About this dataset

Context

The dataset tabulates the Santa Maria 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 Santa Maria 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 Santa Maria was 110,125, a 0.44% increase year-by-year from 2021. Previously, in 2021, Santa Maria population was 109,647, a decline of 0.09% compared to a population of 109,742 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Santa Maria increased by 33,118. In this period, the peak population was 110,125 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 Santa Maria is shown in this column.
• Year on Year Change: This column displays the change in Santa Maria 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 Santa Maria Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Santa Maria median household income by race. The dataset can be utilized to understand the racial distribution of Santa Maria income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Santa Maria, CA median household income breakdown by race betwen 2012 and 2022
• Median Household Income by Racial Categories in Santa Maria, CA (2022)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Santa Maria median household income by race. You can refer the same here

Resident Population in Santa Maria-Santa Barbara, CA (MSA) was 444.50000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in Santa Maria-Santa Barbara, CA (MSA) reached a record high of 448.08300 in January of 2020 and a record low of 424.21800 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in Santa Maria-Santa Barbara, CA (MSA) - last updated from the United States Federal Reserve on July of 2025.

About this dataset

Context

The dataset tabulates the Santa Maria household income by age. The dataset can be utilized to understand the age-based income distribution of Santa Maria income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Santa Maria, CA annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Santa Maria, CA household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Santa Maria income distribution by age. You can refer the same here

ABSTRACT. Tallitroides topitotum (Burt, 1934) (Talitridae) is one of the representatives of Amphipoda that has adapted to terrestrial environment. This research aimed to describe the population structure of this talitrid in the morphometric and reproductive aspects. The study was carried out in the central region of Rio Grande do Sul, Brazil, from June 2020 to May 2021. The talitrids were monthly collected with pitfalls exposed for 48 hours and they were sexed, photographed, and measured (body length, cephalic length, and marsupium length - in millimeters). Ovigerous females had their eggs counted and measured. In addition, we correlated the size data and the sampling latitudes of T. topitotum from previous and present studies. A total of 492 individuals were collected, from which 62 were ovigerous females, 188 non-ovigerous females, and 242 juveniles. No male was sampled. There was a significant correlation between body and head lengths and body and marsupium lengths in ovigerous females. We found no correlation between morphometric data and female fertility. Although not significant, there was a positive correlation between the size of talitrids and the latitude of occurrence. Body size can be inferred from head size, and marsupium size from body size, as both are strongly correlated. The absence of relationship between body size and fecundity may be related to environmental factors that can affect the talitrid reproduction. A clear relationship between the mean body size of T. topitotum and the latitude of their occurrence can be obtained with more studies conducted with a large sampling.

As biparental care is crucial for breeding success in Procellariiformes seabirds (i.e., albatrosses and petrels), these species are expected to be choosy during pair formation. However, the choice of partners is limited in small-sized populations, which might lead to random pairing. In Procellariiformes, the consequences of such limitations for mating strategies have been examined in a single species. Here, we studied mate choice in another Procellariiforme, Bulwer’s petrel Bulweria bulwerii, in the Azores (ca 70 breeding pairs), where the species has suffered a dramatic population decline. We based our approach on both a 11-year demographic survey (capture-mark-recapture) and a genetic approach (microsatellites, n = 127 individuals). The genetic data suggest that this small population is not inbred and did not experience a genetic bottleneck. Moreover, pairing occurred randomly with respect to genetic relatedness, we detected no extrapair parentage (n = 35 offspring), and pair fecundity was unrelated to relatedness between partners. From our demographic survey, we detected no assortative mating with respect to body measurements and breeding experience and observed very few divorces, most of which were probably forced. This contrasts with the pattern previously observed in the much larger population from the Selvagens archipelago (assortative mating with respect to bill size and high divorce rate). We suggest that the Bulwer’s petrels from the Azores pair with any available partner and retain it as long as possible despite the fact that reproductive performance did not improve with pair common experience, possibly to avoid skipping breeding years in case of divorce. We recommend determining whether decreased choosiness during mate choice also occurs in reduced populations of other Procellariiform species. This might have implications for the conservation of small threatened seabird populations.

Methods Field work was conducted on Vila islet, Santa Maria island, Azores archipelago, from 2002 to 2012 included. Adults were captured in their nesting burrows each year during incubation, and ringed for identification. Chicks were ringed before fledging. These capture-mark-recapture sessions enabled us to know the life-history of each ringed individual, year after year, that is, the nest it was occupying (nesting cavities were marked with individual numbers), whether or not it was breeding, the outcomes of its breeding attempts, the identity of its social partner(s) and its offspring. Adults were measured (wing length using a stopped ruler to the nearest mm; tarsus length, culmen length and bill depth at the gonys using a vernier calliper to the nearest 0.1 mm).

Blood samples (50-100 µl) were collected from adults upon their first capture in 2002, 2003 and 2004. . Chicks were sampled a few days after hatching. We extracted bird DNA using the QIAmp Tissue Kit (QIAGEN). Eleven microsatellite loci (autosomal loci Bb2, Bb3, Bb7, Bb10, Bb12, Bb20, Bb21, Bb22, Bb23, Bb25, plus the sex-linked Bb11, Molecular Ecology Resources Primer Development Consortium 2010) were amplified by Polymerase Chain Reaction (PCR). Genotypes (number of base pairs at each allele for each locus) were analysed using GeneMapper 4.0 (Applied Biosystems). 118 adults (57 males, 61 females), including those that were genotyped, plus the offspring from 2002 to 2004 included, were sexed using molecular methods (Fridolfsson and Ellegren 1999, cited in our MS). The sex of 48 other adults (18 males, 30 females), including some chicks that later recruited into the breeding population, was inferred from that of their partner for which molecular sexing had been conducted.

To check if the demographic bottleneck experienced by Bulwer’s petrels in the Azores was associated with a genetic bottleneck, we used the BOTTLENECK software, which relies on the method of Cornuet and Luikart (1996, cited in our MS). Relatedness between social partners was estimated using MER (Wang 2002; version 3 downloadable from http://www.zoo.cam.ac.uk/ioz), after excluding the sex-linked locus Bb11.

We tested if there was an assortative mating based on body measurements or structural body size (PC1 scores of a Principal Component Analysis conducted on wing length, tarsus length and culmen length). To do this, we used two methods. First, we considered the pairs that were observed each year and we analysed our study years separately, after conducting Generalized Linear Models (GLMs) or Spearman rank correlations, according to whether or not the conditions for GLMs were met (that is, whether or not model residuals were normally distributed, Kéry and Hatfield 2003, cited in our MS). Second, we considered all the sexed pairs that were observed in our study together. In this situation, however, a given individual could be involved in several pair bonds (after e.g., the death of its former partner and/or a divorce). To overcome this problem, we used the MIXED procedure of SAS (with the Kenward-Roger degrees of freedom method, SAS Institute 2020), an equivalent of Generalized Linear Mixed Models which allows accounting for the correlations between observations concerning the same individual, can use data from individuals for which there are missing observations, allows within-individual effects to consist of continuous variables and to vary for the same individual, and analyses the data in their original form. To do this, we considered female (male) identity as a random effect.

To test whether pairing occurred at random with respect to genetic relatedness, we compared the relatedness of pair mates with that of male-female pairs drawn at random using a resampling procedure implemented in RESAMPLING PROCEDURES Version 1.3 (Howell 2001, cited in our MS), to account for non-independence of individual pairs. The procedure was repeated 5000 times.

To conduct parentage analyses, we compared chick genotypes with those of their social parents, and we excluded paternity (maternity) when the genotype of a chick mismatched that of its social father (mother) at two loci at least. A single mismatch between offspring and parental genotypes was interpreted as a mutation.

Only birds known to have made at least one breeding attempt in the past were used when calculating mate fidelity rates and determining the causes of divorce. Mate fidelity was defined as 1 minus the probability of divorce, the latter parameter being the total number of divorces divided by the total number of pair × years when both previous partners survive from one year to the next during the study period (Black 1996, cited in our MS).

To determine whether (1) reproductive performance (i.e., the probability of fledging chick) increased with pair common experience and (2) whether the probability of divorce depended on pair common experience and previous reproductive performance, we performed logistic regerssions for repeated measures (GENMOD procedure of SAS, binomial distribution, logit link, with the pair as the 'repeated' subject). Results from these logistic regressions were obtained from the models using generalized estimating equations (GEE).

More details are given in the main text of our MS.

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