15. Microclimate and niche constructionism in tropical bat caves: A case study from Mount Elgon, Kenya
Bibliographic record
Abstract
Significant impacts on cave microclimate from large populations of the bat Rousettus aegyptiacus have been documented in three simple caves in pyroclastic rock of Mount Elgon National Park, Kenya, one of which, Kitum Cave, with few bats, acts as a control, indicating microclimatic variations in the absence of significant biological activity. Seven days of temperature logger records, and on-site mapping of rock and air temperature, humidity, and air flow provide the basis for modeling of heat, water, and CO2 production and dispersion. Internal temperatures in the presence of bats in Mackingeny Cave and Ngwarisha Cave rise to ~18 °C above ambient (from ~12 °C to ~30 °C), but in the control site by only ~2 °C. Excess bat-generated energy is dissipated by conduction to rock and by ongoing air circulation, the strongest of which accompanies bat entry and exit flights. In Kitum Cave, temperatures that are substantially lower than bat thermo-neutral zone raise concern for Allee effects on long-term colony fitness: Modeling indicates that a population of at least 100,000 bats should promote colony vitality. Metabolic outputs were modeled to yield corrosional potential: At these population densities, were the caves in limestone, rates of surface denudation caused directly by metabolic outputs would be 1 m in ~80,000 yr. These results confirm that tropical bats can be effective niche constructionists, by optimizing microclimatic roost conditions, by longer-term bioerosional optimization of rock surfaces for roosting, and by long-term niche engineering through zoo-speleogenetic enlargement of roost volume.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".