Dispersers\u2019 habitat detection and settling abilities modulate the effect of habitat amount on metapopulation resilience

Louise Riotte-Lambert,Fabien Laroche

doi:10.1007/s10980-021-01197-8

Abstract

ContextMetapopulation theory makes useful predictions for conservation in fragmented landscapes. For randomly distributed habitat patches, it predicts that the ability of a metapopulation to recover from low occupancy level (the “metapopulation capacity”) linearly increases with habitat amount. This prediction derives from describing the dispersal between two patches as a function of their features and the distance separating them only, without interaction with the rest of the landscape. However, if individuals can stop dispersal when hitting a patch (“habitat detection and settling” ability), the rest of habitat may modulate the dispersal between two patches by intercepting dispersers (which constitutes a “shadow” effect).ObjectivesWe aim at evaluating how habitat detection and settling ability, and the subsequent shadow effect, can modulate the relationship between the metapopulation capacity and the habitat amount in the metapopulation.MethodsConsidering two simple metapopulation models with contrasted animal movement types, we used analytical predictions and simulations to study the relationship between habitat amount and metapopulation capacity under various levels of dispersers’ habitat detection and settling ability.ResultsIncreasing habitat detection and settling ability led to: (i) larger metapopulation capacity values than expected from classic metapopulation theory and (ii) concave habitat amount–metapopulation capacity relationship.ConclusionsOverlooking dispersers’ habitat detection and settling ability may lead to underestimating the metapopulation capacity and misevaluating the conservation benefit of increasing habitat amount. Therefore, a further integration of our mechanistic understanding of animals’ displacement into metapopulation theory is urgently needed.

Highlights

The spatial distributions of many animals living in fragmented habitats can be represented as metapopulations, i.e. networks of populations connected by dispersal and undergoing frequent extinction/re-colonization
We focus on deriving kP—the metapopulation capacity (‘‘MC’’ below; Hanski and Ovaskainen 2000)—as a function of (i) dispersers’ movement ability T, defined as the average distance dispersers can travel before stopping, limited by the energy available for dispersal, and (ii) dispersers’ habitat detection and settling ability (HDSA) q, defined as the probability of settling when encountering a patch. q 1⁄4 0 means that the disperser is unable to differentiate patches from the matrix, whereas q 1⁄4 1 means that the disperser can distinguish a patch from the matrix with perfect accuracy and always stops when encountering a patch
We theoretically showed for the random walk model that better habitat detection ability (HDSA) always increased the metapopulation capacity (MC), but that the MC was never larger than 1 for both dispersal models

Summary

Introduction

The spatial distributions of many animals living in fragmented habitats can be represented as metapopulations, i.e. networks of populations connected by dispersal and undergoing frequent extinction/re-colonization (e.g. snails: Lamy et al 2013, frogs: Chandler et al 2015, beetles: Laroche et al 2018, voles: Sutherland et al 2014). Spatially-explicit stochastic patch occupancy models (SPOMs), like the Incidence Function models (Hanski 1994) and the spatially realistic Levins model (Hanski and Gaggiotti 2004), contribute to bridge this gap by analyzing the impact of metapopulation spatial structure on its persistence. They allow deriving the expected time to extinction of the metapopulation, i.e. extinction of all local populations, and the ‘metapopulation capacity’ (Hanski and Ovaskainen 2000), which quantifies its ability to recover from low occupancy level. It allows the evaluation of metapopulation persistence in a changing environment (Shen et al 2015; Che-Castaldo and Neel 2016), the ranking of patches’ (Blazquez-Cabrera et al 2014; Rubio et al 2014) and corridors’ conservation values (Brodie et al 2016; Foster et al 2017), and the comparison of metapopulation configurations (Schnell et al 2013; Larrey-Lassalle et al 2018)

Methods

Results

Discussion

Conclusion