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Record W2308239974 · doi:10.1130/2015.2516(17)

15. Microclimate and niche constructionism in tropical bat caves: A case study from Mount Elgon, Kenya

2015· book-chapter· en· W2308239974 on OpenAlexaff
Joyce Lundberg, Donald A. McFarlane

Bibliographic record

VenueGeological Society of America Special Papers · 2015
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsMountGeographyCaveMicroclimateNicheEcologyArchaeologyBiologyEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.024
GPT teacher head0.223
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2015
Admission routes1
Has abstractyes

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