Simulating gross primary production across a chronosequence of coastal Douglas-fir forest stands with a production efficiency model

Abstract Eddy-covariance (EC) measurements in three different-aged Douglas-fir ( Pseudotsuga menziesii [Mirb.] Franco var. menziesii ) stands were used to calibrate a production efficiency model (PEM) and explore the sources of error in simulated annual gross primary production ( P g ). Parameters were derived on a daily time scale, assessing absorbed photosynthetically active radiation ( Q a ), maximum gross photosynthetic efficiency ( ɛ g max ), and functions of environmental stress. Despite similar climate, ɛ g max varied between sites in correspondence with measurements of site index derived from forest inventory, suggesting that landscape variation of ɛ g max is controlled mainly by non-climatic factors and ranges approximately between 1.28 and 4.42 g C MJ −1 . Within stands, daily variation of P g was most strongly controlled by decreasing ɛ g with increasing Q a . We therefore devised a method of incorporating the nonlinear light response (NLR) that is apparent within stands into the model, while preserving the linearity in the relationship between annual P g and Q a across stands that is assumed in conventional PEMs. The ability to match observed seasonal and inter-annual variability of P g improved by taking into account antecedent effects of cumulative heat on plant development. An ecosystem-specific model (i.e., fitted collectively to all stands) explained 81, 95, and 97% of the monthly variation of P g in regenerating, juvenile, and mature stands, respectively. The model was able to collectively explain 96% of variability of annual total P g with a root mean squared error 130 g C m −2  yr −1 , constituting 6% of the mean and 113% of the standard deviation at the mature site. The capacity to predict inter-annual variability of P g was strongly limited by discrepancies that persisted for days-to-weeks at a time, which implies that poor model skill was caused, to uncertain degrees, by inadequate representation of acclimation to environmental stress, and discrepancies between measurement and footprint-weighted conditions.