Climate‐niche factor analysis: a spatial approach to quantifying species vulnerability to climate change

Climate change vulnerability assessments are an important tool for understanding the threat that climate change poses to species and populations, but do not generally yield insight into the spatial variation in vulnerability throughout a species’ habitat. We demonstrate how to adapt the method of ecological‐niche factor analysis (ENFA) to objectively quantify aspects of species sensitivity to climate change. We then expand ENFA to quantify aspects of exposure and vulnerability to climate change as well, using future projections of global climate models. This approach provides spatially‐explicit insight into geographic patterns of vulnerability, relies only on readily‐available spatial data, is suitable for a wide range of species and habitats, and invites comparison between different species. We apply our methods to a case study of two species of montane mammals, the American pika Ochotona princeps and the yellow‐bellied marmot Marmota flaviventris.     

Using fuzzy logic to determine the vulnerability of marine species to climate change

Marine species are being impacted by climate change and ocean acidification, although their level of vulnerability varies due to differences in species' sensitivity, adaptive capacity and exposure to climate hazards. Due to limited data on the biological and ecological attributes of many marine species, as well as inherent uncertainties in the assessment process, climate change vulnerability assessments in the marine environment frequently focus on a limited number of taxa or geographic ranges. As climate change is already impacting marine biodiversity and fisheries, there is an urgent need to expand vulnerability assessment to cover a large number of species and areas. Here, we develop a modelling approach to synthesize data on species‐specific estimates of exposure, and ecological and biological traits to undertake an assessment of vulnerability (sensitivity and adaptive capacity) and risk of impacts (combining exposure to hazards and vulnerability) of climate change (including ocean acidification) for global marine fishes and invertebrates. We use a fuzzy logic approach to accommodate the variability in data availability and uncertainties associated with inferring vulnerability levels from climate projections and species' traits. Applying the approach to estimate the relative vulnerability and risk of impacts of climate change in 1074 exploited marine species globally, we estimated their index of vulnerability and risk of impacts to be on average 52 ± 19 SD and 66 ± 11 SD, scaling from 1 to 100, with 100 being the most vulnerable and highest risk, respectively, under the ‘business‐as‐usual' greenhouse gas emission scenario (Representative Concentration Pathway 8.5). We identified 157 species to be highly vulnerable while 294 species are identified as being at high risk of impacts. Species that are most vulnerable tend to be large‐bodied endemic species. This study suggests that the fuzzy logic framework can help estimate climate vulnerabilities and risks of exploited marine species using publicly and readily available information.     

Hotspots of uncertainty in land‐use and land‐cover change projections: a global‐scale model comparison

Model‐based global projections of future land‐use and land‐cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global‐scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.     

Robust dynamics of Amazon dieback to climate change with perturbed ecosystem model parameters

Climate change science is increasingly concerned with methods for managing and integrating sources of uncertainty from emission storylines, climate model projections, and ecosystem model parameterizations. In tropical ecosystems, regional climate projections and modeled ecosystem responses vary greatly, leading to a significant source of uncertainty in global biogeochemical accounting and possible future climate feedbacks. Here, we combine an ensemble of IPCC‐AR4 climate change projections for the Amazon Basin (eight general circulation models) with alternative ecosystem parameter sets for the dynamic global vegetation model, LPJmL. We evaluate LPJmL simulations of carbon stocks and fluxes against flux tower and aboveground biomass datasets for individual sites and the entire basin. Variability in LPJmL model sensitivity to future climate change is primarily related to light and water limitations through biochemical and water‐balance‐related parameters. Temperature‐dependent parameters related to plant respiration and photosynthesis appear to be less important than vegetation dynamics (and their parameters) for determining the magnitude of ecosystem response to climate change. Variance partitioning approaches reveal that relationships between uncertainty from ecosystem dynamics and climate projections are dependent on geographic location and the targeted ecosystem process. Parameter uncertainty from the LPJmL model does not affect the trajectory of ecosystem response for a given climate change scenario and the primary source of uncertainty for Amazon ‘dieback’ results from the uncertainty among climate projections. Our approach for describing uncertainty is applicable for informing and prioritizing policy options related to mitigation and adaptation where long‐term investments are required.     

The exposure,sensitivity and vulnerability of natural vegetation in China to climate thermal variability (1901–2013): An indicator-based approach

Vulnerability assessments can be helpful in assessing the impact of climate change on natural ecosystems and are expected to support adaptation and/or mitigation strategies in the 21st century. A challenge when conducting such assessments is the integration of the multi-level properties and processes of ecosystems into an assessment framework. Focusing on the primary stresses of climate thermal variability (at both upper and lower extremes), this study proposes a quantitative indicator system—following the IPCC framework of vulnerability assessment—that assesses the impact of historical climate change, during 1901–2013, on the natural terrestrial vegetation types in China. The final output of the vulnerability assessment was expressed as a composite index, composed of ecosystem exposure, sensitivity and resilience to climate thermal change, and including biological, ecological and spatial traits of vegetation types in the assessment. The exposure to temperature variability was generally higher in January than in July, and higher in non-arborous vegetation types than forests. In contrast, sensitivity was higher for forests, wetlands and alpine tundra regions, especially for small areas and areas with scattered patterns. Original forests—especially those distributed in the north—had lower resilience than other vegetation types. The vulnerability of natural vegetation types in China to the temperature variability of the past century was very low to moderate, with a few exceptions, including tropical mangroves and the semi-arid to arid vegetation types in northwestern China, which had high vulnerability. Vulnerability was stronger in winter than in summer. Our results are generally in accord with the scenario-based projections on the geographical pattern of vegetation vulnerability to climate change, and revealed the difference caused by not considering moisture. The risks for these fragmented and narrow-range ecosystems are highlighted, and the importance of natural resilience is stressed for the assessment of vegetation vulnerability to climate change. Given the inadequate coverage of the natural reserve network in China (after the large investment in recent decades) found in the high-vulnerability vegetation types (with a few exceptions), the assessment of natural resilience of ecosystems could be critical for the optimal design of socio-economic strategies in response to the impacts of future climate change.     

气候变化背景下野生动物脆弱性评估方法研究进展

脆弱性评估是研究气候变化影响野生动物的重要内容,识别野生动物脆弱性,是适应和减缓气候变化影响的关键和基础。开展气候变化背景下野生动物的脆弱性评估工作,目的是为了确定易受气候变化影响的物种和明确导致物种脆弱性的因素,其评估结果有助于人类认识气候变化对野生动物的影响,为野生动物适应气候变化保护对策的制定提供科学依据。对野生动物而言(物种),脆弱性是物种受气候变化影响的程度,包括暴露度、敏感性和适应能力三大要素。其中,暴露度是由气候变化引起的外在因素,如温度、降雨量、极值天气等;敏感性是受物种自身因素影响,如种间关系、耐受性等;适应能力是物种通过自身调整来减小气候变化带来的影响,如迁移或扩散到适宜生境的能力、塑性反应和进化反应等。对近期有关气候变化背景下野生动物脆弱性评估方法予以综述,比较每种评估方法所选取指标的差异,总结在脆弱性评估中遇到的不确定性指标的处理方法,以及脆弱性评估结果在野生动物适应气候变化对策中的应用。通过总结野生动物脆弱性评估方法,以期为气候变化背景下评估我国野生动物资源的脆弱性提供参考方法。     

Multi‐model comparison highlights consistency in predicted effect of warming on a semi‐arid shrub

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi‐model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi‐model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.     

Climate change vulnerability for species—Assessing the assessments

Climate change vulnerability assessments are commonly used to identify species at risk from global climate change, but the wide range of methodologies available makes it difficult for end users, such as conservation practitioners or policymakers, to decide which method to use as a basis for decision‐making. In this study, we evaluate whether different assessments consistently assign species to the same risk categories and whether any of the existing methodologies perform well at identifying climate‐threatened species. We compare the outputs of 12 climate change vulnerability assessment methodologies, using both real and simulated species, and validate the methods using historic data for British birds and butterflies (i.e. using historical data to assign risks and more recent data for validation). Our results show that the different vulnerability assessment methods are not consistent with one another; different risk categories are assigned for both the real and simulated sets of species. Validation of the different vulnerability assessments suggests that methods incorporating historic trend data into the assessment perform best at predicting distribution trends in subsequent time periods. This study demonstrates that climate change vulnerability assessments should not be used interchangeably due to the poor overall agreement between methods when considering the same species. The results of our validation provide more support for the use of trend‐based rather than purely trait‐based approaches, although further validation will be required as data become available.     

Species vulnerability to climate change: impacts on spatial conservation priorities and species representation

Climate change may shrink and/or shift plant species ranges thereby increasing their vulnerability and requiring targeted conservation to facilitate adaptation. We quantified the vulnerability to climate change of plant species based on exposure, sensitivity and adaptive capacity and assessed the effects of including these components in complementarity‐based spatial conservation prioritisation. We modelled the vulnerability of 584 native plant species under three climate change scenarios in an 11.9 million hectare fragmented agricultural region in southern Australia. We represented exposure as species' geographical range under each climate change scenario as quantified using species distribution models. We calculated sensitivity as a function of the impact of climate change on species' geographical ranges. Using a dispersal kernel, we quantified adaptive capacity as species' ability to migrate to new geographical ranges under each climate change scenario. Using Zonation, we assessed the impact of individual components of vulnerability (exposure, sensitivity and adaptive capacity) on spatial conservation priorities and levels of species representation in priority areas under each climate change scenario. The full vulnerability framework proved an effective basis for identifying spatial conservation priorities under climate change. Including different dimensions of vulnerability had significant implications for spatial conservation priorities. Incorporating adaptive capacity increased the level of representation of most species. However, prioritising sensitive species reduced the representation of other species. We conclude that whilst taking an integrated approach to mitigating species vulnerability to climate change can ensure sensitive species are well‐represented in a conservation network, this can come at the cost of reduced representation of other species. Conservation planning decisions aimed at reducing species vulnerability to climate change need to be made in full cognisance of the sensitivity of spatial conservation priorities to individual components of vulnerability, and the trade‐offs associated with focussing on sensitive species.     

Projecting marine species range shifts from only temperature can mask climate vulnerability

Climate change is causing range shifts in many marine species, with implications for biodiversity and fisheries. Previous research has mainly focused on how species' ranges will respond to changing ocean temperatures, without accounting for other environmental covariates that could affect future distribution patterns. Here, we integrate habitat suitability modeling approaches, a high‐resolution global climate model projection, and detailed fishery‐independent and ‐dependent faunal datasets from one of the most extensively monitored marine ecosystems—the U.S. Northeast Shelf. We project the responses of 125 species in this region to climate‐driven changes in multiple oceanographic factors (e.g., ocean temperature, salinity, sea surface height) and seabed characteristics (i.e., rugosity and depth). Comparing model outputs based on ocean temperature and seabed characteristics to those that also incorporated salinity and sea surface height (proxies for primary productivity and ocean circulation features), we explored how an emphasis on ocean temperature in projecting species' range shifts can impact assessments of species' climate vulnerability. We found that multifactor habitat suitability models performed better in explaining and predicting species historical distribution patterns than temperature‐based models. We also found that multifactor models provided more concerning assessments of species' future distribution patterns than temperature‐based models, projecting that species' ranges will largely shift northward and become more contracted and fragmented over time. Our results suggest that using ocean temperature as a primary determinant of range shifts can significantly alter projections, masking species' climate vulnerability, and potentially forestalling proactive management.     

An integrated risk assessment for climate change: analysing the vulnerability of sharks and rays on Australia's Great Barrier Reef

An Integrated Risk Assessment for Climate Change (IRACC) is developed and applied to assess the vulnerability of sharks and rays on Australia's Great Barrier Reef (GBR) to climate change. The IRACC merges a traditional climate change vulnerability framework with approaches from fisheries ecological risk assessments. This semi‐quantitative assessment accommodates uncertainty and can be applied at different spatial and temporal scales to identify exposure factors, at‐risk species and their key biological and ecological attributes, critical habitats a`nd ecological processes, and major knowledge gaps. Consequently, the IRACC can provide a foundation upon which to develop climate change response strategies. Here, we describe the assessment process, demonstrate its application to GBR shark and ray species, and explore the issues affecting their vulnerability to climate change. The assessment indicates that for the GBR, freshwater/estuarine and reef associated sharks and rays are most vulnerable to climate change, and that vulnerability is driven by case‐specific interactions of multiple factors and species attributes. Changes in temperature, freshwater input and ocean circulation will have the most widespread effects on these species. Although relatively few GBR sharks and rays were assessed as highly vulnerable, their vulnerability increases when synergies with other factors are considered. This is especially true for freshwater/estuarine and coastal/inshore sharks and rays. Reducing the impacts of climate change on the GBR's sharks and rays requires a range of approaches including mitigating climate change and addressing habitat degradation and sustainability issues. Species‐specific conservation actions may be required for higher risk species (e.g. the freshwater whipray, porcupine ray, speartooth shark and sawfishes) including reducing mortality, preserving coastal catchments and estuarine habitats, and addressing fisheries sustainability. The assessment identified many knowledge gaps concerning GBR habitats and processes, and highlights the need for improved understanding of the biology and ecology of the sharks and rays of the GBR.     

Beyond just sea‐level rise: considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change

Due to their position at the land‐sea interface, coastal wetlands are vulnerable to many aspects of climate change. However, climate change vulnerability assessments for coastal wetlands generally focus solely on sea‐level rise without considering the effects of other facets of climate change. Across the globe and in all ecosystems, macroclimatic drivers (e.g., temperature and rainfall regimes) greatly influence ecosystem structure and function. Macroclimatic drivers have been the focus of climate change‐related threat evaluations for terrestrial ecosystems, but largely ignored for coastal wetlands. In some coastal wetlands, changing macroclimatic conditions are expected to result in foundation plant species replacement, which would affect the supply of certain ecosystem goods and services and could affect ecosystem resilience. As examples, we highlight several ecological transition zones where small changes in macroclimatic conditions would result in comparatively large changes in coastal wetland ecosystem structure and function. Our intent in this communication is not to minimize the importance of sea‐level rise. Rather, our overarching aim is to illustrate the need to also consider macroclimatic drivers within vulnerability assessments for coastal wetlands.     

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.     

Geographical adaptation prevails over species‐specific determinism in trees’ vulnerability to climate change at Mediterranean rear‐edge forests

Climate change may reduce forest growth and increase forest mortality, which is connected to high carbon costs through reductions in gross primary production and net ecosystem exchange. Yet, the spatiotemporal patterns of vulnerability to both short‐term extreme events and gradual environmental changes are quite uncertain across the species’ limits of tolerance to dryness. Such information is fundamental for defining ecologically relevant upper limits of species tolerance to drought and, hence, to predict the risk of increased forest mortality and shifts in species composition. We investigate here to what extent the impact of short‐ and long‐term environmental changes determines vulnerability to climate change of three evergreen conifers (Scots pine, silver fir, Norway spruce) and two deciduous hardwoods (European beech, sessile oak) tree species at their southernmost limits of distribution in the Mediterranean Basin. Finally, we simulated future forest growth under RCP 2.6 and 8.5 emission scenarios using a multispecies generalized linear mixed model. Our analysis provides four key insights into the patterns of species’ vulnerability to climate change. First, site climatic marginality was significantly linked to the growth trends: increasing growth was related to less climatically limited sites. Second, estimated species‐specific vulnerability did not match their a priori rank in drought tolerance: Scots pine and beech seem to be the most vulnerable species among those studied despite their contrasting physiologies. Third, adaptation to site conditions prevails over species‐specific determinism in forest response to climate change. And fourth, regional differences in forests vulnerability to climate change across the Mediterranean Basin are linked to the influence of summer atmospheric circulation patterns, which are not correctly represented in global climate models. Thus, projections of forest performance should reconsider the traditional classification of tree species in functional types and critically evaluate the fine‐scale limitations of the climate data generated by global climate models.     

Probabilistic measures of climate change vulnerability,adaptation action benefits,and related uncertainty from maximum temperature metric selection

Predictions of the projected changes in species distributions and potential adaptation action benefits can help guide conservation actions. There is substantial uncertainty in projecting species distributions into an unknown future, however, which can undermine confidence in predictions or misdirect conservation actions if not properly considered. Recent studies have shown that the selection of alternative climate metrics describing very different climatic aspects (e.g., mean air temperature vs. mean precipitation) can be a substantial source of projection uncertainty. It is unclear, however, how much projection uncertainty might stem from selecting among highly correlated, ecologically similar climate metrics (e.g., maximum temperature in July, maximum 30‐day temperature) describing the same climatic aspect (e.g., maximum temperatures) known to limit a species’ distribution. It is also unclear how projection uncertainty might propagate into predictions of the potential benefits of adaptation actions that might lessen climate change effects. We provide probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty stemming from the selection of four maximum temperature metrics for brook trout (Salvelinus fontinalis), a cold‐water salmonid of conservation concern in the eastern United States. Projected losses in suitable stream length varied by as much as 20% among alternative maximum temperature metrics for mid‐century climate projections, which was similar to variation among three climate models. Similarly, the regional average predicted increase in brook trout occurrence probability under an adaptation action scenario of full riparian forest restoration varied by as much as .2 among metrics. Our use of Bayesian inference provides probabilistic measures of vulnerability and adaptation action benefits for individual stream reaches that properly address statistical uncertainty and can help guide conservation actions. Our study demonstrates that even relatively small differences in the definitions of climate metrics can result in very different projections and reveal high uncertainty in predicted climate change effects.     

Using model‐data fusion to interpret past trends,and quantify uncertainties in future projections,of terrestrial ecosystem carbon cycling

Uncertainties in model projections of carbon cycling in terrestrial ecosystems stem from inaccurate parameterization of incorporated processes (endogenous uncertainties) and processes or drivers that are not accounted for by the model (exogenous uncertainties). Here, we assess endogenous and exogenous uncertainties using a model‐data fusion framework benchmarked with an artificial neural network (ANN). We used 18 years of eddy‐covariance carbon flux data from the Harvard forest, where ecosystem carbon uptake has doubled over the measurement period, along with 15 ancillary ecological data sets relative to the carbon cycle. We test the ability of combinations of diverse data to constrain projections of a process‐based carbon cycle model, both against the measured decadal trend and under future long‐term climate change. The use of high‐frequency eddy‐covariance data alone is shown to be insufficient to constrain model projections at the annual or longer time step. Future projections of carbon cycling under climate change in particular are shown to be highly dependent on the data used to constrain the model. Endogenous uncertainties in long‐term model projections of future carbon stocks and fluxes were greatly reduced by the use of aggregated flux budgets in conjunction with ancillary data sets. The data‐informed model, however, poorly reproduced interannual variability in net ecosystem carbon exchange and biomass increments and did not reproduce the long‐term trend. Furthermore, we use the model‐data fusion framework, and the ANN, to show that the long‐term doubling of the rate of carbon uptake at Harvard forest cannot be explained by meteorological drivers, and is driven by changes during the growing season. By integrating all available data with the model‐data fusion framework, we show that the observed trend can only be reproduced with temporal changes in model parameters. Together, the results show that exogenous uncertainty dominates uncertainty in future projections from a data‐informed process‐based model.     

Regional analysis of drought and heat impacts on forests: current and future science directions

Accurate assessments of forest response to current and future climate and human actions are needed at regional scales. Predicting future impacts on forests will require improved analysis of species‐level adaptation, resilience, and vulnerability to mortality. Land system models can be enhanced by creating trait‐based groupings of species that better represent climate sensitivity, such as risk of hydraulic failure from drought. This emphasizes the need for more coordinated in situ and remote sensing observations to track changes in ecosystem function, and to improve model inputs, spatio‐temporal diagnosis, and predictions of future conditions, including implications of actions to mitigate climate change.     

Vulnerability of the global terrestrial ecosystems to climate change

Climate change has far‐reaching impacts on ecosystems. Recent attempts to quantify such impacts focus on measuring exposure to climate change but largely ignore ecosystem resistance and resilience, which may also affect the vulnerability outcomes. In this study, the relative vulnerability of global terrestrial ecosystems to short‐term climate variability was assessed by simultaneously integrating exposure, sensitivity, and resilience at a high spatial resolution (0.05°). The results show that vulnerable areas are currently distributed primarily in plains. Responses to climate change vary among ecosystems and deserts and xeric shrublands are the most vulnerable biomes. Global vulnerability patterns are determined largely by exposure, while ecosystem sensitivity and resilience may exacerbate or alleviate external climate pressures at local scales; there is a highly significant negative correlation between exposure and sensitivity. Globally, 61.31% of the terrestrial vegetated area is capable of mitigating climate change impacts and those areas are concentrated in polar regions, boreal forests, tropical rainforests, and intact forests. Under current sensitivity and resilience conditions, vulnerable areas are projected to develop in high Northern Hemisphere latitudes in the future. The results suggest that integrating all three aspects of vulnerability (exposure, sensitivity, and resilience) may offer more comprehensive and spatially explicit adaptation strategies to reduce the impacts of climate change on terrestrial ecosystems.     

Microbial models with data‐driven parameters predict stronger soil carbon responses to climate change

Long‐term carbon (C) cycle feedbacks to climate depend on the future dynamics of soil organic carbon (SOC). Current models show low predictive accuracy at simulating contemporary SOC pools, which can be improved through parameter estimation. However, major uncertainty remains in global soil responses to climate change, particularly uncertainty in how the activity of soil microbial communities will respond. To date, the role of microbes in SOC dynamics has been implicitly described by decay rate constants in most conventional global carbon cycle models. Explicitly including microbial biomass dynamics into C cycle model formulations has shown potential to improve model predictive performance when assessed against global SOC databases. This study aimed to data‐constrained parameters of two soil microbial models, evaluate the improvements in performance of those calibrated models in predicting contemporary carbon stocks, and compare the SOC responses to climate change and their uncertainties between microbial and conventional models. Microbial models with calibrated parameters explained 51% of variability in the observed total SOC, whereas a calibrated conventional model explained 41%. The microbial models, when forced with climate and soil carbon input predictions from the 5th Coupled Model Intercomparison Project (CMIP5), produced stronger soil C responses to 95 years of climate change than any of the 11 CMIP5 models. The calibrated microbial models predicted between 8% (2‐pool model) and 11% (4‐pool model) soil C losses compared with CMIP5 model projections which ranged from a 7% loss to a 22.6% gain. Lastly, we observed unrealistic oscillatory SOC dynamics in the 2‐pool microbial model. The 4‐pool model also produced oscillations, but they were less prominent and could be avoided, depending on the parameter values.     

Multiple axes of ecological vulnerability to climate change

Observed ecological responses to climate change are highly individualistic across species and locations, and understanding the drivers of this variability is essential for management and conservation efforts. While it is clear that differences in exposure, sensitivity, and adaptive capacity all contribute to heterogeneity in climate change vulnerability, predicting these features at macroecological scales remains a critical challenge. We explore multiple drivers of heterogeneous vulnerability across the distributions of 96 vegetation types of the ecologically diverse western US, using data on observed climate trends from 1948 to 2014 to highlight emerging patterns of change. We ask three novel questions about factors potentially shaping vulnerability across the region: (a) How does sensitivity to different climate variables vary geographically and across vegetation classes? (b) How do multivariate climate exposure patterns interact with these sensitivities to shape vulnerability patterns? (c) How different are these vulnerability patterns according to three widely implemented vulnerability paradigms—niche novelty (decline in modeled suitability), temporal novelty (standardized anomaly), and spatial novelty (inbound climate velocity)—each of which uses a distinct frame of reference to quantify climate departure? We propose that considering these three novelty paradigms in combination could help improve our understanding and prediction of heterogeneous climate change responses, and we discuss the distinct climate adaptation strategies connected with different combinations of high and low novelty across the three metrics. Our results reveal a diverse mosaic of climate change vulnerability signatures across the region's plant communities. Each of the above factors contributes strongly to this heterogeneity: climate variable sensitivity exhibits clear patterns across vegetation types, multivariate climate change data reveal highly diverse exposure signatures across locations, and the three novelty paradigms diverge widely in their climate change vulnerability predictions. Together, these results shed light on potential drivers of individualistic climate change responses and may help to inform effective management strategies.