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

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Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates. / Cavada, Nathalie; Ciolli, Marco; Rocchini, Duccio; Barelli, Claudia; Marshall, Andrew R.; Rovero, Francesco.

In: ECOLOGICAL APPLICATIONS, Vol. 27, No. 1, 04.01.2017, p. 235-243.

Research output: Contribution to journalArticlepeer-review

Harvard

Cavada, N, Ciolli, M, Rocchini, D, Barelli, C, Marshall, AR & Rovero, F 2017, 'Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates', ECOLOGICAL APPLICATIONS, vol. 27, no. 1, pp. 235-243. https://doi.org/10.1002/eap.1438

APA

Cavada, N., Ciolli, M., Rocchini, D., Barelli, C., Marshall, A. R., & Rovero, F. (2017). Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates. ECOLOGICAL APPLICATIONS, 27(1), 235-243. https://doi.org/10.1002/eap.1438

Vancouver

Cavada N, Ciolli M, Rocchini D, Barelli C, Marshall AR, Rovero F. Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates. ECOLOGICAL APPLICATIONS. 2017 Jan 4;27(1):235-243. https://doi.org/10.1002/eap.1438

Author

Cavada, Nathalie ; Ciolli, Marco ; Rocchini, Duccio ; Barelli, Claudia ; Marshall, Andrew R. ; Rovero, Francesco. / Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates. In: ECOLOGICAL APPLICATIONS. 2017 ; Vol. 27, No. 1. pp. 235-243.

Bibtex - Download

@article{1885fed271ed4a638c310942f905b1c4,
title = "Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates",
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.",
keywords = "abundance, basal area, GIS, Landsat, primates, remote sensing, spatially explicit models, tropical forest, Udzungwa",
author = "Nathalie Cavada and Marco Ciolli and Duccio Rocchini and Claudia Barelli and Marshall, {Andrew R.} and Francesco Rovero",
note = "{\textcopyright} 2016, Ecological Society of America. This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy. Further copying may not be permitted; contact the publisher for details.",
year = "2017",
month = jan,
day = "4",
doi = "10.1002/eap.1438",
language = "English",
volume = "27",
pages = "235--243",
journal = "ECOLOGICAL APPLICATIONS",
issn = "1051-0761",
publisher = "Ecological Society of America",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates

AU - Cavada, Nathalie

AU - Ciolli, Marco

AU - Rocchini, Duccio

AU - Barelli, Claudia

AU - Marshall, Andrew R.

AU - Rovero, Francesco

N1 - © 2016, Ecological Society of America. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

PY - 2017/1/4

Y1 - 2017/1/4

N2 - 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.

AB - 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.

KW - abundance

KW - basal area

KW - GIS

KW - Landsat

KW - primates

KW - remote sensing

KW - spatially explicit models

KW - tropical forest

KW - Udzungwa

UR - http://www.scopus.com/inward/record.url?scp=85008311724&partnerID=8YFLogxK

U2 - 10.1002/eap.1438

DO - 10.1002/eap.1438

M3 - Article

AN - SCOPUS:85008311724

VL - 27

SP - 235

EP - 243

JO - ECOLOGICAL APPLICATIONS

JF - ECOLOGICAL APPLICATIONS

SN - 1051-0761

IS - 1

ER -