Logo image
Back
Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates
Journal article   Peer reviewed

Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates

N Cavada, M Ciolli, D Rocchini, C Barelli, Andrew R Marshall and F Rovero
Ecological Applications, Vol.27(1), pp.235-243
2017
DOI: https://doi.org/10.1002/eap.1438
url
https://doi.org/10.1002/eap.1438View
Published Version

Abstract

abundance basal area GIS Landsat primates remote sensing spatially explicit models tropical forest Udzungwa
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. © 2016 by the Ecological Society of America.

Details

Metrics

604 Record Views
12 Times Cited - Web of Science

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
International collaboration
Web Of Science research areas
Ecology
Environmental Sciences

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#13 Climate Action
#15 Life on Land

Source: InCites

Logo image