This dataset describes environmental conditions at 135 Saiga antelope calving sites (from a total of 214) in Kazakhstan where the predictor variables required for the modelling were available at sufficient resolution. Data collected included climatic variables associated with haemorrhagic septicaemia in the literature, including humidity, temperature and precipitation. Indicators of vegetation biomass, phenology and length of the winter preceding calving were represented using the Normalised Difference Vegetation Index (NDVI), snow depth and snow presence data.
Saiga antelope are susceptible to mass mortality events (MME), the most severe of which are caused by haemorrhagic septicaemia following infection by the bacteria Pasteurella multocida. These die-off events tend to occur in May during calving, when saigas gather in dense aggregations. As the bacteria is a commensal organism, which may live harmlessly in the respiratory tract of the saiga, it is believed that an environmental trigger is involved in a shift to virulence in the pathogen or reduction in immune-competence in the host.
The attached data show environmental conditions at a set of calving sites of the Betpak-dala population of saigas. This population, one of three in Kazakhstan, is located in the central provinces of the country and is the only one in which massive haemorrhagic septicaemia outbreaks have been recorded. At most of the recorded sites, calving progressed normally, whilst at others mass mortality events occurred during calving or just afterwards, namely in 1981, 1988 and 2015. A set of environmental predictor variables was used to model the probability of an MME at calving aggregations. The dataset, modelling process and results are described in Kock et al. (2018): http://advances.sciencemag.org/content/4/1/eaao2314
A related shapefile of the full set of 214 sites, and metadata concerning site characteristics and the provenance of the location data is available at:
The attached dataset and site metadata in the above-mentioned Shapefile attribute table can be combined using the variable ID in order to merge the environmental data with information on the calving and MME sites.
Publication date: 2018-03-09
Given the large geographic scale of the study, range of variables to be covered, long time series coverage and high resolution, the daily ERA-Interim reanalysis dataset, produced at the European Centre for Medium-Range Weather Forecasts (ECMWF), was selected as a source of climate data (Dee et al., 2011). Daily aggregates (means, maxima, minima, or totals) were generated from raw 3 or 6 hourly data for temperature, dew point temperature, soil moisture, snow depth, maximum wind gust and precipitation. Due to uncertainties in precipitation data from this source, interpolated gauge-based products from the Global Precipitation Climatology Centre (GPCC), which are independent of ERA data, were also used, although these were available only from 1988 onwards (Schamm et al., 2014).
Absolute NDVI values at calving/MME sites were obtained using a dataset of 7 day filtered and smoothed MODIS NDVI products from the University of Natural Resources and Life Sciences, Vienna (Vuolo et al., 2012). These data are included in the attached dataset but were not used in final models, they cover the period 2001-2016 and cover a subset of 94 sites.
NDVI anomaly data based on SPOT and PROBA-V data were supplied by the Université Catholique de Louvain-LifeWatch Wallonia-Brussels (WB) project (Radoux et al., 2015). Snow anomaly data (unusual values at the date of observation compared to mean probability of snow presence) were used to look for unusual variation in winter (e.g. winter length, severity, sudden cold shocks) and were derived by LifeWatch WB from filtered MODIS data (Rousseau et al., 2015). These datasets cover periods from 1998-2016 and 2000-2016 respectively and thus include only 2015 MME sites and a set of controls - covering a subset of 96 & 95 sites.