This collection contains replication material for Dahir et al. “Surveillance Cameras Are Most Prevalent in Racially Diverse Neighborhoods Across Ten US Cities". Our analysis code is available at Github (https://github.com/Changing-Cities-Research-Lab/surveillance-replication).

This paper analyzes the geography of super-gentrification in US cities, the further intensification of class upgrading after a neighborhood has already gentrified. Building a national longitudinal tract database of gentrification intensity indicators, we analyze where this process has occurred across the 45 most populous metropolitan regions. We develop a method for quantifying metro-specific gentrification indices, then compare the class and racial demographics of super-gentrified tracts against other kinds of affluent places. We also interpret these national patterns with a case study of gentrification’s broader geographies in greater New York City. While super-gentrification is most commonly researched in global mega-cities, we found a wider geography including substantial suburban and smaller city patterns. We also found that super-gentrified neighborhoods are less racially diverse than other gentrified neighborhoods, and are more demographically similar to historically affluent (but not recently gentrified) neighborhoods. The study contributes a national comparative analysis of gentrification intensity patterns, and a longitudinal analysis of what happens after a neighborhood has already gentrified.

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

According to figures recently released by the United States Census, America’s largest metro areas are currently gaining population at impressive rates. The growth in these areas is in fact driving much of the population growth across the nation. Upon closer examination of the data, this growth is the result of two very different migrations – one coming from the location choices of Americans themselves, the other shaped by where new immigrants from outside the United States are heading.While many metro areas are attracting a net-inflow of migrants from other parts of the country, in several of the largest metros – New York, Los Angeles., and Miami, especially – there is actually a net outflow of Americans to the rest of the country. Immigration is driving population growth in these places. Sunbelt metros like Houston, Dallas, and Phoenix, and knowledge hubs like Austin, Seattle, San Francisco, and the District of Columbia are gaining much more from domestic migration.This map charts overall or net migration – a combination of domestic and international migration. Most large metros, those with at least a million residents, had more people coming in than leaving. The metros with the highest levels of population growth due to migration are a mix of knowledge-based economies and Sunbelt metros, including Houston, Dallas, Miami, District of Columbia, San Francisco, Seattle, and Austin. Eleven large metros, nearly all in or near the Rustbelt, had a net outflow of migrants, including Chicago, Detroit, Memphis, Philadelphia, and Saint Louis.Source: Atlantic Cities

The term 'community garden' in the United States can mean a few different things. For example, they can function as gathering places for the community and/or neighbors, however, they can also resemble the allotment gardens, often found in Europe, used by individuals and families. Of course, some cities are home to more community gardens than others. In 2022, Spokane, Washington, had the most with 4.8 community gardens per 1,000 residents.

Home Owners’ Loan Corporation (HOLC) maps illustrated patterns of segregation in United States cites in the 1930s. As the causes and drivers of demographic and land use segregation vary over years, these maps provide an important spatial lens in determining how patterns of segregation spatially and temporally developed during the course of the past century. Using a high-resolution land-use time series (1937-2018) of Denver Colorado USA, in conjunction with 80 years of U.S. Census data, we found divergent land-use and demographics patterns across HOLC categories were both pre-existent to the establishment of HOLC mapping, and continued to develop over time. Over this period, areas deemed “declining” or “hazardous” had more diverse land use compared “desirable” areas. “Desirable” areas were dominated by one land-use type (single-family residential), while single-family residential diminished in prominence in the “declining/hazardous” areas. This divergence became more established decades after HOLC mapping, with impact to racial metrics and low-income households. We found changes in these demographic patterns also occurred between 2000 and 2019, highlighting how processes like gentrification can develop from both rapid demographic and land-use changes. This study demonstrates how the legacy of urban segregation develops over decades and can simultaneously persist in some neighborhoods while providing openings for fast-paced gentrification in others.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show the birth and citizenship status by Neighborhood Statistical Areas E02 and E06 in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, Super District, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

TotPop_e

# Total population, 2017

TotPop_m

# Total population, 2017 (MOE)

Native_e

# U.S. Native, 2017

Native_m

# U.S. Native, 2017 (MOE)

pNative_e

% U.S. Native, 2017

pNative_m

% U.S. Native, 2017 (MOE)

BornUS_e

# Born in the United States, 2017

BornUS_m

# Born in the United States, 2017 (MOE)

pBornUS_e

% Born in the United States, 2017

pBornUS_m

% Born in the United States, 2017 (MOE)

BornState_e

# Born in state of residence, 2017

BornState_m

# Born in state of residence, 2017 (MOE)

pBornState_e

% Born in state of residence, 2017

pBornState_m

% Born in state of residence, 2017 (MOE)

BornDiffState_e

# Born in different state, 2017

BornDiffState_m

# Born in different state, 2017 (MOE)

pBornDiffState_e

% Born in different state, 2017

pBornDiffState_m

% Born in different state, 2017 (MOE)

BornTerr_e

# Born in Puerto Rico, U.S. Island Areas, or born abroad to American parent(s), 2017

BornTerr_m

# Born in Puerto Rico, U.S. Island Areas, or born abroad to American parent(s), 2017 (MOE)

pBornTerr_e

% Born in Puerto Rico, U.S. Island Areas, or born abroad to American parent(s), 2017

pBornTerr_m

% Born in Puerto Rico, U.S. Island Areas, or born abroad to American parent(s), 2017 (MOE)

ForBorn_e

# Foreign born, 2017

ForBorn_m

# Foreign born, 2017 (MOE)

pForBorn_e

% Foreign born, 2017

pForBorn_m

% Foreign born, 2017 (MOE)

Naturalized_e

# Naturalized U.S. citizen, 2017

Naturalized_m

# Naturalized U.S. citizen, 2017 (MOE)

pNaturalized_e

% Naturalized U.S. citizen, 2017

pNaturalized_m

% Naturalized U.S. citizen, 2017 (MOE)

NotNaturalized_e

# Not a U.S. citizen, 2017

NotNaturalized_m

# Not a U.S. citizen, 2017 (MOE)

pNotNaturalized_e

% Not a U.S. citizen, 2017

pNotNaturalized_m

% Not a U.S. citizen, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

Peatlands are subject to increased pressure from environmental and land-use change, particularly in temperate regions such as the US Midwest. Bryophytes dominate the ground cover of peatlands and play a key role in their functioning. Effective management and restoration of degraded peatlands requires good understanding of their bryophyte communities, and how these are shaped by environmental conditions. Furthermore, bryophytes are sensitive indicators of environmental conditions. We monitored microhabitat characteristics (hydrology, hydrochemistry, abundance of vascular vegetation, microtopography) alongside bryophyte community composition in nine kettle hole mires in Ohio (USA). We found that the most important drivers of bryophyte community composition and diversity were water level and hydrochemistry. Sampling locations showing poor fen characteristics (high water level, pH and electrical conductivity) were associated with generalist pleurocarpous mosses (indicator species: Amblystegium serpens) and lower species richness. Where bog conditions prevailed, Sphagnum species dominated, and Sphagnum fallax and the liverwort Cephalozia sp. were indicator species.

This map shows population density of the United States. Areas in darker magenta have much higher population per square mile than areas in orange or yellow. Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. The map's layers contain total population counts by sex, age, and race groups for Nation, State, County, Census Tract, and Block Group in the United States and Puerto Rico. From the Census:"Population density allows for broad comparison of settlement intensity across geographic areas. In the U.S., population density is typically expressed as the number of people per square mile of land area. The U.S. value is calculated by dividing the total U.S. population (316 million in 2013) by the total U.S. land area (3.5 million square miles).When comparing population density values for different geographic areas, then, it is helpful to keep in mind that the values are most useful for small areas, such as neighborhoods. For larger areas (especially at the state or country scale), overall population density values are less likely to provide a meaningful measure of the density levels at which people actually live, but can be useful for comparing settlement intensity across geographies of similar scale." SourceAbout the dataYou can use this map as is and you can also modify it to use other attributes included in its layers. This map's layers contain total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, State, County, Census Tract, Block Group boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, State, County, Census Tract, Block GroupNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This map is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

Knowing the racial and ethnic composition of a community is often one of the first steps in understanding, serving, and advocating for various groups. This information can help enforce laws, policies, and regulations against discrimination based on race and ethnicity. These statistics can also help tailor services to accommodate cultural differences.This multi-scale map shows the most common race/ethnicity living within an area. Map opens at tract-level in Los Angeles, CA but has national coverage. Zoom out to see counties and states.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available. The data on race were derived from answers to the question on race that was asked of individuals in the United States. The Census Bureau collects racial data in accordance with guidelines provided by the U.S. Office of Management and Budget (OMB), and these data are based on self-identification. The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. The categories represent a social-political construct designed for collecting data on the race and ethnicity of broad population groups in this country, and are not anthropologically or scientifically based. Learn more here.

The importance of microhabitat traits such as floral availability is well known; however, forest bee spatial dynamics have been variably studied across local to broad geographic scales. Past literature suggests that landscape factors from proximate to distal are important in determining forest bee community metrics, including richness, abundance, and taxonomic composition. Leveraging the interest and assistance of citizen science volunteers, we employed standard bee bowl trap transects across Maryland, Delaware, northern Virginia, and the District of Columbia and identified correlations between bee community composition, local and regional landcover, and broader geospatial patterns. We also identified the partial contributions of both specific species and sampling sites to total beta diversity. Various landcover metrics were significantly related to bee community structure, with bee abundance positively and negatively correlated with forest and wetland cover, respectively. In general, l..., Data were collected by citizen science volunteers using bee bowl transects across 99 forest sites in Maryland, Delaware, Washington, D.C., and northern Virginia, USA, in 2014. Characteristics of the forest bee community, including nesting and trophic groups, were correlated with landcover at varying spatial scales (200- and 1000-m buffer from transect origin) and broader biogeographic parameters., , # Geographic drivers more important than landscape composition in predicting bee beta diversity and community structure

Ecosphere

Data were collected by citizen science volunteers using bee bowl transects across 99 forest sites in Maryland, Delaware, Washington, D.C., and northern Virginia, USA, in 2014. Characteristics of the forest bee community, including nesting and trophic groups, were correlated with landcover at varying spatial scales (200- and 1000-m buffer from transect origin) and broader biogeographic parameters.

Site_Data

Data table includes site name (Site), geographic coordinates in decimal degrees (Latitude, Longitude), the sampled State (State) and county (County), and geographic coordinates in meters north (Northing) and east (Easting) of UTM Zone 18 origin. Remaining variables include proportion of land cover types (open water, developed, forest, agriculture and early successional, wetlands) identified within 200- or 1000-m buffer of sampling transect origin. ...

In the past four centuries, the population of the Thirteen Colonies and United States of America has grown from a recorded 350 people around the Jamestown colony in Virginia in 1610, to an estimated 346 million in 2025. While the fertility rate has now dropped well below replacement level, and the population is on track to go into a natural decline in the 2040s, projected high net immigration rates mean the population will continue growing well into the next century, crossing the 400 million mark in the 2070s. Indigenous population Early population figures for the Thirteen Colonies and United States come with certain caveats. Official records excluded the indigenous population, and they generally remained excluded until the late 1800s. In 1500, in the first decade of European colonization of the Americas, the native population living within the modern U.S. borders was believed to be around 1.9 million people. The spread of Old World diseases, such as smallpox, measles, and influenza, to biologically defenseless populations in the New World then wreaked havoc across the continent, often wiping out large portions of the population in areas that had not yet made contact with Europeans. By the time of Jamestown's founding in 1607, it is believed the native population within current U.S. borders had dropped by almost 60 percent. As the U.S. expanded, indigenous populations were largely still excluded from population figures as they were driven westward, however taxpaying Natives were included in the census from 1870 to 1890, before all were included thereafter. It should be noted that estimates for indigenous populations in the Americas vary significantly by source and time period. Migration and expansion fuels population growth The arrival of European settlers and African slaves was the key driver of population growth in North America in the 17th century. Settlers from Britain were the dominant group in the Thirteen Colonies, before settlers from elsewhere in Europe, particularly Germany and Ireland, made a large impact in the mid-19th century. By the end of the 19th century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. It is also estimated that almost 400,000 African slaves were transported directly across the Atlantic to mainland North America between 1500 and 1866 (although the importation of slaves was abolished in 1808). Blacks made up a much larger share of the population before slavery's abolition. Twentieth and twenty-first century The U.S. population has grown steadily since 1900, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. Since WWII, the U.S. has established itself as the world's foremost superpower, with the world's largest economy, and most powerful military. This growth in prosperity has been accompanied by increases in living standards, particularly through medical advances, infrastructure improvements, clean water accessibility. These have all contributed to higher infant and child survival rates, as well as an increase in life expectancy (doubling from roughly 40 to 80 years in the past 150 years), which have also played a large part in population growth. As fertility rates decline and increases in life expectancy slows, migration remains the largest factor in population growth. Since the 1960s, Latin America has now become the most common origin for migrants in the U.S., while immigration rates from Asia have also increased significantly. It remains to be seen how immigration restrictions of the current administration affect long-term population projections for the United States.

This map displays areas of unprotected biodiversity importance (AUBIs) for species in the lower 48 United States that are protected by the Endangered Species Act and/or considered to be in danger of extinction. It is part of the Map of Biodiversity Importance (MoBI) data collection, a series of maps that identify areas of high importance for protecting species from extinction in the contiguous United States.Building on habitat suitability models for 2,493 of the nation’s most imperiled species, and information on range size and degree of protection derived from those models, the MoBI project provides a series of maps that can help inform conservation efforts. This map depicts areas of unprotected biodiversity importance (AUBIs) - that is, areas that scored highest for summed protection-weighted range-size rarity (PWRSR) for Critically Imperiled (categorized by NatureServe as “G1”), Imperiled (“G2”), and ESA-listed (i.e., species listed as Endangered or Threatened under the Endangered Species Act) species in the following groups:Vertebrates (birds, mammals, amphibians, reptiles, freshwater fishes; 326 species) Freshwater invertebrates (mussels and crayfishes; 233 species) Pollinators (bumblebees, solitary bees, butterflies, and skippers; 63 species) Vascular plants (1,871 species)Values of "1" identify areas where under-protected and range-restricted species are most likely to occur, including areas where the presence of multiple imperiled species contributes to higher scores. These areas are of interest to conservationists due to both the restricted range sizes and need for protection from threats such as habitat loss.Habitat models for most species were generated using the random forest algorithm. Data to train the models came from the NatureServe Network (e.g. state Natural Heritage Programs) supplemented by data from Global Biodiversity Information Facility, and other publicly available sources of population and locality data. Environmental predictors used for the modeling include representations of terrain, climate, land cover, soils, and hydrology. The modeling resolution for terrestrial species was either 30-m (most species) or 330-m (some wide-ranging species). Models for aquatic species used the medium resolution National Hydrography Dataset (NHD) as the modeling unit. For species not amenable to random forest modeling, habitat maps were derived by buffering locality data and/or building simple deductive models based on habitat information. NatureServe converted habitat maps to a 330-m raster to provide a consistent unit of aggregation and avoid revealing the precise location of sensitive species. Range-size rarity for each species in the inverse of the total area mapped as habitat (using the 330-m raster). Protection-weighted range-size rarity (PWRSR) maps combine information on both range-size rarity and the degree to which habitat for the species in protected. Protected habitat was defined as that occurring within protected areas managed for biodiversity (i.e., Gap Status 1 and 2 lands in the USGS Protected Areas Database; PAD-US 4.0). Each species was assigned a PWRSR score equal to the product of range-size rarity and the percent of habitat that is unprotected. The PWRSR raster sums these scores for all species with habitat that overlaps a cell. We delineated AUBIs by then selecting all pixels where summed PWRSR ≥ 0.0005, an inclusive value designed to highlight areas of conservation value. A PWRSR score of 0.0005 corresponds to a single species with a range of 1,000 km2 that is 50% unprotected, a single species with a range of 20 km2 that is 1% unprotected, or multiple co-occurring species with lower PWRSR scores. Not that full protected species do not contribute to PWRSR scores.These data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the NatureServe Network.For more information, see:Hamilton, H., Smyth, R.L., Young, B.E., Howard, T.G., Tracey, C., Breyer, S., Cameron, D.R., Chazal, A., Conley, A.K., Frye, C. and Schloss, C. (2022), Increasing taxonomic diversity and spatial resolution clarifies opportunities for protecting imperiled species in the U.S.. Ecological Applications. Accepted Author Manuscript e2534. https://doi.org/10.1002/eap.2534Note that the above citation is based on the MoBI 2020 product and does not reflect the most current information. Please contact NatureServe for more information.This data supersedes the MoBI 2020 data which can be found here. A summary of changes between MoBI 2020 and 2024:Species included: MoBI 2024 includes 2,493 species, compared to 2,216 in MoBI 2024. Due to a combination of taxonomic updates and global rank/ESA status changes, 177 species from the 2020 product were removed while 454 species were added to this 2020 product. All taxonomic groups included in MoBI 2020 are included in the 2024 product, with the addition of several solitary bee genera.Scale changes: We increased the resolution from 990-m to 330-m for all MoBI products. Due to this resolution increase, we recommend caution conducting direct comparisons between the MoBI 2020 and MoBI 2024 products.To download data as a layer package, navigate here.

Coral-associated microbial communities play a vital role in underpinning the health and resilience of reef ecosystems. Previous studies have demonstrated that the microbial communities of corals are affected by multiple factors, mainly focusing on host species and geolocation. However, up-to-date, insight into how the coral microbiota is structured by vast geographic distance with rich taxa is deficient. In the present study, the coral microbiota in six stony coral species collected from the coastal area of three countries, including United States of America (USA), Australia and Fiji, was used for analysis. It was found that the geographic influence on the coral microbiota was stronger than the coral host influence, even though both were significant. Interestingly, the contribution of the deterministic process to bacterial community composition increased as geographical distance grew. A total of 65 differentially abundant features of functions in coral microbial communities were identified to be associated with three geolocations. While in the same coastal area of USA, the similar relationship of coral microbiota was consistent with the phylogenetic relationship of coral hosts. In contrast to the phylum Proteobacteria, which was most abundant in other coral species in USA, Cyanobacteria was the most abundant phylum in Orbicella faveolata. The above findings may help to better understand the multiple natural driving forces shaping the coral microbial community to contribute to defining the healthy baseline of the coral microbiome.

Given that an estimated 0.6% of the U.S. population is transgender (trans) and that large health disparities for this population have been documented, government and research organizations are increasingly expanding measures of sex/gender to be trans inclusive. Options suggested for trans community surveys, such as expansive check-all-that-apply gender identity lists and write-in options that offer maximum flexibility, are generally not appropriate for broad population surveys. These require limited questions and a small number of categories for analysis. Limited evaluation has been undertaken of trans-inclusive population survey measures for sex/gender, including those currently in use. Using an internet survey and follow-up of 311 participants, and cognitive interviews from a maximum-diversity sub-sample (n = 79), we conducted a mixed-methods evaluation of two existing measures: a two-step question developed in the United States and a multidimensional measure developed in Canada. We found very low levels of item missingness, and no indicators of confusion on the part of cisgender (non-trans) participants for both measures. However, a majority of interview participants indicated problems with each question item set. Agreement between the two measures in assessment of gender identity was very high (K = 0.9081), but gender identity was a poor proxy for other dimensions of sex or gender among trans participants. Issues to inform measure development or adaptation that emerged from analysis included dimensions of sex/gender measured, whether non-binary identities were trans, Indigenous and cultural identities, proxy reporting, temporality concerns, and the inability of a single item to provide a valid measure of sex/gender. Based on this evaluation, we recommend that population surveys meant for multi-purpose analysis consider a new Multidimensional Sex/Gender Measure for testing that includes three simple items (one asked only of a small sub-group) to assess gender identity and lived gender, with optional additions. We provide considerations for adaptation of this measure to different contexts.