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.
Amazonian rivers are usually suggested as dispersal barriers, limiting biogeographic units. This is evident in a widely accepted Areas of Endemism (AoEs) hypothesis proposed for Amazonian birds. We empirically test this hypothesis based on quantitative analyses of species distribution. We compiled a database of bird species and subspecies distribution records, and used this dataset to identify AoEs through three different methods. Our results show that the currently accepted Amazonian AoEs are not consistent with areas identified, which were generally congruent among datasets and methods. Some Amazonian rivers represent limits of AoEs, but these areas are not congruent with those previously proposed. However, spatial variation in species composition is correlated with largest Amazonian rivers. Overall, the previously proposed Amazonian AoEs are not consistent with the evidence from bird distribution. However, the fact that major rivers coincide with breaks in species composition suggest they can act as dispersal barriers, though not necessarily for all bird taxa. This scenario indicates a more complex picture of the Amazonian bird distribution than previously imagined.
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.
Comparisons of species richness among assemblages using different sample sizes may produce erroneous conclusions due to the strong positive relationship between richness and sample size. A current way of handling the problem is to standardize sample sizes to the size of the smallest sample in the study. A major criticism about this approach is the loss of information contained in the larger samples. A potential way of solving the problem is to apply extrapolation techniques to smaller samples, and produce an estimated species richness expected to occur if sample size were increased to the same size of the largest sample. We evaluated the reliability of 11 potential extrapolation methods over a range of different data sets and magnitudes of extrapolation. The basic approach adopted in the evaluation process was a comparison between the observed richness in a sample and the estimated richness produced by estimators using a sub‐sample of the same sample. The Log‐Series estimator was the most robust for the range of data sets and sub‐sample sizes used, followed closely by Negative Binomial, SO‐J1, Logarithmic, Stout and Vandermeer, and Weibull estimators. When applied to a set of independently replicated samples from a species‐rich assemblage, 95% confidence intervals of estimates produced by the six best evaluated methods were comparable to those of observed richness in the samples. Performance of estimators tended to be better for species‐rich data sets rather than for those which contained few species. Good estimates were found when extrapolating up to 1.8‐2.0 times the size of the sample. We suggest that the use of the best evaluated methods within the range of indicated conditions provides a safe solution to the problem of losing information when standardizing different sample sizes to the size of the smallest sample.
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