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Record W2973295828 · doi:10.1111/jbi.13697

Species–area relationships in continuous vegetation: Evidence from Palaearctic grasslands

2019· article· en· W2973295828 on OpenAlexaff
Jürgen Dengler, Thomas J. Matthews, Manuel J. Steinbauer, Sebastian Wolfrum, Steffen Boch, Alessandro Chiarucci, Timo Conradi, Iwona Dembicz, Corrado Marcenò, Itziar García‐Mijangos, Arkadiusz Nowak, David Štorch, Werner Ulrich, Juan Antonio Campos, Laura Cancellieri, Marta Carboni, Giampiero Ciaschetti, Pieter De Frenne, Jiří Doležal, Christian Dolnik, Franz Essl, Edy Fantinato, Goffredo Filibeck, John‐Arvid Grytnes, Riccardo Guarino, Behlül Güler, Monika Janišová, Ewelina Klichowska, Łukasz Kozub, Анна Куземко, Michael Manthey, Anne Mimet, Alireza Naqinezhad, Christian Pedersen, Robert K. Peet, Vincent Pellissier, Remigiusz Pielech, Leonardo Rosati, Massimo Terzi, Orsolya Valkó, Denys Vynokurov, Hannah J. White, Manuela Winkler, Idoia Biurrun

Bibliographic record

VenueJournal of Biogeography · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersState Fund for Fundamental Research of UkraineVedecká Grantová Agentúra MŠVVaŠ SR a SAVNarodowe Centrum NaukiEusko JaurlaritzaBayerische ForschungsallianzMinistero dell’Istruzione, dell’Università e della RicercaGrantová Agentura České RepublikySlovenská Akadémia Vied
KeywordsBiomeLichenVegetation (pathology)EcologyRange (aeronautics)Sampling (signal processing)GrasslandTaxonHabitatBiologyGeographyEcosystem

Abstract

fetched live from OpenAlex

Abstract Aim Species–area relationships (SARs) are fundamental scaling laws in ecology although their shape is still disputed. At larger areas, power laws best represent SARs. Yet, it remains unclear whether SARs follow other shapes at finer spatial grains in continuous vegetation. We asked which function describes SARs best at small grains and explored how sampling methodology or the environment influence SAR shape. Location Palaearctic grasslands and other non‐forested habitats. Taxa Vascular plants, bryophytes and lichens. Methods We used the GrassPlot database, containing standardized vegetation‐plot data from vascular plants, bryophytes and lichens spanning a wide range of grassland types throughout the Palaearctic and including 2,057 nested‐plot series with at least seven grain sizes ranging from 1 cm 2 to 1,024 m 2 . Using nonlinear regression, we assessed the appropriateness of different SAR functions (power, power quadratic, power breakpoint, logarithmic, Michaelis–Menten). Based on AICc, we tested whether the ranking of functions differed among taxonomic groups, methodological settings, biomes or vegetation types. Results The power function was the most suitable function across the studied taxonomic groups. The superiority of this function increased from lichens to bryophytes to vascular plants to all three taxonomic groups together. The sampling method was highly influential as rooted presence sampling decreased the performance of the power function. By contrast, biome and vegetation type had practically no influence on the superiority of the power law. Main conclusions We conclude that SARs of sessile organisms at smaller spatial grains are best approximated by a power function. This coincides with several other comprehensive studies of SARs at different grain sizes and for different taxa, thus supporting the general appropriateness of the power function for modelling species diversity over a wide range of grain sizes. The poor performance of the Michaelis–Menten function demonstrates that richness within plant communities generally does not approach any saturation, thus calling into question the concept of minimal area.

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 score0.004
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.226
Teacher spread0.207 · 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".

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Citations95
Published2019
Admission routes1
Has abstractyes

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