Monitoring framework for SK-B MPA
Bibliographic record
Abstract
The SG̲áan K̲ínghlas-Bowie (SK̲-B) Seamount Marine Protected Area (MPA) (Figure 1) is co-managed by the Haida Nation and the Government of Canada to conserve and protect the unique biodiversity and biological productivity of the area. In 2019, the SK̲-B MPA management board published the management plan detailing the ecological conservation goals and objectives of the MPA. In the associated research document (Du Preez et al. in prep1), a monitoring framework is proposed summarizing the ecological monitoring options. It provides an ecosystem review and lists indicators (ecosystem components and metrics), protocols (tools), and strategies related to the SK̲-B MPA conservation objectives. Indicator groupings were generated for biological, environmental, and stressor ecosystem components, incorporating anticipated changes and specific indicator species where appropriate. Metrics for monitoring indicator groupings were described, then linked to standard protocols and strategies (i.e., tools and programs) used in the respective scientific fields (e.g., ecology, geology, oceanography). Information and best practices for designing a monitoring program, such as existing baseline data, statistics, sampling design, feasibility, and data management were also discussed. Trophic structure and ecosystem function were examined through a conceptual food web model. The proposed monitoring framework was then evaluated against the ecological conservation objectives to support adaptive and iterative re-evaluation of plans as an essential part of the MPA management process. The key result of the monitoring framework was connecting the four major components (i.e., the ecological objectives and the monitoring indicators, protocols, and strategies; Figure 5 and Table 1). Priorities and combinations were recommended to address the six ecological operational objectives, with the caveat that some information is unknowable at this time and that new or improved information (e.g., resolved through monitoring) should feed back into the frameworks and plans. While the SK̲-B MPA seamounts are some of the best-studied seamounts in the Northeast Pacific, monitoring of the area is still in its infancy, and there are many uncertainties and knowledge gaps. Limitations of the monitoring framework include the remoteness and size of SK̲-B MPA and the lack of comparable reference sites. Some uncertainties were handled in the associated research document (Du Preez et al. in prep1) by grouping potential ecosystem component indicators. Other uncertainties can be addressed through revisiting the management plan at a future date as part of an adaptive (iterative and responsive) management process. However, a lot of information is unknowable at this point but will potentially be resolved through continued and future baseline monitoring and research (e.g., past, current, and anticipated states, including natural variability, especially as they relate to existing and future climate change impacts). Other uncertainties related to the monitoring framework include changes in the taxonomy of deep-sea species, future changes in sampling technologies, and biological responses to stressors (e.g., the nature of the disturbance, cascading effects, lag time, duration). Recommendations from the meeting include using the SK̲-B MPA monitoring framework to inform the development of a detailed monitoring plan (with stages of baseline and long-term monitoring), including a data management plan. It was highlighted that the future SK̲-B MPA ecological monitoring plan incorporates data and information collected by the other monitoring programs (e.g., on human activities, transient species, and climate change) to effectively interpret the cumulative effects of the conservation management measures, stressors, and unmanageable changes (i.e., beyond the scope of MPA spatial management). It was highlighted that extractive sampling, while contrary to the conservation goals, is likely essential for monitoring changes in trophic structure and ecosystem function. Metrics of gut content analysis and trophic biomarkers were proposed as additional methods for monitoring changes in trophic structure. It was noted that the SK̲-B MPA monitoring framework may support the development of monitoring frameworks and plans for other protected areas (especially in the case of the proposed large Pacific Offshore seamount and hydrothermal vent MPA to the south).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".