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Does a warmer lake mean smaller benthic algae? Evidence against the importance of temperature–size relationships in natural systems

2010· article· en· W2064670808 on OpenAlexaff
Julie Vanden Byllaardt, Hélène Cyr

Bibliographic record

VenueOikos · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBenthic zoneDiatomEcologyLittoral zoneAlgaeBiologyEcosystemBenthosInvertebrateEnvironmental science

Abstract

fetched live from OpenAlex

Ectotherms decrease in size with increasing ambient temperature. Temperature–size relationships (TSR) have been observed experimentally in a wide range of animals, algae, protozoans and bacteria. However, it is still unclear whether temperature is an important factor controlling the size of organisms in natural populations. In this study, we used natural variability in water temperature in the nearshore areas of a single lake to test TSR in populations of benthic diatoms. We deployed standard tile substrates at 5 m depth (similar light availability) at cold and warm sites that were exposed to different hydrodynamic forces. We compared cell sizes of three species of diatoms ( Achnanthidium minutissimum, Gomphonema acuminatum and Gyrosigma acuminatum ) at these sites. Counter to the TSR, diatom cells at warm sites were either larger ( Achnanthidium, Gomphonema ) or similar in size ( Gyrosigma ) compared to those at colder sites. Diatom size was also related to site exposure (hydrodynamic forces), but differently for species with different architectures. TSR were not detectable in the field for these three species of benthic diatom, even when tested within a single ecosystem at a given time of the year. The size of benthic diatoms, however, varied in a predictable way between sites, and such differences could affect the functioning of these primary producers in different parts of the littoral zone.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.239
Teacher spread0.220 · 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.

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

Citations12
Published2010
Admission routes1
Has abstractyes

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