About this dataset

Context

The dataset tabulates the East Bay township population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for East Bay township. The dataset can be utilized to understand the population distribution of East Bay township by age. For example, using this dataset, we can identify the largest age group in East Bay township.

Key observations

The largest age group in East Bay Township, Michigan was for the group of age 60 to 64 years years with a population of 1,205 (10.35%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in East Bay Township, Michigan was the 85 years and over years with a population of 221 (1.90%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the East Bay township is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of East Bay township total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for East Bay township Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the East Bay township population by year. The dataset can be utilized to understand the population trend of East Bay township.

Content

The dataset constitues the following datasets

• East Bay Township, Michigan Population Dataset: Yearly Figures, Population Change, and Percent Change Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

The dataset tabulates the East Bay township population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of East Bay township across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of East Bay township was 11,693, a 0.09% increase year-by-year from 2021. Previously, in 2021, East Bay township population was 11,683, an increase of 0.67% compared to a population of 11,605 in 2020. Over the last 20 plus years, between 2000 and 2022, population of East Bay township increased by 1,803. In this period, the peak population was 11,693 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the East Bay township is shown in this column.
• Year on Year Change: This column displays the change in East Bay township population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for East Bay township Population by Year. You can refer the same here

U.S. Census Bureau QuickFacts statistics for East Bay township, Grand Traverse County, Michigan. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by Census ZIP Code Tabulation Areas and normalized by 2018 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents.

Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset.

Dataset is cumulative and covers cases going back to March 2nd, 2020 when testing began. It is updated daily.

B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2018 ACS estimates for population provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents.

C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset each day.

D. HOW TO USE THIS DATASET Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Cases dropped altogether for areas where acs_population < 1000

Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are polygonal representations of USPS ZIP Code service area routes. Read how the Census develops ZCTAs on their website.

This dataset is a filtered view of another dataset You can find a full dataset of cases and deaths summarized by this and other geographic areas.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases and deaths summarized by ZIP code tabulation area are no longer being updated. This data is currently through 9/6/2023 and will not include any new data after this date.

EBMUD Water DIstribution Network Node List supplement to Masters Thesis by Veronica Li Aguirre. Thesis title: Advancement of Network Analysis Methods to Improve Infrastructure Resilience and Equity - A Case Study of Water and Wastewater Utility Services for the San Francisco East Bay Community. File name: nodes_census_joined.csv

List includes all nodes of original EBMUD water network build. Node list contains columns: Node ID, Longitude, Latitude, Degree Centrality, Betweenness Centrality, geometry, index_right, State, County, Tract, Name, B01003_001, B19013_001, layer, path.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of East Bay township by race. It includes the distribution of the Non-Hispanic population of East Bay township across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of East Bay township across relevant racial categories.

Key observations

Of the Non-Hispanic population in East Bay township, the largest racial group is White alone with a population of 10,775 (94.19% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the East Bay township
• Population: The population of the racial category (for Non-Hispanic) in the East Bay township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of East Bay township total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for East Bay township Population by Race & Ethnicity. You can refer the same here

Characteristics of participants at each round of the study compared to study region population.

Population-adjusted prevalence of antibodies from COVID-19 vaccination in Round 3 within race/ethnicity and age groups and prevalence differences between non-White and White individuals.

About this dataset

Context

The dataset tabulates the population of East Bay township by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for East Bay township. The dataset can be utilized to understand the population distribution of East Bay township by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in East Bay township. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for East Bay township.

Key observations

Largest age group (population): Male # 50-54 years (612) | Female # 60-64 years (721). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the East Bay township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the East Bay township is shown in the following column.
• Population (Female): The female population in the East Bay township is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in East Bay township for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for East Bay township Population by Gender. You can refer the same here

Objective: The purpose of this study was to examine whether a common, non-invasive, muscular fitness field test was a better predictor of bone strength compared to body mass. Methods: Hierarchical multiple regression analyses were used to determine the amount of variance that peak power explained for bone strength of the tibia compared to body mass. Peak power was estimated from maximal vertical jump height using Sayer’s equation. Peripheral quantitative computed tomography scans were used to assess bone strength measures. Results: Peak power (ꞵ=0.541, p<0.001) contributed more to the unique variance in bone strength index for compression compared to body mass (ꞵ=-0.102, p=0.332). For polar strength strain index, the beta coefficient for body mass remained significant (ꞵ=0.257, p<0.006), however, peak power’s contribution was similar (ꞵ=0.213, p= 0.051). Conclusion: Compared to body mass, peak power was a better predictor for trabecular bone strength but similar to body mass for cortical bone strength. These data provide additional support for the development of a vertical jump test as a simple, objective, valid and reliable measure to monitor bone strength among youth and adult populations. Methods Recruitment and Participant Characteristics: A convenience sample of 142 participants (79 F, 63 M) (13.3% African American/Black, 17.9% Latina/o, 28.6% White, 27.6% Asian/Pacific Islander, 1.0% American Indian or Alaskan Native and 11.7% Mixed Race or Unknown) was recruited for this observational, cross-sectional study, from the faculty, staff, and students at a mid-sized regional university. Participants were recruited through flyers, emails to the university community, and word-of-mouth advertisement. Participants received no compensation for participation. A general health and demographic survey was completed by all participants prior to the start of data collection to determine age, sex, and ethnicity of the participants. Participants were excluded if they had a history of any diseases that might influence bone health (endocrine diseases, gastrointestinal disorders, and eating disorders), were under 18 years of age, smoked, or were pregnant. All participants were informed of the risks and benefits of the study and provided written informed consent. The study was approved by the California State University, East Bay Institutional Review Board (IRB) (CSUEB-IRB-2016-223-F). The study was pre-registered at the Center for Open Science OSF (DOI: 10.17605/OSF.IO/B5QZC). Peripheral quantitative computed tomography (pQCT) (XCT 2000 Stratec Medizintechnik, Pforzheim, Germany) scans were used to assess bone strength measures of the dominant tibia. Maximal jump height was measured using a Vertec™ (JUMPUSA.com, Sunnyvale, CA).

Original provider: University of California, Irvine

Dataset credits: Vimoksalehi Lukoschek, University of California, Irvine

Abstract: Marine megafauna populations in coastal waters are increasingly threatened by anthropogenic impacts. Moreton Bay, a large embayment in south-east Queensland, lies adjacent to one of the fastest growing regions in Australia and has a resident population of bottlenose dolphins, Tursiops aduncus. Evaluation of the effectiveness of any proposed management strategy requires robust population abundance estimates.

We estimated abundances of bottlenose dolphins in central eastern Moreton Bay (350 km2) using two commonly used abundance estimation methods for cetaceans: photo-identification mark-recapture and line-transect surveys. Mark-recapture data were analyzed in CAPTURE using a model that allowed capture probabilities to vary between sampling events and between individuals. Based on an estimated 76% of the population identifiable photographically, total abundance estimates were 673 ± 130 s.e. (1997) and 818 ± 152 s.e. (1998). Line-transect data, analyzed using DISTANCE, gave an abundance estimate of 407 ± 113.5 s.e. (2000). These abundance estimates are large compared with many other coastal bottlenose dolphin populations. The line-transect surveys comprised a pilot study, and the lower line-transect abundance estimate is probably best attributable to methodological issues. In particular, smaller mean group size was estimated for the line-transects surveys (2.85 ± 0.29 s.e.) than the mark-recapture surveys (4.87 ± 0.39 s.e., 1997; 5.78 ± 0.73 s.e., 1998), and line-transect group sizes were probably underestimated. In addition, the line-transect detection probability (g(o)) was assumed to be one but was almost certainly less than one. However, the possibility of an actual decline in population size cannot be ruled out. Coefficients of variation (CV) were lower for mark-recapture than for line-transect surveys, however, CVs of line-transect estimates could be lowered through improved survey design. We evaluated the power of these surveys to detect trends in potential population declines for bottlenose dolphins in Moreton Bay and make recommendations for ongoing monitoring strategies.

Supplemental information: Effort data do not include date/time.

MU abbreviations are as in Table 1. Solid lines demarcate the four major clusters discovered by Paetkau et al., 1999 [4], which correspond to our Structure results for K = 4. From left to right, these are: the Hudson Complex, the Canadian Arctic Archipelago, Norwegian Bay, and the Polar Basin. Dotted lines denote the west–east clusters within the Basin and the Archipelago detected by K-means clustering in GenoDive. These six clusters include additional east–west substructure within the Archipelago and within the Polar Basin. DS is an admixture zone showing affinity for both Hudson Complex and the Archipelago, with southern samples tending to belong to the Hudson Complex cluster and northern samples tending to belong to the Eastern Archipelago cluster. LP has been excluded from all comparisons because it deviates significantly from Hardy–Weinberg equilibrium. For mitochondrial DNA, MC, VM, and NW were omitted because sample sizes were too small (i.e., N ≤ 3, k = 1) to accurately estimate haplotype frequencies.

description: The federal and state endangered California clapper rail, Rallus longirostris obsoletus. is a species that, until very recently, was on the verge of extinction. This secretive marsh bird's decline began over 100 years ago in the pristine marshes of San Francisco Bay (Bay) and the California coast (Fig. 1). In the earlier part of this century, the rail was found as far north as Humboldt Bay pd as far south as Morro Bay (Gill 1979) (Fig. 2). In the early 80s, the last known pair of rails outside of the Bay was seen at Elkhorn Slough in Monterey County. During the first half of this century, exploitation of the Bay's natural resources, including unrestricted filling and diking of the tidal marshes, began shrinking the rail's habitat in San Pablo Bay, Central and South San Francisco Bay from over 51,000 hectares to less than 9,000 hectares that now remain today (Dedrick 1993). The cumulative effects from this continued loss of critical habitat, combined with recent threats from increased predation, probable contamination, and other stresses associated with expanding urban growth, has created a crisis for our bay's indigenous rail. After the rail was listed as Endangered under the authority of the Endangered Species Act by the U.S. Fish and Wildlife Service (Service) in 1970, censuses of the population in the Bay were initiated. In the early 1970s, Gill estimated the total California clapper rail population at 4200 to 6000 individuals (1979). Surveys for the rail continued into the 80s (Moss 1980), with Harvey providing an estimate of 1200-1500 rails in 1981. The survey by Harvey was more accurate than the Gill estimate because an actual count was made, as compared to an average density which Gill applied to all suitable habitat. Subsequent censuses were sporadic and incomplete (Harvey 1987) until the Service, led by the San Francisco Bay National Wildlife Refuge (Refuge) began winter high tide surveys of South San Francisco Bay (South Bay) in 1988 (Foerster 1989). The Service began to suspect that the rail was in serious decline after the Refuge conducted a thorough survey of major South Bay marshes in the winter of 1988-89 and estimated a total population of only 700 rails. It was discovered that populations of rails in marshes on the east side of the bay were suffering the greatest declines and that predation by non-native predators was implicated as a primary factor (Foerster 1989). This hypothesis was confirmed by data collected by the Refuge and subsequently an Environmental Assessment and Predator Management Plan was implemented in March 1991 (Foerster and Takekawa 1991). Since 1988, the Refuge has continued to conduct annual winter high tide surveys of South Bay rail populations and some San Pablo Bay (North Bay) subpopulations (Figs. 2 and 3), with the assistance of the California Department of Fish and Game (CDFG) and other local organizations such as the San Francisco Bay Bird Observatory. This report summarizes data collected between November 1989 and January 1993, encompassing four annual winter surveys. During the last two years, the Refuge also initiated research into several factors which were implicated in rail population decline. The factors which were identified as significantly affecting rail survival included predation by non-native predators (Foerster and Takekawa 1991), and high levels of heavy metals in prey species (Lonzarich, et al. 1992). Continued analysis of these factors by the Service will culminate in a several reports to be released in late 1994.; abstract: The federal and state endangered California clapper rail, Rallus longirostris obsoletus. is a species that, until very recently, was on the verge of extinction. This secretive marsh bird's decline began over 100 years ago in the pristine marshes of San Francisco Bay (Bay) and the California coast (Fig. 1). In the earlier part of this century, the rail was found as far north as Humboldt Bay pd as far south as Morro Bay (Gill 1979) (Fig. 2). In the early 80s, the last known pair of rails outside of the Bay was seen at Elkhorn Slough in Monterey County. During the first half of this century, exploitation of the Bay's natural resources, including unrestricted filling and diking of the tidal marshes, began shrinking the rail's habitat in San Pablo Bay, Central and South San Francisco Bay from over 51,000 hectares to less than 9,000 hectares that now remain today (Dedrick 1993). The cumulative effects from this continued loss of critical habitat, combined with recent threats from increased predation, probable contamination, and other stresses associated with expanding urban growth, has created a crisis for our bay's indigenous rail. After the rail was listed as Endangered under the authority of the Endangered Species Act by the U.S. Fish and Wildlife Service (Service) in 1970, censuses of the population in the Bay were initiated. In the early 1970s, Gill estimated the total California clapper rail population at 4200 to 6000 individuals (1979). Surveys for the rail continued into the 80s (Moss 1980), with Harvey providing an estimate of 1200-1500 rails in 1981. The survey by Harvey was more accurate than the Gill estimate because an actual count was made, as compared to an average density which Gill applied to all suitable habitat. Subsequent censuses were sporadic and incomplete (Harvey 1987) until the Service, led by the San Francisco Bay National Wildlife Refuge (Refuge) began winter high tide surveys of South San Francisco Bay (South Bay) in 1988 (Foerster 1989). The Service began to suspect that the rail was in serious decline after the Refuge conducted a thorough survey of major South Bay marshes in the winter of 1988-89 and estimated a total population of only 700 rails. It was discovered that populations of rails in marshes on the east side of the bay were suffering the greatest declines and that predation by non-native predators was implicated as a primary factor (Foerster 1989). This hypothesis was confirmed by data collected by the Refuge and subsequently an Environmental Assessment and Predator Management Plan was implemented in March 1991 (Foerster and Takekawa 1991). Since 1988, the Refuge has continued to conduct annual winter high tide surveys of South Bay rail populations and some San Pablo Bay (North Bay) subpopulations (Figs. 2 and 3), with the assistance of the California Department of Fish and Game (CDFG) and other local organizations such as the San Francisco Bay Bird Observatory. This report summarizes data collected between November 1989 and January 1993, encompassing four annual winter surveys. During the last two years, the Refuge also initiated research into several factors which were implicated in rail population decline. The factors which were identified as significantly affecting rail survival included predation by non-native predators (Foerster and Takekawa 1991), and high levels of heavy metals in prey species (Lonzarich, et al. 1992). Continued analysis of these factors by the Service will culminate in a several reports to be released in late 1994.

Background: Management requires a robust understanding of between- and within-species genetic variability, however such data are still lacking in many species. For example, although multiple population genetics studies of the peregrine falcon (Falco peregrinus) have been conducted, no similar studies have been done of the closely-related prairie falcon (F. mexicanus) and it is unclear how much genetic variation and population structure exists across the species’ range. Furthermore, the phylogenetic relationship of F. mexicanus relative to other falcon species is contested. We utilized a genomics approach (i.e., genome sequencing and assembly followed by single nucleotide polymorphism genotyping) to rapidly address these gaps in knowledge.

Results: We sequenced the genome of a single female prairie falcon and generated a 1.17 Gb (gigabases) draft genome assembly. We generated maximum likelihood phylogenetic trees using complete mitochondrial genomes as well as nuclear protein-coding genes. This process provided evidence that F. mexicanus is an outgroup to the clade that includes the peregrine falcon and members of the subgenus Hierofalco. We annotated > 16,000 genes and almost 600,000 high-quality single nucleotide polymorphisms (SNPs) in the nuclear genome, providing the raw material for a SNP assay design featuring > 140 gene-associated markers and a molecular-sexing marker. We subsequently genotyped ~ 100 individuals from California (including the San Francisco East Bay Area, Pinnacles National Park and the Mojave Desert) and Idaho (Snake River Birds of Prey National Conservation Area). We tested for population structure and found evidence that individuals sampled in California and Idaho represent a single panmictic population.

Conclusions: Our study illustrates how genomic resources can rapidly shed light on genetic variability in understudied species and resolve phylogenetic relationships. Furthermore, we found evidence of a single, randomly mating population of prairie falcons across our sampling locations. Prairie falcons are highly mobile and relatively rare long-distance dispersal events may promote gene flow throughout the range. As such, California’s prairie falcons might be managed as a single population, indicating that management actions undertaken to benefit the species at the local level have the potential to influence the species as a whole.

Violent and property crime rates per 100,000 population for San Mateo County and the State of California. The total crimes used to calculate the rates for San Mateo County include data from: Sheriff's Department Unincorporated, Atherton, Belmont, Brisbane, Broadmoor, Burlingame, Colma, Daly City, East Palo Alto, Foster City, Half Moon Bay, Hillsborough, Menlo Park, Millbrae, Pacifica, Redwood City, San Bruno, San Carlos, San Mateo, South San Francisco, Bay Area DPR, BART, Union Pacific Railroad, and CA Highway Patrol.

About this dataset

Context

The dataset tabulates the East Bay township Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of East Bay township, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of East Bay township.

Key observations

Among the Hispanic population in East Bay township, regardless of the race, the largest group is of Mexican origin, with a population of 80 (40.20% of the total Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the East Bay township
• Population: The population of the specific origin for Hispanic or Latino population in the East Bay township is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of East Bay township total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for East Bay township Population by Race & Ethnicity. You can refer the same here

Along the East Asian-Australasian flyway (EAAF), waterbirds are threatened by a wide range of human activities. Studies have shown that wintering populations of many species have declined in Australia and Japan; however, long term data along China’s coast are limited. In this study, we analyzed data collected from monthly bird surveys to quantify population trends of wintering waterbirds from 1998 to 2017 in the Deep Bay area, South China. Of the 42 species studied, 12 declined, while nine increased significantly. Phylogenetic comparative analysis revealed that population trends were negatively correlated to reliance on the Yellow Sea and body size. Further, waterbird species breeding in Southern Siberia declined more than those breeding in East Asia. These findings, coupled with a relatively high number of increasing species, support the continual preservation of wetlands in the Deep Bay area. This study provides another case study showing that data collected from wintering sites provide insights on the threats along migratory pathway and inform conservation actions. As such, we encourage population surveys in the EAAF to continue, particularly along the coast of China.

Worldwide, cetaceans are impacted by human activities, and those populations that occur in shallow-nearshore habitats are particularly vulnerable. We present the results of the first long-term study on the responses of a coastal population of endangered Irrawaddy dolphins to widespread habitat changes. We particularly investigated their responses in terms of distribution and abundance. Boat-based, line-transect surveys were conducted during 12 discrete survey periods in 7 survey years spanning a 15-year period (totaling 78 days and 4,630 km of effort) in Balikpapan Bay, East Kalimantan, Indonesia. Irrawaddy dolphins were sighted on 136 occasions. Through DISTANCE analysis, a decrease in population density in the inner Bay area was observed from 0.45 dolphins/km2 in 2000–2001 (CV = 24%) to 0.34 and 0.32 dolphins/km2 in 2008 and 2015 (CV = 31% and 25%). A shift in distribution was noted between the periods 2000–2002 and 2008–2015 with significantly lower occurrence in the lower Bay segment compared to upper Bay segments. No sightings were made in the outer Bay area in later years, which coincided with increased shipping traffic in these areas. A peak in stranding events in 2016 and 2018 followed extremely high phenol levels within Bay waters in 2015 and a large-scale oil spill in 2018. The mean annual mortality rates of 0.67 Irrawaddy dolphins/year is unsustainable based on the lower potential biological removal (PBR) values for best abundance estimates of 2015 (Ndistance = 45 and Nmark–recapture = 73). Other threats to local dolphins include unsustainable fishing, underwater noise caused by construction, particularly piling activities. The research helped to identify Balikpapan Bay as an Important Marine Mammal Area by the IUCN MMPA Taskforce. Serious concerns remain for the concrete plans to move Indonesia’s capital city to the area north of the Bay, in terms of increased shipping traffic and harbor construction in the upper Bay segments that represent primary dolphin habitat. We recommend that protected areas be assigned for marine mammals and artisanal fisheries and shipping traffic and piling activities be excluded from these areas. We also recommend a legislated requirement of a mitigation protocol compulsory for piling and seismic activities within Indonesia.

INDICATOR DEFINITION Count of all adult females, fully weaned pups and dead pups hauled out on, or close to, the day of maximum cow numbers, set for October 15.

TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system.

This indicator is one of: CONDITION

RATIONALE FOR INDICATOR SELECTION Elephant seals from Macquarie Island are long distance foragers who can utilise the Southern Ocean both west as far as Heard Island and east as far as the Ross Sea. Thus their populations reflect foraging conditions across a vast area.

The slow decline in their numbers (-2.3% annually from 1988-1993) suggests that their ocean foraging has been more difficult in recent decades. Furthermore, interactions with humans are negligible due to the absence of significant overlap in their diet with commercial fisheries. This suggests that changes in 'natural' ocean conditions may have altered aspects of prey availability. It is clear that seal numbers are changing in response to ocean conditions but at the moment these conditions cannot be specified.

DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial Scale: Five beaches on Macquarie Island (lat54 degrees 37' 59.9' S, long 158 degrees 52' 59.9' E): North Head to Aurora Point; Aurora Point to Caroline Cove; Garden Cove to Sandy Bay; Sandy Bay to Waterfall Bay; Waterfall Bay to Hurd Point.

Frequency: Annual census on 15th October

Measurement Technique: Monitoring the Southern Elephant Seal population on Macquarie island requires a one day whole island adult female census on October 15 and a daily count of cow numbers, fully weaned pups and dead pups on the west and east isthmus beaches throughout October.

Daily cow counts during October, along the isthmus beaches close to the Station, provide data to identify exactly the day of maximum numbers. The isthmus counts are recorded under the long-established (since 1950) harem names. Daily counts allow adjustment to the census totals if the day of maximum numbers of cows ashore happens to fall on either side of October 15. Personnel need to be dispersed around the island by October 15 so that all beaches are counted for seals on that day. This has been achieved successfully for the last 15 years.

On the day of maximum haul out (around 15th October) the only Elephant seals present are cows, their young pups and adult males. The three classes can be readily distinguished and counted accurately. Lactating pups are not counted, their numbers are provided by the cow count on a 1:1 proportion. The combined count of cows, fully weaned pups and dead pups provides an index of pup production.

The count of any group is made until there is agreement between counts to better than +/- 5%. Thus there is always a double count as a minimum; the number of counts can reach double figures when a large group is enumerated. The largest single group on Macquarie Island is that at West Razorback with greater than 1,000 cows; Multiple counts are always required there.

RESEARCH ISSUES Much research has been done already to acquire demographic data so that population models can be produced. Thus there will be predicted population sizes for elephant seals on Macquarie Island in 2002 onwards and the annual censuses will allow these predictions to be tested against the actual numbers. The censuses are also a check on the population status of this endangered species.

LINKS TO OTHER INDICATORS