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Record W2330428476 · doi:10.1139/cjfr-2013-0331

Stand age and soils as drivers of plant functional traits and aboveground biomass in secondary tropical dry forest

2014· article· en· W2330428476 on OpenAlexvenueno aff
Justin M. Becknell, Jennifer S. Powers

Bibliographic record

VenueCanadian Journal of Forest Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceNational Aeronautics and Space Administration
KeywordsEdaphicBiomass (ecology)Tropical and subtropical dry broadleaf forestsEcologyForest dynamicsEnvironmental scienceSecondary forestPlant communitySoil waterTropicsBiologyAgronomyGeographyForestrySpecies richness

Abstract

fetched live from OpenAlex

The distribution of tropical forest biomass across the landscape is poorly understood, particularly in increasingly common secondary tropical forests. We studied the landscape-scale distribution of edaphic properties, plant community characteristics, and aboveground biomass (AGB) in secondary tropical dry forests in northwest Costa Rica. We used structural equation modeling to examine conceptual models of relationships among these factors, with data from 84 0.1 ha plots. Stand age and soils explained 33%–60% of the variation in community-weighted mean values of foliar traits including specific leaf area, foliar nitrogen, phosphorus, and δ 13 C. Aboveground biomass ranged from 1.7 to 409 Mg·ha −1 among plots between 5 and >100 years old. Stand age alone explained 46% of the variation in AGB among plots, while a model including age, soil pH, traits, and forest type explained 58%. Stand age was the most important variable explaining the distribution of AGB and community characteristics in secondary forests. We speculate that plot size, landscape heterogeneity, disturbance history, and stand dynamics contribute to the unexplained variation in AGB across the landscape.

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 score0.713
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.259
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

Citations223
Published2014
Admission routes1
Has abstractyes

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