农业灌溉和施肥在陆面过程模型中的参数化方法研究进展

全球气候变化和人口激增背景下,灌溉和施肥成为保证粮食产量的重要途径,同时也深刻改变着陆地生态系统水循环、能量流动和物质循环过程。在陆面过程模型(LSM)中耦合灌溉和施肥方案对清晰把握陆-气相互作用、保障全球粮食安全有重要意义。本文分别回顾了灌溉和施肥(氮肥)在LSM参数化过程中的3个关键参量(方式、用量和时间)的表达方法,指出了当前受到灌溉和施肥关键参量高时空分辨率数据匮乏的影响,LSM中的灌溉和施肥方案与实际农业生产方式有所偏离,难以充分反映灌溉和施肥对粮食产量、生态环境和局部气候的影响。最后,提出了LSM中灌溉和施肥方案的未来优化方向：1)考虑作物间的水分需求差异,对灌溉阈值进行差异化设置,正确评估不同作物的水资源消耗总量和强度;2)充分利用施肥灌溉的地面观测记录和日益丰富的区域格网数据,发展更加贴合实际农业操作的参数化方案,准确揭示灌溉和施肥的经济、生态和气候等效应;3)综合作物类型、物候阶段、土壤基础肥力等因素,发展施肥诊断方案作为模型的补充方案,提升模型在氮肥数据匮乏地区的应用性和模拟准确性。     

区域气候变化统计降尺度研究进展

统计降尺度方法(the Statistical Downscaling Methods, SDM)是为合理预测区域尺度的气候变化情景而提出的新型研究方法。统计降尺度法利用多年大气环流的观测资料建立大尺度气候要素和区域气候要素之间的统计关系,并用独立的观测资料检验这种关系的合理性。把这种关系应用于大气环流模式(Global atmospheric general circulation models, GCMs)中输出大尺度气候信息,来预估区域未来的气候变化情景(如气温和降水)。同时,10a来降尺度方法在生态过程模拟以及气候变化与生态预报关系拟合研究方面也取得一定进展。对统计降尺度方法概念的内涵和外延、基本原理和操作步骤的创新研究方面进行了综述,归纳了该方法在模拟区域气候变化中的应用进展、研究热点及发展趋势,介绍了降尺度在生态预报中的相关应用,为相关研究提供参考。     

区域气候变化情景下气候变率对我国水稻产量影响的模拟研究

利用中国随机天气模型将中国区域气候模式ＲＣＭ与作物模式ＣＥＲＥＳ－Ｒｉｃｅ相连接，模拟了３种气候变率（０％，１０％，２０％）水平下未来气候（２０５０年，假定此时ＣＯ２浓度为５５０ｍｇ／Ｌ）对我国水稻主产区（广州，长沙，南京）灌溉水稻和雨养水稻在考虑ＣＯ２肥效与否条件下的产量，模拟结果表明；（１）气候变率对水稻产量的影响因经营方式和研究地区的不同而有差异，对灌溉水稻来说，气候变率对其产量有负面影响，     

基于CENTURY模型的荒漠草原ANPP对气候变化响应的模拟

荒漠草原是内蒙古草原主要类型之一,生态系统脆弱,对气候变化响应敏感。利用四子王旗生态气象观测试验站50年的气候数据、近5年的土壤调查资料和7年草地样方资料对CENTURY模型适用性进行检验;分析了过去50年地上净初级生产力(ANPP)与26个气象因子之间的相互关系;最后基于区域气候模式系统PRECIS输出的2021—2050年SRES B2、A2和A1B气候排放情景数据,分析未来50年研究区ANPP的可能变化趋势。结果表明:CENTURY模型能够成功模拟这类荒漠草原的季节动态和年际变化;生长季内逐月地上生物量模拟值与观测值之间具有较好的相关性(R2=0.66,P0.01);在过去50年中,四子王旗荒漠草原温度增加,降水略增,ANPP增加;模拟的ANPP随气温和降水的变化呈现出明显的变化规律;相关分析表明,ANPP与降水量呈显著正相关,与年极端最高气温和年蒸发量呈显著负相关,与其余气象因子相关性不显著;在SRES B2、A2和A1B情景下,未来四子王旗荒漠草原平均最高气温、最低气温和降水量均呈现增加趋势,ANPP年际间波动较大,但总体增加趋势明显,分别较基准时段增加了2.55%、4.19%和9.44%;3种情景下,未来四子王旗气候变化均对ANPP产生正面影响。本研究实现了CENTURY模型在内蒙古荒漠草原上的应用,弥补了由于西部荒漠草原区缺乏连续长期野外试验数据及生态系统异质性而导致无法对草地生产力进行准确动态评估的不足。     

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。     

地下滴灌条件下三倍体毛白杨根区土壤水分动态模拟

在根系分布试验观测的基础上,提出了三倍体毛白杨一维根系吸水模型,在考虑根系吸水情况下利用HYDRUS模型模拟了地下滴灌条件下三倍体毛白杨根区的土壤水分动态,通过田间试验对模型进行验证,并利用HYDRUS研究了不同灌水技术参数对土壤湿润模式的影响.结果表明：在灌溉结束和水分再分布24 h后,土壤含水量模拟结果的相对平均绝对误差(RMAE)分别为7.8%和6.0%,均方根误差(RMSE)分别为0.036和0.026 cm³·cm^-3,说明HYDRUS模型能很好地模拟地下滴灌条件下三倍体毛白杨根区的短期土壤水分动态,且所建根系吸水模型合理;与2、4 L·h^-1的滴头流速和连续性灌溉相比,流速1 L·h^-1和脉冲式灌溉(每隔30 min灌水30 min)能增大土壤湿润体体积,且可以减少水分深层渗漏量,因此,对试验地三倍体毛白杨根区进行地下滴灌应首选流速1 L·h^-1的脉冲式灌溉.     

农田灌溉对印度区域气候的影响模拟

为满足人类对粮食的需求,全球灌溉农田面积迅速扩张,农田灌溉对区域气候的影响引起广泛地关注。利用区域环境系统集成模式(RIEMS2.0)和最新的土地利用变化资料,选取农田灌溉面积最大的印度区域作为研究区域,进行雨养农田和灌溉农田的对比试验,探讨农田灌溉对区域气候的影响。结果表明:(1)农田灌溉使得印度区域年平均气温降低1.4℃,年平均降水率增加0.35mm/d。农田灌溉对印度区域气候的影响存在明显的季节波动,季风前期及6月份该区域气候对下垫面变化的响应最为敏感;7-9月各气候要素变化较小。(2) 农田灌溉使得印度区域地表净辐射增加,且地表净辐射在潜热通量和感热通量之间的分配发生了较大的改变,潜热通量增加,感热通量减少;对地表起冷却作用;同时由于土壤湿度增加,蒸散作用增强,大气中水汽含量增加,潜热不稳定能量增加,导致对流性降水增加。     

未来气候变化对福建省水稻产量影响的模拟

为了科学评价未来气候变化对福建省水稻产量的影响,将福建省划分为3个稻区,选取66个样点,7个代表性品种,以及2种典型浓度路径(中端稳定路径RCP4.5和高端路径RCP8.5),利用BCC_CSM(Beijing Climate Center Climate System Model)气候模式,基于这2种典型浓度路径情景RCP(Representative Concentration Pathway)下的气候预估结果,结合作物生长模型CERES-Rice,分雨养与灌溉两种情形,模拟分析气候变化对水稻生产的影响。结果表明:未来气候变化情景下福建省各站点水稻生育期将明显缩短,生育期内平均温度均有所升高;不考虑CO2肥效作用时,无论早稻、后季稻、单季稻,其产量相对于基准年份均普遍减产,减产幅度不超过12%,其中雨养水稻的减产幅度略高于灌溉水稻;不同情景下水稻产量变化也有所差别,其中RCP8.5情景下水稻的减产幅度明显大于RCP4.5情景;而在考虑CO2肥效作用时,模拟结果比较乐观,各研究站点普遍表现为增产,最大增产幅度可达15.2%。     

基于MaxEnt模型的中国马尾松分布格局及未来变化

气候因素是影响物种分布的决定性因素之一。根据现有的马尾松分布数据和19个全球气候因子变量数据,依托QGIS 2.18.3和ArcGIS 10.1等软件,运用MaxEnt模型,模拟了马尾松的现分布区,并对其未来分布进行预测,同时对影响马尾松的气候变量进行了分析。结果表明:(1)影响马尾松分布的19个气候变量中,最干燥月的降水量(bio14)和最冷季度的平均温度(bio11)对马尾松分布的影响贡献率超过70%;(2)依托气候数据,对马尾松未来分布进行预测,其未来的分布面积增加,增比为35.82%;(3)使用QGIS 2.18.3软件对未来的气候因子变化进行预测,结果显示,气候变化情况与马尾松未来分布格局相吻合。研究表明,马尾松适应能力较强,未来的气候变化对其分布呈正向影响。     

中国植物分布模拟研究现状

在过去的20年里, 物种分布模型已广泛应用于动植物地理分布的模拟研究。该文以植物物种分布模拟为例, 利用中国知网、维普网以及Web of Science文献数据库的检索与统计, 分析了2000-2018年间, 中国研究人员利用各种物种分布模型对植物物种分布模拟研究的发文量、模拟模型、物种类型、数据来源、研究目的等信息。最终共收集到366篇有效文献, 分析表明2011年以来中国的物种分布模型应用发展迅速, 且以最近5年最为迅猛, 在生态学、中草药业、农业和林业等行业部门应用广泛。在使用的33种模型中, 应用最广的为最大熵模型(MaxEnt)。有一半研究的环境数据仅包含气候数据, 另一半研究不仅包含气候数据还包括地形与土壤等数据; 环境及物种数据的来源多样, 国际及本土数据库均得到使用。模拟涉及有明确清单的562个植物种, 既有木本植物(52.7%), 也有草本植物(41.8%), 其中中草药、果树、园林植物、农作物等占比较高。研究目的主要集中在过去、现在和未来气候变化对植物种分布的影响及预测, 以及物种分布评估与生物多样性评价(包括入侵植物风险评估)两大方面。预测物种潜在分布范围与气候变化影响等基础研究, 与模拟物种适生区与推广种植等应用研究并重, 物种分布模型在生态学与农业、林业和中草药业等多学科、多行业开展多种应用, 多物种、多模型和多来源数据共同参与模拟与比较, 开发新的机理性物种分布模型, 拓展新的物种分布模拟应用领域, 是今后研究的重点发展方向。     

Modelled biophysical impacts of conservation agriculture on local climates

Including the parameterization of land management practices into Earth System Models has been shown to influence the simulation of regional climates, particularly for temperature extremes. However, recent model development has focused on implementing irrigation where other land management practices such as conservation agriculture (CA) has been limited due to the lack of global spatially explicit datasets describing where this form of management is practiced. Here, we implement a representation of CA into the Community Earth System Model and show that the quality of simulated surface energy fluxes improves when including more information on how agricultural land is managed. We also compare the climate response at the subgrid scale where CA is applied. We find that CA generally contributes to local cooling (~1°C) of hot temperature extremes in mid‐latitude regions where it is practiced, while over tropical locations CA contributes to local warming (~1°C) due to changes in evapotranspiration dominating the effects of enhanced surface albedo. In particular, changes in the partitioning of evapotranspiration between soil evaporation and transpiration are critical for the sign of the temperature change: a cooling occurs only when the soil moisture retention and associated enhanced transpiration is sufficient to offset the warming from reduced soil evaporation. Finally, we examine the climate change mitigation potential of CA by comparing a simulation with present‐day CA extent to a simulation where CA is expanded to all suitable crop areas. Here, our results indicate that while the local temperature response to CA is considerable cooling (>2°C), the grid‐scale changes in climate are counteractive due to negative atmospheric feedbacks. Overall, our results underline that CA has a nonnegligible impact on the local climate and that it should therefore be considered in future climate projections.     

Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration

Vegetation albedo is a critical component of the Earth's climate system, yet efforts to evaluate and improve albedo parameterizations in climate models have lagged relative to other aspects of model development. Here, we calculated growing season albedos for deciduous and evergreen forests, crops, and grasslands based on over 40 site‐years of data from the AmeriFlux network and compared them with estimates presently used in the land surface formulations of a variety of climate models. Generally, the albedo estimates used in land surface models agreed well with this data compilation. However, a variety of models using fixed seasonal estimates of albedo overestimated the growing season albedo of northerly evergreen trees. In contrast, climate models that rely on a common two‐stream albedo submodel provided accurate predictions of boreal needle‐leaf evergreen albedo but overestimated grassland albedos. Inverse analysis showed that parameters of the two‐stream model were highly correlated. Consistent with recent observations based on remotely sensed albedo, the AmeriFlux dataset demonstrated a tight linear relationship between canopy albedo and foliage nitrogen concentration (for forest vegetation: albedo=0.01+0.071%N, r²=0.91; forests, grassland, and maize: albedo=0.02+0.067%N, r²=0.80). However, this relationship saturated at the higher nitrogen concentrations displayed by soybean foliage. We developed similar relationships between a foliar parameter used in the two‐stream albedo model and foliage nitrogen concentration. These nitrogen‐based relationships can serve as the basis for a new approach to land surface albedo modeling that simplifies albedo estimation while providing a link to other important ecosystem processes.     

Quantifying irrigation cooling benefits to maize yield in the US Midwest

Irrigation is an important adaptation strategy to improve crop resilience to global climate change. Irrigation plays an essential role in sustaining crop production in water‐limited regions, as irrigation water not only benefits crops through fulfilling crops' water demand but also creates an evaporative cooling that mitigates crop heat stress. Here we use satellite remote sensing and maize yield data in the state of Nebraska, USA, combined with statistical models, to quantify the contribution of cooling and water supply to the yield benefits due to irrigation. Results show that irrigation leads to a considerable cooling on daytime land surface temperature (?1.63°C in July), an increase in enhanced vegetation index (+0.10 in July), and 81% higher maize yields compared to rainfed maize. These irrigation effects vary along the spatial and temporal gradients of precipitation and temperature, with a greater effect in dry and hot conditions, and decline toward wet and cool conditions. We find that 16% of irrigation yield increase is due to irrigation cooling, while the rest (84%) is due to water supply and other factors. The irrigation cooling effect is also observed on air temperature (?0.38 to ?0.53°C) from paired flux sites in Nebraska. This study highlights the non‐negligible contribution of irrigation cooling to the yield benefits of irrigation, and such an effect may become more important in the future with continued warming and more frequent droughts.     

Including albedo in time‐dependent LCA of bioenergy

Albedo change during feedstock production can substantially alter the life cycle climate impact of bioenergy. Life cycle assessment (LCA) studies have compared the effects of albedo and greenhouse gases (GHGs) based on global warming potential (GWP). However, using GWP leads to unequal weighting of climate forcers that act on different timescales. In this study, albedo was included in the time‐dependent LCA, which accounts for the timing of emissions and their impacts. We employed field‐measured albedo and life cycle emissions data along with time‐dependent models of radiative transfer, biogenic carbon fluxes and nitrous oxide emissions from soil. Climate impacts were expressed as global mean surface temperature change over time (?T) and as GWP. The bioenergy system analysed was heat and power production from short‐rotation willow grown on former fallow land in Sweden. We found a net cooling effect in terms of ?T per hectare (?3.8 × 10^–11 K in year 100) and GWP₁₀₀ per MJ fuel (?12.2 g CO₂e), as a result of soil carbon sequestration via high inputs of carbon from willow roots and litter. Albedo was higher under willow than fallow, contributing to the cooling effect and accounting for 34% of GWP₁₀₀, 36% of ?T in year 50 and 6% of ?T in year 100. Albedo dominated the short‐term temperature response (10–20 years) but became, in relative terms, less important over time, owing to accumulation of soil carbon under sustained production and the longer perturbation lifetime of GHGs. The timing of impacts was explicit with ?T, which improves the relevance of LCA results to climate targets. Our method can be used to quantify the first‐order radiative effect of albedo change on the global climate and relate it to the climate impact of GHG emissions in LCA of bioenergy, alternative energy sources or land uses.     

Observation of irrigation‐induced climate change in the Midwest United States

Irrigated agriculture alters near‐surface temperature and humidity, which may mask global climate change at the regional scale. However, observational studies of irrigation‐induced climate change are lacking in temperate, humid regions throughout North America and Europe. Despite unknown climate impacts, irrigated agriculture is expanding in the Midwest United States, where unconfined aquifers provide groundwater to support crop production on coarse soils. This is the first study in the Midwest United States to observe and quantify differences in regional climate associated with irrigated agricultural conversion from forests and rainfed agriculture. To this end, we established a 60 km transect consisting of 28 stations across varying land uses and monitored surface air temperature and relative humidity for 31 months in the Wisconsin Central Sands region. We used a novel approach to quantify irrigated land use in both space and time with a database containing monthly groundwater withdrawal estimates by parcel for the state of Wisconsin. Irrigated agriculture decreased maximum temperatures and increased minimum temperatures, thus shrinking the diurnal temperature range (DTR) by an average of 3°C. Irrigated agriculture also decreased the vapor pressure deficit (VPD) by an average of 0.10 kPa. Irrigated agriculture significantly decreased evaporative demand for 25% and 66% of study days compared to rainfed agriculture and forest, respectively. Differences in VPD across the land‐use gradient were highest (0.21 kPa) during the peak of the growing season, while differences in DTR were comparable year‐round. Interannual variability in temperature had greater impacts on differences in DTR and VPD across the land‐use gradient than interannual variability in precipitation. These regional climate changes must be considered together with increased greenhouse gas emissions, changes to groundwater quality, and surface water degradation when evaluating the costs and benefits of groundwater‐sourced irrigation expansion in the Midwest United States and similar regions around the world.     

Radiative forcing of natural forest disturbances

Forest disturbances are major sources of carbon dioxide to the atmosphere, and therefore impact global climate. Biogeophysical attributes, such as surface albedo (reflectivity), further control the climate‐regulating properties of forests. Using both tower‐based and remotely sensed data sets, we show that natural disturbances from wildfire, beetle outbreaks, and hurricane wind throw can significantly alter surface albedo, and the associated radiative forcing either offsets or enhances the CO₂ forcing caused by reducing ecosystem carbon sequestration over multiple years. In the examined cases, the radiative forcing from albedo change is on the same order of magnitude as the CO₂ forcing. The net radiative forcing resulting from these two factors leads to a local heating effect in a hurricane‐damaged mangrove forest in the subtropics, and a cooling effect following wildfire and mountain pine beetle attack in boreal forests with winter snow. Although natural forest disturbances currently represent less than half of gross forest cover loss, that area will probably increase in the future under climate change, making it imperative to represent these processes accurately in global climate models.     

北方典型农牧交错带草地开垦对地表辐射收支与水热平衡的影响

以北方典型农牧交错带草原和农田生态系统的涡度相关数据为基础,对比分析了生长季两种不同土地利用类型的辐射和水热通量之异同,揭示了草地开垦影响地表辐射收支与水热平衡的机制。结果表明:在植被生长季(5月—9月),草地开垦引起太阳总辐射增加了10.74%,短波反射辐射减少了14.20%,净辐射增加了35.16%;在水热通量方面,草地开垦引起潜热通量日积分平均值增加了0.20MJ/m~2,同时显热通量减少了0.09 MJ/m~2;生长季内地表反照率减小,表征地表吸收太阳辐射增加,有升高气温的趋势;非生长季内地表反照率增加,有降低气温趋势,此外地表反照率与土壤湿度存在负指数关系;波文比在植被生长早期和末期增加,生长旺期减小,说明草地开垦与影响着近地表大气状态,从而改变了区域气候。     

Baseline time accounting: Considering global land use dynamics when estimating the climate impact of indirect land use change caused by biofuels

Purpose

Current estimations of the climate impact from indirect land use change (ILUC) caused by biofuels are heavily influenced by assumptions regarding the biofuel production period. The purpose of this paper is to propose a new method (baseline time accounting) that takes global land use dynamics into account that is consistent with the global warming potential, that is applicable to any phenomenon causing land use change, and that is independent of production period assumptions.

Methods

We consider ILUC in two forms. The first is called “accelerated expansion” and concerns ILUC in regions with an expanding agricultural area. The second is called “delayed reversion” and concerns ILUC in regions with a decreasing agricultural area. We use recent trends in international land use and projections of future land use change to assess how ILUC from biofuels will alter the development in global agricultural land use dynamics compared to the existing trend (i.e., the baseline development). We then use the definition of the global warming potential to determine the CO₂ equivalence of the change in land use dynamics.

Results and discussion

We apply baseline time accounting to two existing ILUC studies in the literature. With current trends in global agricultural land use, the method significantly reduces the estimated climate impact in the previous ILUC studies (by more than half). Sensitivity analyses show that results are somewhat sensitive to assumptions regarding carbon sequestration and assumptions regarding postreversion ecosystems.

Conclusions

The global dynamic development in land use has important implications for the time accounting step when estimating the climate impact of ILUC caused by biofuel production or other issues affecting land use. Ignoring this may lead to erroneous conclusions about the actual climate impact of ILUC. Several land use projections indicate that the global agricultural area will keep expanding up to and beyond 2050. We therefore recommend to apply the baseline time accounting concept as an integrated part of future ILUC studies and to update the results on a regular basis.     

Climate change decreases the cooling effect from postfire albedo in boreal North America

Fire is a primary disturbance in boreal forests and generates both positive and negative climate forcings. The influence of fire on surface albedo is a predominantly negative forcing in boreal forests, and one of the strongest overall, due to increased snow exposure in the winter and spring months. Albedo forcings are spatially and temporally heterogeneous and depend on a variety of factors related to soils, topography, climate, land cover/vegetation type, successional dynamics, time since fire, season, and fire severity. However, how these variables interact to influence albedo is not well understood, and quantifying these relationships and predicting postfire albedo becomes increasingly important as the climate changes and management frameworks evolve to consider climate impacts. Here we developed a MODIS‐derived ‘blue sky’ albedo product and a novel machine learning modeling framework to predict fire‐driven changes in albedo under historical and future climate scenarios across boreal North America. Converted to radiative forcing (RF), we estimated that fires generate an annual mean cooling of ?1.77 ± 1.35 W/m² from albedo under historical climate conditions (1971–2000) integrated over 70 years postfire. Increasing postfire albedo along a south–north climatic gradient was offset by a nearly opposite gradient in solar insolation, such that large‐scale spatial patterns in RF were minimal. Our models suggest that climate change will lead to decreases in mean annual postfire albedo, and hence a decreasing strength of the negative RF, a trend dominated by decreased snow cover in spring months. Considering the range of future climate scenarios and model uncertainties, we estimate that for fires burning in the current era (2016) the cooling effect from long‐term postfire albedo will be reduced by 15%–28% due to climate change.     

A pantropical analysis of the impacts of forest degradation and conversion on local temperature

Temperature is a core component of a species' fundamental niche. At the fine scale over which most organisms experience climate (mm to ha), temperature depends upon the amount of radiation reaching the Earth's surface, which is principally governed by vegetation. Tropical regions have undergone widespread and extreme changes to vegetation, particularly through the degradation and conversion of rainforests. As most terrestrial biodiversity is in the tropics, and many of these species possess narrow thermal limits, it is important to identify local thermal impacts of rainforest degradation and conversion. We collected pantropical, site‐level (<1 ha) temperature data from the literature to quantify impacts of land‐use change on local temperatures, and to examine whether this relationship differed aboveground relative to belowground and between wet and dry seasons. We found that local temperature in our sample sites was higher than primary forest in all human‐impacted land‐use types (N = 113,894 daytime temperature measurements from 25 studies). Warming was pronounced following conversion of forest to agricultural land (minimum +1.6°C, maximum +13.6°C), but minimal and nonsignificant when compared to forest degradation (e.g., by selective logging; minimum +1°C, maximum +1.1°C). The effect was buffered belowground (minimum buffering 0°C, maximum buffering 11.4°C), whereas seasonality had minimal impact (maximum buffering 1.9°C). We conclude that forest‐dependent species that persist following conversion of rainforest have experienced substantial local warming. Deforestation pushes these species closer to their thermal limits, making it more likely that compounding effects of future perturbations, such as severe droughts and global warming, will exceed species' tolerances. By contrast, degraded forests and belowground habitats may provide important refugia for thermally restricted species in landscapes dominated by agricultural land.