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Research Article
The range of the Desert Monitor Varanus griseus caspius (Eichwald, 1831) in Central Asia
expand article infoYuliya A. Zima, Vassiliy A. Fedorenko
‡ Institute of Zoology of the Ministry of Science and Higher Education of the Republic of Kazakhstan, Almaty, Kazakhstan
Open Access

Abstract

In more recent publications, the range of Central Asian Desert Monitor Varanus griseus caspius (Eichwald, 1831) for entire Central Asia, as well as for individual republics, is shown either quite conditionally and in general terms, or distribution maps are presented as points of findings. We summarized and analyzed all available sources of information regarding the distribution of the Central Asian Desert Monitor, plotted the existing points of findings on the map, conducted modeling of the potential distribution of the monitor, and, comparing the result with the map of the anthropogenic modified landscape, delineated the modern range of the Desert Monitor in Central Asia. Modeling the potential range using various environmental factor variables allowed us to supplement factual data and construct a more detailed map of the Desert Monitor’s range. The modeling was performed using the maximum entropy method in the Maxent software. The modeling results showed that the most significant factors for model construction were the mean temperature of the driest quarter, as well as the normalized difference vegetation index (March), precipitation during the warmest quarter, precipitation (March), and solar radiation (April). Consolidating all available information, we were able to update the distribution map of the Desert Monitor in Central Asia, and the classification of findings by periods along with the anthropogenic landscape map allowed us to assess the degree of its range change since the mid-last century. It was found that the monitor’s range has significantly decreased in many regions due to the “expansion” of an anthropogenically altered landscape. The most considerable range reduction, relative to the past, occurred in Uzbekistan, Tajikistan, and Kyrgyzstan. To a lesser extent, the range decreased in Turkmenistan, and very slightly in Kazakhstan.

Highlights

  • Data analysis allowed updating the distribution map of the Desert Monitor in Central Asia, revealing a significant decrease in its range in many regions since the mid-last century.

  • The study revealed that the main reasons for the shrinking range of the Desert Monitor in Central Asia are associated with the transformation of anthropogenic landscapes, particularly in Uzbekistan, Tajikistan, and Kyrgyzstan.

  • Modeling showed that the primary factors determining the distribution of the Desert Monitor are climatic conditions, such as the mean temperature of the driest quarter, the normalized difference vegetation index for March, precipitation during the warmest quarter, precipitation for March, and solar radiation in April.

  • The resulting map of the Desert Monitor’s distribution in Central Asia serves as an important tool for conservation and management of this species’ populations, as well as for the development of nature conservation programs.

  • Further field research is necessary to confirm modeling predictions and identify areas where monitor presence has not been previously confirmed, especially in regions where the monitor’s range remains incompletely explored.

Keywords

anthropogenic impact, Central Asia, Desert Monitor, ecological niche models, environmental factors, GIS, MaxEnt, range dynamics, species distribution modelling

Introduction

The range of the Desert Monitor Varanus griseus occupies most of the Sahara-Gobi Desert region. From west to east, it is distributed from North Africa, the Middle East, Central and South Asia to Pakistan and northwestern India. The southern limits of the Desert Monitor are reached in Yemen, and to the north, it extends to the Aral Sea and the city of Kyzylorda in the southern part of Kazakhstan. Within the entire range of the Desert Monitor, three subspecies are distinguished: the nominative subspecies V. g. griseus (Daudin, 1803), inhabiting the western part of the range – from northwestern Africa to the Zagros in Iran; the Central Asian subspecies V. g. caspius (Eichwald, 1831), occurring in the сentral part of the species’ range – from the Zagros to the mountain systems of Pakistan; and the Indian subspecies V. g. koniecznyi Mertens, 1954, native to the eastern part of the range – in the plains of Pakistan and northwestern India (Stanner 2004).

The Central Asian Desert Monitor V. g. caspius was described by E.I. Eichwald in 1831 from the eastern coast of the Caspian Sea, the Dardzha peninsula in Turkmenistan (Eichwald 1831). More than half a century later, in the 1860–1880s, the first findings of the Desert Monitor in other parts of Central Asia had been published: from the vicinity of the city of Khujand (Khojent) in Tajikistan, the foothills of the Kopetdag in Turkmenistan, and from the eastern part of the Kyzylkum desert in Uzbekistan (Zaroudnoï 1885; Nikolsky 1899; Chernov 1959). For the territory of Kazakhstan, the first record of the Desert Monitor on the western shore of the Aral Sea was published by M.A. Levanevsky (1894), and N.A. Zarudny (1915) reported on the occurrence of the Desert Monitor in 1890 on the island of Kaska-Kulan on the eastern coast of the Aral Sea.

At the beginning of the 20th century, data began to appear from other regions, mostly from Turkmenistan, but they were still scarce and did not provide a complete understanding of the distribution of the Desert Monitor in Central Asia. Targeted studies on the Central Asian Desert Monitor were only initiated in the second half of the 20th century. During this period, numerous works were published that quite fully reflected the distribution of the Central Asian Desert Monitor in the region (Paraskiv 1956; Chernov 1959; Bogdanov 1960; Yakovleva 1961; Bogdanov 1962; Salikhbaev et al. 1967; Rumin 1968; Yadgarov 1968; Vashetko and Kamalova 1974; Kamalova 1978; Said-Aliev 1979; Shammakov 1981; Zakharidze 1981; Khodzhaev 1982; Makeev 1982; Tsellarius 1982; Ataev 1985; Kovshar 1986; Bondarenko 1989a, 1989b; Brushko et al. 1990; Tsellarius et al. 1991; Brushko 1995; Tsellarius et al. 1995; Tsellarius and Tsellarius 1996; Tsellarius et al. 1997).

In the 21st century, field studies were conducted to clarify the distribution of the population in Kazakhstan (Chirikova et al. 2019) and Uzbekistan (Khodzhaev et al. 2019; Abduraupov et al. 2021). For the territories of Turkmenistan, Tajikistan, and Kyrgyzstan, only fragmentary data on the modern distribution of the Desert Monitor exist (Milko and Panfilov 2007; Shestopal 2008; Solovyova et al. 2013; Davletbakov et al. 2015; Shestopal and Rustamov 2018; Khidirov et al. 2021).

Despite the significant amount of information on the distribution of the Desert Monitor available today, information on the species’ regional status remains uncertain. The expansion of anthropogenic impacted landscapes leads to the “displacement” of the monitor from its previous habitats. In this regard, it is necessary to conduct monitoring of the populations of this species, especially in those territories for which there are no current data.

The aim of this study is to define the boundaries of the modern distribution of the Desert Monitor for the territory of Central Asia. In more recent publications, the range for entire Central Asia, as well as for individual republics, is depicted either in an approximate and very generalized manner (Stanner 2004; Ananjeva et al. 2006), or distribution maps are presented as individual points of occurrence (Milko and Panfilov 2007; Chirikova et al. 2019; Abduraupov et al. 2021), or in the form of an atlas with a square grid (Sindaco and Jeremčenko 2008). The only exception is Uzbekistan, for which the most detailed range of the Desert Monitor has been published (Nuridjanov et al. 2016).

In this work, we summarized and analyzed all available sources of information regarding the distribution of the Central Asian Desert Monitor, plotted the existing points of findings on the map, conducted modeling of the potential distribution of the monitor, and, comparing the result with the map of the anthropogenic modified landscape, delineated the modern range of the Desert Monitor in Central Asia.

Material and methods

Information sources

The material for this work included data on the findings of the Desert Monitor, gathered from various literature and electronic sources, materials from various zoological collections, personal communications from colleagues, survey data and our own observations from 2008 to 2023. In this work, ‘Central Asia’ refers to the territories of five republics: Kazakhstan, Uzbekistan, Kyrgyzstan, Turkmenistan, and Tajikistan. However, to understand the distribution of the Desert Monitor, including beyond the southern borders of Central Asia, and to use this information for more accurate modeling, data on its locations were also collected for neighboring countries – Iran and Afghanistan. For locations, where precise coordinates were not available, coordinates were determined using satellite and topographical maps based on the textual description of these findings. This only applied to somewhat precise data; in other cases, the information was not used. All collected locality records were classified into three categories (see Table 1). Previously unpublished locality records are presented in the supplementary materials (Suppl. material 1).

Table 1.

Sources of information on the locations of the Desert Monitor. Coll. – collection; PC – personal communication; ZIN – Zoological Museum of the Zoological Institute of the Russian Academy of Sciences; ZMMU – Zoological Museum of Moscow University.

Country Records before 1950 Records in the period 1950–2000 Records after 2000
Kazakhstan (Levanevsky 1894; Zarudny 1915) (Bandyukov 1986; Erofeev 1986; Kovshar 1986; Mityaev 1986; Nikolaev and Badenko 1986; Stogov et al. 1986; Suslov 1986; Brushko et al. 1990; Brushko 1995; Matyukhin 2014; Chirikova et al. 2019) (Dzhikibaev 2010; Chirikova et al. 2019; GBIF 2023)
PC, M.A. Kulemin
PC, G.S. Nazarbek
This study (see Suppl. material 1)
Uzbekistan (Bogdanov 1882; Zarudny 1915; Kaluzhina 1951; Andrushko 1953; Zakhidov 1971) (Paraskiv 1956; Bogdanov 1960; Salikhbaev et al. 1967; Ishunin 1968; Yadgarov 1968; Karpenko 1970; Vashetko and Kamalova 1974; Yadgarov and Vashetko 1978; Popov 1981; Khodzhaev 1982; Bondarenko 1989a; Tsellarius et al. 1991) (Bogdanov 1992; Karmanov et al. 2001; Kholikov 2011; Abduraupov 2013; Nuridjanov et al. 2016; Abduraupov et al. 2021; GBIF 2023)
Coll. ZIN PC, D.A. Nuridjanov
Coll. ZMMU Coll. ZMMU
PC, D.A. Nuridjanov PC, D.A. Nuridjanov
Kyrgyzstan (Yakovleva 1961; Yakovleva 1964; Milko and Panfilov 2007) (Milko and Panfilov 2007; Davletbakov et al. 2015)
PC, K. Beyshebaev PC, D.A. Milko
Tajikistan (Chernov 1959; Said-Aliev 1979) (Chernov 1959; Pereshkolnik 1968; Said-Aliev 1979; GBIF 2023) (Solovyova et al. 2013; Khidirov et al. 2021; GBIF 2023)
Coll. ZIN
Turkmenistan (Kashkarov and Kurbatov 1929; Moritz 1929; Sergeev 1939; Bogdanov 1956; Bogdanov 1962; Ataev 1985; GBIF 2023) (Bogdanov 1956; Kostin 1956; Rustamov 1956; Rustamov and Ptushenko 1959; Bogdanov 1962; Karavaev and Belousov 1981; Shammakov 1981; Zakharidze 1981; Makeev 1982; Ataev 1985; Makarov 1985; Tsellarius et al. 1991; Marochkina et al. 2012; GBIF 2023) (Shestopal 2008; Shestopal and Rustamov 2018; Shestopal and Geokbatyrova 2021)
Coll. ZIN
Coll. ZIN
Iran (Moritz 1929; Šmid et al. 2014; GBIF 2023) (Nilson and Andren 1981; Kami 2005; GBIF 2023) (Fathnia et al. 2009; Sadeghi and Torki 2011; Böhme et al. 2015; Pour et al. 2016; Sanchooli 2018; GBIF 2023)
Afghanistan (Wajgner et al. 2016) (GBIF 2023) (Jablonski et al. 2021; GBIF 2023)

Modeling the potential distribution

The modeling was performed using the maximum entropy method in the Maxent software version 3.4.4 (Phillips et al. 2004; Phillips et al. 2006; Phillips and Dudík 2008; Phillips et al. 2023). The program calculates the statistical probability distributions of the species’ presence based on georeferenced species occurrence points and raster variables that contain information about environmental factors. As a result, Maxent produces a raster image where each cell corresponds to the value of the predicted suitability index for the environmental conditions for the species.

The Maxent analysis suggests a random distribution of species registration points (Phillips et al. 2009). However, in practice, this is often not the case – the points are concentrated near transportation infrastructure objects and populated areas. To reduce the overfitting of the model and the resulting bias towards the most studied areas, the initial set of points was rarefied with a minimum distance of 15 km, 25 km and 50 km. Rarefaction was performed in ArcGIS, version 10.4.1, using the SDMtoolbox, version 1.1. For modeling, after filtering, 352, 261 and 149 points were used, respectively. Spatial data on environmental parameters used for extrapolation are given in Table 2.

Table 2.

Environmental variables used for modeling.

Code Description
ELEV Elevation map from WorldClim version 2.1 dataset (Fick and Hijmans 2017).
BIO1 – BIO19 WorldClim version 2.1 climate data (Fick and Hijmans 2017): annual mean temperature (BIO1), mean diurnal range (BIO2), isothermality (BIO2/BIO7) (×100) (BIO3), temperature seasonality (standard deviation ×100) (BIO4), max temperature of warmest month (BIO5), min temperature of coldest month (BIO6), temperature annual range (BIO5-BIO6) (BIO7), mean temperature of wettest quarter (BIO8), mean temperature of driest quarter (BIO9), mean temperature of warmest quarter (BIO10), mean temperature of coldest quarter (BIO11), annual precipitation (BIO12), precipitation of wettest month (BIO13), precipitation of driest month (BIO14), precipitation seasonality (coefficient of variation) (BIO15), precipitation of wettest quarter (BIO16), precipitation of driest quarter (BIO17), precipitation of warmest quarter (BIO18), precipitation of coldest quarter (BIO19).
TMIN1 – TMIN12 Monthly climate maps WorldClim version 2.1 (Fick and Hijmans 2017): minimum temperature (°C) (TMIN), average temperature (°C) (TMEAN), maximum temperature (°C) (TMAX), precipitation (mm) (PREC), solar radiation (kJ m-2 day-1) (SRAD), water vapor pressure (kPa) (VAPR).
TMEAN1 – TMEAN12
TMAX1 – TMAX12
PREC1 – PREC12
SRAD1 – SRAD12
VAPR1 – VAPR12
NDVI3 – NDVI9 Normalized Difference Vegetation Index (NDVI) maps for the months March to September (2015) from Surface Reflectance Climate Data Record (CDR) version 5 (Vermote 2019).
SWI_TC Soil Water Index (SWI) maps (topography (SWI_TC) and wetland fraction (SWI_WF)) version 1.0.1 provided by «Copernicus Global Land Service» (CGLS) (Albergel et al. 2008).
SWI_WF
SOIL_SAND Soil profile data maps, average values at 0–5 cm depth (sand content (SOIL_SAND), clay content (SOIL_CLAY), silt content (SOIL_SILT), coarse fragment content (SOIL_CRSE) and bulk density fine earth (SOIL_BULK)), available on the SoilGrids portal maintained by ISRIC - World Soil Information (https://soilgrids.org).
SOIL_CLAY
SOIL_SILT
SOIL_CRSE
SOIL_BULK

A total of 106 raster environmental variables were used for modeling. All rasters were standardized to a uniform size (26–50°N, 39–93°E) and resolution (cell size 0.0083) in ArcGIS.

Among many variables, some may correlate significantly, leading to inflated values of predicted environmental suitability for the species (Jueterbock et al. 2016; Lissovsky and Dudov 2020). Therefore, modeling was conducted in two stages: (1) with the full set of variables (106 rasters) and (2) using a reduced set of variables, excluding those that were highly correlated and made no contribution to the construction of the first model. Correlation was assessed using Pearson’s coefficient (r), calculated for all variables in RStudio 2022.12.0 Build 353 using the ENMTools package. For each pair of variables with a Pearson’s coefficient modulus greater than 0.7 (r ≥ |0.7|), indicating high correlation (Manzoor et al. 2018; Borzée et al. 2024), the variable contributing less (Percent contribution) to the first model was excluded.

Modeling was conducted using three variants of the original point sets: with rarefaction distances of 15 km, 25 km, and 50 km. In the first stage, Maxent’s main settings were configured to use all classes of numerical features, as the dataset included more than 80 presence points for the species (Merow et al. 2013). The output format was chosen with a double logarithmic transformation (Cloglog). In the basic settings, random seed generation was enabled, and the number of iterations by cross-validation was set to 10. In the advanced settings, the number of iterations was increased to the maximum value of 5000 to allow the model to reach convergence (Stohlgren et al. 2011).

In the second stage, modeling was again conducted for the three rarefaction variants, but some settings were adjusted. Various combinations of feature classes were specified: L, LQ, H, LQH, LQHP, LQHPT (L – linear, Q – quadratic, P – product, T – threshold, H – hinge) (Borzée et al. 2024). Additionally, in the advanced settings, the regularization multiplier was set in the range of 0.5 to 5 with increments of 0.5. Each model was run with 10 iterations, resulting in a total of 1800 models.

The plausibility of the models was assessed using the AUC (Area Under the ROC Curve) (Felding and Bell 1997) values obtained from the modeling results, as well as the TSS (True Skill Statistic) (Allouche et al. 2006). TSS was calculated in RStudio for each model. The best Maxent settings were chosen based on the highest average value between average test AUC and average TSS. From the 10 iterations of models with these settings, the one with the highest average between test AUC and TSS was selected (Fig. 1).

Construction of the range

To draw the maps, the QGIS (Quantum GIS) program version 3.24.3-Tisler was used. The geographic background was composed of the ELEV elevation map and the hill shade map generated on its basis, the NDVI map for July, the Landsat Tree Canopy Version 4 forest map (Sexton et al. 2013), the map of water bodies from Esri, Garmin International, Inc. (https://www.arcgis.com/home/item.html?id=e750071279bf450cbd510454a80f2e63), the Digital Chart of the World’s rivers map for various countries available at Diva-GIS (http://www.diva-gis.org/gdata), as well as the OpenStreetMap administrative borders map loaded into QGIS using the QuickOSM module.

To construct the Desert Monitor range, all points of its findings were classified into three groups, the final species distribution modelling (SDM), as well as a map of anthropogenic impact Landsat-Derived Global Rainfed and Irrigated-Cropland Product 30 m V001 (Teluguntla et al. 2023) were applied on the geographic substrate. In the display settings of the SDM, areas with habitat suitability indices below the threshold were excluded, and the remaining display area was converted to monochrome. The value according to the rule “Equal training sensitivity and specificity (Cloglog)” was used as the cut-off threshold – the maximum threshold value obtained from the Maxent calculation. In the display settings of the anthropogenic impact map, only areas of human settlements and irrigated croplands were left visible. Based on these maps and occurrence records, as well as Google Maps satellite images, a vector map of the Desert Monitor range was manually drawn. The resulting range with all collected occurrence records is shown in Fig. 2.

Results

Following the comprehensive analysis of all available data sources, a total of 820 occurrences of the Desert Monitor were collected, with 354 in Kazakhstan, 9 in Kyrgyzstan, 180 in Uzbekistan, 45 in Tajikistan, 140 in Turkmenistan, 86 in Iran, and 9 in Afghanistan.

In the first stage of modeling using the full set of variables, Maxent analysis showed that at rarefaction distances of 15, 25, and 50 km, 49, 38, and 46 variables, respectively, contributed zero to the model; 44, 54, and 40 variables contributed less than one percent each; and only 13, 14, and 20 variables contributed more than one percent (Suppl. material 2: table S2).

After filtering the list of variables – excluding those that were highly correlated or contributed nothing to the first model (Suppl. material 2: table S3) – 24, 26, and 26 variables were identified as the most significant at rarefaction distances of 15, 25, and 50 km, respectively. The highest average value between average test AUC and average TSS was obtained for the rarefaction of the initial point set at 15 km, with Maxent settings where feature classes were specified as LQH and the regularization multiplier was set to 0.5. The AUC and TSS values for all combinations of Maxent settings are given in Suppl. material 2: table S4. The list of environmental variables used in the best iteration of the final model, as well as their contributions, is provided in Table 3.

The Area Under the Curve (AUC) of the final model was 0.93, indicating that its predictive value is “excellent” (AUC > 0.90) (Araujo et al. 2005). The True Skill Statistic (TSS) value of the same model was 0.78, which is considered “useful” (0.6 < TSS < 0.8) (Coetzee et al. 2009).

The cutoff threshold according to the “Equal training sensitivity and specificity (Cloglog)” rule, as determined by Maxent, was 0.34 for the best model. The resulting model, with values below the threshold excluded and with overlaid zones of human settlements and irrigated croplands, is shown in Fig. 1.

As a result of summarizing all the gathered literature, collection, and survey data on the actual findings of the Desert Monitor, as well as our own materials, and comparing them with the obtained model and map of anthropogenic impact, the range of the Desert Monitor in Central Asia was delineated (Fig. 2).

Table 3.

List of environmental variables of the best model and their percentage contribution to the model construction.

Variable Percent contribution Variable Percent contribution Variable Percent contribution Variable Percent contribution
BIO9 25.3 SRAD6 2.8 BIO7 0.7 BIO2 0.1
NDVI3 14.6 BIO8 2.1 BIO17 0.6 NDVI7 0.1
BIO18 13.6 ELEV 2 NDVI8 0.6 NDVI9 0.1
PREC3 10.9 NDVI4 1.8 SWI_TC 0.4 SOIL_CRSE 0.1
SRAD4 10.5 SWI_WF 1.8 NDVI05 0.4
VAPR6 5.7 SOIL_CLAY 0.9 SOIL_SAND 0.4
SRAD9 3.2 PREC11 0.8 NDVI6 0.4
Figure 1. 

Best final SDM of the Desert Monitor. Yellow dots show the points used for modeling, dark gray areas represent human settlements and irrigated croplands.

Figure 2. 

Range of the Desert Monitor in Central Asia.

Discussion

As shown in Table 3, five variables contributed 75% to the construction of the final model. These are: the mean temperature of the driest quarter (BIO9), the Normalized Difference Vegetation Index for March (NDVI3), precipitation during the warmest quarter (BIO18), precipitation for March (PREC3), and solar radiation in April (SRAD3). The mean temperature of the driest quarter alone accounted for more than 25% of the model’s contribution. Climatic factors have complex interdependencies with each other and the biology of the species, making the limiting effect of any given variable multifaceted. It can be hypothesized that a decrease in the mean temperature of the driest quarter—typically the summer months when egg incubation occurs—may result in embryos not developing sufficiently before the onset of autumn cooling. The significant contribution of “solar radiation for April” and “NDVI for March” to the model is likely crucial, especially in the northern parts of the range. By this time, the soil must warm up enough to allow the monitors to emerge from their hibernation burrows by early April. Furthermore, vegetation presence is linked to the availability of food resources at the time of their emergence. Precipitation of warmest quarter and precipitation for March humidity level, which, on one hand, also affects the availability and abundance of food objects during spring activity, on the other hand – on the moisture balance in the soil at the depth of the nesting chamber, where a sufficient soil moisture reserve must be formed to preserve the monitor’s clutches during the summer months. However, the limiting effects of these factors can vary greatly across different parts of the species’ range. For instance, while the length of the warm season is critical in the north, where embryos must develop before the autumn cold snap, this is not as relevant in the southern areas, where frost is absent.

The distribution model is calculated based on a limited set of variables and cannot account for all environmental factors. Therefore, the result is hypothetical and does not always match the actual situation – that is, despite the high suitability of certain territories according to the model, in reality the animal may be absent from these areas for various reasons, including natural and human-induced ones. In our case, such areas include a significant part of the right bank of the Syr Darya River (up to the Karatau range) in Kazakhstan, for which the model predicts very high habitat suitability for the Desert Monitor. However, despite the dense human population of this area and its frequent visits by scientists, the Desert monitor has only been recorded south of the 42nd latitude. The most straightforward explanation for this could be the anthropogenic factor – the entire right bank of the Syr Darya River, north of the known records of the Desert monitor, is home to one of Kazakhstan’s largest cities, Shymkent, as well as many villages and roads. Moreover, a large part of this territory is currently or was recently used in agriculture. The negative attitude of the local human population towards this animal cannot be ignored. An encountered Desert Monitor often causes fear among residents. As a result, despite its protected status (in Kazakhstan, the Desert Monitor is listed in the Red Book), the animal is usually killed. Our surveys and observations have confirmed this in most cases (Brushko and Chirikova 2010; Nuridjanov et al. 2016; Chirikova et al. 2019).

In the vicinity of the city of Kyzylorda, despite the presence of several Desert Monitor observations, including a relatively recent one (Dzhikibaev 2010), it seems the Desert Monitor does not live there. This is indicated by both the modeling results and the scant number of findings from this area. Surveys of residents and inspectors confirmed that the Desert Monitor is not found in these regions. Such a distant encounter from the main habitats of the Desert Monitor can be explained by intentional or accidental transportation of the animal. The same point of view was previously expressed by Z.K. Brushko (1995). The authors are aware of two such cases: in one instance, a Desert Monitor climbed into the engine compartment of a car in the Eastern Kyzylkum Desert and revealed itself only after stopping more than a hundred kilometers away (B.M. Gubin, pers. comm.), and in another, an individual was transported to the city of Shymkent in the back of a truck along with sand. In our view, the record at the western foothills of the Karatau range, near the settlement of Zharykbas (Leontyevka) (Chirikova et al. 2019), has a similar nature.

Several records of the Desert Monitor were known near the Aral Sea in the past. It is difficult to assess the reliability of this data and the nature of the stay of the noted individuals, however, it can be confidently stated that today the Desert Monitor is not found in these territories. This is evidenced by both the data from Uzbek colleagues (Nuridjanov et al. 2016; Abduraupov et al. 2021; R.A. Nazarov, pers. comm.), and its absence in the fauna lists of the “Barsakelmes State Nature Reserve” on the Kazakh territory of the Aral Sea.

Classifying the points by historical periods well demonstrates the pace of data accumulation on the distribution of the Desert Monitor. However, the local nature of research at different times does not fully allow assessing the changes at the boundaries of the entire range. Nevertheless, comparing the distribution model, the time and places of locality records, as well as the map of settlements and irrigated arable lands, it can be concluded that in the territory of Uzbekistan, large areas of the Desert Monitor’s range disappeared in the second half of the 20th century as a result of anthropogenic landscape transformation – this includes the entire Tashkent and Syrdarya regions, the eastern part of the Jizzakh region, significant territories along the river floodplains in the Samarkand, Kashkadarya, and Bukhara regions, as well as much of the Fergana Valley, where the Desert Monitor, apparently, has only survived in narrow sections along the southern, northeast, and northwest foothills, unsuitable for agriculture, and on a small segment in the central part of the valley (R.A. Nazarov, pers. comm.).

For Tajikistan, current observations/records are known from the foothill areas. The plains and historical areas of the Desert Monitor’s range along the river valleys have been completely transformed by agricultural activity.

In Turkmenistan, the Desert Monitor inhabits a larger territory of the country. However, despite its widespread distribution and abundance, which even led to its removal from the latest edition of the republic’s Red Book (Shammakov and Geokbatyrova 2016), we managed to find about two dozen references of records in recent publications. According to the modeling results and the absence of encounters, the Desert Monitor does not live in the northern part of Turkmenistan around the Kara-Bogaz-Gol Bay and the Sarykamysh Lake. Additionally, areas previously suitable for the Desert Monitor have been anthropogenically transformed – these are the northern foothills of the Kopetdag mountain system and the territories around the cities of Tejen and Mary.

Regarding modeling, relatively recently, the range of the Desert Monitor in Central Asia was modeled (Malakhov and Chirikova 2018), using a methodology different from ours. That study focuses on identifying the biological characteristics of the Desert Monitor, however, the correspondence of the obtained model to the actual distribution of the species is not discussed, nor the threshold for the suitability of the environmental conditions, which the authors consider to be the species distribution boundary. To compare the results, we attempted to classify the raster image of the model from this article and “draw” a boundary on the model for two values: 50% – as a median value on the scale, and 40% – a value at which the majority of the initial points are “captured” by the model. At 50%, many observational records of the Desert Monitor in Kazakhstan, Uzbekistan, and Tajikistan were outside the model, while at 40%, the entire territory of Kazakhstan south of the latitude of Kyzylorda is displayed as “suitable” habitat for the Desert Monitor, including the Mangyshlak Peninsula and the Moynkum Desert east of the Karatau range. That is, under different assumptions of suitability from the model, either a large number of Desert Monitor locality records “fall out,” or the model outlines suitable territories too “broadly,” which poorly corresponds to actual data, especially regarding the northern boundary of the species’ range.

The territory of Iran in our work is captured incidentally and adjunctly, nonetheless, we have the opportunity to compare the calculation results with the recent work of Shadloo et al. (2021). This work uses a different set of species presence points and a different modeling methodology. The model in the mentioned article agrees well with ours in the western part of Iran but differs in the eastern part – in our case, the model demonstrates “suitable” conditions on more plain areas. In the work of Malakhov and Chirikova (2018), observational records from the territory of Iran were not used, therefore the model in this area is the result of extrapolation and shows the opposite result – plain territories have weak suitability or are unsuitable at all, while mountainous areas are indicated as highly suitable.

Thus, it can be stated that different modeling methodologies can provide varied, sometimes opposite, results. The primary criterion for evaluating the modeling result in such cases should be the model’s correspondence to credible and contemporary actual data, in cases, of course, when such data are available.

Conclusions

Consolidating all available information, we were able to update the distribution map of the Desert Monitor in Central Asia, and the classification of findings by periods along with the anthropogenic landscape map allowed us to assess the degree of its range change since the mid-last century. It was found that the monitor’s range has significantly decreased in many regions due to the “expansion” of an anthropogenically altered landscape. The most considerable range reduction, relative to the past, occurred in Uzbekistan, Tajikistan, and Kyrgyzstan. To a lesser extent, the range decreased in Turkmenistan, and very slightly in Kazakhstan.

Based on the available data, the contemporary range of the Desert Monitor in Central Asian countries appears as follows. In Kazakhstan, the Desert Monitor is widespread in the extreme south of the country in the Turkistan and Kyzylorda regions along the right and left banks of the Syr Darya River. To the west of the Syr Darya, the Desert Monitor inhabits the Kyzylkum sands up to the republic’s border on the south and west of this section, reaching its northern distribution limits at the latitude of the settlement Baygekum village (44.3°). To the east of the Syr Darya River, the Desert Monitor’s range in the south is limited by the Shardara reservoir, in the southeast by the Keles River, and in the northeast by an approximate line from the Koksaray reservoir to the city of Saryagash. In Uzbekistan, the Desert monitor inhabits a significant part of the republic to the east and south of the city of Nukus. The northern boundary of its distribution here runs approximately from the southern part of the Tabakum sands to the east of the city of Nukus, south of the Tasqudyk sands, through the Central Kyzylkum desert, along the northern foothills of the Bukantau range, and further northeast towards the Tasqudyk, well to the southeast of the Taspan village. The Desert Monitor is absent from cultivated river valleys around major cities: Tashkent, Gulistan, Jizzakh, Khujand, Fergana, Andijan, Urgench, Bahara, Samarkand, Karshi, and Termez. In the Fergana Valley, the Desert Monitor is found on narrow strips of foothills. In Kyrgyzstan, the Desert Monitor has only survived in small areas in the south of the Jalal-Abad and Batken regions along the lower belt of mountains along the northeastern and southern edges of the Fergana Valley, respectively. In Turkmenistan, the Desert monitor inhabits a large part of the country. The northern boundary of its distribution here runs in a broken curve from the city of Turkmenbashi, south of the Kara-Bogaz-Gol Bay, south of the city of Gyzylgaya, crosses the Uchtagan sands, south of the Gaplaňgyr Nature Reserve, along the northern edge of the Karakum sands towards city of Dashoguz. The Desert Monitor is absent from cultivated areas along the Karakum Canal and the Amu Darya River floodplain, as well as from the river floodplains around the cities of Ashgabat, Tejen, Mary, Dashoguz, and Turkmenabat. In Tajikistan, the Desert Monitor inhabits the plain and low mountain parts of the western half of the republic, including a small section of the foothill zone in the south of the Fergana Valley, while absent from the agriculturally transformed river valleys of the Syr Darya, Kafirnigan, Vakhsh, Kyzylsu, and Panj rivers.

Modeling the potential range using various environmental factor variables allowed us to supplement factual data and construct a more detailed map of the Desert Monitor’s range. The modeling results showed that the most significant factors for model construction were the mean temperature of the driest quarter, as well as the normalized difference vegetation index for March, precipitation during the warmest quarter and precipitation for March, and solar radiation in April. It can be assumed that these factors are limiting for the distribution of the Desert Monitor.

To verify the predictions of the obtained Desert Monitor distribution model, future field surveys are necessary in territories where its occurrence has not been previously confirmed. In Kazakhstan, these are the areas of the Kyzylkum desert south of the city of Kyzylorda; in Uzbekistan – the northeastern part of the Kyzylkum desert south of Taspan village and the sands from the city of Nukus along the right bank of the Amu Darya River towards the cities of Zarafshan and Bukhara; and in Turkmenistan – the northern part of the Balkan and Lebap Region, and Dashoguz Region.

To conserve the Desert Monitor in all Central Asian republics, it is necessary to implement social national, and international programs aimed at raising awareness among local populations about the species as a whole, its threats, vulnerabilities, and the importance of its conservation.

Acknowledgements

We express our gratitude to colleagues who provided information on Desert Monitor encounter sites, including M.A. Kulemin, G.S. Nazarbek, B.M. Gubin, D.A. Milko, K. Beishebaev, as well as to V.F. Orlova and N.B. Ananjeva for the opportunity to work with the collections of the Zoological Museum of Moscow University and the Zoological Museum of the Zoological Institute of the Russian Academy of Sciences. Special thanks to D.A. Nuridjanov and M.A. Gritsyna for allowing the use and publication of a substantial volume of data on Uzbekistan. We also sincerely appreciate everyone who participated in joint expeditions or assisted us in gathering factual information during this multi-year project. We are grateful to M. Auliya and R.A. Nazarov for valuable remarks, corrections and useful advice on writing this article, and to the anonymous reviewers, whose comments and suggestions have contributed to the improvement of this work.

Author contributions

YuZ and VF co-led the study design and wrote the manuscript. YuZ conducted field research, collected field materials and survey data, and processed literature. VF conducted modeling of the potential species distribution and created the maps. YuZ and VF analyzed the data and interpreted the results.

Data accessibility statement

Previously unpublished species occurrence points used in this study are provided in the supplemental materials. All references to published data are provided in the “Material and methods” section.

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Supplementary materials

Supplementary material 1 

New locality records of the Desert Monitor (table S1) (.docx)

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Supplementary material 2 

List of environmental variables used in the first calculation and their percentage contribution to the model construction (table S2). List of environmental variables used in the final calculation (table S3). AUC, TSS and average between them for all models with various combinations of feature classes and regularization multiplier (table S4) (.xlsx)

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