Science and community monitoring to inform management of phosphorus in the Canadian Lake Erie watersheds
Bibliographic record
Abstract
Algal blooms in Lake Erie have worsened in severity, extent, and duration due to agricultural phosphorus (P) loading. Science, policy, and community action are needed to identify and implement feasible solutions for freshwater protections. To improve conservation action for P-reduction in the Lake Erie watershed, we must understand landscape drivers that enhance P runoff across agricultural regions with seasonal and spatial variation. The goals of this thesis were to assess landscape determinants of seasonal P variations in the understudied Canadian Lake Erie watershed, and to develop tools to amplify community stream monitoring and environmental engagement. A seven-year dataset revealed that P loading has significantly increased on an annual basis (2012 – 2019) in the late-winter-early-spring (February, March, April) in three separate Lake Erie tributaries (the East Sydenham River, the Thames River, and the Grand River). I demonstrated that year-over-year increases in tile drainage area on farms explained 23 – 49% of the year-over-year increases in P loading; however, I found the effects of tile drainage were moderated by differences in soil texture, land use/land cover, regional climate, and urban point sources. Using a nested-watershed approach for one year in 13 monitoring stations of the mixed-use Grand River watershed, I determined that row crop area was a strong predictor of P concentration in the Grand River when fields are bare, soils exposed, and nutrient assimilation by biota nonexistent. I recommend the use of year-round field cover by cover crops as a conservation measure to attenuate P-enrichment in the Grand River since sub-watersheds with increased cover crop proportion had reduced P concentrations and turbidity throughout the year. In another one-year study of 13 sub-watersheds across a gradient row crop and tile-drained sub-watersheds of the East Sydenham River and Grand River, I provided quantitative evidence of the negative effects of intensively row crop and tile-drained sub-watersheds on soluble reactive P export during late-winter-early-spring, and particulate loss during the growing season. To engage agricultural communities in monitoring P export from farms, I developed a bioassay that uses stream algae to determine P concentration in low-order streams. This method involving substrate rods (i.e., Peristix) is cost-effective, requires very little training, and yielded data that were significantly related to P concentrations in agricultural streams in the growing season. I recommend that environmental agencies and landowners use this bioassay to identify areas to implement conservation measures to reduce P export from the Lake Erie watershed. For greater application of community-science tools, I created the CommunityFIRST framework, which emphasizes feasible community collaborations, inclusive volunteerism involving a range of abilities and knowledge systems, using tools developed in this thesis that are relevant to the community’s environmental issues and carried out in a supportive and trusting environment between researchers and community members. My research integrates science and community methods to advance our understanding of agricultural land management and seasonal P loading from the Canadian Lake Erie watershed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".