Human induced fish declines in North America, how do agricultural pesticides compare to other drivers?
Bibliographic record
Abstract
Numerous anthropogenic factors, historical and contemporary, have contributed to declines in the abundance and diversity of freshwater fishes in North America. When Europeans first set foot on this continent some five hundred years ago, the environment was ineradicably changed. Settlers brought with them diseases, animals, and plants via the Columbian Exchange, from the old world to the new, facilitating a process of biological globalization. Invasive species were thus introduced into the Americas, displacing native inhabitants. Timber was felled for ship building and provisioning for agriculture, resulting in a mass land conversion for the purposes of crop cultivation. As European colonization expanded, landscapes were further modified to mitigate against floods and droughts via the building of dams and levees. Resources have been exploited, and native populations have been overfished to the point of collapse. The resultant population explosion has also resulted in wide-spread pollution of aquatic resources, particularly following the industrial and agricultural revolutions. Collectively, these activities have influenced the climate and the climate, in turn, has exacerbated the effects of these activities. Thus, the anthropogenic fingerprints are undeniable, but relatively speaking, which of these transformative factors has contributed most significantly to the decline of freshwater fishes in North America? This manuscript attempts to address this question by comparing and contrasting the preeminent drivers contributing to freshwater fish declines in this region in order to provide context and perspective. Ultimately, an evaluation of the available data makes clear that habitat loss, obstruction of streams and rivers, invasive species, overexploitation, and eutrophication are the most important drivers contributing to freshwater fish declines in North America. However, pesticides remain a dominant causal narrative in the popular media, despite technological advancements in pesticide development and regulation. Transitioning from organochlorines to organophosphates/carbamates, to pyrethroids and ultimately to the neonicotinoids, toxicity and bioaccumulation potential of pesticides have all steadily decreased over time. Concomitantly, regulatory frameworks designed to assess corresponding pesticide risks in Canada and the USA have become increasingly more stringent and intensive. Yet, comparatively, habitat loss continues unabated as agricultural land is ceded to the frontier of urban development, globalized commerce continues to introduce invasive species into North America, permanent barriers in the form of dams and levees remain intact, fish are still being extracted from native habitats (commercially and otherwise), and the climate continues to change. How then should we make sense of all these contributing factors? Here, we attempt to address this issue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".