What drives population fluctuations of European ground squirrels in Hungary?

Abstract Background Understanding and predicting the dynamics of rodent populations, particularly for endangered or pest species, requires knowledge of potential background factors. We collected long-term data on the abundance of European ground squirrels, Spermophilus citellus, in 64 colonies as part of the Hungarian Biodiversity Monitoring System to investigate the abiotic factors that could explain the spatial and temporal dynamics of populations in Hungary. We used information theory-based modelling and multi-model inference to investigate the effects of environmental and climate variables on relative population densities, using monitoring records of burrow counts as proxies for density collected annually between 2000 and 2018. We examined data carefully for any clues of inconsistencies, errors, or missing data, which resulted in 57% of the complete dataset. Results Our generalised additive models (GAM) with splines identified geographical location, year, and principal components reflecting winter temperature, precipitation, and summer temperature to affect population densities. Contour plots derived from the best GAM model uncovered increasing density in the Kiskunság and Kisalföld meso-regions with favourable water management characteristics and decreasing densities in the northern and southern regions, which in fact, cover floodplain of River Tisza and other flash-flood regions. Ambient temperature and precipitation of the wettest and warmest months during hibernation and summer contributed most to counts fluctuations, though these factors were dwarfed by spatial effects. Increased temperatures and aridification in the Kiskunság, because of the warming Pannonian ecoregion, seemed to positively affect counts. Conclusions Analysis of our reduced dataset indicated unidentified local factors or a spatial effect on demographic variability of ground squirrel populations. Those fluctuations underscore the necessity of research and management on local populations to identify the reasons of declines and adapt management accordingly to stop or change decreasing population trajectories.