Chart and table of population level and growth rate for the Sacramento metro area from 1950 to 2025. United Nations population projections are also included through the year 2035.

About this dataset

Context

The dataset tabulates the Sacramento 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 Sacramento 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 2023, the population of Sacramento was 422, a 0.71% decrease year-by-year from 2022. Previously, in 2022, Sacramento population was 425, a decline of 0% compared to a population of 425 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Sacramento decreased by 107. In this period, the peak population was 529 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. 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 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Sacramento is shown in this column.
• Year on Year Change: This column displays the change in Sacramento 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 Sacramento Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Sacramento, CA population pyramid, which represents the Sacramento population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Sacramento, CA, is 26.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Sacramento, CA, is 20.7.
• Total dependency ratio for Sacramento, CA is 46.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Sacramento, CA is 4.8.

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 for the Sacramento population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Sacramento for the selected age group is shown in the following column.
• Population (Female): The female population in the Sacramento for the selected age group is shown in the following column.
• Total Population: The total population of the Sacramento for the selected age group is shown in the following column.

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 Sacramento Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Sacramento population by year. The dataset can be utilized to understand the population trend of Sacramento.

Content

The dataset constitues the following datasets

• Sacramento, CA 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/.

In 2021, the population of the Sacramento-Roseville Folsom metropolitan are was about 2.41 million people. This was a slight increase from the previous year, when the population was about 2.4 million people.

U.S. Census Bureau QuickFacts statistics for West Sacramento city, California. 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 dataset listing California counties by population for 2024.

Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central Valley salmonid monitoring (Johnson et al. 2017; IEP 2019).

 Note that the genetic data provided here may be included in other data repositories. Regarding Sacramento trawl activities, refer to the Interagency Ecological Program: Over four decades of juvenile fish monitoring data from the San Francisco Estuary, collected by the Delta Juvenile Fish Monitoring Program, 1976-2020

 Package ID: edi.244.8 

 Literature Cited

 Dekar, M., P. Brandes, J. Kirsch, L. Smith, J. Speegle, P. Cadrett and M. Marshall. 2013. USFWS Delta Juvenile Fish Monitoring Program Review. Background Document. Prepared for IEP Science Advisory Group, June 2013. US Fish and Wildlife Service, Stockton Fish and Wildlife Office, Lodi, CA. 224 p.
 Fisher, F.W. 1992. Chinook Salmon, Oncorhynchus tshawytscha, growth and occurrence in the Sacramento-San Joaquin River system. California Department of Fish and Game, Inland Fisheries Divisions, draft office report, Redding. 
 Harvey, B.N., D.P. Jacobson, M.A. Banks. 2014. Quantifying the uncertainty of a juvenile Chinook Salmon Race Identification Methyod for a Mixed-Race Stock. North American Journal of Fisheries Management. 
 IEP, Interagency Ecological Program. 2019. Interagency Ecological Program Science Strategy 2020-2024: Invenstment Priorities for Interagency Collaborative Science.
 Johnson, R.C., S. Windell, P. L. Brandes, J. L. Conrad, J. Ferguson, P. A. L. Goertler, B. N. Harvey, J.Heublein, J. A. Israel, D. W. Kratville, J. E. Kirsch, R. W. Perry, J. Pisciotto, W. R. Poytress, K. Reece, and B. G. Swart. 2017. Increasing the management value of life stage monitoring networks for three imperiled fishes in California's regulated rivers: case study Sacramento Winter-run Chinook salmon. San Francisco Estuary and Watershed Science 15: 1-41.
 National Marine Fisheries Service (NMFS). 2013. Endangered and Threatened Species: Designation of a Nonessential Experimental Population of Central Valley Spring-Run Chinook Salmon Below Friant Dam in the San Joaquin River, CA. Federal Register 70: 79622, December 31, 2013.

This dataset includes data on the growth, fecundity, and survival of Giant Gartersnakes (Thamnophis gigas) in the Sacramento Valley of California from 1995-2017. In addition, the dataset includes R code to replicate the Integral Projection Model construction and analysis presented in the paper Demographic drivers of population growth in a threatened snake by Rose et al. published in Journal of Wildlife Management in 2019.

These data support the following publication: Rose, J.P., Ersan, J.S., Wylie, G.D., Casazza, M.L. and Halstead, B.J., 2019. Demographic factors affecting population growth in giant gartersnakes. The Journal of Wildlife Management, 83(7), pp.1540-1551.

1. Declines in diversity among populations managed together have diminished aggregate stability through a decreased portfolio effect. Although the portfolio effect has been quantified in a variety of ways, management recommendations for the recovery of lost diversity rarely specify the stability benefits possible through such improvements. 2. We introduce a metric, the Diversity Deficit (DD), that relates past losses and potential gains in aggregate stability to the changes in population diversity (i.e., covariability among population time series). We illustrate use of this DD in retrospective analyses of the aggregate Sacramento River Fall-run Chinook salmon stock (Oncorhynchus tshawytscha), and project potential future improvements in stability through population diversity. 3. In the retrospective analysis, we removed individual time series from the stability calculations to determine their effects on times and locations of past losses in diversity and stability. We found an early threefold loss in stock stability resulting from the presence of a single tributary, the Sacramento River mainstem. Other shifts in stability resulted from an increase in variability of a single population, and from the synchronizing effects of low ocean survival that led to the 2008-09 fishery closure. Only one, smaller increase in the DD (i.e., in lost stability) was due to portfolio-wide increases in covariabilities among tributary abundances. 4. In a prospective analysis using the DD applied to California salmon, we found that increasing biodiversity to the point of population independence and to its early high value would have reduced the probability of triggering a fishery closure. 5. Synthesis and applications. Analyses with the DD illustrate a means to identify the times and locations of losses in population diversity, and to quantify how much restoration of population diversity could increase stability, thus benefit resource services. Here the benefit was a reduction in the probability of falling below a critical management threshold leading to fishery closure, but other tangible benefits (e.g., reduction in probability of extinction) would be possible.12-Jun-2017

Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central Valley salmonid monitoring (Johnson et al. 2017; IEP 2019).

   Note that the genetic data provided here may be included in other data repositories. Regarding Sacramento trawl activities, refer to the Interagency Ecological Program: Over four decades of juvenile fish monitoring data from the San Francisco Estuary, collected by the Delta Juvenile Fish Monitoring Program, 1976-2020

   Package ID: edi.244.8 

   Literature Cited

   Dekar, M., P. Brandes, J. Kirsch, L. Smith, J. Speegle, P. Cadrett and M. Marshall. 2013. USFWS Delta Juvenile Fish Monitoring Program Review. Background Document. Prepared for IEP Science Advisory Group, June 2013. US Fish and Wildlife Service, Stockton Fish and Wildlife Office, Lodi, CA. 224 p.
   Fisher, F.W. 1992. Chinook Salmon, Oncorhynchus tshawytscha, growth and occurrence in the Sacramento-San Joaquin River system. California Department of Fish and Game, Inland Fisheries Divisions, draft office report, Redding. 
   Harvey, B.N., D.P. Jacobson, M.A. Banks. 2014. Quantifying the uncertainty of a juvenile Chinook Salmon Race Identification Methyod for a Mixed-Race Stock. North American Journal of Fisheries Management. 
   IEP, Interagency Ecological Program. 2019. Interagency Ecological Program Science Strategy 2020-2024: Invenstment Priorities for Interagency Collaborative Science.
   Johnson, R.C., S. Windell, P. L. Brandes, J. L. Conrad, J. Ferguson, P. A. L. Goertler, B. N. Harvey, J.Heublein, J. A. Israel, D. W. Kratville, J. E. Kirsch, R. W. Perry, J. Pisciotto, W. R. Poytress, K. Reece, and B. G. Swart. 2017. Increasing the management value of life stage monitoring networks for three imperiled fishes in California's regulated rivers: case study Sacramento Winter-run Chinook salmon. San Francisco Estuary and Watershed Science 15: 1-41.
   National Marine Fisheries Service (NMFS). 2013. Endangered and Threatened Species: Designation of a Nonessential Experimental Population of Central Valley Spring-Run Chinook Salmon Below Friant Dam in the San Joaquin River, CA. Federal Register 70: 79622, December 31, 2013.

Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central Valley salmonid monitoring (Johnson et al. 2017; IEP 2019).

   Literature Cited

   Dekar, M., P. Brandes, J. Kirsch, L. Smith, J. Speegle, P. Cadrett and M. Marshall. 2013. USFWS Delta Juvenile Fish Monitoring Program Review. Background Document. Prepared for IEP Science Advisory Group, June 2013. US Fish and Wildlife Service, Stockton Fish and Wildlife Office, Lodi, CA. 224 p.
   Fisher, F.W. 1992. Chinook Salmon, Oncorhynchus tshawytscha, growth and occurrence in the Sacramento-San Joaquin River system. California Department of Fish and Game, Inland Fisheries Divisions, draft office report, Redding. 
   Harvey, B.N., D.P. Jacobson, M.A. Banks. 2014. Quantifying the uncertainty of a juvenile Chinook Salmon Race Identification Methyod for a Mixed-Race Stock. North American Journal of Fisheries Management. 
   IEP, Interagency Ecological Program. 2019. Interagency Ecological Program Science Strategy 2020-2024: Invenstment Priorities for Interagency Collaborative Science.
   Johnson, R.C., S. Windell, P. L. Brandes, J. L. Conrad, J. Ferguson, P. A. L. Goertler, B. N. Harvey, J.Heublein, J. A. Israel, D. W. Kratville, J. E. Kirsch, R. W. Perry, J. Pisciotto, W. R. Poytress, K. Reece, and B. G. Swart. 2017. Increasing the management value of life stage monitoring networks for three imperiled fishes in California's regulated rivers: case study Sacramento Winter-run Chinook salmon. San Francisco Estuary and Watershed Science 15: 1-41.
   National Marine Fisheries Service (NMFS). 2013. Endangered and Threatened Species: Designation of a Nonessential Experimental Population of Central Valley Spring-Run Chinook Salmon Below Friant Dam in the San Joaquin River, CA. Federal Register 70: 79622, December 31, 2013.

Conservation of species facing environmental change requires an understanding of interpopulation physiological variation. However, physiological data is often scarce and therefore pooled across populations and species, erasing potentially important variability between populations. Interpopulation variation in thermal physiology has been observed within the Salmonidae family, although it has not been associated with seasonally distinct migratory phenotypes (i.e., seasonal runs). To resolve whether thermal physiology is associated with life-history strategy we acclimated four Sacramento River juvenile Chinook salmon populations (Coleman fall-run, Feather River fall- and spring-run and Sacramento River Winter-run) exhibiting different seasonal migratory phenotypes (fall-, spring- and winter-run), at 11, 16 and 20°C and assessed variation in growth rate, critical thermal maxima and temperature-dependent metabolic traits. We identified population differences in the physiological parameters measured and found compelling evidence that the critically endangered and endemic Sacramento River winter-run Chinook population exhibits thermal physiology associated with its early-migration life-history strategy. Acclimation to warm temperatures limited the growth and metabolic capacity of winter-run Chinook salmon, highlighting the risk of future environmental warming to this endemic population. Methods There are two sets of metabolic data contained herein. The first (2022_CJFAS_Dryad_Metabolic_Dataset.csv) and the second (NOAA_Preliminary_Data-Winter-Run_Small.csv) were captured using different equipment due to their size. Metabolic data from the first dataset were gathered in the following way, methods for collection of the second dataset are appended to the bottom of this section. Fish underwent metabolic trials in one of four, 5 L automated swim tunnel respirometers (Loligo, Denmark). The four tunnels were split into two paired systems with two tunnels sharing a single sump and heat pump. Water for each swim tunnel system was pumped (PM700, Danner USA) from the sump into an aerated water bath surrounding each swim tunnel, and then returned to the sump. Sumps were supplied with non-chlorinated fresh water from a designated well and aerated with air stones. The temperature of the sump (and therefore the swim tunnels) was maintained (±0.5°C) by circulating water through a heat pump (model DSHP-7; Aqua Logic Delta Star, USA) using a high-volume water pump (Sweetwater SHE 1.7 Aquatic Ecosystems, USA). In addition, each sump contained a thermostatically controlled titanium heater (TH-800; Finnex, USA). Swim tunnels and associated sump systems were cleaned and sanitized with bleach weekly to reduce potential for bacterial growth. Dissolved oxygen saturation within the swim tunnels was measured using fibre-optic dipping probes (Loligo OX11250) which continuously recorded data via AutoResp™ software (version 2.3.0). Oxygen probes were calibrated weekly using a two-point, temperature-paired calibration method. Water velocity of the swim tunnels was quantified and calibrated using a flowmeter (Hontzcsh, Germany) and regulated using a variable frequency drive controller (models 4x and 12K; SEW Eurodrive, USA). The velocity (precision <1 cm s-1) for each tunnel was controlled remotely using the Autoresp™ program and a DAQ-M data acquisition device (Loligo, Denmark). Swim tunnels were surrounded by shade cloth to reduce disturbance of the fish. Fish were remotely and individually monitored using infrared cameras (QSC1352W; Q-see, China) connected to a computer monitor and DVR recorder. Oxygen consumption rates for both routine and maximum metabolic rates were captured using intermittent respirometry(Brett 1964). Flush pumps (Eheim 1048A, Germany) for each tunnel pumped aerated fresh water through the swim chamber and was automatically controlled via the AutoResp™ software and DAQ-M system. This system would seal the tunnel and enable the measurement of oxygen consumption attributable to the fish. Oxygen saturation levels were not allowed to drop below 80% and restored within three minutes once the flush pump was activated. Oxygen saturation data from AutoResp™ was transformed to oxygen concentration using the following equation: Where %O2Sat is the oxygen saturation percentage reported from AutoResp™; αO2 is the coefficient temperature-corrected oxygen solubility (mgO2 L-1 mmHg-1); and BP is the barometric pressure (mmHg). Oxygen concentration (milligrams of oxygen per liter) was measured every second and regressed over time, the coefficient of this relationship (milligrams of oxygen per liter per second) was then converted to metabolic rate (milligrams of oxygen per kilogram per minute, Equation 3). Where R is the calculated coefficient of oxygen over time; V is the volume of the closed respirometer; M is the mass of the fish in kilograms and ’60’ transforms the rate from per second to per minute. An allometric scaling exponent was not incorporated due to similarity in fish sizes and to maximize comparability with metabolic data from the Mokelumne Hatchery (CA) fall-run population (Poletto et al. 2017). Routine Metabolic Rate Prior to routine metabolic rate (RMR) trials fish were fasted to ensure a post-prandial state. Fish reared at 16 or 20°C were fasted for 24 hours, while fish acclimated to 11°C were fasted for 48 hours. Fish were then transferred into a swim tunnel respirometer between 13:00 and 17:00. After a 30-minutes at their acclimation temperature the temperature was adjusted at 2°C h-1 to the test temperature (8 – 26°C). Automated intermittent flow respirometry began 30 minutes after the test temperature was achieved and continued overnight. Measurement periods ranged from 900 to 1800 seconds in duration, flush periods were 180-300 seconds. Periods varied in length in response to fish size and test temperature to ensure oxygen saturation was kept high (>80%) during the trial. A small circulation pump (DC30A-1230, Shenzhen Zhongke, China) ensured that water was mixed without disturbing the fish. Fish activity was monitored by overhead infra-red cameras and measurement periods when the fish were active were discarded. RMR was calculated by averaging the three lowest RMR values(Poletto et al. 2017). RMR measurements were concluded by 08:00 ± 40 min. Maximum Metabolic Rate A modified critical swimming velocity protocol was used to elicit maximal metabolic rate (MMR)(Poletto et al. 2017). Tunnel speed was increased gradually from 0 to 30 cm s-1 over an ~2 min period and held there for 20 min. For each subsequent 20-min measurement period, tunnel velocity was increased 10% up to a maximum of 6 cm s-1 per step. Fish were swum until exhausted and unable to swim. Swimming metabolism was measured by sealing the tunnel for approximately 16 minutes of the 20-minute measurement period. When a fish became impinged upon the back screen (>2/3 of body in contact with screen) the tunnel velocity was stopped for ~1 minute and then gradually returned to the original speed over 2 minutes. A fish was determined to be exhausted if it became impinged twice within the same velocity step. At this point the tunnel impellor was stopped to allow for recovery. The highest metabolic rate measured over a minimum of 5 minutes during active swimming was taken as the MMR. Post-experiment, the tunnel was returned to the acclimation temperature and fish were transferred to a recovery tank and monitored. In seeking evidence of metabolic collapse at near-critical temperatures, some metabolic trials were conducted at temperatures exceeding the tolerance of the fish. These mortality events represent potential lethal upper limits for sub-acute thermal persistence (Fig. S1). Data from fish which did not survive the trial or recovery were not used in analysis. After a 24-hour recovery period fish were euthanized in a buffered solution of MS-222 (0.5g/L). Measurements for mass (g), fork length (cm) and total length (cm) were taken, and Fulton’s condition factor was calculated. Aerobic scope (AS) was calculated as the difference between a fish’s RMR and MMR. Thermal optimums (TOPT) were defined as the temperature when aerobic scope was maximized, and calculated as the root-value of the derivative of the quadratic function describing the relationship between AS and test temperature. Growth Data Growth measurements were initiated in mid to late spring when all populations would still be rearing prior to outmigration. Growth data were gathered every two weeks by measuring a sample of 30 fish from each treatment (n=15 per tank, n = 1528 total measurements). Fish were not individually marked and therefore growth rate was calculated across individuals. Fish were arbitrarily netted from their treatment tank and transferred to an aerated five-gallon bucket until measured. Fish were air exposed for ~15-20 seconds to measure mass (± 0.01 grams, Ohaus B3000D) and fork length (± 0.1 cm) and then placed into a second bucket for recovery before returning to their original treatment tank. Fish were netted and measured by the same experimenter across all sampling days. Condition factor was calculated as Fulton's condition factor (K) using the equation K = 100*Mass/Fork Length^3. Critical Thermal Maxima Critical Thermal Maximum (CTMax) values were quantified according to established methods, briefly described below1. We placed six 4L Pyrex beakers in a fiberglass bath tray (1m x 2m x .2m). Beakers were aerated with an air stone to ensure both adequate oxygen saturation and circulation of water within the beaker. The volume of water in each individual beaker (approx. 2.5 L) was calibrated to ensure even heating across all CTMax beakers (0.33°C/min). Two pumps (PM700, Danner USA) were used to circulate water: one pump recirculated water across three heaters (Process Technology S4229/P11), while the other distributed heated water through the CTMax bath

About this dataset

Context

The dataset tabulates the Sacramento County 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 Sacramento County. The dataset can be utilized to understand the population distribution of Sacramento County by age. For example, using this dataset, we can identify the largest age group in Sacramento County.

Key observations

The largest age group in Sacramento County, CA was for the group of age 30 to 34 years years with a population of 126,467 (7.98%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Sacramento County, CA was the 80 to 84 years years with a population of 25,879 (1.63%). 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 Sacramento County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Sacramento County 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 Sacramento County Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the West Sacramento 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 West Sacramento. The dataset can be utilized to understand the population distribution of West Sacramento by age. For example, using this dataset, we can identify the largest age group in West Sacramento.

Key observations

The largest age group in West Sacramento, CA was for the group of age 30 to 34 years years with a population of 4,544 (8.34%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in West Sacramento, CA was the 85 years and over years with a population of 503 (0.92%). 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 West Sacramento is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of West Sacramento 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 West Sacramento Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Sacramento County population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Sacramento County.

Content

The dataset constitues the following two datasets across these two themes

• Sacramento County, CA Population Breakdown by Gender
• Sacramento County, CA Population Breakdown by Gender and Age

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 population of Sacramento by race. It includes the population of Sacramento across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Sacramento across relevant racial categories.

Key observations

The percent distribution of Sacramento population by race (across all racial categories recognized by the U.S. Census Bureau): 39.33% are white, 12.60% are Black or African American, 0.82% are American Indian and Alaska Native, 19.51% are Asian, 1.81% are Native Hawaiian and other Pacific Islander, 12.81% are some other race and 13.12% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 (excluding ethnicity) for the Sacramento
• Population: The population of the racial category (excluding ethnicity) in the Sacramento is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Sacramento 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 Sacramento Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the West Sacramento population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of West Sacramento.

Content

The dataset constitues the following two datasets across these two themes

• West Sacramento, CA Population Breakdown by Gender
• West Sacramento, CA Population Breakdown by Gender and Age

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

Key observations

Of the Non-Hispanic population in Sacramento County, the largest racial group is White alone with a population of 657,865 (54.63% 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 Sacramento County
• Population: The population of the racial category (for Non-Hispanic) in the Sacramento County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Sacramento County 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 Sacramento County Population by Race & Ethnicity. You can refer the same here