Effects of breeding versus winter habitat loss and fragmentation on the population dynamics of a migratory songbird
Bibliographic record
Abstract
Many migratory species are in decline and understanding these declines is challenging because individuals occupy widely divergent and geographically distant habitats during a single year and therefore populations across the range are interconnected in complex ways. Network modeling has been used to show, theoretically, that shifts in migratory connectivity patterns can occur in response to habitat or climate changes and that habitat loss in one region can affect sub-populations in regions that are not directly connected. Here, we use a network model, parameterized by integrating long-term monitoring data with direct tracking of -100 individuals, to explain population trends in the rapidly declining Wood Thrush (Hylocichla mustelina) and to predict future trends. Our model suggests that species-level declines in Wood Thrush are driven primarily by tropical deforestation in Central America but that protection of breeding habitat in some regions is necessary to prevent shifts in migratory connectivity and to sustain populations in all breeding regions. The model illustrates how shifts in migratory connectivity may lead to unexpected population declines in key regions. We highlight current knowledge gaps that make modeling full life-cycle population demographics in migratory species challenging but also demonstrate that modeling can inform conservation while these gaps are being filled.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".