When is a marine protected area network ecologically coherent? A case study from the North‐east Atlantic
Bibliographic record
Abstract
ABSTRACT Creating representative networks of marine protected areas (MPAs) as part of an ecosystem‐based management approach is generally advocated to protect the full spectrum of marine ecosystems and vulnerable species. Core objectives for designing MPA networks incorporate issues of scale, size and spacing. Ascertaining when such objectives have been met, and thus when a network can be judged to be ecologically coherent, presents an ongoing challenge. This paper presents a broad‐scale approach to assess the degree of ecological coherence within one such network. In 2013 an independent ecological coherence assessment was requested by OSPAR, the Regional Seas Convention for the North‐east Atlantic, of the ecological coherence of its regional MPA network. As is often the case in the marine environment, the data were not sufficiently comprehensive or spatially inclusive to allow for a thorough assessment for the entire region. Consequently two levels of testing were applied: (1) basic tests applied to the whole OSPAR maritime area; and (2) a more sophisticated second level of tests directed at specific OSPAR regions and subregions for which more complete datasets were available. The former considered major gaps based on basic distribution thresholds and representativity (both biogeographic and bathymetric). The latter focused on broad‐scale habitat presence to determine replication, adequacy and connectivity. On the basis of these tests it was concluded that while the OSPAR MPA network as a whole is not ecologically coherent, nonetheless significant progress towards global targets, such as protection of 10% of marine and coastal environments under the Convention on Biological Diversity (CBD) has been made. Gaps in the distribution of MPAs were identified, particularly under‐representation of certain biogeographic provinces and bathymetric zones of depths greater than 75 m including bathyal (200–3000 m) and abyssal (3000–6000 m) depths. To address such gaps, a cooperative region by region approach will be required by the OSPAR Parties to identify where positioning new MPAs can enhance ecological coherence. A limitation of the 2013 assessment was that it did not consider which MPAs are designated and managed for the protection of specific features; rather, it assumed that all features that fall within an MPA are protected. With a redevelopment of the OSPAR MPA database underway it is hoped that reporting of such details by Parties will improve, allowing for more specificity in future analyses. However, as the first such assessment of its kind our two‐level approach serves as a case study of a pragmatic example of how assessments of ecologically coherent networks might be undertaken using GIS to contribute to systematic conservation planning. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".