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.     

Predicting potential climate change impacts with bioclimate envelope models: a palaeoecological perspective

Aim We assess the realism of bioclimate envelope model projections for anticipated future climates by validating ecosystem reconstructions for the late Quaternary with fossil and pollen data. Specifically, we ask: (1) do climate conditions with no modern analogue negatively affect the accuracy of ecosystem reconstructions? (2) are bioclimate envelope model projections biased towards under‐predicting forested ecosystems? (3) given a palaeoecological perspective, are potential habitat projections for the 21st century within model capabilities? Location Western North America. Methods We used an ensemble classifier modelling approach (RandomForest) to spatially project the climate space of modern ecosystem classes throughout the Holocene (at 6000, 9000, 11,000, 14,000, 16,000, and 21,000 YBP) using palaeoclimate surfaces generated by two general circulation models (GFDL and CCM1). The degree of novel arrangement of climate variables was quantified with the multivariate Mahalanobis distance to the nearest modern climatic equivalent. Model projections were validated against biome classifications inferred from 1460 palaeoecological records. Results Model accuracy assessed against independent palaeoecology data is generally low for the present day, increases for 6000 YBP, and then rapidly declines towards the last glacial maximum, primarily due to the under‐prediction of forested biomes. Misclassifications were closely correlated with the degree of climate dissimilarity from the present day. For future projections, no‐analogue climates unexpectedly emerged in the coastal Pacific Northwest but were absent throughout the rest of the study area. Main conclusions Bioclimate envelope models could approximately reconstruct ecosystem distributions for the mid‐ to late‐Holocene but proved unreliable in the Late Pleistocene. We attribute this failure to a combination of no‐analogue climates and a potential lack of niche conservatism in tree species. However, climate dissimilarities in future projections are comparatively minor (similar to those of the mid‐Holocene), and we conclude that no‐analogue climates should not compromise the accuracy of model predictions for the next century.     

Improving species distribution models for climate change studies: variable selection and scale

Statistical species distribution models (SDMs) are widely used to predict the potential changes in species distributions under climate change scenarios. We suggest that we need to revisit the conceptual framework and ecological assumptions on which the relationship between species distributions and environment is based. We present a simple conceptual framework to examine the selection of environmental predictors and data resolution scales. These vary widely in recent papers, with light inconsistently included in the models. Focusing on light as a necessary component of plant SDMs, we briefly review its dependence on aspect and slope and existing knowledge of its influence on plant distribution. Differences in light regimes between north‐ and south‐facing aspects in temperate latitudes can produce differences in temperature equivalent to moves 200 km polewards. Local topography may create refugia that are not recognized in many climate change SDMs using coarse‐scale data. We argue that current assumptions about the selection of predictors and data resolution need further testing. Application of these ideas can clarify many issues of scale, extent and choice of predictors, and potentially improve the use of SDMs for climate change modelling of biodiversity.     

Modeling climate change impacts on overwintering bald eagles

Bald eagles (Haliaeetus leucocephalus) are recovering from severe population declines, and are exerting pressure on food resources in some areas. Thousands of bald eagles overwinter near Puget Sound, primarily to feed on chum salmon (Oncorhynchus keta) carcasses. We used modeling techniques to examine how anticipated climate changes will affect energetic demands of overwintering bald eagles. We applied a regional downscaling method to two global climate change models to obtain hourly temperature, precipitation, wind, and longwave radiation estimates at the mouths of three Puget Sound tributaries (the Skagit, Hamma Hamma, and Nisqually rivers) in two decades, the 1970s and the 2050s. Climate data were used to drive bald eagle bioenergetics models from December to February for each river, year, and decade. Bald eagle bioenergetics were insensitive to climate change: despite warmer winters in the 2050s, particularly near the Nisqually River, bald eagle food requirements declined only slightly (<1%). However, the warming climate caused salmon carcasses to decompose more rapidly, resulting in 11% to 14% less annual carcass biomass available to eagles in the 2050s. That estimate is likely conservative, as it does not account for decreased availability of carcasses due to anticipated increases in winter stream flow. Future climate-driven declines in winter food availability, coupled with a growing bald eagle population, may force eagles to seek alternate prey in the Puget Sound area or in more remote ecosystems.     

Contribution of crop model structure,parameters and climate projections to uncertainty in climate change impact assessments

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple‐ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple‐ensemble probabilistic assessment, the median of simulated yield change was ?4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple‐ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.     

Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?

Modelling strategies for predicting the potential impacts of climate change on the natural distribution of species have often focused on the characterization of a species’ bioclimate envelope. A number of recent critiques have questioned the validity of this approach by pointing to the many factors other than climate that play an important part in determining species distributions and the dynamics of distribution changes. Such factors include biotic interactions, evolutionary change and dispersal ability. This paper reviews and evaluates criticisms of bioclimate envelope models and discusses the implications of these criticisms for the different modelling strategies employed. It is proposed that, although the complexity of the natural system presents fundamental limits to predictive modelling, the bioclimate envelope approach can provide a useful first approximation as to the potentially dramatic impact of climate change on biodiversity. However, it is stressed that the spatial scale at which these models are applied is of fundamental importance, and that model results should not be interpreted without due consideration of the limitations involved. A hierarchical modelling framework is proposed through which some of these limitations can be addressed within a broader, scale‐dependent context.     

Potential impacts of climatic change on the breeding and non-breeding ranges and migration distance of European Sylvia warblers

Aim  To explore the potential impacts of climatic change on species with different migratory strategies using Sylvia warblers breeding in Europe as a ‘model’ species group. Location  Europe and Africa. Methods  Climate response surfaces and generalized additive models (GAMs) were used to model relationships between species recorded breeding and non‐breeding ranges and recent climate. Species potential future breeding and non‐breeding ranges were simulated for three scenarios of late 21st‐century climate. The simulated potential future and present ranges were compared in terms of their relative extent and overlap, as well as their location. The impact of any shifts in potential range location on migration distance were quantified. Results  Potential breeding ranges consistently showed a shift northwards, whereas potential non‐breeding ranges showed no consistent directional shift, even when trans‐Saharan migrants were considered separately from resident/short‐distance or partial migrants. Future potential range extent relative to simulated recent range extent varied considerably among species, although on average range extent increased. Overlap between future and recent simulated range was generally low, averaging < 36% for both breeding and non‐breeding ranges. Overlap was consistently less for range‐restricted than for widespread species. Migration distance increased generally, by about twice as much in the case of trans‐Saharan migrant species than for short‐distance migrants. In many cases potential future non‐breeding areas were simulated in regions far from the species present non‐breeding area, suggesting that new migration strategies and routes may need to be developed in response to climatic change. Main conclusions  Migratory species can be expected to suffer greater negative impacts from climatic change than species that are resident or undertake only short‐distance or partial migrations. Trans‐Saharan migrants face the greatest potential increases in migration distances, whereas range‐restricted species are expected to experience major population reductions because of the limited, or in some cases lack of, overlap between their present and potential future ranges.     

Spatial resolution impacts projected plant responses to climate change on topographically complex islands

Aim

Understanding how grain size affects our ability to characterize species responses to ongoing climate change is of crucial importance in the context of an increasing awareness for the substantial difference that exists between coarse spatial resolution macroclimatic data sets and the microclimate actually experienced by organisms. Climate change impacts on biodiversity are expected to peak in mountain areas, wherein the differences between macro and microclimates are precisely the largest. Based on a newly generated fine-scale environmental data for the Canary Islands, we assessed whether data at 100 m resolution is able to provide more accurate predictions than available data at 1 km resolution. We also analysed how future climate suitability predictions of island endemic bryophytes differ depending on the grain size of grids.

Location

Canary Islands.

Time period

Present (1979–2013) and late-century (2071–2100).

Taxa

Bryophytes.

Methods

We compared the accuracy and spatial predictions using ensemble of small models for 14 Macaronesian endemic bryophyte species. We used two climate data sets: CHELSA v1.2 (~1 km) and CanaryClim v1.0 (100 m), a downscaled version of the latter utilizing data from local weather stations. CanaryClim also encompasses future climate data from five individual model intercomparison projects for three warming shared socio-economic pathways.

Results

Species distribution models generated from CHELSA and CanaryClim exhibited a similar accuracy, but CanaryClim predicted buffered warming trends in mid-elevation ridges. CanaryClim consistently returned higher proportions of newly suitable pixels (8%–28%) than CHELSA models (0%–3%). Consequently, the proportion of species predicted to occupy pixels of uncertain suitability was higher with CHELSA (3–8 species) than with CanaryClim (0–2 species).

Main conclusions

The resolution of climate data impacted the predictions rather than the performance of species distribution models. Our results highlight the crucial role that fine-resolution climate data sets can play in predicting the potential distribution of both microrefugia and new suitable range under warming climate.     

Choice of baseline climate data impacts projected species' responses to climate change

Climate data created from historic climate observations are integral to most assessments of potential climate change impacts, and frequently comprise the baseline period used to infer species‐climate relationships. They are often also central to downscaling coarse resolution climate simulations from General Circulation Models (GCMs) to project future climate scenarios at ecologically relevant spatial scales. Uncertainty in these baseline data can be large, particularly where weather observations are sparse and climate dynamics are complex (e.g. over mountainous or coastal regions). Yet, importantly, this uncertainty is almost universally overlooked when assessing potential responses of species to climate change. Here, we assessed the importance of historic baseline climate uncertainty for projections of species' responses to future climate change. We built species distribution models (SDMs) for 895 African bird species of conservation concern, using six different climate baselines. We projected these models to two future periods (2040–2069, 2070–2099), using downscaled climate projections, and calculated species turnover and changes in species‐specific climate suitability. We found that the choice of baseline climate data constituted an important source of uncertainty in projections of both species turnover and species‐specific climate suitability, often comparable with, or more important than, uncertainty arising from the choice of GCM. Importantly, the relative contribution of these factors to projection uncertainty varied spatially. Moreover, when projecting SDMs to sites of biodiversity importance (Important Bird and Biodiversity Areas), these uncertainties altered site‐level impacts, which could affect conservation prioritization. Our results highlight that projections of species' responses to climate change are sensitive to uncertainty in the baseline climatology. We recommend that this should be considered routinely in such analyses.     

Validation of species–climate impact models under climate change

Increasing concern over the implications of climate change for biodiversity has led to the use of species–climate envelope models to project species extinction risk under climate‐change scenarios. However, recent studies have demonstrated significant variability in model predictions and there remains a pressing need to validate models and to reduce uncertainties. Model validation is problematic as predictions are made for events that have not yet occurred. Resubstituition and data partitioning of present‐day data sets are, therefore, commonly used to test the predictive performance of models. However, these approaches suffer from the problems of spatial and temporal autocorrelation in the calibration and validation sets. Using observed distribution shifts among 116 British breeding‐bird species over the past ～20 years, we are able to provide a first independent validation of four envelope modelling techniques under climate change. Results showed good to fair predictive performance on independent validation, although rules used to assess model performance are difficult to interpret in a decision‐planning context. We also showed that measures of performance on nonindependent data provided optimistic estimates of models' predictive ability on independent data. Artificial neural networks and generalized additive models provided generally more accurate predictions of species range shifts than generalized linear models or classification tree analysis. Data for independent model validation and replication of this study are rare and we argue that perfect validation may not in fact be conceptually possible. We also note that usefulness of models is contingent on both the questions being asked and the techniques used. Implementations of species–climate envelope models for testing hypotheses and predicting future events may prove wrong, while being potentially useful if put into appropriate context.     

Disciplinary reporting affects the interpretation of climate change impacts in global oceans

Climate change is affecting marine ecosystems, but different investigative approaches in physical, chemical, and biological disciplines may influence interpretations of climate‐driven changes in the ocean. Here, we review the ocean change literature from 2007 to 2012 based on 461 of the most highly cited studies in physical and chemical oceanography and three biological subdisciplines. Using highly cited studies, we focus on research that has shaped recent discourse on climate‐driven ocean change. Our review identified significant differences in spatial and temporal scales of investigation among disciplines. Physical/chemical studies had a median duration of 29 years (n = 150) and covered the greatest study areas (median 1.41 × 10⁷ km², n = 148). Few biological studies were conducted over similar spatial and temporal scales (median 8 years, n = 215; median 302 km², n = 196), suggesting a more limited ability to separate climate‐related responses from natural variability. We linked physical/chemical and biological disciplines by tracking studies examining biological responses to changing ocean conditions. Of the 545 biological responses recorded, a single physical or chemical stressor was usually implicated as the cause (59%), with temperature as the most common primary stressor (44%). The most frequently studied biological responses were changes in physiology (31%) and population abundance (30%). Differences in disciplinary studies, as identified in this review, can ultimately influence how researchers interpret climate‐related impacts in marine systems. We identified research gaps and the need for more discourse in (1) the Indian and other Southern Hemisphere ocean basins; (2) research themes such as archaea, bacteria, viruses, mangroves, turtles, and ocean acidification; (3) physical and chemical stressors such as dissolved oxygen, salinity, and upwelling; and (4) adaptive responses of marine organisms to climate‐driven ocean change. Our findings reveal that highly cited biological studies are rarely conducted on scales that match those of physical and chemical studies. Rather, we suggest a need for measuring responses at biologically relevant scales.     

气候变化对传染病爆发流行的影响研究进展

全球气候变化已影响到传染病发生、传播与变化的各个环节,从病原体及其携带者、传播途径和人体自身抵抗力等方面直接或间接影响传染病的发病趋势,从而对人类健康造成了巨大的威胁。所以加强对气候变化与传染病间关系、预测预报研究,对进一步认识、预防和控制传染病的爆发流行具有重要意义。本文首先阐述了全球气候变化对生物物种的地理分布和人类健康的影响,气候变化改变了生物物种的地理分布范围,增加了某些物种的潜在分布区域,并造成生物物侯期的改变;同时,极端气候事件成为导致种群数量波动的一个重要驱动力。气候变化对人类健康有直接和间接影响,它使得传染病发病率增加、传染病分布范围扩大、人群对疾病易感性增强。文章重点评述了气候变化对疟疾、登革热、霍乱、流行性乙型脑炎、流感、SARS、肠道传染病、鼠疫、血吸虫病等常见传染病流行机制和传播过程的影响研究进展。评述了传染病和气象因子关系分析中常用的定性和定量分析方法,传统的研究多以定性分析为主,方法较单一;目前,利用流行病学资料与同期的气象因子进行单因素相关分析、多元回归分析是常用的研究方法;主成分回归分析、逐步判别分析、灰色关联分析法、RS和GIS等方法近年来逐渐得到应用;数学建模、实验室生物学仿真实验方法是今后需强化的方向。提出了该研究领域国内外研究普遍存在和亟待解决的问题,针对目前的研究现状和存在的问题,提出了未来的研究重点和发展方向。     

Reducing uncertainty in projections of extinction risk from climate change

Aim Concern over the implications of climate change for biodiversity has led to the use of species–climate ‘envelope’ models to forecast risks of species extinctions under climate change scenarios. Recent studies have demonstrated significant variability in model projections and there remains a need to test the accuracy of models and to reduce uncertainties. Testing of models has been limited by a lack of data against which projections of future ranges can be tested. Here we provide a first test of the predictive accuracy of such models using observed species’ range shifts and climate change in two periods of the recent past. Location Britain. Methods Observed range shifts for 116 breeding bird species in Britain between 1967 and 1972 (t₁) and 1987–91 (t₂) are used. We project range shifts between t₁ and t₂ for each species based on observed climate using 16 alternative models (4 methods × 2 data parameterizations × 2 rules to transform probabilities of occurrence into presence and absence records). Results Modelling results were extremely variable, with projected range shifts varying both in magnitude and in direction from observed changes and from each other. However, using approaches that explore the central tendency (consensus) of model projections, we were able to improve agreement between projected and observed shifts significantly. Conclusions Our results provide the first empirical evidence of the value of species–climate ‘envelope’ models under climate change and demonstrate reduction in uncertainty and improvement in accuracy through selection of the most consensual projections.     

Dispersal limitation and spatial scale affect model based projections of Pinus uncinata response to climate change in the Pyrenees

Species Distribution Models (SDMs) were employed to assess the potential impact of climate change on the distribution of Pinus uncinata in the Pyrenees, where it is the dominant tree species in subalpine forest and alpine tree lines. Predicting forest response to climate change is a challenging task in mountain regions but also a conservation priority. We examined the potential impact of spatial scale on SDM projections by conducting all analyses at four spatial resolutions. We further examined the potential effect of dispersal constraints by applying a threshold distance of maximal advancement derived from a spatially explicit, individual‐based simulation model of tree line dynamics. Under current conditions, SDMs including climatic factors related to stress or growth limitation performed best. These models were then employed to project P. uncinata distribution under two emission scenarios, using data generated from several regional climate models. At the end of this century, P. uncinata is expected to migrate northward and upward, occupying habitat currently inhabited by alpine plant species. However, consideration of dispersal limitation and/or changing the spatial resolution of the analysis modified the assessment of climate change impact on mountain ecosystems, especially in the case of estimates of colonization and extinction at the regional scale. Our study highlights the need to improve the characterization of biological processes within SDMs, as well as to consider simultaneously different scales when assessing potential habitat loss under future climate conditions.     

Disparity in elevational shifts of European trees in response to recent climate warming

Predicting climate‐driven changes in plant distribution is crucial for biodiversity conservation and management under recent climate change. Climate warming is expected to induce movement of species upslope and towards higher latitudes. However, the mechanisms and physiological processes behind the altitudinal and latitudinal distribution range of a tree species are complex and depend on each tree species features and vary over ontogenetic stages. We investigated the altitudinal distribution differences between juvenile and adult individuals of seven major European tree species along elevational transects covering a wide latitudinal range from southern Spain (37°N) to northern Sweden (67°N). By comparing juvenile and adult distributions (shifts on the optimum position and the range limits) we assessed the response of species to present climate conditions in relation to previous conditions that prevailed when adults were established. Mean temperature increased by 0.86 °C on average at our sites during the last decade compared with previous 30‐year period. Only one of the species studied, Abies alba, matched the expected predictions under the observed warming, with a maximum abundance of juveniles at higher altitudes than adults. Three species, Fagus sylvatica, Picea abies and Pinus sylvestris, showed an opposite pattern while for other three species, such as Quercus ilex, Acer pseudoplatanus and Q. petraea, we were no able to detect changes in distribution. These findings are in contrast with theoretical predictions and show that tree responses to climate change are complex and are obscured not only by other environmental factors but also by internal processes related to ontogeny and demography.     

Insights from modeling studies on how climate change affects invasive alien species geography

Climate change and biological invasions are threatening biodiversity and ecosystem services worldwide. It has now been widely acknowledged that climate change will affect biological invasions. A large number of studies have investigated predicted shifts and other changes in the geographic ranges of invasive alien species related to climate change using modeling approaches. Yet these studies have provided contradictory evidence, and no consensus has been reached. We conducted a systematic review of 423 modeling case studies included in 71 publications that have examined the predicted effects of climate change on those species. We differentiate the approaches used in these studies and synthesize their main results. Our results reaffirm the major role of climate change as a driver of invasive alien species distribution in the future. We found biases in the literature both regarding the taxa, toward plants and invertebrates, and the areas of the planet investigated. Despite these biases, we found for the plants and vertebrates studied that climate change will more frequently contribute to a decrease in species range size than an increase in the overall area occupied. This is largely due to oceans preventing terrestrial invaders from spreading poleward. In contrast, we found that the ranges of invertebrates and pathogens studied are more likely to increase following climate change. An important caveat to these findings is that researchers have rarely considered the effects of climate change on transport, introduction success, or the resulting impacts. We recommend closing these research gaps, and propose additional avenues for future investigations, as well as opportunities and challenges for managing invasions under climate change.     

Marine species in ambient low‐oxygen regions subject to double jeopardy impacts of climate change

We have learned much about the impacts of warming on the productivity and distribution of marine organisms, but less about the impact of warming combined with other environmental stressors, including oxygen depletion. Also, the combined impact of multiple environmental stressors requires evaluation at the scales most relevant to resource managers. We use the Gulf of St. Lawrence, Canada, characterized by a large permanently hypoxic zone, as a case study. Species distribution models were used to predict the impact of multiple scenarios of warming and oxygen depletion on the local density of three commercially and ecologically important species. Substantial changes are projected within 20–40 years. A eurythermal depleted species already limited to shallow, oxygen‐rich refuge habitat (Atlantic cod) may be relatively uninfluenced by oxygen depletion but increase in density within refuge areas with warming. A more stenothermal, deep‐dwelling species (Greenland halibut) is projected to lose ~55% of its high‐density areas under the combined impacts of warming and oxygen depletion. Another deep‐dwelling, more eurythermal species (Northern shrimp) would lose ~4% of its high‐density areas due to oxygen depletion alone, but these impacts may be buffered by warming, which may increase density by 8% in less hypoxic areas, but decrease density by ~20% in the warmest parts of the region. Due to local climate variability and extreme events, and that our models cannot project changes in species sensitivity to hypoxia with warming, our results should be considered conservative. We present an approach to effectively evaluate the individual and cumulative impacts of multiple environmental stressors on a species‐by‐species basis at the scales most relevant to managers. Our study may provide a basis for work in other low‐oxygen regions and should contribute to a growing literature base in climate science, which will continue to be of support for resource managers as climate change accelerates.