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Record W4319040370 · doi:10.1111/fwb.14049

Vertical distribution patterns of zooplankton across a gradient of fish predation in boreal lakes

2023· article· en· W4319040370 on OpenAlexafffund
Sabrina Gignac Brassard, Milla Rautio, Andrea Bertolo

Bibliographic record

VenueFreshwater Biology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversité de MontréalUniversité du Québec à ChicoutimiUniversité du Québec à Trois-RivièresUniversité du Québec à Montréal
FundersGroupe de recherche interuniversitaire en limnologieNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsEpilimnionZooplanktonPredationPhytoplanktonDaphniaEcologyDiel vertical migrationPlanktonThermoclineEnvironmental scienceBiologyBorealOceanographyHypolimnionEutrophicationNutrientGeology

Abstract

fetched live from OpenAlex

Abstract Different physical factors such as solar radiation and wind impose strong vertical gradients in lake water columns and have important consequences on the distribution of aquatic organisms, from bacteria to fish. The heterogeneous vertical distribution of zooplankton in lakes is a clear example of a pattern related to these vertical gradients. Although the vertical distribution of zooplankton is probably controlled by multiple factors including light, resources, and predation, we still lack an integrated view of the interplay among these potential drivers, which are most often analysed separately. Moreover, most studies focussed on temperate lakes in which both phytoplankton resources and predation risk from fish are highest in the epilimnion, whereas only a few investigated boreal lakes, where phytoplankton is often more abundant in the metalimnion than in the epilimnion and cold‐stenothermic fishes prey on zooplankton below the warm epilimnetic layers. Here we investigated zooplankton vertical distribution in the epi‐ and metalimnetic waters of 17 boreal lakes that varied in fish predation, resource quantity and quality, and optical properties. The lakes were sampled at midday and midnight at five different depths in the well‐lit epi‐ and metalimnetic layers, where the strongest gradient in light (and predation risk) were expected. We used a multi‐model inference approach combined with linear modelling to examine the relative effects of physical factors (light attenuation, temperature and moon phase), resources (chlorophyll‐ a and polyunsaturated fatty acids), and predation (fish and Chaoborus ) on depth selection in four zooplankton taxa. All taxa showed a heterogeneous vertical distribution that in most cases had a clear diel migration pattern. Both predation and resources (food and temperature gradient) were associated with vertical depth selection by zooplankton, but each taxon had different responses to each factor. Vertebrate predators were associated with the vertical distribution of Daphnia , Calanoida, and Cyclopoida, but—unexpectedly—all these taxa reduced their diel vertical migration patterns in the presence of fish. Invertebrate predators were better correlated with the distribution of Cyclopoida, which tended to be shallower when Chaoborus was abundant. Food abundance seemed important for Holopedium glacialis and Calanodia, while the temperature gradient was partly associated with both Daphnia spp. and Calanoida distribution. Finally, we found an intriguing, albeit weak, positive association between the vertical distribution of Cyclopoida and that of food quality. These results showed that taxa less vulnerable to predators such as H. glacialis perform inverse DVM, and that this is at least partly related to moon phase rather than to fish predation. They also suggest that boreal lakes colonised by stenothermic planktivorous fish behave differently from the classical DVM paradigm, given that visual predators do not have access to fully lit layers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.984

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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".

Quick stats

Citations8
Published2023
Admission routes2
Has abstractyes

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