Anthropogenic climate change will impact a variety of natural processes across scales in forest ecosystems, affecting their structure, composition and functioning. The mechanisms governing these processes are frequently characterized by nonlinearities and threshold behavior, highlighting the importance of local variability in exposure levels and conditions particularly in complex mountainous landscapes. Assessing climate change impacts in such ecosystems thus requires a fine grained approach embracing multi-scale heterogeneity and complexity, key components of ecosystem resilience, e.g. with regard to possible climate-induced shifts in the disturbance regime. Modeling is a primary tool to address complexity; however, forest ecosystem models have traditionally focused on particular dimensions of ecological complexity (i.e., functional complexity in physiological models; structural and compositional aspects in gap models; spatial complexity in landscape models). Here we addressed the question of how to bridge this gap and scale detailed physiological and structural processes to larger scales in a dynamic modeling framework, in order to model landscape level ecosystem structure and function under climate change as emerging system properties. We furthermore scrutinized the utility of our approach by conducting an evaluation of widely different temperate forest ecosystems in the US Pacific Northwest and the Eastern Alps in Europe.

Results/Conclusions

We developed a model of forest ecosystem dynamics adopting a hierarchical multi-scale approach, granting scalability from individual trees to forest landscapes. As the core process of forest dynamics we modeled spatially explicit individual tree competition for resources by means of a modified field of neighborhood approach, combined with a generalized model of tree physiology. We evaluated model performance with regard to indicators representing different dimensions of ecological complexity. Functional aspects (e.g., productivity) were evaluated with FIA plot data over wide ecological gradients, while aspects of structural and compositional complexity were tested against long-term vegetation studies. Our results showed generally good agreement between modeled and empirical data for the initial suite of indicators examined. In addition, the ability to encompass spatial complexity was evaluated by analyzing the scalability of the approach. Using a highly optimized implementation of the pattern-based individual tree model we found that computation scaled linearly with the number of individuals, making it suitable for landscape-scale simulations. In conclusion, the current study presents a step towards an improved consideration of ecological complexity in modeling, strengthening predictive capacities for ecosystem dynamics in complex forest landscapes under climate change.