Extremely thin but very robust: Surprising cryptogam trait combinations at the end of the leaf economics spectrum

Abstract

Leaf economics spectrum (LES) describes the fundamental trade-offs between leaf structural, chemical, and physiological investments. Generally, structurally robust thick leaves with high leaf dry mass per unit area (LMA) exhibit lower photosynthetic capacity per dry mass (A _mass). Paradoxically, "soft and thin-leaved" mosses and spikemosses have very low A _mass, but due to minute-size foliage elements, their LMA and its components, leaf thickness (LT) and density (LD), have not been systematically estimated. Here, we characterized LES and associated traits in cryptogams in unprecedented details, covering five evolutionarily different lineages. We found that mosses and spikemosses had the lowest LMA and LT values ever measured for terrestrial plants. Across a broad range of species from different lineages, A _mass and LD were negatively correlated. In contrast, A _mass was only related to LMA when LMA was greater than 14 g cm^{- 2}. In fact, low A _mass reflected high LD and cell wall thickness in the studied cryptogams. We conclude that evolutionarily old plant lineages attained poorly differentiated, ultrathin mesophyll by increasing LD. Across plant lineages, LD, not LMA, is the trait that represents the trade-off between leaf robustness and physiology in the LES.

Keywords: Investment strategy; LMA estimation bias; Leaf density; Leaf structural traits; Non-seed plants; Trait trade-offs.

Fig. 1

Bivariate relationships between leaf structural traits. (a) LMA vs leaf density. (b) LMA vs leaf thickness. (c) Leaf density vs leaf thickness. (d) LMA vs cell wall thickness. (e) Leaf density vs cell wall thickness. (f) Leaf dry to fresh mass ratio vs cell wall thickness. In (a), (b), and (d), the dashed line indicates 14 g m⁻² in LMA. PA, Plagiochila asplenioides; MP, Marchantia polymorpha; SR, Syntrichia ruralis; PU, Plagiomnium undulatum; SMo, Selaginella moellendorffii; SU, Selaginella uncinata; SA, Selaginella apoda; SMa, Selaginella martensii; AR, Adiantum raddianum; DE, Dryopteris erythrosora; NC, Nephrolepis cordifolia; CS, Cucumis sativus; PV, Phaseolus vulgaris. The outlier in (a) is colored orange and indicated with arrows. Error bars indicate ± SE (n = 3). Error bars are not presented for leaf density (LD) in all related panels, because LD was calculated based on average of LMA and leaf thickness from different leaves. Non-linear relationships in form of y = ax^b were used to fit the data in (a), (c), and (d), and y = aexp (x/t) + y₀ was used to fit the data in (f). Linear regressions were used to fit the data in (b) and (e). In (b), no relationship was found in the range of LMA > 14 g m⁻². Statistical relationships were considered significant at P < 0.05.

Fig. 2

Bivariate relationships between leaf chemical and structural traits. (a) N_mass vs LMA. (b) P_mass vs LMA. (c) N_mass vs leaf thickness. (d) P_mass vs leaf thickness. (e) N_mass vs leaf density. (f) P_mass vs leaf density. (g) N_mass vs cell wall thickness. (h) P_mass vs cell wall thickness. Error bars indicate ± SE (n = 3). Error bars are not presented for leaf density (LD) in all related panels, because LD was calculated based on average of LMA and leaf thickness from different leaves. Species abbreviations as in Fig. 1. Non-linear relationships in form of y = ax^b were used to fit the data in (c−g). Linear regression was used to fit the data in (h).

Fig. 3

Bivariate relationships between Amass and other functional traits. (a) A_mass vs N_mass. (b) A_mass vs LMA. (c) A_area vs leaf thickness. (d) A_mass vs leaf density. (e) A_mass vs cell wall thickness. (f) A_mass vs leaf dry to fresh mass ratio. The outliers in (a) and (f) are colored orange and indicated with arrows. Error bars indicate ± SE (n = 3). Error bars are not presented for leaf density (LD) in the related panel, because LD was calculated based on average of LMA and leaf thickness from different leaves. Species abbreviations as in Fig. 1. Non-linear relationships in form of y = aexp(-x/t) + y₀ was used to fit the data in (b) and (d), and y = ax^b were used to fit the data in (e) and (f, lower line). In (f), ferns and seed plants, bryophytes and spikemosses were fitted separately. Linear regressions were used to fit the data in (a) and (f upper line).

Fig. 4

Images of semithin (a) and ultrathin transmission electron microscopy cross-sections (b), and heatmap of leaf economic traits values (c) in five representative species. Heatmap represents the log(x+1)-tranformed values of all traits. LMA, leaf dry mass per unit area; LT, leaf thickness; LD, leaf density; CWT, cell wall thickness; D/F, leaf dry to fresh mass ratio; A_area, area-based photosynthesis; A_mass, mass-based photosynthesis; PNUE, photosynthetic nitrogen use efficiency; N_area, nitrogen per area; N_mass, nitrogen per mass; C_area, carbon per area; C_mass, carbon per mass; P_area, phosphorus per area; P_mass, phosphorus per mass. The hotter color represents higher values and cooler ones represents lower values. Row scale was performed (scale method ZeroToOne) to visualize patterns of each trait along with evolution time.