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Record W77897399

Female diving beetles in antagonistic coevolutionary arms race with male suckers

2005· article· en· W77897399 on OpenAlexaboutno aff
Johannes Bergsten, Kelly B. Miller

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicColeoptera Taxonomy and Distribution
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyPhylogenetic treeSexual dimorphismPhylogeneticsZoologyPopulationEvolutionary biologyRace (biology)DemographyPaleontology
DOInot available

Abstract

fetched live from OpenAlex

Sexual conflict can lead to antagonistic coevolution between the sexes, but empirical examples are few. In this thesis secondary sexual characters in diving beetles are interpreted in the light of sexual conflict theory. Whether the male tarsal suction cups and female dorsal modifications are involved in a coevolutionary arms race is tested in two ways. First eight populations of a species with dimorphic females that varied in frequency of the morphs were investigated and male tarsal characteristics quantified. The frequency of female morphs is shown to be significantly correlated to the average number and size of male tarsal suction cups in the population, a prediction of the arms race hypothesis. Second, the hypothesis is tested in a phylogenetic perspective by optimizing the secondary sexual characters on a phylogeny. A full taxonomic revision of the genus Acilius is presented, including new synonyms, lectotype designations, geographic distributions based on more than five thousand examined museum specimens and the description of a new species from northeastern USA. Specimens of all species (except one possibly extinct that failed to be found in Yunnan, China 2000), were field collected between 2000 and 2003 in Sardinia, Sweden, Russia, Honshu and Hokkaido in Japan, New York, Maryland, California and Alberta. Three genes (CO1, H3 and Wingless) were sequenced from the fresh material as well as scoring a morphological character matrix all of which was used to derive a robust and complete hypothesis of the phylogenetic relationship in the group. The phylogeny was derived using Bayesian phylogenetics with Markov Chain Monte Carlo techniques and received a posterior probability of 0.85. Changes in male and female characters turned out to be perfectly correlated across the phylogeny, providing one of the best empirical examples to date of an antagonistic arms race between the sexes in a group of organisms. Finally, a review of a pitfall to phylogenetic analysis known under the name long-branch attraction (LBA), is provided. The problem is well known theoretically but has been questioned to occur in real data, and LBA has been in the core center of the hard debate between parsimony and likelihood advocates since different inference methods vary in sensitivity to the phenomenon. Most important conclusions from the review are; LBA is very common in real data, and is most often introduced with the inclusion of outgroups that almost always provide long branches, pulling down long terminal ingroup branches towards the root. Therefore it is recommended to always run analyses with and without outgroups. Taxon sampling is very important to avoid the pitfall as well as including different kind of data, especially morphological data, i.e. many LBA-affected conclusions have recently been reached by analyses of few taxa with complete genomes. Long-branch extraction (incl. outgroup exclusion), methodological disconcordance (parsimony vs modelbased), separate partition analyses (morphology vs molecules, codon positions, genes, etc), parametric simulation (incl. random outgroups), and split graphs are available relevant methods for the detection of LBA that should be used in combinations, because none alone is enough to stipulate LBA.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.199
Teacher spread0.187 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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