Spatial Pattern Analysis Reveals Randomness Among Carnivore Depredation of Livestock

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.

Wolf depredation hotspots in France: Clustering analyses accounting for livestock availability

“…During the third period (2004-2012), a weak significant aggregation was only observed over the lowest r values, and turned into significant repulsion between 15 and 30 km. This was typical of a clustered pattern, with hotspots producing coldspot interzones (Hoffmann et al, 2019). These hotspots could correspond to the south-east and south areas where depredations were particularly densely distributed (Fig.…”

“…In contrast, accounting for sheep availability (and therefore for sheep distribution too) was necessary to identify the typical clustered pattern (i.e. aggregation followed by repulsion; Hoffmann et al, 2019) through Kinhom and to provide a finer-grained analysis of hotspots through the Kulldorff statistic. This is of great interest for management when allocating prevention tools or derogations of wolf removals for the most pressing situations.…”

“…Spatial autocorrelation quantifies the level of clustering or regularity (i.e. repulsion) of a depredation pattern (Baddeley et al, 2015;Hoffmann et al, 2019) and allows the identification of hotspots or coldspots, where events are unusually aggregating or scarce. Risk modelling aims to predict locations of future depredations, by quantifying the relationship between ecological or non-ecological features and depredation numbers or occurrences (Miller, 2015).…”

“…Spatial autocorrelation is often presented as a preliminary step to risk modelling and as of limited interest if applied alone (Gastineau, Robert, Sarrazin, Mihoub, & Quenette, 2019;Hoffmann et al, 2019). This may explain why risk modelling is more popular (e.g.…”

“…To avoid arbitrary thresholds, some studies have applied spatial statistical analyses (e.g. Gastineau et al, 2019;Hoffmann et al, 2019;Packer et al, 2019). The spatial statistical analyses consider depredation events as a spatial point pattern, i.e.…”