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Record W4415901294 · doi:10.1016/j.isci.2025.113936

Narrow thresholds of canopy disturbance determine the microclimate buffering potential of tropical forests

2025· article· en· W4415901294 on OpenAlexfundno aff
Michael J. W. Boyle, Joseph R. Williamson, Stephen J. Rossiter, Marion Pfeifer, Rosie Drinkwater, Joel S. Woon, Louise A. Ashton, Michiel van Breugel, Paul Eggleton, Theodore A. Evans, Owen T. Lewis, Sarab S. Sethi, Eleanor M. Slade, Arthur Y. C. Chung, Robert M. Ewers

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
FundersScleroderma Association of British ColumbiaMinistry of Education - SingaporeUniversity Grants CommitteeNational Natural Science Foundation of ChinaUniversity of Hong KongYayasan Sime Darby
KeywordsCanopyMicroclimateUnderstoryDisturbance (geology)Tree canopyTropical climateInvertebrateBiodiversity

Abstract

fetched live from OpenAlex

The importance of protecting logged and recovering tropical forests has gained much attention. Disturbed forests can, however, have hotter microclimates, exacerbating the effects of future climate change. Using thermal imaging we captured understory surface temperatures along a gradient of tropical forest disturbance, and compared these to the upper thermal limits (CT max ) of invertebrates within the same forests. Surface temperatures exceeding the CT max of invertebrate groups occurred once canopy cover fell below 76%. In highly degraded forests, surface niche space was reduced by 22% for the most sensitive taxa, and this doubled following simulated warming of +3°C. In contrast, all invertebrate groups were buffered in sites that retained 80% canopy cover or higher even following severe warming. We demonstrate a narrow threshold of canopy disturbance beyond which microclimate buffering is significantly diminished. These findings illustrate the importance of conserving high canopy cover forests to protect tropical biodiversity in a hotter future.

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 categoriesInsufficient 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 score0.364
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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