Comparing approaches for estimating ecological connectivity at a local scale in a marine system
Bibliographic record
Abstract
Connections among habitat patches through propagule dispersal are critical for designing effective networks of marine protected areas. To meet targets, managers need a diverse toolkit for translating patterns of connectivity to actionable metrics through specific size, spacing, and siting recommendations. Measuring ecological connectivity in the marine realm is particularly challenging because of water movement and the lack of distinct physical boundaries. Additionally, tracking most propagules is not logistically feasible. Here, we compared 3 approaches of increasing complexity for predicting potential ecological connectivity (measured as passive dispersal by ocean currents) of kelps and 2 resident invertebrates, the dominant macrograzer Strongylocentrotus droebachiensis and a destructive invasive epiphyte (Membranipora membranacea) among habitat patches along the NW Atlantic coast of Canada. The 3 approaches differ in the complexity of estimating ocean currents: current speed depth-averaged over time (1D); current velocity decomposed into along-shore and cross-shore components depth-averaged over time (2D); and spatially modelled current velocity derived from a 3D hydrodynamic model (3D). We found that the 1D approach was adequate for taxa with a short competent propagule duration (CPD), but that dispersal for the 2D and 3D approaches were most similar for medium-long CPD dispersers at the scale of management units, likely because they both account for the directionality of currents, whereas the 1D approach does not. This research helps bridge the gap between connectivity research and ocean management by demonstrating that the 2D approach requires less data, time, and resources while still providing adequate outputs at the scale of management units.
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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".