Although Brazil is a megadiverse country and thus a conservation priority, no study has yet quantified conservation gaps in the Brazilian protected areas (PAs) using extensive empirical data. Here, we evaluate the degree of biodiversity protection and knowledge within all the Brazilian PAs through a gap analysis of vertebrate, arthropod and angiosperm occurrences and phylogenetic data. Our results show that the knowledge on biodiversity in most Brazilian PAs remain scant as 71% of PAs have less than 0.01 species records per km2. Almost 55% of Brazilian species and about 40% of evolutionary lineages are not found in PAs, while most species have less than 30% of their geographic distribution within PAs. Moreover, the current PA network fails to protect the majority of endemic species. Most importantly, these results are similar for all taxonomic groups analysed here. The methods and results of our countrywide assessment are suggested to help design further inventories in order to map and secure the key biodiversity of the Brazilian PAs. In addition, our study illustrates the most common biodiversity knowledge shortfalls in the tropics.
Aim The knowledge of biodiversity facets such as species composition, distribution and ecological niche is fundamental for the construction of biogeographic hypotheses and conservation strategies. However, the knowledge on these facets is affected by major shortfalls, which are even more pronounced in the tropics. This study aims to evaluate the effect of sampling bias and variation in collection effort on Linnean, Wallacean and Hutchinsonian shortfalls and diversity measures as species richness, endemism and beta-diversity. Location Brazil.Methods We have built a database with over 1.5 million records of arthropods, vertebrates and angiosperms of Brazil, based on specimens deposited in scientific collections and on the taxonomic literature. We used null models to test the collection bias regarding the proximity to access routes. We also tested the influence of sampling effort on diversity measures by regression models. To investigate the Wallacean shortfall, we modelled the geographic distribution of over 4000 species and compared their observed distribution with models. To quantify the Hutchinsonian shortfall, we used environmental Euclidean distance of the records to identify regions with poorly sampled environmental conditions. To estimate the Linnean shortfall, we measured the similarity of species composition between regions close to and far from access routes. Results We demonstrated that despite the differences in sampling effort, the strong collection bias affects all taxonomic groups equally, generating a pattern of spatially biased sampling effort. This collection pattern contributes greatly to the biodiversity knowledge shortfalls, which directly affects the knowledge on the distribution patterns of diversity.Main conclusions The knowledge on species richness, species composition and endemism in the Brazilian biodiversity is strongly biased spatially. Despite differences in sampling effort for each taxonomic group, roadside bias affected them equally. Species composition similarity decreased with the distance from access routes, suggesting collection surveys at sites far from roads could increase the probability of sampling new geographic records or new species.
We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE), based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units.
Forests with Araucaria angustifolia (Bert.) O. Kuntze trees are endangered in Brazil, and information on the diversity of soil spider families associated to these environments is practically inexistent. The present study was set up to evaluate the abundance and diversity of soil spider families in natural and reforested Araucaria forests, impacted or not by fire, and to identify the most efficient method to collect these organisms. The study was conducted in four areas: native forest with predominance of Araucaria (NF); Araucaria reforestation (R); Araucaria reforestation submitted to an accidental fire (RF); and native grass pasture with native Araucaria and submitted to an intense accidental fire (NPF). Considering both sampling methods (Monolith and Pitfall traps), 20 spider families were identified. The pitfall trap method was more effective as it captured 19 out of the 20 recorded families, while the Monolith method extracted only ten spider families. Spider family abundance and Shannon's diversity index (H) were affected by the employed collection method; the values for these attributes were always higher for the NF and lower for the NPF. Correspondence analysis (CA) showed a spatial separation among spider familiy assemblages from the different studied areas. It is suggested that changes in the abundance of soil spider families in Araucaria forests are mainly caused by recurrent human intervention over the last few years.
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