Research on marine coastal impacts to promote ecosystem-based management : nonnative species in northeast Pacific estuaries
Bibliographic record
Abstract
Ecosystem-based management (EBM) offers a holistic evaluation of tradeoffs between human activities, but this offer rests upon a foundation of science. In this thesis, I assessed and advanced the knowledge-base for EBM in five ways, focusing on nonnative species in estuarine ecosystems. In Chapter 2, I tested for the comprehensiveness of research that connects the impacts of anthropogenic activities to changes in ecosystem service production, employing a literature review of estuarine ecosystems. Research on these connections virtually never included the relationship of activities to ecosystem services production, presenting an impressive gap in research for evaluating tradeoffs using EBM. I addressed the sufficiency of existing information regarding nonnative species in eelgrass beds in Chapter 3. I tested the relationship of nonnative species in British Columbia’s (BC) eelgrass beds with arrival pathways and environmental selection factors. There were few (12) nonnatives in BC’s eelgrass; all associated most commonly with aquaculture facilities and warm temperatures. Existing reports included the majority of nonnatives: only one species, the bamboo worm Clymenella torquata, represented a new record, as I described in Chapter 4. Impacts of nonnatives are difficult to limit after invasion. In Chapter 5, I developed an approach for characterizing the potential economic impacts of nonnatives. I focused on European green crab, a nonnative species that has not yet arrived in Puget Sound, Washington. At a range of invasion densities and increasing calorie diets, I calculated a value-at-risk to shellfish harvest ranging from $1.6 - $41 million USD. Such calculations can aid in preparation for impending invasion by motivating prevention and mitigation efforts. Nonnative management is often based on the available understanding of the impacts on native species. In Chapter 6, I assessed available research on the impact of nonnative seagrass, Zostera japonica, in northeast Pacific estuaries. My results suggested existing studies that quantitatively test Z. japonica impacts are insufficient to comprehensively assess the effects of this invasion. My dissertation research highlights the need for research to determine the ecosystem role of nonnatives in their invaded range through analysis of quantitative studies across broad scales.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".