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Aim: How the ability of plants to move towards newly favourable habitats (dispersal limitation) impacts the change of biome distribution and transition under fast climate warming is still debated. Analysing vegetation change in the past may help to clarify the relative importance of underlying ecological processes such as climate, biotic interactions, and dispersal. In this study, we investigated how dispersal limitation affected the distribution of European forests in the last 18,000 years. Location: Southern and Central Europe. Taxon: Spermatophyta. Methods: Using the LPJ‐GM 2.0 model (an extension of LPJ‐GUESS), we simulated European vegetation from the end of the Last Glacial Maximum (18.5 ka) to the current time (0 ka). Using biome reconstructions from pollen data as reference, we compared the performance of two dispersal modes: with no migration constraints or seed limitation (free dispersal mode), and with plant establishment depending on seed dynamics and dispersal (dispersal limitation mode). Results: The model run, including migration processes, was better at capturing the post‐glacial expansion of European temperate forests (and the longer persistence of boreal forests) than the setting assuming free dispersal, especially during periods of rapid warming. This suggests that a number of (temperate) tree taxa experienced delayed occupancy of climatically suitable habitats due to a limited dispersal capacity, i.e., post‐glacial migration lags. Main Conclusions: Our results show that including migration processes in model simulations allows for more realistic reconstructions of forest patterns under rapid climate change, with consequences for future projections of carbon sequestration and climate reconstructions with vegetation feedback, assisted migration and forest conservation. [ABSTRACT FROM AUTHOR]

Introduction: The ability of species to colonize newly suitable habitats under rapid climate change can be constrained by migration processes, resulting in a shift of the leading edge lagging behind the ameliorating climate, i.e. migration lag. The importance and extent of such migration lags during the forest expansion after the Last Glacial Maximum (LGM) are still debated. Similarly, the relative importance of the main drivers of post-glacial vegetation dynamics (temperature, dispersal limitation, and competition) is still discussed in the literature. Methods: We used the dynamic global vegetation model LPJ-GM 2.0 to reconstruct the range shifts of 16 competing major European tree species after the LGM (18.5 ka BP) until recent times (0 ka BP). We simulated two dispersal modes by allowing free establishment whenever the climatic conditions suited the species (free dispersal), or by accounting for migration processes in the simulated vegetation dynamics (dispersal limitation). We then calculated thermal and range shift velocities, competition at establishment, thermal and dispersal lags for each species and dispersal mode. Finally, we compared our simulated range shift velocities with pollen-derived migration rates. Results: The simulation assuming limited dispersal resulted in more accurate migration rates as compared to pollen-derived migration rates and spreading patterns. We found no marked migration lags in the post-glacial establishment of pioneer species (Pinus sylvestris and Betula pubescens). Under the free dispersal mode, the remaining temperate species expanded rapidly and almost synchronously across central Europe upon climate warming (Bolling-Allerod interstadial). Differently, the northward spread of temperate species simulated under dispersal limitation happened mainly during the Holocene and in successive waves, with late spreaders (e.g. Fraxinus excelsior) experiencing multi-millennial dispersal lags and higher competition. Discussion: Our simulation under dispersal constraints suggests that the postglacial tree expansion in Europe was mainly driven by species-specific thermal requirements and dispersal capacity, which in turn affected the order of taxa establishment and thus the degree of competition. Namely, taxa with less cold-tolerance and relatively low dispersal ability experienced the highest migration lags, whereas the establishment of pioneer species was mostly in equilibrium with the climate. [ABSTRACT FROM AUTHOR]

The prediction of species geographic redistribution under climate change (i.e. range shifts) has been addressed by both experimental and modelling approaches and can be used to inform efficient policy measures on the functioning and services of future ecosystems. Dynamic global vegetation models (DGVMs) are considered state-of-the art tools to understand and quantify the spatio-temporal dynamics of ecosystems at large scales and their response to changing environments. They can explicitly include local vegetation dynamics relevant to migration (establishment, growth, seed (propagule) production), species-specific dispersal abilities and the competitive interactions with other species in the new environment. However, the inclusion of more detailed mechanistic formulations of range shift processes may also widen the overall uncertainty of the model. Thus, a quantification of these uncertainties is needed to evaluate and improve our confidence in the model predictions. In this study, we present an efficient assessment of parameter and model uncertainties combining low-cost analyses in successive steps: local sensitivity analysis, exploration of the performance landscape at extreme parameter values, and inclusion of relevant ecological processes in the model structure. This approach was tested on the newly implemented migration module of the state-of-the-art DGVM LPJ-GM, focusing on European forests. Estimates of post-glacial migration rates obtained from pollen and macrofossil records of dominant European tree taxa were used to test the model performance. The results indicate higher sensitivity of migration rates to parameters associated with the dispersal kernel (dispersal distances and kernel shape) compared to plant traits (germination rate and maximum fecundity) and highlight the importance of representing rare long-distance dispersal events via fat-tailed kernels. Overall, the successful parametrization and model selection of LPJ-GM will allow plant migration to be simulated with a more mechanistic approach at larger spatial and temporal scales, thus improving our efforts to understand past vegetation dynamics and predict future range shifts in a context of global change. [ABSTRACT FROM AUTHOR]

The prediction of species geographic redistribution under climate change (i.e. range shifts) has been addressed by both experimental and modelling approaches and can be used to inform efficient policy measures on the functioning and services of future ecosystems. Dynamic Global Vegetation Models (DGVMs) are considered state-of-the art tools to understand and quantify the spatio-temporal dynamics of ecosystems at large scales and their response to changing environments. They can explicitly include local vegetation dynamics relevant to migration (establishment, growth, seed production), species-specific dispersal abilities and the competitive interactions with other species in the new environment. However, the inclusion of more detailed mechanistic formulations of range shift processes may also widen the overall uncertainty of the model. Thus, a quantification of these uncertainties is needed to evaluate and improve our confidence in the model predictions. In this study, we present an efficient assessment of parameter and model uncertainties combining low-cost analyses in successive steps: local sensitivity analysis, exploration of the performance landscape at extreme parameter values, and inclusion of relevant ecological processes in the model structure. This approach was tested on the newly-implemented migration module of the state-of-the-art DGVM, LPJ-GM 1.0. Estimates of post-glacial migration rates obtained from pollen and macrofossil records of dominant European tree taxa were used to test the model performance. The results indicate higher sensitivity of migration rates to parameters associated with the dispersal kernel (dispersal distances and kernel shape) compared to plant traits (germination rate and maximum fecundity) and highlight the importance of representing rare long-distance dispersal events via fat-tailed kernels. Overall, the successful parametrization and model selection of LPJ-GM will allow simulating plant migration with a more mechanistic approach at larger spatial and temporal scales, thus improving our efforts to understand past vegetation dynamics and predict future range shifts in a context of global change. [ABSTRACT FROM AUTHOR]

Microbial communities of methane (CH4) producing methanogens and consuming methanotrophs play an important role for Earth's atmospheric CH4 budget. Despite their global significance, knowledge on how much they control the spatial variation in CH4 fluxes from peatlands is poorly understood. We studied variation in CH4 producing and consuming communities in a natural peatland dominated by Eriophorum vaginatum, via a metagenomics approach using custom designed hybridization-based oligonucleotide probes to focus on taxa and functions associated with methane cycling. We hypothesized that sites with different magnitudes of methane flux are occupied by structurally and functionally different microbial communities, despite the dominance of a single vascular plant species. To investigate this, nine plant-peat mesocosms dominated by the sedge Eriophorum vaginatum, with varying vegetation coverage, were collected from a temperate natural wetland and subjected to a simulated growing season. During the simulated growing season, measurements of CH4 emission, carbon dioxide (CO2) exchange and δ13C signature of emitted CH4 were made. Mesocosms 1 through 9 were classified into three categories according to the magnitude of CH4 flux. Gross primary production and ecosystem respiration followed the same pattern as CH4 fluxes, but this trend was not observed in net ecosystem exchange. We observed that genetic functional potential was of minor importance in explaining spatial variability of CH4 fluxes with only small shifts in taxonomic community and functional genes. In addition, a higher β-diversity was observed in samples with high CH4 emission. Among methanogens, Methanoregula, made up over 50% of the community composition. This, in combination with the remaining hydrogenotrophic methanogens matched the δ13C isotopic signature of emitted CH4. However, the presence of acetoclastic and methylotrophic taxa and type I, II and Verrucomicrobia methanotrophs indicates that the microbial community holds the ability to produce and consume CH4 in multiple ways. This is important in terms of future climate scenarios, where peatlands are expected to alter in nutrient status, hydrology, and peat biochemistry. Due to the high functional potential, we expect the community to be highly adaptive to future climate scenarios. [ABSTRACT FROM AUTHOR]

Aim: Climate change impacts forest functioning and services through two inter‐related effects. First, it impacts tree growth, with effects, for example, on biomass production. Second, climate change also reshuffles community composition, with further effects on forest functioning. However, the relative importance of these two effects has rarely been studied. Here, we developed a new modelling approach to investigate these relative importances for forest productivity. Location: Eleven forest sites in central Europe. Time period: Historical (1990) and end‐of‐21st‐century climate‐like conditions. We simulated 2,000 years of forest dynamics for each set of conditions. Major taxa studied: Twenty‐five common tree species in European temperate forests. Methods: We coupled species distribution models and a forest succession model, working at complementary spatial and temporal scales, to simulate the climatic filtering that shapes potential tree species pools, the biotic filtering that shapes realized communities and the functioning of these realized communities in the long‐term. Results: Under an average temperature increase (relative to 1901–1990) of between 1.5 and 1.7 °C, changes in simulated forest productivity were caused mostly by changes in the growth of persisting tree species. With an average temperature increase of 3.6–4.0 °C, changes in simulated productivity at sites that currently have a mild climate were again caused predominantly by changes in tree species growth. However, at the warmest and coldest sites, changes in productivity were related mostly to shifts in species composition. In general, at the coldest sites, forest productivity is likely to be enhanced by climate change, whereas at the warmest sites the productivity might increase or decrease depending on the future precipitation regime. Main conclusions: A combination of two complementary modelling approaches that address questions at the interface between biogeography, community ecology and ecosystem functioning, reveals that climate change‐driven community reshuffling in the long term might be crucially important for ecosystem functioning. [ABSTRACT FROM AUTHOR]

While deforestation and forest degradation have gained attention in recent years not least at the UN climate negotiations, a third "de", i.e., defaunation, has to a great extent been overlooked. Human-induced faunal loss does not only reduce tree species diversity, but also significantly erodes key ecosystem services and functions and further disadvantages local communities. In this article, we analyze these impacts, and the associated multi-level governance gaps, through a case study of Nigeria's Cross River State and make suggestions for more encompassing conservation approaches that take defaunation into account. To this end, we analyze the interplay between current forest governance and REDD+ in Cross River State and local hunting of forest fauna. Drawing on Ostrom's social-ecological systems framework and a mixed-methods approach, we identify shortcomings and gaps of international and domestic forest governance, for instance, the ongoing expansion of agriculture in forest areas, a lack of collective action on forest fauna conservation at the local level, as well as conflicts amongst key actors at the sub-national level. Current REDD+ governance in Cross River State largely fails to address fauna loss and local hunting practices, but also affect allocation and access of environmental benefits and burdens for local people. [ABSTRACT FROM AUTHOR]

Dynamic global vegetation models are a common tool to assess the effect of climate and land use change on vegetation. Though most applications of dynamic global vegetation models use plant functional types, some also simulate species occurrences. While the current development aims to include more processes, e.g. the nitrogen cycle, the models still typically assume an ample seed supply allowing all species to establish once the climate conditions are suitable. Pollen studies have shown that a number of plant species lag behind in occupying climatological suitable areas (e.g. after a change in the climate) as they need to arrive at and establish in the newly suitable areas. Previous attempts to implement migration in dynamic vegetation models have allowed for the simulation of either only small areas or have been implemented as a post-process, not allowing for feedbacks within the vegetation. Here we present two novel methods simulating migrating and interacting tree species which have the potential to be used for simulations of large areas. Both distribute seeds between grid cells, leading to individual establishment. The first method uses an approach based on fast Fourier transforms, while in the second approach we iteratively shift the seed production matrix and disperse seeds with a given probability. While the former method is computationally faster, it does not allow for modification of the seed dispersal kernel parameters with respect to terrain features, which the latter method allows. We evaluate the increase in computational demand of both methods. Since dispersal acts at a scale no larger than 1 km, all dispersal simulations need to be performed at maximum at that scale. However, with the currently available computational power it is not feasible to simulate the local vegetation dynamics of a large area at that scale. We present an option to decrease the required computational costs through a reduction in the number of grid cells for which the local dynamics are simulated only along migration transects. Evaluation of species patterns and migration speeds shows that simulating along transects reduces migration speed, and both methods applied on the transects produce reasonable results. Furthermore, using the migration transects, both methods are sufficiently computationally efficient to allow for large-scale DGVM simulations with migration. [ABSTRACT FROM AUTHOR]

Abstract Livestock production is important for local food security and as a source of income in sub-Saharan Africa. The human population of the region is expected to double by 2050, and at the same time climate change is predicted to negatively affect grazing resources vital to livestock. Therefore, it is essential to model the potential grazing output of sub-Saharan Africa in both present and future climatic conditions. Standard tools to simulate plant productivity are dynamic vegetation models (DVMs). However, as they typically allocate carbon to plant growth at an annual time step, they have a limited capability to simulate grazing. Here, we present a novel implementation of daily carbon allocation for grasses into the DVM Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) and apply this to study the grazing potential for the Kordofan region in Sudan. The results show a latitudinal split in grazing resources, where the northern parts of Kordofan are unexploited and southern parts are overused. Overall, we found that the modeled grazing potential of Kordofan is 16% higher than the livestock usage reported in the Food and Agricultural Organization of the United Nations, indicating a mitigation potential in the form of a spatial relocation of the herds. [ABSTRACT FROM AUTHOR]

Dynamic vegetation models are a common tool to assess the effect of climate and land use change on vegetation. While the current development aims to include more processes, e.g. the nitrogen cycle, the models still typically assume an ample seed supply allowing all species to establish once the climate conditions are suitable. A number of species have been shown to lag behind in occupying climatological suitable areas (e.g. after a change in the climate) as they need to arrive and establish at the newly suitable areas. Previous attempts to implement migration in dynamic vegetation models have allowed simulating either only small areas or have been implemented as post process, not allowing for feedbacks within the vegetation. Here we present two novel methods simulating migrating and interacting tree species which have the potential to be used for continental simulations. Both distribute seeds between grid cells leading to individual establishment. The first method uses an approach based on Fast Fourier transform while in the second approach we iteratively shift the seed production matrix and disperse seeds with a given probability. While the former method is computationally marginally faster, it does not allow for modification of the seed dispersal kernel parameters with respect to terrain features, which the latter method allows. We evaluate the increase in computational demand of both methods. Since dispersal acts at a scale no larger than 1 km, all dispersal simulations need to be performed at least at that scale. However, with the current available computational power it is not feasible to simulate the vegetation dynamics of a whole continent at that scale. We present an option to decrease the required computational costs, reducing the number of grid cells where the local dynamics is computed by simulating it only along migration transects. Evaluation of species patterns and migration speeds shows that although the simulation along transects reduces the migration speed slightly, both methods are reliable. Furthermore, both methods are sufficiently computationally efficient to allow large scale DGVM simulations with migration on entire continents. [ABSTRACT FROM AUTHOR]

European managed grasslands are amongst the most productive in the world. Besides temperature and the amount and timing of precipitation, grass production is also highly controlled by applications of nitrogen fertilizers and land management to sustain a high productivity. Since management characteristics of pastures vary greatly across Europe, land-use intensity and their projections are critical input variables in earth system modeling when examining and predicting the effects of increasingly intensified agricultural and livestock systems on the environment. In this study, we aim to improve the representation of pastures in the dynamic global vegetation model LPJ-GUESS. This is done by incorporating daily carbon allocation for grasses as a foundation to further implement daily land management routines and land-use intensity data into the model to discriminate between intensively and extensively used regions. We further compare our new simulations with leaf area index observations, reported regional grassland productivity, and simulations conducted with the vegetation model ORCHIDEE-GM. Additionally, we analyze the implications of including pasture fertilization and daily management compared to the standard version of LPJ-GUESS. Our results demonstrate that grassland productivity cannot be adequately captured without including land-use intensity data in form of nitrogen applications. Using this type of information improved spatial patterns of grassland productivity significantly compared to standard LPJ-GUESS. In general, simulations for net primary productivity, net ecosystem carbon balance and nitrogen leaching were considerably increased in the extended version. Finally, the adapted version of LPJ-GUESS, driven with projections of climate and land-use intensity, simulated an increase in potential grassland productivity until 2050 for several agro-climatic regions, most notably for the Mediterranean North, the Mediterranean South, the Atlantic Central and the Atlantic South. [ABSTRACT FROM AUTHOR]

Satellite derived normalized difference vegetation index (NDVI) is a common data source for monitoring regional and global ecosystem properties. In dry lands it has contributed to estimation of inter-annual and seasonal vegetation dynamics and phenology. However, due to the spectral properties of NDVI it can be affected by clouds which can introduce missing data in the time series. Remotely sensed soil moisture has in contrast to NDVI the benefit of being unaffected by clouds due to the measurements being made in the microwave domain. There is therefore a potential in combining the remotely sensed NDVI with remotely sensed soil moisture to enhance the quality and estimate the missing data. We present a step towards the usage of remotely sensed soil moisture for estimation of savannah NDVI. This was done by evaluating the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture and three of its individual products with respect to their relative performance. The individual products are from the advance scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), and the Land Parameter Retrieval Model-Advanced Microwave Scanning Radiometer-Earth Observing System (LPRM-AMSR-E). Each dataset was used to simulate NDVI, which was subsequently compared to remotely sensed NDVI from MODIS. Differences in their ability to estimate NDVI indicated that, on average, CCI soil moisture differs from its individual products by showing a higher average correlation with measured NDVI. Overall NDVI modelled from CCI soil moisture gave an average correlation of 0.81 to remotely sensed NDVI which indicates its potential to be used to estimate seasonal variations in savannah NDVI. Our result shows promise for further development in using CCI soil moisture to estimate NDVI. The modelled NDVI can potentially be used together with other remotely sensed vegetation datasets to enhance the phenological information that can be acquired, thereby, improving the estimates of savannah vegetation phenology. [ABSTRACT FROM AUTHOR]

Biogeochemical models use meteorological forcing data derived with different approaches (e.g. based on interpolation or reanalysis of observation data or a hybrid hereof) to simulate ecosystem processes such as gross primary productivity (GPP). This study assesses the impact of different widely used climate datasets on simulated gross primary productivity and evaluates the suitability of them for reproducing the global and regional carbon cycle as mapped from independent GPP data. We simulate GPP with the biogeochemical model LPJ-GUESS using six historical climate datasets (CRU, CRUNCEP, ECMWF, NCEP, PRINCETON, and WFDEI). The simulated GPP is evaluated using an observation-based GPP product derived from eddy covariance measurements in combination with remotely sensed data. Our results show that all datasets tested produce relatively similar GPP simulations at a global scale, corresponding fairly well to the observation-based data with a difference between simulations and observations ranging from -50 to 60 g m-2 yr-1. However, all simulations also show a strong underestimation of GPP (ranging from -533 to -870 g m-2 yr-1) and low temporal agreement (r < 0.4) with observations over tropical areas. As the shortwave radiation for tropical areas was found to have the highest uncertainty in the analyzed historical climate datasets, we test whether simulation results could be improved by a correction of the tested shortwave radiation for tropical areas using a new radiation product from the International Satellite Cloud Climatology Project (ISCCP). A large improvement (up to 48%) in simulated GPP magnitude was observed with bias corrected shortwave radiation, as well as an increase in spatio-temporal agreement between the simulated GPP and observation-based GPP. This study conducts a spatial inter-comparison and quantification of the performances of climate datasets and can thereby facilitate the selection of climate forcing data over any given study area for modelling purposes. [ABSTRACT FROM AUTHOR]

Background: Late blight (caused by Phytophthora infestans) is a devastating potato disease that has been found to occur earlier in the season over the last decades in Fennoscandia. Up until now the reasons for this change have not been investigated. Possible explanations for this change are climate alterations, changes in potato production or changes in pathogen biology, such as increased fitness or changes in gene flow within P. infestans populations. The first incidence of late blight is of high economic importance since fungicidal applications should be typically applied two weeks before the first signs of late blight and are repeated on average once a week. Methods: We use field observations of first incidence of late blight in experimental potato fields from five sites in Sweden and Finland covering a total of 30 years and investigate whether the earlier incidence of late blight can be related to the climate. Results: We linked the field data to meteorological data and found that the previous assumption, used in common late blight models, that the disease only develops at relative humidity levels above 90% had to be rejected. Rather than the typically assumed threshold relationship between late blight disease development and relative humidity we found a linear relationship. Our model furthermore showed two distinct responses of late blight to climate. At the beginning of the observation time (in Sweden until the early 90s and in Finland until the 2000s) the link between climate and first incidence was very weak. However, for the remainder of the time period the link was highly significant, indicating a change in the biological properties of the pathogen which could for example be a change in the dominating reproduction mode or a physiological change in the response of the pathogen to climate. Conclusions: The study shows that models used in decision support systems need to be checked and re-parametrized regularly to be able to capture changes in pathogen biology. While this study was performed with data from Fennoscandia this new pathogen biology and late blight might spread to (or already be present at) other parts of the world as well. The strong link between climate and first incidence together with the presented model offers a tool to assess late blight incidence in future climates. [ABSTRACT FROM AUTHOR]

In the 21st century, climate change in combination with increasing demand, mainly from population growth, will exert greater pressure on the ecosystems of the Sahel to supply food and feed resources. The balance between supply and demand (annual biomass required for human consumption) serves as a key metric for quantifying basic resource shortfalls over broad regions. Here we apply an exploratory modelling framework to analyze the variations in the timing and geography of different NPP (net primary production) supply-demand scenarios (with distinct assumptions determining supply and demand) for the 21st century Sahel. We achieve this by coupling a simple NPP supply model (forced with projections from four representative concentration pathways) with a global, reduced-complexity demand model (driven by socio-economic data and assumptions derived from five shared socio-economic pathways). For the scenario that deviates least from current socio-economic and climate trends, we find that per capita NPP outstrips its supply in the 2070s, while by 2050, half the countries in the Sahel experience NPP shortfalls. We also find that despite variations in the timing of the onset of NPP shortfalls, demand cannot consistently be met across the majority of scenarios. Moreover, large between-country variations are shown across the scenarios where by the year 2050, some countries consistently experience shortage, others surplus while yet others shift from surplus to shortage. At the local level (i.e. grid cell) hotspots of total NPP shortfall consistently occur in the same locations across all scenarios, but vary in size and magnitude. These hotspots are linked to population density and high demand. For all scenarios, total simulated NPP supply doubles by 2050 but is outpaced by increasing demand due to a combination of population growth and adoption of a diets rich in animal products. Finally, variations in the timing of onset and end of supply shortfalls stem from the assumptions that underpin the shared socio-economic pathways rather than the representative concentration pathways. Our results suggest that the UN sustainable development goals for eradicating hunger are at high risk for failure. This emphasizes the importance of policy interventions such as the implementation of sustainable and healthy diets, family planning, reducing yield gaps, and encouraging transfer of resources to impoverished areas via trade relations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Forests are major carbon stores on a global scale but there are significant uncertainties about changes in carbon flux through time and the relative contributions of drivers such as land use, climate and atmospheric CO. We used the dynamic vegetation model LPJ-GUESS to test the relative influence of CO increase, temperature increase and management on carbon storage in living biomass in an unmanaged European temperate deciduous forest. The model agreed well with living biomass reconstructed from forest surveys and maximum biomass values from other studies. High-resolution climate data from both historical records and general circulation models were used to force the model and was manipulated for some simulations to allow relative contributions of individual drivers to be assessed. Release from management was the major driver of carbon storage for most of the historical period, whereas CO took over as the most important driver in the last 20 years. Relatively, little of the observed historical increase in carbon stocks was attributable to increased temperature. Future simulations using IPCC RCP4.5 and RCP8.5 scenarios indicated that carbon stocks could increase by as much as 3 kg C m by the end of the century, which is likely to be driven by CO increase. This study suggests that unmanaged semi-natural woodland in Europe can be a major potential carbon sink that has been previously underestimated. Increasing the area of unmanaged forest would provide carbon sink services during recovery from timber extraction, while long-term protection would ensure carbon stocks are maintained. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aim To project the potential European distribution of seven broad habitat categories (needle-leaved, broad-leaved, mixed and mediterranean forest, urban, grassland and cropland) in order to assess effects of land use, climate change and increase in CO2 on predicted habitat changes up to the year 2050. Location Europe. Method We modelled the response of European vegetation to changes in land use, climate and CO2 by combining the land-use model Dyna- CLUE (based on the CORINE land-cover data) and the dynamic vegetation model LPJ-GUESS. Two reforestation options were explored: maintaining the current range of tree species ( EFI) or promoting naturally occurring tree species ( NAT). Climate data from two general circulation models and two SRES scenarios ( A2 and B1) were used. The broad habitat types were classified according to a combination of land use and the dominant plant species. Results Our models predicted that croplands and grasslands are expected to decrease due to land-use change. Although climate change has a negative effect on needle-leaved forest, it is expected to maintain its area or even increase in the EFI reforestation option while mediterranean, broad-leaved and mixed forests are expected to increase markedly. All investigated drivers have shown some effect, but land use is the dominant contributor to broad habitat change except for needle-leaved and mixed which are mainly influenced by climate change. Main conclusions 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. [ABSTRACT FROM AUTHOR]

Aim Vegetation dynamics and the competitive interactions involved are assumed to restrict the ability of species to migrate. But in most migration modelling approaches disturbance-driven succession and competition processes are reduced to simple assumptions or are even missing. The aim of this study was to test a combination of a migration model and a dynamic vegetation model to estimate the migration of tree species controlled by climate, environment and local species dynamics such as succession and competition. Location Europe. Methods To estimate the effect of vegetation dynamics on the migration of European beech and Norway spruce, we developed a post-process migration tool ( LPJ-CATS). This tool integrates outputs of the migration model CATS and the dynamic vegetation model LPJ-GUESS. The model LPJ-CATS relies on a linear dependency between the dispersal kernel and migration rate and is based on the assumption that competition reduces fecundity. Results Simulating potential migration rates with the CATS model, which does not account for competition and disturbance, resulted in mean Holocene migration rates of 435 ± 55 and 330 ± 95 m year−1 for the two species Picea abies and Fagus sylvatica, respectively. With LPJ-CATS, these mean migration rates were reduced to 250 ± 75 and 170 ± 60 m year−1 for spruce and beech, respectively. Moreover, LPJ-CATS simulated migration pathways of these two species that generally comply well with those documented in the palaeo-records. Main conclusions Our 'hybrid' modelling approach allowed for the simulation of generally realistic Holocene migration rates and pathways of the two study species on a continental scale. It suggests that competition can considerably modify spread rates, but also the magnitude of its effect depends on how close climate conditions are to the niche requirements of a particular species. [ABSTRACT FROM AUTHOR]

Aim To reconstruct spatial and temporal patterns of European fire activity during the Holocene and to explore their potential drivers, by relating biomass burning to simulated biotic and abiotic parameters. Location Europe. Methods Holocene fire activity was investigated based on 156 sedimentary charcoal records from lakes and peat bogs across Europe. Charcoal data covering the last 9000 years were statistically compared with palaeoclimate data derived from the Max Planck Institute for Meteorology/ University of Wisconsin-Madison Earth System Model, with vegetation and fire indices simulated with the dynamic vegetation model lpj- guess and with two independent scenarios of past anthropogenic land-cover change. Results The combined sedimentary charcoal records suggest that there was little fire activity during the early and the middle Holocene compared with recent millennia. A progressive increase in fire frequency began around 3500 cal. yr bp and continues into the late Holocene. Biomass burning rose sharply from 250 cal. yr bp onwards, reaching a maximum during the early Industrial Era and then declining abruptly. When considering the whole Holocene, the long-term control of fire is best explained by anthropogenic land-cover change, litter availability and temperature-related parameters. Main conclusions While the general patterns found across Europe suggest the primary role of vegetation, precipitation and temperature-related parameters in explaining fire dynamics during the early Holocene, the increase in fire activity observed in the mid-late Holocene is mainly related to anthropogenic land-cover changes, followed by vegetation and temperature-related parameters. The 20th-century decline in biomass burning seems to be due to increased landscape fragmentation and active fire suppression policies. Our hypothesis that human activities played a primary role in Holocene biomass burning across Europe could be tested by improved palaeoclimate reconstructions and more refined representations of anthropogenic fires in climate and vegetation models. [ABSTRACT FROM AUTHOR]

Plant functional response groups (PFGs) are now widely established as a tool to investigate plant—environment relationships. Different statistical methods to form PFGs are used in the literature. One way is to derive emergent groups by classifying species based on correlation of biological attributes and subjecting these groups to tests of response to environmental variables. Another way is to search for associations of occurrence data, environmental variables and trait data simultaneously. The fourth-corner method is one way to assess the relationships between single traits and habitat factors. We extended this statistical method to a generally applicable procedure for the generation of plant functional response groups by developing new randomization procedures for presence/absence data of plant communities. Previous PFG groupings used either predefined groups or emergent groups i.e. classifications based on correlations of biological attributes (Lavorel et al Trends Ecol Evol 12:474–478, 1997), of the global species pool and assessed their functional response. However, since not all PFGs might form emergent groups or may be known by experts, we used a permutation procedure to optimise functional grouping. We tested the method using an artificial test data set of virtual plants occurring in different disturbance treatments. Direct trait-treatment relationships as well as more complex associations are incorporated in the test data. Trait combinations responding to environmental variables could be clearly distinguished from non-responding combinations. The results are compared with the method suggested by Pillar (J Veg Sci 10:631–640) for the identification of plant functional groups. After exploring the statistical properties using an artificial data set, the method is applied to experimental data of a greenhouse experiment on the assemblage of plant communities. Four plant functional response groups are formed with regard to differences in soil fertility on the basis of the traits canopy height and spacer length. [ABSTRACT FROM AUTHOR]

Land use and climate changes induce shifts in plant functional diversity and community structure, thereby modifying ecosystem processes. This is particularly true for litter decomposition, an essential process in the biogeochemical cycles of carbon and nutrients. In this study, we asked whether changes in functional traits of living leaves in response to changes in land use and climate were related to rates of litter potential decomposition, hereafter denoted litter decomposability, across a range of 10 contrasting sites. To disentangle the different control factors on litter decomposition, we conducted a microcosm experiment to determine the decomposability under standard conditions of litters collected in herbaceous communities from Europe and Israel. We tested how environmental factors (disturbance and climate) affected functional traits Of living leaves and how these traits then modified litter quality and subsequent litter decomposability. Litter decomposability appeared proximately linked to initial litter quality, with particularly clear negative correlations with lignin-dependent indices (litter lignin concentration, lignin : nitrogen ratio, and fiber component). Litter quality was directly related to community-weighted mean traits. Lignin-dependent indices of litter quality were positively correlated with community-weighted mean leaf dry matter content (LDMC), and negatively correlated with community-weighted mean leaf nitrogen concentration (LNC). Consequently, litter decomposability was correlated negatively with community-weighted mean LDMC, and positively with community-weighted mean LNC. Environmental factors (disturbance and climate) influenced community-weighted mean traits. Plant communities experiencing less frequent or less intense disturbance exhibited higher community-weighted mean LDMC, and therefore higher litter lignin content and slower litter decomposability. LDMC therefore appears as a powerful marker of both changes in land use and of the pace of nutrient cycling across 10 contrasting sites. [ABSTRACT FROM AUTHOR]

The analysis of co-occurrence matrices is a common practice to evaluate community structure. The observed data are compared with a “null model”, a randomised co-occurrence matrix derived from the observation by using a statistic, e.g. the C-score, sensitive to the pattern investigated. The most frequently used algorithm, “sequential swap”, has been criticised for not sampling with equal frequencies thereby calling into question the results of earlier analysis. The bias of the “sequential swap” algorithm when used with the C-score was assessed by analysing 291 published presence-absence matrices. In 152 cases, the true p-value differed by >5% from the p-value generated by an uncorrected “sequential swap”. However, the absolute value of the difference was rather small. Out of the 291 matrices, there were only 5 cases in which an incorrect statistical decision would have been reached by using the uncorrected p-value (3 at the p<0.05 and 2 at the p<0.01 level), and in all 5 of these cases, the true p-value was close to the significance level. Our results confirm analytical studies of Miklos and Podani which show that the uncorrected swap gives slightly conservative results in tests for competitive segregation. However, the bias is very small and should not distort the ecological interpretation. We also estimated the number of iterations needed for the “sequential swap” to generate accurate p-values. While most authors do not exceed a number of 104 iterations, the suggested minimum number of swaps for 29 out of the 291 tested matrices is greater than 104. We recommend to use 30 000 “sequential swaps” if the required sample size is not assessed otherwise. [ABSTRACT FROM AUTHOR]

Carbon storage dynamics in vegetation and soil are determined by the balance of carbon influx and turnover. Estimates of these opposing fluxes differ markedly among different empirical datasets and models leading to uncertainty and divergent trends. To trace the origin of such discrepancies through time and across major biomes and climatic regions, we used a model-data fusion framework. The framework emulates carbon cycling and its component processes in a global dynamic ecosystem model, LPJ-GUESS, and preserves the model-simulated pools and fluxes in space and time. Thus, it allows us to replace simulated carbon influx and turnover with estimates derived from empirical data, bringing together the strength of the model in representing processes, with the richness of observational data informing the estimations. The resulting vegetation and soil carbon storage and global land carbon fluxes were compared to independent empirical datasets. Results show model-data agreement comparable to, or even better than, the agreement between independent empirical datasets. This suggests that only marginal improvement in land carbon cycle simulations can be gained from comparisons of models with current-generation datasets on vegetation and soil carbon. Consequently, we recommend that model skill should be assessed relative to reference data uncertainty in future model evaluation studies. [ABSTRACT FROM AUTHOR]

Abstract: The influence of different drivers on changes in North American and European boreal forests biomass burning (BB) during the Holocene was investigated based on the following hypotheses: land use was important only in the southernmost regions, while elsewhere climate was the main driver modulated by changes in fuel type. BB was reconstructed by means of 88 sedimentary charcoal records divided into six different site clusters. A statistical approach was used to explore the relative contribution of (a) pollen‐based mean July/summer temperature and mean annual precipitation reconstructions, (b) an independent model‐based scenario of past land use (LU), and (c) pollen‐based reconstructions of plant functional types (PFTs) on BB. Our hypotheses were tested with: (a) a west‐east northern boreal sector with changing climatic conditions and a homogeneous vegetation, and (b) a north‐south European boreal sector characterized by gradual variation in both climate and vegetation composition. The processes driving BB in boreal forests varied from one region to another during the Holocene. However, general trends in boreal biomass burning were primarily controlled by changes in climate (mean annual precipitation in Alaska, northern Quebec, and northern Fennoscandia, and mean July/summer temperature in central Canada and central Fennoscandia) and, secondarily, by fuel composition (BB positively correlated with the presence of boreal needleleaf evergreen trees in Alaska and in central and southern Fennoscandia). Land use played only a marginal role. A modification towards less flammable tree species (by promoting deciduous stands over fire‐prone conifers) could contribute to reduce circumboreal wildfire risk in future warmer periods. [ABSTRACT FROM AUTHOR]