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Aim: Despite increasing evidence for plant growth often being limited by sink (meristem) activity rather than source (photosynthesis) activity, all currently available dynamic global vegetation models (DGVMs) simulate plant growth via source-limited processes. For a given climatic region, this may lead to an overestimation of carbon stock per unit surface area, particularly if a model fails to correctly predict forest cover. Our aim is to improve the Lund-Potsdam-Jena (LPJ) DGVM by replacing the source-limited (SoL) tree growth algorithm by a sink-limited (SiL) one. Location: Our analysis focuses on the cold tree line at high latitudes and altitudes. We study two altitudinal transects in the Swiss Alps and the northern tree line. Methods: We limit annual net primary productivity of the LPJ DGVM by an algorithm based on the annual sum of growing degree-days (GDD), assuming that maximum plant growth is reached asymptotically with increasing GDD. Results: Comparing simulation results with observational data, we show that the locations of both the northern and the alpine tree line are estimated more accurately when using a SiL algorithm than when using the commonly employed SoL algorithm. Also, simulated carbon stocks decrease in a more realistic manner towards the tree line when the SiL algorithm is used. This has far-reaching implications for estimating and projecting present and future carbon stocks in temperature-limited ecosystems. Main conclusions: In the range of 60-80°N over Europe and Asia, carbon stored in vegetation is estimated to be c. 50% higher in the LPJ standard version (LPJ-SoL) compared with LPJ-SiL, resulting in a global difference in estimated biomass of 25 Pg (c. 5% of the global terrestrial standing biomass). Similarly, the simulated elevation of the upper tree line in the European Alps differs by c. 400m between the two model versions, thus implying an additional overestimation of carbon stored in mountain forests around the world.

Source: Scopus

Background - Despite empirical support for an increase in ecosystem productivity with species diversity in synthetic systems, there is ample evidence that this relationship is dependent on environmental characteristics, especially in structurally more complex natural systems. Empirical support for this relationship in forests is urgently needed, as these ecosystems play an important role in carbon sequestration. Methodology/Principal Findings - We tested whether tree wood production is positively related to tree species richness while controlling for climatic factors, by analyzing 55265 forest inventory plots in 11 forest types across five European countries. On average, wood production was 24% higher in mixed than in monospecific forests. Taken alone, wood production was enhanced with increasing tree species richness in almost all forest types. In some forests, wood production was also greater with increasing numbers of tree types. Structural Equation Modeling indicated that the increase in wood production with tree species richness was largely mediated by a positive association between stand basal area and tree species richness. Mean annual temperature and mean annual precipitation affected wood production and species richness directly. However, the direction and magnitude of the influence of climatic variables on wood production and species richness was not consistent, and vary dependent on forest type. Conclusions - Our analysis is the first to find a local scale positive relationship between tree species richness and tree wood production occurring across a continent. Our results strongly support incorporating the role of biodiversity in management and policy plans for forest carbon sequestration.

Background: Despite empirical support for an increase in ecosystem productivity with species diversity in synthetic systems, there is ample evidence that this relationship is dependent on environmental characteristics, especially in structurally more complex natural systems. Empirical support for this relationship in forests is urgently needed, as these ecosystems play an important role in carbon sequestration. Methodology/Principal Findings: We tested whether tree wood production is positively related to tree species richness while controlling for climatic factors, by analyzing 55265 forest inventory plots in 11 forest types across five European countries. On average, wood production was 24% higher in mixed than in monospecific forests. Taken alone, wood production was enhanced with increasing tree species richness in almost all forest types. In some forests, wood production was also greater with increasing numbers of tree types. Structural Equation Modeling indicated that the increase in wood production with tree species richness was largely mediated by a positive association between stand basal area and tree species richness. Mean annual temperature and mean annual precipitation affected wood production and species richness directly. However, the direction and magnitude of the influence of climatic variables on wood production and species richness was not consistent, and vary dependent on forest type. Conclusions: Our analysis is the first to find a local scale positive relationship between tree species richness and tree wood production occurring across a continent. Our results strongly support incorporating the role of biodiversity in management and policy plans for forest carbon sequestration. © 2013 Vilà et al.

Dynamic vegetation models are important tools in ecological research, but not all processes of vegetation dynamics are captured adequately. Tree mortality is often modeled as a function of growth efficiency and maximum age. However, empirical studies have shown for different species that slow-growing trees may become older than fast-growing trees, implying a correlation of mortality with growth rate and size rather than age. We used the ecosystem model LPJ-GUESS to compare the standard age-dependent mortality with two size-dependent mortality approaches. We found that all mortality approaches, when calibrated, yield a realistic pattern of growing stock and Plant Functional Type (PFT) distribution at five study sites in Switzerland. However, only the size-dependent approaches match a third pattern, i.e. the observed negative relationship between growth rate and longevity. As a consequence, trees are simulated to get older at higher than at lower altitudes/latitudes. In contrast, maximum tree ages do not change along these climatic gradients when the standard age-dependent mortality is used. As tree age and size determine forest structure, our more realistic mortality assumptions improved forest biomass estimation, but indicate a potential decline of carbon storage under climate change. We conclude that tree mortality should be modeled as a function of size rather than age. (C) 2012 Elsevier B.V. All rights reserved.

Source: Scopus

Source: Scopus

Recent temperature observations suggest a general warming trend that may be causing the range of tree species to shift to higher latitudes and altitudes. Since biotic interactions such as herbivory can change tree species composition, it is important to understand their contribution to vegetation changes triggered by climate change. To investigate the response of forests to climate change and herbivory by wild ungulates, we used the forest gap model ForClim v2.9.6 and simulated forest development in three climatically different valleys in the Swiss Alps. We used altitudinal transects on contrasting slopes covering a wide range of forest types from the cold (upper) to the dry (lower) treeline. This allowed us to investigate (1) altitudinal range shifts in response to climate change, (2) the consequences for tree species composition, and (3) the combined effect of climate change and ungulate herbivory. We found that ungulate herbivory changed species composition and that both basal area and stem numbers decreased with increasing herbivory intensity. Tree species responded differently to the change in climate, and their ranges did not change concurrently, thus causing a succession to new stand types. While climate change partially compensated for the reductions in basal area caused by ungulate herbivory, the combined effect of these two agents on the mix of the dominant species and forest type was non-compensatory, as browsing selectively excluded species from establishing or reaching dominance and altered competition patterns, particularly for light. We conclude that there is an urgent need for adaptive forest management strategies that address the joint effects of climate change and ungulate herbivory.

Models of carbon cycling in terrestrial ecosystems contain formulations for the dependence of respiration on temperature, but the sensitivity of predicted carbon pools and fluxes to these formulations and their parameterization is not well understood. Thus, we performed an uncertainty analysis of soil organic matter decomposition with respect to its temperature dependency using the ecosystem model LPJ-GUESS. We used five temperature response functions (Exponential, Arrhenius, Lloyd-Taylor, Gaussian, Van’t Hoff). We determined the parameter confidence ranges of the formulations by nonlinear regression analysis based on eight experimental datasets from Northern Hemisphere ecosystems. We sampled over the confidence ranges of the parameters and ran simulations for each pair of temperature response function and calibration site. We analyzed both the long-term and the short-term heterotrophic soil carbon dynamics over a virtual elevation gradient in southern Switzerland. The temperature relationship of Lloyd-Taylor fitted the overall data set best as the other functions either resulted in poor fits (Exponential, Arrhenius) or were not applicable for all datasets (Gaussian, Van’t Hoff). There were two main sources of uncertainty for model simulations: (1) the lack of confidence in the parameter estimates of the temperature response, which increased with increasing temperature, and (2) the size of the simulated soil carbon pools, which increased with elevation, as slower turn-over times lead to higher carbon stocks and higher associated uncertainties. Our results therefore indicate that such projections are more uncertain for higher elevations and hence also higher latitudes, which are of key importance for the global terrestrial carbon budget., cited By 5

Forests are experiencing an environment that changes much faster than during the past several hundred years. In addition, the abiotic factors determining forest dynamics vary depending on its location. Forest modeling thus faces the new challenge of supporting forest management in the context of environmental change. This review focuses on three types of models that are used in forest management: empirical (EM), process-based (PBM) and hybrid models. Recent approaches may lead to the applicability of empirical models under changing environmental conditions, such as (i) the dynamic state-space approach, or (ii) the development of productivity-environment relationships. Twenty-five process-based models in use in Europe were analyzed in terms of their structure, inputs and outputs having in mind a forest management perspective. Two paths for hybrid modeling were distinguished: (i) coupling of EMs and PBMs by developing signal-transfer environment-productivity functions; (ii) hybrid models with causal structure including both empirical and mechanistic components. Several gaps of knowledge were identified for the three types of models reviewed. The strengths and weaknesses of the three model types differ and all are likely to remain in use. There is a trade-off between how little data the models need for calibration and simulation purposes, and the variety of input-output relationships that they can quantify. PBMs are the most versatile, with a wide range of environmental conditions and output variables they can account for. However, PBMs require more data making them less applicable whenever data for calibration are scarce. EMs, on the other hand, are easier to run as they require much less prior information, but the aggregated representation of environmental effects makes them less reliable in the context of environmental changes. The different disadvantages of PBMs and EMs suggest that hybrid models may be a good compromise, but a more extensive testing of these model

Source: Scopus

Climate change and biological invasions are key processes affecting global biodiversity, yet their effects have usually been considered separately. Here, we emphasise that global warming has enabled alien species to expand into regions in which they previously could not survive and reproduce. Based on a review of climate-mediated biological invasions of plants, invertebrates, fishes and birds, we discuss the ways in which climate change influences biological invasions. We emphasise the role of alien species in a more dynamic context of shifting species' ranges and changing communities. Under these circumstances, management practices regarding the occurrence of 'new' species could range from complete eradication to tolerance and even consideration of the 'new' species as an enrichment of local biodiversity and key elements to maintain ecosystem services.

Ecologists have long been searching for mechanisms of species coexistence, particularly since G.E. Hutchinson raised the 'paradox of the plankton'. A promising approach to solve this paradox and to explain the coexistence of many species with strong niche overlap is to consider over-compensatory density regulation with its ability to generate endogenous population fluctuations.Previous work has analysed the role of over-compensation in coexistence based on analytical approaches. Using a spatially explicit time-discrete simulation model, we systematically explore the dynamics and conditions for coexistence of two species. We go beyond the analytically accessible range of models by studying the whole range of density regulation from under- to very strong over-compensation and consider the impact of spatial structure and temporal disturbances. In particular, we investigate how coexistence can emerge in different types of population growth models.We show that two strong competitors are able to coexist if at least one species exhibits over-compensation. Analysing the time series of population dynamics reveals how the differential responses to density fluctuations of the two competitors lead to coexistence: The over-compensator generates density fluctuations but is the inferior competitor at strong amplitudes of those fluctuations; the competitor, therefore, becomes frequent and dampens the over-compensator's amplitudes, but it becomes inferior under dampened fluctuations.These species interactions cause a dynamic alternation of community states with long-term persistence of both species. We show that a variety of population growth models is able to reproduce this coexistence although the particular parameter ranges differ among the models. Spatial structure influences the probability of coexistence but coexistence is maintained for a broad range of dispersal parameters.The flexibility and robustness of coexistence through over-compensation emphasize the importance of non

Understanding the impacts of environmental changes on species survival is a major challenge in ecological research, especially when shifting from single- to multispecies foci. Here, we apply a spatially explicit two-species simulation model to analyze the effects of geographic range shifting and habitat isolation on different coexistence mechanisms. The model explicitly considers dispersal, local competition, and growth on a single resource. Results highlight that both range shifting and habitat isolation severely impact coexistence. However, the strength of these impacts depends on the underlying coexistence mechanisms. Neutrally coexisting species are particularly sensitive to habitat isolation, while stabilized coexistence through overcompensatory density regulation is much more sensitive to range shifting. We conclude that, at the community level, the response to environmental change sensitively depends on the underlying coexistence mechanisms. This suggests that predictions and management recommendations should consider differences between neutral versus stabilized community structures whenever possible.

Capercaillie (Tetrao urogallus) is a large, endangered forest grouse species with narrow habitat preferences and large spatial requirements that make it susceptible to habitat changes at different spatial scales. Our aim was to evaluate the relative power of variables relating to forest versus landscape structure in predicting capercaillie occurrence at different spatial scales. We investigated capercaillie-habitat relationships at the scales of forest stand and forest-stand mosaic in 2 Swiss regions. We assessed forest structure from aerial photographs in 52 study plots each 5 km2. We classified plots into one of 3 categories denoting the observed local population trend (stable, declining, extinct), and we compared forest structure between categories. At the stand scale, we used presence-absence data for grid cells within the plots to build predictive habitat models based on logistic regression. At this scale, habitat models that included only variables relating to forest structure explained the occurrence of capercaillie only in part, whereas variables selected by the models differed between regions. Including variables relating to landscape features improved the models significantly. At the scale of stand mosaic, variables describing forest structure (e.g., mean canopy cover, proportion of open forest, and proportion of multistoried forest) differed between plot categories. We conclude that small-scale forest structure has limited power to predict capercaillie occurrence at the stand scale, but that it explains well at the scale of the stand mosaic. Including variables for landscape structure improves predictions at the forest-stand scale. Habitat models built with data from one region cannot be expected to predict the species occurrence in other regions well. Thus, multiscale approaches are necessary to better understand species-habitat relationships. Our results can help regional authorities and forest-management planners to identify areas where suitable habita

Predictive habitat distribution models are normally assumed to sacrifice generality for precision and reality. Nevertheless, such models are often applied to predict the distribution of a species outside the area for which the model has been calibrated. We investigated how the geographic extent of the data used for calibration influenced the performance of habitat distribution models applied on independent data. We took a multi-scale logistic regression approach by varying the grain size to develop six habitat models for capercaillie Tetrao urogallus in Switzerland: three regional models, for the northern Pre-Alps, eastern Central Alps and Jura mountains, respectively, and three pooled models, each using data from two of the three regions. The six models were validated with data from the region(s) not used for model building. We used Cohen's Kappa and the area under the receiver operating characteristics curve as accuracy measures. The regional models performed well in the region where they had been calibrated, but poorly to moderately well in the other regions. The pooled models classified almost as well in their calibration regions as the corresponding regional models, but generally better when validated on data from the independent region. Hence, models built with data from single regions provide less certain predictions of species' distributions in other regions. We recommend building more general models using data pooled from several regions, when the aim is to predict species' distributions in independent regions

Forest patch models have been applied to simulate forest development and long-term forest succession in many studies. The main focus of these simulations has been on species composition and biomass in natural forests, but these models could also become useful for the prediction of other structural forest patterns such as size distributions. Up to now, most of these models have been validated by approaches such as comparison of simulation results with potential natural vegetation (PNV), or national forest inventory data. While these approaches may be appropriate to validate the simulated species composition, they are not sufficient in testing the prediction of other structural patterns. Thus, little is known about the accuracy of forest patch models in simulating structural forest patterns such as size distribution of different forest types. For this reason, we tested the forest patch model ForClim against empirical data from three Swiss mountain forests. The objectives of this study were: (i) to investigate the performance of ForClim in simulating structural forest patterns and (ii) to assess the influence of the regeneration, growth and mortality submodels of ForClim on the simulation results.Several shortcomings of the model were identified and quantified. In particular, the stress-induced mortality implemented in ForClim was found to overestimate the actual mortality rates. The excessive mortality was most likely caused by an inaccurate growth function or an overestimation of light competition. Once the stress-induced mortality was reduced, ForClim was able to reproduce structural forest patterns in an accurate manner. Based on these encouraging results, we suggest that ForClim as well as other forest patch models could become important tools for further applications in forest research