Variations of leaf longevity in tropical moist forests predicted by a trait‐driven carbon optimality model

Abstract

Leaf longevity (LL) varies more than 20-fold in tropical evergreen forests, but it remains unclear how to capture these variations using predictive models. Current theories of LL that are based on carbon optimisation principles are challenging to quantitatively assess because of uncertainty across species in the 'ageing rate:' the rate at which leaf photosynthetic capacity declines with age. Here, we present a meta-analysis of 49 species across temperate and tropical biomes, demonstrating that the ageing rate of photosynthetic capacity is positively correlated with the mass-based carboxylation rate of mature leaves. We assess an improved trait-driven carbon optimality model with in situLL data for 105 species in two Panamanian forests. We show that our model explains over 40% of the cross-species variation in LL under contrasting light environment. Collectively, our results reveal how variation in LL emerges from carbon optimisation constrained by both leaf structural traits and abiotic environment.

Novel Representation of Leaf Phenology Improves Simulation of Amazonian Evergreen Forest Photosynthesis in a Land Surface Model

“…Data from three out of the four Amazonian sites analyzed here support the light‐controlled leaf flushing hypothesis of Guan et al () and support the environment‐controlled leaf phenology hypotheses. The underlying mechanisms are probably that the tropical evergreen forests of this study are not water limited (Guan et al, ), having evolved deep root systems giving access to deep soil moisture reservoir (Christoffersen et al, ; da Rocha et al, ; Oliveira et al, ), and the dry‐season replacement of lower‐ V c,max old leaves with the newly mature leaves of higher V c,max might be a long‐term evolutionary strategy to optimize the carbon gain (Xu et al, ). However, the opposite behavior of GF‐Guy with respect to the other sites is not explained by the variable‐date scheme, while the fixed‐date simulations both correlate well with the site observations.…”

“…Xu et al () established a leaf age‐related V c,max function based on field data that were collected in 20 tropical evergreen forests. Differing from the default leaf efficiency model described above (black dotted line in Figure b), this function gives a V c,max which increases rapidly for the first 2 months and then declines continuously with leaf age, after reaching the maximum V c,max (Figure a).…”

“…When LAI reaches this maximum value, no more carbon is allocated to leaves (as described in Section A5 of Krinner et al, ). Xu et al () observed that canopy leaf mass per area (LMA) differ significantly among Amazonian evergreen forest sites (Figure S1), but understory LMA (Figure S1) and maximum carboxylation rate ( V c,max ) (Figure S2) only differ slightly. So, a constant maximum V c,max value (51.4 μmol·m −2 ·s −1 ) was prescribed at all the four sites, and we also ignore the differences of understory LMA.…”

“…This study focuses on the process of coincident production of new leaves and litterfall of old leaves, which implies that more carbon gets allocated to new leaves and that the shedding rate of old leaves is increased just before to produce a younger leaf age demography in the canopy; this process is important in producing the higher photosynthetic rates of the dry season (Chave et al, ; de Weirdt et al, ; Wagner et al, ; Wu et al, ; Xu et al, ). This argument is supported by field observations from Wu et al () and by Xu et al () of ontogeny‐associated PC. In this paper, we (1) illustrate the fact that current LSMs do not capture the seasonality of tropical evergreen forests phenology; (2) explore environmental drivers that trigger new leaf formation and old leaf shedding in Amazonian evergreen forests; (3) link leaf phenology with the seasonality of carbon allocation to leaves, roots, and sapwood in the equations of the ORCHIDEE LSM, with a parsimonious modeling strategy; and (4) simulate the seasonality of canopy leaf phenology, carbon and water fluxes, and litterfall and evaluate model results with observations.…”

“…In the canopy photosynthesis submodel of the ORCHIDEE LSM, the shifts in carbon allocation to new leaves and the litterfall of old leaves are defined by empirical functions of SWdown and VPD , respectively, combined with an ontogeny‐dependent leaf PC function (Xu et al, ). Model results are tested in two steps.…”