Least concern to endangered: Applying climate change projections profoundly influences the extinction risk assessment for wild Arabica coffee

Arabica coffee (Coffea arabica) is a key crop in many tropical countries and globally provides an export value of over US$13 billion per year. Wild Arabica coffee is of fundamental importance for the global coffee sector and of direct importance within Ethiopia, as a source of harvestable income and planting stock. Published studies show that climate change is projected to have a substantial negative influence on the current suitable growing areas for indigenous Arabica in Ethiopia and South Sudan. Here we use all available future projections for the species based on multiple general circulation models (GCMs), emission scenarios, and migration scenarios, to predict changes in Extent of Occurrence (EOO), Area of Occupancy (AOO), and population numbers for wild Arabica coffee. Under climate change our results show that population numbers could reduce by 50% or more (with a few models showing over 80%) by 2088. EOO and AOO are projected to decline by around 30% in many cases. Furthermore, present‐day models compared to the near future (2038), show a reduction for EOO of over 40% (with a few cases over 50%), although EOO should be treated with caution due to its sensitivity to outlying occurrences. When applying these metrics to extinction risk, we show that the determination of generation length is critical. When applying the International Union for Conservation of Nature's Red list of Threatened Species (IUCN Red List) criteria, even with a very conservative generation length of 21 years, wild Arabica coffee is assessed as Threatened with extinction (placed in the Endangered category) under a broad range of climate change projections, if no interventions are made. Importantly, if we do not include climate change in our assessment, Arabica coffee is assessed as Least Concern (not threatened) when applying the IUCN Red List criteria.     

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.     

Climate Change Impacts on the Future Distribution of Date Palms: A Modeling Exercise Using CLIMEX

Climate is changing and, as a consequence, some areas that are climatically suitable for date palm (Phoenix dactylifera L.) cultivation at the present time will become unsuitable in the future. In contrast, some areas that are unsuitable under the current climate will become suitable in the future. Consequently, countries that are dependent on date fruit export will experience economic decline, while other countries’ economies could improve. Knowledge of the likely potential distribution of this economically important crop under current and future climate scenarios will be useful in planning better strategies to manage such issues. This study used CLIMEX to estimate potential date palm distribution under current and future climate models by using one emission scenario (A2) with two different global climate models (GCMs), CSIRO-Mk3.0 (CS) and MIROC-H (MR). The results indicate that in North Africa, many areas with a suitable climate for this species are projected to become climatically unsuitable by 2100. In North and South America, locations such as south-eastern Bolivia and northern Venezuela will become climatically more suitable. By 2070, Saudi Arabia, Iraq and western Iran are projected to have a reduction in climate suitability. The results indicate that cold and dry stresses will play an important role in date palm distribution in the future. These results can inform strategic planning by government and agricultural organizations by identifying new areas in which to cultivate this economically important crop in the future and those areas that will need greater attention due to becoming marginal regions for continued date palm cultivation.     

气候变化对物种分布影响模拟中的不确定性组分分割与制图——以油松为例

物种分布模型是预测评估气候变化对物种分布影响的主要工具。为了降低物种分布模型在预测过程中的不确定性,近期有学者提出了采用组合预测的新方法,即采用多套建模数据、模型技术,模型参数,以及环境情景数据对物种分布进行预测,构成物种分布预测集合。但是,组合预测中各组分对变异的贡献还知之甚少,因此有必要把变异组分来源进行分割,以更有效地利用组合预测方法来降低模型预测中的不确定性。以油松为例,采用8个生态位模型,9套模型训练数据,3个GCM模型和一个SRES(A2)排放情景,模型分析了油松当前(1961-1990年)和未来气候条件下3个时间段(2010-2039年,2040-2069年,2070-2099年)的潜在分布。共计得到当前分布预测数据72套,未来每个时间段分布数据216套。采用开发的ClimateChina软件进行当前和未来气候数据的降尺度处理。采用Kappa、真实技巧统计方法(TSS)和接收机工作特征曲线下的面积(AUC)对模型预测能力进行评估。结果表明,随机森林(RF)、广义线性模型(GLM),广义加法模型(GAM)、多元自适应样条函数(MARS)以及助推法(GBM)预测效果较好,几乎不受建模数据之间差异的影响。混合判别分析模型(MDA)对建模数据之间的差异非常敏感,甚至出现建模失败现象。采用三因素方差分析方法对组合预测中的不确定性来源进行变异分割,结果表明,模型之间的差异对模拟预测结果不确定性的贡献最大且所占比例极高,而建模数据之间的差异贡献最小,GCM贡献居中。研究将有助于加深对物种分布模拟预测中不确定性的认识。     

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

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

Conservation management and sustainable harvest quotas are sensitive to choice of climate modelling approach for two marine gastropods

Aim

To establish the robustness of two alternative methods for predicting the future ranges and abundances for two wild‐harvested abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814): single atmosphere–ocean general circulation model (GCM) or ensemble‐averaged GCM forecasts.

Location

South Australia.

Methods

We assessed the ability of 20 GCMs to simulate observed seasonal sea surface temperature (SST) between 1980–1999, globally, and regionally for the Indian and Pacific Oceans south of the Equator. We used model rankings to characterize a set of representative climate futures, using three different‐sized GCM ensembles and two individual GCMs (the Parallel Climate Model and the Community Climate System Model, version 3.0). Ecological niche models were then coupled to physiological information to compare forecast changes in area of occupancy, population size and harvest area based on forecasts using the various GCM selection methods, as well as different greenhouse gas emission scenarios and climate sensitivities.

Results

We show that: (1) the skill with which climate models reproduce recent SST records varies considerably amongst GCMs, with multimodel ensemble averages showing closer agreement to observations than single models; (2) choice of GCM, and the decision on whether or not to use ensemble‐averaged climate forecasts, can strongly influence spatiotemporal predictions of range, abundance and fishing potential; and (3) comparable hindcasting skill does not necessarily guarantee that GCM forecasts and ecological and evolutionary responses to these forecast changes, will be similar amongst closely ranked models.

Conclusion

By averaging across an ensemble of seven highly ranked skilful GCMs, inherent uncertainties stemming from GCM differences are incorporated into forecasts of change in species range, abundance and sustainable fishing area. Our results highlight the need to make informed and explicit decisions on GCM choice, model sensitivity and emission scenarios when exploring conservation options for marine species and the sustainability of future harvests using ecological niche models.

     

东北地区5个物种潜在栖息地变化与优化保护规划

气候变化广泛影响着物种多样性及其分布变迁。优化模型模拟结果,获取气候变化影响下的优先保护区域将为制定应对气候变化的物种保护政策或行动提供理论依据,提升保护绩效。选取东北地区五种代表性动物,包括黑熊(Ursus thibetanus)、驼鹿(Alces alces)、水獭(Lutra lutra)、紫貂(Martes zibellina)及黑嘴松鸡(Tetrao parvirostris);结合最大熵模型(Maxent)模拟在不同RCP情景下未来3个年代(2030s,2050s,2070s)的物种潜在栖息地。根据九个常用气候模式的评价结果,获取东北地区合适的气候模式,了解气候变化对物种潜在栖息地的影响,同时开展物种保护规划,识别保护空缺,为应对气候变化、保持生物多样性提供支持。结果显示,在气候变化背景下物种潜在栖息地面积整体呈现下降趋势,但不同气候模式之间存在差异;评价结果推荐CCSM4、Nor ESM1-M、Had GEM2-AO及GFDL-CM3气候模式,推荐在东北地区使用以上气候模式进行物种未来潜在分布的研究。5个物种潜在栖息地平均面积变化率分别为-62.16%,-73.93%,-78.46%(2030s,2050s,2070s)。综合5个重点保护物种的保护优先区,大兴安岭的呼中、汗马与额尔古纳国家级自然保护区,延边地区的天佛指山、老爷岭东北虎、珲春东北虎与汪清原麝国家级自然保护区,长白山国家级自然保护区是气候变化下物种保护的热点区域。     

Patterns and Variability of Projected Bioclimatic Habitat for Pinus albicaulis in the Greater Yellowstone Area

Projected climate change at a regional level is expected to shift vegetation habitat distributions over the next century. For the sub-alpine species whitebark pine (Pinus albicaulis), warming temperatures may indirectly result in loss of suitable bioclimatic habitat, reducing its distribution within its historic range. This research focuses on understanding the patterns of spatiotemporal variability for future projected P.albicaulis suitable habitat in the Greater Yellowstone Area (GYA) through a bioclimatic envelope approach. Since intermodel variability from General Circulation Models (GCMs) lead to differing predictions regarding the magnitude and direction of modeled suitable habitat area, nine bias-corrected statistically down-scaled GCMs were utilized to understand the uncertainty associated with modeled projections. P.albicaulis was modeled using a Random Forests algorithm for the 1980–2010 climate period and showed strong presence/absence separations by summer maximum temperatures and springtime snowpack. Patterns of projected habitat change by the end of the century suggested a constant decrease in suitable climate area from the 2010 baseline for both Representative Concentration Pathways (RCPs) 8.5 and 4.5 climate forcing scenarios. Percent suitable climate area estimates ranged from 2–29% and 0.04–10% by 2099 for RCP 8.5 and 4.5 respectively. Habitat projections between GCMs displayed a decrease of variability over the 2010–2099 time period related to consistent warming above the 1910–2010 temperature normal after 2070 for all GCMs. A decreasing pattern of projected P.albicaulis suitable habitat area change was consistent across GCMs, despite strong differences in magnitude. Future ecological research in species distribution modeling should consider a full suite of GCM projections in the analysis to reduce extreme range contractions/expansions predictions. The results suggest that restoration strageties such as planting of seedlings and controlling competing vegetation may be necessary to maintain P.albicaulis in the GYA under the more extreme future climate scenarios.     

Mopane (Colophospermum mopane): A potential winner under climate change in southern Africa

Distribution and abundance under climate change of particularly non-timber forest product tree species is vital since they sustain many livelihoods, especially in rural sub-Saharan Africa. The aim of the study was to determine the current and future natural range of mopane (Colophospermum mopane (J. Kirk ex Benth.) J. Léonard, Fabaceae), a dominant tree species in mopane woodlands of southern Africa. An ensemble model was built in ‘biomod2’ from eight algorithms and used to estimate the current and future distribution. Seven bioclimatic variables and 269 occurrence records were used to calibrate individual models that were later combined into an ensemble model. The ensemble model was projected to two time periods, 2041–2060 and 2081–2100, under two shared socio-economic pathways (SSPs), SSP2-4.5 and SSP5-8.5, and three general circulation models (GCMs). The ensemble model showed high performance (KAPPA = 0.770, ROC = 0.961, TSS = 0.792, ACCURACY = 0.900). A map of the current distribution shows occurrence predominantly in low-lying areas, including the Zambezi, Save and Limpopo valleys, Okavango and Cuvelai basins, and in southern and central Mozambique. Projection maps show expansion under all SSPs, GCMs and time periods. Averaged across GCMs in 2041–2060, the range expanded by 22.37% under SSP2-4.5, and by 19.94% under SSP5-8.5. In 2081–2100, the range expanded by 20.43% under SSP2-4.5, and by 27.62% under SSP5-8.5. Notably, the range expansion was highest under SSP5-8.5, an SSP that envisages unmitigated greenhouse gas release and the largest mean global temperature increase. It is highly likely that mopane is not directly threatened by climate change. Indirect climate change threats, however, remain uncertain.     

Uncertainties in the projection of species distributions related to general circulation models

Ecological Niche Models (ENMs) are increasingly used by ecologists to project species potential future distribution. However, the application of such models may be challenging, and some caveats have already been identified. While studies have generally shown that projections may be sensitive to the ENM applied or the emission scenario, to name just a few, the sensitivity of ENM‐based scenarios to General Circulation Models (GCMs) has been often underappreciated. Here, using a multi‐GCM and multi‐emission scenario approach, we evaluated the variability in projected distributions under future climate conditions. We modeled the ecological realized niche (sensu Hutchinson) and predicted the baseline distribution of species with contrasting spatial patterns and representative of two major functional groups of European trees: the dwarf birch and the sweet chestnut. Their future distributions were then projected onto future climatic conditions derived from seven GCMs and four emissions scenarios using the new Representative Concentration Pathways (RCPs) developed for the Intergovernmental Panel on Climate Change (IPCC) AR5 report. Uncertainties arising from GCMs and those resulting from emissions scenarios were quantified and compared. Our study reveals that scenarios of future species distribution exhibit broad differences, depending not only on emissions scenarios but also on GCMs. We found that the between‐GCM variability was greater than the between‐RCP variability for the next decades and both types of variability reached a similar level at the end of this century. Our result highlights that a combined multi‐GCM and multi‐RCP approach is needed to better consider potential trajectories and uncertainties in future species distributions. In all cases, between‐GCM variability increases with the level of warming, and if nothing is done to alleviate global warming, future species spatial distribution may become more and more difficult to anticipate. When future species spatial distributions are examined, we propose to use a large number of GCMs and RCPs to better anticipate potential trajectories and quantify uncertainties.     

Implications of future climate and atmospheric CO2 content for regional biogeochemistry,biogeography and ecosystem services across East Africa

We model future changes in land biogeochemistry and biogeography across East Africa. East Africa is one of few tropical regions where general circulation model (GCM) future climate projections exhibit a robust response of strong future warming and general annual‐mean rainfall increases. Eighteen future climate projections from nine GCMs participating in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment were used as input to the LPJ dynamic global vegetation model (DGVM), which predicted vegetation patterns and carbon storage in agreement with satellite observations and forest inventory data under the present‐day climate. All simulations showed future increases in tropical woody vegetation over the region at the expense of grasslands. Regional increases in net primary productivity (NPP) (18–36%) and total carbon storage (3–13%) by 2080–2099 compared with the present‐day were common to all simulations. Despite decreases in soil carbon after 2050, seven out of nine simulations continued to show an annual net land carbon sink in the final decades of the 21st century because vegetation biomass continued to increase. The seasonal cycles of rainfall and soil moisture show future increases in wet season rainfall across the GCMs with generally little change in dry season rainfall. Based on the simulated present‐day climate and its future trends, the GCMs can be grouped into four broad categories. Overall, our model results suggest that East Africa, a populous and economically poor region, is likely to experience some ecosystem service benefits through increased precipitation, river runoff and fresh water availability. Resulting enhancements in NPP may lead to improved crop yields in some areas. Our results stand in partial contradiction to other studies that suggest possible negative consequences for agriculture, biodiversity and other ecosystem services caused by temperature increases.     

Are Plant Species Able to Keep Pace with the Rapidly Changing Climate?

Future climate change is predicted to advance faster than the postglacial warming. Migration may therefore become a key driver for future development of biodiversity and ecosystem functioning. For 140 European plant species we computed past range shifts since the last glacial maximum and future range shifts for a variety of Intergovernmental Panel on Climate Change (IPCC) scenarios and global circulation models (GCMs). Range shift rates were estimated by means of species distribution modelling (SDM). With process-based seed dispersal models we estimated species-specific migration rates for 27 dispersal modes addressing dispersal by wind (anemochory) for different wind conditions, as well as dispersal by mammals (dispersal on animal''s coat – epizoochory and dispersal by animals after feeding and digestion – endozoochory) considering different animal species. Our process-based modelled migration rates generally exceeded the postglacial range shift rates indicating that the process-based models we used are capable of predicting migration rates that are in accordance with realized past migration. For most of the considered species, the modelled migration rates were considerably lower than the expected future climate change induced range shift rates. This implies that most plant species will not entirely be able to follow future climate-change-induced range shifts due to dispersal limitation. Animals with large day- and home-ranges are highly important for achieving high migration rates for many plant species, whereas anemochory is relevant for only few species.     

The impact of climate change on the distribution of two threatened Dipterocarp trees

Two ecologically and economically important, and threatened Dipterocarp trees Sal (Shorea robusta) and Garjan (Dipterocarpus turbinatus) form mono‐specific canopies in dry deciduous, moist deciduous, evergreen, and semievergreen forests across South Asia and continental parts of Southeast Asia. They provide valuable timber and play an important role in the economy of many Asian countries. However, both Dipterocarp trees are threatened by continuing forest clearing, habitat alteration, and global climate change. While climatic regimes in the Asian tropics are changing, research on climate change‐driven shifts in the distribution of tropical Asian trees is limited. We applied a bioclimatic modeling approach to these two Dipterocarp trees Sal and Garjan. We used presence‐only records for the tree species, five bioclimatic variables, and selected two climatic scenarios (RCP4.5: an optimistic scenario and RCP8.5: a pessimistic scenario) and three global climate models (GCMs) to encompass the full range of variation in the models. We modeled climate space suitability for both species, projected to 2070, using a climate envelope modeling tool “MaxEnt” (the maximum entropy algorithm). Annual precipitation was the key bioclimatic variable in all GCMs for explaining the current and future distributions of Sal and Garjan (Sal: 49.97 ± 1.33; Garjan: 37.63 ± 1.19). Our models predict that suitable climate space for Sal will decline by 24% and 34% (the mean of the three GCMs) by 2070 under RCP4.5 and RCP8.5, respectively. In contrast, the consequences of imminent climate change appear less severe for Garjan, with a decline of 17% and 27% under RCP4.5 and RCP8.5, respectively. The findings of this study can be used to set conservation guidelines for Sal and Garjan by identifying vulnerable habitats in the region. In addition, the natural habitats of Sal and Garjan can be categorized as low to high risk under changing climates where artificial regeneration should be undertaken for forest restoration.     

Projected Changes in Terrestrial Carbon Storage in Europe under Climate and Land-use Change, 1990–2100

Changes in climate and land use, caused by socio-economic changes, greenhouse gas emissions, agricultural policies and other factors, are known to affect both natural and managed ecosystems, and will likely impact on the European terrestrial carbon balance during the coming decades. This study presents a comprehensive European Union wide (EU15 plus Norway and Switzerland, EU*) assessment of potential future changes in terrestrial carbon storage considering these effects based on four illustrative IPCC-SRES storylines (A1FI, A2, B1, B2). A process-based land vegetation model (LPJ-DGVM), adapted to include a generic representation of managed ecosystems, is forced with changing fields of land-use patterns from 1901 to 2100 to assess the effect of land-use and cover changes on the terrestrial carbon balance of Europe. The uncertainty in the future carbon balance associated with the choice of a climate change scenario is assessed by forcing LPJ-DGVM with output from four different climate models (GCMs: CGCM2, CSIRO2, HadCM3, PCM2) for the same SRES storyline. Decrease in agricultural areas and afforestation leads to simulated carbon sequestration for all land-use change scenarios with an average net uptake of 17–38 Tg C/year between 1990 and 2100, corresponding to 1.9–2.9% of the EU*s CO₂ emissions over the same period. Soil carbon losses resulting from climate warming reduce or even offset carbon sequestration resulting from growth enhancement induced by climate change and increasing atmospheric CO₂ concentrations in the second half of the twenty-first century. Differences in future climate change projections among GCMs are the main cause for uncertainty in the cumulative European terrestrial carbon uptake of 4.4–10.1 Pg C between 1990 and 2100.     

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.     

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。     

Downy mildew (Plasmopara viticola) epidemics on grapevine under climate change

As climate is a key agro‐ecosystem driving force, climate change could have a severe impact on agriculture. Many assessments have been carried out to date on the possible effects of climate change (temperature, precipitation and carbon dioxide concentration changes) on plant physiology. At present however, likely effects on plant pathogens have not been investigated deeply. The aim of this work was to simulate future scenarios of downy mildew (Plasmopara viticola) epidemics on grape under climate change, by combining a disease model to output from two general circulation models (GCMs). Model runs corresponding to the SRES‐A2 emissions scenario, characterized by high projections of both population and greenhouse gas emissions from present to 2100, were chosen in order to investigate impacts of worst‐case scenarios, among those currently available from IPCC. Three future decades were simulated (2030, 2050, 2080), using as baseline historical series of meteorological data collected from 1955 to 2001 in Acqui Terme, an important grape‐growing area in the north‐west of Italy. Both GCMs predicted increase of temperature and decrease of precipitation in this region. The simulations obtained by combining the disease model to the two GCM outputs predicted an increase of the disease pressure in each decade: more severe epidemics were a direct consequence of more favourable temperature conditions during the months of May and June. These negative effects of increasing temperatures more than counterbalanced the effects of precipitation reductions, which alone would have diminished disease pressure. Results suggested that, as adaptation response to future climate change, more attention would have to be paid in the management of early downy mildew infections; two more fungicide sprays were necessary under the most negative climate scenario, compared with present management regimes. At the same time, increased knowledge on the effects of climate change on host–pathogen interactions will be necessary to improve current predictions.     

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

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

Climate change may have limited effect on global risk of potato late blight

Weather affects the severity of many plant diseases, and climate change is likely to alter the patterns of crop disease severity. Evaluating possible future patterns can help focus crop breeding and disease management research. We examined the global effect of climate change on potato late blight, the disease that caused the Irish potato famine and still is a common potato disease around the world. We used a metamodel and considered three global climate models for the A2 greenhouse gas emission scenario for three 20‐year time‐slices: 2000–2019, 2040–2059 and 2080–2099. In addition to global analyses, five regions were evaluated where potato is an important crop: the Andean Highlands, Indo‐Gangetic Plain and Himalayan Highlands, Southeast Asian Highlands, Ethiopian Highlands, and Lake Kivu Highlands in Sub‐Saharan Africa. We found that the average global risk of potato late blight increases initially, when compared with historic climate data, and then declines as planting dates shift to cooler seasons. Risk in the agro‐ecosystems analyzed, varied from a large increase in risk in the Lake Kivu Highlands in Rwanda to decreases in the Southeast Asian Highlands of Indonesia.     

Climate change and voltinism in Californian insect pest species: sensitivity to location,scenario and climate model choice

Experimental studies of the impact of climatic change are hampered by their inability to consider multiple climate change scenarios and indeed often consider no more than simple climate sensitivity such as a uniform increase in temperature. Modelling efforts offer the ability to consider a much wider range of realistic climate projections and are therefore useful, in particular, for estimating the sensitivity of impact predictions to differences in geographical location, and choice of climate change scenario and climate model projections. In this study, we used well‐established degree‐day models to predict the voltinism of 13 agronomically important pests in California, USA. We ran these models using the projections from three Atmosphere–Ocean Coupled Global Circulation Models (AOCGCMs or GCMs), in conjunction with the SRES scenarios. We ran these for two locations representing northern and southern California. We did this for both the 2050s and 2090s. We used anova to partition the variation in the resulting voltinism among time period, climate change scenario, GCM and geographical location. For these 13 pest species, the choice of climate model explained an average of 42% of the total variation in voltinism, far more than did geographical location (33%), time period (17%) or scenario (1%). The remaining 7% of the variation was explained by various interactions, of which the location by GCM interaction was the strongest (5%). Regardless of these sources of uncertainty, a robust conclusion from our work is that all 13 pest species are likely to experience increases in the number of generations that they complete each year. Such increased voltinism is likely to have significant consequences for crop protection and production.