Where will species go? Incorporating new advances in climate modelling into projections of species distributions

Bioclimatic models are the primary tools for simulating the impact of climate change on species distributions. Part of the uncertainty in the output of these models results from uncertainty in projections of future climates. To account for this, studies often simulate species responses to climates predicted by more than one climate model and/or emission scenario. One area of uncertainty, however, has remained unexplored: internal climate model variability. By running a single climate model multiple times, but each time perturbing the initial state of the model slightly, different but equally valid realizations of climate will be produced. In this paper, we identify how ongoing improvements in climate models can be used to provide guidance for impacts studies. In doing so we provide the first assessment of the extent to which this internal climate model variability generates uncertainty in projections of future species distributions, compared with variability between climate models. We obtained data on 13 realizations from three climate models (three from CSIRO Mark2 v3.0, four from GISS AOM, and six from MIROC v3.2) for two time periods: current (1985–1995) and future (2025–2035). Initially, we compared the simulated values for each climate variable (P, T_max, T_min, and T_mean) for the current period to observed climate data. This showed that climates simulated by realizations from the same climate model were more similar to each other than to realizations from other models. However, when projected into the future, these realizations followed different trajectories and the values of climate variables differed considerably within and among climate models. These had pronounced effects on the projected distributions of nine Australian butterfly species when modelled using the BIOCLIM component of DIVA-GIS. Our results show that internal climate model variability can lead to substantial differences in the extent to which the future distributions of species are projected to change. These can be greater than differences resulting from between-climate model variability. Further, different conclusions regarding the vulnerability of species to climate change can be reached due to internal model variability. Clearly, several climate models, each represented by multiple realizations, are required if we are to adequately capture the range of uncertainty associated with projecting species distributions in the future.     

Seasonal and synoptic climatic drivers of tree growth in the Bighorn Mountains,WY, USA (1654–1983 CE)

In the United States’ (US) Northern Rockies, synoptic pressure systems and atmospheric circulation drive interannual variation in seasonal temperature and precipitation. The radial growth of high-elevation trees in this semi-arid region captures this temperature and precipitation variability and provides long time series to contextualize instrumental-era variability in synoptic-scale climate patterns. Such variability in climate patterns can trigger extreme climate events, such as droughts, floods, and forest fires, which have a damaging impact on human and natural systems. We developed 11 tree-ring width (TRW) chronologies from multiple species and sites to investigate the seasonal climatic drivers of tree growth in the Bighorn Mountains, WY. A principal component analysis of the chronologies identified 54% of shared common variance (1894–2014). Tree growth (expressed by PC1) was driven by multiple seasonal climate variables: previous October and current July temperatures, as well as previous December and current April precipitation, had a positive influence on growth, whereas growth was limited by July precipitation. These seasonal growth-climate relationships corresponded to circulation patterns at higher atmospheric levels over the Bighorn Mountains. Tree growth was enhanced when the winter jet stream was in a northward position, which led to warmer winters, and when the spring jet stream was further south, which led to wetter springs. The second principal component, explaining 19% of the variance, clustered sites by elevation and was strongly related to summer temperature. We leverage this summer temperature signal in our TRW chronologies by combining it with an existing maximum latewood density (MXD) chronology in a nested approach. This allowed us to reconstruct Bighorn Mountains summer (June, July, and August) temperature (BMST) back to 1654, thus extending the instrumental temperature record by 250 years. Our BMST reconstruction explains 39–53% of the variance in regional summer temperature variability. The 1830s were the relatively coolest decade and the 1930s were the warmest decade over the reconstructed period (1654–1983 CE) – which excludes the most recent 3 decades. Our results contextualize recent drivers and trends of climate variability in the US Northern Rockies, which contributes to the information that managers of human and natural systems need in order to prepare for potential future variability.     

Regional analysis of the impacts of climate change on cheatgrass invasion shows potential risk and opportunity

Interactions between climate change and non-native invasive species may combine to increase invasion risk to native ecosystems. Changing climate creates risk as new terrain becomes climatically suitable for invasion. However, climate change may also create opportunities for ecosystem restoration on invaded lands that become climatically unsuitable for invasive species. Here, I develop a bioclimatic envelope model for cheatgrass ( Bromus tectorum ), a non-native invasive grass in the western US, based on its invaded distribution. The bioclimatic envelope model is based on the Mahalanobis distance using the climate variables that best constrain the species' distribution. Of the precipitation and temperature variables measured, the best predictors of cheatgrass are summer, annual, and spring precipitation, followed by winter temperature. I perform a sensitivity analysis on potential cheatgrass distributions using the projections of 10 commonly used atmosphere–ocean general circulation models (AOGCMs) for 2100. The AOGCM projections for precipitation vary considerably, increasing uncertainty in the assessment of invasion risk. Decreased precipitation, particularly in the summer, causes an expansion of suitable land area by up to 45%, elevating invasion risk in parts of Montana, Wyoming, Utah, and Colorado. Conversely, increased precipitation reduces habitat by as much as 70%, decreasing invasion risk. The strong influence of precipitation conditions on this species' distribution suggests that relying on temperature change alone to project future change in plant distributions may be inadequate. A sensitivity analysis provides a framework for identifying key climate variables that may limit invasion, and for assessing invasion risk and restoration opportunities with climate change.     

Modeled response of the West Nile virus vector Culex quinquefasciatus to changing climate using the dynamic mosquito simulation model

Climate can strongly influence the population dynamics of disease vectors and is consequently a key component of disease ecology. Future climate change and variability may alter the location and seasonality of many disease vectors, possibly increasing the risk of disease transmission to humans. The mosquito species Culex quinquefasciatus is a concern across the southern United States because of its role as a West Nile virus vector and its affinity for urban environments. Using established relationships between atmospheric variables (temperature and precipitation) and mosquito development, we have created the Dynamic Mosquito Simulation Model (DyMSiM) to simulate Cx. quinquefasciatus population dynamics. The model is driven with climate data and validated against mosquito count data from Pasco County, Florida and Coachella Valley, California. Using 1-week and 2-week filters, mosquito trap data are reproduced well by the model (P < 0.0001). Dry environments in southern California produce different mosquito population trends than moist locations in Florida. Florida and California mosquito populations are generally temperature-limited in winter. In California, locations are water-limited through much of the year. Using future climate projection data generated by the National Center for Atmospheric Research CCSM3 general circulation model, we applied temperature and precipitation offsets to the climate data at each location to evaluate mosquito population sensitivity to possible future climate conditions. We found that temperature and precipitation shifts act interdependently to cause remarkable changes in modeled mosquito population dynamics. Impacts include a summer population decline from drying in California due to loss of immature mosquito habitats, and in Florida a decrease in late-season mosquito populations due to drier late summer conditions.     

An objective tropical Atlantic sea surface temperature gradient index for studies of south Amazon dry-season climate variability and change

Future changes in meridional sea surface temperature (SST) gradients in the tropical Atlantic could influence Amazon dry-season precipitation by shifting the patterns of moisture convergence and vertical motion. Unlike for the El Niño-Southern Oscillation, there are no standard indices for quantifying this gradient. Here we describe a method for identifying the SST gradient that is most closely associated with June–August precipitation over the south Amazon. We use an ensemble of atmospheric general circulation model (AGCM) integrations forced by observed SST from 1949 to 2005. A large number of tropical Atlantic SST gradient indices are generated randomly and temporal correlations are examined between these indices and June–August precipitation averaged over the Amazon Basin south of the equator. The indices correlating most strongly with June–August southern Amazon precipitation form a cluster of near-meridional orientation centred near the equator. The location of the southern component of the gradient is particularly well defined in a region off the Brazilian tropical coast, consistent with known physical mechanisms. The chosen index appears to capture much of the Atlantic SST influence on simulated southern Amazon dry-season precipitation, and is significantly correlated with observed southern Amazon precipitation.We examine the index in 36 different coupled atmosphere–ocean model projections of climate change under a simple compound 1% increase in CO₂. Within the large spread of responses, we find a relationship between the projected trend in the index and the Amazon dry-season precipitation trends. Furthermore, the magnitude of the trend relationship is consistent with the inter-annual variability relationship found in the AGCM simulations. This suggests that the index would be of use in quantifying uncertainties in climate change in the region.     

Recent climate hiatus revealed dual control by temperature and drought on the stem growth of Mediterranean Quercus ilex

A better understanding of stem growth phenology and its climate drivers would improve projections of the impact of climate change on forest productivity. Under a Mediterranean climate, tree growth is primarily limited by soil water availability during summer, but cold temperatures in winter also prevent tree growth in evergreen forests. In the widespread Mediterranean evergreen tree species Quercus ilex, the duration of stem growth has been shown to predict annual stem increment, and to be limited by winter temperatures on the one hand, and by the summer drought onset on the other hand. We tested how these climatic controls of Q. ilex growth varied with recent climate change by correlating a 40‐year tree ring record and a 30‐year annual diameter inventory against winter temperature, spring precipitation, and simulated growth duration. Our results showed that growth duration was the best predictor of annual tree growth. We predicted that recent climate changes have resulted in earlier growth onset (?10 days) due to winter warming and earlier growth cessation (?26 days) due to earlier drought onset. These climatic trends partly offset one another, as we observed no significant trend of change in tree growth between 1968 and 2008. A moving‐window correlation analysis revealed that in the past, Q. ilex growth was only correlated with water availability, but that since the 2000s, growth suddenly became correlated with winter temperature in addition to spring drought. This change in the climate–growth correlations matches the start of the recent atmospheric warming pause also known as the ‘climate hiatus’. The duration of growth of Q. ilex is thus shortened because winter warming has stopped compensating for increasing drought in the last decade. Decoupled trends in precipitation and temperature, a neglected aspect of climate change, might reduce forest productivity through phenological constraints and have more consequences than climate warming alone.     

Projecting species’ vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?

Species distribution models (SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross‐validation skill indicates extrapolative skill. We compared projected vulnerability for 29 wetland‐dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross‐validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross‐validation results were correlated with extrapolation results, the use of cross‐validation performance metrics to guide modeling choices where knowledge is limited was supported.     

Modelling biome shifts and tree cover change for 2050 in West Africa

Aim Africa is expected to face severe changes in climatic conditions. Our objectives are: (1) to model trends and the extent of future biome shifts that may occur by 2050, (2) to model a trend in tree cover change, while accounting for human impact, and (3) to evaluate uncertainty in future climate projections. Location West Africa. Methods We modelled the potential future spatial distribution of desert, grassland, savanna, deciduous and evergreen forest in West Africa using six bioclimatic models. Future tree cover change was analysed with generalized additive models (GAMs). We used climate data from 17 general circulation models (GCMs) and included human population density and fire intensity to model tree cover. Consensus projections were derived via weighted averages to: (1) reduce inter‐model variability, and (2) describe trends extracted from different GCM projections. Results The strongest predicted effect of climate change was on desert and grasslands, where the bioclimatic envelope of grassland is projected to expand into the desert by an area of 2 million km². While savannas are predicted to contract in the south (by 54 ± 22 × 10⁴ km²), deciduous and evergreen forest biomes are expected to expand (64 ± 13 × 10⁴ km² and 77 ± 26 × 10⁴ km²). However, uncertainty due to different GCMs was particularly high for the grassland and the evergreen biome shift. Increasing tree cover (1–10%) was projected for large parts of Benin, Burkina Faso, Côte d’Ivoire, Ghana and Togo, but a decrease was projected for coastal areas (1–20%). Furthermore, human impact negatively affected tree cover and partly changed the direction of the projected change from increase to decrease. Main conclusions Considering climate change alone, the model results of potential vegetation (biomes) show a ‘greening’ trend by 2050. However, the modelled effects of human impact suggest future forest degradation. Thus, it is essential to consider both climate change and human impact in order to generate realistic future tree cover projections.     

Climate and climatic variability at Lake Myvatn

The climate of the Lake Myvatn region is examined through the use of weather station data, using the years from 1971 to 2000 as a reference period. Variations in mean monthly temperature and precipitation at Reykjahlid (Myvatn) are compared with variations at seven other weather stations in north east Iceland. The area is drier and colder than coastal stations and exhibits a seasonal cycle in temperature that is larger than found at the coast. The temperature is significantly influenced by the number of sunlight hours only during the summer months. During summer, the influence of a sea breeze circulation can be clearly identified.The variability of climate since 1936 is also examined in comparison with the seven other weather stations. It is found that temperature variations at the different stations are highly correlated, but for precipitation the correlation is significant but much weaker.The influence of the large scale atmospheric circulation on temperature and precipitation at Lake Myvatn is also examined. It is found that the air temperature at Lake Myvatn is most sensitive to an east-west dipole in the large scale sea surface pressure field, i.e. to a pattern that is very different from the spatial pattern associated with the North Atlantic Oscillation (NAO). Precipitation at Lake Myvatn is to some degree influenced by the NAO, but generally precipitation is associated with northerly winds and cold temperatures whereas southerly winds at Lake Myvatn are likely to be drier.     

Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates

Africa is predicted to be highly vulnerable to 21st century climatic changes. Assessing the impacts of these changes on Africa's biodiversity is, however, plagued by uncertainties, and markedly different results can be obtained from alternative bioclimatic envelope models or future climate projections. Using an ensemble forecasting framework, we examine projections of future shifts in climatic suitability, and their methodological uncertainties, for over 2500 species of mammals, birds, amphibians and snakes in sub‐Saharan Africa. To summarize a priori the variability in the ensemble of 17 general circulation models, we introduce a consensus methodology that combines co‐varying models. Thus, we quantify and map the relative contribution to uncertainty of seven bioclimatic envelope models, three multi‐model climate projections and three emissions scenarios, and explore the resulting variability in species turnover estimates. We show that bioclimatic envelope models contribute most to variability, particularly in projected novel climatic conditions over Sahelian and southern Saharan Africa. To summarize agreements among projections from the bioclimatic envelope models we compare five consensus methodologies, which generally increase or retain projection accuracy and provide consistent estimates of species turnover. Variability from emissions scenarios increases towards late‐century and affects southern regions of high species turnover centred in arid Namibia. Twofold differences in median species turnover across the study area emerge among alternative climate projections and emissions scenarios. Our ensemble of projections underscores the potential bias when using a single algorithm or climate projection for Africa, and provides a cautious first approximation of the potential exposure of sub‐Saharan African vertebrates to climatic changes. The future use and further development of bioclimatic envelope modelling will hinge on the interpretation of results in the light of methodological as well as biological uncertainties. Here, we provide a framework to address methodological uncertainties and contextualize results.     

The impact of climate change on the physical characteristics of the larger lakes in the English Lake District

1. The larger lakes of the English Lake District have been the subject of intensive scientific study for more than 60 years. Year‐to‐year variations in the weather have recently been shown to have a major effect on their physical characteristics. The area is mild but very wet and the dynamics of the lakes are strongly influenced by the movement of weather systems across the Atlantic. 2. Here, we combine the results of long‐term measurements and the projections from a Regional Climate Model (RCM) to assess the potential impact of climate change on the surface temperature and residence times of the lakes. 3. The RCM outputs used were produced by the U.K. Hadley Centre and are based on the IPCC ‘A2’ scenario for the emission of greenhouse gases. These suggest that winters in the area will be very much milder and wetter by the 2050s and that there will be a pronounced reduction in the summer rainfall. 4. An analysis of the meteorological data acquired between 1940 and 2000 shows that there have been progressive increases in the winter air temperature and in the rainfall which are correlated with the long‐term change in the North Atlantic Oscillation. The trends reported during the summer were less pronounced and were correlated with the increased frequency of anticyclonic days and a decrease in the frequency of westerly days in the British Isles. 5. A simple model of the year‐to‐year variations in surface temperatures showed that the highest winter temperatures were recorded in the deeper lakes and the highest summer temperatures in the lakes with the shallowest thermoclines. When this model was used to predict the surface temperatures of the lakes in the 2050s, the greatest winter increase (+1.08 °C) was observed in the shallowest lake and the greatest summer increase (+2.18 °C) in the lake with the shallowest thermocline. 6. The model used to estimate the seasonal variation in the residence time of the lakes showed that the most pronounced variations were recorded in lakes with a short residence time. Average winter residence times ranged from a minimum of 10 days to a maximum of 436 days and average summer values from a minimum of 23 days to a maximum of 215 days. When this model was used to predict the residence time of the lakes in the 2050s, the greatest winter decrease (−20%) was observed in the smallest lake and the greatest summer increase (+92%) in the lake with the shortest residence time. 7. The results are discussed in relation to trends reported elsewhere in Europe and the impact of changes in the atmospheric circulation on the dynamics of the lakes. The most serious limnological effects were those projected for the summer and included a general increase in the stability of the lakes and a decrease in the flushing rate of the lakes with short residence times.     

Climate change scenarios and models yield conflicting predictions about the future risk of an invasive species in North America

• 1 The pea leafminer Liriomyza huidobrensis (Blanchard) (Diptera: Agromyzidae) is an invasive species in North America and a serious economic pest on a wide variety of crops. We developed a bioclimatic envelope model (BEM) for this species and examined the envelope's potential location in North America under various future climates.
• 2 We compared the future bioclimatic envelopes for L. huidobrensis using either simple scenarios comprising uniform changes in temperature/precipitation or climate projections from general circulation models (GCMs). Our simple scenarios were: (i) an increase of 0.1°C per degree in latitude with a 20% increase in summer precipitation and a 20% decrease in winter precipitation and (ii) an overall increase of 3°C everywhere, also with the same changes in precipitation. For GCM‐modelled climate change, we used the Canadian Centre for Climate Modelling and Analysis GCM (CGCM2) and the Hadley Centre climate model (HadCM3), each in combination with two scenarios from the Special Report on Emissions Scenarios (A2 and B2).
• 3 The BEM results using the simple scenarios were more similar to each other than to the results obtained using GCM projections. The results were also qualitatively different (i.e. spatially different and divergent) depending on which GCM‐scenario combination was used.
• 4 This modelling exercise illustrates that: (i) results using first approximation simple climate change scenarios can give predictions very different from those that use GCM‐modelled climate projections (comprising a result that has worrying implications for empirical impact research) and that (ii) different GCM‐models using the same scenario can give very different results (implying strong model dependency in projected biological impacts).

     

Sensitivity of salmonid freshwater life history in western US streams to future climate conditions

We projected effects of mid‐21st century climate on the early life growth of Chinook salmon (Oncorhynchus tshawytscha) and steelhead (O. mykiss) in western United States streams. Air temperature and snowpack trends projected from observed 20th century trends were used to predict future seasonal stream temperatures. Fish growth from winter to summer was projected with temperature‐dependent models of egg development and juvenile growth. Based on temperature data from 115 sites, by mid‐21st century, the effects of climate change are projected to be mixed. Fish in warm‐region streams that are currently cooled by snow melt will grow less, and fish in suboptimally cool streams will grow more. Relative to 20th century conditions, by mid‐21st century juvenile salmonids' weights are expected to be lower in the Columbia Basin and California Central Valley, but unchanged or greater in coastal and mountain streams. Because fish weight affects fish survival, the predicted changes in weight could impact population fitness depending on other factors such as density effects, food quality and quantity changes, habitat alterations, etc. The level of year‐to‐year variability in stream temperatures is high and our analysis suggests that identifying effects of climate change over the natural variability will be difficult except in a few streams.     

Potential impact of climate change on selected agricultural crops in north-eastern Austria

The vulnerability and adaptation of major agricultural crops to various soils in north‐eastern Austria under a changing climate were investigated. The CERES crop model for winter wheat and the CROPGRO model for soybean were validated for the agrometeorological conditions in the selected region. The simulated winter wheat and soybean yields in most cases agreed with the measured data. Several incremental and transient global circulation model (GCM) climate change scenarios were created and used in the study. In these scenarios, annual temperatures in the selected region are expected to rise between 0.9 and 4.8 °C from the 2020s to the 2080s. The results show that warming will decrease the crop‐growing duration of the selected crops. For winter wheat, a gradual increase in air temperature resulted in a yield decrease. Incremental warming, especially in combination with an increase in precipitation, leads to higher soybean yield. A drier climate will reduce soybean yield, especially on soils with low water storage capacity. All transient GCM climate change scenarios for the 21st century, including the adjustment for only air temperature, precipitation and solar radiation, projected reductions of winter wheat yield. However, when the direct effect of increased levels of CO₂ concentration was assumed, all GCM climate change scenarios projected an increase in winter wheat yield in the region. The increase in simulated soybean yield for the 21st century was primarily because of the positive impact of warming and especially of the beneficial influence of the direct CO₂ effect. Changes in climate variability were found to affect winter wheat and soybean yield in various ways. Results from the adaptation assessments suggest that changes in sowing date, winter wheat and soybean cultivar selection could significantly affect crop production in the 21st century.     

云南鹤庆盆地末次盛冰期的孢粉记录与古季风

通过研究相当于末次盛冰期鹤庆古湖泊沉积物4．6－9．0m段的孢粉记录,对该区末次盛冰期阶段的植被与古季风变迁模式进行了恢复。该区末次盛冰期冷湿的气候特点与同时东部干旱的草原植被、青藏、高原的荒漠植被和黄土高原区风尘堆积存在明显差异,而与滇池的气候记录有较好的一致性。冰期内部的气候波动与深海氧同位素记录有较好的可比性。冰期冷锋强度的增加,与北方冬季风的经常入侵和冰期青藏高原的冷源效应有关。     

Uncertainty in predicting range dynamics of endemic alpine plants under climate warming

Correlative species distribution models have long been the predominant approach to predict species’ range responses to climate change. Recently, the use of dynamic models is increasingly advocated for because these models better represent the main processes involved in range shifts and also simulate transient dynamics. A well‐known problem with the application of these models is the lack of data for estimating necessary parameters of demographic and dispersal processes. However, what has been hardly considered so far is the fact that simulating transient dynamics potentially implies additional uncertainty arising from our ignorance of short‐term climate variability in future climatic trends. Here, we use endemic mountain plants of Austria as a case study to assess how the integration of decadal variability in future climate affects outcomes of dynamic range models as compared to projected long‐term trends and uncertainty in demographic and dispersal parameters. We do so by contrasting simulations of a so‐called hybrid model run under fluctuating climatic conditions with those based on a linear interpolation of climatic conditions between current values and those predicted for the end of the 21st century. We find that accounting for short‐term climate variability modifies model results nearly as differences in projected long‐term trends and much more than uncertainty in demographic/dispersal parameters. In particular, range loss and extinction rates are much higher when simulations are run under fluctuating conditions. These results highlight the importance of considering the appropriate temporal resolution when parameterizing and applying range‐dynamic models, and hybrid models in particular. In case of our endemic mountain plants, we hypothesize that smoothed linear time series deliver more reliable results because these long‐lived species are primarily responsive to long‐term climate averages.     

The subtle role of climate change on population genetic structure in Canada lynx

Anthropogenically driven climatic change is expected to reshape global patterns of species distribution and abundance. Given recent links between genetic variation and environmental patterns, climate change may similarly impact genetic population structure, but we lack information on the spatial and mechanistic underpinnings of genetic–climate associations. Here, we show that current genetic variability of Canada lynx (Lynx canadensis) is strongly correlated with a winter climate gradient (i.e. increasing snow depth and winter precipitation from west‐to‐east) across the Pacific‐North American (PNO) to North Atlantic Oscillation (NAO) climatic systems. This relationship was stronger than isolation by distance and not explained by landscape variables or changes in abundance. Thus, these patterns suggest that individuals restricted dispersal across the climate boundary, likely in the absence of changes in habitat quality. We propose habitat imprinting on snow conditions as one possible explanation for this unusual phenomenon. Coupling historical climate data with future projections, we also found increasingly diverging snow conditions between the two climate systems. Based on genetic simulations using projected climate data (2041–2070), we predicted that this divergence could lead to a threefold increase in genetic differentiation, potentially leading to isolated east–west populations of lynx in North America. Our results imply that subtle genetic structure can be governed by current climate and that substantive genetic differentiation and related ecological divergence may arise from changing climate patterns.     

Increasing influence of heat stress on French maize yields from the 1960s to the 2030s

Improved crop yield forecasts could enable more effective adaptation to climate variability and change. Here, we explore how to combine historical observations of crop yields and weather with climate model simulations to produce crop yield projections for decision relevant timescales. Firstly, the effects on historical crop yields of improved technology, precipitation and daily maximum temperatures are modelled empirically, accounting for a nonlinear technology trend and interactions between temperature and precipitation, and applied specifically for a case study of maize in France. The relative importance of precipitation variability for maize yields in France has decreased significantly since the 1960s, likely due to increased irrigation. In addition, heat stress is found to be as important for yield as precipitation since around 2000. A significant reduction in maize yield is found for each day with a maximum temperature above 32 °C, in broad agreement with previous estimates. The recent increase in such hot days has likely contributed to the observed yield stagnation. Furthermore, a general method for producing near‐term crop yield projections, based on climate model simulations, is developed and utilized. We use projections of future daily maximum temperatures to assess the likely change in yields due to variations in climate. Importantly, we calibrate the climate model projections using observed data to ensure both reliable temperature mean and daily variability characteristics, and demonstrate that these methods work using retrospective predictions. We conclude that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target.     

Predicting impacts of Arctic climate change: Past lessons and future challenges

General circulation models predict increases in temperature and precipitation in the Arctic as the result of increases in atmospheric carbon dioxide concentrations. Arctic ecosystems are strongly constrained by temperature, and may be expected to be markedly influenced by climate change. Perturbation experiments have been used to predict how Arctic ecosystems will respond to global climatic change, but these have often simulated individual perturbations (e.g. temperature alone) and have largely been confined to the short Arctic summer. The importance of interactions between global change variables (e.g. CO₂, temperature, precipitation) has rarely been examined, and much experimentation has been short-term. Similarly, very little experimentation has occurred in the winter when General circulation models predict the largest changes in climate will take place. Recent studies have clearly demonstrated that Arctic ecosystems are not dormant during the winter and thus much greater emphasis on experimentation during this period is essential to improve our understanding of how these ecosystems will respond to global change. This, combined with more long-term experimentation, direct observation of natural vegetation change (e.g. at the tundra/taiga boundary) and improvements in model predictions is necessary if we are to understand the future nature and extent of Arctic ecosystems in a changing climate.     

The effect of past changes in inter‐annual temperature variability on tree distribution limits

Aim The northern limits of temperate broadleaved species in Fennoscanndia are controlled by their requirements for summer warmth for successful regeneration and growth as well as by the detrimental effects of winter cold on plant tissue. However, occurrences of meteorological conditions with detrimental effects on individual species are rare events rather than a reflection of average conditions. We explore the effect of changes in inter‐annual temperature variability on the abundances of the tree species Tilia cordata, Quercus robur and Ulmus glabra near their distribution limits using a process‐based model of ecosystem dynamics. Location A site in central Sweden and a site in southern Finland were used as examples for the ecotone between boreal and temperate forests in Fennoscandia. The Finnish site was selected because of the availability of varve‐thickness data. Methods The dynamic vegetation model LPJ‐GUESS was run with four scenarios of inter‐annual temperature forcing for the last 10,000 years. In one scenario the variability in the thickness of summer and winter varves from the annually laminated lake in Finland was used as a proxy for past inter‐annual temperature variability. Two scenarios were devised to explore systematically the effect of stepwise changes in the variance and shape parameter of a probability distribution. All variability scenarios were run both with and without the long‐term trend in Holocene temperature change predicted by an atmospheric general circulation model. Results Directional changes in inter‐annual temperature variability have significant effects on simulated tree distribution limits through time. Variations in inter‐annual temperature variability alone are shown to alter vegetation composition by magnitudes similar to the magnitude of changes driven by variation in mean temperatures. Main conclusions The varve data indicate that inter‐annual climate variability has changed in the past. The model results show that past changes in species abundance can be explained by changes in the inter‐annual variability of climate parameters as well as by mean climate. Because inter‐annual climatic variability is predicted to change in the future, this component of climate change should be taken into account both when making projections of future plant distributions and when interpreting vegetation history.