The Impact of Climate Warming on Organism Populations in US
Bibliographic record
Abstract
Climate warming is often assessed through rising maximum temperatures, yet trends in minimum temperatures can have equally profound ecological impacts, especially in colder regions. This study primarily focuses on Michigan, while also incorporating data from Alaska, Washington, Maine, and Minnesota to examine long-term changes in minimum temperatures and their effects on cold-adapted wildlife. Minimum monthly temperature records from 1896 to 2025 were sourced from NOAA's National Centers for Environmental Information (NCEI), and wildlife observation data—centered on species such as the Canada lynx (Lynx canadensis) were analyzed. Our objective was to identify temporal patterns in minimum temperature increases and assess their potential correlation with shifts in species distribution. Statistical modeling and time-series analysis revealed a consistent upward trend in minimum winter temperatures across all studied regions. These warming trends align with observed changes in animal populations, including range contractions of the lynx and altered behavior and survival rates in other cold-adapted species. Reduced snow cover persistence and milder winters appear to be reshaping ecological dynamics in these northern habitats. Our findings highlight the critical need to include minimum temperature data in climate-impact assessments and underscore the vulnerability of northern species to even subtle forms of warming. Ongoing monitoring and adaptive management strategies are essential for preserving ecological integrity in these changing environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".