Abundance patterns of mammals across Russia explained by remotely sensed vegetation productivity and snow indices

Author:

Razenkova Elena1ORCID,Dubinin Maxim12,Pidgeon Anna M.1,Hobi Martina L.13,Zhu Likai14,Bragina Eugenia V.1,Allen Andrew M.5,Clayton Murray K.6,Baskin Leonid M.7,Coops Nicholas C.8,Radeloff Volker C.1

Affiliation:

1. SILVIS Lab, Department of Forest and Wildlife Ecology University of Wisconsin‐Madison Madison Wisconsin USA

2. NextGIS Moscow Russia

3. Swiss Federal Institute for Forest Snow and Landscape Research WSL, Stand Dynamics and Silviculture Group Birmensdorf Switzerland

4. Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences East China Normal University Shanghai China

5. Department of Animal Ecology and Physiology, Institute for Water and Wetland Research Radboud University Nijmegen The Netherlands

6. Department of Statistics University of Wisconsin‐Madison Madison Wisconsin USA

7. Severtsov Institute of Ecology and Evolution Moscow Russia

8. Integrated Remote Sensing Studio, Department of Forest Resources Management University of British Columbia Vancouver British Columbia Canada

Abstract

AbstractAimPredicting biodiversity responses to global changes requires good models of species' distributions. Both environmental conditions and human activities determine population density patterns. However, quantifying the relationship between wildlife population densities and their underlying environmental conditions across large geographical scales has remained challenging. Our goal was to explain the abundances of mammal species based on their response to several remotely sensed indices including the Dynamic Habitat Indices (DHIs) and the novel Winter Habitat Indices (WHIs).LocationRussia, the majority of regions.TaxonEight mammal species.MethodsWe estimated average population densities for each species across Russia from 1981 to 2010 from winter track counts. The DHIs measure vegetative productivity, a proxy for food availability. Our WHIs included the duration of snow‐free ground, duration of snow‐covered ground and the start, end and length of frozen season. In models, we included elevation, climate conditions, human footprint index. We parameterized multiple linear regression and applied best‐subset model selection to determine the main factors influencing population density.ResultsThe DHIs were included in some of the top‐twelve models of every species, and in the top model for moose, wild boar, red fox and wolf, so they were important for species at all trophic levels. The WHIs were included in top models for all species except roe deer, demonstrating the importance of winter conditions. The duration of frozen ground without snow and the end of frozen season were particularly important. Our top models performed well for all the species (R2adj 0.43–0.87).Main ConclusionsThe combination of the DHIs and the WHIs with climate and human‐related variables resulted in high explanatory power. We show that vegetation productivity and winter conditions are key drivers of variation in population density of eight species across Russia.

Publisher

Wiley

Subject

Ecology,Ecology, Evolution, Behavior and Systematics

Reference108 articles.

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