Ecological Niche Modeling of the Main - Forming Species in the

R. Pshegusov (  [email protected] ) 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 , BAM diagram, Maxent, Environmental predictor

Posted Date: August 17th, 2021

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

Page 1/29 Abstract

Background: Ecological niche modeling of the main forest-forming species within the same geographic range contributes signifcantly 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 sufciently studied.

Methods: We conducted studies within the physiographic boundaries of the Caucasus, including Russian Federation, , Armenia, and Azerbaijan. Our studies focused on ecological niche modeling of pure fr, spruce, pine, , hornbeam, and 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 infuence 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 infuence 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 signifcantly 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 efciency of ENM as a method for assessing the geographic distribution of species was confrmed by numerous studies of different and grass species. For many species, the predicted geographic ranges corresponded to the actual distribution of 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 feld of ENM is the study of the divergence in ecological niches of sympatric species within the same geographic range. Such studies contribute signifcantly 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.

Page 2/29 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 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 fnal 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, fr, 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 unifed 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 .

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 fr 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 , 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).

Page 3/29 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 of the Greater Caucasus is generally humid subtropical in the South- ( coast of ) 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 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, fr 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 . 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 (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 , and Asia Minor. In the Last Glacial Maximum, the most suitable areas for hornbeam forests occupied the Black Sea region of Turkey and western (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 confned 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 fltered the occurrence data from the GBIF database to exclude geographic records outside the natural range of the species (urban and rural areas, 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 rarefed (using "clean

Page 4/29 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-ftting 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 GBIF after Expedition Records in the records fltering records analysis

Abies DOI10.15468/dl.v2ff6j 497 52 17 69 nordmanniana

Picea orientalis DOI:10.15468/dl.dkaz3a 194 32 12 44

Pinus DOI10.15468/dl.ymbrx9 147 108 15 123 sylvestris

Fagus DOI10.15468/dl.zvhjhs 3009 130 42 172 orientalis

Carpinus DOI10.15468/dl.a4yhh3 1967 114 23 137 betulus

Betula DOI10.15468/dl.wny9k8 70 52 6 58 litwinowii

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 overftting of the models and select predictors most important for modeling, VIF (Variance Infation 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).

Page 5/29 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/dm3 2.22

silt Proportion of silt particles (≥ 0.002 mm and ≤ 0.05 mm) in the fne earth 2.32 fraction, g/kg

sand Proportion of sand particles (> 0.05 mm) in the fne earth fraction, g/kg 1.71

clay Proportion of clay particles (< 0.002 mm) in the fne 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 fne 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 efcient 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 defnes 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 fve-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 fle to a netCDF fle 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.

Page 6/29 At the frst 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 identifed 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 signifcantly limit the actual distribution of species by limiting population processes. To consider the infuence 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 specifcity 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 specifc to the training data (Warren and Seifert 2011). AUC values for each model were averaged across fve 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

Page 7/29 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 fve replicate runs Species model A Model BA Model BAM Model

AUCTrain AUCTest AUCTrain AUCTest AUCTrain AUCTest

Abies 0.97 0.94 0.98 0.94 0.98 0.95 nordmanniana

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 0.92 0.87 0.95 0.87 0.96 0.89 betulus

Betula 0.98 0.95 0.98 0.95 0.98 0.92 litwinowii

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 quantifes 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.

Page 8/29 Table 4 Contribution of environmental variables to the Maxent models of distribution of the main forest-forming species in the Caucasus Environmental A Model BA Model BAM Model variable PC, PI, % Suitable PC, PI, Suitable PC, PI, Suitable % values % % values % % values

Abies nordmanniana embergerQ 46.2 22.4 120– 10.1 19.5 120– 8.2 2.5 120– 200 210 210

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– 1.1 19.7 330– 0.1 0.3 330– 470 500 500

Picea orientalis - - - 65.3 5.5 0.3–1 35.1 1.1 0.4–1 occurrence

Movement factor, ------40.7 78.8 0–10 km

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 - - - 55 32.1 0.6–1 27.8 5.5 0.6–1 occurrence

Pinus sylvestris - - - 16.8 11.3 0.2–1 16.7 10.5 0.4–1 occurrence

Abies - - - 15.7 8.6 0.2–1 9.1 3.9 0.1–1 nordmanniana occurrence

Movement factor, ------34.2 48.1 0–6 km

Pinus sylvestris

TRI 66.7 42.4 80–550 19.9 4.6 350– 15.9 22.7 80–600 550

PC (percentage contribution) is the contribution to construction of models; PI (permutation importance) is the permutation coefcient

TRI is the Terrain roughness index; embergerQ is the Emberger's pluviothermic quotient; sand is the proportion of sand particles in the fne 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

Page 9/29 Environmental A Model BA Model BAM Model variable PC, PI, % Suitable PC, PI, Suitable PC, PI, Suitable % values % % values % % values

PETDriestQuarter, 12.2 13.1 15–20 0.4 2 15–20 0.1 0.6 15–20 mm/month

Betula pendula - - - 17.2 18.9 0.2–1 3.3 6.4 0.3–1 occurrence

Carpinus betulus - - - 15.5 3.3 0.1–1 14.7 0.8 0.1–1 occurrence

Betula litwinowii - - - 14 13.2 0.2–1 12.9 5.2 0.2–1 occurrence

Picea orientalis - - - 10.8 12.6 0.5–1 6.4 4.9 0.1–1 occurrence

Movement factor, ------39.5 27 0–10 km

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 - - - 40.6 4.9 0.3–1 42 11.3 0.2–1 occurrence

Picea orientalis - - - 12.1 18 0.1–1 2.5 5.2 0.1–1 occurrence

Movement factor, ------33.4 33 0–10 km

Carpinus betulus

TRI 60.3 52.4 35– 0.2 0.7 35–220 0.3 6.7 30–200 200

PETWettestQuarter, 15.9 21.2 105– 0.2 2.3 105– 0 0.3 105– mm/month 130 130 130

Pinus sylvestris - - - 30.5 13.6 0.1–1 11.6 2.2 0.1–1 occurrence

PC (percentage contribution) is the contribution to construction of models; PI (permutation importance) is the permutation coefcient

TRI is the Terrain roughness index; embergerQ is the Emberger's pluviothermic quotient; sand is the proportion of sand particles in the fne 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

Page 10/29 Environmental A Model BA Model BAM Model variable PC, PI, % Suitable PC, PI, Suitable PC, PI, Suitable % values % % values % % values

Movement factor, ------39.9 30.8 0–1 km

Betula litwinowii

TRI 47.3 27.5 120– 3.1 0.3 125– 2.6 12.2 100– 570 620 620

embergerQ 20.2 13.4 100– 0 0 100– 0.1 1.2 100– 170 175 175

Betula pendula - - - 45.9 22.6 0.4–1 29.6 28 0.5–1 occurrence

Pinus sylvestris - - - 11.5 4.9 0.1–1 21.6 15.4 0–1 occurrence

Movement factor, ------27.4 7.7 0–20 km

Betula pendula

TRI 47.4 51.9 90– 0 0 85–450 0.1 1.4 85–450 350

embergerQ 25.3 16.1 90– 0.1 0 85–210 0 1.4 90–200 180

Pinus sylvestris - - - 32.5 0 0.4–1 34.6 14.9 0.5–1 occurrence

Betula litwinowii - - - 20.8 48.2 0.4–1 11.3 19.2 0.4–1 occurrence

Movement factor, ------24.9 4.2 0–20 km

PC (percentage contribution) is the contribution to construction of models; PI (permutation importance) is the permutation coefcient

TRI is the Terrain roughness index; embergerQ is the Emberger's pluviothermic quotient; sand is the proportion of sand particles in the fne 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 fve 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 11/29 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 signifcantly 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 defciency (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 fne earth fraction for Abies nordmanniana. Suitable soils for fr 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 km2 Optimal 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 12/29 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 (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 fr 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 fr 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 signifcantly masked the infuence 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 13/29 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 infuence 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 signifcantly 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 signifcantly 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, fr 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 infuence on the distribution of pine and fr 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 14/29 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 signifcantly 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, signifcantly decreased. Areas optimal for Fagus orientalis with a relatively low infuence 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 signifcantly, 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 infuence 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 signifcant effect of topographic conditions and water regime on the potential distribution of the studied species. Our results showed that the acceptable habitats for pure fr 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 fr forests in northwestern Turkey (Coban 2020), which showed that pure fr forests mainly occurred between 1000 and 1600 m above sea level on

Page 15/29 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 fr 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 (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 signifcant infuence 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 16/29 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) confrmed that the water regime signifcantly 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 defciency 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 signifcantly 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 fnding supported previous reports (Nishimura 2006; Litvinskaya and Salina 2012) on the convergence of suitable environmental conditions for spruce and fr.

Page 17/29 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 fr-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 fr-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 fr to recover faster after felling and fres could also limit the distribution of fr-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 fr forests prefer wetter habitats. Thus, in BA Models, embergerQ still retained a signifcant effect on the distribution of pure fr 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 fr 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 fr-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 fr also formed stable mixed stands in the fnal 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 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 signifcant 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 fnding 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 fr-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 18/29 hornbeam forests in the Central Caucasus by 6% in the frst 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. Signifcant infuence 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 fr-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 infuence 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 fnal maps of the forest-forming species ranges to their real distribution in the Caucasus with the possibility of practical application. Therefore, we defned 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 fr, 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 signifcant 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 19/29 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 signifcantly 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 infuence 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 infuence 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 infuenced the distribution of Carpinus betulus in the Caucasus. Initially, small optimal area and low mobility of the species (only 0–1 km) signifcantly 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 fr 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 20/29 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 fr 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 fr 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 infuence 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 21/29 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 the fnal manuscript.

Acknowledgements

Not applicable.

Supplementary Information

Supplementary material available at Supplementary Information (SI).pdf

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Figures

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Location of the study area and climate classifcation scheme of the Caucasus. We constructed the climate classifcation 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 classifcation 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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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)

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