Global forest management data for 2015 at a 100 m resolution.

Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki ( https://www.geo-wiki.org/ ). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.

Using ergodic theory to assess the performance of ecosystem models.

Ecosystem simulation models are designed to assess the flux of energy, water, carbon and nitrogen according to a given vegetation type. The reliability of the modeled results is determined by model validations. Model validations are typically done using classical statistical methods like regression analysis of predicted versus observed values, paired t statistics and error assessment procedures to characterize the quality of current and future model predictions. All these validation efforts concentrate on static aspects of the model but fail to describe the model dynamics. In this paper, we introduce methods from ergodic theory to analyze the dynamic behavior of ecosystem models. We describe (1) how the attractor representation of model behavior can be reconstructed from a time series of model outputs, and (2) what we can learn from the attractor to assess the model dynamics. As an application example, we provide simulation results for two important pine forest ecosystems in Austria, i.e., 23 Cembran pine and 16 Scots pine stands. These stands were simulated with three model parameterizations: one representing a generic, evergreen needle-leaf forest, and two species- specific parameter sets, one for Cembran pine and one for Scots pine. First, we applied standard validation methods to get static measures of model accuracy and precision. Next, we used ergodic theory to assess model dynamics. A comparison of both analyses reveals important issues related to model dynamics, such as the finding that the occurrence of instabilities in model behavior may not be detected by standard validation methods. Using ergodic theory, we were able to reconstruct the attractors of model behavior. In the attractor describing model dynamics for Cembran pine, simulated with the generic, evergreen needle-leaf forest parameter set, we detected instability in model behavior. We identified this instability as a riddled basin configuration, which is a strong indicator for the occurrence of a chaotic model behavior that may result in random predictions. Our results suggest that ergodic theory is a useful tool for assessing inconsistencies in the dynamics of ecosystem model simulations that have not been detected by standard statistical validation methods.