Elevational specialization and the monitoring of the effects of climate change in insects: Beetles in a Brazilian rainforest mountain
Bibliographic record
Abstract
Mountains have provided important insights on the impacts of climate change on species distribution. Organisms living in tropical mountains are expected to specialize in narrow temperature limits (demonstrating low thermal tolerance), often with narrow elevational distributions relative to temperate species, and may shift their elevational range in response to climate change. Importantly, insects are sensitive, and respond rapidly, to temperature variation, making them suitable bioindicators to monitor the effects of climate change. However, to monitor the effects of climate change in mountains it is important to understand present elevational distribution and other ecological characteristics of local insect populations. In this context, we suggest a method to identify beetle taxa that can be used to monitor climate change effects in tropical mountainous insect species. We illustrate the method by describing the elevational distribution of different beetle groups, associating this distribution with species’ thermal range in a tropical mountain forest in Southeast Brazil. Sampling was conducted at Serra dos Órgãos National Park, RJ, Brazil, in the Atlantic Rainforest, one of the main global biodiversity hotspots. In order to systematically sample beetle diversity across elevations, we used flight interception ‘Malaise’ traps at fifteen different sites, from 130 m to 2170 m a.s.l., over three consecutive months during the rainy season. To investigate species’ climatic niches, we recorded climatic variables for this period. We collected 2963 individuals of 272 species, belonging to six Coleoptera groups over a temperature gradient that decreased about 0.5 °C for each 100 m in elevation. Considering the thermal tolerance of species from tropical mountains and their narrow elevational range and abundance, five Coleoptera species belonging to Cerambycidae, Eumolpinae (Chrysomelidae), Lampyridae and Phengodidae were considered suitable bioindicators, and the Eumolpinae and Lampyridae were the ones with the narrowest elevational range. We suggest that the use of abundant species (or groups of species) with narrow elevational range as bioindicators can be valuable to monitor the effects of climate change on the biota, allowing us to evaluate how species are responding to changes over time.
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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 | 0.001 | 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".