Spatial Pattern Analysis Reveals Randomness Among Carnivore Depredation of Livestock

Claire F Hoffmann,Robert A Montgomery,Bernard M Kissui

doi:10.3389/fevo.2019.00478

Claire F Hoffmann, Robert A Montgomery + Show 1 more

Open Access

https://doi.org/10.3389/fevo.2019.00478

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Abstract

Carnivore depredation of livestock is a global problem which negatively impacts both agropastoral livelihoods and carnivore population viability. Given the gravity of this issue, research has increasingly focused on applied techniques capable of quantifying the factors that increase the risk of livestock depredation. One such technique is risk modeling. This multivariate approach is designed to produce predictions of the spatial configuration of depredation so as to prioritize interventionist activities. Thus, the efficacy of subsequent interventions is, in part, dependent upon the accuracy of the predictions deriving from the risk models. The predictability of spatial patterns in carnivore depredation of livestock is influenced by the degree of spatial autocorrelation evident in the data distributions. We conducted a multi-year assessment to quantify the degree of spatial autocorrelation within livestock depredation data. We centered our study in the Maasai steppe of Tanzania, which experiences some of the highest rates of human-carnivore conflict in the world. We applied three geostatistical measures to assess spatial clustering in data describing livestock depredation by lions (Panthera leo), leopards (Panthera pardus), spotted hyenas (Crocuta crocuta), black-backed jackals (Canis mesomelas), and cheetahs (Acinonyx jubatus) at the household (i.e., livestock enclosure) scale. Using an ordinal spatial scan statistic, a Bernoulli spatial scan statistic, and the Getis-Ord local spatial statistic, we found that the spatial patterns in carnivore depredation of livestock tended not to significantly differ from random. As the predictive ability of spatial risk models may be limited where spatial patterns of carnivore depredation of livestock do not statistically differ from random, explicitly assessing such patterns is an important component of conflict mitigation efforts. We discuss the inferences of this analysis for the optimization of interventionist activities intending to develop sustainable solutions for human-carnivore conflict.

Highlights

Large carnivore hunting and killing of domesticated livestock represents one of the most common triggers of human-carnivore conflict globally (Mizutani, 1999; Frank et al, 2005; Maggi et al, 2014)
There are a number of models used to predict spatial patterns in carnivore depredation of livestock, which are often referred to as risk models
We evaluated the degree of spatial autocorrelation inherent to these data using the ordinal spatial scan statistic, the Bernoulli spatial scan statistic, and the Getis-Ord local spatial statistic

Summary

Introduction

Large carnivore hunting and killing of domesticated livestock represents one of the most common triggers of human-carnivore conflict globally (Mizutani, 1999; Frank et al, 2005; Maggi et al, 2014). Within this context, people who have experienced livestock losses will often retaliate against those carnivores perceived to be responsible or in an effort to prevent future livestock losses. There are a number of models used to predict spatial patterns in carnivore depredation of livestock, which are often referred to as risk models These models generally fall into one of three categories including correlation modeling, spatial interpolation, and spatial associations (Miller, 2015). The calculations of clustering or dispersion are carried out via a comparison of the data to a completely spatial random pattern (Aldstadt, 2010; Chakraborty, 2011; Diggle, 2014)

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