晋北地区土地利用覆被格局的演变与模拟

区域土地利用覆被变化及未来发展情景对区域土地管理和可持续发展具有重要意义。以地处农牧交错带、土地利用覆被变化剧烈的晋北地区为研究区,获取其2010、2015年的土地利用覆被(Land use/land cover,LULC)数据,选取高程、人口、经济、气温、降水等9种影响因素作为驱动因子,采用CLUE-S模型拟合研究区2015年的土地覆被格局并判断拟合精度,在此基础上,分别设置了3种社会经济发展情景,模拟这些情景下研究区2020年的土地利用覆被格局演变。结果表明:1)晋北地区土地利用覆被以耕地、林地和草地为主,各类型土地主要呈西北斜向的条带状分布;2)Logistic回归模型可以很好地提取LULC与驱动因子之间的关系,反映不同的驱动因素对不同的土地利用类型分布格局的影响效果及程度;3)CLUE-S模型在晋北地区土地利用覆被格局的拟合上有较好的精度,模拟Kappa系数值达0.89,表明该模型能够很好地模拟晋北地区的土地利用覆被;4)情景模拟结果表明,研究区生态保护情景(c)下的土地利用覆被格局明显优于维持现状情景(a)和经济优先情景(b),建议在未来土地开发利用过程中,应当减缓工矿用地增...     

2.5D Morphogenesis: modeling landuse and landcover dynamics in the Ecuadorian Amazon

The Ecuadorian Amazon, lying in the headwaters of the Napo and Aguarico River valleys, is experiencing rapid change in Land Use and Land Cover (LULC) conditions and regional landscape diversity uniquely tied to the spontaneous agricultural colonization of the Oriente region of northeastern Ecuador beginning in the mid to late 1970s. Spontaneous colonization occurred on squattered lands located adjacent to oil company roads and in government development sectors composed of multiple 50 ha land parcels organized into `piano key' shaped family farms or fincas. Portions of these fincas were deforested for agricultural extensification depending upon the age of the finca and several site and situation factors. Because fincas are managed at the household level as spatially discrete, temporally independent units, land conversion at the finca-level is recognized as the chief proximate cause of deforestation within the region.Focusing on the spatial and temporal dynamics of deforestation, agricultural extensification, and plant succession at the finca-level, and urbanization at the community-level, a cell-based morphogenetic model of Land Use and Land Cover Change (LULCC) was developed as the foundation for a predictive model of regional LULCC dynamics and landscape diversity. Here, LULC characteristics are determined using a time-series of remotely sensed data (i.e., Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS)) using an experimental [semi-traditional] (hybrid unsupervised-supervised) classification scheme resulting in a time-series data set including LULC images for 1973, 1986, 1989, 1996, and 1999. Pixel histories of LULC type across the time-series were integrated into LULC trajectories and converted into seed or input data sets for LULC modeling to alternate time periods and for model validation. LULC simulations, achieved through cellular automata (CA) methodologies, were run on an annual basis to the year 2010 using 1973 as the initial conditions and the satellite time-series as the `check points' in the simulations. The model was developed using the Imagine Spatial Modeler of the ERDAS image processing software, and enhanced using the Spatial Modeler Language (SML). The model works by (a) simulating the present by extrapolating from the past using the image time-series, (b) validating the simulations via the remotely sensed time-series of past conditions and through field observations of current conditions, (c) allowing the model to iterate to the year 2010, and (d) comparing model outputs to an autoregressive time-series approach for annual conditions that are compared via paired t-tests of pattern metrics run at the landscape-level to define compositional and structural differences between successive model outputs.     

Implications of biofuel policy‐driven land cover change for rainfall erosivity and soil erosion in the United States

Large‐scale conversion of traditional agricultural cropping systems to biofuel cropping systems is predicted to have significant impact on the hydrologic cycle. Changes in the hydrologic cycle lead to changes in rainfall and its erosive power, and consequently soil erosion that will have onsite impacts on soil quality and crop productivity, and offsite impacts on water quality and quantity. We examine regional change in rainfall erosivity and soil erosion resulting from biofuel policy‐induced land use/land cover (LULC) change. Regional climate is simulated under current and biofuel LULC scenarios for the period 1979–2004 using the Weather Research Forecast (WRF) model coupled to the NOAH land surface model. The magnitude of change in rainfall erosivity under the biofuel scenario is 1.5–3 times higher than the change in total annual rainfall. Over most of the conterminous United States (~56%), the magnitude of the change in erosivity is between ?2.5% and +2.5%. A decrease in erosivity of magnitude 2.5–10% is predicted over 23% of the area, whereas an increase of the same magnitude is predicted over 14% of the area. Corresponding to the changes in rainfall erosivity and crop cover, a decrease in soil loss is predicted over 60% of the area under the biofuel scenario. In Kansas and Oklahoma, the states in which a large fraction of land area is planted with switchgrass under the biofuel scenario, soil loss is estimated to decrease 12% relative to the baseline. This reduction in soil loss is due more to changes in the crop cover factor than changes in rainfall or rainfall erosivity. This indicates that the changes in LULC, due to future cellulosic biofuel feedstock production, can have significant implications for regional soil and water resources in the United States and we recommend detailed investigation of the trade‐offs between land use and management options.     

Effects of urbanization and industrialization on agricultural land use in Shandong Peninsula of China

China is the most populated country in the world with slightly more than half of the population is still living in rural areas. In the past couple of decades, rapid urbanization and industrialization have significantly changed the land use/land cover (LULC) pattern in rural areas, particularly those around the big cities in eastern China. Shandong Peninsula, a traditional agriculture area, also has witnessed rapid urbanization and industrialization. Analysis of land use/land cover change in this area, specially the change of agricultural lands, would help us better understand the interaction between government's policies and farmers’ economic interests.This paper developed a method to extract single-cropping land, double-cropping land and other land use/land cover categories for 1978, 1999 and 2006 from seasonal variations in Normalized Vegetation Index (NDVI) during a crop calendar year. Spatial analysis results indicated significant changes of arable lands and other land use/land cover categories due to the urbanization and industrialization. The most possible reason is due to the continuous adjustment of government's policies and shift of farmer's economic interests. Results from this study would help government make wise decisions in the near future to mitigate urban sprawl and industrial development while maintain enough agricultural production.     

Impact of LUCC on landscape pattern in the Yangtze River Basin during 2001–2019

Evaluating the influences of LUCC (Land Use/Land Cover Change) on landscape pattern is significant for understanding and improving ecological environment system management. This study used landscape pattern as an important indicator to estimate the impacts of the LUCC in the Yangtze River Basin from 2001 to 2019. Based on the remote sensing images of LULC (Land Use/Land Cover) in the Yangtze River Basin in 2001–2019, the dynamic attitude and transfer matrix of LULC, and landscape pattern indices were employed to analyze the LUCC and the impact of LULC on the landscape pattern of the Yangtze River Basin. The results of LUCC show that the main LUCC in the Yangtze River Basin during 2001–2019 is mainly manifested by the increase of water body, forest, wetland, crop/natural vegetation mosaic (NVM) and urban, among which the forest increased the most by 62,635 km². The areas of grassland and cropland are decreasing, with the grassland decreasing the most. Forest, crop/NVM and cropland are transformed into grassland, which complements the lack of grassland to some extent. LUCC in the Yangtze River Basin is most intense between grassland and forest. Landscape pattern shows: Grassland occupies an important advantage in the whole landscape structure. Forest, grassland and urban landscapes are seriously fragmented, and their LSI (Landscape Shape Index) is more complex than others. The connectivity between various landscape types is weakened, and the degree of landscape fragmentation is increased, but the LULC structure is becoming more and more abundant. The areas with high landscape fragmentation value have richer landscape diversity and diverse LULC types. There is a strong correlation between grassland and the four landscape pattern indices. The change of landscape pattern in the Yangtze River Basin is influenced by natural factors and LUCC in the Yangtze River Basin, and the change caused by human activities is the main driving factor of LUCC.     

克里雅河中游土地利用/覆被与景观格局变化研究

干旱内陆河流在维系绿洲形成与发展,连接干旱区绿洲各类生态子系统中具有举足轻重的作用。以克里雅河中游为研究区,基于遥感影像分析了1995年、2005年和2015年3个时期的土地利用/覆被与景观格局变化特征,采用缓冲区分析法,揭示了土地利用/覆被与景观格局的时空变化特征,用土地覆被转移指数模型计算了土地利用转移方向。研究结果表明:1)1995—2015年,克里雅河中游耕地持续增加,草地和水体呈减少趋势,耕地增加和水体减少主要发生在河道附近,草地退化在绿洲边缘较严重。在河道附近,耕地主要由水体和草地转入,水体主要转出到耕地,而在绿洲边缘草地主要转出为其他用地;2)从景观水平看,研究区整体景观具有破碎化趋势,景观多样性降低,分离度变大,整个景观向均匀化发展,此变化在河道附近和绿洲边缘较明显。从类型水平看,耕地斑块数量持续增加,有向连片生成的趋势。草地斑块分离度越来越大,逐渐失去了在绿洲中的优势。水体有破碎化趋势;3)从土地转移指数看,研究区土地覆被总体变差;河道附近土地覆被经历了退化-改善的变化过程,而其他缓冲带则是持续退化,尤其绿洲边缘退化程度最为严重。     

Projecting global land-use change and its effect on ecosystem service provision and biodiversity with simple models

Background

As the global human population grows and its consumption patterns change, additional land will be needed for living space and agricultural production. A critical question facing global society is how to meet growing human demands for living space, food, fuel, and other materials while sustaining ecosystem services and biodiversity [1].

Methodology/Principal Findings

We spatially allocate two scenarios of 2000 to 2015 global areal change in urban land and cropland at the grid cell-level and measure the impact of this change on the provision of ecosystem services and biodiversity. The models and techniques used to spatially allocate land-use/land-cover (LULC) change and evaluate its impact on ecosystems are relatively simple and transparent [2]. The difference in the magnitude and pattern of cropland expansion across the two scenarios engenders different tradeoffs among crop production, provision of species habitat, and other important ecosystem services such as biomass carbon storage. For example, in one scenario, 5.2 grams of carbon stored in biomass is released for every additional calorie of crop produced across the globe; under the other scenario this tradeoff rate is 13.7. By comparing scenarios and their impacts we can begin to identify the global pattern of cropland and irrigation development that is significant enough to meet future food needs but has less of an impact on ecosystem service and habitat provision.

Conclusions/Significance

Urban area and croplands will expand in the future to meet human needs for living space, livelihoods, and food. In order to jointly provide desired levels of urban land, food production, and ecosystem service and species habitat provision the global society will have to become much more strategic in its allocation of intensively managed land uses. Here we illustrate a method for quickly and transparently evaluating the performance of potential global futures.     

The transparency,reliability and utility of tropical rainforest land‐use and land‐cover change models

Land‐use and land‐cover (LULC) change is one of the largest drivers of biodiversity loss and carbon emissions globally. We use the tropical rainforests of the Amazon, the Congo basin and South‐East Asia as a case study to investigate spatial predictive models of LULC change. Current predictions differ in their modelling approaches, are highly variable and often poorly validated. We carried out a quantitative review of 48 modelling methodologies, considering model spatio‐temporal scales, inputs, calibration and validation methods. In addition, we requested model outputs from each of the models reviewed and carried out a quantitative assessment of model performance for tropical LULC predictions in the Brazilian Amazon. We highlight existing shortfalls in the discipline and uncover three key points that need addressing to improve the transparency, reliability and utility of tropical LULC change models: (1) a lack of openness with regard to describing and making available the model inputs and model code; (2) the difficulties of conducting appropriate model validations; and (3) the difficulty that users of tropical LULC models face in obtaining the model predictions to help inform their own analyses and policy decisions. We further draw comparisons between tropical LULC change models in the tropics and the modelling approaches and paradigms in other disciplines, and suggest that recent changes in the climate change and species distribution modelling communities may provide a pathway that tropical LULC change modellers may emulate to further improve the discipline. Climate change models have exerted considerable influence over public perceptions of climate change and now impact policy decisions at all political levels. We suggest that tropical LULC change models have an equally high potential to influence public opinion and impact the development of land‐use policies based on plausible future scenarios, but, to do that reliably may require further improvements in the discipline.     

大兴安岭人为火发生影响因素及气候变化下的趋势

我国重要的北方针叶林地区大兴安岭是林火高发地区.受气候变暖影响,该地区林火发生频率将会发生显著变化.模拟人为火的发生分布与影响因素之间的关系、加强气候变化下人为火的发生分布预测,对于林火管理和减少森林碳损失具有重要作用.本文采用点格局分析方法,基于大兴安岭1967—2006年的火烧数据,建立人为火空间分布与影响因素之间的关系模型,该模型以林火发生次数为因变量,选取非生物因子(年均温和降水量、坡度、坡向和海拔)、生物因子(植被类型)和人为活动因子(距离道路距离、距离居民点距离、道路密度)共9个因子为自变量.并采用RCP 2.6和RCP 8.5气候情景数据代替当前气候情景预测2050年大兴安岭人为火的空间分布状况.结果表明: 点格局模型能够较好地模拟人为火发生分布与空间变量的关系,可以预测未来气候下人为火的发生概率.其中,气候因子对人为火的发生具有明显的控制作用,植被类型、海拔和人为活动等因子对人为火的发生也具有重要影响.林火发生预测结果表明,未来气候变化下,南部地区的林火发生概率将进一步增加,北部和沿主要道路干线附近将成为新的人为火高发区.与当前相比,2050年大兴安岭人为火的发生概率将增加72.2%～166.7%.在未来气候情景下,人为火的发生更多受气候和人为活动因素的控制.     

The Relative Impacts of Climate and Land-Use Change on Conterminous United States Bird Species from 2001 to 2075

Species distribution models often use climate data to assess contemporary and/or future ranges for animal or plant species. Land use and land cover (LULC) data are important predictor variables for determining species range, yet are rarely used when modeling future distributions. In this study, maximum entropy modeling was used to construct species distribution maps for 50 North American bird species to determine relative contributions of climate and LULC for contemporary (2001) and future (2075) time periods. Species presence data were used as a dependent variable, while climate, LULC, and topographic data were used as predictor variables. Results varied by species, but in general, measures of model fit for 2001 indicated significantly poorer fit when either climate or LULC data were excluded from model simulations. Climate covariates provided a higher contribution to 2001 model results than did LULC variables, although both categories of variables strongly contributed. The area deemed to be “suitable” for 2001 species presence was strongly affected by the choice of model covariates, with significantly larger ranges predicted when LULC was excluded as a covariate. Changes in species ranges for 2075 indicate much larger overall range changes due to projected climate change than due to projected LULC change. However, the choice of study area impacted results for both current and projected model applications, with truncation of actual species ranges resulting in lower model fit scores and increased difficulty in interpreting covariate impacts on species range. Results indicate species-specific response to climate and LULC variables; however, both climate and LULC variables clearly are important for modeling both contemporary and potential future species ranges.     

Forecasting the Effects of Land Use Scenarios on Farmland Birds Reveal a Potential Mitigation of Climate Change Impacts

Climate and land use changes are key drivers of current biodiversity trends, but interactions between these drivers are poorly modeled, even though they could amplify or mitigate negative impacts of climate change. Here, we attempt to predict the impacts of different agricultural change scenarios on common breeding birds within farmland included in the potential future climatic suitable areas for these species. We used the Special Report on Emissions Scenarios (SRES) to integrate likely changes in species climatic suitability, based on species distribution models, and changes in area of farmland, based on the IMAGE model, inside future climatic suitable areas. We also developed six farmland cover scenarios, based on expert opinion, which cover a wide spectrum of potential changes in livestock farming and cropping patterns by 2050. We ran generalized linear mixed models to calibrate the effects of farmland cover and climate change on bird specific abundance within 386 small agricultural regions. We used model outputs to predict potential changes in bird populations on the basis of predicted changes in regional farmland cover, in area of farmland and in species climatic suitability. We then examined the species sensitivity according to their habitat requirements. A scenario based on extensification of agricultural systems (i.e., low-intensity agriculture) showed the greatest potential to reduce reverse current declines in breeding birds. To meet ecological requirements of a larger number of species, agricultural policies accounting for regional disparities and landscape structure appear more efficient than global policies uniformly implemented at national scale. Interestingly, we also found evidence that farmland cover changes can mitigate the negative effect of climate change. Here, we confirm that there is a potential for countering negative effects of climate change by adaptive management of landscape. We argue that such studies will help inform sustainable agricultural policies for the future.     

Assessing impact of forest landscape dynamics on migratory corridors: a case study of two protected areas in Himalayan foothills

Contiguity of protected areas (PAs) is a critical factor to promote well being of the native flora, fauna and life support system to humans. Such contiguity cannot be guaranteed without providing a path or ‘a corridor’ through forested landscapes that includes natural land cover and undisturbed patches. Incidentally, the Himalayan foothills have greater pressure on these landscapes due to high human dependence for livelihood. This pressure is expected to increase in the coming years altering the potential corridors between PAs. The PA managers need flexible processing, modeling and decision tools to propose a range of acceptable corridors between the PAs and ensure their sustainable health. Such flexible tools can be utilized in future to modify for taking decision to conserve the patches connecting patches and adapt as per changing landscapes. This article describes utility of geospatial modeling tools to assess the status of corridors in light of changing landscapes between Rajaji and Jim Corbett National Park, the two most important PAs in the Himalayan foothills. The work has been carried out in four stages, first—using satellite data land use land cover (LULC) maps were prepared for year 1990, 2000 and 2005, second—Land Change Modeler (LCM) was used for LULC change analysis, third—Multi Layer Perceptron Neural Network (MLPNN) was used to predict the status of LULC for 2015 and 2020, and fourth—using temporal morphology of the areas behaving both as barrier and easiness, friction surface cost was calculated to identify least cost pathways (LCPs)/migratory corridors between the PAs. The LULC maps for 1990, 2000 and 2005 were evaluated using accuracy assessment (80%) and Khat statistics (>0.79). The change prediction model was validated by comparing actual LULC of 2005 with predicted LULC of 2005 and the agreement was 71%. The LCP has shifted with the predicted change in the classes. The corridor has shifted by 0.5–3 km towards the south and has come closer to the agriculture fields and river channels.     

Area-based scenario development in land-use change modeling: A system dynamics-assisted approach for mixed agricultural-residential landscapes

This study aimed to enhance land use and land cover (LULC) change models by addressing their main limitations, which include the lack of accountability and temporal stability of driving forces. Additionally, the study aimed to create area-based scenarios to forecast future LULCs, rather than solely relying on distribution-based scenarios. To accomplish this goal, the study developed a coupled System Dynamics (SD) and Cellular Automata (CA) modeling system to simulate possible LULC changes in the Gavkhooni Basin, central Iran. The study utilized LULC maps from Landsat images in 2001, 2011, and 2021 to analyze spatio-temporal land use changes in the region. Agricultural and residential transition suitability layers were produced using a spatial Multi-Criteria Evaluation procedure and applied to inform the CA model in the proper allocation of LULC changes. Three interconnected water supply, agricultural, and residential area projection subsystems were developed using system dynamics method to determine land requirements for LULC conversions from 2020 to 2041, taking into account factors such as water availability, land suitability, agricultural labor force, and economic development. Ten scenarios were developed based on changes in the key variables affecting the limiting factors, such as climatic conditions and water management policies, to project agricultural and residential areas in the future. The CA's spatial allocation informed by transition suitability layers was found to be satisfactory with a Kappa-location value of 0.85. The subsystems were competent in projecting water supply with Mean Absolute Error (MAE) values of 6.57% and the dynamics of agricultural and residential areas with MAE values of 2.94%, whereas those of the Markovian Chain model were found to be 23.02% and 7.5% for agricultural and residential areas, respectively. The study found that available agricultural areas varied significantly between 86.53 and 1480 sq.km under different climatic conditions, irrigation efficiency, and agricultural water assignment coefficients between 2024 and 2033. Residential area demand was found to be increasing with different rates under the scenarios between 47.40 and 73.01 sq.km. The SD-CA coupled framework presented in this research can be viewed as a decision support system to develop compensatory strategies for better management and planning of agricultural and residential lands.     

中国土地利用空间格局动态变化模拟——以规划情景为例

土地利用变化研究在环境可持续发展研究领域中具有重要的地位,其空间分布格局的变化影响到生物地球化学循环、气候变化、生物多样性等。采用土地利用动态变化模型Dyna-CLUE模拟了在规划情景下中国土地利用变化未来空间分布格局。将土地利用类型分为六大类,即耕地、草地、林地、建设用地、水域和其它用地。驱动因子包括地形地貌、气候、社会交通等方面,对动态驱动因子如气温、降水、人口交通等,考虑了其在未来情景下的发展趋势。基于土地利用类型与驱动因子之间的定量关系和土地利用类型之间的转换规则等,模拟出至2020年中国土地利用分布格局。结果表明,至2020年,中国东南部、黄淮海平原、四川盆地等地区耕地面积将增加,东北、西北等农牧交错区、农林交错区和沙漠边缘耕地面积将会呈轻度减少趋势;林地面积将增加1417.91万hm2,主要发生在中国东北部以及西南部水热条件好的地区;中国草地在面积上保持稳定,空间上中东部、东南地区草地面积减少,内蒙古中部,青海东部,四川盆地北缘区和青藏高原等地面积增加;建设用地增加531.76万hm2,主要发生在中国的东部地区。     

Land use/cover change and its drivers: a case in the watershed of Lake Kasumigaura, Japan

Land use/land cover (LULC) changes in the watershed (2,157 km²) of Lake Kasumigaura during 1979–1996 (Period-1: 1979–1990, Period-2: 1990–1996) were analyzed, and their socio-economic and biophysical drivers were compared using time-series, high-quality GIS datasets in order to examine the characteristics of a model forecasting the future LULC. The changes occurred over an area of more than 90 km² during the overall period at changing rates of 0.22% year⁻¹ in Period-1 and 0.25% year⁻¹ in Period-2. Forestland decreased most in both periods at changing rates of 0.45% year⁻¹ in Period-1 and 0.61% year⁻¹ in Period-2. However, predominant changing patterns differed, i.e., from forest to golf course in Period-1 and from forest to artificial field in Period-2. Particularly in Period-2, a significant LULC change was observed in an area of high population increase on the edge of an already high-population area. Relationships examined among LULC change, population, and rate of population change suggested that the urbanized area was highly resistant to LULC change, and that such change was less frequent in areas of population decline. Statistical analyses indicated that the most influential drivers for total LULC changes were population in Period-1 and distance from the Tokyo Station in Period-2. Since the change potentials differed between the periods, we could not assume a stationary process for the corresponding drivers. Somewhat low S values (indices for demonstrability) show that LULC changes in the watershed of Lake Kasumigaura occurred rather randomly, probably resulting in fragmentation of the landscape.     

The interplay of climate and land use change affects the distribution of EU bumblebees

Bumblebees in Europe have been in steady decline since the 1900s. This decline is expected to continue with climate change as the main driver. However, at the local scale, land use and land cover (LULC) change strongly affects the occurrence of bumblebees. At present, LULC change is rarely included in models of future distributions of species. This study's objective is to compare the roles of dynamic LULC change and climate change on the projected distribution patterns of 48 European bumblebee species for three change scenarios until 2100 at the scales of Europe, and Belgium, Netherlands and Luxembourg (BENELUX). We compared three types of models: (1) only climate covariates, (2) climate and static LULC covariates and (3) climate and dynamic LULC covariates. The climate and LULC change scenarios used in the models include, extreme growth applied strategy (GRAS), business as might be usual and sustainable European development goals. We analysed model performance, range gain/loss and the shift in range limits for all bumblebees. Overall, model performance improved with the introduction of LULC covariates. Dynamic models projected less range loss and gain than climate‐only projections, and greater range loss and gain than static models. Overall, there is considerable variation in species responses and effects were most pronounced at the BENELUX scale. The majority of species were predicted to lose considerable range, particularly under the extreme growth scenario (GRAS; overall mean: 64% ± 34). Model simulations project a number of local extinctions and considerable range loss at the BENELUX scale (overall mean: 56% ± 39). Therefore, we recommend species‐specific modelling to understand how LULC and climate interact in future modelling. The efficacy of dynamic LULC change should improve with higher thematic and spatial resolution. Nevertheless, current broad scale representations of change in major land use classes impact modelled future distribution patterns.     

Characterizing drought stress and trait influence on maize yield under current and future conditions

Global climate change is predicted to increase temperatures, alter geographical patterns of rainfall and increase the frequency of extreme climatic events. Such changes are likely to alter the timing and magnitude of drought stresses experienced by crops. This study used new developments in the classification of crop water stress to first characterize the typology and frequency of drought‐stress patterns experienced by European maize crops and their associated distributions of grain yield, and second determine the influence of the breeding traits anthesis‐silking synchrony, maturity and kernel number on yield in different drought‐stress scenarios, under current and future climates. Under historical conditions, a low‐stress scenario occurred most frequently (ca. 40%), and three other stress types exposing crops to late‐season stresses each occurred in ca. 20% of cases. A key revelation shown was that the four patterns will also be the most dominant stress patterns under 2050 conditions. Future frequencies of low drought stress were reduced by ca. 15%, and those of severe water deficit during grain filling increased from 18% to 25%. Despite this, effects of elevated CO₂ on crop growth moderated detrimental effects of climate change on yield. Increasing anthesis‐silking synchrony had the greatest effect on yield in low drought‐stress seasonal patterns, whereas earlier maturity had the greatest effect in crops exposed to severe early‐terminal drought stress. Segregating drought‐stress patterns into key groups allowed greater insight into the effects of trait perturbation on crop yield under different weather conditions. We demonstrate that for crops exposed to the same drought‐stress pattern, trait perturbation under current climates will have a similar impact on yield as that expected in future, even though the frequencies of severe drought stress will increase in future. These results have important ramifications for breeding of maize and have implications for studies examining genetic and physiological crop responses to environmental stresses.     

气候变化情景下裸冠菊在中国的潜在适生区分布预测

外来入侵植物裸冠菊(Gymnocoronis spilanthoides)具有较强的入侵适应性能快速繁殖扩散,会对本土物种的生长繁殖及本地生态安全、景观格局等产生不良影响。基于265个有效分布点和7个环境变量,调整优化预测模型的调控倍频和特征组合参数,应用MaxEnt、ArcGIS、R软件预测当前和未来(2050s, 2070s)不同气候情景(SSP126, SSP245,SSP370, SSP585)下裸冠菊在中国的潜在地理分布,定量分析其适生区的空间变化及质心移动轨迹,最后采用受试者工作特征(ROC)曲线下面积(AUC)和测试遗漏率评估模型的精确性。未来气候模式选择中国国家气候中心开发的CMIP6中BCC-CSM2-MR。结果表明：(1)模型预测结果极准确,各组模型的AUC值均高于0.97;(2)最干季降水量(bio17)、最冷季度平均温(bio11)、温度季节性变化(bio4)和最暖季度平均降雨量(bio18)是影响裸冠菊地理分布的主导气候因子;(3)当前气候条件下,裸冠菊的总适生区面积达到191.18×10⁴km²,约占国土总面积的1...     

气候变化背景下我国农业热量资源的变化趋势及适应对策

根据区域气候模式PRECIS输出的未来A2气候情景（2011-2050年）以及基准气候时段（1961-1990年）的逐日资料,对2011-2050年我国农业热量资源的变化趋势进行了预测.结果表明: 与1961-1990年相比,未来A2气候情景下,2011-2050年我国大部分地区的平均无霜期日数延长趋势明显,主要表现为终霜冻日的提前和初霜冻日的推迟;各地日均气温稳定通过0 ℃的持续日数也明显延长,大部分地区延长了1～14 d,其中2041-2050年,青藏地区大部、长江中下游地区大部、甘新地区西部和西南地区北部均可延长49 d;我国大部分地区≥0 ℃积温均呈增加趋势.为适应未来农业热量资源的变化,应进一步调整农业种植制度、优化农业生产布局和发展生物技术等,以实现我国农业的可持续发展.     

Assessing effects of forecasted climate change on the diversity and distribution of European higher plants for 2050

The rapidly increasing atmospheric concentrations of greenhouse gases may lead to significant changes in regional and seasonal climate patterns. Such changes can strongly influence the diversity and distribution of species and, therefore, affect ecosystems and biodiversity. To assess these changes we developed a model, called euromove. The model uses climate data from 1990 to 2050 as compiled from the image 2 model, and determines climate envelopes for about 1400 plant species by multiple logistic regression analysis. The climate envelopes were applied to the projected climate to obtain predictions about plant diversity and distributions by 2050. For each European grid cell, euromove calculates which species would still occur in forecasted future climate conditions and which not. The results show major changes in biodiversity by 2050. On average, 32% of the European plant species that were present in a cell in 1990 would disappear from that cell. The area, in which 32% or more of the 1990 species will disappear, takes up 44% of the modelled European area. Individual responses of the plant species to the forecasted climate change were diverse. In reviewing possible future trends, we found that plant species, in general, would find their current climate envelopes further northeast by 2050, shifting ranges that were comparable with those ranges in other studies.