Faunal surrogates for forest species conservation: A systematic niche-based approach

The accelerating decline of biodiversity paralleled by limited resources available for conservation requires methods for systematically prioritizing conservation efforts. Surrogate species, serving as proxies for the presence or ecological requirements of other species, have thus been proposed in a variety of conceptual approaches, all requiring the selection of representative species on which conservation efforts will be focused. Yet, apart from their inherent ecological limitations, surrogate species approaches often suffer from methodological issues with selection criteria being ill-defined and selection procedures solely expert-based, which makes them irreproducible and prone to bias. We used a niche-based selection algorithm to identify a set of faunal focal species for promoting biodiversity in temperate forests, using the state of Baden-Wurttemberg (Southwestern Germany) as example region. Based on a literature-based categorization of each species' resource requirements we identified - from candidate species of five taxonomic groups - species sets that represented all predefined forest structural components with the most sensitive species. In addition, we examined the effect of variance introduced by expert scoring (of bird species' sensitivity) on the stability of set composition. Candidate species were defined for mammals (N = 24), birds (27), herpetofauna (17), diurnal butterflies (36) and saproxylic beetles (36). The resulting focal-species sets consisted of six (herpetofauna) up to thirteen (diurnal butterflies) species, representing the main forest structural requirements of the faunal forest community at different spatial scales. Non-metric multidimensional scaling showed that the "resource-space" covered by the selected species, both of the multi-taxon and the taxon-specific sets, encompassed the one of the non-selected candidate species, except for mammals. Differences in expert scoring had a major effect on set composition, but dissimilarity between sets decreased with an increasing number of included experts and reached convergence after considering the scoring of 10 and more persons. Niche-based species selection proved valuable for systematic surrogate selection, as it requires a clear definition of the conservation-targets and environmental components to be represented by the surrogate set. The selected algorithm helps objectifying the selection process according to predefined criteria, which can be flexibly chosen so as to maximize different traits (e.g. sensitivity, flagship-characteristics) in the resulting surrogate set. However, given the high sensitivity, expert scoring (where necessary) should never be based on only one or a few experts. Our proposed sensitivity analysis can help identifying the minimal number of experts required for reaching stability in set composition.