Evaluating cumulative effects of anthropogenic inputs in Prince Edward Island estuaries using the mummichog (<i>Fundulus heteroclitus</i>)
Bibliographic record
Abstract
Estuarine eutrophication as a result of agricultural land use, including the use of chemical fertilizers, is increasing worldwide. Prince Edward Island (PEI), Canada has very high agricultural intensity by international standards with approximately 44% of the land area under production, and some watersheds in excess of 75% agricultural land-use. The type of agriculture is also intensive with primarily row crops that have high chemical fertilizer and pesticide usage. In light of these stressors, the hypothesis of this study was that mummichog (Fundulus heteroclitus) population parameters would change with point and nonpoint source pollution, and that multivariate statistics could be used to draw associations with specific stressors. Fish were sampled on a monthly basis from May through August at 7 estuaries spanning a range of land use, nutrient, and contaminant loadings. A suite of environmental variables were simplified into 3 principal components: PC1 representing agricultural land use, N loading, and plant habitat, PC2 being dominated by sediment sand and silt distribution, and PC3 largely reflecting P loading and sediment organic matter. There were significant differences in abundance of both adult and young-of-the-year mummichog, and these changes associated most strongly with PC1, the largely N-driven agricultural influences. In contrast, somatic variables such as liver and gonad size did not show strong association with the environmental quality principal component scores. The sand and silt PC2 appeared to have the opposite association with the biological data, with siltier environments correlating to older, larger, less dense populations of mummichog. Although pesticide residues were detected in estuarine sediment, there was no clear relationship between these and watershed agricultural intensity or biochemical indicators. There was, however, a strong relationship between agricultural environmental variables (PC1) and in vitro steroid production that is suggestive of a potential chemical effect. Eutrophication appeared to be a primary stressor affecting mummichog populations, as nutrient enrichment was associated with changes in habitat variables and these in turn were associated with high mummichog density. Thus, mummichog population demographics appear to have use as an indicator of adverse or worsening conditions in estuaries. We concluded that, based on the subset of environmental factors evaluated, the nonpoint-source inputs of sediments and nutrients exerted the greatest influence on mummichog populations in PEI estuaries.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".