Response of plant diversity and soil physicochemical properties to different gap sizes in a Pinus massoniana plantation

As one means of close-to-nature management, forest gaps have an important impact on the ecological service function of plantations. To improve the current situation of P. massoniana plantations, three different sizes of forest gaps (large gaps, medium gaps and small gaps) were established to observe whether gap setting can improve the soil fertility and plant diversity of forest plantations. The results showed that compared with the control, the soil organic matter content of different soil layers increased significantly in the medium forest gap and large forest gap. The content of soil organic matter in the surface layer of the middle gap had the largest increase (80.64%). Compared with the control, the content of soil-available potassium between different soil layers decreased significantly by 15.93% to 25.80%. The soil hydrolysable nitrogen reached its maximum under the medium gap. Soil moisture showed significant changes among different gap treatments, different soil layers and their interaction, decreasing significantly in large gaps and small gaps but increasing significantly in medium gaps. The soil bulk density decreased significantly compared with the control, and the surface soil reached the minimum in the medium gap. There were different plant species in forest gaps of different sizes, and shrub layer plants were more sensitive to gap size differences than herb layer plants. The plant diversity indices of the shrub layer increased significantly and showed a maximum under the medium gap. The plant diversity of the herb layer showed the opposite trend, and the Shannon-Wiener index, Simpson index and Pielou index were significantly lower than those of the control. RDA showed that different gap treatments had significant effects on the distribution of plants under the forest. Soil available potassium, soil moisture and soil bulk density affected the distribution and diversity of plants under the forest, serving as the limiting factors of plant growth. In forest management, if we strictly consider the improvement of plant diversity and soil physicochemical properties, these results suggest that a medium gap should be established in a plantation for natural restoration.

Rodent-mediated plant seed dispersal: what happens to the seeds after entering the gaps with different sizes?

In general, it is accepted that gap formation significantly affects the placement of scatter-hoarded seeds by small rodents, but the effects of different forest gap sizes on the seed-eating and scatter-hoarding behaviors of small rodents remain unclear. Thus, we examined the effects of a closed canopy forest, forest edge, and gaps with different sizes on the spatial dispersal of Quercus variabilis acorns and cache placement by small rodents using coded plastic tags in the Taihang Mountains, China. The seeds were removed rapidly and there were significant differences in the seed-eating and caching strategies between the stand types. We found that Q. variabilis acorns were usually eaten after being removed from the closed canopy forest and forest edges. By contrast, the Q. variabilis acorns in the forest gap stands were more likely to be scatter hoarded. The dispersal distances of Q. variabilis acorns were significantly longer in the forest gap plots compared with the closed canopy and forest edge plots. However, the proportions of scatter-hoarded seeds did not increase significantly as the gap size increased. In small-scale oak reforestation projects or research, creating small gaps to promote rodent-mediated seed dispersal may effectively accelerate forest recovery and successional processes.

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.

Using forest gap models and experimental data to explore long-term effects of tree diversity on the productivity of mixed planted forests

Key message In this exploratory study, we show how combining the strength of tree diversity experiment with the long-term perspective offered by forest gap models allows testing the mixture yielding behavior across a full rotation period. Our results on a SW France example illustrate how mixing maritime pine with birch may produce an overyielding (i.e., a positive net biodiversity effect). Context Understanding the link between tree diversity and stand productivity is a key issue at a time when new forest management methods are investigated to improve carbon sequestration and climate change mitigation. Well-controlled tree diversity experiments have been set up over the last decades, but they are still too young to yield relevant results from a long-term perspective. Alternatively, forest gap models appear as appropriate tools to study the link between diversity and productivity as they can simulate mixed forest growth over an entire forestry cycle. Aims We aimed at testing whether a forest gap model could first reproduce the results from a tree diversity experiment, using its plantation design as input, and then predict the species mixing effect on productivity and biomass in the long term. Methods Here, we used data from different forest experimental networks to calibrate the gap model ForCEEPS for young pine (Pinus pinaster) and birch (Betula pendula) stands. Then, we used the refined model to compare the productivity of pure and mixed pine and birch stands over a 50-year cycle. The mixing effect was tested for two plantation designs, i.e., species substitution and species addition, and at two tree densities. Results Regarding the comparison with the experiment ORPHEE (thus on the short term), the model well reproduced the species interactions observed in the mixed stands. Simulations showed an overyielding (i.e., a positive net biodiversity effect) in pine-birch mixtures in all cases and during the full rotation period. A transgressive overyielding was detected in mixtures resulting from birch addition to pine stands at low density. These results were mainly due to a positive mixing effect on pine growth being larger than the negative effect on birch growth. Conclusion Although this study remains explorative, calibrating gap models with data from monospecific stands and validating with data from the manipulative tree diversity experiment (ORPHEE) offers a powerful tool for further investigation of the productivity of forest mixtures. Improving our understanding of how abiotic and biotic factors, including diversity, influence the functioning of forest ecosystems should help to reconsider new forest managements optimizing ecosystem services.

Loss of total phenols from leaf litter of two shrub species: dual responses to alpine forest gap disturbance during winter and the growing season

Aims Alpine forest gaps can control understory ecosystem processes by manipulating hydrothermal dynamics. Here, we aimed to test the role of alpine forest gap disturbance on total phenol loss (TPL) from the decomposing litter of two typical shrub species (willow, Salix paraplesia Schneid., and bamboo, Fargesia nitida (Mitford) Keng f.). Methods We conducted a field litterbag experiment within a representative fir (Abies faxoniana Rehd.) forest based on ‘gap openness treatments’ (plot positions in the gap included the gap center south, gap center north, canopy edge, expanded edge and closed canopy). The TPL rate and litter surface microbial abundance (fungi and bacteria) of the two shrub species were measured during the following periods over 2 years: snow formation (SF), snow cover (SC), snow melting (ST), the early growing season (EG) and the late growing season (LG). Important Findings At the end of the study, we found that snow cover depth, freeze–thaw cycle frequency and the fungal copies g−1 to bacterial copies g−1 ratio had significant effects on litter TPL. The abundances of fungi and bacteria decreased from the gap center to the closed canopy during the SF, SC, ST and LG periods and showed the opposite trend during the EG periods. The rate of TPL among plot positions closely followed the same trend as microbial abundance during the first year of incubation. In addition, both species had higher rates of TPL in the gap center than at other positions during the first winter, first year and entire 2-year period. These findings suggest that alpine forest gap formation accelerates litter TPL, although litter TPL exhibits dual responses to gap disturbance during specific critical periods. In conclusion, reduced snow cover depth and duration during winter warming under projected climate change scenarios or as gaps vanish may slow litter TPL in alpine biomes.

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.