Average Dispersal Success: Linking Home Range, Dispersal, And Metapopulation Dynamics To Reserve Design
Bibliographic record
Abstract
Spatially explicit models for populations are often difficult to tackle mathematically and, in addition, require detailed data on individual movement behavior that are not easily obtained. An approximation known as the "average dispersal success" provides a tool for converting complex models, which may include stage structure and a mechanistic description of dispersal, into a simple matrix model. This simpler matrix model has two key advantages. First, it is easier to parameterize from the types of empirical data typically available to conservation biologists, such as survivorship, fecundity, and the fraction of juveniles produced in a study area that also recruit within the study area. Second, it is more amenable to theoretical investigation. Here, we use the average dispersal success approximation to develop estimates of the critical reserve size for systems comprising single patches or simple metapopulations. The quantitative approach can be used for both plants and animals; however, to provide a concrete example of the technique's utility, we focus on a special case pertinent to animals. Specifically, for territorial animals, we can characterize such an estimate of minimum viable habitat area in terms of the number of home ranges that the reserve contains. Consequently, the average dispersal success framework provides a framework through which home range size, natal dispersal distances, and metapopulation dynamics can be linked to reserve design. We briefly illustrate the approach using empirical data for the swift fox (Vulpes velox).
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".