:
– To address some fundamental questions of ecology, using reference studies in an environment largely untouched by civilization, including characteristics of natural forests, the
biodiversity of different habitats, landscape heterogeneity, issues of biogeography,
phenology, and population biology of selected species. Key hypotheses are:
• Low human impact and naturalness are better predictors of species richness than
biogeographical factors such as latitude, size of area or regional climate1.
• A natural landscape is less fragmented than a cultural landscape and thus facilitates
greater mobility and greater niche overlap of the different species.
• Habitat selection by species in a natural landscape differs from those found in a landscape modified by humans, as is the case in Central Europe for example.
• The population density of a particular species is often higher in a human-dominated
landscape than in a natural landscape2.
– To evaluate the conservation value of the region including presence of a near-pristine
landscape, occurrence of species which are threatened elsewhere, analysis of communities
in primeval habitats as reference for the assessment of anthropogenic impact on species
communities in Europe.
– To conduct impact assessments in areas where timber has been harvested previously
(comparison of faunas between undisturbed and managed taiga, composition of species
and dominance of species).
– To conduct impact assessments of open-cast gold mining in stream valleys including
analysis of water quality, sediment load, and animal communities, up- and downstream of
the mining (fish communities and benthos).
– To develop ecologically sound natural resource management strategies (forest management, non-timber-forest-products3).
1
2
3
related to the habitat diversity hypothesis presented by GASTON and BLACKBURN (2000)
due to the buffer effect (aggregation on few optimal patches), SUTHERLAND (1998)
examples are nuts of Pinus sibirica, berries, medicinal plants; deer antlers, …
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Project area
Mongolia is a country characterized by a rather distinct zonation of vegetation types. The
Khentii Mountains of Northern Mongolia, where the Siberian forest belt borders the steppe,
represent a unique and greatly untouched ecosystem. The Khentii Mountains have been
subdivided in two subprovinces, the Western and Eastern Khentii (SAVIN et al. 1988). In the
Western Khentii the tree line and the permafrost starts at a lower altitude than in the EastKhentii (Geokriologicheskie usloviya MNR 1974), resulting in different mountain forest
types with different typological structures.
The study site is situated in the buffer zone of the Strictly Protected Area of Khan Khentii,
in the “forest steppe” which forms the southern extension of the Siberian taiga forest.
Natural forest growing on permafrost soils is found on the northern slopes, while the southern
slopes receiving greater amounts of solar radiation, are naturally covered with steppe
vegetation due to the relatively dry conditions. In this transition zone elements of the boreal
conifer forests meet the floristic elements of the Central-Asiatic steppe. That particular
region in Mongolia is very much exposed to future development because of its valuable
timber, water, and productive pasture resources. A greater number of mesophilic elements
are found in the Western Khentii. For example, the Siberian Fir (Abies sibirica) occurs only
in the Western Khentii and the Siberian Spruce (Picea obovata) forms forest communities
only in the Western Khentii. The herbaceous flora exhibits similar differences in the two
subprovinces. According to TSEDENDASCH (1995) and TRETER (1997) the most important
factor influencing the formation of closed forest communities in the Khentii is the topography (slope and exposition). This hypothesis is supported by our observations and our
preliminary vegetation assessments. However, important additional factors are precipitation, radiation, soil depth and permafrost. The main tree species occurring in the Western
Khentii mountains are Siberian Larch (Larix sibirica), Siberian Pine (Pinus cembra sibirica),
Scots Pine (Pinus sylvestris), Birch species (Betula patyphylla most common, also Betula
gmelinii and Betula fruticosa), Siberian Spruce (Picea sibirica). Also found are Poplars
(Populus tremula most common, also Populus diversifolia) and Elm (Ulmus pumila). The
main vegetation types in the core area of the protected Western Khentii are boreal virgin
forest, bog and alpine Tundra.The vegetation classification is given in MÜHLENBERG et al.
(2000). Initially, more or less in line with HILBIG and KNAPP (1983), eight vegetation types
were identified. However, we found it more useful to develop a hierarchical classification
(Table 1).
It was not possible to classify forest sites based on the herbaceous flora. Significant differences of the coverage in different habitats (Kruskal-Wallis-Test, P < 0.05) showed only three
characteristic plant species for the Pine forest and five species for the Betula–Larix forest,
while 108 plant species were indifferent. This result appears to be typical of a heterogenous
natural landscape. It would be difficult, if not impossible to develop such a classification
using satellite imagery.
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Michael Mühlenberg et al.
Table 1. The hierarchical classification system of the different habitats around the research station.
Grasslands (with seven subdivisions)
Mountain dry steppe (G1a) and meadow steppe (G1b)
Herb meadow on the terrace in the river valley
Meadow on the river terrace with shrubs of Padus asiatica, Salix sp.
Wet meadow with Salix sp. and Betula fusca shrubs
Wet grassland dominated by Carex sp.
Peat meadow
River bank with Carex sp., Equisetum fluviatile and Calamagrostis purpurea
Riparian woodland (with five subdivisions)
Dense Betula fusca shrub and Salix sp. in the river valley
Salix sp. shrubs on the river bank (R2a) and Salix sp. shrub thickets with Pinus sylvestris,
Larix sibirica, Padus asiatica (R2b)
Open riparian forest with Larix sibirica and Betula platyphylla with shrub layer
Picea obovata–riparian forest
Populus laurifolia–riparian forest, mixed with Padus asiatica, Crataegus sanguinea,
Cornus alba, Salix sp.
Mountain forest (with five subdivisions)
Larix sibirica–Betula platyphylla forest with different successional stages
Mixed forest with dominant conifers (Pinus sylvestris, Abies sibirica, Picea obovata,
Larix sibirica, Pinus sibirica, Betula platyphylla)
Pinus sylvestris forest and Populus tremula–stands
Picea obovata–Abies sibirica forest
Pinus sibirica forest (“dark taiga”)
3
Assessment methods
3.1
Biodiversity
G1
G2
G3
G4
G5
G6
G7
R1
R2
R3
R4
R5
F1
F2
F3
F4
F5
Biodiversity is investigated in different taxa: higher plants, birds, small mammals, some
groups of insects, and fish. Biodiversity research requires assessment of organisms including
their occurrence, abundance and distribution. Even for selected taxa it is obviously impossible
to assess all the species within such a big area. Therefore one needs to use sampling
techniques adapted to the different kinds of species. Appropriate sampling techniques were
used for all groups (Table 2).
For. Snow Landsc. Res. 78, 1/2 (2004)
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Table 2. Sampling methods used at the Khonin Nuga research station.
1 Sherman traps; 2 IMS, it is a joint programme of the three German ornithological research stations
Wilhelmshaven, Radolfzell, and Hiddensee using standardised methods (Constant Effort Sites) in order
to pool the data from different study sites in Eurasia. 3 Angle count method for assessing basal area/ha
after BITTERLICH (1948, using the Dendrometer of KRAMER and AKÇA 1995)
Field of research
Botanical surveys
Entomological
surveys
Small mammal
surveys
Ornithological
surveys
Stream ecological
surveys
Forest surveys
Methods used in Khonin Nuga
Mapping of plant communities in 10 x 10 m2 plots according to a matrix of
different ecological factors (variables: slope exposition, canopy closure, soil
depth), applying the Braun-Blanquet method.
Standardised catch of butterflies with the same effort (one hour netting along
transects in one study plot of 0.5 ha within the chosen habitat, late morning with
sunshine and no wind) in 6 different habitats, twice per month for the whole
season (May–September). Two habitat types each with 4 replicates in 2002.
Standardised catch with live traps1 arranged both in a trapping web of 148 traps
in three habitats and in a grid of 100 x 100 m each with 121 traps, operating each
for a four-days period monthly. In addition 20 m-ditches with two pitfalls each
were established in nine habitats especially to collect shrews (Sorex-species).
Mistnetting in two habitats (108 m each) according to the integrated monitoring
of songbird populations2 for the whole season (May till August); mapping along
transects in different habitats in spring time (May until July). Hole-nesting birds
were surveyed along transects each 1200 m in length in four forest types, the
census was conducted twice in each habitat in May and June.
Electro-fishing in the Eröö-river and its tributaries, sample stations of 3–30 m
length corresponding to three most typical fish habitats were marked out in
advance in the river and fished each 3–5 times. Qualitative samples were taken in
addition with gill nets by 12–50 mesh sizes and cast nets; fishing with lines
occasionally; measurements of the fishes, investigations of the ectoparasites;
survey of benthos-community (assessment of relative abundances, with particular
interest in stoneflies); standard measurements of physical and chemical
parameters including turbidity by photometer.
Stratified random sampling: in four forest types 40–60 points were scattered
randomly at which variables of forest structure, including the description of dead
wood were evaluated. At each point trees were sampled by plot less method with
the help of a dendrometer3. The survey of the variables was prepared by working
sheets. In addition dead wood was estimated with methods described by KIRBY
et al. (1998).
An assortment of sampling methods are available which allow an estimate of the species
richness with affordable sampling effort (COLWELL and CODDINGTON 1994). Cumulative
species curves may be used to compare different habitats. The method is known as rarefaction
(see COLWELL 1997).
To deal with the large map scales, the forest was stratified into different types using a
range of vegetation attributes. The types were mapped using a 4 km2 (2 x 2 km) grid4 within
an area of about 140 km2. The mapped area covers 35 grid cells. The areas of the different
vegetation types are shown in Figure 1.
4
following the 1:50 000 universal transverse mercator (UTM) grid
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Michael Mühlenberg et al.
800
ha on 140 km2 research area
700
600
a Mountain steppe/meadow steppe (G1)
b Peat meadow/wet grassland with Salix
(G4–6)
c Betula fusca + Salix shrub (R1, R2)
d Riparian forest types (R3, R4, R5)
e Pinus sylvestris forest with Larix sibirica,
Betula platyphylla, Populus tremula,
Abies, Picea, Pinus sibirica (F2, F3)
f Larix sibirica–Betula platyphylla forest
(F1)
g Picea–Abies forest (F4)
h Pinus sibirica forest (F5)
500
400
300
200
100
0
a
b
c
d
e
f
vegetations types
g
h
Fig. 1. Areas of different vegetation types in the 35 grid cells of the study site of the Khonin Nuga
region. The dominant vegetation is the Larix–Betula forest with its different successional stages.
Each grid cell was sampled using two parallel transects 500 m apart. The vegetation
formation was mapped on the spot and located using a global positioning system (GPS). The
natural regeneration was separately assessed.
3.2
Sampling and monitoring forests
Forest composition and structure was investigated at large scales, using stratified random
sampling. The forest types were classified according to the dominant tree species, resulting in
4 different strata: Larix–Betula forest with different successional stages (F1), Picea–Abies
forest (F4), Pinus sibirica forest (F5), and Populus laurifolia riparian forest (R5). In order to
describe the structure, 40 to 60 sample plots were distributed at random within each of the
strata. In each plot variables of forest structure were assessed, including the dead wood. In
total 184 points were sampled.
The dynamics of a forest ecosystem is influenced by tree growth which in turn is a reaction
to the specific environmental conditions existing on the site. Tree growth data, obtained in a
variety of ways, are essential for predicting the consequences of harvesting decisions. The
limited availability of research funds and the increasing complexity of the questions that are
being addressed by research, necessitate a continuous evaluation of the optimum design of
growth trials. Forest management objectives are continually changing. This requires data
that permit prediction of forest growth for any set of site conditions and management objectives.
Three types of growth trials were established. Permanent plots are established for collecting data for a particular silvicultural program. The plots are remeasured, usually at
regular intervals, until harvesting. Temporary plots, measured only once, provide age-based
information about relevant state variables which is used to construct a yield table, again
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assuming normal or representative silviculture. Interval plots are remeasured at least once,
thus providing an average rate of change in response to a given set of initial conditions.
After each remeasurement, a decision was taken whether to abandon the trial or maintain it
for another growth interval.
3.2.1 Permanent plots
One of the advantages of a database derived from permanent plots is the potential to
describe polymorphic growth patterns by evaluating the data of each plot separately and by
expressing the parameters of a growth model as a function of specific site variables. In this
way, it is possible to develop non-disjoint polymorphic growth models (CLUTTER et al. 1983;
KAHN 1994) and disjoint polymorphic site index equations, which depict the site-specific
development of certain forest variables over age. A recent example of a polymorphic height
model is presented by JANSEN et al. (1996). Many of the existing yield tables are based on
permanent plots (SCHOBER 1987; JANSEN et al. 1996; ROJO and MONTERO 1996).
A disadvantage of the permanent plot design is the high maintenance cost of the research
infrastructure and the long wait for data. The object of the trial is not always achieved, as
plots may be destroyed prematurely by wind or fire, or by unauthorized cutting.
3.2.2 Temporary plots
Temporary plots may provide a quick solution in a situation were nothing is known about
forest growth. They are measured only once, but cover a wide range of ages and growing
sites. Thus, the sequence of remeasurements in time is substituted by simultaneous point
measurements in space. This method has been used extensively during the 19th century
(KRAMER 1988, p. 97; ASSMANN 1953; WENK et al. 1990, p. 116)5. Temporary plots are still
being used today for constructing growth models in situations where empirical data are not
available (BIBER 1996).
For this purpose, increment cores may be taken from a reference tree (usually the last
five years are evaluated). To explain variations in diameter growth, it is necessary to evaluate
the neighbourhood constellation in the immediate vicinity of the tree. The reference tree
should be positioned in the centre of a competition area, the size of which depends on the
tree density. Temporary plots are often useful for establishing relationships between
variables. The main limitation of temporary plots, when increment cores are not used, is the
fact that they cannot provide information about the rate of change of a known state
variable, thus preventing the use of some contemporary techniques of growth modelling
(GARCÍA 1988).
5
During the 19th century, the “Weiserverfahren” and the “Streifenverfahren” were the most popular
methods for obtaining growth information rapidly (KRAMER 1988, p. 97). In the approach known as
“Weiserverfahren” the growth of single trees was reconstructed using stem analysis techniques.
Another method known as the Streifenverfahren was used to gather data in numerous normally
stocked temporary plots of different ages and site qualities for developing yield tables (BAUR 1877).
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Michael Mühlenberg et al.
3.2.3 Interval plots
A compromise may be achieved by using a system of growth trials which maintains the
advantages of permanent plots, i.e. obtaining rates of change of known initial states, as well
as temporary plots, i.e. broad coverage of initial states and minimum wait for data.
Interval plots are measured at least twice, the interval between the measurements being
sufficiently long to absorb the short-term effects of abnormal climatic extremes. The interval
is a period of undisturbed growth. Measurements should coincide with a thinning operation,
to obtain data not only about tree growth, but at the same time about the change of state
variables resulting from a silvicultural operation. The thinning effects may be assessed at the
initial (t1) or at the final (t2) measurement, or at both occasions. The concept is illustrated in
Figure 2.
GARCÍA (1988) proposed a multi-dimensional system of differential equations, in which
the future development of a forest depends solely on the present state. To be able to develop
such a model, it is necessary to have data describing initial states as well as the associated
changes of the state variables.
W
*
W2
a
∆W
*
*
b
a
W1
*
t1
t2
t
∆t
Fig. 2. Two successive measurements for obtaining the change of a state variable W resulting from a) a
thinning and b) natural growth.
3.3
Forest regeneration
Natural regeneration is an important element of forest dynamics. Accordingly, the distribution
of the density, height and browse damage is often assessed in forest ecological surveys. The
method employed in Khonin Nuga involves 10 m2 circular sample plots (KIRCHHOFF 2003).
The sapling representing the sample plot with its height and species is the one nearest to the
center of the sample plot. The illustration in Figure 3 shows the representative sapling
(Abies with a height of 58 cm) plus two saplings within the circular plot which are used to
determine the sapling density, which is equal to 3000 plants per ha.
The class frequencies derived from the representative trees represent area proportions
(STAUPENDAHL 1997). A disadvantage of this otherwise effective method is the difficulty,
due to the small plot size, to capture rare species.
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Sample point
Abies/58
“Representative” sapling
nearest to sample point;
used to determine species
and height.
Circular plot (r = 1.78 m).
Number of saplings in plot
defines sapling density.
Fig. 3. Schematic representation of a regeneration sample plot showing the tree species and sapling
height in cm. The representative sapling in this example is Abies, with a height of 58 cm; the sapling
density is 3000 plants/ha.
4
Preliminary results
4.1
Species diversity
More than 1150 plant species characteristic of the steppe ecosystem, 253 bird and more than
50 ungulate species were identified in the protected area of the Khentii. Prominent large
mammals are the Maral (Cervus elaphus maral), Moose (Alces alces), Siberian Roe Deer
(Capreolus pygargus), Musk Deer (Moschus moschiferus), Wild Boar (Sus scrofa), Brown
Bear (Ursos arctos), Wolf (Canis lupus), Lynx (Lynx lynx), Wolverine (Gulo gulo), Sable
(Martes zibellina) (READING et al. 1994). Except for the Musk Deer all other mentioned
species may occur in Europe. Thus the area under study can serve in some way as a reference
area representing natural conditions in Europe. Considering the entire fauna about half of
the species encountered in the study area are palearctic. Table 3 presents an overview of the
biodiversity found in Khonin Nuga and a comparison with findings from other areas in
Europe. 50 percent of the butterfly species and 51 percent of the bird species found in
Khonin Nuga are palaearctic and occur also in Central Europe (Fig. 4).
The analysis of cumulative species curves for butterflies shows that, in order to evaluate
the species diversity in different habitats, it is necessary to capture at least 2000 individuals.
Rarefaction curves are suitable for describing differences between habitats. They compare species numbers at the same sample size, in our case with the same amount of captured
individuals. Figure 5 shows the rarefaction curve of the butterfly community in two habitats.
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Michael Mühlenberg et al.
Table 3. Comparison of known species numbers in different regions.
1 KARSHOLT and RAZOWSKI 1996, Lepidoptera of Europe; 2 JONSSON 1992, Vögel Europas; 3 HAGEMEIJER and BLAIR 1997, EBCC Atlas of European Breeding Birds; 4 BfN Rote Liste Deutschlands,
1998; 5 bird species number in Germany inclusive guests: 515 species; 6 DENNIS 1992; 7 GREENWOOD
et al. 1993; 8 MÖCHBAYAR 1999; 9 Redkie Zivotnye Mongolii (pozvonocnye), Moskva 1996; 10 DAWAA
et al. 1994: Kommentierte Checkliste der Vögel und Säuger der Mongolei; 11 ULZIJCHUTAG 1989; 12 own
data 1998–2002. BV = Brutvögel = Breeding bird species.
Region
Number of known
butterfly species
Number of known
bird species
Known species
number of higher
plants
Total land
area (km2)
Europe
Germany
Great Britain
Mongolia
4681
1854
626
(207)8
12 500
2691
1494
282311
10 531 000
357 042
241 752
1 565 000
Khonin Nuga12
146
4692, 500 BV3
2883, 260 BV5
215 BV7
415 (322 BV)9, 440
(360 BV)10
162, 123BV
553
140
Species cumulative curve of butterflies in West Khentei
500
140
400
120
Number of species
Number of species
600
300
200
100
100
80
60
40
20
China
Germany
Britain
Sweden
Finland
Russia
Europe
Mongolia
0
0
0
1000 2000 3000 4000 5000 6000 7000
Number of individuals
Fig. 4. Species richness and distribution of butterflies. Left: the black part of the columns indicates the
number of species shared with Mongolia (only the butterfly assemblage of Khonin Nuga is presented
for Mongolia); right: cumulative curve for butterfly species in West Khentei, species pooled from the
catch of 2000 and 2001. Broken lines indicate the 95%-confidence limit.
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120
Herb Meadow
Expected number of species
100
80
Mountain Dry Steppe
60
40
20
0
0
500
1000
1500
2000
Fig. 5. Rarefaction curves showing the species richness of herb meadow (habitat G2) and mountain dry
steppe (habitat G1). Data are pooled from 4 replicates of each habitat in 2002.
Single Linkage Cluster
Distance = 1 – Morisita Horn
Cluster Diagramm Butterflies 2002 (n = 3027)
HM1
MDS2
MDS1
MDS3
MDS4
HM2
HM3
HM4
0.10
0.15
0.20
0.25
0.30
0.35
Distance
Fig. 6. Cluster diagram of the dissimilarity indices (1 – Morisita Horn) for the butterfly assemblages of
the 4 herb meadow plots (HM) and of the 4 mountain dry steppe plots (MDS).
Altogether 2114 individuals in 111 species were found in the Herb Meadow (G2) type
and 913 individuals in 95 species in the Mountain Dry Steppe (G1), in the sample of the year
2002. Habitat G2 has a higher species richness than habitat G1.
The butterfly population was used to test the faunistic similarity between different
habitats. As an example the butterfly community of two habitats which appear particularly
different is compared: a moist herb meadow with tall grass (G2, G3) and a mountain dry
steppe with short grass (G1). Twice per month, between May and August 2002, the butterfly
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Michael Mühlenberg et al.
assemblages were assessed on four sampling areas on each vegetation type using the standard
sampling method. The result is presented using a cluster diagram (Fig. 6). The cluster
diagram shows the dissimilarity indices (1 – Morisita Horn) for the butterfly assemblages of
the four herb meadow plots (HM) and of the four mountain dry steppe plots (MDS). The
ANOVA test reveals no overall differences between the plots (R = 4.07, p = 0.089). These
preliminary results confirm a high similarity between different habitats in the studied natural
landscape.
The similarity indices of the small mammal communities between different habitats are
surprisingly high as well, indicating the same large overlapping of species in the habitats.
These findings support the hypothesis of greater mobility and greater niche overlap in a
natural landscape (refer to the Chapter 1).
4.2
Forest spatial structure and diversity
The old-growth forest (Pinus sibirica – taiga, F5) exceeds all other forest types in basal area
of living and dead wood, but for cavity-nesting birds the holes in Betula trees are most
important. Different forest types (e.g. successional stages of Larix–Betula and Picea–Pinus)
can be grouped together due to fire disturbances considering the tree species composition.
Riparian woodland (R2, R3, R5) sustains the highest biodiversity but is most restricted in
area.
4.2.1 Habitat trees
The types of damage investigated were fire, wind and rot. Wind caused breakages, rot hollow
trees. Most of fire damages and most of the hollow trees are found in the Betula–Larix forest
stands (F1). Most of the tree hollows are provided by Betula platyphylla trees (BAI et al.
2003). Betula is therefore a key species for cavity nesting birds. The Pinus sibirica forest
exceeds the other forest types, both in total basal area and number of big-diameter trees.
Clustering these samples according to the basal area of the tree species confirms the
stratification by methods of vegetation analysis (Fig. 7a, cluster 1). Pinus sibirica forest is
most clearly separated from other forest types. The mixture of Betula–Larix forest stands
with Picea–Pinus and Populus riparian forest at right hand in the cluster documents the high
influence of fire, in all stands Betula platyphylla as a pioneer tree is represented with a rather
high basal area. Some plots of Picea–Abies–Pinus forest (green colour) are grouped together
like a low disturbed conifer forest (in the cluster 1 right of the Pinus sibirica block). If
clustering is done with variables of structure, the picture changes (Fig. 7b, cluster 2): the
sample plots are now not grouped in the vegetation formations (the classified 4 forest
types). The mixture reflects more the high dynamics in the natural landscape.
A relevant feature of interest to biologists is the occurrence of “wildlife trees”, representing particularly big trees, dead or broken trees, trees with hollows and fire damage.
Clustering with these variables leads to the cluster 3 (Fig. 7c). Consider the significant different picture of the cluster 3 in comparison with cluster 1. One conclusion is that mapping
of vegetation or interpretation of satellite photos according to vegetation classification
methods (e.g. dominant tree species) does not necessarily delineate important stands
for conservation purposes. It may be concluded that for evaluating conservation values,
terrestrial assessment is also needed.
For. Snow Landsc. Res. 78, 1/2 (2004)
105
Fig. 7 a. Three clusters created with different sets of variables. The colour assigns the sample point to one
of the four stratified forest types. Cluster analysis grouping of 184 point samples according to the basal
area of the tree species. Orange = samples in Betula–Larix forests, green = samples in Picea–Abies–
Pinus forest, brown = samples of Pinus sibirica forest, blue = samples in riparian forest with Salix and
Populus laurifolia.
Fig. 7 b. Three clusters created with different sets of variables. The colour assigns the sample point to one
of the four stratified forest types. Cluster analysis grouping of 184 point samples according to the diameter classes of the trees. Orange = samples in Betula–Larix forests, green = samples in Picea–Abies–
Pinus forest, brown = samples of Pinus sibirica forest, blue = samples in riparian forest with Salix and
Populus laurifolia.
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Michael Mühlenberg et al.
Fig. 7 c. Three clusters created with different sets of variables. The colour assigns the sample point to one
of the four stratified forest types. Cluster analysis grouping of 184 point samples according to relevant
variables of conservation. The variables are dbh > 50cm, dead-, broken-, hollow-, fire-tree. Orange =
samples in Betula–Larix forests, green = samples in Picea–Abies–Pinus forest, brown = samples of Pinus
sibirica forest, blue = samples in riparian forest with Salix and Populus laurifolia.
4.2.2 Forest spatial structure in Sangstai Forest
The Sangstai Forest, representing of old-growth forest, was studied in greater detail. Oldgrowth forests are found in places with very low fire frequency. In the Khentii region fire did
not affect the remote mountain ridges with wet mossy ground vegetation and shallow soil
layers. Another region not affected by fire is situated in the river valley between water
bodies where riparian woodland is found.
The “structure” of a forest may be defined by the spatial distribution of the tree positions,
by the spatial mingling of the different tree species and by the spatial arrangement of the
tree dimensions. The spatial structure is one of the characteristic attributes of a forest. The
problem is to characterize and describe forests with different spatial characteristics more
accurately, using affordable assessment techniques. The Sangstai plot in the Khentii may be
used to demonstrate an approach to describe the spatial forest structure and diversity
(Fig. 8).
L- and Pair correlation functions are useful for describing forest structures, but they
require datasets with known tree positions (STOYAN and STOYAN 1992; PRETZSCH 2001;
POMMERENING 2002). Such data are hardly ever available in practice and this precludes
their use. Aggregate indices, such as the spatial index proposed by CLARK and EVANS
(1954), can provide a first general impression of the structure of a particular forest, but they
cannot be used to describe the great variety of spatial arrangements (ZENNER and HIBBS
2000). This problem is especially serious in very irregular forests where small-scale structural
characteristics are highly variable (ALBERT 1999).
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For this reason, three types of neighbourhood-based parameters are used, which are
known as Contagion, Mingling and Differentiation. The parameters can be used to provide a
comprehensive description of the spatial structure of a forest. Assessment and description
may be tree-based or point-based. In the tree-based approach a sample tree closest to a
sample point is chosen as reference tree and the attributes of its immediate neighbours (size,
species) and the regularity of their positions are related to the reference tree. In the
point-based approach, the structural attributes of a neighbourhood group of trees (variation
of tree species and sizes; regularity of tree positions) is assessed at each sample point.
Species
Abies sibirica
Larix sibirica
Picea obovata
Pinus sibirica
Total
Trees per ha
280
8
40
212
540
Basal area per ha
4.22
1.64
0.64
34.69
41.20
Fig. 8. Sangstai plot with buffer showing tree positions (left) and the corresponding plot data with trees
(N/ha) and basal areas (G/ha) per hectare listed for the four species occurring in the plot (right).
Red = samples in Betula–Larix forests, green = samples in Picea–Abies forests, spotted = samples of
Pinus sibirica forests, blue = samples in riparian forests with Salix and Populus Laurifolia.
Contagion
The variable contagion Wi describes the degree of regularity of the spatial distribution of the
four trees nearest to a reference tree i6. Wi is based on the classification of the angles αj
between these four neighbours. A reference quantity is the standard angle α0, which is
expected in a regular point distribution. The binary random variable vj is determined by
comparing each αj with the standard angle α0. The Contagion is then defined as the proportion of angles αj between the four neighbouring trees which are smaller than the standard
angle α0:
4
Wi =
6
1
∑ v j with
4 j=1
⎧1, α j < α0
and
vj = ⎨
⎩0, otherwise
0 ≤ Wi ≤ 1
(1)
For details refer to GADOW et al. (1998). Four neighbours have proved to be most suitable based on
practical considerations in connection with the field assessment methods (ALBERT 1999; HUI and
HU 2001).
108
Michael Mühlenberg et al.
Wi = 0 indicates that the trees in the vicinity of the reference tree are positioned in a regular
manner, whereas Wi = 1 points to an irregular or clumped distribution. With four
neighbours, there are five possible values that Wi can assume. The estimator for the
Contagion of a given forest is W the arithmetic mean of all Wi-values. Although the
Contagion mean value W is quite informative for characterizing a point distribution, it is
often advisable to study the distribution of the Wi -values which reveals the structural
variability in a given forest (Table 4).
Table 4. Distribution of the variable “contagion” which describes the degree of regularity of the spatial
distribution of the four trees nearest to a reference tree i. The spatial distribution is random with a small
proportion of very clumped neighborhoods.
W
0.00
0.25
0.50
0.75
1.00
All species
0.00
0.21
0.54
0.22
0.02
Abies sibirica
0.00
0.16
0.57
0.24
0.01
Larix sibirica
0.00
0.50
0.50
0.00
0.00
Picea obovata
0.00
0.50
0.40
0.10
0.00
Pinus sibirica
0.00
0.23
0.53
0.23
0.02
Based on the work by HUI and GADOW (2002), the spatial distribution may be characterized
as random, although about two percent of the trees are situated in a neighbourhood with a
very clumped distribution (W = 1.0). Neighbourhoods with a very regular distribution
(W = 0) are not encountered.
Species mingling
Species diversity has become a very important aspect of forest management and conservation
and a number of parameters are available to describe it. An example is the Shannon-Weaver
index which has been used in ecological applications by PIELOU (1977, p. 293). We propose
to evaluate the species diversity in the vicinity of a reference tree and define mingling as
the proportion of the n nearest neighbours that do not belong to the same species as the
reference tree (GADOW and FÜLDNER 1992), specifically:
Mi =
1 4
∑v j
4 j =1
with
⎧1, neighbour j belongs to the same species as reference tree i
vj = ⎨
⎩0, otherwise
and
0 ≤ Mi ≤1
(2)
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For. Snow Landsc. Res. 78, 1/2 (2004)
With four neighbours, the mingling attribute Mi can assume five values. Table 5 presents the
mingling distributions for all trees and for each of the four tree species in the Sangstai plot.
Table 5. Distribution of the variable “mingling” which describes the degree of regularity of the spatial
distribution of the four trees nearest to a reference tree i.
M
0.00
0.25
0.50
0.75
1.00
All species
0.13
0.22
0.22
0.26
0.16
Abies sibirica
0.23
0.23
0.26
0.21
0.07
Larix sibirica
0.00
0.00
0.00
0.00
1.00
Picea obovata
0.00
0.00
0.00
0.40
0.60
Pinus sibirica
0.04
0.26
0.23
0.30
0.17
The mingling distribution for all species shows a variety of mingling constellations. 13% of
the trees, for example, occur in pure groups and 22% in groups where half of the trees are of
the same species. As expected, the rare species (Larix and Picea) have the highest mingling
values. The most frequent species Abies and Pinus occur in all the different mingling constellations. Abies sibirica is frequently found in pure groups.
Size differentiation and dominance
The tree attribute “dominance” of neighbours was proposed by HUI et al. (1998) to relate
the relative dominance of a given tree species to the immediate neighbourhood. We define
dominance as the proportion of the n nearest neighbours of a given reference tree which are
smaller than the reference tree, which is calculated in the same way as the previous treebased structural parameters:
1 4
∑vj
4 j =1
⎧1, neighbour j is smaller than reference tree i
with v j = ⎨
⎩0, otherwise
Ui =
and
(3)
0 ≤ Ui ≤ 1
With four neighbours, Ui can assume five values. Low U-values indicate dominance. The
dominance criterion is useful if we wish to describe the relative dominance of a particular
tree species. The highest value of 1 means that the tree is the smallest one in its immediate
neighbourhood. Figure 9 shows the results for the two most common species, Pinus sibirica
and Abies sibirica.
The high dominance values of Pinus sibirica can be expected as this species is represented
with a basal area of almost 35 m2/ha and less trees per ha than Abies sibirica which is represented by a basal area of only 4.22 m2/ha. Pinus sibirica and Larix sibirica are mostly
dominant while Abies sibirica and Picea are more subdominant or suppressed.
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Michael Mühlenberg et al.
0.5
0.4
P
0.3
0.2
0.1
0.0
0.00
0.25
0.50
0.75
1.00
U
Fig. 9. Distribution of dominance values. Pinus sibirica (shaded columns) occurs mostly as a dominant
tree while Abies sibirica (white columns) is mostly subdominant.
4.3
Forest regeneration
Sustainable development of a natural or managed forest ecosystem depends on the ability of
the system to regenerate itself. The recruitment potential is a key factor in the Southern
Taiga forests which are regularly affected by sometimes very destructive and large scale
wildfires. To evaluate this potential, KIRCHHOFF (2003) made an assessment of the natural
regeneration in three different forest environments. Figure 10 presents an impression of
three assessment sites and a graph of the terrain features and occurrence of the different
tree species. The corresponding distributions of regeneration density classes for the different
tree species are also shown.
Sample sites were chosen with three specific questions in mind:
1) What is the capability of the forest to recolonize burnt areas?
2) What is the regeneration potential in the managed forests, which were heavily exploited
towards the end of the 20th century and where mostly Larix sibirica was cut?
3) What is the regeneration potential in the virgin dark taiga forests, a very sensitive ecosystem which is dominated by Pinus sibirica and Abies sibirica?
This first assessment done by KIRCHHOFF (2003) suggests that forest regeneration is not
endangered in any of these three problem sites.
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For. Snow Landsc. Res. 78, 1/2 (2004)
East
West
Pinus sylvestris
Larix sibirica
Populus tremula
Betula platyphylla
60
Fig. 10 a. Burnt forest Sharlang
Altitude 1000–1100 m, slope 20–30°; regeneration
in strips parallel with slope; two regeneration age
classes are found.
percent
50
40
30
20
10
0
no regen. Betula Populus Pinus
Larix
platyphylla
sylvestris sibirica
no regeneration
5000–10000 per ha
South
1000–5000 per ha
10000–50000 per ha
North
Betula platyphylla
Larix sibirica
50
percent
40
Fig. 10 b. Managed forest Hausberg
Altitude 900 m, moderate slope; regeneration in
clumps; regeneration of Abies sibirica.
30
20
10
0
no regen. Betula
Abies
Pinus
Larix
platyphylla sibirica sylvestris sibirica
no regeneration
5000–10000 per ha
1000–5000 per ha
10000–50000 per ha
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Michael Mühlenberg et al.
West
East
Pinus sibirica
Abies sibirica
Picea obovata
60
50
percent
Fig. 10 c. Virgin taiga forest Sangstai
Altitude 1500 m, flat; dense forest with few gaps;
regeneration in clumps.
40
30
20
10
0
no regen.
no regeneration
5000–10000 per ha
Abies
sibirica
Pinus
sibirica
1000–5000 per ha
10000–50000 per ha
Fig. 10 a–c. Three examples of areas where natural regeneration was assessed (KIRCHHOFF 2003).
5
Discussion
Biological diversity describes the variety of life at different levels of biological organisation
(SPELLERBERG and SAWYER 1999). Inventorying biodiversity involves the surveying, sorting,
cataloguing, quantifying and mapping of entities such as genes, individuals, populations,
species, habitats, biotopes, ecosystems and landscapes or their components, and the synthesis
of the resulting information for the analysis of processes (HEYWOOD 1995)7. Based on
practical considerations, assessment and analysis are usually concentrated on the species
level. We selected in our study sites some taxa from which we have some knowledge for
comparison and the experts available to work in the field. It is not our aim, to approach an
“All Taxa Biodiversity Inventory” (JANZEN and HALLWACHS 1994).
Half of the species found in our project area are palearctic, i.e. the area under study
serves in some way as a reference area with natural conditions for Europe, e.g. the still existing
coexistence of all big mammals (top carnivores and big herbivores) in an unchanged landscape. Our study of biodiversity aims to find answers to the following questions:
7
Global Biodiversity Assessment, (GBA; HEYWOOD 1995)
For. Snow Landsc. Res. 78, 1/2 (2004)
113
1) What are the relationships between certain attributes of forest structure and the variables
describing biodiversity? Can forest structure attributes be used to predict biodiversity?
2) What is the biogeographic significance of the Khentii region in the international context?
3) What is the conservation value of the Khentii region in the national and international
context?
4) Is it possible to use biodiversity indicators to describe human impacts in the region?
Clearly, the answers to these questions may turn out to be dependent on the spatial scale of
our work. Until now, research in Khonin Nuga has concentrated on the vascular plants, birds,
small mammals, butterflies, grasshoppers, fish and stoneflies. To be able to deal with the
questions (1) and (2), it was necessary to develop a classification and description of the
vegetation and habitat types. Concerning question (1), relationships have been established
between the structural variety of the vegetation and the number of species richness (e.g.
KARR and ROTH 1971; WILLSON 1974; MÜHLENBERG 1980; ARNOLD 1983; NILSSON et al.
1988; JEDRZEJEWSKA et al. 1994; KUJAWA 1997; SULLIVAN et al. 2001; LOHR et al. 2002).
Animal groups that exploit the environment in three dimensions are most sensitive to plant
community structure, which has been shown in the classical study by MACARTHUR and
MACARTHUR (1961); MACARTHUR (1964) who found a correlation between foliage height
diversity and bird species richness.
In our area the Larix–Betula forest (F1) as the most extended forest harbours the richest
breeding bird community (41 out of 109 species), followed from the riparian woodland (R5)
with 20 species out of 109. 18 breeding bird species are recorded in the Pinus sibirica forest
(F5) (WICHMANN 2001). In the successional series of F1 the bird species richness increases
from burned area to young forest to old forest, indicating an increase in biodiversity with
increasing structure (WICHMANN 2001). BOURSKI (1996) confirmed a highest richness in the
breeding bird assemblage for the flood-plain (corresponding to our riparian woodlands),
decreasing to the taiga forest and last to the burned areas.
The three modes of clustering our sample points of forest structure show us, that basal
area of tree species or simple variables like diameter are not useful to predict biodiversity.
For that approach a set of specific variables has to be measured on a large scale.
The Khentii mountains are part of the “Transbaikal region” (for bird studies see
KOZLOVA 1930; GLADKOV and SELIVONIN 1963; BOLD 1984; VASILCHENKO 1987). The
species richness of birds and trees is known to be higher in the eastern Palearctic than in the
western part due to different histories in the two biogeographical regions (MÖNKKÖNEN
1994; MÖNKKÖNEN and VIRO 1997). Our botanical survey shows that the forested northern
slopes are home to more western Eurosiberian and Uralosiberian flora elements whereas on
the steppe of the southern slopes East-Asian elements from the Mandshurian-AltaianDahurian area are dominating (DULAMSUREN and MÜHLENBERG 2003).
The national conservation value of the Khentii region (question 3) is highlighted by mapping of the plant species out of the Red Data Book of Mongolia (MÜHLENBERG et al. 2000).
The international conservation value is documented by the presence of many palearctic
species of which the populations in Europe are threatened (MÜHLENBERG et al. 2000;
WOYCIECHOWSKI et al. 2001). About three quarters of the palearctic butterflies, half of the
palearctic species of birds and a third of the palearctic plant species found in Khonin Nuga
have some threat status in Central Europe (BfN 1996, 1998). The overall conservation value
of the region exists because of the huge natural landscape itself (>> 20 000 km2) which is not
yet altered by humans. The unmanaged forests provide fallen timber and a great amount of
woody debris what is generally seen as being of conservation value (JONSELL et al. 1998;
KLAUSNITZER 1999; IRMLER et al. 1996; JONSSON and KRUYS 2001; MACNALLY et al. 2002;
GÖTMARK and THORELL 2003).
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Michael Mühlenberg et al.
Question (4) relating to the use of biodiversity indicators to describe human impacts is
still under investigation. The importance and potential effects of many proposals for forest
habitats has to be ascertained for the Khentii region (e.g. LANDRES et al. 1988; PEARSON
and CASSOLA 1992; WEAVER 1995; NILSSON et al. 1995; STORK et al. 1997; DUFRÊNE and
LEGENDRE 1997; NIEMELÄ 1997; JONSSON and JONSELL 1999; LINDENMAYER et al. 1999;
KERR et al. 2000; MIKUSINSKI et al. 2001; TAYLOR and DORAN 2001; RAINIO and NIEMELÄ
2003). This goes beyond ecological considerations and implies political ones as well. Only
large samples can help to understand the correlation between forest structure and biodiversity. One of the future challenges is the development of a sustainable management system
for the forest resources which does not yet exist in Mongolia. Concerning mature forest
habitat and the maintenance of coarse woody debris for biodiversity, European guidelines
for sustainable management cannot easily be adapted. Alternative guidelines need to be
developed and evaluated for this very unique ecosystem.
Acknowledgements
We acknowledge the generous support of the Mongolian project partners, especially the Faculty
of Biology of the National University of Mongolia and the German Technical Service (GTZ) in
Mongolia. The field data were collected by students from Mongolia, Taiwan and Germany; regeneration data were collected by B. Kirchhoff; tree structure data by A. Gradel. H. Heydecke helped
with data processing using the software developed by Chen BoWang. We are grateful for useful
comments received from two anonymous referees.
6
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Accepted April 9, 2004