Assessing the Ecological Effects of Multiple Stressors in River and Stream Ecosystems
Bibliographic record
Abstract
Freshwater ecosystems are subjected to a multitude of stressors resulting from human population growth, urbanization, and climate change. One class of stressors facing freshwater ecosystems is contaminants (some of which are pollutants), which come from a variety of point and non-point sources (e.g., agriculture, wastewater, stormwater) and human activities (e.g., fishing, recreation). Despite widespread recognition that freshwater ecosystems are subject to multiple anthropogenic contaminants, there is still a need to understand their effects as a mixture, and at multiple levels of biological organization (i.e., sub-organismal to ecosystem-level effects). The goal of my doctoral research is to understand the sources, sinks, and ecological impacts of plastics and other chemical contaminants as a mixture, in the context of multiple stressors. I use both field and laboratory approaches to understand patterns of contamination and effects of stressors in two systems, one large estuary in Northern Vietnam, and a smaller, urbanized tributary in Toronto, Ontario, Canada. In my first two data chapters, I use observational field data to demonstrate that plastic (macroplastic and microplastics) and other contaminant stressors (anthropogenic debris, metals, polycylic aromatic hydrocarbons, road salt) are ubiquitous in the environment and are associated with human activities (e.g., fishing) and types of land-use (e.g., urban, roads). I then explore the ecological impacts of these contaminant mixtures using common ecological indices, community analysis, and a laboratory experiment. I leverage field data to demonstrate that elevated stressors are negatively associated with ecological effects (i.e., canopy cover, mangrove diameter, crab burrows, taxonomic diversity, incidence of tolerant taxa). Using an experimental approach, I demonstrate that, although exposure to some stressors leads to individual level mortality, the presence of mixtures does not worsen these effects. Collectively my research contributes to our understanding of the sources, fate, and ecological effects of multiple stressors, demonstrating the ways in which technologies and existing approaches can be adapted to take a more environmentally relevant approach to understand them. Future work should continue to investigate the impacts of locally relevant mixtures on local ecosystems to identify effects, monitor changes, and evaluate the efficacy of management and conservation efforts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".