The Elusive Turkestan Lynx at the Northwestern Edge of Geographic Range: Current Suitable Habitats and Distribution Forecast in the Climate Change
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
- 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′).
2.2. Occurrence Data
2.2.1. Records Search and Analysis
2.2.2. Field Data Collection
2.2.3. Camera Trapping
2.2.4. Data Classification Methodology
2.3. Environmental Predictors
2.3.1. Current Environmental Predictors
2.3.2. Projected (Future) Climatic Predictors
2.4. MaxEnt
2.5. Habitat Distribution Percentage
3. Results
3.1. GPS Mapping Results
3.1.1. Species Distribution Models (SDMs)
3.1.2. Species Distribution Forecasts (SDFs)
3.2. Habitat Suitability
4. Discussion
4.1. Data Classification
4.2. Notes on the Turkestan Lynx’s Taxonomic Status
4.3. Analysis of Present Distribution
4.4. Analysis of Predicted Movements (SDF)
4.5. Habitat Suitability, Protected Areas and Conservation
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AUC | Area Under the Curve value |
CITES | International Convention on the Limitation of Trade in Endangered Species of Flora and Fauna |
CRS | Coordinate Reference Systems |
EN | Endangered species or subspecies |
GCM | Global Climate Model |
GIS | Geographic Information System |
GPS | Global Positioning System |
HDP | Habitat distribution percentage |
IC | Independent captures |
IPSL-CM6A-LR | L’Institut Pierre-Simon Laplace Coupled Model, ver. 6, low resolution |
LC | Least Concern species |
MaxEnt | Maximum entropy modelling |
MIROC | Model for Interdisciplinary Research on Climate |
MRI-ESM | Meteorological Research Institute Earth System Model |
MS | Manuscript |
NPWC | National Parks and Wildlife Conservation Act 2029 |
NT | Near threatened species or subspecies |
OPF | Occurrence Presence Factor |
PA | Protected natural area |
SCALP | Conference on the Status and Conservation of the Alpine Lynx Population |
SDF | Species’ distribution forecast |
SDM | Species’ distribution model |
SIC | Independent captures per season, i.e., per 100 traps days |
SNNP | State National Nature Park |
SNR | State Nature Reserve |
UNESCO | The United Nations Educational, Scientific and Cultural Organization |
VU: D | Vulnerable declining species or subspecies |
Appendix A
Predictor Name | Source | Explanation | Citation |
---|---|---|---|
BIO1_2_5min–BIO19_2_5min; tmin1–tmin12; tmax1–tmax12; tavg1–tavg12; srad1–srad12; prec1–prec12; Wcaltitude | Worldclim (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] |
FAOCC | FAO Geonetwork (http://www.fao.org/geonetwork/) (accessed on 1 June 2022) | This predictor represents the global climate classes. | |
LC12asc2; VE4 | Geospatial 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). | |
GlobalRiversProxy2 | A 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] |
GlobalBigRivers11 | Global major rivers (https://www.arcgis.com/home/item.html?id=44e8358cf83a4b43bc863646cd695945) (accessed on 1 June 2022) | This predictor represents all global large rivers. | |
GlobalCities2 | Global 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. | |
GlobalLakes2 | Global 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_3 | Global 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. | |
WorldSoil2 | Global 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. | |
WorldProtectedAreasMerged4 | Global 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; GlobalBirdDensity2 | Global 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] |
GlobalRoadsProxy2 | Global 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. | |
HII1 | Human 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. | |
WorldSlope1 | Slope. (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_DEC | Global 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] |
Variable | Percent Contribution | Permutation Importance |
---|---|---|
WorldSoil2 | 41.9 | 44.5 |
WorldSlope1 | 15.8 | 0.4 |
World_MIN_RH_MAR | 7.1 | 0 |
World_MAX_RH_OCT | 5.4 | 0 |
World_MAX_RH_SEP | 4.1 | 0 |
WCaltitude | 3.6 | 5.1 |
srad6 | 3.1 | 0.2 |
WorldProtectedAreasMerged4 | 3 | 4.8 |
tmax2 | 1.6 | 2.3 |
World_MAX_RH_DEC | 1.5 | 0 |
tavg11 | 1.3 | 0 |
World_MIN_RH_OCT | 1.3 | 0 |
Prec09 | 1.2 | 5.5 |
srad8 | 1.1 | 1 |
tmax3 | 0.8 | 0.5 |
BIO1_2_5min | 0.7 | 0 |
BIO7_2_5min | 0.7 | 0.5 |
tmax12 | 0.4 | 0 |
tavg3 | 0.4 | 0.1 |
srad12 | 0.4 | 0 |
FAOCC1 | 0.4 | 4.1 |
tmax11 | 0.4 | 0.5 |
tavg10 | 0.4 | 0 |
Prec12 | 0.4 | 3 |
tavg1 | 0.3 | 0.3 |
BIO14_2_5min | 0.3 | 0.6 |
tmin11 | 0.3 | 0 |
tmax10 | 0.3 | 0.1 |
srad11 | 0.2 | 4.8 |
srad7 | 0.2 | 0 |
GlobalBigRivers11 | 0.2 | 0.7 |
BIO4_2_5min | 0.2 | 0 |
tmax1 | 0.1 | 0.4 |
World_MIN_RH_JUL | 0.1 | 0.2 |
World_MIN_RH_JAN | 0.1 | 0.4 |
World_MAX_RH_APR | 0.1 | 0 |
BIO5_2_5min | 0.1 | 0 |
FFNov2020_3 | 0.1 | 4.8 |
VE4 | 0.1 | 0.7 |
BIO2_2_5min | 0.1 | 0.5 |
GlobalLakes2 | 0.1 | 0.5 |
Variable | Percent Contribution | Permutation Importance |
---|---|---|
Bio11_2000 | 41 | 47.6 |
Bio17_2000 | 28.8 | 14.6 |
Bio7_2000 | 27.1 | 29.7 |
Bio16_2000 | 1.6 | 2.4 |
Bio10_2000 | 1.3 | 3.6 |
Bio12_2000 | 0.3 | 2.1 |
Bio1_2000 | 0 | 0 |
Variable | Percent contribution | Permutation importance |
---|---|---|
Bio17_IPSL2100 | 35.8 | 14.1 |
Bio11_IPSL2100 | 28.6 | 31.1 |
Bio7_IPSL2100 | 20.7 | 24.6 |
Bio16_IPSL2100 | 7.3 | 2.7 |
Bio10_IPSL2100 | 6.9 | 2.8 |
Bio12_IPSL2100 | 0.4 | 1.9 |
Bio1_IPSL2100 | 0.3 | 22.9 |
Variable | Percent contribution | Permutation importance |
---|---|---|
Bio7_MRI2100 | 37 | 27.7 |
Bio11_MRI2100 | 29.1 | 41.3 |
Bio17_MRI2100 | 27.5 | 5.4 |
Bio10_MRI2100 | 2.9 | 9.5 |
Bio12_MRI2100 | 2.3 | 9 |
Bio16_MRI2100 | 1.1 | 7 |
Bio1_MRI2100 | 0 | 0 |
Variable | Percent contribution | Permutation importance |
---|---|---|
Bio17_MIROC2100 | 27.9 | 7.6 |
Bio7_MIROC2100 | 24.2 | 13.7 |
Bio11_MIROC2100 | 23 | 50.6 |
Bio1_MIROC2100 | 19.2 | 15.9 |
Bio12_MIROC2100 | 2.2 | 4.2 |
Bio10_MIROC2100 | 2.2 | 1.4 |
Bio16_MIROC2100 | 1.2 | 6.7 |
Appendix B
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Ridge | Occurrence | TOTAL | ||
---|---|---|---|---|
C1 (n) | C2 (n) | C3 (n) | ||
Northern Tien Shan | 122 | 127 | 36 | 285 |
Western Tien Shan | 31 | 18 | 13 | 62 |
Zhetisu Alatau | 9 | 2 | 1 | 12 |
TOTAL | 162 | 147 | 50 | 359 |
Turkestan Lynx | Region | Country | All Data Merged 132 Predictors Contribution Percentage | SDF Current Contribution Percentage | SDF IPSL Sum Contribution Percentage | SDF MRI Sum Contribution Percentage | SDF MIROC Sum Contribution Percentage |
Central Asia | Kazakhstan | 28.5 | 5.63 | 3.66 | 6.8 | 4.85 | |
Kyrgyzstan | 21.71 | 1.21 | 0.73 | 1.1 | 1.36 | ||
China | 21.28 | 9.49 | 6.58 | 4.1 | 8.23 | ||
Uzbekistan | 2.16 | 0.37 | 0.21 | 0.25 | 0.28 | ||
Tajikistan | 3.33 | 0.84 | 0.42 | 0.54 | 0.64 | ||
Turkmenistan | 0.16 | 0.07 | 0.02 | 0.05 | 0.04 | ||
Afghanistan | 0.03 | 0.77 | 0.49 | 0.35 | 0.53 | ||
South Asia | India | 0.09 | 0.4 | 0.08 | 0.17 | 0.35 | |
Pakistan | 0.08 | 0.74 | 0.21 | 0.25 | 0.67 | ||
Nepal | 0.04 | 0.03 | 0 | 0 | 0.01 | ||
Bhutan | 0.01 | 0 | 0 | 0 | 0 | ||
Countries with other lynx species and subspecies | 19.59 | 80.45 | 87.6 | 86.39 | 83.04 |
Scenario | Move Northwards | Move Southwards | Move towards Higher Altitudes | Move towards Lower Altitudes | Predicted Distribution Range Decreased | Predicted Distribution Range Increased |
---|---|---|---|---|---|---|
MIROC | NO (it centralizes) | NO (it centralizes) | YES | YES (especially in the southern range) | YES | YES (only around the Northern Tien Shan Mountain range) |
MRI | YES | NO | YES | YES (but not in mountainous regions towards central Kazakhstan) | YES (in southern areas) | YES (in northern areas) |
IPSL | NO | YES | YES (in southern areas) | YES (in northern areas) | YES (in northern areas) | YES (in southern areas) |
Region | Trap Days | Species | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Turkestan Lynx | Tolai Hare | Roe Deer | Siberian Ibex | Red Tree Squirrel | Wild Boar | Red Deer | |||||||||
IC | SIC | IC | SIC | IC | SIC | IC | SIC | IC | SIC | IC | SIC | IC | SIC | ||
Northern Tien Shan | 11,479 | 67 | 0.58 | 105 | 0.91 | 260 | 2.26 | 539 | 4.69 | 101 | 0.88 | 162 | 1.41 | 228 | 1.98 |
Western Tien Shan | 353 | 1 | 0.28 | - | - | 3 | 0.85 | 75 | 21.24 | - | - | 25 | 7.08 | 9 | 2.55 |
Zhetisu Alatau | 182 | 6 | 3.29 | - | - | - | - | 40 | 21.97 | - | - | - | - | - | - |
TOTAL | 12,014 | 74 | 0.61 | 105 | 0.87 | 263 | 2.19 | 654 | 5.44 | 101 | 0.84 | 187 | 1.55 | 237 | 1.97 |
Regions | Protected Areas | SDM 132 Predictors | SDF Current | SDF IPSL | SDF MRI | SDF MIROC | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
HDP | OPF | HDP | OPF | HDP | OPF | HDP | OPF | HDP | OPF | ||
Northern Tien Shan | Ile-Alatau SNNP | 15.18 | 36.38 | 19.94 | 15.31 | 23.25 | 13.39 | 18.55 | 8.63 | 19.11 | 16.3 |
Almaty SNR | 4.57 | 15.65 | 5.89 | 6.66 | 6.33 | 5.44 | 4.79 | 3.10 | 6.42 | 8.33 | |
Kolsai Kolderi SNNP | 12.90 | 19.98 | 17.44 | 8.91 | 20.07 | 7.80 | 16.55 | 4.84 | 17.26 | 10.12 | |
Sharyn SNNP | 7.97 | 21.20 | 2.92 | 2.56 | 2.81 | 1.88 | 3.27 | 1.64 | 2.47 | 2.49 | |
Western Tien Shan | Aksu-Zhabagly SNR | 8.26 | 16.91 | 7.83 | 5.29 | 10.91 | 5.60 | 12.40 | 4.80 | 10.13 | 7.85 |
Sairam-Ugam SNNP | 9.63 | 18.87 | 13.58 | 8.78 | 18.35 | 9.02 | 15.60 | 5.78 | 13.65 | 10.13 | |
Karatau SNR | 0.42 | 3.05 | 1.79 | 4.26 | 1.16 | 2.09 | 1.99 | 2.71 | 0.84 | 2.29 | |
Zhetisu Alatau | Altyn Emel SNNP | 12.52 | 18.97 | 4.61 | 2.30 | 4.40 | 1.67 | 7.50 | 2.15 | 3.61 | 2.07 |
Zhongar Alatau SNNP | 28.54 | 18.04 | 25.99 | 5.42 | 12.72 | 2.02 | 19.36 | 2.31 | 26.50 | 6.35 |
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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
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 StyleBizhanova, 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