Bibliographic record
Abstract
Estuaries are highly productive and diverse ecosystems that are the site of many anthropogenic activities. While only comprising 3% of the British Columbia (BC) coastline, there are over 400 individual estuaries, making management difficult and highlighting the need for regional approaches. A literature review was used to compile anthropogenic activities, including climate change and other associated stressors (physical, chemical, or biological processes that have the potential to change an ecosystem) relevant to estuarine environments throughout the BC coast across terrestrial, marine, and atmospheric zones. Based on the literature review, 45 available coastwide spatial data layers were compiled and used to assess the spatial overlap, frequency, or intensity of activities and stressors relevant to 439 estuaries and their associated watersheds. All estuaries assessed had at least one marine or terrestrial activity present. The most marine activities were identified in estuaries in the Strait of Georgia and the Skeena River estuary, while the most terrestrial activities occurred in estuaries in the Strait of Georgia, the Skeena River estuary, the Kitimat estuary, and estuaries along the west coast of Vancouver Island closer to population centers. A cluster analysis was used to characterize the estuaries based on the type and intensity of activities that occur. Five clusters of estuaries and the associated characteristic activities were identified. The analysis identified one cluster of estuaries that had high numbers of anthropogenic activities and tended to be associated with activities in the watershed (e.g., agriculture, dams) or that were associated with populated areas (e.g., wastewater outflow, marinas). The other four clusters were associated primarily with marine activities. Two clusters had intermediate numbers of activities, one with more forestry and crab and shrimp fishing, and the other having more salmon fishing. The last two clusters had low numbers of activities - one cluster was associated with shipping and recreational boating, and the other had no association with any one activity. Climate change (air temperature change, stream temperature change, precipitation change, and/or sea level rise) is expected to impact all estuaries to varying extents. In particular, the highest degree of stream temperature increase is expected in estuaries at the end of long inlets (e.g., Bute Inlet, Observatory Inlet). The estuaries that are projected to experience the highest degree of sea level rise are around Haida Gwaii. Available coastwide spatial data for salmon, other ecologically significant fish species, sensitive fish habitat, and other habitat characteristics (e.g., presence of eelgrass, macroalgae, and saltmarsh) was compiled. The presence of fish and fish habitat varied among estuaries. There were observations of at least one species of Pacific salmon since 1990 for 76% of the estuaries, and 20% of the estuaries had all five species present. Estuaries with the highest salmon biomass and richness were associated with the most human activities. Dungeness Crab was likely present in 84% of the estuaries. Eelgrass was present in 43% of the estuaries, understory kelp in 82%, and saltmarsh in 81%. All three biogenic habitat types were present in 36% of the estuaries. The assessment of fish and fish habitats highlighted coastwide data gaps, which limited the assessment of all species/habitats of interest, particularly for non-commercial species. This analysis provides a significant data resource and initial assessment to help focus further estuarine conservation and management efforts. It can be used to identify estuaries that face similar types of concerns, potential management options, and relevant partners and resource users. This work is the first step towards a comprehensive assessment of how human activities may combine and cumulate to impact estuaries. Although this work is not a cumulative impacts assessment, future analysis could combine the outputs from this work with vulnerability scores of individual estuarine species to quantify an overall risk to each estuary.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".