The impacts of overwintered green frog larvae on wood frog embryo mortality under a wetter climate
Bibliographic record
Abstract
Abstract For many species, the distribution and quantity of habitat is likely to be altered under climate change scenarios. Globally, temperatures are expected to increase, but changes in precipitation will be variable with some locations expected to receive more and other locations to receive less. The amount and timing of precipitation has a strong influence on wetland hydroperiod, which is a critical factor in determining wetland use by species of pond‐breeding amphibians. Past research has focused on the direct effects of reductions in precipitation and shorter hydroperiods, such as larvae desiccation, but little attention has been given to the indirect consequences of increased precipitation and longer hydroperiods, such as changes in predation pressure. We used wood frogs ( Lithobates sylvaticus ), a species that typically breeds in ephemeral (temporary) wetlands, to test the hypothesis that increased precipitation and permanent wetlands would indirectly increase mortality in wood frog embryos through increases in the density of embryo predators, second‐year green frog larvae ( Lithobates clamitans ), limnephilid and phryganeid caddisfly larvae and red‐spotted newts ( Notophthalmus viridescens ). We conducted translocation experiments in 2014 and 2015. We monitored wood frog embryo hatch success and mortality from predation using open (predator access) and closed (predators excluded) cages in 16 wetlands in each year. We observed no difference in embryo hatch success between ephemeral and permanent wetlands, with water temperature, not pH , affecting hatch rates. Wood frog embryos in permanent wetlands with overwintered second‐year green frog larvae had higher mortality from predation (0.80, 95% confidence intervals 0.65–0.91) than permanent wetlands (0.42, CI 0.26–0.60) and ephemeral wetlands (0.37, CI 0.31–0.45) that lacked predatory green frog larvae. Embryo mortality from predation increased with increasing green frog larvae density. Regression models showed that ponds with larvae densities >2.5 larvae m −2 resulted in 90% embryo mortality. Caddisfly larvae and red‐spotted newt density had no effect, and pond water temperature had a weak negative effect on embryo mortality from predation. Under a wetter and warmer climate, springtime occupancy of predatory green frog larvae is likely to increase, resulting in a reduction in the amount of suitable wood frog breeding habitat. Our research suggests that indirect effects of climate change mediated through biotic interactions may play a critical role in the distribution of ephemeral wetland species.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".