Methods for prioritizing protected areas using individual and aggregate rankings

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 dem...

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Bibliographic Details
Published in:Environmental conservation 2020-06, Vol.47 (2), p.113-122
Main Authors: Carvalho, Fabio, Brown, Kerry A, Gordon, Adam D, Yesuf, Gabriel U, Raherilalao, Marie Jeanne, Raselimanana, Achille P, Soarimalala, Voahangy, Goodman, Steven M
Format: Article
Language:English
Subjects:
Algorithms
Biodiversity
Conservation
Decision making
Developing countries
Forests
Habitats
LDCs
Markov chains
Natural resources
Principal components analysis
Protected areas
Regression analysis
Resource conservation
Search engines
Spatial data
Species diversity
Taxonomy
Variables
Vegetation
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Summary: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.
ISSN:0376-8929
1469-4387
DOI:10.1017/S0376892920000090