2026/04/01 by Xueqin Li, X. Chen, Shudong Zhang +3 · 1 voice
Agricultural and Biological Sciences · Environmental Science · #Fern and Epiphyte Biology #Ecology and Vegetation Dynamics Studies #Plant Water Relations and Carbon Dynamics
paper · doi:10.1002/ajb2.70188
openalex publication_date 2026/04/01 · openalex created_date 2026/04/08 · openalex updated_date 2026/07/31
Abstract Premise Leaf respiration is a key metabolic process controlling leaf carbon balance. However, empirical data on leaf respiration, particularly in the light ( R L ), remain scarce for ferns, and no study has systematically compared respiration–trait relationships between ferns and seed plants. Methods We measured seven leaf traits, including leaf respiration in the dark ( R D ), R L , light‐saturated photosynthetic rate ( A sat ), leaf N and P content, leaf dry mass per unit area (LMA), and leaf dry mass content (LDMC) in 19 ferns. These fern data were analyzed in comparison with seed plant data from the Glopnet data set. Results Light inhibition of leaf respiration averaged 37%. Ferns had significantly lower LMA, A sat , and R D than seed plants. Standardized major axis analyses (SMA) revealed the slopes of R D vs. LMA, A sat , and P differed significantly between ferns and seed plants, while R D ‐N relationships shared common slopes, but their intercept differed markedly. Generalized linear models identified LMA and R D as the strongest predictors of R L , jointly explaining 70.6% of its variation. Conclusions The terrestrial understory ferns employ a distinct “low‐cost, slow‐cycling” strategy—characterized by a unique combination of low leaf construction cost (LMA) and low metabolic rates (photosynthesis and respiration), which deviates from the traditional fast‐slow leaf economics spectrum. The divergent respiration–trait relationships suggest that applying trait‐based models parameterized solely for seed plants to ferns may introduce uncertainty into ecosystem models. This highlights the value of incorporating fern‐specific trait relationships to refine projections of the global carbon cycle.