Density‐dependent dispersal suggests a genetic measure of habitat suitability
Bibliographic record
Abstract
Recent research shows that density dependence should result in predictable movements between habitats of different suitability, depending on whether population densities are increasing or decreasing. When population densities are increasing, habitats become filled in order of their suitability, resulting in a net flow from high suitability to low suitability. When populations decrease in density, the reverse can happen. These patterns suggest that genetic information can be used to infer habitat suitability since individual‐based genetic assignment tests permit high resolution assessments of migration. We used replicated landscapes to study fishers ( Martes pennanti ) during a population increase and predicted that there should be a net flow of individuals from areas of shallow to deep snow, since snow depth has previously been linked to fisher fitness. A total of 769 fishers were sampled from 35 different landscapes and profiled at 16 microsatellite loci. From assignment tests, we inferred five genetic populations. By assigning each of the 35 landscapes to one of these five populations, we were able to determine the proportion of immigrants to each. Consistent with our prediction, there was a positive relationship between the proportion of immigrants and snow depth. The best model of fisher habitat suitability was one with both snow depth and the proportion of coniferous forest in landscapes. Our findings suggest that where population trend is known, genetic information can be used to measure habitat suitability.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".