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. Stochastic patch occupancy models 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 fulfil their potential to guide conservation if parameters could be extrapolated to data-deficient landscapes and species.