Wilting Wildflowers and Bummed-Out Bees: Climate Change Threatens U.S. State Symbols
Bibliographic record
Abstract
Abstract Species designated as state symbols in the United States carry cultural importance and embody historical heritage. However, they are threatened by climate change and even face the risk of local or global extinction. The responses of these species to climate change have received little attention. In this study, we examine the effects of climate change on 64 state flowers and 68 state insects in the United States by employing correlative species distribution models (SDMs). We select a variety of commonly used SDM algorithms to construct an ensemble forecasting framework aimed at predicting the potential climatic habitats for each species under both historical (1981-2010) and future (2071-2100) climate scenarios (SSP1-2.6 and SSP5-8.5), and how these changes might influence the habitat suitability of flower and insect species within their symbolic states and across the United States. Our results indicate that 30 − 66% of state flowers and 18 − 51% of state insects are projected to experience substantial losses of climatically suitable habitat within their symbolic states. Under the high-emissions scenario (SSP5-8.5), ten state flowers and three state insects are likely to face local extinction by the 2080s. Although most of these species may find suitable habitats in other states, only two are projected to have such areas located adjacent to their current symbolic states, potentially limiting natural dispersal. Nationally, 85% of flower species and 71 − 79% of insect species are expected to shift their suitable habitat both poleward and uphill, with the magnitude of latitudinal and elevational shifts significantly greater under SSP5-8.5 than under SSP1-2.6. These findings highlight the vulnerability of culturally significant species to climate change and underscore the urgency of integrating climate adaptation into conservation planning. Proactive, forward-looking conservation and management strategies may be critical for preserving cultural heritage and maintaining ecosystem resilience.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".