Open AccessThe strong influence of collection bias on biodiversity knowledge shortfalls of B razilian terrestrial biodiversity
Author(s) -
Oliveira Ubirajara,
Paglia Adriano Pereira,
Brescovit Antonio D.,
Carvalho Claudio J. B.,
Silva Daniel Paiva,
Rezende Daniella T.,
Leite Felipe Sá Fortes,
Batista João Aguiar Nogueira,
Barbosa João Paulo Peixoto Pena,
Stehmann João Renato,
Ascher John S.,
Vasconcelos Marcelo Ferreira,
De Marco Paulo,
LöwenbergNeto Peter,
Dias Priscila Guimarães,
Ferro Viviane Gianluppi,
Santos Adalberto J.
Publication year - 2016
Publication title -
diversity and distributions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.918
H-Index - 118
eISSN - 1472-4642
pISSN - 1366-9516
DOI - 10.1111/ddi.12489
Subject(s) - biodiversity , species richness , sampling bias , sampling (signal processing) , ecology , beta diversity , geography , distribution (mathematics) , global biodiversity , species diversity , diversity (politics) , niche , biology , sample size determination , statistics , mathematical analysis , mathematics , filter (signal processing) , sociology , computer science , anthropology , computer vision
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.