DOI: 10.5061/dryad.p5hqbzkx3
title: Data from: Differential vulnerability of key threatened mammals to climate and land cover changes in the Central Himalayas

description: Anthropogenic climate change affects biological diversity by altering
 their suitable habitat ranges. The Himalayan region is one of the
 world's most sensitive biodiversity hotspots to global climate
 change. The Chitwan–Annapurna Landscape (CHAL) in the Central Himalayas
 serves as a vital north‒south linkage among the protected areas in central
 Nepal, and provides suitable habitats for threatened mammals in different
 ecological zones, such as snow leopards (in the alpine zone), Himalayan
 red panda (in the temperate zone), and one-horned rhinoceros (in the
 lowland tropical zone). The biodiversity of CHAL is threatened by climate
 change and land use alterations. This study assessed the potential impacts
 of climate and land cover changes on the above three key threatened
 mammals in CHAL by employing maximum entropy (MaxEnt) modeling to predict
 the current potential habitat and project it for future climate change
 scenarios under different greenhouse gas concentrations. Further, we used
 the cellular automata and Markov-Chain models to simulate and predict the
 temporal and spatial changes in land cover of CHAL. Our results indicate
 that the snow leopard and Himalayan red panda will experience significant
 vulnerability than the one-horned rhinoceros in all future climate
 scenarios. Approximately 36.3% and 41.8% of the suitable habitat of the
 snow leopard and 32.5% and 56% of the Himalayan red panda in CHAL are
 projected to be lost in 2050 and 2070, respectively, under RCP6.0. Climate
 refugia, representing areas of suitable habitat for 2070 (under the
 RCP6.0) in CHAL, are projected to cover 958 km2 (80.37% of the current
 range), 1,052 km2 (43.73% of the current range), and 2,375 km2 (58.21% of
 the current range) for one-horned rhinoceros, Himalayan red panda and snow
 leopard, respectively. Among the land cover attributes in CHAL, snow cover
 is predicted to decrease by 24% in 2070. Our findings indicate that
 species inhabiting alpine and temperate environments are more susceptible
 to human-induced climate change than those inhabiting lowland tropical
 areas. These findings will help to implement the adaptation actions that
 are crucial to address future conservation challenges arising from climate
 and land cover change.
description: Occurrence records of the snow leopard, one-horned rhinoceros and
 Himalayan red panda were collected from the published literature, grey
 literature, national survey database, and field surveys (December 2019)
 (Ale et al., 2014; Bista et al.2016, Bista et al., 2017; Chetri et
 al.2019; Thapa et al. 2018b; Thapa and Puri 2019; DNPWC, 2009; DNPWC
 2020).
description: # Differential vulnerability of key threatened mammals to climate and land
 cover changes in the Central Himalayas
 [https://doi.org/10.5061/dryad.p5hqbzkx3](https://doi.org/10.5061/dryad.p5hqbzkx3) ## Description of the data and file structure Data set consists species name (sp_name), ecological zone (sp_bio_zone_elv), bioclimatic (ext_bio_1 to ext_bio_19), and topographic (ext_elv and ext_slp) first row. The data set included topographic and bioclimatic data for the three species: Snow leopard (Panthera_unica), Himalayan red panda (Ailurus_fulgens), and One-horned rhinoceros (Rhinoceros_unicornis) in the first column (sp_name).  Second column consists of ecological zones (sp_bio_zone_elv), which includes name of the ecological zone and their elevational range; alpine ecological zone (alpine_zone(3000m-5500m)), temperate ecological zone (temperate_zone(2100m-3800m)), and tropical ecological zone (tropical_zone(110m-850m). Temperature variables are in column from ext_bio_1 to ext_bio_11 that included ext_bio_1 =  Bio1 – Annual Mean Temperature: °C (Degrees Celsius); ext_bio_2= Bio2 – Mean Diurnal Range (Mean of monthly [max temp - min temp]): °C (Degrees Celsius);  ext_bio_3 = Bio3 – Isothermality (Bio2/Bio7): Dimensionless; ext_bio_4 = Bio4 – Temperature Seasonality (Standard Deviation * 100): °C (Degrees Celsius) ; ext_bio_5= Bio5 – Max Temperature of Warmest Month: °C (Degrees Celsius) ; ext_bio_6 = Bio6 – Min Temperature of Coldest Month: °C (Degrees Celsius) ; ext_bio_7 = Bio7 – Temperature Annual Range (Bio5 - Bio6): °C (Degrees Celsius) ; ext_bio_8 = Bio8 – Mean Temperature of Wettest Quarter: °C (Degrees Celsius);  ext_bio_9 = Bio9 – Mean Temperature of Driest Quarter: °C (Degrees Celsius) ; ext_bio_10 = Bio10 – Mean Temperature of Warmest Quarter: °C (Degrees Celsius) ; ext_bio_11 = Bio11 – Mean Temperature of Coldest Quarter: °C (Degrees Celsius); Precipitation variables from ext_bio_11 to bio_ext_19 which included ext_bio_1 = Bio11 Bio12 – Annual Precipitation: mm/year (Millimeters per year); ext_bio_13 = Bio13 – Precipitation of Wettest Month: mm/month (Millimeters per month); ext_bio_14 = Bio14 – Precipitation of Driest Month: mm/month (Millimeters per month); ext_bio_15 = Bio15 – Precipitation Seasonality (Coefficient of Variation): Dimensionless; ext_bio_16 = Bio16 – Precipitation of Wettest Quarter: mm/quarter (Millimeters per quarter); ext_bio_17 = Bio17 – Precipitation of Driest Quarter: mm/quarter (Millimeters per quarter); ext_bio_18 = Bio18 – Precipitation of Warmest Quarter: mm/quarter (Millimeters per quarter); ext_bio_19 = Bio19 – Precipitation of Coldest Quarter: mm/quarter (Millimeters per quarter). Topographic variables included elevation in meters (ext_elv) and slope in degrees (ext_slp) **Sharing/Access information** Due to high risk of illegal poaching affecting these species, their occurrence data have not been included for open access. However, these data can be obtained from corresponding author upon, with a description of their intened purpose (Author :Arjun Thapa ; email : thapa.nature@gmail.com).