不同气象数据空间内插对区域玉米生长模拟结果的影响

作物模型区域模拟已成为作物模型应用的一个新方向。运用作物模型进行区域研究时,遇到的问题之一就是输入模型的空间数据质量问题,研究不同空间内插法获得的气象数据对作物模型区域模拟结果的影响,可以为区域模拟对输入数据的敏感性研究提供一定的参考。利用区域校准的CERES-Maize模型,将3类内插方法(几何内插、统计内插、动力模型内插)产生的网格化天气数据分别输入到CERES-Maize模型中,模拟了50km×50km网格水平下1961—1990年我国玉米生产状况,并选取1980—1990年模拟的平均产量与同期农调队调查产量进行比较,以了解区域模拟中,不同空间内插方法所得的逐日气象数据对区域模拟结果的影响。结果表明:(1)作物模型区域应用时,所采用的3种内插方法都能满足作物模型区域模拟对网格化天气数据的要求,采用3种天气数据的区域模拟结果都能反映出玉米平均产量的空间变化特征,与网格调查平均产量之间具有极显著的相关关系,但采用不同内插天气数据对模拟结果造成了8%以内的偏差。(2)采用不同内插天气数据,在进行作物区域模拟时,各方法的模拟结果之间呈极显著的相关关系,但这些模拟结果之间,在全国大部分地区是差异显著。     

利用GIS和多变量分析估算青藏高原月降水

随着空间降水信息需求的日益增加,空间降水插值已被广泛应用。降水区域不同,插值方法不同;时间尺度不同,插值方法也不相同。适合于所有地区的通用降水插值模型是不存在的。青藏高原自然地理特征独特,分析高原降水的时空格局意义重要。以青藏高原及其周边地区140个气象站点的月降水信息及其该地区的数字高程数据(DEM)为基础,利用G IS工具,对比分析了五种插值方法在青藏高原不同降水年份(以1998年、1997年分别代表丰水及欠水年份)的干湿季(1998年的干湿季分别以12月份和8月份为代表,1997年的干湿季分别以1月份和7月份为代表)月降水插值中的应用,并对整个高原地区的干季和湿季的月降水进行制图。这5种插值方法分别是:克里金插值法、反距离加权法、样条法、混合插值法Ⅰ和混合插值法Ⅱ,前3种插值方法未考虑海拔高度对降水的影响,而混合插值法则将高程作为降水的重要影响因子。结果表明:①在干季,无论是丰水还是欠水年份,月降水量都比较少,高程对降水量的影响较小,在月降水插值时可不考虑高程的影响,克里金法的月降水插值精度最高。②在湿季,月降水量较多,高程的影响较大,混合插值法比局部插值法及克里金插值法的精度高,尤以混合插值法Ⅱ(多元回归和样条法的综合)的精度最高。③干季,整个高原的月降水很少,西部和北部降水最少,东部和南部相对较多;湿季,高原的月降水较多,空间格局表现为由东南向西北递减。     

Back to the future: using historical climate variation to project near‐term shifts in habitat suitable for coast redwood

Studies that model the effect of climate change on terrestrial ecosystems often use climate projections from downscaled global climate models (GCMs). These simulations are generally too coarse to capture patterns of fine‐scale climate variation, such as the sharp coastal energy and moisture gradients associated with wind‐driven upwelling of cold water. Coastal upwelling may limit future increases in coastal temperatures, compromising GCMs’ ability to provide realistic scenarios of future climate in these coastal ecosystems. Taking advantage of naturally occurring variability in the high‐resolution historic climatic record, we developed multiple fine‐scale scenarios of California climate that maintain coherent relationships between regional climate and coastal upwelling. We compared these scenarios against coarse resolution GCM projections at a regional scale to evaluate their temporal equivalency. We used these historically based scenarios to estimate potential suitable habitat for coast redwood (Sequoia sempervirens D. Don) under ‘normal’ combinations of temperature and precipitation, and under anomalous combinations representative of potential future climates. We found that a scenario of warmer temperature with historically normal precipitation is equivalent to climate projected by GCMs for California by 2020–2030 and that under these conditions, climatically suitable habitat for coast redwood significantly contracts at the southern end of its current range. Our results suggest that historical climate data provide a high‐resolution alternative to downscaled GCM outputs for near‐term ecological forecasts. This method may be particularly useful in other regions where local climate is strongly influenced by ocean–atmosphere dynamics that are not represented by coarse‐scale GCMs.     

New insights into global patterns of ocean temperature anomalies: implications for coral reef health and management

Aim Coral reefs are widely considered to be particularly vulnerable to changes in ocean temperatures, yet we understand little about the broad‐scale spatio‐temporal patterns that may cause coral mortality from bleaching and disease. Our study aimed to characterize these ocean temperature patterns at biologically relevant scales. Location Global, with a focus on coral reefs. Methods We created a 4‐km resolution, 21‐year global ocean temperature anomaly (deviations from long‐term means) database to quantify the spatial and temporal characteristics of temperature anomalies related to both coral bleaching and disease. Then we tested how patterns varied in several key metrics of disturbance severity, including anomaly frequency, magnitude, duration and size. Results Our analyses found both global variation in temperature anomalies and fine‐grained spatial variability in the frequency, duration and magnitude of temperature anomalies. However, we discovered that even during major climatic events with strong spatial signatures, like the El Niño–Southern Oscillation, areas that had high numbers of anomalies varied between years. In addition, we found that 48% of bleaching‐related anomalies and 44% of disease‐related anomalies were less than 50 km², much smaller than the resolution of most models used to forecast climate changes. Main conclusions The fine‐scale variability in temperature anomalies has several key implications for understanding spatial patterns in coral bleaching‐ and disease‐related anomalies as well as for designing protected areas to conserve coral reefs in a changing climate. Spatial heterogeneity in temperature anomalies suggests that certain reefs could be targeted for protection because they exhibit differences in thermal stress. However, temporal variability in anomalies could complicate efforts to protect reefs, because high anomalies in one year are not necessarily predictive of future patterns of stress. Together, our results suggest that temperature anomalies related to coral bleaching and disease are likely to be highly heterogeneous and could produce more localized impacts of climate change.     

Patterns of ant species richness along elevational gradients in an arid ecosystem

Aim In this study, we examine patterns of local and regional ant species richness along three elevational gradients in an arid ecosystem. In addition, we test the hypothesis that changes in ant species richness with elevation are related to elevation‐dependent changes in climate and available area. Location Spring Mountains, Nevada, U.S.A. Methods We used pitfall traps placed at each 100‐m elevational band in three canyons in the Spring Mountains. We compiled climate data from 68 nearby weather stations. We used multiple regression analysis to examine the effects of annual precipitation, average July precipitation, and maximum and minimum July temperature on ant species richness at each elevational band. Results We found that patterns of local ant species richness differed among the three gradients we sampled. Ant species richness increased linearly with elevation along two transects and peaked at mid‐elevation along a third transect. This suggests that patterns of species richness based on data from single transects may not generalize to larger spatial scales. Cluster analysis of community similarity revealed a high‐elevation species assemblage largely distinct from that of lower elevations. Major changes in the identity of ant species present along elevational gradients tended to coincide with changes in the dominant vegetation. Regional species richness, defined here as the total number of unique species within an elevational band in all three gradients combined, tended to increase with increasing elevation. Available area decreased with increasing elevation. Area was therefore correlated negatively with ant species richness and did not explain elevational patterns of ant species richness in the Spring Mountains. Mean July maximum and minimum temperature, July precipitation and annual precipitation combined to explain 80% of the variation in ant species richness. Main conclusions Our results suggest that in arid ecosystems, species richness for some taxa may be highest at high elevations, where lower temperatures and higher precipitation may support higher levels of primary production and cause lower levels of physiological stress.     

河北省年均降水量插值方法比较

以河北省及临近区域120个气象观测站点1971～2000年均降水量数据为基础,选择其中的40个作为检验站点,其余站点分别取80、40、20个作为插值站点,采用局部插值、整体插值、多元线性回归、综合模拟等多种插值模型讨论了降水空间插值问题,主要结论如下:插值站点数、模型类型、模型参数都会影响插值精度.局部插值模型相对误差最小值出现在Spline、IDW模型中,其次为Kridging模型,而整体模型Trend、多元线性回归模型误差均较大,但综合了局部插值模型和统计模型的综合模型一定程度上能改善插值精度及误差分布.河北省80和40个站点的最优插值模型为综合模型,20个站点的最优插值模型为IDW2.     

Seasonal weather patterns drive population vital rates and persistence in a stream fish

Climate change affects seasonal weather patterns, but little is known about the relative importance of seasonal weather patterns on animal population vital rates. Even when such information exists, data are typically only available from intensive fieldwork (e.g., mark–recapture studies) at a limited spatial extent. Here, we investigated effects of seasonal air temperature and precipitation (fall, winter, and spring) on survival and recruitment of brook trout (Salvelinus fontinalis) at a broad spatial scale using a novel stage‐structured population model. The data were a 15‐year record of brook trout abundance from 72 sites distributed across a 170‐km‐long mountain range in Shenandoah National Park, Virginia, USA. Population vital rates responded differently to weather and site‐specific conditions. Specifically, young‐of‐year survival was most strongly affected by spring temperature, adult survival by elevation and per‐capita recruitment by winter precipitation. Low fall precipitation and high winter precipitation, the latter of which is predicted to increase under climate change for the study region, had the strongest negative effects on trout populations. Simulations show that trout abundance could be greatly reduced under constant high winter precipitation, consistent with the expected effects of gravel‐scouring flows on eggs and newly hatched individuals. However, high‐elevation sites would be less vulnerable to local extinction because they supported higher adult survival. Furthermore, the majority of brook trout populations are projected to persist if high winter precipitation occurs only intermittently (≤3 of 5 years) due to density‐dependent recruitment. Variable drivers of vital rates should be commonly found in animal populations characterized by ontogenetic changes in habitat, and such stage‐structured effects may increase population persistence to changing climate by not affecting all life stages simultaneously. Yet, our results also demonstrate that weather patterns during seemingly less consequential seasons (e.g., winter precipitation) can have major impacts on animal population dynamics.     

The influence of climate on the spatial patterning of Neotropical plant families

Aim Climate is recognized for the significant role it plays in the global distribution of plant species diversity. We test the extent to which two aspects of climate, namely temperature and precipitation, explain the spatial distribution of high taxonomic groupings (plant families) at a regional spatial resolution (the Neotropics). Our goal is to provide a quantitative and comparative framework for identifying the local effects of climate on the familial composition of tropical forests by identifying the influence of climate on the number of individuals and the number of species within a given family. Location One hundred and forty‐four 0.1‐ha forest transect sites from the Neotropics (19.8°N–27.0°S and 40.1°W–105.1°W). Data were originally collected by A.H. Gentry. Methods Spatial variability in the abundance (density) and species richness of 159 tropical plant families across a range of predominately lowland Neotropical landscapes were attributed to eight temperature and precipitation measures using the eigen analysis method of two‐field joint single‐value decomposition. Results Climate significantly affects the within‐clade diversity of several ecologically important Neotropical plant families. Intrafamily abundance and richness covary with temperature in some families (e.g. Fabaceae) and with precipitation in others (e.g. Bignoniaceae, Arecaceae), with differing climatic preferences observed even among co‐occurring families. In addition, the family‐level composition of Neotropical forests, in both abundance and richness, appears to be influenced more by temperature than by precipitation. Among lowland families, only Asteraceae increased in species richness with decreasing temperature, although several families, including Melastomataceae and Rubiaceae, are more abundant at lower temperatures. Main conclusions Although plant diversity is known to vary as a function of climate at the species level, we document clear climatic preferences even at the rank of family. Temperature plays a stronger role in governing the familial composition of tropical forests, particularly in the richness of families, than might be expected given its narrow annual and diurnal range in the tropics. Although other environmental or geographic variables that covary with temperature may be more causally linked to diversity differences than temperature itself, the results nonetheless identify the taxonomic components of tropical forest composition that may be most affected by future climatic changes.     

Bioclimatic and vegetation mapping of a topographically complex oceanic island applying different interpolation techniques

Different spatial interpolation techniques have been applied to construct objective bioclimatic maps of La Palma, Canary Islands. Interpolation of climatic data on this topographically complex island with strong elevation and climatic gradients represents a challenge. Furthermore, meteorological stations are not evenly distributed over the island, with few stations at high elevations. We carried out spatial interpolations of the compensated thermicity index (Itc) and the annual ombrothermic Index (Io), in order to obtain appropriate bioclimatic maps by using automatic interpolation procedures, and to establish their relation to potential vegetation units for constructing a climatophilous potential natural vegetation map (CPNV). For this purpose, we used five interpolation techniques implemented in a GIS: inverse distance weighting (IDW), ordinary kriging (OK), ordinary cokriging (OCK), multiple linear regression (MLR) and MLR followed by ordinary kriging of the regression residuals. Two topographic variables (elevation and aspect), derived from a high-resolution digital elevation model (DEM), were included in OCK and MLR. The accuracy of the interpolation techniques was examined by the results of the error statistics of test data derived from comparison of the predicted and measured values. Best results for both bioclimatic indices were obtained with the MLR method with interpolation of the residuals showing the highest R ² of the regression between observed and predicted values and lowest values of root mean square errors. MLR with correction of interpolated residuals is an attractive interpolation method for bioclimatic mapping on this oceanic island since it permits one to fully account for easily available geographic information but also takes into account local variation of climatic data.     

逐日气象要素空间插值方法的比较

采用距离反比权重法(IDW)、协克里格法(CK)和薄盘样条法(TPS) 3种不同的空间插值方法,对我国1951-2005年气象数据完整的559个气象站点逐月第15日的平均基本气象要素(最高气温、最低气温、日照时数和降水量)进行了插值分析与评价.结果表明：3种插值方法中,TPS法对最高气温和最低气温插值的根均方差（RMSE）最小(1.02 ℃和1.12 ℃)、R²最大(0.9916和0.9913);不同季节中,TPS法对秋季最高气温、夏季最低气温进行插值的RMSE均最小(0.83℃、0.86 ℃),R²均为秋季最高.对于日照时数和降水量而言,TPS法的RMSE最小(0.59 h和1.01 mm)、R²最大(0.9118和0.8135);不同季节中,TPS法对冬季日照时数进行插值的RMSE最小(0.49 h)、R²最大(0.9293),TPS法对冬季降水量进行插值的RMSE最小(0.33 mm),IDW法对夏季降水量进行插值的RMSE最小(2.01 mm),CK法对春季降水量进行插值的R²最大(0.8781).TPS法可作为我国大量逐日基本气象要素的最优空间插值方法.     

Influence of bioclimatic variables on tree-line conifer distribution in the Greater Yellowstone Ecosystem: implications for species of conservation concern

Aim Tree‐line conifers are believed to be limited by temperature worldwide, and thus may serve as important indicators of climate change. The purpose of this study was to examine the potential shifts in spatial distribution of three tree‐line conifer species in the Greater Yellowstone Ecosystem under three future climate‐change scenarios and to assess their potential sensitivity to changes in both temperature and precipitation. Location This study was performed using data from 275 sites within the boundaries of Yellowstone and Grand Teton national parks, primarily located in Wyoming, USA. Methods We used data on tree‐line conifer presence from the US Forest Service Forest Inventory and Analysis Program. Climatic and edaphic variables were derived from spatially interpolated maps and approximated for each of the sites. We used the random‐forest prediction method to build a model of predicted current and future distributions of each of the species under various climate‐change scenarios. Results We had good success in predicting the distribution of tree‐line conifer species currently and under future climate scenarios. Temperature and temperature‐related variables appeared to be most influential in the distribution of whitebark pine (Pinus albicaulis), whereas precipitation and soil variables dominated the models for subalpine fir (Abies lasiocarpa) and Engelmann spruce (Picea engelmannii). The model for whitebark pine substantially overpredicted absences (as compared with the other models), which is probably a result of the importance of biological factors in the distribution of this species. Main conclusions These models demonstrate the complex response of conifer distributions to changing climate scenarios. Whitebark pine is considered a ‘keystone’ species in the subalpine forests of western North America; however, it is believed to be nearly extinct throughout a substantial portion of its range owing to the combined effects of an introduced pathogen, outbreaks of the native mountain pine beetle (Dendroctonus ponderosae), and changing fire regimes. Given predicted changes in climate, it is reasonable to predict an overall decrease in pine‐dominated subalpine forests in the Greater Yellowstone Ecosystem. In order to manage these forests effectively with respect to future climate, it may be important to focus attention on monitoring dry mid‐ and high‐elevation forests as harbingers of long‐term change.     

淮河流域双季稻气候适宜度及其变化趋势

选取淮河流域33个县市1961-2005年的逐日气象数据,运用模糊数学和空间插值方法,对淮河流域双季稻的温度、降水、日照和气候适宜度进行了研究.结果表明:温度和降水是淮河流域双季稻生长的关键气候因子,早、晚稻各气候因子适宜度地域差异明显;其中,早稻气候适宜度由中部平原向东部沿海和西部山区递减,晚稻气候适宜度由南向北递减;温度适宜度以0.01(10a)~(-1)的速率上升,说明温度对淮河流域水稻生长发育产生正效应;日照适宜度以>0.02(10a)~(-1)的速率下降,总的气候适宜度呈下降趋势.

Abstract：

Based on the 1961-2005 meteorological data from thirty three stations in Huaihe River basin, and by using fuzzy mathematics and spatial interpolation methods, the climate suitability of double-cropping rice in this basin were studied. Temperature and precipitation were the key climate factors affecting the growth of double-cropping rice, and the climate suitability of both early-and late rice had strong regional characters. The climate suitability of early rice decreased from central plain area to east coast and west mountain area, whereas that of late rice decreased from south area to north area. Temperature suitability increased at a rate of 0. 01 (10a)~(-1), sug-gesting that temperature had positive effects on the growth and development of double-cropping rice in Huaihe River Basin. Sunlight suitability decreased at a rate higher than 0. 02 (10a)~(-1).The overall climate suitability had a decreasing trend.     

Evolutionary and ecological causes of species richness patterns in North American angiosperm trees

Climate and evolutionary factors (e.g. diversification, time‐for‐speciation, niche conservatism) are both thought to be major drivers of species richness in regional assemblages. However, few studies have simultaneously investigated the relative effects of climate and evolutionary factors on species richness across a broad geographical extent. Here, we assess their relative effects on species richness of angiosperm trees across North America. Species richness of angiosperm trees in 1175 regional assemblages were related to climate and phylogenetic structure using a structural equation modeling (SEM) approach. Climate was quantified based on the mean temperature of the coldest month and mean annual precipitation. Evolutionary factors (time‐for‐speciation vs diversification) were inferred from phylogeny‐based measures of mean root distance, phylogenetic species variability, and net relatedness index. We found that at the continental scale, species richness is correlated with temperature and precipitation with approximately similar strength. In the SEM with net relatedness index and phylogenetic species variability and with all the 1175 quadrats, the total direct effect size of phylogenetic structure on species richness is greater than the total direct effect size of climate on species richness by a factor of 3.7. The specific patterns of phylogenetic structure (i.e. greater phylogenetic distances in more species rich regions) are consistent with the idea that time and niche conservatism drive richness patterns in North American angiosperm trees. We conclude that angiosperm tree species richness in regional assemblages in North America is more strongly related to patterns of phylogenetic relatedness than to climatic variation. The results of the present study support the idea that climatic and evolutionary explanations for richness patterns are not in conflict, and that evolutionary processes explain both the relationship between climate and richness and substantial variation in richness that is independent of climate.     

Mapping Atmospheric Moisture Climatologies across the Conterminous United States

Spatial climate datasets of 1981–2010 long-term mean monthly average dew point and minimum and maximum vapor pressure deficit were developed for the conterminous United States at 30-arcsec (~800m) resolution. Interpolation of long-term averages (twelve monthly values per variable) was performed using PRISM (Parameter-elevation Relationships on Independent Slopes Model). Surface stations available for analysis numbered only 4,000 for dew point and 3,500 for vapor pressure deficit, compared to 16,000 for previously-developed grids of 1981–2010 long-term mean monthly minimum and maximum temperature. Therefore, a form of Climatologically-Aided Interpolation (CAI) was used, in which the 1981–2010 temperature grids were used as predictor grids. For each grid cell, PRISM calculated a local regression function between the interpolated climate variable and the predictor grid. Nearby stations entering the regression were assigned weights based on the physiographic similarity of the station to the grid cell that included the effects of distance, elevation, coastal proximity, vertical atmospheric layer, and topographic position. Interpolation uncertainties were estimated using cross-validation exercises. Given that CAI interpolation was used, a new method was developed to allow uncertainties in predictor grids to be accounted for in estimating the total interpolation error. Local land use/land cover properties had noticeable effects on the spatial patterns of atmospheric moisture content and deficit. An example of this was relatively high dew points and low vapor pressure deficits at stations located in or near irrigated fields. The new grids, in combination with existing temperature grids, enable the user to derive a full suite of atmospheric moisture variables, such as minimum and maximum relative humidity, vapor pressure, and dew point depression, with accompanying assumptions. All of these grids are available online at http://prism.oregonstate.edu, and include 800-m and 4-km resolution data, images, metadata, pedigree information, and station inventory files.     

Watershed‐scale climate influences productivity of Chinook salmon populations across southcentral Alaska

The ecosystems supporting Pacific salmon (Oncorhynchus spp.) are changing rapidly as a result of climate change and habitat alteration. Understanding how—and how consistently—salmon populations respond to changes at regional and watershed scales has major implications for fisheries management and habitat conservation. Chinook salmon (O. tshawytscha) populations across Alaska have declined over the past decade, resulting in fisheries closures and prolonged impacts to local communities. These declines are associated with large‐scale climate drivers, but uncertainty remains about the role of local conditions (e.g., precipitation, streamflow, and stream temperature) that vary among the watersheds where salmon spawn and rear. We estimated the effects of these and other environmental indicators on the productivity of 15 Chinook salmon populations in the Cook Inlet basin, southcentral Alaska, using a hierarchical Bayesian stock‐recruitment model. Salmon spawning during 2003–2007 produced 57% fewer recruits than the previous long‐term average, leading to declines in adult returns beginning in 2008. These declines were explained in part by density dependence, with reduced population productivity following years of high spawning abundance. Across all populations, productivity declined with increased precipitation during the fall spawning and early incubation period and increased with above‐average precipitation during juvenile rearing. Above‐average stream temperatures during spawning and rearing had variable effects, with negative relationships in many warmer streams and positive relationships in some colder streams. Productivity was also associated with regional indices of streamflow and ocean conditions, with high variability among populations. The cumulative effects of adverse conditions in freshwater, including high spawning abundance, heavy fall rains, and hot, dry summers may have contributed to the recent population declines across the region. Identifying both coherent and differential responses to environmental change underscores the importance of targeted, watershed‐specific monitoring and conservation efforts for maintaining resilient salmon runs in a warming world.     

Geographic range determinants of two commercially important marine molluscs

Aim We modelled the spatial abundance patterns of two abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814) inhabiting inshore rocky reefs to better understand the importance of current sea surface temperature (SST) (among other predictors) and, ultimately, the effect of future climate change, on marine molluscs. Location Southern Australia. Methods We used an ensemble species distribution modelling approach that combined likelihood‐based generalized linear models and boosted regression trees. For each modelling technique, a two‐step procedure was used to predict: (1) the current probability of presence, followed by (2) current abundance conditional on presence. The resulting models were validated using an independent, spatially explicit dataset of abalone abundance patterns in Victoria. Results For both species, the presence of reef was the main driver of abalone occurrence, while SST was the main driver of spatial abundance patterns. Predictive maps at c. 1‐km resolution showed maximal abundance on shallow coastal reefs characterized by mild winter SSTs for both species. Main conclusions Sea surface temperature was a major driver of abundance patterns for both abalone species, and the resulting ensemble models were used to build fine‐resolution predictive range maps (c. 1 km) that incorporate measures of habitat suitability and quality in support of resource management. By integrating this output with structured spatial population models, a more robust understanding of the potential impacts of threatening human processes such as climate change can be established.     

High community turnover and dispersal limitation relative to rapid climate change

Aim Many competing hypotheses seek to identify the mechanisms behind species richness gradients. Yet, the determinants of species turnover over broad scales are uncertain. We test whether environmental dissimilarity predicts biotic turnover spatially and temporally across an array of environmental variables and spatial scales using recently observed climate changes as a pseudo‐experimental opportunity. Location Canada. Methods We used an extensive database of observation records of 282 Canadian butterfly species collected between 1900 and 2010 to characterize spatial and temporal turnover based on Jaccard indices. We compare relationships between spatial turnover and differences in an array of relevant environmental conditions, including aspects of temperature, precipitation, elevation, primary productivity and land cover. Measurements were taken within 100‐, 200‐ and 400‐km grid cells, respectively. We tested the relative importance of each variable in predicting spatial turnover using bootstrap analysis. Finally, we tested for effects of temperature and precipitation change on temporal turnover, including distinctly accounting for turnover under individual species’ potential dispersal limitations. Results Temperature differences between areas correlate with spatial turnover in butterfly assemblages, independently of distance, sampling differences or the spatial resolution of the analysis. Increasing temperatures are positively related to biotic turnover within quadrats through time. Limitations on species dispersal may cause observed biotic turnover to be lower than expected given the magnitude of temperature changes through time. Main conclusions Temperature differences can account for spatial trends in biotic dissimilarity and turnover through time in areas where climate is changing. Butterfly communities are changing quickly in some areas, probably reflecting the dispersal capacities of individual species. However, turnover is lower through time than expected in many areas, suggesting that further work is needed to understand the factors that limit dispersal across broad regions. Our results illustrate the large‐scale effects of climate change on biodiversity in areas with strong environmental gradients.     

Spatial scale affects novel and disappeared climate change projections in Alaska

The formation of novel and disappeared climates between the last glacial maximum (LGM) and the present is important to consider to understand the expansion and contraction of species niches and distributions, as well as the formation and loss of communities and ecological interactions over time. Our choice in climate data resolution has the potential to complicate predictions of the ecological impacts of climate change, since climate varies from local to global scales and this spatial variation is reflected in climate data. To address this issue, we downscaled LGM and modern (1975–2005) 30‐year averaged climate data to 60‐m resolution for the entire state of Alaska for 10 different climate variables, and then upsampled each variable to coarser resolutions (60 m to 12 km). We modeled the distributions of novel and disappeared climates to evaluate the locations and fractional area of novel and disappeared climates for each of our climate variables and resolutions. Generally, novel and disappeared climates were located in southern Alaska, although there were cases where some disappeared climates existed within coastal and interior Alaska. Climate resolution affected the fractional area of novel and disappeared climates in three patterns: As the spatial resolution of climate became coarser, the fractional area of novel and disappeared climates (a) increased, (b) decreased, or (c) had no explainable relationship. Overall, we found the use of coarser climate data increased the fractional area of novel and disappeared climates due to decreased environmental variability and removal of climate extremes. Our results reinforce the importance of downscaling coarse climate data and suggest that studies analyzing the effects of climate change on ecosystems may overestimate or underestimate their conclusions when utilizing coarse climate data.     

Regional unified model‐based leaf unfolding prediction from 1960 to 2009 across northern China

Using first leaf unfolding data of Salix matsudana, Populus simonii, Ulmus pumila, and Prunus armeniaca, and daily mean temperature data during the 1981–2005 period at 136 stations in northern China, we fitted unified forcing and chilling phenology models and selected optimum models for each species at each station. Then, we examined performances of each optimum local species‐specific model in predicting leaf unfolding dates at all external stations within the corresponding climate region and selected 16 local species‐specific models with maximum effective predictions as the regional unified models in different climate regions. Furthermore, we validated the regional unified models using leaf unfolding and daily mean temperature data beyond the time period of model fitting. Finally, we substituted gridded daily mean temperature data into the regional unified models, and reconstructed spatial patterns of leaf unfolding dates of the four tree species across northern China during 1960–2009. At local scales, the unified forcing model shows higher simulation efficiency at 83% of data sets, whereas the unified chilling model indicates higher simulation efficiency at 17% of data sets. Thus, winter temperature increase so far has not yet significantly influenced dormancy and consequent leaf development of deciduous trees in most parts of northern China. Spatial and temporal validation confirmed capability and reliability of regional unified species‐specific models in predicting leaf unfolding dates in northern China. Reconstructed leaf unfolding dates of the four tree species show significant advancements by 1.4–1.6 days per decade during 1960–2009 across northern China, which are stronger for the earlier than the later leaf unfolding species. Our findings suggest that the principal characteristics of plant phenology and phenological responses to climate change at regional scales can be captured by phenological and climatic data sets at a few representative locations.     

Large‐scale spatial variation in feather corticosterone in invasive house sparrows (Passer domesticus) in Mexico is related to climate

Ecologists frequently use physiological tools to understand how organisms cope with their surroundings but rarely at macroecological scales. This study describes spatial variation in corticosterone (CORT) levels in feathers of invasive house sparrows (Passer domesticus) across their range in Mexico and evaluates CORT–climate relationships with a focus on temperature and precipitation. Samples were collected from 49 sites across Mexico. Feather CORT (CORT_f) was measured using methanol‐based extraction and radioimmunoassay. Relationships between CORT_f and spatial and climate variables were examined using simple linear regressions. Ordination was used on climate data, CORT_f was plotted against the resulting axes, and univariate regression trees were used to identify important predictors of CORT_f. Universal kriging interpolation was used to illustrate spatial variation in CORT_f across Mexico. Correlations with ordination axes showed that high CORT_f was associated with low precipitation during the rainy season and low dry season temperatures. Specifically, CORT_f was negatively related to May precipitation and January and July minimum temperatures, and positively related to April deuterium excess and June minimum temperatures. CORT_f was higher in second‐year birds compared to after‐hatch years and after‐second years. House sparrows had higher CORT_f levels in the hot, dry, north‐central region of Mexico, and CORT_f was negatively related to temperature and precipitation. House sparrows molt primarily from August–September but climate conditions throughout the year were important predictors of CORT_f, suggesting that conditions outside of molt can carry over to influence energetics during feather growth. These data suggest that dry conditions are challenging for house sparrows in Mexico, supporting previous work showing that precipitation is an important predictor of broad‐scale CORT variation. This work highlights the utility of CORT_f for evaluating the influence of physiology on current avian range limits; furthermore, these data may allow us to predict future changes in species distributions.