Vieses e lacunas no (des)conhecimento espacial dos primatas da Mata Atlântica

• Ano de defesa: 2022
• Autor(a) principal: Silva, Nicolas Bosco da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Mato Grosso; Brasil; Instituto de Biociências (IB); UFMT CUC - Cuiabá; Programa de Pós-Graduação em Ecologia e Conservação da Biodiversidade
• Palavras-chave em Português: Perda de biodiversidade; Primatas; Viés; Fragmentação; Macroecologia; Lacunas de conhecimento; CNPQ::CIENCIAS BIOLOGICAS::ECOLOGIA; Biodiversity loss; Primates; Bias; Fragmentation; Macroecology; knowledge gaps
• Link de acesso: http://ri.ufmt.br/handle/1/5920

The human-induced effects on biodiversity losses are clear across the world. In the Neotropics, the main conservation issue is how human-induced deforestation affects biodiversity at different levels of landscape fragmentation on a wide spatial scale. To answer this question, we need biodiversity data that demonstrate large-scale sampling of biodiversity in an equitable way. However, the availability of large-scale biodiversity data is still limited and space-time biased, especially in biodiverse regions such as the Neotropics. Here, (1) we make the first assessment of biases and sample coverage gaps in the data at multispatial scales. With this proposal, we question how these gaps and biases might (2) affect our predictive ability to assess the functional relationship between deforestation and biodiversity trends for a highly dynamic biome subject to deforestation, such as the Atlantic Forest. For this, we used an extensive primate sampling database for the Atlantic Forest with data from individual occurrences and in community sampling, to assess sampling biases at different spatial scales and biases and gaps in primate sampling coverage in different climatic environments. and landscaped. From this context, we divided this dissertation into two segments: the 1° chapter aims to highlight the biases and sample gaps in terms of the scale and spatial resolution studied; the 2° chapter has the prerogative that primate sampling data have gaps and biases and if used to answer macroecological questions will result in incomplete inferences. Finally, we hope to show that generating inferences for spatially broad (macroecological) issues, with biased data, can be detrimental to large-scale conservation decision making.