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Record W2768822493 · doi:10.17863/cam.15482

Palynological insights into the Mid Jurassic dinoflagellate radiation

2017· dissertation· en· W2768822493 on OpenAlexaboutno aff
Nickolas J. Wiggan

Bibliographic record

VenueApollo (University of Cambridge) · 2017
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsnot available
Fundersnot available
KeywordsPalynologyDinoflagellatePaleontologyGeologyGeographyEarth scienceOceanographyEcologyBiologyPollen

Abstract

fetched live from OpenAlex

Dinoflagellates are a diverse group of flagellated eukaryotes, the majority of which are marine plankton, and are one of the most important groups of primary producers in the world’s oceans. The dinoflagellate fossil record is based on their zygotic resting cysts; this record indicates that the Bajocian of the Mid Jurassic (~170–168 Ma) represents a critical interval in dinoflagellate evolutionary history, marked by a rapid increase in the diversity of cysts from the family Gonyaulacaceae. From the Bajocian onwards, the Gonyaulacaceae have remained one of the most diverse and abundant groups of dinoflagellate cysts in the fossil record. Even so, Bajocian dinoflagellate cysts themselves have received relatively little study, leaving the patterns of this radiation unresolved. In this thesis, I examine the Bajocian diversification of gonyaulacacean dinoflagellate cysts in northwest Europe via quantitative palynological analysis, and relate this into a broader stratigraphic and palaeoenvironmental context. The dinoflagellate cyst record of the three key study areas demonstrates an increase in gonyaulacacean dinoflagellate cyst diversity through the Bajocian, irrespective of differing palaeoenvironmental settings. However, palynological and sedimentological data record systematic changes in lithostratigraphic composition and/or depositional environment which reflect changes in sea level. The integration of these data with biostratigraphic records indicates that the pattern of the radiation in Europe was strongly influenced by sea level, with the increase in gonyaulacacean diversity mirroring a major second-order transgression. On a finer scale, the main pulses of first appearances correlate with third-order transgressive episodes. A rise in sea level, coupled with changes in the tectonic configuration of ocean gateways, appears to have controlled the pattern of diversification in Europe. These palaeoceanographic changes may have enhanced water-mass transfer between Europe, the northwest Tethys Ocean, and the Hispanic Corridor, which promoted the floral interchange of dinoflagellates. Comparison to global data demonstrates that gonyaulacacean dinoflagellate cysts increased in diversity on a global scale through the Mid Jurassic. Whilst sea level rise and associated large-scale palaeoenvironmental shifts appear to have controlled the pattern of dinoflagellate cyst appearances in several regions (e.g. eastern Canada, Israel), there is no direct correlation between dinoflagellate cyst diversity and sea level rise on a global scale. Within dinoflagellate cyst assemblages from Europe, the spatial and temporal distribution of key taxa can be related to differing palaeoenvironmental settings of the basins studied; but certain patterns may reflect wider palaeoenvironmental drivers. Carbon isotope records generated for this thesis, and their correlation to other European sections, demonstrate that the Early Bajocian in Europe was marked by a positive shift in δ13C. Previous work has linked this carbon cycle perturbation to a phase of enhanced continental weathering and associated run-off. In several European basins, the Early Bajocian was marked by an acme of the genus Dissiliodinium; this genus may have bloomed in response to elevated nutrient levels. Intriguingly, a similar pattern is seen within dinoflagellate cyst assemblages from Australia, indicating this interval of palaeoenvironmental change may have had a global extent.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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