Biome‐scale signatures of land‐use change on raptor abundance: insights from single‐visit detection‐based models
Bibliographic record
Abstract
Summary Declines in raptor populations often result from the transformation of natural habitats to anthropogenic land uses, but the rate of population change can vary greatly among species. Declines associated with land transformation have been linked to loss of foraging habitat, prey resources and nest sites due to expanding cultivation, overgrazing and disturbance of nests and persecution by humans. We combined extensive road‐survey counts of raptors, large‐scaleGISdata sets and a single‐visit conditional likelihoodN‐mixture model to generate biome‐scale projections of abundance as a function of environmental covariates while correcting for detection error and other forms of zero inflation. This approach was employed to investigate how land‐use transformations in the threatened Cerrado savannas and Pantanal wetlands in Brazil have affected the populations of raptors on a large scale (>300 000 km2). We predicted that predominance of land uses with fewer or less accessible prey and scarcer nesting sites would sustain smaller raptor populations. Twelve species were encountered sufficiently to estimate abundance, while 20 others were encountered too infrequently to permit abundance estimation. Detection of all 12 species was influenced by time of day, with variable species‐specific effects that followed expectations based on foraging and flight behaviour. Abundance of most species was negatively influenced by conversion of natural habitats to pastures, an effect that held even for generalist species considered poor indicators of habitat quality, but was not universally impacted by urbanization and soya beans, sugarcane and Eucalyptus plantations, confirming the expectation that some species may tolerate these habitats. Spatial projections of abundance appeared realistic for most species. Synthesis and applications. Protection of the remaining natural habitats is essential to prevent further decline of raptor populations in the Brazilian Cerrado and Pantanal, and restoration of unproductive pastures into natural habitat could prove an efficient strategy to recover diminished raptor populations. The conditional likelihood single‐visit approach is a valid and useful tool for measuring population size and for making detection‐corrected inferences of abundance over large geographical scales with sensible research budgets. Incorporating the approach into a multispecies framework would allow future studies to make important inferences for entire communities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".