Global Vegetation Patterns Research Articles

Human land use is comparable to climate as a driver of global plant occurrence and abundance across life forms

AbstractAimHistorically, climate has been a dominant driver of global vegetation patterns. Recently, ecological understanding has been updated to acknowledge the influence of human land use (the dominant driver of biodiversity change) in shaping global vegetation patterns. We test whether Raunkiær's life form, a plant classification system designed to reflect climatic drivers, affects how plants respond to both land use and climate.LocationForty‐one countries across six continents.Time period1990 to 2013.Major taxa studiedTerrestrial plants.MethodsCombining data from the biodiversity and land use database PREDICTS, and plant trait databases TRY and BIEN, we use generalized linear mixed models with weighted effects coding to test whether Raunkiær's life form affects plant response to land use and climate in over 4800 species at over 300 sites globally.ResultsWe provide evidence that human land use is comparable to climate in influencing life form occurrence and that land use produces divergent outcomes across life forms.Main conclusionsCombined with climatic suitability, land use acts as a filter contracting the realized niche of trees and expanding the realized niche of disturbance‐tolerant species. Our results highlight the fundamental role of human activity in shaping species' distribution.

Global biome patterns of the Middle and Late Pleistocene

AbstractOur primary aim was to assess the hypothesis that distinctive features of the patterns of vegetation change during successive Quaternary glacial–interglacial cycles reflect climatic differences arising from forcing differences. We addressed this hypothesis using 207 half‐degree resolution global biome pattern simulations, for time slices between 800 and 2 ka, made using the LPJ‐GUESS dynamic global vegetation model. Simulations were driven using ice‐core atmospheric CO2 concentrations, Earth's obliquity, and outputs from a pre‐industrial and 206 palaeoclimate experiments; four additional simulations were driven using projected future CO2 concentrations. Climate experiments were run using HadCM3. Using a rule‐based approach, above‐ground biomass and leaf area index of LPJ‐GUESS plant functional types were used to infer each grid cell's biome. The hypothesis is supported by the palaeobiome simulations. To enable comparisons with the climatic forcing, multivariate analyses were performed of global vegetation pattern dissimilarities between simulations. Results showed generally similar responses to glacial–interglacial climatic variations during each cycle, although no two interglacials or glacials had identical biome patterns. Atmospheric CO2 concentration was the strongest driver of the dissimilarity patterns. Dissimilarities relative to the time slice with the lowest atmospheric CO2 concentration show the log‐linear relationship to atmospheric CO2 concentration expected of an index of ecocarbon sensitivity. For each simulation, extent and total above‐ground biomass of each biome were calculated globally and for three longitudinal segments corresponding to the major continental regions. Mean and minimum past extents of forest biomes, notably Temperate Summergreen Forest, in the three major continental regions strongly parallel relative tree diversities, hence supporting the hypothesis that past biome extents played an important role in determining present diversity. Albeit that they reflect the climatic consequences only of the faster Earth system components, simulated potential future biome patterns are unlike any during the past 800 ky, and likely will continue to change markedly for millennia if projected CO2 concentrations are realised.

Water availability creates global thresholds in multidimensional soil biodiversity and functions.

Soils support an immense portion of Earth's biodiversity and maintain multiple ecosystem functions which are essential for human well-being. Environmental thresholds are known to govern global vegetation patterns, but it is still unknown whether they can be used to predict the distribution of soil organisms and functions across global biomes. Using a global field survey of 383 sites across contrasting climatic and vegetation conditions, here we showed that soil biodiversity and functions exhibited pervasive nonlinear patterns worldwide and are mainly governed by water availability (precipitation and potential evapotranspiration). Changes in water availability resulted in drastic shifts in soil biodiversity (bacteria, fungi, protists and invertebrates) and soil functions including plant-microbe interactions, plant productivity, soil biogeochemical cycles and soil carbon sequestration. Our findings highlight that crossing specific water availability thresholds can have critical consequences for the provision of essential ecosystem services needed to sustain our planet.

The Role of Fungi in Weed Biocontrol: A Review

Weeds are one of the major threats to the agricultural sector as well as to the natural environment, and it is important to control them. They cause serious threats to commercial crop production, global vegetation, and human health. Invasion by exotic weed species imposes a significant impact on native plant communities and their biodiversity, leading to major changes in global vegetation patterns. Biological control has become popular during the past few decades. In agricultural systems, weeds are controlled using chemicals. However, it has some limitations in natural systems. Furthermore, the use of biological control for weeds increased due to increased public awareness of the negative impacts of chemical herbicides and the increase of herbicide-resistant weeds. Biological control of weeds focuses on the use of co-evolved natural enemies such as fungi, bacteria, and viruses. Nowadays, biological control of weeds with fungi was popular because it is environmentally safe, sustainable and has beneficial applications. This review focuses on fungi as potential biocontrol agents for weeds and the success achieved in case studies.

Reconstructing Past Global Vegetation With Random Forest Machine Learning, Sacrificing the Dynamic Response for Robust Results

AbstractVegetation is an important component in the Earth system, providing a direct link between the biosphere and atmosphere. As such, a representative vegetation pattern is needed to accurately simulate climate. We attempt to model global vegetation (biomes) with a data‐driven approach, to test if this allows us to create robust global and regional vegetation patterns. This not only provides quantitative reconstructions of past vegetation cover as a climate forcing, but also improves our understanding of past land cover‐climate interactions which have important implications for the future. By using a Random Forest (RF) machine learning tool, we train the vegetation reconstruction with available biomized pollen data of present and past conditions to produce broad‐scale vegetation patterns for the preindustrial (PI), the mid‐Holocene (MH, ∼6,000 years ago), and the Last Glacial Maximum (LGM, ∼21,000 years ago). We test the method's robustness by introducing a systematic temperature bias based on existing climate model spread and compare the result with that of LPJ‐GUESS, an individual‐based dynamic global vegetation model. The results show that the RF approach is able to produce robust patterns for periods and regions well constrained by evidence (the PI and the MH), but fails when evidence is scarce (the LGM). The apparent robustness of this method is achieved at the cost of sacrificing the ability to model dynamic vegetation response to a changing climate.

Litter removal through fire – A key process for wetland vegetation and ecosystem dynamics

Fire is a major driver of global vegetation patterns. It strongly reduces litter and thus alters physical and chemical properties of the environment. Studies investigating the interplay of fire and litter are scarce, and wetland ecosystems are strongly under-represented in research focusing on litter dynamics. We present data on short-term effects of fires in floodplain wetlands along the Amur River in the Russian Far East, an area with a high fire recurrence rate. We analysed vegetation and plant growth patterns as well as soil temperature and nutrient concentrations on recently burnt and unburnt control plots.Directly after fire, litter was reduced by more than 50% on burnt plots. This effect was no longer visible 15 months after fire, probably due to the high productivity of the floodplain ecosystem. Litter was found to act as a key determinant in the net of direct and indirect fire effects, by influencing early plant growth patterns of herbs and grasses. Furthermore, litter removal through fire significantly increased plant species diversity and soil temperature. Contrary, N and P concentrations in living plant biomass of grasses and herbs decreased with decreasing litter cover. Combustion during burning seems to be responsible for the negative direct fire impacts on nutrient concentrations, which were found for N and Mg.Litter removal through fire can strongly affect diversity patterns, dominance structures, and nutrient cycling in wetlands. With increasing fire frequency in the course of global change, significant structural and compositional changes in herbaceous wetland vegetation must be anticipated and the studied ecosystem may shift to reinforced N-limitation.

Global vegetation patterns of the past 140,000 years

AbstractAimInsight into global biome responses to climatic and other environmental changes is essential to address key questions about past and future impacts of such changes. By simulating global biome patterns 140 ka to present, we aimed to address important questions about biome changes during this interval.LocationGlobal.TaxonSpermatophyta.MethodsUsing the LPJ‐GUESS dynamic global vegetation model, we made 89 simulations driven using ice‐core atmospheric CO2 concentrations, Earth's obliquity, and outputs from a pre‐industrial and 88 palaeoclimate experiments run using HadCM3. Experiments were run for 81 time slices between 1 and 140 ka, seven ‘hosing’ experiments also being run, using a 1‐Sv freshwater flux to the North Atlantic, for time slices corresponding to Heinrich Events H0–H6. Using a rule‐based approach, based on carbon mass and leaf area index of the LPJ‐GUESS plant functional types, the biome was inferred for each grid cell. Biomes were mapped, and the extent and total vegetation biomass of each biome, and total global vegetation biomass, estimated.ResultsSubstantial changes in biome extents and locations were found on all vegetated continents. Although the largest magnitude changes were in Eurasia, important changes were seen in tropical latitudes and the Southern Hemisphere. Total global extent of most biomes varied on multi‐millennial (orbital) time scales, although some (e.g. Tropical Raingreen Forest) responded principally to the c. 100‐kyr glacial–interglacial cycle and others (e.g. Temperate Broad‐leaved Evergreen Forest) mainly to the c. 20‐kyr precession cycle. Many also responded to millennial contrasts between stadial (‘hosing’) and interstadial climates, with some (e.g. Tropical Evergreen Forest) showing stronger responses than to the multi‐millennial changes.Main conclusionsNo two time slices had identical biome patterns. Even equivalent Holocene and last interglacial time slices, and the last and penultimate glacial maxima, showed important differences. Only a small proportion of global land area experienced no biome change since 140 ka; many places experienced multiple biome changes. These modelling experiments provided little evidence for long‐term biome stability.

Retrieval of global terrestrial solar-induced chlorophyll fluorescence from TanSat satellite

The first Chinese Carbon Dioxide Observation Satellite Mission (TanSat), which was launched on December 21, 2016, is intended to measure atmospheric CO2 concentration. The high spectral resolution (0.044 nm) and high SNR (360 at 15.2 mW m−1 sr−1 nm−1) measurements in the region of the O2-A band of the Atmospheric Carbon dioxide Grating Spectroradiometer (AGCS) module onboard TanSat make it possible to retrieve solar-induced chlorophyll fluorescence (SIF) from TanSat observations at the global scale. This paper aims to explore the potential of the TanSat data for global SIF retrieval. A singular vector decomposition (SVD) statistical method was employed to retrieve SIF using radiance over a micro spectral window (∼2 nm) around the Fe Fraunhofer lines (centered at 758.8 nm). The global SIF at 758.8 nm was successfully retrieved with a low residual error of 0.03 mW m−1 sr−1 nm−1. The results show that the spatial and temporal patterns of the retrieved SIF agree well with the global terrestrial vegetation pattern. The monthly SIF products retrieved from the TanSat data were compared with other remote sensing datasets, including OCO-2 SIF, MODIS NDVI, EVI and GPP. The overall consistency between TanSat and OCO-2 SIF products (R2 = 0.86) and the consistency of the spatial patterns and temporal variations between the TanSat SIF and MODIS vegetation indices and GPP enhance our confidence in the potential and feasibility of TanSat data for SIF retrieval. TanSat, therefore, provides a new opportunity for global sampling of SIF at fine spatial resolution (2 km × 2 km), thus improving photosynthesis observations from space.

A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations

Abstract. Spaceborne microwave remote sensing is widely used to monitor global environmental changes for understanding hydrological, ecological, and climate processes. A new global land parameter data record (LPDR) was generated using similar calibrated, multifrequency brightness temperature (Tb) retrievals from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). The resulting LPDR provides a long-term (June 2002–December 2015) global record of key environmental observations at a 25 km grid cell resolution, including surface fractional open water (FW) cover, atmosphere precipitable water vapor (PWV), daily maximum and minimum surface air temperatures (Tmx and Tmn), vegetation optical depth (VOD), and surface volumetric soil moisture (VSM). Global mapping of the land parameter climatology means and seasonal variability over the full-year records from AMSR-E (2003–2010) and AMSR2 (2013–2015) observation periods is consistent with characteristic global climate and vegetation patterns. Quantitative comparisons with independent observations indicated favorable LPDR performance for FW (R ≥ 0.75; RMSE ≤ 0.06), PWV (R ≥ 0.91; RMSE ≤ 4.94 mm), Tmx and Tmn (R ≥ 0.90; RMSE ≤ 3.48 °C), and VSM (0.63 ≤ R ≤ 0.84; bias-corrected RMSE ≤ 0.06 cm3 cm−3). The LPDR-derived global VOD record is also proportional to satellite-observed NDVI (GIMMS3g) seasonality (R ≥ 0.88) due to the synergy between canopy biomass structure and photosynthetic greenness. Statistical analysis shows overall LPDR consistency but with small biases between AMSR-E and AMSR2 retrievals that should be considered when evaluating long-term environmental trends. The resulting LPDR and potential updates from continuing AMSR2 operations provide for effective global monitoring of environmental parameters related to vegetation activity, terrestrial water storage, and mobility and are suitable for climate and ecosystem studies. The LPDR dataset is publicly available at http://files.ntsg.umt.edu/data/LPDR_v2/.

Periglacial fires and trees in a continental setting of Central Canada, Upper Pleistocene

Fire is a key factor controlling global vegetation patterns and carbon cycling. It mostly occurs under warm periods during which fuel builds up with sufficient moisture, whereas such conditions stimulate fire ignition and spread. Biomass burning increased globally with warming periods since the last glacial era. Data confirming periglacial fires during glacial periods are very sparse because such climates are likely too cold to favour fires. Here, tree occurrence and fires during the Upper Pleistocene glacial periods in Central Canada are inferred from botanical identification and calibrated radiocarbon dates of charcoal fragments. Charcoal fragments were archived in sandy dunes of central Saskatchewan and were dated >50000-26600 cal BP. Fragments were mostly gymnosperms. Parallels between radiocarbon dates and GISP2-δ¹⁸O records deciphered relationships between fire and climate. Fires occurred either hundreds to thousands of years after Dansgaard-Oeschger (DO) interstadial warming events (i.e., the time needed to build enough fuel for fire ignition and spread) or at the onset of the DO event. The chronological uncertainties result from the dated material not precisely matching the fires and from the low residual ¹⁴C associated with old sample material. Dominance of high-pressure systems and low effective moisture during post-DO coolings likely triggered flammable periglacial ecosystems, while lower moisture and the relative abundance of fuel overshadowed lower temperatures for fire spread. Laurentide ice sheet (LIS) limits during DO events are difficult to assess in Central Canada due to sparse radiocarbon dates. Our radiocarbon data set constrains the extent of LIS. Central Saskatchewan was not covered by LIS throughout the Upper Pleistocene and was not a continental desert. Instead, our results suggest long-lasting periods where fluctuations of the northern tree limits and fires after interstadials occurred persistently.

Environmental factors control and climate change impact on forest type: Dong PraYa Yen-KhaoYai world heritage in Thailand

Climate is a major determinant of global vegetation patterns and has a significant influence on the distribution and structure of forest ecosystems. Dong PraYa Yen-KhaoYai Forest Complex has been a UNESCO natural world heritage site since 2007, but little is known about its plant community. Our study aims to identify each plant community within the world heritage area and calculate its potential for carbon content. We determine both the relationship between forest type and both physio-chemical soil properties and climate change impact. We employed allometric equations to calculate aboveground biomass and both cluster analysis and canonical correspondence analysis (CCA) to examine the relationship between forest type and physiochemical soil properties. An equation for each physical parameter was used to predict the forest model. The climate scenario under A2 and B2 was applied to calculate future predominant forest types. Our results reveal that the forest ecosystems at Tab Lan (TL) have the highest species count (332 species) followed by Pang Srida (PD), KhaoYai (KY), Dong Yai (DY), and Tapraya (TY), with 293, 271, 169, and 99 species, respectively. We found KY to have the highest recorded carbon storage value at 2507.6 tC/ha followed by TL, PD, TY, and DY (1613.8, 1269.1, 844 and 810.7 tC/ha, respectively). Cluster analysis results indicated that the dominant species in each forest type is different. Moreover, CCA revealed that soil organic matter (SOM) and soil acid-base indicators are the best parameters to establish correlation for each forest type. Based on our results, future climate predictions show a negative impact on evergreen forests, but a positive one on deciduous ones.

Multiscale comparison of spatial patterns using two-dimensional cross-spectral analysis: application to a semi-arid (gapped) landscape

Spectral analysis allows the characterization of temporal (1D) or spatial (2D) patterns in terms of their scale (frequency) distribution. Cross-spectral analysis can also be used to conduct independent correlation analyses at different scales between two variables, even in the presence of a complex superposition of structures, such as structures that are shifted, have different scales or have different levels of anisotropy. These well-grounded approaches have rarely been applied to two-dimensional ecological datasets. In this contribution, we illustrate the potential of the method. We start by providing a basic methodological introduction, and we clarify some technical points concerning the computation of two-dimensional coherency and phase spectra and associated confidence intervals. First, we illustrate the method using a simple theoretical model. Next, we present a real world application: the case of patterned (gapped) vegetation in SW Niger. In this example, we investigate the functional relationships between topography and the spatial distribution of two shrub species, Combretum micranthum G. Don. and Guiera senegalensis J.F. Gmel. We show that both the global vegetation pattern and the distribution of C. micranthum are independent at all analyzable scales (i.e., from 10 to 50 m) from possible relief-induced determinisms. Additionally, the two dominant shrub species form distinct patches, thus suggesting separate niches.

Understanding a flammable planet – climate, fire and global vegetation patterns

Understanding a flammable planet – climate, fire and global vegetation patterns

Global vegetation and climate: Self-beneficial effects, climate forcings and climate feedbacks

Vegetation strongly affects climate by influencing the exchanges of energy and moisture between the land and atmosphere. This paper uses climate modelling studies to discuss four perspectives on the influence of vegetation on climate through biophysical properties of the land surface; (i) the extent to which present-day patterns of climate are modified by the presence of vegetation, and the importance of this for the vegetation itself; (ii) anthropogenic vegetation change as a driver (forcing) of climate change; (iii) the physiological impact of elevated CO 2 on vegetation as a forcing of climate change through the surface energy budget; and (iv) the responses of vegetation to radiatively-forced climate change and resulting feedbacks on the climate change itself. Contemporary vegetation increases continental precipitation while generally reducing temperature extremes, and this is crucial for maintaining present-day global vegetation patterns. Mid-latitude deforestation has acted to cool the climate by increasing surface albedo, while continued tropical deforestation may exert a warming and reduce precipitation. Elevated CO 2 may cause a warming through reduced transpiration by plants in addition to the greenhouse warming. Forest die-back may accelerate a projected precipitation reduction in Amazonia, while expansion of the boreal forests may provide a positive feedback on local warming.

The global distribution of ecosystems in a world without fire

This paper is the first global study of the extent to which fire determines global vegetation patterns by preventing ecosystems from achieving the potential height, biomass and dominant functional types expected under the ambient climate (climate potential). To determine climate potential, we simulated vegetation without fire using a dynamic global-vegetation model. Model results were tested against fire exclusion studies from different parts of the world. Simulated dominant growth forms and tree cover were compared with satellite-derived land- and tree-cover maps. Simulations were generally consistent with results of fire exclusion studies in southern Africa and elsewhere. Comparison of global 'fire off' simulations with landcover and treecover maps show that vast areas of humid C(4) grasslands and savannas, especially in South America and Africa, have the climate potential to form forests. These are the most frequently burnt ecosystems in the world. Without fire, closed forests would double from 27% to 56% of vegetated grid cells, mostly at the expense of C(4) plants but also of C(3) shrubs and grasses in cooler climates. C(4) grasses began spreading 6-8 Ma, long before human influence on fire regimes. Our results suggest that fire was a major factor in their spread into forested regions, splitting biotas into fire tolerant and intolerant taxa.

Ecosystem as a text: information analysis of the global vegetation pattern

Let us consider a text, which is written in the English language. At the zero level of perception (or description), we know only the number of symbols ( n=29: 26 letters, 1 blank, 1 comma and 1 full stop). Then the information per symbol I (0) log 2 29≈4.85 bits. At the second level of perception we take into account the frequencies of the symbols (letters); then I (1)≈4.03 bits. At the next levels we take into account double, triple, etc. correlations, i.e., words of two letters, three letters, etc., so that the information per letter are I (2)/2≈3.32 bits, I (3)/3≈3.10 bits,…. The redundancy of information, R ( i) , and its cost, C ( i) , are equal to R (0)=0, R (1)=0.15, R (2)=0.30, R (3)=0.35, and C (0)=1, C (1)=1.18, C (2)=1.43, C (3)=1.54. Let us consider a description of the global vegetation pattern (GVP). At the zero level of description we have a number of significant biomes or vegetation types, n. In accordance with Bazilevich n=30, then I (0) log 2 30≈4.9 bits. At the second level we take into account the relative areas covered by biomes. Then I (1)≈4.41 bits, R 1=0.1, and C 1=1.11. At the next level of description we consider the spatial correlations between different pairs of biomes, then I (2)/2≈3.6 bits, R 2=0.265, and C 2=1.36. Note that in addition to their areas, the biomes are also characterised by three parameters: annual productivity P i , living biomass B i and dead organic matter D i . Weighting them in relation to biome area σ i we define the frequencies p i P =P iσ i/ ∑ i=1 30 P iσ i,p i B =B iσ i/ ∑ i=1 30 B iσ i and p i D =D iσ i/ ∑ i=1 30 D iσ i . The description in terms of productivity, living or dead organic matter can be considered as description at the first level with information per letter defined as (I (1)) P, B, D =− ∑ i=1 n p i P, B, D log 2 p i P, B, D . Then (1) for productivity: ( I (1)) P=3.61 bits, C 2 P=1.32, R 2 P=0.24; (2) for living biomass: ( I (1)) B=3.27 bits, C 2 B=1.5, R 2 B=0.33; (3) for dead organic matter: ( I (1)) D=4.13 bits, C 2 D=1.17, R 2 D=0.16. One can see that information about the biome productivity, living biomass and dead organic matter is more valuable than the information about the biome areas distribution. Information about the distribution of living biomass has the maximal cost ( C 2 B=1.5).

Exergy of solar radiation: global scale

A new concept regarding the exergy of solar radiation [Ecol. Model. 145 (1991) 101] is applied to the analysis of satellite data describing the seasonal (monthly) dynamics of four components of the global radiation balance: incoming and outgoing short- and long-wave solar radiation. Stated in general terms, the incoming and outgoing radiation are described by the energy spectra E in( ν) and E out( ν) for any point of the globe. We assume that there exists a non-linear operator which transforms E in( ν) into E out( ν). Its properties are defined by the characteristics of a reflecting surface (ocean, ice, land, vegetation, etc.). It is suggested that exergy, which is properly a generalised Kullback measure of the information increment, is such an operator. It is also shown that such standard values as the radiation balance and albedo are characteristics of a linear transformation operator. Using NASA satellite data to calculate the global distribution of the annual mean of exergy, we see that the domains with maximal values of exergy correspond to the main upwellings of the ocean. If thermodynamics analogies are applied to the process of interaction of solar radiation with an “active” planetary surface, in particular with vegetation cover, then the difference between the radiation balance and the exergy can be considered as the increment of internal energy. It is shown that the global pattern of this value is very similar to the global vegetation pattern.

“Forest–grass” global vegetation model with forest age structure

In the paper [Ecol. Modell. 124 (1999) 131], a set of “simplest” models of the global vegetation pattern (GVP) was suggested. All these models are based on the simple probabilistic “urn” schemes and their dynamics were sufficiently complex to present nonlinear phenomena such as multiple equilibriums and hysteresis. Although these schemes are simple, sufficiently complex behaviour is implied. Expressed in terms of equations with biologically motivated parameters, only three equilibriums corresponding to “grass ”, transition “grass–forest” and “forest” zones exist. Stability domains of these equilibriums do not intersect in the parameter space. Since at each geographical point, the GVP dynamics is described by the same dynamical system with different parameters; then this difference is defined by the change in annual temperature and precipitation. We also assume that at any point there are both tree and grass “seeds”. In order to reduce the number of independent parameters, we formulate two additional hypotheses. Let τ m and τ l , i.e. the age of maturity and the life span for trees. Then, (1) the ratio k τ = τ m / τ l must be constant for coniferous and deciduous trees and (2) τ m = τ ml ( T). Both these hypotheses have been tested. As a result only one parameter, which governs the intensity of competition within a mature tree cohort, remained free and it could be used for the tuning purposes. Since the final GVP substantially depends on the border shape between forest, grass and mixed biomes in the climatic space {temperature, precipitation}; then in order to obtain better coincidence between the real and modelled GVP, we should modify the classic Lieth diagram. After calibrating parameters using climatic and vegetation data we developed a model, which predicts vegetation distribution in satisfactory agreement with observation for the present day. It is also shown that a “relaxation” time (time to reach equilibrium) for a transition zone is more than one order greater than the time for “pure” grass and forests equilibriums. This means that a real transformation of the GVP under climate change is a very slow process.

Lotka–Volterra models and the global vegetation pattern

How can such a classic object of mathematical ecology as the Lotka–Volterra competition model for a community of n competing species be used for description of the global vegetation dynamics under the climate change? We assume that the present spatial distribution of global vegetation is a stable equilibrium solution of corresponding Lotka–Volterra equations. The problem is how to construct some discrete structure on a continuous set of parameters, in this context remaining in a framework of a continuous model description? The problem can be solved if a dynamical system with multiple equilibria is considered. In this case, a continuous quasi-stationary change of parameters induces a jump from one to another equilibrium, since they have different stability domain in the parametric space. On this conceptual base a special class of Lotka–Volterra equations is developed and a biologically interpreted procedure for estimating their coefficients is suggested. For this we must have maps of the annual production and biomass, a life span of each vegetation type, and a map of the current geographical distribution of different vegetation, i.e. the current global vegetation pattern (GVP). The latter is needed for the construction of the ‘elementary map’, which is an important part of the model. Another important part is the formula, which describes an annual production dependence on the temperature and precipitation (for instance, Lieth's formula can be used). Considering a one-dimensional particular case for n=2 we have the analytical formulas describing a shift of borders between two vegetation zones under the climate change. It was shown that two different types of the transition zones, namely, ‘soft’ and ‘hard’, exist in this case. If the soft zone is characterised by the continuous and smooth replacement of one type of vegetation by another, then the hard zone is a typical ‘fractal’ structure with a mosaic of different vegetation. A special calibration procedure for the Lotka–Volterra model describing the dynamics of one-dimensional GVP with n types of vegetation is suggested. It is based on the stability theorem for a system with multiple equilibria and a special averaging method applying to real geographical distributions of the annual production and the biomasses. The results are used for estimating the shift of one transition zone between taiga and steppe in the Central Siberia under the climate change (CO 2-doubling scenario).

Large-Scale Vegetation Feedbacks on a Doubled CO2Climate

Changes in vegetation cover are known to influence the climate system by modifying the radiative, momentum, and hydrologic balance of the land surface. To explore the interactions between terrestrial vegetation and the atmosphere for doubled atmospheric CO2 concentrations, the newly developed fully coupled GENESIS–IBIS climate–vegetation model is used. The simulated climatic response to the radiative and physiological effects of elevated CO2 concentrations, as well as to ensuing simulated shifts in global vegetation patterns is investigated. The radiative effects of elevated CO2 concentrations raise temperatures and intensify the hydrologic cycle on the global scale. In response, soil moisture increases in the mid- and high latitudes by 4% and 5%, respectively. Tropical soil moisture, however, decreases by 5% due to a decrease in precipitation minus evapotranspiration. The direct, physiological response of plants to elevated CO2 generally acts to weaken the earth’s hydrologic cycle by lowering transpiration rates across the globe. Lowering transpiration alone would tend to enhance soil moisture. However, reduced recirculation of water in the atmosphere, which lowers precipitation, leads to more arid conditions overall (simulated global soil moisture decreases by 1%), particularly in the Tropics and midlatitudes. Allowing structural changes in the vegetation cover (in response to changes in climate and CO2 concentrations) overrides the direct physiological effects of CO2 on vegetation in many regions. For example, increased simulated forest cover in the Tropics enhances canopy evapotranspiration overall, offsetting the decreased transpiration due to lower leaf conductance. As a result of increased circulation of moisture through the hydrologic cycle, precipitation increases and soil moisture returns to the value simulated with just the radiative effects of elevated CO2. However, in the highly continental midlatitudes, changes in vegetation cover cause soil moisture to decline by an additional 2%. Here, precipitation does not respond sufficiently to increased plant-water uptake, due to a limited source of external moisture into the region. These results illustrate that vegetation feedbacks may operate differently according to regional characteristics of the climate and vegetation cover. In particular, it is found that CO2 fertilization can cause either an increase or a decrease in available soil moisture, depending on the associated changes in vegetation cover and the ability of the regional climate to recirculate water vapor. This is in direct contrast to the view that CO2 fertilization will enhance soil moisture and runoff across the globe: a view that neglects changes in vegetation structure and local climatic feedbacks.