Sequential settlement and site dependence in a migratory raptor
Bibliographic record
Abstract
Habitat selection models, such as the ideal free, ideal despotic, site-dependent, and conspecific cuing models, are of great importance to behavioral ecologists given their capability to predict habitat distributions and to link individual behavior to population processes. However, there have been relatively few field tests of their predictions. We tested the 4 models by studying the process of sequential settlement on territory in 2 distant populations of a migratory raptor, the black kite Milvus migrans. Results were mainly consistent with the site-dependent model: on arrival, kites settled on progressively lower quality territories, and earlier arriving individuals were older, larger, and in better body condition than later arriving ones, leading to a state-dependent arrival sequence also predicted by a previous theoretical model of settlement pattern. Occupation of superior territories by superior phenotypes resulted in cascading advantages for earlier arriving individuals in terms of subsequent reproductive performance. At the population level, the populations expanded/retracted from lower quality sites during population increases/declines. The above scenario was consistent across the 2 populations, and a review of the literature uncovered a remarkably consistent picture of state-dependent arrival, progressive monopolization of best quality sites, and cascading effects on subsequent breeding performance. We propose as a general paradigm of sequential settlement the following process: 1) arrival date is a reliable surrogate of phenotype quality, 2) early-arriving individuals have preferential access to the best quality sites and partners, 3) the above conditions cascade into a number of benefits ultimately related to higher fitness for earlier arriving individuals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".