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Below‐ground plant species richness: new insights from <scp>DNA</scp>‐based methods

2012· article· en· W2160310937 on OpenAlexafffund
Meelis Pärtel, Inga Hiiesalu, Maarja Öpik, Scott D. Wilson

Bibliographic record

VenueFunctional Ecology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Regina
FundersEuropean Regional Development FundNatural Sciences and Engineering Research Council of CanadaEuropean Commission
KeywordsSpecies richnessVegetation (pathology)EcologyPlant communityBiologyEcosystemBiodiversity

Abstract

fetched live from OpenAlex

Summary 1. Ideas about mechanisms controlling plant species richness are founded on empirical studies of above‐ground vegetation. In many ecosystems, however, the majority of vegetation (e.g. 50–90% in temperate grasslands) occurs below‐ground as roots, rhizomes and shoot bases. Whether the richness patterns described for above‐ground vegetation also hold for the large below‐ground component is still unknown. 2. Here, we provide a concise overview how the use of DNA ‐based techniques might alter our perception of richness patterns in plant communities. We focus mainly on temperate grasslands, but new patterns should also arise in other community types (except epiphyte‐rich ecosystems). 3. We hypothesise that DNA ‐based measurements of below‐ground plant richness will reveal that richness is greater below‐ than above‐ground because many perennial plants persist below‐ground even in the temporary absence of above‐ground shoots, and because the roots and rhizomes of plant individuals occupy larger areas than do shoots. Consequently, the species–area relationship may show steeper slopes for below‐ground than above‐ground richness. Further, above‐ground richness may not be a constant proportion of below‐ground richness, so the ratio of below/above‐ground richness may vary along environmental gradients of productivity, disturbance and heterogeneity. 4. We also hypothesize that the often‐observed decrease in above‐ground richness with increasing productivity may not occur for below‐ground richness, partly for the reasons noted above, and partly because of differences between above‐ and below‐ground resources. Light is supplied largely in one dimension and does not persist in the environment for later uptake, whereas nutrients and water are supplied in three dimensions and can persist in the soil. Together, these differences should allow more niche differentiation and greater species richness below‐ground. 5. Current DNA ‐based methods that allow measurement of below‐ground richness in the field are likely to reveal patterns different from those well‐documented for above‐ground richness and may also produce new insights about plant community structure and function.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.998

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.027
GPT teacher head0.254
Teacher spread0.227 · 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; both teacher heads agree on what is shown here.

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

Citations39
Published2012
Admission routes2
Has abstractyes

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