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Abstract Detail



Physiology

Gilman, Ian [1], Edwards, Erika [2].

Distinguishing CAM photosynthesis with machine learning.

Crassulacean Acid Metabolism (CAM) has evolved at least 35 times independently throughout the plant kingdom-in lineages as distantly related as isoetes and orchids. Although we recognize many, repeated origins of other modifications to the standard C3 pathway, such as C4 photosynthesis, a variety of CAM phenotypes have evolved in an extremely diverse set of ecological circumstances. CAM is suspected to have played a role in the massive diversification of tropical forest lineages, such as the bromeliads and orchids, but also in arid-adapted lineages like the Portulacineae (Carophyllales) and Euphorbiaceae. Furthermore, the types of CAM (full CAM, majority of carbon fixation by CAM pathway; low level CAM, majority of carbon fixation by C3 pathway, with constitutive, small degree of CAM activity; and facultative CAM, flexible CAM metabolism that is expressed only in response to a a stressor such as drought) may not strictly represent a continuum of transitional states on the path to full CAM. Rather, these types may often represent stable phenotypes that have evolved for different, but not unrelated, reasons. Disentangling and distinguishing the types of CAM and the problems that they solve in disparate lineages and ecologies therefore precludes a full understanding of the evolution of CAM photosynthesis. Here, we show how machine learning techniques can be used to distinguish and place boundaries on different types of CAM.


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1 - Yale University, Ecology And Evolutionary Biology, Osborn Memorial Labs, 165 Prospect Street, New Haven, CT, 06511, United States
2 - Yale University, Ecology And Evolutionary Biology, Osborn Memorial Labs, 165 Prospect Street, New Haven, CT, 06511, USA

Keywords:
CAM
Machine Learning
Portulacineae
Bromeliadaceae.

Presentation Type: Oral Paper
Session: 44, Physiology
Location: 114/Mayo Civic Center
Date: Wednesday, July 25th, 2018
Time: 2:30 PM
Number: 44005
Abstract ID:554
Candidate for Awards:None


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