气候变化对中国东北主要森林类型的影响

应用林窗模型-FAREAST,模拟未来气候变化对中国东北主要类型森林演替动态的影响.根据大气环流模型ECHAM5-OM和HadCM3预测的气候变化资料,模拟选择了目前气候情景、增暖情景、增暖且降水变化情景3种气候情景.结果表明:维持目前气候不变,东北森林树种组成和森林生物量基本维持动态平衡.气候增暖不利于东北主要森林类型生长,主要针叶树种比例下降,阔叶树比例增加;温带针阔混交林垂直分布带有上移的趋势;增暖幅度越大,变化越明显.气候增暖基础上考虑降水变化,东北森林水平分布带有北移的趋势,降水对低海拔温带针阔混交林影响不大.     

区域气候模式与作物模型联接的影响评估模拟实验及不确定性分析

利用英国Hadley中心开发的区域气候模式RCMPRECIS(网格分辨率50km×50km),与经过田间试验资料和历史气候资料验证和校准过的CERES系列作物模式相结合,就区域气候模式与作物模式联接的影响评估方法及其不确定性进行了评估。结果表明,相对于大气环流模型来说,区域气候模式与作物模型的结合省去了随机天气发生器的中间环节,减小了不确定性产生的因素。在站点模拟上,该方法在平原地区的模拟效果较好,而山区的模拟效果较差,但如果能用实测天气数据对模拟的天气数据进行验证,模拟效果明显提高。在区域模拟上,该方法可以较好地体现出产量变化的空间分布规律,但由于空间数据的限制,模拟产量与实际产量的偏差较站点水平要大。     

考虑气候因子变化的湖泊富营养化模型研究进展

气候因子是影响湖泊营养状态和进程的主要自然因素.在全球气候变化的趋势下,将气候因子的变化纳入湖泊富营养化模型中,可以为湖泊演化趋势分析和环境管理决策提供技术支持.本文首先分析了气温、降水、光照和大气等气候因子对湖泊富营养化的影响,进而对考虑气候因子变化的数理统计与分析模型、生态动力学模型、系统生态学模型及智能算法等的研究进行了综述.在此基础上,对完善气候因子变化下湖泊营养状态变化的模型研究进行了展望：1)加强气候因子作用于湖泊营养状态的机理研究;2)选择合适的气候模拟模型,合理设置气候变化情景,在不同模型嵌套时保证时空尺度的匹配;3)以水动力学模型为基础,耦合生态模型及智能算法等,并结合良好的气候模拟模型,以精确模拟预测气候变化下湖泊富营养化的演化过程和趋势.     

农田灌溉对气候的影响研究综述

过去200年全球灌溉农田面积迅速扩张,灌溉对气候的影响逐渐受到世界各国研究者的关注。回顾了过去有关灌溉对气候的影响研究,归纳了前人的研究手段,指出了目前研究中存在的问题和困难,并提出了未来灌溉对气候的影响研究应该注重如下几个方面:1)同时利用观测数据分析方法和模型模拟研究方法进行灌溉对区域气候的影响,并将两者的结果进行对比分析,以求做到互相验证;2)对于缺乏地面观测数据或者地面数据受其他因素(比如:城市化)影响大的区域,建议利用遥感观测数据进行灌溉对地表参数的影响研究;3)注意对灌溉四大属性(灌溉位置、灌水量、灌溉方式和灌溉时间)的精确模拟,可以考虑耦合气候模型和作物模型进行模拟研究,并注意区分灌溉旱地和灌溉水田。4)提倡利用多模式集合的方式研究灌溉对区域气候的影响,以减少灌溉对气候影响(强度和方向)模拟结果的不确定性;5)未来也应该考虑模拟灌溉和气候变化间的互馈影响。     

土壤-植物-大气连续体水热、CO2通量估算模型研究进展

土壤-植物-大气连续体(SPAC)水热、CO2通量的准确估算对理解陆地和大气的物质和能量交换过程有着重要意义.重点阐述了基于过程的土壤-植物-大气连续体水热、CO2通量模型,综述了统计模型、综合模型及基于遥感的模型的发展过程.其中水热通量统计模型包括基于温度和湿度以及基于温度和辐射的方法;CO2通量统计模型包括基于气候因子或蒸散因子以及基于光能利用率的方法.水热通量过程模型包括大叶、双源、多源和多层的水热传输物理模型;CO2通量过程模型包括叶片尺度及由大叶、双叶和多层方法扩展到冠层尺度的生理生态模型以及光合-蒸腾耦合模型.综合模型包括生物物理模型、生物化学模型和生物地理模型.统计模型形式简单,资料易得,对大范围的水热通量模拟具有指导意义;过程模型准确的揭示了水热和CO2通量传输的物理和生理过程,是大尺度综合模型的基础.未来生态系统水热、CO2通量估算模型将集成各种技术手段进行多尺度网络观测和大尺度机理模拟.     

气候变暖对西北雨养农业及农业生态影响研究进展

以全球年平均地表气温升髙为主要特征的全球气候变暖给农业、农业生态和区域粮食安全带来严峻挑战。气候变暖对农业发展、农业生态的影响已成为社会各界关注的热点。气候变暖对作物生育期、形态特征、植物生理、产量形成和品质的影响及其机理的研究,是认识气候变暖对农业影响,制定应对气候变化策略的科学基础。本文在给出西北区域气候变化基本特征的基础上,综述了气候变暖对西北旱作区主要粮食作物、经济作物和特色林果生长发育、生理生态、产量和品质影响研究的进展,以及气候变暖对农田生态环境、农业气象灾害及病虫害影响的主要进展。提出了以往研究中存在的问题,展望了未来西北地区应对全球变暖的农业研究重点,即:充分利用模拟、试验、观测手段,揭示气候变化多因子对主要农作物的综合影响;探索气候变暖对主要作物生理生态的影响;开展农业气象灾害对气候变暖的响应特征研究,开展农业气象灾害风险评估与应对技术研究;进行精细化动态农业种植区划、农业结构布局及种植制度方面应对气候变暖的技术策略研究。     

大尺度陆地生态系统科学研究的理论基础及其技术体系之探讨

大尺度的陆地生态系统科学研究是在生物多样性保护、应对全球气候变化、区域生态环境治理及全球社会经济可持续发展的科技需求背景下应运而生的生态学及生态系统科学前沿领域,并在我国的生态文明建设社会实践中快速发展。本文在系统论述大尺度陆地生态系统科学研究的科技使命基础上,重点梳理和探讨了区域及大陆尺度的生态系统科学研究理论基础和方法学问题,进而基于宏生态系统生态学理论,新提出了宏观生态系统科学的基础理论、科学概念及其研究方法体系的逻辑框架。本文重点探讨了: 1) 基于生态系统的系统学特性,发展多维视角的“生态系统科学概念网络体系”;2) 基于生态系统整体性和涌现性理论,发展“生态系统状态变化分析理论体系”;3) 基于生态系统属性及状态概念,发展生态系统结构-过程-功能-服务级联关系的“整合研究方法学理论体系”的学科内涵及应用问题,进而论述了发展区域及大陆尺度宏观生态系统科学研究的方法体系,提出构建新一代大陆尺度生态系统观测研究实验网络,完善“联网观测-联网实验-数值模拟-知识融合”四位一体的科学研究技术体系的必要性。     

生物多样性与生态系统服务情景模拟研究进展

生物多样性和生态系统服务情景模拟是指对未来生物多样性和生态系统服务变化轨迹的定量估计,二者相互关联并为长期、稳定的保护和恢复生态系统提供了重要科学依据。梳理生物多样性以及生态系统服务预测情景的核心观点,讨论基于生物多样性和生态系统服务情景模拟的保护决策支持途径,以期服务于我国生物多样性与生态系统服务预测研究的发展和深化。研究凝练结果如下：物种分布模型需要进行更规范的评价以明晰其对具体对象的适用性,生态系统预测模型亟待在关系结构的基础上嵌入更多的生态系统过程和社会经济过程,生态系统服务评估模型有必要强化对生物多样性、生态系统服务、人类福祉级联特征的刻画;全球气候变化驱动了未来区域生物多样性的大幅改变;土地利用则是陆地生态系统服务预测中的核心驱动变量。生态区划与区域尺度情景模拟、景观尺度下的生态安全格局构建、基于社会生态网络的社区适应三点重要展望方向将对基于情景模拟的我国生态系统保护决策提供重要的理论和实践支持。     

气候变化条件下苦参在我国潜在分布区的预测分析

为了解气候变化情景下苦参在中国的潜在分布区变化,探讨生物气候因子与苦参适宜分布格局的关系.该文通过收集苦参的地理分布点并结合19项生态因子,运用最大熵模型(MaxEnt)和地理信息系统(ArcGIS)对末次盛冰期、当前气候、未来气候三种气候情景下苦参在我国适生区的分布格局进行模拟,并分析影响苦参生长的主导生态因子.结果...     

模拟植物多样性的大尺度分布：从气候和生产力推知的一种可能性（英文）

 鉴于全球植被/生物群区在现状气候条件下已经被很好地模拟并在未来气候变化情景下得到很好的预测,人们有必要和急需模拟大尺度（区域、洲际至全球）植物多样性的分布格局。陆地生物圈模型的发展（从生物地理模型和生物地球化学模型到动态和耦合的植被模型）,气候-生物多样性相互关系和生产力-生物多样性相互关系研究成果的增多,以及基于现有生物多样性调查的全球生物多样性理论和经验制图的进步,加大了模拟大尺度植物多样性格局的可能性。本文的目的是：综述当前气候-生物多样性相互关系和生产力-生物多样性相互关系的主要研究成果以及大尺度     

Strengthening forecasts of climate change impacts with multi‐model ensemble averaged projections using MAGICC/SCENGEN 5.3

Climate output from general circulation models (GCMs) is being used with increasing frequency to explore potential climate change impacts on species’ distributional range shifts and extinction probability. However, different GCMs do not perform equally well in their ability to hindcast the key climatic factors that potentially influence species distributions. Previous research has demonstrated that multi‐model ensemble forecasts perform better than any single GCM in simulating observed conditions at a global scale. MAGICC/SCENGEN 5.3 is a freeware climate model ‘emulator’ that generates multi‐model ensemble forecasts, conditional on regional and/or global performance, for up to twenty GCMs. In combination with a new application ‘M/SGridder’, this software can be used to produce down‐scaled ensemble forecasts, which minimize climate‐model‐related uncertainty, for a range of ecological problems.     

Species Distribution Models and Impact Factor Growth in Environmental Journals: Methodological Fashion or the Attraction of Global Change Science

In this work, I evaluate the impact of species distribution models (SDMs) on the current status of environmental and ecological journals by asking the question to which degree development of SDMs in the literature is related to recent changes in the impact factors of ecological journals. The hypothesis evaluated states that research fronts are likely to attract research attention and potentially drive citation patterns, with journals concentrating papers related to the research front receiving more attention and benefiting from faster increases in their impact on the ecological literature. My results indicate a positive relationship between the number of SDM related articles published in a journal and its impact factor (IF) growth during the period 2000–09. However, the percentage of SDM related papers in a journal was strongly and positively associated with the percentage of papers on climate change and statistical issues. The results support the hypothesis that global change science has been critical in the development of SDMs and that interest in climate change research in particular, rather than the usage of SDM per se, appears as an important factor behind journal IF increases in ecology and environmental sciences. Finally, our results on SDM application in global change science support the view that scientific interest rather than methodological fashion appears to be the major driver of research attraction in the scientific literature.     

Static species distribution models in dynamically changing systems: how good can predictions really be?

It is widely acknowledged that species respond to climate change by range shifts. Robust predictions of such changes in species’ distributions are pivotal for conservation planning and policy making, and are thus major challenges in ecological research. Statistical species distribution models (SDMs) have been widely applied in this context, though they remain subject to criticism as they implicitly assume equilibrium, and incorporate neither dispersal, demographic processes nor biotic interactions explicitly. In this study, the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections were tested. A spatially explicit multi‐species dynamic population model was built, incorporating species‐specific and interspecific ecological processes, environmental stochasticity and climate change. Species distributions were sampled in different scenarios, and SDMs were estimated by applying generalised linear models (GLMs) and boosted regression trees (BRTs). Resulting model performances were related to prevailing ecological processes and temporal dynamics. SDM performance varied for different range dynamics. Prediction accuracies decreased when abrupt range shifts occurred as species were outpaced by the rate of climate change, and increased again when a new equilibrium situation was realised. When ranges contracted, prediction accuracies increased as the absences were predicted well. Far‐dispersing species were faster in tracking climate change, and were predicted more accurately by SDMs than short‐dispersing species. BRTs mostly outperformed GLMs. The presence of a predator, and the inclusion of its incidence as an environmental predictor, made BRTs and GLMs perform similarly. Results are discussed in light of other studies dealing with effects of ecological traits and processes on SDM performance. Perspectives are given on further advancements of SDMs and for possible interfaces with more mechanistic approaches in order to improve predictions under environmental change.     

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate‐mediated range shifts

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad‐scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment‐only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment‐only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions.     

Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities

Aim Species distribution models (SDMs) have been used to address a wide range of theoretical and applied questions in the terrestrial realm, but marine‐based applications remain relatively scarce. In this review, we consider how conceptual and practical issues associated with terrestrial SDMs apply to a range of marine organisms and highlight the challenges relevant to improving marine SDMs. Location We include studies from both marine and terrestrial systems that encompass many geographic locations around the globe. Methods We first performed a literature search and analysis of marine and terrestrial SDMs in ISI Web of Science to assess trends and applications. Using knowledge from terrestrial applications, we critically evaluate the application of SDMs in marine systems in the context of ecological factors (dispersal, species interactions, aggregation and ontogenetic shifts) and practical considerations (data quality, alternative modelling approaches and model validation) that facilitate or create difficulties for model application. Results The relative importance of ecological factors to be considered when applying SDMs varies among terrestrial and marine organisms. Correctly incorporating dispersal is frequently considered an important issue for terrestrial models, but because there is greater potential for dispersal in the ocean, it is often less of a concern in marine SDMs. By contrast, ontogenetic shifts and feeding have received little attention in terrestrial SDM applications, but these factors are important to many marine SDMs. Opportunities also exist for applying more advanced SDM approaches in the marine realm, including mechanistic ecophysiological models, where water balance and heat transfer equations are simpler for some marine organisms relative to their terrestrial counterparts. Main conclusions SDMs have generally been under‐utilized in the marine realm relative to terrestrial applications. Correlative SDM methods should be tested on a range of marine organisms, and we suggest further development of methods that address ontogenetic shifts and feeding interactions. We anticipate developments in, and cross‐fertilization between, coupled correlative and process‐based SDMs, mechanistic eco‐physiological SDMs, and spatial population dynamic models for climate change and species invasion applications in particular. Comparisons of the outputs of different model types will provide insight that is useful for improved spatial management of marine species.     

Choice of baseline climate data impacts projected species' responses to climate change

Climate data created from historic climate observations are integral to most assessments of potential climate change impacts, and frequently comprise the baseline period used to infer species‐climate relationships. They are often also central to downscaling coarse resolution climate simulations from General Circulation Models (GCMs) to project future climate scenarios at ecologically relevant spatial scales. Uncertainty in these baseline data can be large, particularly where weather observations are sparse and climate dynamics are complex (e.g. over mountainous or coastal regions). Yet, importantly, this uncertainty is almost universally overlooked when assessing potential responses of species to climate change. Here, we assessed the importance of historic baseline climate uncertainty for projections of species' responses to future climate change. We built species distribution models (SDMs) for 895 African bird species of conservation concern, using six different climate baselines. We projected these models to two future periods (2040–2069, 2070–2099), using downscaled climate projections, and calculated species turnover and changes in species‐specific climate suitability. We found that the choice of baseline climate data constituted an important source of uncertainty in projections of both species turnover and species‐specific climate suitability, often comparable with, or more important than, uncertainty arising from the choice of GCM. Importantly, the relative contribution of these factors to projection uncertainty varied spatially. Moreover, when projecting SDMs to sites of biodiversity importance (Important Bird and Biodiversity Areas), these uncertainties altered site‐level impacts, which could affect conservation prioritization. Our results highlight that projections of species' responses to climate change are sensitive to uncertainty in the baseline climatology. We recommend that this should be considered routinely in such analyses.     

Incorporating intraspecific variation into species distribution models improves distribution predictions,but cannot predict species traits for a wide-spread plant species

The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its potential distribution under future climate scenarios. A core, rarely tested, assumption of SDMs is that all populations will respond equivalently to climate. Few studies have examined this assumption, and those that have rarely dissect the reasons for intraspecific differences. Focusing on the arctic-alpine cushion plant Silene acaulis, we compared predictive accuracy from SDMs constructed using the species’ full global distribution with composite predictions from separate SDMs constructed using subpopulations defined either by genetic or habitat differences. This is one of the first studies to compare multiple ways of constructing intraspecific-level SDMs with a species-level SDM. We also examine the contested relationship between relative probability of occurrence and species performance or ecological function, testing if SDM output can predict individual performance (plant size) and biotic interactions (facilitation). We found that both genetic- and habitat-informed SDMs are considerably more accurate than a species-level SDM, and that the genetic model substantially differs from and outperforms the habitat model. While SDMs have been used to infer population performance and possibly even biotic interactions, in our system these relationships were extremely weak. Our results indicate that individual subpopulations may respond differently to climate, although we discuss and explore several alternative explanations for the superior performance of intraspecific-level SDMs. We emphasize the need to carefully examine how to best define intraspecific-level SDMs as well as how potential genetic, environmental, or sampling variation within species ranges can critically affect SDM predictions. We urge caution in inferring population performance or biotic interactions from SDM predictions, as these often-assumed relationships are not supported in our study.     

Effects of ecohydrological variables on current and future ranges,local suitability patterns,and model accuracy in big sagebrush

Forecasting of species and ecosystem responses to novel conditions, including climate change, is one of the major challenges facing ecologists at the start of the 21st century. Climate change studies based on species distribution models (SDMs) have been criticized because they extend correlational relationships beyond the observed data. Here, we compared conventional climate‐based SDMs against ecohydrological SDMs that include information from process‐based simulations of water balance. We examined the current and future distribution of Artemisia tridentata (big sagebrush) representing sagebrush ecosystems, which are widespread in semiarid western North America. For each approach, we calculated ensemble models from nine SDM methods and tested accuracy of each SDM with a null distribution. Climatic conditions included current conditions for 1970–1999 and two IPCC projections B1 and A2 for 2070–2099. Ecohydrological conditions were assessed by simulating soil water balance with SOILWAT, a daily time‐step, multiple layer, mechanistic, soil water model. Under current conditions, both climatic and ecohydrological SDM approaches produced comparable sagebrush distributions. Overall, sagebrush distribution is forecasted to decrease, with larger decreases under the A2 than under the B1 scenario and strong decreases in the southern part of the range. Increases were forecasted in the northern parts and at higher elevations. Both SDM approaches produced accurate predictions. However, the ecohydrological SDM approach was slightly less accurate than climatic SDMs (?1% in AUC, ?4% in Kappa and TSS) and predicted a higher number of habitat patches than observed in the input data. Future predictions of ecohydrological SDMs included an increased number of habitat patches whereas climatic SDMs predicted a decrease. This difference is important for understanding landscape‐scale patterns of sagebrush ecosystems and management of sagebrush obligate species for future conditions. Several mechanisms can explain the diverging forecasts; however, we need better insights into the consequences of different datasets for SDMs and how these affect our understanding of future trajectories.     

High-resolution bioclimatic dataset derived from future climate projections for plant species distribution modeling

Species distribution modeling is playing an increasingly prominent role in ecology and global change biology, owing to its potential to predict species range shifts, biodiversity losses, and biological invasion risks for future climates. Such models are now well-established as important tools for biological conservation. However, the lack of high-resolution data for future climate scenarios has seriously limited their application, particularly because of the scale gap between general circulation models (GCMs) and species distribution models (SDMs). A recently introduced change-factor downscaling technique provides a convenient way to build high-resolution datasets from GCM projections. Here, we present a high-resolution (10’ × 10’) global bioclimatic dataset (BioPlant) for plant species distribution. The 15 bioclimatic variables we select are considered those most eco-physiologically relevant. They can be easily calculated from climatic variables common to all GCM projections. In addition to the traditional classes of variables regarding temperature and precipitation, the BioPlant dataset emphasizes the interactions between temperature and precipitation, particularly within plant growing seasons. A preliminary visual analysis shows that variations among GCMs are more significant on a species range scale than on a global scale. Thus, the formerly advocated ensemble modeling method should be applied not only to different SDMs, but also to various GCMs. Statistic analysis suggests that divergent behavior among GCM variations for temperature class variables and classes of precipitation variables requires special attention. Our dataset may provide a common platform for ensemble modeling, and can serve as an example to develop higher-resolution regional datasets.     

Scale Interactions and Regional Climate: Examples from the Susquehanna River Basin

One of the most difficult problems faced by climatologists is how to translate global climate model (GCM) output into regional- and local-scale information that health and environmental effects researchers can use. It will be decades before GCMs will be able to resolve scales small enough for most effects research, so climatologists have developed climate downscaling methods to bridge the gap between the global and local scales. There are two main streams of climate downscaling research. First, high-resolution, limited-area climate models can be embedded in the coarse-scale GCMs, producing much finer resolution climate data. Second, empirical downscaling techniques develop transfer functions linking the large-scale atmospheric circulation generated by the GCMs to surface data. Examples of both types of downscaling, aimed at improving projections of future climate in the Susquehanna River Basin (the Mid-Atlantic Region of the United States), are presented. A third case is also described in which an even higher-resolution nested atmospheric model is being developed and linked to a hydrologic model system, with the ultimate goal of simulating the environmental response to climate forcing at all time and space scales.