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Article

The Elusive Turkestan Lynx at the Northwestern Edge of Geographic Range: Current Suitable Habitats and Distribution Forecast in the Climate Change

1
Department of Biodiversity and Bioresources, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan
2
Laboratory of Theriology, Institute of Zoology, Almaty 050060, Kazakhstan
3
Wildlife Without Borders Public Fund, Almaty 050063, Kazakhstan
4
Department of Animal Science, Wageningen University and Research, 6708 PB Wageningen, The Netherlands
5
IUCN Small Mammal Specialist Group (SMSG), IUCN, Rue Mauverney 28, 1196 Gland, Switzerland
6
IUCN Species Survival Commission (SSC), IUCN, Rue Mauverney 28, 1196 Gland, Switzerland
7
Department of Geospatial Engineering, Satpaev Kazakh National Research Technical University, Almaty 050000, Kazakhstan
8
Department of Research and Mountain Agrobiocenosis, Ile-Alatau National Park, Almaty 050067, Kazakhstan
9
Almaty State Nature Reserve, Almaty 041600, Kazakhstan
10
Kolsai Kolderi National Park, Saty 041422, Kazakhstan
11
School of Biological Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(15), 9491; https://doi.org/10.3390/su14159491
Submission received: 10 June 2022 / Revised: 23 July 2022 / Accepted: 29 July 2022 / Published: 2 August 2022
(This article belongs to the Special Issue Recent Advances in Global Climate and Ecology Change)

Abstract

:
The Turkestan lynx (Lynx lynx isabellina Blyth, 1847) is a rare and understudied subspecies of the Eurasian lynx occupying the mountains of Central and South Asia. This elusive felid’s northwestern range includes the Tien Shan and Zhetisu Alatau mountains in the border region of Kazakhstan, China, Kyrgyzstan, and Uzbekistan. As the first step to conserve this vulnerable carnivore, we have conducted the first full-scale research from 2013 until 2022 on its distribution in this region. Using 132 environmental predictors of 359 lynx sightings, we have created species habitat distribution models across the lynx’s northwestern range using machine learning approaches (Maximum Entropy—MaxEnt). Additionally, we created species distribution forecasts based on seven bio-climatic environmental predictors with each three different future global climate model scenarios. To validate these forecasts, we have calculated the changes in the lynx distribution range for the year 2100, making the first species distribution forecast for the Turkestan lynx in the area. Additionally, it provides insight into the possible effects of global climate change on this lynx population. Based on these distribution models, the lynx population in the Northern and Western Tien Shan and Zhetisu Alatau plays a significant role in maintaining the stability of the whole subspecies in its northwestern and global range, while the distribution forecast shows that most lynx distribution ranges will reduce in all future climate scenarios, and we might face the Turkestan lynx’s significant distribution range decline under the ongoing and advancing climate change conditions. For a future (year 2100) warming scenario of 3 deg. C (GCM IPSL), we observe a decrease of 35% in Kazakhstan, 40% in Kyrgyzstan, and 30% in China as the three countries with the highest current predicted distribution range. For a milder temperature increase of 1.5–2 deg. C. (GCM MRI), we observe an increase of 17% Kazakhstan, decrease of 10% in Kyrgyzstan, and 57% in China. For a cooling scenario of approx. 1–1.5 deg. C (GCM MIROC), we observe a decrease of 14% Kazakhstan, increase of 11% in Kyrgyzstan, and a decrease of 13% in China. These modeled declines indicate the necessity to create new and expand the existing protected areas and establish ecological corridors between the countries in Central and South Asia.

1. Introduction

The development of industry and agriculture, as well as a significant increase in the world’s population, have led to an increase in anthropogenic pressure on natural ecosystems, fauna and flora [1,2,3]. In particular, human activities have precipitated habitat loss and fragmentation, followed by isolation, loss of genetic diversity and biodiversity, decline in population sizes, and extinction of wild animals [4,5,6,7,8]. Among vertebrates, a number of large carnivorous mammals are threatened with extinction due to their lower population density and reproductive rates. Moreover, some of the main threats to carnivores are related to people’s prevailing negative attitude towards these animals, which may result in their direct or indirect extermination [9].
The Eurasian lynx (Lynx lynx L., 1758) is a felid species especially affected by human activities [10]. For instance, as a species that was once widespread throughout Europe, it became extinct in the region in the 19th century due to habitat loss and persecution [11,12]. The lynx is still considered a rare or endangered species, even after its re-introduction in a number of countries [13,14]. The case of lynx disappearance in Europe is a good example that indicates the need for measures to study and preserve this species and its subspecies in other habitats, taking into account the evolving industry and intensive developing of natural areas.
The Turkestan lynx, also known as Himalayan lynx, Central Asian lynx (Lynx lynx isabellina, also referred to as Lynx lynx isabellinus Blyth, 1847) is an elusive subspecies of the Eurasian lynx that occupies the mountains of Central and South Asia. Being rare and understudied throughout all 11 countries of its habitat, the lynx is listed in Appendix II of the International CITES Convention. In Central Asia, this carnivore is listed in the Red Data Book of Kazakhstan as “a rare subspecies, the range and number of which are decreasing” (III category) [15]; Kyrgyzstan as a “subspecies close to a vulnerable position” (NT, VI category), [16]; Uzbekistan as a “vulnerable declining subspecies” (II category—VU: D) [17]; Turkmenistan as “an extinct or endangered species or subspecies” (Category I) [18]; Tajikistan as an “endangered subspecies” (EN) [19]; China as an “endangered subspecies” (EN A1cd) [20]; and in Afghanistan, the lynx is considered “vulnerable” [21,22]. In South Asia, the lynx is common in the northern part of Pakistan, and it is considered to be of “Least Concern” (LC) in the region [23]. However, the current status and distribution in Pakistan is unknown [24], which confirms the need to update the Red List in Pakistan [23,25]. In India, the lynx is on the verge of extinction [26,27], and is listed in Plan I—protected by the National Indian Wildlife Protection Act since 1972. The lynx is listed in the Red Data Book of Nepal as a “vulnerable subspecies” (Category Vulnerable B1a; D2; protected under the National NPWC Act 2029) [28]. In Bhutan, there is no information available to determine lynx status (Data Deficiency).
The study is complicated by the inaccessibility of lynx habitats, as well as its secretive lifestyle. As it is exceedingly elusive, this form of the lynx is poorly studied, especially in the northwest of its global range, where it inhabits the mountains of Northern and Western Tien Shan and Dzhungarian (Zhetisu) Alatau [29]. The mentioned mountains are located in the border region of Kazakhstan, China, Kyrgyzstan, and Uzbekistan.
Most of the early studies conducted on a local scale in Kazakhstan in the 20th century were mainly related to the general distribution of the species, indicating the lynx presence in a particular mountain range [30,31,32], or contain fragmentary data on lynx sightings obtained while studying other mammalian species [33,34,35,36,37,38,39,40,41]. The situation is similar in neighboring Kyrgyzstan [16,40,42,43,44,45,46,47,48,49,50,51,52,53] and Uzbekistan [54,55,56,57,58,59,60,61,62,63,64,65]. More extensive studies in China by Ablimit et al. [66] indicate that, in the northwestern region, which includes the Xinjiang Autonomous Region, suitable lynx habitats in this region have decreased by 50% since the 1950s. At the same time, data on lynx in China are still insufficient [67], and detailed studies on lynx distribution are needed in the country [68].
In the northwest of its distribution, one of the key lynx habitats in Central Asia, the Turkestan lynx populations are understudied and heavily affected by habitat degradation and fragmentation, prey base depletion, poaching, and conflict with livestock breeders (fur trade and retaliatory killings due to livestock depredation) [69]. With the intensive regional development, the habitats may reduce in size and quality, thus increasing the extinction rate of lynx populations and subpopulations in these mountains [70]. As the first step in preserving this felid, we will predict the subspecies’ distribution, as it is an important component of populations and species conservation planning, and a variety of modeling techniques have been developed for this purpose [71].
Our study goals are twofold: (i) relying on 132 relevant environmental characteristics and our recently obtained more accurate lynx occurrence records, assess the habitat suitability for the lynx populations and create the first subspecies distribution model (SDM) in its northwest range; (ii) based on seven bio-climatic environmental characteristics, modelling a forecast on the lynx habitat distribution—subspecies distribution forecast (SDF) for the year 2100, thus evaluating the effect of climate change on the lynx’s distribution range. For this purpose, we will apply the above-mentioned models, also used in different modifications by other researchers to study the Eurasian lynx distribution in the world [72,73,74,75].

2. Materials and Methods

2.1. Study Area

We collected the data from the Northern Tien Shan, Western Tien Shan, and the Zhetisu Alatau mountains (Figure 1). We conducted our research within Kazakhstan, located in the most northwestern edge of the Turkestan lynx’s geographical range, and analyzed the survey and literature data from China, Kyrgyzstan, and Uzbekistan.
  • In the Northern Tien Shan, our study area covered the mountain ranges—Ile Alatau (N 42°58′–43°47′, E 74°53′–78°54′), Kungei Alatau (N 42°47′–43°01′, E 77°18′–78°59′), and Terskei Alatau (N 42°28′–42°46′, E 79°13′–80°07′), located in the border area of Kazakhstan and Kyrgyzstan, and Uzynkara (Ketmen) (N 43°02′–43°25′, E 79°17′–80°40′), located in the border area of Kazakhstan and China. In the Xinjiang part of Northern Tien Shan, we considered the felid occurrences in the Tomur (N 41°54′, E 80°18′), Kalajun–Kuerdening (N 42°59′–43°05′, E 82°22′–82°59′), Bayinbuluke (N 42°47′, E 84°09′) and Bogda (N 43°51′, E 88°09′) areas. We also studied Kyrgyz Alatau (N 42°38′–42°40′, E 73°14′–74°35′), which is located between Kazakhstan and Kyrgyzstan, and is an interjacent ridge between Northern and Western Tien Shan.
  • In the Western Tien Shan, we checked the lynx occurrences in the ranges of Karatau (N 42°53′, E 69°58′), located in Kazakhstan; Karzhantau (N 41°50′–42°03′, E 69°50′–69°58′), Talas Alatau (N 42°19′–42°25′, E 70°22′–71°51′) and Ugam (N 41°49′–42°04′, E 70°16′–70°25′), located in the border region of Kazakhstan and Uzbekistan; Pskem (N 41°45′–42°02′, E 70°22′–70°41′) and Chatkal (N 41°10′–41°23′, E 69°49′–70°29′), located in the border between Uzbekstan and Kyrgyzstan.
  • In the Zhetisu Alatau, located in the border region of Kazakhstan and China, we surveyed the ridges of Sholak (N 43°56′, E 77°56′), Degeres (N 44°00′, E 78°10′), Matai (N 44°10′, E 78°26′), Altynemel (N 44°13′, E 78°34′), Koyandytau (N 44°21′, E 78°49′), Toksanbay (N 44°43′, E 79°04′), Baskantau (N 44°04′, E 80°26′), Karatau (N 44°57′, E 79°38′), Bedzhintau (N 45°02′, E 79°58′), and Tyshkantau (N 44°37′, E 80°08′).
The Tien Shan ridges have a latitudinal or nearly latitudinal strike [76]. The climate in the mountains is continental; nevertheless, the complexity and vegetation of the relief cause contrasts in temperature and the degree of moisture [77,78]. Thus, the average annual air temperatures in Tien Shan decrease to 6.5 °C below zero [79]; at the same time, the southern slopes of the ridges can be 5–10 °C warmer than the northern ones [80].
Most of the plant species are found in the mid-altitude mountain forest belt. Above 2000–2200 m, deciduous forests give way to spruce on mountain–forest dark-colored soils with a high (up to 10%) humus content [81,82]. The important component of the spruce forests on the northern slope of the Northern Tien Shan ridges, particularly for the lynx distribution, is the endemic species, the Schrenk’s spruce (Picea shrenkiana) [83,84]; in the Western Tien Shan—the juniper shrubs (Juniperus pseudosabina, J. sibirica); and in the Zhetisu Alatau—the Schrenk’s spruce mixed with the Siberian fir (Abies sibirica) [30].
In addition to the Turkestan lynx, many other mammalian species inhabit Tien Shan and Zhetisu Alatau. Among them, there are carnivores (potential lynx competitors), in particular, representatives of the family Canidae—the wolf (Canis lupus) and the fox (Vulpes vulpes); the family Ursidae—the Tien Shan brown bear (Ursus arctos isabellinus); the Mustelidae—the short-tailed weasel (Mustela erminea), the least weasel (Mustela nivalis), stone marten (Martes foina), and Asian badger (Meles leucurus); and Felidae—snow leopard (Panthera uncia), manul (Otocolobus manul), and African wildcat (Felis lybica). The lynx preys on the tolai hare (Lepus tolai) and mountain hare (Lepus timidus), the Siberian roe deer (Capreolus pygargus), the grey marmot (Marmota baibacina), the Siberian red squirrel (Sciurus vulgaris exalbidus), and the Turkestan red pika (Ochotona rutila), as well as the Siberian ibex (Capra sibirica), wild boar (Sus scrofa), red deer (Cervus elaphus), and birds [2,30,31,32,41,44,45,67,85,86].
We collected the majority of occurrence data in specially protected natural areas (PAs): Ile-Alatau State National Natural Park (SNNP), Almaty State Nature Reserve (SNR), Kolsai Kolderi SNNP, Sharyn SNNP, Altyn Emel SNNP, Zhongar Alatau SNNP, Aksu-Zhabagly SNR, Sairam-Ugam SNNP, and Karatau SNR, and in the adjacent territories. The lynx populations in the PAs were comparably more stable, with a lower level of human disturbance and activities noted. General environmental conditions given in average for lynx locations within our study area are presented in Appendix A (Table A1).

2.2. Occurrence Data

2.2.1. Records Search and Analysis

We searched extensively for observational records within our study area. The literature data used in this study were collected mainly from zoological and ecological reports of the Institute of Zoology of Kazakhstan and Specially Protected Natural Areas (PAs), zoological articles, books, conference materials, methodological manuals, etc. In order to determine the modern lynx distribution, in the Northern Tien Shan, we frequently used the records in the diaries of the inspectors (rangers) of protected areas. In the Xinjiang part of Tien Shan, we mainly relied on the data given in the papers and reports of Ablimit et al. [42], the Leading Group for Application of WNH of Xinjiang Uygur Autonomous Region [87], and Tancheng Dong et al. [88], and we also examined the locations mentioned in craniological notes of lynx skulls by A. Regel (collected in 1880; Skulls No. 1164, 1284, 1287, 1325; Zoological Institute of Russian Academy of Science, St. Petersburg, Russia). One location presented in the gbif.org database was examined as well (accessed on 16 April 2022). In the Western Tien Shan, we often relied on the occurrence data by Bykova et al. [67] and Shakula et al. [89] and the oral communication of S.V. Baskakova. In the Zhetisu Alatau, one location given by V.A. Zhiryakov and B.R. Baidavletov [32] was considered for the lynx occurrence data. The reliability of all regions of lynx encounters, provided in scientific papers and reports, were reviewed and verified. Oral communications were taken from experienced researchers in the area.

2.2.2. Field Data Collection

During research from 2013 until 2022, we relied on traditional methods of field mammalogical research [90,91], which included visual observations and identification of various lynx and their prey traces (paw and hoof prints, prey remains, feces, scratches on trees and rocks, etc.) with their registration on the GPS. We typically collected the data on field trips during winter, as it was more attainable to observe tracks on snow.
Within the Kazakh part of our study area, we completed horse-walking routes with a total length of 1426 km, with 586 km in the Ile Alatau, 185 km in the Kungei Alatau, 74 km in the Terskei Alatau, 32 km in the Uzynkara, and 54 km in the Kyrgyz Alatau ridges of Northern Tien Shan; 108 km in the Talas Alatau and 47 km in the Ugam ridge of Western Tien Shan; and 340 km in the Zhetisu Alatau.

2.2.3. Camera Trapping

Research with the use of camera traps was mainly carried out in areas where the level of anthropogenic pressure was reduced. The camera traps (models Bushnell, Reconyx, Seelock, ScoutGuard, BolyGuard, Browning) were installed in 143 locations from 2013 to 2022 in the most potential lynx habitats (Figure 2), which we selected taking into account our own and survey data, as well as the presence of its prey. For the calculation of lynx and its prey abundance in the study area, we used the formula below (see the results in the Section 4.5).
The average abundance was calculated using this formula:
SIC = Total   IC Total   camera   trap   days × 100 ,
where IC means independent captures, SIC means IC per season (IC per 100 trap days), and camera trap days represents the number of applied camera traps multiplied by the number of days they were installed for.

2.2.4. Data Classification Methodology

We considered all field data obtained both by using traditional field methods and camera trapping, as well as data from a survey of PA staff about encounters of tracks and the lynx individuals themselves. Considering the fact that the study area occupied a large scale, we followed the data interpretation criteria according to the SCALP categories for the classification according to their level of reliability (C1; C2; C3) [92,93] (see Table 1 and Figure 3).
C1 (Category 1): “Confirmed occurrence”—our observations (traditional field methods, camera trapping) and survey (questionnaire) data: verified and reliable data such as (1) dead lynx, (2) captured lynx, (3) videos and photographs of lynx, or (4) samples (e.g., fur).
C2 (Category 2): “Probable occurrence”—data checked and confirmed by a specialist (inspector (ranger), hunting biologist, protected area employee, our data), such as (1) remains of livestock or (2) a wild animal killed by a lynx, (3) documented traces of a lynx or other signs of vital activity, (4) feces, or (5) documented (recorded) and confirmed lynx calls.
C3 (Category 3): “Unconfirmed data” of category 2, such as remains of domestic or wild animals, footprints, feces, or lynx calls, and all unverifiable information, such as encounters of lynx by local residents without attached evidence.
In order to present a more complete modeled distribution, here we merged all occurrence data, and created the SDM using a total of 359 occurrence points (see Figure 4). For completion, and possible future research, we also created the same SDMs, using the same predictor set, for all the three different C-categories separately, presented in Appendix B.

2.3. Environmental Predictors

2.3.1. Current Environmental Predictors

In this study, we created species distribution models (SDMs) based on 132 environmental predictors. These 132 environmental predictors aim to best describe the surrounding environmental and habitat conditions of the studied subspecies (Turkestan lynx (Lynx lynx isabellina)) (see [94]). These are the building blocks of the SDMs, apart from the collected occurrence data [95]. These 132 predictors have been retrieved by MS from Steiner and Huettmann (in-press) [8]. They describe, e.g., the temperature, precipitation, relative humidity, soil conditions, snowfall, proximity to streets, cities and rivers, wildfires, vegetation cover, climate classes, prey densities, etc., on a global scale. All details and sources of the 132 predictors can be found in Appendix A, Table A1 (Reproduced from Steiner and Huettmann (in-press)) [8]. Each environmental predictor has an accuracy of 2.5 km2 with a pixel size of 0.04166666666666666435x − 0.04166666666666666435 decimal degrees (CRS: EPSG:4326—WGS 84). This is believed to be the most complete set of environmental predictors publicly available to this date. For details on how to standardize and prepare environmental predictors for SDMs please see Steiner and Huettmann, in-press (Chapter 3) [8].

2.3.2. Projected (Future) Climatic Predictors

We created species distribution forecasts (SDFs) based on seven environmental predictors. These SDFs aim to predict the distribution of Turkestan lynx for the year 2100 using bio-climatic environmental predictors only. These predictors follow the same technical details (pixel size, accuracy) as the ones used for the SDMs above. The reason for only using 7 predictors for these SDFs compared to the 132 predictors for the SDMs is that significantly less data are available for the year 2100 than 2000–2020. Since 2100 is in the relatively far future, for the models here we used three different scenarios. These three scenarios aim to cover three different climate directions that are likely to be approached by 2100. The three scenarios utilized in this study are Global Climate Models (GCMs) that have been downloaded from www.WorldClim.org (accessed on 1 June 2022) (MIROC6, MRI-ESM2-0, and IPSL-CM6A-LR). Similarly, as described by Steiner and Huettmann (in-press—Chapter 7) [8], the MIROC6 may be considered as the low temperature increase scenario, or even as a certain cooling scenario [96]. MRI-ESM2-0 is considered as a low–medium temperature increase as an approximate global increase of 2 degrees Celsius [97]. One might refer to it as a “business as usual” model. Lastly, IPSL-CM6A-LR is considered as a medium–high increase in temperature by approximately three degrees Celsius [98]. Additionally, we created an SMD for the year 2000 with the same predictor set as the SDFs for 2100 in order to best compare current and predicted future distribution ranges.

2.4. MaxEnt

For this study, we used the Maximum Entropy software MaxEnt (https://biodiversityinformatics.amnh.org/open_source/maxent/) (accessed on 1 June 2022) version 3.4.4 to create the SDMs and SDFs (following Machine Learning approaches) (see [99] and references within). A thorough description of the workflow for MaxEnt SDMs can be found in Chapter 3 (Steiner and Huettmann, in-press) [8]. In coarse, the data must first be compiled, cleaned up, and standardized before they can be used for SDMs in MaxEnt. Once the models have been created with MaxEnt, their visualization has been improved and enriched by MS using ArcGIS Pro (version 2.7) (https://pro.arcgis.com/en/pro-app/2.8/get-started/download-arcgis-pro.htm) (accessed on 1 June 2022), and Open-Source GIS—QGIS (versions 3.10 and 3.22.7) (https://qgis.org/en/site/forusers/download.html) (accessed on 1 June 2022). For these models, we have used the standard settings for Maxent; this includes the number of iterations set to 500, and maximum number of background points set to 10,000. Additionally, for all other settings we have used the default option, which includes the feature selection, where we used “Auto Features”, including “Hinge Features”, “Product Features”, “Quadratic Features”, and “Linear Features”.

2.5. Habitat Distribution Percentage

In order to assess the spatial distribution of the models and to provide an overview of in which countries the predicted distribution occurrences can be found, we summarized the spatial information and presented it in percentage per country. This has been carried out by utilizing the QGIS tool “Zonal Statistics”, which calculates the sum of all raster cells (pixels) within a given polygon (in our case countries). Subsequently, the outcomes of this analysis have been converted into percentage per country for better interpretation.

3. Results

3.1. GPS Mapping Results

3.1.1. Species Distribution Models (SDMs)

SDMs have been created for all occurrence datasets available. They have been created for all C-datasets individually (C1, C2, and C3), and for all available data merged. The individual SDMs for all C-datasets can be found in Appendix B (Figure A6, Figure A7 and Figure A8). For a more holistic overview, here we present the SDMs of all available data merged. Figure 4 illustrates the predicted distribution range for the Tien Shan–Alai region.
The SDM indicates that the highest predicted occurrence of Turkestan lynx can be found near the Parkent district of Tashkent Region (Uzbekistan), in the Uzgen district (Kyrgyzstan), near the Naryn River in the Naryn region (Kyrgyzstan), around the Issyk Kul (Kyrgyzstan), in Kemin (Kyrgyzstan), and throughout the Almaty region (Kazakhstan) except the northwest areas near Balkhash Lake. For details, see the Discussion Section 4.3. The top predictors contributing most to the creation of Figure 4 are World Soil Characteristics (with 41.9%), Slope (15.8%), Minimum Relative Humidity of March 2020 (7.1%), Maximum Relative Humidity of October 2020 (5.4%), Maximum Relative Humidity of September 2020 (4.1%), Altitude (3.6%), Solar Radiation of June 2021 (3.1%), and Proximity to World Protected Areas (3%). The reported Area Under the Curve (AUC) for this model was 0.991.

3.1.2. Species Distribution Forecasts (SDFs)

Similar to the SDM, SDFs have been created for all C-datasets, which can be found in Appendix B (Figure A9, Figure A10, Figure A11, Figure A12, Figure A13, Figure A14, Figure A15, Figure A16, Figure A17, Figure A18, Figure A19 and Figure A20). In Figure 5, Figure 6, Figure 7 and Figure 8, we present the SDFs for the year 2100 using seven bio-climate environmental predictors for all three included climate scenarios (IPSL, MRI, and MIROC). Similar to the above, here we present an overview of the Tien Shan–Alai region. For details about Figures below, see the Discussion Section 4.4. The top predictors and the corresponding contribution percentages for Figure 5, Figure 6, Figure 7 and Figure 8 can be found in Table A3, Table A4, Table A5 and Table A6.
The reported Area Under the Curve (AUC) values for these SDFs were 0.982, 0.973, 0.982, and 0.984 for Figure 5, Figure 6, Figure 7 and Figure 8, respectively.

3.2. Habitat Suitability

Based on the contribution percentage of each country in the SDM and SDF models seen in Table 2 below, we evaluated which countries provide the most favorable habitats for the lynx’s present and predicted future distribution. Table 2 was created using the “Zonal statistics” tool in QGIS.
It can be seen that Kazakhstan, Kyrgyzstan and China have the highest contribution in lynx distribution in both SDM and SDF models. In the case of using 132 predictors, models showed that Kazakhstan has the greatest percentage (28.5%) of suitable lynx habitat, followed by Kyrgyzstan (21.71%) and China (21.28%). The countries mentioned have the Northern Tien Shan and Zhetisu Alatau mountains with suitable vegetation cover, water access, and an abundance of smaller prey species. The Western Tien Shan mountains, shared between Kazakhstan, Kyrgyzstan, Uzbekistan and the northmost part of Tajikistan, also provide a favorable habitat for the lynx populations, which also indicates the contribution of Uzbekistan and Tajikistan (2.16% and 3.33%, respectively). According to the models’ estimation, the contribution percentage of other countries in Central and South Asia differ insignificantly and provide less habitat area for the felid. The mentioned row of countries with other species and subspecies was based on models automatically running the calculations for suitable habitats throughout the world in all of the other countries, including other continents, and is not relevant for our evaluation. For the evaluation of predicted lynx movements (SDF) based on seven bio-climatic predictors, see the Discussion Section 4.4 and Section 4.5.

4. Discussion

4.1. Data Classification

For the standardization process and usage of data obtained on lynx sightings over a large area, it is important to collect and classify the data on lynx at the local scale according to their reliability. First proposed by Molinari-Jobin et al. [92], the method to classify the data in three categories was approved and applied by a number of researchers studying lynx in mountainous conditions [91,100,101]. For this research, we also used three sets of data according to their reliability—C1 (Confirmed data), C2 (Probable occurrence) and C3 (Unconfirmed data). After evaluating the models created on these three sets of data, we noted the accuracy of all three categories of data collected laid over models. Moreover, the models of C3 data in the area of Kazakhstan and Kyrgyzstan, shown in Figure A8 and Figure A17, seem to be highly precise and even more accurate compared to models of C2 data, illustrated in Figure A7 and Figure A13, in the aforementioned countries. Notedly, models calculated on the C2 and C3 data do not provide the distribution information of lynx in China. As we did not obtain the C2 and C3 data from Xinjiang and the country has a different set of environmental predictors classification, the extrapolation did not cover the Xinjiang Tien Shan area for our distribution models. The accurate models created based on combined data provide the necessary insight into the predicted current and future lynx habitat distribution and dispersal.

4.2. Notes on the Turkestan Lynx’s Taxonomic Status

For the study area of this manuscript, we excluded the Altai region (N 48°45′–51°55′, E 83°05′–90°35′), located in the border region of Kazakhstan, Russia, Mongolia and China. This is due to the unclear taxonomical position of the Turkestan and Altai lynx (Lynx lynx wardi Lydekker, 1904) and where the border between the two subspecies is located in the region. According to our own observations, the differences in physical characteristics of the lynx inhabiting the Altai mountains and Zhetisu Alatau–Tien Shan mountains are distinct, with the Altai lynx being larger in size than the Turkestan lynx. Still, we included the Saur (N 47°12′, E 85°05′) and Tarbagatai (N 47°14′, E 82°13′) mountain system in the SDM and SDF maps, as the Saur–Tarbagatai lynx population has less distinguishable traits from the Turkestan lynx. Yet, some researchers believe the Saur–Tarbagatai population can still be identified as the Altai lynx, as the felid in this region frequently migrates to the Altai mountains (oral commination by Starikov S.). Thus, it is legal in Kazakhstan to hunt the lynx in the hunting grounds of the Saur mountains.
It should be noted that the subspecies differentiation between the Turkestan and Altai lynx has never been specially studied. At the same time, there is an assumption about the possibility of the Altai subspecies being identical or very close to the Turkestan lynx [29], while some researchers do not classify the Altai lynx as a separate subspecies and also consider this felid to be the Turkestan lynx [102]. For conservation purposes, it is essential that we conduct special studies in this direction. In order to determine the position of the Turkestan lynx as a subspecies and possibly strengthen its conservation status, we have conducted comparative morphometric research on lynx skulls and started molecular–genetic research, the results of which might help us to gain insight into the taxonomic position of the lynx in the region.

4.3. Analysis of Present Distribution

Considering the increasing ecological challenges for the Turkestan lynx populations such as habitat loss and fragmentation, mostly caused by overgrazing, deforestation, prey base depletion, infrastructure development, unregulated tourism, poaching, and retaliatory killings by herders [69,103], the evaluation of the elusive felid’s distribution and suitable habitats holds essential value for the creation of conservation strategies. Furthermore, assessment of the potential current lynx distribution and predicted dispersal according to a changing climate with the subsequent protection measures will also lead to the conservation of important habitats for other plant and animal species. In the northwest part of Turkestan lynx’s global range, where there has been no prior specialized and large-scale research conducted, we used our data and survey occurrence records of various lynx populations and created the first highly predictive habitat distribution models for the lynx in the region. For the current and predicted distribution forecast models, we relied on 132 environmental and 7 bio-climatic predictors, respectively. Thus, the models based on the predictors mentioned, illustrated in Figure 4 and Figure 5, provide an ostensive characterization of present favorable lynx habitat that will accelerate the application of conservation management to areas crucial to the Turkestan lynx.
Figure 4 shows the model based on all lynx observation records obtained, combined with 132 environmental predictors, where we can visually see that lynx sightings fall within regions that our model confirms to be highly potential habitats. In particular, these regions include the Northern Tien Shan and Zhetisu Alatau areas in Kazakhstan, and the Western Tien Shan area in the border of Kazakhstan and Uzbekistan.
Notwithstanding the lack of spatial data from other Tien Shan ridges in Kyrgyzstan and China and their absence from the Western Tien Shan and Alai ridges in Tajikistan due to the fact that we did not collect data from this country, our model suggests that the distribution of lynx populations could include suitable areas in those regions as well. In particular, in Kyrgyzstan, the model presents the most propitious habitats to be located in the Ile Alatau and Kungei Alatau north to the Issyk Kul in the Northern Tien Shan, and especially in the Terskei Alatau east and south of Issyk Kul between the Northern and Inner Tien Shan, as well as in the Talas Alatau, Chatkal and Koksu ranges in the Western Tien Shan and the Fergana range between Western and Inner Tien Shan. In China, the model shows that the most highly favorable habitat is located in the Kalajun area of Northern Tien Shan in Xinjiang, near the Tekes River’s mouth. In Tajikistan, the average level of predicted occurrence is estimated to be in the Alai range at the upper stream of Zeravshan River, near the Komarou Nature Refuge.

4.4. Analysis of Predicted Movements (SDF)

The figures of the SDF models, based on seven bio-climatic predictors, indicate that the highest predicted distribution index of the Turkestan lynx, according to the current distribution, can be found in the following areas: the Ile Alatau, Kungei Alatau, Terskei Alatau and Uzynkara ranges in the Almaty region (Kazakhstan); in the Ile Alatau and Terskei Alatau ranges in the Issyk-Kul region (Kyrgyzstan); in the Ugam, Pskem and Chatkal ranges in the Tashkent region (Uzbekistan) and Jalal-Abad region (Kyrgyzstan); and Talas Alatau range in the Turkistan region (Kazakhstan). Additional hotspots can be found in the central part of Tajikistan, the northeastern part of Afghanistan, and the far north of Pakistan. Inspecting Figure 6, it can be identified that the described hotspots seem to increase towards the south-western distribution (near Tajikistan and the far western part of Tien Shan), and also near Almaty. However, the distribution seems to decrease in the northern parts (near Zhetisu Alatau (Kazakhstan)) and in the very southern parts (near Pakistan). Inspecting Figure 7, and comparing it to Figure 5, it can be identified that the described hotspots seem to change following similar patterns observed for the IPSL scenario (Figure 6), with the difference being that there is a significant increase observed near the area of Taldykorgan (Kazakhstan), which is not visible for the IPSL scenario. Lastly, for the MIROC scenario, it seems that the distribution hotspot near Almaty increases and densifies in the far western part of Tien Shan. Additionally, the southern distribution seems to decrease.
Overall, it can be observed that following the IPSL scenario, the Turkestan lynx distribution is expected to densify, move northwards when following the MRI scenario, and again densify for the MIROC scenario. Details of the observed shifts in the predicted distribution ranges (likelihood of occurrence) can be seen in Table 3 below.
According to our SDF models based on seven bio-climatic predictors, shown in Table 2, the greatest contributions to the lynx populations are presented by China (9.49%), followed by Kazakhstan (5.63%) and Kyrgyzstan (1.21%). Kazakhstan seems to be the only country with the possible expansion of suitable habitats in the case of MRI—a possible temperature increase of approximately two degrees—but the opposite is observed with Kyrgyzstan in the case of the cooling MIROC scenario. With over 93% of the country occupied by mountainous areas [104], Tajikistan is estimated to provide 0.84% of suitable habitats for the lynx, which reduces insignificantly in cases of possible climate change. Afghanistan and Pakistan, with the presence of the Pamir and Hindu Kush mountains, follow with a similar current contribution of 0.77% and 0.74%, respectively.
Overall, in all the countries, it is observed that the lynx habitats might significantly shrink in size in the case of the IPSL scenario (significant warming scenario). Considering the calculations of the most possible MRI scenario, the predicted climate change might result in habitat reduction in all countries except Kazakhstan. This, as presented in Table 3, proves that the lynx moves northwards in the case of the MRI scenario, and we are likely to observe a significant distribution range decline in several lynx habitats with the future climate change. It must be noted that these models are “minimum-estimates”, meaning that with the low number of future predictors available, here we present a possible approximation of the future distribution range using solely seven climate predictors. We encourage future research to develop more future predictors for more robust future Species Distribution Models.

4.5. Habitat Suitability, Protected Areas and Conservation

The high percentage of favorable habitat in Kazakhstan, shown in Table 2, can be explained by a larger area of mountains in the northwest range considered for research. More precisely, the country has the largest part of the Northern Tien Shan and Zhetisu Alatau mountains. As these mountains continue in the east direction into China, the models suggest that the ridges in Xinjiang present a highly favorable environment for the lynx population and their distribution, which can be especially seen in the SDF columns of Table 2. In Kyrgyzstan, despite the lack of data used, the presence of Northern Tien Shan ridges, and a major part of the Western and Inner Tien Shan mountains, allowed the models to estimate that the country provides necessary lynx habitat; this can also be applied for the Western Tien Shan in Uzbekistan, with a high predicted lynx occurrence index in the country.
It is clear that the higher lynx occurrence indices in the study area depend on a number of environmental conditions, such as climate, land cover (coniferous forest and shrubs), access to a water source (the river and lake network), limited human disturbance, terrain (elevation and the level of annual snowfall, as well as the level of snow line), abundance of prey, and the presence of protected areas in these lynx habitats (see Figure A1, Figure A2, Figure A3, Figure A4 and Figure A5 and Table A2, Table A3, Table A4, Table A5 and Table A6 of Appendix A). Figure A1, Figure A2, Figure A3, Figure A4 and Figure A5 additionally represent the omission of the models. Table A2, Table A3, Table A4, Table A5 and Table A6 additionally represent the contribution percentage of the corresponding model, representing the “importance” of all included environmental predictors for building the model (retrieved from the HTML MaxEnt output).
The abundance of prey plays an important role in lynx stability, especially during winter [30], while extensive hunting for the prey may also result in a decline in the lynx population. During our research, we observed that in the Northern Tien Shan, during the years where the prey species, in particular, tolai hare, were declining in number, a similar tendency was noted in the lynx. In the habitats where the prey species are abundant, the lynx populations seem to be more stable, as well (Table 4).
In the Northern Tien Shan, where most of our camera traps were installed, the lynx was more frequently registered by camera traps in the Kungei Alatau range compared to the Ile Alatau (on average, 0.74 and 0.44 lynx individuals per 100 trap days in Kungei Alatau and Ile Alatau ranges, respectively). In Kungei Alatau, during the period of our research, we noted a higher abundance of the tolai hare, the main lynx prey in the Tien Shan (on average, 2.34 hares per 100 trap days). In Ile Alatau, the Siberian roe deer, another key lynx prey, was frequently caught on camera (on average, 3.33 individuals per 100 trap days). In the Western Tien Shan and Zhetisu Alatau, where the camera traps were stationed for a shorter period of time, we noticed the lynx to be more frequent in the habitats with more roe deer, tolai hare (for the Western Tien Shan) and mountain hare (for the Zhetisu Alatau) present. Thus, in the territory with a relatively high abundance of prey and a smaller number of disturbance factors, a higher abundance of lynx can be traced.
Higher lynx occurrences can be noticed in habitats with protected areas within all of our study area. The level of general population stability can especially depend on the level of protection these PAs provide. In order to evaluate the contribution made by PAs for habitat suitability, we calculated the percentage of PAs laid over habitats our models estimated to be suitable for the lynx populations in the south-east of Kazakhstan, as well as the X-fold increase in predicted lynx occurrence indices of the PAs compared to the rest of Kazakhstan (as seen in Table 5 below). The calculations were made using the “Zonal statistics” tool in QGIS.
In Kazakhstan, the model based on the 132 predictors suggests that the highly favorable habitats can be found in the Northern Tien Shan at the Ile-Alatau and Kolsai Kolderi National Parks and in the Zhetisu Alatau at the Zhongar Alatau and Altyn Emel National Parks. With the calculations of SDF models based on seven predictors, it can be seen that the Ile-Alatau, Kolsai Kolderi, and Zhongar Alatau National Parks and the Sairam-Ugam National Park in the Western Tien Shan play a significant role in conserving the lynx habitats in future climate change scenarios. In the case of Nature Reserves, where the level of protection is the highest, Almaty Nature Reserve provides the key habitats to the lynx populations despite having much less territory compared to the other National Parks in the Northern Tien Shan.
In China, the possible lynx habitats are protected in the Tomur Peak National Nature Reserve, Kalajun Provincal Park, Kuerdening National Nature Reserve, and Bayinbuluke National Nature Park, with our models estimating the most suitable habitats being located in the Kalajun Provincal Park area. In Kyrgyzstan, the models show that the relevant lynx habitats are located at and protected by the Chong-Kemin National Park on the Ile Alatau and Kungei Alatau ranges of Northern Tien Shan, the Besh-Aral Reserve on the Chatkal Valley of Western Tien Shan, and the Issyk-Kul and Naryn Reserves and the Saymaluu-Tash National Park on the Fergana range in the Inner Tien Shan; in the Karatal-Japyryk Nature Reserve in the Inner Tien Shan the lynx number seems to slightly increase over the years [56]. In Uzbekistan, the Ugam Chatkal Nature Park, Chatkal Nature Reserve and Ugam Chatkal Reserve in the Western Tien Shan maintain the habitats for the lynx and its prey species [67]. In Tajikistan, SDM shows the occurrences being mostly near the Komarou Nature Refuge.
As the threats to the lynx populations are still present and increasing in the mountains, the key solution for conservation of the felid and other animal and plant species, as well as their habitats, is to create new PAs in the areas where they are most necessary, as well as expand the territory of the existing PAs. In Kazakhstan, one of key countries for the lynx habitats, we recommend creation of Uzynkara Nature Reserve in the Uzynkara range of Northern Tien Shan, while in this and Terskei Alatau range there is no PA present yet. As the Ile-Alatau National Park is located in the vicinity and partially in the territory of Almaty megapolis, it is also important to increase the size of the park and strengthen its protection system.

5. Conclusions

In summary, our results describe the present distribution and future dispersal for the year 2100 of Turkestan lynx at the northernmost part of their global geographical range—in the Northern and Western Tien Shan and Zhetisu Alatau mountains. The highly predictive distribution models provide the data on suitable habitats in our study area, as well as within all 11 countries of its distribution. According to the SDM models on 132 environmental predictors and our subsequent calculations, we noted the most suitable lynx habitats to be located in Kazakhstan, followed by Kyrgyzstan and China. Based on seven bio-climatic environmental predictors, we estimate a lynx habitat reduction in most predicted future climate scenarios.
With the significant proportion of suitable habitat relevant to the lynx population located in the northwestern part of its global range, it is evident that the Northern and Western Tien Shan and Zhetisu Alatau mountains are crucial for lynx population stability and future survival. For the conservation of the lynx and other rare animals, it is essential to create new and expand existing protected areas, make a collaboration between the countries to study this felid, and create ecological corridors between the mountain regions and countries contributing to lynx distribution in Central and South Asia.

Author Contributions

Conceptualization, N.B., A.G., N.R., Y.G., K.S. and S.A.M.S.; methodology, N.B., A.G., M.B., T.Z., S.S., A.S., S.B. and A.B.; software, M.S. and N.R.; formal analysis, N.B., M.S., A.G. and Y.G.; data curation, A.G., N.B. and M.B.; funding acquisition, A.G. and N.B.; writing—original draft preparation, N.B.; writing—review and editing, N.B., M.S., A.G., N.R. and S.A.M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Institute of Zoology of Ministry of Education and Science of the Republic of Kazakhstan, the Program number OR11465437—“Development of a national electronic data bank on the scientific zoological collection of the Republic of Kazakhstan, ensuring their effective use in science and education” (2021–2022); the Rufford Small Grants, Rufford Foundation, United Kingdom, the Project ID 29126-1-Bizhanova Nazerke—“Population and conservation status of the Turkestan lynx (Lynx lynx isabellina Blyth, 1847) in the Kazakh part of the Northern Tien Shan” (2020–2021).

Data Availability Statement

The data are available online at https://arcg.is/1T0qjy for the SDFs, and at https://arcg.is/18PvPj0 for the SDMs.

Acknowledgments

The authors express their sincere gratitude to the administration, researchers, game managers, inspectors and other employees of the Almaty Reserve, Ile-Alatau National Park, Kolsai Kolderi National Park, Sharyn National Park, Altyn Emel National Park, Aksu-Zhabagly Nature Reserve, Sairam-Ugam National Park and al-Farabi Kazakh National University, and other organizations involved for their help in the preparation and implementation of these studies. In particular, from the Ile-Alatau SNNP—from the Department of Research and Mountain Agrobiocenosis—Saltanat Userbayeva, Berik Nesipbai, the Department of Protection and Regeneration of the Wildlife—Bauyrzhan Rustemkhanuly, Meirzhan Kistykbayev, Sergey Strebkov, Andrey Segayev, Ulan Estemesov, Askar Kali, and others; from the Almaty SNR—Altynbek Dzhanyspayev, Zhanbolat Baimukhanov, Alexander Matvienko, and others; from the Kolsai Kolderi SNNP—Khamit Akhmetov, and others; from the Department of Biodiversity and bioresources at al-Farabi Kazakh National University—Meruyert Kurmanbayeva, Sandugash Mankibayeva, Ruslan Salmurzauly, and the Kuandyk Saparov who has passed away during the pandemic, and who we included as a co-author of this manuscript; from the “Ecological Education” group—Elena Udartseva; from the Wild Nature NGO—Svetlana Baskakova, from Wildlife Without Borders NGO—Dina Konysbayeva, and others.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

AUCArea Under the Curve value
CITESInternational Convention on the Limitation of Trade in Endangered Species of Flora and Fauna
CRSCoordinate Reference Systems
ENEndangered species or subspecies
GCMGlobal Climate Model
GISGeographic Information System
GPSGlobal Positioning System
HDPHabitat distribution percentage
ICIndependent captures
IPSL-CM6A-LRL’Institut Pierre-Simon Laplace Coupled Model, ver. 6, low resolution
LCLeast Concern species
MaxEntMaximum entropy modelling
MIROCModel for Interdisciplinary Research on Climate
MRI-ESMMeteorological Research Institute Earth System Model
MSManuscript
NPWCNational Parks and Wildlife Conservation Act 2029
NTNear threatened species or subspecies
OPFOccurrence Presence Factor
PAProtected natural area
SCALPConference on the Status and Conservation of the Alpine Lynx Population
SDFSpecies’ distribution forecast
SDMSpecies’ distribution model
SICIndependent captures per season, i.e., per 100 traps days
SNNPState National Nature Park
SNRState Nature Reserve
UNESCOThe United Nations Educational, Scientific and Cultural Organization
VU: DVulnerable declining species or subspecies

Appendix A

Table A1. Environmental predictors.
Table A1. Environmental predictors.
Predictor NameSourceExplanation Citation
BIO1_2_5min–BIO19_2_5min; tmin1–tmin12; tmax1–tmax12; tavg1–tavg12; srad1–srad12; prec1–prec12; WcaltitudeWorldclim (https://www.worldclim.org/data/worldclim21.html (accessed on 1 June 2022))These datasets represent most of the climate data utilized for the SDM.[105]
FAOCCFAO Geonetwork (http://www.fao.org/geonetwork/) (accessed on 1 June 2022)This predictor represents the global climate classes.
LC12asc2; VE4Geospatial Information Authority of Japan (https://www.gsi.go.jp/kankyochiri/gm_global_e.html) (accessed on 1 June 2022)These predictors represent the global land cover (LC12asc2), and the global vegetation cover (VE4).
GlobalRiversProxy2A Simple Global River Bank full Width & Depth Database (http://gaia.geosci.unc.edu/rivers/) (accessed on 1 June 2022)This predictor represents all global mid-size and large rivers.[106]
GlobalBigRivers11Global major rivers (https://www.arcgis.com/home/item.html?id=44e8358cf83a4b43bc863646cd695945) (accessed on 1 June 2022)This predictor represents all global large rivers.
GlobalCities2Global Cities (https://hub.arcgis.com/datasets/6996f03a1b364dbab4008d99380370ed_0?geometry=-65.394%2C25.931%2C73.737%2C49.818) (accessed on 1 June 2022)This predictor represents all global cities.
GlobalLakes2Global Lakes and Wetlands Database (GLWD) (http://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/es/c/1043160/) (accessed on 1 June 2022)This predictor represents all global lakes and wetlands.
GlobalSnowCoverMonthJan2021_7; FFJan2020_3; FFFeb2020_3; FFMar2020_3; FFMay2020_3, FFJun2020_3; FFJul2020_3; FFAug2020_3; FFSep2020_3; FFOct2020_3; FFNov2020_3; FFJan2021_3Global Snow Cover and Forest fires (https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD10C1_M_SNOW and https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD14A1_M_FIRE&year=2020) (accessed on 1 June 2022)These predictors mainly represent the Global snow cover in the month of January, and the forest fire information for nearly all months of 2020 with exception of April and December as these months were not available.
WorldSoil2Global Soil characteristics map (https://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/HWSD_Data.html?sb=4) (accessed on 1 June 2022)This predictor represents all global soil types and its characteristics.
WorldProtectedAreasMerged4Global Protected areas. (https://www.protectedplanet.net/en/search-areas?geo_type=region&filters%5Bis_type%5D%5B%5D=terrestrial) (accessed on 1 June 2022)This predictor represents all global protected areas merged into one shapefile.[107]
WorldMammaldensity4; WorldRodentDensity3; WorldThreatenedMammalDensity3; GlobalBirdDensity2Global Mammal density. Proximity maps for the world mammal density, world rodent density, world threatened mammal density (https://biodiversitymapping.org/index.php/mammals/) (accessed on 1 June 2022)These predictors mainly represent global biodiversity densities. In detail, they contain the world mammal density, world rodent density, world bird density, and the world’s threatened mammal density. [108,109]
GlobalRoadsProxy2Global Roads—Socioeconomic data and applications center (SEDAC)—Data center in NASA’s Earth Observatory System Data and Information System (EOSDIS) (https://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-v1/data-download) (accessed on 1 June 2022)This predictor represents the global proximity to all world’s roads. Minor roads may not be included.
HII1Human Influence Index (HII). (https://sedac.ciesin.columbia.edu/data/set/wildareas-v2-human-influence-index-geographic/data-download) (accessed on 1 June 2022)This predictor represents the global Human Influence index.
WorldSlope1Slope. (https://scholarworks.alaska.edu/handle/11122/7151) (accessed on 1 June 2022)This predictor represents the global terrestrial and aquatic slope.[94]
World_MAX_RH_JAN–World_MAX_RH_DEC; World_MIN_RH_JAN–World_MIN_RH_DECGlobal Monthly Relative Humidity. (http://palebludata.com/?q=data) (accessed on 1 June 2022)This predictor set represents the global maximum and minimum relative humidity for the months January to December of the year 2020.[110]
Figure A1. SDM on 132 environmental predictors of all data combined.
Figure A1. SDM on 132 environmental predictors of all data combined.
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Table A2. Percent contribution of 132 environmental predictors to the SDM, all data combined.
Table A2. Percent contribution of 132 environmental predictors to the SDM, all data combined.
VariablePercent ContributionPermutation Importance
WorldSoil241.944.5
WorldSlope115.80.4
World_MIN_RH_MAR7.10
World_MAX_RH_OCT5.40
World_MAX_RH_SEP4.10
WCaltitude3.65.1
srad63.10.2
WorldProtectedAreasMerged434.8
tmax21.62.3
World_MAX_RH_DEC1.50
tavg111.30
World_MIN_RH_OCT1.30
Prec091.25.5
srad81.11
tmax30.80.5
BIO1_2_5min0.70
BIO7_2_5min0.70.5
tmax120.40
tavg30.40.1
srad120.40
FAOCC10.44.1
tmax110.40.5
tavg100.40
Prec120.43
tavg10.30.3
BIO14_2_5min0.30.6
tmin110.30
tmax100.30.1
srad110.24.8
srad70.20
GlobalBigRivers110.20.7
BIO4_2_5min0.20
tmax10.10.4
World_MIN_RH_JUL0.10.2
World_MIN_RH_JAN0.10.4
World_MAX_RH_APR0.10
BIO5_2_5min0.10
FFNov2020_30.14.8
VE40.10.7
BIO2_2_5min0.10.5
GlobalLakes20.10.5
Figure A2. SDF on 7 bio-climatic environmental predictors, year 2000, all data combined.
Figure A2. SDF on 7 bio-climatic environmental predictors, year 2000, all data combined.
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Table A3. Percent contribution of 7 bio-climatic environmental predictors to the SDF, year 2000, all data combined.
Table A3. Percent contribution of 7 bio-climatic environmental predictors to the SDF, year 2000, all data combined.
VariablePercent ContributionPermutation Importance
Bio11_20004147.6
Bio17_200028.814.6
Bio7_200027.129.7
Bio16_20001.62.4
Bio10_20001.33.6
Bio12_20000.32.1
Bio1_200000
Figure A3. SDF on 7 bio-climatic environmental predictors, IPSL scenario, year 2100, all data combined.
Figure A3. SDF on 7 bio-climatic environmental predictors, IPSL scenario, year 2100, all data combined.
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Table A4. Percent contribution of 7 bio-climatic environmental predictors to the SDF, IPSL scenario, year 2100, all data combined.
Table A4. Percent contribution of 7 bio-climatic environmental predictors to the SDF, IPSL scenario, year 2100, all data combined.
VariablePercent contributionPermutation importance
Bio17_IPSL210035.814.1
Bio11_IPSL210028.631.1
Bio7_IPSL210020.724.6
Bio16_IPSL21007.32.7
Bio10_IPSL21006.92.8
Bio12_IPSL21000.41.9
Bio1_IPSL21000.322.9
Figure A4. SDF on 7 bio-climatic environmental predictors, MRI scenario, year 2100, all data combined.
Figure A4. SDF on 7 bio-climatic environmental predictors, MRI scenario, year 2100, all data combined.
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Table A5. Percent contribution of 7 bio-climatic environmental predictors to the SDF, MRI scenario, year 2100, all data combined.
Table A5. Percent contribution of 7 bio-climatic environmental predictors to the SDF, MRI scenario, year 2100, all data combined.
VariablePercent contributionPermutation importance
Bio7_MRI21003727.7
Bio11_MRI210029.141.3
Bio17_MRI210027.55.4
Bio10_MRI21002.99.5
Bio12_MRI21002.39
Bio16_MRI21001.17
Bio1_MRI210000
Figure A5. SDF on 7 bio-climatic environmental predictors, MIROC scenario, year 2100, all data combined.
Figure A5. SDF on 7 bio-climatic environmental predictors, MIROC scenario, year 2100, all data combined.
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Table A6. Percent contribution of 7 bio-climatic environmental predictors to the SDF, MIROC scenario, year 2100, all data combined.
Table A6. Percent contribution of 7 bio-climatic environmental predictors to the SDF, MIROC scenario, year 2100, all data combined.
VariablePercent contributionPermutation importance
Bio17_MIROC210027.97.6
Bio7_MIROC210024.213.7
Bio11_MIROC21002350.6
Bio1_MIROC210019.215.9
Bio12_MIROC21002.24.2
Bio10_MIROC21002.21.4
Bio16_MIROC21001.26.7

Appendix B

Figure A6. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C1 data on 132 environmental predictors created with Maxent for the Tien Shan—Alai region.
Figure A6. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C1 data on 132 environmental predictors created with Maxent for the Tien Shan—Alai region.
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Figure A7. * Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C2 data on 132 environmental predictors created with Maxent for the Tien Shan—Alai region.
Figure A7. * Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C2 data on 132 environmental predictors created with Maxent for the Tien Shan—Alai region.
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Figure A8. * Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C3 data on 132 environmental predictors created with Maxent for the Tien Shan—Alai region.
Figure A8. * Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C3 data on 132 environmental predictors created with Maxent for the Tien Shan—Alai region.
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* Authors’ note: Due to the fact that China has a different set of environmental predictors and we did not have C2 and C3 data from the Xinjiang Tien Shan, the lynx distribution did not extrapolate to China from the existing C2 and C3 data from Kazakhstan, as shown in Figure A7 and Figure A8.
Figure A9. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C1 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2000.
Figure A9. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C1 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2000.
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Figure A10. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C1 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario IPSL.
Figure A10. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C1 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario IPSL.
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Figure A11. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C1 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario MRI.
Figure A11. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C1 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario MRI.
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Figure A12. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C1 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario MIROC.
Figure A12. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C1 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario MIROC.
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Figure A13. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C2 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2000.
Figure A13. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C2 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2000.
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Figure A14. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C2 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario IPSL.
Figure A14. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C2 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario IPSL.
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Figure A15. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C2 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario MRI.
Figure A15. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C2 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario MRI.
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Figure A16. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C2 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario MIROC.
Figure A16. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C2 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario MIROC.
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Figure A17. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C3 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2000.
Figure A17. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C3 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2000.
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Figure A18. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C3 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario IPSL.
Figure A18. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C3 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario IPSL.
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Figure A19. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C3 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario MRI.
Figure A19. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C3 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario MRI.
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Figure A20. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C3 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario MIROC.
Figure A20. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on C3 data on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—scenario MIROC.
Sustainability 14 09491 g0a20

References

  1. Khan, S.M.; Page, S.; Ahmad, H.; Harper, D. Anthropogenic influences on the natural ecosystem of the Naran Valley in the western Himalayas. Pak. J. Bot. 2012, 44, 231–238. [Google Scholar]
  2. Bizhanova, N.A.; Grachev, Y.A.; Saparov, K.A.; Grachev, A.A. Rasprostranenie, chislennost’ i nekotorye osobennosti ekologii krupnykh khischnykh mlekopitayuschikh v Kazahstane: Analiticheskiy obzor [Distribution, abundance and some features of the ecology of large carnivores in Kazakhstan: Analytical review]. Eurasian J. Ecol. 2017, 3, 96–111. (In Russian) [Google Scholar] [CrossRef]
  3. Bowler, D.E.; Bjorkman, A.D.; Dornelas, M.; Myers-Smith, I.H.; Navarro, L.M.; Niamir, A.; Supp, S.R.; Waldock, C.; Winter, M.; Vellend, M.; et al. Mapping human pressures on biodiversity across the planet uncovers anthropogenic threat complexes. People Nat. 2020, 2, 380–394. [Google Scholar] [CrossRef] [Green Version]
  4. Forester, D.J.; Machlist, G.E. Modeling human factors that affect the loss of biodiversity. Conserv. Biol. 1996, 10, 1253–1263. [Google Scholar] [CrossRef]
  5. Scanes, C.G. Human activity and habitat loss: Destruction, fragmentation, and degradation. In Animals and Human Society; Scanes, C.G., Toukhsati, S.R., Eds.; Academic Press: Cambridge, MA, USA, 2018; pp. 451–482. ISBN 9780128052471. [Google Scholar] [CrossRef]
  6. Cepic, M.; Bechtold, U.; Wilfing, H. Modelling human influences on biodiversity at a global scale—A human ecology perspective. Ecol. Model. 2022, 465, 109854. [Google Scholar] [CrossRef]
  7. Tollefson, J. Humans are driving one million species to extinction. Nature 2019, 569, 171–172. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Steiner, M.; Huettmann, F. Sustainable Squirrel Conservation: A Modern Re-Assessment of Family Sciuridae; Springer Nature: Cham, Switzerland, in press.
  9. Somerville, K. Humans and Lions: Conflict, Conservation and Coexistence; Routledge: London, UK, 2019; 260p, ISBN 9781138558038. [Google Scholar]
  10. Ripari, L.; Premier, J.; Belotti, E.; Bluhm, H.; Breitenmoser-Würsten, C.; Bufka, L.; Červený, J.; Drouet-Hoguet, N.; Fuxjäger, C.; Jędrzejewski, W.; et al. Human disturbance is the most limiting factor driving habitat selection of a large carnivore throughout Continental Europe. Biol. Conserv. 2022, 266, 109446. [Google Scholar] [CrossRef]
  11. Nowell, K.; Jackson, P. Wild Cats: Status Servey and Conservation Action Plan; IUCN/SSC Cat Specialist Group: Gland, Switzerland, 1996; 421p, ISBN Z-8317-0045-0. [Google Scholar]
  12. Breitenmoser, U.; Breitenmoser-Würsten, C. Der Luchs: Ein Grossraubtier in der Kulturlandschaft [The Lynx: A Large Carnivore in the Cultural Landscape]; Salm Verlag: Wohlen/Bern, Switzerland, 2008; 537p. (In German) [Google Scholar]
  13. Kaczensky, P.; Chapron, G.; von Arx, M.; Huber, D.; Andrén, H.; Linnell, J. (Eds.) Status, Management and Distribution of Large Carnivores—Bear, Lynx, Wolf & Wolverine–in Europe; Istituto di Ecologia Applicata and IUCN/SSC Large Carnivore Initiative for Europe: Rome, Italy, 2012; 72p. [Google Scholar] [CrossRef]
  14. Melovski, D. Status and Distribution of the Balkan Lynx (Lynx lynx martinoi MIRIC, 1978) and Its Prey. Master’s Thesis, Faculty of Natural Sciences, University of Montenegro, Podgorica, Montenegro, 2012; 82p. [Google Scholar]
  15. Meldebekov, A.M. (Ed.) Animals, Part 1: Vertebrates (Collective of authors). In The Red Book of the Republic of Kazakhstan, 4th ed.; Grachev, Yu.A. Rys’ [Lynx]; DPS, Institute of Zoology: Almaty, Kazakhstan, 2010; Volume 1, pp. 254–255. 324p, ISBN 9965-32-738-6. (In Kazakh and Russian). [Google Scholar]
  16. Shukurov, E.D.; Kasybekov, E.S. (Eds.) Part 2 (Collective of authors). In The Red Data Book of the Republic of Kyrgyzstan, 2nd ed.; State Agency for Environmental Protection and Forestry under the Government of the Kyrgyz Republic, Institute of Biology and Soil of the National Academy of Sciences of the Kyrgyz Republic, Aleyne Ecological Movement of Kyrgyzstan: Bishkek, Kyrgyzstan, 2006; Mammals/Katayevskiy V.N. Lynx lynx; pp. 508–509. 544p, ISBN 9967-23-367-2. [Google Scholar]
  17. Azimov, J.A.; Lynx, E.A.V. (Eds.) Animals (Collective of authors). In The Red Data Book of the Republic of Uzbekistan; Chinor ENK, Uzbek Zoology Institute: Tashkent, Uzbekistan, 2009; Esipov A.V. Lynx; Volume 2, pp. 192–193. 218p. [Google Scholar]
  18. Annabayramov, B.; Saparmyradov, J.; Karyyeva, O.; Potayeva, A.; Atayev, K.; Kokanova, E.; Durdyev, S.; Rustamov, E.; Shammakov, S. (Eds.) Invertebrate and vertebrate animals (Collective of authors). In The Red Data Book of the Republic of Turkmenistan, 3rd ed.; Ilim: Ashgabat, Turkmenistan, 2011; Volume 2, 384p. [Google Scholar]
  19. The Red Data Book of Tajikistan: Flora and Fauna, 2nd ed; Kurbonov, S.; Toshev, A. (Eds.) Donish: Dushanbe, Tajikistan, 2015; 535p, ISBN 978-9975-45-07-7. [Google Scholar]
  20. Wang, S.; Xie, Y. (Eds.) China Species Red List; Higher Education Press: Beijing, China, 2004; Volume 1, 224p, ISBN 7-04-015584-2. [Google Scholar]
  21. Habibi, K. Mammals of Afghanistan; Zoo Outreach Organization: Coinmbatore, India, 2003; 168+ivp. [Google Scholar]
  22. Christopher, C.S. (Ed.) Biodiversity Profile of Afghanistan. An. Output of the National Capacity Needs Self-Assessment for Global Environment Management (NCSA) for Afghanistan; Asif Zaidi (Programme Manager); United Nations Environment Programme: Kabul, Afghanistan, 2008; 150p, Available online: http://hdl.handle.net/20.500.11822/33553 (accessed on 1 June 2022).
  23. Sheikh, K.M.; Molur, S. (Eds.) Status and Red List of Pakistan’s Mammals. Based on the Conservation Assessment and Management Plan; IUCN Pakistan’s Biodiversity Programme: Karachi, Pakistan, 2004; 312p. [Google Scholar]
  24. Din, J.U.; Nawaz, M.A. Status of the Himalayan lynx in district Chitral, NWFP, Pakistan. J. Anim. Plant. Sci. 2010, 20, 17–22, ISSN 1018-7081. [Google Scholar]
  25. Din, J.U.; Zimmermann, F.; Ali, M.; Ali Shah, K.; Ayub, M.; Khan, S. Population assessment of Himalayan lynx (Lynx lynx isabellinus) and conflict with humans in the Hindu Kush mountain range of District Chitral, Pakistan. J. Biodivers. Environ. Sci. (JBES) 2015, 6, 31–39. [Google Scholar]
  26. Chundawat, R.S.; Rawat, G.S. Indian Cold Deserts: A Status Report on Biodiversity; Wildlife Institute of India: Dehradun, India, 1994. [Google Scholar]
  27. Kotia, A.; Angmo, K.; Rawat, G.S. Sighting of a Eurasian lynx near Chushul Village in Ladakh, India; CATnews #54; Spring: Berlin/Heidelberg, Germany, 2011; pp. 18–19. ISSN 1027-2992. [Google Scholar]
  28. Jnawali, S.R.; Baral, H.S.; Lee, S.; Acharya, K.P.; Upadhyay, G.P.; Pandey, M.B.; Subedi, N.; Joshi, D.; Griffith, J.; Amin, R. The Status of Nepal Mammals: The National Red List Series; Department of National Parks and Wildlife Conservation: Kathmandu, Nepal, 2011; 267p, ISBN 978-0-900881-60-2. [Google Scholar]
  29. Heptner, V.G.; Sludskiy, A.A. Mlekopitayuschie Sovetskogo Soyuza: Posobie Dlya Universitetov: Uchebnoe Posobie; [Mammals of the Soviet Union: A manual for universities: Textbook]. In Carnivores (Hyenas and Cats); Vysshaya shkola [Higher school]: Moscow, Russia, 1972; 3 Volumes; Volume 2, Part 2, 553p. (In Russian) [Google Scholar]
  30. Fedosenko, A.K. Rys’ [Lynx]. In Mammals of Kazakhstan; Nauka: Almaty, Kazakhstan, 1982; Volume 3, Part 2, pp. 194–203. (In Russian) [Google Scholar]
  31. Zhiryakov, V.A. Turkestanskaya rys’ v Zailiyskom Alatau [Turkestan lynx in the Ile Alatau]. Selevinia #1 1995, 43–49, ISSN 2789-6404. [Google Scholar]
  32. Zhiryakov, V.A.; Baidavletov, R.Z. Lynx: Regional Features of Ecology, Use and Protection; Matyushkin, E.N., Vaysfeld, M.A., Eds.; Kazakhstan: Moscow, Russia, 2003; 523p, ISBN 5-02-002789-8. [Google Scholar]
  33. Shnitnikov, V.N. Mlekopitayushhie Semirech’ya [Mammals of Zhetisu]; Publishing House of the Aacademy of Science of the USSR: Moscow, Russia, 1936; 323p. (In Russian) [Google Scholar]
  34. Ognev, S.I. Mlekopitayushhie Tsentral’nogo Tyan’-Shanya (Zailiyskiy i Kungey Alatau) [Mammals of the Central Tien Shan (Ile and Kungei Alatau)]; Publishing House of the Moscow Society of Naturalists: Moscow, Russia, 1940; 86p. (In Russian) [Google Scholar]
  35. Shulpin, L.M. Materialy po mlekopitayuschim i gadam Talasskogo Alatau [Materials on mammals and reptiles of Talas Alatau]. In News of Academy of Science of Kazakh SSR; Zoology Series: Almaty, Kazakhstan, 1948; Volume 7. (In Russian) [Google Scholar]
  36. Kuznetsov, B.A. Mlekopitayuschie Kazahstana [Mammals of Kazakhstan]; Publishing House of Moscow Society of Naturalists: Moscow, Russia, 1948; Volume 2, 226p. (In Russian) [Google Scholar]
  37. Afanasyev, A.V.; Bazhanov, V.S.; Korelov, M.N.; Sludskiy, A.A.; Strautman, E.I. Zveri Kazakhstana [Animals of Kazakhstan]; Publishing House of Academy of Science of Kazakh SSR: Almaty, Kazakhstan, 1953; 530p. (In Russian) [Google Scholar]
  38. Afanasyev, A.V. Zoogeorgrafia Kazakhstana (Na osnove rasprostraneniya mlekopitayuschikh) [Zoogeography of Kazakhstan (Based on mammals’ distribution)]; Publishing House of Academy of Science of Kazakh SSR: Almaty, Kazakhstan, 1960; 261p. (In Russian) [Google Scholar]
  39. Sludskiy, A.A. Otryad khischnye [Order Carnivora]. In Animals of Kazakhstan; Afanasyev, A.V., et al., Eds.; Edition of AS of Kazakh SSR: Almaty, Kazakhstan, 1953; pp. 303–449. (In Russian) [Google Scholar]
  40. Sludskiy, A.A. Rasprostranenie i chislennost’ dikikh koshek v SSSR [Distribution and number of wild cats in the USSR]. In Commercial Mammals of Kazakhstan. Proceedings of the Institute of Zoology; Nauka KazSSR: Almaty, Kazakhstan, 1973; Volume 34, pp. 5–106. (In Russian) [Google Scholar]
  41. Fedosenko, A.K.; Zhiryakov, V.A. Vzaimootnosheniya khischnikov i dikikh kopytnykh v Severnom Tyan’-Shane i Dzhungarskom Alatau [Relations between predators and wild ungulates in the Northern Tien Shan and Dzhungar Alatau]. In Ekologicheskie osnovy okhrany i ratsional’noe ispol’zovanie khischnykh mlekopitayuschikh [Ecological Fundamentals of Protection and Rational Use of Carnivorous Mammals]; Nauka Publishing House: Moscow, Russia, 1979; pp. 72–74. (In Russian) [Google Scholar]
  42. Yanushevich, A.I.; Ayzin, B.M.; Kydyraliev, A.K.; Umrikhina, G.S.; Fedyanina, T.F.; Shukurov, E.D.; Grebenyuk, R.V.; Tokobayev, M.M. Mlekopitayuschie Kirgizii [Mammals of Kyrgyzstan]; Ilim: Frunze, Kyrgyzstan, 1972; 463p. (In Russian) [Google Scholar]
  43. Severtsov, N.A. Puteshestviya po Turkestanskomu krayu i issledovanie gornoy strany Tyan’-Shanya [Travels around the Turkestan region and exploring the mountainous country of the Tien Shan]; Committed on Behalf of the Imperial Russian Society of Geography: St.-Petersburg, Russia, 1873; 462p. (In Russian) [Google Scholar]
  44. Dementyev, D.P. Katalog kollektsii pozvonochnykh zoologicheskogo kabineta Kirgizskogo gos. muzeyya kraevedeniya [Catalog of Collections of Vertebrates of the Zoological Office of the Kyrgyz State Local History Museum]; Part I. Mammalia, Issue 2; Carnivora: Frunze, Kazan, 1940. (In Russian) [Google Scholar]
  45. Dementyev, D.P. Nekotorye dannye o rasprostranenii mlekopitayuschikh v Kirgizskoy SSR [Some data on the distribution of mammals in the Kyrgyz SSR]. In Proceedings of Kyrgyz Pedagogical Institute; Kyrgyz Pedagogical Institute: Frunze, Kyrgyzstan, 1947; Volume 2, pp. 41–49. (In Russian) [Google Scholar]
  46. Dementyev, D.P.; Tyurin, P.S. Fauna okhotnich’e-promyslovykh mlekopitayuschikh khrebta Kungey Alatau (v predelakh Kirgizskoy SSR) [The fauna of hunting and commercial mammals of the Kungei Alatau Range (within the Kyrgyz SSR)]. In Trudy Instituta zoologii i parazitologii Kirgizskoi FAN SSSR [Proceedings of Institute of zoology and parasitology of FAS of USSR]; Kyrgyz branch of the Academy of Sciences of the USSR: Frunze, Kyrgyzstan, 1954; Volume 2, pp. 131–160. (In Russian) [Google Scholar]
  47. Kuznetsov, B.A. Zveri Kirgizii [Animals of Kyrgyzstan]; Publishing House of Moscow Society of Naturalists: Moscow, Russia, 1948; 209p. (In Russian) [Google Scholar]
  48. Shukurov, E.J. Zoogeografia Kirgizstana [Zoogeorraphy of Kyrgyzstan]; Aleine Plus: Bishkek, Kyrgyzstan, 2016; 186p, ISBN 978-9967-08-602-9. (In Russian) [Google Scholar]
  49. Chelpakova, Z.M.; Davletbakov, A.T.; Kustareva, L.A. (Eds.) Zhivotnyy mir Kyrgyzstana [Fauna of Kyrgyzstan]; Al-Salam Publishing House: Bishkek, Kyrgyzstan, 2011; 264p, ISBN 978-9967-435-96-4. (In Russian) [Google Scholar]
  50. Lazkov, G.A.; Davletbakov, A.T.; Milko, D.A.; Ganybekova, M.R. (Eds.) Atlas of Flora and Fauna of the Protected Areas in Central Tien Shan (Kyrgyz Republic); United Nations Development Program in the Kyrgyz Republic: Bishkek, Kyrgyzstan, 2016; 320p, ISBN 978-9967-32-135-9. (In Russian) [Google Scholar]
  51. Koshkarev, E.P.; Vyrypayev, V.A. Izmenenija v populyatsiyakh nekotorykh redkikh i promyslovykh mlekopitayuschikh Kirgizii v poslednee desyatiletie XX v. [Changes in the populations of some rare and commercial mammals in Kyrgyzstan in the last decade of the 20th century]. Bull. Mosc. Soc. Nat. 2002, 107, 3–15. ISSN 0366-1318(In Russian) [Google Scholar]
  52. Tytar, V.; DeKastle, A. Mountain Ghosts: Protecting Snow Leopards and Other Animals of the Tien Shan Mountains of Kyrgyzstan (as Well as Studying Butterflies as Indicators of Climate Change): Report; Hammer, M., Ed.; I.I Schmalhausen Institute of Zoology of the National Academy of Sciences of Ukraine: Kyiv, Ukraine; Plateau Perspectives and American University of Central Asia, Biosphere Expeditions: Bishkek, Kyrgyzstan, 2017; 79p. [Google Scholar]
  53. Choroev, B.K.; Karipova, N.T.; Omuraliev, T.O.; Toktosunov, T.A. K bioekologii Turkestanskoi Rysi, Turkestan lynx, v Karatal-Zhapyrykskom Gosudarstvennom Zapovednike [To the bioecology of the Turkestan lynx in the Karatal-Japyryk State Reserve]. Sci. New Technol. Kyrg. 2009, 217–220. (In Russian) [Google Scholar] [CrossRef]
  54. Ishunin, G.I. Mlekopitayushchiye (khishchnyye i kopytnyye). Fauna Uzbekskoy SSR [Mammals (Carnivores and Ungulates). Fauna of UzSSR]; Publishing House of Academy of Sciences of the Uzbek SSR: Tashkent, Uzbekistan, 1961; Volume 3, 230p. (In Russian) [Google Scholar]
  55. Ishunin, G.I. Promyslovyye zhivotnyye Uzbekistana [The Game Animals of Uzbekistan]; Mehnat Publishing House: Tashkent, Uzbekistan, 1987; 238p. (In Russian) [Google Scholar]
  56. Plyaskin, V.E. Redkiye vidy koshach’ikh Chatkal’skoy doliny Zapadnogo Tyan’-Shanya” [Rare Species of Cats in the Chatkal Valley, Western Tien Shan]. In The Conference Proceedings “Okhrana i vosproizvodstvo zhivotnogo mira Uzbekistana” [Animals Protection and Restoration in Uzbekistan]; Tashkent, Uzbekistan, 1982; pp. 41–42. [Google Scholar]
  57. Mitropolsky, O.V. Bioraznoobraziye Zapadnogo Tyan’-Shanya. Materialy po izucheniyu ptits i mlekopitayushchikh v basseynakh rek Chirchik i Akhangaran (Uzbekistan, Kazakhstan) [Biodiversity of Western Tien Shan. Data on the Mammals and Birds in the Chirchik and Akhangaran Rivers Basins, Uzbekistan, Kazakhstan]; Tashkent-Bishkek, Uzbekistan, 2005; 166p. [Google Scholar]
  58. Ryabinina, M.A.; Esipov, A.V. K pitaniyu turkestanskoy rysi [On the Diet of the Turkestan Lynx]. In Ekologiya rasteniy i zhivotnykh zapovednikov Uzbekistana [Ecology of Plants and Animals in the Reserves of Uzbekistan]; FAN Publishing House: Tashkent, 1983; pp. 91–93. [Google Scholar]
  59. Lesnyak, A.P.; Ishunin, G.I.; Esipov, A.V.; Alimov, L.A. Koshki v pushnom promysle Uzbekistana [Cats in the Fur Trade of Uzbekistan]. In Okhota i okhrana prirody Uzbekistana [Hunting and Nature Protection in Uzbekistan]; Tashkent, Uzbekistan, 1984; pp. 57–64. (In Russian) [Google Scholar]
  60. Taryannikov, V.I. Rasprostraneniye, biologiya i sovremennoye sostoyaniye chislennosti redkikh khischnykh mlekopitayuschikh Zapadnogo Gissara [The Distribution, Biology and Contemporary Status of the Population of Rare Mammal Predators in the Western Part of the Gissar Range]. In Ekologiya, okhrana i akklimatizatsiya pozvonochnykh v Uzbekistane [Ecology, Protection and Acclimatisation of Vertebrates in Uzbekistan]; Tashkent, Uzbekistan, 1986; pp. 107–109. (In Russian) [Google Scholar]
  61. Esipov, A.V.; Golovtsov, D.E.; Bykova, E.A. Opyt primeneniya fotolovushek v Chatkal’skom gosudarstvennom biosfernom zapovednike (Uzbekistan) [The Experience of the Camera Trapping in Chatkal Biosphere Nature Reserve, Uzbekistan]. In The Conference Proceedings: Okruzhayuschaya sreda i menedzhment prirodnykh resursov [Environment and Natural Resource Management]; Tyumen State University: Tyumen, Russia, 2014; pp. 96–98. (In Russian) [Google Scholar]
  62. Esipov, A.V.; Golovtsov, D.E.; Bykova, E.A. Bykova, E.A. Materialy k faune mlekopitayuschikh i ptits zapadnoy chasti Chatkal’skogo khrebta po dannym fotolovushek [Materials on the Bird and Mammal Fauna in the Western Part of the Chatkal Range Based on Camera Trap Data]. Tyumen State Univ. Herald. Nat. Resour. Use Ecol. 2015, 141–150, ISSN 1562-2983. [Google Scholar]
  63. Bykova, E.A.; Esipov, A.V.; Golovtsov, D.E. K izucheniyu redkikh vidov mlekopitayuschikh Uzbekistana s pomosch’yu fotolovushek [On the Study of Rare Mammal Species in Uzbekistan with the Use of Camera Traps]. In The Conference Proceedings: Biologicheskiye i strukturno-funktsional’nyye osnovy izucheniya i sokhraneniya bioraznoobraziya Uzbekistana [The Biological, Structural and Functional Principles of Study and Conservation of Biodiversity in Uzbekistan]; Institute of the Gene Pool of Plants and Animals: Tashkent, Uzbekistan, 2015; pp. 912–996. (In Russian) [Google Scholar]
  64. Bykova, E.A.; Golovtsov, D.E.; Esipov, A.V. The Turkestan lynx in the Chatkal Range, Western Tien Shan, Uzbekistan. Tyumen State Univ. Herald. Nat. Resour. Use Ecol. 2018, 4, 92–107. [Google Scholar] [CrossRef]
  65. Bykova, E.A.; Esipov, A.V.; Ten, A.G. O rasprostranenii Turkestanskoy rysi v Uzbekistane [On the distribution of Turkestan lynx in Uzbekistan]. In The Conference Proceedings: Ecosystem Services and Natural Resource Management; VektorBuk Publishing House, Tyumen State University: Tyumen, Russia, 2020; pp. 160–165. (In Russian) [Google Scholar]
  66. Ablimit, A.; Hu, D.F.; Ai, M. Study on the ecology, distribution, resource and protection strategies of Lynx lynx in Xinjiang. Arid Zone Res. 1998, 15, 38–43. [Google Scholar]
  67. Tang, X.; Tang, S.; Li, X.; Menghe, D.; Bao, W.; Xiang, C.; Gao, F.; Bao, W. A study of population size and activity patterns and their relationship to the prey species of the Eurasian Lynx using a camera trapping approach. Animals 2019, 9, 864. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Bao, W. Eurasian lynx in China–present status and conservation challenges. Cat News Spec. 2010, 5, 22–25. [Google Scholar]
  69. Bizhanova, N.A.; Grachev, A.A.; Saparbayev, S.K.; Grachev, Y.A.; Bespalov, M. Problemy sokhraneniya Turkestanskoy rysi (Lynx lynx isabellina) v Severnom Tiyan-Shane [Issues on conservation of the Turkestan lynx (Lynx lynx isabellina) in the Northern Tien Shan]. News Natl. Acad. Sci. Repub. Kazakhstan. Biol. Med. Ser. 5–6 2021, 9–30. (In Russian) [Google Scholar] [CrossRef]
  70. Pearson, R.G. Species’ Distribution Modeling for Conservation Educators and Practitioners. Synthesis; American Museum of Natural History: New York, NY, USA, 2008; 47p, Available online: http://ncep.amnh.org (accessed on 1 May 2022).
  71. Guisan, A.; Thuiller, W. Predicting Species Distribution: Offering More than Simple Habitat Models. Ecol. Lett. 2005, 8, 993–1009. [Google Scholar] [CrossRef]
  72. Zimmermann, F. Conservation of the Eurasian Lynx (Lynx Lynx) in a Fragmented Landscape—Habitat Models, Dispersal, and Potential Distribution. Ph.D. Thesis, Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland, 2004; 179p. [Google Scholar]
  73. Hetherington, D.A.; Miller, D.R.; Macleod, C.D.; Gorman, M.L. A potential habitat network for the Eurasian lynx Lynx lynx in Scotland. Mammal. Rev. 2008, 38, 285–303. [Google Scholar] [CrossRef]
  74. Hemmingmoore, H.; Aronsson, M.; Åkesson, M.; Persson, J.; Andrén, H. Evaluating habitat suitability and connectivity for a recolonizing large carnivore. Biol. Conserv. 2020, 242, 108352. [Google Scholar] [CrossRef]
  75. Olson, L.E.; Bjornlie, N.; Hanvey, G.; Holdbrook, J.D.; Ivan, J.S.; Jackson, S.; Kertson, B.; King, T.; Lucid, M.; Murrary, D.; et al. Improved prediction of Canada lynx distribution through regional model transferability and data efficiency. Ecol. Evol. 2021, 11, 1667–1690. [Google Scholar] [CrossRef] [PubMed]
  76. Gvozdetskiy, N.A.; Mikhailov, N.I. Fizicheskaja Geografija SSSR [Physical Geography of the USSR]; State Publishing House of Geographical Literature: Moscow, Russia, 1963; 146p. (In Russian) [Google Scholar]
  77. Gvozdetskiy, N.A.; Nikolayev, V.A. Kazakhstan. Ocherk prirody [Kazakhstan. Essay on Nature]; Mysl’ Publishing House: Moscow, Russia, 1971; 258p. (In Russian) [Google Scholar]
  78. Lukyanov, O. Otchet o gornom pokhode pyatoy kategorii slozhnosti po Severnomu Tyan’-Shanyu (Kirgizskiy hrebet), sovershennom s 30 iyulya po 28 avgusta 2004 g. [Report on a Mountain Hike of the Fifth Category of Complexity in the Northern Tien Shan (Kyrgyz ridge), Made from 30 July to 28 August 2004]; Ufa, Russia, 2004. (In Russian) [Google Scholar]
  79. Vilesov, E.N.; Naumenko, A.A.; Veselova, L.K.; Aubekerov, B.Z. Fizicheskaya geografiya Kazakhstana [Physical Geography of Kazakhstan]; Naumenko, A.A., Ed.; Textbook; Kazakh University Publishing House: Almaty, Kazakhstan, 2009; pp. 142–166. ISBN 9965-30-836-5. (In Russian) [Google Scholar]
  80. Vukolov, V.N. Po Severnomu Tyan’-Shanyu: Gornye turistskie marshruty po Zailiyskomu i Kungey Alatau [In the Northern Tien Shan: Mountain Tourist Routes along the Ile and Kungei Alatau]. In Training Manual; Profizdat Publishing House: Moscow, Russia, 1991; p. 27. (In Russian) [Google Scholar]
  81. Mazirov, M.A.; Vasenev, I.I.; Ilakhun, A. Agroekologicheskaya, pochvennaya i klimaticheskaya otsenka gornoy sistemy Tyan’-Shanya [Agroecological, soil and climatic assessment of the Tien Shan mountain system]. Achiev. Sci. Technol. Agro-Ind. Complex 2013, 2, 32–34. ISSN 0235-2451(In Russian) [Google Scholar]
  82. Dimeyeva, L.A.; Ussen, K.; Sultanova, B.M.; Islamgulova, A.F.; Zverev, N.E.; Imanalinova, A.A.; Masimzhan, M.; Ablaikhanov, E.T. Fitotsenoticheskoe raznoobrazie gornykh khrebtov i mezhgornykh dolin vostochnoy chasti Severnogo Tyan’-Shanya [Phytocoenotic diversity of mountain ranges and intermountain valleys of the eastern part of the Northern Tien Shan]. In Proceedings of the XV International Scientific and Practical Conference “Problems of Botany of Southern Siberia and Mongolia”, Barnaul, Russia, 23–26 October 2016; Altai State University Publishing House: Barnaul, Russia, 2016; pp. 29–33. [Google Scholar]
  83. Liu, G.-F.; Zang, R.-G.; Guo, Z.-J.; Ayoufu, B.; Zhang, X.-P.; Cheng, K.-W.; Bai, Z.-Q. Species richness patterns of Picea schrenkiana var. tianschanica communities along an altitudinal gradient at different longitudes in Xinjiang of Northwest China. Chin. J. Appl. Ecol. 2008, 19, 1407–1413. [Google Scholar]
  84. Kelgenbaev, N.S.; Mambetov, B.T.; Bukeikhanov, A.N.; Besschetnov, V.P. Raspredelenie el’nikov po klassam zhizneustoychivosti v gornykh lesakh Severnogo Tyan’-Shanya [Distribution of spruce forests by classes of vitality in the mountain forests of the Northern Tien Shan]. Izvestia OGAU 2016, 2, 137–140. ISSN 2073-0853(In Russian) [Google Scholar]
  85. Zhiryakov, V.A. Vliyanie krupnykh khischnikov na populyatsii dikikh mlekopitayuschikh v Alma-Atinskom zapovednike [The impact of large predators on the populations of wild mammals in the Almaty Reserve]. In Ekologicheskie osnovy okhrany i racional’noe ispol’zovanie khischnykh mlekopitayuschikh [Ecological Bases of Protection and Rational Use of Carnivorous Mammals]; Nauka: Moscow, Russia, 1979; pp. 37–39. (In Russian) [Google Scholar]
  86. Vyrypayev, V.A. Voprosy strategii po otnosheniyu k nekotorym vidam khischnykh mlekopitayuschikh Issyk-Kul’skoy oblasti [Issues of strategy in relation to some species of carnivorous mammals in the Issyk-Kul region]. In Vzaimodeystvie bioticheskikh komponentov i sredy v nekotorykh ekosistemakh Tyan’-Shanya [Interaction of Biotic Components and the Environment in Some Ecosystems of the Tien Shan]; Ilim: Frunze, Kyrgyzstan, 1983; pp. 125–129. (In Russian) [Google Scholar]
  87. UNESCO. World Heritage Nomination Natural Heritage. China. Xinjiang Tianshan: Report; Ministry of Housing and Urban-Rural Development of the People’s Republic of China: Beijing, China, 2012. [Google Scholar]
  88. Dong, T.; Chu, H.; Wu, H.; Wang, Y.; Ge, Y.; Bu, L. Monitoring birds and mammals through camera traps in Mount Kalamaili Ungulate Nature Reserve, Xinjiang. Biodivers. Sci. 2014, 22, 804–807. (In Chinese) [Google Scholar] [CrossRef]
  89. Shakula, G.B.; Shakula, F.V. Mammals of Aksu-Zhabagly Reserve. Shymkent, Kazakhstan. 2022; unpublished manuscript. Manuscript ptovided by Baskakova S. and Shakula G.B. [Google Scholar]
  90. Novikov, G.A. Polevye issledovaniya po ekologii nazemnykh pozvonochnykh [Field Research on the Ecology of Terrestrial Vertebrates], 2nd ed.; Soviet Science Publishing House: Moscow, Russia, 1953; 502p. (In Russian) [Google Scholar]
  91. Breitenmoser, U.; Breitenmoser-Würsten, C.; von Arx, M.; Zimmermann, F.; Ryser, A.; Angst, A.; Molinari-Jobin, A.; Molinari, P.; Linnell, J.; Siegenthaler, A.; et al. Guidelines for the Monitoring of Lynx; KORA-Bericht: Bern, Switzerland, 2006; 31p, ISSN 1422-5123. [Google Scholar]
  92. Molinari-Jobin, A.; Molinari, P.; Breitenmoser-Würsten, C.; Wölfl, M.; Stanisa, C.; Fasel, M.; Stahl, P.; Vandel, J.-M.; Rotelli, L.; Kaczensky, P.; et al. Pan-Alpine Conservation Strategy for the Lynx. No. 130, SCALP. In Nature and Environment; Council of Europe: Strasbourg, France, 2003; ISBN 92-871-5111-3. [Google Scholar]
  93. Molinari-Jobin, A.; Kéry, M.; Marboutin, E.; Marucco, F.; Zimmermann, F.; Molinari, P.; Frick, H.; Fuxjäger, C.; Wölfl, S.; Bled, F.; et al. Mapping range dynamics from opportunistic data: Spatiotemporal modelling of the lynx distribution in the Alps over 21 years. Anim. Conserv. 2017, 21, 168–180. [Google Scholar] [CrossRef] [Green Version]
  94. Sriram, S.; Huettmann, F. A Global Model of Predicted Peregrine Falcon (Falco peregrinus) Distribution with Open Source GIS Code and 104 Open Access Layers for use by the global public. Earth Syst. Sci. Data Discuss. 2017, 1–39. [Google Scholar] [CrossRef] [Green Version]
  95. Lissovsky, A.A.; Dudov, S.V. Species-distribution modeling: Advantages and limitations of its application. 2. MaxEnt. Biol. Bull. Rev. 2021, 11, 265–275. [Google Scholar] [CrossRef]
  96. Tatebe, H.; Ogura, T.; Nitta, T.; Komuro, Y.; Ogochi, K.; Takemura, T.; Sudo, K.; Sekiguchi, M.; Abe, M.; Saito, F.; et al. Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geosci. Model. Dev. 2019, 12, 2727–2765. [Google Scholar] [CrossRef] [Green Version]
  97. Yukimoto, S.; Kawai, H.; Koshiro, T.; Oshima, N.; Yoshida, K.; Urakawa, S.; Tsujino, H.; Deushi, M.; Tanaka, Y.; Hosaka, M.; et al. The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2.0: Description and basic evaluation of the physical component. J. Meteorol. Soc. Jpn. Ser. II 2019, 97, 931–965. [Google Scholar] [CrossRef] [Green Version]
  98. Boucher, O.; Servonnat, J.; Albright, A.L.; Aumont, O.; Balkanski, Y.; Bastrikov, V.; Bekki, S.; Bonnet, R.; Bony, S.; Bopp, L.; et al. Presentation and evaluation of the IPSL-CM6A-LR climate model. J. Adv. Modeling Earth Syst. 2020, 12, e2019MS002010. [Google Scholar] [CrossRef]
  99. Elith, J.; Phillips, S.J.; Hastie, T.; Dudík, M.; Chee, Y.E.; Yates, C.J. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 2011, 17, 43–57. [Google Scholar] [CrossRef]
  100. Zimmermann, F.; Molinari-Jobin, A.; Weber, J.-M.; Capt, S.; Ryser, A.; Angst, C.; Siegenthaler, A.; von Wattenwyl, K.; Breitenmoser-Würsten, C.; Breitenmoser, U. Monitoring Bericht Raubtiere Schweiz 2004; KORA Bericht Nr. 29; Kora: Bern, Switzerland, 2005; 60p. [Google Scholar]
  101. Fležar, U.; Pičulin, A.; Bartol, M.; Černe, R.; Stergar, M.; Krofel, M. Eurasian lynx (Lynx lynx) monitoring with camera traps in Slovenia in 2018-2019. In Research Report; Biotehniška fakulteta, Zavod za gozdove Slovenije: Ljubljana, Slovenia, 2019; 20p. [Google Scholar]
  102. Kitchener, A.C.; Breitenmoser-Würsten, C.; Eizirik, E.; Gentry, A.; Werdelin, L.; Wilting, A.; Yamaguchi, N.; Abramov, A.V.; Christiansen, P.; Driscoll, C.; et al. A revised taxonomy of the Felidae. The final report of the Cat Classification Task Force of the IUCN/SSC Cat Specialist Group. Cat News Spec. Issue 2017, 11, 80. [Google Scholar]
  103. Breitenmoser, U.; Breitenmoser-Würsten, C.; Lanz, T.; von Arx, M.; Antonevich, A.; Bao, W.; Avgan, B. Lynx lynx (errata version published in 2017). In The IUCN Red List of Threatened Species 2015; 2017; Available online: https://www.iucnredlist.org/species/12519/121707666 (accessed on 1 May 2022).
  104. Kayumov, A.; Novikov, V. (Eds.) The Third National Communication of the Republic of Tajikistan under the UN Framework Convention on Climate Change; The Government of the Republic of Tajikistan: Dushanbe, Tajikistan, 2014; 167p. (In Russian) [Google Scholar]
  105. Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
  106. Andreadis, K.; Schumann, G.; Pavelsky, T. A simple global river bankfull width and depth database. Water Resour. Res. 2013, 49, 7164–7168. [Google Scholar] [CrossRef]
  107. UNEP-WCMC and IUCN. Protected Planet: The World Database on Protected Areas (WDPA) [Online Report]; UNEP-WCMC and IUCN: Cambridge, UK, 2020; Available online: https://livereport.protectedplanet.net/ (accessed on 1 June 2022).
  108. Jenkins, C.N.; Pimm, S.L.; Joppa, L.N. Global Patterns of Terrestrial Vertebrate Diversity and Conservation. Proc. Natl. Acad. Sci. USA 2013, 110, E2602–E2610. [Google Scholar] [CrossRef] [Green Version]
  109. Pimm, S.L.; Jenkins, C.N.; Abell, R.; Brooks, T.M.; Gittleman, J.L.; Joppa, L.N.; Raven, P.H.; Roberts, C.M.; Sexton, J.O. The biodiversity of species and their rates of extinction, distribution, and protection. Science 2014, 344, 1246752. [Google Scholar] [CrossRef] [PubMed]
  110. Jones, P.; Wint, W. European Union’s Seventh Framework Programme for Research, Technological Development and Demonstration under; Data set produced by Waen Associates for Environmental Research Group Oxford, Limited, funded by the International Research Consortium on Dengue Risk Assessment, Management and Surveillance (IDAMS); Waen Associates for Environmental Research Group Oxford, Limited: Oxford, UK, 2015. [Google Scholar]
Figure 1. Study area—northwestern range of Turkestan lynx distribution.
Figure 1. Study area—northwestern range of Turkestan lynx distribution.
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Figure 2. Locations of camera traps in the study area, 2013–2022.
Figure 2. Locations of camera traps in the study area, 2013–2022.
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Figure 3. The Turkestan lynx (Lynx lynx isabellina Blyth, 1847) occurrences in the study area according to our and survey observations data, 2013–2022.
Figure 3. The Turkestan lynx (Lynx lynx isabellina Blyth, 1847) occurrences in the study area according to our and survey observations data, 2013–2022.
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Figure 4. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on 132 environmental predictors created with Maxent for the Tien Shan—Alai region.
Figure 4. Turkestan lynx (Lynx lynx isabellina Blyth, 1847) SDM based on 132 environmental predictors created with Maxent for the Tien Shan—Alai region.
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Figure 5. Turkestan lynx SDM based on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2000.
Figure 5. Turkestan lynx SDM based on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2000.
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Figure 6. Turkestan lynx SDM based on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—IPSL.
Figure 6. Turkestan lynx SDM based on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—IPSL.
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Figure 7. Turkestan lynx SDM based on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—MRI.
Figure 7. Turkestan lynx SDM based on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—MRI.
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Figure 8. Turkestan lynx SDM based on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—MIROC.
Figure 8. Turkestan lynx SDM based on 7 bio-climatic environmental predictors created with Maxent for the Tien Shan—Pamir-Alai region for the year 2100—MIROC.
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Table 1. The lynx observations in the study area (according to the SCALP categories) from 2013 to 2022.
Table 1. The lynx observations in the study area (according to the SCALP categories) from 2013 to 2022.
RidgeOccurrenceTOTAL
C1 (n)C2 (n)C3 (n)
Northern Tien Shan12212736285
Western Tien Shan31181362
Zhetisu Alatau92112
TOTAL16214750359
Table 2. Lynx habitat distribution percentage by country.
Table 2. Lynx habitat distribution percentage by country.
Turkestan LynxRegionCountryAll Data Merged 132 Predictors Contribution PercentageSDF Current Contribution PercentageSDF IPSL Sum Contribution PercentageSDF MRI Sum Contribution PercentageSDF MIROC Sum Contribution Percentage
Central AsiaKazakhstan28.55.633.666.84.85
Kyrgyzstan21.711.210.731.11.36
China21.289.496.584.18.23
Uzbekistan2.160.370.210.250.28
Tajikistan3.330.840.420.540.64
Turkmenistan0.160.070.020.050.04
Afghanistan0.030.770.490.350.53
South AsiaIndia0.090.40.080.170.35
Pakistan0.080.740.210.250.67
Nepal0.040.03000.01
Bhutan0.010000
Countries with other lynx species and subspecies19.5980.4587.686.3983.04
Table 3. The movement trends of lynx according to the three global climate model scenarios.
Table 3. The movement trends of lynx according to the three global climate model scenarios.
ScenarioMove NorthwardsMove SouthwardsMove towards Higher AltitudesMove towards Lower AltitudesPredicted Distribution Range DecreasedPredicted Distribution Range Increased
MIROCNO
(it centralizes)
NO
(it centralizes)
YESYES
(especially in the southern range)
YESYES
(only around the Northern Tien Shan Mountain range)
MRIYESNOYESYES
(but not in mountainous regions towards central Kazakhstan)
YES
(in southern areas)
YES
(in northern areas)
IPSLNOYESYES
(in southern areas)
YES
(in northern areas)
YES
(in northern areas)
YES
(in southern areas)
Table 4. Independent captures (IC) and index of the average abundance per 100 camera trap days (IC per season—SIC) of the lynx and its main prey species in the study area, 2013–2022.
Table 4. Independent captures (IC) and index of the average abundance per 100 camera trap days (IC per season—SIC) of the lynx and its main prey species in the study area, 2013–2022.
RegionTrap DaysSpecies
Turkestan LynxTolai HareRoe DeerSiberian IbexRed Tree SquirrelWild BoarRed Deer
ICSICICSICICSICICSICICSICICSICICSIC
Northern Tien Shan11,479670.581050.912602.265394.691010.881621.412281.98
Western Tien Shan35310.28--30.857521.24--257.0892.55
Zhetisu Alatau18263.29----4021.97------
TOTAL12,014740.611050.872632.196545.441010.841871.552371.97
Table 5. Lynx habitat distribution percentage (HDP) between the PAs in the south-east of Kazakhstan and Occurrence Presence Factor (OPF) within all of Kazakhstan (X-fold increase in lynx presence compared to the rest of the country).
Table 5. Lynx habitat distribution percentage (HDP) between the PAs in the south-east of Kazakhstan and Occurrence Presence Factor (OPF) within all of Kazakhstan (X-fold increase in lynx presence compared to the rest of the country).
RegionsProtected AreasSDM 132 PredictorsSDF CurrentSDF IPSLSDF MRISDF MIROC
HDPOPFHDPOPFHDPOPFHDPOPFHDPOPF
Northern Tien ShanIle-Alatau SNNP15.1836.3819.9415.3123.2513.3918.558.6319.1116.3
Almaty SNR4.5715.655.896.666.335.444.793.106.428.33
Kolsai Kolderi SNNP12.9019.9817.448.9120.077.8016.554.8417.2610.12
Sharyn SNNP7.9721.202.922.562.811.883.271.642.472.49
Western Tien ShanAksu-Zhabagly SNR8.2616.917.835.2910.915.6012.404.8010.137.85
Sairam-Ugam SNNP9.6318.8713.588.7818.359.0215.605.7813.6510.13
Karatau SNR0.423.051.794.261.162.091.992.710.842.29
Zhetisu AlatauAltyn Emel SNNP12.5218.974.612.304.401.677.502.153.612.07
Zhongar Alatau SNNP28.5418.0425.995.4212.722.0219.362.3126.506.35
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Bizhanova, N.; Steiner, M.; Rametov, N.; Grachev, A.; Grachev, Y.; Bespalov, M.; Zhaparkulov, T.; Saparbayev, S.; Sailaukhanuly, A.; Bespalov, S.; et al. The Elusive Turkestan Lynx at the Northwestern Edge of Geographic Range: Current Suitable Habitats and Distribution Forecast in the Climate Change. Sustainability 2022, 14, 9491. https://doi.org/10.3390/su14159491

AMA Style

Bizhanova N, Steiner M, Rametov N, Grachev A, Grachev Y, Bespalov M, Zhaparkulov T, Saparbayev S, Sailaukhanuly A, Bespalov S, et al. The Elusive Turkestan Lynx at the Northwestern Edge of Geographic Range: Current Suitable Habitats and Distribution Forecast in the Climate Change. Sustainability. 2022; 14(15):9491. https://doi.org/10.3390/su14159491

Chicago/Turabian Style

Bizhanova, Nazerke, Moriz Steiner, Nurkuisa Rametov, Alexey Grachev, Yuri Grachev, Maxim Bespalov, Tungyshbek Zhaparkulov, Saltore Saparbayev, Amanbol Sailaukhanuly, Sergey Bespalov, and et al. 2022. "The Elusive Turkestan Lynx at the Northwestern Edge of Geographic Range: Current Suitable Habitats and Distribution Forecast in the Climate Change" Sustainability 14, no. 15: 9491. https://doi.org/10.3390/su14159491

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