Journal
LANDSCAPE ECOLOGY
Volume 36, Issue 4, Pages 973-987Publisher
SPRINGER
DOI: 10.1007/s10980-021-01196-9
Keywords
Scale of response; Tree cover; Spatial scale; Multi-scale model; Mammal; Herbivore; Brazil
Funding
- CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior, Brazil) COFECUB (Comite Francais d'Evaluation de la Cooperation Universitaire et Scientifique) [Sv 875-17, 23038.001818/2020-70]
- CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior, Brazil) (Campus France) [Sv 875-17, 23038.001818/2020-70]
- CAPES [001, Capes-PrInt 41/2017, 88881.311897/2018-01]
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) [301306/2018-4]
- Pesquisa Ecologica de Longa DuracAo (PELD/ILTER) Planalto da Bodoquena
- University of Angers (FragHerb project)
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The optimal scale of effect for the occurrence of terrestrial mammalian herbivores in Brazilian savannas was found to be 0.5 to 2 km, with the scale of effect being similar among three of the species. The way forest habitat is operationally defined significantly influences the probability of detecting the scale of effects, especially for forest-specialist species.
Context Determining the appropriate scale at which to study species' interactions with their environment is a great challenge. Objective We investigated the spatial extent at which landscape structure affects the occurrence of four species of terrestrial mammalian herbivores in the Brazilian savannas and examined whether those scales could be explained by species ecological traits and how forest habitat was operationally defined. Methods Using maps of forest cover, camera trapping and occupancy modelling, we determined the relations between three landscape metrics (percentage of forest cover, patch density and edge density) and the occurrence of four species. To determine the optimal scale of effect for each species, we computed landscape metrics at different spatial extents (from 0.5 to 10 km radius) from camera trap locations and for three forest maps, each using different operational definitions of a forest pixel: minimum of tree cover of 25, 50 or 75%. Results The occupancy models revealed scales of effect of 0.5 to 2 km with the scale of effect being similar among three of the species. However, the probability of a scale of effect being detected depended upon how forest is operationally defined, being greater when forest was defined with greater tree cover, particularly for forest-specialist species. Conclusions Besides biological traits, the way habitat is operationally defined shapes our ability to detect the scale of effects. Thus, it is necessary not to adopt a multi-scale approach, but also to use multiple operational definitions of habitat, considering particularities of how each species interact with their environment.
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