Contribution of crop model structure,parameters and climate projections to uncertainty in climate change impact assessments

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple‐ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple‐ensemble probabilistic assessment, the median of simulated yield change was ?4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple‐ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.     

Synergistic effects of climate and land‐use change influence broad‐scale avian population declines

Climate and land‐use changes are expected to be the primary drivers of future global biodiversity loss. Although theory suggests that these factors impact species synergistically, past studies have either focused on only one in isolation or have substituted space for time, which often results in confounding between drivers. Tests of synergistic effects require congruent time series on animal populations, climate change and land‐use change replicated across landscapes that span the gradient of correlations between the drivers of change. Using a unique time series of high‐resolution climate (measured as temperature and precipitation) and land‐use change (measured as forest change) data, we show that these drivers of global change act synergistically to influence forest bird population declines over 29 years in the Pacific Northwest of the United States. Nearly half of the species examined had declined over this time. Populations declined most in response to loss of early seral and mature forest, with responses to loss of early seral forest amplified in landscapes that had warmed over time. In addition, birds declined more in response to loss of mature forest in areas that had dried over time. Climate change did not appear to impact populations in landscapes with limited habitat loss, except when those landscapes were initially warmer than the average landscape. Our results provide some of the first empirical evidence of synergistic effects of climate and land‐use change on animal population dynamics, suggesting accelerated loss of biodiversity in areas under pressure from multiple global change drivers. Furthermore, our findings suggest strong spatial variability in the impacts of climate change and highlight the need for future studies to evaluate multiple drivers simultaneously to avoid potential misattribution of effects.     

气候变化对鸟类影响的研究进展

气候变化对生物多样性的影响已成为热点问题.本文以鸟类为研究对象,根据鸟类受气候变化影响的最新研究成果,综述了气候变化对鸟类的分布、物候和种群等方面的影响.结果表明,在气候变化影响下,鸟类分布向高纬度或高海拔区移动,速度比以往加快,繁殖地和非繁殖地的分布移动变化并不相同,并且多数分布范围缩小,物候期发生复杂变化,种群数量下降明显.文章还讨论了该领域主要的预测和评估方法,以及进化适应等生物因素对气候变化预测结果的影响,除了以往单一的相关性模型外,目前应用最多的是集成模型,而未来最具发展潜力的是机理模型.进化适应方面的研究近来取得新进展,证实了生物个体积极应对气候变化影响的事实,从而对人为模型预测的准确性带来挑战.文章最后进行了总结和展望,结合国外研究经验和我国实际情况,提出一些建议:由于气候变化的影响及其研究是长期性的,从而对鸟类的历史监测数据提出很高的要求,当前我国急需建立一套长期、全面和可靠的鸟类数据监测系统;此外,人们需要综合评估现有各种预测模型的可靠性,在此基础上探索新的研究方法.     

Frog survival and population viability in an agricultural landscape with a drying climate

Amphibians are the most threatened class of vertebrate in the world. Although a number of causes of the amphibian decline phenomenon are emerging, there is a need for robust demographic data to be able to monitor current and future threats such as climate change. Despite this, few studies on amphibians have the life-history data available to undertake these analyses and fewer still have looked at the challenges to population viability posed by fragmentation—a feature inherent in agricultural landscapes where the matrix is highly modified. Our aim was to investigate the population viability of a large burrowing frog in an agricultural landscape. Specifically, we aimed to investigate the future persistence of populations under a range of scenarios including populations connected by various levels of dispersal and reduced rainfall. We used the life-history parameters of Heleioporus albopunctatus, a frog species widely distributed in the extensively cleared agricultural regions of south-western Australia. We investigated the viability of 24 partially connected populations under a range of scenarios using the program Vortex Version 10.1.6.0. Metapopulations were consistently more robust to extinction than isolated local populations. Both meta- and local populations were more susceptible to increases in age-specific mortality rates than to variation in the estimated ability of H. albopunctatus to disperse between breeding ponds, the survival rate of dispersers, or the frequency of drought. Our results reinforce the importance of metapopulations for survival in fragmented landscapes and point to the need to manage amphibian breeding ponds across landscapes to ensure high survival rates, particularly for juveniles.     

Integrated population modeling reveals the impact of climate on the survival of juvenile emperor penguins

Early‐life demographic traits are poorly known, impeding our understanding of population processes and sensitivity to climate change. Survival of immature individuals is a critical component of population dynamics and recruitment in particular. However, obtaining reliable estimates of juvenile survival (i.e., from independence to first year) remains challenging, as immatures are often difficult to observe and to monitor individually in the field. This is particularly acute for seabirds, in which juveniles stay at sea and remain undetectable for several years. In this work, we developed a Bayesian integrated population model to estimate the juvenile survival of emperor penguins (Aptenodytes forsteri), and other demographic parameters including adult survival and fecundity of the species. Using this statistical method, we simultaneously analyzed capture–recapture data of adults, the annual number of breeding females, and the number of fledglings of emperor penguins collected at Dumont d'Urville, Antarctica, for the period 1971–1998. We also assessed how climate covariates known to affect the species foraging habitats and prey [southern annular mode (SAM), sea ice concentration (SIC)] affect juvenile survival. Our analyses revealed that there was a strong evidence for the positive effect of SAM during the rearing period (SAMR) on juvenile survival. Our findings suggest that this large‐scale climate index affects juvenile emperor penguins body condition and survival through its influence on wind patterns, fast ice extent, and distance to open water. Estimating the influence of environmental covariates on juvenile survival is of major importance to understand the impacts of climate variability and change on the population dynamics of emperor penguins and seabirds in general and to make robust predictions on the impact of climate change on marine predators.     

Disentangling climate change from air pollution effects on epiphytic bryophytes

At the interface between atmosphere and vegetation, epiphytic floras have been largely used as indicators of air quality. The recovery of epiphytes from high levels of SO₂ pollution has resulted in major range changes, whose interpretation has, however, been challenged by concomitant variation in other pollutants as well as climate change. Here, we combine historical and contemporary information on epiphytic bryophyte species distributions, climatic conditions, and pollution loads since the 1980s in southern Belgium to disentangle the relative impact of climate change and air pollution on temporal shifts in species composition. The relationship between the temporal variation of species composition, climatic conditions, SO₂, NO₂, O₃, and fine particle concentrations, was analyzed by variation partitioning. The temporal shift in species composition was such, that it was, on average, more than twice larger than the change in species composition observed today among communities scattered across the study area. The main driver, contributing to 38% of this temporal shift in species composition, was the variation of air quality. Climate change alone did not contribute to the substantial compositional shifts in epiphytic bryophyte communities in the course of the last 40 years. As a consequence of the substantial drop of N and S loads over the last decades, present-day variations of epiphytic floras were, however, better explained by the spatial variation of climatic conditions than by extant pollution loads. The lack of any signature of recolonization delays of formerly polluted areas in the composition of modern floras suggests that epiphytic bryophytes efficiently disperse at the landscape scale. We suggest that a monitoring of epiphyte communities at 10-year intervals would be desirable to assess the impact of raising pollution sources, and especially pesticides, whose impact on bryophytes remains poorly documented.     

气候变化对我国7种植物潜在分布的影响

利用CART(分类和回归树)模型及A2和B2气候情景,模拟分析气候变化对瘿椒树、岩高兰、延龄草、星叶草、天麻、蝟实和秃杉分布范围及空间格局影响.结果显示:气候变化下,就目前适宜分布范围,瘿椒树呈增加趋势,其它植物呈缩小趋势;就新适宜及总适宜分布范围,蔚实、延龄草和瘿椒树呈增加趋势,星叶草和岩高兰呈减小趋势,天麻和秃杉在...     

Livestock and food security: vulnerability to population growth and climate change

Livestock production is an important contributor to sustainable food security for many nations, particularly in low‐income areas and marginal habitats that are unsuitable for crop production. Animal products account for approximately one‐third of global human protein consumption. Here, a range of indicators, derived from FAOSTAT and World Bank statistics, are used to model the relative vulnerability of nations at the global scale to predicted climate and population changes, which are likely to impact on their use of grazing livestock for food. Vulnerability analysis has been widely used in global change science to predict impacts on food security and famine. It is a tool that is useful to inform policy decision making and direct the targeting of interventions. The model developed shows that nations within sub‐Saharan Africa, particularly in the Sahel region, and some Asian nations are likely to be the most vulnerable. Livestock‐based food security is already compromised in many areas on these continents and suffers constraints from current climate in addition to the lack of economic and technical support allowing mitigation of predicted climate change impacts. Governance is shown to be a highly influential factor and, paradoxically, it is suggested that current self‐sufficiency may increase future potential vulnerability because trade networks are poorly developed. This may be relieved through freer trade of food products, which is also associated with improved governance. Policy decisions, support and interventions will need to be targeted at the most vulnerable nations, but given the strong influence of governance, to be effective, any implementation will require considerable care in the management of underlying structural reform.     

气候变化对马尾松潜在分布影响预估的多模型比较

物种分布模型被广泛应用于评估气候变化对物种分布的影响。随着计算机和统计学的发展, 模拟物种分布的模型层出不穷, 但对这些模型的相对表现知之甚少, 因此需要对其进行对比分析, 以便更可靠地评估气候变化的影响。该文采用3个比较新颖的组合集成学习(ensemble learning)模型(随机森林(random forest, RF)、广义助推法和NeuralEnsembles)、3个常规模型(广义线性模型、广义加法模型和分类回归树)、3个大气环流模型(global circulation model, GCM) (MIROC32_medres, JP; CCCMA_CGCM3, CA; BCCR-BCM2.0, NW)和一个气体排放情景(SRES_A2), 模拟分析了马尾松(Pinus massoniana)历史基准气候(1961-1990)和未来3个不同时期(2010-2039, 2020s; 2040-2069, 2050s; 2070-2099, 2080s)的潜在分布。基于环境阈值方法选择物种不发生区, 依据ClimateChina软件进行当前和未来气候数据的降尺度处理, 采用接收机工作特征曲线(receiver operator characteristic, ROC)下的面积(area under the curve, AUC)、Kappa值和真实技巧统计法(true skill statistic, TSS)以及马尾松种子区划范围来评价模型的预测精度。结果表明: 6个物种分布模型都具有较高的预测精度, 但组合集成学习模型的预测精度稍高于其他常规模型, 其中RF的预测精度最高。3个GCM和6个模型模拟条件下, 马尾松对气候变化的响应格局既有一致性也有异同性。一致性表现在: 随着时间的推移, 马尾松分布区将逐渐向北迁移, 未来潜在分布区的面积将逐渐增加; 异同性表现在: 在不同模型和不同气候情景下, 马尾松潜在分布区的迁移距离和面积变化幅度不同, 其中NW模式下预测的变化幅度小于CA和JP模式; RF模型预测的分布区迁移距离和面积变化幅度最大。随着时间的推移, 未来马尾松的18个潜在分布空间预测图(6个模型 × 3 GCM)之间的差异也逐渐增大, 其中空间不一致性地区主要集中发生在马尾松潜在分布区的北部和西部边缘地带。模型本身不同的构建原理以及GCM之间的差异是导致预测结果存在差异的主要原因。     

Predicting differential effects of climate change at the population level with life-cycle models of spring Chinook salmon

Habitat conditions mediate the effects of climate, so neighboring populations with differing habitat conditions may differ in their responses to climate change. We have previously observed that juvenile survival in Snake River spring/summer Chinook salmon is strongly correlated with summer temperature in some populations and with fall streamflow in others. Here, we explore potential differential responses of the viability of four of these populations to changes in streamflow and temperature that might result from climate change. First, we linked predicted changes in air temperature and precipitation from several General Circulation Models to a local hydrological model to project streamflow and air temperature under two climate‐change scenarios. Then, we developed a stochastic, density‐dependent life‐cycle model with independent environmental effects in juvenile and ocean stages, and parameterized the model for each population. We found that mean abundance decreased 20–50% and the probability of quasi‐extinction increased dramatically (from 0.1–0.4 to 0.3–0.9) for all populations in both scenarios. Differences between populations were greater in the more moderate climate scenario than in the more extreme, hot/dry scenario. Model results were relatively robust to realistic uncertainty in freshwater survival parameters in all scenarios. Our results demonstrate that detailed population models can usefully incorporate climate‐change predictions, and that global warming poses a direct threat to freshwater stages in these fish, increasing their risk of extinction. Because differences in habitat may contribute to the individualistic population responses we observed, we infer that maintaining habitat diversity will help buffer some species from the impacts of climate change.     

Predicting population consequences of ocean climate change for an ecosystem sentinel,the seabird Cassin's auklet

Forecasting the ecological effects of climate change on marine species is critical for informing greenhouse gas mitigation targets and developing marine conservation strategies that remain effective and increase species' resilience under changing climate conditions. Highly productive coastal upwelling systems are predicted to experience substantial effects from climate change, making them priorities for ecological forecasting. We used a population modeling approach to examine the consequences of ocean climate change in the California Current upwelling ecosystem on the population growth rate of the planktivorous seabird Cassin's auklet (Ptychoramphus aleuticus), a demographically sensitive indicator of marine climate change. We use future climate projections for sea surface temperature and upwelling intensity from a regional climate model to forecast changes in the population growth rate of the auklet population at the important Farallon Island colony in central California. Our study projected that the auklet population growth rate will experience an absolute decline of 11–45% by the end of the century, placing this population on a trajectory toward extinction. In addition, future changes in upwelling intensity and timing of peak upwelling are likely to vary across auklet foraging regions in the California Current Ecosystem (CCE), producing a mosaic of climate conditions and ecological impacts across the auklet range. Overall, the Farallon Island Cassin's auklet population has been declining during recent decades, and ocean climate change in this century under a mid‐level emissions scenario is projected to accelerate this decline, leading toward population extinction. Because our study species has proven to be a sensitive indicator of oceanographic conditions in the CCE and a powerful predictor of the abundance of other important predators (i.e. salmon), the significant impacts we predicted for the Cassin's auklet provide insights into the consequences that ocean climate change may have for other plankton predators in this system.     

Designing ecological climate change impact assessments to reflect key climatic drivers

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive – such as means or extremes – can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the ‘model space’ approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling.     

Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models

Sea ice conditions in the Antarctic affect the life cycle of the emperor penguin (Aptenodytes forsteri). We present a population projection for the emperor penguin population of Terre Adélie, Antarctica, by linking demographic models (stage‐structured, seasonal, nonlinear, two‐sex matrix population models) to sea ice forecasts from an ensemble of IPCC climate models. Based on maximum likelihood capture‐mark‐recapture analysis, we find that seasonal sea ice concentration anomalies (SIC_a) affect adult survival and breeding success. Demographic models show that both deterministic and stochastic population growth rates are maximized at intermediate values of annual SIC_a, because neither the complete absence of sea ice, nor heavy and persistent sea ice, would provide satisfactory conditions for the emperor penguin. We show that under some conditions the stochastic growth rate is positively affected by the variance in SIC_a. We identify an ensemble of five general circulation climate models whose output closely matches the historical record of sea ice concentration in Terre Adélie. The output of this ensemble is used to produce stochastic forecasts of SIC_a, which in turn drive the population model. Uncertainty is included by incorporating multiple climate models and by a parametric bootstrap procedure that includes parameter uncertainty due to both model selection and estimation error. The median of these simulations predicts a decline of the Terre Adélie emperor penguin population of 81% by the year 2100. We find a 43% chance of an even greater decline, of 90% or more. The uncertainty in population projections reflects large differences among climate models in their forecasts of future sea ice conditions. One such model predicts population increases over much of the century, but overall, the ensemble of models predicts that population declines are far more likely than population increases. We conclude that climate change is a significant risk for the emperor penguin. Our analytical approach, in which demographic models are linked to IPCC climate models, is powerful and generally applicable to other species and systems.     

The impact of climate change on the distribution of rare and endangered tree Firmiana kwangsiensis using the Maxent modeling

The upsurge in anthropogenic climate change has accelerated the habitat loss and fragmentation of wild animals and plants. The rare and endangered plants are important biodiversity elements. However, the lack of comprehensive and reliable information on the spatial distribution of these organisms has hampered holistic and efficient conservation management measures. We explored the consequences of climate change on the geographical distribution of Firmiana kwangsiensis (Malvaceae), an endangered species, to provide a reference for conservation, introduction, and cultivation of this species in new ecological zones. Modeling of the potential distribution of F. kwangsiensis under the current and two future climate scenarios in maximum entropy was performed based on 30 occurrence records and 27 environmental variables of the plant. We found that precipitation‐associated and temperature‐associated variables limited the potentially suitable habitats for F. kwangsiensis. Our model predicted 259,504 km² of F. kwangsiensis habitat based on 25 percentile thresholds. However, the high suitable habitat for F. kwangsiensis is only about 41,027 km². F. kwangsiensis is most distributed in Guangxi''s protected areas. However, the existing reserves are only 2.7% of the total suitable habitat and 4.2% of the high suitable habitat for the plant, lower than the average protection area in Guangxi (7.2%). This means the current protected areas network is insufficient, underlining the need for alternative conservation mechanisms to protect the plant habitat. Our findings will help identify additional F. kwangsiensis localities and potential habitats and inform the development and implementation of conservation, management, and cultivation practices of such rare tree species.     

量化气候和土地利用变化对生态资产变化的相对贡献——以房山区为例

生态资产与人类福祉密切相关,开展生态资产评估并定量区分气候和人类活动对生态资产变化的相对贡献,对于评估区域生态文明建设成效、生态补偿、干部离任的自然资产审计等均具有重要意义。在单位面积价值当量因子方法的基础上,重新定义了标准生态服务价值当量因子,并构建了一个能够定量区分气候变化和土地利用变化对生态资产变化相对贡献的方法,以北京市房山区为例,分析了2000年至2019年房山区生态资产的变化,以及气候变化和土地利用变化对生态资产变化的相对贡献,结果表明:(1)房山区2019年生态资产总价值177.14亿元。森林、草地、农田和湿地的生态资产分别占生态资产总价值的82.33%、11.76%、5.25%和0.095%。(2)房山在2000-2019年期间,生态资产总价值增加了2.275亿元,气候变化使得房山区的生态资产总价值增加了2.689亿元,而土地利用变化使得生态资产总价值减少了0.414亿元。(3)房山区生态资产西高东低,其中霞云岭乡生态资产总价值最高;琉璃河镇的生态资产增加最多,而拱辰街道下降最显著。过去20年是房山区社会经济快速发展的时期,由于气候变化和生态保护与修复使得生态资产增加,抵消了由于建设用地扩张所带来的生态资产损失。     

气候变化对鄱阳湖白鹤越冬种群数量变化的影响

分析了1983—2011年鄱阳湖国家级自然保护区越冬白鹤种群数量的年际变化趋势,检验了白鹤种群动态与繁殖地和越冬地气候变化的相关性,气候变量包括月平均气温、月平均最高气温、月平均最低气温和月降水量。研究结果表明,鄱阳湖国家级自然保护区内的白鹤年最大数量平均为(2 130±153)只,呈显著地线性增长趋势(R2=0.454,F=22.441,df=28,P=0.000),但年际波动较大。在越冬地,越冬当年10月、11月、12月的气候变量与白鹤种群数量没有显著的相关性,但越冬初期10月份和越冬后期翌年3月份的气温变量与第4年、第5年、第6年及第7年的白鹤种群数量存在显著的正相关,表明越冬地气候对白鹤种群大小的影响存在时滞效应。越冬初期和末期可能是白鹤补充能量的关键阶段,而且越冬初期的气候可能也与冬季食物的数量或质量相关,因此这两个阶段的适宜气温可能有利于个体尤其是幼鹤的存活,使更多的个体参加繁殖,由于白鹤的性成熟年龄在3—5a,因此其对白鹤种群增长的有利影响会在3a以后表现出来。白鹤种群数量变化与繁殖地繁殖期的降水量没有显著的相关性,而与7月份的气温变量存在显著的正相关。多元线性回归分析结果表明,6a前的10月份平均最低温度、2a前的10月最高温度及5a前的10月平均气温是白鹤种群数量变化的显著预测因子,共同解释了鄱阳湖国家级自然保护区白鹤种群数量变化的74.8%(F=23.807,df=27,P=0.000)。     

人参潜在地理分布以及气候变化对其影响预测

本文以人参为研究对象,基于人参分布点位数据和22个气候环境因子数据,运用BioMod2平台10个物种分布模型对当前我国东北地区人参潜在生境分布进行预测.以受试者工作特征曲线(ROC)为权重集成10个模型的模拟结果,构建组合模型,并基于该模型预测了IPCC 第五次评估报告中RCP 8.5、RCP 6.0、RCP 4.5和RCP 2.6等4种排放情景下21世纪50和70年代人参潜在分布范围.结果表明: 在基准气候条件下,人参适宜生境面积占研究区总面积的10.4%,此类地区主要分布于研究区东北部长白山地区以及小兴安岭东南部区域的森林地带.在未来不同的排放情景下研究区人参的适宜生境变化显著,总体上分布范围将有一定程度的缩小.同时参与建模的10种模型在统计学精度、预测结果以及变量权重上都有差异.模型精度计算结果表明,MAXENT模拟效果最好,GAM、RF和ANN次之,SRE模拟精度最低.本文构建的组合模型在一定程度上提高了现有物种分布模型的预测精度,从而使模拟效果更优.