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\"In celebration of the 50th anniversary of the Apollo 11 landing, the endlessly-mysterious moon is explored in this reprint short science fiction anthology from award-winning editor and anthologist Neil Clarke ... On July 20, 1969, mankind made what had only years earlier seemed like an impossible leap forward: when Apollo 11 became the first manned mission to land on the moon, and Neil Armstrong the first person to step foot on the lunar surface. While there have only been a handful of new missions since, the fascination with our planet's satellite continues, and generations of writers and artists have imagined the endless possibilities of lunar life. From adventures in the vast gulf of space between the earth and the moon, to journeys across the light face to the dark side, to the establishment of permanent residences on its surface, science fiction has for decades given readers bold and forward-thinking ideas about our nearest interstellar neighbor and what it might mean to humankind, both now and in our future. [This book] collects the best stories written in the fifty years since mankind first stepped foot on the lunar surface, serving as a shining reminder that the moon is and always has been our most visible and constant example of all the infinite possibility of the wider universe\"-- Provided by publisher.

For the millions of people incarcerated in United States’ prisons and jails during the COVID-19 pandemic, isolation took many forms, including medical isolation for those sick with COVID-19, quarantine for those potentially exposed, and prolonged facility-wide lockdowns. Incarcerated people’s lived experience of isolation during the pandemic has largely gone undocumented. Through interviews with 48 incarcerated people and 27 staff at two jails and one prison in geographically diverse locations in the United States, we document the implementation of COVID-19 isolation policies from the perspective of those that live and work in carceral settings. Incarcerated people were isolated from social contact, educational programs, employment, and recreation, and lacked clear communication about COVID-19-related protocols. Being isolated, no matter the reason, felt like punishment and was compared to solitary confinement—with resultant long-term, negative impacts on health. Participants detailed isolation policies as disruptive, detrimental to mental health, and dehumanizing for incarcerated people. Findings point to several recommendations for isolation policy in carceral settings. These include integrating healthcare delivery into isolation protocols, preserving social relationships during isolation, promoting bidirectional communication about protocols and their effect between facility leadership and incarcerated people. Most importantly, there is an urgent need to re-evaluate the current approach to the use of isolation in carceral settings and to establish external oversight procedures for its use during pandemics.

Phenology offers critical insights into the responses of species to climate change; shifts in species’ phenologies can result in disruptions to the ecosystem processes and services upon which human livelihood depends. To better detect such shifts, scientists need long-term phenological records covering many taxa and across a broad geographic distribution. To date, phenological observation efforts across the USA have been geographically limited and have used different methods, making comparisons across sites and species difficult. To facilitate coordinated cross-site, cross-species, and geographically extensive phenological monitoring across the nation, the USA National Phenology Network has developed in situ monitoring protocols standardized across taxonomic groups and ecosystem types for terrestrial, freshwater, and marine plant and animal taxa. The protocols include elements that allow enhanced detection and description of phenological responses, including assessment of phenological “status”, or the ability to track presence–absence of a particular phenophase, as well as standards for documenting the degree to which phenological activity is expressed in terms of intensity or abundance. Data collected by this method can be integrated with historical phenology data sets, enabling the development of databases for spatial and temporal assessment of changes in status and trends of disparate organisms. To build a common, spatially, and temporally extensive multi-taxa phenological data set available for a variety of research and science applications, we encourage scientists, resources managers, and others conducting ecological monitoring or research to consider utilization of these standardized protocols for tracking the seasonal activity of plants and animals.

Climate change is predicted to alter the current climate suitability under which plant species and communities occur. Predictions of change have focused on individual species or entire communities, but theory indicates plants will not respond uniformly to climate change within or between communities. We developed models of the current climate suitability (the baseline) of 66 plant species characteristic of 29 plant communities of the arid Southwest, made predictions of climate suitability for the species under two climate change scenarios for the years 2041–2060 (Community Climate System Model version 1.4 [CCSM4] global climate model [GCM], Representative Concentration Pathway [RCP] 4.5 and 8.5 scenarios), and calculated changes in suitability between the future scenarios and baseline for each species. Climate change exposure for the entire community was then evaluated as the composite change of the predicted future climate suitability of the communities' characteristic species. Loss of 25% or more of favorable climate suitability was predicted for 39 (RCP4.5) and 51 (RCP8.5) species within their communities. The proportion of the study area with all species in a community having unfavorable suitability was 17.9% (RCP4.5) and 21.3% (RCP8.5) compared to 6.2% for baseline. We show that suitable climates for species within a plant community are not expected to be a single community‐wide trajectory, but rather changes in climate suitability will be unique to the species and not experienced uniformly across the extant communities. This decoupling of plant species within their traditional plant communities may lead to a cascade of unanticipated ecological responses and unprecedented challenges to resource management. Our study results can inform hypotheses of the future successional track of plant communities, characteristic species, and the decisions resource managers must make for management.

Geospatial data and tools evolve as new technologies are developed and landscape change occurs over time. As a result, these data may become outdated and inadequate for supporting critical habitat-related work across the international boundary in the Sonoran and Mojave Deserts Bird Conservation Region (BCR 33) due to the area’s complex vegetation communities and the discontinuity in data availability across the United States (US) and Mexico (MX) border. This research aimed to produce the first 30 m continuous land cover map of BCR 33 by prototyping new methods for desert vegetation classification using the Random Forest (RF) machine learning (ML) method. The developed RF classification model utilized multitemporal Landsat 8 Operational Land Imager spectral and vegetation index data from the period of 2013–2020, and phenology metrics tailored to capture the unique growing seasons of desert vegetation. Our RF model achieved an overall classification F-score of 0.80 and an overall accuracy of 91.68%. Our results portrayed the vegetation cover at a much finer resolution than existing land cover maps from the US and MX portions of the study area, allowing for the separation and identification of smaller habitat pockets, including riparian communities, which are critically important for desert wildlife and are often misclassified or nonexistent in current maps. This early prototyping effort serves as a proof of concept for the ML and data fusion methods that will be used to generate the final high-resolution land cover map of the entire BCR 33 region.

A long‐standing goal of invasion biology is to identify factors driving highly variable impacts of non‐native species. Although hypotheses exist that emphasize the role of evolutionary history (e.g., enemy release hypothesis & defense‐free space hypothesis), predicting the impact of non‐native herbivorous insects has eluded scientists for over a century. Using a census of all 58 non‐native conifer‐specialist insects in North America, we quantified the contribution of over 25 factors that could affect the impact they have on their novel hosts, including insect traits (fecundity, voltinism, native range, etc.), host traits (shade tolerance, growth rate, wood density, etc.), and evolutionary relationships (between native and novel hosts and insects). We discovered that divergence times between native and novel hosts, the shade and drought tolerance of the novel host, and the presence of a coevolved congener on a shared host, were more predictive of impact than the traits of the invading insect. These factors built upon each other to strengthen our ability to predict the risk of a non‐native insect becoming invasive. This research is the first to empirically support historically assumed hypotheses about the importance of evolutionary history as a major driver of impact of non‐native herbivorous insects. Our novel, integrated model predicts whether a non‐native insect not yet present in North America will have a one in 6.5 to a one in 2,858 chance of causing widespread mortality of a conifer species if established (R2 = 0.91) Synthesis and applications. With this advancement, the risk to other conifer host species and regions can be assessed, and regulatory and pest management efforts can be more efficiently prioritized. This research is the first to empirically support historically assumed hypotheses about the importance of evolutionary history as a major driver of impact of non‐native herbivorous insects. We discovered that evolutionary history between native and novel hosts, life history traits of the novel host, and the evolutionary relationship between non‐native and coevolved insects on a shared host were more predictive of impact than traits of the invading insect. Our novel, integrated model predicts whether a non‐native insect not yet present in North America will have a one in 6.5 to a one in 2,858 chance of causing widespread mortality of a conifer species if established.

Introduction:Choosing an appropriate electronic data capture system (EDC) is a critical decision for all randomized controlled trials (RCT). In this paper, we document our process for developing and implementing an EDC for a multisite RCT evaluating the efficacy and implementation of an enhanced primary care model for individuals with opioid use disorder who are returning to the community from incarceration.Methods:Informed by the Knowledge-to-Action conceptual framework and user-centered design principles, we used Claris Filemaker software to design and implement CRICIT, a novel EDC that could meet the varied needs of the many stakeholders involved in our study.Results:CRICIT was deployed in May 2021 and has been continuously iterated and adapted since. CRICIT’s features include extensive participant tracking capabilities, site-specific adaptability, integrated randomization protocols, and the ability to generate both site-specific and study-wide summary reports.Conclusions:CRICIT is highly customizable, adaptable, and secure. Its implementation has enhanced the quality of the study’s data, increased fidelity to a complicated research protocol, and reduced research staff’s administrative burden. CRICIT and similar systems have the potential to streamline research activities and contribute to the efficient collection and utilization of clinical research data.

Little research has explored relationships between prenatal substance use policies and rates of maternal mortality across all 50 states, despite evidence that prenatal substance use elevates risk of maternal death. This study, utilizing publicly available data, revealed that state-level mandated testing laws predicted maternal mortality after controlling for population characteristics.

In the arid Mojave Desert, the phenological response of vegetation is largely dependent upon the timing and amount of rainfall, and maps of annual plant cover at any one point in time can vary widely. Our study developed relative annual plant growth models as proxies for annual plant cover using metrics that captured phenological variability in Moderate-Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) satellite images. We used landscape phenologies revealed in MODIS data together with ecological knowledge of annual plant seasonality to develop a suite of metrics to describe annual growth on a yearly basis. Each of these metrics was applied to temporally-composited MODIS-EVI images to develop a relative model of annual growth. Each model was evaluated by testing how well it predicted field estimates of annual cover collected during 2003 and 2005 at the Mojave National Preserve. The best performing metric was the spring difference metric, which compared the average of three spring MODIS-EVI composites of a given year to that of 2002, a year of record drought. The spring difference metric showed correlations with annual plant cover of R2 = 0.61 for 2005 and R2 = 0.47 for 2003. Although the correlation is moderate, we consider it supportive given the characteristics of the field data, which were collected for a different study in a localized area and are not ideal for calibration to MODIS pixels. A proxy for annual growth potential was developed from the spring difference metric of 2005 for use as an environmental data layer in desert tortoise habitat modeling. The application of the spring difference metric to other imagery years presents potential for other applications such as fuels, invasive species, and dust-emission monitoring in the Mojave Desert.

Russian-thistle (Salsola tragus L.), is a nonnative, C4 photosynthesizing, annual plant that infests disturbed and natural areas in the arid U.S. Southwest. Land managers of natural areas may need to decide whether a S. tragus infestation is potentially harmful and whether it should be actively managed. One factor informing that decision is an understanding of the conditions under which this weed emerges and establishes and how those processes affect where and when infestations occur. We studied S. tragus establishment on aeolian (windblown) sandy soils at Petrified Forest National Park, AZ. Our sites were a previously disturbed sand sheet and a semistabilized sand dune. Measurements in plots on these sites over two growing seasons revealed a similar number of S. tragus seedlings emerging on both sites early in the 2015 growing season. As the season progressed, S. tragus cover (seedling survival and growth) was lower on the sand dune, except for a plot placed entirely on a coppice mound. In 2016, S. tragus seedling emergence and development of cover, measured on plots at both sites, was exceptionally low, as was summer rainfall. A growth chamber assay of seedling emergence from soil and litter samples collected at each site showed emergence was greatest from samples collected where S. tragus litter remained on the soil surface, and otherwise was infrequent. Our study suggests that S. tragus emergence and early establishment are sensitive to low precipitation and that soil-surface microtopography and grass and shrub cover may be determinants of the spatial pattern of infestation on sandy soils. As aeolian sands occur throughout drylands of the U.S. Southwest, deeper understanding of the conditions under which S. tragus seedlings emerge and establish can inform management of this invasive annual in those habitats.