Modelling temperature-dependent larval development and\nsubsequent demographic Allee effects in adult populations of the alpine butterfly Parnassius smintheus
Bibliographic record
Abstract
Climate change has been attributed as a driver of changes to ecological systems worldwide and understanding the effects of climate change at individual, population, community, and ecosystem levels has become a primary concern of ecology. One avenue toward understanding the impacts of climate change on an ecosystem is through the study of environmentally sensitive species. Butterflies are sensitive to climatic changes due to their reliance on environmental cues such as temperature and photoperiod, which regulate the completion of life history stages. As such, the population dynamics of butterflies may offer insight into the impacts of climate change on the health of an ecosystem. In this paper we study the effects of rearing temperature on the alpine butterfly Parnassius smintheus (Rocky Mountain Apollo), both directly through individual phenological changes and indirectly through adult reproductive success at the population level. Our approach is to formulate a mathematical model of individual development parameterized by experimental data and link larval development to adult reproductive success. A Bernoulli process model describes temperature-dependent larval phenology, and a system of ordinary differential equations is used to study impacts on reproductive success. The phenological model takes field temperature data as its input and predicts a temporal distribution of adult emergence, which in turn controls the dynamics of the reproductive success model. We find that warmer spring and summer temperatures increase reproductive success, while cooler temperatures exacerbate a demographic Allee effect, suggesting that observed yearly fluctuations in P. smintheus population size may be driven by inter-annual temperature variability. Model predictions are validated against mark-recapture field data from 2001 and 2003 − 2009.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".