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Record W2130811880 · doi:10.1111/1365-2745.12132

Scale‐dependent relationships between tree species richness and ecosystem function in forests

2013· article· en· W2130811880 on OpenAlexaff
Ryan A. Chisholm, Helene C. Muller‐Landau, Kassim Abdul Rahman, Daniel P. Bebber, Yue Bin, Stephanie Bohlman, Norman A. Bourg, Joshua S. Brinks, Sarayudh Bunyavejchewin, Nathalie Butt, Honglin Cao, Min Cao, Dairón Cárdenas, Li‐Wan Chang, Jyh‐Min Chiang, George B. Chuyong, Richard Condit, H. S. Dattaraja, Stuart J. Davies, Álvaro Duque, Christine Fletcher, I. A. U. N. Gunatilleke, Savitri Gunatilleke, Zhanqing Hao, Rhett D. Harrison, Robert W. Howe, Chang‐Fu Hsieh, Stephen P. Hubbell, Akira Itoh, David Kenfack, Somboon Kiratiprayoon, Andrew J. Larson, Juyu Lian, Dunmei Lin, Haifeng Liu, James A. Lutz, Keping Ma, Yadvinder Malhi, Sean M. McMahon, William J. McShea, Madhava Meegaskumbura, Salim Mohd Razman, Michael D. Morecroft, Christopher J. Nytch, Alexandre A. Oliveira, Geoffrey G. Parker, Sandeep Pulla, Ruwan Punchi‐Manage, Hugo Romero‐Saltos, Weiguo Sang, Jon Schurman, Sheng‐Hsin Su, Raman Sukumar, I‐Fang Sun, H. S. Suresh, Sylvester Tan, Duncan W. Thomas, Sean C. Thomas, Jill Thompson, Renato Valencia, Amy Wolf, Sandra Yap, Wanhui Ye, Zuoqiang Yuan, Jess K. Zimmerman

Bibliographic record

VenueJournal of Ecology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of ChinaHSBC Bank USASmithsonian InstitutionNational Science Foundation
KeywordsSpecies richnessBiomass (ecology)ProductivityEcologyBiodiversitySpatial ecologyEcosystemBiologyEnvironmental science

Abstract

fetched live from OpenAlex

Summary The relationship between species richness and ecosystem function, as measured by productivity or biomass, is of long‐standing theoretical and practical interest in ecology. This is especially true for forests, which represent a majority of global biomass, productivity and biodiversity. Here, we conduct an analysis of relationships between tree species richness, biomass and productivity in 25 forest plots of area 8–50 ha from across the world. The data were collected using standardized protocols, obviating the need to correct for methodological differences that plague many studies on this topic. We found that at very small spatial grains (0.04 ha) species richness was generally positively related to productivity and biomass within plots, with a doubling of species richness corresponding to an average 48% increase in productivity and 53% increase in biomass. At larger spatial grains (0.25 ha, 1 ha), results were mixed, with negative relationships becoming more common. The results were qualitatively similar but much weaker when we controlled for stem density: at the 0.04 ha spatial grain, a doubling of species richness corresponded to a 5% increase in productivity and 7% increase in biomass. Productivity and biomass were themselves almost always positively related at all spatial grains. Synthesis . This is the first cross‐site study of the effect of tree species richness on forest biomass and productivity that systematically varies spatial grain within a controlled methodology. The scale‐dependent results are consistent with theoretical models in which sampling effects and niche complementarity dominate at small scales, while environmental gradients drive patterns at large scales. Our study shows that the relationship of tree species richness with biomass and productivity changes qualitatively when moving from scales typical of forest surveys (0.04 ha) to slightly larger scales (0.25 and 1 ha). This needs to be recognized in forest conservation policy and management.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.217
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations365
Published2013
Admission routes1
Has abstractyes

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