Threatened birds, climate change, and human footprint: protected areas network in Neotropical grassland hotspot.

Climate change (CC) and human footprint (HF) shape species spatial patterns and may affect the effectiveness of Protected Areas (PAs) network. Spatial patterns of threatened bird species of Subtropical-temperate hotspots in Southeastern South American grasslands are relevant biodiversity features to guide conservation policies. However, the PAs network covers less than 1% of grassland areas and does not overlap areas with the most suitable environmental conditions for threatened birds. Our aim was to find the most environmentally suitable areas for both current and future threatened birds (2050 and 2070) in Entre Ríos. We applied Systematic Conservation Planning protocols with Ecological Niche Models (ENMs) and ZONATION using distribution interaction function and HF as a cost. Then we overlapped binary maps to find priority areas among time periods. HF showed a more fragmented spatial configuration. The PAs network may include environmentally suitable conditions for threatened birds in CC scenarios and HF. We found areas that showed more connectivity in landscape prioritization over time and ensure high-quality environmental conditions for birds. We concluded that the effectiveness of the PAs network could be improved by overlapping priority areas. Our approach provides a knowledge base as a contribution to conservation-related decisions by considering HF and CC.

Species distribution models and empirical test: Comparing predictions with well-understood geographical distribution of Bothrops alternatus in Argentina.

Species distribution models (SDMs) estimate the geographical distribution of species although with several limitations due to sources of inaccuracy and biases. Empirical tests arose as the most important steps in scientific knowledge to assess the efficiency of model predictions, which are poorly rigorous in SDMs. A good approach to the empirical distribution (ED) of a species can be obtained from comprehensive empirical knowledge, that is, well-understood distributions gathered from large amount of data generated with appropriate spatial and temporal samples coverage. The aims of this study were to (a) compare different SDMs predictions with an ED; and (b) evaluate if fuzzy global matching (FGM) could be used as an index to compare SDMs predictions and ED. Six algorithms with 5 and 20 variables were used to assess their accuracy in predicting the ED of the venomous snake Bothrops alternatus (Viperidae). Its entire distribution is known, thanks to thorough field surveys across Argentina, with 1,767 records. ED was compared with SDMs predictions using Map Comparison Kit. SDMs predictions showed important biases in all methods used, from 70% sub-estimation to 40% over-estimation of ED. BIOCLIM predicted ≈31% of B. alternatus ED. DOMAIN predicted 99% of ED, but over-estimated 40% of the area. GLM with five variables calculated 75% of ED, while Genetic Algorithm for Rule-set Prediction showed ≈60% of ED; the last two presenting overpredictions in areas with favorable climatic conditions but not inhabited by the species. MaxEnt and RF were the only methods to detect isolated populations in the southern distribution of B. alternatus. Although SDMs proved useful in making predictions about species distribution, predictions need validation with expert maps knowledge and ED. Moreover, FGM showed a good performance as an index with values similar to True Skill Statistic, so that it could be used to relate ED and SDMs predictions.