Rafael de Andrade Moral is Professor of Statistics at Maynooth University. His research interests include the development and application of statistical modelling techniques to Ecology, Wildlife Management, and Agriculture. He is also interested in the computational implementation of statistical models, especially as R code. 

Wildlife Monitoring Is About How We Count Needles in a Haystack

Monitoring wildlife is both essential and fundamentally messy. Ecosystems (both natural and human-altered) can support endangered, damaging, and economically valuable species, yet estimating animal abundance remains difficult, because detection is imperfect, data are either very expensive to obtain or very sparse, and a number of assumptions often dominate inference. Wildlife counting methods are like looking for a needle in a haystack with the needle usually hiding from you, or occasionally attacking you… This brings about conceptual, technological, and societal challenges: what should indicators indicate? How many indicators are too many? How relevant is spatial and/or temporal heterogeneity? In this talk, I will frame wildlife monitoring as a statistical problem with escalating levels of difficulty and will show how to estimate animal abundance when we can identify individuals, see them but not identify who’s who, or only infer their presence from indirect evidence. Across these settings, I will show how statistical models can be adapted to accommodate real-world constraints. Real case studies include monitoring deer, collared peccaries, foxes, giant anteaters, and wild boars. I will also illustrate how movement, behaviour, and landscape structure can affect data collection, and showcase a counterintuitive result of a common practice aimed at avoiding bias that ends up introducing more bias in camera trap surveys. Ultimately, I will attempt to substantiate an argument for a pragmatic approach to wildlife monitoring that acknowledges conceptual, technological, and societal constraints, while equipping statisticians to ask better questions of imperfect data in the real world.