Potential Vegetation Changes Research Articles

Grazing capacity change on a small Mediterranean-type South African offshore island following the control of invasive alien European rabbits (Oryctolagus cuniculus)

Invasive alien species (IAS) are one of the most important factors driving extinction rates worldwide. Among the IAS, the impacts of introduced mammals on native biodiversity are most severe, with the European rabbit (Oryctolagus cuniculus) being the most problematic. Globally, European rabbits have been introduced to five continents and more than 800 islands or island groups where they are a threat to biodiversity and human livelihoods. The impacts of European rabbits on biodiversity and human livelihoods have been extensively studied across the globe. However, some regions of the world, such as the offshore islands along Africa’s southern coast, have been sparsely studied. In this study, we monitored change in grazing capacity on a South African offshore island, Robben Island, following an attempt to eradicate European rabbits between 2008 and 2009. Grazing capacity on the island increased following rabbit control, driven by the recovery of palatable plant species in response to release from herbivory. There was no change in grazing capacity at sites under Eucalyptus plantations because of the inhibitory effects of the alien trees on understory vegetation dynamics. We believe that grazing capacity changes on Robben Island are indicative of potential vegetation changes on other southern African offshore islands.

Simulation model of vegetation dynamics by combining static and dynamic data using the gated recurrent unit neural network-based method

The simulation of vegetation dynamics is essential for guiding regional ecological remediation and environmental management. Recent progress in deep learning methods has provided possible solutions to vegetation simulations. The gated recurrent unit (GRU) is one of the latest deep learning algorithms that can effectively process dynamic data. However, static and dynamic data, which typically coexist in the datasets of vegetation dynamic changes, are typically processed indistinguishably. To efficiently extract spatiotemporal patterns and improve our ability to simulate potential vegetation changes, we introduced GRU into vegetation simulation and further amended the original structure of GRU according to the characteristics of the simulation dataset. The new model, the vegetation dynamics model (VDM), can independently process static and dynamic data using a more appropriate algorithm, thereby improving the simulation accuracy. Moreover, we presented a model test applied in the Luntai Desert-Oasis Ecotone in Northwest China and compared the performance of the VDM with baseline models. The results showed that the VDM produced a 7.51% higher coefficient of determination (R2) value, 7.51% higher adjusted R2 value, 16.67% lower mean squared error, and 10.78% lower mean absolute error than those of the GRU, which is the best baseline model. The proposed VDM is the first GRU-based simulation model of vegetation dynamics that has the potential to detect the time-order characteristics of dynamic factors by comprehensively considering the static information that affects vegetation changes. Moreover, the flexibility of the VDM, in combination with the wide availability of data from different data sources, aids the broader application of the VDM.

Impact of climate zone migration on geographical distribution of indigenous vegetation in Northeast China

Based on the relationship between climate and vegetation, this study uses the time lag of vegetation response to climate change, simulates the dynamic response process of different vegetation types to climate change, retrieves relevant literature and collates data, and deduces the actual occurrence and geographic distribution of vegetation under the current climate potential and future climate change scenarios. This experiment infer the adaptation relationship between plant geographical distribution and climatic conditions. The results show that the plants in Northeast China are generally more adaptable to climate change. The areas with poor adaptability are mainly at the junction of forest shrubs and grasslands. The geographical distribution of plants has changed, accounting for about 5%. The grassland ecosystem in the bush-grassland transition zone in the Greater Xing’an Mountains is also less adaptable to climate, accounting for about 35%. The vegetation in these areas tends to degenerate. The geographical distribution may change. The adjustment of plants in Northeast China to the future climate change is usually reasonable. 84% of the vegetation changes are positive changes. Especially in the eastern regions, the vegetation deduction conditions will be improved in the future, and the vegetation coverage in these regions may be improved. About 79% of the potential changes in vegetation can adapt to the future climate. Nevertheless, the grassland ecosystems in the Greater Xing’an Mountains and parts of Inner Mongolia are less adaptable to future climate change and tend to degrade. It provides new ideas for assessing vegetation adaptability or ecosystem adaptability. Research on the interaction between climate change and terrestrial ecosystems of different scales needs to be strengthened in the context of global change.

Future hydrological constraints of the Montseny brook newt (Calotriton arnoldi) under changing climate and vegetation cover.

The Montseny brook newt (Calotriton arnoldi) is a critically endangered amphibian species which inhabits a small 20 km2 holm oak and beech forest area in NE Spain. Calotriton arnoldi strictly lives in running waters and might be highly vulnerable to hydrological perturbations expected to occur under climate and vegetation cover changes. Knowledge about the potential response of the species habitat to environmental changes can help assessing the actions needed for its conservation. Based on knowledge of the species and supported by observations, we proposed daily low and high streamflow event thresholds for the viability of C. arnoldi. We used the rainfall–runoff model PERSiST to simulate changes in the frequency and duration of these events, which were predicted under two climate and four vegetation cover scenarios for near‐future (2031–2050) and far‐future (2081–2100) periods in a reference catchment. All future scenarios projected a significant decrease in annual streamflow (from 21% to as much as 67%) with respect to the reference period. The frequency and length of low streamflow events will dramatically increase. In contrast, the risk of catastrophic drift linked to high streamflow events was predicted to decrease. The potential change in vegetation toward an expansion of holm oak forests will be more important than climate changes in determining threshold low flow conditions. We thus demonstrated that consideration of potential changes in vegetation and not only changes in climate variables is essential in simulating future streamflows. This study shows that future low streamflow conditions will pose a severe threat for the survival of C. arnoldi and may help taking management actions, including limiting the expansion of holm oak forest, for ameliorating the species habitat and help its conservation.

Spatiotemporal Analysis for NDVI Time Series Using Local Binary Pattern and Daubechies Wavelet Transform

NDVI time series has shown to be very efficient for vegetation change dynamic analysis over a long period. However, noise and illumination variations present significant challenges to perform an accurate change detection. This paper aims at capturing global vegetation change dynamics within 16-days MODIS-NDVI time series by considering the inter-annual variations. To determine the appropriate scale that characterizes the long term variation, an efficient way relying on wavelet transform is used. First, the Daubechies 4 wavelet transform is employed to perform a multi-scale decomposition to extract the inter-annual variations and remove noise. Second, critical point theory is used to identify a set of points indicating potential vegetation change within time series, which, allows a time series reduction. Then, for each critical point, LBP code is computed to characterize the corresponding local patterns, which provides the ability to deal with illumination variations. Based on the extracted features, a change map is produced by computing similarity between neighboring time series, assessing dynamic vegetation change over the period of study. Experiment results using NDVI time series show clearly the potential of the proposed approach to detect change.

Climatic Disequilibrium Threatens Conservation Priority Forests

AbstractWe test the hypothesis that climatic changes since 1800 have resulted in unrealized potential vegetation changes that represent a “climatic debt” for many ecosystems. Caledonian pinewoods, an EU priority forest type, are used as a model system to explore potential impacts of two centuries of climatic change upon sites of conservation importance and surrounding landscapes. Using methods that estimate topographic microclimate, current and preindustrial climates were estimated for 50 m grid cells and simulations made using a dynamic vegetation model. Core Caledonian pinewood areas are now less suitable for growth of pine and more favorable for oak than in 1800, whereas landscapes as a whole are on average more favorable for both. The most favorable areas for pine are now mainly outside areas designated to conserve historical pinewoods. A paradigm shift is needed in formulating conservation strategies to avoid catastrophic losses of this habitat, and of many others globally with trees or other long‐lived perennials as keystone species.

Climatic thresholds shape northern high‐latitude fire regimes and imply vulnerability to future climate change

Boreal forests and arctic tundra cover 33% of global land area and store an estimated 50% of total soil carbon. Because wildfire is a key driver of terrestrial carbon cycling, increasing fire activity in these ecosystems would likely have global implications. To anticipate potential spatiotemporal variability in fire‐regime shifts, we modeled the spatially explicit 30‐yr probability of fire occurrence as a function of climate and landscape features (i.e. vegetation and topography) across Alaska. Boosted regression tree (BRT) models captured the spatial distribution of fire across boreal forest and tundra ecoregions (AUC from 0.63–0.78 and Pearson correlations between predicted and observed data from 0.54–0.71), highlighting summer temperature and annual moisture availability as the most influential controls of historical fire regimes. Modeled fire–climate relationships revealed distinct thresholds to fire occurrence, with a nonlinear increase in the probability of fire above an average July temperature of 13.4°C and below an annual moisture availability (i.e. P‐PET) of approximately 150 mm. To anticipate potential fire‐regime responses to 21st‐century climate change, we informed our BRTs with Coupled Model Intercomparison Project Phase 5 climate projections under the RCP 6.0 scenario. Based on these projected climatic changes alone (i.e. not accounting for potential changes in vegetation), our results suggest an increasing probability of wildfire in Alaskan boreal forest and tundra ecosystems, but of varying magnitude across space and throughout the 21st century. Regions with historically low flammability, including tundra and the forest–tundra boundary, are particularly vulnerable to climatically induced changes in fire activity, with up to a fourfold increase in the 30‐yr probability of fire occurrence by 2100. Our results underscore the climatic potential for novel fire regimes to develop in these ecosystems, relative to the past 6000–35 000 yr, and spatial variability in the vulnerability of wildfire regimes and associated ecological processes to 21st‐century climate change.

Projected Future Vegetation Changes for the Northwest United States and Southwest Canada at a Fine Spatial Resolution Using a Dynamic Global Vegetation Model.

Future climate change may significantly alter the distributions of many plant taxa. The effects of climate change may be particularly large in mountainous regions where climate can vary significantly with elevation. Understanding potential future vegetation changes in these regions requires methods that can resolve vegetation responses to climate change at fine spatial resolutions. We used LPJ, a dynamic global vegetation model, to assess potential future vegetation changes for a large topographically complex area of the northwest United States and southwest Canada (38.0–58.0°N latitude by 136.6–103.0°W longitude). LPJ is a process-based vegetation model that mechanistically simulates the effect of changing climate and atmospheric CO2 concentrations on vegetation. It was developed and has been mostly applied at spatial resolutions of 10-minutes or coarser. In this study, we used LPJ at a 30-second (~1-km) spatial resolution to simulate potential vegetation changes for 2070–2099. LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. The fine spatial-scale vegetation simulations resolve patterns of vegetation change that are not visible at coarser resolutions and these fine-scale patterns are particularly important for understanding potential future vegetation changes in topographically complex areas.

Using the Köppen classification to quantify climate variation and change: An example for 1901–2010

The Köppen climate classification was developed based on the empirical relationship between climate and vegetation. This type of climate classification scheme provides an efficient way to describe climatic conditions defined by multiple variables and their seasonalities with a single metric. Compared with a single variable approach, the Köppen classification can add a new dimension to the description of climate variation. Further, it is generally accepted that the climatic combinations identified with the Köppen classification are ecologically relevant. The classification has therefore been widely used to map geographic distribution of long term mean climate and associated ecosystem conditions. Over the recent years, there has also been an increasing interest in using the classification to identify changes in climate and potential changes in vegetation over time. These successful applications point to the potential of using the Köppen classification as a diagnostic tool to monitor changes in the climatic condition over various time scales. This work used a global temperature and precipitation observation dataset to reveal variations and changes of climate over the period 1901–2010, demonstrating the power of the Köppen classification in describing not only climate change, but also climate variability on various temporal scales. It is concluded that the most significant change over 1901–2010 is a distinct areal increase of the dry climate (B) accompanied by a significant areal decrease of the polar climate (E) since the 1980s. The areas of spatially stable climate regions for interannual and interdecadal variations are also identified, which have practical and theoretical implications.

Characterizing land condition variability in Ferlo, Senegal (2001–2009) using multi-temporal 1-km Apparent Green Cover (AGC) SPOT Vegetation data

The ecosystem state or ‘land condition’ can be characterized by a set of attributes, which show variations at different temporal scales. A multi-resolution analysis (MRA) based on the wavelet transform (WT) has been implemented to examine the land condition of a dryland region in Ferlo (Senegal) over the period 2001–2009. This methodology has proven to be useful for smoothing time series while considering those temporal resolutions that incorporate information about the vegetation dynamics. For this purpose, time series of the 1-km Apparent Green Cover (AGC) from the 10-day composites SPOT Vegetation (VGT) data are analyzed. Two relevant outputs from the MRA, A 1 (de-noised) and the A 6 (inter-annual) components have served us for characterizing the annual vegetation production and assess the long-term variation, respectively. In a first stage, the vegetation seasonality (or intra-annual variation) over the area is described by using several metrics related to vegetation phenology derived from the de-noised time series ( A 1). In a second phase, the temporal variability of the inter-annual component series ( A 6) is accomplished to detect potential vegetation changes over the considered period. A Mann–Kendall test has been applied to confirm the significance of the observed inter-annual changes. A higher number of significant pixels (86%) are obtained when considering the inter-annual component in the trend analysis instead of the original time-series (47%). The results confirm a general greening up over the period 2001–2009, not fully explained by precipitation, as well as rather local negative trends. The Rain-Use Efficiency (RUE) ratio computed using the AGC as a proxy of vegetation production has been considered to further analyze the detected changes. Inter-annual changes in RUE provide a potential method of separating vegetation declines due to lack of rainfall from declines associated with degradation. Some spots of negative values in inter-annual RUE changes are found in certain areas for which several evidence of land degradation have been documented.

Drought-Induced Multifactor Decline of Scots Pine in the Pyrenees and Potential Vegetation Change by the Expansion of Co-occurring Oak Species

Episodes of drought-induced tree dieback have been recently observed in many forest areas of the world, particularly at the dry edge of species distributions. Under climate change, those effects could signal potential vegetation shifts occurring over large geographical areas, with major impacts on ecosystem form and function. In this article, we studied the effect of a single drought episode, occurred which in summer 2005, on a Scots pine population in central Pyrenees (NE Spain). Our main objective was to study the environmental correlates of forest decline and vegetation change at the plot level. General and generalized linear models were used to study the relationship between canopy defoliation, mortality and recruitment, and plot characteristics. A drought-driven multifactor dieback was observed in the study forest. Defoliation and mortality were associated with the local level of drought stress estimated at each plot. In addition, stand structure, soil properties, and mistletoe infection were also associated with the observed pattern of defoliation, presumably acting as long-term predisposing factors. Recruitment of Scots pine was low in all plots. In contrast, we observed abundant recruitment of other tree species, mostly Quercus ilex and Q. humilis, particularly in plots where Scots pine showed high defoliation and mortality. These results suggest that an altitudinal upwards migration of Quercus species, mediated by the dieback of the currently dominant species, may take place in the studied slopes. Many rear-edge populations of Scots pine sheltered in the mountain environments of the Iberian Peninsula could be at risk under future climate scenarios.

Development of Vegetation Models to Predict the Potential Effect of Groundwater Withdrawals on Forested Wetlands

We developed vegetation models that, when linked to groundwater-hydrology models and landscape-level applications, can be used to predict the potential effect of groundwater-level declines on the distribution of wetland-forest communities, individual wetland species, and wetland-indicator groups. An upland-to-wetland vegetation gradient, comprising 201 forest plots located in five different study basins and classified as either upland pine-oak, pitch pine lowland, pine-hardwood lowland, hardwood swamp, or cedar swamp, paralleled variations in water-level. Water levels, woody-species composition, the percentage of wetland- and upland-indicator species, and soil properties varied among the five vegetation types. Because of the functional relationship of hydrology with its correlated soil variables, hydrology represented a good proxy for the complex hydrologic-edaphic gradient associated with the upland-to-wetland vegetation gradient. Two types of vegetation models were developed to predict potential changes in vegetation associated with water-level declines. Logistic regression models predicted the probability of encountering the different vegetation types and 29 community-indicator species in relation to water level. Simple regression models predicted the relative abundance and richness of wetland-and upland-indicator species as a function of water level.

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.

Germination Strategies and Patterns of Annual Species in the Temperate Semiarid Region of China

Seed germination traits play a key role in the persistence and dynamics of annual desert plants, which enables predicting potential vegetation changes corresponding to environmental changes. Information on seed germination of annual species in the temperate semiarid region of China is limited. A laboratory study was conducted to evaluate the seed germination behavior of 17 annual species collected from a wide range of habitats in the Wulanaodu region of Horqin sandy land. Measurements were conducted on freshly collected seeds and on samples subjected to chilling and dry storage for 150 days and 365 days. Three species achieved germination percentages greater than 90% in any storage condition. Eight species had an after-ripening mechanism, and freshly collected seeds could mature and germinate in a single growing season. Three species had strong innate dormancy, and this fraction (18–50%) of seeds did not germinate even after dry storage for 365 days. Two species had short-lived seeds, whose longevity was less than one year. The majority of annual species showed potential for rapid germination and a unimodal germination curve with time, expressed as the proportion of seed germinated per day. A minority of the species had long germination duration and exhibited a multimodal process. Seventeen annual species produced four germination patterns and two germination strategies based on germination percentages and processes. Species with high germination percentages, short germination duration and a unimodal curve, exhibited risk-taking strategy in a growing season. Species with a low final germination fraction or long germination duration and a multimodal curve or both, showed a risk avoiding strategy. The adaptability to uncertain environments of some annual species in temperate semiarid region is discussed.

Modelling the potential effects of climate change on calcareous grasslands in Atlantic Europe

Aim This study aims to assess the sensitivity of calcareous grassland vegetation to climate change and to indicate the most probable direction of change. Location The study area was a region of Britain, Ireland, France and Spain, centred on the Bay of Biscay, which was defined using a land classification based on climatic criteria. Methods Vegetation was sampled in the field, with additional data collected on soils, climate, management and land cover. The vegetation samples were ordinated by detrended correspondence analysis in order to explore the main gradients present and as a basis for modelling changes. Environmental data were summarized by ordination techniques, with the scores generated used to predict the current vegetation score on the first two ordination axes by multiple regression. The model was then manipulated to represent a 2 °C increase in temperature and resulting shifts in the vegetation samples in terms of their species composition assessed. Results There was a good general agreement between the original vegetation ordination axis scores and those predicted by the model, the latter of which were based on environmental data alone. Following a 2 °C increase in temperature, the predicted changes in the ordination space were demonstrated to be subtle, consisting of small shifts towards vegetation associated with warmer conditions, representing distances 100 km or less on the ground. Main conclusions The models are simple but nevertheless provide a useful basis for the investigation of potential vegetation change. The shifts in the ordination space represent more minor changes than those predicted in previous studies. This suggests that the potential for major change is lower when environmental factors such as soil and management are considered in addition to climate. The potential for change is also reduced when vegetation is considered as a whole rather than on an individual species basis, due to both interspecific interactions and interactions with environmental factors acting as constraints.

Integrated scenarios of acidification and climate change in Asia and Europe

Two integrated assessment models, one for climate change on a global scale (IMAGE 2) and another for the regional analysis of the impacts of acidifying deposition (RAINS), have been linked to assess the impacts of reducing sulphur emission on ecosystems in Asia and Europe. While such reductions have the beneficial effect of reducing the deposition of acidifying compounds and thus the exceedance of critical loads of ecosystems, they also reduce the global level of sulphate aerosols and thus enhance the impact of increased emissions of greenhouse gases, and consequently increase the risk of potential vegetation changes. The calculations indicate that about 70% of the ecosystems in Asia would be affected by either acid deposition or climate change in the year 2100 (up from 20% in 1990) for both sulphur emission scenarios (controlled and uncontrolled), whereas in Europe the impacted area would remain at a level of about 50%, with a dip early next century. More generally, the effects of reducing sulphur emissions and thus enhancing climate change would about balance for the Asian region, whereas for Europe the desirable impact of sulphur emission reductions would greatly outweigh its undesirable effects.

The effects of disturbance on marsh seed banks

Seed numbers and the species composition of seed banks (germinable seeds) from a marsh adjacent to the Great Salt Lake were compared among five vegetation types prior to a drawdown, during a drawdown, and prior to fire, after fire, and after restoration of normal water levels. Substrate samples were processed in the greenhouse under submersed and moist soil treatments to simulate the two germination conditions found in the field. After the fire, seed movement into the different vegetation types was also estimated. Numbers of germinable seeds were not depleted during the drawdown, possibly owing to increased salinity and the presence of standing vegetation. Fire had little effect on seed banks and subsequent seedling response. In general, seed banks were not affected by disturbance (e.g., burning, drawdown). The movement of seeds into the different vegetation types indicated that seed ingress could be important when one considers potential vegetation change. Seed banks of open water sites contained few germinable seeds when compared with Scirpus lacustris, S. maritimus, Distichlis spicata, and Typha spp. sites. Open water sites were devoid of vegetation and had few physical barriers, and seeds continued to move (air, water) across these areas until a barrier was reached, e.g., sites with vegetation.