Abstract

Forests have climate change mitigation potential since they sequester carbon. However, their carbon sink strength might depend on management. As a result of the balance between CO2 uptake and emission, forest net ecosystem exchange (NEE) reaches optimal values (maximum sink strength) at young stand ages, followed by a gradual NEE decline over many years. Traditionally, this peak of NEE is believed to be concurrent with the peak of primary production (e.g., gross primary production, GPP); however, in theory, this concurrence may potentially vary depending on tree species, site conditions and the patterns of ecosystem respiration (Reco). In this study, we used eddy-covariance (EC)-based CO2 flux measurements from 8 forest sites that are dominated by Norway spruce (Picea abies L.) and built machine learning models to find the optimal age of ecosystem productivity and that of CO2 sequestration. We found that the net CO2 uptake of Norway spruce forests peaked at ages of 30-40 yrs. Surprisingly, this NEE peak did not overlap with the peak of GPP, which appeared later at ages of 60-90 yrs. The mismatch between NEE and GPP was a result of the Reco increase that lagged behind the GPP increase associated with the tree growth at early age. Moreover, we also found that newly planted Norway spruce stands had a high probability (up to 90%) of being a C source in the first year, while, at an age as young as 5 yrs, they were likely to be a sink already. Further, using common climate change scenarios, our model results suggest that net CO2 uptake of Norway spruce forests will increase under the future climate with young stands in the high latitude areas being more beneficial. Overall, the results suggest that forest management practices should consider NEE and forest productivity separately and harvests should be performed only after the optimal ages of both the CO2 sequestration and productivity to gain full ecological and economic benefits. How to cite: Zhao, J., Lange, H., and Meissner, H.: Mismatch between the optimal ages for ecosystem productivity and net CO2 sequestration in Norway spruce forests, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4257, https://doi.org/10.5194/egusphere-egu21-4257, 2021.

Abstract

Soil respiration is an important ecosystem process that releases carbon dioxide into the atmosphere. While soil respiration can be measured continuously at high temporal resolutions, gaps in the dataset are inevitable, leading to uncertainties in carbon budget estimations. Therefore, robust methods used to fill the gaps are needed. The process-based non-linear least squares (NLS) regression is the most widely used gap-filling method, which utilizes the established relationship between the soil respiration and temperature. In addition to NLS, we also implemented three other methods based on: 1) artificial neural networks (ANN), driven by temperature and moisture measurements, 2) singular spectrum analysis (SSA), relying only on the time series itself, and 3) the expectation-maximization (EM) approach, referencing to parallel flux measurements in the spatial vicinity. Six soil respiration datasets (2017–2019) from two boreal forests were used for benchmarking. Artificial gaps were randomly introduced into the datasets and then filled using the four methods. The time-series-based methods, SSA and EM, showed higher accuracies than NLS and ANN in small gaps (<1 day). In larger gaps (15 days), the performance was similar among NLS, SSA and EM; however, ANN showed large errors in gaps that coincided with precipitation events. Compared to the observations, gap-filled data by SSA showed similar degree of variances and those filled by EM were associated with similar first-order autocorrelation coefficients. In contrast, data filled by both NLS and ANN exhibited lower variance and higher autocorrelation than the observations. For estimations of the annual soil respiration budget, NLS, SSA and EM resulted in errors between −3.7% and 5.8% given the budgets ranged from 463 to 1152 g C m−2 year−1, while ANN exhibited larger errors from −11.3 to 16.0%. Our study highlights the two time-series-based methods which showed great potential in gap-filling carbon flux data, especially when environmental variables are unavailable.

Abstract

Three methods for extracting DNA were tested on otoliths, scales, fins, and gill tissue from European whitefish [Coregonus lavaratus (L.)]. The aim was to find time-efficient and affordable ways to simultaneously extract DNA suitable for conservation genetic studies from a large number of samples and different tissues. A rapid low-cost method led to 97 % success of microsatellite amplification in otoliths and 100 % in scales. High amplification success was achieved with fin (97 %) and gill (99 %) tissue using a salt lysis-based protocol. A commercial extraction kit delivered good results with all tissues. The findings are useful for conservation genetic studies using both contemporary and archived samples.