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Data for: Carbon-concentrating mechanisms are a key trait in lichen ecology and distribution


Koch, Natalia; Lendemer, James; Stanton, Daniel (2023), Data for: Carbon-concentrating mechanisms are a key trait in lichen ecology and distribution, Dryad, Dataset,


Carbon-concentrating mechanisms (CCMs) are a widespread phenomenon in photosynthetic organisms. In vascular plants, the evolution of CCMs (C4 and 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 presence or absence of CCMs leads to opposite responses to temperature and precipitation in green algal lichens, and with 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.


This dataset consists of data on lichen communities from temperate eastern North America. It comprises presence/absence species occurrence data from intensive expert-based inventories of 630 one-hectare plots. The plots were sampled during three large-scale regional lichen biodiversity inventory projects led by James Lendemer and colleagues: 208 plots in the southern Appalachian Mountains (see Tripp et al. 2019), 204 plots in the northern Appalachian Mountains of Pennsylvania (see Lendemer and Coyle 2021), and 215 plots in the Mid-Atlantic Coastal Plain (see McMullin et al. 2019). 

Methods of site delimitation and sampling are described fully in the publications cited for each project. In brief, each plot was delimited at one-hectare, allowing for an irregular shape to ensure each consisted of a single vegetation type (e.g., swamp forest, spruce-fir forest). Within each plot an expert-based inventory of all lichens was carried out using a Floristic Habitat Sampling approach to more effectively detect total cryptogam biodiversity compared to randomized plot-based methods (e.g., Bowering et al. 2018). All lichens regardless of growth form and size (i.e., macrolichens and microlichens), occuring on all substrates (e.g., bark, leaves, soil, rock, wood), were included in the sampling and the final dataset included a total of 1,208 species.

All vouchers were transferred to The New York Botanical Garden (NY) and the identifications were confirmed by one of us (James Lendemer) to maintain consistent and standardized taxonomy, as well as minimize errors in initial stages of field identification. All voucher data can be accessed in the NYBG institutional KEMu database (; see Lendemer et al. 2019). The digital voucher data were exported from KEMu and used to build a presence/absence matrix of occurrences of each species at each site.

Using the species occurrence matrix as a reference, photobionts were identified for each lichen species based on published literature (such as Muggia et al. 2018, Sanders and Masumoto 2021). When possible, photobionts were reported at the level of algal or cyanobacterial genera. The small number of tripartite lichens (containing both green algal and cyanobacterial symbionts) were classified based on the primary (green) photobiont and analyzed separately. Where more than one primary photobiont genus has been reported for a lichen species, the most common association as determined from the literature was used. In the rare cases when photobiont identity to genus could not be scored, records were not assigned a CCM status. Photobiont genera were further classified according to the reported presence or absence of CCM structures such as pyrenoids or carboxysomes (in Dryad upon acceptance). In the cases where pyrenoid presence varies infra-generically (e.g. Elliptochloris), taxa were scored as pyrenoid-present, considering that the presence of this structure was possible.

The macroenvironmental variables were drawn from the ClimateNA v6.40a software package ( based on methods described by Wang et al. (2016), extracting annual values for Mean Annual Temperature (MAT), Mean Annual Precipitation (MAP) and Hogg’s climate moisture index (CMI). The CMI derives from the annual precipitation (P) minus the potential evapotranspiration (PET), considering a well-vegetated landscape with adequate soil moisture (CMI = P – PET, Hogg 1997), so higher CMI indicates wet conditions and lower values, drought conditions. The environmental data is downscaled from 800x800m resolution PRISM data and includes annual averages for 1991-2020 (Wang et al. 2016).

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NSF Dimensions Awards, Award: 1542639

NSF Dimensions Awards, Award: 1542629

NSF DEB Awards, Award: 1145511

NPS PMIS, Award: 236370

NSF DEB Awards, Award: 2115190

NSF DEB Awards, Award: 2115191