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The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

The strong influence of collection bias on biodiversity knowledge shortfalls ofBrazilian terrestrial biodiversity

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Oliveira, Ubirajara, Paglia, Adriano Pereira, Brescovit, Antonio D., de 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., de Vasconcelos, Marcelo Ferreira, De Marco, Paulo, Löwenberg‐Neto, Peter, Dias, Priscila Guimarães, Ferro, Viviane Gianluppi, Santos, Adalberto J.·2016·Diversity and Distributions

Abstract

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.

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Metadata

Authors
Oliveira, U., Paglia, A. P., Brescovit, A. D., de Carvalho, C. J. B., Silva, D. P., Rezende, D. T., Leite, F. S. F., Batista, J. A. N., Barbosa, J. P. P. P., Stehmann, J. R., Ascher, J. S., de Vasconcelos, M. F., De Marco, P., Löwenberg‐Neto, P., Dias, P. G., Ferro, V. G., & Santos, A. J.
Venue
Diversity and Distributions
Publisher
Wiley
Published
2016-09-30
Volume
22
Issue
12
Pages
1232-1244
ISSN
  • 0967-9952 (ISSN-L)
  • 1366-9516
  • 1472-4642
OpenAlex
W2525919422
Cited by
371
References
36
FWCI
16.11
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: The strong influence of collection bias on biodiversity knowledge shortfalls ofBrazilian terrestrial biodiversity. https://app.cochat.ai/papers/10.1111/ddi.12489
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Oliveira, U., Paglia, A. P., Brescovit, A. D., de Carvalho, C. J. B., Silva, D. P., Rezende, D. T., Leite, F. S. F., Batista, J. A. N., Barbosa, J. P. P. P., Stehmann, J. R., Ascher, J. S., de Vasconcelos, M. F., De Marco, P., Löwenberg‐Neto, P., Dias, P. G., Ferro, V. G., & Santos, A. J. (2016). The strong influence of collection bias on biodiversity knowledge shortfalls ofBrazilian terrestrial biodiversity. Diversity and Distributions. https://doi.org/10.1111/ddi.12489

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.