DOI: 10.5061/dryad.cjsxksngd
title: Data from: Red pandas on the move: Weather and disturbance effects on habitat specialists

description: Challenging weather events can make winters harsh for habitat and diet
 specialists. They may also incur high energetic costs due to reduced
 availability of food resources and elevated predation risk. Using GPS
 satellite collars we tracked the movement of the red panda Ailurus fulgens
 in the Himalayas, and evaluated the effects of weather and disturbances on
 their movement patterns and habitat use. We also analyzed the nutritional
 content of their key diet plant species. The mean daily distance travelled
 by red pandas was 748 ± 40 m (median 573 m), with no detectable effect of
 weather conditions and snow age. However, males travelled further than
 females when there was snow on the ground (β = 410.5, p &lt; 0.02).
 Red pandas moved between 2,528 and 3,250 m during the study period with
 the mean elevation 2,857 ± 107 m when snow was on the ground and 2,816 ±
 99 m without snow. A group of disturbances such as distance to
 settlements, herding stations, and roads, and geo-physical variables
 affected their habitat use when the forest was covered with snow as they
 occupied areas away from human settlements (β = 0.36, p = 0.03), exhibited
 affinity for high elevations (β = 0.37, p = 0.02), and avoided steep slope
 (β = -0.21, p = 0.04). These movement patterns suggest a risk aversion
 strategy with males' behaviour appearing to be driven by reproductive
 instincts. Additionally, the distribution of their major dietary
 vegetation varied across the elevation gradient, leading to differences in
 the nutritional content of the diet, which might also have some effects on
 habitat use. Overall we found that despite red pandas exhibiting risk
 aversion behaviour, challenging weather events like snowfall could
 exacerbate the adverse effects on these habitat specialists.
description: Please refer to the materials and methods section of the article
 for the details.  
description: # Red pandas on the move: weather and disturbance effects on habitat
 specialists
 [https://doi.org/10.5061/dryad.cjsxksngd](https://doi.org/10.5061/dryad.cjsxksngd) ## Description of the data and file structure This is based on GPS telemetry of 10 red pandas which were tracked for four months (1 December 2019  ̶  31 March 2020). The weather data, except temperature, was collected in the field, while temperature was recorded by in-built sensors in the GPS collars. Geo-physical and disturbance variables were retrieved using the ArcMap. The details of the methodology used for data collection are presented in the article. ### Files and variables #### File: Data.xlsx **Description:** Data is presented in two sheets: Data_*DistanceVsVariables and Data*_SnowCoverVsVariables. ##### Variables Data_*DistanceVsVariables *sheet contains eight columns (variables) and 218 rows, with each row representing data collected for an individual red panda over a 24 hour. * Animal_id: This includes the id of 10 study animals. * Sex: Male, Female * Age: Adult, Subadult * Distance: Daily travel distance (m) covered by an individual in 24 hours * Precipitation: Clear, Snowfall, Rainfall * Snow_cover: Yes, No * Snow_age: Fresh, Old, No_snow * Temperature: Mean daily temperature (oC) *Data*_SnowCoverVsVariablessheet includes 14 columns (variables) and 704 rows, where each row corresponds to data from a specific location at a particular time for an individual red panda. Given the endangered status of the study animals, the coordinates of their location are not included in this sheet. * Animal_id: This includes the id of 10 study animals. * Sex: Male, Female * Precipitation: Clear, Snowfall, Rainfall * Snow_cover: Yes, No * Snow_age: Fresh, Old, No_snow * Temperature: Mean daily temperature (oC) * Solar: Solar radiation (KW/m2) * NDVI: Normalized Difference Vegetation Index * Road: Distance to road (m) * Settlement: Distance to settlement (m) * Herding shed: Distance to herding shed (m) * Elevation: Elevation (m) * Aspect: Aspect (o) * Slope: Slope (o)