Benefits of genetic data for spatial conservation planning in coastal habitats
Bibliographic record
Abstract
Coastal marine environments are subject to a variety of anthropogenic pressures that can negatively impact habitats and the biodiversity they harbor. Conservation actions such as marine protected areas, marine reserves, and other effective area-based conservation measures, are pivotal tools for protecting coastal biodiversity. However, to be effective, conservation area networks must be planned through a systematic conservation planning (SCP) approach. Recently, such approaches have begun to orient their goals toward the conservation of different biodiversity facets and to integrate different types of data. In this review, we illustrate how genetic data and molecular techniques can bring useful knowledge for SCP approaches that are both more comprehensive (sampling the full range of biodiversity) and more adequate (ensuring the long-term persistence of biodiversity). With an emphasis on coastal organisms and habitats, we focus on phylogenetic analysis, the estimation of neutral and adaptive intraspecific genetic diversity at different spatial levels (alpha, beta, and gamma), the study of connectivity and dispersal, and the information obtainable from environmental DNA techniques. For each of these applications, we discuss the benefits of its integration into SCP for coastal systems, its strengths and weaknesses, and the aspects yet to be developed.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".