Anders Lundström

Overview of methods and tools for evaluating future woody biomass availability in European countries

Key messageThis analysis of the tools and methods currently in use for reporting woody biomass availability in 21 European countries has shown that most countries use, or are developing, National Forest Inventory-oriented models whereas the others use standwise forest inventory--oriented methods.ContextKnowledge of realistic and sustainable wood availability in Europe is highly relevant to define climate change mitigation strategies at national and European level, to support the development of realistic targets for increased use of renewable energy sources and of industry wood. Future scenarios at European level highlight a deficit of domestic wood supply compared to wood consumption, and some European countries state they are harvesting above the increment.AimsSeveral country-level studies on wood availability have been performed for international reporting. However, it remains essential to improve the knowledge on the projection methods used across Europe to better evaluate forecasts.MethodsAnalysis was based on descriptions supplied by the national correspondentsinvolved in USEWOOD COST Action (FP1001), and further enriched with additionaldata from international reports that allowedcharacterisation of the forests in these countries for the same base year.ResultsMethods currently used for projecting wood availability were described for 21 European countries. Projection systems based on National Forest Inventory (NFI) data prevail over methods based on forest management plans. Only a few countries lack nationwide projection tools, still using tools developed for specific areas.ConclusionsA wide range of NFI-based systems for projecting wood availability exists, being under permanent improvement. The validation of projection forecasts and the inclusion of climate sensitive growth models into these tools are common aims for most countries. Cooperation among countries would result in higher efficiency when developing and improving projection tools and better comparability among them.

Evaluating citizen science data for forecasting species responses to national forest management

Abstract The extensive spatial and temporal coverage of many citizen science datasets (CSD) makes them appealing for use in species distribution modeling and forecasting. However, a frequent limitation is the inability to validate results. Here, we aim to assess the reliability of CSD for forecasting species occurrence in response to national forest management projections (representing 160,366 km2) by comparison against forecasts from a model based on systematically collected colonization–extinction data. We fitted species distribution models using citizen science observations of an old‐forest indicator fungus Phellinus ferrugineofuscus. We applied five modeling approaches (generalized linear model, Poisson process model, Bayesian occupancy model, and two MaxEnt models). Models were used to forecast changes in occurrence in response to national forest management for 2020‐2110. Forecasts of species occurrence from models based on CSD were congruent with forecasts made using the colonization–extinction model based on systematically collected data, although different modeling methods indicated different levels of change. All models projected increased occurrence in set‐aside forest from 2020 to 2110: the projected increase varied between 125% and 195% among models based on CSD, in comparison with an increase of 129% according to the colonization–extinction model. All but one model based on CSD projected a decline in production forest, which varied between 11% and 49%, compared to a decline of 41% using the colonization–extinction model. All models thus highlighted the importance of protected old forest for P. ferrugineofuscus persistence. We conclude that models based on CSD can reproduce forecasts from models based on systematically collected colonization–extinction data and so lead to the same forest management conclusions. Our results show that the use of a suite of models allows CSD to be reliably applied to land management and conservation decision making, demonstrating that widely available CSD can be a valuable forecasting resource.