Vegetation productivity summarized by the Dynamic Habitat Indices explains broad-scale patterns of moose abundance across Russia

Elena Razenkova,Murray K Clayton,Andrew M Allen,Eugenia V Bragina,Volker C Radeloff,Maxim Dubinin,Martina L Hobi,Leonid M Baskin,Nicholas C Coops,Anna M Pidgeon

doi:10.1038/s41598-019-57308-8

Abstract

Identifying the factors that determine habitat suitability and hence patterns of wildlife abundances over broad spatial scales is important for conservation. Ecosystem productivity is a key aspect of habitat suitability, especially for large mammals. Our goals were to a) explain patterns of moose (Alces alces) abundance across Russia based on remotely sensed measures of vegetation productivity using Dynamic Habitat Indices (DHIs), and b) examine if patterns of moose abundance and productivity differed before and after the collapse of the Soviet Union. We evaluated the utility of the DHIs using multiple regression models predicting moose abundance by administrative regions. Univariate models of the individual DHIs had lower predictive power than all three combined. The three DHIs together with environmental variables, explained 79% of variation in moose abundance. Interestingly, the predictive power of the models was highest for the 1980s, and decreased for the two subsequent decades. We speculate that the lower predictive power of our environmental variables in the later decades may be due to increasing human influence on moose densities. Overall, we were able to explain patterns in moose abundance in Russia well, which can inform wildlife managers on the long-term patterns of habitat use of the species.

Highlights

Identifying the factors that determine habitat suitability and patterns of wildlife abundances over broad spatial scales is important for conservation
The Dynamic Habitat Indices (DHIs) can be computed from a range of satellite datasets including the Moderate Resolution Imaging Spectroradiometer (MODIS), which is aboard NASA’s Terra and Aqua satellites, developed to monitor the environmental conditions of Earth[14]
We aimed to a) explain patterns of moose (Alces alces) density across Russia based on remotely sensed measures of vegetation productivity, environmental variables, elevation, and human influence, and b) examine if the relationship between average moose density versus productivity and temperature differed among the last decade of Soviet time, the first decade after the collapse of the Soviet Union, and the second decade after the collapse, given the changing population trends and socioeconomic conditions during these periods (Fig. 1c)

Summary

Introduction

Identifying the factors that determine habitat suitability and patterns of wildlife abundances over broad spatial scales is important for conservation. Habitat and land cover maps based on remotely sensed data are powerful predictors of species occurrence patterns over large areas[6,7,8]. The relative importance of the DHIs to predict abundance is yet to be tested though, because abundance depends on many factors besides productivity, including availability of forage in space and time, necessary climate conditions for survival and reproduction, as well as predation and harvest pressure. Moose abundance data were available at the scale of Russia’s administrative regions from 1981 to 2010 This time period is interesting because Russia has undergone radical political and economic changes since 1981, including the collapse of the Soviet Union in 1991, and the transition from a socialist government to a market economy. After 2000, populations of many wildlife species rebounded, potentially due to increasing habitat availability on abandoned agricultural fields

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