Landscape-scale concordance between local ecological knowledge for tropical wild species and remote sensing of land cover

Author:

Takasaki Yoshito1ORCID,Coomes Oliver T.2ORCID,Abizaid Christian3ORCID,Kalacska Margaret2ORCID

Affiliation:

1. Graduate School of Economics, University of Tokyo, Tokyo 113-0033, Japan

2. Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada

3. Department of Geography & Planning and School of Environment, University of Toronto, Toronto, ON M5S 3G3, Canada

Abstract

Monitoring the status of species is crucial for biodiversity conservation and sustainable resource management in tropical forests, but conventional in situ monitoring methods are impractical over large scales. Scientists have resorted to two potentially complementary approaches: local ecological knowledge (LEK) and remote sensing. To gauge the potential of combining LEK and remote sensing for assessing species status at landscape scales, a large-scale assessment of the reliability of both measures is critical but hampered by the lack of ground-level data. We conducted a landscape-scale assessment of LEK and remote sensing, using a survey of over 900 communities (a near census in our study area) and nearly 4,000 households in 235 randomly selected communities in the Peruvian Amazon—the largest LEK survey as yet undertaken in tropical forests. The survey collected LEK data on the presence of 20 indicator species from both community leaders/elders and randomly sampled households. We assessed LEK and remotely sensed land cover—forest cover and nonmain channel open water—as proxies for species habitat, across species (game, fish, and timber), over time (current and historical), and by indigeneity (Indigenous peoples and mestizos). Overall, LEK and remotely sensed land cover corroborate each other well. Concordance is highest for the current status of game species reported by sampled households, as is the concordance of historical LEK from Indigenous community leaders/elders. The results point to the promise of combining LEK and remote sensing in monitoring the status of species in data-poor tropical forests.

Funder

MEXT | Japan Society for the Promotion of Science

Gouvernement du Canada | Social Sciences and Humanities Research Council of Canada

University of Toronto

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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