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Record W3049498585 · doi:10.1111/geb.13165

A cross‐scale assessment of productivity–diversity relationships

2020· article· en· W3049498585 on OpenAlexaff
Dylan Craven, Masha T. van der Sande, Carsten Meyer, Katharina Gerstner, Joanne M. Bennett, Darren P. Giling, Jes Hines, Helen R. P. Phillips, Felix May, Katherine H. Bannar‐Martin, Jonathan M. Chase, Petr Keil

Bibliographic record

VenueGlobal Ecology and Biogeography · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsFisheries and Oceans Canada
FundersDeutsche Forschungsgemeinschaft
KeywordsProductivitySpecies richnessBiodiversityEcologyPrimary productionBiomass (ecology)EcosystemSpatial ecologySpatial variabilityGeographyEnvironmental scienceEcosystem diversityBiology

Abstract

fetched live from OpenAlex

Abstract Aim Biodiversity and ecosystem productivity vary across the globe, and considerable effort has been made to describe their relationships. Biodiversity and ecosystem functioning research has traditionally focused on how experimentally controlled species richness affects net primary productivity ( S → NPP) at small spatial grains. In contrast, the influence of productivity on richness (NPP → S ) has been explored at many grains in naturally assembled communities. Mismatches in spatial scale between approaches have fuelled debate about the strength and direction of biodiversity–productivity relationships. Here, we examine the direction and strength of the influence of productivity on diversity (NPP → S ) and the influence of diversity on productivity ( S → NPP) and how these vary across spatial grains. Location Contiguous USA. Time period 1999–2015. Major taxa studied Woody species (angiosperms and gymnosperms). Methods Using data from North American forests at grains from local (672 m 2 ) to coarse spatial units (median area = 35,677 km 2 ), we assess relationships between diversity and productivity using structural equation and random forest models, while accounting for variation in climate, environmental heterogeneity, management and forest age. Results We show that relationships between S and NPP strengthen with spatial grain. Within each grain, S → NPP and NPP → S have similar magnitudes, meaning that processes underlying S → NPP and NPP → S either operate simultaneously or that one of them is real and the other is an artefact. At all spatial grains, S was one of the weakest predictors of forest productivity, which was largely driven by biomass, temperature and forest management and age. Main conclusions We conclude that spatial grain mediates relationships between biodiversity and productivity in real‐world ecosystems and that results supporting predictions from each approach (NPP → S and S → NPP) serve as an impetus for future studies testing underlying mechanisms. Productivity–diversity relationships emerge at multiple spatial grains, which should widen the focus of national and global policy and research to larger spatial grains.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.351

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 science0.0000.000
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 head0.014
GPT teacher head0.247
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.

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

Citations63
Published2020
Admission routes1
Has abstractyes

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