Research Article |
Corresponding author: David Tarkhnishvili ( david_tarkhnishvili@iliauni.edu.ge ) Academic editor: Günter Gollmann
© 2021 Natia Barateli, David Tarkhnishvili, Giorgi Iankoshvili, Luka Kokiashvili, Nikoloz Dvali, Zurab Janiashvili.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Barateli N, Tarkhnishvili D, Iankoshvili G, Kokiashvili L, Dvali N, Janiashvili Z (2021) Fine-scale analysis of habitat occupancy by Kura lizard (Darevskia portschinskii) and its daughter parthenogenetic form (Darevskia dahli). Herpetozoa 34: 71-81. https://doi.org/10.3897/herpetozoa.34.e63072
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Two species of rock lizards, the parthenogenetic D. dahli and the sexually reproducing D. portschinskii, coexist in a rocky outcrop in an area of ca. 1 ha, in the vicinity of Tbilisi, Georgia; the location has been well-known since the middle 1960s. The population density of the parthenogenetic lizard is five times higher than that of the sexual breeder. We studied the distribution of active lizards in space and time over three consecutive years, during the spring and autumn activity periods, to explore spatial and temporal differences between the species on a fine spatial scale. We studied the influence of temperature, humidity, and quantitative characteristics of the surface and the distance from permanent water source on the spatial distribution of D. dahli and D. portschinskii. Darevskia portschinskii was less dependent on the distance from the water source and more evenly distributed in space and time than D. dahli. Despite potential competitive interactions, the species did not avoid each other on the microhabitat scale, suggesting that the observed ecological differences are not caused by a niche shift. More individuals of the sexual breeder than individuals of the parthenogen were found in suboptimal habitats. This feature may increase the evolutionary success of D. portschinskii in a long-term perspective.
competition, ecological niche, parthenogenesis, rock lizards, territoriality
Comparing ecological niches of closely related species is a challenging task. A species’ spatial distribution and activity dynamics depend on its ecological preferences. Simultaneously, the realized niche in
Parthenogenetic lizards have hybrid origins, and they commonly coexist with their ancestral bisexual forms (
One of such hybrid parthenogenetic lizards is Darevskia dahli from the Lesser Caucasus Mountains, the patrilineal ancestor of which is the bisexual Darevskia portschinskii and the matrilineal ancestor is Darevskia mixta (
Remote sensing analysis showed that the shift of the spatial distribution of the two species is related to different thermal preferences and to different preferred humidity: D. portschinskii prefers higher temperatures than D. dahli, and the distribution of D. dahli is limited to the areas where annual rainfall level is above a certain threshold (
For deeper insight into this question, it is essential to understand how the species share space and other resources on the microgeographic scale and whether competition for the resources or behavioral interactions shape the realized niche of either species. Here, several interacting processes may play a role.
Here, we examined whether ecological niches of D. dahli and D. portschinskii are displaced on a microgeographic scale (within a semi-isolated habitat covering ca. 1 hectare) and what can be the reason for the differences (if any): type of territorial behavior, population density, or competition. We tested two complementary hypotheses: (1) the differences in the habitat use are related to mutual avoidance of the coexisting species, and/or (2) the differences are due to differential territorial behavior; alternatively, habitat requirements might not differ or differ for reasons independent of the intra- or interspecific competition.
A rock outcrop near Tbilisi (Georgia) was selected as a study site. This area is described in previous papers (
We divided the study area into 108 squared plots with a surface of 10 × 10 m each (Fig.
Standard deviation, average and maximal values of slope and convergence index in each sample plot were calculated using the Zonal Statistics tool in QGIS 3.12. The slope represents the inclination angle to the horizontal surface in degrees; the maximum value of slope (MSlope), characterizing each plot, was used in final calculations. The convergence index calculates an index of convergence/divergence regarding overland flow (2004–2016 QGIS Development Team). Our study used the standard deviation of convergence index (SDCI), which measures the unevenness of the rocky surface or density of clefts that the lizards are using as hiding shelters.
In each field session (27 field sessions altogether – Suppl. material
For each sighted individual, we recorded: age (adult or juvenile); species (D. dahli vs D. portschinskii); sex (for D. portschinskii); time of the day and the number of the plot. All individuals with the distance from the back surface of the hip to the tip of snout exceeding 5 cm were treated as adults.
The numbers of active D. dahli and D. portschinskii were recorded for each of 108 plots 100 m2 each, during each of 27 field sessions. Hence, altogether we had 2,916 observations for each species. Surface temperature and humidity were scored for most of these observations. The entire observation table is shown in the appendix (Suppl. material
Because males of D. portschinskii might have spatial distribution different from females and juveniles of the same species, due to potentially more expressed territorial behavior, we first tested significance in differences in distribution between D. dahli, males of D. portschinskii, and females + juveniles of D. portschinskii across the 108 plots, using Kolmogorov-Smirnov Lambda test. Since the differences were significant for different species but insignificant for different ages and sexes of D. portschinskii, we clumped together all individuals of the latter species for further analyses.
In order to evaluate the significance of each of the factors that potentially influence the presence and activity of the lizards (the number of recorded individuals at a plot during a given session; NL; the analysis separately ran for D. dahli and a unified sample of D. portschinskii) we applied generalized linear mixed models (GLMM). Because of the excess of NL equal to zero, before applying this method, we removed from the dataset all plots where fewer than three lizards were recorded during the entire period of the study, reducing the number of observations to 1,483. This was done to achieve better correspondence of the NL distribution throughout the plots and sessions to Poisson distribution, recommended for this kind of data. The plots were set as “subjects” and the sessions as “repeated measures.” Target (NL) was set to Poisson distribution with the logarithmic link function. Nine predictors (year, date, time of the day, surface type, humidity and temperature of the ground surface, averaged air temperature, slope, and convergence index) were set as fixed factors; the session combined with the plot was set as a random factor. Covariance type was set to “Variance component” for the subject and to “first-order autoregressive” for the random component (assuming autocorrelation between the sessions close in time).
Classification tree analysis (CHAID algorithm) was an additional method applied for inferring predictive models for both species. In this analysis, we included the occurrence of a potential competitor (PC) in the set of predictors to infer whether and how the presence of PC may influence the occurrence of the target species, given similar environmental conditions. In this analysis, we used a complete dataset (2,916 observations, including those on the plots where the lizards were never recorded). Parent and Child nodes were set to 100 and 50. Significance levels for splitting nodes and merging categories were set to 0.05, growth limits to 2 levels, the maximum number of iterations to 10,000, and Chi-square statistics calculated using likelihood ratio. For validation, the entire dataset was randomly separated into the training (75%) and test (25%) datasets; squared correlation (R2) between the observed mean in the training and test datasets was used as an index of the model quality.
.
In this equation, O21 is the number of individuals of species 2 whose resources are consumed by a single individual of species 1; p1i is the index of the utilization of resource category i by species 1. We considered a single observation as a “resource category” and estimated pxi as NL for species x in this observation. Before that, we conducted this procedure separately for individual plots and survey sessions and estimated the “overall” overlap indices as the product of the two latter ones (
All calculations were done using IBM SPSS v. 23.0 and Excel 2010 for Windows.
Darevskia dahli were recorded at 78 out of 108 studied plots, and D. portschinskii at 40 plots (Fig.
Both Pearson correlation and Spearman rank correlation coefficients showed a significant negative correlation of the number of records of D. dahli with the distance from the brook, surface humidity, and convergence index, and positive correlation with an average slope of the plot (Suppl. material
Dependence of the average and 95% confidence intervals for the number of recorded individuals (NL) of D. dahli (solid lines) and D. portschinskii (dashed lines) at a plot during a given session on: a: distance from the brook (m), b: year, c: air temperature (°C), d: days passed after 1st April, e: substrate type and f: average slope index on the 3 cm pixel scale (averaged for a plot).
The analysis based on the assumption that the individual plots are “subjects”, individual sessions are “repeated measures”, and NL has Poisson distribution with Log function, suggested a significant effect of all predictors, except surface temperature and the convergence index on the occurrence of D. dahli, and presence of temporal autocorrelation explaining the occurrence dynamics (Table
Source of variance | Type | F (DD) | F (DP) | df1 | df2 | Sig (DD) | Sig (DP) |
---|---|---|---|---|---|---|---|
Corrected model | 22.34 | 2.18 | 20 | 1.17 | <0.001 | 0.002 | |
Distance from the brook | Fixed in space | 9.75 | 8.79 | 1 | 1.17 | 0.002 | 0.003 |
Surface type | Fixed in space | 3.66 | 1.59 | 11 | 1.17 | <0.001 | 0.097 |
Convergence Index | Fixed in space | 0.87 | 0.21 | 1 | 1.17 | 0.352 | 0.645 |
Average Slope | Fixed in space | 6.03 | 0.39 | 1 | 1.17 | 0.014 | 0.535 |
Year | Fixed in time | 30.90 | 0.67 | 2 | 1.17 | <0.001 | 0.513 |
Date | Fixed in time | 28.99 | 1.32 | 1 | 1.17 | <0.001 | 0.250 |
Air Temperature | Fixed in time | 10.17 | 0.03 | 1 | 1.17 | <0.001 | 0.864 |
Surface Humidity | Varying | 16.00 | 1.50 | 1 | 1.17 | <0.001 | 0.221 |
Surface Temperature | Varying | 2.89 | 0.01 | 1 | 1.17 | 0.089 | 0.960 |
Coefficients (for significant predictors only) | |||||||
b (DD) | b (DP) | Sig(DD) | Sig(DP) | ||||
Surface = forest | -2.764 | N/A | 0.004 | N/A | |||
Surface = grassy rock | -1.269 | N/A | 0.050 | N/A | |||
Surface = rock | 0.765 | 0.070 | 0.012 | 0.016 | |||
Surface = Gravel | N/A | 0.126 | N/A | 0.002 | |||
Distance from the brook | -0.006 | 0.001 | 0.002 | 0.003 | |||
Date (days from 1 April) | -0.013 | N/A | <0.001 | N/A | |||
Surface Humidity | -0.025 | N/A | <0.001 | N/A | |||
Air Temperature | -0.089 | N/A | 0.001 | N/A | |||
Average Slope | 0.022 | N/A | 0.014 | N/A |
R2 value estimated from the node means of the training and test CHAID trees for D. dahli was 0.859 (17 nodes), and for D. portschinskii 0.575 (11 nodes).
The classification tree showed 3–4 variables that significantly influenced NL of D. dahli (Suppl. material
The distance from the brook was also a primary variable affecting NL for D. portschinskii. Simultaneously, given the same distance from the brook, NL (D. portschinskii) positively correlated with NL (D. dahli), suggesting that similarity of the requirements to the environmental conditions, except distance from the permanent water source, outweigh potential negative interactions between the two species.
The asymmetric index of niche overlap calculated for the utilization of space and time was: for dp/dd 0.543, and for dd/dp 0.053. If we ignore time and only count the overlap in space use, the respective coefficients would be 2.69 (dp/dd) and 0.10 (dd/dp). Symmetric Pianka’s niche overlap index reached 0.224 for individual observations and 0.487 for individual plots irrespective of the observation time.
Competition is a significant force, able to lead to one of the competitor’s extinction if the differences between the utilized resources are insufficient (
Remote sensing analysis (
Why do environmental conditions, including surface type and characteristics, proximity of water source, and air temperature, affect the occurrence of D. dahli more markedly than that of D. portschinskii? Tsellarius (
Territoriality has another interesting consequence: the territorial species, which is more commonly found in suboptimal habitats and time, may better adapt to changing conditions, if previously optimal parts of the habitat will be lost. Hence, the advantage of sexual reproduction, along with the well-known factor of genetic diversity (
In spite of these differences, the overlap of spatio-temporal niches between D. dahli and D. portschinskii is substantial. This may potentially cause interspecific competition (
These differences, which are related to the temperature and humidity optima, reported here and in earlier observations and modeling results (
Although the species do not avoid each other on the microhabitat scale, on the macrogeographic scale the mutual negative impacts are obvious (
This study shows that negative interspecific interactions do not necessarily trigger niche shift and character displacement and that the simultaneous presence of negative interactions and ecological differences between closely related species cannot be taken as an argument for character displacement. The studied species do not actively avoid the competitor, which suggests that their long-lasting coexistence is a result of metapopulation dynamics and sources-sinks type of spatial structure (
The research was supported by Shota Rustaveli National Science Foundation (Award# 217478).The authors appreciate Akaki Nadaraia for assisting in drone operation in field conditions and Eduard Galoyan for useful suggestions during the field work. Günter Gollmann and two anonymous reviewers provided useful comments on the first draft of the paper.
Table S1
Data type: Microsoft Excel table, full data of the records and physical conditions
Explanation note: Full record for the three years of the observation. Pl – the number of a plot (see Fig.
Figure S1
Data type: PNG figure, classification tree describing habitat preferences of the studied species
Explanation note: Classification tree (=decision tree), describing relation of occurrence of D. dahli (dd) and D. portschinskii (dp) to the most important environmental variables. Method – CHAID; Maximum depth – 2 steps; minimum size of parental node – 100; minimum size of child node – 50; Validation: 75% training observations, 25% test observations. The total number of observation – 2917. Test tree is shown. Variables/ predictors: dd – number of observed D. dahli; dp- number of observed D. portschinskii; Dis. From River in Meters – the distance of a plot from the brook; slmax – average slope at a plot; cst – convergence index; Surface – surface type (see Fig.