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Density-dependent mortality occurs in the evolution of even-aged populations when these approach crown closure age. This density-dependent mortality is regulated by the so-called "3/2 power law of self-thinning" that assumes a constant slope for the line relating the log of stand density with the log of the average tree size, the self-thinning line or maximum size–density relationship, MSDR. A good estimate of the self-thinning line is therefore an essential component to any forest growth model. Two concepts for the MSDR have emerged: (1) a static upper limit for the species; and (2) a dynamic self-thinning line influenced by several factors (e.g., management techniques, site quality and/or genetics). The objective of this study was to estimate a new static self-thinning line based on the quadratic mean diameter at breast height (Reineke's self-thinning line) for the generalized use in maritime pine growth models in Portugal. Data from 41 observations obtained in nine long-term permanent experimental trials of maritime pine species were carefully selected from a data set of 186 plots as being under self-thinning. Two methods were used: OLS and mixed linear models. An exploratory analysis on the impact of each environmental variable on the slope and intercept of the self-thinning line led to the selection of a subset of environmental variables later used in an all possible regressions algorithm to find the subsets leading to the lowest values of Akaike information criterion (AIC). The OLS procedure showed that the differences between the plots could be explained by site index, by climate variables (e.g., evaporation or climatic indices) and the use of more than one covariable slightly improved the fit. Nevertheless, the best MSDR line fitted with mixed linear models (ln N = 12.97158 − 1.83926 ln dg) having the plot random effect in the intercept, largely outperformed the best OLS model and is therefore recommended for generalized use in forest growth models. [ABSTRACT FROM AUTHOR]

The necessity for accurate biomass estimates is greater than ever for the sustainable management of forest resources, which is an increasingly pressing matter due to climate change. The most used method to estimate biomass for operational purposes is through allometric equations. Typically, each country develops their own models to be applied at the local scale because it is more convenient. But, for Quercus suber, a joint regional model can be more beneficial, since the species is distributed across the Mediterranean and is challenging to account for due to felling limitations and the nature of mature cork biomass itself. We found that these characteristics are reflected in the biomass datasets and compatibility was, perhaps, the largest impediment to such a model. The use of dummy variables to differentiate between countries, as well as compromises in the limits of biomass compartments, allowed us to develop two joint models to estimate aboveground biomass in Portugal, Spain and Tunisia. One model as a function of diameter and another as a function of diameter and total tree height. In addition, we developed a separate model for roots (modelling efficiency of fitting = 0.89), since it was not possible to assure additivity of the whole tree. All coefficients were estimated using Seemingly Unrelated Regressions (SUR) and model fitting assured additivity in the aboveground compartments—leaves and woody biomass (modelling efficiency of fitting = 0.89 and 0.93, respectively). This work proves that it is possible to have a biologically sound and efficient model for the three countries, despite differences in the observed allometric patterns. [ABSTRACT FROM AUTHOR]

The wildfire regime in Portugal has been responsible for millions of hectares of burnt area, and Alvares parish is no exception. In 2017, a severe wildfire burnt 60% of its area. Land abandonment has been increasing since the mid 20th century, and a large fraction of the forest area belongs to quasi-absent landowners. This has given rise to large, almost unbroken expanses of undermanaged forests that, in combination with rugged topography, originates a landscape prone to large, intense wildfires. Thus, a change in landscape composition and structure capable of reducing flammability and promoting fuel discontinuity is urgently needed. A fire spread simulator and a forest growth simulator were combined to show the impact of improving management at landscape level. It was assumed that the probability of large wildfires may be reduced by setting aside forest area for the implementation of a fuel break network (FBN) and increasing the area under sustainable forest management. Three levels of management intensity were simulated by restricting the area of Quasi-absent non-industrial owners to 34.5%, 20.1%, and 8.5% of the Alvares forest area, in favor of increasing the area of active and semi-active non-industrial owners (current, moderate, and high management scenarios). Different FBN extents, representing four levels of network implementation priority were combined with the management levels, resulting in 12 scenarios. To evaluate the impact of fire, simulations assuming no-fire, no-FBN, and current management intensity were performed, whereas the impact of operation costs was assessed assuming reduced costs for silvicultural operations. Per hectare simulations were then scaled up to the parish level and volume harvested and net present values were used to compare the management improvement scenarios. Results showed that fire has major repercussions on forest income, but these impacts can be minimized. Intensifying forest management and implementing the first priority FBN segments originated substantial improvements in financial outcome from timber production, close to those obtained for the full FBN implementation. Results also evidenced contrasting contributions from industrial and non-industrial owners with the later evidencing unbalanced cash-flows derailing the possibility for interesting forest incomes. The coupling of fire and forest growth simulations can be an interesting approach to assess the impact of different management and policy scenarios and inform policies. [ABSTRACT FROM AUTHOR]