Living in risky landscapes: delineating management units in multithreat environments for effective species conservation

Abstract

Summary 1. Managing threatened species to reduce their extinction risk is a widely used, yet challenging, means of halting biodiversity loss. Species show complex spatial patterns of extinction risk, due to spatial variation in both threats and vulnerability across their ranges. Conservation practitioners, however, rarely consider this spatial variation and routinely apply uniform conservation schemes, either throughout the species’ ranges, or following administrative borders that do not match ecological boundaries. Most of these schemes are experience-based (e.g. expert opinion) and thus difficult to replicate. 2. We accounted for spatial variation in species’ threats by using multivariate techniques [i.e. cluster analyses and multidimensional scaling (MDS)] to delineate management units for more effective conservation. We grouped breeding territories of the endangered Egyptian vulture, according to interterritory similarity in presence and intensity of their threats. 3. The first three MDS axes explained 62% of the data variation. The first axis separated territories in protected areas, with low human presence, but high risk of illegal poisoning from areas highly dominated by humans. The second axis classified territories regarding the density of sheep/goats and griffon vultures and the presence of wind farms. The third axis confronted territories in protected areas with those in unprotected areas with wind farms. 4. We obtained 18 statistically supported groups (i.e. management units) including 86% of the territories. Territories within the same group were geographically close, agreeing with the underlying spatial autocorrelation of threats. However, six groups (33%) were distributed over more than one administrative region, which will require inter-regional coordination for cost-effective conservation. 5. Synthesis and applications. Our results show wide spatial variation for species’ threats and suggest incorporation of this heterogeneity into conservation schemes. We demonstrate how multivariate statistics, coupled with uncertainty analysis, can be employed in a systematic and repeatable way to deal with the heterogeneous landscapes of risk that species face across their ranges. Our approach allows researchers and managers to delineate management units according to similarity in species’ threats for any targeted organization level (e.g. individuals, territories, populations). The results can be visualized in Euclidean and geographical spaces for better interpretation, allowing managers to design more effective conservation actions.