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Evolutionary Biology
https://doi.org/10.1007/s11692-023-09603-6
& Schemske, 2005; Barrett & Hoekstra, 2011; Niewi-
arowski & Roosenburg, 1993). As a result, they have the
power to inuence the distributional limits of populations
and species as well as phenotypic evolution and functional
diversity (Sexton et al., 2009). Additionally, ecological vari-
ables may directly inuence speciation (McKinnon et al.,
2004). According to ecological speciation models, repro-
ductive isolation emerges as an adaptive result of diverse
natural selection (Rundle & Nosil, 2005; Schluter, 2001).
In this context, speciation coexists with divergence in niche
occupancy along at least one axis of multidimensional niche
space (Cox et al., 2014; Gómez-Rodríguez et al., 2015).
Even when non-ecological reasons produce reproductive
isolation, multifarious selection can speed up and stabilize
speciation, resulting in the secondary accumulation of eco-
logical dierences across lineages (Arnqvist et al., 2000;
Burke & Arnold, 2001; Chevin et al., 2014; Mank, 2007;
Owens et al., 1999). Furthermore, ecological restrictions
may also contribute to speciation if evolutionary lineages
separate geographically during a period of environmental
Introduction
Ecological factors play a signicant role in speciation by
providing sources of selection that lead to microevolution-
ary change and by restricting organismal functions (Angert
Somaye Vaissi
s.vaissi@razi.ac.ir
1 Department of Biology, Faculty of Science, Razi University,
Baghabrisham, Kermanshah, Iran
2 Kelkit Sema Doğan Vocational School of Health Services,
Department of Medical Services and Techniques, Gümüşhane
University, Kelkit, Türkiye 29600, Turkey
3 Department of Biology, Faculty of Science, Hacettepe
University, Ankara, Türkiye, Turkey
4 Universita Di Corsica, Université de Corse Pascal Paoli,
Corsica, France
5 College of Biology and the Environment, LASER, Nanjing
Forestry University, Nanjing, China
Abstract
The degree to which closely related species conserve or diverge in their niche characteristics may provide insight into evo-
lutionary and diversication processes, as well as some understanding of broad-scale biogeographic patterns. In this study,
we used an ensemble of 10 algorithms to predict the distributions of 12 Ablepharus species in geographical (G)-space for
the recent time and the future (2081–2100) climate conditions. Niche overlap, equivalency, and similarity tests in envi-
ronmental (E)-space were used to test niche divergence and conservatism. The consensus model showed that temperature
seasonality (n = 6) and annual precipitation (n = 3) were the major factors predicting the distribution of 75% (9 out of 12)
of the species. The ensemble models based on ssp585 predict that 83.33% of the species (n = 10) may have contracted
their ranges, especially at lower altitudes along their southern margins. They may have also expanded northward during
this period. However, in the ssp126, most species’ distribution ranges may remain unchanged despite contractions and
expansions. Based on multivariate niche analyses, the distributions of 28 and 11 paired comparisons explored in this study
signicantly diverged and conserved. We found that closely related species inhabit dierent habitats and respond to their
climatic characteristics, indicating that ecology has a signicant inuence on speciation.
Keywords Ablepharus · Ecology · Distribution · Threats · Climate change · Conservation
Received: 1 October 2022 / Accepted: 28 March 2023
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023
Climatic Niche Divergence and Conservatism Promote Speciation in
Snake-Eyed Skinks (Sauria: Scincidae): New Insight into the Evolution
and Diversification of Ablepharus Species
SomayeVaissi1· MuammerKurnaz2· Mehmet KürşatŞahin3· AxelHernandez4,5
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Evolutionary Biology
change while retaining ancestral niche anities (niche con-
servatism) (Wiens, 2004). In this instance, vicariant specia-
tion occurs when ancestral distributional ranges split and
shift (for example, during periods of orogeny or warming)
as the new species cannot adapt to the novel environmental
conditions that would otherwise enable gene ow to persist
(Shepard & Burbrink, 2008; Wiens, 2004). Therefore, func-
tional lineage diversication and the level of niche simi-
larity across members of a phylogenetic group should be
inuenced by the respective contributions of niche conser-
vatism and niche divergence throughout speciation events
(Culumber & Tobler, 2016). However, diversication still
lacks a clear understanding of the relative importance of
these components (Culumber & Tobler, 2016).
Ecologists and evolutionary biologists appreciate quanti-
fying niche dierentiation among closely related and para-
patric species because it provides a solid basis for future
experimental and observational work and raises questions
about the “mechanistic underpinnings of broad-scale geo-
graphical patterns” (Wellenreuther et al., 2012). In the
past, studies of the niche have required in-depth examina-
tions of an organism’s needs for its local habitat (Galetto,
2005). Recently, regional environmental information has
been made available due to the accessibility of global cli-
mate and land cover Geographic Information System (GIS)
and remote-sensing data. Including these data in ecologi-
cal niche modeling (ENMs hereafter) provides insight into
ecological and evolutionary concerns by comparing niche
similarities among species and predicting how they will
respond to environmental changes (Broennimann et al.,
2012; Guisan et al., 2014; Kozak et al., 2008; Soberón,
2007). There are, however, signicant conceptual and statis-
tical challenges associated with determining niche similar-
ity based on occurrence records that cover a geographically
representative range of species. For instance, most ENMs
have environmental variables (such as temperature) that
are frequently spatially correlated, so signicant niche
divergence may be confused with geographical distance
(McCormack et al., 2010). The spatial autocorrelation prob-
lem cannot be ignored when comparing niche conservatism
versus divergence, however, it may be addressed by using
null models (McCormack et al., 2010; Warren et al., 2008).
Realized environmental niches may be directly measured
and compared in the environmental (E)-space using mul-
tivariate statistics to satisfy the growing demand for robust
approaches for understanding niche dierences in evolu-
tionary and community contexts (Broennimann et al., 2012).
Recent ordination null tests can evaluate niche conservatism
or divergence hypotheses by applying a kernel estimation
to smooth out biases caused by inevitable sampling eorts
(Broennimann et al., 2012).
The small, secretive, and oviparous snake-eyed skinks in
the genus Ablepharus Fitzinger, 1823 (Family: Scincidae)
are an excellent study system for investigating the impacts
of climate on genetic structure as well as how divergence and
subsequent speciation processes have been driven over time.
They show a wide geographical distribution in southeastern
Europe, southwest Asia, and central Asia (Karamiani et al.,
2022; Mirza et al., 2022) and occur in dierent environmen-
tal conditions including sub-Mediterranean deciduous for-
ests, shrubs, and steppes with limestone, sandstone,basalt,
loess, and calcareous sand substrates (Herczeg et al., 2004;
Jovanovic Glavas et al., 2022; Karamiani et al., 2022;
Ljubisavljević et al., 2002). This genus contains 11 species
(Karamiani et al., 2022), however, a recent systematic study
found it to be synonymous with Asymblepharus (Mirza et
al., 2022). This resulted in the transfer of eight species from
Asymlepharus to Ablepharus (Mirza et al., 2022). These
species’ habitat requirements and potential distributions
under climate change, as well as the role of climate inu-
ences in their speciation, have received comparatively little
attention. In this sense, Sanchooli (2016) investigated the
distribution of A. bivittatus in Iran and concluded that A.
bivittatus prefers suitable habitats in the northern regions
due to habitat competition with A. pannonicus. Karamiani
et al. (2018) explored the potential distribution of A. graya-
nus and A. pannonicus species in the present as well as the
mid-Holocene and Last Glacial Maximum periods. These
authors concluded that these two species had a wider and
more suitable distribution range in the past compared to the
present time. Kurnaz and Yousefkhani (2021) also evaluated
the ecological niches of A. budaki and A. anatolicus in terms
of biogeography and found that both species had dierent
niche requirements. Finally, Vaissi (2022a) projected A.
pannonicus distribution in Iran in the present and the future.
As a result of this study, A. pannonicus will probably lose
habitat on the eastern and southern margins, and its distribu-
tion patterns may shift to higher elevations.
The study of niche relationships can provide new insights
into ecological distinctiveness and the mechanisms behind
species evolution (McCormack et al., 2010; Raxworthy et
al., 2007; Schluter, 2009; Wiens & Graham, 2005). Our
study explores the relationship between climatic niche vari-
ation and speciation. This addresses the question of whether
Ablepharus species maintained the same niche, as well as
whether niche divergence or niche conservation inuenced
their diversication process. We also investigate the rela-
tionships between recent Ablepharus distributions and
observed climate, as well as potential future species distri-
butions. For this purpose, ENMs based on climatic variables
were developed in concert with occurrence data in the geo-
graphic space (G-space hereafter) to project species’ recent
and future distribution range (2081–2100). We then used
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Evolutionary Biology
an ordination null test of PCA-environment in E-space to
assess whether these closely related species occupy habitats
that are more similar or more dierent than expected based
on background environmental divergence. These results
provide a multidimensional and comprehensive picture of
niche variation within Ablepharus species when combined
with phylogeographic relationships (Bozkurt & Olgun,
2020; Karamiani et al., 2021; Mirza et al., 2022; Poulaka-
kis et al., 2005, 2013; Skourtanioti et al., 2016). Further-
more, recent conservation eorts and future research into
the impacts of climate change on biodiversity depend on a
knowledge of the primary ecological restrictions on species
distribution (Aguirre-Gutiérrez et al., 2015).
Materials and methods
Study area and species data
Figure 1 displays the study area, which included the entire
range of Ablepharus species, and covers Asia and south-
ern Europe, as well as possibly northern Africa (Anderson,
1999; Fuhn, 1969; Karamiani et al., 2018; Khan, 2002;
Vyas, 2011). We used 12 of the 19 known species in this
study because the occurrence records were sucient for the
analysis (Table 1). We obtained occurrence records from a
variety of sources; detailed information is available in Table
S1. Then, we removed records that were duplicates, had
dubious taxonomic identication, or had ambiguous locali-
ties (such as the centers of countries or states). Finally, we
used occurrence data that were more than 500 m apart in
order to reduce the impacts of spatial autocorrelation (Boria
et al., 2014) using the R package spThin (Aiello-Lammens
et al., 2015). Table 1 lists the species, their conservation sta-
tus, and the number of occurrence records before and after
the analysis. Besides Table S1, Fig. 1 also displays the geo-
graphic coordinates of these occurrences.
Climatic variables
Six climatic variables were employed to predict the species
niche under recent (1970–2000) and future (2081–2100) cli-
mate change projections: annual mean temperature (BIO1
hereafter); temperature seasonality (BIO4 hereafter); the
Fig.1 Study area. The occurrence records of 12 species of Ablepharus with dierent colours are shown on the map
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Evolutionary Biology
Validation + 3 Repetitions), Classication Tree Analysis
(CTA, CV.tree = 50, 5 Fold Cross-Validation), Flexible Dis-
criminant Analysis (FDA), Generalized Additive Models
(GAM, algo = ’GAM_mgcv’), Generalized Boosted Mod-
els (GBM, n.trees = 1000, 3 Fold Cross-Validation), Gener-
alized Linear Models (GLM, type = ’quadratic’, interaction.
level = 1, the stepwise procedure using Akaike Information
Criterion (AIC) criteria), Maximum Entropy (MaxEnt.
Phillips, maximum iterations = 500, https://biodiversityin-
formatics.amnh.org/open_source/maxent/), Multivariate
Adaptive Regression splines (MARS, simple with no inter-
action), Random Forest (RF, n.trees = 1000), and Surface
Range Envelops (SRE, quant = 0.025) (Guisan et al., 2018).
These modeling algorithms need both presence and
absence datasets, but it might be dicult to nd the real
absence data. Thus, following Wisz and Guisan (2009),
Barbet-Massin et al. (2012), and Guisan et al. (2018), we
generated 500 pseudo-absences at random within the study
area. We repeated the procedure three times to address
the potential sample bias in the pseudo-absence genera-
tion because this process of pseudo-absence generation is
a stochastic procedure caused by the random selection of
the pseudo-absences (Allouche et al., 2006). The models
are calibrated on 80% of the data (training set) and evalu-
ated on the remaining 20% (validation set). The entire
procedure is repeated four times. The mean of the impor-
tant variables was determined for the models. The higher
mean indicates the variable is more signicant (Guisan et
al., 2018). By default, the True Skill Statistics (TSS), Area
Under Curve-Receiver Operating Characteristics statistics
(AUC), and Cohen’s kappa (KAPPA) metrics are used to
evaluate each model (Allouche et al., 2006; Cohen, 1960;
Fielding & Bell, 1997; Hanley & McNeil, 1982). The TSS
and KAPPA values range from − 1 to + 1, with 1 indicating
perfect agreement and 0.60 to 0.90 indicating fair to good
max temperature of the warmest month (BIO5 hereafter),
annual precipitation (BIO12 hereafter); precipitation of dri-
est month (BIO14 hereafter); and precipitation seasonality
(BIO15 hereafter). These bioclimatic variables were down-
loaded from the WorldClim v2.1 (https://www.worldclim.
org) with 30-second spatial resolution raster grids. BIO1 and
BIO12 were selected as they were found to have the most
signicant eects on reptile richness and range distribution
in the study region (Kafash et al., 2020; Vaissi, 2022). BIO4,
BIO5, BIO14, and BIO15 were selected because they are
biologically signicant, have weak global correlations, and
may reveal environmental variables that impact distribu-
tions (Jarvie & Svenning, 2018).
The future data were obtained by averaging two global
climate models (GCMs) from the Goddard Institute for
Space Studies and Meteorological Research Institute for
the years 2081–2100: GISS-E2-1-G (Kelley et al., 2020)
and MRI-ESM2-0 (Yukimoto et al., 2019). These models
have the best performance among the 32 models for Eur-
asia (Sun et al., 2022). The lowest and the highest limits
of the shared socioeconomic pathways (ssps) from the
Coupled Model Intercomparison Project Phase 6 (CMIP6)
(Eyring et al., 2016), 126 (lowest emissions, smallest tem-
perature increase) and 585 (highest emissions, largest tem-
perature increase), were analyzed to predict future climate
conditions.
Species distribution modelling
We used the “biomod2” package (v 3.4.6) in the R (v
4.2.0) programming language and followed default set-
tings recommended by Guisan et al. (2018) in the G-space
to simulate species distribution using an ensemble of 10
algorithms (Thuiller et al., 2016). These algorithms were
Articial Neural Networks (ANN, CV.ann = 5, 5 Fold Cross
Table 1 The number of Ablepharus species (N = 12) occurrence records before and after correcting to a resolution of 30 arc seconds (1 km), con-
servation status under IUCN criteria as well as the quality of ensemble models. TSS: true skill statistic, AUC: area under the receiver operating
characteristic curve, and KAPPA: Cohen’s Kappa. LC: Least Concern. NA: Not Evaluated
No. Species IUCN status Occurrence records (N) Ensemble models quality
Before correcting After correcting TSS AUC KAPPA
1Ablepharus alaicus LC 50 35 0.98 1 0.95
2Ablepharus anatolicus NA 74 69 0.99 1 0.96
3Ablepharus bivittatus LC 121 116 0.90 0.98 0.82
4Ablepharus budaki LC 100 91 0.98 1 0.97
5Ablepharus chernovi LC 75 74 0.94 0.99 0.88
6Ablepharus deserti LC 126 117 0.92 0.99 0.88
7Ablepharus grayanus LC 12 12 0.93 0.99 0.87
8Ablepharus himalayanus LC 31 28 0.96 10.96
9Ablepharus kitaibelii LC 457 388 0.97 1 0.96
10 Ablepharus pannonicus LC 71 69 0.93 0.99 0.83
11 Ablepharus rueppellii LC 247 204 0.99 1 0.99
12 Ablepharus sikimmensis LC 25 24 0.99 1 0.98
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Evolutionary Biology
approach. Climatic variables are changed into two-dimen-
sional spaces dened by two principal components. The
two-dimensional environmental space is then projected
onto grid cells with a diameter of 100 × 100 and bounded by
minimum and maximum PCA values in the background. A
kernel density function is used to estimate a smoothed den-
sity of occurrences for each species in each grid cell. The
overlap between niches was calculated using Schoener’s D
and Hellinger’s I metrics, which range from 0 (no overlap)
to 1 (complete overlap) (Broennimann et al., 2012).
We performed equivalency and similarity tests to com-
pare the overlap of the two observed niches to the overlap of
simulated niches to test if niche overlap was statistically sig-
nicant (Schoener’s D). The niche equivalency test deter-
mines if two niches are equivalent by randomly permuting
occurrences between ranges. The niche similarity test deter-
mines if the species’ niches are more or less similar than
anticipated by chance, using random shifts of niches within
accessible conditions in the study region (Broennimann
et al., 2012; Di Cola et al., 2017; Warren et al., 2008). We
tested niche conservatism (alternative = higher, i.e. the niche
overlap is more equivalent/similar than random) and niche
divergence (alternative = lower, i.e. the niche overlap is less
equivalent/similar than random) by the “alternative” option
at ecospat (Broennimann et al., 2012; Di Cola et al., 2017).
To test each hypothesis, we performed 1000 permutations.
R (v 4.2.0) was used to perform all of the analyses (www.r-
project.org).
Results
Species distribution modelling
TSS, AUC, and KAPPA values ranging from 0.82 to 1
indicate high-quality ensemble models (Table 1). Figure 2
shows the map of suitable habitats for 12 Ablepharus spe-
cies over their entire range, based on six climatic factors
for recent and future climate projections (2081–2100) under
SSP126 and SSP585 scenarios. For the 12 Ablepharus spe-
cies, Table 2 provides the mean of variable importance (%)
predicted by the 10 algorithms. The most important vari-
ables impacting the distribution of species were determined
to be BIO4 for six species, BIO12 for three species, BIO5
for two species, and BIO15 for one species (Table 2). Cli-
mate change will have a range of impacts on Ablepharus
species, with some increasing their ranges, some decreasing
their ranges, and others staying relatively unaected (Fig. 2,
Fig. S1, Table 3). Changes in the ranges of Ablepharus spe-
cies over time are shown in Fig. S1 and Table 3, including
habitat stability, habitat gain or expansion, and habitat loss
model performance (Allouche et al., 2006). AUC values of
0.90 and above are regarded as good, 0.60 and 0.90 are con-
sidered medium, while 0.60 and below are considered poor
(Phillips et al., 2006).
Ensemble models are created using the BIOMOD Ensem-
bleModeling() function. We consider two “ensembling”
options—committee averaging and weighted mean—to
reduce the number of outputs (Guisan et al., 2018). We mix
all models (i.e. all techniques, all pseudo-absences sam-
pling, and all cross-validation runs) to produce our ensem-
bles of models (Guisan et al., 2018). TSS was used as the
evaluation reference for committee building and dening
weights. This means that only models with a TSS greater
than or equal to 0.8 are kept to build the nal ensemble
(Guisan et al., 2018).
Species range change
We evaluated and displayed the range changes over future
climate scenarios for each of the species using the BIO-
MOD (Range Size) function in the biomod2 package. This
function generates two outputs: a spatial map that highlights
where species may gain or lose suitable conditions, and a
table with summary data of species range change. More
specically, from both output types, we can get information
about four absolute metrics: “Loss” denotes the number of
pixels that the studied species is predicted to lose as a result
of climate change; “Absent” denotes the number of pixels
that the studied species is currently not occupying and that
are also predicted to be unsuitable under a specic climatic
scenario; and “Stable” denotes the number of pixels that the
studied species is currently occupying and that are also pre-
dicted to remain occupied into the future, and “Gain,“ which
indicates the number of pixels that the studied species is
currently not occupying but are expected to be occupied in
the future. Finally, from these four metrics, three additional
relative metrics were obtained, including “Percentage loss,“
which corresponds to the percentage of currently occupied
sites to be lost and is calculated as (Loss/(Loss + Stable);
“Percentage gain,“ which corresponds to the percentage
of new sites taking into account the species’ current dis-
tribution size and calculated as (Gain/(Loss + Stable), and
“Range change,“ which represents the overall projection
outcome and is equal to percentage gain − percentage loss
(Guisan et al., 2018).
Niche analyses
Niche analysis was carried out separately for each of the 12
Ablepharus species using the R package ecospat (Di Cola et
al., 2017). The E-space was evaluated utilizing a PCA-envi-
ronment (PCA-env hereafter) framework and an ordination
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Evolutionary Biology
Table 2 Mean of variable importance (%) by the 10 algorithms for the 12 species of Ablepharus
No. Species BIO1 BIO4 BIO5 BIO12 BIO14 BIO15
1Ablepharus alaicus 20.37 26.97 14.72 5.45 19.49 12.96
2Ablepharus anatolicus 20.11 19.83 20.73 20.21 8.97 10.12
3Ablepharus bivittatus 14.48 37.72 12.48 10.71 8.15 16.04
4Ablepharus budaki 22.01 19.69 13.71 25.20 11.79 7.57
5Ablepharus chernovi 17.44 24.84 18.48 14.88 11.82 12.50
6Ablepharus deserti 23.76 24.29 14.83 8.66 10.05 18.39
7Ablepharus grayanus 22.60 18.81 15.74 7.16 9.47 26.20
8Ablepharus himalayanus 15.32 15.13 14.50 30.42 15.51 9.08
9Ablepharus kitaibelii 21.47 31.36 13.85 21.53 5.02 6.73
10 Ablepharus pannonicus 20.59 30.40 14.72 13.18 7.68 13.40
11 Ablepharus rueppellii 24.00 14.68 28.00 2.56 14.90 15.83
12 Ablepharus sikimmensis 17.74 11.37 18.92 32.65 46.60 14.70
Annual mean temperature (BIO1); temperature seasonality (BIO4); max temperature of war mest month (BIO5); annual precipitation (BIO12);
precipitation of driest month (BIO14); and precipitation seasonality (BIO15)
Table 3 Species range change (SRC) of 12 species of Ablepharus in recently suitable habitats (gain/loss) by 2081–2100 under optimistic (ssp126)
and pessimistic (ssp585) scenarios
No. Species ssp126 ssp585
Lost Gain SRC Lost Gain SRC
1Ablepharus alaicus 33.34 17.89 -15.45 52.19 26.94 -25.24
2Ablepharus anatolicus 35.94 18.26 -17.67 91.96 35.36 -56.60
3Ablepharus bivittatus 17.97 30.94 12.96 26.55 91.59 65.03
4Ablepharus budaki 30.47 30.16 − 0.30 83.12 37.68 -45.44
5Ablepharus chernovi 30.35 22.49 -7.46 62.62 23.49 -39.13
6Ablepharus deserti 6.89 74.00 67.11 14.97 177.81 162.83
7Ablepharus grayanus 26.26 4.86 -21.40 65.47 14.82 -50.65
8Ablepharus himalayanus 49.05 4.09 -44.95 75.53 3.94 -71.89
9Ablepharus kitaibelii 22.78 67.66 44.87 72.21 91.40 19.19
10 Ablepharus pannonicus 31.17 23.16 -8.01 54.75 34.76 -19.99
11 Ablepharus rueppellii 55.39 9.51 -45.87 83.45 15.37 -68.07
12 Ablepharus sikimmensis 39.49 10.02 -29.47 76.86 6.42 -70.44
Fig. 2 Recent and future
(2081–2100) habitat suitability
for 12 species of Ablepharus
based on the consensus model
under optimistic (ssp126) and
pessimistic (ssp585) scenarios. A:
Ablepharus alaicus; B: Ablepha-
rus anatolicus; C: Ablepharus
bivittatus; D: Ablepharus budaki;
E: Ablepharus chernovi; F:
Ablepharus deserti; G: Ablepha-
rus grayanus; H: Ablepharus
himalayanus; I: Ablepharus
kitaibelii; J: Ablepharus pannoni-
cus; K: Ablepharus rueppellii;L:
Ablepharus sikimmensis
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Evolutionary Biology
Israel, as well as a portion of South Uzbekistan, Tajikistan,
eastern Afghanistan, and northern Pakistan have medium
to high habitat suitability (Fig. 2D). Climate change causes
habitat gain at high latitudes in both scenarios, though most
A. budaki habitats remain stable under the ssp126. In the
ssp585, habitat losses (83.12%) may be greatest in the
countries surrounding the eastern Mediterranean as well as
southern Türkiye (Table 3 and Fig. S1D).
A. chernovi BIO4 (24.84%) and BIO5 (18.48%) are the
two most important variables aecting the A. chernovi dis-
tribution, respectively (Table 2). The habitat suitability map
under recent climate conditions indicates that Northern and
eastern Mediterranean countries, Türkiye, the Caucasus, Iran
(except central and southern regions), northern and eastern
Afghanistan, north and northwest of Pakistan to Tajikistan
and southern Kazakhstan, and southeast Uzbekistan have
high habitat suitability (Fig. 2E). The majority of the recent
distribution range may be preserved by future climate under
the ssp126, although the southern margin at lower latitudes
may be lost especially under the ssp585 (62.62%). On the
other hand, the northern margin at higher latitudes may gain
new habitat (Table 3 and Fig. S1E).
A. deserti BIO4 (24.29%) and BIO1 (23.76%) are the two
most important variables aecting the A. deserti distribution,
respectively (Table 2). The habitat suitability map under
recent climate conditions indicates that all of Kazakhstan’s
southern areas, a small portion of southwestern Russia,
western Turkmenistan, north and northwest of Uzbekistan,
west Kyrgyzstan, eastern Türkiye, western Armenia, and
northwest, parts of the north and northeast of Iran have high
habitat suitability (Fig. 2F). The majority of the recent dis-
tribution range may be preserved due to the eects of future
climate on both scenarios (especially ssp126), but southern
margins at lower latitudes will be lost (especially ssp585:
14.97%) and northern margins at higher latitudes may gain
new habitat (especially ssp585: 177.81%) (Table 3 and Fig.
S1F).
A. grayanus BIO15 (26.20%) and BIO1 (22.60%) are the
two most important variables aecting the A. grayanus dis-
tribution, respectively (Table 2). The habitat suitability map
under recent climate conditions indicates that major north
and northwest India, east Pakistan, a small part of the west
and south Afghanistan, parts of the south and southeast
of Iran, small parts of the central and southern regions of
Saudi Arabia, east and south margins of the Mediterranean,
western Sudan, eastern Chad, and southern Niger have
high habitat suitability (Fig. 2G). The majority of this spe-
cies distribution range may be lost at the southern margin,
or contraction. The following are the details responses of 12
species to climate change:
A. alaicus BIO4 (26.97%) and BIO1 (20.37%) are the two
most important variables aecting the A. alaicus distribu-
tion, respectively (Table 2). The habitat suitability map
under recent climate conditions indicates that the eastern
edges to the southeast of Kazakhstan, Kyrgyzstan, Tajikistan
(except southwest), northeast of Pakistan, and the northern
margin of the Kunlun and Qilian Mountains in China have
medium to high habitat suitability (Fig. 2A). According to
the models, eastern Türkiye, Armenia, and small parts of
southwest and southeast Russia also showed high habitat
suitability (Fig. 2A). In the future, the distribution range
of A. alaicus may contract at the margins of its range and
may expand toward the north, but these changes may be
relatively small, with most of the existing range remaining
unchanged (Table 3 and Fig. S1A).
A. anatolicus BIO5 (20.73%) and BIO12 (20.21%) are the
two most important variables aecting the A. anatolicus
distribution, respectively (Table 2). The habitat suitability
map under recent climate conditions indicates that coun-
tries around the Mediterranean, including southern Greece,
Cyprus, the west, southwest, and southern Türkiye, western
Syria, and Lebanon have high habitat suitability (Fig. 2B).
Climate change would not signicantly aect the A. anatoli-
cus distribution under the ssp126, however, the ssp585 may
result in the loss of 91.96% of the species’ suitable habitats
in the distribution region (Table 3 and Fig. S1B).
A. bivittatus BIO4 (37.72%) and BIO15 (16.04%) are the
two most important variables aecting the A. bivittatus dis-
tribution, respectively (Table 2). The habitat suitability map
under recent climate conditions indicates that Iran (except
central parts), Afghanistan (except southwest), southeast-
ern Turkmenistan to southwestern Uzbekistan, west Tajiki-
stan, Azerbaijan, Armenia, Türkiye (except northern strip),
Syria, western and eastern Iraq to northern Saudi Arabia,
and Kuwait have high habitat suitability (Fig. 2C). ssp126
predicts that the species’ distribution may remain stable;
however, ssp585 predicts 91.59% of the species habitats
may be gained at high and especially low latitudes, such as
north Africa (Table 3 and Fig. S1C).
A. budaki BIO12 (25.20%) and BIO1 (22.01%) are the
two most important variables aecting the A. budaki dis-
tribution, respectively (Table 2). The habitat suitability
map under recent climate conditions indicates that the
countries around the Mediterranean, including southern
Greece, Cyprus, the west, southwest, and southern margins
of Türkiye, western Syria, Lebanon, and northern Palestine/
1 3
Evolutionary Biology
Mediterranean coastal countries in North Africa (Egypt and
Libya) have high habitat suitability (Fig. 2K). Future cli-
mate change may lose the distribution range of this species
at the southern margins, especially under the ssp585 sce-
nario (83.45%) (Table 3 and Fig. S1K).
A. sikimmensis BIO12 (31.61%) and BIO5 (18.92%) are
the two most important variables aecting the A. sikim-
mensis distribution, respectively (Table 2). The habitat suit-
ability map under recent climate conditions indicates that
a small part of northern India, Nepal, Bhutan, and north
and west Myanmar have high habitat suitability (Fig. 2L).
Future climate change may lead to the loss of the majority
of the distribution range of this species at the margins, espe-
cially under the ssp585 (76.86%) (Table 3 and Fig. S1L).
Niche analyses
The sum of the rst two axes of the PCA-env explained
between 69.55% and 92.61% variation in the environmental
study for all pairwise comparisons among Ablepharus spe-
cies (Table 4, Fig. S3). The D and I metric was used to ana-
lyze pairwise niche overlap, which ranged from no (D and
I = 0) or very limited (D and I = 0.004-0.1), moderate (D and
I = 0.11–0.51) to substantial overlap (D and I = 0.52–0.76)
(Table 4, Fig. S2). Table 4 summarizes the niche equiva-
lency and similarity tests. In the niche equivalency test, the
hypothesis of niche divergence was accepted (p < 0.05) in 28
pairwise comparisons among Ablepharus species, whereas
the hypothesis of niche conservatism was accepted in 11
paired comparisons (Table 4). In the niche similarity test,
only the 21 paired comparisons for conservatism hypoth-
eses showed signicant values (Table 4).
Discussion
In this study, the climatic niche variations among Ablepha-
rus species were explained using ENMs and multivariate
niche analyses. We specically explored the question of
whether the Ablepharus species evolve by maintaining dif-
ferent ecological niches (divergence) or by inhabiting a sim-
ilar ecological niche (conservatism). Based on the overall
pattern of niche conservatism and niche divergence, as well
as the distinct evidence that temperature and precipitation
variables account for the crucial dierences between the cli-
mate niches of the Ablepharus species, it is hypothesized
that climate-mediated adaptation is a signicant factor in
the speciation process in these snake-eyed skinks.
Migration movements of reptiles are remarkably limited
compared to other large vertebrate groups (Hickling et al.
especially under the ssp585 (65.47%) due to future climate
change (Table 3 and Fig. S1G).
A. himalayanus BIO12 (30.42%) and BIO14 (15.51%) are
the two most important variables aecting the A. himalaya-
nus distribution, respectively (Table 2). The habitat suitabil-
ity map under recent climate conditions indicates that east
Tajikistan, a small part of eastern Afghanistan, northern bor-
ders of Pakistan and India, Nepal, northern borders of Bhu-
tan and Myanmar have high habitat suitability (Fig. 2H).
Most of the species’ distribution range may be lost at the
margins due to future climate change, especially under the
ssp585 (75.53%) (Fig. S1H).
A. kitaibelii BIO4 (31.36%) and BIO12 (21.53%) are the
two most important variables aecting the A. kitaibelii dis-
tribution, respectively (Table 2). The habitat suitability map
under recent climate conditions indicates that northern and
eastern Mediterranean countries, southern, western, and
northern Black Sea border, southern European countries
toward central Europe, northern Iran, Georgia, and north-
ern and western borders of Azerbaijan have high habitat
suitability (Fig. 2I). The majority of the recent distribution
range may be preserved due to the eects of future climate
under the ssp126, but southern margins at lower latitudes
may be lost under the ssp585 (72.21%) and northern mar-
gins at higher latitudes may gain new habitat (especially
ssp585: 91.40%) (Table 3 and Fig. S1F).
A. pannonicus BIO4 (30.40%) and BIO1 (20.59%) are the
two most important variables aecting the A. pannonicus
distribution, respectively (Table 2). The habitat suitability
map under recent climate conditions indicates that Iran
(except narrow strip in the southeast and central plains),
Azerbaijan, southwest to southeast Turkmenistan, south-
west Tajikistan, southeast Uzbekistan, northern and eastern
Afghanistan, Pakistan (except southern areas), Iraq (except
southern areas), Syria, northern Jordan, northern Saudi
Arabia, Türkiye (except northern strip) have high habitat
suitability (Fig. 2J). Although climate change may result in
habitat loss at the distribution margins (especially ssp585:
54.75%) and habitat gains at high and low latitudes (espe-
cially ssp585: 34.76%), the majority of the distribution
range of this species may be preserved (especially ssp126)
(Table 3 and Fig. S1J).
A. rueppellii BIO5 (28.00%) and BIO1 (24.00%) are the
two most important variables aecting the A. rueppellii dis-
tribution, respectively (Table 2). The habitat suitability map
under recent climate conditions indicates that western Med-
iterranean countries including southern Lebanon, Israel/
Palestine, and western Jordan as well as a strip of southern
1 3
Evolutionary Biology
No. Species Schoener’s
D
Hellinger’s I E (D) S (D) E (C) S (C) Correlation
circle (%)
PC1 PC2
1Ablepharus alaicus vs. A. anatolicus 0.01 0.01 0.007 0.78 0.99 0.22 81.89 9.07
2Ablepharus alaicus vs. A. bivittatus 0.17 0.25 0.001 0.77 1 0.19 74.87 9.99
3Ablepharus alaicus vs. A. budaki 0 0 0.97 0.95 1 1 69.68 16.22
4Ablepharus alaicus vs. A. chernovi 0.14 0.20 0.001 0.94 1 0.06 55.94 19.49
5Ablepharus alaicus vs. A. deserti 0.21 0.37 0.001 0.86 1 0.13 49.49 30.97
6Ablepharus alaicus vs. A. grayanus 0 0 1 1 0.14 0.01 71.49 16.47
7Ablepharus alaicus vs. A. himalayanus 0.01 0.02 0.001 0.78 1 0.25 63.85 19.01
8Ablepharus alaicus vs. A. kitaibelii 0.02 0.03 0.19 0.70 0.88 0.29 60.58 17.75
9Ablepharus alaicus vs. A. pannonicus 0.01 0.01 0.005 0.77 0.99 0.23 81.89 9.07
10 Ablepharus alaicus vs. A. rueppellii 0 0 0.93 1 1 1 75.56 16.82
11 Ablepharus alaicus vs. A. sikimmensis 0 0 0.93 1 1 1 70.08 20.67
12 Ablepharus anatolicus vs. A. bivittatus 0.04 0.06 0.006 0.61 1 0.37 56.55 31.83
13 Ablepharus anatolicus vs. A. budaki 0.71 0.76 1 1 0.001 0.001 51.03 19.8
14 Ablepharus anatolicus vs. A. chernovi 0.29 0.46 0.001 0.80 1 0.17 57.79 23.71
15 Ablepharus anatolicus vs. A. deserti 0.06 0.08 0.001 0.73 1 0.26 64.02 28.59
16 Ablepharus anatolicus vs. A. grayanus 0.0004 0.0006 0.05 0.76 0.91 0.23 62.52 23.44
17 Ablepharus anatolicus vs. A. himalayanus 0 0 0.93 1 1 1 55.71 34.2
18 Ablepharus anatolicus vs. A. kitaibelii 0.48 0.69 0.99 0.99 0.009 0.006 66.29 15.21
19 Ablepharus anatolicus vs. A. pannonicus 0.03 0.03 0.02 0.87 0.97 0.12 44.21 38.74
20 Ablepharus anatolicus vs. A. rueppellii 0.12 0.15 0.001 0.86 1 0.12 62.95 17.78
21 Ablepharus anatolicus vs. A. sikimmensis 0.02 0.02 0.003 0.98 0.99 0.19 50.90 38.88
22 Ablepharus bivittatus vs. A. budaki 0.15 0.19 0.001 1 1 0.004 53 28.02
23 Ablepharus bivittatus vs. A. chernovi 0.56 0.60 1 0.97 0.001 0.03 58.78 18.53
24 Ablepharus bivittatus vs. A. deserti 0.52 0.57 1 1 0.001 0.002 61.61 20.05
25 Ablepharus bivittatus vs. A. grayanus 0.55 0.58 0.91 0.94 0.05 0.03 62.35 19.02
26 Ablepharus bivittatus vs. A. himalayanus 0.05 0.06 0.14 1 0.86 0.08 51.57 37.54
27 Ablepharus bivittatus vs. A. kitaibelii 0.14 0.15 0.02 1 0.98 0.003 51.43 30.26
28 Ablepharus bivittatus vs. A. pannonicus 0.66 0.81 0.55 1 0.42 0.001 60.41 15.67
29 Ablepharus bivittatus vs. A. rueppellii 0.11 0.20 0.001 1 1 0.003 56.11 28.25
30 Ablepharus bivittatus vs. A. sikimmensis 0 0 1 1 0.009 0.34 47.11 42.17
31 Ablepharus budaki vs. A. chernovi 0.35 0.45 0.07 0.56 0.92 0.41 50.75 21.35
32 Ablepharus budaki vs. A. deserti 0.06 0.08 0.001 0.52 1 0.45 59.06 27.38
33 Ablepharus budaki vs. A. grayanus 0.11 0.12 0.36 0.77 0.70 0.22 51.20 22.81
34 Ablepharus budaki vs. A. himalayanus 0.01 0.02 0.001 0.68 1 0.34 45.35 33.26
35 Ablepharus budaki vs. A. kitaibelii 0.54 0.69 0.97 0.92 0.01 0.07 61.62 16.67
36 Ablepharus budaki vs. A. pannonicus 0.22 0.23 0.18 10.84 0.002 37.32 32.65
37 Ablepharus budaki vs. A. rueppellii 0.25 0.38 0.001 1 1 0.008 54.53 26.44
38 Ablepharus budaki vs. A. sikimmensis 0.02 0.03 0.002 0.99 0.99 0.08 49.15 28.45
39 Ablepharus chernovi vs. A. deserti 0.36 0.40 0.88 0.92 0.11 0.09 50.04 30.55
40 Ablepharus chernovi vs. A. grayanus 0.28 0.31 0.68 0.99 0.34 0.01 57.08 18.27
41 Ablepharus chernovi vs. A. himalayanus 0.01 0.01 0.18 10.78 0.16 42.09 40.26
42 Ablepharus chernovi vs. A. kitaibelii 0.62 0.65 10.98 0.001 0.02 57.21 20.21
43 Ablepharus chernovi vs. A. pannonicus 0.39 0.52 0.62 1 0.42 0.005 56.42 18.44
44 Ablepharus chernovi vs. A. rueppellii 0.10 0.15 0.001 1 1 0.01 58.89 19.98
45 Ablepharus chernovi vs. A. sikimmensis 0 0 0.96 0.82 1 1 48.69 35.37
46 Ablepharus deserti vs. A. grayanus 0.18 0.20 0.47 0.64 0.50 0.35 59.50 26.95
47 Ablepharus deserti vs. A. himalayanus 0 0 0.94 0.98 1 1 51.62 34.69
48 Ablepharus deserti vs. A. kitaibelii 0.14 0.16 0.001 0.58 1 0.42 57.21 20.21
49 Ablepharus deserti vs. A. pannonicus 0.32 0.42 0.19 1 1 0.41 52.12 31.74
50 Ablepharus deserti vs. A. rueppellii 0.009 0.01 0.001 0.97 1 0.07 61.03 20.12
Table 4 Schoener’s D and Hellinger’s I index and p-values for the equivalency (E) and similarity (S) test for niche divergence (D) and conserva-
tism (C). The correlation circle illustrates the changing relevance between two species ranges along the rst two principal axes
1 3
Evolutionary Biology
Climate-related species distribution models can help us
predict how dierent species may react to future climate
changes (Araújo et al., 2006). The results of this study
showed that climatic variables aect the distribution of
Ablepharus species through a wide distribution range. Sea-
sonal temperature variations (for six species), precipitation
during the year (for three species), maximum temperature of
warmest month (for two species) and precipitation seasonal-
ity (for one species) are the primary factors contributing to
the understanding of their distribution preferences (Table 2).
This demonstrates how temperature and precipitation have a
signicant impact on where these species are found. There-
fore, the results of this study may also shed new light on
how future climate change may aect mid-latitude lizards.
However, the genus Ablepharus has been inuenced by a
variety of biotic and abiotic factors that have shaped its
distribution limits (Kurnaz, 2022). Therefore, it is impor-
tant to understand that the models used in this study do not
take into account additional factors that may aect lizard
declines, such as habitat loss and fragmentation, anthropo-
genic pollution, parasitism and disease, and invasive species
predation (Todd et al., 2010). For instance, several studies
have shown that habitat loss and fragmentation have a det-
rimental eect on how lizard species disperse (Bell et al.,
2010; Clark et al., 1999; Delaney et al., 2010; Stow et al.,
2001; Templeton et al., 2001). Restricted migration can lead
to inbreeding, decreasing populations, and a loss of genetic
diversity (Andersen et al., 2004; Gilpin, 1991; Hastings
& Harrison, 1994; Lacy & Lindenmayer, 1995; Leimu et
al., 2010; Matesanz et al., 2017; Schlaepfer et al., 2018;
Senior et al., 2021). Therefore, more study is necessary,
especially in light of climate change, as there are no studies
2006). However, when looking at climate change models,
this distribution pattern is somewhat dicult to be neutral
(Araújo et al., 2006). On the other hand, the population
ecology of Ablepharus species has received less attention,
and only a few studies have been published about their
activities. Long-term recaptures of the A. kitaibelii by Ver-
gilov et al. (2022) indicate that it is largely sedentary, per-
haps not engaging in many moves over a distance of more
than 60 m, and typically keeping a small range. Based on
Table 3; Fig. 2 and Fig. S1, the present study, used the limit-
less dispersion hypothesis assumption, and forecasted range
loss and gain for species by 2081–2100.
Based on ssp585, the eSDM ndings show that 83.33%
of the species (A. alaicus, A. anatolicus, A. budaki, A. cher-
novi, A. grayanus, A. himalayanus, A. kitaibelii, A. pan-
nonicus, A. rueppellii, A. sikimmensis) contracted their
recent distribution, particularly along their southern mar-
gins at lower altitudes. Meanwhile, they may also shown
the northward expansion. On the other hand, some species
expanded their range to north (A. deserti) or south (A. bivit-
tatus). However, in the ssp126, most species’ distribution
ranges remain unchanged despite contractions and expan-
sions (Fig. S1, Table 3). According to Moreno-Rueda et al.
(2012), other factors, such as geographic barriers, may also
have an impact on the rate of a species’ migration to the
north. Therefore, more investigation in these areas is needed
for the species under consideration. Similar to these results,
monitoring in the southern range of the species revealed
that several lowland populations either became extinct in 10
years or saw a reduction in density of more than 50% during
a warm spell (Le Galliard et al., 2012).
No. Species Schoener’s
D
Hellinger’s I E (D) S (D) E (C) S (C) Correlation
circle (%)
PC1 PC2
51 Ablepharus deserti vs. A. sikimmensis 0 0 0.93 0.99 1 1 60.70 26.96
52 Ablepharus grayanus vs. A. himalayanus 0 0 0.95 1 0.03 0.79 60.48 30.70
53 Ablepharus grayanus vs. A. pannonicus 0.51 0.63 0.43 1 0.59 0.002 47.09 41.94
54 Ablepharus grayanus vs. A. rueppellii 0.06 0.14 0.14 1 0.80 0.003 49.74 20.52
55 Ablepharus grayanus vs. A. sikimmensis 0 0 0.94 0.93 1 1 40.06 29.49
56 Ablepharus himalayanus vs. A. kitaibelii 0.08 0.08 0.77 0.98 0.28 0.03 60.09 26.45
57 Ablepharus himalayanus vs. A. pannonicus 0 0 0.92 1 1 1 59.70 20.97
58 Ablepharus himalayanus vs. A. rueppellii 0.04 0.08 0.05 1 0.94 0.01 51.21 36.01
59 Ablepharus himalayanus vs. A. sikimmensis 0.10 0.21 0.009 1 1 0.003 65.93 22.64
60 Ablepharus kitaibelii vs. A. pannonicus 0.04 0.06 0.02 0.83 0.98 0.18 64.28 22.24
61 Ablepharus kitaibelii vs. A. rueppellii 0.28 0.40 1 1 0.001 0.002 51.91 30.01
62 Ablepharus kitaibelii vs. A. sikimmensis 0.01 0.02 0.18 1 0.90 0.13 67.71 15.79
63 Ablepharus pannonicus vs. A. rueppellii 0.08 0.17 0.001 1 1 0.006 50.55 27.15
64 Ablepharus pannonicus vs. A. sikimmensis 0 0 1 1 0.05 0.98 58.48 26.61
65 Ablepharus rueppellii vs. A. sikimmensis 0.08 0.09 0.04 0.82 0.94 0.15 50.79 33.84
Table 4 (continued)
1 3
Evolutionary Biology
can be considered a case study for this fact (Karamiani et
al., 2021).
A study on the populations of A. kitaibelli using two
mitochondrial markers, cytochrome b and 16 S rRNA genes
showed that A. budaki and A. chernovi taxa can be consid-
ered as valid species either subspecies as previously known
(Poulakakis et al., 2005). In addition, another study by Pou-
lakakis et al. (2013) examined the phylogenetic relation-
ships between the A. kitaibelli, A. budaki and A. chernovi
taxa through a limited and narrow distribution area includ-
ing Cyprus, Syria and Türkiye countries. The A. budaki, A.
kitaibelli, A. chernovi, A. pannonicus, and A. ruepellii spe-
cies, which are regarded as the A. kitaibelli complex, were
compared phylogenetically in subsequent research employ-
ing nuDNA, and mtDNA (Skourtanioti et al., 2016). This
study suggested that the A. anatolicus taxon, which was
a subspecies of the A. budaki taxon, is in fact a separate
and valid species. Based on estimated divergence times,
the complex originated in the Middle Miocene (around 16
Mya) and most species diverged in the Late Miocene. This
hypothesis implies that the A. kitaibelii species complex
originated in Anatolia by inferring that its current distribu-
tion pattern is a result of multiple dispersals and vicariance
events associated with the Anatolian Diagonal, Mid-Aegean
Trench, and mountain chain formation in southern and east-
ern Anatolia (Skourtanioti et al., 2016). Elsewhere, another
recent phylogenetic and morphological study by Bozkurt
and Olgun (2020) examined all Ablepharus taxa distrib-
uted in Türkiye and conrmed the full species status of A.
anatolicus. These authors also suggested that A. bivittatus
taxon should be included in the genus Asymblepharus. On
the other hand, Karamiani et al. (2021) investigated the phy-
logeny of Ablepharus species from Iraqi and Iranian popu-
lations and mentioned the existence of a phylogenetically
distinct species found in the eastern Iraq regions and the
western and southwestern Iran regions. Hence, Mirza et al.
(2022) while introducing the new genus Protoblepharus,
revealed the necessity of considering all species belonging
to the genus Asymblepharus as a synonym of Ablepharus.
Our study claries the signicance of niche conservatism
and divergence in shaping diversity patterns and provides
insight into the evolutionary history of the genus Ablepha-
rus. We recommend that future investigations that take into
account microhabitat and other parameters may be neces-
sary to fully comprehend these speciation processes. They
might also allow for new phylogenetic analysis studies on
evolution and climate.
The development of climatic niche divergence occurs
when the environmental conditions (such as elevation varia-
tions) are mutually unfavourable for adjacent populations.
This can limit gene ow between the populations, leading
to reproductive isolation and then speciation (Jezkova &
on Ablepharus species in this area. Furthermore, non-cli-
matic variables may play a signicant role in forecasting
taxonomic ranges, and their inclusion in models, along with
feedback interactions between variables, is anticipated to
enhance future estimations of species extinction or decline
(Coudun et al., 2006; Luoto et al., 2006). Examples of such
variables include habitat distribution in space, habitat man-
agement, nutritional considerations, and anthropogenic dis-
turbance (Storfer, 2003). Therefore, a multi-factorial study
would be required to explore these complex linkages.
If each species occupies its unique Grinnellian niche on a
macro-scale, then ENMs approaches may be used to assess
the potential distribution of a species derived from its real-
ized niche (Elith & Leathwick, 2009; Guisan & Thuiller,
2005; Thrasher et al., 1917). ENMs may be used to evalu-
ate speciation processes and even for species delimitation
when combined with phylogenetic data (Hawlitschek et
al., 2011; Jakob et al., 2010). Because systematics has a
signicant biogeographical component, ENMs techniques
are currently growing increasingly crucial in phylogenetic
study (Raxworthy et al., 2007). The genus Ablepharus,
however, received little attention from phylogenetic, paleo-
geographic, and phylogeographic investigations, and no one
has performed a comprehensive analysis that incorporates
ecological data. However, several examples of European
organisms whose geographic range displays northern-south-
ern shifts due to the response of the climatological oscilla-
tions (Villa & Delno, 2019; Weiss & Ferrand, 2007) might
be a suitable explanation for Mediterranean based Ablepha-
rus species (A. kitaibelii, A. ruepellii, A. budaki, A. cher-
novi). On the other hand, it is believed that central Asia is
the origin of the genus Ablepharus (Karamiani et al., 2021).
As a result of it, Asiatic Ablepharus species diverged in the
early Miocene and displayed various evolutionary history
patterns (Karamiani et al., 2021). The dispersal routes of the
genus are thought to be related to the geologic and tectonic
formations of the Iranian Plateau, the rise of the Caucasus
mountains, the collision of the Indian plate with Eurasia and
the rise of the Himalaya mountains and the Tibetan plateau
(Ghorbani, 2021; Tripathy-Lang et al., 2013). This situation
enabled the distribution of the genus in a wide range, and
as a result of it, local conditions, such as altitudinal prefer-
ences aided the speciation process of the Ablepharus spe-
cies. Because the orogeny of the western Asia mountains
might have provided dierent bioclimatological niche char-
acteristics in lower and higher elevations. For instance, the
warm and humid climate in northwest Iran caused a remark-
able fragmentation of oristic and faunistic elements (Ben-
nett, 1990) and as a consequence of it, most populations of
Ablepharus had expanded or sheltered in cool habitats. The
migration of A. bivittatus from northwest to lower latitudes
1 3
Evolutionary Biology
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speciation through climatic-niche divergence. In climatic-
niche conservatism, dierent populations of a species are
separated by a zone of unfavourable climatic conditions,
either via dispersal over this zone or vicariance (i.e., popula-
tions that formerly occurred within the zone of now unsuit-
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Niche conservatism (climatic or not) may be the main force
behind allopatry and, consequently, the beginning of allo-
patric speciation. Understanding the degree to which closely
related and/or partially sympatric taxa deviate in their niche
characteristics is necessary for identifying the processes
driving broad-scale biogeographic patterns (Cuervo et al.,
2021; Wiens et al., 2010). Our ndings supported the idea
that ecology played a role in speciation by demonstrating
that closely related Ablepharus taxa occur in and respond to
climatic features of various habitats (Zink, 2014). Accord-
ing to the ndings of this study, both niche divergence and
niche conservatism can contribute to the evolution of these
species. Niche equivalency and similarity test based on
Schoener’s D statistic and Hellinger’s I in E-space show that
the actual Grinnellian niches of the 28 paired comparisons
evaluated in this study had signicantly divergent potential
distributions (see Table 4). There is a possibility that the cur-
rent pattern of distribution is the result of vicariance-driven
allopatric speciation followed by secondary sympatry. As a
result of the niche overlap test, the niche equivalency and
similarity tests, which involved 11 and 21 pairwise compari-
sons, niche conservatism is supported by the fact that the
biological heterogeneity between these species results from
an allopatric speciation process (see Table 4). This strongly
implies that biotic interactions have an impact on the pro-
cess of niche diversication.
Supplementary Information The online version contains
supplementary material available at https://doi.org/10.1007/s11692-
023-09603-6.
Author Contributions All authors contributed to the study conception
and design. Material preparation and data collection were performed
by Somaye Vaissi, Muammer Kurnaz, Mehmet Kürşat Şahin, analysis
was performed by Somaye Vaissi. The rst draft of the manuscript was
written by Somaye Vaissi, Muammer Kurnaz, Mehmet Kürşat Şahin
and Axel Hernandez and all authors commented on previous versions
of the manuscript. All authors read and approved the nal manuscript.
Data Availability All data generated or analyzed during this study are
included in this published article and its supplementary information
les.
Declarations
Conflict of interest Its preparation was not supported by any external
funding source, nor are there any conicts of interest in connection
with our authorship.
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