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Ecological Niche Modeling of the Main Forest-Forming Species in the Caucasus

Authors:
  • Tembotov Institute of Ecology of Mountain Territories RAS
  • Tembotov Institute of Ecology of Mountain Territories RAS

Abstract and Figures

Background: Ecological niche modeling of the main forest-forming species within the same geographic range contributes significantly to understanding the coexistence of species and the regularities of formation of their current spatial distribution. The main abiotic and biotic environmental variables, as well as species dispersal capability, affecting the spatial distribution of the main forest-forming species in the Caucasus, have not been sufficiently studied. Methods: We conducted studies within the physiographic boundaries of the Caucasus, including Russian Federation, Georgia, Armenia, and Azerbaijan. Our studies focused on ecological niche modeling of pure fir, spruce, pine, beech, hornbeam, and birch forests through species distribution modeling and the concept of the BAM (Biotic-Abiotic-Movement) diagram. We selected 648 geographic records of pure forests occurrence. ENVIREM and SoilGrids databases, statistical tools in R, Maxent were used to assess the influence of abiotic, biotic, and movement factors on the spatial distribution of the forest-forming species. Results: Geographic expression of fundamental ecological niches of the main forest-forming species depended mainly on topographic conditions and water regime. Competitor influence reduced the potential ranges of the studied species by 1.2–1.7 times to the geographic expression of their realized ecological niches. Movement factor significantly limited the areas suitable for pure forests (by 1.2–1.8 times compared with geographic expression of realized ecological niches), except for birch forests. Conclusion: Distribution maps, modeled by abiotic, biotic variables and movement factor, were the closest to the real distribution of the forest-forming species in the Caucasus. Biotic and movement factors should be considered in modeling studies of forest ecosystems if models are to have biological meaning and reality.
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Ecological Niche Modeling of the Main Forest-
Forming Species in the Caucasus
R. Pshegusov ( p_rustem@inbox.ru )
Tembotov Institute of Ecology of Mountain Territories of Russian Academy of Science
https://orcid.org/0000-0002-6204-2690
F. Tembotova
Tembotov Institute of Ecology of Mountain Territories of Russian Academy of Science
V. Chadaeva
Tembotov Institute of Ecology of Mountain Territories of Russian Academy of Science
Y. Sablirova
Tembotov Institute of Ecology of Mountain Territories of Russian Academy of Science
M. Mollaeva
Tembotov Institute of Ecology of Mountain Territories of Russian Academy of Science
A. Akhomgotov
Tembotov Institute of Ecology of Mountain Territories of Russian Academy of Science
Research
Keywords: Distribution modeling, Pure forests, BAM diagram, Maxent, Environmental predictor
DOI: https://doi.org/10.21203/rs.3.rs-796514/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.  Read
Full License
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Abstract
Background: Ecological niche modeling of the main forest-forming species within the same geographic
range contributes signicantly to understanding the coexistence of species and the regularities of formation
of their current spatial distribution. The main abiotic and biotic environmental variables, as well as species
dispersal capability, affecting the spatial distribution of the main forest-forming species in the Caucasus,
have not been suciently studied.
Methods: We conducted studies within the physiographic boundaries of the Caucasus, including Russian
Federation, Georgia, Armenia, and Azerbaijan. Our studies focused on ecological niche modeling of pure r,
spruce, pine, beech, hornbeam, and birch forests through species distribution modeling and the concept of
the BAM (Biotic-Abiotic-Movement) diagram. We selected 648 geographic records of pure forests
occurrence. ENVIREM and SoilGrids databases, statistical tools in R, Maxent were used to assess the
inuence of abiotic, biotic, and movement factors on the spatial distribution of the forest-forming species.
Results: Geographic expression of fundamental ecological niches of the main forest-forming species
depended mainly on topographic conditions and water regime. Competitor inuence reduced the potential
ranges of the studied species by 1.2–1.7 times to the geographic expression of their realized ecological
niches. Movement factor signicantly limited the areas suitable for pure forests (by 1.2–1.8 times compared
with geographic expression of realized ecological niches), except for birch forests.
Conclusion: Distribution maps, modeled by abiotic, biotic variables and movement factor, were the closest to
the real distribution of the forest-forming species in the Caucasus. Biotic and movement factors should be
considered in modeling studies of forest ecosystems if models are to have biological meaning and reality.
Background
Ecological niche modeling (ENM) or Species distribution modeling (SDM), based on Machine Learning
Algorithms and statistical data processing, is currently an important part of research on the species
potential distribution. The eciency of ENM as a method for assessing the geographic distribution of plant
species was conrmed by numerous studies of different tree and grass species. For many species, the
predicted geographic ranges corresponded to the actual distribution of plants and their ecological and
biological characteristics (Ebeling et al. 2008; Carvalho et al. 2017; Li et al. 2017; Zurell and Engler 2019;
Bowen and Stevens 2020). In addition, one of the reasons for the popularity of the ENM method in species
distribution studies is the availability and accessibility of global databases on biological diversity and
environmental variables (Ortega-Huerta and Peterson 2008; Peterson et al. 2011). An emerging eld of ENM
is the study of the divergence in ecological niches of sympatric species within the same geographic range.
Such studies contribute signicantly to understanding the coexistence of species and the formation of their
current spatial distribution (Pirayesh et al. 2017; Hemami et al. 2020). The studies are especially relevant for
dominant species, which largely determine the structure, species composition, and dynamics of plant
communities.
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In the Caucasus, the main forest-forming species of coniferous forests are
Abies nordmanniana
(Steven)
Spach,
Picea orientalis
(L.) Peterm., and
Pinus sylvestris
L. The main forest-forming species of deciduous
forests are
Fagus orientalis
Lipsky,
Carpinus betulus
L.,
Betula litwinowii
Doluch., and
B. pendula
Roth.
These species are of great economic importance and high conservation value for mountain regions of the
Caucasus. At the same time,
Abies nordmanniana
and
Picea orientalis
are endangered due to bacterial
diseases and outbreaks of forest pests. We studied highly productive pure forest stands of the main forest-
forming species. In their nal stages of development, these stable plant communities are reliable indicators
of the most suitable habitats for the studied species. Accordingly, using pure forests as species occurrence
data in distribution modeling increases the probability of detecting species in the predicted area. Despite
previous studies on GIS mapping of spruce, r, pine, and beech forests in different regions of the Caucasus
(Komarova et al. 2016; Sablirova et al. 2016; Shevchenko and Geraskina 2019; Aliev et al. 2020), there is no
unied concept of their spatial distribution and coexistence. We lack a clear understanding of climatic,
landscape, soil, and biotic variables that contribute to the potential distribution of the species in the
Caucasus Mountains.
In this study, we aimed to help gain a better understanding of how abiotic (climatic variables, topographic
parameters, edaphic indicators), biotic (competitors) and movement (species dispersal capability) factors
affect the spatial distribution of the main forest-forming species in the Caucasus. Our study focused on the
ecological niche modeling approach through SDM and the concept of the BAM (Biotic-Abiotic-Movement)
diagram (Soberón and Peterson 2005; Peterson 2006; Peterson and Nakazawa 2008; Peterson et al. 2011;
Peterson and Soberón 2012). In this paper, we used a comparative analysis of the ecological niche models
constructed on different sets of environmental data. These were abiotic conditions for A Models;
competitors and abiotic conditions for BA Models; abiotic conditions, competitors, and accessibility of areas
(species dispersal capabilities) for BAM Models. We assumed that competitors, along with abiotic
conditions, have an important effect on the spatial distribution of the forest-forming species in the
Caucasus. The approach to the formalization of the movement factor suggested that the territories with the
highest predicted probability of pure forests occurrences are the most accessible for the studied species.
The main modeling tool was Maxent (Maxent software for species habitat modeling), from which we
produced the models of the potential species distribution in the Caucasus and analyzed the factors
determining this distribution. Our study also aims to formulate promote the preservation of the threatened
species
Abies nordmanniana
and
Picea orientalis
by addressing the following two questions: 1) What areas
are more probable to occur r and spruce forests in the Caucasus? 2) What environmental conditions and
territories are most suitable for the restoration of highly productive forests of threatened species?
Methods
Study area
The physiographic boundaries of the Caucasus (38 to 47° North and 36 to 50° East) consist of the Greater
Caucasus, the Lesser Caucasus, and the Transcaucasian Depression (Kura-Araks Lowland in the east and
Colchis Lowland in the west), including the territory of the Russian Federation, Georgia, Armenia, and
Azerbaijan (Fig.1).
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The study area covered about 390 thousand km2 in the range from − 28 m (Caspian Lowland) to 5642 m
(Mount Elbrus) above sea level. The prevailing climate of the Greater Caucasus is generally humid
subtropical in the South-Western Caucasus (Black Sea coast of Russia) and humid warm summer
continental in the North-Western Caucasus. The climate of the Central and Eastern Greater Caucasus
(Russian territory in the north and Georgian territory in the south of the Main Caucasian Ridge) is continental
and cool, humid (or even alpine) in the highlands, warm, humid in the middle mountains, and hot, dry on the
plains. The prevailing climate in the foothills and low mountains of the Lesser Caucasus (mountain system
in Georgia, Armenia, and Azerbaijan) is humid subtropical in the west and dry subtropical in the east. In the
middle mountains, a continental climate prevails (drier to the east and southeast). The climate of the Kura-
Araks Lowland and the Colchis Lowland, separating the Greater Caucasus and the Lesser Caucasus, is dry
continental and humid subtropical, respectively.
Fir forests of
Abies nordmanniana
concentrate in the mountains of the Western Greater Caucasus
(Litvinskaya and Salina 2012). In this area, r forests were prone to deforestation, bacterial diseases, and
the negative impact of climate change (Akatov et al. 2013; Bebiya 2015; Gornov et al. 2018). The entire
native range of
Abies nordmanniana
encompasses the Russian Caucasus, Georgia, and the northeastern
Turkey. Spruce forests of
Picea orientalis
mainly cover the mountains of the Western Caucasus, occupying
no more than 5% of the forested area (Litvinskaya and Salina 2012). Outbreaks of
Ips typographus
(Linnaeus, 1758) and
Dendroctonus micans
(Kugelann, 1794) against the background of climate changes
caused the spruce forest dieback throughout the native range (Tufekcioglu et al. 2008; Akinci and Erşen
2016; Güney et al. 2019; Pukinskaya et al. 2019).
Pinus sylvestris
is a widespread species of Eurasia, the
ecological plasticity of which determined the typological diversity and the vast area of pine forests in the
Caucasus (Yermakov et al. 2019). The main productive pure pine forests are located in the Central Greater
Caucasus. The native range of
Fagus orientalis
covers the Greater and Lesser Caucasus, as well as Crimea,
Northern Iran, Turkey, Greece, Bulgaria, and Syria (Aliev et al. 2020; Dagtekin et al. 2020). The Western
Caucasus, northern Turkey, and northern Iran were refugia for
Fagus orientalis
during the Last Glacial
Maximum (Dagtekin et al. 2020).
Carpinus betulus
is common species in the Caucasus, Mainland Europe,
and Asia Minor. In the Last Glacial Maximum, the most suitable areas for hornbeam forests occupied the
Black Sea region of Turkey and western Anatolia (Koç et al. 2021). The native range of
Betula litwinowii
mainly covers the northern slopes of the Greater Caucasus, as well as Transcaucasia, Turkey, and northern
Iran. Birch forests of the northern and southern slopes of the Greater Caucasus are usually conned to the
steep slopes at the upper border of the forest belt (Akhalkatsi et al. 2006; Kessel et al. 2020).
Betula pendula
is common in most of Europe, from the northern regions to southern areas, where the species mainly occurs
in the mountains (Beck et al. 2016).
Species occurrence data and environmental variables
Species occurrence data were sourced from the Global Biodiversity Information Facility (GBIF) and the
expedition research in the Central and Western Greater Caucasus in 2012–2021. We ltered the occurrence
data from the GBIF database to exclude geographic records outside the natural range of the species (urban
and rural areas, trees in landscape design), as well as those that were not the presence points of pure forests
(Table 1). Geographic records were further checked for duplicates and were spatially rareed (using "clean
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duplicate" function from library ntbox in R (Osorio-Olvera et al. 2020)). We selected no more than one point
per grid cell (cell size of 1 km2) to avoid model over-tting and to ensure the validity of the statistical
analysis. In total, 648 species occurrence records were retained in the study area.
Table 1
Species occurrence data used in the study
Species DOI from the GBIF GBIF
records GBIF after
ltering Expedition
records Records in the
analysis
Abies
nordmanniana
DOI10.15468/dl.v2ff6j 497 52 17 69
Picea orientalis
DOI:10.15468/dl.dkaz3a 194 32 12 44
Pinus
sylvestris
DOI10.15468/dl.ymbrx9 147 108 15 123
Fagus
orientalis
DOI10.15468/dl.zvhjhs 3009 130 42 172
Carpinus
betulus
DOI10.15468/dl.a4yhh3 1967 114 23 137
Betula
litwinowii
DOI10.15468/dl.wny9k8 70 52 6 58
Betula pendula
DOI10.15468/dl.ezr54q 86 32 13 45
Total 5970 520 128 648
We used a set of 16 climatic and two topographic variables from the ENVIronmental Rasters for Ecological
Modeling database (ENVIREM 2021)). Many of these environmental variables, such as evapotranspiration
parameters, are directly related to the ecological or physiological processes that determine the distribution of
plant species (Title and Bemmels 2018). We also used 11 edaphic variables from the SoilGrids database
(SoilGrids250m version 2.0) (Poggio et al. 2021). Edaphic variables such as bulk density, cation exchange
capacity, coarse fragment volumetric, proportion of clay, sand and silt particles, total nitrogen, soil pH, soil
organic carbon content, organic carbon density, and organic carbon stocks were extracted for Interval II
(depth of 5–15 cm) and adapted to an ASCII standard format with a spatial resolution of 30 seconds (~ 1
km2).
To prevent overtting of the models and select predictors most important for modeling, VIF (Variance
Ination Factor) test in R was run to assess variable correlation, including latent correlations. VIF test
constrained predictors to only 12 non-correlated variables for model outputs (threshold VIF  3). They were
four climatic variables, one topographic variable, and seven edaphic variables (Table 2).
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Table 2
Environmental variables selected by VIF test
Variable Description, units VIF
embergerQ Emberger's pluviothermic quotient 1.91
PETDriestQuarter Mean monthly potential evapotranspiration of driest quarter, mm/month 2.37
PETWettestQuarter Mean monthly potential evapotranspiration of wettest quarter, mm/month 1.78
PETColdestQuarter Mean monthly potential evapotranspiration of coldest quarter, mm/month 2.06
TRI Terrain roughness index 2.24
cfvo Volumetric fraction of coarse fragments (> 2 mm), cm3/dm32.22
silt Proportion of silt particles ( 0.002 mm and  0.05 mm) in the ne earth
fraction, g/kg 2.32
sand Proportion of sand particles (> 0.05 mm) in the ne earth fraction, g/kg 1.71
clay Proportion of clay particles (< 0.002 mm) in the ne earth fraction, g/kg 1.84
nitro Total nitrogen (N), cg/kg 2.39
ocd Organic carbon density, hg/m³ 2.32
soc Soil organic carbon content in the ne earth fraction, dg/kg 2.10
Model development and evaluation
Model development was conducted in R package dismo (Hijmans et al. 2017) using Maxent (ver. 3.4.3)
(Steven et al. 2017) for each of the studied species. Maxent is one of the most ecient modeling methods,
especially in prediction based on presence-only data (Elith et al. 2006; Phillips and Dudík 2008; Dube et al.
2015; Yi et al. 2018; Iverson et al. 2019; Komori et al. 2019). Maxent generates the probability of species
occurrences from the distribution of predictor values. The territories with the highest probability of species
occurrences are considered the most suitable. Extrapolation of the probabilities of species occurrence to the
study area with the logistic format of output data results in a probability distribution map in the range from
0 to 1. Maxent denes the importance of environmental variables in species distribution and constructs the
response curves that illustrate the relationship between a particular variable and the predicted probability of
suitable conditions for a species. The program was used with the Auto(LQHP) feature type and 1000
iterations. We used a ve-fold cross-validation method in which 75% of occurrence records were the training
samples, and 25% were kept as test samples (Phillips and Dudík 2008).
Visualization of the probabilities of species occurrence to the study area was carried out according to the
ranked values of the standard Maxent palette in gradation of colors from blue (occurrence “0”) to red
(occurrence “1”) by converting the output Maxent le to a netCDF le with subsequent visualization in the
PanoplyWin program (PanoplyWin 2021). For potentially suitable habitats of the species, values of 0.5–1
were acceptable; for optimal habitats, the species could be detected with a probability of 0.8–1.
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At the rst stage of modeling (Step 3), the input data used to run the species distribution models were the
occurrence records and the environmental variables selected by VIF test (Fig.2.). According to BAM diagram
(Soberón and Peterson 2005; Peterson 2006; Peterson and Nakazawa 2008; Peterson et al. 2011; Peterson
and Soberón 2012), A Models represented areas with suitable abiotic conditions that could be considered as
a geographic expression of fundamental ecological niches of species. The analyzed abiotic factors imposed
physiological restrictions on the ability of species to persist in the identied territory. In our opinion, the
modeling of species distribution based only on abiotic environmental variables is the closest to the concept
of SDM.
Species distribution modeling, along with abiotic factors, should include an analysis of biotic factors
(Soberón and Peterson 2005; Peterson and Soberón 2012), which are positive and negative interactions with
other species. Species distribution models constructed with abiotic and biotic variables (BA Models)
corresponded to the geographic expression of the realized niche of the species. In our opinion, such
modeling, based on abiotic and biotic environmental variables, was the closest to the ENM concept. One of
the most important biotic factors for plant species is competitors, which can signicantly limit the actual
distribution of species by limiting population processes. To consider the inuence of competing species, in
accordance with the correlative approach to ecological niche modeling, it is possible to include the
geography of other species in single-species models (Soberón and Peterson 2005). To do this, we re-
modeled the spatial distribution of each species using the abiotic environmental variables and the
previously obtained probability distribution maps of other species as biotic environmental layers.
In our study, movement factor (species dispersal capability, accessibility of areas) represented the part of
the Caucasus that was most accessible to the studied species. Optimal areas were territories with a
probability of species occurrence of 0.8–1 in BA Models. We displayed the accessibility of areas through the
distance from these territories, where the probability of species occurrence was higher than 0.5 (0.5
threshold of habitat suitability). We used the obtained raster of distances as an additional layer for
modeling. BAM Models corresponded to the geographic expression of the realized niche of the species
which was the closest to their real distribution.
Model evaluation consisted of area under the receiver operating characteristic (Area under the curve, AUC)
as a measure of predictive success. AUC values provide information about the sensitivity and specicity of
the model for classifying data compared to random (AUC  0.5) (Fielding and Bell 1997). We used AUC
values from test (AUCTest) and training (AUCTrain) data. Minimum difference between training and test
data indicated that the models were not over-parameterized to be overly specic to the training data (Warren
and Seifert 2011). AUC values for each model were averaged across ve replicates that differed in 25% of
the test data, which were occurrence records randomly separated from the original presence points.
Results
Species distribution modeling by abiotic environmental
variables
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Models constructed with 12 selected abiotic variables showed a reliable prediction. AUCTrain and AUCTest
ranged from 0.92 to 0.98 and from 0.87 to 0.95, respectively (Table3). The difference between AUCTrain
and AUCTest were quite low (0.03–0.06).
Table 3
Evaluation of Maxent models using AUC values averaged over ve replicate runs
Species model A Model BA Model BAM Model
AUCTrain AUCTest AUCTrain AUCTest AUCTrain AUCTest
Abies
nordmanniana
0.97 0.94 0.98 0.94 0.98 0.95
Picea orientalis
0.94 0.90 0.97 0.93 0.98 0.95
Pinus sylvestris
0.93 0.87 0.95 0.87 0.95 0.89
Fagus orientalis
0.92 0.87 0.94 0.89 0.95 0.88
Carpinus
betulus
0.92 0.87 0.95 0.87 0.96 0.89
Betula
litwinowii
0.98 0.95 0.98 0.95 0.98 0.92
Betula pendula
0.93 0.88 0.94 0.89 0.95 0.88
For all seven species, one of the variables with the greatest percentage contribution was Terrain roughness
index (TRI) (Table4). TRI quanties local vertical topographic heterogeneity by calculating the average
elevation difference between a particular site and its eight neighbor sites (Riley et al. 1999; Rózycka et al.
2016). Using a 0.5 threshold of habitat suitability, suitable TRI values for
Abies nordmanniana
,
Picea
orientalis
and
Betula pendula
ranged from nearly level (80–90) to moderately rugged (350–425) areas
according to Riley et al. (1999). Suitable conditions for
Fagus orientalis
and
Carpinus betulus
were the lower
TRI values ranging between level (35–50) and intermediately rugged (200–235) areas. The upper TRI values
for
Pinus sylvestris
and
Betula litwinowii
corresponded to highly rugged areas.
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Table 4
Contribution of environmental variables to the Maxent models of distribution of the main forest-forming
species in the Caucasus
Environmental
variable A Model BA Model BAM Model
PC,
%PI, % Suitable
values PC,
%PI,
%Suitable
values PC,
%PI,
%Suitable
values
Abies nordmanniana
embergerQ 46.2 22.4 120–
200 10.1 19.5 120–
210 8.2 2.5 120–
210
TRI 22.3 7.2 80–425 0.5 3.5 80–450 0.1 0.3 80–470
sand, g/kg 11.2 9.1 350–
470 1.1 19.7 330–
500 0.1 0.3 330–
500
Picea orientalis
occurrence - - - 65.3 5.5 0.3–1 35.1 1.1 0.4–1
Movement factor,
km - - - - - - 40.7 78.8 0–10
Picea orientalis
TRI 35.9 8.7 80–430 0 0.6 80–510 0.3 2.2 80–510
embergerQ 22 34.9 75–220 0.1 0.3 80–255 0.2 0.4 80–255
Fagus orientalis
occurrence - - - 55 32.1 0.6–1 27.8 5.5 0.6–1
Pinus sylvestris
occurrence - - - 16.8 11.3 0.2–1 16.7 10.5 0.4–1
Abies
nordmanniana
occurrence
- - - 15.7 8.6 0.2–1 9.1 3.9 0.1–1
Movement factor,
km - - - - - - 34.2 48.1 0–6
Pinus sylvestris
TRI 66.7 42.4 80–550 19.9 4.6 350–
550 15.9 22.7 80–600
PC (percentage contribution) is the contribution to construction of models; PI (permutation importance)
is the permutation coecient
TRI is the Terrain roughness index; embergerQ is the Emberger's pluviothermic quotient; sand is the
proportion of sand particles in the ne earth fraction; PETDriestQuarter and PETWettestQuarter are the
mean monthly potential evapotranspiration of the driest and the wettest quarter, respectively
Species occurrence is an acceptable probability of competing species occurrence (in the range from 0 to
1), at which the analyzed species can be found on the same site of the study area with a probability of
0.5 or higher
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Environmental
variable A Model BA Model BAM Model
PC,
%PI, % Suitable
values PC,
%PI,
%Suitable
values PC,
%PI,
%Suitable
values
PETDriestQuarter,
mm/month 12.2 13.1 15–20 0.4 2 15–20 0.1 0.6 15–20
Betula pendula
occurrence - - - 17.2 18.9 0.2–1 3.3 6.4 0.3–1
Carpinus betulus
occurrence - - - 15.5 3.3 0.1–1 14.7 0.8 0.1–1
Betula litwinowii
occurrence - - - 14 13.2 0.2–1 12.9 5.2 0.2–1
Picea orientalis
occurrence - - - 10.8 12.6 0.5–1 6.4 4.9 0.1–1
Movement factor,
km - - - - - - 39.5 27 0–10
Fagus orientalis
TRI 55 42.7 50–235 0.4 1 55–245 0.6 1.8 55–275
embergerQ 20.8 18.3 80–375 0.7 0 80–370 0.3 0.6 80–370
Carpinus betulus
occurrence - - - 40.6 4.9 0.3–1 42 11.3 0.2–1
Picea orientalis
occurrence - - - 12.1 18 0.1–1 2.5 5.2 0.1–1
Movement factor,
km - - - - - - 33.4 33 0–10
Carpinus betulus
TRI 60.3 52.4 35–
200 0.2 0.7 35–220 0.3 6.7 30–200
PETWettestQuarter,
mm/month 15.9 21.2 105–
130 0.2 2.3 105–
130 0 0.3 105–
130
Pinus sylvestris
occurrence - - - 30.5 13.6 0.1–1 11.6 2.2 0.1–1
PC (percentage contribution) is the contribution to construction of models; PI (permutation importance)
is the permutation coecient
TRI is the Terrain roughness index; embergerQ is the Emberger's pluviothermic quotient; sand is the
proportion of sand particles in the ne earth fraction; PETDriestQuarter and PETWettestQuarter are the
mean monthly potential evapotranspiration of the driest and the wettest quarter, respectively
Species occurrence is an acceptable probability of competing species occurrence (in the range from 0 to
1), at which the analyzed species can be found on the same site of the study area with a probability of
0.5 or higher
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Environmental
variable A Model BA Model BAM Model
PC,
%PI, % Suitable
values PC,
%PI,
%Suitable
values PC,
%PI,
%Suitable
values
Movement factor,
km - - - - - - 39.9 30.8 0–1
Betula litwinowii
TRI 47.3 27.5 120–
570 3.1 0.3 125–
620 2.6 12.2 100–
620
embergerQ 20.2 13.4 100–
170 0 0 100–
175 0.1 1.2 100–
175
Betula pendula
occurrence - - - 45.9 22.6 0.4–1 29.6 28 0.5–1
Pinus sylvestris
occurrence - - - 11.5 4.9 0.1–1 21.6 15.4 0–1
Movement factor,
km - - - - - - 27.4 7.7 0–20
Betula pendula
TRI 47.4 51.9 90–
350 0 0 85–450 0.1 1.4 85–450
embergerQ 25.3 16.1 90–
180 0.1 0 85–210 0 1.4 90–200
Pinus sylvestris
occurrence - - - 32.5 0 0.4–1 34.6 14.9 0.5–1
Betula litwinowii
occurrence - - - 20.8 48.2 0.4–1 11.3 19.2 0.4–1
Movement factor,
km - - - - - - 24.9 4.2 0–20
PC (percentage contribution) is the contribution to construction of models; PI (permutation importance)
is the permutation coecient
TRI is the Terrain roughness index; embergerQ is the Emberger's pluviothermic quotient; sand is the
proportion of sand particles in the ne earth fraction; PETDriestQuarter and PETWettestQuarter are the
mean monthly potential evapotranspiration of the driest and the wettest quarter, respectively
Species occurrence is an acceptable probability of competing species occurrence (in the range from 0 to
1), at which the analyzed species can be found on the same site of the study area with a probability of
0.5 or higher
For ve species, one of the main environmental predictors was Emberger's pluviothermic quotient
(embergerQ) (Table4). EmbergerQ is based on the combined dynamic of evapotranspiration and the
extreme annual temperature amplitude and synthesizes temperature and humidity of climate with higher
values for more humid conditions (Emberger 1955; Daget et al. 1988). In our study, pure spruce, beech and
birch (
Betula pendula
) forests occurred mostly in sub-humid and humid areas (0.5 threshold of habitat
Page 12/29
suitability).
Betula litwinowii
and
Abies nordmanniana
preferred only humid habitats with embergerQ of at
least 100 and 120, respectively.
In terms of percentage contribution and permutation importance, mean monthly potential
evapotranspiration of the driest quarter (PETDriestQuarter) and the wettest quarter (PETWettestQuarter)
contributed signicantly to the distribution models of pine and hornbeam forests, respectively. Potential
evapotranspiration indicates the maximum amount of moisture that evaporates per unit time by a unit
surface of vegetation cover in the absence of moisture deciency (Allen et al. 1998). It largely depends on
precipitation, solar radiation, air temperature, and wind speed. The suitable values of PETDriestQuarter and
PETWettestQuarter for
Pinus sylvestris
and
Carpinus betulus
, respectively, were in the range of rather low
values.
Edaphic factors were less important in geographic distribution of the studied species, except the proportion
of sand particles in the ne earth fraction for
Abies nordmanniana
. Suitable soils for r forests should
contain 35–50% sand, which corresponds to loamy soils (Kaufmann and Cutler 2008).
According to A Models,
Carpinus betulus
,
Fagus orientalis
and
Pinus sylvestris
were able to occupy more
than 40 thousand km2 each (Table5). The potential ranges of these species largely overlapped throughout
the Caucasus and covered the mountain regions of the Greater and Lesser Caucasus. At the same time, 7.5
thousand km2 of optimal areas for hornbeam forests were mainly concentrated from the foothills to the
middle mountains of the Western Greater Caucasus and, partly, on the Black Sea coast of Georgia with a
humid subtropical climate. In the Central and Eastern Greater Caucasus and the Lesser Caucasus, the
habitats optimal for
Carpinus betulus
were limited to relatively small areas in the foothills, low and middle
mountains with a warm summer continental or hemiboreal climate. Probability distribution map of
hornbeam forests by A Model was presented in Supplementary Information (SI Fig.1a).
Table 5
Areas of acceptable and optimal habitats for the main forest-forming species in the Caucasus by the
Maxent models
Species Acceptable areas, thousand km2Optimal areas, thousand km2
A Model BA Model BAM Model А Model BA Model BAM Model
Abies nordmanniana
17.7 14.3 11.9 6.5 4.8 4.0
Picea orientalis
16 12.9 9.0 5.3 3.2 2.8
Pinus sylvestris
41.3 30.9 21.1 9.3 8.9 5.5
Fagus orientalis
44.4 33.2 18.9 12.2 7.2 7.0
Carpinus betulus
42.2 26.2 15.7 7.5 6.2 5.2
Betula litwinowii
24.9 15 13.7 9.5 5.4 5.1
Betula pendula
32.8 22.8 22.4 10.4 5.2 4.7
Page 13/29
Optimal habitats of
Fagus orientalis
(about 12 thousand km2) were scattered in the foothills, low and middle
mountains of the South-Western Caucasus and the Black Sea coast of Georgia (humid subtropical climate),
in the middle mountains of the North-Western Caucasus and the Georgian part of the Central Greater
Caucasus (warm summer continental and humid subtropical climate) (SI Fig.2a). With a high probability,
pure beech forests were predicted in the west of the Lesser Caucasus and the Eastern Caucasus on the
border of Russia and Azerbaijan (humid subtropical and warm summer continental climate).
Areas optimal for
Pinus sylvestris
(about 9 thousand km2) were widespread in the middle mountains and
highlands of the Greater Caucasus with an oceanic climate (the Western Caucasus) or, most commonly, a
warm summer continental or hemiboreal climate (the Central and Eastern Caucasus) (SI Fig.3a). In the
Eastern Greater Caucasus, pure pine forests were predicted in the low mountains of the Caspian Sea coast
(hot summer continental climate). In the Lesser Caucasus, areas optimal for
Pinus sylvestris
were found in
the mountain regions with a warm summer continental climate.
According to A Models,
Abies nordmanniana
and
Picea orientalis
occupied the smallest potentially
acceptable and optimal areas among the forest-forming species in the Caucasus (Table5). Optimal areas of
r and spruce forests covered the middle mountains and highlands of the Western Caucasus and the
southern slopes of the Central Greater Caucasus (humid subtropical and warm summer continental climate)
(SI Fig.4a, 5a). On the northern slopes of the Central Greater Caucasus, habitats optimal for
Abies
nordmanniana
and
Picea orientalis
were limited to small areas in the upper reaches of mountain gorges. A
Models predicted the smallest acceptable and optimal areas for r and spruce forests in the Eastern Greater
Caucasus and Lesser Caucasus with a more continental climate.
Area of acceptable habitats for
Betula pendula
exceeded that for
Betula litwinowii
, while the areas of
optimal habitats for both species were almost the same. Areas optimal for
Betula pendula
were
concentrated in the middle mountains and highlands of the Greater Caucasus and the western part of the
Lesser Caucasus with a warm summer continental or hemiboreal climate (SI Fig.6a). The main distribution
area of
Betula litwinowii
covered the mountain regions of the Greater Caucasus, where the probability of
species occurrence was higher in the middle mountains and highlands of the North-Western Caucasus and
the Georgian part of the Central Greater Caucasus with a humid subtropical and warm summer continental
climate (SI Fig.7a).
Ecological niche modeling by abiotic and biotic
environmental variables
AUCTrain and AUCTest values for BA Models mainly exceed those for A Models, indicating their better
predictive success (Table3). The differences between AUCTrain and AUCTest values were fairly low (0.04–
0.08).
According to ВA Models, the most important ecological predictors in the potential distribution of the main
forest-forming species in the Caucasus were biotic factors (competitors), which signicantly masked the
inuence of abiotic variables (Table4). The competition from
Picea orientalis
had the largest percentage
contribution in ВA Model of
Abies nordmanniana
distribution. The habitats were considered suitable for
Page 14/29
Abies nordmanniana
(0.5 threshold of habitat suitability) if the probability of
Picea orientalis
occurrence on
the sites was 0.3 and higher. This proved the similarity of ecological niches of these species. At the same
time, the main competing species for
Picea orientalis
in the Caucasus was
Fagus orientalis
. Pure spruce
forests formation was possible in the sites with a probability of beech forests occurrence of 0.6–1.
Pinus
sylvestris
was a relatively weak competitor for other forest-forming species except for
Carpinus betulus
,
Betula pendula
and
B. litwinowii
. In turn, the studied species had a relatively weak inuence on the
distribution of pine forests (percentage contribution of about 11–17%). The main competing species for
Fagus orientalis
was
Carpinus betulus
, to a lesser extent
Picea orientalis
.
Betula litwinowii
and
B. pendula
competed with each other for distribution in the Caucasus.
The acceptable values of climatic variables in BA Models were almost the same as in A Models, but TRI
values for most species increased. The contribution of abiotic factors to the construction of species
distribution models signicantly decreased. The exceptions were embergerQ for
Abies nordmanniana
and
TRI for
Pinus sylvestris
, the percentage contribution of which decreased only to 10 and 20%, respectively.
According to ВA Models, the areas of acceptable and optimal habitats of the most main forest-forming
species in the Caucasus decreased by 1.1–1.7 times (Table5). For
Carpinus betulus
, the optimal area
decreased to the greatest extent in the Eastern Greater Caucasus and Georgia, including the central part of
the Greater Caucasus and the west of the Lesser Caucasus (SI Fig.1b). The areas of optimal habitats of
Fagus orientalis
decreased fairly evenly throughout the entire potential range of pure beech forests (SI
Fig.2b). BA Model predicted reduction in the area optimal for pine forests in the North-Western Caucasus (SI
Fig.3b). Optimal habitats of
Abies nordmanniana
and
Picea orientalis
decreased throughout the entire
potential range, but especially in the Lesser Caucasus (SI Fig.4b, 5b). BA Models still predicted small
optimal areas for both species in the upper reaches of mountain gorges on the northern slopes of the
Central Caucasus. BA Model demonstrated the largest reduction in the optimal habitats of
Betula pendula
(twice as compared to A Model), which were concentrated mainly in the highlands (SI Fig.6b). Areas optimal
for
Betula litwinowii
most signicantly decreased on the northern slopes of the Central and North-Western
Greater Caucasus, as well as in the northeast of Georgia (SI Fig.7b).
Ecological niche modeling by abiotic, biotic, and movement
environmental factors
In our study, the movement factor was characterized by the distances (km) from optimal habitats (sites with
a probability of species occurrence of 0.8–1), where the probability of species occurrence was higher than
0.5. The distances were determined in a straight line, taking into account the terrain. BAM Models
constructed with abiotic, biotic and movement factors, in general, showed the most reliable prediction
(Table3). Movement factor was the most important ecological predictor in the potential distribution of
hornbeam, r and pine forests (Table4). However, the total contribution of the biotic factor in BAM Models
of
Pinus sylvestris
(37.3%) and
Abies nordmanniana
was not much less than that of the movement factor.
TRI and embergerQ still retained an inuence on the distribution of pine and r forests, respectively.
According to BAM Models, competition from other forest-forming species remained the most important
factor in the distribution of beech, spruce and birch forests in the Caucasus (Table4).
Page 15/29
Betula litwinowii
and
B. pendula
were the most “mobile” forest-forming species in the Caucasus. The
distance of territories suitable for pure birch forests was up to 20 km from optimal habitats. BAM Models
predicted a slight decrease in the areas acceptable and optimal for
Betula litwinowii
and
B. pendula
compared to BA Models (Table5, SI Fig.6c, 7c).
Ten-kilometer distances of suitable habitats from the sites with optimal environmental conditions
signicantly limited the initially large potential ranges of
Fagus orientalis
and
Pinus sylvestris
(by 1.8 and
1.5 times compared to BA Models) (Table5, SI Fig.2c, 3c). The relatively small predicted range of
Abies
nordmanniana
decreased only by 1.2 times. BAM Model predicted new small areas optimal for this species
in the west of the Lesser Caucasus (SI Fig.4c). Nevertheless, the total area of habitats optimal for
Abies
nordmanniana
, as well as for
Pinus sylvestris
, signicantly decreased. Areas optimal for
Fagus orientalis
with a relatively low inuence of movement factor decreased to the least extent (only by 1.03 times
compared to BA Model).
The smallest predicted distribution from optimal habitats was for pure hornbeam (0–1 km) and spruce (0–6
km) forests (Table4). According to ВAM Models,
Carpinus betulus
and
Picea orientalis
tended to reduce the
area of potential distribution in the Caucasus by 1.7 and 1.4 times compared to BA Models. The areas of
optimal habitats of
Picea orientalis
did not decrease signicantly, while for
Carpinus betulus
, the reduction
was 1.2 times (Table5, SI Fig.1c, 5c).
Discussion
A Model
The aim of this study was to assess the inuence of abiotic, biotic and movement factors on the spatial
distribution of the main forest-forming species in the Caucasus by modeling the geographic expression of
their fundamental and realized ecological niches. We revealed a signicant effect of topographic conditions
and water regime on the potential distribution of the studied species. Our results showed that the acceptable
habitats for pure r forests were relatively gentle slopes (between nearly level and moderately rugged) with
loamy soils in humid conditions (Table4). Pure spruce forests also potentially occurred on relatively gentle
slopes in sub-humid and humid conditions. Optimal habitats of both species were mainly located in the
middle mountains and highlands of the Western Caucasus and the Georgian part of the Central Greater
Caucasus with a humid subtropical and warm summer continental climate (SI Fig.4a, 5a). These results are
in line with Shevchenko and Geraskina (2019), who observed that in the North-Western Greater Caucasus,
the modern potential areas of
Abies nordmanniana
and
Picea orientalis
almost completely coincided. The
authors concluded that the main limiting factors in the distribution of these drought-sensitive species in the
region were the precipitation in the driest month, as well as the altitude (Shevchenko and Geraskina 2019).
Previous research also revealed a high sensitivity to climate humidity of
Abies nordmanniana
in the
Caucasus (Litvinskaya and Salina 2012) and
Picea orientalis
in Turkey (Ucarcı and Bilir 2018). According to
Akatov et al. (2013), the suitable average annual precipitation for
Abies nordmanniana
ranged from 700 to
2500 mm. Our result is also consistent with a previous study of r forests in northwestern Turkey (Coban
2020), which showed that pure r forests mainly occurred between 1000 and 1600 m above sea level on
Page 16/29
mountain slopes with a steepness of about 10–20°. Litvinskaya and Salina (2012) observed that in the
Western Greater Caucasus, optimal conditions for
Abies nordmanniana
and
Picea orientalis
forests were
formed at altitudes of 1200–1600 m and up to 1500–1700 m, respectively. Usta and Yılmaz (2020) found
that in the Trabzon mountains (northeastern Turkey), slope steepness and altitude positively correlated with
the distribution of
Picea orientalis
. The authors suggested that negative anthropogenic interventions could
limit spruce forests to steep slopes unsuitable for agriculture and settlement (Usta and Yılmaz 2020). Our
studies of the importance of edaphic factors in r forest distribution was also supported by Litvinskaya and
Salina (2012), who highlighted that
Abies nordmanniana
is sensitive to deteriorating soil conditions and
prefers loamy soils.
In our studies,
Pinus sylvestris
mainly depended on the topographic factor TRI; the percentage contribution
of climatic factors to the species distribution model was relatively low (Table4). Acceptable habitats of pure
pine forests were located in a wide range of mountain slope steepness and altitude from nearly level to
highly rugged areas with fairly low mean monthly potential evapotranspiration of the driest quarter. Areas
optimal for
Pinus sylvestris
mainly included the middle mountains and highlands of the Greater Caucasus
with warm summer continental, hemiboreal, oceanic or hot summer continental climates (SI Fig.3a). The
wide ecological range of
Pinus sylvestris
by temperature and humidity gradients, climate continentality,
underlying rocks and soil is in line with previous studies of pine forests in the Dagestan Republic (Eastern
Greater Caucasus, Russia) by Ermakov et al. (2019). The authors showed that pine forests were distributed
in the middle mountains and highlands at an altitude of 1600–2500 m (Ermakov et al. 2019), which is
consistent with our results. Researchers also highlighted the drought resistance of
Pinus sylvestris
(Usta and
Yılmaz 2020) and its tolerance to excessive moisture (Rakhmatullina et al. 2017). Rakhmatullina et al.
(2017) and Arslan and Örücü (2019) used Maxent models to analyze the contribution of environmental
factors to the distribution of pine forests in the Southern Ural (Republic of Bashkortostan, Russia) and
Turkey, respectively. They revealed a signicant inuence of the maximum temperature of the warmest
month, which may be due to climatic differences between these regions and the Caucasus.
Our results showed that the potential ranges of
Fagus orientalis
and
Carpinus betulus
largely overlapped
throughout the study area, while the area of optimal habitats for beech forests was almost twice that for
hornbeam forests (Table5). Optimal areas for both species covered the foothills, low and middle mountains
(from level to intermediately rugged areas) of the Western Greater Caucasus and the Black Sea coast of
Georgia (Table4, SI Fig.1a, 2a). Moreover, the Georgian part of the Central Greater Caucasus, the Eastern
Caucasus and the west of the Lesser Caucasus also included optimal sites for beech forests. The low frost
resistance of these species (Shevchenko and Geraskina 2019) probably explains the relatively low upper
limit of the distribution of beech and hornbeam forests in the Caucasus Mountains. Usta and Yilmaz (2020)
also reported that slope steepness and altitude were negatively correlated with the distribution of
Carpinus
orientalis
on the Karadağ Mass, Turkey.
Fagus orientalis
preferred mainly sub-humid and humid bioclimatic
conditions, while
Carpinus betulus
occurred in conditions with rather low suitable values of mean monthly
potential evapotranspiration of the wettest quarter. This result coincided with Jensen et al. (2008), who
showed that in central and northern Europe, a drier and warmer climate (annual precipitation of less than
600 mm and mean July temperature above 18°C) favored the distribution of
Carpinus betulus
, whereas
beech forests prevailed in more humid regions. Based on modeling the range of
Fagus orientalis
with
Page 17/29
environmental data of the present, past and future climates, Dagtekin et al. (2020) also showed that drier
climate and higher temperatures will limit future distribution of this species. Previous studies in the North-
Western Greater Caucasus (Shevchenko and Geraskina 2019) and Anatolia, Turkey (Koç et al. 2021)
conrmed that the water regime signicantly affected the current distribution of beech and hornbeam
forests. Shevchenko and Geraskina (2019) reported that beech forests were mainly distributed in areas
where the annual precipitation was not less than 600 mm (Shevchenko and Geraskina 2019). According
Packham et al. (2012), beech trees have a shallow root system which makes them sensitive to the moisture
deciency during the drought period.
Birch forests of
Betula pendula
occurred mainly in the sub-humid and humid bioclimatic zones of the
Greater and Lesser Caucasus with a warm summer continental or hemiboreal climate, while
Betula
litwinowii
preferred humid habitats in the North-Western and Central Greater Caucasus with a humid
subtropical and warm summer continental climate (Table4, SI Fig.6a, 7a). Both species were common in
the middle mountains and highlands; however, the probability of
Betula litwinowii
occurrence was higher in
more rugged areas. This result supported previous reports that on the northern and southern slopes of the
Greater Caucasus,
Betula litwinowii
usually formed the upper border of the forest belt (1500–2800 m above
sea level) on steep slopes (Akhalkatsi et al. 2006; Kessel et al. 2020). In addition, Akatov (2009) and Hansen
et al. (2017) concluded that in the Western and Central Greater Caucasus, the upper limits of
Betula
litwinowii
tended to increase in an uphill direction. Beck et al. (2016) associated
Betula pendula
distribution
in southern Europe (mainly in mountain regions) with its sensitivity to summer drought, which did not
contradict our conclusion about the importance of the water regime in the distribution of the species.
BA Model
In our study, the contribution of biotic ecological predictors signicantly exceeded the contribution of abiotic
variables to construction the models of the species distribution in the Caucasus (Table4). Areas of
geographic expression of realized ecological niches of species were 1.2–1.7 times smaller than the areas of
geographic expression of their fundamental ecological niches (Table5). This result is consistent with
previous opinions and conclusions (Keane and Crawley 2002; Soberón and Peterson 2005; Peterson et al.
2011; Peterson and Soberón 2012; Atwater et al. 2018; etc.) that positive and negative interactions between
species should be considered in ENM or SDM studies if models are to have biological meaning and reality.
Present study revealed that in the Caucasus,
Picea orientalis
was the main competitor to
Abies
nordmanniana
in the same areas, while the main species limiting the distribution of
Picea orientalis
was
Fagus orientalis
(to a lesser extent
Abies nordmanniana
and
Pinus sylvestris
) (Table4). In turn, the main
competitor of
Fagus orientalis
was
Carpinus betulus
, while the ecological niche of
Carpinus betulus
was
most similar to that of the
Pinus sylvestris
. The ecological niches of both birch species were similar;
Pinus
sylvestris
was also a competitor species to
Betula pendula
and
B. litwinowii
.
Our results (SI Fig.4b, 5b), as well as species distribution modeling in the North-Western Caucasus
(Shevchenko and Geraskina 2019), showed that the potential ranges of
Abies nordmanniana
and
Picea
orientalis
almost completely coincided. This nding supported previous reports (Nishimura 2006;
Litvinskaya and Salina 2012) on the convergence of suitable environmental conditions for spruce and r.
Page 18/29
Therefore, Shevchenko and Geraskina (2019) suggested that
Abies nordmanniana
and
Picea orientalis
could form mixed communities within the entire range of dark coniferous forests of the North-Western
Caucasus. At the same time, in the Caucasus, the areas of pure spruce forests, as well as r-spruce co-
dominated forests, were relatively small (Litvinskaya and Salina 2012; Shevchenko and Geraskina 2019). In
our opinion, the similarity and highly competitive nature of the ecological niches of the two species
determined the low probability of the occurrence of r-spruce co-dominated forests (Table4). Probably,
Abies nordmanniana
displaced
Picea orientalis
from territories suitable for both species. Gokturk and Tıraş
(2020) also reported that in the mixed stands of Ovacik Forests of Artvin, Turkey,
Picea orientalis
tended
toward a clumped distribution, avoiding a tree-wise mixture with
Abies nordmanniana
and
Pinus sylvestris
.
Accordingly, in such mixed communities,
Picea orientalis
was not competitive and could only thrive in
groups. Anthropogenic effect and the ability of r to recover faster after felling and res could also limit the
distribution of r-spruce forests in the Caucasus (Shevchenko and Geraskina 2019). In addition, according
to our data (Table4), as well as Litvinskaya and Salina (2012) report, the difference in the real ranges of
Abies nordmanniana
and
Picea orientalis
was also due to the fact that r forests prefer wetter habitats.
Thus, in BA Models, embergerQ still retained a signicant effect on the distribution of pure r forests.
The distribution of pure spruce forests in the Caucasus was also limited by the presence of pure beech
forests in habitats suitable for both species (Table4).
Fagus orientalis
is the most widespread shade
tolerant deciduous species in the Caucasus. Its potential range covered the area of coniferous-broad leaved
forests of the North-Western Caucasus (Shevchenko and Geraskina 2019) and the potential range of dark
coniferous forests throughout the Caucasus (SI Fig.2b, 4b, 5b). The range of embergerQ values in habitats
suitable for beech forests was wider than those for spruce and r forests. At the same time,
Fagus orientalis
preferred rather gentle slopes located lower in altitude. This was probably why the upper TRI values for pure
spruce forests in BA Model shifted to the range of highly rugged areas (Table4). In our study, despite the
similarity of ecological niches,
Fagus orientalis
was not a competitive species for
Abies nordmanniana
. This
result is consistent with a previous study of r-beech co-dominated forests in the Northwest of Turkey
(Coban 2020), which showed that shade tolerance of both species provided a high degree of their spatial
mingling. In the Western Caucasus, beech and r also formed stable mixed stands in the nal stages of
forest development (Litvinskaya and Salina 2012; Gornov et al. 2018).
Present study revealed that the most important ecological predictor in the potential distribution of
Fagus
orientalis
was
Carpinus betulus
. According to Sikkema et al. (2016) and Gornov et al. (2018), the hornbeam
is a fast growing tree species with long distance seed distribution that prefers sunny habitats, but at the
same time, it is one of the most shade tolerant native trees in Europe. In mixed forests, this species can be a
dangerous invader (Sikkema et al. 2016). Jensen et al. (2008) showed that in central and northern Europe,
there was a signicant negative correlation at the local scale between relative areas of
Fagus orientalis
and
Carpinus betulus
due to the competitive relationship between the two species. This nding was also
supported by Yakhyayev et al. (2021), who reported that in the northern regions of Azerbaijan, complex
cuttings in the secondary hornbeam stands were an effective measure for regenerating the natural beech
stands.
Carpinus betulus
was observed in pure groups in r-beech co-dominated forest of the northwestern
Turkey, where it was not competitive compared to both shade tolerant species (Coban 2020). However,
Carpinus betulus
replaced beech forests at the felling sites, which caused an increase in the area of
Page 19/29
hornbeam forests in the Central Caucasus by 6% in the rst decade of the 21st century (Tembotova et al.
2012).
At the same time, the ecological niche of
Carpinus betulus
was most similar to the ecological niche of
Pinus
sylvestris
, which was probably largely due to the relative drought resistance of both species (Table4). In
turn, there were no strong competitors for
Pinus sylvestris
among the studied species. Signicant inuence
on the species distribution was retained by TRI, the lower values of which shifted to the range of moderately
rugged areas. Like spruce forests, according BA Model, pure pine forests were concentrated on steeper
slopes at higher altitudes. Based on the studies by Coban (2020) and Gokturk and Tıraş (2020), we assumed
that light demanding
Pinus sylvestris
was able to avoid competition due to concentration in the upper layer
of stands and exclusion from suppression by shade tolerant species. Thus, Coban (2020) showed that
Pinus sylvestris
demonstrated random distribution and spatial association with other species in r-beech
forests of the northwestern Turkey. The author also concluded that the pioneer character of this species
allowed its establishment early in the succession stage (Coban 2020). Ecological plasticity of
Pinus
sylvestris
and its ability to occupy habitats unsuitable for other species (Table4, SI Fig.3a) were also
important in reducing competition with other species.
Similarity of ecological niches of both birch species was due to their similar requirements for relief
conditions, temperature and water regimes. These species often form mixed stands of the upper forest belt
in the Caucasus Mountains, below which there is a belt of pure pine or birch-pine forests. Accordingly, the
inuence of the biotic factor caused the displacement of pure birch forests upward and to steeper slopes
(Table4).
BAM Model
According to Peterson et al. (2011), movement factor (M set of environmental conditions) represented the
geographic regions accessible for the species for a certain period. Analysis of this factor, along with sets of
biotic and abiotic environmental conditions, made it possible to establish the "occupied distributional area"
(Soberón and Peterson 2005; Peterson et al. 2011) or the geographic expression of the species realized
niches, which is the closest to their real distribution. In our study, we aimed to approximate the nal maps of
the forest-forming species ranges to their real distribution in the Caucasus with the possibility of practical
application. Therefore, we dened the geographic regions accessible for the species as the distances from
the sites with the most optimal conditions, where the probability of species occurrence was higher than 0.5.
We considered these distances as an indicator of species mobility. Birch forests were the most “mobile” in
the Caucasus (0–20 km of accessible areas from optimal habitats), followed by r, beech and pine forests
(0–10 km), and spruce forests (0–6 km). Areas suitable for hornbeam forests were the most compact (only
0–1 km from optimal habitats).
We revealed a signicant effect of movement factor on the potential distribution of the main forest-forming
species in the Caucasus, with the exception of
Betula litwinowii
and
B. pendula
. BAM Models predicted a
relatively low contribution of movement factor to the distribution of pure birch forests. Competition from
each other and
Pinus sylvestris
, as well as mountain terrain and water regime, mainly determined the
modern ranges of both species in the Caucasus. The acceptable area for
Betula pendula
exceeded that for
Page 20/29
B. litwinowii
(Table5, SI Fig.6c, 7c) due to lesser dependence on the habitat humidity and the slope
steepness. At the same time, the area of optimal habitats for
Betula litwinowii
exceeded that for
B. pendula
because of the large occupied territories in the relatively humid highlands of the Western and Central
Caucasus. Geographic expression of the realized niches of
Betula litwinowii
and
B. pendula
, which is the
closest to their real distribution, was the upper forest belt in the highlands throughout the Caucasus.
Movement factor signicantly limited the areas of suitable habitats of widespread forest-forming species in
the Caucasus (
Pinus sylvestris
,
Fagus orientalis
and
Carpinus betulus
) (Table5, SI Fig.1c, 2c, 3c). According
to A Models, the geographic expression of fundamental ecological niches of these species covered more
than 40 thousand km2 throughout the Caucasus, but the inuence of biotic and movement factors reduced
this area by 2–2.7 times.
Pinus sylvestris
with a wide ecological range in main environment gradients,
spread from nearly level to highly rugged areas with warm summer continental, hemiboreal, oceanic, or hot
summer continental climates. Among studied forest-forming species, there were no strong competitors for
Pinus sylvestris
. However, the complex inuence of biotic and movement factors shifted the distribution of
pure pine forests to more local areas in the highlands of the Greater and Lesser Caucasus.
Although
Fagus orientalis
preferred more humid bioclimatic conditions than
Carpinus betulus
, the potential
ranges of these species largely overlapped throughout the Caucasus and there was a competitive
relationship between them.
Carpinus betulus
mainly limited the distribution of
Fagus orientalis
only in
disturbed beech forests (e.g. felling sites) due to its rapid growth and renewal. Nevertheless, the competition
from
Carpinus betulus
, and, to a lesser extent, the species mobility (0–10 km from optimal habitats), limited
the real distribution of
Fagus orientalis
to more compact areas within the boundaries of its potential
distribution (foothills, low and middle mountains of the Greater and Lesser Caucasus). To the greatest
extent, the movement factor inuenced the distribution of
Carpinus betulus
in the Caucasus. Initially, small
optimal area and low mobility of the species (only 0–1 km) signicantly limited the geographic expression
of the realized niche of
Carpinus betulus
to relatively small suitable and optimal sites from the foothills to
the middle mountains of the Western Greater Caucasus and the Lesser Caucasus.
Due to the dependence of
Abies nordmanniana
and
Picea orientalis
on factors of water regime, their
predicted ranges in the Caucasus were initially relatively small (Table5) and almost completely coincided.
At the same time, competition from other forest-forming species (
Fagus orientalis
,
Pinus sylvestris
,
Abies
nordmanniana
) and relatively low mobility of
Picea orientalis
(0–6 km) limited its “occupied distributional
area to the small territories in the highlands of the potential range (SI Fig.5c). Thus, the highlands of the
Western Caucasus and the Georgian part of the Central Greater Caucasus, as well as the highlands of the
western Lesser Caucasus and the borders of Russia and Azerbaijan can be recommended for conservation
and restoration of
Picea orientalis
in the Caucasus.
Abies nordmanniana
is able to displace
Picea orientalis
from territories suitable for both species, especially in disturbed ecosystems. Therefore, the real distribution
of
Abies nordmanniana
in the Caucasus was mainly determined by habitat humidity and species mobility
(0–10 km). Areas suitable and optimal for pure r forests, which can be recommended for conservation and
restoration of
Abies nordmanniana
, were compacted to the middle mountains and highlands throughout the
Western Greater Caucasus, Georgian part of the Central Greater Caucasus and the west of the Lesser
Caucasus (SI Fig.4c).
Page 21/29
Conclusions
In our study, the potential spatial distribution of the main forest-forming species in the Caucasus depended
oncompetitors,species dispersal capability andabiotic variables (topographicconditions and water
regime). Areas of geographic expression of realized ecological niches of species, modeled by abiotic and
biotic variables, were 1.2–1.7 times smaller than the areas of geographic expression of fundamental
ecological niches, modeled only by abiotic variables. Movement factorreduced the areas of geographic
expression of realized ecological niches by 1.2–1.5times(
Abies nordmanniana
,
Picea orientalis
and
Pinus
sylvestris
) and 1.7–1.8times(
Fagus orientalis
and
Carpinus betulus
), but almost did not affect the potential
distribution of
Betula litwinowii
and
B. pendula
.Distribution maps,modeled by abiotic, biotic variables and
movement factor, were the closest to the real distribution of the forest-forming species in the Caucasus.
Acceptable habitats for pure r and spruce forests were relatively gentle slopes in humid (and sub-humid
for
Picea orientalis
) conditions in the middle mountains and highlands of the regions with a humid
subtropical and warm summer continental climate. Since
Abies nordmanniana
is able to displace
Picea
orientalis
from areas suitable for both species, its“occupied distributional area”was mainly determined by
habitat humidity and species mobility (0–10 km from optimal habitats).Competition from
Fagus orientalis
,
Pinus sylvestris
, and
Abies nordmanniana
, as well asspecies mobility (0–6 km),limited the real distribution
of
Picea orientalis
to relatively small highland territories. Optimal habitats of both species were concentrated
in theWestern Greater Caucasus, Georgian part of the Central Greater Caucasus, and in the west of the
Lesser Caucasus, wherewe recommend the conservation and restoration ofr and spruce forests.
Pinus sylvestris
, with a wide ecological range in main environment gradients and lack of competitors among
separate studied forest-forming species, can spread from nearly level to highly rugged areas with warm
summer continental, hemiboreal, oceanic, or hot summer continental climates. However, the complex
inuence of biotic and movement factors shifted the distribution of pure pine forests to the highlands of the
Greater and Lesser Caucasus.
Although thegeographic expression of fundamental ecological niches of pure beech and hornbeam forests
largely overlapped throughout the study area, and there was competitive relationship between them,
Fagus
orientalis
preferred more humid bioclimatic conditions. Competition from
Carpinus betulus
(especially in
disturbed forests) and species mobility (0–10 km from) limited thedistributionof pure beech forests in the
foothills, low and middle mountains of the Greater and Lesser Caucasus. Low species mobility (0–1 km)
were compacted thedistributionof
Carpinus betulus
to relatively small areas from the foothills to the middle
mountains of the Western Caucasus and the Lesser Caucasus.
Fundamental ecological niches of both birch species were similar; however,
Betula litwinowii
preferred wetter
habitats in more rugged areas. Competition with each otherand
Pinus sylvestris
, as well as mountain terrain
and water regime, mainly determined the “occupied distributional area” of both species in the upper forest
belt of the highlands throughout the Caucasus.
Declarations
Page 22/29
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Availability of data and materials
Part of the datasets used and/or analyzed during the current study is publicly available in the Global
Biodiversity Information Facility (GBIF) on the above-mentioned DOI. Part of the datasets is available from
the corresponding author on reasonable request.
Competing interests
The authors declare that they have no competing interests.
Funding
This work was supported by the state assignment “Patterns of the Spatiotemporal Dynamics of Meadow
and Forest Ecosystems in Mountainous Areas (Russian Western and Central Caucasus)”, No. 075-00347-19-
00
Authors’ contributions
The idea of research belongs to PR, FT and VCh. PR developed the distribution models and maps. YS, MM
and AA made a literature review and data processing. Statistical treatment, analysis of the results, and the
writing of the paper were made by PR, FT and VCh. The authors read and approved thenal manuscript.
Acknowledgements
Not applicable.
Supplementary Information
Supplementary material available at Supplementary Information (SI).pdf
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Scholarship Online, pp 60–73.https://doi.org/10.1093/oso/9780198824268.003.0006
Figures
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Figure 1
Location of the study area and climate classication scheme of the Caucasus. We constructed the climate
classication scheme using monthly mean temperature and precipitation data from WorldClim v2.0. based
on the SagaGis algorithm of Conrad et al. (2015). Köppen-Geiger climate classication and map color
scheme was used from Peel et al. (2007): BSk is a cold semi-arid climate (cold steppe climate); Cfa is a
humid subtropical climate; Cfb is an oceanic climate; Csa is a Mediterranean hot summer climate; Csb is a
Mediterranean warm or cool summer climate; Dfa is a hot summer continental climate; Dfb is a warm
summer continental or hemiboreal climate; Dfc is a cool summer continental climate; Dsa is a hot dry
summer continental climate; Dsb is a warm dry summer continental or hemiboreal climate; Dsc is a cool dry
summer continental climate; ET is an alpine climate (tundra climate)
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Figure 2
Theoretical framework of the study. Step 1 – selecting environmental layers for modeling; Step 2 – removal
of correlated variables using VIF test; Step 3 – modeling by abiotic environmental variables (A Models); Step
4 – extraction of species distribution models as biotic environmental layers; Step 5 – modeling by abiotic
and biotic environmental variables (BA Models); Step 6 – extraction of species distribution models with a
probability of species occurrence of 0.8–1 from BA Models and creating a raster of distances from the
optimal habitats; Step 7 – modeling based on abiotic, biotic and movement components of species
ecological niches (BAM Models)
Supplementary Files
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SupplementaryInformationSI.pdf
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