DOI: 10.5061/dryad.5dv41ns8h
title: Data for: Carbon-concentrating mechanisms are a key trait in lichen ecology and distribution

description: 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.
description: 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
 (http://sweetgum.nybg.org/science/vh/; 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 (<em>http://tinyurl.com/ClimateNA</em>) 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).
description: Any program that opens csv files.