Hidden Functional Complexity in the Flora of an Early Land Ecosystem.
NEW PHYTOLOGIST(2024)
John Innes Ctr
Abstract
The first land ecosystems were composed of organisms considered simple in nature, yet the morphological diversity of their flora was extraordinary. The biological significance of this diversity remains a mystery largely due to the absence of feasible study approaches. To study the functional biology of Early Devonian flora, we have reconstructed extinct plants from fossilised remains in silico. We explored the morphological diversity of sporangia in relation to their mechanical properties using finite element method. Our approach highlights the impact of sporangia morphology on spore dispersal and adaptation. We discovered previously unidentified innovations among early land plants, discussing how different species might have opted for different spore dispersal strategies. We present examples of convergent evolution for turgor pressure resistance, achieved by homogenisation of stress in spherical sporangia and by torquing force in Tortilicaulis-like specimens. In addition, we show a potential mechanism for stress-assisted sporangium rupture. Our study reveals the deceptive complexity of this seemingly simple group of organisms. We leveraged the quantitative nature of our approach and constructed a fitness landscape to understand the different ecological niches present in the Early Devonian Welsh Borderland flora. By connecting morphology to functional biology, these findings facilitate a deeper understanding of the diversity of early land plants and their place within their ecosystem.
MoreTranslated text
Key words
3D reconstructions,early land plants,finite element method (FEM),first land ecosystems,functional paleobotany,mechanical stress,plant evolution
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined