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Abstract Detail



Paleobotany

Harbert, Robert [1], Escapa, Ignacio [2].

Quantitative paleobotanical climate estimation in the Mesozoic: a CRACLE based approach.

Mesozoic paleoclimatic inference based on plants has been mostly developed on the basis of the presence/absence of a small number of taxa considered as indicators of particular climatic features. However, many lineages that are present in the Jurassic have modern representatives and therefore climate estimation based on the taxonomic composition of a fossil flora is theoretically possible. Here we develop a quantitative probabilistic estimate of climate based on the taxonomic composition of plant fossils from an Early Jurassic locality from Patagonia using CRACLE modeling and the modern distribution of modern relatives of fossil plants. The plant association from Cerro Bayo locality is largely dominated by conifers and ferns, and despite being preserved mostly as impressions, the specimens show a high definition of morphological features, often reaching the cellular level. The collections are large and include several hundred specimens, many of which have been placed phylogenetically into families with extant representatives. Some of these fossil species have a well-defined phylogenetic position within the family crown group (e.g. Austrohamia), while other species show less definition and are related to the total group. These results are a first attempt at modeling Mesozoic climate using CRACLE. Future work in this area will refine these estimates through phylogenetic placement of other fossil species, adding fossil taxa to the model, and potentially building niche estimates for fossil taxa without extant lineages.


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1 - Stonehill College, Biology, 320 Washington St., North Easton, MA, 02357, United States
2 - MEF-CONICET, Fontana 140, Trelew Chubut, U, 9100, Argentina

Keywords:
paleoclimate
mesozoic
Patagonia
CRACLE
Paleobotany.

Presentation Type: Oral Paper
Session: PL6, Paleobotany: Paleozoic/Mesozoic Paleobotany
Location: /
Date: Thursday, July 22nd, 2021
Time: 10:30 AM(EDT)
Number: PL6003
Abstract ID:206
Candidate for Awards:None


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