Towards ecosystem-based management : integrating stakeholder values in decision-making and improving the representation of ecosystems in ecosystem models
Bibliographic record
Abstract
Ecosystem-based management (EBM) is increasingly seen as the new paradigm for managing the use of marine resources and ecosystems. Although EBM has been defined in theory, its implementation has faced challenges worldwide. This research aims to examine two approaches to contribute to the operationzalization of EBM by incorporating stakeholder values in the decision-making process, and by better representing ecosystem dynamics in ecosystem models. First, I illustrate a decision-making framework for EBM rooted in structured decision-making (SDM), a well-known systematic approach for planning and stakeholder-consultation processes. SDM helps to identify the values of the constituents and define objectives and indicators that are consistent with those values. I demonstrate how SDM can enable managers to evaluate the performance of management alternatives using indicators specifically chosen to reflect values. This can help managers make more systematic, transparent and informed decisions with respect to the use of marine resources. As a case study, I apply SDM to the marine planning process on the west coast of Vancouver Island (WCVI). Second, as ecosystem models play an important role in EBM, I strive to improve the representation of marine ecosystems using ecosystem models in Ecopath with Ecosim (EwE). I focus on incorporating mediating effects and species reintroductions, both common situations that can strongly influence ecosystem dynamics. These considerations are essential when applying holistic approaches to management but they are not generally included in EwE. I use EwE to model the reintroduction of sea otters (Enhydra lutris) and the mediating effects provided by kelp forests in nearshore ecosystems of the WCVI. Because EwE does not have the functionality to represent reintroductions, I created two scenarios to work around the assumptions of Ecospace on the initial state of the ecosystem. In addition, I demonstrate how mediating effects can be represented using the ‘mediation’ function in Ecosim. These methods and results can contribute to advance EBM on the WCVI and offer insights to other marine planning processes. Both strengths and limitations of this work are presented and analyzed.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".