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Methods for prioritizing protected areas using individual and aggregate rankings

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Methods for prioritizing protected areas using individual and aggregate rankings. / Carvalho, Fabio; Brown, Kerry A.; Gordon, Adam D.; Yesuf, Gabriel U.; Raherilalao, Marie Jeanne; Raselimanana, Achille P.; Soarimalala, Voahangy; Goodman, Steven M.

In: Environmental Conservation, Vol. 47, No. 2, 01.06.2020, p. 113-122.

Research output: Contribution to journalJournal article

Harvard

Carvalho, F, Brown, KA, Gordon, AD, Yesuf, GU, Raherilalao, MJ, Raselimanana, AP, Soarimalala, V & Goodman, SM 2020, 'Methods for prioritizing protected areas using individual and aggregate rankings', Environmental Conservation, vol. 47, no. 2, pp. 113-122. https://doi.org/10.1017/S0376892920000090

APA

Carvalho, F., Brown, K. A., Gordon, A. D., Yesuf, G. U., Raherilalao, M. J., Raselimanana, A. P., Soarimalala, V., & Goodman, S. M. (2020). Methods for prioritizing protected areas using individual and aggregate rankings. Environmental Conservation, 47(2), 113-122. https://doi.org/10.1017/S0376892920000090

Vancouver

Carvalho F, Brown KA, Gordon AD, Yesuf GU, Raherilalao MJ, Raselimanana AP et al. Methods for prioritizing protected areas using individual and aggregate rankings. Environmental Conservation. 2020 Jun 1;47(2):113-122. https://doi.org/10.1017/S0376892920000090

Author

Carvalho, Fabio ; Brown, Kerry A. ; Gordon, Adam D. ; Yesuf, Gabriel U. ; Raherilalao, Marie Jeanne ; Raselimanana, Achille P. ; Soarimalala, Voahangy ; Goodman, Steven M. / Methods for prioritizing protected areas using individual and aggregate rankings. In: Environmental Conservation. 2020 ; Vol. 47, No. 2. pp. 113-122.

Bibtex

@article{34cfad56a0ea4e2fbb87b2468339914a,
title = "Methods for prioritizing protected areas using individual and aggregate rankings",
abstract = "Despite their legal protection status, protected areas (PAs) can benefit from priority ranks when ongoing threats to their biodiversity and habitats outpace the financial resources available for their conservation. It is essential to develop methods to prioritize PAs that are not computationally demanding in order to suit stakeholders in developing countries where technical and financial resources are limited. We used expert knowledge-derived biodiversity measures to generate individual and aggregate priority ranks of 98 mostly terrestrial PAs on Madagascar. The five variables used were state of knowledge (SoK), forest loss, forest loss acceleration, PA size and relative species diversity, estimated by using standardized residuals from negative binomial models of SoK regressed onto species diversity. We compared our aggregate ranks generated using unweighted averages and principal component analysis (PCA) applied to each individual variable with those generated via Markov chain (MC) and PageRank algorithms. SoK significantly affected the measure of species diversity and highlighted areas where more research effort was needed. The unweighted- and PCA-derived ranks were strongly correlated, as were the MC and PageRank ranks. However, the former two were weakly correlated with the latter two. We recommend using these methods simultaneously in order to provide decision-makers with the flexibility to prioritize those PAs in need of additional research and conservation efforts.",
keywords = "conservation triage, expert knowledge, forest loss acceleration, Madagascar, Markov chain algorithm, PageRank algorithm",
author = "Fabio Carvalho and Brown, {Kerry A.} and Gordon, {Adam D.} and Yesuf, {Gabriel U.} and Raherilalao, {Marie Jeanne} and Raselimanana, {Achille P.} and Voahangy Soarimalala and Goodman, {Steven M.}",
year = "2020",
month = jun
day = "1",
doi = "10.1017/S0376892920000090",
language = "English",
volume = "47",
pages = "113--122",
journal = "Environmental Conservation",
issn = "0376-8929",
publisher = "CAMBRIDGE UNIV PRESS",
number = "2",

}

RIS

TY - JOUR

T1 - Methods for prioritizing protected areas using individual and aggregate rankings

AU - Carvalho, Fabio

AU - Brown, Kerry A.

AU - Gordon, Adam D.

AU - Yesuf, Gabriel U.

AU - Raherilalao, Marie Jeanne

AU - Raselimanana, Achille P.

AU - Soarimalala, Voahangy

AU - Goodman, Steven M.

PY - 2020/6/1

Y1 - 2020/6/1

N2 - Despite their legal protection status, protected areas (PAs) can benefit from priority ranks when ongoing threats to their biodiversity and habitats outpace the financial resources available for their conservation. It is essential to develop methods to prioritize PAs that are not computationally demanding in order to suit stakeholders in developing countries where technical and financial resources are limited. We used expert knowledge-derived biodiversity measures to generate individual and aggregate priority ranks of 98 mostly terrestrial PAs on Madagascar. The five variables used were state of knowledge (SoK), forest loss, forest loss acceleration, PA size and relative species diversity, estimated by using standardized residuals from negative binomial models of SoK regressed onto species diversity. We compared our aggregate ranks generated using unweighted averages and principal component analysis (PCA) applied to each individual variable with those generated via Markov chain (MC) and PageRank algorithms. SoK significantly affected the measure of species diversity and highlighted areas where more research effort was needed. The unweighted- and PCA-derived ranks were strongly correlated, as were the MC and PageRank ranks. However, the former two were weakly correlated with the latter two. We recommend using these methods simultaneously in order to provide decision-makers with the flexibility to prioritize those PAs in need of additional research and conservation efforts.

AB - Despite their legal protection status, protected areas (PAs) can benefit from priority ranks when ongoing threats to their biodiversity and habitats outpace the financial resources available for their conservation. It is essential to develop methods to prioritize PAs that are not computationally demanding in order to suit stakeholders in developing countries where technical and financial resources are limited. We used expert knowledge-derived biodiversity measures to generate individual and aggregate priority ranks of 98 mostly terrestrial PAs on Madagascar. The five variables used were state of knowledge (SoK), forest loss, forest loss acceleration, PA size and relative species diversity, estimated by using standardized residuals from negative binomial models of SoK regressed onto species diversity. We compared our aggregate ranks generated using unweighted averages and principal component analysis (PCA) applied to each individual variable with those generated via Markov chain (MC) and PageRank algorithms. SoK significantly affected the measure of species diversity and highlighted areas where more research effort was needed. The unweighted- and PCA-derived ranks were strongly correlated, as were the MC and PageRank ranks. However, the former two were weakly correlated with the latter two. We recommend using these methods simultaneously in order to provide decision-makers with the flexibility to prioritize those PAs in need of additional research and conservation efforts.

KW - conservation triage

KW - expert knowledge

KW - forest loss acceleration

KW - Madagascar

KW - Markov chain algorithm

KW - PageRank algorithm

U2 - 10.1017/S0376892920000090

DO - 10.1017/S0376892920000090

M3 - Journal article

VL - 47

SP - 113

EP - 122

JO - Environmental Conservation

JF - Environmental Conservation

SN - 0376-8929

IS - 2

ER -