DOI: 10.5061/dryad.pg4f4qrjw
title: Hidden treasure of the Gobi: understanding how water limits range use of khulan in the Mongolian Gobi

description: Most large herbivores in arid landscapes need to drink which constrains
 their movements and makes them vulnerable to disturbance. Asiatic wild ass
 or khulan (Equus hemionus) were widespread and abundant throughout the
 arid landscapes of Central Asia and Mongolia, but have undergone dramatic
 population declines and range constrictions; denying khulan access to
 water is believed to have played a major role. Mongolia’s South Gobi
 Region now houses the world largest remaining khulan population, but is
 undergoing rapid land use changes. Khulan water use is poorly understood,
 largely due to the difficulty of mapping waterpoints used by khulan
 throughout their exceptionally large ranges, prone to high variations in
 precipitation. We used the special movement path characteristics of GPS
 tagged khulan to show us where water is located. We identified 367
 waterpoints, 53 of which were of population importance, characterized the
 seasonal and circadian use, and identified snow cover as the most
 important variable predicting khulan visits during the non-growing season,
 and vegetation greenness during the growing season. Our results provide a
 data layer to help guide a regional khulan conservation strategy, allow
 predictions for other part of the global khulan range, and illustrates the
 overall importance of waterpoints for dryland herbivores.
description: <strong>Water point
 characterization</strong> To identify unique waterpoints
 with similar patterns of use, we applied  ahierarchical cluster analysis (Ward
 algorithm) to data on waterpoint use (the number of total
 visits, the number of unique individuals of the visits, and the number of
 years the waterpoint was visited). We plotted the relationship between the
 number of clusters and the within-cluster variation to assess the
 appropriate number of clusters. <strong>The influence of environmental
 variables on waterpoint use and visitation
 rates</strong> Environmental
 variables To test which environmental
 variables influence waterpoint visitation rates, we obtained values for
 precipitation, snow cover, vegetation greenness (as a proxy for plant
 water content), and ambient temperature from the following
 sources: Precipitation from the Global
 Precipitation Climatology Centre’s GPCC First Guess Daily product<sup>75</sup> available on a 1-degree grid (roughly 110km longitude x 82 km latitude, at the latitude of the study area) and available open source at: https://opendata.dwd.de/climate_environment/GPCC/html/gpcc_firstguess_daily_doi_download.html. We had previously found good agreement of this landscape-scale precipitation data with localized rain information from a weather station at the Oyu Tolgoi mine site (JP unpubl. data) and evidence of rain from camera collar images<sup>56</sup>. For each khulan, we calculated the daily rainfall as the mean of the grid values which were intersected by its hourly GPS positions. Vegetation greenness based on the Normalized Vegetation Index (NDVI) from the 250m Grid, 16-day composite MODIS/Terra Vegetation Indices product (Version 6, MOD13Q1, open source available from the SGS website at: https://e4ftl01.cr.usgs.gov/MOLT/MOD13Q1.006/. We mosaiced and re-projected the tiles (from <i>Sin</i> to <i>Geographic</i>), extracted the NDVI index and NASA’s pixel reliability layer<sup>76</sup>. We simplified the pixel reliability layer by converting all categories except “<i>Good data</i>” to missing-data values, then used the resulting layer as a mask to exclude questionable pixels from the NDVI data. We subsequently loaded the layers into PostgreSQL database tables, using a custom R function and NASA’s <i>MODIS Reprojection Tool</i> available at: https://lpdaac.usgs.gov/tools/modis_reprojection_tool. For each khulan we calculated the daily mean NDVI based on 9 pixels around each hourly GPS position. Snow cover from the MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 6 (MOD10A1; available at: http://nsidc.org/data/mod10a1). Satellite images of the Gobi in winter are often obscured by cloud, sometimes over very large areas for days on end. Matching khulan track locations with individual pixels from the daily snow cover images resulted in time series with many missing values. We therefore calculated a 5-day running mean of the proportion of snow cover for the entire khulan range (100% minimum convex polygon of all track positions, Fig. 1) for each day, and the same daily snow cover values were assigned to each animal. Ambient temperature from the hourly temperatures measured by each khulan collar (as a rough proxy for the temperatures experienced by the animals). For each khulan we calculated the daily mean of the collar temperature and assigned individual daily means to each khulan. GAM modeling To test the effect of abiotic variables on water use, we modelled the probability of a waterpoint visit by a khulan on a given day as a binary variable in a generalized additive mixed effects model<sup>77</sup>. We tested the effects of climate variables snow cover, temperature, rainfall and NDVI as fixed effects in two separate models because snow and rain / NDVI are more or less mutually exclusive. For the growing season (May to September) we tested for the effect of temperature, rainfall and NDVI and for the non-growing season (October – April) for the effect of temperature and snow cover. These variables were standardized to units of standard deviations so that their effect sizes represented their relative importance. We included a random intercept for individual ID and random slopes for each individual for the effect of each abiotic variable. To account for spatial autocorrelation, we also included a Duchon spline term for the interaction of latitude and longitude of the centroid of each individual’s daily movement track (Fig. S2 in SOM). Because the response was a binary variable, we specified the family argument as binomial. These models were fitted with the R package <i>gamm4</i><sup>77</sup> (R script provided in the SOM).
description: <strong>Water point
 characterization: given the population level importance of these
 waterpoints we did remove the GPS
 coordinates</strong>
 <strong>T<em>he influence of environmental variables on waterpoint use and visitation rates: the GPS coordinates represent the </em></strong><em>centroid of each individual’s daily movement track </em>