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Record W2590763270 · doi:10.1093/jpe/rtx013

Species abundance distributions as a proxy for the niche–neutrality continuum

2017· article· en· W2590763270 on OpenAlexfundno aff
Xianzhi Wang, M. D. Farnon Ellwood, Dexiecuo Ai, Renyi Zhang, Gang Wang

Bibliographic record

VenueJournal of Plant Ecology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersSpecial Fund for Agro-scientific Research in the Public InterestNational Natural Science Foundation of ChinaMcGill University
KeywordsRelative species abundanceEcologyAbundance (ecology)Rare speciesEcological nicheNeutral theory of molecular evolutionInterspecific competitionNicheBiological dispersalCommon speciesCommunityRelative abundance distributionSpecies diversityBiologyEcosystemPopulationHabitat

Abstract

fetched live from OpenAlex

Species abundance distributions (SADs) are often used to verify mechanistic theories underlying community assembly. However, it is now accepted that SADs alone are not sufficient to reveal biological mechanisms. Recent attention focuses on the relative importance of stochastic dispersal processes versus deterministic processes such as interspecific competition and environmental filtering. Here, we combine a study of the commonness and rarity of species (i.e. the SAD) with mechanistic processes underlying community composition. By comparing the occurrence frequencies of each and every species with its abundance, we quantify the relative contributions of common and rare species to the maintenance of community structure. Essentially, we relate the continuum between commonness and rarity with that of niches and neutrality. An individual-based, spatially explicit model was used to simulate local communities in niche spaces with the same parameters. We generated sets of assemblages from which species were eliminated in opposing sequences: from common to rare and from rare to common, and investigated the relationship between the abundance and frequency of species. We tested the predictions of our model with empirical data from a field experiment in the environmentally homogeneous alpine meadows of the Qinghai–Tibetan plateau. Our simulations support the widespread notion that common species maintain community structure, while rare species maintain species diversity, in both local and regional communities. Our results, both from theoretical simulations and from empirical observations, revealed positive correlations between the abundance of a particular species and its occurrence frequency. SAD curves describe a continuum between commonness and rarity. Removing species from the ‘rare’ end of this continuum has little effect on the similarity of communities, but removing species from the ‘common’ end of the continuum causes significant increases in beta diversity, or species turnover, between communities. In local communities distributed in a homogenous habitat, species located at the ‘common’ end of the continuum should be selected by environmental filtering, with niche space partitioning governed by interspecific competition. Conversely, species located at the ‘rare’ end of the continuum are most likely subject to stochastic dispersal processes. Species situated at intermediate locations on this continuum are therefore determined by niche and neutral processes acting together. Our results suggest that, in homogeneous habitats, SAD curves describing the common-rare continuum may also be used to describe the continuum between niches and neutrality.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.018
GPT teacher head0.274
Teacher spread0.256 · 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 designTheoretical or conceptual
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

Citations17
Published2017
Admission routes1
Has abstractyes

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