Magnitude and timing of density reduction are key for the resilience to severe drought in conifer-broadleaf mixed forests in Central Europe

Key message We applied a modified forest gap model (ForClim) to depict changes in stand water transpiration via density reduction as a forest adaptation strategy. This approach is the key to analyzing the ecological resilience to drought, stress-induced mortality, and economic efficiency of managed mixed forest stands in Central Europe. The results show that specific geographic conditions and forest composition define the optimal stand density of drought-resilient forests. Context Reducing stand density has been recognized as a valid strategy to increase forest resilience to drought. Moreover, to develop adaptive management strategies (AMS) under climate change, it is crucial to consider not only drought resilience but also the economic efficiency of alternative AMS proposed to alleviate drought effects. Aims To analyze how decreased inter-tree competition among overstorey trees affects stand vulnerability to drought and its expected yield. Methods We integrated experimental thinning data and historical responses to drought years in a climate-sensitive forest gap model, ForClim. We tested a business as usual (BAU) and three alternative AMS (“do-nothing,” low- and high-intensity overstorey removal) in mixed stands of Norway spruce (Picea abies), silver fir (Abies alba), and European beech (Fagus sylvatica) along an elevational gradient of 520–1020 m a.s.l. in Central Europe. Results High-intensity overstorey removal in mixed stands of all three species considerably increased forest volume growth resilience to drought and decreased stress-induced mortality by two-thirds vis à vis a “do-nothing” strategy. In sites including only conifer species, forest resilience was equally improved by high- and low-intensity overstorey removal compared to that in the BAU strategy. Regarding the timber economy, high-intensity overstorey removal resulted in a higher economic revenue of mixed stands (~ 22% higher net present value than other strategies) on the high-elevation sites (> 1000 m a.s.l.). Conclusion Modifying forest density and structure by overstorey removal is principally suitable to increase forest resilience to drought and improve its economic efficiency. The magnitude of the effect however depends on the geographical setting and forest composition.

Using Microwave Profile Radar to Estimate Forest Canopy Leaf Area Index: Linking 3D Radiative Transfer Model and Forest Gap Model

Profile radar allows direct characterization of the vertical forest structure. Short-wavelength, such as Ku or X band, microwave data provide opportunities to detect the foliage. In order to exploit the potential of radar technology in forestry applications, a helicopter-borne Ku-band profile radar system, named Tomoradar, has been developed by the Finnish Geospatial Research Institute. However, how to use the profile radar waveforms to assess forest canopy parameters remains a challenge. In this study, we proposed a method by matching Tomoradar waveforms with simulated ones to estimate forest canopy leaf area index (LAI). Simulations were conducted by linking an individual tree-based forest gap model ZELIG and a three-dimension (3D) profile radar simulation model RAPID2. The ZELIG model simulated the parameters of potential local forest succession scene, and the RAPID2 model utilized the parameters to generate 3D virtual scenes and simulate waveforms based on Tomoradar configuration. The direct comparison of simulated and collected waveforms from Tomoradar could be carried out, which enabled the derivation of possible canopy LAI distribution corresponding to the Tomoradar waveform. A 600-m stripe of Tomoradar data (HH polarization) collected in the boreal forest at Evo in Finland was used as a test, which was divided into 60 plots with an interval of 10 m along the trajectory. The average waveform of each plot was employed to estimate the canopy LAI. Good results have been found in the waveform matching and the uncertainty of canopy LAI estimation. There were 95% of the plots with the mean relative overlapping rate (RO) above 0.7. The coefficients of variation of canopy LAI estimates were less than 0.20 in 80% of the plots. Compared to lidar-derived canopy effective LAI estimation, the coefficient of determination was 0.46, and the root mean square error (RMSE) was 1.81. This study established a bridge between the Ku band profile radar waveform and the forest canopy LAI by linking the RAPID2 and ZELIG model, presenting the uncertainty of forest canopy LAI estimation using Tomoradar. It is worth noting that since the difference of backscattering contribution is caused by both canopy structure and tree species, similar waveforms may correspond to different canopy LAI, inducing the uncertainty of canopy LAI estimation, which should be noticed in forest parameters estimation with empirical methods.

Towards understanding predictability in ecology: A forest gap model case study

Underestimation of uncertainty in ecology runs the risk of producing precise, but inaccurate predictions. Most predictions from ecological models account for only a subset of the various components of uncertainty, making it diffcult to determine which uncertainties drive inaccurate predictions. To address this issue, we leveraged the forecast-analysis cycle and created a new state data assimilation algorithm that accommodates non-normal datasets and incorporates a commonly left-out uncertainty, process error covariance. We evaluated this novel algorithm with a case study where we assimilated 50 years of tree-ring-estimated aboveground biomass data into a forest gap model. To test assumptions about which uncertainties dominate forecasts of forest community and carbon dynamics, we partitioned hindcast variance into five uncertainty components. Contrary to the assumption that demographic stochasticity dominates forest gap dynamics, we found that demographic stochasticity alone massively underestimated forecast uncertainty (0.09% of the total uncertainty) and resulted in overconfident, biased model predictions. Similarly, despite decades of reliance on unconstrained “spin-ups” to initialize models, initial condition uncertainty declined very little over the forecast period and constraining initial conditions with data led to large increases in prediction accuracy. Process uncertainty, which up until now had been diffcult to estimate in mechanistic ecosystem model projections, dominated the prediction uncertainty over the forecast time period (49.1%), followed by meteorological uncertainty (32.5%). Parameter uncertainty, a recent focus of the modeling community, contributed 18.3%. These findings call into question our conventional wisdom about how to improve forest community and carbon cycle projections. This foundation can be used to test long standing modeling assumptions across fields in global change biology and specifically challenges the conventional wisdom regarding which aspects dominate uncertainty in the forest gap models.