Predictors of extinction risk in large tropical forest mammals: From global to local.

Animals; Conservation of Natural Resources; Ecosystem; Endangered Species; Extinction, Biological; Forests; Tropical Climate; Mammals/physiology; Extinction risks; Global scale; Global-local; Large-scale studies; Local scale; Mammal species; Policy makers; Regional scale; Small scale; Tropical forest; Mammals; Multidisciplinary

Abstract :

[en] Studies can only guide conservation if their findings are informative at the scales at which practitioners and policy-makers operate. Yet, it is rarely tested whether large-scale studies reach similar conclusions to the smaller-scale studies on which conservation traditionally relies. We examine whether predictors of extinction risk are consistent across global, regional, and local scales, for 210 tropical forest mammal species (≥1 kg) that existed during the last 130,000 years, in 64 tropical forests, across three biogeographical realms. We found consistent predictors of extinction risk (body mass, generation length, diet, brain volume, and scansoriality) when analyses differed only in their spatial resolution. However, predictors differed when analyses also varied in their temporal extent. Macroecological findings about extinction risk can, thus, inform conservation at smaller scales, but they risk misidentifying threatened species if differences in temporal extent are not recognized.

Disciplines :

Environmental sciences & ecology

Author, co-author :

Schowanek, Simon D ; Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences (NMBU), Ås, Norway

Sheil, Douglas ; Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences (NMBU), Ås, Norway ; Forest Ecology and Management, Wageningen University and Research, Wageningen, Netherlands ; Center for International Forestry Research (CIFOR), Kota Bogor, Indonesia

Beaudrot, Lydia ; Department of Integrative Biology, Michigan State University, Michigan, USA ; Ecology, Evolution, and Behavior Program, Michigan State University, Michigan, USA

Dupont, Pierre ; Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences (NMBU), Ås, Norway

Espinosa, Santiago ; Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México ; Facultad de Ciencias Exactas y Naturales, Pontificia Universidad Católica del Ecuador, Quito, Ecuador

Estienne, Vittoria ; Nouabalé-Ndoki National Park, Wildlife Conservation Society - Congo Program, Brazzaville, Republic of the Congo

Fa, Julia E ; Center for International Forestry Research (CIFOR), Kota Bogor, Indonesia ; Department of Natural Sciences, School of Science and the Environment, Manchester Metropolitan University, Manchester, UK

Geldmann, Jonas ; Center for Macroecology, Evolution and Climate, Globe Institute, University of Copenhagen, Copenhagen, Denmark

Jansen, Patrick A ; Wildlife Ecology and Conservation Group, Wageningen University & Research, Wageningen, Netherlands ; Smithsonian Tropical Research Institute, Panamá, República de Panamá ; Department of Biology, Utrecht University, Padualaan 8, Utrecht 3584 CH, Netherlands

Johnson, Steig E ; Department of Anthropology & Archaeology, University of Calgary, Calgary, Canada

More authors (18 more)

Language :

English

Title :

Predictors of extinction risk in large tropical forest mammals: From global to local.

Publication date :

17 July 2026

Journal title :

Science Advances

eISSN :

2375-2548

Publisher :

American Association for the Advancement of Science, United States

Volume :

Issue :

Pages :

eaeb7543

Peer reviewed :

Peer Reviewed verified by ORBi

Development Goals :

15. Life on land

Additional URL :

https://www.science.org/doi/pdf/10.1126/sciadv.aeb7543

Funders :

Norges Forskningsråd

Funding text :

We thank R. \u00D8stergaard Pedersen for the help regarding phylogenetic imputations, and R. Taylor Lemoine for the help regarding biogeography. We thank S. Dey for the help with Nimble and Bayesian analyses. We thank A. Mtui, J. Salvador, and the many people who helped to collect the camera trap data used in this study. This work was funded by the Research Council of Norway (grant NFR301075; D.S. and R.B.).

Available on ORBi :

since 07 August 2026

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