Local context and connectivity determine the response of zooplankton communities to salt contamination
Bibliographic record
Abstract
Abstract Salt, primarily NaCl, is commonly used to de‐ice winter roads. The usage of road salt is steadily increasing as the area of urbanised land expands, and chloride concentrations in many lakes and flowing waters near roads have been steadily increasing through time. In some waterbodies, these concentrations are reaching levels that are toxic to freshwater organisms, such as zooplankton which are integral to the structure and function of freshwater communities. We currently have a poor understanding of the impact of road salt contamination on zooplankton communities, and particularly the variation in response among lakes. Zooplankton communities differ in factors that could influence the impacts of elevated chloride, such as local species composition, exposure history and regional connectivity. Assessing and predicting how increasing road salt usage affects natural, freshwater zooplankton communities requires studies that investigate how communities vary in the impacts of elevated chloride, and in their response to dispersal. We conducted a field mesocosm experiment that examined the effects of salt (NaCl) addition on two different zooplankton communities, which were also subjected to repeated introductions of zooplankton dispersers from the regional species pool. These treatments allowed us to determine whether communities differed in the impacts of salt, and how the effects of salt differed between communities that did and did not receive zooplankton dispersers. We found variation in the effects of salt and dispersal between our experimental communities. For one lake community, salt drove zooplankton species composition towards dominance by littoral cladocerans. Zooplankton dispersal into this community ameliorated these impacts of salt by returning species composition to a state more similar to our “Control” communities. Conversely, for the other lake community, salt alone had minimal effects on zooplankton, while the combination of salt and dispersal led to declines in adult copepods. Our experiment found that the impacts of salt varied between two zooplankton communities, causing a range of negative, positive and neutral community effects, and that dispersal from the regional species pool could serve as an ameliorating or exacerbating influence. Efforts to understand and predict the impacts of road salt on freshwater communities would therefore benefit from an evaluation of the potential role played by local (e.g., exposure history and biotic interactions) and regional (e.g., connectivity to possible sources of immigrants) context in the response of communities to elevated chloride.
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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 | 0.001 |
| 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".