A Biophysical Model and Network Analysis of Invertebrate Community Dispersal Reveals Regional Patterns of Seagrass Habitat Connectivity
Bibliographic record
Abstract
The dispersal of marine organisms is a critical process for the maintenance of biodiversity and ecosystem functioning across a seascape. Understanding the patterns of habitat connectivity that arise from the movement of multiple species can highlight the role of regional processes in maintaining local community structure. However, quantifying the probability and scale of dispersal for marine organisms remains a challenge. Here, we use a biophysical model to simulate dispersal, and we conduct a network analysis to predict connectivity patterns across scales for the community of invertebrates associated with seagrass habitat in British Columbia, Canada. We found many possible connections and few isolated habitat meadows, but the probability of most connections was low. Most habitat connections occurred within 3 days of dispersal time over short distances, indicating potential limits to long distance dispersal and little effect of species-specific dispersal abilities on the potential spatial extent of habitat connectivity. We then highlight the different roles that individual seagrass meadows can play in maintaining network connectivity. We also identify clusters of connected meadows and use these clusters to estimate the spatial scale of community dynamics. The connectivity patterns generated by our dispersal simulations highlight the importance of considering marine communities in their broad seascape context, with applications for the prioritization and conservation of habitat that maintains connectivity.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".