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Record W2984967191 · doi:10.1111/geb.13029

Species niches, not traits, determine abundance and occupancy patterns: A multi‐site synthesis

2019· article· en· W2984967191 on OpenAlexafffund
Nicholas A. C. Marino, Régis Céréghino, Benjamin Gilbert, Jana S. Petermann, Diane S. Srivastava, Paula M. de Omena, Fabiola Ospina Bautista, Laura Melissa Guzman, Gustavo Q. Romero, M. Kurtis Trzcinski, Ignacio M. Barberis, Bruno Corbara, Vanderlei J. Debastiani, Olivier Dézerald, Pavel Kratina, Céline Leroy, Arthur Andrew Meahan MacDonald, Guillermo Montero, Valério D. Pillar, Barbara A. Richardson, M.J. Richardson, Stanislas Talaga, Ana Z. Gonçalves, Gustavo C. O. Piccoli, Merlijn Jocqué, Vinicius F. Farjalla

Bibliographic record

VenueGlobal Ecology and Biogeography · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorAgence Nationale de la RechercheFundação de Amparo à Pesquisa do Estado de São PauloFondation pour la Recherche sur la BiodiversiteInternational Institute of Tropical ForestryNational Science FoundationRoyal Society of EdinburghRoyal SocietyUniversidad Nacional de RosarioCarnegie Trust for the Universities of ScotlandConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsNicheEcological nicheEcologyOccupancyBiologyAbundance (ecology)TraitNiche segregationHabitatRange (aeronautics)Niche differentiationTaxonOptimal distinctiveness theoryEnvironmental niche modelling

Abstract

fetched live from OpenAlex

Abstract Aim Locally abundant species are usually widespread, and this pattern has been related to properties of the niches and traits of species. However, such explanations fail to account for the potential of traits to determine species niches and often overlook statistical artefacts. Here, we examine how trait distinctiveness determines the abilities of species to exploit either common habitats (niche position) or a range of habitats (niche breadth) and how niche position and breadth, in turn, affect abundance and occupancy. We also examine how statistical artefacts moderate these relationships. Location Sixteen sites in the Neotropics. Time period 1993–2014. Major taxa studied Aquatic invertebrates from tank bromeliads. Methods We measured the environmental niche position and breadth of each species and calculated its trait distinctiveness as the average trait difference from all other species at each site. Then, we used a combination of structural equation models and a meta‐analytical approach to test trait–niche relationships and a null model to control for statistical artefacts. Results The trait distinctiveness of each species was unrelated to its niche properties, abundance and occupancy. In contrast, niche position was the main predictor of abundance and occupancy; species that used the most common environmental conditions found across bromeliads were locally abundant and widespread. Contributions of niche breadth to such patterns were attributable to statistical artefacts, indicating that effects of niche breadth might have been overestimated in previous studies. Main conclusions Our study reveals the generality of niche position in explaining one of the most common ecological patterns. The robustness of this result is underscored by the geographical extent of our study and our control of statistical artefacts. We call for a similar examination across other systems, which is an essential task to understand the drivers of commonness across the tree of life.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.013
GPT teacher head0.218
Teacher spread0.205 · 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 designMeta-analysis
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

Citations20
Published2019
Admission routes2
Has abstractyes

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