Territory Sizes and Patterns of Habitat Use by Forest Birds Over Five Decades: Ideal Free or Ideal Despotic?
Bibliographic record
Abstract
Relations among territoriality, abundance and habitat suitability are fundamental to the ecology of many animal populations. Theory suggests two classes of possible responses to increasing abundance in territorial species: (1) the ideal free distribution (IFD), which predicts smaller territory sizes and decreased fitness as individuals adaptively pack into suitable habitats, and (2) the ideal despotic distribution (IDD), which predicts stable territory sizes and fitness in preferred habitats for dominant individuals and increased use of marginal habitats, reduced fitness and changes in territory sizes for subordinate individuals. We analysed the territory sizes and locations of seven migratory songbird species occupying a 10-ha plot in the Hubbard Brook Experimental Forest, New Hampshire, USA over a 52-year period. Species varied in abundance over years from twofold to 22-fold, and all species displayed clear patterns of habitat preference within the study plot. Consistent with IFD, and contrary to IDD, territory sizes decreased with local abundance for all species, irrespective of habitat preferences. There was at least a twofold variation in territory size within years. Conformity of territory size to predictions of the IFD argues for the efficacy of territorial defence in songbirds and has general consequences for population dynamics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".