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Record W4392654009 · doi:10.5194/egusphere-egu24-18509

Exploring the Links between Testate Amoeba Traits and Eutrophication in Lakes

2024· preprint· en· W4392654009 on OpenAlexaffabout
Helen Roe, Andrew L. Macumber, Stephen Prentice, Timothy Patterson, Carl D. Sayer, David Emson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsTestate amoebaeEutrophicationAmoeba (genus)EcologyEnvironmental scienceBiologyMicrobiologyNutrientPeat

Abstract

fetched live from OpenAlex

There is considerable potential to apply traits-based approaches to the subfossil remains of shell-forming micro-organisms which preserve well in sediments and whose short generation times make it possible to achieve high-temporal resolution in palaeoecological studies. In this paper we review progress in applying traits-based approaches to freshwater testate amoebae (Arcellinida), a diverse group of protists which are abundant in lakes and are valuable palaeoecological indicators. Drawing on published studies from the last ~10 years, we describe the methodologies which have been applied to delimit testate amoeba (TA) traits and review the challenges associated with their measurement and interpretation. We also showcase the results of ongoing work in seven lakes (UK, Canada) which aims to (i) examine the character and causes of trait-based variability in palaeolimnological settings; (ii) apply novel biometric approaches to aid in trait delimitation; and (iii) explore the potential for combining phylogenetic with advanced morphometric approaches to better understand the ecological and evolutionary significance of TA traits. We applied geometric morphometric analysis to define test size and shape indices and summarise testate amoeba community dynamics along a temporal gradient of eutrophication in a large shallow lake in Scotland, UK. Cluster analysis of test size and shape indices yielded three assemblages, each dominated by a single shape: elongate, spherical and ovoid. When plotted stratigraphically, we observed increases in spherical tests, decreases in elongate tests and shrinking of test size coeval with eutrophication. Decreases in the elongate cluster may reflect benthic conditions with reduced oxygen levels, while increases in the spherical cluster are likely associated with an expanding macrophyte community that promoted pelagic and epibiotic life habits. Shrinking of test size may be a stress response to eutrophication and/or warming temperatures. Tracking community dynamics using test size and shape indices was found to be as effective as using traditional species-based approaches to summarize key palaeolimnological changes, with the added benefit of being free of taxonomic bias. The approach thus shows significant potential for future studies of aquatic community change in nutrient-impacted lakes. To further investigate the functional significance of the Arcellinida shape groups, we examined the phylogenetic signal of morphological traits in elongate Difflugia species which occur in eutrophic and mesotrophic lakes. Previous phylogenetic work has shown that whilst overall test morphology (e.g., spherical or elongate) is generally conserved in Arcellinida lineages, the taxonomic significance of other traits (e.g., size, ornamentation, mixotrophy/heterotrophy metabolism type) is not well understood. Our analyses revealed two clades which could be reliably separated by test size and the presence/absence of mixotrophy. This suggests that test size may reflect trophic level, with smaller taxa occupying lower trophic levels. In addition to having larger tests, elongate mixotrophic Difflugia are characterised by wide, flat bases and inflation of the lower part of the test. These morphological traits may provide additional space for endosymbionts and/or increased surface area to aid light transmission. Continued research into the ecological and evolutionary significance of morphological traits will serve to strengthen palaeoecological inferences, increasing the importance of lacustrine testate Arcellinida as environmental proxies.

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)0.002
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 score0.100
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.245
Teacher spread0.191 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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