Integrating fine‐scale seafloor mapping and spatial pattern metrics into marine conservation prioritization
Bibliographic record
Abstract
Abstract Marine protected area (MPA) planning often relies on scientific principles that help ensure that an area selected for conservation will effectively protect biodiversity. Capturing ecological processes in MPA network planning has received increased attention in recent years. High‐resolution seafloor maps, which show patterns in seafloor bio‐physical characteristics, can support our understanding of ecological processes. In part, owing to a global lack of high‐resolution seafloor maps, studies that aim to integrate seascape spatial pattern and conservation prioritization often focus on shallow biogenic habitats with less attention paid to deeper benthic seascapes (benthoscapes) mapped using acoustic techniques. Acoustic seafloor mapping strategies yield the spatial information required to extend conservation prioritization research into these environments, making incorporating seafloor ecological processes into conservation prioritization increasingly achievable. Here, a new method is proposed and tested that combines benthoscape mapping, landscape ecology metrics and a conservation decision support tool to prioritize areas with structural and potential connectivity value in MPA placement. Using a case study in eastern Canada, benthoscape composition and configuration were quantified using spatial pattern metrics and integrated into Marxan. Results illustrate how large patches of seafloor habitat in close proximity to neighbouring patches can be preferentially selected when benthoscape configuration is considered. The flexibility of the method for including relevant spatial pattern metrics or species‐specific movement data is discussed to illustrate how benthic habitat maps can improve existing conservation planning methods and complement existing and future work to support marine biodiversity conservation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".