Potential Vegetation Distribution Research Articles

Modelling of potential vegetation identifies diverging expectable outcomes of river floodplain widening

River re-naturalisations are at the forefront of conservation efforts. The hope is that these interventions will benefit both local ecosystems and facilitate flood mitigation. While hydrological modelling has been a standard procedure in assessing the outcomes of river re-naturalisations, vegetation modelling has not always been performed as part of these assessments. We hypothesised that the use of potential vegetation modelling, i.e. the modelling of self-sustainable vegetation that can survive after the intervention, can provide insight into vegetation outcome of river re-naturalisation and can thus support vegetation restoration and conservation planning. We investigated the utility of potential vegetation modelling under a specific, widespread element of river re-naturalisation: river floodplain widening at three study sites along the Tisza River in Hungary. We applied potential vegetation modelling, in addition to hydrological and groundwater modelling, to assess the expected vegetation outcome of the river floodplain widening. Flood frequency and duration were assessed by the HEC-RAS hydrological model. Based on the output this hydrological model provided, expected values of the water-related explanatory variables (including groundwater level) were calculated. Statistical relationships encompassed by the existing mulitple potential vegetation (MPV) models of Hungary were applied to the environmental variable sets corresponding to conditions before and after floodplain widening (pre- and post-treatment, respectively), including water-related and other explanatory variables. This resulted in predicted potential vegetation distribution for pre-treatment and post-treatment conditions, which were then compared via ordinations and PERMANOVA. At two of the study sites, post-treatment potential vegetation prediction typically showed vegetation types requiring wetter and less saline conditions when compared to the pre-treatment potential vegetation distribution. This pattern corresponds to general expectations given river floodplain widening. However, at one of the sites, the potentiality of saline and non-saline steppe vegetation was actually more pronounced under the expected post-treatment conditions than those before widening. The strengthening of the potentiality of dry vegetation types can be explained by the minor environmental differences related to the microrelief. As the studied sites were all located in the lowlands where geomorphological variation is small, the effect of these minor geomorphological differences on post-treatment potential vegetation would have remained hidden without applying MPV models. In conclusion, scenarios that employ MPV models help predict river restoration outcomes more accurately and can help identify factors that might otherwise be overlooked. Thus, when combined with physical modelling of river flow, their use can aid in the restoration and landscape planning decisions in river re-naturalisation projects.

Simulation of potential vegetation distribution in arid areas at regional scale

Awareness of potential natural vegetation (PNV) is imperative in ecological restoration. Extensive global and national PNV simulation with coarse spatial resolution is increasingly seen to mitigate land degradation under climate change. However, precise PNV modeling in drylands at a small regional scale with high enough spatial resolution usually receives little attention. This study developed a precise regional multiple PNV framework in drylands based on climatic variables, soil properties, groundwater depth, and topographical variables through a maximum entropy algorithm (MaxEnt). The effects of three different input variable combinations and five background point numbers on PNV prediction were compared. Applying the model to the lower Tarim River Basin, Northwest China, the results showed that the model developed incorporating groundwater depth exhibited better performance in regional PNV simulation, and the AUC of the tree, grass, and shrub was improved by 0.21, 0.18 and 0.09, respectively. Furthermore, the results showed that the probability of growing natural vegetation decreased with the increase of distance from the river within 100 m; and the probability of occurring tree, grass, and shrub was higher when the groundwater depth was close to −3 m, −2 m, and −7 m, respectively. Moreover, the suitable habitat of the tree, grass, and shrub was mainly concentrated in the riparian zone, accounting for 34.5%, 21.9%, and 72.4% of the study area, respectively. Comparisons of reconstructed PNV and existing land use patterns revealed that the lower Tarim River still has enormous land potential for ecological restoration. The model developed here can assist in precise ecosystem restoration in drylands.

Quantifying the Potential Vegetation Distribution under Climate Change: The Case of Cryptomeria fortunei in Dongting Lake Watershed, China

Potential vegetation distribution is an important study in environmental sciences. We utilized the Mixed Least Squares–Total Least Squares (MLS-TLS) method and the Signal Mode Decomposition method and the Ecological Niche model to identify the inter-correlations of internal climate change factors and constructed an environmental factor response regression model. We identified the resonance periods and trend relationships among climate factors (temperature, precipitation, and evapotranspiration) and found that the evapotranspiration of the watershed interferes with the correlation between temperature and precipitation on a five-year scale. The specific change degree of extreme climate indicators in the region was quantified by the Range of Variability Approach, among which the precipitation indicators were all below 33% (low change). There were significant differences between the key bioclimatic variables and Aspect of the development of suitable vegetation habitats. The difference between the Aspect and average daily air temperature is the main contributor to the spatial distribution of vegetation, and the mutual contribution is 76.19%. Our regression model can effectively simulate the potential distribution of vegetation (r = 0.854). Compared to the MaxEnt model, our regression model can quantitatively and intuitively provide suitable habitat values for Cryptomeria fortunei at any given location in the basin. Under future scenarios (2021–2040), suitable habitat for Cryptomeria fortunei in the eastern and western regions of the basin is projected to deteriorate further. The research results can provide some help for policymakers to eliminate the potential adverse effects of future climate change on regional ecology.

Study on the Ecological Degradation of Lashihai Area based on Potential Vegetation

Ecological degradation is a common problem around the world which has a profound impact on the sustainable development of mankind. This paper selects Lashihai basin as the study case, and uses Logistic stepwise regression to simulate the original ecology of the potential vegetation in the area as a reference system for the definition and analysis of the subsequent degree of ecological degradation and its distribution characteristics. The analysis yields four main results. (1) The strong human disturbance areas in the Lashihai region are concentrated in the Lashihai basin, and the main impact factors are roads, residential areas and cultivated lands. (2) Besides lake, there are eight potential vegetation types in Lashihai, among which evergreen coniferous forest is the dominant community, and the other seven planting types of potential vegetation include warm meadow, grass, beach grass, evergreen broad-leaved shrubbery, deciduous broad-leaved shrubbery, warm steppe and alpine grassland. (3) The elevation and average phosphorus content have significant effects on the distribution of potential vegetation, while the different vegetation types have differential sensitivities to environmental factors. (4) On the whole, the degree of ecological degradation in the basin is relatively light, in which the proportion of non-degraded areas accounts for nearly half, the area of mild degradation is about one-fourth, the moderately degraded area is concentrated in areas with strong human disturbance, accounting for only 18.64%, and the severe degradation is rare, occupying an area of only 3.17%.

Projected Changes in Terrestrial Vegetation and Carbon Fluxes under 1.5 °C and 2.0 °C Global Warming

The terrestrial ecosystem plays a vital role in regulating the exchange of carbon between land and atmosphere. This study investigates how terrestrial vegetation coverage and carbon fluxes change in a world stabilizing at 1.5 °C and 2 °C warmer than pre-industrial level. Model results derived from 20 Earth System Models (ESMs) under low, middle, and high greenhouse emission scenarios from CMIP5 and CMIP6 are employed to supply the projected results. Although the ESMs show a large spread of uncertainties, the ensemble means of global LAI are projected to increase by 0.04 ± 0.02 and 0.08 ± 0.04 in the 1.5 and 2.0 °C warming worlds, respectively. Vegetation density is projected to decrease only in the Brazilian Highlands due to the decrease of precipitation there. The high latitudes in Eurasia are projected to have stronger increase of LAI in the 2.0 °C warming world compared to that in 1.5 °C warming level caused by the increase of tree coverage. The largest zonal LAI is projected around 70° N while the largest zonal NPP is projected around 60° N and equator. The zonally inhomogeneous increase of vegetation density and productivity relates to the zonally inhomogeneous increase of temperature, which in turn could amplify the latitudinal gradient of temperature with additional warming. Most of the ESMs show uniform increases of global averaged NPP by 10.68 ± 8.60 and 15.42 ± 10.90 PgC year−1 under 1.5 °C and 2.0 °C warming levels, respectively, except in some sparse vegetation areas. The ensemble averaged NEE is projected to increase by 3.80 ± 7.72 and 4.83 ± 10.13 PgC year−1 in the two warming worlds. The terrestrial ecosystem over most of the world could be a stronger carbon sink than at present. However, some dry areas in Amazon and Central Africa may convert to carbon sources in a world with additional 0.5 °C warming. The start of the growing season in the northern high latitudes is projected to advance by less than one month earlier. Five out of 10 CMIP6 ESMs, which use the Land Use Harmonization Project (LUH2) dataset or a prescribed potential vegetation distribution to constrain the future change of vegetation types, do not reduce the model uncertainties in projected LAI and terrestrial carbon fluxes. This may suggest the challenge in optimizing the carbon fluxes modeling in the future.

Scenarios of potential vegetation distribution in the different gradient zones of Qinghai-Tibet Plateau under future climate change

The spatial distribution of potential vegetation types in Qinghai-Tibet Plateau presents a significant vertical zonation. Explicating the vertical differences of potential vegetation distribution under future climate change in Qinghai-Tibet Plateau is an important issue for understanding the response of terrestrial ecosystem to climate change. Based on the observed climate data in 1981–2010 (T0), the scenario data of RCP 2.6, RCP 4.5 and RCP 8.5 released by CMIP5 in 2011–2040 (T1), 2041–2070 (T2) and 2071–2100 (T3), and the digital elevation model (DEM) data, the Holdridge life zone (HLZ) model has been improved to simulate the scenarios of potential vegetation distribution in the different gradient zones of Qinghai-Tibet plateau. The shift model of mean center has been improved to calculate the shift direction and distance of mean center in the potential vegetation types. The ecological diversity index was introduced to compute the ecological diversity change of potential vegetation. The simulated results show that there are 17 potential vegetation types in Qinghai-Tibet Plateau. Wet tundra, high-cold moist forest and nival are the major potential vegetation types and cover 56.26% of the total area of Qinghai-Tibet Plateau. Under the three scenarios, the nival would have the largest decreased area that would be decreased by 3.340 × 104 km2 per decade, and the high-cold wet forest would have the greatest increased area that would be increased by 3.340 × 104 km2 on average per decade from T0 to T3. The potential vegetation types distributed in the alpine zone would show the fastest change ratio (11.32% per decade) and that in low mountain and other zone would show the slowest change ratio (7.54% per decade) on average. The ecological diversity and patch connectivity of potential vegetation would be decreased by 0.108% and 0.290% per decade on average from T0 to T3. In general, the potential vegetation types distributed in the high elevation area generally have a higher sensitivity to climate change in Qinghai-Tibet plateau in the future.

Integrating Habitat Suitability and the Near-Nature Restoration Priorities into Revegetation Plans Based on Potential Vegetation Distribution

Selecting optimal revegetation patterns and filtering priority areas can improve the effectiveness and efficiency of revegetation planning, particularly in areas with severe vegetation damage. However, few people include optimal revegetation patterns and priority restoration areas into revegetation plans. The Near-Nature restoration pays attention to “based on nature” ideas, guiding the degraded ecosystems to reorganize and achieving sustainable restoration through self-regulation. In this study, we conducted a field survey of the native vegetation communities in the Yanhe River catchment, and the data obtained were used to construct the potential distribution suitability of the habitat and screen the priority areas through the combination of MaxEnt and prioritizr models. We drew a heat map of species richness by simulating the potential distribution of 60 native species. The results showed that the potentially suitable habitats for forest cover were distributed in the southern part of the Yanhe River catchment; the potentially suitable habitats for herbaceous plant species were located in the center and the northwest parts of the study area; the potentially suitable habitats for shrub plant species in this area were larger than that of the forest, and herbaceous plants species were distributed in many zones of the study area. This study demonstrated that shrubs and herbaceous plant species in parts of the Loess Plateau should be considered as the pioneer plants of revegetation in future revegetation plans. Moreover, we also mapped the priority area of the Near-Nature restoration based on the richness of the potential native species. The procedure followed in this study could provide guidance for revegetation planning and manual management in the regions where vegetation damage occurs.

THE CREATION OF BIOCLIMATIC VEGETATION MAP TO DEVELOP SUSTAINABLE AGRO FORESTRY IN BA AND KONE RIVER BASIN, VIETNAM

Bioclimate and natural vegetation have a long - term relationship that identify the potential vegetation distribution at different areas. For that reason, bioclimatic classification system was applied to the territory of Ba and Kone river basin, Vietnam. The precipitation and temperature dataset of Ba and Kone river basin was collected from 17 climate, hydrology, rain gauge stations which allowed to create a bioclimatic map at a scale of 1:250.000. Three bioclimatic factors of thermal-moisture basic conditions such as annual temperature (TN), annual rainfall (RN), length of dry season (n) are selected as criteria system of Ba and Kone river basin’s bioclimate. In order to describe the relationships between bioclimatic variables and zonal vegetation units, the resulting map presented 12 bioclimatic units corresponding distribution of vegetation from low to high altitudes. By building bioclimatology map in Ba and Kone river basin, the government can develop sustainable agro forestry in Central Highlands and South Central Coast of Vietnam.

A GIS simulation of potential vegetation in China under different climate scenarios at the end of the 21st century

The study of potential vegetation can reveal the impact of climate on changes in vegetation patterns. It is the starting point for studying vegetation-environmental classification and relationships, and it is the key point for studying global change and terrestrial ecosystems. By using the Comprehensive Sequential Classification System (CSCS) and the meteorological data under the four climate change scenarios from the IPCC5 publication, the present paper carries out a GIS simulation study of the spatial distribution of potential vegetation in China at the end of the 21st century. The results indicate that under the four climate scenarios at the end of the 21st century: (1) The potential vegetation in China shows significant horizontal and vertical distribution, which corresponds well to those of natural topographic features. (2) There are 40 classes of potential vegetation in China. Tropical-extrarid tropical desert (VIIA), which has no corresponding condition of growth in China, is commonly lacking, and differences exist among the potential vegetation classes and among the ratios of the classes under different scenarios. (3) From the perspective of categories, temperate forest is the most widely distributed, and savanna is the least widely distributed. Together with the strengthening of the radiation intensity according to RCP2.6 → RCP4.5 → RCP6.0 → RCP8.5, the area covered by cold-dry potential vegetation decreases as the area covered by warm-humid potential vegetation increases. As a result, the areas of tundra and alpine steppe, frigid desert, steppe, and temperate humid grassland tend to decrease, and those of semi-desert, temperate forest, sub-tropical forest, tropical forest, warm desert, and savanna tend to increase. Moreover, the potential vegetation in China at the end of the 21st century would change at different levels and in different directions when compared with that at the end of the 20th century. (4) In the same period, potential vegetation in different regions shows differences in their sensitivity to climate change, and by the end of the 21st century, 30.73% of land in China would be classified as a sensitive region, which highly corresponds to the current ecologically vulnerable zone, and whose potential vegetation easily evolves along with changes of climate scenarios.

High-resolution modelling closes the gap between data and model simulations for Mid-Holocene and present-day biomes of East Africa

East Africa hosts a striking diversity of terrestrial ecosystems, which vary both in space and time due to complex regional topography and a dynamic climate. The structure and functioning of these ecosystems under this environmental setting can be studied with dynamic vegetation models (DVMs) in a spatially explicit way. Yet, regional applications of DVMs to East Africa are rare and a comprehensive validation of such applications is missing. Here, we simulated the present-day and mid-Holocene vegetation of East Africa with the DVM, LPJ-GUESS and we conducted an exhaustive comparison of model outputs with maps of potential modern vegetation distribution, and with pollen records of local change through time. Overall, the model was able to reproduce the observed spatial patterns of East African vegetation. To see whether running the model at higher spatial resolutions (10′×10′) contribute to resolve the vegetation distribution better and have a better comparison scale with the observational data (i.e. pollen data), we run the model with coarser spatial resolution (0.5°×0.5°) for the present-day as well. Both the area- and point-wise comparison showed that a higher spatial resolution allows to better describe spatial vegetation changes induced by the complex topography of East Africa. Our analysis of the difference between modelled mid-Holocene and modern-day vegetation showed that whether a biome shifts to another is best explained by both the amount of change in precipitation it experiences and the amount of precipitation it received originally. We also confirmed that tropical forest biomes were more sensitive to a decrease in precipitation compared to woodland and savanna biomes and that Holocene vegetation changes in East Africa were driven not only by changes in annual precipitation but also by changes in its seasonality.

Simulating potential vegetation distribution in a mountainous region of the Eastern Tibetan Plateau: A case study of Barkam County

The main objective of this study is to simulate the potential vegetation types on the basis of environmental parameters. The paper took Barkam County in a mountainous region of the Eastern Tibetan Plateau as the study area. The vegetation distribution was mapped in 1994 and 2007 based on TM remote sensing images by object-oriented interpretation method. We overlaid the two maps to find out the vegetation patches which have not changed, and took them as stable types. Fifty per cent of the stable patches were randomly sampled to operate the logistic regression with related environmental parameters; others were used as test data of simulated results. Seven environmental parameters were mapped, including elevation, slope, aspect, surface curvature, solar radiation, temperature and precipitation, based on DEM data and meteorological site data by GIS technology. The relationship between the spatial distribution of vegetation and environmental variables were quantified by logistic regression. The distribution pr...

Statistical modelling of a new global potential vegetation distribution

The potential natural vegetation (PNV) distribution is required for several studies in environmental sciences. Most of the available databases are quite subjective or depend on vegetation models. We have built a new high-resolution world-wide PNV map using a objective statistical methodology based on multinomial logistic models. Our method appears as a fast and robust alternative in vegetation modelling, independent of any vegetation model. In comparison with other databases, our method provides a realistic PNV distribution in agreement with respect to BIOME 6000 data. Among several advantages, the use of probabilities allows us to estimate the uncertainty, bringing some confidence in the modelled PNV, or to highlight the regions needing some data to improve the PNV modelling. Despite our PNV map being highly dependent on the distribution of data points, it is easily updatable as soon as additional data are available and provides very useful additional information for further applications.

Modelling effective thermal climate for mountain forests in the Bavarian Alps: Which is the best model?

AbstractQuestion: Which thermal climate model performs best in predicting the combined effects of temperature and radiation on forest vegetation in the Bavarian Alps?Location: Bavarian Alps, Germany.Methods: In order to find the best model for effective thermal climate for the Bavarian Alps, we analysed models using the following predictors derived from climate data and/or a digital elevation model: (a) temperature variables only, (b) temperature plus slope aspect and inclination, and (c) temperature plus potential global solar radiation. Models were tested by linear regression against four response variables based on average Ellenberg indicator values for temperature (cover weighted/unweighted, with/without bryophytes), which were computed for 2280 georeferenced relevés from the vegetation database BERGWALD. We optimized (b) by empirically searching for thermally most favourable slope aspect and inclination.Results: Closest model fit was achieved for unweighted temperature values based on vascular plants without bryophytes. Model fit (adj. R2) increased from using temperature alone to temperature–radiation, to temperature–aspect–inclination as predictors. The best spatially explicit model for predicting temperature values (adj. R2=0.57) was based on the variable combination mean temperature in the growing season (May to September), slope aspect (optimal aspect 195°) and inclination (optimal slope 30°).Conclusion: Combining mean temperatures and relief variables in GIS allows creation of predictive maps of mountain forest response to thermal climate. Applied to climate change scenarios, our model can forecast potential vegetation distribution in the future. The superiority of simple empirical relief factors over a widely used model of potential radiation casts doubt on the meaningfulness of the latter for vegetation studies.

Quantitative biome reconstruction using modern and late Quaternary pollen data from the southern part of the Russian Far East

In this study we present a recent compilation of 286 modern surface pollen spectra from the southern part of the Russian Far East (42–54°N, 131–141°E) and use it to test the biome reconstruction method. Seventy terrestrial pollen taxa were assigned to plant functional types and then classified to eight regional biomes. When applied to 286 surface pollen spectra, the method assigns about 70% (201 sites) of the samples to the cool mixed forest biome, 17% – to the taiga, 2% – to the cool conifer forest, 3% – to the temperate deciduous forest, and 7% – to the steppe. The steppe reconstruction is characteristic of the pollen spectra from the agricultural areas around Lake Khanka. A visual comparison shows good agreement between pollen-derived biomes and actual vegetation distribution in the region. However, pollen and botanical data, compared with the potential vegetation distribution simulated from the modern climate dataset using the BIOME1 model, demonstrate that spatial distribution of cool mixed forest is underrepresented in the model simulation. The model sets the mean temperature of the coldest month of −15 °C as the factor limiting distribution of the temperate deciduous broadleaf taxa, while vegetation and pollen data from the region demonstrate that this limit should be lowered to −26 °C. Application of the method to the Gur 3–99 pollen record (50°00 ′N, 137°03 ′E) demonstrates that tundra vegetation predominated around the site prior to 14 ka BP (1 ka = 1000 cal. years). However, the local presence of boreal trees and mixed forest-tundra vegetation is suggested by relatively high taiga scores. Soon after 14 ka BP the scores of taiga become slightly higher than tundra scores. During 11.4–10.5 ka BP a cool conifer forest is reconstructed. Establishment of the full interglacial conditions is marked by the onset of cool mixed forest by 10.5 ka BP. Between 10.3 and 2.5 ka BP the scores of temperate deciduous forest are close to those of cool mixed forest and become distinctly lower during the late Holocene.

Projected potential vegetation change in China under the SRES A2 and B2 scenarios

The ability of seven global coupled ocean-atmosphere models to reproduce East Asian monthly surface temperature and precipitation climatologies during 1961–1990 is evaluated. January and July climate differences during the 2050s and 2090s relative to 1961–1990 projected by the seven-model ensemble under the Special Report on Emission Scenarios (SRES) A2 and B2 scenarios are then briefly discussed. These projections, together with the corresponding atmospheric CO2 concentrations under the SRES A2 and B2 scenarios, are subsequently used to drive the biome model BIOME3 to simulate potential vegetation distribution in China during the 2050s and 2090s. It is revealed that potential vegetation belts during the 2050s shift northward greatly in central and eastern China compared to those during 1961–1990. In contrast, potential vegetation change is slight in western China on the whole. The spatial pattern of potential vegetation during the 2090s is generally similar to that during the 2050s, but the range of potential vegetation change against 1961–1990 is more extensive during the 2090s than the 2050s, particularly in western China. Additionally, there exists model-dependent uncertainty of potential vegetation change under the SRES A2 scenario during the 2090s, which is due to the scatter of projected climate change by the models. The projected change in potential vegetation under the SRES A2 scenario during the 2090s is attributable to surface temperature change south of 35°N and to the joint changes of surface temperature, precipitation, and atmospheric CO2 concentration north of 35°N.

Modeling distribution changes of vegetation in China under future climate change

Climatic change will result in great changes in vegetation. In this paper, a biogeographical model, the BIOME1, was used to predict potential vegetation distribution in China under climate change. Firstly, the BIOME1 was validated according to the climate–vegetation relationships in China. Kappa statistics showed that the validated BIOME1 was able to capture the geographical patterns of vegetation more accurately. Then, the validated BIOME1 was used to predict the distribution of vegetation of China under two climatic scenarios produced by a Regional Circulation Model, RegCM2/CN. The simulation results showed obvious northward shifts of the boreal, temperate deciduous and evergreen and tropical forests, a large expansion of tropical dry forest/savanna and reduction of tundra on the Tibetan Plateau. Three vulnerable regions sensitive to climate changes are pointed out, i.e., Northern China, the Tibetan Plateau and Southwestern China (mainly Hengduan Mountains in Yunnan Province and west of Sichuan Province). In recent decades, China has experienced dramatic industrialization and population growth, which exert strong pressure on the environment of China. The consequences of climate changes warrant more attention for maintaining a sustainable environment for China.

Heat and water balances of the bare soil surface and the potential distribution of vegetation in the Loess Plateau, China

Spatial and temporal changes of heat and water balances are crucial to planting to combat desertification in the Loess Plateau of China. A three-layer soil model and meteorological data from 43 observatories were used to estimate the heat and water balances of the bare soil surface. Results indicate that the annual mean of sensible heat flux ranged from 13 to 36 W m −2 and the latent heat flux from 14 W m −2 (179 mm yr −1) to 40 W m −2 (521 mm yr −1). Water retention and restriction of evaporation from the soil surface are affected by the yellow loessial soil including medium loam, light loam and sandy loam, but not heavy loam. The distribution of fractional extractable water and the existing vegetation have been compared to reveal the potential vegetation distribution leading to the aridity distribution in the plateau.

Simulating past and future dynamics of natural ecosystems in the United States

Simulations of potential vegetation distribution, natural fire frequency, carbon pools, and fluxes are presented for two DGVMs (Dynamic Global Vegetation Models) from the second phase of the Vegetation/Ecosystem Modeling and Analysis Project. Results link vegetation dynamics to biogeochemical cycling for the conterminous United States. Two climate change scenarios were used: a moderately warm scenario from the Hadley Climate Centre and a warmer scenario from the Canadian Climate Center. Both include sulfate aerosols and assume a gradual CO2 increase. Both DGVMs simulate a reduction of southwestern desert areas, a westward expansion of eastern deciduous forests, and the expansion of forests in the western part of the Pacific Northwest and in north‐central California. Both DGVMs predict an increase in total biomass burnt in the next century, with a more pronounced increase under the Canadian scenario. Under the Hadley scenario, both DGVMs simulate increases in total carbon stocks. Under the Canadian scenario, both DGVMs simulate a decrease in live vegetation carbon. We identify similarities in model behavior due to the climate forcing and explain differences by the different structure of the models and their different sensitivity to CO2. We compare model output with data to enhance our confidence in their ability to simulate potential vegetation distribution and ecosystem processes. We compare changes in the area of drought‐induced decreases in vegetation density with a spatial index derived from the Palmer Drought Severity Index to illustrate the ability of the vegetation to cope with water limitations in the future and the role of the CO2 fertilization effect.

Responses of vegetation structure and primary production of a forest transect in eastern China to global change

AbstractAim A regional model of vegetation dynamics was enhanced to include biogeochemical cycling of nitrogen and was then applied to a forest transect in east China (FTEC) in order to investigate the responses of the transect to possible global change.Location Eastern China.Methods Biomass and nitrogen concentration of green and nongreen portions of vegetation, moisture contents of three soil layers, and total and available soil nitrogen are included as state variables in the enhanced model. The model was parameterized and validated against field observations of biomass, productivity, plant and soil nitrogen concentration, nitrogen uptake, a vegetation index derived from satellite remote sensing and digital maps of vegetation and soil distributions along a forest transect in eastern China (FTEC). The model was applied to FTEC in order to investigate the responsive characteristics of the ecosystems to global climatic change. Scenarios of climate change under doubled CO2 produced by seven general circulation models (GCM) were used to drive the model.Results The simulations indicated that the model is capable of simulating accurately potential vegetation distribution and net primary productivity under contemporary climatic conditions. The simulations for GCM‐projected future climate scenarios with doubled atmospheric CO2 concentration predicted that broadleaf forests would increase, but conifer forests, shrubs and grasses would decrease; and that deciduous forests would have the largest relative increase, but evergreen shrubs would have the largest decrease.Conclusions The overall effects of doubling CO2 and climatic changes on FTEC were to produce an increased net primary productivity (NPP) at equilibrium for all seven GCM scenarios. The inclusion of nitrogen dynamics in the model imposes more constraint on the responses of FTEC to climatic change than the previous version of the model without nitrogen dynamics. Temperature exerts a stronger control on NPP than precipitation, as indicated by the negative correlations between NPP and temperature. The southern portion of FTEC, at latitudes less than 33 °N, show much larger increases in annual NPP than in the north. However, the predicted range of NPP increases is much larger in the north than in the south.

Climate Change Effects on Vegetation Distribution and Carbon Budget in the United States

The Kyoto protocol has focused the attention of the public and policymarkers on the earth’s carbon (C) budget. Previous estimates of the impacts of vegetation change have been limited to equilibrium “snapshots” that could not capture nonlinear or threshold effects along the trajectory of change. New models have been designed to complement equilibrium models and simulate vegetation succession through time while estimating variability in the C budget and responses to episodic events such as drought and fire. In addition, a plethora of future climate scenarios has been used to produce a bewildering variety of simulated ecological responses. Our objectives were to use an equilibrium model (Mapped Atmosphere‐Plant‐ Soil system, or MAPSS) and a dynamic model (MC1) to (a) simulate changes in potential equilibrium vegetation distribution under historical conditions and across a wide gradient of future temperature changes to look for consistencies and trends among the many future scenarios, (b) simulate time-dependent changes in vegetation distribution and its associated C pools to illustrate the possible trajectories of vegetation change near the high and low ends of the temperature gradient, and (c) analyze the extent of the US area supporting a negative C balance. Both models agree that a moderate increase in temperature produces an increase in vegetation density and carbon sequestration across most of the US with small changes in vegetation types. Large increases in temperature cause losses of C with large shifts in vegetation types. In the western states, particularly southern California, precipitation and thus vegetation density increase and forests expand under all but the hottest scenarios. In the eastern US, particularly the Southeast, forests expand under the more moderate scenarios but decline under more severe climate scenarios, with catastrophic fires potentially causing rapid vegetation conversions from forest to savanna. Both models show that there is a potential for either positive or negative feedbacks to the atmosphere depending on the level of warming in the climate change scenarios.