Climate Change and Local Host Availability Drive the Northern Range Boundary in the Rapid Expansion of a Specialist Insect Herbivore, Papilio cresphontes
Bibliographic record
Abstract
Species distributions, abundance, and interactions have always been influenced by human activity and are currently experiencing rapid change. Biodiversity benchmark surveys traditionally require intense human labor inputs to find, identify, and record organisms limiting the rate and impact of scientific enquiry and discovery. Recent emergence and advancement of monitoring technologies have improved biodiversity data collection to a scale and scope previously unimaginable. Community science web platforms, smartphone applications, and technology assisted identification have expedited the speed and enhanced the volume of observational data all while providing open access to these data worldwide. How to integrate and leverage the data into valuable information on how species are changing in space and time requires new best practices in computational and analytical approaches. Here we integrate data from three community science repositories to explore how a specialist herbivore distribution changes in relation to host plant distributions and other environmental factors. We generate a series of temporally explicit species distribution models to generate range predictions for a specialist insect herbivore ( Papilio cresphontes ) and three predominant host-plant species. We find that this insect species has experienced rapid northern range expansion, likely due to a combination of the range of its larval host plants and climate changes in winter. This case study shows rapid data collection through large scale community science endeavors can be leveraged through thoughtful data integration and transparent analytic pipelines to inform how environmental change impacts where species are and their interactions for a more cost effective method of biodiversity benchmarking.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".