Disentangling how climate change can affect an aquatic food web by combining multiple experimental approaches
Bibliographic record
Abstract
Predicting the biological effects of climate change presents major challenges due to the interplay of potential biotic and abiotic mechanisms. Climate change can create unexpected outcomes by altering species interactions, and uncertainty over the ability of species to develop in situ tolerance or track environmental change further hampers meaningful predictions. As multiple climatic variables shift in concert, their potential interactions further complicate ecosystem responses. Despite awareness of these complexities, we still lack controlled experiments that manipulate multiple climatic stressors, species interactions, and prior exposure of species to future climatic conditions. Particularly studies that address how changes in water availability interact with other climatic stressors to affect aquatic ecosystems are still rare. Using aquatic insect communities of Neotropical tank bromeliads, we combined controlled manipulations of drought length and species interactions with a space-for-time transplant (lower elevations represent future climate) and a common garden approach. Manipulating drought length and experiment elevation revealed that adverse effects of drought were amplified at the warmer location, highlighting the potential of climatic stressors to synergistically affect communities. Manipulating the presence of omnivorous tipulid larvae showed that negative interactions from tipulids, presumably from predation, arose under drought, and were stronger at the warmer location, stressing the importance of species interactions in mediating community responses to climate change. The common garden treatments revealed that prior community exposure to potential future climatic conditions did not affect the outcome. In this powerful experiment, we demonstrated how complexities arise from the interplay of biotic and abiotic mechanisms of climate change. We stress that single species can steer ecological outcomes, and suggest that focusing on such disproportionately influential species may improve attempts at making meaningful predictions of climate change impacts on food webs.
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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) | 0.000 | 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".