Evidence map›Paper›PMID 36161957›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2022

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

Yoshito Takasaki, Oliver T Coomes, Christian Abizaid, Margaret Kalacska

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Use of community characteristics to predict hunting and game harvests in western Amazonian forests.Conservation biology : the journal of the Society for Conservation Biology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yoshito TakasakiGraduate School of Economics, University of Tokyo, Tokyo 113-0033, Japan.ORCID 0000-0002-4324-4668
Oliver T CoomesDepartment of Geography, McGill University, Montreal, QC H3A 0B9, Canada.ORCID 0000-0002-8220-8758
Christian AbizaidDepartment of Geography & Planning and School of Environment, University of Toronto, Toronto, ON M5S 3G3, Canada.ORCID 0000-0002-1672-3006
Margaret KalacskaDepartment of Geography, McGill University, Montreal, QC H3A 0B9, Canada.ORCID 0000-0002-1676-481X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ForestsRemote Sensing TechnologyAnimalsBiodiversityConservation of Natural ResourcesEcosystemPeruReproducibility of ResultsTropical ClimateWaterWaterAmazoniaconcordancelocal ecological knowledgeremote sensing

Identifiers

PMID36161957
PMCPMC9546624

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.