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Abstract Carbon‐concentrating mechanisms (CCMs) are a widespread phenomenon in photosynthetic organisms. In vascular plants, the evolution of CCMs ([C44‐carbon compound] and crassulacean acid metabolism [CAM]) is associated with significant shifts, most often to hot, dry and bright, or aquatic environments. If and how CCMs drive distributions of other terrestrial photosynthetic organisms, remains little studied. Lichens are ecologically important obligate symbioses between fungi and photosynthetic organisms. The primary photosynthetic partner in these symbioses can include CCM‐presenting cyanobacteria (as carboxysomes), CCM‐presenting green algae (as pyrenoids) or green algae lacking any CCM. We use an extensive dataset of lichen communities from eastern North America, spanning a wide climatic range, to test the importance of CCMs as predictors of lichen ecology and distribution. We show that the presence or absence of CCMs leads to opposite responses to temperature and precipitation in green algal lichens, and different responses in cyanobacterial lichens. These responses contrast with our understanding of lichen physiology, whereby CCMs mitigate carbon limitation by water saturation at the cost of efficient use of vapor hydration. This study demonstrates that CCM status is a key functional trait in obligate lichen symbioses, equivalent in importance to its role in vascular plants, and central for studying present and future climate responses.
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This content will become publicly available on March 20, 2026
Fermi calculations enable quick downselection of target genes and process optimization in photosynthetic systems
Abstract Understanding how photosynthetic organisms including plants and microbes respond to their environment is crucial for optimizing agricultural practices and ensuring food and energy security, particularly in the context of climactic change and sustainability. This perspective embeds back-of-the-envelope calculations across a photosynthetic organism design and scale up workflow. Starting from the whole system level, we provide a recipe to pinpoint key genetic targets, examine the logistics of detailed computational modeling, explore environmentally driven phenotypes, and feasibility as an industrial biofuel production chassis. While complex computer models or high throughput in vivo studies often dominate scientific inquiry, this perspective highlights the power of simple calculations as a valuable tool for initial exploration and evaluating study feasibility. Fermi calculations are defined as quick, approximate estimations made using back-of-the-envelope calculations and straightforward reasoning to achieve order-of-magnitude accuracy, named after the physicist Enrico Fermi. We show how Fermi calculations, based on fundamental principles and readily available data, can offer a first pass understanding of metabolic shifts in plants and microbes in response to environmental and genetic changes. We also discuss how Fermi checks can be embedded in data-driven advanced computing workflows to enable bio-aware machine learning. Lastly, an understanding of state-of-the-art is necessary to guide study feasibility and identifying key levers to maximize cost to return ratios. Combining biology- and resource- aware Fermi calculations, this proposed approach enables researchers to prioritize resource allocation, identify gaps in predictions and experiments, and develop intuition about how observed responses of plants differ between controlled laboratory environments and industrial conditions.
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- Award ID(s):
- 1943310
- PAR ID:
- 10595638
- Publisher / Repository:
- American Society of Plant Biologists
- Date Published:
- Journal Name:
- Plant Physiology
- ISSN:
- 0032-0889
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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