The role of marine habitat mapping in ecosystem-based management
Bibliographic record
Abstract
Abstract Cogan, C. B., Todd, B. J., Lawton, P., and Noji, T. T. 2009. The role of marine habitat mapping in ecosystem-based management. – ICES Journal of Marine Science, 66: 2033–2042. Ecosystem-based management (EBM) and the related concept of large marine ecosystems (LMEs) are sometimes criticized as being too broad for many management and research applications. At the same time, there is a great need to develop more effectively some substantive scientific methods to empower EBM. Marine habitat mapping (MHM) is an example of an applied set of field methods that support EBM directly and contribute essential elements for conducting integrated ecosystem assessments. This manuscript places MHM practices in context with biodiversity models and EBM. We build the case for MHM being incorporated as an explicit and early process following initial goal-setting within larger EBM programmes. Advances in MHM and EBM are dependent on evolving technological and modelling capabilities, conservation targets, and policy priorities within a spatial planning framework. In both cases, the evolving and adaptive nature of these sciences requires explicit spatial parameters, clear objectives, combinations of social and scientific considerations, and multiple parameters to assess overlapping viewpoints and ecosystem functions. To examine the commonalities between MHM and EBM, we also address issues of implicit and explicit linkages between classification, mapping, and elements of biodiversity with management goals. Policy objectives such as sustainability, ecosystem health, or the design of marine protected areas are also placed in the combined MHM–EBM context.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".