Applications of Dispersal Diversity on Food Web Stability Through a Synthesis of Current Literature and Observational Study
Bibliographic record
Abstract
Dispersal is a key mechanism that allows for spatially separated populations to interact across space and time. Rates of dispersal have been identified as a key factor shaping the stability of ecological communities. Dispersal diversity is the component of diversity that encompasses species dispersal abilities, driven by variation in dispersal-linked traits and condition-dependent movement behaviours. Frameworks that incorporate spatial dynamics often have not considered this source of diversity, opting for simpler methods of accounting for dispersal, but recent theoretical research has pushed for explicit inclusion of dispersal diversity within spatially structured (meta)communities. In my first chapter I reviewed literature that supports the stabilizing role of dispersal diversity and compiled intrinsic and extrinsic sources of variation that could be used to monitor stability in a trophic food web. In chapter two I tested whether local dispersal diversity predicted local community stability in the marine fish metacommunity of the Newfoundland and Labrador shelves, using dispersal trait measurements to quantify dispersal diversity and determine its impact on community stability over time. The results from chapter two support the stabilizing role of dispersal diversity, and that dispersal diversity can be measured applying the same methods as for functional diversity. This research highlights the importance of dispersal diversity for community stability, how dispersal diversity can be measured, and provides direction for future spatial studies on what traits to consider when accounting for dispersal diversity. Incorporating dispersal diversity into spatial models could provide better information to decision makers for managing spatially connected regions.
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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".