Connectivity mediates the spatial ecological impacts of a glyphosate-based herbicide in experimental metaecosystems
Bibliographic record
Abstract
Metacommunity ecology has shown that connectivity is important for the persistence of a species locally and across connected ecosystems, however we do not know if ecological effects in freshwater ecosystems exposed to biocides leaking from agriculture depend on metaecosystem connectivity. We experimentally replicated metaecosystems in the laboratory using gradostats as a model system. We tested the effects of connectivity, in terms of node distance from the pollutant-source, flow rate, and a glyphosate-based herbicide, on phytoplankton productivity, diversity and stability. Gradostats were composed of interconnected equally spaced nodes where resources and phytoplankton move directionally along a gradient of increasing distance from the source of the polluting herbicide. We hypothesised that ecological effects would be stronger in the node situated closer to the point of herbicide input, but that flow would suppress phytoplankton populations in distant nodes. Overall, RoundUp impacted phytoplankton productivity and stability by reducing algal biomass and abundances. This occurred especially in the node closest to the diluted herbicide point-source and under high flow, where species abundances were heavily suppressed by the effects of the rapidly flowing herbicide. At low flow on the other hand, distant nodes where buffered from the effects of the slow-moving herbicide. No differences in beta and gamma diversity among replicate metaecosystems was found; however, a significant loss of alpha diversity in all metaecosystems occurred through time until the end of the experiment. Together, these results point to the importance of considering aquatic connectivity in management plans for monitoring and mitigating unintended ecological consequences of agrochemical runoff.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".