Climate warming mediates negative impacts of rapid pond drying for three amphibian species
Bibliographic record
Abstract
Anthropogenic climate change will present both opportunities and challenges for pool-breeding amphibians. Increased water temperature and accelerated drying may directly affect larval growth, development, and survival, yet the combined effects of these processes on larvae with future climate change remain poorly understood. Increased surface temperatures are projected to warm water and decrease water inputs, leading to earlier and faster wetland drying. So it is often assumed that larvae will experience negative synergistic impacts with combined warming and drying. However, an alternative hypothesis is that warming-induced increases in metabolic rate and aquatic resource availability might compensate for faster drying rates, generating antagonistic larval responses. We conducted a mesocosm experiment to test the individual and interactive effects of pool permanency (permanent vs. temporary) and water temperature (ambient vs. (+) -3 degrees C) on three anurans with fast-to-slow larval development rates (Great Basin spadefoot [Spea intermontana], Pacific chorus frog [Pseudacris regilla], and northern red-legged frog [Rana aurora]). We found that although tadpoles in warmed pools reached metamorphosis 15-17 days earlier, they did so with little cost (< 2 mm) to size, likely due to greater periphyton growth in warmed pools easing drying-induced resource competition. Warming and drying combined to act antagonistically on early growth (P = 0.06) and survival (P = 0.06), meaning the combined impact was less than the sum of the individual impacts. Warming and drying acted additively on time to and size at metamorphosis. These nonsynergistic impacts may result from cotolerance of larvae to warming and drying, as well as warming helping to offset negative impacts of drying. Our results indicate that combined pool warming and drying may not always be harmful for larval amphibians. However, they also demonstrate that antagonistic responses are difficult to predict, which poses a challenge to proactive conservation and management. Our study highlights the importance of considering the nature of multiple stressor interactions as amphibians are exposed to an increasing number of anthropogenic threats.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".