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Record W2928052381 · doi:10.1111/ele.13259

Species‐rich boreal forests grew more and suffered less mortality than species‐poor forests under the environmental change of the past half‐century

2019· letter· en· W2928052381 on OpenAlexafffundabout
Masumi Hisano, Han Y. H. Chen, Eric B. Searle, Peter B. Reich

Bibliographic record

VenueEcology Letters · 2019
Typeletter
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Ontario
KeywordsBiodiversityTaigaEcologyClimate changeBiomass (ecology)EcosystemSpecies diversityGeographyForest ecologyGlobal biodiversityBorealEnvironmental changeBiology

Abstract

fetched live from OpenAlex

Abstract Climate and other global environmental changes are major threats to ecosystem functioning and biodiversity. However, the importance of plant diversity in mitigating the responses of functioning of natural ecosystems to long‐term environmental change remains unclear. Using inventory data of boreal forests of western Canada from 1958 to 2011, we found that aboveground biomass growth increased over time in species‐rich forests but decreased in species‐poor forests, and importantly, aboveground biomass loss from tree mortality was smaller in species‐rich than species‐poor forests. A further analysis indicated that growth of species‐rich (but not species‐poor) forests was statistically positively associated with rising CO 2 , and that mortality in species‐poor forests increased more as climate moisture availability decreased than it did in species‐rich forests. In contrast, growth decreased and mortality increased as the climate warmed regardless of species diversity. Our results suggest that promoting high tree diversity may help reduce the climate and environmental change vulnerability of boreal forests.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score1.000

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 science0.0010.001
Research integrity0.0010.001
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 head0.200
Teacher spread0.183 · 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.

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

Citations59
Published2019
Admission routes3
Has abstractyes

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