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Record W2040531938 · doi:10.1111/fwb.12313

Interactive effects of higher temperature and dissolved organic carbon on planktonic communities in fishless mountain lakes

2014· article· en· W2040531938 on OpenAlexafffund
Paul Weidman, David W. Schindler, Patrick L. Thompson, Rolf D. Vinebrooke

Bibliographic record

VenueFreshwater Biology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsMcGill UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Conservation Association
KeywordsPhytoplanktonCopepodPlanktonDissolved organic carbonEnvironmental scienceZooplanktonEcologyAbundance (ecology)Montane ecologyEnvironmental chemistryParticulatesPhosphorusChemistryNutrientBiology

Abstract

fetched live from OpenAlex

Summary We tested the hypothesis that higher temperature and dissolved organic carbon ( DOC ) concentration increase dissolved and particulate carbon (C) relative to phosphorus (P), thereby reducing algal food quality for P‐limited cladocerans while not affecting N‐limited copepods. Also, we expected alpine zooplankton to respond more strongly than those from warmer montane lakes to increased water temperature. Plankton from two alpine lakes and two montane lakes were incubated in vitro for 30 days at 10 or 17 °C and with ambient or +80% DOC , which was achieved by concentrating humic substances from each lake via reverse osmosis. Dissolved organic carbon amendments and warming significantly increase particulate C : P under montane, but not alpine conditions. While higher water temperature and DOC separately reduced phytoplankton abundance, together they increased phytoplankton by stimulating uptake of P. Warming stimulated only D aphnia while suppressing the abundance of the calanoid copepod H esperodiaptomus when they originated from the three coldest lakes. Particulate C : P was positively correlated with D aphnia abundance and negatively correlated with H esperodiaptomus , probably due to greater P‐retention by D aphnia . Our findings highlight the importance of interactions between the ecological effects of higher temperature and increased inputs of terrestrial organic matter to forecasts of the net impact of global warming on mountain lakes. Such predictions may be confounded if they are derived solely from the expected sum of single effects by each climatic factor.

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 score0.044
Threshold uncertainty score0.978

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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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

Citations22
Published2014
Admission routes2
Has abstractyes

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