Structure and parameter uncertainty in centennial projections of forest community structure and carbon cycling

Abstract

Secondary forest regrowth shapes community succession and biogeochemistry for decades, including in the Upper Great Lakes region. Vegetation models encapsulate our understanding of forest function, and whether models can reproduce multidecadal succession patterns is an indication of our ability to predict forest responses to future change. We test the ability of a vegetation model to simulate C cycling and community composition during 100 years of forest regrowth following standreplacing disturbance, asking (a) Which processes and parameters are most important to accurately model Upper Midwest forest succession? (b) What is the relative importance of model structure versus parameter values to these predictions? We ran ensembles of the Ecosystem Demography model v2.2 with different representations of processes important to competition for light. We compared the magnitude of structural and parameter uncertainty and assessed which sub-model-parameter combinations best reproduced observed C fluxes and community composition. On average, our simulations underestimated observed net primary productivity (NPP) and leaf area index (LAI) after 100 years and predicted complete dominance by a single plant functional type (PFT). Out of 4,000 simulations, only nine fell within the observed range of both NPP and LAI, but these predicted unrealistically complete dominance by either early hardwood or pine PFTs. A different set of seven simulations were ecologically plausible but under-predicted observed NPP and LAI. Parameter uncertainty was large; NPP and LAI ranged from ~0% to >200% of their mean value, and any PFT could become dominant. The two parameters that contributed most to uncertainty in predicted NPP were plant-soil water conductance and growth respiration, both unobservable empirical coefficients. We conclude that (a) parameter uncertainty is more important than structural uncertainty, at least for ED-2.2 in Upper Midwest forests and (b) simulating both productivity and plant community composition accurately without physically unrealistic parameters remains challenging for demographic vegetation models. K E Y W O R D S canopy radiative transfer, demographic vegetation modeling, forest succession, temperate deciduous forest, terrestrial carbon cycle, uncertainty analysis | 6081 SHIKLOMANOV et AL.

Liana optical traits increase tropical forest albedo and reduce ecosystem productivity

“…The relevant parameters are again further described in Table 1. The complete description of the radiative transfer model of ED2 can be found in Appendix A, in Longo et al (2019a), or in Shiklomanov et al (2020).…”

Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)

“…Model structure complexity varies among TBMs and also depends on the user configuration choices: different formulations of the same process can co-exist within a TBM. This complexity results from the necessary compromise between an accurate representation of the reality on the one hand and the computational demand and observational requirements on the other (Shiklomanov et al 2020). Model intercomparison studies have demonstrated that discrepancies in the representation of key processes such as forest structure (Fisher et al 2018) or photosynthesis (Rogers et al 2017) lead to significant uncertainties in the projections of critical variables such as the overall land carbon sequestration capacity (Friedlingstein et al 2014;Lovenduski and Bonan 2017;Friedlingstein et al 2006).…”

“…Previous sensitivity analyses have underlined the critical importance of parameter uncertainty for the projections of ecosystem demography and productivity (Dietze et al 2014;Massoud et al 2019;Raczka et al 2018;Wramneby et al 2008). In a recent comparative study, parameter uncertainty was even shown to drive the overall model uncertainty (Shiklomanov et al 2020). Among model parameters, allometric coefficients scale the shape and mass of the plants or of its components with their size (Chave et al 2014).…”

The <i>fortedata</i> R package: open-science datasets from a manipulative experiment testing forest resilience

“…1). Data from the field experiment also inform a series of modeling experiments; specifically, data included in this package are used to initialize, calibrate, and validate dynamic vegetation model simulations of forest function and its responses to disturbance (e.g., Shiklomanov et al, 2021).…”

“…Disturbance alters multiple carbon (C) cycling processes and, as a result, may affect forest C uptake and storage (Williams et al, 2016). The magnitude, timing, and duration of changes in the C cycle following disturbance vary among forests (Amiro et al, 2010;Luo and Weng, 2011;Coomes et al, 2012;Hicke et al, 2012;Gough et al, 2013;Peters et al, 2013;Vanderwel et al, 2013;Flower and Gonzalez-Meler, 2015;Gu et al, 2019). These responses may differ as a function of disturbance severity, type, and frequency along with the physical, structural, and biological properties of the affected ecosystem (Amiro et al, 2010;Williams et al, 2012;Scheuermann et al, 2018;Rebane et al, 2019;Atkins et al, 2020a).…”