ALBERT

Description: The forest, savanna, and grassland biomes, and the transitions between them, are expected to undergo major changes in the future, due to global climate change. Dynamic Global Vegetation Models (DGVMs) are very useful to understand vegetation dynamics under present climate, and to predict its changes under future conditions. However, several DGVMs display high uncertainty in predicting vegetation in tropical areas. Here we perform a comparative analysis of three different DGVMs (JSBACH, LPJ-GUESS-SPITFIRE and aDGVM) with regard to their representation of the ecological mechanisms and feedbacks that determine the forest, savanna and grassland biomes, in an attempt to bridge the knowledge gap between ecology and global modelling. Model outcomes, obtained including different mechanisms, are compared to observed tree cover along a mean annual precipitation gradient in Africa. Through these comparisons, and by drawing on the large number of recent studies that have delivered new insights into the ecology of tropical ecosystems in general, and of savannas in particular, we identify two main mechanisms that need an improved representation in the DGVMs. The first mechanism includes water limitation to tree growth, and tree-grass competition for water, which are key factors in determining savanna presence in arid and semi-arid areas. The second is a grass-fire feedback, which maintains both forest and savanna occurrences in mesic areas. Grasses constitute the majority of the fuel load, and at the same time benefit from the openness of the landscape after fires, since they recover faster than trees. Additionally, these two mechanisms are better represented when the models also include tree life stages (adults and seedlings), and distinguish between fire-prone and shade-tolerant savanna trees, and fire-resistant and shade-intolerant forest trees. Including these basic elements could improve the predictive ability of the DGVMs, not only under current climate conditions but also and especially under future scenarios.

Description: Living organisms respond both to current and previous environments, which can have important consequences on population dynamics. However, there is little experimental evidence based on long-term field studies of the effects of previous environments on the performance of individuals. We tested the hypothesis that trees that establish under different environmental conditions perform differently under similar post-establishment conditions. We used the slow-growing, evergreen Mediterranean oak Quercus ilex subsp. rotundifolia as target species. We analyzed the effects of previous environments, competition effects and tradeoffs among life-history traits (survival, growth, and reproduction). We enhanced seedling establishment for three years by reducing abiotic environmental harshness by means of summer irrigation and artificial shading in 12 experimental plots, while four plots remained as controls. Then these treatments were interrupted for ten years. Seedlings under ameliorated environmental conditions survived and grew faster during early establishment. During the post-management period, previous treatments 1) did not have any effect on survival, 2) experienced a slower above-ground growth, 3) decreased root biomass as indicated from reflectivity of Ground Penetration Radar, 4) increased acorn production mostly through a greater canopy volume and 5) increased acorn production effort. The trees exhibited a combination of effects related to acclimation for coping with abiotic stress and effects of intra-specific competition. In accordance with our hypothesis, tree performance overall depended on previous environmental conditions, and the response was different for different life-history traits. We recommend early management because it increased plot cover, shortened the time to attain sexual maturity and increased the amount of acorn production. Plots such as those assessed in this study may act as sources of propagules in deforested agricultural landscapes thus aiding natural establishment of new plants.

Description: The forest, savanna, and grassland biomes, and the transitions between them, are expected to undergo major changes in the future due to global climate change. Dynamic global vegetation models (DGVMs) are very useful for understanding vegetation dynamics under the present climate, and for predicting its changes under future conditions. However, several DGVMs display high uncertainty in predicting vegetation in tropical areas. Here we perform a comparative analysis of three different DGVMs (JSBACH, LPJ-GUESS-SPITFIRE and aDGVM) with regard to their representation of the ecological mechanisms and feedbacks that determine the forest, savanna, and grassland biomes, in an attempt to bridge the knowledge gap between ecology and global modeling. The outcomes of the models, which include different mechanisms, are compared to observed tree cover along a mean annual precipitation gradient in Africa. By drawing on the large number of recent studies that have delivered new insights into the ecology of tropical ecosystems in general, and of savannas in particular, we identify two main mechanisms that need improved representation in the examined DGVMs. The first mechanism includes water limitation to tree growth, and tree–grass competition for water, which are key factors in determining savanna presence in arid and semi-arid areas. The second is a grass–fire feedback, which maintains both forest and savanna presence in mesic areas. Grasses constitute the majority of the fuel load, and at the same time benefit from the openness of the landscape after fires, since they recover faster than trees. Additionally, these two mechanisms are better represented when the models also include tree life stages (adults and seedlings), and distinguish between fire-prone and shade-tolerant forest trees, and fire-resistant and shade-intolerant savanna trees. Including these basic elements could improve the predictive ability of the DGVMs, not only under current climate conditions but also and especially under future scenarios.