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Comparing tropical forest tree size distributions with the predictions of metabolic ecology and equilibrium models

2006· letter· en· W2165903616 on OpenAlexaff
Helene C. Muller‐Landau, Richard Condit, Kyle E. Harms, Christian O. Marks, Sean C. Thomas, Sarayudh Bunyavejchewin, George B. Chuyong, Leonardo Co, Stuart J. Davies, Robin B. Foster, Savitri Gunatilleke, I. A. U. N. Gunatilleke, Térese B. Hart, Stephen P. Hubbell, Akira Itoh, Abd Rahman Kassim, David Kenfack, James V. LaFrankie, Daniel Lagunzad, Hua Seng Lee, Elizabeth Losos, Jean‐Remy Makana, Tatsuhiro Ohkubo, Cristián Samper, Raman Sukumar, I‐Fang Sun, M. N. Nur Supardi, Sylvester Tan, Duncan W. Thomas, Jill Thompson, Renato Valencia, Martha Isabel Vallejo, Gorky Villa Muñoz, Takuo Yamakura, Jess K. Zimmerman, Handanakere Shavaramaiah Dattaraja, Shameema Esufali, Pamela Hall, Fangliang He, Consuelo Hernández, Somboon Kiratiprayoon, H. S. Suresh, Christopher Wills, Peter S. Ashton

Bibliographic record

VenueEcology Letters · 2006
Typeletter
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of AlbertaUniversity of Toronto
Fundersnot available
KeywordsEcologyTropical forestTree (set theory)BiologyEnvironmental scienceMathematics

Abstract

fetched live from OpenAlex

Tropical forests vary substantially in the densities of trees of different sizes and thus in above-ground biomass and carbon stores. However, these tree size distributions show fundamental similarities suggestive of underlying general principles. The theory of metabolic ecology predicts that tree abundances will scale as the -2 power of diameter. Demographic equilibrium theory explains tree abundances in terms of the scaling of growth and mortality. We use demographic equilibrium theory to derive analytic predictions for tree size distributions corresponding to different growth and mortality functions. We test both sets of predictions using data from 14 large-scale tropical forest plots encompassing censuses of 473 ha and > 2 million trees. The data are uniformly inconsistent with the predictions of metabolic ecology. In most forests, size distributions are much closer to the predictions of demographic equilibrium, and thus, intersite variation in size distributions is explained partly by intersite variation in growth and mortality.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.691

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.190
Teacher spread0.177 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations214
Published2006
Admission routes1
Has abstractyes

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