Changing estuaries and impacts on juvenile salmon: A systematic review
Bibliographic record
Abstract
Estuaries are productive ecosystems providing important habitat for a diversity of species, yet they also experience intense levels of anthropogenic development. To inform decision-making, it is essential to understand the pathways of impacts of particular human activities, especially those that affect species such as salmon, which have high ecological, social-cultural and economic values. Salmon systems provide an opportunity to build from the substantial body of research on responses to estuary developments and take stock of what is known. We conducted a systematic English-language literature review on the responses of juvenile salmon to anthropogenic activities in estuaries and nearshore areas asking: what has been studied, where are the major knowledge gaps and how do stressors affect salmon? We found a substantial body of research (n = 167 studies; 1,369 comparative tests) to help understand responses of juvenile salmon to 24 activities and their 14 stressors. Across studies, 82% of the research was conducted in the eastern Pacific (Oregon and Washington, USA and British Columbia, Canada) showing a limited geographical scope. Using a semiquantitative approach to summarize the literature, including a weight-of-evidence metric, we found a range of results from low to moderate-high confidence in the consequences of the stressors. For example, we found moderate-high confidence in the negative impacts of pollutants and sea lice and moderate confidence in negative impacts from connectivity loss and changes in flow. Our results suggest that overall, multiple anthropogenic activities cause negative impacts across ecological scales. However, our results also reveal knowledge gaps resulting from minimal research on particular species (e.g. sockeye salmon), regions (e.g. Atlantic) or stressors (e.g. entrainment) that would be expedient areas for future research. With estuaries acting as a nexus of biological and societal importance and hotspots of ongoing development, the insights gained here can contribute to informed decision-making.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".