Exploring the effects of spatial autocorrelation when identifying key drivers of wildlife crop-raiding.

Anna Songhurst,Tim Coulson

doi:10.1002/ece3.837

Anna Songhurst, Tim Coulson

Open Access

https://doi.org/10.1002/ece3.837

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Abstract

Few universal trends in spatial patterns of wildlife crop-raiding have been found. Variations in wildlife ecology and movements, and human spatial use have been identified as causes of this apparent unpredictability. However, varying spatial patterns of spatial autocorrelation (SA) in human–wildlife conflict (HWC) data could also contribute. We explicitly explore the effects of SA on wildlife crop-raiding data in order to facilitate the design of future HWC studies. We conducted a comparative survey of raided and nonraided fields to determine key drivers of crop-raiding. Data were subsampled at different spatial scales to select independent raiding data points. The model derived from all data was fitted to subsample data sets. Model parameters from these models were compared to determine the effect of SA. Most methods used to account for SA in data attempt to correct for the change in P-values; yet, by subsampling data at broader spatial scales, we identified changes in regression estimates. We consequently advocate reporting both model parameters across a range of spatial scales to help biological interpretation. Patterns of SA vary spatially in our crop-raiding data. Spatial distribution of fields should therefore be considered when choosing the spatial scale for analyses of HWC studies. Robust key drivers of elephant crop-raiding included raiding history of a field and distance of field to a main elephant pathway. Understanding spatial patterns and determining reliable socio-ecological drivers of wildlife crop-raiding is paramount for designing mitigation and land-use planning strategies to reduce HWC. Spatial patterns of HWC are complex, determined by multiple factors acting at more than one scale; therefore, studies need to be designed with an understanding of the effects of SA. Our methods are accessible to a variety of practitioners to assess the effects of SA, thereby improving the reliability of conservation management actions.

Highlights

One of the biggest challenges in conservation today is managing situations where people and wildlife utilize the same space and compete for similar resources (Balmford et al 2001; Sitati et al 2005; Woodroffe 2005)
Ecology and Evolution published by John Wiley & Sons Ltd
This study aims to illustrate methods that are accessible to a variety of practitioners and can be used to explore the effects of spatial autocorrelation (SA) on wildlife crop-raiding data in order to facilitate the design of future human–wildlife conflict (HWC) studies

Summary

Introduction

One of the biggest challenges in conservation today is managing situations where people and wildlife utilize the same space and compete for similar resources (Balmford et al 2001; Sitati et al 2005; Woodroffe 2005). It is not surprising that when these animals live in areas surrounded by a burgeoning human population, they frequently compete with humans for limited resources such as space, water, and food (Hoare 2000; Conover 2002; Hegel et al 2009). Despite an increase in the extent of HWC situations (Hoare 1999b; Madden 2004), it is still difficult to reliably predict where conflict is going to occur, or in the case of crop-raiding, for example, what makes a field susceptible to attack (Smith and Kasiki 1999; Sitati et al 2003; Hegel et al 2009).

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