Search

The COVID-19 pandemic has dramatically altered people’s travel behavior, in particular outdoor activities, including walking. Their behavior changes may have prolonged effects after the pandemic, and such changes vary by the context and are related to the characteristics of the built environment. But empirical studies about the relationships between pedestrians and the built environment during the pandemic are lacking. This study explores how COVID-19 and related travel restrictions have affected the relationship between pedestrian traffic volume and the built environment. We estimate daily pedestrian volumes for all signalized intersections in Salt Lake County, Utah, U.S.A., from pedestrian push-button log data between January 2019 and October 2020. Multilevel spatial filtering models show that the COVID-19 pandemic has altered the relationship between pedestrian traffic volume and the built environment. During the pandemic, the higher the number of COVID-19 cases, the less (or more negative) the effects of density, street connectivity, and destination accessibility on pedestrian volume being observed. The exception is access to urban parks, as it became more significant in increasing pedestrian activities during the pandemic. The models also highlight the negative impacts of the pandemic in economically disadvantaged areas. Our findings could help urban and transportation planners find effective interventions to promote active transportation and physical activity amid the global pandemic.

International audience; Lake Bottom oxbow is a partially-filled oxbow lake within an alluvium filled sandstone canyon reach of the Dolores River in eastern Utah, USA. Two adjacent sediment cores were obtained from the Lake Bottom oxbow to better understand the depositional and environmental history of the site and region. Depositional ages were determined using radiocarbon dating (14C) of pollen extracts, optically stimulated luminescence (OSL) of sand, concentration of 137Cs and 210Pb radioisotopes, and the first occurrence of pollen from human-introduced crops (Zea mays) and trees and shrubs (Tamarix) found in the oxbow sediments. Age control from OSL, short-lived radioisotopes, and pollen are stratigraphically consistent and fit the geomorphic character of the partially-filled oxbow, which suggests it is a late Holocene feature. In contrast, the 14C results appear to be contaminated by old carbon and produced age overestimates of 3000–12,000 years. Our findings suggest that multiple dating methods should be employed when applying radiocarbon dating to settings that are susceptible to contamination by recycled old carbon, especially when dating pollen extracts that are difficult to determine organic purity. The OSL results suggest initial oxbow formation by 860 ± 270 yr (±2σ) and then rapidly infilled with fluvial sediment. The OSL age control indicates the oxbow formed during the Medieval Climate Anomaly, a time of enhanced summer moisture and active flooding in the Dolores River watershed. Similar low-elevation records in the arid Colorado Plateau are rare, making this site an important contribution to the regional paleoenvironmental record.

Criminal activities motivated by hate are the most extreme form of bias against people. While hating a class of people and organizing in hate groups to express feelings against those people are not illegal, hate crimes, violent and non-violent, are illegal. However, there remains much to be learned about geographic patterns of hate crimes and facilitating environments. This exploratory research examines hate crime occurrences aggregated to counties in the conterminous United States and attempts to explain resulting patterns using socioeconomic and ideological correlates with traditional and spatial statistics. Geographical patterns of hate crimes in the Unites States are found to be a complicated phenomenon.

The spread of agriculture is a major driver of social and environmental change throughout the Holocene, yet experimental and ethnographic data indicate that farming is less profitable than foraging, so why would individuals choose to adopt agriculture leading to its expansion? Ideal distribution models offer one framework to answer this question: Individuals should adopt less profitable subsistence strategies and occupy more marginal environments when local population density increases competition to the point where the suitability of the best strategies and habitats becomes equal to what can be gained in poorer strategies and habitats. Coupling radiocarbon-dated archaeological sites with a validated measure of agricultural suitability, we evaluate the emergence of farming in the Basin-Plateau region of North America. Our results show that farming first occurs in the more suitable Colorado Plateau physiographic region, and only spreads into the less suitable Great Basin after population density on the Plateau increases. This produces an approximate 300- to 400-year lag between the onset of farming on the Plateau and in the Basin. These findings support the ideal distribution hypothesis for the spread of farming, and suggest a general socioecological process that may help explain global patterns in the timing and tempo of agricultural expansions.

Criminal activities motivated by hate are the most extreme form of bias against people. While hating a class of people and organizing in hate groups to express feelings against those people are not illegal, hate crimes, violent and non-violent, are illegal. However, there remains much to be learned about geographic patterns of hate crimes and facilitating environments. This exploratory research examines hate crime occurrences aggregated to counties in the conterminous United States and attempts to explain resulting patterns using socioeconomic and ideological correlates with traditional and spatial statistics. Geographical patterns of hate crimes in the Unites States are found to be a complicated phenomenon.

Background Transgenerational epigenetic risks associated with complex health outcomes, such as autism spectrum disorder (ASD), have attracted increasing attention. Transgenerational environmental risk exposures with potential for epigenetic effects can be effectively identified using space-time clustering. Specifically applied to ancestors of individuals with disease outcomes, space-time clustering characterized for vulnerable developmental stages of growth can provide a measure of relative risk for disease outcomes in descendants. Objectives (1) Identify space-time clusters of ancestors with a descendent with a clinical ASD diagnosis and matched controls. (2) Identify developmental windows of ancestors with the highest relative risk for ASD in descendants. (3) Identify how the relative risk may vary through the maternal or paternal line. Methods Family pedigrees linked to residential locations of ASD cases in Utah have been used to identify space-time clusters of ancestors. Control family pedigrees of none-cases based on age and sex have been matched to cases 2:1. The data have been categorized by maternal or paternal lineage at birth, childhood, and adolescence. A total of 3,957 children, both parents, and maternal and paternal grandparents were identified. Bernoulli space-time binomial relative risk (RR) scan statistic was used to identify clusters. Monte Carlo simulation was used for statistical significance testing. Results Twenty statistically significant clusters were identified. Thirteen increased RR (> 1.0) space-time clusters were identified from the maternal and paternal lines at a p-value geographic legacy, is a powerful tool for studying transgenerational effects that may be epigenetic in nature. Our novel use of space-time clustering can be applied to any disease where family pedigree data is available.

There has been an increasing body of literature analyzing eviction in different cities and contexts in urban studies, public health, sociology, geography, and housing studies. Still, little has bee...

This research proposes a methodology for extracting and analyzing geographical perspectives of terrorists and insurgents from online textual media. The information age has afforded organizations pl...

How might subsistence strategies structure the costs and benefits of habitat selection and, therefore, drive settlement patterning? We explore this question within an Ideal Distribution framework, ...

The American Automobile Association (AAA) conducted a 1-year pilot study of running a self-driving shuttle bus in the old downtown of Las Vegas between November 1, 2017, and October 31, 20...

BACKGROUND AND AIM: Space-time cluster analyses are commonly used in spatial epidemiology. There is evidence of transgenerational factors associated with ASD. Space-time clusters of parents and gra...

Paleoecological investigations using the pollen-plant functional trait linkage are increasing in value as new insights to past ecological function and dynamics are revealed. These retrospective approaches link pollen sequences to plant functional trait measurements to reveal long-term changes in ecosystem properties that are difficult to resolve using traditional paleoecological methods. Despite these methodological improvements and the newfound perspectives, there has yet to be thorough testing of whether transforming pollen to ecological function tracks functional trait distributions in geographic space. We assess this in North America by linking surface pollen samples to measurements of three functional traits that represent major axes of plant ecological strategy. Pollen-derived estimates of function were first used to investigate occupied trait space at different scales. These estimates were used to reconstruct the latitudinal functional diversity gradient of North America, and results were compared to the continent’s functional diversity gradient estimated from tree assemblages and gradients based on pollen richness and evenness. Results indicated that the patterns in pollen-based function sufficiently track ecological function in trait and geographic space and the macroecological biodiversity pattern was reconstructed, although there were minor differences between the slopes of the functional diversity and each of the pollen index gradients. Taken together, the outcomes of our investigation indicate reliability in extending the pollen-plant functional trait linkage into deeper time, at least for examining North American functional dynamics.

Escape routes keep firefighters safe by providing efficient evacuation pathways from the fire line to safety zones. Effectively utilizing escape routes requires a precise understanding of how much time it will take firefighters to traverse them. To improve this understanding, we collected GPS-tracked travel rate data from US Interagency Hotshot &ldquo, Type 1&rdquo, Crews during training in 2019. Firefighters were tracked while hiking, carrying standard loads (e.g., packs, tools, etc.) along trails with a precisely-measured terrain slope derived from airborne lidar. The effects of the slope on the instantaneous travel rate were assessed by three models generated using non-linear quantile regression, representing low (bottom third), moderate (middle third), and high (upper third) rates of travel, which were validated using k-fold cross-validation. The models peak at about a &minus, 3&deg, (downhill) slope, similar to previous slope-dependent travel rate functions. The moderate firefighter travel rate model mostly predicts faster movement than previous slope-dependent travel rate functions, suggesting that firefighters generally move faster than non-firefighting personnel while hiking. Steepness was also found to have a smaller effect on firefighter travel rates than previously predicted. The travel rate functions produced by this study provide guidelines for firefighter escape route travel rates and allow for more accurate and flexible wildland firefighting safety planning.

As sensor measurements emerge in urban water systems, data-driven unsupervised machine learning algorithms have drawn tremendous interest in event detection and hydraulic water level and flow prediction recently. However, most of them are applied in water distribution systems and few studies consider using unsupervised cluster analysis to group the time-series hydraulic-hydrologic data in stormwater urban drainage systems. To improve the understanding of how cluster analysis contributes to flooding location detection, this study compared the performance of K-means clustering, agglomerative clustering, and spectral clustering in uncovering time-series water depth dissimilarity. In this work, the water depth datasets are simulated by an urban drainage model and then formatted for a clustering problem. Three standard performance evaluation metrics, namely the silhouette coefficient index, Calinski&ndash, Harabasz index, and Davies&ndash, Bouldin index are employed to assess the clustering performance in flooding detection under various storms. The results show that silhouette coefficient index and Davies&ndash, Bouldin index are more suitable for assessing the performance of K-means and agglomerative clustering, while the Calinski&ndash, Harabasz index only works for spectral clustering, indicating these clustering algorithms are metric-dependent flooding indicators. The results also reveal that the agglomerative clustering performs better in detecting short-duration events while K-means and spectral clustering behave better in detecting long-duration floods. The findings of these investigations can be employed in urban stormwater flood detection at the specific junction-level sites by using the occurrence of anomalous changes in water level of correlated clusters as flood early warning for the local neighborhoods.

As sensor measurements emerge in urban water systems, data-driven unsupervised machine learning algorithms have been drawn tremendous interest in infrastructure monitoring, flow prediction, and pollutant warning recently. However, most of them are applied in water distribution systems, and few studies consider using unsupervised clustering analysis to group the time-series hydraulic-hydrologic data at urban drainage systems. To improve the understanding of how clustering analysis contributes to detecting urban flooding events, this study compared the performance of K-means Clustering, Agglomerative Clustering, and Spectral Clustering in uncovering time-series water depth similarity and finally identified the number of clusters with maximum performance scores. In this work, the water depth datasets are simulated by a real-world SWMM model and then formatted for a clustering problem. Three standard performance evaluation scores, the SCI, CHI, and DBI, are employed to assess the clustering performance under six artificial rainfalls and two recorded storms. The results indicate that SCI and DBI are appropriate for assessing the performance of K-means Clustering and Agglomerative Clustering, while CHI only works for Spectral Clustering. Noticeably, it was found that the number of clusters is negatively related to the dataset length, but less correlated with the dataset magnitude.

The carbon sink potential of peatlands depends on the balance of carbon uptake by plants and microbial decomposition. The rates of both these processes will increase with warming but it remains unclear which will dominate the global peatland response. Here we examine the global relationship between peatland carbon accumulation rates during the last millennium and planetary-scale climate space. A positive relationship is found between carbon accumulation and cumulative photosynthetically active radiation during the growing season for mid- to high-latitude peatlands in both hemispheres. However, this relationship reverses at lower latitudes, suggesting that carbon accumulation is lower under the warmest climate regimes. Projections under Representative Concentration Pathway (RCP)2.6 and RCP8.5 scenarios indicate that the present-day global sink will increase slightly until around AD 2100 but decline thereafter. Peatlands will remain a carbon sink in the future, but their response to warming switches from a negative to a positive climate feedback (decreased carbon sink with warming) at the end of the twenty-first century.

Hate in the United States today is narrowly understood but widely used as a politically charged term. Recently, political blame-placing on outsiders such as immigrants has bred a climate of hate an...

Cooling during most of the past two millennia has been widely recognized and has been inferred to be the dominant global temperature trend of the past 11,700 years (the Holocene epoch). However, long-term cooling has been difficult to reconcile with global forcing, and climate models consistently simulate long-term warming. The divergence between simulations and reconstructions emerges primarily for northern mid-latitudes, for which pronounced cooling has been inferred from marine and coastal records using multiple approaches. Here we show that temperatures reconstructed from sub-fossil pollen from 642 sites across North America and Europe closely match simulations, and that long-term warming, not cooling, defined the Holocene until around 2,000 years ago. The reconstructions indicate that evidence of long-term cooling was limited to North Atlantic records. Early Holocene temperatures on the continents were more than two degrees Celsius below those of the past two millennia, consistent with the simulated effects of remnant ice sheets in the climate model Community Climate System Model 3 (CCSM3). CCSM3 simulates increases in 'growing degree days'-a measure of the accumulated warmth above five degrees Celsius per year-of more than 300 kelvin days over the Holocene, consistent with inferences from the pollen data. It also simulates a decrease in mean summer temperatures of more than two degrees Celsius, which correlates with reconstructed marine trends and highlights the potential importance of the different subseasonal sensitivities of the records. Despite the differing trends, pollen- and marine-based reconstructions are correlated at millennial-to-centennial scales, probably in response to ice-sheet and meltwater dynamics, and to stochastic dynamics similar to the temperature variations produced by CCSM3. Although our results depend on a single source of palaeoclimatic data (pollen) and a single climate-model simulation, they reinforce the notion that climate models can adequately simulate climates for periods other than the present-day. They also demonstrate that amplified warming in recent decades increased temperatures above the mean of any century during the past 11,000 years.

Airborne laser scanning (ALS) data enable accurate modeling and mapping of aboveground biomass (AGB), but the limited spatial and temporal extents of ALS data collection limit the capacity for broad-scale carbon accounting. Conversely, while space-based remote sensing instruments provide increased spatial and temporal coverage, it can be difficult to directly link field-level vegetation biometrics to satellite data due to coarser spatial resolution and positional uncertainty. The combined use of ALS and satellite remote sensing data may offer a solution to efficient, accurate, and consistent AGB mapping across time and space. Such airborne-spaceborne data fusion has been demonstrated successfully in high-biomass settings; however, the unique structural conditions of dryland woodland ecosystems, with open canopies and low leaf area indices, pose mapping challenges that require further study. These challenges are particularly acute with large footprint spaceborne lidar, where short, widely-spaced trees may limit the capacity for accurate AGB estimation. In this study, we present a scaled methodological framework for linking field-measured woodland AGB to ALS data and, in turn, linking ALS-modeled AGB to satellite data, using pinon-juniper woodlands in southeastern Utah as a case study. We compare the effectiveness of this scaling approach using two satellite sensors, Landsat 8 OLI (multispectral) and GEDI (lidar). Since the predicted outputs of our local-scale model are being used as inputs to our regional-scale model, we also demonstrate an approach for propagating uncertainty throughout this nested, multiscale analytical framework, leveraging the inherent variability within a random forest's decision trees. Given the positional uncertainty of GEDI footprints, we test a range of different footprint sizes for their relative effects on ALS-GEDI AGB model accuracy. Our local-scale (field-ALS) predictive model was able to account for 74% of variance in AGB, and estimate AGB with a root mean squared error (RMSE) of 14 Mg/ha, a mean absolute error (MAE) of 11.09 Mg/ha. Our regional-scale (ALS-Landsat/GEDI) analysis with propagated uncertainty revealed that the combined use of Landsat and GEDI metrics produced the best predictive model (R2 = 0.68; RMSE = 12.71 Mg/ha; MAE = 9.40 Mg/ha), followed by Landsat-only metrics (R2 = 0.66, RMSE = 13.08 Mg/ha; MAE = 9.71 Mg/ha), and GEDI-only metrics (R2 = 0.49, RMSE = 16.01 Mg/ha; MAE = 12.14 Mg/ha). These results suggest that Landsat may be better-suited than GEDI for estimating AGB in woodland environments where low canopy covers and short trees limit the capacity for precisely characterizing vegetation structure within large-footprint, waveform lidar data. The footprint size analysis revealed that larger simulated footprints (e.g., 30 m radius and greater) produced higher GEDI model accuracies; however, increasing footprint radii beyond 30 m does not significantly increase model accuracy. This research represents an important step forward in improving our capacity for reliably mapping woodland AGB, and provides an early test case for the application of GEDI data to woodland AGB mapping.

International audience; The European Pollen Database (EPD) is a community effort to archive and make available pollen sequences from across the European continent. Pollen sequences provide records that may be used to infer past vegetation and vegetation change. We present here maps based on 828 sites from the EPD giving an overview of changes in postglacial pollen assemblages in Europe over the past 15,000 years. The maps show the distribution and abundance of 54 different pollen taxa at 500 year intervals, supported by new age-depth models and associated chronological uncertainty analysis. Results show the individualistic patterns of spread of different pollen taxa, and provide a standardized dataset for further analysis, defining a spatial context for the study of past plant and vegetation changes and other aspects of environmental history in Europe.

Many studies have used observational data to explore associations between El Niño–Southern Oscillation (ENSO) and western North America (NA) hydroclimate at regional spatial scales. However, relationships between tropical Pacific sea surface temperature (SST) variability and western NA hydroclimate at local scales using reanalysis data are less understood. Here, the current understanding of relationships between large-scale tropical Pacific SST variability and western NA hydroclimate is extended to localized headwaters. To accomplish this, high-resolution reanalysis data (i.e., monthly mean surface precipitation rate, 2-m temperature, 850-mb specific humidity, and 500-mb omega) were used for gridpoint correlation analyses with Niño-3.4 SST and El Niño Modoki indices. Reanalysis time series data were provided by the National Centers for Environmental Prediction North American Regional Reanalysis (NARR) product. To validate the accuracy of NARR surface data, observational Livneh precipitation and temperature data were used. Resulting correlations between tropical Pacific indices and NARR surface precipitation and 2-m temperature are consistent with previous research both spatially and temporally, indicating that the strongest correlations occur primarily over southwestern NA during the winter (DJF). The results herein demonstrate the potential of high-resolution reanalysis data to reveal distinct correlations over topographically complex watersheds in the U.S. Intermountain West (IMW) over the recent record, 1979–2015. The use of the high-resolution NARR product as a viable option to explore western NA hydroclimate is demonstrated here.

The extinction of California’s flightless duck, Chendytes lawi, stands out in the faunal history of North America because it involved a marine animal that disappeared in the late Holocene, not in the terminal Pleistocene when humans arrived from Asia, nor with the more recent entry of Europeans and associated resource exploitation. Here we evaluate the duck’s ecology by combining a stable isotopic evaluation of its feeding niche with records of human predation and climatic variation to evaluate (1) alternative influences on the duck’s extinction and (2) possible ecological consequences of its disappearance. Results indicate that a mid-late Holocene increase in human population density coincided with a decrease in mean sea surface temperatures (SST) and an increase in SST variance, all of which were contemporaneous with the demise of the flightless duck ca. 2500 cal BP. Climate appears to be a main exogenous driver of this extinction event by not only negatively impacting flightless duck food availability, but positively impacting one of the duck’s main predators: humans. Isotopic findings indicate the duck had a unique feeding ecology that overlapped equally with analog species reliant mostly on marine invertebrates (sea otters [Enhydra lutris]) and on fish (harbor seals [Phoca vitulina]). This dietary niche overlap suggests the duck’s extinction could have opened foraging opportunities for harbor seals and sea otters. Otter numbers appear to have increased coincident with the duck’s decline which also correlates with a decrease in size and abundance of red abalone (Haliotis rufescens), one of the otter’s preferred prey. These correlations suggest that new feeding opportunities may have emerged for otters in the face of the duck’s extinction, encouraging an increase in otter populations, which then had negative impacts on abalone that also were targeted by humans. Overall findings suggest the possibility of an unanticipated prehistoric precursor to the historic baseline of this nearshore ecosystem.

Large parks—including regional parks, state parks, and national forests and parks—have particular health, social, and environmental benefits. Thus, promoting equal access to large parks is increasingly becoming a goal of environmental justice activists, planners, and policymakers. Disadvantaged populations (e.g., low-income people of color) have worse walking access to large parks than more privileged groups and might rely on public transportation to access such parks. But empirical studies on whether access to large parks via public transit is justly distributed are lacking. In this paper, we examine the relationship between a novel measure of public transit access to large parks (the T2P index) and neighborhood-level disadvantage (income, race/ethnicity, and age). Using network analysis with public transit feed data and park location data, we calculate the T2P index for every census block group in the 15 largest metropolitan statistical areas (MSAs) in the U.S. West. We find some evidence of environmental injustice. A spatial filtering model shows that T2P access increases when a neighborhood has a larger share of non-Hispanic Whites and a smaller share of older adults, but that median household income is not associated with T2P in the entire sample. We also find that some regions present significant environmental injustices in T2P whereas others have fewer to no injustices. Transit agencies and park planners could use our T2P index and findings to prioritize transit investment for disadvantaged populations and promote healthy living.

Water extraction for anthropogenic use has become a major flux in the hydrological cycle. With increasing demand for water and challenges supplying it in the face of climate change, there is a pressing need to better understand connections between human populations, climate, water extraction, water use, and its impacts. To understand these connections, we collected and analyzed stable isotopic ratios of more than 800 urban tap water samples in a series of semiannual water surveys (spring and fall, 2013 to 2015) across the Salt Lake Valley (SLV) of northern Utah. Consistent with previous work, we found that mean tap water had a lower 2H and 18O concentration than local precipitation, highlighting the importance of nearby montane winter precipitation as source water for the region. However, we observed strong and structured spatiotemporal variation in tap water isotopic compositions across the region which we attribute to complex distribution systems, varying water management practices and multiple sources used across the valley. Water from different sources was not used uniformly throughout the area and we identified significant correlation between water source and demographic parameters including population and income. Isotopic mass balance indicated significant inter- and intra-annual variability in water losses within the distribution network due to evaporation from surface water resources supplying the SLV. Our results demonstrate the effectiveness of isotopes as an indicator of water management strategies and climate impacts within regional urban water systems, with potential utility for monitoring, regulation, forensic and a range of water resource research. This article is protected by copyright. All rights reserved.

Predictive models are central to both archaeological research and cultural resource management. Yet, archaeological applications of predictive models are often insufficient due to small training data sets, inadequate statistical techniques, and a lack of theoretical insight to explain the responses of past land use to predictor variables. Here we address these critiques and evaluate the predictive power of four statistical approaches widely used in ecological modeling-generalized linear models, generalized additive models, maximum entropy, and random forests-to predict the locations of Formative Period (2100-650 BP) archaeological sites in the Grand Staircase-Escalante National Monument. We assess each modeling approach using a threshold-independent measure, the area under the curve (AUC), and threshold-dependent measures, like the true skill statistic. We find that the majority of the modeling approaches struggle with archaeological datasets due to the frequent lack of true-absence locations, which violates model assumptions of generalized linear models, generalized additive models, and random forests, as well as measures of their predictive power (AUC). Maximum entropy is the only method tested here which is capable of utilizing pseudo-absence points (inferred absence data based on known presence data) and controlling for a non-representative sampling of the landscape, thus making maximum entropy the best modeling approach for common archaeological data when the goal is prediction. Regression-based approaches may be more applicable when prediction is not the goal, given their grounding in well-established statistical theory. Random forests, while the most powerful, is not applicable to archaeological data except in the rare case where true-absence data exist. Our results have significant implications for the application of predictive models by archaeologists for research and conservation purposes and highlight the importance of understanding model assumptions.

Housing insecurity affects millions of Americans. Many cases in which individuals or families lack secure housing are the result of involuntary residential displacement, which often comes in the form of eviction and eviction threat from rental residences. This study takes a spatial analysis approach to understand patterns of evictions filed in Salt Lake County, Utah at the block group level. Modeling the geography of housing security attributes in urban areas is key to identifying inequality issues in potentially segregated regions. Two regression models are constructed that provide insight into inequalities based on race/ethnicity and socioeconomic vulnerability. The models show that there are clear inequalities in Salt Lake County, whereby those living in block groups of minority populations are affected by eviction at a substantially higher rate than those living in majority White population block groups. There is also a higher likelihood of threat of eviction if residents are already economically stressed. The implications of these findings are not limited to individual or family suffering, but also have negative community and larger social effects. Potential pathways to alleviating these issues are discussed in the conclusions section.

The Department of Justice has the authority to settle litigation against the United States. In doing so, the Attorney General can create policy that is difficult or impossible for successor administration to reverse. This paper reexamines the assumptions of prior literature concluding that the settlement authority poses grave separation of powers concerns, and instead shows why settlement—as part of the executive branch's litigation authority—is a proper part of executive decision-making. It concludes by demonstrating how the settlement authority could be harmonized with administrative law norms of transparency and judicial review.

Few studies of walkability include both perceived and audited walkability measures. We examined perceived walkability (Neighborhood Environment Walkability Scale—Abbreviated, NEWS-A) and audited walkability (Irvine–Minnesota Inventory, IMI) measures for residents living within 2 km of a “complete street”—one renovated with light rail, bike lanes, and sidewalks. For perceived walkability, we found some differences but substantial similarity between our final scales and those in a prior published confirmatory factor analysis. Perceived walkability, in interaction with distance, was related to complete street active transportation. Residents were likely to have active transportation on the street when they lived nearby and perceived good aesthetics, crime safety, and traffic safety. Audited walkability, analyzed with decision trees, showed three general clusters of walkability areas, with 12 specific subtypes. A subset of walkability items (n = 11), including sidewalks, zebra-striped crosswalks, decorative sidewalks, pedestrian signals, and blank walls combined to cluster street segments. The 12 subtypes yielded 81% correct classification of residents’ active transportation. Both perceived and audited walkability were important predictors of active transportation. For audited walkability, we recommend more exploration of decision tree approaches, given their predictive utility and ease of translation into walkability interventions.

Concern is growing that global climate change will have widespread impact on the world’s terrestrial ecosystems, but future impacts are imperfectly constrained by ecosystem models and direct observations. All available data, including records of past ecological change, need to be utilized to assess the range of potential future outcomes. Here we show that pervasive ecosystem transformations occurred in response to warming and associated climatic changes during the last glacial-to-interglacial transition, which was of comparable magnitude to climatic change projected to occur in the next 100 to 150 years under high-emission scenarios. We used data from 596 published paleoecological records to examine compositional and structural changes in global terrestrial vegetation since the last glacial period, and to project the magnitude of ecosystem transformations under different emission scenarios in the future. Our results indicate that terrestrial ecosystems are highly sensitive to temperature change, and suggest that without major reductions in greenhouse-gas emissions to the atmosphere (i.e., in line with those targeted in the 2015 Paris Agreement), most terrestrial ecosystems worldwide are at risk of major transformation, with accompanying disruption of ecosystem services and impacts on biodiversity.

The specific temporal patterns of antecedent conditions associated with fire occurrence in the Great Basin and Upper Colorado River Basin are poorly understood. Using 25 years of combined fire and climate data, we identified unique antecedent patterns of climate conditions prior to fires in the Great Basin and Upper Colorado River Basin. Five distinct antecedent patterns of climate related to fire were found within the region; with these antecedent patterns we were able to construct models of fire danger. The occurrence of these antecedent patterns varies both spatially and temporally, and appears to be driven by drought severity. We used a Maximum Entropy approach to model the spatial extent and strength of these fire-climate patterns, and the associated fire danger. This approach provides land managers with a practical way to assess fire danger at a relatively fine spatial scale and also gives researchers a tool for assessing future fire danger.

Live fuel moisture content (LFMC), the ratio of water mass to dry mass contained in live plant material, is an important fuel property for determining fire danger and for modeling fire behavior. Remote sensing estimation of LFMC often relies on an assumption of changing water and stable dry mass over time. Fundamental understanding of seasonal variation in plant water and dry mass is needed to explain the spectral expression of LFMC changes over time. We conducted a five-month experiment to continuously measure field LFMC samples, biochemical components of dry matter, and leaf spectroscopic data for two species common in the western U.S., lodgepole pine ( Pinus contorta Douglas ex Loudon) and big sagebrush ( Artemisia tridentata Nutt). Our results showed that new lodgepole pine needles initially had higher LFMC and a smaller proportion of dry mass, but differences between new and old needles converged as the new needles matured. New needle dry mass had strong temporal trends, and dry mass explained more variation in LFMC than water in both new and old needles. Sagebrush leaves exhibited decreasing trends in LFMC, but water and dry mass comparably contributed to LFMC seasonal variation. Spectroscopic analysis using partial least squares regression (PLSR) showed good modeling accuracy for LFMC temporal variation in new needles (R 2 = 0.94, RMSE = 5.84%), old needles (R 2 = 0.72, RMSE = 3.51%), and sagebrush (R 2 = 0.91, RMSE = 21.03%). Spectral variation in response to changing LFMC and dry mass was difficult to isolate from broader spectral trends due to chlorophyll absorption, leaf structure, water absorption, and co-varied biochemical components. Our results stress cautious spectral interpretation and wavelength selection for LFMC estimation in some species (e.g. lodgepole pine), since temporal changes in spectra may dominantly reflect temporal variation in dry mass, pigments, and/or structure rather than water content. Since new needles should have stronger spectral expression at the canopy scale, differing temporal trends in new and old lodgepole pine needles provides an additional complicating factor for remote monitoring of LFMC.

We used a database capturing large wildfires (> 405 ha) in the western U.S. to document regional trends in fire occurrence, total fire area, fire size, and day of year of ignition for 1984–2011. Over the western U.S. and in a majority of ecoregions, we found significant, increasing trends in the number of large fires and/or total large fire area per year. Trends were most significant for southern and mountain ecoregions, coinciding with trends toward increased drought severity. For all ecoregions combined, the number of large fires increased at a rate of seven fires per year, while total fire area increased at a rate of 355 km2 per year. Continuing changes in climate, invasive species, and consequences of past fire management, added to the impacts of larger, more frequent fires, will drive further disruptions to fire regimes of the western U.S. and other fire-prone regions of the world.