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Sloths from above: assessing the local abundance of the Southern Sloth (Bradypus crinitus) using drones

Grant number: 23/03892-1
Support Opportunities:BIOTA-FAPESP Program - Regular Research Grants
Duration: March 01, 2024 - February 28, 2026
Field of knowledge:Biological Sciences - Ecology - Applied Ecology
Principal Investigator:Adriano Garcia Chiarello
Grantee:Adriano Garcia Chiarello
Host Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
Associated researchers:Danilo Boscolo ; Fabiano Rodrigues de Melo ; Paloma Marques Santos ; Silvio Frosini de Barros Ferraz


Sloths are strictly arboreal, folivorous mammals. Six species occur in Brazil, two of which are endemic to the Atlantic Forest and are threatened with extinction due to deforestation and associated impacts. These two threatened species are the northern maned sloth (Bradypus torquatus), found between Bahia and Sergipe and the southern maned sloth (B. crinitus), a recently described species that occurs between Espírito Santo and Rio de Janeiro. Previous studies indicate that these sloths prefer humid forests (dense ombrophilous) located from sea level to about 1000 m altitude. It is not known, however, what degree of forest disturbance is tolerated by these two species, although radio-telemetry-based studies indicate that secondary forests may be suitable habitat for them. The objective of this study is therefore focused on elucidating how the local abundance of the newly described B. crinitus varies in native forests at different degrees of natural regeneration. We will conduct the study in the lower-montane micro-region of ES, where there are populations of this species and evidence of forest regeneration in the last 20-30 years, according to MapBiomas. The study region includes protected areas and areas specially protected by the Forest Code (permanent preservation areas and legal reserves), allowing comparisons of abundance in forests at different successional stages inserted in matrices with varying degrees of forest cover. In an innovative way, we will estimate local abundance using drones equipped with thermal sensors (infrared) suitable for the detection of cryptic animals. The abundance will be estimated using hierarchical N-mixture models that model detection failures without the need for differentiation of detected individuals. In addition to forest quality, other potentially influential predictors related to landscape composition and configuration will be measured and evaluated. The data to be obtained are relevant to stimulate conservation projects and to inform public policies involving forest restoration in public and private areas. (AU)

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