No abstract

Up to now, global conservation priorities are far from incorporating megadiverse invertebrate taxa. Thus, an important emerging field in biological conservation is how we might manage landscape to preserve insects. In this study, we analyze the efficacy of Italian reserve network for protecting multiple saproxylic beetles, considering both nationally designated areas and Natura 2000 sites. We selected 150 species inhabiting the Italian territory from the European Red List for saproxylic beetles, on the basis of distribution data availability. For each species, a vulnerability score was assigned according to their Red List status, and the species' distributions data were used to perform an irreplaceability analysis. Our analyses show that conservation targets based on geographic range extent are achieved for only 7% of the considered species. We find that 13 species are not represented in any protected area: among these, two click beetle species (Elateridae) are listed in the International Union for Conservation of Nature threatened categories (i.e. Ampedus quadrisignatus EN and Ampedus brunnicornis VU). Our analyses on protected area effectiveness for the conservation of saproxylic beetles showed that nationally designated protected areas are more irreplaceable than a random selection of cells. Surprisingly, the addition of Natura 2000 sites did not improve the species representation. Moreover, these reserves include sites that are not more irreplaceable than a random selection of cells. We identify some currently unprotected areas that protection could prevent from future extinctions and ensure a favorable conservation status of saproxylic beetles. In particular, we find an important stronghold for beetle conservation, which obtained a high irreplaceability score, in the Adige river basin. We recommend the designation of new reserves in this area to complement the existing network and to help guarantee invertebrate saproxylic fauna protection.

Aim  Defining priority areas for conservation is essential to minimize biodiversity loss, but the adoption of different methods for describing species distributions influences the outcomes. In order to provide a robust basis for the conservation of freshwater turtles in Africa, we compared the effect that different species‐mapping approaches had on derived patterns of species richness, species vulnerability and protected‐area representativeness.Location  Africa.Methods  We adopted three different approaches with increasing complexity for generating species distribution maps. The first approach was based on the geographic intersection of species records and grid squares; the second on the union of local convex polygons; and the third on inductive distribution modelling techniques. We used distribution maps, generated using these three approaches, to determine conservation priorities based on geographic patterns of species richness and vulnerability, as well as for conducting gap and irreplaceability analyses.Results  We obtained markedly different distribution maps using the three methods, which in turn caused differences in conservation priorities. The grid‐square approach underestimated range sizes and species richness, while the polygon approach overestimated these attributes. The distribution modelling approach provided the most realistic outcome in terms of diversity patterns, by minimizing both commission and omission errors. An integrated map of conservation priority – derived by combining individual measures of priority based on the distribution modelling approach – identified the Gulf of Guinea coast and the Albertine Rift as major priority areas.Main conclusions  Each species‐mapping approach has both advantages and disadvantages. The choice of the most appropriate approach in any given situation depends on the availability of locality records and on the relative importance of mitigating omission and commission errors. Our findings suggest that in most circumstances, the use of distribution modelling has many advantages relative to the other approaches. The priority areas identified in this study should be considered for targeting efforts to conserve Africa freshwater turtles in the coming years.

Aim  Conservation managers are increasingly looking for modelled projections of species distributions to inform management strategies; however, the coarse resolution of available data usually compromises their helpfulness. The aim of this paper is to delineate and test different approaches for converting coarse‐grain occurrence data into high‐resolution predictions, and to clarify the conceptual circumstances affecting the accuracy of downscaled models. Location  We used environmental data from a real landscape, southern Africa, and simulated species distributions within this landscape. Methods  We built 10 virtual species at a resolution of 5 arcmin, and for each species we simulated atlas range maps at four decreasing resolutions (15, 30, 60, 120 arcmin). We tested the ability of three downscaling strategies to produce high‐resolution predictions using two modelling techniques: generalized linear models and generalized boosted models. We calibrated reference models with high‐resolution data and we compared the relative reduction of predictive performance in the downscaled models by using a null model approach. We also estimated the applicability of downscaling procedures to different situations by using distribution data for Mediterranean reptiles. Results  All reference models achieved high performance measures. For all strategies, we observed a reduction of predictive performance proportional to the degree of downscaling. The differences in evaluation indices between reference models and downscaled projections obtained from atlases at 15 and 30 arcmin were never statistically significant. The accuracy of projections scaled down from 60 arcmin largely depended on the combination of approach and algorithm adopted. Projections scaled down from 120 arcmin gave misleading results in all cases. Main conclusions  Moderate levels of downscaling allow for reasonably accurate results, regardless of the technique used. The most general effect of scaling down coarse‐grain data is the reduction of model specificity. The models can successfully delineate a species’ environmental association up until a 12‐fold downscaling, although with an increasing approximation that causes the overestimation of true distributions. We suggest appropriate procedures to mitigate the commission error introduced by downscaling at intermediate levels (approximately 12‐fold). Reductions of grain size > 12‐fold are discouraged.

No abstract