Integrating seascape resistances and gene flow to produce area-based metrics of functional connectivity for marine conservation planning
Bibliographic record
Abstract
Abstract Context Prioritizing regions that facilitate connectivity among populations is an essential principle for conservation planning. However, the lack of conspicuous geographical and environmental features that constrain dispersal and gene flow throughout life history challenges the characterization of dispersal pathways within a three-dimensional marine realm. Objectives To elucidate regions of high connectivity value in the marine environment, we develop a novel approach that integrates estimates of spatial genetic structure with representation of regions of high dispersal potential for meroplankton, incorporating elements of pelagic larval and benthic adult life history. Methods Spatial patterns of connectivity were characterized using circuit theory as an inverse function oceanographic- and habitat-based resistance to movement. We integrate emergent spatial patterns of connectivity with population genetic data to account for realized patterns of gene flow across a seascape. We apply this approach to four broadly distributed species in the Northwest Atlantic. Results Estimates of resistance to gene flow revealed multiple connectivity barriers not observed in oceanographic or habitat models. Comparison of isolation-by-distance versus isolation-by-resistance revealed genetic variation was best explained by seascape resistance in three of four species, supporting the resistance-based assessments of connectivity. Our approach identified areas of high and low connectivity value for each species, with overlap generally associated with geographic pinch points and areas of low genetic exchange. Conclusions By integrating spatial interpolations of gene flow and estimated pathways for dispersal, we develop a novel area-based metric of connectivity that considers life-history based structural constraints to dispersal and observed genetic variation. Outputs from this workflow can reveal regions of connectivity for conservation planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".