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Certain general facets of biotic response to climate change, such as shifts in phenology and geographic distribution, are well characterized; however, it is not clear whether the observed similarity of responses across taxa will extend to variation in other population-level processes. We examined population response to climatic variation using long-term incidence data (collected over 42 years) encompassing 149 butterfly species and considerable habitat diversity (10 sites along an elevational gradient from sea level to over 2,700 m in California). Population responses were characterized by extreme heterogeneity that was not attributable to differences in species composition among sites. These results indicate that habitat heterogeneity might be a buffer against climate change and highlight important questions about mechanisms maintaining interpopulation differences in responses to weather. Despite overall heterogeneity of response, population dynamics were accurately predicted by our model for many species at each site. However, the overall correlation between observed and predicted incidence in a cross validation analysis was moderate (Pearson's r = 0.23, SE 0.01), and 97% of observed data fell within the predicted 95% credible intervals. Prediction was most successful for more abundant species as well as for sites with lower annual turnover. Population-level heterogeneity in response to climate variation and the limits of our predictive power highlight the challenges for a future of increasing climatic variability., (© 2019 John Wiley & Sons Ltd.)

Climate and land-use changes are expected to be the primary drivers of future global biodiversity loss. Although theory suggests that these factors impact species synergistically, past studies have either focused on only one in isolation or have substituted space for time, which often results in confounding between drivers. Tests of synergistic effects require congruent time series on animal populations, climate change and land-use change replicated across landscapes that span the gradient of correlations between the drivers of change. Using a unique time series of high-resolution climate (measured as temperature and precipitation) and land-use change (measured as forest change) data, we show that these drivers of global change act synergistically to influence forest bird population declines over 29 years in the Pacific Northwest of the United States. Nearly half of the species examined had declined over this time. Populations declined most in response to loss of early seral and mature forest, with responses to loss of early seral forest amplified in landscapes that had warmed over time. In addition, birds declined more in response to loss of mature forest in areas that had dried over time. Climate change did not appear to impact populations in landscapes with limited habitat loss, except when those landscapes were initially warmer than the average landscape. Our results provide some of the first empirical evidence of synergistic effects of climate and land-use change on animal population dynamics, suggesting accelerated loss of biodiversity in areas under pressure from multiple global change drivers. Furthermore, our findings suggest strong spatial variability in the impacts of climate change and highlight the need for future studies to evaluate multiple drivers simultaneously to avoid potential misattribution of effects., (© 2019 John Wiley & Sons Ltd.)

Phenological changes have been observed in a variety of systems over the past century. There is concern that, as a consequence, ecological interactions are becoming increasingly mismatched in time, with negative consequences for ecological function. Significant spatial heterogeneity (inter-site) and temporal variability (inter-annual) can make it difficult to separate intrinsic, extrinsic and stochastic drivers of phenological variability. The goal of this study was to understand the timing and variability in breeding phenology of Adélie penguins under fixed environmental conditions and to use those data to identify a "null model" appropriate for disentangling the sources of variation in wild populations. Data on clutch initiation were collected from both wild and captive populations of Adélie penguins. Clutch initiation in the captive population was modelled as a function of year, individual and age to better understand phenological patterns observed in the wild population. Captive populations displayed as much inter-annual variability in breeding phenology as wild populations, suggesting that variability in breeding phenology is the norm and thus may be an unreliable indicator of environmental forcing. The distribution of clutch initiation dates was found to be moderately asymmetric (right skewed) both in the wild and in captivity, consistent with the pattern expected under social facilitation. The role of stochasticity in phenological processes has heretofore been largely ignored. However, these results suggest that inter-annual variability in breeding phenology can arise independent of any environmental or demographic drivers and that synchronous breeding can enhance inherent stochasticity. This complicates efforts to relate phenological variation to environmental variability in the wild. Accordingly, we must be careful to consider random forcing in phenological processes, lest we fit models to data dominated by random noise. This is particularly true for colonial species where breeding synchrony may outweigh each individual's effort to time breeding with optimal environmental conditions. Our study highlights the importance of identifying appropriate null models for studying phenology., (© 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.)

Evidence of climate-change-driven shifts in plant and animal phenology have raised concerns that certain trophic interactions may be increasingly mismatched in time, resulting in declines in reproductive success. Given the constraints imposed by extreme seasonality at high latitudes and the rapid shifts in phenology seen in the Arctic, we would also expect Antarctic species to be highly vulnerable to climate-change-driven phenological mismatches with their environment. However, few studies have assessed the impacts of phenological change in Antarctica. Using the largest database of phytoplankton phenology, sea-ice phenology, and Adélie Penguin breeding phenology and breeding success assembled to date, we find that, while a temporal match between Penguin breeding phenology and optimal environmental conditions sets an upper limit on breeding success, only a weak relationship to the mean exists. Despite previous work suggesting that divergent trends in Adélie Penguin breeding phenology are apparent across the Antarctic continent, we find no such trends. Furthermore, we find no trend in the magnitude of phenological mismatch, suggesting that mismatch is driven by interannual variability in environmental conditions rather than climate-change-driven trends, as observed in other systems. We propose several criteria necessary for a species to experience a strong climate-change-driven phenological mismatch, of which several may be violated by this system., (© 2017 by the Ecological Society of America.)

In this paper, I present and discuss a novel approach to parameterize a gravity model of migration using Bayesian hierarchical models with random intercepts that are free to vary by country of origin, destination, and directed origin-destination country pairs. I then utilize this model to project transboundary migration flows between African countries to the horizon 2050. To do so, I use data on projected future crop yields and water availability from the ISIMIP2b scenarios in combination with projections on future economic and demographic trends from the Shared Socio-Economic Pathways (SSPs). The results indicate that over the period 2010–2050 between 8 to 17 millions people are projected to migrate internationally on the African continent. Yet, only a small portion of these migrants will be induced to move because of climate change. To the contrary, comparisons between SSPs scenarios suggests that economic development will have a far larger impact on projected level of international migration on the continent than climate change. [ABSTRACT FROM AUTHOR]

In this paper, I present and discuss a novel approach to parameterize a gravity model of migration using Bayesian hierarchical models with random intercepts that are free to vary by country of origin, destination, and directed origin-destination country pairs. I then utilize this model to project transboundary migration flows between African countries to the horizon 2050. To do so, I use data on projected future crop yields and water availability from the ISIMIP2b scenarios in combination with projections on future economic and demographic trends from the Shared Socio-Economic Pathways (SSPs). The results indicate that over the period 2010–2050 between 8 to 17 millions people are projected to migrate internationally on the African continent. Yet, only a small portion of these migrants will be induced to move because of climate change. To the contrary, comparisons between SSPs scenarios suggests that economic development will have a far larger impact on projected level of international migration on the continent than climate change.

Climate and land use change are two of the primary threats to global biodiversity; however, each species within a community may respond differently to these facets of global change. Although it is typically assumed that species use the habitat that is advantageous for survival and reproduction, anthropogenic changes to the environment can create ecological traps, making it critical to assess both habitat selection (e.g. where species congregate on the landscape) and the influence of selected habitats on the demographic processes that govern population dynamics.We used a long‐term (1958–2011), large‐scale, multi‐species dataset for waterfowl that spans the United States and Canada to estimate species‐specific responses to climate and land use variables in a landscape that has undergone significant environmental change across space and time. We first estimated the effects of change in climate and land use variables on habitat selection and population dynamics for nine species. We then hypothesized that species‐specific responses to environmental change would scale with life‐history traits, specifically: longevity, nesting phenology and female breeding site fidelity.We observed species‐level heterogeneity in the demographic and habitat selection responses to climate and land use change, which would complicate community‐level habitat management. Our work highlights the importance of multi‐species monitoring and community‐level analysis, even among closely related species.We detected several relationships between life‐history traits, particularly nesting phenology, and species' responses to environmental change. One species, the early‐nesting northern pintail (Anas acuta), was consistently at the extreme end of responses to land use and climate predictors and has been a species of conservation concern since their population began to decline in the 1980s. They, and the blue‐winged teal, also demonstrated a positive habitat selection response to the proportion of cropland on the landscape that simultaneously reduced abundance the following year, indicative of susceptibility to ecological traps.By distilling the diversity of species' responses to environmental change within a community, our methodological approach and findings will help improve predictions of community responses to global change and can inform multi‐species management and conservation plans in dynamic landscapes that are based on simple tenets of life‐history theory. [ABSTRACT FROM AUTHOR]

We developed spatial Bayesian hierarchical models to assess potential climate change impacts on suitable habitat for five important tree species in the Pacific northwestern United States (California, Oregon, and Washington). Individual‐species models were fit with presence–absence data from forest inventory field plots and spatial relationships were specified through a conditional autoregressive model. This modeling approach allowed us to visualize uncertainty in response curves, map current and future prediction uncertainty, and provide interval estimates for change. Upward elevational or northward latitudinal shifts in climatically suitable habitat were projected for all species. Climate change impacts were the most damaging for noble fir (Abies procera), for which 79%–100% of the current range was projected to become climatically unsuitable by the 2080s. Although coastal Douglas‐fir (Pseudotsuga menziesii var. menziesii) has been projected by others to gain habitat in Canada, within our study area we projected a net loss of climatically suitable habitat (ca. 8000–31,400 km2) under three of four future climate scenarios. A net loss in habitat was also projected for Oregon white oak (Quercus garryana) under three of four scenarios, with 40%–60% of the current range becoming unsuitable. Although there was no net loss of habitat for forest land blue oak under any scenario, other factors like competition may inhibit blue oak (Quercus douglasii) and white oak from occupying areas projected to increase in climatic suitability. Additionally, between 13% and 32% of blue oak's current range was projected to become unsuitable; some of these areas aligned with dieback following the 2012–2015 California drought, which our data set predates. Unlike the other four species, we projected a 17%–25% increase in climatically suitable habitat for California black oak (Quercus kelloggii), although 1%–20% of the current range was still projected to become unsuitable. Our findings indicate that, although some species will face more pressure in tracking climatically suitable habitat than others, climate change will impact the location of suitable habitat for many species. [ABSTRACT FROM AUTHOR]

Abstract We developed spatial Bayesian hierarchical models to assess potential climate change impacts on suitable habitat for five important tree species in the Pacific northwestern United States (California, Oregon, and Washington). Individual‐species models were fit with presence–absence data from forest inventory field plots and spatial relationships were specified through a conditional autoregressive model. This modeling approach allowed us to visualize uncertainty in response curves, map current and future prediction uncertainty, and provide interval estimates for change. Upward elevational or northward latitudinal shifts in climatically suitable habitat were projected for all species. Climate change impacts were the most damaging for noble fir (Abies procera), for which 79%–100% of the current range was projected to become climatically unsuitable by the 2080s. Although coastal Douglas‐fir (Pseudotsuga menziesii var. menziesii) has been projected by others to gain habitat in Canada, within our study area we projected a net loss of climatically suitable habitat (ca. 8000–31,400 km2) under three of four future climate scenarios. A net loss in habitat was also projected for Oregon white oak (Quercus garryana) under three of four scenarios, with 40%–60% of the current range becoming unsuitable. Although there was no net loss of habitat for forest land blue oak under any scenario, other factors like competition may inhibit blue oak (Quercus douglasii) and white oak from occupying areas projected to increase in climatic suitability. Additionally, between 13% and 32% of blue oak's current range was projected to become unsuitable; some of these areas aligned with dieback following the 2012–2015 California drought, which our data set predates. Unlike the other four species, we projected a 17%–25% increase in climatically suitable habitat for California black oak (Quercus kelloggii), although 1%–20% of the current range was still projected to become unsuitable. Our findings indicate that, although some species will face more pressure in tracking climatically suitable habitat than others, climate change will impact the location of suitable habitat for many species.

An important and largely unaddressed issue in studies of biotic-abiotic relationships is the extent to which closely related species, or species living in similar habitats, have similar responses to weather. We addressed this by applying a hierarchical, Bayesian analytical framework to a long-term data set for butterflies which allowed us to simultaneously investigate responses of the entire fauna and individual species. A small number of variables had community-level effects. In particular, higher total annual snow depth had a positive effect on butterfly occurrences, while spring minimum temperature and El Niño-Southern Oscillation (ENSO) sea-surface variables for April-May had negative standardized coefficients. Our most important finding was that variables with large impacts at the community-level did not necessarily have a consistent response across all species. Species-level responses were much more similar to each other for snow depth compared to the other variables with strong community effects. This variation in species-level responses to weather variables raises important complications for the prediction of biotic responses to shifting climatic conditions. In addition, we found that clear associations with weather can be detected when considering ecologically delimited subsets of the community. For example, resident species and non-ruderal species had a much more unified response to weather variables compared to non-resident species and ruderal species, which suggests local adaptation to climate. These results highlight the complexity of biotic-abiotic interactions and confront that complexity with methodological advances that allow ecologists to understand communities and shifting climates while simultaneously revealing species-specific variation in response to climate.

Climate and land‐use changes are expected to be the primary drivers of future global biodiversity loss. Although theory suggests that these factors impact species synergistically, past studies have either focused on only one in isolation or have substituted space for time, which often results in confounding between drivers. Tests of synergistic effects require congruent time series on animal populations, climate change and land‐use change replicated across landscapes that span the gradient of correlations between the drivers of change. Using a unique time series of high‐resolution climate (measured as temperature and precipitation) and land‐use change (measured as forest change) data, we show that these drivers of global change act synergistically to influence forest bird population declines over 29 years in the Pacific Northwest of the United States. Nearly half of the species examined had declined over this time. Populations declined most in response to loss of early seral and mature forest, with responses to loss of early seral forest amplified in landscapes that had warmed over time. In addition, birds declined more in response to loss of mature forest in areas that had dried over time. Climate change did not appear to impact populations in landscapes with limited habitat loss, except when those landscapes were initially warmer than the average landscape. Our results provide some of the first empirical evidence of synergistic effects of climate and land‐use change on animal population dynamics, suggesting accelerated loss of biodiversity in areas under pressure from multiple global change drivers. Furthermore, our findings suggest strong spatial variability in the impacts of climate change and highlight the need for future studies to evaluate multiple drivers simultaneously to avoid potential misattribution of effects. [ABSTRACT FROM AUTHOR]

Interactions between drought and insect defoliation may dramatically alter forest function under novel climate and disturbance regimes, but remain poorly understood. We empirically tested two important hypotheses regarding tree responses to drought and insect defoliation: (a) trees exhibit delayed, persistent, and cumulative growth responses to these stressors; (b) physiological feedbacks in tree responses to these stressors exacerbate their impacts on tree growth. These hypotheses remain largely untested at a landscape scale, yet are critical for predicting forest function under novel future conditions, given the connection between tree growth and demographic processes such as mortality and regeneration.We developed a Bayesian hierarchical model to quantify the ecological memory of tree growth to past water deficits and insect defoliation events, derive antecedent variables reflecting the persistent and cumulative effects of these stressors on current growth, and test for their interactive effects. The model was applied to extensive tree growth, weather, and defoliation survey data from western and eastern regions of the Canadian boreal forest impacted by recent drought and defoliation events and characterized by contrasting tree compositions, climates, and insect defoliators.Results revealed persistent negative tree growth responses to past water (all trees) and defoliation (host trees) stress lasting 3–6 and 10–12 years, respectively, depending on study region. Accounting for the ecological memory of tree growth to water and defoliation stress allowed for detection of interactions not previously demonstrated. Contrary to expectations, we found evidence for positive interactions among non‐host trees likely due to reduced water stress following defoliation events. Regional differences in ecological memory to water stress highlight the role of climate in shaping forest responses to drought.Synthesis. Study results suggest negative feedbacks in tree responses to drought and insect attack may be weaker than predicted for defoliator‐dominated boreal forest systems. Instead, insect defoliation may offset the impacts of water deficit on boreal tree growth by reducing transpirational water demand. This offset mimics increased resistance to drought following forest thinning and may lessen growth and mortality losses due to increased aridity and more severe insect damage forecast for the boreal forest under global change. [ABSTRACT FROM AUTHOR]

Understanding the genetic bases underlying climate adaptation is a key element to predict the potential of species to face climate warming. Although substantial climate variation is observed at a micro-geographic scale, most genomic maps of climate adaptation have been established at broader geographical scales. Here, by using a Pool-Seq approach combined with a Bayesian hierarchical model that control for confounding by population structure, we performed a genome–environment association (GEA) analysis to investigate the genetic basis of adaptation to six climate variables in 168 natural populations of Arabidopsis thaliana distributed in south-west of France. Climate variation among the 168 populations represented up to 24% of climate variation among 521 European locations where A. thaliana inhabits. We identified neat and strong peaks of association, with most of the associated SNPs being significantly enriched in likely functional variants and/or in the extreme tail of genetic differentiation among populations. Furthermore, genes involved in transcriptional mechanisms appear predominant in plant functions associated with local climate adaptation. Globally, our results suggest that climate adaptation is an important driver of genomic variation in A. thaliana at a small spatial scale and mainly involves genome-wide changes in fundamental mechanisms of gene regulation. The identification of climate-adaptive genetic loci at a micro-geographic scale also highlights the importance to include within-species genetic diversity in ecological niche models for projecting potential species distributional shifts over short geographic distances.

Aim: Environmental changes that amplify rates of site or patch occupancy turnover can increase risks of decline in spatially structured populations. We asked whether local habitat and meso‐scale climate influenced site occupancy turnover rates in four American pika (Ochotona princeps) metapopulations. We focused on winter cold stress, which is a proposed driver of American pika extinction risk but has been rarely studied. Location: Oregon, Northern California, and Idaho. Methods: We developed Bayesian hierarchical multiseason site occupancy models that accounted for both false‐negative and false‐positive survey detection errors to explore the winter stress turnover hypothesis. We used remotely sensed meso‐scale (1 km) snowpack and temperature data and fine‐grained local habitat attributes as covariates to model site persistence and colonization rates. Results: The estimated magnitude of imperfect detection was greater than previously reported for the species. After accounting for imperfect detection, we found no evidence of declines in site occupancy over the 5‐year study period, but our models provided evidence that pika site occupancy turnover can be high (>50% between some years) and apparently exacerbated by winter cold stress, summer heat stress and variation in site habitat quality. However, strength of evidence varied among metapopulations, suggesting influential local contingencies, as reported previously for the species. Main conclusions: Our empirical results suggest that the American pika may be vulnerable to wintertime turnover, negatively exacerbated by climatic events, with implications for future persistence given forecasted snowpack declines across the species' range. Our models suggest a more nuanced dynamism to persistence and extinction risk than the simple scenario of inexorable, monotonic range contraction offered by static range‐wide distribution models, and we suggest several ways to strengthen these insights with future studies. [ABSTRACT FROM AUTHOR]

Understanding the genetic bases underlying climate adaptation is a key element to predict the potential of species to face climate warming. Although substantial climate variation is observed at a micro-geographic scale, most genomic maps of climate adaptation have been established at broader geographical scales. Here, by using a Pool-Seq approach combined with a Bayesian hierarchical model that control for confounding by population structure, we performed a genome–environment association (GEA) analysis to investigate the genetic basis of adaptation to six climate variables in 168 natural populations of Arabidopsis thaliana distributed in south-west of France. Climate variation among the 168 populations represented up to 24% of climate variation among 521 European locations where A. thaliana inhabits. We identified neat and strong peaks of association, with most of the associated SNPs being significantly enriched in likely functional variants and/or in the extreme tail of genetic differentiation among populations. Furthermore, genes involved in transcriptional mechanisms appear predominant in plant functions associated with local climate adaptation. Globally, our results suggest that climate adaptation is an important driver of genomic variation in A. thaliana at a small spatial scale and mainly involves genome-wide changes in fundamental mechanisms of gene regulation. The identification of climate-adaptive genetic loci at a micro-geographic scale also highlights the importance to include within-species genetic diversity in ecological niche models for projecting potential species distributional shifts over short geographic distances. [ABSTRACT FROM AUTHOR]

The spatiotemporal dynamics of multiple animal populations may exhibit the hierarchical dynamic patterns including regional common trends or spatial synchrony and local variability. Environmental drivers such as climatic variability, operating on large spatial scales, have been proposed to be a driver of the spatial synchrony of avian and mammalian populations (i.e., the Moran effect). On the other hand, demographic responses to heterogeneous, local environments may result in substantial local population variability (i.e., variance across multiple populations). This study used the Bayesian hierarchical spatiotemporal dynamic model to determine the spatiotemporal dynamic patterns of 27 eastern wild turkey ( Meleagris gallopavo silvestris ) populations scattered across Mississippi, USA, from 1994 to 2005. The regional effects of seasonal climate and relative amount of forest in habitats were also evaluated to test the Moran effect. Twenty seven wild turkey populations had a spatial synchrony with the strength and spatial scale similar to those of other avian populations in North America. Warming spring or spring-autumn climate appeared to reduce wild turkey relative abundance; however, winter climate did not influence the dynamics of wild turkey populations in Mississippi. Average proportion of forest affected the average relative abundance of wild turkeys in a nonlinear manner, with its positive effects switching to a lessened constant effect beyond the threshold of 60% forest cover. However, regional climatic variability was unlikely to synchronize wild turkey populations in Mississippi. This study proposed a hypothesis concerning regional habitat quality degradation, which may cause regional population declines and synchronize the dynamics of animal populations. [ABSTRACT FROM AUTHOR]

Extreme precipitation plays a major role in flooding events and their occurrence and intensity are expected to increase. Because climate models are the only tools for providing quantitative projections of precipitation, flood risk management for the future climate may be based on the simulation of such events. The goal of the paper is to develop a spatiotemporal statistical model for extremes that is particularly suited to climate model outputs, which are transient and lie on a regular grid. Using the statistical model proposed, projected precipitation return levels for the coming climate over North America are estimated. [ABSTRACT FROM AUTHOR]

Responses of forest ecosystems to increased atmospheric CO2 concentration have been studied in few free-air CO2 enrichment ( FACE) experiments during last two decades. Most studies focused principally on the overstory trees with little attention given to understory vegetation. Despite its small contribution to total productivity of an ecosystem, understory vegetation plays an important role in predicting successional dynamics and future plant community composition. Thus, the response of understory vegetation in Pinus taeda plantation at the Duke Forest FACE site after 15-17 years of exposure to elevated CO2, 6-13 of which with nitrogen (N) amendment, was examined. Aboveground biomass and density of the understory decreased across all treatments with increasing overstory leaf area index ( LAI). However, the CO2 and N treatments had no effect on aboveground biomass, tree density, community composition, and the fraction of shade-tolerant species. The increases of overstory LAI (~28%) under elevated CO2 resulted in a reduction of light available to the understory (~18%) sufficient to nullify the expected growth-enhancing effect of elevated CO2 on understory vegetation. [ABSTRACT FROM AUTHOR]

Reconstruction of pre-instrumental, late Holocene climate is important for understanding how climate has changed in the past and how climate might change in the future. Statistical prediction of paleoclimate from tree ring widths is challenging because tree ring widths are a one-dimensional summary of annual growth that represents a multi-dimensional set of climatic and biotic influences. We develop a Bayesian hierarchical framework using a nonlinear, biologically motivated tree ring growth model to jointly reconstruct temperature and precipitation in the Hudson Valley, New York. Using a common growth function to describe the response of a tree to climate, we allow for species-specific parameterizations of the growth response. To enable predictive backcasts, we model the climate variables with a vector autoregressive process on an annual timescale coupled with a multivariate conditional autoregressive process that accounts for temporal correlation and cross-correlation between temperature and precipitation on a monthly scale. Our multi-scale temporal model allows for flexibility in the climate response through time at different temporal scales and predicts reasonable climate scenarios given tree ring width data. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Predicting how climate change is hkely to interact with myriad other Stressors that threaten species of conservation concern is an essential challenge in aquatic ecosystems. This study provides a framework to accomplish this task in salmon-bearing streams of the northwestern United States, where land-use-related reductions in riparian shading have caused changes in stream thermal regimes, and additional warming from projected climate change may result in significant losses of coldwater fish habitat over the next century. Predatory, nonnative smallmouth bass have also been introduced into many northwestern streams, and their range is likely to expand as streams warm, presenting an additional challenge to the persistence of threatened Pacific salmon. The goal of this work was to forecast the interactive effects of climate change, riparian management, and nonnative species on stream-rearing salmon and to evaluate the capacity of restoration to mitigate these effects. We intersected downscaled global climate forecasts with a local-scale water temperature model to predict mid- and end-of-century temperatures in streams in the Columbia River basin. We compared one stream that is thermally impaired due to the loss of riparian vegetation and another that is cooler and has a largely intact riparian corridor. Using the forecasted stream temperatures in conjunction with fish-habitat models, we predicted how stream-rearing chinook salmon and bass distributions would change as each stream warmed. In the highly modified stream, endof- century warming may cause near total loss of chinook salmon-rearing habitat and a complete invasion of the upper watershed by bass. In the less modified stream, bass were thermally restricted from the upstream-most areas. In both systems, temperature increases resulted in higher predicted spatial overlap between stream-rearing chinook salmon and potentially predatory bass in the early summer (two- to fourfold increase) and greater abundance of bass. We found that riparian restoration could prevent the extirpation of chinook salmon from the more altered stream and could also restrict bass from occupying the upper 31 km of salmon-rearing habitat. The proposed methodology and model predictions are critical for prioritizing climate-change adaptation strategies before salmonids are exposed to both warmer water and greater prédation risk by nonnative species. [ABSTRACT FROM AUTHOR]

Although climate acts as a fundamental constraint on the distribution of organisms, understanding how this relationship between climate and distribution varies over a species' range is critical for addressing the potential impacts of accelerated climate change on biodiversity. Bioclimatic niche models provide compelling evidence that many species will experience range shifts under scenarios of global change, yet these broad, macroecological perspectives lack specificity at local scales, where unique combinations of environment, biota, and history conspire against generalizations. We explored how these idiosyncrasies of place affect the climate-distribution relationship of the American pika (Ochotona princeps) by replicating intensive field surveys across bioclimatic gradients in eight U.S. national parks. At macroecological scales, the importance of climate as a constraint on pika distribution appears unequivocal; forecasts suggest that the species' range will contract sharply in coming decades. However, the species persists outside of its modeled bioclimatic envelope in many locations, fueling uncertainty and debate over its conservation status. Using a Bayesian hierarchical approach, we modeled variation in local patterns of pika distribution along topographic position, vegetation cover, elevation, temperature, and precipitation gradients in each park landscape. We also accounted for annual turnover in site occupancy probabilities. Topographic position and vegetation cover influenced occurrence in all parks. After accounting for these factors, pika occurrence varied widely among parks along bioclimatic gradients. Precipitation by itself was not a particularly influential predictor. However, measures of heat stress appeared most influential in the driest parks, suggesting an interaction between the strength of climate effects and the position of parks along precipitation gradients. The combination of high elevation, cold temperatures, and high precipitation lowered occurrence probabilities in some parks, suggesting an upper elevational limit for pikas in some environments. Our results demonstrate that the idiosyncrasies of place influence both the nature and strength of the climate-distribution relationship for the American pika. Finegrained, but geographically extensive, studies replicated across multiple landscapes offer insights important to assessing the impacts of climate change that otherwise may be masked at macroecological scales. The hierarchical approach to modeling provides a coherent conceptual and technical framework for gaining these insights. [ABSTRACT FROM AUTHOR]

. Climate change may lead to changes in several aspects of the distribution of climate variables, including changes in the mean, increased variability and severity of extreme events. We propose the use of spatiotemporal quantile regression as a flexible and interpretable method for simultaneously detecting changes in several features of the distribution of climate variables. The spatiotemporal quantile regression model assumes that each quantile level changes linearly in time, permitting straightforward inference on the time trend for each quantile level. Unlike classical quantile regression which uses model-free methods to analyse a single quantile or several quantiles separately, we take a model-based approach which jointly models all quantiles, and thus the entire response distribution. In the spatiotemporal quantile regression model, each spatial location has its own quantile function that evolves over time, and the quantile functions are smoothed spatially by using Gaussian process priors. We propose a basis expansion for the quantile function that permits a closed form for the likelihood and allows for residual correlation modelling via a Gaussian spatial copula. We illustrate the methods by using temperature data for the south-east USA from the years 1931-2009. For these data, borrowing information across space identifies more significant time trends than classical non-spatial quantile regression. We find a decreasing time trend for much of the spatial domain for monthly mean and maximum temperatures. For the lower quantiles of monthly minimum temperature, we find a decrease in Georgia and Florida, and an increase in Virginia and the Carolinas. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of climate change in its full richness requires the knowledge of long temperature time series. Although long-term, widely distributed temperature observations are not available, there are other forms of data, known as climate proxies, that can have a statistical relationship with temperatures and have been used to infer temperatures in the past before direct measurements. We propose a Bayesian hierarchical model to reconstruct past temperatures that integrates information from different sources, such as proxies with different temporal resolution and forcings acting as the external drivers of large scale temperature evolution. Additionally, this method allows us to quantify the uncertainty of the reconstruction in a rigorous manner. The reconstruction method is assessed, using a global climate model as the true climate system and with synthetic proxy data derived from the simulation. The target is to reconstruct Northern Hemisphere temperature from proxies that mimic the sampling and errors from tree ring measurements, pollen indices, and borehole temperatures. The forcing series used as covariates are solar irradiance, volcanic aerosols, and greenhouse gas concentrations. The Bayesian model was successful in integrating these different sources of information in creating a coherent reconstruction. Within the context of this numerical testbed, a statistical process model that includes the external forcings can improve the quality of a hemispheric reconstruction when long time scale proxy information is not available. This article has supplementary material online. [ABSTRACT FROM AUTHOR]

Currently, there is a tremendous scientific research effort in the area of climate change. In this paper, our motivation is to improve the understanding of historical climatic events such as the Little Ice Age (LIA), a period of relatively cold weather around 1450–1850 AD. Although the LIA is well documented in Europe, its extent and timing are not known in areas of the globe where climatological records were not kept during this period. To study the climate, which predates historical records, proxy climate records must be used. A proxy record for the timing of climatic cooling events are the ages of the moraines left behind by glacial advances. Unfortunately, to determine the ages of these moraines in alpine environments there is little material available but lichens. Hence, lichenometry was developed to determine the ages of glacial landforms by using lichen measurements. To our knowledge, this article provides the first attempt at deriving a comprehensive statistical model for lichenometry. Our model foundation is based on extreme value theory because only the largest lichens are measured in lichenometry studies. This application is novel to extreme value theory because the quantities of interest (the ages of climatic events) are not the measured quantities (lichen diameters), i.e., it is a inverse problem. We model the lichen measurements with the generalized extreme value (GEV) distribution, upon which a Bayesian hierarchical model is built. The hierarchical model enables estimation of the hidden covariate ages of the moraines. The model also allows for pooling of data from different locations and evaluation of spatial differences in lichen growth. Parameter inference is obtained using a straightforward Markov Chain Monte Carlo method. Our procedure is applied to data gathered from the Cordillera Real region in Bolivia. [ABSTRACT FROM AUTHOR]

The impacts of climate change and extreme weather events (e.g. frost-, heat-, drought-, and heavy rainfall events) on the continuous phenological development over the entire seasonal cycle remained poorly understood. Previous studies mainly focused on modeling key phenological transition dates (e.g. discrete timing of spring bud-break and fall senescence) based on aggregated climate variables (e.g. mean temperature, growing-degree days). We developed and evaluated a Bayesian Hierarchical Space-Time model for Land Surface Phenology (BHST-LSP) to synthesize remotely sensed vegetation greenness with climate covariates at a daily temporal scale from 1981 to 2014 across the entire conterminous United States. The BHST-LSP model incorporated both temporal and spatial information and exhibited high predictive power in simulating daily phenological development with an overall out-of-sample R2 of 0.80 ± 0.17 and 0.72 ± 0.20 for spring and fall phenology, respectively. The overall out-of-sample normalized root mean square errors were 9.3% ± 6.1% and 9.9% ± 5.2% between the observed and predicted vegetation greenness for spring and fall phenology, respectively. We found that a fast increase of temperature can accelerate the speed of spring green-up while a slow decrease of temperature can lead to a decelerated fall brown-down. Increasing accumulated precipitation can benefit daily phenological development over an entire growing season, while extreme rainfall events can have the opposite effects. More frequent frost events could slow spring leaf expansion and accelerate fall leaf senescence. Impacts of extreme heat events were complex and depended on water availability. Cropland in the Midwest as well as evergreen needleleaf forest along the coastal regions showed relatively strong resistance to drought events compared to other land cover types. The BHST-LSP model can be used to forecast vegetation phenology given future climate projection, thus providing valuable information for adopting climate change adaptation and mitigation measures. • A Bayesian model was developed to simulate continuous phenological development. • Rate of daily temperature changes impact speed of both spring and fall phenology. • Increased precipitation benefit phenological development over an entire season. • Frost events slow spring leaf expansion and accelerate fall leaf senescence. • Cropland and evergreen forest show resistance to drought events. [ABSTRACT FROM AUTHOR]

While new mobile monitoring technology has revolutionized our ability to measure pollutant levels over large regions, statistical methods for making inferences from data collected by these mobile systems are still being developed. We introduce a new capture–recapture model to answer key inferential questions from data collected by mobile monitoring systems. We apply our new method to characterize populations of natural gas (NG) leaks in urban areas using data collected by atmospheric methane analyzers placed on Google Street View cars. Leaks in urban NG distribution systems correspond to an economic loss, are a potential safety hazard, and are climate altering because NG is primarily composed of methane, a potent greenhouse gas. The new calibration capture–recapture (CCR) model combines data from controlled methane release experiments and data collected from mobile air monitors to enable inference for several NG leak population characteristics, including the number of undetected leaks and the total methane output rate in a surveyed region. Our methodology is a novel application of capture–recapture modeling. The CCR model addresses challenges associated with using a capture–recapture model to analyze data collected by a mobile monitoring system such as a variable sampling effort. We develop a Markov chain Monte Carlo algorithm for parameter estimation and apply the CCR model to data collected in two U.S. cities. The CCR model provides a new framework for inferring the total number of leaks in NG distribution systems and offers critical insights for informing intelligent infrastructure repair policy that is both cost effective and environmentally friendly. [ABSTRACT FROM AUTHOR]