Ecological Modelling 368 (2018) 377–390 Contents lists available at ScienceDirect Ecological Modelling journa l h om epa ge: www.elsevier.com/locate/ecolmodel Global sensitivity analysis of a dynamic vegetation model: Model sensitivity depends on successional time, climate and competitive interactions Nica Huber * , Harald Bugmann, Valentine Lafond Forest Ecology, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zurich, Universitätsstrasse 16, CH-8092 Zurich, Switzerland a r t i c l e i n f o Article history: Received 24 April 2017 Received in revised form 24 November 2017 Accepted 15 December 2017 Keywords: Europe ForClim Forest gap model Morris screening method a b s t r a c t Accurately predicting the dynamics of tree species productivity as well as their ranges at large scales is of key importance for assessing the impact of global change on forests. Dynamic vegetation models (DVMs), particularly forest gap models (FGMs), have been suggested as suitable tools for such joint predictions. However, DVMs generally feature a large number of parameters that need to be estimated and may cause considerable uncertainty in model outputs. In addition, model sensitivity may depend on environmental conditions, stand composition and development stage. We systematically evaluated the parameter sen- sitivity on simulated basal area of the state-of-the art FGM ForClim along a wide ecological gradient to analyze model behavior and identify key parameters and processes that cause the highest variability in model output. We applied the revised Morris screening method at 30 representative sites across Europe, and compared results for monospecific and mixed stands at two system states in time, i.e. early and late succession (dynamic equilibrium). The most influential parameters were related to tree establishment, the water and light regimes, growth and temperature, whereby the relative parameter influence of the latter strongly varied with climate. Further, model sensitivity differed between monospecific and mixed stands as well as between early and late succession, reflecting the differential influence of ecological processes with stand structure. We conclude that the parameter sensitivity of complex models should be analyzed individually for several system states of interest. We recommend to focus the further develop- ment (process representation and calibration) and analysis of FGMs on process representations related to establishment, water limitations and phenology to improve the robustness of model predictions. We provide recommendations for specific improvements of FGMs to better represent range dynamics. © 2017 Elsevier B.V. All rights reserved. 1. Introduction Accurately predicting tree species productivity as well as tree species ranges and range shifts at large scales is of key impor- tance for assessing the impact of global change on ecosystems and the multiple ecosystem services they provide (e.g., Hanewinkel et al., 2013; Montoya and Raffaelli, 2010). Climate change has been shown to affect the geographical range of tree species (Walter et al., 2002) and productivity (Boisvenue and Running, 2006). Since forests are long-lived ecosystems, it is of key interest to understand * Corresponding author. E-mail addresses: nica.huber@usys.ethz.ch (N. Huber), harald.bugmann@usys.ethz.ch (H. Bugmann), valentine.lafond@usys.ethz.ch (V. Lafond). transient dynamics (i.e., non-equilibrium situations) that result from changes in environmental drivers, succession, and large- scale disturbances. Moreover, while studying species range shifts, impacts of interspecific competition should be considered instead of focusing on climatic changes only (e.g., Araujo and Luoto, 2007; Brooker et al., 2007; Bullock et al., 2000; Meier et al., 2012; Nieto- Lugilde et al., 2015). Currently, two broad approaches are used to model for- est productivity and plant distributions, namely correlative and process-based approaches. To model plant distributions, correlative species distribution models (SDM) relate species’ presence-absence data to environmental predictor variables (Elith and Leathwick, 2009; Guisan and Zimmermann, 2000) and use these correlations to project species’ occurrence probabilities under changing conditions. They however assume that species dis- https://doi.org/10.1016/j.ecolmodel.2017.12.013 0304-3800/© 2017 Elsevier B.V. All rights reserved.