Demographic change in the northern forest

This brief examines the population redistribution in the Northern Forest, which includes thirty-four counties scattered across northern and central Maine, New Hampshire, New York, and Vermont. Authors Ken Johnson, Susan Stewart, and Miranda Mockrin report that the population of the Northern Forest grew modestly between 2000 and 2010, and the population gains were greatest in recreational areas and least in manufacturing areas. Racial and ethnic diversity is also growing in the Northern Forest, and the population is getting older due to aging in place among current residents and net outmigration among younger populations

Forests in Flux: The Effects of Demographic Change on Forest Cover in New England and New York

This brief contributes to a better understanding of the linkages between demographic and forest cover change so as to inform policy efforts aimed at maintaining existing forested areas in and around sprawling urban centers. Authors Mark Ducey, Kenneth Johnson, Ethan Belair, and Miranda Mockrin report that forest cover has declined throughout New England and New York over the last decade. In rural areas, forest loss is primarily due to commercial timber harvesting and represents a temporary change. Conversely, forest cover decline in urban areas is usually the result of development and is likely to be permanent. Forest cover change is strongly linked to demographic variables throughout this region. Forest cover loss is most pronounced along the urban fringe, where population growth is greatest. Amenity-rich rural areas are also experiencing high rates of population growth and regionally-high rates of forest cover loss. However, the causes of forest cover change in these areas are less certain. Forest cover change has the potential to impact ecosystem services important to both local residents and the larger region

Changes to Rural Migration in the COVID-19 Pandemic☆

Media stories highlighted accounts of migration away from city centers towards more rural destinations during the COVID-19 pandemic, but systematic research about how the pandemic changed migration in more rural destinations is only starting to emerge. This paper relies on U.S. Postal Service change-of-address data to describe whether and how established domestic migration systems changed during the COVID-19 pandemic, focusing on differences across the rural–urban gradient and by outdoor recreation resources. We find little evidence of massive urban exodus. We do find that out-migration from rural counties declined post-pandemic onset and has stayed low in the 3 years since, stemming the tide of net population loss in many rural places. Most rural counties that experienced net population loss prior to the pandemic saw either less net loss or net gains during the pandemic. Rural recreation counties experienced greater gains through both decreased out-migration and increased in-migration in the first year of the pandemic; but by year three, differences between rural recreation and non-recreation counties had balanced. Overall, counties across Rural America saw notable change to pre-pandemic migration patterns. This shift may benefit rural areas through long-term population stability and/or growth but might also exacerbate housing and childcare shortages

Wildlife-Associated Recreation Trends in the United States A Technical Document Supporting the Forest Service 2010 RPA Assessment

The Forest and Rangeland Renewable Resources Planning Act (RPA) of 1974 requires periodic assessments of the condition and trends of the Nation’s renewable natural resources. In this report, we document recent and historical trends in hunting and wildlife watching to fulfill RPA requirements. Using data from the U.S. Department of the Interior, Fish and Wildlife Service’s National Survey of Fishing, Hunting, and Wildlife-Associated Recreation we present historical trends back to 1955 as well as recent changes from the past 10 to 20 years to evaluate changes in recreation since the 2000 RPA Assessment. We report on several attributes of wildlife recreation, including number of participants, days participating, recreation on public and private land, and economic expenditures. We found that participation in wildlife-associated recreation continues to change, with fewer Americans taking part in hunting and wildlife watching over the past 20 years. Total days devoted to recreation have declined along with number of participants, but the annual expenditures per participant and days of recreation per participant have generally risen or remained stable. We discuss variation in participation among types of hunting and across RPA Regions of the United States. Documenting and understanding these changes in wildlife-associated recreation is essential to ensure the continued successful management of wildlife resources

Wildlife-Associated Recreation Trends in the United States A Technical Document Supporting the Forest Service 2010 RPA Assessment

The Forest and Rangeland Renewable Resources Planning Act (RPA) of 1974 requires periodic assessments of the condition and trends of the Nation’s renewable natural resources. In this report, we document recent and historical trends in hunting and wildlife watching to fulfill RPA requirements. Using data from the U.S. Department of the Interior, Fish and Wildlife Service’s National Survey of Fishing, Hunting, and Wildlife-Associated Recreation we present historical trends back to 1955 as well as recent changes from the past 10 to 20 years to evaluate changes in recreation since the 2000 RPA Assessment. We report on several attributes of wildlife recreation, including number of participants, days participating, recreation on public and private land, and economic expenditures. We found that participation in wildlife-associated recreation continues to change, with fewer Americans taking part in hunting and wildlife watching over the past 20 years. Total days devoted to recreation have declined along with number of participants, but the annual expenditures per participant and days of recreation per participant have generally risen or remained stable. We discuss variation in participation among types of hunting and across RPA Regions of the United States. Documenting and understanding these changes in wildlife-associated recreation is essential to ensure the continued successful management of wildlife resources

Social Vulnerability of the People Exposed to Wildfires in U.S. West Coast States

Understanding of the vulnerability of populations exposed to wildfires is limited. We used an index from the U.S. Centers for Disease Control and Prevention to assess the social vulnerability of populations exposed to wildfire from 2000–2021 in California, Oregon, and Washington, which accounted for 90% of exposures in the western United States. The number of people exposed to fire from 2000–2010 to 2011–2021 increased substantially, with the largest increase, nearly 250%, for people with high social vulnerability. In Oregon and Washington, a higher percentage of exposed people were highly vulnerable (\u3e40%) than in California (~8%). Increased social vulnerability of populations in burned areas was the primary contributor to increased exposure of the highly vulnerable in California, whereas encroachment of wildfires on vulnerable populations was the primary contributor in Oregon and Washington. Our results emphasize the importance of integrating the vulnerability of at-risk populations in wildfire mitigation and adaptation plans

The global wildland–urban interface

The wildland–urban interface (WUI) is where buildings and wildland vegetation meet or intermingle1,2. It is where human–environmental conflicts and risks can be concentrated, including the loss of houses and lives to wildfire, habitat loss and fragmentation and the spread of zoonotic diseases3. However, a global analysis of the WUI has been lacking. Here, we present a global map of the 2020 WUI at 10 m resolution using a globally consistent and validated approach based on remote sensing-derived datasets of building area4 and wildland vegetation5. We show that the WUI is a global phenomenon, identify many previously undocumented WUI hotspots and highlight the wide range of population density, land cover types and biomass levels in different parts of the global WUI. The WUI covers only 4.7% of the land surface but is home to nearly half its population (3.5 billion). The WUI is especially widespread in Europe (15% of the land area) and the temperate broadleaf and mixed forests biome (18%). Of all people living near 2003–2020 wildfires (0.4 billion), two thirds have their home in the WUI, most of them in Africa (150 million). Given that wildfire activity is predicted to increase because of climate change in many regions6, there is a need to understand housing growth and vegetation patterns as drivers of WUI change

Using structure locations as a basis for mapping the wildland urban interface

The wildland urban interface (WUI) delineates the areas where wildland fire hazard most directly impacts human communities and threatens lives and property, and where houses exert the strongest influence on the natural environment. Housing data are a major problem for WUI mapping. When housing data are zonal, the concept of a WUI neighborhood can be captured easily in a density measure, but variations in zone (census block) size and shape introduce bias. Other housing data are points, so zonal issues are avoided, but the neighborhood character of the WUI is lost if houses are evaluated individually. Our goal was to develop a consistent method to map the WUI that is able to determine where neighborhoods (or clusters of houses) exist, using just housing location and wildland fuel data. We used structure and vegetation maps and a moving window analysis, with various window sizes representing neighborhood sizes, to calculate the neighborhood density of both houses and wildland vegetation. Mapping four distinct areas (in WI, MI, CA and CO) the method resulted in amounts of WUI comparable to those of zonal mapping, but with greater precision. We conclude that this hybrid method is a useful alternative to zonal mapping from the neighborhood to the landscape scale, and results in maps that are better suited to operational fire management (e.g., fuels reduction) needs, while maintaining consistency with conceptual and U.S. policy-specific WUI definitions.Keywords: Structure locations, Wildland urban interface, MappingKeywords: Structure locations, Wildland urban interface, Mappin

Spatial and temporal residential density patterns from 1940 to 2000 in and around the Northern Forest of the Northeastern United States

Over the past 60 years, housing growth has outpaced population growth in the United States. Conservationists are concerned about the far-reaching environmental impacts of housing development, particularly in rural areas. We use clustering analysis to examine the pattern and distribution of housing development since 1940 in and around the Northern Forest, a heavily forested region with high amenity and recreation use in the Northeastern United States. We find that both proximity to urban areas and an abundance of natural amenities are associated with housing growth at the neighborhood level in this region. In the 1970s, counterurbanization led to higher rates of growth across rural areas. The Northern Forest now has extensive interface between forest vegetation and residential development, which has the potential to profoundly alter the ecological and social benefits of these forests.Keywords: Housing density, Amenity growth, Sprawl, Housing growth, Cluster analysis, Northern ForestKeywords: Housing density, Amenity growth, Sprawl, Housing growth, Cluster analysis, Northern Fores

Examining sociodemographic correlates of opioid use, misuse, and use disorders in the All of Us Research Program

BACKGROUND: The All of Us Research Program enrolls diverse US participants which provide a unique opportunity to better understand the problem of opioid use. This study aims to estimate the prevalence of opioid use and its association with sociodemographic characteristics from survey data and electronic health record (EHR). METHODS: A total of 214,206 participants were included in this study who competed survey modules and shared EHR data. Adjusted logistic regressions were used to explore the associations between sociodemographic characteristics and opioid use. RESULTS: The lifetime prevalence of street opioids was 4%, and the nonmedical use of prescription opioids was 9%. Men had higher odds of lifetime opioid use (aOR: 1.4 to 3.1) but reduced odds of current nonmedical use of prescription opioids (aOR: 0.6). Participants from other racial and ethnic groups were at reduced odds of lifetime use (aOR: 0.2 to 0.9) but increased odds of current use (aOR: 1.9 to 9.9) compared with non-Hispanic White participants. Foreign-born participants were at reduced risks of opioid use and diagnosed with opioid use disorders (OUD) compared with US-born participants (aOR: 0.36 to 0.67). Men, Younger, White, and US-born participants are more likely to have OUD. CONCLUSIONS: All of Us research data can be used as an indicator of national trends for monitoring the prevalence of receiving prescription opioids, diagnosis of OUD, and non-medical use of opioids in the US. The program employs a longitudinal design for routinely collecting health-related data including EHR data, that will contribute to the literature by providing important clinical information related to opioids over time. Additionally, this data will enhance the estimates of the prevalence of OUD among diverse populations, including groups that are underrepresented in the national survey data