Associate Editor Matt Hayward discussed the importance of the recent paper from Lindsey Rich and colleagues ‘Using camera trapping and hierarchical occupancy modelling to evaluate the spatial ecology of an African mammal community’ with his PhD student, Lilian Sales (supervised by Rafael Loyola of the Conservation Biogeography Lab at the Federal University of Goias, Brazil) and below are her views on this paper.
Monitoring wildlife is challenging. There is no silver bullet management action aimed at sustaining biodiversity and beneficial for all species anywhere. Uncertainties permeate all sampling protocols and some of them can really threaten the effectiveness of management plans (as illustrated in the recent debate on the use of indices in ecology by Nimmo and colleagues and Hayward and colleagues). In addition, human–wildlife conflicts are uneven throughout landscapes and therefore mapping conservation priority areas is fundamental.
In this exciting paper, Rich et al. use a non-conventional approach to understand the distribution drivers of a terrestrial mammal community in southern Africa. On one hand, their approach explicitly accounts for uncertainty related to detectability issues. A species’ non-detection is not equivalent to its absence, the latter a fact rarely considered in ecological field studies. On the other hand, the use of hierarchical Bayesian models enabled them to integrate data among biological scales of species, groups and communities. This is thus one of the first large-scale assessments of wildlife communities’ distribution patterns and drivers.
The results from this study have direct implications to management initiatives, for highlighting the importance of protected areas and grasslands to sustain southern African biodiversity, and may guide conservation trade-off decisions. Areas with high conservation value for a large number of species can posteriorly be mapped to allow us to mitigate the impacts associated with anthropogenic change. Overall, the use of camera trap protocols, coupled with multi-scale occupancy models in a hierarchical fashion, is a promising tool for cost-effective biodiversity monitoring. We hope ecologists will move toward using such robust tools in analyzing their data henceforth.
Lilian is halfway through her PhD modelling the changing ecological niche of Amazonian fauna under different future climate scenarios. She has published papers on primates from Brazilian Atlantic Forest fragments using occupancy models.