Comprehensive demographic dataset for Valencia Park, San Diego, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Demographics, population, housing, income, education, schools, and geography for ZIP Code 91355 (Valencia, CA). Interactive charts load automatically as you scroll for improved performance.

Blocks are typically bounded by streets, roads or creeks. In cities, a census block may correspond to a city block, but in rural areas where there are fewer roads, blocks may be limited by other features. The Census Bureau established blocks covering the entire nation for the first time in 1990.There are less number of Census Blocks within Los Angeles County in 2020 Census TIGER/Line Shapefiles, compared in 2010.Updated:1. June 2023: This update includes 2022 November Santa Clarita City annexation and the addition of "Kinneloa Mesa" community (was a part of unincorporated East Pasadena). Added new data fields FIP_CURRENT to CITYCOMM_CURRENT to reflect new/updated city and communities. Updated city/community names and FIP codes of census blocks that are in 2022 November Santa Clarita City annexation and new Kinneloa Mesa community (look for FIP_Current, City_Current, Comm_Current field values)2. February 2023: Updated few Census Block CSA values based on Demographic Consultant inquiry/suggestions3. April 2022: Updated Census Block data attribute values based on Supervisorial District 2021, Service Planning Area 2022, Health District 2022 and ZIP Code Tabulation Area 2020Created: March 2021How This Data is Created? This census geographic file was downloaded from Census Bureau website: https://www2.census.gov/geo/tiger/TIGER2020PL/STATE/06_CALIFORNIA/06037/ on February 2021 and customized for LA County. New data fields are added in the census blocks 2020 data and populated with city/community names, LA County FIPS, 2021 Supervisorial Districts, 2020 Census Zip Code Tabulation Area (ZCTA) and some administrative boundary information such as 2022 Health Districts and 2022 Service Planning Areas (SPS) are also added. "Housing20" field value and "Pop20" field value is populated with PL 94-171 Redistricting Data Summary File: Decennial Census P.L. 94-171 Redistricting Data Summary Files. Similarly, "Feat_Type" field is added and populated with water, ocean and land values. Five new data fields (FIP_CURRENT to CITYCOMM_CURRENT) are added in June 2023 updates to accommodate 2022 Santa Clarita city annexation. City/community names and FIP codes of census blocks affected by 2022 November Santa Clarita City annexation are assigned based on the location of block centroids. In June 2023 update, total of 36 blocks assigned to the City of Santa Clarita that were in Unincorporated Valencia and Castaic. Note: This data includes 3 NM ocean (FEAT_TYPE field). However, user can use a definition query to remove those. Data Fields: 1. STATE (STATEFP20): State FIP, "06" for California, 2. COUNTY (COUNTYFP20): County FIP "037" for Los Angeles County, 3. CT20: (TRACTCE20): 6-digit census tract number, 4. BG20: 7-digit block group number, 5. CB20 (BLOCKCE20): 4-digit census block number, 6. CTCB20: Combination of CT20 and CB20, 7. FEAT_TYPE: Land use types such as water bodies, ocean (3 NM ocean) or land, 8. FIP20: Los Angeles County FIP code, 9. BGFIP20: Combination of BG20 and FIP20, 10. CITY: Incorporated city name, 11. COMM: Unincorporated area community name and LA City neighborhood, also known as "CSA", 12. CITYCOMM: City/Community name label, 13. ZCTA20: Parcel specific zip codes, 14. HD12: 2012 Health District number, 15. HD_NAME: Health District name, 16. SPA22: 2022 Service Planning Area number, 17. SPA_NAME: Service Planning Area name, 18. SUP21: 2021 Supervisorial District number, 19. SUP_LABEL: Supervisorial District label, 20. POP20: 2020 Population (PL 94-171 Redistricting Data Summary File - Total Population), 21. HOUSING20: 2020 housing (PL 94-171 Redistricting Data Summary File - Total Housing),22. FIP_CURRENT: Los Angeles County 2023 FIP code, as of June 2023,23. BG20FIP_CURRENT: Combination of BG20 and 2023 FIP, as of June 2023,24. CITY_CURRENT: 2023 Incorporated city name, as of June 2023,25. COMM_CURRENT: 2023 Unincorporated area community name and LA City neighborhood, also known as "CSA", as of June 2023,26. CITYCOMM_CURRENT: 2023 City/Community name label, as of June 2023.

Aim Biogeographic boundaries and genetic structuring have important effects on the inferences and interpretation of effective population size (Ne) temporal variations, a key genetics parameter. We reconstructed the historical demography and divergence history of a vulnerable coastal high-trophic shark using population genomics and assessed our ability to detect recent bottlenecks events. Location Western and Central Indo-Pacific (IPA), Western Tropical Atlantic (WTA), Eastern Tropical Pacific (EPA) Taxon Carcharhinus leucas (Müller & Henle, 1839) Methods A DArTcapTM approach was used to sequence 475 samples and assess global genetic structuring. Three demographic models were tested on each population, using an ABC-RF framework coupled with coalescent simulations, to investigate within-cluster structure. Divergence times between clusters were computed, testing multiple scenarios, with fastsimcoal. Ne temporal variations were reconstructed with STAIRWAYPLOT. Coalescent simulations wer..., Sample collection and DNA extraction A subsample of the dataset of Devloo-Delva et al. (2023) was used for this study, representing 475 C. leucas sampled between 1985 and 2019 from 18 locations covering its distribution (except for West Africa; Supplementary Material 1). DNA was extracted with the Qiagen Blood and Tissue kit, following standard protocol (Qiagen Inc., Valencia, California, USA). After bait design and bioinformatic filtering (see following sections), the dataset comprised 16 sampling locations with at least five individuals (309 individuals; Fig. 1, Table 1) covering the WTA, IPA, and EPA. Sampling locations with mostly adults were preferentially selected to limit relatedness effects. SNP selection for bait design The approach used for bait design is described in Devloo-Delva et al. (2023). Briefly, a subset of 219 sample libraries were genotyped using the DArTseqTM approach (Cruz et al., 2013; Feutry et al., 2017, 2020, Supplementary material 1). From this dataset, 3,400..., , # Original FASTQ files of "Global genetic diversity and historical demography of the Bull Shark"

Publication:

dataset DOI:10.5061/dryad.9zw3r22mn

The original 512 FASTQ files (475 individuals) and their metadata, including the final list of 309 individuals used to generate the results of this study, using DArTcap sequencing and based on the Dataset used in Devloo-Delva et al. 2023 ( )

## Description of the data and file structure

There are 512 individual Bull Shark FastQ files, unfiltered and including technical replicates. These files have a unique ID, which allows identifying the samples' metadata in the provided Tab delimited text. The final dataset used in the study (309 individuals) is also indicated in the metadata file, allowing to reproduce the results using STACKS (Genotyping-by-Sequencing analysis software).

There are no abbreviations in the metadata file.

## Sharing/Access information

This dataset is a subset of the dataset produced in :

Devloo-Delva...

The existence of distinct traits in island versus mainland populations offers opportunities to gain insights into how eco-evolutionary processes operate under natural conditions. We used two island colonization events in the white-winged fairywren (Malurus leucopterus) to investigate the genomic and demographic origin of melanic plumage. This avian species is distributed across most of Australia, and males of the mainland subspecies (M. l. leuconotus) exhibit a blue nuptial plumage in contrast to males of two island subspecies – M. l. leucopterus on Dirk Hartog Island and M. l. edouardion Barrow Island – that exhibit a black nuptial plumage. We used reduced-representation sequencing to explore differentiation and demographic history in this species and found clear patterns of divergence between mainland and island populations, with additional substructuring on the mainland. Divergence between the mainland and Dirk Hartog was approximately 10 times more recent th...

In temperate regions, one of the most critical determinants of present range-wide genetic diversity was the Pleistocene climate oscillations, the most recent one created by the last glacial maximum (LGM). This study aimed to describe Nereocystis luetkeana genetic structure across its entire range (Alaska to California) and test different models of population connectivity within the Salish Sea. This region was colonized after the LGM and has been under increased disturbance in recent decades. We utilized microsatellite markers to study N. luetkeana genetic diversity at 53 sites across its range. Using higher sampling density in the Salish Sea, we employed a seascape genetics approach and tested isolation by hydrodynamic transport and environment models. At the species distribution scale, we found four main groups of genetic co-ancestry, Alaska; Washington with Vancouver Island's outer coast and Juan de Fuca Strait; Washington's inner Salish Sea; and Oregon with California. The highest allelic richness (AR) levels were found in California, near the trailing range edge, although AR was also high in Alaska. The inner Salish Sea region had the poorest diversity across the species distribution. Nevertheless, a pattern of isolation by hydrodynamic transport and environment was supported in this region. The levels of allelic richness and genetic differentiation suggest that during the LGM, bull kelp had both northern and southern glacial refugia in Haida Gwaii and Central California, respectively. Genetic diversity in Northern California sites seems resilient to recent disturbances, whereas the low levels of genetic diversity in the inner Salish Sea are concerning. Methods Sampling natural populations We sampled fifty-three sites across the geographic range of N. luetkeana, ranging from Herring Island, Alaska (59.65°N, 151.59°W) to Cambria Bay, California (35.53°N, 121.09°W), from May 2016 to August 2017. We collected a higher sampling site density inside the Salish Sea, the inner water body composed of the Strait of Georgia in British Columbia, Canada, and Puget Sound in Washington, USA (Figure 1, Table 1). Within each site, the average number of specimens collected was 40, ranging from 7 to 51. We sampled specimens haphazardly, separated by at least 2 meters, by cutting 2-4 cm pieces of blade tissue in a non-destructive manner. We wiped the sampled blade tissue to remove epiphytes before storing the tissue in silica gel desiccant for preservation until DNA extraction. Next, we used a Tissue Lyser II (Qiagen, Valencia, CA) to homogenize the silica-dried tissue to a fine powder before extracting DNA using the DNeasy Nucleospin 96 Plant Kit II (Machery-Nagel, Duren, Germany) following the kit protocol. Microsatellite loci genotyping We characterized microsatellite regions for N. luetkeana and used seven of the resulting microsatellite loci (Ner-2, Ner-4, Ner-6, Ner-9, Ner-11, Ner-13, and Ner-14, see article online supplementary material). We prepared PCRs in a total reaction volume of 15 µL comprised of 10 µM primer, 10 mM dNTP's per base (Promega, Madison, WI), 25 mM MgCl2, 3.0 µl 5X PCR buffer, and 0.5 U GoTaq Polymerase. Thermocycler conditions consisted of a 5-minute denaturation step at 95ºC, followed by 33 cycles of 20 seconds each at 95ºC, 20 seconds at an annealing temperature of 57ºC–61ºC, 30 seconds at 72ºC followed by a final elongation step of 20 minutes at 72ºC using an Eppendorf thermocycler (Eppendorf, USA). We sized microsatellite PCR fragments using fragment analysis on a 96-capillary DNA sequencer ABI 3730xl at the Madison Biotechnologies Center. We scored the resulting microsatellite fragments with STRand (https://www.vgl.ucdavis.edu/informatics/strand.php) and binned them into integer allele codes with the R (R Core Team, 2016) package “MsatAllele” (Alberto, 2009). The presence of null alleles was evaluated with MICRO-CHECKER v.2.2.3 (Van Oosterhout et al., 2004).