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Heat waves are becoming more frequent due to climate change. Summer heat waves can be particularly deadly in cities, where temperatures are already inflated by abundant impervious, dark surfaces (i.e., the heat island effect). Urban heat waves might be ameliorated by planting and maintaining urban forests. Previous observational research has suggested that conifers may be particularly effective in cooling cities. However, the observational nature of these studies has prevented the identification of the direct and indirect mechanisms that drive this differential cooling. Here, we develop a systems dynamics representation of urban forests to model the effects of the percentage cover of either conifers or broadleaf trees on temperature. Our model includes physiological and morphological differences between conifers and broadleaf trees, and physical feedback among temperature and energy fluxes. We apply the model to a case study of Vancouver, BC, Canada. Our model suggests that in temperate rainforest cities, conifers may by 1.0 °C cooler than broadleaf trees; this differential increases to 1.2 °C when percentage tree cover increases from 17% to 22% and to 1.7 °C at 30% cover. Our model suggests that these differences are due to three key tree traits: leaf area index, leaf boundary layer resistance, and dry mass per leaf area. Creating urban forests that optimize these three variables may not only sequester CO2 to mitigate global climate change but also be most effective at locally minimizing deadly urban heat waves. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Anthropogenic greenhouse gas emissions are increasing the frequency of deadly heat waves. Heat waves are particularly devastating in cities, where air pollution is high and air temperatures are already inflated by the heat island effect. Determining how cities can ameliorate extreme summer temperature is thus critical to climate adaptation. Tree planting has been proposed to ameliorate urban temperatures, but its effectiveness, particularly of coniferous trees in temperate climates, has not been established. Here, we use remote sensing data (Landsat 8), high-resolution land cover data, and Bayesian models to understand how different tree and land cover classes affect summer surface temperature in Metro Vancouver, Canada. Although areas dominated by coniferous trees exhibited the lowest albedo (95% CrI 0.08–0.08), they were significantly (12.2 °C) cooler than areas dominated by buildings. Indeed, we found that for conifers, lower albedo was associated with lower surface temperatures. Planting and maintaining coniferous trees in cities may not only sequester CO2 to mitigate global climate change, but may also ameliorate higher temperatures and deadly heat waves locally. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Adaptation to heterogeneous environments can occur via phenotypic plasticity, but how often this occurs is unknown. Reciprocal transplant studies provide a rich dataset to address this issue in plant populations because they allow for a determination of the prevalence of plastic versus canalized responses. From 31 reciprocal transplant studies, we quantified the frequency of five possible evolutionary patterns: (1) canalized response-no differentiation: no plasticity, the mean phenotypes of the populations are not different; (2) canalized response-population differentiation: no plasticity, the mean phenotypes of the populations are different; (3) perfect adaptive plasticity: plastic responses with similar reaction norms between populations; (4) adaptive plasticity: plastic responses with parallel, but not congruent reaction norms between populations; and (5) nonadaptive plasticity: plastic responses with differences in the slope of the reaction norms. The analysis included 362 records: 50.8% life-history traits, 43.6% morphological traits, and 5.5% physiological traits. Across all traits, 52% of the trait records were not plastic, and either showed no difference in means across sites (17%) or differed among sites (83%). Among the 48% of trait records that showed some sort of plasticity, 49.4% showed perfect adaptive plasticity, 19.5% adaptive plasticity, and 31% nonadaptive plasticity. These results suggest that canalized responses are more common than adaptive plasticity as an evolutionary response to environmental heterogeneity. [ABSTRACT FROM AUTHOR]

Soil process models such as RothC typically assume soil organic carbon (SOC) is in equilibrium at the beginning of each simulation run. This is not likely to be true in the real world, since recalcitrant SOC pools (notably, humified material) take many decades to re-stabilize after a land use change. The equilibrium assumption stems from a spinup method in which the model is run under a single land use until all SOC pools stabilize. To overcome this, we demonstrate an alternative spinup procedure that accounts for historical land use changes. The "steady-state" and "historical" spinup methods both impute unknown C inputs such that the modeled SOC matches empirical measurements at the beginning of the simulation and set initial SOC fractions. Holding all other parameters equal, we evaluated how each spinup affects SOC projections in simulations of agricultural land use change in the U.S. state of Vermont. We found that projected SOC trajectories for all land use scenarios are sensitive to the spinup procedure. These differences are due to disparities in imputed below-ground plant-derived carbon between the two procedures. Compared to the steady-state, imputed C in the historical spinup is higher for land uses that increase SOC (e.g., adoption of regenerative practices) and lower for land uses that decrease SOC (e.g., a transition from pasture to crops), due to the time window within which land use changes are assumed to have occurred. The novel historical spinup procedure captures important dynamics commonly missing in previous studies, representing an advancement in soil process modeling. [ABSTRACT FROM AUTHOR]

The Lake Champlain basin is a critical ecological and socioeconomic resource of the northeastern United States and southern Quebec, Canada. While general circulation models (GCMs) provide an overview of climate change in the region, they lack the spatial and temporal resolution necessary to fully anticipate the effects of rising global temperatures associated with increasing greenhouse gas concentrations. Observed trends in precipitation and temperature were assessed across the Lake Champlain basin to bridge the gap between global climate change and local impacts. Future shifts in precipitation and temperature were evaluated as well as derived indices, including maple syrup production, days above 32.2°C (90°F), and snowfall, relevant to managing the natural and human environments in the region. Four statistically downscaled, bias-corrected GCM simulations were evaluated from the Coupled Model Intercomparison Project phase 5 (CMIP5) forced by two representative concentration pathways (RCPs) to sample the uncertainty in future climate simulations. Precipitation is projected to increase by between 9.1 and 12.8 mm yr−1 decade−1 during the twenty-first century while daily temperatures are projected to increase between 0.43° and 0.49°C decade−1. Annual snowfall at six major ski resorts in the region is projected to decrease between 46.9% and 52.4% by the late twenty-first century. In the month of July, the number of days above 32.2°C in Burlington, Vermont, is projected to increase by over 10 days during the twenty-first century. [ABSTRACT FROM AUTHOR]

Projections of vegetation distribution that incorporate the transient responses of vegetation to climate change are likely to be more efficacious than those that assume an equilibrium between climate and vegetation. We examine the non-equilibrium dynamics of a temperate forest region under historic and projected future climate change using the dynamic ecosystem model LPJ-GUESS. We parameterized LPJ-GUESS for the New England region of the United Sates utilizing eight forest cover types that comprise the regionally dominant species. We developed a set of climate data at a monthly-step and a 30-arc second spatial resolution to run the model. These datasets consist of past climate observations for the period 1901-2006 and three general circulation model projections for the period 2007-2099. Our baseline (1971-2000) simulation reproduces the distribution of forest types in our study region as compared to the National Land Cover Data 2001 (Kappa statistic = 0.54). Under historic and nine future climate change scenarios, maple-beech-basswood, oaks and aspen-birch were modeled to move upslope at an estimated rate of 0.2, 0.3 and 0.5 m yr from 1901 to 2006, and continued this trend at an accelerated rate of around 0.5, 0.9 and 1.7 m yr from 2007 to 2099. Spruce-fir and white pine-cedar were modeled to contract to mountain ranges and cooler regions of our study region under projected future climate change scenarios. By the end of the 21 century, 60% of New England is projected to be dominated by oaks relative to 21% at the beginning of the 21 century, while northern New England is modeled to be dominated by aspen-birch. In mid and central New England, maple-beech-basswood, yellow birch-elm and hickories co-occur and form novel species associations. In addition to warming-induced northward and upslope shifts, climate change causes more complex changes in our simulations, such as reversed conversions between forest types that currently share similar bioclimatic ranges. These results underline the importance of considering community interactions and transient dynamics in modeling studies of climate change impacts on forest ecosystems. [ABSTRACT FROM AUTHOR]

Savannas commonly consist of a discontinuous cover of overstory trees and a groundcover of grasses. Savanna models have previously demonstrated that vegetation feedbacks on fire frequency can limit the density of overstory trees, thereby maintaining savannas. Positive feedbacks of either savanna trees alone or trees and grasses together on fire frequency have been shown to result in a stable savanna equilibrium. Grass feedbacks on fire frequency, in contrast, have resulted in stable equilibria in either a grassland or forest state, but not in a savanna. These results, however, were derived from a system of differential equations that assumes that fire occurrence is strictly deterministic and that vegetation losses due to fire are continuous in time. We develop an alternative formulation of the grass-fire feedback model that assumes that fires are discrete and occur stochastically in time to examine the influence of these assumptions on the predicted state of the system. We show that incorporating fire as a discrete event can produce a recurring temporal refuge in which both grass and trees co-occur in a stable, bounded savanna. In our model, tree abundance is limited without invoking demographic bottlenecks in the transition from fire-sensitive to fire-resistant life history stages. An increasing strength of grass feedback on fire results in regular, predictable fires, which suggests that the system can also be modeled using a set of difference equations. We implement this discrete system using modified Leslie/Gower difference equations and demonstrate the existence of a bounded savanna state in this model framework. Our results confirm the potential for grass feedbacks to result in stable savannas, and indicate the importance of modeling fire as a discrete event rather than as a loss rate that is continuous in time. [Copyright &y& Elsevier]

Theory predicts that wildfires will encounter spatial thresholds where different drivers may become the dominant influence on continued fire spread. Studying these thresholds, however, is limited by a lack of sufficiently detailed data sets. To address this problem, we searched for scale thresholds in data describing wildfire size at the Avon Park Air Force Range, south-central Florida. We used power-law statistics to describe the “heavy-tail” of the fire size distribution, and quantile regression to determine how the edges of data distributions of fire size were related to climate. Power-law statistics revealed a heavy-tail, a pattern consistent with scale threshold theory, which predicts that large fires will be rare because only fires that cross all thresholds will become large. Results from quantile regression suggested that different climate conditions served as critical thresholds, influencing wildfire size at different spatial scales. Modeling at higher quantiles (≥75th) implicated drought as driving the spread of larger fires, whereas modeling at lower quantiles (≤25th) implicated that wind governed the spread of smaller fires. Fires of intermediate size were negatively associated with relative humidity. Our results are consistent with the idea that fire spread involves scale thresholds, with the small-scale drivers allowing fires to spread after ignition, but with further spread only being possible when large-scale drivers are favorable. These results suggest that other data sets that have heavy-tailed distributions may contain patterns generated by scale thresholds, and that these patterns may be revealed using quantile regression. [ABSTRACT FROM AUTHOR]

Aim To project the distribution of three major forest types in the northeastern USA in response to expected climate change. Location The New England region of the United States. Methods We modelled the potential distribution of boreal conifer, northern deciduous hardwood and mixed oak–hickory forests using the process-based BIOME4 vegetation model parameterized for regional forests under historic and projected future climate conditions. Projections of future climate were derived from three general circulation models forced by three global warming scenarios that span the range of likely anthropogenic greenhouse gas emissions. Results Annual temperature in New England is projected to increase by 2.2–3.3 °C by 2041–70 and by 3.0–5.2 °C by 2071–99 with corresponding increases in precipitation of 4.7–9.5% and 6.4–11.4%, respectively. We project that regional warming will result in the loss of 71–100% of boreal conifer forest in New England by the late 21st century. The range of mixed oak–hickory forests will shift northward by 1.0–2.1 latitudinal degrees ( c. 100–200 km) and will increase in area by 149–431% by the end of the 21st century. Northern deciduous hardwoods are expected to decrease in area by 26% and move upslope by 76 m on average. The upslope movement of the northern deciduous hardwoods and the increase in oak–hickory forests coincide with an approximate 556 m upslope retreat of the boreal conifer forest by 2071–99. In our simulations, rising atmospheric CO2 concentrations reduce the losses of boreal conifer forest in New England from expected losses based on climatic change alone. Main conclusion Projected climate warming in the 21st century is likely to cause the extensive loss of boreal conifer forests, reduce the extent of northern hardwood deciduous forests, and result in large increases of mixed oak–hickory forest in New England. [ABSTRACT FROM AUTHOR]

Savanna models that are based on recurrent disturbances such as fire result in nonequilibrium savannas, but these models rarely incorporate vegetation feedbacks on fire frequency or include more than two states (grasses and trees). We develop a disturbance model that includes vegetation-fire feedbacks, using a system of differential equations to represent three main components of savannas: grasses, fire-tolerant savanna trees, and fire-intolerant forest trees. We investigate the stability of savannas in the presence of positive feedbacks of fire frequency with (1) grasses, (2) savanna trees, and (3) grasses and savanna trees together while also allowing for negative feedbacks of forest trees on fire frequency. We find that positive feedbacks between fire frequency and savanna trees, alone or together with grasses, can stabilize savannas, blocking the conversion of savannas to forests. Negative feedbacks of forest trees on fire frequency shift the range of parameter space that supports savannas, but they do not generally alter our results. We propose that pyrogenic trees that modify characteristics of fire regimes are ecosystem engineers that facilitate the persistence of savannas, generating both threshold fire frequencies with rapid changes in community composition when these thresholds are crossed and hystereses with bistable community states. [ABSTRACT FROM AUTHOR]

• Fire disturbance can mediate the invasion of ecological communities by nonnative species. Nonnative plants that modify existing fire regimes may initiate a positive feedback that can facilitate their continued invasion. Fire-sensitive plants may successfully invade pyrogenic landscapes if they can inhibit fire in the landscape. • Here, we investigated whether the invasive shrub Brazilian pepper ( Schinus terebinthifolius) can initiate a fire-suppression feedback in a fire-dependent pine savanna ecosystem in the southeastern USA. • We found that prescribed burns caused significant (30–45%) mortality of Brazilian pepper at low densities and that savannas with more frequent fires contained less Brazilian pepper. However, high densities of Brazilian pepper reduced fire temperature by up to 200°C, and experienced as much as 80% lower mortality. • A cellular automaton model was used to demonstrate that frequent fire may control low-density populations, but that Brazilian pepper may reach a sufficient density during fire-free periods to initiate a positive feedback that reduces the frequency of fire and converts the savanna to an invasive-dominated forest. [ABSTRACT FROM AUTHOR]

Montane forests in the northeastern United States have experienced symptoms of declining vigor, such as branch dieback and increased mortality, over the last half-century. These declines have been attributed to the cumulative impacts of acid deposition, but reconstructing these declines from tree-ring records has proved difficult because of confounding factors that affect low-frequency growth patterns, including climate and natural growth trajectories following disturbance. We obtained tree-ring records of red spruce (Picea rubens Sarg.) and sugar maple (Acer saccharum L.) from three elevations on Bolton Mountain, Vermont, and applied traditional dendroclimatological analyses that revealed a profound declining growth–climate correlation since ca. 1970 for sugar maple but much less so for red spruce. We then applied a new multifaceted statistical approach that conservatively detrends tree-ring records by minimizing the influences of tree size, age, and canopy disturbances on radial growth. In contrast with the traditional analysis, this approach yielded chronologies that were consistently correlated with climate but with important exceptions. Low-elevation sugar maple suffered distinct episodes of slow growth, likely because of insect defoliators, and also a progressive decline since ca. 1988. Red spruce experienced subdecadal episodes of decline that may be related to freeze–thaw events known to injure foliage but showed no evidence of a progressive decline. This analysis was supported by a forest plot resurvey that indicated major declines in these species. [ABSTRACT FROM AUTHOR]

Detecting latitudinal range shifts of forest trees in response to recent climate change is difficult because of slow demographic rates and limited dispersal but may be facilitated by spatially compressed climatic zones along elevation gradients in montane environments. We resurveyed forest plots established in 1964 along elevation transects in the Green Mountains (Vermont) to examine whether a shift had occurred in the location of the northern hardwood-boreal forest ecotone (NBE) from 1964 to 2004. We found a 19% increase in dominance of northern hardwoods from 70% in 1964 to 89% in 2004 in the lower half of the NBE. This shift was driven by a decrease (up to 76%) in boreal and increase (up to 16%) in northern hardwood basal area within the lower portions of the ecotone. We used aerial photographs and satellite imagery to estimate a 91- to 119-m upslope shift in the upper limits of the NBE from 1962 to 2005. The upward shift is consistent with regional climatic change during the same period; interpolating climate data to the NBE showed a 1.1°C increase in annual temperature, which would predict a 208-m upslope movement of the ecotone, along with a 34% increase in precipitation. The rapid upward movement of the NBE indicates little inertia to climatically induced range shifts in montane forests; the upslope shift may have been accelerated by high turnover in canopy trees that provided opportunities for ingrowth of lower elevation species. Our results indicate that high-elevation forests may be jeopardized by climate change sooner than anticipated. [ABSTRACT FROM AUTHOR]

• Ecological and biological processes can change from one state to another once a threshold has been crossed in space or time. Threshold responses to incremental changes in underlying variables can characterize diverse processes from climate change to the desertification of arid lands from overgrazing. • Simultaneously estimating the location of thresholds and associated ecological parameters can be difficult: ecological data are often ‘noisy’, which can make the identification of the locations of ecological thresholds challenging. • We illustrate this problem using two ecological examples and apply a class of statistical models well-suited to addressing this problem. We first consider the case of estimating allometric relationships between tree diameter and height when the trees have distinctly different growth modes across life-history stages. We next estimate the effects of canopy gaps and dense understory vegetation on tree recruitment in transects that transverse both canopy and gap conditions. • The Bayesian change-point models that we present estimate both threshold locations and the slope or level of ecological quantities of interest, while incorporating uncertainty in the change-point location into these estimates. This class of models is suitable for problems with multiple thresholds and can account for spatial or temporal autocorrelation. [ABSTRACT FROM AUTHOR]

• Recent empirical studies have found evidence of increased biomass production (‘overyielding’) in species mixtures relative to monoculture, but the interpretation of these results remains controversial, in part, because of the lack of a theoretical expectation. • Here, we examined the expected frequency and stability of overyielding species mixtures using Lotka–Volterra models of species dynamics in two- and four-species systems in conjunction with community, population, and specific rate of biomass production (SRP) definitions of overyielding. • Overyielding plant mixtures represented > 55% of potential species assemblages under community definitions and approximately 100% of species were either overyielding or underyielding under the population definition. Our species simulations approached their equilibria in 1–2 yr, supporting the relevancy of an equilibrial analysis. The range of parameter space that we explored produced realistic values of plot biomass, supporting their biological relevance. • We show that overyielding is expected to be common under community definitions and population definitions. Overyielding, under community or population definitions, does not imply an actual increase in the specific rate of biomass production. In addition, assemblages of overyielding and underyielding species under all three definitions can be stable over time with underyielding species persisting in the presence of overyielding species. [ABSTRACT FROM AUTHOR]

Global warming can potentially influence ecological communities through altered disturbance regimes in addition to increased temperatures. We investigate the response of pine savannas in the southeastern United States to global warming using a simple Lotka-Volterra competition model together with predicted changes to fire and hurricane disturbance regimes with global climate change. In the southeastern United States, decreased frequency of both fires and hurricanes with global warming will shift pine savannas toward a forested state. A CO2 fertilization effect that increases the growth rate of tree populations will also push southeastern landscapes from open savannas towards closed forests. Transient dynamics associated with climate driven changes in vegetation will last on the order of decades to a century. In our model, the sensitivity of savannas to relative changes in the frequency of fire versus hurricanes is linearly dependent on the growth rate and mortality of trees in fire and hurricane disturbances. [ABSTRACT FROM AUTHOR]

Global warming can potentially influence ecological communities through altered disturbance regimes in addition to increased temperatures. We investigate the response of pine savannas in the southeastern United States to global warming using a simple Lotka‐Volterra competition model together with predicted changes to fire and hurricane disturbance regimes with global climate change. In the southeastern United States, decreased frequency of both fires and hurricanes with global warming will shift pine savannas toward a forested state. A CO2 fertilization effect that increases the growth rate of tree populations will also push southeastern landscapes from open savannas towards closed forests. Transient dynamics associated with climate driven changes in vegetation will last on the order of decades to a century. In our model, the sensitivity of savannas to relative changes in the frequency of fire versus hurricanes is linearly dependent on the growth rate and mortality of trees in fire and hurricane disturbances. [ABSTRACT FROM AUTHOR]

1 Tree diversity in forests may be maintained by variability in seedling recruitment. Although forest ecologists have emphasized the importance of canopy gaps in generating spatial variability that might promote tree regeneration, the effects of canopy gaps on seedling recruitment may be offset by dense forest understories. 2 Large annual fluctuations in recruitment processes, coupled with the long life of forest trees, can provide an alternative mechanism for maintaining tree diversity. Evaluating the relative importance of spatial vs. temporal variability in recruitment, however, requires estimates of seedling survival that are extensive on both scales. 3 Estimates of survival derived from conventional statistical methods require that individual seedlings are monitored through time, and are therefore often limited in extent. We demonstrate a relatively fast and easy alternative approach to measuring survival that uses repeated counts of individuals in quadrats. 4 Annual seedling counts were used to quantify both the effects of small canopy gaps and understorey shrubs on the survival of Acer rubrum (red maple) seedlings and the magnitude of residual spatial and temporal variability in seedling survival. 5 We found that a dense understorey had a greater effect on seedling survival than did canopy gaps. Our canopy gaps had only a slight positive effect on seedling survival, and their benefit was offset by a large negative effect of the understorey shrub Rhododendron maximum. 6 Annual fluctuations in recruitment processes were seven times greater than spatial variability across transects and the effect of individual years on seedling survival was larger than even that of understorey shrubs. 7 The long life span of trees coupled with large annual variability in recruitment success across species may maintain the diversity of forest trees through a storage effect. [ABSTRACT FROM AUTHOR]

Seed and seedling predation may differentially affect competitively superior tree species to increase the relative recruitment success of poor competitors and contribute to the coexistence of tree species. We examined the effect of seed and seedling predation on the seedling recruitment of three tree species,Acer rubrum(red maple),Liriodendron tulipifera(yellow poplar), andQuercus rubra(northern red oak), over three years by manipulating seed and seedling exposure to predators under contrasting microsite conditions of shrub cover, leaf litter, and overstory canopy. Species rankings of seedling emergence were constant across microsites, regardless of exposure to seed predators, but varied across years.A. rubrumhad the highest emergence probabilities across microsites in 1997, butQ. rubrahad the highest emergence probabilities in 1999. Predators decreased seedling survival uniformly across species, but did not affect relative growth rates (RGRs).Q. rubrahad the highest seedling survivorship across microsites, whileL. tulipiferahad the highest RGRs. Our results suggest that annual variability in recruitment success contributes more to seedling diversity than differential predation across microsites. We synthesized our results from separate seedling emergence and survival experiments to project seedling bank composition. With equal fecundity assumed across species,Q. rubradominated the seedling bank, capturing 90% of the regeneration sites on average, followed byA. rubrum(8% of sites) andL. tulipifera(2% of sites). When seed abundance was weighted by species-specific fecundity, seedling bank composition was more diverse;L. tulipiferacaptured 62% of the regeneration sites, followed byA. rubrum(21% of sites) andQ. rubra(17% of sites). Tradeoffs between seedling performance and fecundity may promote the diversity of seedling regeneration by increasing the probability of inferior competitors capturing regeneration sites. [ABSTRACT FROM AUTHOR]

Over 100 native herbaceous forest perennials are harvested in the United States. Management of these populations for continued survival requires information on the long-term effects of varied harvesting levels on populations of forest perennials. Allium tricoccum Ait. is a species that is harvested throughout its range for its edible bulb. We examined the recovery of A. tricoccum populations over a 5-year period following experimental harvests of different intensity to predict the number of years required to return to pre-harvest levels and to establish guidelines for sustainable harvesting. Our harvest treatments included removal of 0, 25, 50, 75, and 100% of plants within experimental plots. Treatments were repeated at three sites. Population recovery was monitored for 4 years at two sites and 7 years at one site. We found a relationship between the level of harvest and the negative impacts on populations of A. tricoccum, with limited population recovery in the 4 years following harvesting. We projected recovery times as a function of harvest level using a population projection model. Deterministic projections of recovery time ranged from 148 years for a 95% harvest to 2.5 years for a 5% harvest. Incorporating stochasticity into our projections increased the uncertainty in our estimates of recovery time. For example the recovery time from a 5% harvest ranged from 1 to 89 years (95% confidence limit). Based on our results, a 10% harvest once every 10 years would, on average, be a sustainable level of harvest for A. tricoccum in southern Appalachian forests. [Copyright &y& Elsevier]

Disturbances that are strongly linked to global climatic cycles may occur in a regular, predictable manner that affects composition and distribution of ecological communities. The El Niño Southern Oscillation (ENSO) influences worldwide precipitation patterns and has occurred with regular periodicity over the last 130000 years. We hypothesized that ENSO, through effects on local weather conditions, has influenced frequency and extent of fires within Everglades National Park (Florida, USA). Using data from 1948 to 1999, we found that the La Niña phase of ENSO was associated with decreased dry-season rainfall, lowered surface water levels, increased lightning strikes, more fires, and larger areas burned. In contrast, the El Niño phase was associated with increased dry-season rainfall, raised surface water levels, decreased lightning strikes, fewer fires, and smaller areas burned. Shifts between ENSO phases every few years have likely influenced vegetation through periodic large-scale fires, resulting in a prevalence of fire-influenced communities in the Everglades landscape. [ABSTRACT FROM AUTHOR]

Spatial heterogeneity in microenvironments may provide unique regeneration niches for trees and may promote forest diversity. We examined how heterogeneity in understory cover, mineral nutrients, and moisture and their interactions with canopy gaps contribute to the coexistence of three common, co-occuring tree species. We measured survival and height growth of 1080 seedlings of Acer rubrum (red maple), Liriodendron tulipifera (yellow poplar), and Quercus rubra (red oak) that were planted in one of five understory treatments: removal of understory vegetation, trenched, trenched plus removal of understory vegetation, fertilization, and a control. Understory treatments were replicated in 12 paired gap and canopy environments. Survivorship varied among species, with Q. rubra having the highest probability of surviving beyond the 1135-day experiment (probability = 0.64), followed by A. rubrum (probability = 0.27) and L. tulipifera (probability = 0.07). Although canopy gaps and understory treatments had large effects on survivorship, species survival rankings changed little across microenvironments; Q. rubra had the highest survival in all microenvironments. In contrast to survival, L. tulipifera had a relative growth rate for height that was three times greater than that of A. rubrum and Q. rubra in high-resource microenvironments. There was broad overlap among species in relative growth rates in the remaining seven microenvironments, with no clear top-ranked species. Differences in seedling growth and survival across these 10 microenvironments may contribute to the coexistence of two of the three species studied, L. tulipifera and Q. rubra, but not A. rubrum. Q. rubra had higher survival than A. rubrum and L. tulipifera in all microenvironments, but L. tulipifera tended to grow faster than A. rubrum and Q. rubra in high-resource microenvironments. Despite the generally poor performance of A. rubrum, it was the only surviving species in some quadrats at the end of the experiment, indicating that stochastic effects, in conjunction with broad niche overlap, may also contribute to species coexistence. The importance of stochastic effects will probably increase when differential fecundity across these three species is considered because the high fecundity of A. rubrum offsets survival and growth disadvantages of its seedlings through their greater total abundance. [ABSTRACT FROM AUTHOR]

Examines the effects of fire regimes on subsequent hurricane-related mortality of Savanna pines in Florida. Interactions of large scale disturbances; Rate of mortality; Sources of mortality.

Recurrent fires are integral to the function of many ecosystems worldwide. The management of fire-frequented ecosystems requires the application of fire at the appropriate frequency and seasonality, but establishing the natural fire regime for an ecosystem can be problematic. Historical records of fires are often not available, and surrogates for past fires may not exist. We suggest that the relationship between climate and fire can provide an alternative means for inferring past fire regimes in some ecosystems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the article, the author reflects on the importance of increasing the efficiency of crops due to a continuous increase in the world population. He discusses the results of the study by crop physiologist Deng J. regarding the crop growth. He also states that the model assumptions in the Deng and others' study of optimum water and nutrient resource availability leads to several issues related to multiple limitations of plant growth.

The northeastern USA has experienced a dramatic increase in total and extreme precipitation over the past 30 years, yet how precipitation will evolve across the Northeast by the end of the twenty-first century remains uncertain. To examine the future of precipitation across the Northeast, we use the Weather Research and Forecasting (WRF) regional climate model driven by the National Center for Atmospheric Research Community Earth System Model (CESM) to simulate precipitation for historical (1976–2005) and future (2070–2099) periods. We compare precipitation from CESM-WRF hindcasts to gridded observations (Daymet), finding a 4.6% dry bias and 7.7% wet bias for total and extreme precipitation, respectively. CESM-WRF projections have increases in both total (9.7%) and extreme (51.6%) precipitation by the end of the twenty-first century, with winter having the largest increases in total precipitation (16.4%) and extreme precipitation (109.3%). These results are consistent with additional WRF simulations forced with the Max Planck Institute Earth System Model and the North American Coordinated Regional Downscaling Experiment archive. To investigate the drivers of precipitation change, we analyze several atmospheric variables and find that the projected increases in extreme precipitation are strongly related to increasing precipitable water over the eastern USA and the Atlantic Ocean. Understanding projected increases in total and extreme precipitation is critical for stakeholders to prepare for the impacts of intensified precipitation. [ABSTRACT FROM AUTHOR]

Interdisciplinary research that links human and natural systems is critical to addressing complex environmental and ecological problems. A growing number of interdisciplinary research teams investigate coupled natural-human systems, but the degree to which they actually examine two-way linkages between the systems is limited. We examined aspects of interdisciplinary teams that were explicitly funded to conduct research including such linkages by considering attributes of team leaders, team members, and analysis methods employed. Our objective was to investigate the degree to which interdisciplinary teams studying coupled natural-human systems publish research that displays two-way linkages between systems. Our analysis shows that team members' academic disciplines and the types of analysis methods that interdisciplinary teams apply play a crucial role in the success of the team in publishing articles that include two-way linkages. We found that the success of developing two-way linkages is enhanced when teams include leaders and/ or members from interdisciplinary academic disciplines (e.g., planning departments, sustainability, environmental economics, biological and ecological engineering, and individuals affiliated with more than one academic department from different discipline categories). Additionally, the presence of social science members increases the likelihood of two-way linkages, whereas the presence of physical science or biological/life science members decreases this likelihood. Among articles that included two-way linkages, essentially all utilized a conceptual-/literature-review approach, or included simulation model analysis. Based on these findings, we conclude that interdisciplinary teams are not a mere sum of people from different academic disciplines, but a group of people who have the ability to incorporate different disciplines conceptually and analytically. To move forward, it is important to acknowledge that becoming an interdisciplinary researcher takes deliberative work. Educational programs that train students and early career scholars with flexible thinking and analytical capacities may be the key to furthering coupled natural-human systems research. [ABSTRACT FROM AUTHOR]

Wildfires are often governed by rapid changes in seasonal rainfall. Therefore, measuring seasonal rainfall on a temporally finescale should facilitate the prediction of wildfire regimes. To explore this hypothesis, daily rainfall data over a 58-yr period (1950--2007) in south-central Florida were transformed into cumulative rainfall anomalies (CRAs). This transformation allowed precise estimation of onset dates and durations of the dry and wet seasons, as well as a number of other variables characterizing seasonal rainfall. These variables were compared with parameters that describe ENSO and a wildfire regime in the region (at the Avon Park Air Force Range). Onset dates and durations were found to be highly variable among years, with standard deviations ranging from 27 to 41 days. Rainfall during the two seasons was distinctive, with the dry season having half as much as the wet season despite being nearly 2 times as long. The precise quantification of seasonal rainfall led to strong statistical models describing linkages between climate and wildfires: a multiple-regression technique relating the area burned with the seasonal rainfall characteristics had an of 0.61, and a similar analysis examining the number of wildfires had an of 0.56. Moreover, the CRA approach was effective in outlining how seasonal rainfall was associated with ENSO, particularly during the strongest and most unusual events (e.g., El Niño of 1997/98). Overall, the results presented here show that using CRAs helped to define the linkages among seasonality, ENSO, and wildfires in south-central Florida, and they suggest that this approach can be used in other fire-prone ecosystems. [ABSTRACT FROM AUTHOR]

Presents a study that determined whether survival probabilities at early life history transitions decrease when seeds or seedlings are at high densities or close to adults of the same species in a temperate forest. Quantification of seed dispersal and seedling emergence; Relationship between conspecific seed density and seed germination; Correlation between latitude and the proportion of tree species experiencing density-dependent mortality.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Global change may induce shifts in plant community distributions at multiple spatial scales. At the ecosystem scale, such shifts may result in movement of ecotones or vegetation boundaries. Most indicators for ecosystem change require timeseries data, but here a new method is proposed enabling inference of vegetation boundary movement from one 'snapshot' (e.g. an aerial photograph or satellite image) in time. The method compares the average spatial position of frontrunners of both communities along the vegetation boundary. Mathematical analyses and simulation modeling show that the average frontrunner position of retreating communities is always farther away from a so-called optimal vegetation boundary as compared to that of the expanding community. This feature does not depend on assumptions about plant dispersal or competition characteristics. The method is tested with snapshot data of a northern hardwood-boreal forest mountain ecotone in Vermont, a forest-mire ecotone in New Zealand and a subalpine treeline-tundra ecotone in Montana. The direction of vegetation boundary movement is accurately predicted for these case studies, but we also discuss potential caveats. With the availability of snapshot data rapidly increasing, the method may provide an easy tool to assess vegetation boundary movement and hence ecosystem responses to changing environmental conditions. [ABSTRACT FROM AUTHOR]