Factors controlling long-term carbon dioxide exchange between a Douglas-fir stand and the atmosphere identified using an artificial neural network approach

It is critical to have long-term carbon dioxide (CO2) flux observations in forest ecosystems to understand how changing climate can affect forest carbon (C) stocks and CO2 exchange between forests and the atmosphere. In this study, fifteen years (2002–2016) of continuous eddy-covariance flux and climate measurements in an intermediate-aged Douglas-fir stand on the east coast of Vancouver Island, Canada, were analyzed. First, the eddy covariance-measured CO2 fluxes were partitioned into gross primary production and ecosystem respiration using two artificial neural networks. Second, the responses of net ecosystem production, gross primary production and ecosystem respiration to interannual climate variability, including five El Niño-Southern Oscillation events, were determined. Three hyper-parameters (number of layers, hidden units, and batch size) of each artificial neural network were set by Bayesian optimization using sequential model-based optimization while the remaining hyper-parameters were taken from the literature. The first artificial neural network was fitted using only nighttime CO2 flux data and applied to estimate nighttime and daytime ecosystem respiration values, and the second one was used to gap-fill gross primary production values. In addition, a predictor analysis was done to investigate the most influential predictors (i.e., environmental variables) within seasons and years. When applied to half-hourly data, the ecosystem respiration model had an R2 of 0.43, whereas the gross primary production model had an R2 of 0.80. The stand was a moderate C sink (average net ecosystem production of 118 ± 404 g C m−2 year−1) during the entire study period, except for the years 2002–2006 when the stand was a moderate C source. The mean annual values of gross primary production and ecosystem respiration were 1649 ± 157 g C m−2 year−1 and 1531 ± 410 g C m−2 year−1, respectively. Our analysis showed that soil temperature was the most important predictor for the ecosystem respiration model, and photosynthetically active irradiance was the most important predictor for the gross primary production model. However, during dry periods in late summer, soil moisture became the most important predictor. Interannual variability of net ecosystem production was only slightly affected by annual total precipitation, mean soil temperature and mean air temperature. Instead, it depended on spring mean air temperature (start of the growing season), summer total precipitation (indicative of water deficiency) and mean summer air temperature. El Niño and La Niña events generally resulted in lower and higher annual net ecosystem production, respectively.