Inclusion of trophic interactions increases the vulnerability of an alpine butterfly species to climate change
Bibliographic record
Abstract
Climate change is expected to have significant and complex impacts on ecological communities. In addition to direct effects of climate on species, there can also be indirect effects through an intermediary species, such as in host-plant interactions. Indirect effects are expected to be more pronounced in alpine environments because these ecosystems are sensitive to temperature changes and there are limited areas for migration of both species (i.e. closed systems), and because of simpler trophic interactions. We tested the hypothesis that climate change will reduce the range of an alpine butterfly (Parnassius smintheus) because of indirect effects through its host plant (Sedum sp.). To test for direct and indirect effects, we used the simulations of climate change to assess the distribution of P. smintheus with and without Sedum sp. We also compared the projected ranges of P. smintheus to four other butterfly species that are found in the alpine, but that are generalists feeding on many plant genera. We found that P. smintheus gained distributional area in climate-only models, but these gains were significantly reduced with the inclusion of Sedum sp. and in dry-climate scenarios which resulted in a reduction in net area. When compared to the more generalist butterfly species, P. smintheus exhibited the largest loss in suitable habitat. Our findings support the importance of including indirect effects in modelling species distributions in response to climate change. We highlight the potentially large and still neglected impacts climate change can have on the trophic structure of communities, which can lead to significant losses of biodiversity. In the future, communities will continue to favour species that are generalists as climate change induces asynchronies in the migration of species.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".