Landscape-scale concordance between local ecological knowledge for tropical wild species and remote sensing of land cover
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".