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Record W4410912881 · doi:10.1101/2025.05.27.656387

What determines trophic niche breadth? A global analysis of freshwater fishes using isospaces

2025· preprint· en· W4410912881 on OpenAlexaff
Friedrich W. Keppeler, Tommaso Giarrizzo, Carmen G. Montaña, Alphonse Adité, Şenol Akın, Ronaldo Angelini, Caroline C. Arantes, Evanilde Benedito, Thethela Bokhutlo, Rana W. El‐Sabaawi, Alexandre Garcia, Ivan González-Bergonzoni, David J. Hoeinghaus, Joel C. Hoffman, Olaf P. Jensen, Erik Jeppesen, R. Keller Kopf, Craig A. Layman, Bryan M. Maitland, Shin‐ichiro S. Matsuzaki, Jill A. Olin, Gordon Paterson, Yasmín Quintana, Carlos Eduardo de Rezende, Ashley Trudeau, Paulo Arthur A. Trindade, Thomas F. Turner, Eugenia Zandonà, Kirk O. Winemiller

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of Victoria
FundersDivision of Graduate EducationFundação Amparo e Desenvolvimento da PesquisaConselho Nacional de Desenvolvimento Científico e TecnológicoNational Geographic SocietyTexas Parks and Wildlife DepartmentU.S. Environmental Protection AgencyNational Science Foundation
KeywordsTrophic levelNicheEcologyGeographyBiologyFishery

Abstract

fetched live from OpenAlex

Abstract Trophic niche is one of the most tractable dimensions of a species niche. Several hypotheses have been proposed to explain global variation in species’ trophic niches, but empirical tests are limited. Stable isotope analysis (SIA) of δ¹³C (a proxy for basal sources) and δ¹ N (a proxy for vertical trophic position) has increasingly been used to estimate trophic niche variation. We performed SIA on a dataset of over 33,000 stable isotope samples from freshwater fishes across six ecoregions. For 541 populations (358 species), we estimated the size of δ¹³C × δ¹ N isospace—a bivariate representation of trophic niche breadth. We evaluated isospace variation in relation to environmental factors, species traits, and sampling scale, testing both novel and long-standing hypotheses about the drivers of trophic niche variation. A Boosted Regression Tree Model best predicted isospace, with environmental and trait-based variables among the strongest predictors. Fish isospaces were broader in regions with warmer temperatures, higher humidity, and precipitation variability, suggesting that more productive, diverse, and seasonal ecosystems are associated with broader trophic niches. Conversely, isospace size declined with increasing basin fish richness, which may reflect heightened interspecific competition or historical competitive exclusion. Isospace size was slightly smaller in large lakes compared to rivers, streams, and small lakes. Within-population variation in body size was strongly and positively associated with isospace size, highlighting the role of ontogenetic dietary shifts. Fish with truncate-rounded fins had broader isospaces than those with forked-lunate fins, likely due to the former having more limited movement and greater spatial isolation that resulted in greater between-individual niche variation. Primary consumers had larger isospaces than intermediate and top consumers. Predators showed relationships consistent those observed from analysis encompassing all trophic guilds, but non-predators deviated with regards to four variables—solar radiation, basin richness, average body size, and caudal fin aspect ratio. Isospace size increased with the number of sampled individuals, habitats and years surveyed. Although isospace patterns have well-documented limitations as trophic ecology metrics, our findings nonetheless conform with several longstanding hypotheses for trophic niche variation and stimulate new ideas about environmental and biological drivers of niche breadth.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.240
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; 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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