A monitoring framework for SGáan Kínghlas-Bowie Seamount Marine Protected Area, British Columbia, Canada
Bibliographic record
Abstract
The SG̲áan K̲ínghlas-Bowie Seamount Marine Protected Area (SK̲-B MPA) is co-managed by the Haida Nation (as represented by the Council of the Haida Nation, CHN) and the Government of Canada (as represented by the Minister of Fisheries and Oceans Canada, DFO) 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 of the MPA. In this research document, we provide an ecosystem review and list indicators (ecosystem components and metrics), protocols (e.g., tools), and strategies related to monitoring the SK̲-B MPA conservation objectives. Indicator ecosystem component groupings were generated for biological, environmental, and stressor ecosystem components, incorporating anticipated changes (e.g., climate change, recovery from fisheries) and specific indicator species where appropriate. Metrics for ecosystem component groupings were described, then linked to standard protocols and strategies 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. Ecosystem function and trophic structure 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. A key result of the monitoring framework is connecting the four major components (i.e., the ecological objectives and the monitoring indicators, protocols, and strategies). Priorities and combinations are 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. The information in this paper was presented in support of a Canadian Science Advisory process (peer-reviewed May 3–5, 2022) and will be used by practitioners and managers to develop an appropriate and effective monitoring plan for the SK̲-B MPA. This monitoring framework covers a great deal of generally and regionally relevant information and may support the development of monitoring frameworks and plans for other protected areas, especially in the case of the proposed Tang.ɢwan – ḥačxwiqak – Tsigis (TḥT) 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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".