Effects of Climate Change on the Distribution of White-footed mouse (Peromyscus leucopus), an Ecologically and Epidemiologically Important Species
Bibliographic record
Abstract
Peromyscus leucopus (White-footed mouse) is a common species found throughout the eastern United States and a key component of midwestern ecosystems. Recently, the species has been expanding its range from the Lower Peninsula of Michigan, to the Upper Peninsula, possibly due to increasing temperatures. Given the shifting environmental conditions, understanding current environmental determinants of current P. leucopus distribution can help predict how the species will respond to global climate change. Such insight in turn, is both important for understanding how N. American species communities are likely to be influenced by ongoing climate change and also for applied local conservation efforts. Data on the presence/absence of P. leucopus and environmental variables including elevation, land cover, and climate, such as temperature, and precipitation, were used to predict habitat suitability and current distribution in Michigan. We assessed the fit of a model that uses maximum entropy approach (MaxEnt) to relate presence to environmental variables by using a cross-validation process and the receiver operating characteristic. Response curves were used to illustrate the relationship between each of the environmental variables and the probability of presence of P. leucopus. And a jackknife test was used to identify those environmental layers that were most important in predicting White-footed mice distribution. Future temperature and precipitation layers were used to predict the possible future distribution of White-footed mice in Michigan and northward. Our analyses indicated that the final model provided a reasonably good fit to the current distribution of the species. Average minimum temperature of April was the environmental layer that contributed most to predicting the current distribution of White-footed mice, whereas, February precipitation reduced the gain of the model most when omitted from the analysis. April average minimum temperature and April precipitation were both positively related to the probability of presence of P. leucopus. The importance of temperature and precipitation suggests that the distribution of this ecologically important species is going to change under future climatic regimes. Indeed fitting the present model to future conditions indicates that the species will expand dramatically northward in the next 50-70 years with many Canadian areas north of Michigan becoming suitable habitat for P. leucopus by 2050.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".