Modeling post-fire regeneration patterns under different restoration scenarios to improve forest recovery in degraded ecosystems

Abstract

BackgroundChanges in disturbance regimes triggered by land use and climate change can significantly alter forest ecosystems by modifying the distribution of some species and hindering post-disturbance tree regeneration dynamics. Applied nucleation (AN) could be a valuable active restoration approach for promoting natural recovery in forest ecosystems affected by stand-replacing disturbances as it improves seed availability and microsite conditions. ObjectivesThe study aimed to investigate the potential of AN under different scenarios in a mountain forest ecosystem of the Northwestern Italian Alps dominated by Scots pine (Pinus sylvestris L.). The area was affected by a large stand-replacing fire in 2005 and post-fire salvage logging that amplified ecosystem degradation and dampened natural tree regeneration. MethodsWe assessed the main drivers guiding natural post-fire natural recovery and identified suitable sites for tree regeneration through a machine learning correlative model (Bayesian Additive Regression Tree, BART). Specifically, we used several environmental predictors (e.g., topography, wind direction, and distance from seed trees) to model the occurrence of natural tree regeneration. We predicted the probability of tree regeneration presence at landscape scale under the current situation (fire followed by salvage logging) and a set of AN scenarios characterized by an increase in nuclei density, since distance from seed trees emerged as the most important driver for natural tree regeneration. Starting from the situation 16 years after the fire, we reclassified the prediction raster into a binary map of intervention priority (priority and non-priority patches), using the probability value that maximized the model accuracy (true skill statistic; TSS) as threshold. Patches with scarce pine regeneration were considered as high intervention priority sites for AN. These predictions made it possible to assess the most efficient active management scenario in terms of promoting forest recovery. ConclusionsThe simulations showed the positive effects of AN on natural tree regeneration and the importance of site selection for plantations, proving that AN could be a promising post-fire management technique that can minimize human interventions and their associated economic and ecological costs. To our knowledge, this work is the first AN simulation in a temperate mountain ecosystem. The selection of favorable sites can be further improved by considering fine-scale characteristics through field experiments and cross-scale integration.