30 years of terrestrial insect richness patterns across elevation: What have we learned? A global meta‐analysis
Bibliographic record
Abstract
Understanding elevation variation in biodiversity is a classic question in ecology and has implications for understanding climate change impacts on mountain ecosystems. While insects are the largest group of animals, the global trend in insect species richness with elevation is unknown. To date, single studies and taxa-specific syntheses have provided no single picture, finding variable patterns of insect richness with elevation. A global synthesis across systems would provide a better understanding of how insect species richness changes with elevation and the possible environmental correlates of those patterns. We used published studies of terrestrial insect elevation gradients from 1990 to 2020 to ask: How do insect species richness change with elevation, and which environmental variables best explain this relationship statistically? With 1486 sites spanning 151 species richness-elevation gradients from 80 studies from four diverse insect taxonomic groups and five biomes, we found that overall proportional richness reached a low-elevation plateau and then decreased. We also show that mean annual temperature and seasonality best explain this trend. We suggest best practices and areas of interest for the future of insect richness-elevation studies, including underrepresented groups, geographic areas, and more standardized methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".