Thermal tolerances and species interactions determine the elevational distributions of insects
Bibliographic record
Abstract
Abstract Aim Physiological limits to thermal extremes are often thought to determine the abundance and geographical distribution of species, but more recent evidence suggests that species interactions might be equally important. Moreover, the relative importance of these constraints might shift with changing abiotic conditions, such as climate change. Here, we explore the relative importance of physiological tolerances to heat and species interactions in determining the distribution of insects along two elevational gradients. The gradients contrast in precipitation but not temperature, allowing us to separate these two climatic factors. Location Montane rainforest in Costa Rica. Time period 2015–2016. Major taxa studied Bromeliad‐dwelling aquatic insect larvae. Methods We estimated the elevational preferences of five insect taxa by surveying 170 bromeliads along the moist Atlantic and the dry Pacific slopes of Monteverde and determined their critical thermal maxima (CTmax) experimentally. We determined whether taxon‐specific heat tolerances predicted their elevational preferences, using Deming regressions, and tested whether potential predators mediated effects on the elevation of insect distributions, using structural equation models. Results On the moist Atlantic slope, heat tolerances of insects explained their elevational distributions: taxa with high heat tolerances preferred low elevations where conditions were warmest, whereas taxa with low heat tolerances preferred high elevations where conditions were coldest. In contrast, on the drier Pacific slope, the elevational abundance pattern of many insects reflected negative interactions from crane fly larvae. These larvae are known to become predatory in drought conditions and were disproportionally abundant at low elevations on the Pacific slope. Main conclusions We show that under drought, indirect effects mediated by species interactions can override any direct physiological effects of environmental conditions on insect distributions. The relative importance of limits to physiological tolerance and species interactions thus depends on the environmental context, an important insight given that environmental conditions are expected to shift with climate change.
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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 | 0.000 | 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 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".