Dispersal Limitation, Climate Change, and Practical Tools for Butterfly Conservation in Intensively Used Landscapes
Bibliographic record
Abstract
Pollinators, such as butterflies, contribute to vital ecosystem services, but are susceptible to changing thermal regimes associated with recent climate change. While butterflies are responding to climate changes in many ways, they are not keeping pace. Rapid climate changes are leading to an accumulation of climate debts (or loss of climatic habitat) at continental scales. Climate change mediated shifts in distribution depend on many factors, but particularly on species-specific dispersal abilities and availability of larval host plants. We measured geographical variation in mobility for butterfly species across North America relative to their conservation status and the intensity of human land use. We identified areas where the rate and variability of recent climatic changes have been relatively low and could be managed for pollinator conservation, potentially augmenting existing protected area networks. Using the Yellowstone to Yukon region as a case study, we outline differences between connectivity analyses that incorporate (i) human footprint, (ii) human footprint in conjunction with climate change considerations, and (iii) human footprint in conjunction with climate change considerations weighted by species mobility and richness. All three approaches yield different connectivity recommendations. Conservation management efforts to enhance climate change-related dispersal should focus on improving landscape connectivity based on species-specific mobility, richness, and climate change, as well as landscape permeability. Improving connectivity is particularly vital in areas where mobility and landscape permeability are low but species are at greatest risk of extinction. Mobility matters when considering efforts to mitigate climate change impacts on butterflies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".