Predicting Suitable Habitat for Glipa (Coleoptera: Mordellidae: Mordellinae) Under Current and Future Climates Using MaxEnt Modeling
Bibliographic record
Abstract
Beetles of the family Mordellidae, important global pollinators, include Glipa, the third largest genus, which retains plesiomorphic traits related to pollination and is mainly found between 38° S–38° N. Existing studies on Glipa focus largely on taxonomy and systematics. The ecological response of Glipa to climate change remains poorly understood. Our objective was to investigate how the distribution of Glipa may respond to climate change using a species-level MaxEnt based model with 297 geographic distribution data points and seven bioclimatic environmental variables. The study showed that the MaxEnt model had a high predictive accuracy, with an Area Under the Curve (AUC) value of 0.963. The maximum temperature of the warmest month, mean annual precipitation, and mean precipitation of the driest quarter were the three most important factors affecting the distribution of Glipa. Currently, the suitable distribution areas of Glipa are mainly located in East Asia, Southeast Asia, eastern North America, South America, and central and western Africa. Under future climate scenarios, the area of suitable habitat is expected to increase gradually as global temperatures rise. Under the SSP585 scenario in the 2070s, the suitable habitat area is projected to expand by 53.89% compared to the present. Additionally, the centroid of suitable habitat is expected to shift northward. This study not only deepens the understanding of the distribution patterns of Glipa and their response to climate change but also provides important scientific evidence for the conservation of pollinator diversity.
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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 | 0.000 | 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 | 0.000 | 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 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".