Leveraging plant hydraulics to yield predictive and dynamic plant leaf allocation in vegetation models with climate change

Abstract

Plant functional traits provide a link in process-based vegetation models between plant-level physiology and ecosystem-level responses. Recent advances in physiological understanding and computational efficiency have allowed for the incorporation of plant hydraulic processes in large-scale vegetation models. However, a more mechanistic representation of water limitation that determines ecosystem responses to plant water stress necessitates a re-evaluation of trait-based constraints for plant carbon allocation, particularly allocation to leaf area. In this review, we examine model representations of plant allocation to leaves, which is often empirically set by plant functional type-specific allometric relationships. We analyze the evolution of the representation of leaf allocation in models of different scales and complexities. We show the impacts of leaf allocation strategy on plant carbon uptake in the context of recent advancements in modeling hydraulic processes. Finally, we posit that deriving allometry from first principles using mechanistic hydraulic processes is possible and should become standard practice, rather than using prescribed allometries. The representation of allocation as an emergent property of scarce resource constraints is likely to be critical to representing how global change processes impact future ecosystem dynamics and carbon fluxes and may reduce the number of poorly constrained parameters in vegetation models. K E Y W O R D Sallocation, ecohydrological equilibrium, leaf area, optimization, plant hydraulics, vegetation models, vegetation water demand

The impact of rising CO2and acclimation on the response of US forests to global warming

“…The main advance in our modeling approach was to constrain a mechanistic plant hydraulic-gas exchange model with optimality principles operating at a hierarchy of scales from leaf to forest. In some form, the concept could be scaled up to replace the empirical approach generally utilized in land surface models and ESMs, particularly with the increasing prevalence of demographic vegetation models (20,28,86). These models face the enormous challenge of integrating basic plant ecophysiology with potentially critical impacts of fire regime, land use, soil biogeochemistry, pathogen and pest activity, and the like.…”

“…Most landscapescale models predict stomatal responses from empirical functions fitted to historic conditions (21)(22)(23), but there is no guarantee these physiologically blind functions will predict a changing future with potentially acclimating vegetation (24). Acclimation of tree LA and leaf photosynthetic capacity complicates stomatal regulation, which is known to be coordinated with these internal factors (25)(26)(27)(28). Despite these complexities, it is essential to improve the modeling of stomatal gas exchange, because instantaneous rates of photosynthesis and transpiration underlie long-term projections of NPP and stand water stress.…”

Tree growth sensitivity to climate is temporally variable

“…These responses are only partially captured by the current generation of land carbon models, which tend to underestimate the severity and duration of drought impacts on productivity (Kolus et al 2019), and it remains unclear whether a single modelling framework or statistical approach is capable of improved representation (e.g. Bond-Lamberty et al 2014;Ogle et al 2015;Trugman et al 2019). As we have shown, many causes of variable growth-climate sensitivity likely arise from or can be conceptualised as changes in the supply or allocation of NSC reserves within individual trees (perhaps most consistent with dynamic sensitivity, H2).…”

“…It is clear that species hydraulic traits are of critical importance to understanding variable growth-climate sensitivity, particularly across species (Anderegg et al 2016;Trugman et al 2019;and Fig. 3).…”

Towards better representations of carbon allocation in vegetation: a conceptual framework and mathematical tool

“…However, many parameter values are unknown because they are not necessarily measurable (Trumbore 2006), and increasing model complexity has obscured the identification of the main processes that control overall system behaviour. Perhaps this is why the quest to find the source of uncertainties has been limited to the comparison of numerical outputs of models (De Kauwe et al 2014), and reviews of their conceptual design (Franklin et al 2012;Walker et al 2014;Merganičová et al 2019;Trugman et al 2019). Although these studies have inspired model classifications (e.g.…”