title: 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 fungusPhellinus 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 forP. ferrugineofuscuspersistence. 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.
authors.affiliation: Swedish Species Information Centre Swedish University of Agricultural Sciences (SLU) Uppsala Sweden.
authors.forename: Louise
authors.initials: L
authors.lastname: Mair
authors.affiliation: Swedish Species Information Centre Swedish University of Agricultural Sciences (SLU) Uppsala Sweden.
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Harrison
authors.affiliation: Swedish Species Information Centre Swedish University of Agricultural Sciences (SLU) Uppsala Sweden.
authors.forename: Mari
authors.initials: M
authors.lastname: Jönsson
authors.affiliation: Swedish Species Information Centre Swedish University of Agricultural Sciences (SLU) Uppsala Sweden; Department of Environmental System Analysis Institute of Geoecology Technical University Braunschweig Braunschweig Germany.
authors.forename: Swantje
authors.initials: S
authors.lastname: Löbel
authors.affiliation: Department of Research and Collections Natural History Museum University of Oslo Oslo Norway; Norwegian Institute for Nature Research Oslo Norway.
authors.forename: Jenni
authors.initials: J
authors.lastname: Nordén
authors.affiliation: Natural Resources Institute Finland Vantaa Finland.
authors.forename: Juha
authors.initials: J
authors.lastname: Siitonen
authors.affiliation: Department of Forest Resource Management Swedish University of Agricultural Sciences (SLU) Umeå Sweden.
authors.forename: Tomas
authors.initials: T
authors.lastname: Lämås
authors.affiliation: Department of Forest Resource Management Swedish University of Agricultural Sciences (SLU) Umeå Sweden.
authors.forename: Anders
authors.initials: A
authors.lastname: Lundström
authors.affiliation: Swedish Species Information Centre Swedish University of Agricultural Sciences (SLU) Uppsala Sweden.
authors.forename: Tord
authors.initials: T
authors.lastname: Snäll
keywords: deadwood‐dependent fungi; forestry; global biodiversity information facility; habitat change; land use change; opportunistic data; volunteer recording