Visual encounters on line transect surveys under-detect carnivore species: Implications for assessing distribution and conservation status

• Autor(a) principal: Fragoso, José Manuel Vieira
• Data de Publicação: 2019
• Outros Autores: Gonçalves, Fernando Henrique, Oliveira, Luiz Flamarion Barbosa, Overman, Han, Levi, Taal, Silvius, Kirsten M.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional do INPA
• Texto Completo: https://repositorio.inpa.gov.br/handle/1/14653
• topic: Carnivore; Dog; Guyana; Jaguar; Mustelidae; Nonhuman; Rare Species; Animals; Carnivora; Ecosystem; Environmental Protection; Questionnaires; Animalss; Carnivora; Conservation Of Natural Resources; Ecosystem; Surveys And Questionnaires

We compared the distribution and occurrence of 15 carnivore species with data collected monthly over three years by trained native trackers using both sign surveys and an encounter-based, visual-distance method in a well-preserved region of southern Guyana (Amazon / Guiana Shield). We found that a rigorously applied sign-based method was sufficient to describe the status of most carnivore species populations, including rare species such as jaguar and bush dog. We also found that even when accumulation curves for direct visual encounter data reached an asymptote, customarily an indication that sufficient sampling has occurred to describe populations, animal occurrence and distribution were grossly underestimated relative to the results of sign data. While other researchers have also found that sign are better than encounters or camera traps for large felids, our results are important in documenting the failure of even intensive levels of effort to raise encounter rates sufficiently to enable statistical analysis, and in describing the relationship between encounter and sign data for an entire community of carnivores including felids, canids, procyonids, and mustelids. © Copyright: © 2019 Fragoso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.