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Record W4387116039 · doi:10.1002/ecy.4173

Biological invasions alter the structure of a tropical freshwater food web

2023· article· en· W4387116039 on OpenAlexafffund
Diana M. T. Sharpe, Marisol Valverde, Luis F. De León, Andrew P. Hendry, Mark E. Torchin

Bibliographic record

VenueEcology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsMcGill University
FundersSistema Nacional de InvestigadoresSmithsonian Tropical Research InstituteNatural Sciences and Engineering Research Council of CanadaSecretaría Nacional de Ciencia, Tecnología e InnovaciónNational Geographic Society
KeywordsFood webEcologyGeographyBiologyTrophic level

Abstract

fetched live from OpenAlex

Biological invasions are expected to alter food web structure, but there are limited empirical data directly comparing invaded versus uninvaded food webs, particularly in species-rich, tropical systems. We characterize for the first time the food web of Lake Gatun-a diverse and highly invaded tropical freshwater lake within the Panama Canal. We used stable isotope analysis to reconstruct the trophic structure of the fish community of Lake Gatun and to compare it to that of a minimally invaded reference lake, Lake Bayano. We found significant differences between the trophic structures of these two Neotropical lakes, notably that Lake Gatun's fish community was characterized by a longer food chain, greater isotopic diversity, a broader range of trophic positions and body sizes, and shifts in the isotopic positions of several native taxa relative to Lake Bayano. The degree of isotopic overlap between native and non-native trophic guilds in Lake Gatun was variable, with herbivores exhibiting the lowest (20%-29%) overlap and carnivores the greatest (81%-100%). Overall, our results provide some of the first empirical evidence for the ways in which multiple introduced and native species may partition isotopic space in a species-rich tropical freshwater food web.

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 score0.048
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.001

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 head0.025
GPT teacher head0.235
Teacher spread0.210 · 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

Citations18
Published2023
Admission routes2
Has abstractyes

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