Temporal Non-stationarity of Path-Selection Movement Models and Connectivity: An Example of African Elephants in Kruger National Park

Żaneta Kaszta,Rob Slotow,Samuel A Cushman

doi:10.3389/fevo.2021.553263

Abstract

Effective conservation and land management require robust understanding of how landscape features spatially and temporally affect population distribution, abundance and connectivity. This is especially important for keystone species known to shape ecosystems, such as the African elephant (Loxodonta africana). This work investigates monthly patterns of elephant movement and connectivity in Kruger National Park (KNP; South Africa), and their temporal relationship with landscape features over a 12-month period associated with the occurrence of a severe drought. Based on elephant locations from GPS collars with a short acquisition interval, we explored the monthly patterns of spatial-autocorrelation of elephant movement using Mantel correlograms, and we developed scale-optimized monthly path-selection movement and resistant kernel connectivity models. Our results showed high variability in patterns of autocorrelation in elephant movements across individuals and months, with a preponderance of directional movement, which we believe is related to drought induced range shifts. We also found high non-stationarity of monthly movement and connectivity models; most models exhibited qualitative similarity in the general nature of the predicted ecological relationships, but large quantitative differences in predicted landscape resistance and connectivity across the year. This suggests high variation in space-utilization and temporal shifts of core habitat areas for elephants in KNP. Even during extreme drought, rainfall itself was not a strong driver of elephant movement; elephant movements, instead, were strongly driven by selection for green vegetation and areas near waterholes and small rivers. Our findings highlight a potentially serious problem in using movement models from a particular temporal snapshot to infer general landscape effects on movement. Conservation and management strategies focusing only on certain areas identified by temporarily idiosyncratic models might not be appropriate or efficient as a guide for allocating scarce resources for management or for understanding general ecological relationships.

Highlights

With increasingly negative synergies between changing climate and expanding anthropogenic impacts on ecosystems, land management and conservation face serious challenges to sustain viable populations of many species in many parts of the world
Based on the shape of Mantel correlograms, we distinguished three main patterns of autocorrelation: a gradientlike pattern associated with directional movement, periodic autocorrelation linked to cyclical use of focal areas, and an irregular pattern associated with random use of temporary home ranges (Supplementary Figure 2 and Supplementary Material 2)
This paper evaluated monthly movement patterns of female elephants in Kruger National Park, South Africa, over a period of 12 months to quantify the degree to which movement pattern, factors driving path selection, predicted landscape resistance, and predicted landscape connectivity were stable through time, and the degree to which their variability was related to seasonality, forage quality, water available, rainfall, and their interactions

Summary

Introduction

With increasingly negative synergies between changing climate and expanding anthropogenic impacts on ecosystems, land management and conservation face serious challenges to sustain viable populations of many species in many parts of the world. Effective conservation in this era of rapid global change requires, more than ever, understanding the complex spatial and temporal effects of landscape components on population abundance, distribution and connectivity (Cushman, 2006). Investigations into habitat selection and movement should be conducted at multiple, ecologically relevant scales (Wiens, 1989; Goodwin and Fahrig, 1998; McGarigal et al, 2016b). Reliable knowledge about the multi-variate and multi-scale effects of environmental heterogeneity on organism distribution, abundance and movement can be acquired through robust multi-scale analytical methods supported by empirical data (Cushman et al, 2013; Zeller et al, 2018a)

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