基于SPOT-VGT NDVI的陕北植被覆盖时空变化

利用1998-2010年SPOT-VGT NDVI影像对陕北地区植被的时空变化进行分析.结果表明:研究期间,陕北地区归一化植被指数(NDVI)的季相变化明显,月均最高值出现在8月、最小值出现在1月,月均值的变化幅度在0.14 ～0.46,NDVI月均值为0.28,其年均值总体呈上升趋势;在空间上,植被改善地区集中于陕北中南部,生态环境退化区域集中在长城以北风沙区;气温和降水是影响植被变化的重要气候因子,其与NDVI变化的相关系数分别为0.72和0.58;植被改善明显的区域主要位于坡度15° ～25°的地区,反映出国家退耕还林还草工程对陕北地区生态环境的恢复和改善起到了巨大作用.     

青藏高原植被退化对高原及周边地区大气环流的影响

利用耦合了陆面模式的大气环流模式,结合青藏高原植被退化的范围和程度,通过在模式中修改高原地区(27°—40°N,75°—100°E)的叶面积指数的方法,探讨了植被退化以后对高原及其附近地区上空大气环流的影响。结果表明,该模式对高原地表温度场具有很强的模拟能力,并且能够很好地模拟出青藏高原及附近地区夏季位势高度场的平均特征及南亚高压的位置和强度,但南亚高压中心强度偏大且略微西退。在青藏高原植被出现退化以后,高原整体地表土壤温度和地表2 m空气温度升高,感热通量增加、潜热通量减小,进而改变了高原地区的波文比。地表感热增加导致高原及附近地区500 h Pa高度场降低和200 h Pa高度场升高,并在200 h Pa上存在强大的反气旋性环流异常,导致南亚高压增强和北扩东伸。植被退化造成的青藏高原感热增加导致了高原南部上升运动增强和北部上升运动减弱,同时又引起高原以北地区下沉气流的影响范围扩大,而下沉气流的强度减弱,其结果有助于高原以北的干旱范围扩大,而干旱程度却得到缓解。     

基于MODIS/NDVI的陕北地区植被动态监测与评价

陕北地区从1999年退耕还林试点工程实施以来,区域植被发生很大变化,退耕前后植被动态变化监测成为退耕还林工程评价任务之一,而当前植被恢复监测评价的难点在于如何确定哪些是由于退耕而引起的植被变化。针对此问题,选取适合陕北地区植被变化监测的MODIS/NDVI数据,利用均值变化及趋势分析方法,从不同土地利用/覆被类型和不同坡度植被指数动态变化两方面分析退耕还林对植被动态变化的影响。结论如下:(1)陕北地区平均NDVI从2000-2008年呈现较明显的增长趋势,坡耕地和草地NDVI增长速度相对较快;(2)趋势分析结果显示,陕北绝大部分地区植被恢复良好,植被指数呈明显改善的面积占整个地区面积的64.96%,中度改善占18.58%,其中又以坡耕地、草地植被明显改善面积分别占陕北地区明显改善面积的45.43%和17.10%,坡耕地对陕北地区植被明显改善面积贡献最大;(3)7 15°、15 25°及25 35°坡度植被明显改善面积分别占总改善面积的39.91%、25.81%、2.28%,其中7 25°坡度植被明显改善面积占总面积的65.72%;(4)基于陕北地区近年气候呈暖干化发展趋势,同期降雨并未呈现显著变化,说明非气候因子中退耕还林等人为因素是引起NDVI增长的主要因素,退耕还林对于陕北地区植被恢复有明显促进作用。     

桂西北喀斯特区域植被变化趋势及其对气候和地形的响应

基于1999—2010年的SPOT NDVI数据,分析了河池市植被变化趋势及空间差异,并结合气象和地形数据分析了植被与气候、地形的关系。结果表明:(1)桂西北喀斯特地区植被变化总体上呈恢复趋势,年均气候因子对植被变化的作用不明显;(2)200—500m的海拔范围内植被恢复显著,但400—500m的海拔范围内有小面积植被退化现象,随着海拔增加,植被变化趋于稳定;(3)6—15°的坡度范围内植被恢复最显著,而2—6°及大于25°坡度范围存在植被退化的现象;(4)不同坡向上的植被恢复差异不明显,但随着坡向由阴坡转阳坡,植被总体恢复呈减小趋势。喀斯特地区人类生态建设取得一定成效,但由于人类活动的负面影响,在海拔400—500m、坡度大于25°的阳坡区域仍存在植被减少的现象。     

稻田甲烷排放模型研究——模型的验证

模型的有效性检验是模型应用于估计区域尺度稻田甲烷排放量的基本前提 ,尤其是针对多种不同的土壤、气候以及农业管理方式等可能影响稻田甲烷排放的环境条件下的模型检验。利用覆盖全国主要水稻产区的 94个甲烷排放观测案例对稻田甲烷排放模型 (CH4 MOD)进行了验证。这些观测区域分布范围北至北京 (4 0°30′N,116°2 5′E) ,南至广州 (2 3°0 8′N,113°2 0′E) ,东起杭州 (30°19′N,12 0°12′E) ,西到四川的土主 (2 9°4 0′N,10 3°5 0′E)。既有双季稻 ,也有单季稻 ,稻田灌溉及施肥方式也多种多样 ,对我国水稻生产具有较广泛的代表性。观测获得的稻田甲烷排放季节总量从 3.1kg C/hm2到 76 1.7kg C/hm2 ,平均值为199.4 (± 187.3) kg C/hm2 ;相应的模拟值分别为 13.9、82 4 .3和 2 2 4 .6 (± 187.0 ) kg C/hm2。模拟值与实测值的线性相关系数(r2 )为 0 .84 (n=94 ,p<0 .0 0 1)。CH4 MOD模型能够通过较少的输入参数有效地模拟我国主要农作方式下的稻田甲烷排放     

陕北能源富集区工业化过程与生态风险格局

能源开采对地表生境的破坏、地下作业导致的采空沉降、能源化工企业三废污染等是导致能源地区环境恶化的主要因素.但在更大尺度上,矿区工业化与区域生态格局相互作用关系驱动机理比较复杂,时空节律与尺度同步性难以判定.以处于生态恢复背景下的陕北能源富集地区为例,基于夜间灯光和NDVI数据分析工业化过程与植被生态风险的时空变化特征,并采用土地利用类型空间变异、降水量与径流量减少趋势、地震发生频次等进行不同尺度的综合分析,探讨工业化过程对生态风险的影响.结果表明:能源区工业化直接增加区域生态压力的假设能否成立与实证角度和尺度放缩有关,在植被恢复背景下,工业化对植被格局的影响不能在较大尺度分析中显现;陕北能源富集区生态环境呈现整体好转、局部恶化的格局,植被格局与工业化过程交互机理存在单向性,并且具有时空不同步性,小尺度与大尺度的观测结果并不一致;工业化过程在较小区域内仍强烈影响局地生态安全,区域生态环境建设的政策导向应整体与局部并重.     

中国盛夏褐飞虱北迁过程的动态数值模拟

以控制和影响褐飞虱Nilaparvata lugens (Stal)迁飞过程的生理生态参数为依据,应用时空分辨率较高的中尺度数值预报模式-MM4和三维轨迹计算方法对我国盛夏褐飞虱的北迁过程进行了动态模拟。模拟结果及其与实测虫情资料的对比研究表明：（1）我国盛夏褐飞虱北迁的虫源地主要在22.5°N～27°N,110°E～116°E之间。（2）空中迁飞路径有三条：主径取32°方位角,副径分别取10°和75°方位角。（3）降落虫汇区有三个：主降区为长江中下游稻区,副降区分别为鄂西北、川东北稻区和浙东南、闽北稻区。（4）理论模拟与实际虫情普查分析比较吻合,说明该模型可作为迁飞害虫灾变机制研究的一个重要工具。     

河南省近30 年植被指数与气象因子的相关性分析

GIMMS NDVI3g 数据因其具有半月合成及长时间序列的优势, 被国内外学者广泛应用于植被指数研究。因此利用河南省1982—2013 年该数据集和省内及周边共32 个气象站点的逐月气象数据, 采用均值法、克里金插值和相关性分析等方法, 分析了河南省近30 年来的植被指数与主要气象因子(温度、降雨量)的相关性。结果表明: (1)近30 年来,河南省年平均NDVI 呈波动性上升趋势, 其年增长率为0.002, 总体植被覆盖增加; (2)在全球变暖的大环境下, 省内年均气温显著增加, 其年平均增长率为0.038, 而降雨量则逐年减少, 其年下降率为2.151; (3)年平均NDVI 与年平均温度呈一般正相关, 两者增减基本同步, 而与降雨量的年际变化则存在一定的滞后性, 体现在当年降雨增多作用于来年植被NDVI 上升明显。(4)在植被覆盖较差的中部地区, 植被NDVI 与气温、降雨的相关性较高, 在植被覆盖较好的东部和西部地区, 其NDVI 与气温、降雨的相关性相对较低, 该结果为土地遭破坏区域的生态恢复提供了理论参考。     

基于MODIS NDVI数据的陕西省植被绿度时空变化及人类活动影响

气候和人类活动是控制和影响植被空间分布及其变化的基本驱动力。利用2001-2018年的MODIS NDVI和1999-2018年的降雨时间序列数据,分析了陕西省NDVI的时空变化规律。采用TSS-RESTREND （Time Series Segmentation and Residual Trend）算法剥离了气候要素（降雨）对植被NDVI的影响,分析了人类活动对植被变化的影响程度和区域。（1）2001-2018年间,陕西省NDVI呈显著增加,全省平均增加速率为0.006/a;（2）相比18年来的平均值,77.29%的区域大于均值。其中,陕北的榆林市、延安市大于均值的区域较大,分别为97.52%和89.03%,秦巴山区次之,为73.91%。2012年之后,NDVI高值向北逐年推进趋势明显。（3）全省NDVI增加的区域达71.77%,而陕北地区的增加量明显大于关中平原区和陕南秦巴山地,其中陕北的榆林NDVI增加区域为72.11%,延安为86.44%,均超过了全省平均水平。（4）总体上陕西全省呈变绿趋势。榆林市和延安市的变绿区域明显多于关中平原和秦巴山地,延安和榆林的剧烈增长区域分别为55.46%和34.34%,而陕南为41.03%,说明处于湿润气候区的陕南地区也有显著变绿趋势。     

退耕还林(草)过程中陕北地区NDVI时空变异及其驱动因子的地理探测

地表植被作为生态环境变化的敏感因子,对维持区域生态稳定性具有重要作用.基于退耕还林(草)生态工程实施过程中2000-2019年陕北地区的MODIS NDVI数据,结合地形、地貌、气候、土壤和植被等环境因子,探究NDVI时空变异特征,并运用地理探测器模型对植被NDVI影响因子及其影响程度进行探测,最终确定主要环境因子对N...     

Inclusion of soil data improves the performance of bioclimatic envelope models for insect species distributions in temperate Europe

Aim To analyse the effect of the inclusion of soil and land‐cover data on the performance of bioclimatic envelope models for the regional‐scale prediction of butterfly (Rhopalocera) and grasshopper (Orthoptera) distributions. Location Temperate Europe (Belgium). Methods Distributional data were extracted from butterfly and grasshopper atlases at a resolution of 5 km for the period 1991–2006 in Belgium. For each group separately, the well‐surveyed squares (n = 366 for butterflies and n = 322 for grasshoppers) were identified using an environmental stratification design and were randomly divided into calibration (70%) and evaluation (30%) datasets. Generalized additive models were applied to the calibration dataset to estimate occurrence probabilities for 63 butterfly and 33 grasshopper species, as a function of: (1) climate, (2) climate and land‐cover, (3) climate and soil, and (4) climate, land‐cover and soil variables. Models were evaluated as: (1) the amount of explained deviance in the calibration dataset, (2) Akaike’s information criterion, and (3) the number of omission and commission errors in the evaluation dataset. Results Information on broad land‐cover classes or predominant soil types led to similar improvements in the performance relative to the climate‐only models for both taxonomic groups. In addition, the joint inclusion of land‐cover and soil variables in the models provided predictions that fitted more closely to the species distributions than the predictions obtained from bioclimatic models incorporating only land‐cover or only soil variables. The combined models exhibited higher discrimination ability between the presence and absence of species in the evaluation dataset. Main conclusions These results draw attention to the importance of soil data for species distribution models at regional scales of analysis. The combined inclusion of land‐cover and soil data in the models makes it possible to identify areas with suitable climatic conditions but unsuitable combinations of vegetation and soil types. While contingent on the species, the results indicate the need to consider soil information in regional‐scale species–climate impact models, particularly when predicting future range shifts of species under climate change.     

Effectiveness of land use and disturbance measures,compared to climate,soil, and topography,for modeling relative abundances of tree species in the eastern United States

Environmental factors control species distributions and abundances, but effectiveness of land use and disturbance variables for modeling species generally is unknown compared to climate, soil, and topography variables. Therefore, I used predictor variables from categories of 1) land use and disturbance, 2) climate, and 3) soil, topography, and wind speed to model the relative abundances (i.e., percentage of all trees) of 65 common tree species in the eastern United States, with a contrast to presence-absence models of species distributions. First, I modeled variables within each category to identify the five most important variables. Then, I combined variables from each category to isolate most important variables, based on five model combinations of input variables from each category, ranging from one (i.e., three total) to five (i.e., 15 total) variables. From the five models of combined categories for each tree species, I identified the model with the greatest R² value. Overall, climate variables were most important for tree species models with one and two input variables from each category, but land use and disturbance variables were most important for models with three to five input variables from each category. Although a range of R² values occurred by species and number of input model variables, 32 species had best models with greatest R² values of 0.50 to 0.81. For all best species models, the most important variables were temperature of the warmest quarter, historical fire return interval for all fires, agricultural area during years 1850 to 1997, and precipitation of the driest month. Current land cover classes, which are accessible and the most commonly modeled land use variables, were not important for modeling tree species abundances or distributions. Climate variables were most important for modeling species distributions. Results support the concept that while climate sets soft boundaries on distributions, relative abundances within distributions are affected by other filters. Future modeling may establish other important land use and disturbance variables, or refinements within the important variables of historical fire return interval and agricultural area over time, advancing integration of both land use and climate variables into studies.     

The contributions of topoclimate and land cover to species distributions and abundance: fine‐resolution tests for a mountain butterfly fauna

Aim Models relating species distributions to climate or habitat are widely used to predict the effects of global change on biodiversity. Most such approaches assume that climate governs coarse‐scale species ranges, whereas habitat limits fine‐scale distributions. We tested the influence of topoclimate and land cover on butterfly distributions and abundance in a mountain range, where climate may vary as markedly at a fine scale as land cover. Location Sierra de Guadarrama (Spain, southern Europe) Methods We sampled the butterfly fauna of 180 locations (89 in 2004, 91 in 2005) in a 10,800 km² region, and derived generalized linear models (GLMs) for species occurrence and abundance based on topoclimatic (elevation and insolation) or habitat (land cover, geology and hydrology) variables sampled at 100‐m resolution using GIS. Models for each year were tested against independent data from the alternate year, using the area under the receiver operating characteristic curve (AUC) (distribution) or Spearman's rank correlation coefficient (r_s) (abundance). Results In independent model tests, 74% of occurrence models achieved AUCs of > 0.7, and 85% of abundance models were significantly related to observed abundance. Topoclimatic models outperformed models based purely on land cover in 72% of occurrence models and 66% of abundance models. Including both types of variables often explained most variation in model calibration, but did not significantly improve model cross‐validation relative to topoclimatic models. Hierarchical partitioning analysis confirmed the overriding effect of topoclimatic factors on species distributions, with the exception of several species for which the importance of land cover was confirmed. Main conclusions Topoclimatic factors may dominate fine‐resolution species distributions in mountain ranges where climate conditions vary markedly over short distances and large areas of natural habitat remain. Climate change is likely to be a key driver of species distributions in such systems and could have important effects on biodiversity. However, continued habitat protection may be vital to facilitate range shifts in response to climate change.     

Integrated approaches to climate-crop modelling: needs and challenges

This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate-vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO2), ozone (03) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation may be affected by changes in runoff as a direct consequence of climate change, and may also be affected by climate-related changes in demand for water for other uses. It is, therefore, necessary to consider the interactions between the responses of several impacts sectors to climate change. Overall, there is a strong case for a much closer coupling between models of climate, crops and hydrology, but this in itself poses challenges arising from issues of scale and errors in the models. A strategy is proposed whereby the pursuit of a fully coupled climate-chemistry-crop-hydrology model is paralleled by continued use of separate climate and land surface models but with a focus on consistency between the models.     

Climate and satellite-derived land cover for predicting breeding bird distribution in the Great Lakes Basin

Aim We examined relationships between breeding bird distribution of 10 forest songbirds in the Great Lakes Basin, large‐scale climate and the distribution of land cover types as estimated by advanced very high resolution radiometer (AVHRR) and multi‐spectral scanner (MSS) land cover classifications. Our objective was to examine the ability of regional climate, AVHRR (1 km resolution) land cover and MSS (200 m resolution) land cover to predict the distribution of breeding forest birds at the scale of the Great Lakes Basin and at the resolution of Breeding Bird Atlas data (5–10 km²). Specifically we addressed the following questions. (1) How well do AVHRR or MSS classifications capture the variation in distribution of bird species? (2) Is one land cover classification more useful than the other for predicting distribution? (3) How do models based on climate compare with models based on land cover? (4) Can the combination of both climate and land cover improve the predictive ability of these models. Location Modelling was conducted over the area of the Great Lakes Basin including parts of Ontario, Canada and parts of Illinois, Indiana, Michigan, New York, Ohio, Pennsylvania Wisconsin, and Minnesota, USA. Methods We conducted single variable logistic regression with the forest classes of AVHRR and MSS land cover using evidence of breeding as the response variable. We conducted multiple logistic regression with stepwise selection to select models from five sets of explanatory variables (AVHRR, MSS, climate, AVHRR + climate, MSS + climate). Results Generally, species were related to both AVHRR and MSS land cover types in the direction expected based on the known local habitat use of the species. Neither land cover classification appeared to produce consistently more intuitive results. Good models were generated using each of the explanatory data sets examined here. And at least one but usually all five variable sets produced acceptable or excellent models for each species. Main conclusions Both climate and large scale land cover were effective predictors of the distribution of the 10 forest bird species examined here. Models generated from these data had good classification accuracy of independent validation data. Good models were produced from all explanatory data sets or combinations suggesting that the distribution of climate, AVHRR land cover, and MSS land cover all captured similar variance in the distribution of the birds. It is difficult to separate the effects of climate and vegetation on the species’ distributions at this scale.     

Modelling climate response to historical land cover change

In order to estimate the effect of historical land cover change (deforestation) on climate, we perform a set of experiments with a climate system model of intermediate complexity – CLIMBER-2. We focus on the biophysical effect of the land cover change on climate and do not explicitly account for the biogeochemical effect. A dynamic scenario of deforestation during the last millennium is formulated based on the rates of land conversion to agriculture. The deforestation scenario causes a global cooling of 0.35 °C with a more notable cooling of the northern hemisphere (0.5 °C). The cooling is most pronounced in the northern middle and high latitudes, especially during the spring season. To compare the effect of deforestation on climate with other forcings, climate responses to the changing atmospheric CO₂ concentration and solar irradiance are also analysed. When all three factors are taken into account, dynamics of northern hemisphere temperature during the last 300 years within the model are generally in agreement with the observed (reconstructed) temperature trend. We conclude that the impact of historical land cover changes on climate is comparable with the impact of the other climate forcings and that land cover forcing is important for reproducing historical climate change.     

Land-cover data improve bioclimatic models for anurans and turtles at a regional scale

Aim We investigated whether accounting for land cover could improve bioclimatic models for eight species of anurans and three species of turtles at a regional scale. We then tested whether accounting for spatial autocorrelation could significantly improve bioclimatic models after statistically controlling for the effects of land cover. Location Nova Scotia, eastern Canada. Methods Species distribution data were taken from a recent (1999–2003) herpetofaunal atlas. Generalized linear models were used to relate the presence or absence of each species to climate and land‐cover variables at a 10‐km resolution. We then accounted for spatial autocorrelation using an autocovariate or third‐order trend surface of the geographical coordinates of each grid square. Finally, variance partitioning was used to explore the independent and joint contributions of climate, land cover and spatial autocorrelation. Results The inclusion of land cover significantly increased the explanatory power of bioclimatic models for 10 of the 11 species. Furthermore, including land cover significantly increased predictive performance for eight of the 11 species. Accounting for spatial autocorrelation improved model fit for rare species but generally did not improve prediction success. Variance partitioning demonstrated that this lack of improvement was a result of the high correlation between climate and trend‐surface variables. Main conclusions The results of this study suggest that accounting for the effects of land cover can significantly improve the explanatory and predictive power of bioclimatic models for anurans and turtles at a regional scale. We argue that the integration of climate and land‐cover data is likely to produce more accurate spatial predictions of contemporary herpetofaunal diversity. However, the use of land‐cover simulations in climate‐induced range‐shift projections introduces additional uncertainty into the predictions of bioclimatic models. Further research is therefore needed to determine whether accounting for the effects of land cover in range‐shift projections is merited.     

An attack on two fronts: predicting how changes in land use and climate affect the distribution of stream macroinvertebrates

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Biotic interactions improve prediction of boreal bird distributions at macro-scales

Aim The role of biotic interactions in influencing species distributions at macro‐scales remains poorly understood. Here we test whether predictions of distributions for four boreal owl species at two macro‐scales (10 × 10 km and 40 × 40 km grid resolutions) are improved by incorporating interactions with woodpeckers into climate envelope models. Location Finland, northern Europe. Methods Distribution data for four owl and six woodpecker species, along with data for six land cover and three climatic variables, were collated from 2861 10 × 10 km grid cells. Generalized additive models were calibrated using a 50% random sample of the species data from western Finland, and by repeating this procedure 20 times for each of the four owl species. Models were fitted using three sets of explanatory variables: (1) climate only; (2) climate and land cover; and (3) climate, land cover and two woodpecker interaction variables. Models were evaluated using three approaches: (1) examination of explained deviance; (2) four‐fold cross‐validation using the model calibration data; and (3) comparison of predicted and observed values for independent grid cells in eastern Finland. The model accuracy for approaches (2) and (3) was measured using the area under the curve of a receiver operating characteristic plot. Results At 10‐km resolution, inclusion of the distribution of woodpeckers as a predictor variable significantly improved the explanatory power, cross‐validation statistics and the predictive accuracy of the models. Inclusion of land cover led to similar improvements at 10‐km resolution, although these improvements were less apparent at 40‐km resolution for both land cover and biotic interactions. Main conclusions Predictions of species distributions at macro‐scales may be significantly improved by incorporating biotic interactions and land cover variables into models. Our results are important for models used to predict the impacts of climate change, and emphasize the need for comprehensive evaluation of the reliability of species–climate impact models.     

Assessing ecosystem threats from global and regional change: hierarchical modeling of risk to sagebrush ecosystems from climate change, land use and invasive species in Nevada, USA

Global change poses significant challenges for ecosystem conservation. At regional scales, climate change may lead to extensive shifts in species distributions and widespread extirpations or extinctions. At landscape scales, land use and invasive species disrupt ecosystem function and reduce species richness. However, a lack of spatially explicit models of risk to ecosystems makes it difficult for science to inform conservation planning and land management. Here, I model risk to sagebrush ( Artemisia spp.) ecosystems in the state of Nevada, USA from climate change, land use/land cover change, and species invasion. Risk from climate change is based on an ensemble of 10 atmosphere-ocean general circulation model (AOGCM) projections applied to two bioclimatic envelope models (Mahalanobis distance and Maxent). Risk from land use is based on the distribution of roads, agriculture, and powerlines, and on the spatial relationships between land use and probability of cheatgrass Bromus tectorum invasion in Nevada. Risk from land cover change is based on probability and extent of pinyon-juniper ( Pinus monophylla; Juniperus spp.) woodland expansion. Climate change is most likely to negatively impact sagebrush ecosystems at the edges of its current range, particularly in southern Nevada, southern Utah, and eastern Washington. Risk from land use and woodland expansion is pervasive throughout Nevada, while cheatgrass invasion is most problematic in the northern part of the state. Cumulatively, these changes pose major challenges for conservation of sagebrush and sagebrush obligate species. This type of comprehensive assessment of ecosystem risk provides managers with spatially explicit tools important for conservation planning.