SPOM review: Broadening applications of stochastic patch occupancy models over three decades

Aim: Stochastic patch occupancy models (SPOMs) are a type of
spatial population simulation. They are arguably well suited to guide
conservation in human-altered landscapes, but their appropriateness for a
wide range of species and landscape types has often been questioned. Here,
we provide an overview of how SPOM research has expanded over the last
three decades and discuss the untapped potential for these models to
inform current conservation strategies. Location: Worldwide
Methods: We carried out a systematic review of studies that have fitted
SPOMs to real species and landscapes. We assessed temporal trends in
SPOMs’ use in conservation and management studies, their taxonomic and
geographic coverage, and the attributes of studied landscapes. We
quantitatively and qualitatively evaluated whether the authors’ modelling
choices reflected the perceived advantages and disadvantages of SPOMs.
Results: The proportion of SPOMs used to answer conservation
questions has increased over time. Questions of where, when and how to
conserve have all been addressed, sometimes considering additional aspects
such as cost-effectiveness and climate change. Taxonomic diversity
coverage has increased over time, and SPOMs have been used in landscapes
with a higher proportion of suitable habitat. They have, however, been
predominantly applied in temperate biomes. Few studies have explored
parameter extrapolation in taxonomically and ecologically related species
with mixed results. Main conclusions: Over the past three
decades, authors have exploited the simplicity and flexibility of SPOMs to
answer a broad range of questions with practical implications. The use of
SPOMs in less fragmented landscapes, and for an increasing range of taxa,
suggests that the strictest definitions of their applicability can be
challenged. SPOMs have untapped potential for informing conservation under
climate change. Given the urgent need to plan for large numbers of species
with limited data for fitting, SPOMs could better fulfill their potential
to guide conservation if parameters could be extrapolated to
data-deficient landscapes and species.