Spatially explicit species distribution models: A missed opportunity in conservation planning?

Sami Domisch,Florian Borgwardt,Annett Wetzig,Martin Friedrichs,Sonja C Jähnig,Thomas Hein,Simone D Langhans,Gwen Iacona

doi:10.1111/ddi.12891

Abstract

AbstractAimSystematic conservation planning is vital for allocating protected areas given the spatial distribution of conservation features, such as species. Due to incomplete species inventories, species distribution models (SDMs) are often used for predicting species’ habitat suitability and species’ probability of occurrence. Currently, SDMs mostly ignore spatial dependencies in species and predictor data. Here, we provide a comparative evaluation of how accounting for spatial dependencies, that is, autocorrelation, affects the delineation of optimized protected areas.LocationSoutheast Australia, Southeast U.S. Continental Shelf, Danube River Basin.MethodsWe employ Bayesian spatially explicit and non‐spatial SDMs for terrestrial, marine and freshwater species, using realm‐specific planning unit shapes (grid, hexagon and subcatchment, respectively). We then apply the software gurobi to optimize conservation plans based on species targets derived from spatial and non‐spatial SDMs (10%–50% each to analyse sensitivity), and compare the delineation of the plans.ResultsAcross realms and irrespective of the planning unit shape, spatially explicit SDMs (a) produce on average more accurate predictions in terms of AUC, TSS, sensitivity and specificity, along with a higher species detection probability. All spatial optimizations meet the species conservation targets. Spatial conservation plans that use predictions from spatially explicit SDMs (b) are spatially substantially different compared to those that use non‐spatial SDM predictions, but (c) encompass a similar amount of planning units. The overlap in the selection of planning units is smallest for conservation plans based on the lowest targets and vice versa.Main conclusionsSpecies distribution models are core tools in conservation planning. Not surprisingly, accounting for the spatial characteristics in SDMs has drastic impacts on the delineation of optimized conservation plans. We therefore encourage practitioners to consider spatial dependencies in conservation features to improve the spatial representation of future protected areas.

Highlights

In the light of the ongoing decline in global biodiversity (Pimm et al, 2014), the implementation of protected areas in the terrestrial, ma‐ rine and freshwater realms is yet the most widely used conservation approach to reduce the loss of biodiversity
We show that (a) spatially explicit pre‐ dictions of species’ probabilistic habitat suitability outperform those derived from non‐spatial species distribution models (SDMs) across the three realms, each with a specific landscape configuration and distinct species dispersal and connectivity characteristics
Broennimann, Davison, and Guisan (2014), who found that spatial autocorrelation itself has only a minor relative effect on model pre‐ diction accuracy, our analyses reveal that accounting for spatial autocorrelation leads to a substantial improve‐ ment of model outputs

Summary

Introduction

In the light of the ongoing decline in global biodiversity (Pimm et al, 2014), the implementation of protected areas in the terrestrial, ma‐ rine and freshwater realms is yet the most widely used conservation approach to reduce the loss of biodiversity. Recently the focus has shifted towards consid‐ ering environmental and ecological processes, which are essential for securing species persistence (e.g., Klein et al, 2009) Such processes shape the distribution and abundance of species (Pressey, Cabeza, Watts, Cowling, & Wilson, 2007) with connectivity playing a para‐ mount and distinct role in terrestrial (Lockwood, 2010), marine (Carr et al, 2003), and freshwater ecosystems (Hermoso, Filipe, Segurado, & Beja, 2018). Incorporating spatial connectivity in the planning pro‐ cess has, important implications for designing protected areas (Daigle et al, 2018; van Teeffelen, Cabeza, & Moilanen, 2006; Weeks, 2017) This fact is reflected in the software that is used in conservation planning, such as marxan (Ball, Possingham, & Watts, 2009) or zonation (Lehtomäki & Moilanen, 2013). Among other parameters, the selection of potential planning units on algorithms that account for their spatial connectivity

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