Incorporating Social Information to Improve the Precision of Models of Avian Habitat Use
Bibliographic record
Abstract
Correlations between habitat measures and animal distributions are not always applicable outside the study area that generated them. In such cases, the particularities of these correlations likely arise because only use of the local habitat has been quantified, rather than actual habitat selection, as the distribution models do not account for the behavior of animals in choice. The addition of covariates accounting for selection strategies could improve the precision and accuracy of correlative models of habitat use, but this conjecture has received little empirical attention. To evaluate this possibility, we re-assess previously developed habitat-use models for abundance of males of three grassland birds by explicitly including two measures of selection behavior: the “propensity to aggregate” and “propensity to use social information.” Habitat-use models for Nelson's Sharp-tailed Sparrow (Ammodramus nelsoni) were not improved by either behavioral variable. However, models for two other species, the Bobolink (Dolichonyx oryzivorus) and Savannah Sparrow (Passerculus sandwichensis), improved substantially through reduced prediction error (assessed with cross-validation) and were much more likely to be an appropriate model (by reducing the deviance of the fitted models). These results indicate that habitat-selection models can be an improvement over correlative habitat-use models. In our case, these improvements were limited to two species in which individuals use their conspecifics as cues of local habitat quality. However, numerous other measures of selection behavior can be included to improve upon certain habitat-use models, particularly when those models depart unexplainedly from optimality theory.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".