Predicting distribution overlaps between <i>Dendroctonus adjunctus</i> Blandford 1897 and six <i>Pinus</i> species in Mexico under global climate change
Bibliographic record
Abstract
Species that coexist nowadays will not necessarily match their distributions in the future due to different climate suitability. The aim of this study was to identify potential distribution areas where the bark beetle Dendroctonus adjunctus and six of its host tree species overlap under different climate change scenarios. Potential distribution maps were built with species presence data using the MaxLike R library. For each projection, we used WorldClim bioclimatic variables, current and future (2050, 2070) condition climate data, two greenhouse gas concentration scenarios (RCP 4.5, RCP 8.5), and three general circulation models. The results show that the projected current potential distribution area of the bark beetle extends over 216 000 km2. This potential distribution range spans across 28 of the 32 Mexican states, eight of which have not yet reported the insect's presence. Of the 72 overlapping maps that we made, the largest covers more than 118 000 km2 for Pinus duranguensis, while all future projections show a reduction in spatial coincidence. Given future climatic scenarios, D. adjunctus will probably reach higher altitudinal sites. The information contained in this study can be used to identify areas to prioritize monitoring, management, plant sanitation treatment, and reforestation strategies in Mexican pine forests.
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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 | 0.000 |
| 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".