Changes in water quality related to permafrost thaw may significantly impact zooplankton in small Arctic lakes
Bibliographic record
Abstract
Rising temperatures are leading to permafrost thaw over vast areas of the northern hemisphere. In the Canadian Arctic, permafrost degradation is causing significant changes in surface water quality due to the release of solutes that can alter conductivity, water clarity, and nutrient levels. For this study, we examined how changes in water quality associated with permafrost thaw might impact zooplankton, a group of organisms that play an important role in the food web of Arctic lakes. We conducted a biological and water quality survey of 37 lakes in the Mackenzie Delta region of Canada's Northwest Territories. We then used this data set to develop models linking variation in the abundance, diversity, and evenness of zooplankton communities to physicochemical, biological, and spatial variables. Subsequently, we used these models to predict how zooplankton communities might respond as water quality is altered by permafrost thaw. Our models explained 47%, 68%, and 69% of the variation in zooplankton abundance, diversity, and evenness, respectively. Importantly, the most parsimonious models always included variables affected by permafrost thaw, such as calcium and conductivity. Predictions based on our models suggest significant increases in zooplankton abundance (1.6-3.6 fold) and decreases in diversity (1.2-1.7 fold) and evenness (1.1-1.4 fold) in response to water quality changes associated with permafrost thaw. These changes are in line with those described for significant perturbations such as eutrophication, acidification, and the introduction of exotic species such as the spiny water flea (Bythotrephes). Given their important role in aquatic food webs, we expect these changes in zooplankton communities will have ramifications for organisms at higher (fish) and lower (phytoplankton) trophic positions in Arctic lakes.
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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) | 0.020 | 0.002 |
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; both teacher heads agree on what is shown here.
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".