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

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

Decadal change in wetland–woodland boundaries during the late 20th century reflects climatic trends

Wetlands are important and restricted habitats for dependent biota and play vital roles in landscape function, hydrology and carbon sequestration. They are also likely to be one of the most sensitive components of the terrestrial biosphere to global climate change. An understanding of relationships between wetland persistence and climate is imperative for predicting, mitigating and adapting to the impacts of future climate change on wetland extent and function. We investigated whether mire wetlands had contracted, expanded or remained stable during 1960–2000. We chose a study area encompassing a regional climatic gradient in southeastern Australia, specifically to avoid confounding effects of water extraction on wetland hydrology and extent. We first characterized trends in climate by examining data from local weather stations, which showed a slight increase in precipitation and marked decline in pan evaporation over the relevant period. Remote sensing of vegetation boundaries showed a marked lateral expansion of mires during 1961–1998, and a corresponding contraction of woodland. The spatial patterns in vegetation change were consistent with the regional climatic gradient and showed a weaker co‐relationship to fire history. Resource exploitation, wildland fires and autogenic mire development failed to explain the observed expansion of mire vegetation in the absence of climate change. We therefore conclude that the extent of mire wetlands is likely to be sensitive to variation in climatic moisture over decadal time scales. Late 20th‐century trends in climatic moisture may be related primarily to reduced irradiance and/or reduced wind speeds. In the 21st century, however, net climatic moisture in this region is projected to decline. As mires are apparently sensitive to hydrological change, we anticipate lateral contraction of mire boundaries in coming decades as projected climatic drying eventuates. This raises concerns about the future hydrological functions, carbon storage capacity and unique biodiversity of these important ecosystems.     

Modeling plant species distributions under future climates: how fine scale do climate projections need to be?

Recent studies suggest that species distribution models (SDMs) based on fine‐scale climate data may provide markedly different estimates of climate‐change impacts than coarse‐scale models. However, these studies disagree in their conclusions of how scale influences projected species distributions. In rugged terrain, coarse‐scale climate grids may not capture topographically controlled climate variation at the scale that constitutes microhabitat or refugia for some species. Although finer scale data are therefore considered to better reflect climatic conditions experienced by species, there have been few formal analyses of how modeled distributions differ with scale. We modeled distributions for 52 plant species endemic to the California Floristic Province of different life forms and range sizes under recent and future climate across a 2000‐fold range of spatial scales (0.008–16 km²). We produced unique current and future climate datasets by separately downscaling 4 km climate models to three finer resolutions based on 800, 270, and 90 m digital elevation models and deriving bioclimatic predictors from them. As climate‐data resolution became coarser, SDMs predicted larger habitat area with diminishing spatial congruence between fine‐ and coarse‐scale predictions. These trends were most pronounced at the coarsest resolutions and depended on climate scenario and species' range size. On average, SDMs projected onto 4 km climate data predicted 42% more stable habitat (the amount of spatial overlap between predicted current and future climatically suitable habitat) compared with 800 m data. We found only modest agreement between areas predicted to be stable by 90 m models generalized to 4 km grids compared with areas classified as stable based on 4 km models, suggesting that some climate refugia captured at finer scales may be missed using coarser scale data. These differences in projected locations of habitat change may have more serious implications than net habitat area when predictive maps form the basis of conservation decision making.     

Climate versus weather extremes: Temporal predictor resolution matters for future rather than current regional species distribution models

Aim

Climate is considered a major driver of species distributions. Long‐term climatic means are commonly used as predictors in correlative species distribution models (SDMs). However, this coarse temporal resolution does not reflect local conditions that populations experience, such as short‐term weather extremes, which may have a strong impact on population dynamics and local distributions. We here compare the performance of climate‐ and weather‐based predictors in regional SDMs and their influence on future predictions, which are increasingly used in conservation planning.

Location

South‐western Germany.

Methods

We built different SDMs for 20 Orthoptera species based on three predictor sets at a regional scale for current and future climate scenarios. We calculated standard bioclimatic variables and yearly and seasonal sets of climate change indicating variables of weather extremes. As the impact of extreme events may be stronger for habitat specialists than for generalists, we distinguished species’ degrees of specialization. We computed linear mixed‐effects models to identify significant effects of algorithm, predictor set and specialization on model performance and calculated correlations and geographical niche overlap between spatial predictions.

Results

Current predictions were rather similar among all predictor sets, but highly variable for future climate scenarios. Bioclimatic and seasonal weather predictors performed slightly better than yearly weather predictors, though performance differences were minor. We found no evidence that specialists are more sensitive to weather extremes than generalists.

Main conclusions

For future projections of species distributions, SDM predictor selection should not solely be based on current performances and predictions. As long‐term climate and short‐term weather predictors represent different environmental drivers of a species’ distribution, we argue to interpret diverging future projections as complements. Even if similar current performances and predictions might imply their equivalency, favouring one predictor set neglects important aspects of future distributions and might mislead conservation decisions based on them.

     

Impact of landscape predictors on climate change modelling of species distributions: a case study with Eucalyptus fastigata in southern New South Wales,Australia

Aim We consider three questions. (1) How different are the predicted distribution maps when climate‐only and climate‐plus‐terrain models are developed from high‐resolution data? (2) What are the implications of differences between the models when predicting future distributions under climate change scenarios, particularly for climate‐only models at coarse resolution? (3) Does the use of high‐resolution data and climate‐plus‐terrain models predict an increase in the number of local refugia? Location South‐eastern New South Wales, Australia. Methods We developed two species distribution models for Eucalyptus fastigata under current climate conditions using generalized additive modelling. One used only climate variables as predictors (mean annual temperature, mean annual rainfall, mean summer rainfall); the other used both climate and landscape (June daily radiation, topographic position, lithology, nutrients) variables as predictors. Predictions of the distribution under current climate and climate change were then made for both models at a pixel resolution of 100 m. Results The model using climate and landscape variables as predictors explained a significantly greater proportion of the deviance than the climate‐only model. Inclusion of landscape variables resulted in the prediction of much larger areas of existing optimal habitat. An overlay of predicted future climate on the current climate space indicated that extrapolation of the statistical models was not occurring and models were therefore more robust. Under climate change, landscape‐defined refugia persisted in areas where the climate‐only model predicted major declines. In areas where expansion was predicted, the increase in optimal habitat was always greater with landscape predictors. Recognition of extensive optimal habitat conditions and potential refugia was dependent on the use of high‐resolution landscape data. Main conclusions Using only climate variables as predictors for assessing species responses to climate change ignores the accepted conceptual model of plant species distribution. Explicit statements justifying the selection of predictors based on ecological principles are needed. Models using only climate variables overestimate range reduction under climate change and fail to predict potential refugia. Fine‐scale‐resolution data are required to capture important climate/landscape interactions. Extrapolation of statistical models to regions in climate space outside the region where they were fitted is risky.     

Climate Change and Crop Exposure to Adverse Weather: Changes to Frost Risk and Grapevine Flowering Conditions

The cultivation of grapevines in the UK and many other cool climate regions is expected to benefit from the higher growing season temperatures predicted under future climate scenarios. Yet the effects of climate change on the risk of adverse weather conditions or events at key stages of crop development are not always captured by aggregated measures of seasonal or yearly climates, or by downscaling techniques that assume climate variability will remain unchanged under future scenarios. Using fine resolution projections of future climate scenarios for south-west England and grapevine phenology models we explore how risks to cool-climate vineyard harvests vary under future climate conditions. Results indicate that the risk of adverse conditions during flowering declines under all future climate scenarios. In contrast, the risk of late spring frosts increases under many future climate projections due to advancement in the timing of budbreak. Estimates of frost risk, however, were highly sensitive to the choice of phenology model, and future frost exposure declined when budbreak was calculated using models that included a winter chill requirement for dormancy break. The lack of robust phenological models is a major source of uncertainty concerning the impacts of future climate change on the development of cool-climate viticulture in historically marginal climatic regions.     

Consistent response of vegetation dynamics to recent climate change in tropical mountain regions

Global climate change has emerged as a major driver of ecosystem change. Here, we present evidence for globally consistent responses in vegetation dynamics to recent climate change in the world's mountain ecosystems located in the pan‐tropical belt (30°N–30°S). We analyzed decadal‐scale trends and seasonal cycles of vegetation greenness using monthly time series of satellite greenness (Normalized Difference Vegetation Index) and climate data for the period 1982–2006 for 47 mountain protected areas in five biodiversity hotspots. The time series of annual maximum NDVI for each of five continental regions shows mild greening trends followed by reversal to stronger browning trends around the mid‐1990s. During the same period we found increasing trends in temperature but only marginal change in precipitation. The amplitude of the annual greenness cycle increased with time, and was strongly associated with the observed increase in temperature amplitude. We applied dynamic models with time‐dependent regression parameters to study the time evolution of NDVI–climate relationships. We found that the relationship between vegetation greenness and temperature weakened over time or was negative. Such loss of positive temperature sensitivity has been documented in other regions as a response to temperature‐induced moisture stress. We also used dynamic models to extract the trends in vegetation greenness that remain after accounting for the effects of temperature and precipitation. We found residual browning and greening trends in all regions, which indicate that factors other than temperature and precipitation also influence vegetation dynamics. Browning rates became progressively weaker with increase in elevation as indicated by quantile regression models. Tropical mountain vegetation is considered sensitive to climatic changes, so these consistent vegetation responses across widespread regions indicate persistent global‐scale effects of climate warming and associated moisture stresses.     

Designing ecological climate change impact assessments to reflect key climatic drivers

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive – such as means or extremes – can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the ‘model space’ approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling.     

Soil moisture's underestimated role in climate change impact modelling in low‐energy systems

Shifts in precipitation regimes are an inherent component of climate change, but in low‐energy systems are often assumed to be less important than changes in temperature. Because soil moisture is the hydrological variable most proximally linked to plant performance during the growing season in arctic‐alpine habitats, it may offer the most useful perspective on the influence of changes in precipitation on vegetation. Here we quantify the influence of soil moisture for multiple vegetation properties at fine spatial scales, to determine the potential importance of soil moisture under changing climatic conditions. A fine‐scale data set, comprising vascular species cover and field‐quantified ecologically relevant environmental parameters, was analysed to determine the influence of soil moisture relative to other key abiotic predictors. Soil moisture was strongly related to community composition, species richness and the occurrence patterns of individual species, having a similar or greater influence than soil temperature, pH and solar radiation. Soil moisture varied considerably over short distances, and this fine‐scale heterogeneity may contribute to offsetting the ecological impacts of changes in precipitation for species not limited to extreme soil moisture conditions. In conclusion, soil moisture is a key driver of vegetation properties, both at the species and community level, even in this low‐energy system. Soil moisture conditions represent an important mechanism through which changing climatic conditions impact vegetation, and advancing our predictive capability will therefore require a better understanding of how soil moisture mediates the effects of climate change on biota.     

Eco-hydrological controls on summertime convective rainfall triggers

Triggers of summertime convective rainfall depend on numerous interactions and feedbacks, often compounded by spatial variability in soil moisture and its impacts on vegetation function, vegetation composition, terrain, and all the complex turbulent entrainment processes near the capping inversion. To progress even within the most restricted and idealized framework, many of the governing processes must be simplified and parameterized. In this work, a zeroth‐order representation of the dynamical processes that control convective rainfall triggers – namely land surface fluxes of heat and moisture – is proposed and used to develop a semianalytical model to explore how differential sensitivities of various ecosystems to soil moisture states modify convective rainfall triggers. The model is then applied to 4 years (2001–2004) of half‐hourly precipitation, soil moisture, environmental, and eddy‐covariance surface heat flux data collected at a mixed hardwood forest (HW), a maturing planted loblolly pine forest (PP), and an abandoned old field (OF) experiencing the same climatic and edaphic conditions. We found that the sensitivity of PP to soil moisture deficit enhances the trigger of convective rainfall relative to HW and OF, with enhancements of about 25% and 30% for dry moisture states, and 5% and 15% for moist soil moisture states, respectively. We discuss the broader implications of these findings on potential modulations of convective rainfall triggers induced by projected large‐scale changes in timberland composition within the Southeastern United States.     

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.     

Incorporating microclimate into species distribution models

Species distribution models (SDMs) have rapidly evolved into one of the most widely used tools to answer a broad range of ecological questions, from the effects of climate change to challenges for species management. Current SDMs and their predictions under anthropogenic climate change are, however, often based on free‐air or synoptic temperature conditions with a coarse resolution, and thus fail to capture apparent temperature (cf. microclimate) experienced by living organisms within their habitats. Yet microclimate operates as soon as a habitat can be characterized by a vertical component (e.g. forests, mountains, or cities) or by horizontal variation in surface cover. The mismatch between how we usually express climate (cf. coarse‐grained free‐air conditions) and the apparent microclimatic conditions that living organisms experience has only recently been acknowledged in SDMs, yet several studies have already made considerable progress in tackling this problem from different angles. In this review, we summarize the currently available methods to obtain meaningful microclimatic data for use in distribution modelling. We discuss the issue of extent and resolution, and propose an integrated framework using a selection of appropriately‐placed sensors in combination with both the detailed measurements of the habitat 3D structure, for example derived from digital elevation models or airborne laser scanning, and the long‐term records of free‐air conditions from weather stations. As such, we can obtain microclimatic data with a relevant spatiotemporal resolution and extent to dynamically model current and future species distributions.     

Marine influence controls plant phenological dynamics in Mediterranean Mexico

Aims To assess the role of moisture in phenological timing in the mediterranean coastal flora of Baja California, and specifically to assess the role of coastal fog and ocean-derived moisture in plant phenology. Moisture seems to be the primary driver of flowering times and durations at the arid end of the mediterranean-climate region, where rainfall is often sporadic (temperature and day length can be expected to play a much lesser role as they are not growth limiting). We aimed to understand: What factors drive climatic variation between sites? Are there general flowering patterns allowing us to identify phenological categories? Do flowering patterns vary in relation to site-specific weather? and most importantly, does maritime influence on weather affect flowering dynamics in coastal mediterranean ecosystems?Methods The southernmost extent of the California Floristic Province (in Baja California, Mexico) is a biological diversity hotspot of high endemism and conservation value, with two steep moisture gradients: rainfall (N–S) and coastal fogs (W–E), providing an ideal study system. We installed five weather stations across the moisture gradients, recording data hourly. We monitored flowering phenology in the square kilometer surrounding each weather station from 2010 to 2013. About 86 plant taxa were monitored across the five sites, every 6–8 weeks. Averaged climatic data is presented with general trends in flowering, and specific flowering syndromes were observed. Data for flowering intensity across the sites was analyzed using a principal components analysis.Important findings Data analysis demonstrates a general seasonal pattern in flowering times, but distinct differences in local weather and phenology between the five study sites. Three flowering syndromes are revealed in the flora: (i) water responders or spring bloomers, (ii) day-length responders or fall-blooming taxa and (iii) aseasonal bloomers with no seasonal affinity. The two moisture gradients are the strongest drivers of flowering times. Inland sites showed higher phenological variation than coastal sites where seasonality is dampened by ocean-derived moisture, which extends and buffers perennial plant phenology and is a probable driver of local endemism. Phenological controls vary globally with climate and geography; moisture is the primary driver of phenology in mediterranean climates and fog is an important climatic variable in coastal Mexico.     

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.     

Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia

Climate refugia are regions that animals can retreat to, persist in and potentially then expand from under changing environmental conditions. Most forecasts of climate change refugia for species are based on correlative species distribution models (SDMs) using long‐term climate averages, projected to future climate scenarios. Limitations of such methods include the need to extrapolate into novel environments and uncertainty regarding the extent to which proximate variables included in the model capture processes driving distribution limits (and thus can be assumed to provide reliable predictions under new conditions). These limitations are well documented; however, their impact on the quality of climate refugia predictions is difficult to quantify. Here, we develop a detailed bioenergetics model for the koala. It indicates that range limits are driven by heat‐induced water stress, with the timing of rainfall and heat waves limiting the koala in the warmer parts of its range. We compare refugia predictions from the bioenergetics model with predictions from a suite of competing correlative SDMs under a range of future climate scenarios. SDMs were fitted using combinations of long‐term climate and weather extremes variables, to test how well each set of predictions captures the knowledge embedded in the bioenergetics model. Correlative models produced broadly similar predictions to the bioenergetics model across much of the species' current range – with SDMs that included weather extremes showing highest congruence. However, predictions in some regions diverged significantly when projecting to future climates due to the breakdown in correlation between climate variables. We provide unique insight into the mechanisms driving koala distribution and illustrate the importance of subtle relationships between the timing of weather events, particularly rain relative to hot‐spells, in driving species–climate relationships and distributions. By unpacking the mechanisms captured by correlative SDMs, we can increase our certainty in forecasts of climate change impacts on species.     

Spatial scale,topography and thermoregulatory behaviour interact when modelling species’ thermal niches

The spatial scale at which climate and species’ occupancy data are gathered, and the resolution at which ecological models are run, can strongly influence predictions of species performance and distributions. Running model simulations at coarse rather than fine spatial resolutions, for example, can determine if a model accurately predicts the distribution of a species. The impacts of spatial scale on a model's accuracy are particularly pronounced across mountainous terrain. Understanding how these discrepancies arise requires a modelling approach in which the underlying processes that determine a species’ distribution are explicitly described. Here we use a process‐based model to explore how spatial resolution, topography and behaviour alter predictions of a species thermal niche, which in turn constrains its survival and geographic distribution. The model incorporates biophysical equations to predict the operative temperature (T_e), thermal‐dependent performance and survival of a typical insect, with a complex life‐cycle, in its microclimate. We run this model with geographic data from a mountainous terrain in South Africa using climate data at three spatial resolutions. We also explore how behavioural thermoregulation affects predictions of a species performance and survival by allowing the animal to select the optimum thermal location within each coarse‐grid cell. At the regional level, coarse‐resolution models predicted lower T_e at low elevations and higher T_e at high elevations than models run at fine‐resolutions. These differences were more prominent on steep, north‐facing slopes. The discrepancies in T_e in turn affected estimates of the species thermal niche. The modelling framework revealed how spatial resolution and topography influence predictions of species distribution models, including the potential impacts of climate change. These systematic biases must be accounted for when interpreting the outputs of future modelling studies, particularly when species distributions are predicted to shift from uniform to topographically heterogeneous landscapes.     

Wildfire refugia in forests: Severe fire weather and drought mute the influence of topography and fuel age

Wildfire refugia (unburnt patches within large wildfires) are important for the persistence of fire‐sensitive species across forested landscapes globally. A key challenge is to identify the factors that determine the distribution of fire refugia across space and time. In particular, determining the relative influence of climatic and landscape factors is important in order to understand likely changes in the distribution of wildfire refugia under future climates. Here, we examine the relative effect of weather (i.e. fire weather, drought severity) and landscape features (i.e. topography, fuel age, vegetation type) on the occurrence of fire refugia across 26 large wildfires in south‐eastern Australia. Fire weather and drought severity were the primary drivers of the occurrence of fire refugia, moderating the effect of landscape attributes. Unburnt patches rarely occurred under ‘severe’ fire weather, irrespective of drought severity, topography, fuels or vegetation community. The influence of drought severity and landscape factors played out most strongly under ‘moderate’ fire weather. In mesic forests, fire refugia were linked to variables that affect fuel moisture, whereby the occurrence of unburnt patches decreased with increasing drought conditions and were associated with more mesic topographic locations (i.e. gullies, pole‐facing aspects) and vegetation communities (i.e. closed‐forest). In dry forest, the occurrence of refugia was responsive to fuel age, being associated with recently burnt areas (<5 years since fire). Overall, these results show that increased severity of fire weather and increased drought conditions, both predicted under future climate scenarios, are likely to lead to a reduction of wildfire refugia across forests of southern Australia. Protection of topographic areas able to provide long‐term fire refugia will be an important step towards maintaining the ecological integrity of forests under future climate change.     

Moisture‐induced greening of the South Asia over the past three decades

South Asia experienced a weakening of summer monsoon circulation in the past several decades, resulting in rainfall decline in wet regions. In comparison with other tropical ecosystems, quantitative assessments of the extent and triggers of vegetation change are lacking in assessing climate‐change impacts over South Asia dominated by crops. Here, we use satellite‐based Normalized Difference Vegetation Index (NDVI) to quantify spatial–temporal changes in vegetation greenness, and find a widespread annual greening trend that stands in contrast to the weakening of summer monsoon circulation particularly over the last decade. We further show that moisture supply is the primary factor limiting vegetation activity during dry season or in dry region, and cloud cover or temperature would become increasingly important in wet region. Enhanced moisture conditions over dry region, coinciding with the decline in monsoon, are mainly responsible for the widespread greening trend. This result thereby cautions the use of a unified monsoon index to predict South Asia's vegetation dynamics. Current climate–carbon models in general correctly reproduce the dominant control of moisture in the temporal characteristics of vegetation productivity. But the model ensemble cannot exactly reproduce the spatial pattern of satellite‐based vegetation change mainly because of biases in climate simulations. The moisture‐induced greening over South Asia, which is likely to persist into the wetter future, has significant implications for regional carbon cycling and maintaining food security.     

Modeled ecohydrological responses to climate change at seven small watersheds in the northeastern United States

A cross‐site analysis was conducted on seven diverse, forested watersheds in the northeastern United States to evaluate hydrological responses (evapotranspiration, soil moisture, seasonal and annual streamflow, and water stress) to projections of future climate. We used output from four atmosphere–ocean general circulation models (AOGCMs; CCSM4, HadGEM2‐CC, MIROC5, and MRI‐CGCM3) included in Phase 5 of the Coupled Model Intercomparison Project, coupled with two Representative Concentration Pathways (RCP 8.5 and 4.5). The coarse resolution AOGCMs outputs were statistically downscaled using an asynchronous regional regression model to provide finer resolution future climate projections as inputs to the deterministic dynamic ecosystem model PnET‐BGC. Simulation results indicated that projected warmer temperatures and longer growing seasons in the northeastern United States are anticipated to increase evapotranspiration across all sites, although invoking CO₂ effects on vegetation (growth enhancement and increases in water use efficiency (WUE)) diminish this response. The model showed enhanced evapotranspiration resulted in drier growing season conditions across all sites and all scenarios in the future. Spruce‐fir conifer forests have a lower optimum temperature for photosynthesis, making them more susceptible to temperature stress than more tolerant hardwood species, potentially giving hardwoods a competitive advantage in the future. However, some hardwood forests are projected to experience seasonal water stress, despite anticipated increases in precipitation, due to the higher temperatures, earlier loss of snow packs, longer growing seasons, and associated water deficits. Considering future CO₂ effects on WUE in the model alleviated water stress across all sites. Modeled streamflow responses were highly variable, with some sites showing significant increases in annual water yield, while others showed decreases. This variability in streamflow responses poses a challenge to water resource management in the northeastern United States. Our analyses suggest that dominant vegetation type and soil type are important attributes in determining future hydrological responses to climate change.