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Eucalyptus and maritime pine forests provide most of the wood harvested in Portugal. Due mainly to forest fires maritime pine forest has been converted to eucalyptus plantations. In the central inland of Portugal these species are well represented, and their productivities are quite the same. The objectives of the study were: (1) to assess average wood volume, biomass production and carbon content of eucalyptus plantations and naturally regenerated maritime pine stands; (2) to evaluate biomass nutrients concentration of eucalyptus sprouts selection and maritime pine non-commercial thinning; (3) to analyse these forests cover change in relation to the annual burnt areas; and (4) to produce the species ecological envelope maps to foresee future environmental impacts of these forests current distribution. Simulation models were used to assess wood volume, biomass production and carbon content. Field data was collected to evaluate biomass nutrients concentration and to validated simulated data. Forest inventory data and forest cover maps were used to analyse forest cover change. Biogeographical variables were used to produce species ecological envelope maps. It was found that eucalyptus plantations provide higher mean annual wood volume, biomass production and carbon content. Biomass nutrients concentration in eucalyptus sprouts selection were higher than in maritime pine non-commercial thinning, particularly in calcium and potassium. Overall eucalyptus forests area did not increase substantially but the fires in maritime pine forests gave the opportunity for eucalyptus afforestation. That has precluded the opportunity for introducing native broadleaves to respond to climate changes, improve landscape biodiversity and mitigate fire hazard. Implementing good management practices to prevent losses of soil fertility by nutrients exhaustion, organic matter depletion or erosion were found more relevant in eucalyptus plantations than in naturally maritime pine forest. Species ecological envelope maps and production simulation models are key tools for supporting decision in afforestation planning.