Regional zooplankton biodiversity provides limited buffering of pond ecosystems against climate change
Bibliographic record
Abstract
1. Climate change and other human-driven environmental perturbations are causing reductions in biodiversity and impacting the functioning of ecosystems on a global scale. Metacommunity theory suggests that ecosystem connectivity may reduce the magnitude of these impacts if the regional species pool contains functionally redundant species that differ in their environmental tolerances. Dispersal may increase the resistance of local ecosystems to environmental stress by providing regional species with traits adapted to novel conditions. 2. We tested this theory by subjecting freshwater zooplankton communities in mesocosms that were either connected to or isolated from the larger regional species pool to a factorial manipulation of experimental warming and increased salinity. 3. Compensation by regional taxa depended on the source of stress. Warming tolerant regional taxa partially compensated for reductions in heat sensitive local taxa but similar compensation did not occur under increased salinity. 4. Dispersal-mediated species invasions dampened the effects of warming on summer net ecosystem productivity. However, this buffering effect did not occur in the fall or for periphyton growth, the only other ecosystem function affected by the stress treatments. 5. The results indicate that regional biodiversity can provide insurance in a dynamic environment but that the buffering capacity is limited to some ecosystem processes and sources of stress. Maintaining regional biodiversity and habitat connectivity may therefore provide some limited insurance for local ecosystems in changing environments, but is unable to impart resistance against all sources of environmental stress.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".