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Record W2108008160 · doi:10.1111/geb.12371

Assessing the general patterns of forest structure: quantifying tree and forest allometric scaling relationships in the<scp>U</scp>nited<scp>S</scp>tates

2015· article· en· W2108008160 on OpenAlexfundno aff
Laura Duncanson, Ralph Dubayah, Brian J. Enquist

Bibliographic record

VenueGlobal Ecology and Biogeography · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric AdministrationU.S. Department of AgricultureNational Aeronautics and Space Administration
KeywordsAllometryEcologyTree allometryScalingScale (ratio)Forest ecologyTree (set theory)EcosystemEnvironmental scienceMathematicsBiologyGeographyBiomass (ecology)GeometryCartography

Abstract

fetched live from OpenAlex

Abstract Aim Understanding the drivers of forest structure, function and change is a fundamental problem in both theoretical ecology and applied forestry for carbon mapping and monitoring. An important component of forest ecology research often utilizes allometric equations to scale up local measurements to predict large‐scale forest and ecosystem‐level properties. However, both applied and theoretical allometries in forest ecology (such as metabolic scaling theory, MST ) assume that many scaling relationships are insensitive to broad‐scale climate gradients or species life histories. We aim to test these assumptions by mapping continental‐scale forest allometry across environmental gradients in the U nited S tates. Location U nited S tates. Methods We fit exponents to two allometric relationships in c . 100,000 F orest I nventory A nalysis ( FIA ) field plots: (1) the relationship between the height of an individual tree and its diameter, and (2) plot‐level tree size distributions. We compare fitted exponents to environmental and life‐history variables, such as climate, topography and forest structure, in an attempt to explain allometric variability and deviations from theoretically predicted allometries. Results We find that the structural allometry of forests varies strongly as a function of location in the U nited S tates. Allometric exponents appear to asymptote at approximately where MST theory predicts with increasing forest height, while deviations from MST are partially explained as a function of environmentally driven recruitment limitations and successional status. Main conclusions While we find support for invariant tree and stand allometric scaling relationships in forests that are in steady state with regard to demography and resources, we also find considerable spatial variability in forest allometric relationships when steady‐state conditions are violated. These findings suggest that extensions of metabolic scaling theory should incorporate variation in demographic dynamics in younger successional forests, and factors influencing recruitment limitation.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.267
Teacher spread0.237 · 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

Citations85
Published2015
Admission routes1
Has abstractyes

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