The central importance of ecological spatial connectivity to effective coastal marine protected areas and to meeting the challenges of climate change in the marine environment
Bibliographic record
Abstract
Abstract The several forms of ecological spatial connectivity – population, genetic, community, ecosystem – are among the most important ecological processes in determining the distribution, persistence and productivity of coastal marine populations and ecosystems. Ecological marine protected areas (MPAs) focus on restoring or maintaining marine populations, communities, or ecosystems. All ecological MPAs – no matter their specific focus or objectives – depend for their success on incorporating ecological spatial connectivity into their design, use (i.e. application), and management. Though important, a synthesis of the implications of ecological spatial connectivity for the design, use, and management of MPAs, especially in the face of a changing global climate, does not exist. We synthesize this information and distill it into practical principles for design, use, and management of MPAs and networks of MPAs. High population connectivity among distant coastal ecosystems underscores the critical value of MPA networks for MPAs and the populations and ecosystems between them. High ecosystem connectivity among coastal ecosystems underscores the importance of protecting multiple connected ecosystems within an MPA, maximizing ecosystem connectivity across MPAs, and managing ecosystems outside MPAs so as to minimize influxes of detrimental organisms and materials into MPAs. Connectivity‐informed MPAs and MPA networks – designed and managed to foster the ecological spatial connectivity processes important to local populations, species, communities, and ecosystems – can best address ecological changes induced by climate change. Also, the protections afforded by MPAs from direct, local human impacts may ameliorate climate change impacts in coastal ecosystems inside MPAs and, indirectly, in ecosystems outside MPAs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".