Insectivores exhibit superior microbial transmission efficiency and elevated zoonotic risk by 2035 relative to rodents and bats
Bibliographic record
Abstract
Abstract Wild small mammals represent critical sources of zoonotic infections due to their high diversity, global distribution, and proximity to humans. Nevertheless, significant knowledge gaps persist in characterizing pan-taxonomic microbial richness and sharing dynamics, particularly regarding ecologically critical yet understudied Eulipotyphla (true insectivores). Here we take a macroecological approach to compare how microbial hosting and transmission differ across insectivores, rodents, and bats, and what ecological factors drive such disparities. We find that insectivores host comparable microbial richness to rodents and bats while exhibiting superior intra- and cross-order transmission efficiency. Urban adaptation, geographic range area, and longevity are shared drivers of microbial richness and transmission across these host orders, while greater body mass and shorter gestation time specifically are positive predictors of these outcomes within insectivores. Climate change projections identify insectivores as primary transmission hosts in new high-latitude hotspots by 2035, including parts of the US, Canada, and Russia, posing greater zoonotic threats than rodents or bats. Our findings challenge the prevailing paradigm that prioritizes rodents and bats as special zoonotic reservoirs, establishing insectivores as critical but overlooked players in disease ecology. Collective proactive surveillance of insectivores, rodents, and bats is imperative for forecasting emerging zoonotic threats and informing global risk assessment frameworks.
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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".