Population densities of curassows, guans, and chachalacas (Cracidae): Effects of body size, habitat, season, and hunting
Bibliographic record
Abstract
Understanding the factors that determine population densities is critical for conserving viable populations of threatened species. Half of the 50 species in the family Cracidae have experienced population declines. We conducted a literature review to explore the relations of population densities of cracids with body size, habitat, season, and hunting. We compiled 103 density data points for 27 species in 37 localities from Mexico to Argentina. There was no correlation between body mass and density. The larger cracines tended to have lower densities than penelopines, but densities in both subfamilies spanned a similar range of values. Intraspecific and interspecific densities varied among sites over 2 orders of magnitude (1–100 birds km−2). Some cracids exhibited plasticity in habitat use, with variable densities among habitats. There is evidence that some species performed local movements related to seasonality in rainfall or resource availability, leading to aggregations around water sources during the dry season or around seasonally abundant food sources. Hunting had a negative effect on population densities, but in some cases low to moderate hunting did not cause a decrease in density in comparison to nonhunted sites. Despite having similar ecologies, densities of cracid species are very variable, and each population seems to respond idiosyncratically to local factors, which requires care if data are extrapolated across populations or species. Future studies that aim to characterize cracid populations for conservation purposes should take into account possible intraspecific density variations related to seasonality, local movements, and habitat heterogeneity.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".