Mean annual dynamics of daily net ecosystem productivity (NEP) among sites and ecotypes. Lines represent a cubic smoothing spline applied to the entire record length at each site.
Growing season length (GSL) is a key unifying concept in ecology that can be estimated from eddy covariance-derived estimates of net ecosystem production (NEP). Previous studies disagree on how increasing GSLs may affect NEP in evergreen coniferous forests, potentially due to the variety of methods used to quantify GSL from NEP. We calculated GSL and GSL-NEP regressions at eleven evergreen conifer sites across a broad climatic gradient in western North America using three common approaches: (1) variable length (3–7 days) regressions of day of year versus NEP, (2) a smoothed threshold approach, and (3) the carbon uptake period, followed by a new approach of a method-averaged ensemble. The GSL and the GSL-NEP relationship differed among methods, resulting in linear relationships with variable sign, slope, and statistical significance. For all combinations of sites and methods, the GSL explained between 6% and 82% of NEP with p-values ranging from 0.45 to < 0.01. These results demonstrate the variability among GSL methods and the importance of selecting an appropriate method to accurately project the ecosystem carbon cycling response to longer growing seasons in the future. To encourage this approach in future studies, we outline a series of best practices for GSL method selection depending on research goals and the annual NEP dynamics of the study site(s). These results contribute to understanding growing season dynamics at ecosystem and continental scales and underscore the potential for methodological variability to influence forecasts of the evergreen conifer forest response to climate variability.
Barnard, D.M., Knowles, J.F., Barnard, H.R., Goulden, M.L., Hu, J., Litvak, M.E., Molotch, N.P. (2018): Reevaluating growing season length controls on net ecosystem production in evergreen conifer forests. Scientific Reports 8(1): 17973. DOI: 10.1038/s41598-018-36065-0
This Paper/Book acknowledges NSF CZO grant support.