SEASONAL INTERACTIONS, HABITAT QUALITY, AND POPULATION DYNAMICS IN MIGRATORY BIRDS
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. Historically, studies of habitat selection have focused on quantifying how current patterns of habitat occupancy influence condition and survival within a season. This approach, however, is overly simplistic, especially for migratory birds that spend different periods of the year in geographically distinct places. Habitat occupancy and the resulting condition of individual birds is likely to be affected by events in the previous season, and the consequences of habitat occupancy will influence individuals and populations in subsequent seasons. Thus, for migratory birds, variation in habitat quality (and quantity) needs to be understood in the context of how events interact throughout periods of the annual cycle. Seasonal interactions can occur at the individual level or population level. Individual-level interactions occur when events in one season produce nonlethal, residual effects that carry over to influence individuals the following season. Population-level interactions occur when a change in population size in one season influences per capita rates the following season. We review various methods for estimating seasonal interactions and highlight a number of examples in the literature. Using a variety of techniques, including intrinsic and extrinsic markers, the vast majority of studies to date have measured seasonal interactions at the individual level. Obtaining estimates of density and changes in per capita rates across multiple seasons to determine population-level interactions has been more challenging. Both types of seasonal interactions can influence population dynamics, but predicting their effects requires detailed knowledge of how populations are geographically connected (i.e., migratory connectivity). We recommend that researchers studying habitat occupancy and habitat selection consider how events in previous seasons influence events within a season.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 it