A biome classification of China based on plant functional types and the BIOME3 model

A biome classification for China was established based on plant functional types (PFTs) using the BIOME3 model to include 16 biomes. In the eastern part of China, the PFTs of trees determine mostly the physiognomy of landscape. Biomes range from boreal deciduous coniferous forest/woodland, boreal mixed forest/woodland, temperate mixed forest, temperate broad-leaved deciduous forest, warm-temperate broad-leaved evergreen/mixed forest, warm-temperate/cool-temperate evergreen coniferous forest, xeric woodland/scrub, to tropical seasonal and rain forest, and tropical deciduous forest from north to south. In the northern and western part of China, grass is the dominant PFT. From northeast to west and southwest the biomes range from moist savannas, tall grassland, short grassland, dry savannas, arid shrubland/steppe, desert, to alpine tundra/ice/polar desert. Comparisons between the classification introduced here and the four classifications which were established over the past two decades, i.e. the vegetation classification, the vegetation division, the physical ecoregion, and the initial biome classification have showed that the different aims of biome classifications have resulted in different biome schemes each with its own unique characteristics and disadvantages for global change study. The new biome classification relies not only on climatic variables, but also on soil factor, vegetation functional variables, ecophysiological parameters and competition among the PFTs. It is a comprehensive classification that using multivariables better expresses the vegetation distribution and can be compared with world biome classifications. It can be easily used in the response study of Chinese biomes to global change, regionally and globally.     

Estimating Carbon Flux Phenology with Satellite-Derived Land Surface Phenology and Climate Drivers for Different Biomes: A Synthesis of AmeriFlux Observations

Carbon Flux Phenology (CFP) can affect the interannual variation in Net Ecosystem Exchange (NEE) of carbon between terrestrial ecosystems and the atmosphere. In this study, we proposed a methodology to estimate CFP metrics with satellite-derived Land Surface Phenology (LSP) metrics and climate drivers for 4 biomes (i.e., deciduous broadleaf forest, evergreen needleleaf forest, grasslands and croplands), using 159 site-years of NEE and climate data from 32 AmeriFlux sites and MODIS vegetation index time-series data. LSP metrics combined with optimal climate drivers can explain the variability in Start of Carbon Uptake (SCU) by more than 70% and End of Carbon Uptake (ECU) by more than 60%. The Root Mean Square Error (RMSE) of the estimations was within 8.5 days for both SCU and ECU. The estimation performance for this methodology was primarily dependent on the optimal combination of the LSP retrieval methods, the explanatory climate drivers, the biome types, and the specific CFP metric. This methodology has a potential for allowing extrapolation of CFP metrics for biomes with a distinct and detectable seasonal cycle over large areas, based on synoptic multi-temporal optical satellite data and climate data.     

湖南城步南山的植被及其合理利用

 城步南山是我国南方规模最大的牧场,总面积138,345亩(9,223公顷)。南山地区的植被,根据生态外貌原则划分为常绿针叶林、常绿落叶阔叶混交林,落叶阔叶林,灌丛、竹丛、草丛,农作物,经济林和果园等10个主要类型。现在的草丛是森林遭受破坏的次生植被。本区在开发利用上,应以发展林业为主,实行林牧结合。山地坡度大于35˚的必须发展林业,25—34˚的坡地要林牧兼顾,小于24˚者可以发展牧业,并营造各类防护林。     

High ecosystem stability of evergreen broadleaf forests under severe droughts

Global increase in drought occurrences threatens the stability of terrestrial ecosystem functioning. Evergreen broadleaf forests (EBFs) keep leaves throughout the year, and therefore could experience higher drought risks than other biomes. However, the recent temporal variability of global vegetation productivity or land carbon sink is mainly driven by non‐evergreen ecosystems, such as semiarid grasslands, croplands, and boreal forests. Thus, we hypothesize that EBFs have higher stability than other biomes under the increasingly extreme droughts. Here we use long‐term Standardized Precipitation and Evaporation Index (SPEI) data and satellite‐derived Enhanced Vegetation Index (EVI) products to quantify the temporal stability (ratio of mean annual EVI to its SD), resistance (ability to maintain its original levels during droughts), and resilience (rate of EVI recovering to pre‐drought levels) at biome and global scales. We identified significantly increasing trends of annual drought severity (SPEI range: ?0.08 to ?1.80), area (areal fraction range: 2%–19%), and duration (month range: 7.9–9.1) in the EBF biome over 2000–2014. However, EBFs showed the highest resistance of EVI to droughts, but no significant differences in resilience of EVI to droughts were found among biomes (forests, grasslands, savannas, and shrublands). Global resistance and resilience of EVI to droughts were largely affected by temperature and solar radiation. These findings suggest that EBFs have higher stability than other biomes despite the greater drought exposure. Thus, the conservation of EBFs is critical for stabilizing global vegetation productivity and land carbon sink under more‐intense climate extremes in the future.     

Evaluating Spatial-Temporal Dynamics of Net Primary Productivity of Different Forest Types in Northeastern China Based on Improved FORCCHN

An improved individual-based forest ecosystem carbon budget model for China (FORCCHN) was applied to investigate the spatial-temporal dynamics of net primary productivity of different forest types in northeastern China. In this study, the forests of northeastern China were categorized into four ecological types according to their habitats and generic characteristics (evergreen broadleaf forest, deciduous broadleaf forest, evergreen needleleaf forest and deciduous needleleaf forest). The results showed that distribution and change of forest NPP in northeastern China were related to the different forest types. From 1981 to 2002, among the forest types in northeastern China, per unit area NPP and total NPP of deciduous broadleaf forest were the highest, with the values of 729.4 gC/(m²•yr) and 106.0 TgC/yr, respectively, followed by mixed broadleaf- needleleaf forest, deciduous needleleaf forest and evergreen needleleaf forest. From 1981 to 2002, per unit area NPP and total NPP of different forest types in northeastern China exhibited significant trends of interannual increase, and rapid increase was found between the 1980s and 1990s. The contribution of the different forest type’s NPP to total NPP in northeastern China was clearly different. The greatest was deciduous broadleaf forest, followed by mixed broadleaf- needleleaf forest and deciduous needleleaf forest. The smallest was evergreen needleleaf forest. Spatial difference in NPP between different forest types was remarkable. High NPP values of deciduous needleleaf forest, mixed broadleaf- needleleaf forest and deciduous broadleaf forest were found in the Daxing’anling region, the southeastern of Xiaoxing’anling and Jilin province, and the Changbai Mountain, respectively. However, no regional differences were found for evergreen needleleaf NPP. This study provided not only an estimation NPP of different forest types in northeastern China but also a useful methodology for estimating forest carbon storage at regional and global levels.     

Are There Consistent Grazing Indicators in Drylands? Testing Plant Functional Types of Various Complexity in South Africa’s Grassland and Savanna Biomes

Despite our growing knowledge on plants’ functional responses to grazing, there is no consensus if an optimum level of functional aggregation exists for detecting grazing effects in drylands. With a comparative approach we searched for plant functional types (PFTs) with a consistent response to grazing across two areas differing in climatic aridity, situated in South Africa’s grassland and savanna biomes. We aggregated herbaceous species into PFTs, using hierarchical combinations of traits (from single- to three-trait PFTs). Traits relate to life history, growth form and leaf width. We first confirmed that soil and grazing gradients were largely independent from each other, and then searched in each biome for PFTs with a sensitive response to grazing, avoiding confounding with soil conditions. We found no response consistency, but biome-specific optimum aggregation levels. Three-trait PFTs (e.g. broad-leaved perennial grasses) and two-trait PFTs (e.g. perennial grasses) performed best as indicators of grazing effects in the semi-arid grassland and in the arid savanna biome, respectively. Some PFTs increased with grazing pressure in the grassland, but decreased in the savanna. We applied biome-specific grazing indicators to evaluate if differences in grazing management related to land tenure (communal versus freehold) had effects on vegetation. Tenure effects were small, which we mainly attributed to large variability in grazing pressure across farms. We conclude that the striking lack of generalizable PFT responses to grazing is due to a convergence of aridity and grazing effects, and unlikely to be overcome by more refined classification approaches. Hence, PFTs with an opposite response to grazing in the two biomes rather have a unimodal response along a gradient of additive forces of aridity and grazing. The study advocates for hierarchical trait combinations to identify localized indicator sets for grazing effects. Its methodological approach may also be useful for identifying ecological indicators in other ecosystems.     

Latitudinal patterns of magnitude and interannual variability in net ecosystem exchange regulated by biological and environmental variables

Over the last two and half decades, strong evidence showed that the terrestrial ecosystems are acting as a net sink for atmospheric carbon. However the spatial and temporal patterns of variation in the sink are not well known. In this study, we examined latitudinal patterns of interannual variability (IAV) in net ecosystem exchange (NEE) of CO₂ based on 163 site-years of eddy covariance data, from 39 northern-hemisphere research sites located at latitudes ranging from ∼29°N to ∼64°N. We computed the standard deviation of annual NEE integrals at individual sites to represent absolute interannual variability (AIAV), and the corresponding coefficient of variation as a measure of relative interannual variability (RIAV). Our results showed decreased trends of annual NEE with increasing latitude for both deciduous broadleaf forests and evergreen needleleaf forests. Gross primary production (GPP) explained a significant proportion of the spatial variation of NEE across evergreen needleleaf forests, whereas, across deciduous broadleaf forests, it is ecosystem respiration (Re). In addition, AIAV in GPP and Re increased significantly with latitude in deciduous broadleaf forests, but AIAV in GPP decreased significantly with latitude in evergreen needleleaf forests. Furthermore, RIAV in NEE, GPP, and Re appeared to increase significantly with latitude in deciduous broadleaf forests, but not in evergreen needleleaf forests. Correlation analyses showed air temperature was the primary environmental factor that determined RIAV of NEE in deciduous broadleaf forest across the North American sites, and none of the chosen climatic factors could explain RIAV of NEE in evergreen needleleaf forests. Mean annual NEE significantly increased with latitude in grasslands. Precipitation was dominant environmental factor for the spatial variation of magnitude and IAV in GPP and Re in grasslands.     

Global synthesis of leaf area index observations: implications for ecological and remote sensing studies

Aim We present the first global synthesis of plant canopy leaf area index (LAI) measurements from more than 1000 published estimates representing ～400 unique field sites. LAI is a key variable for regional and global models of biosphere‐atmosphere exchanges of energy, carbon dioxide, water vapour, and other materials. Location The location is global, geographically distributed. Results Biomes with LAI values well represented in the literature included croplands, forests and plantations. Biomes not well represented were deserts, shrublands, tundra and wetlands. Nearly 40% of the records in the database were published in the past 10 years (1991–2000), with a further 20% collected between 1981 and 1990. Mean (± SD) LAI, distributed between 15 biome classes, ranged from 1.3 ± 0.9 for deserts to 8.7 ± 4.3 for tree plantations, with temperate evergreen forests (needleleaf and broadleaf) displaying the highest average LAI (5.1–6.7) among the natural terrestrial vegetation classes. Following a statistical outlier analysis, the global mean (± SD) LAI decreased from 5.2 (4.1) to 4.5 (2.5), with a maximum LAI of 18. Biomes with the highest LAI values were plantations > temperate evergreen forests > wetlands. Those with the lowest LAI values were deserts < grasslands < tundra. Mean LAI values for all biomes did not differ statistically by the methodology employed. Direct and indirect measurement approaches produced similar LAI results. Mean LAI values for all biomes combined decreased significantly in the 1990s, a period of substantially more studies and improved methodologies. Main conclusions Applications of the LAI database span a wide range of ecological, biogeochemical, physical, and climate research areas. The data provide input to terrestrial ecosystem and land‐surface models, for evaluation of global remote sensing products, for comparisons to field studies, and other applications. Example uses of the database for global plant productivity, fractional energy absorption, and remote sensing studies are highlighted.     

Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change

Aim Climate change threatens to shift vegetation, disrupting ecosystems and damaging human well‐being. Field observations in boreal, temperate and tropical ecosystems have detected biome changes in the 20th century, yet a lack of spatial data on vulnerability hinders organizations that manage natural resources from identifying priority areas for adaptation measures. We explore potential methods to identify areas vulnerable to vegetation shifts and potential refugia. Location Global vegetation biomes. Methods We examined nine combinations of three sets of potential indicators of the vulnerability of ecosystems to biome change: (1) observed changes of 20th‐century climate, (2) projected 21st‐century vegetation changes using the MC1 dynamic global vegetation model under three Intergovernmental Panel on Climate Change (IPCC) emissions scenarios, and (3) overlap of results from (1) and (2). Estimating probability density functions for climate observations and confidence levels for vegetation projections, we classified areas into vulnerability classes based on IPCC treatment of uncertainty. Results One‐tenth to one‐half of global land may be highly (confidence 0.80–0.95) to very highly (confidence ≥ 0.95) vulnerable. Temperate mixed forest, boreal conifer and tundra and alpine biomes show the highest vulnerability, often due to potential changes in wildfire. Tropical evergreen broadleaf forest and desert biomes show the lowest vulnerability. Main conclusions Spatial analyses of observed climate and projected vegetation indicate widespread vulnerability of ecosystems to biome change. A mismatch between vulnerability patterns and the geographic priorities of natural resource organizations suggests the need to adapt management plans. Approximately a billion people live in the areas classified as vulnerable.     

植被气候关系与我国的植被分区

气候制约着植被的地理分布,植被是区域气候特征的反映和指示,两者之间存在密不可分的联系.揭示植被与气候之间的关系是正确认识植被分布的前提,是进行植被区划的理论基础.植被区划是植被研究的归纳和总结,是其他自然地理区划和农林业区划的基础.本文在简要回顾中国植被气候关系及植被分区的研究历史的基础上,对我国以往的主要植被分区原则、依据和方案进行了评述,对有争议的主要植被界线进行了讨论.我们认为,在当今我国大部分地区的原生植被已遭到破坏的现实情况下,根据原生植被及其衍生植被类型的分布,确定其分布与限制性气候因子的关系,以此来进行植被带(区)的划分,不仅反映植被气候间密不可分的关系,在实践上也便于操作.尽管在一些植被带的命名、具体界线的划定上有分歧,但最近的中国植被分区方案大都认为我国基本的植被区有8至9个,即针叶林、针阔叶混交林、落叶阔叶林、常绿落叶阔叶混交林、常绿阔叶林以及雨林季雨林、草原、荒漠以及高寒植被.通过分析主要植被带附近的植被、气候等特征,本文认为,1)秦岭淮河线是一条重要的水分气候带,而不是温度带,不是亚热带植被的北界;2)我国亚热带植被的北界基本上沿长江北岸,从杭州湾经太湖、安徽宣城、铜陵经大别山南坡到武汉往西,与WI值130-140 ℃·月一致;3)我国热带区域的面积极小,仅分布在海南岛的东南部和台湾南端及其以南地区; 4) 我国东部地区暖温带的水热条件南北差异甚大,建议以秦岭淮河为界,将暖温带划分为两个植被带,即落叶阔叶疏林带和落叶常绿阔叶混交林带;华北地区的地带性植被为落叶阔叶疏林.最后,本文还强调了对应于气候变化进行动态植被分区的重要性.     

Climate-induced changes in the vegetation pattern of China in the 21st century

Quantifying climate-induced changes in vegetation patterns is essential to understanding land–climate interactions and ecosystem changes. In the present study, we estimated various distributional changes of vegetation under different climate-change scenarios in the 21st century. Both hypothetical scenarios and Hedley RCM scenarios show that the transitional vegetation types, such as shrubland and grassland, have higher sensitivity to climatic change compared to vegetation under extreme climatic conditions, such as the evergreen broadleaf forest or desert, barren lands. Mainly, the sensitive areas in China lie in the Tibetan Plateau, Yunnan-Guizhou Plateau, northeastern plain of China and eco-zones between different vegetations. As the temperature increases, mixed forests and deciduous broadleaf forests will shift towards northern China. Grassland, shrubland and wooded grassland will extend to southeastern China. The RCM-project climate changes generally have caused positive vegetation changes; vegetation cover will probably improve 19% relative to baseline, and the forest will expand to 8% relative to baseline, while the desert and bare ground will reduce by about 13%.     

Forest biomass is strongly shaped by forest height across boreal to tropical forests in China

Aims Forest height is a major factor shaping geographic biomass patterns, and there is a growing dependence on forest height derived from satellite light detecting and ranging (LiDAR) to monitor large-scale biomass patterns. However, how the relationship between forest biomass and height is modulated by climate and biotic factors has seldom been quantified at broad scales and across various forest biomes, which may be crucial for improving broad-scale biomass estimations based on satellite LiDAR.Methods We used 1263 plots, from boreal to tropical forest biomes across China, to examine the effects of climatic (energy and water availability) and biotic factors (forest biome, leaf form and leaf phenology) on biomass–height relationship, and to develop the models to estimate biomass from forest height in China.Important findings (i) Forest height alone explained 62% of variation in forest biomass across China and was far more powerful than climate and other biotic factors. (ii) However, the relationship between biomass and forest height were significantly affected by climate, forest biome, leaf phenology (evergreen vs. deciduous) and leaf form (needleleaf vs. broadleaf). Among which, the effect of climate was stronger than other factors. The intercept of biomass–height relationship was more affected by precipitation while the slope more affected by energy availability. (iii) When the effects of climate and biotic factors were considered in the models, geographic biomass patterns could be well predicted from forest height with an r 2 between 0.63 and 0.78 (for each forest biome and for all biomes together). For most biomes, forest biomass could be well predicted with simple models including only forest height and climate. (iv) We provided the first broad-scale models to estimate biomass from forest height across China, which can be utilized by future LiDAR studies. (v) Our results suggest that the effect of climate and biotic factors should be carefully considered in models estimating broad-scale forest biomass patterns with satellite LiDAR.     

Landscape connectivity of the grassland biome in Mpumalanga,South Africa

The South African grassland biome is one of the most threatened biomes in South Africa. Approximately 45% of the grassland biome area is transformed, degraded or severely invaded by alien plants and the remaining natural areas are highly fragmented. In this fragmented landscape, the connectivity between habitat patches is very important to maintain viable populations. In this study we aimed to quantify connectivity of the grassland biome in Mpumalanga using graph theory in order to identify conservation priorities and to direct conservation efforts. Graph theory‐based connectivity indices have the ability to combine spatially explicit habitat data with species specific dispersal data and can quantify structural and functional connectivity over large landscapes. We used these indices to quantify the overall connectivity of the study area, to determine the influence of abandoned croplands on overall connectivity, and to identify the habitat patches and vegetation types most in need of maintaining overall connectivity. Natural areas were identified using 2008 land cover data for Mpumalanga. Connectivity within the grassland biome of Mpumalanga was analysed for grassland species with dispersal distances ranging from 50 to 1000 m. The grassland habitat patches were mostly well connected, with 99.6% of the total habitat area connected in a single component at a threshold distance of 1000 m. The inclusion of abandoned croplands resulted in a 33% increase in connectivity at a threshold distance of 500 m. The habitat patches most important for maintaining overall connectivity were the large patches of continuous habitat in the upper and lower centres of the study area and the most important vegetation types were the Wakkerstroom Montane Grassland and the Eastern Temperate Freshwater Wetlands. These results can be used to inform management decisions and reserve design to improve and maintain connectivity in this biome.     

气候变化下中国不同植被区总初级生产力对干旱的响应

干旱变化具有明显的空间分异,不同植被类型对干旱的响应亦有差别。开展气候变化下不同植被覆盖类型对干旱响应的差异分析,厘清温升干旱化进程对植被的影响,对了解植被发展动态及预测未来格局有着非常重要的意义。基于1982—2017年的总初级生产力(GPP)数据和同时期东安格利亚大学气候研究中心(CRU)时间序列(TS)气候数据,分析了中国8个植被区GPP和干旱的变化趋势,通过对比标准化降水指数(SPI)和标准化降水蒸散指数(SPEI)的趋势差异识别了典型的温升干旱化区域,在此基础上研究气温上升如何影响GPP对干旱的响应,进一步讨论了不同植被类型对干旱的敏感性差异。结果表明：(1) 36年来8个植被区除青藏高原高寒植被区呈湿润化,其他植被区均呈现变干趋势;(2)气温上升大面积加剧了温带荒漠区和温带草原区的变干趋势;(3)亚热带常绿阔叶林区和热带季风雨林、雨林区的GPP受温度和干旱影响相当,青藏高原高寒植被区和针叶、落叶林混交林区的GPP受温度主导,其他植被区GPP均受干旱主导。     

土地利用约束下中国东部南北样带生产力和植被分布对全球变化的响应

对现有的区域植被动态模拟模型进行了改进,使之包含了土地利用分布格局对植被和生态系统相关过程的影响。改进后的模型被用地研究中国东部南北样带(NSTEC)植被和净第一性生产力对未来气候变化的响应。模拟结果显示土地利用格局对未来气候条件下植被分布的变迁和生产力形成过程有非常显著的影响。与没有土地利用约束的情形相比较,土地利用作为限制条件缓减了植被类型之间的竞争,从而减少了模拟的样带区域内常绿阔叶林,但增加了模拟灌木和草地的分布。土地利用约束使得模拟得到的当前条件下的净第一性生产力更为接近实际情况,且未来气候条件下的生产力改变量更为可信。对未来CO2倍增条件下7个大气环流模型预测的气候情景的模拟结果表明：落叶阔叶林将显著增加,但针叶林、灌木和草原的分布将下降。未来气候条件下NSTEC样带的净第一性生产力总量将增加。预测样带北部的净第一性生产力的变化范围大于样带南部。温度变化比降水变化对样带的生产力具有更强的控制。     

土地利用约束下中国东部南北样带生产力和植被分布对全球变化的响应

对现有的区域植被动态模拟模型进行了改进,使之包含了土地利用分布格局对植被和生态系统相关过程的影响.改进后的模型被用于研究中国东部南北样带(NSTEC)植被和净第一性生产力对未来气候变化的响应.模拟结果显示土地利用格局对未来气候条件下植被分布的变迁和生产力形成过程有非常显著的影响.与没有土地利用约束的情形相比较,土地利用作为限制条件缓减了植被类型之间的竞争,从而减少了模拟的样带区域内常绿阔叶林,但增加了模拟灌木和草地的分布.土地利用约束使得模拟得到的当前条件下的净第一性生产力更为接近实际情况,且未来气候条件下的生产力改变量更为可信.对未来CO2倍增条件下7个大气环流模型预测的气候情景的模拟结果表明:落叶阔叶林将显著增加,但针叶林、灌木和草原的分布将下降.未来气候条件下NSTEC样带的净第一性生产力总量将增加.预测样带北部的净第一性生产力的变化范围大于样带南部.温度变化比降水变化对样带的生产力具有更强的控制.     

Assimilation exceeds respiration sensitivity to drought: A FLUXNET synthesis

The intensification of the hydrological cycle, with an observed and modeled increase in drought incidence and severity, underscores the need to quantify drought effects on carbon cycling and the terrestrial sink. FLUXNET, a global network of eddy covariance towers, provides dense data streams of meteorological data, and through flux partitioning and gap filling algorithms, estimates of net ecosystem productivity (F_NEP), gross ecosystem productivity (P), and ecosystem respiration (R). We analyzed the functional relationship of these three carbon fluxes relative to evaporative fraction (EF), an index of drought and site water status, using monthly data records from 238 micrometeorological tower sites distributed globally across 11 biomes. The analysis was based on relative anomalies of both EF and carbon fluxes and focused on drought episodes by biome and climatic season. Globally P was ≈50% more sensitive to a drought event than R. Network‐wide drought‐induced decreases in carbon flux averaged ?16.6 and ?9.3 g C m^?2 month^?1 for P and R, i.e., drought events induced a net decline in the terrestrial sink. However, in evergreen forests and wetlands drought was coincident with an increase in P or R during parts of the growing season. The most robust relationships between carbon flux and EF occurred during climatic spring for F_NEP and in climatic summer for P and R. Upscaling flux sensitivities to a global map showed that spatial patterns for all three carbon fluxes were linked to the distribution of croplands. Agricultural areas exhibited the highest sensitivity whereas the tropical region had minimal sensitivity to drought. Combining gridded flux sensitivities with their uncertainties and the spatial grid of FLUXNET revealed that a more robust quantification of carbon flux response to drought requires additional towers in all biomes of Africa and Asia as well as in the cropland, shrubland, savannah, and wetland biomes globally.     

An Ecogeographical Regionalization for Biodiversity in China

Ecogeographical regionalization is the basis for spatial differentiation of biodiversity research. In view of the principle of international ecogeographical regionalization, this study has applied multivariate analysis and GIS method and based on some ecogeographical attributes limited to the distribution of plant and vegetation, including climatic factors, such as minimum temperature, mean temperature of the coldest month, mean temperature of the wannest month, annual average temperature, precipitation of the coldest month, precipitation of the wannest month, annual precipitation, CV of annual precipitation, biological factors such as vegetation types, vegetation division types, NPP, fiorisitic types, fauna types, abundance of plant species, genus and endemic genus; soil factors such as soil types, soil pH;topographical factors as longitude, latitude and altitude etc. The ecogeographical regionalization for biodiversity in China was made synthetically by using fuzzy cluster method. Four classes of division were used, viz., biodomain, subbiodomain, biome and bioregion. Five biodomains, seven subbiodomains and eighteen biomes were divided in China as follows: Ⅰ Boreal forest biodomain. Ⅰ A Eurasian boreal forest subbiodomain. Ⅰ A1 Southern Taiga mountain cold-temperate coniferous forest biome; Ⅰ A2 North Asian mixed coniferous-broad-leaved forest biome. Ⅱ Northern steppe and desert biodomain. Ⅱ B Eurasian steppe subbiodomain. Ⅱ BI Inner Asian temperate grass steppe biome; Ⅱ B2 Loess Plateau warm-temperate forest/shmb steppe biome. Ⅱ C Asia-Mrica desert subbiodomain. Ⅱ C1 Mid-Asian temperate desert biome; Ⅱ C2 Mongolian/Inner Asian temperate desert biome. Ⅲ East Asian biodomain. Ⅲ D East Asian deciduous broad-leaved forest subbiodomain. Ⅲ D1 East Asian deciduous broad-leaved forest biome, Ⅲ E East Asian evergreen broad-leaved forest subbiodomain. Ⅲ El East Asian mixed deciduous-evergreen broad-leaved forest biome; Ⅲ E2 East Asian evergreen broad-leaved forest biome; Ⅲ E3 East Asian monsoon evergreen broad-leaved forest biome; Ⅲ FA Western East Asian mountain evergreen broadleaved forest biome. Ⅳ Palaeotropical subdomain. IV F India-Malaysian tropical forest subbiodomain.Ⅳ Fl Northern tropical rain forest/seasonal rain forest biome; Ⅳ F2 Tropical island coral reef vegetation biome. Ⅴ Asian plateau biodomain. Ⅴ G Tibet Plateau subbiodomain. Ⅴ G1 Tibet alpine highcold shrub meadow biome;Ⅴ G2 Tibet alpine high-cold steppe biome; Ⅴ G3 Tibet alpine high-cold desert biome; Ⅴ G4 Tibet alpine temperate steppe biome; Ⅴ G5 Tibet alpine temperate desert biome.     

Modelling biome shifts and tree cover change for 2050 in West Africa

Aim Africa is expected to face severe changes in climatic conditions. Our objectives are: (1) to model trends and the extent of future biome shifts that may occur by 2050, (2) to model a trend in tree cover change, while accounting for human impact, and (3) to evaluate uncertainty in future climate projections. Location West Africa. Methods We modelled the potential future spatial distribution of desert, grassland, savanna, deciduous and evergreen forest in West Africa using six bioclimatic models. Future tree cover change was analysed with generalized additive models (GAMs). We used climate data from 17 general circulation models (GCMs) and included human population density and fire intensity to model tree cover. Consensus projections were derived via weighted averages to: (1) reduce inter‐model variability, and (2) describe trends extracted from different GCM projections. Results The strongest predicted effect of climate change was on desert and grasslands, where the bioclimatic envelope of grassland is projected to expand into the desert by an area of 2 million km². While savannas are predicted to contract in the south (by 54 ± 22 × 10⁴ km²), deciduous and evergreen forest biomes are expected to expand (64 ± 13 × 10⁴ km² and 77 ± 26 × 10⁴ km²). However, uncertainty due to different GCMs was particularly high for the grassland and the evergreen biome shift. Increasing tree cover (1–10%) was projected for large parts of Benin, Burkina Faso, Côte d’Ivoire, Ghana and Togo, but a decrease was projected for coastal areas (1–20%). Furthermore, human impact negatively affected tree cover and partly changed the direction of the projected change from increase to decrease. Main conclusions Considering climate change alone, the model results of potential vegetation (biomes) show a ‘greening’ trend by 2050. However, the modelled effects of human impact suggest future forest degradation. Thus, it is essential to consider both climate change and human impact in order to generate realistic future tree cover projections.     

秦岭太白山桦林的稳定性

本文论述了桦林的稳定性。文中认为桦林地质时期和现代都可形成地带性森林,它是凉温湿润气候的顶极群落,分布在暖温带落叶阔叶林与湿冷生针叶林之间的凉温湿润地带。因此这类林的区系组成无论地质时期或现代,都是以暖温带落叶阔叶林和寒温带针叶林的混合成分为特征。