Modeling the spatial distribution of dung beetles under climate change scenarios: insights based on nesting strategy, body size and period of activity
Bibliographic record
Abstract
Abstract Climate change is expected to exert varying effects on different taxa and species, affecting both their abundance and distribution ranges. Previous studies have used climate niche models (CNMs) to estimate shifts in the distribution of insects, without considering whether the effects of climate change may vary depending on their functional traits (nesting strategy, body size, and period of activity). Dung beetles, a taxonomic group characterized by using mammalian dung as their primary source of food (coprophagy), respond differently to temperature fluctuations depending on their nesting strategy and body size. In this study, we used CNMs to estimate shifts in the distribution ranges of 33 species of dung beetles under climate change scenarios (the shared socioeconomic pathways from the IPCC’s Sixth Assessment Report) for the period 2041–2060 in North America and Central America (excluding Canada due to absence of data). Additionally, we analyzed whether the effects of climate change on the distribution ranges of the studied species are significantly different depending on their functional traits. Our results showed that climate change will negatively affect the distribution range of the majority of the studied species by the middle of this century, with contrasting effects depending on their nesting strategy and body size. The smallest species and dwellers showed an increase in their occurrence probabilities and percentage of highly suitable habitats, whereas larger-bodied species and tunnelers showed a decrease in both. We found no significant differences between diurnal and nocturnal species. Our results show that by incorporating key traits related to temperature response and ecosystem function, we can analyze shifts in species distribution ranges more precisely, enabling the identification of patterns across functional categories and predictions about their future.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".