SATELLITE DETECTION OF BIRD COMMUNITIES IN TROPICAL COUNTRYSIDE
Bibliographic record
Abstract
The future of biodiversity hinges partly on realizing the potentially high conservation value of human-dominated countryside. The characteristics of the countryside that promote biodiversity preservation remain poorly understood, however, particularly at the fine scales at which individual farmers tend to make land use decisions. To address this problem, we explored the use of a rapid remote sensing method for estimating bird community composition in tropical countryside, using a two-step process. First, we asked how fine-grained variation in land cover affected community composition. Second, we determined whether the observed changes in community composition correlated with three easily accessible remote sensing metrics (wetness, greenness, and brightness), derived from performing a tasseled-cap transformation on a Landsat Enhanced Thematic Mapper Plus image. As a comparison, we also examined whether the most commonly used remote sensing indicator in ecology, the Normalized Difference Vegetation Index (NDVI), correlated with community composition. We worked within an agricultural landscape in southern Costa Rica, where the land comprised a complex and highly heterogeneous mosaic of remnant native vegetation, pasture, coffee cultivation, and other crops. In this region, we selected 12 study sites (each < 60 ha) that encompassed the range of available land cover possibilities in the countryside. Within each site, we surveyed bird communities within all major land cover types, and we conducted detailed field mapping of land cover. We found that the number of forest-affiliated species increased with forest cover and decreased with residential area across sites. Conversely, the number of agriculture-affiliated species using forest increased with land area devoted to agricultural and residential uses. Interestingly, we found that the wetness and brightness metrics predicted the number of forest- and agriculture-affiliated species within a site as well as did detailed field-generated maps of land cover. In contrast, NDVI and the closely correlated greenness metric did not correlate with land cover or with bird communities. Our study shows the strong potential of the tasseled-cap transformation as a tool for assessing the conservation value of countryside for biodiversity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 teacher head, 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".