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Climate change and other challenges to the stability and functioning of natural and managed environmental systems are driven by increasing anthropogenic domination of the Earth. Models to forecast the trajectory of climate change and to identify pathways to sustainability require representation of human behaviour and its feedbacks with the climate system. Social climate models (SCMs) are an emerging class of models that embed human behaviour in climate models. We survey existing SCMs and make recommendations for how to integrate models of human behaviour and climate. We suggest a framework for representing human behaviour that consists of cognition, contagion and a behavioural response. Cognition represents the human processing of information around climate change; contagion represents the spread of information, beliefs and behaviour through social networks; and response is the resultant behaviour or action. This framework allows for biases, habituation and other cognitive processes that shape human perception of climate change as well as the influence of social norms, social learning and other social processes on the spread of information and factors that shape decision-making and behaviour. SCMs move beyond the inclusion of human activities in climate models to the representation of human behaviour that determines the magnitude, sign and character of these activities. The development of SCMs is a challenging but important next step in the evolution of Earth system models.

The ecological and evolutionary processes that allow alien species to establish and dominate native communities (i.e., become invasive) have been a rich area of research. Past areas of inquiry have included identifying the traits necessary to invade a community and/or determining how phylogenetic relatedness of the introduced species with the resident community can promote invasive success. Yet despite decades of research, little consensus exists about why particular species successfully invade native communities while others do not. Here we develop a conceptual framework for why only certain introduced species become invasive: optimal differentiation to the edge of trait space (EoTS). We posit that optimal differentiation leading to successful invasion into a community requires that the multi-dimensional trait space of the introduced species exists at the edge of the multi-dimensional trait space of the native community. Species that possess traits that are too different cannot enter the community because of environmental filtering, while species that are too similar will either become integrated into the community but not take over or alternatively never establish. We apply this conceptual framework to species functional traits and discuss how both genetic processes and phylogenetic processes may also result in optimal differentiation to EoTS.

Objectives Information on the prevalence of face mask use to reduce the spread of SARS-CoV-2 is needed to model disease spread and to assess the effectiveness of policies that encourage face mask use. We sought to (1) estimate the prevalence of face mask use in northern Vermont and (2) assess the effect of age and sex on the likelihood of face mask use. Methods We monitored the entrances to public businesses and visually assessed age, sex, and face mask use. We collected 1004 observations during May 16-30, 2020. We calculated estimates of overall face mask use and odds ratios (ORs) for effects by age and sex. Results Of 1004 observations, 758 (75.5%) sampled people used a face mask. Our census-weighted estimate was 74.1%. A higher percentage of females than males wore face masks (83.8% vs 67.6%). The odds of face mask use were lower among males than among females (OR = 0.52; 95% CI, 0.37-0.73). Face mask use generally decreased with decreasing age: 91.4% among adults aged >60, 70.7% among adults aged 26-60, 74.8% among people aged 15-25, and 53.3% among people aged ≤14. The OR of an adult aged >60 wearing a face mask was 14.70 times higher, for young people aged 15-25 was 2.72 times higher, and for adults aged 26-60 was 2.99 times higher than for people aged ≤14. Females aged >60 had the highest percentage of face mask use (96.3%) and males aged ≤14 had the lowest (43.8%). Conclusions Educational efforts promoting the use of face masks should be targeted at males and younger age groups to limit the spread of SARS-CoV-2.

High-resolution, bias-corrected climate data are necessary for climate impact studies at local scales. Gridded historical data are convenient for bias correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data are generally superior climatological measurements, but because the distribution of climate stations is irregular, station data are challenging to incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980–89 and 1980–2014) and two testing sets of RCM data (1990–2014 and 1980–2014). RMSE for all methods varied throughout the year and was larger in cold months, whereas PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980–89 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method performed best in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, whereas EQM is ideal for bias correcting historical climate model output.

High-resolution, daily precipitation climate products that realistically represent extremes are critical for evaluating local-scale climate impacts. A popular bias-correction method, empirical quantile mapping (EQM), can generally correct distributional discrepancies between simulated climate variables and observed data but can be highly sensitive to the choice of calibration period and is prone to overfitting. In this study, we propose a hybrid bias-correction method for precipitation, EQM-LIN, which combines the efficacy of EQM for correcting lower quantiles, with a robust linear correction for upper quantiles. We apply both EQM and EQM-LIN to historical daily precipitation data simulated by a regional climate model over a region in the northeastern USA. We validate our results using a five-fold cross-validation and quantify performance of EQM and EQM-LIN using skill score metrics and several climatological indices. As part of a high-resolution downscaling and bias-correction workflow, EQM-LIN significantly outperforms EQM in reducing mean, and especially extreme, daily distributional biases present in raw model output. EQM-LIN performed as good or better than EQM in terms of bias-correcting standard climatological indices (e.g., total annual rainfall, frequency of wet days, total annual extreme rainfall). In addition, our study shows that EQM-LIN is particularly resistant to overfitting at extreme tails and is much less sensitive to calibration data, both of which can reduce the uncertainty of bias-correction at extremes. National Science Foundation under VT EPSCoR [NSF OIA 1556770] Published version The development of this manuscript was supported by the National Science Foundation under VT EPSCoR Grant No. NSF OIA 1556770.

While climate models have rapidly advanced in sophistication over recent decades, they lack dynamic representation of human behavior and social systems despite strong feedbacks between social processes and climate. The impacts of climate change alter perceptions of risk and emissions behavior that, in turn, influence the rate and magnitude of climate change. Addressing this deficiency in climate models requires a substantial interdisciplinary effort to couple models of climate and human behavior. We suggest a multi-model approach that considers a range of theories and implementations of human behavior and social systems, similar to the multi-model approach that has been used to explore the physical climate system. We describe the importance of linking social factors with climate processes and identify four priorities essential to advancing the development of coupled social-climate models.

Computational models can project how changes in land use and management will affect soil organic carbon (SOC) stocks over time, but these models usually assume an unchanging climate. We investigate how incorporating climate change projections affects carbon sequestration and SOC stocks. We apply the Rothamsted Carbon model (RothC) to study agricultural land use and management transitions in the U.S. state of Vermont, comparing several regenerative farming strategies, as well as afforestation, against business-as-usual. In 11 relatively-homogeneous Ecoregions within the study area, we run simulations for each land management scenario from 2022–2099, under both projected climate change and the static climate normal from 1991–2021. We use downscaled climate projections from four Global Climate Models, forced by RCP 4.5, that bracket the range of likely climate change. We find that rising temperatures decrease SOC stocks compared to static climate runs by 9.1% to 19.9% across management scenarios, leading to net SOC loss even under many regenerative farming scenarios. Other regenerative practices, notably rotational grazing, could maintain or slightly increase SOC through 2099, and old-growth afforestation could increase statewide stocks by up to 4.5 Mt. Although the potential for farmland management to increase SOC over current levels is diminished when accounting for climate change, it remains important to incentivize regenerative agriculture and afforestation, because this may be the only way to avoid SOC losses by end-of-century.

High-resolution, daily precipitation climate products that realistically represent extremes are critical for evaluating local-scale climate impacts. A popular bias-correction method, empirical quantile mapping (EQM), can generally correct distributional discrepancies between simulated climate variables and observed data but can be highly sensitive to the choice of calibration period and is prone to overfitting. In this study, we propose a hybrid bias-correction method for precipitation, EQM-LIN, which combines the efficacy of EQM for correcting lower quantiles, with a robust linear correction for upper quantiles. We apply both EQM and EQM-LIN to historical daily precipitation data simulated by a regional climate model over a region in the northeastern United States. We validate our results using a five-fold cross-validation and quantify performance of EQM and EQM-LIN using skill score metrics and several climatological indices. As part of a high-resolution downscaling and bias-correction workflow, EQM-LIN significantly outperforms EQM in reducing mean, and especially extreme, daily distributional biases present in raw model output. EQM-LIN performed as good or better than EQM in terms of bias-correcting standard climatological indices (e.g., total annual rainfall, frequency of wet days, total annual extreme rainfall). In addition, our study shows that EQM-LIN is particularly resistant to overfitting at extreme tails and is much less sensitive to calibration data, both of which can reduce the uncertainty of bias-correction at extremes.

AIM: Montane environments are sentinels of global change, providing unique opportunities to assess impacts on species diversity. Multiple anthropogenic stressors such as climate change and atmospheric pollution may act concurrently or synergistically in restructuring communities. Thus, a major challenge for conservation is untangling the relative importance of different stressors. Here, we combine long‐term monitoring with multivariate community modelling to estimate the anthropogenic drivers shaping forest tree diversity along an elevational gradient. LOCATION: Camels Hump Mountain, Vermont, USA. METHODS: We used Generalized Dissimilarity Modelling (GDM) to model spatial and temporal turnover in beta diversity along an elevational gradient over a 50‐year period and tested for spatiotemporal shifts in density and elevational distribution of individual species. GDMs were used to predict community turnover as nonlinear functions of changes in elevation, climate and atmospheric pollution. RESULTS: We observed significant shifts in elevational range and density of individual species, which contributed to an overall reduction in the elevational gradient in beta diversity through time. GDMs showed the combined effects of sulphate deposition and temperature as drivers of this temporal reduction in beta diversity. Spatiotemporal changes differed among species, with shifts observed both up‐ and downslope. For example, in a reversal of a previous upslope range contraction, red spruce (Picea rubens Sarg.) increased in density and shifted downslope since the 1990s, occupying warmer, drier climates. MAIN CONCLUSION: Our results demonstrate that global change is impacting the stratification of forest tree diversity along elevational gradients, but the responses of individual species are complex and variable in direction. We suggest abiotic drivers may directly impact individual species while also indirectly altering species interactions along elevational gradients. Our approach modelling the drivers of compositional turnover quantifies the rate and amount of change in beta diversity along environmental gradients and serves as a powerful complement to studying species‐specific responses.

The Lake Champlain Basin has socioeconomic and ecological significance for the Northeastern United States and Quebec, Canada. Temperatures and extreme precipitation events have been increasing across this region over the past three decades. Accurate, high-resolution climate simulations are critical to assessing potential climate change risk in the Lake Champlain Basin. We evaluate the performance of a regional climate model, the Weather Research and Forecasting (WRF) model, to downscale ERA-Interim reanalysis data to 4 km for the Lake Champlain Basin. Specifically, we compare an ensemble of five WRF experiments with different physics configurations using a one-way, triple-nested domain (36, 12, and 4 km) over three 5-year periods (1980–1984, 1995–1999, and 2010–2014) to Daymet, a gridded observational dataset. We find that WRF simulations of the Lake Champlain Basin generally reproduce the observed temperature and precipitation seasonal cycles, but have cold and wet biases. The simulation of mean temperature by WRF is most sensitive to the choice of radiation scheme, while the simulation of mean precipitation is most sensitive to the choice of radiation, cumulus, and microphysics scheme. We find that turning the cumulus scheme on improves the simulation of the precipitation seasonal cycle at a 4 km resolution, but also substantially enhances the wet bias. Using a coarser resolution (36 km) produces smaller regionally averaged precipitation biases, but not improved correlations between simulated and observed monthly precipitation. Both spatial resolution and turning the cumulus scheme off have minor effects on simulated temperature.

General circulation models (GCMs) are essential for projecting future climate; however, despite the rapid advances in their ability to simulate the climate system at increasing spatial resolution, GCMs cannot capture the local and regional weather dynamics necessary for climate impacts assessments. Temperature and precipitation, for which dense observational records are available, can be bias corrected and downscaled, but many climate impacts models require a larger set of variables such as relative humidity, cloud cover, wind speed and direction, and solar radiation. To address this need, we develop and demonstrate an analog-based approach, which we call a “weather estimator.” The weather estimator employs a highly generalizable structure, utilizing temperature and precipitation from previously downscaled GCMs to select analogs from a reanalysis product, resulting in a complete daily gridded dataset. The resulting dataset, constructed from the selected analogs, contains weather variables needed for impacts modeling that are physically, spatially, and temporally consistent. This approach relies on the weather variables’ correlation with temperature and precipitation, and our correlation analysis indicates that the weather estimator should best estimate evaporation, relative humidity, and cloud cover and do less well in estimating pressure and wind speed and direction. In addition, while the weather estimator has several user-defined parameters, a sensitivity analysis shows that the method is robust to small variations in important model parameters. The weather estimator recreates the historical distributions of relative humidity, pressure, evaporation, shortwave radiation, cloud cover, and wind speed well and outperforms a multiple linear regression estimator across all predictands.

The ambition and effectiveness of climate policies will be essential in determining greenhouse gas emissions and, as a consequence, the scale of climate change impacts

Most inland water bodies are not resolved by general circulation models, requiring that lake surface temperatures be estimated. Given the large spatial and temporal variability of the surface temperatures of the North American Great Lakes, such estimations can introduce errors when used as lower boundary conditions for dynamical downscaling. Lake surface temperatures (LSTs) influence moisture and heat fluxes, thus impacting precipitation within the immediate region and potentially in regions downwind of the lakes. For this study, the Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW) was used to simulate precipitation over the six New England states during a 5-yr historical period. The model simulation was repeated with perturbed LSTs, ranging from 10°C below to 10°C above baseline values obtained from reanalysis data, to determine whether the inclusion of erroneous LST values has an impact on simulated precipitation and synoptic-scale features. Results show that simulated precipitation in New England is statistically correlated with LST perturbations, but this region falls on a wet–dry line of a larger bimodal distribution. Wetter conditions occur to the north and drier conditions occur to the south with increasing LSTs, particularly during the warm season. The precipitation differences coincide with large-scale anomalous temperature, pressure, and moisture patterns. Care must therefore be taken to ensure reasonably accurate Great Lakes surface temperatures when simulating precipitation, especially in southeastern Canada, Maine, and the mid-Atlantic region.

Our ability to predict the outcome of invasion declines rapidly as non-native species progress through intertwined ecological barriers to establish and spread in recipient ecosystems. This is largely due to the lack of systemic knowledge on key processes at play as species establish self-sustaining populations within the invaded range. To address this knowledge gap, we present a mathematical model that captures the eco-evolutionary dynamics of native and non-native species interacting within an ecological network. The model is derived from continuous-trait evolutionary game theory (i.e., Adaptive Dynamics) and its associated concept of invasion fitness which depicts dynamic demographic performance that is both trait mediated and density dependent. Our approach allows us to explore how multiple resident and non-native species coevolve to reshape invasion performance, or more precisely invasiveness, over trait space. The model clarifies the role of specific traits in enabling non-native species to occupy realised opportunistic niches. It also elucidates the direction and speed of both ecological and evolutionary dynamics of residing species (natives or non-natives) in the recipient network under different levels of propagule pressure. The versatility of the model is demonstrated using four examples that correspond to the invasion of (i) a horizontal competitive community; (ii) a bipartite mutualistic network; (iii) a bipartite antagonistic network; and (iv) a multi-trophic food web. We identified a cohesive trait strategy that enables the success and establishment of non-native species to possess high invasiveness. Specifically, we find that a non-native species can achieve high levels of invasiveness by possessing traits that overlap with those of its facilitators (and mutualists), which enhances the benefits accrued from positive interactions, and by possessing traits outside the range of those of antagonists, which mitigates the costs accrued from negative interactions. This ‘central-to-reap, edge-to-elude’ trait strategy therefore describes the strategic trait positions of non-native species to invade an ecological network. This model provides a theoretical platform for exploring invasion strategies in complex adaptive ecological networks.

The Intergovernmental Panel on Climate Change assessments (IPCC) Special Report on 1.5 °C of global warming is clear. Nearly all pathways that hold global warming well below 2 °C involve carbon removal ( IPCC, 2015 ). In addition, solar geoengineering is being considered as a potential tool to offset warming, especially to limit temperature until negative emissions technologies are sufficiently matured ( MacMartin et al., 2018 ). Despite this, there has been a reluctance to embrace carbon removal and solar geoengineering, partly due to the perception that these technologies represent what is widely termed a “moral hazard”: that geoengineering will prevent people from developing the will to change their personal consumption and push for changes in infrastructure ( Robock et al., 2010 ), erode political will for emissions cuts ( Keith, 2007 ), or otherwise stimulate increased carbon emissions at the social-system level of analysis ( Bunzl, 2008 ). These debates over carbon removal and geoengineering echo earlier ones over climate adaptation. We argue that debates over “moral hazard” in many areas of climate policy are unhelpful and misleading. We also propose an alternative framework for dealing with the tradeoffs that motivate the appeal to “moral hazard,” which we call “risk-response feedback.”

The northeastern United States has experienced a large increase in precipitation over recent decades. Annual and seasonal changes of total and extreme precipitation from station observations in the Northeast were assessed over multiple time periods spanning 1901–2014. Spatially averaged, both annual total and extreme precipitation across the Northeast increased significantly since 1901, with changepoints occurring in 2002 and 1996, respectively. Annual extreme precipitation experienced a larger increase than total precipitation; extreme precipitation from 1996 to 2014 is 53% higher than from 1901 to 1995. Spatially, coastal areas receive more total and extreme precipitation on average, but increases across the changepoints are distributed fairly uniformly across the domain. Increases in annual total precipitation across the 2002 changepoint are driven by significant total precipitation increases in fall and summer, while increases in annual extreme precipitation across the 1996 changepoint are driven by significant extreme precipitation increases in fall and spring. The ability of gridded observed and reanalysis precipitation data to reproduce station observations was also evaluated. Gridded observations perform well in reproducing averages and trends of annual and seasonal total precipitation, but extreme precipitation trends show significantly different spatial and domain-averaged trends than station data. The North American Regional Reanalysis generally underestimates annual and seasonal total and extreme precipitation means and trends relative to station observations, and also shows substantial differences in the spatial pattern of total and extreme precipitation trends within the Northeast.

ObjectivesInformation on prevalence of face mask usage in response to SARS-CoV-2 is required to both model disease spread and to improve compliance with mask usage. We sought to (1) estimate the prevalence of mask usage in northern Vermont and to (2) assess the effect of age and sex on mask usage.MethodsWe monitored the entrances to businesses and visually assessed individuals’ age, sex, and mask usage from a distance. We collected 1004 observations from 16 May through 30 May 2020 as businesses began to reopen following an extended state-wide lockdown. We analyzed these data using Bayesian random effects logistic regression.Results75.5% of individuals used a mask with significant effects of age and sex on mask usage. Females were more likely than males to wear masks (83.8%, n=488 vs. 67.6%, n=516); the odds of mask usage in males were 53% of those for females. Elders were most likely to wear a mask (91.4%, n=209) followed by young adults (74.8%, n=246), middle-aged adults (70.7%, n=519) and children (53.3%, n=30). The odds of an elder wearing a mask were 16.7 times that of a child, while the odds for young adults and middle-aged adults were ∼3 times greater than for a child. Highest mask usage was in elder females (96.3%, n=109) and lowest mask usage was in male children (43.8%, n=16).ConclusionsWe found high prevalence of mask usage overall, but also large differences in mask usage with age and sex. Females and elders had the highest use of face masks.

This study investigates the extent to which land use and management transitions on Vermont’s farmland could sequester atmospheric carbon in the soil. We weigh the sequestration potential of several types of regenerative agricultural practices against both business as usual and afforestation scenarios using the Rothamsted Carbon Model. We split the study area into 13 Ecoregions for a finer spatial scale of analysis, with key climate, soil, and land use data specified for each. Empirical soil laboratory data are used to initialize the model to mirror current conditions under each of three agricultural land uses (crops, hay, and pasture) in each Ecoregion. We consult experts as well as the literature to parameterize the anticipated effects of alternative agricultural management practices on soil carbon inputs. In the simulation runs, we find that all non-business-as-usual scenarios sequester carbon over time, with a higher rate of sequestration in the decades immediately after a land use or management change. Among the regenerative agriculture scenarios, conversion to rotational grazing offers the highest soil carbon sequestration potential, at 1,269 kt, or 5.3% above current stocks after ten years. Of all scenarios, afforestation of farmland to non-harvested forest stores the most soil carbon, increasing stocks by 6.5% after ten years, and continuing to sequester at a high rate many decades into the future. We discuss tradeoffs and policy implications, especially in the context of the 2020 Vermont Global Warming Solutions Act, and suggest that payments for ecosystem services for farmers sequestering carbon may have strategic value.

Although not considered in climate models, perceived risk stemming from extreme climate events may induce behavioural changes that alter greenhouse gas emissions. Here, we link the C-ROADS climate model to a social model of behavioural change to examine how interactions between perceived risk and emissions behaviour influence projected climate change. Our coupled climate and social model resulted in a global temperature change ranging from 3.4–6.2 °C by 2100 compared with 4.9 °C for the C-ROADS model alone, and led to behavioural uncertainty that was of a similar magnitude to physical uncertainty (2.8 °C versus 3.5 °C). Model components with the largest influence on temperature were the functional form of response to extreme events, interaction of perceived behavioural control with perceived social norms, and behaviours leading to sustained emissions reductions. Our results suggest that policies emphasizing the appropriate attribution of extreme events to climate change and infrastructural mitigation may reduce climate change the most. Human behaviour is an important driver of emissions. A system-dynamics model that couples a psychological model of behaviour with a model of emissions and climate change shows that behaviour can influence global temperature in the year 2100 by up to 1.5 °C.

We aim to understand how small-scale genotypic richness and genotypic interactions influence the biomass and potential invasiveness of the invasive grass, Phalaris arundinacea under two different disturbance treatments: intact plots and disturbed plots, where all the native vegetation has been removed. Specifically, we address the following questions (i) Does genotypic richness increase biomass production? (ii) Do genotypic interactions promote or reduce biomass production? (iii) Does the effect of genotypic richness and genotypic interactions differ in different disturbance treatments? Finally (iv) Is phenotypic variation greater as genotypic richness increases? We conducted a 2-year common garden experiment in which we manipulated genotype richness using eight genotypes planted under both intact and disturbed conditions in a wetland in Burlington, Vermont (44°27′23″N, 73°11′29″W). The experiment consisted of a randomized complete block design of three blocks, each containing 20 plots (0.5 m²) per disturbed treatment. We calculated total plot biomass and partitioned the net biodiversity effect into three components: dominance effect, trait-dependent complementarity and trait-independent complementarity. We calculated the phenotypic variance for each different genotype richness treatment under the two disturbance treatments. Our results indicate that local genotypic richness does not increase total biomass production of the invasive grass P. arundinacea in either intact or disturbed treatments. However, genotypic interactions underlying the responses showed very different patterns in response to increasing genotypic richness. In the intact treatment, genotypic interactions resulted in the observed biomass being greater than the predicted biomass from monoculture plots (e.g., overyielding) and this was driven by facilitation. However, facilitation was reduced as genotypic richness increased. In the disturbed treatment, genotypic interactions resulted in underyielding with observed biomass being slightly less than expected from the performance of genotypes in monocultures; however, underyielding was reduced as genotypic richness increased. Thus, in both treatments, higher genotypic richness resulted in plot biomass nearing the additive biomass from individual monocultures. In general, higher genotypic richness buffered populations against interactions that would have reduced biomass and potentially spread. Phenotypic variance also had contrasting patterns in intact and disturbed treatments. In the intact treatment, phenotypic variance was low across all genotypic richness levels, while in the disturbed treatment, phenotypic variance estimates increased as genotypic richness increased. Thus, under the disturbed treatment, plots with higher genotypic richness had a greater potential response to selection. Therefore, limiting the introduction of new genotypes, even if existing genotypes of the invasive species are already present, should be considered a desirable management strategy to limit the invasive behavior of alien species.

We investigate phase transitions associated with three control methods for epidemics on small world networks. Motivated by the behavior of SARS-CoV-2, we construct a theoretical SIR model of a virus that exhibits presymptomatic, asymptomatic, and symptomatic stages in two possible pathways. Using agent-based simulations on small world networks, we observe phase transitions for epidemic spread related to: 1) Global social distancing with a fixed probability of adherence. 2) Individually initiated social isolation when a threshold number of contacts are infected. 3) Viral shedding rate. The primary driver of total number of infections is the viral shedding rate, with probability of social distancing being the next critical factor. Individually initiated social isolation was effective when initiated in response to a single infected contact. For each of these control measures, the total number of infections exhibits a sharp phase transition as the strength of the measure is varied.

Natural fires ignited by lightning strikes following droughts frequently are posited as the ecological mechanism maintaining discontinuous tree cover and grass‐dominated ground layers in savannas. Such fires, however, may not reliably maintain humid savannas. We propose that savanna trees producing pyrogenic shed leaves might engineer fire characteristics, affecting ground‐layer plants in ways that maintain humid savannas. We explored our hypothesis in a high‐rainfall, frequently burned pine savanna in which the dominant tree, longleaf pine (Pinus palustris), produces resinous needles that become highly flammable when shed and dried. We postulated that pyrogenic needles should have much greater influence on fire characteristics at ground level, and hence post‐fire responses of dominant shrubs and grasses, than other abundant fine fuels (shed oak leaves and grass culms). We further reasoned that these effects should increase with amounts of needles. We managed site conditions that affect fuels (time since fire, dominant vegetation), manipulated amounts of needles in ground‐layer plots, prescribed burned the plots, and measured fire characteristics at ground level. We also measured characteristics of ground‐layer oaks and grasses before, then 2 and 8 months after fires. We tested our hypotheses regarding effects of pyrogenic pine fuels on fire characteristics and vegetation regrowth and explored direct and indirect effects of fuels on fire characteristics and vegetation using a structural equation model. Pine needles influenced fire characteristics, elevating maximum temperature increases, durations of heating above 60°C, and fine fuel consumption considerably above measurements when fuels only included other savanna plants. Presence of pine needles depressed post‐fire numbers of oak stems and grass culms, especially in the interior of grass genets, as well as post‐fire flowering of grasses. The structural equation model indicated strong direct and indirect pathways from pine needles to post‐fire responses of oaks and grasses. The experimental field tests of hypotheses, bolstered by structural equation modeling, indicate pyrogenic fine fuels modify characteristics of prescribed fires at ground level, negatively affecting dominant ground‐layer oaks and grasses. Frequent fires fueled by pyrogenic needles should maintain humid savannas and generate spatial pyrodiversity that affects composition and dynamics of pine savanna ground‐layer vegetation.

The mountain regions of the northeastern United States are a critical socioeconomic resource for Vermont, New York State, New Hampshire, Maine, and southern Quebec. While global climate models (GCMs) are important tools for climate change risk assessment at regional scales, even the increased spatial resolution of statistically downscaled GCMs (commonly ~⅛°) is not sufficient for hydrologic, ecologic, and land-use modeling of small watersheds within the mountainous Northeast. To address this limitation, an ensemble of topographically downscaled, high-resolution (30″), daily 2-m maximum air temperature; 2-m minimum air temperature; and precipitation simulations are developed for the mountainous Northeast by applying an additional level of downscaling to intermediately downscaled (⅛°) data using high-resolution topography and station observations. First, observed relationships between 2-m air temperature and elevation and between precipitation and elevation are derived. Then, these relationships are combined with spatial interpolation to enhance the resolution of intermediately downscaled GCM simulations. The resulting topographically downscaled dataset is analyzed for its ability to reproduce station observations. Topographic downscaling adds value to intermediately downscaled maximum and minimum 2-m air temperature at high-elevation stations, as well as moderately improves domain-averaged maximum and minimum 2-m air temperature. Topographic downscaling also improves mean precipitation but not daily probability distributions of precipitation. Overall, the utility of topographic downscaling is dependent on the initial bias of the intermediately downscaled product and the magnitude of the elevation adjustment. As the initial bias or elevation adjustment increases, more value is added to the topographically downscaled product.

Forest Transition Theory (FTT) suggests that reforestation may follow deforestation as a result of and interplay between changing social, economic and ecological conditions. We develop a simplistic but empirically data driven land use transition agent-based modeling platform, interactive land use transition agent-based model (ILUTABM), that is able to reproduce the observed land use patterns and link the forest transition to parcel-level heuristic-based land use decisions and ecosystem service (ES). The ILUTABM endogenously links landowners’ land use decisions with ecosystem services (ES) provided by the lands by treating both lands and landowners as interacting agents. The ILUTABM simulates both the land use changes resulting from farmers’ decision behaviors as well as the recursive effects of changing land uses on farmers’ decision behaviors. The ILUTABM is calibrated and validated at 30 m × 30 m spatial resolution using National Land Cover Data (NLCD) 1992, 2001 and 2006 across the western Missisquoi watershed, which is located in the north-eastern US with an estimated area of 283 square kilometers and 312 farmers farming on 16% of the total Missisquoi watershed area. This study hypothesizes that farmers’ land use decisions are made primarily based on their summed expected utilities and that impacts of exogenous socio-economic factors, such as natural disasters, public policies and institutional/social reforms, on farmers’ expected utilities can significantly influence the land use transitions between agricultural and forested lands. Monte Carlo experiments under six various socio-economic conditions combined with different ES valuation schemes are used to assess the sensitivities of the ILUTABM. Goodness-of-fit measures confirm that the ILUTABM is able to reproduce 62% of the observed land use transitions. However, the spatial patterns of the observed land used transitions are more clustered than the simulated counterparts. We find that, when farmers value food provisioning Ecosystem Services (ES) more than other ES (e.g., soil and water regulation), deforestation is observed. However, when farmers value less food provisioning than other ES or they value food provisioning and other ES equally, the forest transition is observed. The ILUTABM advances the Forest Transition Theory (FTT) framework by endogenizing the interactions of socio-ecological feedbacks and socio-economic factors in a generalizable model that can be calibrated with empirical data.

We present evidence of increasing persistence in daily precipitation in the northeastern United States that suggests that global circulation changes are affecting regional precipitation patterns. Meteorological data from 222 stations in 10 northeastern states are analyzed using Markov chain parameter estimates to demonstrate that a significant mode of precipitation variability is the persistence of precipitation events. We find that the largest region-wide trend in wet persistence (i.e., the probability of precipitation in 1 day and given precipitation in the preceding day) occurs in June (+0.9% probability per decade over all stations). We also find that the study region is experiencing an increase in the magnitude of high-intensity precipitation events. The largest increases in the 95th percentile of daily precipitation occurred in April with a trend of +0.7 mm/d/decade. We discuss the implications of the observed precipitation signals for watershed hydrology and flood risk.

This analysis uses over 50 years of hourly observations of temperature, relative humidity, and opaque cloud cover and daily precipitation from 11 climate stations across the Canadian Prairies to analyze the monthly, seasonal, and long-term climate coupling in the warm season. On climate time scales, temperature depends on cloud forcing, while relative humidity depends on precipitation. The monthly climate depends on both opaque cloud cover for the current month and precipitation for both the present and past 2 months in summer. Multiple linear regression shows that anomalies of opaque cloud and precipitation explain 60–80% of the variance in the diurnal temperature range, afternoon relative humidity, and lifting condensation level on monthly time scales. We analyze the internal coupling of diurnal climate observables as a further guide to evaluating models. We couple the statistics to simplified energy and water budgets for the Prairies in the growing season. The opaque cloud observations have been calibrated against the incoming shortwave and longwave fluxes. We estimate that the drydown of total water storage on the landscape damps 56% of precipitation anomalies for the growing season on large spatial scales, although this drydown increases evapotranspiration. This couples the climatological surface fluxes to four key observables: cloud forcing, precipitation, temperature, and humidity. We estimate a climatological evaporative fraction of 0.61 for the Prairies. The observational relationships of the coupled Prairie climate system across time scale will be useful for evaluating these coupled processes in models for weather and seasonal forecasting and climate simulation.