Putting Density Back into the Habitat‐Quality Equation: Case Study of an Open‐Nesting Forest Bird
Bibliographic record
Abstract
Ecological traps and other cases of apparently maladaptive habitat selection cast doubt on the relevance of density as an indicator of habitat quality. Nevertheless, the prevalence of these phenomena remains poorly known, and density may still reflect habitat quality in most systems. We examined the relationship between density and two other parameters of habitat quality in an open-nesting passerine species: the Ovenbird (Seiurus aurocapilla). We hypothesized that the average individual bird makes a good decision when selecting its breeding territory and that territory spacing reflects site productivity or predation risk. Therefore, we predicted that density would be positively correlated with productivity (number of young fledged per unit area). Because individual performance is sensitive to events partly determined by chance, such as nest predation, we further predicted density would be weakly correlated or uncorrelated with the proportion of territories fledging young. We collected data in 23 study sites (25 ha each), 16 of which were located in untreated mature northern hardwood forest and seven in stands partially harvested (treated) 1-7 years prior to the survey. Density explained most of the variability in productivity (R(2)= 0.73), and there was no apparent decoupling between density and productivity in treated plots. In contrast, there was no significant relationship between density and the proportion of territories fledging >or=1 young over the entire breeding season. These results suggest that density reflects habitat quality at the plot scale in this study system. To our knowledge this is one of the few studies testing the value of territory density as an indicator of habitat quality in an open-nesting bird species on the basis of a relatively large number of sizeable study plots.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".