Maladaptive Habitat Use of a <scp>N</scp>orth <scp>A</scp>merican <scp>W</scp>oodpecker in Population Decline
Bibliographic record
Abstract
Abstract Rapid anthropogenic habitat changes can lead to non‐ideal habitat use by animals, often resulting in lower fitness and population declines. An extreme case of use and fitness mismatch is an ecological trap where habitat quality cues are disjointed from the true quality of the habitat. Species primarily associated with anthropogenically altered habitat, such as red‐headed woodpeckers (Melanerpes erythrocephalus), may be especially vulnerable to use and fitness mismatch as they encounter novel environmental challenges. We investigated multi‐scale habitat use and nesting success of red‐headed woodpeckers to assess their vulnerability to mismatches between use and fitness as a result of non‐ideal habitat use across multiple scales. We found that habitat characteristics that promote feeding potential such as canopy openness and greater dead limb length appeared paramount and were consistent in use across several spatial scales although reproductive fitness suffered. This contrasts with the assumption that habitat use by nesting birds should instead favor predation avoidance at smaller scales to improve reproductive fitness and suggests that maladaptive, food‐based habitat use by red‐headed woodpeckers in southern Ontario may result in ecological traps for the species. Whether due to poor habitat choices or costly ones in favor of feeding potential, it is vital to consider this behavior in conservation and management plans for this and similar species. We suggest multi‐scale habitat use studies that consider fitness outcomes are critical for species‐at‐risk in human‐modified landscapes.
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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 | 0.000 | 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 | 0.001 | 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) | 0.003 | 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".