Wind and snow-induced damage have been analyzed at stand level for three pine forests in the Central-Eastern Pyrenees (Pinus nigra Arn. salzmanii, Pinus sylvestris L. and Pinus uncinata Ram.). Stand-level models have been then developed for the most affected two species, Pinus sylvestris L. and Pinus uncinata Ram., to describe damage severity. The models were based on data from national forest inventory plots. They included variables related to the spatial location and structure of the stands, being validated using a sub-set of the database (25% of the plots randomly selected). Mountain pine forests (Pinus uncinata Ram.) were the most heavily affected by wind and snow disturbances. For both mountain and Scots pine species, topographic exposure and the severity of the local storm regime had an important effect on the degree of damage. Stand's resistance to wind and snow was found to be dependent on the combined effect of basal area and mean slenderness of the dominant trees. For a given slenderness ratio, damage increased strongly in lower-density stands, particularly in stands with basal areas below 15 m 2 /ha. Stand structure was particularly important to define the resistance of Scots pine stands, which presented a higher vulnerability to wind and snow under higher degree of even-agedness. The models presented in this study provide empirically-based information that can be used to implement silvicultural practices to minimize the risk of those forests to suffer wind and snow-related damages.
In the Mediterranean, non-serotinous pinewoods are suffering an increasing occurrence of high-severity crown fires that usually drive vegetation shifts to fire-adapted communities and a decrease in pine-dominated area. Here we used a case-study approach on a large area dominated by Pinus nigra Arn. ssp. salzmannii burned in 1998 to gain further understanding of the relative 17 importance of different factors related to local topography (elevation, aspect, slope, curvature), pre-18 fire vegetation (land-use history, canopy cover) and fire behavior (burn severity, presence of 19 unburned patches) as drivers of post-fire regeneration dynamics. The results find that pine shows locally resilient responses driven mainly by factors related to fire effects (presence of unburned patches) and the characteristics of the pre-fire vegetation (i.e. stable forest areas). When fire-induced changes from pine dominance to other types of vegetation occurred, landscape 15 years post-fire was dominated by woody vegetation, with some rare grassland communities emerging under very specific conditions (mountain ridges, hilltops and rocky sites). Conversion from forest to shrubland occurred mainly in the most xeric sites (south-facing areas, in some cases with steep slopes) and areas dominated by young pine stands prior to the fire. We found manageable factors such as the prefire structure and composition of the vegetation strongly determine the occurrence of post-fire regeneration trajectories dominated by tree species regeneration. This knowledge can be used to define preventive management strategies oriented to direct regeneration dynamics in anticipation of fire occurrence. At landscape level, managing forest fuels to favor the occurrence of unburned patches and modify their spatial distribution along the burned landscape will favor a more resilient pine response. At stand level, adjusting silvicultural interventions to favor the natural establishment of late-successional tree species will favor post-fire oak regeneration.
Forest aboveground biomass (AGB) estimation over large extents and high temporal resolution is crucial in managing Mediterranean forest ecosystems, which have been predicted to be very sensitive to climate change effects. Although many modeling procedures have been tested to assess forest AGB, most of them cover small areas and attain high accuracy in evaluations that are difficult to update and extrapolate without large uncertainties. In this study, focusing on the Region of Murcia in Spain (11,313 km2), we integrated forest AGB estimations, obtained from high-precision airborne laser scanning (ALS) data calibrated with plot-level ground-based measures and bio-geophysical spectral variables (eight different indices derived from MODIS computed at different temporal resolutions), as well as topographic factors as predictors. We used a quantile regression forest (QRF) to spatially predict biomass and the associated uncertainty. The fitted model produced a satisfactory performance (R2 0.71 and RMSE 9.99 t·ha−1) with the normalized difference vegetation index (NDVI) as the main vegetation index, in combination with topographic variables as environmental drivers. An independent validation carried out over the final predicted biomass map showed a satisfactory statistically-robust model (R2 0.70 and RMSE 10.25 t·ha−1), confirming its applicability at coarser resolutions.
a b s t r a c tThe progressive abandonment of traditional forest management over the last few decades has led to significant densification processes in most Mediterranean pine stands. In parallel, some of these stands have also shown tree-species diversification processes, the occurrence of which is considered essential for future adaptability and resilience to change. Here we aim to gain further understanding of the main factors driving these diversification processes via a case-study approach using the long-term-managed black pine (Pinus nigra Arn. ssp. salzmannii) forests of the Catalan Pre-Pyrenees (NE Spain). For this purpose, we sampled 155 plots distributed in 8 different stands and analyzed the role played by a number of microsite factors and stand attributes (including canopy openness and heterogeneity) on the abundance of seedlings (h < 1.3 m) and saplings (h > 1.3 m; dbh < 7.5 cm) of the main tree-species in the area (i.e. black pine, evergreen oak and marcescent oaks). Results revealed ongoing black pine recruitment limitation processes mainly associated to the high canopy cover of the overstory and the increasing abundance of shrubs, which may compete with pines for light resources. In contrast, we found that current environmental and stand-level conditions favor the progressive advance of the recruitment of evergreen and marcescent oaks, which are able to establish successfully under the dominant pine canopy. However, in the absence of canopy openings, light levels may not allow the established oaks (in particular the evergreen Quercus ilex) to grow and progress to higher developmental stages. Our findings bring deeper insight into the role of stand-level factors regulating species diversification, and can be used by forest managers to adjust their practices (e.g. by modifying the spatial and temporal patterns of silvicultural treatments such as thinnings or selection cuttings) in order to favor this natural process and increase stand resilience.
& Key message We present a novel approach to define pure-and mixed-forest typologies from the comparison of pairs of forest plots in terms of species identity, diameter, and height of their trees. & Context Forest typologies are useful for many purposes, including forest mapping, assessing habitat quality, studying forest dynamics, or defining sustainable management strategies. Quantitative typologies meant for forestry applications normally focus on horizontal and vertical structure of forest plots as main classification criteria, with species composition often playing a secondary role. The selection of relevant variables is often idiosyncratic and influenced by a priori expectations of the forest types to be distinguished. & Aims We present a general framework to define forest typologies where the dissimilarity between forest stands is assessed using coefficients that integrate the information of species composition with the univariate distribution of tree diameters or heights or the bivariate distribution of tree diameters and heights. & Methods We illustrate our proposal with the classification of forest inventory plots in Catalonia (NE Spain), comparing the results obtained using the bivariate distribution of diameters and heights to those obtained using either tree heights or tree diameters only. & Results The number of subtypes obtained using the tree diameter distribution for the calculation of dissimilarity was often the same as those obtained from the tree height distribution or to those using the bivariate distribution. However, classifications obtained using the three approaches were often different in terms of forest plot membership. & Conclusion The proposed classification framework is particularly suited to define forest typologies from forest inventory data and allows taking advantage of the bivariate distribution of diameters and heights if both variables are measured. It can provide support to the development of typologies in situations where fine-scale variability of topographic, climatic, and legacy management factors leads to fine-scale variation in forest structure and composition, including uneven-aged and mixed stands.
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