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Savannah regions are predicted to undergo changes in precipitation patterns according to current climate change projections. This change will affect leaf phenology, which controls net primary productivity. It is of importance to study this since savannahs play an important role in the global carbon cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r2 of 0.6 and root mean squared error of 0.1. We therefore expect similar average results when applying the model to other savannah areas and further expect that it could be used to estimate the productivity of savannah regions. Figures

Coprolites from the Early Cretaceous vertebrate bone-bed at Ariño in Teruel, Spain, were analyzed geochemically and palynologically. They contain various inclusions, such as small bone fragments, abundant plant remains, pollen, and spores. We attribute the coprolites to carnivorous dinosaurs based partly on their morphology together with the presence of bone fragments, and a high content of calcium phosphate (hydroxylapatite) with calcite. Well-preserved pollen and spore assemblages were identified in all coprolite samples and a slightly poorer assemblage was obtained from the adjacent sediments, both indicating an Early Cretaceous (Albian) age. This shows that the coprolites are in situ and also confirms previous age determinations for the host strata. The depositional environment is interpreted as a continental wetland based on the palynoflora, which includes several hydrophilic taxa, together with sparse occurrences of fresh-water algae, such as Ovoidites, and the absence of marine palynomorphs. Although the coprolites of Ariño samples generally are dominated by pollen produced by Taxodiaceae (cypress) and Cheirolepidiaceae (a family of extinct conifers), the sediment samples have a slightly higher relative abundance of fern spores. The distribution of major organic components varies between the coprolite and sediment samples, which is manifest by the considerably higher charcoal percentage within the coprolites. The high quantities of charcoal might be explained by a ground-dwelling species, feeding on smaller vertebrates that complemented its diet with plant material from a paleoenvironment were wild fires were a part of the ecosystem. The state of preservation of the spores and pollen is also more detailed in the coprolites, suggesting that encasement in calcium phosphate may inhibit degradation of sporopollenin.

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 especi

Healthy and productive forests benefit us all, but what are the priorities of those directly managingour Swedish forests? This brief presents a comparison of the preferences of key stakeholdersregarding Swedish forest management and biodiversity protection. According to the SwedishForest Act production and environmental goals should be regarded as equally important. Our studyfinds that forest owners, public forestry officials and employees at industrial forestry companiesand forest owners’ associations prefer management practices that promote production rather thanbiodiversity protection., Policy Brief

Land use is predicted to have the greatest effect on broad habitat distribution according to our simulations. Hence in most parts of Europe mitigating actions should focus on land‐use change rather than climate change. According to our simulation, the effects of the different drivers are not in general additive. In some cases they act synergistically and in some cases antagonistically. The projected habitat changes are a valuable tool for species distribution modelling and are available online.

Wildfires are a major driver of ecosystem development and contributor to carbon emissions in boreal forests. We analyzed the contribution of fires of different fire size classes to the total burned area and suggest a novel fire characteristic, the characteristic fire size, i.e., the fire size class with the highest contribution to the burned area, its relation to bioclimatic conditions, and intra-annual and interannual variation. We used the Canadian National Fire Database (using data from 1960 to 2010) and a novel satellite-based burned area data set (2001 to 2011). We found that the fire size distribution is best explained by a normal distribution in log space in contrast to the power law-based linear fire area relationship which has prevailed in the literature so far. We attribute the difference to previous studies in the scale invariance mainly to the large extent of the investigated ecoregion as well as to unequal binning or limiting the range at which the relationship is analyzed; in this way we also question the generality of the scale invariance for ecoregions even outside the boreal domain. The characteristic fire sizes and the burned area show a weak correlation, indicating different mechanisms behind each feature. Fire sizes are found to depend markedly on the ecoregion and have increased over the last five decades for Canada in total, being most pronounced in the early season. In the late season fire size and area decreased, indicating an earlier start of the fire season.

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

Fire is the most important ecological and forest disturbance agent worldwide, is a major way by which carbon is transferred from the land to the atmosphere, and is globally a significant source of greenhouse gases and aerosols. Wildfires across all major biome types globally consume about 5% of net annual terrestrial primary production per annum, and release about 2-4 Pg C per annum, of which approximately 0.6 Pg C comes from tropical deforestation and below-ground peat fires. The global figure is equivalent to about 20-30% of global emissions from fossil fuels. Tropical savannas comprise the largest areas burned and greatest emissions sources from vegetation wildfires. Fires in Mediterranean forests and shrublands, tropical forests and boreal forests are also significant sources of emissions because they are generally characterised by much higher fuel loads per unit area compared with grasslands. Improved satellite data and sophisticated biogeochemical modeling enables emis-sions assessments on a global scale with fine spatial and temporal resolution. Emissions estimates are still comparable to those based on older inventory-based techniques, but uncertainties remain large. Fires increase during El Niño periods because parts of the tropics where humans use fire as a tool for deforestation experience drought conditions. These spikes contribute to the inter-annual variability of CO2 and CH4 observed in the atmosphere. Recently developed dynamic fire-vegetation models are capable of simulating the extent of wildfires as well as their emissions of CO2 and other greenhouse gases for ambient as well as for projected climatic conditions. The performance of fire-vegetation models however needs to be strongly improved and validated.

Fire is the most important ecological and forest disturbance agent worldwide, is a major way by which carbon is transferred from the land to the atmosphere, and is globally a significant source of greenhouse gases and aerosols. Wildfires across all major biome types globally consume about 5% of net annual terrestrial primary production per annum, and release about 2-4 Pg C per annum, of which approximately 0.6 Pg C comes from tropical deforestation and below-ground peat fires. The global figure is equivalent to about 20-30% of global emissions from fossil fuels. Tropical savannas comprise the largest areas burned and greatest emissions sources from vegetation wildfires. Fires in Mediterranean forests and shrublands, tropical forests and boreal forests are also significant sources of emissions because they are generally characterised by much higher fuel loads per unit area compared with grasslands. Improved satellite data and sophisticated biogeochemical modeling enables emis-sions assessments on a global scale with fine spatial and temporal resolution. Emissions estimates are still comparable to those based on older inventory-based techniques, but uncertainties remain large. Fires increase during El Niño periods because parts of the tropics where humans use fire as a tool for deforestation experience drought conditions. These spikes contribute to the inter-annual variability of CO2 and CH4 observed in the atmosphere. Recently developed dynamic fire-vegetation models are capable of simulating the extent of wildfires as well as their emissions of CO2 and other greenhouse gases for ambient as well as for projected climatic conditions. The performance of fire-vegetation models however needs to be strongly improved and validated.

The savanna biome has the greatest burned area globally. Whereas the global distribution of most biomes can be predicted successfully from climatic variables, this is not so for savannas. Attempts to dynamically model the distribution of savannas, including a realistically varying tree : grass ratio are fraught with difficulties. In a simulation study using the dynamic vegetation model LPJ-GUESS we investigate the effect of fire on the tree : grass ratios as well as the biome distribution on the African continent. We performed simulations at three spatial scales: locally, at four sites inside Kruger National Park (South Africa); regionally, along a precipitation gradient; and for the African continent. We evaluated the model using results of a fire experiment and found that the model underestimates the effect of fire on tree cover slightly. On a regional scale, high frequencies were able to prevent trees from outcompeting grasses in mesic regions between ~700 and 900 mm mean annual precipitation. Across the African continent, incorporation of fire improved notably the simulated distribution of the savanna biome. Our model results confirm the role of fire in determining savanna distributions, a notion that has been challenged by competing theories of tree–grass coexistence.