Dynamic Vegetation Model Research Articles

Model‐based reconstruction of the succession dynamics of a large river floodplain

AbstractMost large rivers in Europe and North America suffered flow regulations and channelization in the 19th and 20th century. To study the effects of the altered site conditions on the development of floodplain vegetation and create a benchmark map for their restoration, we calibrated and applied a dynamic floodplain vegetation model that accounts for the processes recruitment as well as morphodynamic disturbance and physiologic stress on vegetation to reconstruct the succession dynamics of the floodplain vegetation of a segment of the Rhine River from shortly after it was channelized (1872) until today (2016). The model calibration was based on historical maps and hydrologic data.Our simulation demonstrated a steady, one‐way progression of the vegetation communities towards mature phases without regression to younger stages. It was possible to attribute this development to a lack of morphodynamic disturbances strong enough to reset succession and to identify physiological stress caused by long inundations periods as the most relevant controlling factor of succession. The resulting vegetation distribution (2016) can be considered an estimation of the potential natural vegetation (PNV) under altered site conditions.The good agreement of the model results with an expert‐based PNV map showed that our approach is a good alternative to create benchmark maps for floodplain conservation and restoration projects. From a research and practitioners' viewpoint, it has the big advantage over the traditional approach that it allows to analyse different points in time as well as to be comprehensive and reproducible.

Long-term changes in riparian forest cover under a dam-induced flow scheme: the accompanying a numerical modelling perspective

River regulation affects downstream riparian vegetation dynamics. This paper investigates changes in vegetation cover along a steep reach of the Kitakami river in Japan, below the Gosho dam. Historical aerial imagery covering 60-years of both the pre- and post- dam construction phases is used to document land cover evolution. Changes in riparian forest cover are evaluated in relation to the corresponding hydrological regimes. A long-term trend in forest cover encroachment was revealed and a temporal correlation analysis confirmed a clear association with the modified water and sediment regime downstream of the dam. In addition, a numerical simulation of changes in spatial tree distribution using the dynamic riparian vegetation model (DRIPVEM) was undertaken. Validation of the simulation using the Kappa statistic showed a moderate-to-substantial agreement between model output and the photographic record.

Modeling the Vegetation Dynamics of Northern Shrubs and Mosses in the ORCHIDEE Land Surface Model

AbstractParameterizations of plant competition processes involving shrubs, mosses, grasses, and trees were introduced with the recently implemented shrubs and mosses plant functional types in the ORCHIDEE dynamic global vegetation model in order to improve the representation of high latitude vegetation dynamics. Competition is based on light capture for growth, net primary productivity, and survival to cold‐induced mortality during winter. Trees are assumed to outcompete shrubs and grasses for light, and shrubs outcompete grasses. Shrubs are modeled to have a higher survival than trees to extremely cold winters because of thermic protection by snow. The fractional coverage of each plant type is based on their respective net primary productivity and winter mortality of trees and shrubs. Gridded simulations were carried out for the historical period and the 21st century following the RCP4.5 and 8.5 scenarios. We evaluate the simulated present‐day vegetation with an observation‐based distribution map and literature data of boreal shrubs. The simulation produces a realistic present‐day boreal vegetation distribution, with shrubs, mosses north of trees and grasses. Nevertheless, the model underestimated local shrub expansion compared to observations from selected sites in the Arctic during the last 30 years suggesting missing processes (nutrients and microscale effects). The RCP4.5 and RCP8.5 projections show a substantial decrease of bare soil, an increase in tree and moss cover and an increase of shrub net primary productivity. Finally, the impact of new vegetation types and associated processes is discussed in the context of climate feedbacks.

Projecting Forest Dynamics Across Europe: Potentials and Pitfalls of Empirical Mortality Algorithms

Mortality is a key process of forest ecosystem dynamics and functioning strongly altering biomass stocks and carbon residence times. Dynamic vegetation models (DVMs) used to predict forest dynamics are typically based on simple, largely data-free (‘theoretical’) mortality algorithms (MAs). To improve DVM projections, the use of empirically based MAs has been suggested, but little is known about their impact on DVM behavior. A systematic comparison of eight MAs (seven inventory-based, one ‘theoretical’) for the pan-European tree species Pinus sylvestris L. was carried out within the DVM ForClim for present and future climate scenarios at three contrasting sites across Europe. Model accuracy was furthermore evaluated with empirical data from young- and old-growth forests. We found strongly diverging mortality patterns among the MAs for present climate. Based on their behavior, we identified two distinct empirical MA groups that were related to their structure (i.e., variables considered), but not to their geographic origin (i.e., the environmental conditions they were calibrated to). Under climate change, MAs based on a competition index produced ecologically inconsistent results, while MAs based on growth showed more plausible and less extreme behaviors. Furthermore, MAs based on growth reached a higher accuracy for projecting young- and old-growth forest dynamics. Our results demonstrate that using empirical MAs in DVMs has a high potential to better predict forest dynamics, but also a risk of yielding implausible results if their structure is inadequate. For DVM applications across large spatiotemporal scales, we thus suggest using MAs based on growth, particularly under future no-analogue climates.

IMPACTOS DO DESMATAMENTO PROGRESSIVO DA AMAZÔNIA NA PRECIPITAÇÃO DO BRASIL

A Floresta Amazônica possui importância indispensável para o equilíbrio terrestre, pois proporciona diversos serviços ambientais. O desmatamento é uma ameaça imediata a este ecossistema, pois afeta os níveis de evapotranspiração da floresta, tornando o clima local mais quente e seco; podendo influenciar no clima além de seus limites, devido à circulação atmosférica. Este estudo avalia os efeitos de longo prazo do desmatamento progressivo da Amazônia sobre a precipitação no Brasil, através do modelo CCM3-IBIS. Os resultados encontrados mostraram anomalias negativas com maior intensidade nos meses de setembro, outubro e novembro afetando o regime de chuvas em grande parte das Regiões Norte e Centro-Oeste do Brasil. Já as anomalias anuais mostraram a influência da localização da floresta sobre as áreas que tiveram os índices pluviométricos reduzidos, em praticamente toda a Região Norte, leste da Região Nordeste e norte da Região Centro-Oeste. Além disso, os resultados de anomalias negativas anuais demonstraram que o impacto do desmatamento não é linear, pois no cenário de desmatamento total foram observadas anomalias negativas menos intensas que no cenário onde 60% da floresta foi transformada em pastagem. Em contrapartida, nas Regiões Sudeste e Sul prevaleceram as anomalias positivas de precipitação.

Impact of Soil Reflectance Variation Correction on Woody Cover Estimation in Kruger National Park Using MODIS Data

Time-series of imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) has previously been used to estimate woody and herbaceous vegetation cover in savannas. However, this is challenging due to the mixture of woody and herbaceous plant functional types with specific contributions to the phenological signal and variations in soil background reflectance signatures observed from satellite. These factors cause variations in the accuracy and precision of woody cover estimates from different modelling approaches and datasets. Here, woody cover is estimated over Kruger National Park (KNP) from the MODIS 16-day composite time-series data using dry season NDVI/SAVI images and applying NDVIsoil determination methods. The woody cover estimates when NDVIsoil was ignored had R² = 0.40, p < 0.01, slope = 1.01, RMSE (root mean square error) = 15.26% and R² = 0.32, p < 0.03, slope = 0.79, RMSE = 16.39% for NDVIpixel and SAVIpixel, respectively, when compared to field plot data of plant functional type fractional cover. The woody cover estimated from the soil determination methods had a slope closer to 1 for both NDVI and SAVI but also a slightly higher RMSE. For a soil-invariant method, RMSE = 19.04% and RMSE = 17.34% were observed for NDVI and SAVI respectively, while for a soil-variant method, RMSE = 18.28% and RMSE = 19.17% were found for NDVI and SAVI. The woody cover estimated from all models had a high correlation and significant relationship with LiDAR/SAR based estimates and a woody cover map produced by Bucini. Woody cover maps are required for vegetation succession monitoring, grazing impact assessment, climate change mitigation and adaptation research and dynamic vegetation model validation.

A predictive algorithm for wetlands in deep time paleoclimate models

Abstract. Methane is a powerful greenhouse gas produced in wetland environments via microbial action in anaerobic conditions. If the location and extent of wetlands are unknown, such as for the Earth many millions of years in the past, a model of wetland fraction is required in order to calculate methane emissions and thus help reduce uncertainty in the understanding of past warm greenhouse climates. Here we present an algorithm for predicting inundated wetland fraction for use in calculating wetland methane emission fluxes in deep-time paleoclimate simulations. For each grid cell in a given paleoclimate simulation, the algorithm determines the wetland fraction predicted by a nearest-neighbour search of modern-day data in a space described by a set of environmental, climate and vegetation variables. To explore this approach, we first test it for a modern-day climate with variables obtained from observations and then for an Eocene climate with variables derived from a fully coupled global climate model (HadCM3BL-M2.2; Valdes et al., 2017). Two independent dynamic vegetation models were used to provide two sets of equivalent vegetation variables which yielded two different wetland predictions. As a first test, the method, using both vegetation models, satisfactorily reproduces modern day wetland fraction at a course grid resolution, similar to those used in paleoclimate simulations. We then applied the method to an early Eocene climate, testing its outputs against the locations of Eocene coal deposits. We predict global mean monthly wetland fraction area for the early Eocene of 8×106 to 10×106 km2 with a corresponding total annual methane flux of 656 to 909 Tg CH4 yr−1, depending on which of the two different dynamic global vegetation models are used to model wetland fraction and methane emission rates. Both values are significantly higher than estimates for the modern day of 4×106 km2 and around 190 Tg CH4 yr−1 (Poulter et al., 2017; Melton et al., 2013).

LPJ-GM 1.0: simulating migration efficiently in a dynamic vegetation model

Abstract. 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.

Integrating potential natural vegetation and habitat suitability into revegetation programs for sustainable ecosystems under future climate change

Global concern about the restoration of vegetation ecosystems has recently increased. Potential natural vegetation (PNV) and climate adaptation concepts should be integrated into revegetation programs to achieve sustainable ecosystems. The Yanhe Basin in the Loess Plateau of China (7687 km2) has been subjected to intense human activity for centuries. It was selected as the study area because vegetation degradation and restoration are occurring there. The objectives of this study were to (1) evaluate whether the current vegetation pattern is appropriate, and (2) provide a restoration plan for future revegetation programs based on PNV and habitat suitability patterns simulated by the dynamic vegetation model LPJ-GUESS. Current work focused on the parameter calibration and high-resolution climate data of the model over the study area. The comparisons of model performances in the PNV pattern and productivity indicated that parameter calibration was necessary for the model application. PNV of the Yanhe Basin may shift by 21.37–29.67% from 1981–2010 to 2071–2100, mainly in the southern part. Forests may decrease and steppes may increase as the climate becomes drier in the future. Comparisons between an existing land use map and the current PNV pattern indicated that only 40.8% of the forestland was coincident with the current PNV pattern, whereas grassland was a more suitable vegetation type for the rest of the terrain. In contrast, 83.1% of the grassland aligned with the current PNV pattern. Therefore, 16.9% remains to be forested. Current forestland and grassland patterns should be adjusted to cope with future climate change. Broadleaf summer-green shrubs covered a larger area and had higher habitat suitability than forests; they might be the most suitable woody plants for revegetation of the Yanhe Basin. The applied research approach could be extended to other regions undergoing similar revegetation programs and help promote sustainable vegetation management.

Tropical tree height and crown allometries for the Barro Colorado Nature Monument, Panama: a comparison of alternative hierarchical models incorporating interspecific variation in relation to life history traits

Abstract. Tree allometric relationships are widely employed for estimating forest biomass and production and are basic building blocks of dynamic vegetation models. In tropical forests, allometric relationships are often modeled by fitting scale-invariant power functions to pooled data from multiple species, an approach that fails to capture changes in scaling during ontogeny and physical limits to maximum tree size and that ignores interspecific differences in allometry. Here, we analyzed allometric relationships of tree height (9884 individuals) and crown area (2425) with trunk diameter for 162 species from the Barro Colorado Nature Monument, Panama. We fit nonlinear, hierarchical models informed by species traits – wood density, mean sapling growth, or sapling mortality – and assessed the performance of three alternative functional forms: the scale-invariant power function and the saturating Weibull and generalized Michaelis–Menten (gMM) functions. The relationship of tree height with trunk diameter was best fit by a saturating gMM model in which variation in allometric parameters was related to interspecific differences in sapling growth rates, a measure of regeneration light demand. Light-demanding species attained taller heights at comparatively smaller diameters as juveniles and had shorter asymptotic heights at larger diameters as adults. The relationship of crown area with trunk diameter was best fit by a power function model incorporating a weak positive relationship between crown area and species-specific wood density. The use of saturating functional forms and the incorporation of functional traits in tree allometric models is a promising approach for improving estimates of forest biomass and productivity. Our results provide an improved basis for parameterizing tropical plant functional types in vegetation models.

Harmonising plant functional type distributions for evaluating Earth system models

Abstract. Dynamic vegetation models simulate global vegetation in terms of fractional coverage of a few plant functional types (PFTs). Although these models often share the same concept, they differ with respect to the number and kind of PFTs, complicating the comparability of simulated vegetation distributions. Pollen-based vegetation reconstructions are initially only available in the form of time series of individual taxa that are not distinguished in the models. Thus, to evaluate simulated vegetation distributions, the modelling results and pollen-based vegetation reconstructions have to be converted into a comparable format. The classical approach is the method of biomisation, but hitherto PFT-based biomisation methods were only available for individual models. We introduce and evaluate a simple, universally applicable technique to harmonise PFT distributions by assigning them into nine mega-biomes, using only assumptions on the minimum PFT cover fractions and few bioclimatic constraints (based on the 2 m temperature). These constraints mainly follow the limitation rules used in the classical biome models (here BIOME4). We test the method for six state-of-the-art dynamic vegetation models that are included in Earth system models based on pre-industrial, mid-Holocene and Last Glacial Maximum simulations. The method works well, independent of the spatial resolution or the complexity of the models. Large biome belts (such as tropical forest) are generally better represented than regionally confined biomes (warm–temperate forest, savanna). The comparison with biome distributions inferred via the classical biomisation approach of forcing biome models (here BIOME1) with the simulated climate states shows that the PFT-based biomisation is even able to keep up with the classical method. However, as the new method considers the PFT distributions actually calculated by the Earth system models, it allows for a direct comparison and evaluation of simulated vegetation distributions which the classical method cannot do. Thereby, the new method provides a powerful tool for the evaluation of Earth system models in general.

Tree mortality submodels drive simulated long-term forest dynamics: assessing 15 models from the stand to global scale.

Models are pivotal for assessing future forest dynamics under the impacts of changing climate and management practices, incorporating representations of tree growth, mortality, and regeneration. Quantitative studies on the importance of mortality submodels are scarce. We evaluated 15 dynamic vegetation models (DVMs) regarding their sensitivity to different formulations of tree mortality under different degrees of climate change. The set of models comprised eight DVMs at the stand scale, three at the landscape scale, and four typically applied at the continental to global scale. Some incorporate empirically derived mortality models, and others are based on experimental data, whereas still others are based on theoretical reasoning. Each DVM was run with at least two alternative mortality submodels. Model behavior was evaluated against empirical time series data, and then, the models were subjected to different scenarios of climate change. Most DVMs matched empirical data quite well, irrespective of the mortality submodel that was used. However, mortality submodels that performed in a very similar manner against past data often led to sharply different trajectories of forest dynamics under future climate change. Most DVMs featured high sensitivity to the mortality submodel, with deviations of basal area and stem numbers on the order of 10–40% per century under current climate and 20–170% under climate change. The sensitivity of a given DVM to scenarios of climate change, however, was typically lower by a factor of two to three. We conclude that (1) mortality is one of the most uncertain processes when it comes to assessing forest response to climate change, and (2) more data and a better process understanding of tree mortality are needed to improve the robustness of simulated future forest dynamics. Our study highlights that comparing several alternative mortality formulations in DVMs provides valuable insights into the effects of process uncertainties on simulated future forest dynamics.

Enhancing SWAT with remotely sensed LAI for improved modelling of ecohydrological process in subtropics

Vegetation growth in Soil and Water Assessment Tool (SWAT) is a crucial process for quantifying ecohydrological modelling, as it influences evapotranspiration, interception, soil erosion and biomass production. The simplified version of Environmental Policy Integrated Climate (EPIC) in SWAT was originally designed for temperate regions and naturally based on temperature to simulate growth cycles of vegetation. However, tropical or subtropical vegetation growth is mainly controlled by rainfall. Due to this limitation, current SWAT simulations in tropics and subtropics have been facing a series of problems on vegetation dormancy, water balance and sediment yield. Therefore, we proposed an approach to enhance the modelling of SWAT vegetation dynamics with remotely sensed leaf area index (LAI), to finally increase the applicability of SWAT in tropical or subtropical areas. Spatially and temporally continuous LAI products (1 day, 500 m) from Moderate Resolution Imaging Spectroradiometer (MODIS) observations were integrated into SWAT to replace the LAI simulated by built-in EPIC module. Two advanced filter algorithms were employed to derive a downscaled LAI (30 m) to keep a consistent spatial scale with the size of Hydrological Response Units (HRU) and open data (i.e. SRTM, 30 m), and the source code of the plant growth module were correspondingly modified to incorporate the downscaled LAI into SWAT. To examine the performance of our proposed approach, a case study was conducted in a representative middle-scale (6384 km2) subtropical watershed of Meichuan basin, China, and detailed analysis was performed to investigate its ecohydrological effects, such as streamflow, sediment yield and LAI dynamics from 2001 to 2014. Model performances were compared among three scenarios: (1) original SWAT, (2) SWAT with a corrected plant dormancy function, and (3) modified SWAT after integration of MODIS LAI (our proposed method). Results showed that the modified SWAT took advantage of downscaled MODIS LAI and produced more reasonable seasonal curves of vegetation cover factor (C) of plants than the original model. Correspondingly, the modified SWAT substantially improved streamflow and sediment simulations. The findings demonstrated that SWAT model can be a useful tool for simulating ecohydrological process for subtropical ecosystems when integrated with our proposed method.

Redefining temperate forest responses to climate and disturbance in the eastern United States: New insights at the mesoscale

AbstractAimClimate and disturbance alter forest dynamics, from individual trees to biomes and from years to millennia, leaving legacies that vary with local, meso‐ and macroscales. Motivated by recent insights in temperate forests, we argue that temporal and spatial extents equivalent to that of the underlying drivers are necessary to characterize forest dynamics across scales. We focus specifically on characterizing mesoscale forest dynamics because they bridge fine‐scale (local) processes and the continental scale (macrosystems) in ways that are highly relevant for climate change science and ecosystem management. We revisit ecological concepts related to spatial and temporal scales and discuss approaches to gain a better understanding of climate–forest dynamics across scales.LocationEastern USA.Time periodLast century to present.Major taxa studiedTemperate broadleaf forests.MethodsWe review regional literature of past tree mortality studies associated with climate to identify mesoscale climate‐driven disturbance events. Using a dynamic vegetation model, we then simulate how these forests respond to a typical climate‐driven disturbance.ResultsBy identifying compound disturbance events from both a literature review and simulation modelling, we find that synchronous patterns of drought‐driven mortality at mesoscales have been overlooked within these forests.Main conclusionsAs ecologists, land managers and policy‐makers consider the intertwined drivers of climate and disturbance, a focus on spatio‐temporal scales equivalent to those of the drivers will provide insight into long‐term forest change, such as drought impacts. Spatially extensive studies should also have a long temporal scale to provide insight into pathways for forest change, evaluate predictions from dynamic forest models and inform development of global vegetation models. We recommend integrating data collected from spatially well‐replicated networks (e.g., archaeological, historical or palaeoecological data), consisting of centuries‐long, high‐resolution records, with models to characterize better the mesoscale response of forests to climate change in the past and in the future.

Grazing and aridity reduce perennial grass abundance in semi-arid rangelands – Insights from a trait-based dynamic vegetation model

Semi-arid tropical rangelands substantially contribute to livelihoods of subsistence farmers, but are threatened by undesired vegetation shifts due to climate change and overgrazing. Grazing-induced shifts of the grass community composition are often associated with rangeland degradation. To identify sustainable management strategies, a process-based understanding of grass functional diversity and rangeland dynamics is required. We present a new scheme for aDGVM2, a dynamic vegetation model for tropical ecosystems, that distinguishes annual and perennial grasses based on trait trade-offs to improve the representation of rangeland communities. Additionally, the model includes a new scheme that describes selective grazing and grazing effects on grass-layer composition. We tested the new model version for various grazing intensities along a precipitation gradient in South Africa. Mean annual precipitation below 500 mm constrained rangeland productivity and carrying capacity. Increasing grazing intensity reduced rangeland productivity and increased annual grass abundance. Heavy grazing resulted in annual grass dominance. Livestock preferred perennial over annual grasses at low grazing intensities at all except the two driest sites; preference switched to annual grasses at intermediate intensities, and became non-discriminating at high grazing intensities. Rangeland recovery after removal of grazers required 2–15 years. We conclude that management intervention reducing or eliminating grazing pressure during and after stress years is crucial to allow rangeland recovery and avoid permanent degradation.

A tribute to the use of minimalistic spatially-implicit models of savanna vegetation dynamics to address broad spatial scales in spite of scarce data

The savanna biome encompasses a variety of vegetation physiognomies that traduce complex dynamical responses of plants to the rainfall gradients leading from tropical forests to hot deserts. Such responses are shaped by interactions between woody and grassy plants that can be either direct, disturbance-mediated or both. There has been increasing evidence that several vegetation physiognomies, sometimes highly contrasted, may durably coexist under similar rainfall conditions suggesting multi-stability or at least not abrupt transitions. These fascinating questions have triggered burgeoning modelling efforts which have, however, not yet delivered an integrated picture liable to furnish sensible predictions of potential vegetation at broad scales. In this paper, we will recall the key ecological processes and resulting vegetation dynamics that models should take into account. We will also present the main modelling options present in the literature and advocate the use of minimalistic models, capturing only the essentialВ processes while retaining sufficient mathematical tractability and restricting themselves to a minimal set of parameters assessable from the overall literature.

How Does Climate Change Influence the Economic Value of Ecosystem Services in Savanna Rangelands?

Savanna rangelands provide essential ecosystem services to people. Intense land-use and climate change may degrade ecosystems and influence the provision of ecosystem services. Complex dynamic vegetation models can simulate future vegetation and how vegetation may interact with land-use. Yet, identification of best-practice management directives in the face of climate change is challenging and requires consideration of socio-economic aspects. Here, we developed an economic model to describe the value of key ecosystem services, namely fuelwood harvesting and livestock, and coupled it with aDGVM, a vegetation model for tropical ecosystems. We used simulation optimization to identify land-use strategies that maximize economic value to stakeholders in the planning horizon until 2050, and compared it to realistic land-use intensities. We found that realistic intensities exceed optimal intensities, indicating the tragedy of the commons and external stress factors, prevalent in many rural savanna rangelands. We show that a reduction in fuelwood harvesting until 2050 allows vegetation to recover but that recovery is slow. We conclude that strong governance is important in rural savanna rangelands to ensure sustainable use of resources under future climate conditions. The coupled ecological-economic model can serve as tool to develop sustainable land-use strategies in complex socio-ecological systems globally.

How do tree mortality models from combined tree-ring and inventory data affect projections of forest succession?

Tree mortality is caused by complex interactions between multiple biotic and abiotic factors. Processes of tree mortality that are not induced by natural disturbances are often reflected in distinct radial growth patterns of trees, which typically serve as reliable indicators of impending tree mortality. However, it remains unclear whether empirical mortality models that are based on tree size and growth result in more realistic projections of forest succession in dynamic vegetation models (DVMs). We used a combination of tree-ring and inventory data from unmanaged Swiss natural forest reserves to derive species-specific survival models for six Central European tree species (Abies alba, Fagus sylvatica, Larix decidua, Picea abies, Pinus cembra and Quercus spp.). We jointly used 528 tree-ring samples and inventory data from eight forest reserves. We implemented the estimated parameters of the survival models into the DVM ForClim and performed simulations of forest succession that were validated using the inventory data of the forest reserves. Size- and growth-dependent variables (i.e., diameter at breast height and mean ring width) over the last few years prior to tree death were reliable predictors to distinguish between dying and living trees. Very low mean ring widths over several preceding years as well as small and large trees, respectively, reflected low survival probabilities. However, the small sample sizes of small and large trees resulted in considerable uncertainty of the survival probabilities. The implementation of these survival models in ForClim yielded plausible projections in short-term simulations and for some sites improved the predictions compared to the current ForClim version. Stand basal area, however, tended to be overestimated. Long-term simulations of ForClim based on the empirical survival models resulted in realistic predictions only if the uncertainty of the predicted survival probabilities was considered. We conclude that the combination of different data sources in combination with the consideration of intra-specific trait variability yields robust predictions of tree survival probabilities, thus paving the way towards better tree mortality models and more reliable projections of future forest dynamics.

Refining Species Traits in a Dynamic Vegetation Model to Project the Impacts of Climate Change on Tropical Trees in Central Africa

African tropical ecosystems and the services they provide to human society suffer from an increasing combined pressure of land use and climate change. How individual tropical tree species respond to climate change remains relatively unknown. In this study, we refined the species characterization in the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model by replacing plant functional type morpho-physiological traits by species-specific traits. We focus on 12 tropical tree species selected for their importance in both the plant community and human society. We used CARAIB to simulate the current species net primary productivity (NPP), biomass and potential distribution and their changes in the future. Our results indicate that the use of species-specific traits does not necessarily result in an increase of predicted current NPPs. The model projections for the end of the century highlight the large uncertainties in the future of African tropical species. Projected changes in species distribution vary greatly with the general circulation model (GCM) and, to a lesser extent, with the concentration pathway. The question about long-term plant response to increasing CO2 concentrations also leads to contrasting results. In absence of fertilization effect, species are exposed to climate change and might lose 25% of their current distribution under RCP8.5 (12.5% under RCP4.5), considering all the species and climatic scenarios. The vegetation model projects a mean biomass loss of −21.2% under RCP4.5 and −34.5% under RCP8.5. Potential range expansions, unpredictable due to migration limitations, are too limited for offsetting range contraction. By contrast, if the long-term species response to increasing [CO2] is positive, the range reduction is limited to 5%. However, despite a mean biomass increase of 12.2%, a positive CO2 feedback might not prevent tree dieback. Our analysis confirms that species will respond differently to new climatic and atmospheric conditions, which may induce new competition dynamics in the ecosystem and affect ecosystem services.

Climate Regimes Override Micro-Site Effects on the Summer Temperature Signal of Scots Pine at Its Northern Distribution Limits.

Tree growth at northern boreal treelines is generally limited by summer temperature, hence tree rings serve as natural archives of past climatic conditions. However, there is increasing evidence that a changing summer climate as well as certain micro-site conditions can lead to a weakening or loss of the summer temperature signal in trees growing in treeline environments. This phenomenon poses a challenge to all applications relying on stable temperature-growth relationships such as temperature reconstructions and dynamic vegetation models. We tested the effect of differing ecological and climatological conditions on the summer temperature signal of Scots pine at its northern distribution limits by analyzing twelve sites distributed along a 2200 km gradient from Finland to Western Siberia (Russia). Two frequently used proxies in dendroclimatology, ring width and maximum latewood density, were correlated with summer temperature for the period 1901–2013 separately for (i) dry vs. wet micro-sites and (ii) years with dry/warm vs. wet/cold climate regimes prevailing during the growing season. Differing climate regimes significantly affected the temperature signal of Scots pine at about half of our sites: While correlations were stronger in wet/cold than in dry/warm years at most sites located in Russia, differing climate regimes had only little effect at Finnish sites. Both tree-ring proxies were affected in a similar way. Interestingly, micro-site differences significantly affected absolute tree growth, but had only minor effects on the climatic signal at our sites. We conclude that, despite the treeline-proximal location, growth-limiting conditions seem to be exceeded in dry/warm years at most Russian sites, leading to a weakening or loss of the summer temperature signal in Scots pine here. With projected temperature increase, unstable summer temperature signals in Scots pine tree rings might become more frequent, possibly affecting dendroclimatological applications and related fields.