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Forests in the wildland-urban interface (WUI) are of high value but vulnerable to wildland fires due to abundant fire ignitions and flammable forest fuels. Restoring the post-fire Wildland-Urban Interface (WUI) forest landscape is of utmost importance in order to maintain ecosystem service provision. The near-natural restoration strategy is widely employed in vegetation restoration as it enables the formation of healthy, stable, and diverse artificial mixed forests that resemble natural forests. To evaluate the long-term effects of near-natural restoration on the WUI forest landscape, which are largely unclear, we used a wildfire in 2019 near Shenyang City in northeast China as an example and investigated the post-fire forest dynamics under two different scenarios (i.e., natural succession and near-natural restoration) based on the forest landscape model. The results demonstrated that near-natural restoration can significantly accelerate the restoration process in terms of forest biomass, species biodiversity, and age structure. Under the near-natural restoration scenario, the biomass of the burned area can be quickly restored within 20 years after the fire. At the species level, the biomass and proportion of pioneer tree species such as Pinus tabuliformis and Robinia pseudoacacia decreased under the near-natural restoration scenario, while other species started to increase. Then post-fire near-natural planting accelerated the restoration of forest biodiversity, by 2070, the Shannon–Wiener index was predicted to be 1.49 under natural succession and remained at 2.02 under near-natural restoration. In terms of age structure, near-natural restoration shortens the recovery time of fire trails to mature forests. In summary, near-natural restoration accelerates forest recovery in post-fire WUI areas. Our results highlighted the impact of near-natural restoration on forest conservation to inform post-fire forest planning and management practices.

Abstract Climate change has been observed to significantly influence forest growth, community composition, and species distribution ranges. These influences in turn will impose continuous impacts on forest production and carbon (C) storage potential. Forests in the subtropical China that are experiencing rapid regeneration and recovery may suffer multiple threats in the face of future climate change. Understanding how climate change may affect forest C sequestration and species dynamics over time will help formulate better management strategies for maintaining forest productivity and biodiversity. Here, we used a forest landscape model (LANDIS‐II) to evaluate the long‐term effects of current business‐as‐usual (BAU) management and climate projections (current, RCP4.5, and RCP8.5 climate scenarios; IPCC representative concentration pathways [RCPs] scenarios) on above‐ and belowground forest C storage and tree species dynamics in the Sangzhi County in the subtropical China. Our simulations showed a fast‐growing period of forest total C in the first 70 yr, regardless of climate regime. Moderate climate change (RCP4.5 climate scenario) increased soil organic carbon (SOC) (12%) and detrital C (16%) but reduced live C (5%), contributing to a slight augment of 3% in forest C storage compared to the control climate, while severe climate change (RCP8.5 climate scenario) decreased SOC (16%), detrital C (27%), and live C (12%), resulting in a dramatic reduction of 14% in forest C storage, primarily because severe warming‐induced water stress restrained species establishment and regeneration in temperature‐sensitive areas like the lower elevations. Meanwhile, nature reserves in the higher elevations could act as “safe islands” by providing suitable conditions for most tree species, but the logging ban caused higher canopy closure, which in turn inhibit the growth and establishment of shade‐intolerant species. The results also highlighted the positive responses of native “warm species” to climate warming and suggest that using them to replace some conventional coniferous plantation tree species would better mitigate the future climate change. Poor performance of the current BAU management in maintaining forest productivity and diversity suggests that new climate‐adapted management strategies should be designed accordingly.

[Display omitted] • The effects of near-natural restoration on post-fire forest in the wildland-urban interface were quantified. • Near-natural restoration increased post-fire forest biomass and biodiversity more than natural succession. • Near-natural restoration shortens the evolution time of fire trails to mature forest. • Near-natural restoration can accelerate post-fire forest recovery in the wildland-urban interface. Forests in the wildland-urban interface (WUI) are of high value but vulnerable to wildland fires due to abundant fire ignitions and flammable forest fuels. Restoring the post-fire Wildland-Urban Interface (WUI) forest landscape is of utmost importance in order to maintain ecosystem service provision. The near-natural restoration strategy is widely employed in vegetation restoration as it enables the formation of healthy, stable, and diverse artificial mixed forests that resemble natural forests. To evaluate the long-term effects of near-natural restoration on the WUI forest landscape, which are largely unclear, we used a wildfire in 2019 near Shenyang City in northeast China as an example and investigated the post-fire forest dynamics under two different scenarios (i.e., natural succession and near-natural restoration) based on the forest landscape model. The results demonstrated that near-natural restoration can significantly accelerate the restoration process in terms of forest biomass, species biodiversity, and age structure. Under the near-natural restoration scenario, the biomass of the burned area can be quickly restored within 20 years after the fire. At the species level, the biomass and proportion of pioneer tree species such as Pinus tabuliformis and Robinia pseudoacacia decreased under the near-natural restoration scenario, while other species started to increase. Then post-fire near-natural planting accelerated the restoration of forest biodiversity, by 2070, the Shannon–Wiener index was predicted to be 1.49 under natural succession and remained at 2.02 under near-natural restoration. In terms of age structure, near-natural restoration shortens the recovery time of fire trails to mature forests. In summary, near-natural restoration accelerates forest recovery in post-fire WUI areas. Our results highlighted the impact of near-natural restoration on forest conservation to inform post-fire forest planning and management practices. [ABSTRACT FROM AUTHOR]

Forest landscape models (FLMs) have become a valuable tool for projecting broad-scale forest dynamics, but incomplete knowledge about model behavior can make parameterization challenging and outcomes unreliable. FLMs generally model forest growth as a set of interacting processes, and, consequently, predictions can be influenced by process or parameter uncertainty. A sensitivity analysis can potentially help identify sources of uncertainty, but if it does not use global measures of sensitivity nor consider that sensitivity in a process-based model is likely time-dependent, results could be misleading. Our aim was to evaluate the sensitivity of nine key parameters when predicting live aboveground biomass (AGB) with the widely used FLM, LANDIS-II. To fully explore parameter interactions and nonlinear model behavior, we selected a range of parameter values based on LANDIS-II applications in North America that was considerably wider than in previous local sensitivity analyses. Our results showed commonalities with previous studies, which concluded the maximum allowable biomass and maximum annual net primary productivity specified for a species were most influential when predicting AGB. In contrast to earlier work, we also clearly demonstrated how relative importance was time-dependent for all but the least important parameters. Interactions between parameters and with simulation duration generated substantial variability in AGB and number of cohorts established. Results will improve future calibration efforts and may offer insight into opportunities for possible model refinements. This study also suggests, however, that parameters which cannot be calibrated based on empirical data will continue to be a major source of model uncertainty. [ABSTRACT FROM AUTHOR]