Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates. Issue 1 (4th January 2017)

• Record Type: Journal Article
• Authors: Cavada, Nathalie; Ciolli, Marco; Rocchini, Duccio; Barelli, Claudia; Marshall, Andrew R.; Rovero, Francesco
• Is Part Of: Ecological applications. Volume 27:Issue 1(2017)
• Page Start: 235
• Page End: 243
• Publication Date: 2017-01-04
• Subjects: abundance -- basal area -- GIS -- Landsat -- primates -- remote sensing -- spatially explicit models -- tropical forest -- Udzungwa; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1002/eap.1438
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract: Spatially explicit models of animal abundance are a critical tool to inform conservation planning and management. However, they require the availability of spatially diffuse environmental predictors of abundance, which may be challenging, especially in complex and heterogeneous habitats. This is particularly the case for tropical mammals, such as nonhuman primates, that depend on multi‐layered and species‐rich tree canopy coverage, which is usually measured through a limited sample of ground plots. We developed an approach that calibrates remote‐sensing imagery to ground measurements of tree density to derive basal area, in turn used as a predictor of primate density based on published models. We applied generalized linear models (GLM) to relate 9.8‐ha ground samples of tree basal area to various metrics extracted from Landsat 8 imagery. We tested the potential of this approach for spatial inference of animal density by comparing the density predictions for an endangered colobus monkey, to previous estimates from field transect counts, measured basal area, and other predictors of abundance. The best GLM had high accuracy and showed no significant difference between predicted and observed values of basal area. Our species distribution model yielded predicted primate densities that matched those based on field measurements. Results show the potential of using open‐access and global remote‐sensing data to derive an important predictor of animal abundance in tropical forests and in turn to make spatially explicit inference on animal density. This approach has important, inherent applications as it greatly magnifies the relevance of abundance modeling for informing conservation. This is especially true for threatened species living in heterogeneous habitats where spatial patterns of abundance, in relation to habitat and/or human disturbance factors, are often complex and, management decisions, such as improving forest protection, may need to be focused on priority areas.