US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Race/Ethnicity, Educational Attainment, Unemployment, Health Insurance, Disability and Vehicle Access.

The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller the level of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.

Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.

Race/Ethnicity

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WPop: Total population of those who identify as white alone (B01001A).

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PWPop: Percentage of total population that identifies as white alone (B01001A).

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BPop: Total population of those who identify as black or African American alone (B01001B).

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PWPop: Percentage of total population that identifies as black or African American alone (B01001B).

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AmIPop: Total population of those who identify as American Indian and Alaska Native alone (B01001C).

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PAmIPop: Percentage of total population that identifies as American Indian and Alaska Native alone (B01001C).

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APop: Total population of those who identify as Asian alone (B01001D).

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PAPop: Percentage of total population that identifies as Asian alone (B01001D).

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PacIPop: Total population of those who identify as Native Hawaiian and Other Pacific Islander alone (B01001E).

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PPacIPop: Percentage of total population that identifies as Native Hawaiian and Other Pacific Islander alone (B01001E).

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OPop: Total population of those who identify as Some Other Race alone (B01001F).

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POPop: Percentage of total population that identifies as Some Other Race alone (B01001F).

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MPop: Total population of those who identify as Two or More Races (B01001G).

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PMPop: Percentage of total population that identifies as Two or More Races (B01001G).

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WnHPop: Total population of those who identify as White alone, not Hispanic or Latino (B01001H).

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PWnHPop: Percentage of total population that identifies as White alone, not Hispanic or Latino (B01001H).

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LPop: Total population of those who identify as Hispanic or Latino (B01001I).

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PLPop: Percentage of total population that identifies as Hispanic or Latino (B01001I).

Educational Attainment

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EdLHS1824: Total population between the ages of 18 and 24 that has not received a High School degree (S1501).

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PEdLHS1824: Percentage of population between the ages of 18 and 24 that has not received a High School degree (S1501).

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EdLHS1824: Total population between the ages of 18 and 24 that has received a High School degree or equivalent (S1501).

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PEdLHS1824: Percentage of population between the ages of 18 and 24 that has received a High School degree or equivalent (S1501).

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EdSC1824: Total population between the ages of 18 and 24 that has received some amount of college education or an associate’s degree (S1501).

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PEdSC1824: Percentage of population between the ages of 18 and 24 that has received some amount of college education or an associate’s degree (S1501).

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EdB1824: Total population between the ages of 18 and 24 that has received bachelor’s degree or higher (S1501).

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PEdB1824: Percentage of the population between the ages of 18 and 24 that has received bachelor’s degree or higher (S1501).

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EdL9: Total population ages 25 and over that has received less than a ninth grade education (S1501).

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PEdL9: Percentage of population ages 25 and over that has received less than a ninth grade education (S1501).

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Ed912nD: Total population ages 25 and over that has received some degree of education between grades 9 and 12 but has not received a high school degree (S1501).

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PEd912nD: Percentage of population ages 25 and over that has received some degree of education between grades 9 and 12 but has not received a high school degree (S1501).

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EdHS: Total population ages 25 and over that has received a high school degree or equivalent (S1501).

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PEdHS: Percentage of population ages 25 and over that has received a high school degree or equivalent (S1501).

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EdSC: Total population ages 25 and over with some college education but no degree (S1501).

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PEdSC: Percentage of population ages 25 and over with some college education but no degree (S1501).

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EdAssoc: Total population ages 25 and over with an associate’s degree (S1501).

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PEdAssoc: Percentage of population population ages 25 and over with an associate’s degree (S1501).

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EdB: Total population ages 25 and over with bachelor’s degree (S1501).

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PEdB: Percentage of population ages 25 and over with bachelor’s degree (S1501).

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EdG: Total population ages 25 and over with a graduate or professional degree (S1501).

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PEdG: Percentage of population ages 25 and over with a graduate or professional degree (S1501).

Unemployment, Health Insurance, Disability

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UnempR: Unemployment rate among the population ages 16 and over (S2301).

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UnIn: Total non-institutionalized population without health insurance (B27001).

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PUnIn: Percentage of non-institutionalized populations without health insurance (B27001).

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Disab: Total non-institutionalized population with a disability (S1810).

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PDisab: Percentage of non-institutionalized populations with a disability (B27001).

Vehicle Access

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OwnNV: Total number of owner-occupied households without a vehicle (B25044).

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POwnNV: Percentage of owner-occupied households without a vehicle (B25044).

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OwnnV: Total number of owner-occupied households with n numbers of vehicles (B25044).

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POwnnV: Percentage of owner-occupied households with n numbers of vehicles (B25044).

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RentNV: Total number of renter-occupied households without a vehicle (B25044).

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PRentNV: Percentage of renter-occupied households without a vehicle (B25044).

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RentnV: Total number of renter-occupied households with n numbers of vehicles (B25044).

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POwnnV: Percentage of renter-occupied households with n numbers of vehicles (B25044).

The fate of natural populations is mediated by complex interactions among vital rates, which can vary within and among years. While the effects of random, among-year variation in vital rates have been studied extensively, relatively little is known about how periodic, non-random variation in vital rates affects populations. This knowledge gap is potentially alarming as global environmental change is projected to alter common periodic variations, such as seasonality. We investigated the effects of changes in vital-rate periodicity on populations of three species representing different forms of adaptation to periodic environments: the yellow-bellied marmot (Marmota flaviventer), adapted to strong seasonality in snowfall; the meerkat (Suricata suricatta), adapted to inter-annual stochasticity as well as seasonal patterns in rainfall; and the dewy pine (Drosophyllum lusitanicum), adapted to fire regimes and periodic post-fire habitat succession. To assess how changes in periodicity affect population growth, we parameterized periodic matrix population models and projected population dynamics under different scenarios of perturbations in the strength of vital-rate periodicity. We assessed the effects of such perturbations on various metrics describing population dynamics, including the stochastic growth rate, log λS. Overall, perturbing the strength of periodicity had strong effects on population dynamics in all three study species. For the marmots, log λS decreased with increased seasonal differences in adult survival. For the meerkats, density dependence buffered the effects of perturbations of periodicity on log λS. Finally, dewy pines were negatively affected by changes in natural post-fire succession under stochastic or periodic fire regimes with fires occurring every 30 years, but were buffered by density dependence from such changes under presumed more frequent fires or large-scale disturbances. We show that changes in the strength of vital-rate periodicity can have diverse but strong effects on population dynamics across different life histories. Populations buffered from inter-annual vital-rate variation can be affected substantially by changes in environmentally-driven vital-rate periodic patterns; however, the effects of such changes can be masked in analyses focusing on inter-annual variation. As most ecosystems are affected by periodic variations in the environment such as seasonality, assessing their contributions to population viability for future global-change research is crucial. Methods Data collection Yellow-bellied marmot (Marmota flaviventer) We used seasonal data on survival, life-history stages, and recruitment of individual female yellow-bellied marmots. The data were collected between 1976 and 2016 in nine marmot colonies living at 2900 masl in the upper East River Valley near Gothic, Colorado, United States. To collect these demographic data, individuals were live-trapped each year throughout their summer active season, and ear-marked in the first capture event. Meerkat (Suricata suricatta) We used seasonal data on survival, life-history stages and social status, emigration, and recruitment of individual female meerkats. The data were collected by frequently visiting (one to three times per week) wild groups of individually-marked meerkats in the Kuruman River Reserve, South Africa. For this study, we used 20 years of individual data (1997–2016). We also used data on population density, calculated as the number of individuals per km2 of population range at each census. Dewy pine (Drosophyllum lusitanicum) We used data on survival, life-history stages, and reproduction of individual dewy pines. The data were collected annually for nine years between April 2011 and April 2019 on dewy-pine populations occurring in three sites of southern Spain and facing different types of post-fire disturbance: human-disturbed (i.e., heavy persistent browsing) or natural (i.e., little browsing). The seed bank-related vital rates (seed germination or stasis) were estimated from seed-burial and greenhouse germination experiments. In addition, we used data on population density, calculated as the number of aboveground dewy pines per 1-m2 square within a study transect in each site and each post-fire habitat state.

US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Population, Households, Tenure, Cost Burden, Poverty, and Age of Housing Stock.The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller thelevel of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.Data Dictionary - Population, Households, Tenure, Cost Burden, Poverty, Age of Housing Stock· Population: Total Population (B01003)· Households: Total number of households (B25003)· OwnHH: Total number of owner-occupied households (B25003)· RentHH: Total number of renter-occupied households (B25003)· TotalU: Total number of housing units (B25001)· VacantU: Total number of vacant units (B25004)· SeasRecOcU: Total number of Seasonal/Recreational/Occasionally Occupied Units (B25004)· ForSale: Total number of units currently for sale (B25004)· ForRent: Total number of units currently for rent (B25004)· MedianHI: Median Household Income (B25119)· OwnHH3049: Total number of owner-occupied households spending 30-49% of their income on housing costs. (B25095)· POwnHH3049: Percentage of owner-occupied households spending 30-49% of their income on housing costs. (B25095)· OwnHH50: Total number of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)· POwnHH50: Percentage of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)· OwnHH_CB: Total number of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)· POwnHH_CB: Percentage of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)· RenHH3049: Total number of renter-occupied households spending 30-49% of their income on housing costs. (B25070)· PRenHH3049: Percentage of renter-occupied households spending 30-49% of their income on housing costs. (B25070)· RenHH50: Total number of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)· PRenHH50: Percentage of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)· RenHH_CB: Total number of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)· PRenHH_CB: Percentage of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)· Poverty: Population below poverty level. (B17001)· PPoverty: Percentage of population below poverty level. Note poverty status (above or below) is not determined for the entire population. (B17001)· MYearBuilt: Median structure year of construction. (B25035)

The Doyle mule deer (Odocoileus hemionus) herd migrates from a winter range in Honey Lake Valley and Upper Long Valley near Doyle, California along US Highway 395 in Lassen County, California and eastward into Plumas County and Plumas National Forest in the Sierra Nevada Mountains for the summer. Winter range also exists on the Nevada side of the border in Washoe County. Much of the winter range habitat is now deteriorated, lacking vegetation that historically provided forage. Highway 395 is a major barrier to migration, with hundreds of deer being killed annually trying to cross it. Population estimates were ~15,600 in 2019. These data provide the location of winter ranges for mule deer in the Doyle population in California. They were developed from 25 migration sequences collected from a sample size of 14 animals comprising GPS locations collected every 3-13 hours.

Informant’s demographics in the study area.

Parameter estimates (mean, standard deviation, lower and upper 95% credible intervals) of the most parsimonious binomial mixture model to estimate abundance of singing male Aquatic Warblers in the Biebrza valley, Poland, in 2011 – 2013.