A global database on non-volant small mammal composition in natural and human-modified habitats
Bibliographic record
Abstract
Non-volant small mammals, which include small-bodied representatives from several mammal orders, have been used as a model group to test the effects of habitat conversion and edge creation on biodiversity. Small mammals occupy a large variety of habitat types and vegetation strata, and have varied lifestyles and diets. They include species with slow-to fast-life history (the Etruscan shrew Suncus etruscus and European Hare Lepus europaeus, respectively) and with very specialized to very generalist habits and diets (the Atlantic bamboo rat Kannabateomys amblyonyx and house mouse Mus musculus, respectively). There are no databases with global coverage focusing on small mammal composition in natural and human-modified habitats and that include neglected natural habitats (e.g. grasslands and savannas). Here, peer-reviewed articles were searched in the primary literature to synthesize almost half century (1973–2017) of research on small mammal composition in natural forests, grasslands and their natural edges, and in five types of human-modified habitats (human-induced forest edges, human-induced grassland edges, crop fields, clear-cuts and tree plantations). The complete database includes information from 199 peer-reviewed articles. Presence data were obtained for 534 species (including 30 unidentified) in 551 sites distributed in 45 countries, 92 ecoregions, 10 biomes and six realms. Measurements of sampling effort and number of species records (number of individuals, captures) per habitat were also obtained, from which researchers can calculate a measure of abundance standardized by the sampling effort. The database will be useful for researchers interested in local-to broad-scale patterns of alpha- and beta-diversity in natural and human-modified habitats.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.051 | 0.057 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".