Modeling post-fire mortality in pure and mixed forest stands in Portugal—A forest planning-oriented model

Assessing impacts of management strategies may allow designing more resistant forests to wildfires. Planning-oriented models to predict the effect of stand structure and forest composition on mortality for supporting fire-smart management decisions, and allowing its inclusion in forest management optimization systems were developed. Post-fire mortality was modeled as a function of measurable forest inventory data and projections over time in 165 pure and 76 mixed forest stands in Portugal, collected by the 5th National Forest Inventory plots (NFI) plus other sample plots from ForFireS project, intercepted within 2006–2008 wildfire perimeters’ data. Presence and tree survival were obtained by examining 2450 trees from 16 species one year after the wildfire occurrence. A set of logistic regression models were developed under a three-stage modeling system: firstly multiple fixed-effects at stand-level that comprises a sub-model to predict mortality from wildfire; and another for the proportion of dead trees on stands killed by fire. At tree-level due to the nested structure of the data analyzed (trees within stands), a mixed-effect model was developed to estimate mortality among trees in a fire event. The results imply that the variation of tree mortality decreases when tree diameter at breast height increases. Moreover, the relative mortality increases with stand density, higher altitude and steeper slopes. In the same conditions, conifers are more prone to die than eucalyptus and broadleaves. Pure stands of broadleaves exhibit noticeably higher fire resistance than mixed stands of broadleaves and others species compositionThis research was supported by Project UID/AGR/00239/2013, PTDC/AGR-CFL/64146/2006 “Decision support tools for integrating fire and forest management planning” and project FIRE-ENGINE “Flexible Design of Forest Fire Management Systems” (MIT/FSE/0064/2009), both funded by the Portuguese Science Foundation (FCT), and contributes to the activities of the ALTERFOR Project “Alternative models and robust decision-making for future forest management”—H2020-ISIB-2015-2/grant agreement No. 67654, funded by European Union Seventh Framework Programme. This research has received also funding from the European Union’s H2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 691149 (SuFoRun). The authors would like to thank the Portuguese Science Foundation for funding the doctoral scholarships of Brigite Botequim (SFRH/ BD/44830/2008) and the Post Doc grant SFRH/BPD/96806/2013 of Susete Marques. Researcher Jordi Garcia-Gonzalo was supported by a “Ramon y Cajal” research contract from the MINECO (Ref. RYC-2013-14262) and has received funding from CERCA Programme / Generalitat de Catalunya. In addition, the authors wish to acknowledge the Portuguese Forest Service (ICNF) for supplying the perimeters of wildfires and NFI Databases and ForFireS Project for providing the inventory DatabasesS