# Species Distribution Models and Niche Partitioning among Unisexual Darevskia dahli and Its Parental Bisexual (D. portschinskii, D. mixta) Rock Lizards in the Caucasus

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Dataset Acquisition and Preparation of Vector and Raster Layers

#### 2.2. Spatial Thinning of Records and Predictor Variables

#### 2.3. Determination of the Maxent Models Parameters

#### 2.4. Construction of Species Distribution (SDM) and Ecological Niche (ENM) Models

_{ind}) to assess model performance [69,70,71] with the help of the EcoSpat R package [71]. Boyce index lacks those drawbacks which has AUC index [72,73]. It requires only data on lizard occurrence and measures how much the predictive models differ from random distribution. We calculated B

_{ind}for all the 10 models for each lizard; afterwards we averaged the obtained values to get the final estimates. The importance of each predictor variable of SDMs was assessed by analysis of Maxents variable contribution table using the jackknife method [45,46]. Variables with essential impact on the model, i.e., having high values of permutation importance (PI > 5) and/or high values of the percent of contribution (PC > 5) were considered as the most important.

#### 2.5. Comparison of Ecological Niches and Parameters of Habitat Exploitation

_{b}). The difference in the niches breadth was estimated in two steps. First, the uniformity of the niches breadth was tested using multiple comparisons [77]. Then, if the null hypothesis was rejected, niches were compared pairwise [78].

## 3. Results

#### 3.1. Model Performance and Predictor Variables

_{ind}were obtained based on Maxent parameters by default and using the optimal parameters determined by AICc test (Delta AIC < 2) (Supplementary Figure S3). As a result, B

_{ind}significantly differed between these two versions of SDMs. Boyce Index (B

_{ind}± SE) for SDMs of D. dahli, D. portschinskii and D. mixta calculated with Maxent parameters by default were 0.938 (±0.0012), 0.91 (±0.003), 0.91 (0.004), respectively, and for optimal parameters of Maxent, they were 0.95 (±0.004), 0.93 (±0.004) and 0.94 (±0.003), respectively. A statistically significant increase in SDMs performance (B

_{ind}) was found for all studied species: D. dahli (t = 2.82, p = 0.006), D. portschinskii (t = 4.83, p << 0.001) and D. mixta (t = 7.13, p << 0.001). To conclude, the settings by default resulted in more complex and less accurate SDMs.

_{ind}at optimal parameters of Maxent are given in Table 2. These results show that three variables, namely precipitation of the warm quarter (C_PWarmQ), solar irradiation (C_SRad) and elevation (T_EL), are significant for all species. The remaining predictor variables determining the suitable habitats varied among the species. The habitat preferences of D. dahli determined by isothermality (C_ISOT) and distance to road (L_DHW); while the “paternal” species D. portschinskii depends on isothermality (C_ISOT), seasonal coefficient of humidity variation (C_PCoefVar), annual temperature range (C_TAnR), air temperature in dry quarter (C_MTDrQ), distance to road (L_DHW), and vegetation type (L_VEG). The habitat preferences of D. mixta, mainly depend on the seasonal coefficient of humidity variation (C_PCoefVar) and vegetation type (L_VEG). There are three common predictor variables for D. dahli and D. mixta (Table 2). All the variables responsible for D. dahli preferences are relevant for D. portschinskii except for vegetation type.

#### 3.2. Potential Range of Studied Lizards

#### 3.3. Comparative Analysis of Ecological Niches

_{b}= 1.12 (±0.15), the intermediate breadth was in D. dahli, Nb = 0.86 (±0.12), and the smallest breadth was in D. mixta, Nb = 0.48 (±0.08). The pairwise comparison showed that N

_{b}in D. portschinskii was significantly wider (F = 1.31, p = 0.026) while that of the D. mixta was narrower (F = 2.27, p = 0.0001) than in D. dahli. The largest standard deviations of MD (SD MD = 10.1) was for D. portschinskii, the intermediate (SD MD = 9.4) for D. dahli, and smallest (SD MD = 5.1) was obtained for D. mixta. The largest distance between the niche centroids MD = 21.1 was found between the parental species (D. portschinskii and D. mixta), the intermediate MD = 16.5 was observed between the centroids of D. mixta and D. dahli, and the smallest MD = 0.78 was obtained between the centroids of D. portschinskii and D. dahli.

#### 3.4. Shifts of Ecological Niches Centroids along Environmental Gradients

#### 3.5. Statistical Analysis of the Shifts of Niche Centroids

^{−2}day

^{−1}), and large distance from roads (774 ± 34 m).

^{−2}day

^{−1}) (Figure 3h), medium coefficient of humidity variation (44 ± 0.48%) (Figure 3f), low total annual precipitation (630 ± 9mm) (Figure 3e), and low total precipitation in the warm season (192 ± 3 mm) (Figure 3e). In terms of distance roads, D. portschinskii occupies an intermediate position relative to the other lizards (540 ± 17 m) (Figure 3j).

^{−2}day

^{−1}) (Figure 3h), high seasonal variation of humidity (46 ± 0.6%) (Figure 3f), and low average temperatures in dry quarter of year (0.09 ± 0.33 °C) (Figure 3d). Habitats of D. dahli are located close to roads (514 ± 22 m) (Figure 3j).

## 4. Discussion

#### 4.1. Predicted Distribution Range with Optimal Model Parameters

#### 4.2. Niches of the Unisexual and Bisexual Lizards: Breadth, Overlap, Similarity and Shifts

#### 4.3. Mechanisms of Coexistence of Unisexual and Bisexual Forms

## 5. Conclusions

_{ind}) for all studied lizards calculated with optimal Maxent parameters using AICc criterion evidenced that we were able to select the most important environmental predictor variables that determine habitat suitability for lizards. The narrow niche breadth of the “maternal” species D. mixta relative to that of D. dahli, and separation of their habitats confirm that the initial assumption of GP model was fulfilled. However, the fact of the displacement of D. mixta from its native range by polyphyletic clones of D. dahli has not been completely proven and requires further studies. On the other hand, the availability of polyphyletic clones, a significant superiority in the niche breadth of the “paternal” species D. portschinskii, and significant shifts of the niche centroids of clonal forms facilitate co-existence of these species in the Caucasus. We regard the differentiation of unisexual and bisexual lizards’ niches as a mechanism of their survival. Finally, we developed a new methodological approach based on the SDMs and ENMs which can be further applied for studying the niche partitioning in unisexual and its parental bisexual forms. These results can be helpful for conducting future field surveys and can be used by environmental agencies and/or decision makers to preserve natural habitats for rock lizards.

## Supplementary Materials

**a**), (

**c**), (

**e**) are initial clustered data sets; (

**b**), (

**d**), (

**f**) are reduced non–autocorrelated data sets. Dotted areas represent masks used to fit the potential distribution models of Darevskia spp. Figure S2: Evaluation metrics for D. dahli, D. portschinskii и D. mixta resulting from MaxEnt models made across a range of feature-class combinations and regularization multipliers. AICc is the Akaike Information Criterion corrected for small sample sizes, delta AICc (DAIC) is the difference between the AICc of a given model and the AICc of the model with the lowest AICc. Dotted horizontal line represents delta AICc = 2, which delimits models that are generally considered to have substantial support relative to others examined – that is those below the line. Default settings and settings that yielded minimum AICc are indicated with arrows. Legends denote feature classes allowed (L = linear, Q = quadratic, H = hinge, p = product and T = threshold). Note that for these lizards, AICc consistently selected regularization multipliers higher than the default value. Figure S3: Relationships between each of the most important environmental predictors (see Table 2) and the likelihood of D. dahli occurrence. Figure S4: Relationships between each of the most important environmental predictors (see Table 2) and the likelihood of D. portschinskii occurrence. Vegetation type: 1—Mountain forest zone, 2—Mountain meadows, 3—Mountain steppe, 4—Arid mountain steppe, 5—Nival zone, 6—Semi-desert, 7—Cultivated areas, 8—Wetland areas, 9—Alpine zone, 10—Urban areas. Figure S5: Relationships between each of the most important environmental predictors (see Table 2) and the likelihood of D. mixta occurrence. Vegetation types are presented in Figure S4. Figure S6: Correlation between predictor variables and the first two components of the principal component analysis calibrated on the environmental conditions in parental and “daughter” lizards. First and second components explain 81.1% of the total variation. The abbreviations of variables are described in Table S1. Figure S7: Graphic representation of the shift of the niche centroid of the parthenogenetic lizard D. dahli relative to the “maternal” species D. mixta along the most important environmental gradients. The red arrow indicates the direction of niches shift. Vegetation types are presented in Figure S4. Figure S8: Graphic representation of the shift of the niche centroid of the parthenogenetic lizard D. dahli relative to the “paternal” species D. portschinskii along the most important environmental gradients. The red arrow indicates the direction of niches shift. Vegetation types are presented in Figure S4. Figure S9: Comparing the means positions (centroids) of the ecological niches of the studied rock lizards along the gradients of the environment using Post Hoc Tukey HSD test. Differences between each pair of means are presented with 95% family-wise confidence level. The names of the predictor variables (see Table S1) for the panels (

**a**)–(

**j**) are the same as in Figure 3. Table S1: Habitat variables considered in the species distribution models. Table S2: Average Nearest Neighbor Index (ANNI) of the species occurrence data, where n is the number of sampling sites, Z score is the statistic value showing validity of the null hypothesis of a random distribution of points.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**Niche overlaps of D. dahli, D. portschinskii and D. mixta in multidimensional space of predictor variables. Panels (

**a**,

**b**) represent the niche overlap between D. dahli and D. portschinskii along the two first axes of the PCA; (

**c**,

**d**) represent the niche overlap between D. dahli and D. mixta. Shaded areas show the density of the occurrences of D. dahli (

**a**,

**c**), D. portschinskii (

**b**) and D. mixta (

**d**). The solid and dashed lines illustrate, respectively, 100% and 90% of the available (background) environment.

**Figure 3.**The positions of the centroids of the ecological niches of the rock lizards along the environmental gradients with 95% confidence intervals of Tukey HSD. The GLM ANOVA tested the main factor effects of the lizards—(

**a**) F = 46.3; p <<0.01; (

**b**) F = 44.8; p << 0.01; (

**c**) F = 16.5, p << 0.01; (

**d**) F = 22.58; p << 0.01; (

**e**) F = 270.31, p << 0.01; (

**f**) F = 295.6; p << 0.01; (

**g**) F = 66.6; p << 0.01; (

**h**) F = 16.9; p << 0.01; (

**i**) F = 17.6; p <<0.001; (

**j**) F = 22.49; p << 0.01 (F is Fisher’s test; p value is given for the factor effects). Absence of difference between means using Post hoc Tukey HSD test are marked by * (p-values of the all comparison pairs are presented in Figure S9).

**Figure 4.**The percentage of preferred vegetation types with 95% Wald’s confidence intervals. A multiple comparison was performed using the Chi-square (a: 13.4, DF = 2, p = 0.001; b: 17.7; DF = 2, p << 0.001; c: 5.9, DF = 2, p = 0.05; d: 5.84, DF = 2, p = 0.05) and Tukey’s Post hoc tests. Absence of significant differences between the means was designated *.

**Table 1.**The list of predictor variables selected for building SDM of D. dahli, D. portschinskii and D. mixta, where R

_{P}is the largest value of the Pearson’s correlation coefficient; R

^{2}is the coefficient of determination of a linear regression of each predictor variable on all other predictor variables, VIF is Variance Inflation Factor.

Variable | Code | R^{2} | VIF |
---|---|---|---|

D. dahli, R_{P} = 0.63 | |||

Solar irradiation (kJ m^{−2} day^{−1}) | C_SRad | 0.72 | 3.53 |

Precipitation in warmest quarter (mm) | C_PWarmQ | 0.63 | 2.69 |

Elevation (m) | T_EL | 0.42 | 1.72 |

Isothermality, % | C_ISOT | 0.26 | 1.35 |

Distance to road (m) | L_DHW | 0.09 | 1.1 |

D. portschinskii, R_{P} = 0.72 | |||

Solar irradiation (kJ m^{−2} day^{−1}) | C_SRad | 0.86 | 7.28 |

Elevation (m) | T_EL | 0.77 | 4.43 |

Mean temperature in driest quarter (°C) | C_MeanTDrQ | 0.77 | 4.27 |

Precipitation in warmest quarter (mm) | C_PWarmQ | 0.76 | 4.18 |

Annual temperature range (°C) | C_TAnR | 0.72 | 3.56 |

Precipitation seasonality (coefficient of variation) (%) | C_PCoefVar | 0.56 | 2.28 |

Isothermality, % | C_ISOT | 0.56 | 2.27 |

Vegetation type | L_VEG | 0.56 | 0.44 |

Distance to road (m) | L_DHW | 0.12 | 1.14 |

D. mixta, R_{P} = 0.69 | |||

Solar irradiation (kJ m^{−2} day^{−1}) | C_Srad | 0.76 | 4.13 |

Precipitation in warmest quarter | C_PWarmQ | 0.65 | 2.87 |

Precipitation seasonality (coefficient of variation) (%) | C_PCoefVar | 0.54 | 2.16 |

Elevation (m) | T_EL | 0.47 | 1.9 |

Vegetation type | L_VEG | 0.41 | 1.7 |

Distance to road (m) | L_DHW | 0.07 | 1.08 |

**Table 2.**Table of contribution of the most important variables obtained by Maxent SDMs. Significant contributions of variables are bolded, where PC is a percentage of contribution, PI is permutation importance.

Predictor Variables | D. dahli | D. portschinskii | D. mixta | |||
---|---|---|---|---|---|---|

PC | PI | PC | PI | PC | PI | |

C_ISOT | 24.4 | 10.4 | 12.6 | 21.9 | 0.3 | 1 |

C_TAnR | 0.1 | 0.1 | 1.5 | 5.6 | 0.8 | 0.7 |

C_MeanTDrQ | 0.2 | 0 | 9.1 | 8.6 | 0.1 | 0 |

C_PCoefVar | 1.7 | 0.8 | 3.9 | 5.3 | 36.8 | 44.5 |

C_PWarmQ | 30 | 50.2 | 32.7 | 22.3 | 30.1 | 20.4 |

C_SRad | 12.4 | 22.7 | 17.8 | 26.4 | 8.7 | 17.4 |

T_EL | 13.6 | 13.7 | 6.6 | 1.8 | 4.9 | 9.1 |

L_DHW | 9 | 0.1 | 5.4 | 0.1 | 0.2 | 0.1 |

L_VEG | 0.2 | 0.3 | 5.1 | 1 | 8.3 | 0.8 |

Boyce index (±SE) | 0.95(±0.004) | 0.93 (±0.004) | 0.94 (±0.003) |

**Table 3.**Niche overlap assessed using Schoener’s D indices between D. dahli and parental species (D. portschinskii and D. mixta) ranges. Where E is expansion, S is stability, U is unfilling, p-values of the niche similarity test are given for a pairwise comparison.

Parental Species | Schoener’s D Index | p-Value | E | S | U |
---|---|---|---|---|---|

D. mixta | 0.22 | 0.09 | 0.12 | 0.88 | 0.57 |

D. portschinskii | 0.72 | 0.009 | 0.02 | 0.98 | 0.06 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Petrosyan, V.; Osipov, F.; Bobrov, V.; Dergunova, N.; Omelchenko, A.; Varshavskiy, A.; Danielyan, F.; Arakelyan, M. Species Distribution Models and Niche Partitioning among Unisexual *Darevskia dahli* and Its Parental Bisexual (*D. portschinskii*, *D. mixta*) Rock Lizards in the Caucasus. *Mathematics* **2020**, *8*, 1329.
https://doi.org/10.3390/math8081329

**AMA Style**

Petrosyan V, Osipov F, Bobrov V, Dergunova N, Omelchenko A, Varshavskiy A, Danielyan F, Arakelyan M. Species Distribution Models and Niche Partitioning among Unisexual *Darevskia dahli* and Its Parental Bisexual (*D. portschinskii*, *D. mixta*) Rock Lizards in the Caucasus. *Mathematics*. 2020; 8(8):1329.
https://doi.org/10.3390/math8081329

**Chicago/Turabian Style**

Petrosyan, Varos, Fedor Osipov, Vladimir Bobrov, Natalia Dergunova, Andrey Omelchenko, Alexander Varshavskiy, Felix Danielyan, and Marine Arakelyan. 2020. "Species Distribution Models and Niche Partitioning among Unisexual *Darevskia dahli* and Its Parental Bisexual (*D. portschinskii*, *D. mixta*) Rock Lizards in the Caucasus" *Mathematics* 8, no. 8: 1329.
https://doi.org/10.3390/math8081329