Density‐dependent immigration promotes population stability in a long‐distance migratory bird
Bibliographic record
Abstract
Abstract The spatial structure of populations determines the relative importance of reproduction, survival and movement on population dynamics. However, the mechanisms by which local individuals and immigrants interact and the subsequent effects of immigrants on productivity are poorly known. We developed an integrated population model (IPM) to study the extent and consequences of immigration on the dynamics of a neotropical migrant (American redstart, Setophaga ruticilla ) over an 11‐year period in Ontario, Canada. New immigrants represented the majority of the study population each year with higher immigration rates for males than females and for first‐year breeders than breeders in their second year or older. Immigration was negatively density dependent, with immigrants replacing previously established breeders in a compensatory manner following their death or emigration. Because of the tradeoff between immigration and apparent survival, neither had a strong influence on population growth and reproductive output was most strongly correlated with a change in abundance between years. However, if immigration ceased, the study population would become locally extinct within 7 years and thus immigrants were essential for local population persistence. We found no evidence for reduced breeding success when immigrants represented a higher proportion of the study population. Our research highlights the importance of movement in the stability of open populations and the strong correlation between the fates of local breeders and the number of immigrants entering the population. We recommend the use of IPMs to address the spatial scale over which immigration occurs and how different scales influence its contribution to 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 | 0.001 |
| 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 | 0.001 | 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".