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.

Eco-evolutionary optimality as a means to improve vegetation and land-surface models.

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

Organizing principles for vegetation dynamics.

Plants and vegetation play a critical-but largely unpredictable-role in global environmental changes due to the multitude of contributing processes at widely different spatial and temporal scales. In this Perspective, we explore approaches to master this complexity and improve our ability to predict vegetation dynamics by explicitly taking account of principles that constrain plant and ecosystem behaviour: natural selection, self-organization and entropy maximization. These ideas are increasingly being used in vegetation models, but we argue that their full potential has yet to be realized. We demonstrate the power of natural selection-based optimality principles to predict photosynthetic and carbon allocation responses to multiple environmental drivers, as well as how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities and how entropy maximization can identify the most probable outcomes of community dynamics in space- and time-varying environments. Finally, we present a roadmap indicating how these principles could be combined in a new generation of models with stronger theoretical foundations and an improved capacity to predict complex vegetation responses to environmental change.

Carbon pools of an intact forest in Gabon.

Quantitative and qualitative loss of tropical forests prompted international policy agendas to slow down forest loss through reducing emissions from deforestation and forest degradation (REDD)+, ensuring carbon offset payments to developing countries. So far, many African countries lack reliable forest carbon data and monitoring systems as required by REDD+. In this study, we estimate the carbon stocks of a naturally forested landscape unaffected by direct human impact. We used data collected from 34 plots randomly distributed across the Mount Birougou National Park (690 km2) in southern Gabon. We used tree-level data on species, diameter, height, species-specific wood density and carbon fraction as well as site-level data on dead wood, soil and litter carbon to calculate carbon content in aboveground, belowground, dead wood, soil and litter as 146, 28, 14, 186 and 7 Mg ha-1, respectively. Results may serve as a benchmark to assess ecosystem carbon loss/gain for the Massif du Chaillu in Gabon and the Republic of Congo, provide field data for remote sensing and also may contribute to establish national monitoring systems. RESUME: Les pertes qualitatives et quantitatives de forêt tropicale ont poussé les calendriers politiques internationaux à ralentir la perte de forêts au moyen des mécanismes REDD+, qui garantissent le paiement compensatoire des émissions de carbone aux pays en développement. Jusqu'à présent, de nombreux pays africains ne disposent pas encore de données fiables sur le carbone forestier, pas plus que de systèmes de suivi exigés par les REDD+. Dans cet article, nous estimons les stocks de carbone d'un paysage de forêt naturelle non affecté par des impacts humains directs. Nous avons utilisé les données provenant de 34 parcelles réparties au hasard dans le Parc National du Mont Birougou (690 km²), dans le sud du Gabon. Nous avons utilisé trois niveaux de données pour les espèces, le diamètre, la hauteur et la densité spécifique du bois par espèce, et la fraction de carbone ainsi que des données au niveau du site sur le carbone du bois mort, du sol et de la litière pour calculer le contenu en carbone au-dessus du sol, en dessous, dans le bois mort, le sol et la litière, à savoir, respectivement, 146, 28, 14, 186 et 7 mg ha-1. Ces résultats peuvent servir de données de référence pour évaluer la perte ou le gain de carbone de l'écosystème pour le Massif du Chaillu, au Gabon, et en République du Congo, constituer des données de terrain pour la détection à distance et aussi contribuer à établir des systèmes de suivi au niveau national.

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.

Modeling effects of hydrological changes on the carbon and nitrogen balance of oak in floodplains.

We extended the applicability of the ecosystem model BIOME-BGC to floodplain ecosystems to study effects of hydrological changes on Quercus robur L. stands. The extended model assesses floodplain peculiarities, i.e., seasonal flooding and water infiltration from the groundwater table. Our interest was the tradeoff between (a). maintaining regional applicability with respect to available model input information, (b). incorporating the necessary mechanistic detail and (c). keeping the computational effort at an acceptable level. An evaluation based on observed transpiration, timber volume, soil carbon and soil nitrogen content showed that the extended model produced unbiased results. We also investigated the impact of hydrological changes on our oak stands as a result of the completion of an artificial canal network in 1971, which has stopped regular springtime flooding. A comparison of the 11 years before versus the 11 years after 1971 demonstrated that the hydrological changes affected mainly the annual variation across years in leaf area index (LAI) and soil carbon and nitrogen sequestration, leading to stagnation of carbon and nitrogen stocks, but to an increase in the variance across years. However, carbon sequestration to timber was unaffected and exhibited no significant change in cross-year variation. Finally, we investigated how drawdown of the water table, a general problem in the region, affects modeled ecosystem behavior. We found a further amplification of cross-year LAI fluctuations, but the variance in soil carbon and nitrogen stocks decreased. Volume increment was unaffected, suggesting a stabilization of the ecosystem two decades after implementation of water management measures.