Amazonia camtrap: A dataset of mammal, bird, and reptile species recorded with camera traps in the Amazon forest.

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T, Piña, T ORCID: https://orcid.org/0000-0001-6718-9379, Caetano Andrade, V, Santiago, W, Magnusson, W, Campos, Z ORCID: https://orcid.org/0000-0003-0386-1618 and Ribeiro, M ORCID: https://orcid.org/0000-0002-4312-202X 2022, 'Amazonia camtrap: A dataset of mammal, bird, and reptile species recorded with camera traps in the Amazon forest.' , Ecology , e3738.

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Abstract

The Amazon forest has the highest biodiversity on earth. However, information on Amazonian vertebrate diversity is still deficient and scattered across the published, peer-reviewed and grey literature and in unpublished raw data. Camera traps are an effective non-invasive method of surveying vertebrates, applicable to different scales of time and space. In this study, we organized and standardized camera trap records from different Amazon regions to compile the most extensive dataset of inventories of mammal, bird and reptile species ever assembled for the area. The complete dataset comprises 154,123 records of 317 species (185 birds, 119 mammals and 13 reptiles) gathered from surveys from the Amazonian portion of eight countries (Brazil, Bolivia, Colombia, Ecuador, French Guiana, Peru, Suriname and Venezuela). The most frequently recorded species per taxa were: mammals - Cuniculus paca (11,907 records); birds - Pauxi tuberosa (3,713 records); and reptiles - Tupinambis teguixin (716 records). The information detailed in this data paper opens-up opportunities for new ecological studies at different spatial and temporal scales, allowing for a more accurate evaluation of the effects of habitat loss, fragmentation, climate change and other human-mediated defaunation processes in one of the most important and threatened tropical environments in the world. The dataset is not copyright restricted; please cite this data-paper when using its data in publications and we also request that researchers and educators inform us of how they are using this data. [Abstract copyright: This article is protected by copyright. All rights reserved.]

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Ecology
ISSN: 1939-9170
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 29 Jul 2022 13:23
Last Modified: 17 Aug 2022 08:31
URI: https://usir.salford.ac.uk/id/eprint/64044

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