Accounting for Fitness: Combining Survival and Selection when Assessing Wildlife-Habitat Relationships
Bibliographic record
Abstract
Assessing the viability of a population requires understanding of the resources used by animals to determine how those resources affect long-term population persistence. To understand the true importance of resources, one must consider both selection (where a species occurs) and fitness (reproduction and survival) associated with the use of those resources. Failure to do so may result in incorrect assessments of habitat quality and inappropriate management activities. We illustrate the importance of considering both occurrence and fitness metrics when assessing habitat requirements for the endangered greater sage-grouse in Alberta, Canada. This population is experiencing low recruitment, so we assess resource use during the brood-rearing period to identify management priorities. First, we develop logistic regression occurrence models fitted with habitat covariates. Second, we use proportional hazard survival analysis to assess chick survival (fitness component) associated with habitat and climatic covariates. Sage-grouse show strong selection for sage-brush cover at both patch (smaller) and area (larger) spatial scales, and weak selection for forbs at the patch scale only. Drought conditions based on an index combining growing degree days and spring precipitation strongly reduced chick survival. While hens selected for taller grass and more sage-brush cover, only taller grass cover also enhanced chick survival. We show that sage-grouse may not recognize all ecological cues that enhance chick survival. Management activities targeted at providing habitats that sage-grouse are likely to use in addition to those that enhance survival are most likely to ensure the long-term viability of this population. Our techniques account for both occurrence and fitness in habitat quality assessments and, in general, the approach should be applicable to other species or ecosystems.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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".