Demographic and genetic approaches to study dispersal in wild animal populations: A methodological review
Bibliographic record
Abstract
Dispersal is a central process in ecology and evolution. At the individual level, the three stages of the dispersal process (i.e., emigration, transience and immigration) are affected by complex interactions between phenotypes and environmental factors. Condition- and context-dependent dispersal have far-reaching consequences, both for the demography and the genetic structuring of natural populations and for adaptive processes. From an applied point of view, dispersal also deeply affects the spatial dynamics of populations and their ability to respond to land-use changes, habitat degradation and climate change. For these reasons, dispersal has received considerable attention from ecologists and evolutionary biologists. Demographic and genetic methods allow quantifying non-effective (i.e., followed or not by a successful reproduction) and effective (i.e., with a successful reproduction) dispersal and to investigate how individual and environmental factors affect the different stages of the dispersal process. Over the past decade, demographic and genetic methods designed to quantify dispersal have rapidly evolved but interactions between researchers from the two fields are limited. We here review recent developments in both demographic and genetic methods to study dispersal in wild animal populations. We present their strengths and limits, as well as their applicability depending on study objectives and population characteristics. We propose a unified framework allowing researchers to combine methods and select the more suitable tools to address a broad range of important topics about the ecology and evolution of dispersal and its consequences on animal population dynamics and genetics.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".