Relative concentration of the Northern California region's Asian American population. The variable ASIANALN records all individuals who select Asian as their SOLE racial identity in response to the Census questionnaire, regardless of their response to the Hispanic ethnicity question. Both Hispanic and non-Hispanic in the Census questionnaire are potentially associated with the Asian race alone.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as ASIANALN alone to the proportion of all people that live within the 1,207 block groups in the Northern California RRK region that identify as ASIANALN alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of ASIANALN individuals compared to the Northern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then ASIANALN individuals are highly concentrated locally.

20 year Projected Urban Growth scenarios. Base year is 2000. Projected year in this dataset is 2020.

By 2020, most forecasters agree, California will be home to between 43 and 46 million residents-up from 35 million today. Beyond 2020 the size of California's population is less certain. Depending on the composition of the population, and future fertility and migration rates, California's 2050 population could be as little as 50 million or as much as 70 million. One hundred years from now, if present trends continue, California could conceivably have as many as 90 million residents.

Where these future residents will live and work is unclear. For most of the 20th Century, two-thirds of Californians have lived south of the Tehachapi Mountains and west of the San Jacinto Mountains-in that part of the state commonly referred to as Southern California. Yet most of coastal Southern California is already highly urbanized, and there is relatively little vacant land available for new development. More recently, slow-growth policies in Northern California and declining developable land supplies in Southern California are squeezing ever more of the state's population growth into the San Joaquin Valley.

How future Californians will occupy the landscape is also unclear. Over the last fifty years, the state's population has grown increasingly urban. Today, nearly 95 percent of Californians live in metropolitan areas, mostly at densities less than ten persons per acre. Recent growth patterns have strongly favored locations near freeways, most of which where built in the 1950s and 1960s. With few new freeways on the planning horizon, how will California's future growth organize itself in space? By national standards, California's large urban areas are already reasonably dense, and economic theory suggests that densities should increase further as California's urban regions continue to grow. In practice, densities have been rising in some urban counties, but falling in others.

These are important issues as California plans its long-term future. Will California have enough land of the appropriate types and in the right locations to accommodate its projected population growth? Will future population growth consume ever-greater amounts of irreplaceable resource lands and habitat? Will jobs continue decentralizing, pushing out the boundaries of metropolitan areas? Will development densities be sufficient to support mass transit, or will future Californians be stuck in perpetual gridlock? Will urban and resort and recreational growth in the Sierra Nevada and Trinity Mountain regions lead to the over-fragmentation of precious natural habitat? How much water will be needed by California's future industries, farms, and residents, and where will that water be stored? Where should future highway, transit, and high-speed rail facilities and rights-of-way be located? Most of all, how much will all this growth cost, both economically, and in terms of changes in California's quality of life?

Clearly, the more precise our current understanding of how and where California is likely to grow, the sooner and more inexpensively appropriate lands can be acquired for purposes of conservation, recreation, and future facility siting. Similarly, the more clearly future urbanization patterns can be anticipated, the greater our collective ability to undertake sound city, metropolitan, rural, and bioregional planning.

Consider two scenarios for the year 2100. In the first, California's population would grow to 80 million persons and would occupy the landscape at an average density of eight persons per acre, the current statewide urban average. Under this scenario, and assuming that 10% percent of California's future population growth would occur through infill-that is, on existing urban land-California's expanding urban population would consume an additional 5.06 million acres of currently undeveloped land. As an alternative, assume the share of infill development were increased to 30%, and that new population were accommodated at a density of about 12 persons per acre-which is the current average density of the City of Los Angeles. Under this second scenario, California's urban population would consume an additional 2.6 million acres of currently undeveloped land. While both scenarios accommodate the same amount of population growth and generate large increments of additional urban development-indeed, some might say even the second scenario allows far too much growth and development-the second scenario is far kinder to California's unique natural landscape.

This report presents the results of a series of baseline population and urban growth projections for California's 38 urban counties through the year 2100. Presented in map and table form, these projections are based on extrapolations of current population trends and recent urban development trends. The next section, titled Approach, outlines the methodology and data used to develop the various projections. The following section, Baseline Scenario, reviews the projections themselves. A final section, entitled Baseline Impacts, quantitatively assesses the impacts of the baseline projections on wetland, hillside, farmland and habitat loss.

These data include egg mass counts and adult capture-mark-recapture histories for Foothill Yellow-legged frogs at two streams in northern California. Data were collected from the South Fork Eel River and its tributary, Fox Creek, from 1993-2019. Data from Hurdygurdy Creek were collected from 2002-2008.

Relative concentration of the Northern California region's Hispanic/Latino population. The variable HISPANIC records all individuals who select Hispanic or Latino in response to the Census questionnaire, regardless of their response to the racial identity question.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as Hispanic or LatinoAmerican Indian / Alaska Native alone to the proportion of all people that live within the 1,207 block groups in the Northern California RRK region that identify as Hispanic or LatinoAmerican Indian / Alaska native alone. Example: if 5.2% of people in a block group identify as HISPANIC, the block group has twice the proportion of HISPANIC individuals compared to the Northern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then HISPANIC individuals are highly concentrated locally.

Relative concentration of the Northern California region's Hispanic and/or Black, Indigenous or person of color (HSPBIPOC) population. The variable HSPBIPOC is equivalent to all individuals who select a combination of racial and ethnic identity in response to the Census questionnaire EXCEPT those who select "not Hispanic" for the ethnic identity question, and "white race alone" for the racial identity question. This is the most encompassing possible definition of racial and ethnic identities that may be associated with historic underservice by agencies, or be more likely to express environmental justice concerns (as compared to predominantly non-Hispanic white communities). Until 2021, federal agency guidance for considering environmental justice impacts of proposed actions focused on how the actions affected "racial or ethnic minorities." "Racial minority" is an increasingly meaningless concept in the USA, and particularly so in California, where only about 3/8 of the state's population identifies as non-Hispanic and white race alone - a clear majority of Californians identify as Hispanic and/or not white. Because many federal and state map screening tools continue to rely on "minority population" as an indicator for flagging potentially vulnerable / disadvantaged/ underserved populations, our analysis includes the variable HSPBIPOC which is effectively "all minority" population according to the now outdated federal environmental justice direction. A more meaningful analysis for the potential impact of forest management actions on specific populations considers racial or ethnic populations individually: e.g., all people identifying as Hispanic regardless of race; all people identifying as American Indian, regardless of Hispanic ethnicity; etc.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as HSPBIPOC alone to the proportion of all people that live within the 1,207 block groups in the Northern California RRK region that identify as HSPBIPOC alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of HSPBIPOC individuals compared to the Northern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then HSPBIPOC individuals are highly concentrated locally.

The Northern California Community Study investigated the effects of urbanism on social networks and social attitudes. To do so, the study explored the relationship between characteristics and perceptions of neighborhoods, and the acquaintance patterns, social activities, and psychological attitudes of residents of particular neighborhoods in the San Francisco area in 1977. The study focused on the nonminority population. Part 1 (Respondent File) includes information obtained in personal interviews with 1,050 persons living in 50 communities in northern California. Included in this file are two general categories of variables--those describing the respondents' experiences in their neighborhoods and locales, and those recording the respondents' psychological states and feelings of well-being. Part 2 (Name File) contains information about 19,417 persons identified by the survey respondents in Part 1 as being part of their (respondents') social networks. Variables include whether the named individuals lived in the respondents' neighborhoods, and the types of relationships, interactions, and things in common that the respondents had with the individuals they named. Part 3 (Community File) contains a data record prepared for each tract of the sampling frame. The data in the Community file are summary counts for each tract's total population, total household population, total housing units, and selected demographic information, such as the percentage of Black population, percent residing in group quarters, and mean family income. The file also contains opinions gathered from the survey respondents about each community, e.g., ratings of local services, fear of crime, and the effect of the water shortage.

Relative concentration of the Northern California region's Hispanic/Latino population. The variable HISPANIC records all individuals who select Hispanic or Latino in response to the Census questionnaire, regardless of their response to the racial identity question.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as Hispanic or LatinoAmerican Indian / Alaska Native alone to the proportion of all people that live within the 1,207 block groups in the Northern California RRK region that identify as Hispanic or LatinoAmerican Indian / Alaska native alone. Example: if 5.2% of people in a block group identify as HISPANIC, the block group has twice the proportion of HISPANIC individuals compared to the Northern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then HISPANIC individuals are highly concentrated locally.

CDFW BIOS GIS Dataset, Contact: Steve Stone, Description: This dataset depicts the general boundaries of the Northern California Steelhead distinct population segment (DPS) under the U.S. Endangered Species Act, as well as the historical population structure of the species.

CDFW BIOS GIS Dataset, Contact: Steve Stone, Description: This dataset depicts the general boundaries of the Southern OR and Northern CA Coastal Chinook Salmon evolutionarily significant unit (ESU) (i.e., a distinct population segment (DPS) under the U.S. Endangered Species Act) as well as the historical population structure of the species.

Relative concentration of the Northern California region's American Indian population. The variable AIANALN records all individuals who select American Indian or Alaska Native as their SOLE racial identity in response to the Census questionnaire, regardless of their response to the Hispanic ethnicity question. Both Hispanic and non-Hispanic in the Census questionnaire are potentially associated with American Indian / Alaska Native race alone. IMPORTANT: this self reported ancestry and Tribal membership are distinct identities and one does not automatically imply the other. These data should not be interpreted as a distribution of "Tribal people." Numerous Rancherias in the Northern California region account for the wide distribution of very to extremely high concentrations of American Indians outside the San Francisco Bay Area.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as American Indian / Alaska Native alone to the proportion of all people that live within the 1,207 block groups in the Northern California RRK region that identify as American Indian / Alaska native alone. Example: if 5.2% of people in a block group identify as AIANALN, the block group has twice the proportion of AIANALN individuals compared to the Northern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then AIANALN individuals are highly concentrated locally.

Relative concentration of the estimated number of people in the Northern California region that live in a household defined as "low income." There are multiple ways to define low income. These data apply the most common standard: low income population consists of all members of households that collectively have income less than twice the federal poverty threshold that applies to their household type. Household type refers to the household's resident composition: the number of independent adults plus dependents that can be of any age, from children to elderly. For example, a household with four people ' one working adult parent and three dependent children ' has a different poverty threshold than a household comprised of four unrelated independent adults.

Due to high estimate uncertainty for many block group estimates of the number of people living in low income households, some records cannot be reliably assigned a class and class code comparable to those assigned to race/ethnicity data from the decennial Census.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit to the proportion of all people that live within the 1,207 block groups in the Northern California RRK region. See the "Data Units" description below for how these relative concentrations are broken into categories in this "low income" metric.

Characteristics of healthy individuals referred through public health clinics in Santa Clara County, CA, USA who completed tuberculin skin test [TST], QuantiFERON-TB GOLD® interferon-γ release assay, and H. pylori serology.1152 (91%) born in Latin America; TST: tuberculin skin test, +, ≥ 10mm induration (includes 18 prior positives not retested); QFT (QuantiFERON-TB-GOLD® including M. tuberculosis antigens ESAT6 and CFP10 and M. tuberculosis antigen TB7.7; +: ≥0.35 IU/ml IFN-γ difference over unstimulated well.2Latent TB infection [LTBI]: either TST+ or QFT+ for analysis; 7 (2%) individuals reported prophylactic TB treatment 3–34 years prior to enrollment. All factors significant at p

The Kelp Forest Monitoring data record span surveys across 24 years from 1999 through 2023 at 20 locations on the Sonoma-Mendocino Coast, Northern California. Years without data, inclusive: 2002, 2020, 2021. These surveys are ongoing and are conducted by the California Department of Fish and Wildlife dive team with participation from dive program partners at UC Davis, UC Santa Cruz, Cal Poly Humboldt, Sonoma State, and other dive programs and volunteers. Not all sites were surveyed in all years. Surveys prior to 2000 were not conducted by the same teams or with the same methods except that all surveys were done using Scuba along 30 x 2 meter (m) transects randomly placed in the subtidal zone in rocky habitats dominated by bull kelp, Nereocystis luetkeana, forests. These randomly placed band transects surveys were stratified by depth (A=0-15, B=16-30, C=31-45, D=46-60 ft) as we know sea urchin and abalone populations differ by depth.

Data collected include the number of live, dying (in some years during the mass mortality events), and dead sea urchins (red-Mesocentrotus franciscanus and purple-Strongylocentrotus purpuratus), red abalone (Haliotis rufescens), pinto abalone (H. kamtschatkana), flat abalone (H. walallensis), as well as empty abalone shells (again in some years). Additional data collected (if Scuba bottom time and/or air allowed): red abalone size, numbers or presence of associated species such as sea stars and predators, algal group quantification, and presence of bull kelp, substrate type. Data on algae and associated species differed depending on the year and the focus of the studies in response to ecosystem conditions but all years quantified sea urchins and abalones.

These data provide a baseline of biological conditions in the kelp forest before, during and after the major marine heatwave of 2014-2016 in northern California. These data were used to manage the recreational red abalone fishery by the California Department of Fish and Wildlife from 2002 to the closure of the fishery in 2018. These data are from the two counties Sonoma and Mendocino County that had 95% of the bull kelp forests in northern California.

Populations of the chaparral shrub were sampled in southern California and further north in Monterey and Santa Clara counties and it was discovered that postfire regeneration modes were different. The southern California populations had substantial resprouting with some seedling recruitment. The Monterey populations had no resprouting ability and recovery was entirely by seedlings. However, there is an age effect in that when young these northern California populations fail to recruit seedlings due to lack of a seed bank buildup in the short interval since the last fire. These populations likely will be extirpated. I hypothesize that this obligate seeding mode has been selected for because seed reproduction is more reliable when intervals between fires are very long, longer that resprouting shrubs would survive. Support for this is provided by demonstrating substantially higher lightning fire frequencies in southern California than in the Monterey and Santa Clara area.

Considerable research describes the interactions between seagrasses and their sedimentary environment, but there is little information on how populations differ in their innate versus plastic responses to these differences. Here, we test whether sediment contributes to eelgrass population differentiation and the nature of plastic responses to different sediment environments. We do this via a 15-week, fully crossed common garden experiment with two populations and their native sediment types. Plants from the warmer-temperature, clay-dominated site (90% silt + clay, 10% sand) consistently maintained greater biomass than plants from the cooler, sand-dominated site (60% sand, 40% silt + clay). Plants from both populations were highly plastic for root length and clonal shoot size, with both increasing when planted in clay-dominated compared to sand-dominated sediment. Plants from the clay-dominated site grew longer rhizomes in foreign sediment while plants from the sand-dominated site had no change in this plant trait, indicating some measure of home site advantage with respect to sediment conditions. Porewater sulfide also exhibited this pattern where concentrations were very low in clay-dominated sediment for all plants, but in the sand-dominated treatment, only plants native to sand-dominated sediment maintained porewater sulfide concentrations below toxic levels. These patterns may be mediated by microbiome differences between populations as roots from plants native to clay-dominated sediment had more fixed microbiomes between treatments compared to plants native to sand-dominated sediment. These results support that sediment type partially mediates home site advantage in eelgrass populations and suggest differential population responses may be mediated by the associated microbiome. Methods For methods on collecting, generating, and processing the data, please see [DOI: 10.1007/s12237-025-01549-6]

Abbreviations: Charlson Comorbidity Index (CCI), gastroesophageal reflux disease (GERD), proton pump inhibitors (PPIs), H2-receptor antagonists (H2-RAs), hormone replacement therapy (HRT), non-steroidal anti-inflammatory drugs (NSAIDs), squamous cell carcinoma (SCC)Descriptive characteristics of the esophageal and gastric cancer cases and controls from Kaiser Permanente, Northern California, 1997–2011.

Cities are sometimes characterized as homogenous with species assemblages composed of abundant, generalist species having similar ecological functions. Under this assumption, rare species, or species observed infrequently, would have especially high conservation value in cities for their potential to increase functional diversity. Management to increase the number of rare species in cities could be an important conservation strategy in a rapidly urbanizing world. However, most studies of species rarity define rarity in relatively pristine environments where human management and disturbance is minimized. We know little about what species are rare, how many species are rare, and what management practices promote rare species in urban environments. Here, we identified which plants and species of birds and bees that control pests and pollinate crops are rare in urban gardens and assessed how social, biophysical factors, and cross-taxonomic comparisons influence rare species richness. We found overwhelming numbers of rare species, with over 50% of plant cultivars observed classified as rare. Our results highlight the importance of women, older individuals, and gardeners who live closer to garden sites in increasing the number of rare plants within urban areas. Fewer rare plants were found in older gardens and gardens with more bare soil. There were more rare bird species in larger gardens and more rare bee species where canopy cover was higher. We also found that in some cases, rarity begets rarity, with positive correlations found between the number of rare plants and bee species and between bee and bird species. Overall, our results suggest that urban gardens include a high number of species existing at low frequency and that social and biophysical factors promoting rare, planned biodiversity can cascade down to promote rare, associated biodiversity. Methods Study Region We worked in 18 urban community gardens in three counties (Santa Clara, Santa Cruz, and Monterey) in the central coast region of California, USA. The gardens differ in local habitat (structural and compositional diversity of both crop and non-crop species) and landscape context (amount of natural, agricultural, and urban land cover in the surrounding area). All gardens have been cultivated for five to 47 years and range from 444 to 15,525 m2 in size. All of the gardens use organic management practices and prohibit the use of chemical pesticides and insecticides. Gardens were chosen because they represent sites across a gradient of urban, natural, and agricultural landscapes and were separated from each other by >2 km, the farthest distance between gardens was 90 km and the closest was 2 km (Cohen et al., 2020; Egerer et al., 2017; Philpott and Bichier, 2017). Gardener demographic data indicates that gardeners are diverse in their make-up, covering a range of family sizes, education, salary, and food insecurity levels (Egerer et al., 2017; Philpott et al., 2020). Data Collection We provide the following framework (Fig. 1) to help visualize the specific set of questions posed in this study and the data and analyses used to address them. First, we ask which gardener characteristics (Q1), and which local and landscape garden features affect the number of rare plant cultivars (Q2a) and rare bird and bee species (Q2b) in urban community gardens. We include cultivars as distinct types per (Reiss and Drinkwater 2018). Subsequently, we ask if there is an association between the number of rare plant cultivars and the number of rare bird and bee species (Q3), and if the number of rare bird and bee species are also related to one another (Q4).
The data analyzed for this research was collected in two summer field seasons (2015, 2017), from May to September, which is the peak urban garden growing season for the region. Gardener characteristics data (defined below) and gardener self-reported plant data were collected in summer 2017 to address Q1 (Fig. 1). Direct sampling of biodiversity (plants, bees, birds) and garden characteristics was done in summer 2015 to address Q2-4 (Fig. 1). Though structural equation modeling (SEMs) was considered, there is no direct way to compare data from 2017 and 2015 because of the methodological differences outlined below. Thus, separate statistical analyses are conducted for 2017 and 2015 data. We can test the relationship between gardener characteristics and number of rare plant cultivars because gardeners reported what plants they grew in our surveys. We cannot directly test how gardener characteristics influenced the number of rare bird and bee species because gardeners were not asked about these species. Instead, we infer effects of gardener characteristics on bees and birds indirectly via the overall research framework in Figure 1. We explain the specific methods for each type of data collection and the analysis below. Gardener characteristics data We surveyed gardeners from 18 urban community gardens during the 2017 summer field season. Survey questionnaires collected information on gardener demographic information as well as gardening experience and use data (Table 1). Specifically, we surveyed 185 gardeners in total, or six to 14 gardeners per garden (9.5-65% of the gardener population in a site). We only included surveys in our analysis if plant information on the survey was completed (n=162). We administered surveys in English (n=123), Spanish (n=38), and Bosnian (n=1) and either read the survey out loud in person (n=138) or via phone (n=1), and either had the gardener fill out the survey themselves (n=21) or had a gardener read the survey to another gardener (n=1). Two of the surveys did not have information on the method of survey administration. We also note that despite best efforts to surveys gardens equally, uneven gardener availability resulted in unequal gardener sampling across the 18 community gardens, requiring us to calculate the number of rare plant cultivars in gardener-reported data (2017) by gardener surveys rather than by garden as was done in direct field-based data (2015) described below. Gardener-reported plant data Gardeners were asked to identify and list the plant species and cultivars that they planted in their plots. We then classified gardener-reported plants into either crop or ornamental species. Crop species included fruits, vegetables, herbs, and other consumable plants. Ornamental species included plants grown for decorative purposes, such as flowers and non-food providing crops. Though we included plant cultivars as distinct types, gardeners varied in the level of cultivar specificity provided, which we acknowledge is a limitation to our study. We looked up scientific names for common names provided and supplemented these results with direct field-based plant data where researchers identified species and cultivars in the field using methods described in detail below. Garden characteristics data Landscape-level garden data For each garden, we measured the surrounding landscape composition within buffers surrounding gardens at the 0.5, 1, and 3 km scale. We used the 2011 National Land Cover Database (NLCD) (Jin et al. 2015) to calculate the percentage of urban NLCD land cover class using ArcGIS (v. 10.1) (ESRI 2011). Urban land cover was calculated by combining developed low, medium, and high intensity developed land. Urban land cover is correlated with many other land use categories (e.g., natural land), thus we chose to focus on only urban land cover in our models because we were most interested in the effects of urbanization on biodiversity; further, urban land cover has been a significant predictor of biodiversity in previous analyses of these gardens (Quistberg et al. 2016, Egerer et al. 2017). Urban cover at the 1 km scale best predicted pooled species rarity across taxa, exhibiting the lowest AIC of all the scale models (Appendix S1: Table S1), thus the 1 km spatial scale was used for all subsequent analyses. Local-level garden data To collect local-scale garden characteristics, we established a 20 x 20 m plot in the center of each garden. In this plot, we measured canopy cover using a spherical densiometer at the center and N, S, E, and W edges of the plot, counted the number and species of trees and shrubs, and counted the number of trees or shrubs in flower within the plot. We determined age and size of each garden by examining historic Google Earth images and noting the first appearance of the gardens, and then we used ground-truthed GPS points taken from each garden to calculate size. For a few of the gardens older than 35 years, we used historical information gained through community resources or discussions with farm management to determine age. We measured ground characteristics using four 1 x 1 m sub-plots within the 20 x 20 m plots. The 1 x 1 m sub-plots were randomly placed anywhere (including pathways) within the 20 x 20 m plots. Within each 1 x 1 m sub-plot, we measured the height of the tallest herbaceous vegetation and estimated ground cover composition (percent bare soil, rocks, leaf litter, grass, mulch). We repeated sampling once per month between May and September 2015 and calculated the mean value for each environmental variable for each garden at each time point. Field-based biodiversity data Field-based plant data We measured plant biodiversity using the same four 1 x 1 m sub-plots within the 20 x 20 m plots. Within each sub-plot, we identified the species and cultivars of all herbaceous plants and measured the percent cover for each species and cultivar. This was measured once per month for five sampling periods, separated by roughly 21 days. As with gardener-reported plant data, researchers classified field-based plant data into either crop or ornamental species and cultivars. Plants that did not fit crop or ornamental categories were designated weeds. Gardeners were not asked to report any weeds, thus not classified in gardener-reported plant

aSize is defined as total length (TL), and size range the difference between the largest and smallest individual from n individuals sampled.bMean expected Hardy-Weinberg heterozygosity.cRarefied allelic richness.dHeterozygosity estimates obtained from McCraney et al. (2010).Habitat size, location, demographic parameters and genetic diversity measures (across nine microsatellite loci) of 17 tidewater goby populations in northern California.

Microsatellite genotypes for northern California tidewater goby populationsMicrosatellite genotypes in GENEPOP format for northern California tidewater goby populations. Definitions for site abbreviations and geographic coordinates of sampling locations can be found in the accompanying publication.Goby Combined.genepop.txtTidewater Goby Occupancy History Northcoast Haul DataTidewater goby occupancy data for northern California tidewater goby populations. Data includes presence (1) absence(0) from 94 sites between 1897 and 2014. Primary sampling periods are years and secondary sampling sessions are repeat seine hauls within years. For additional descriptions of the data please see accompanying publication.Genetic and geographic distance data for isolation by distance analysisGenetic and geographic distance data for analysis of conformance to an isolation by model of gene flow. Values above the diagonal are geographic distances (km) and values below diagonal are genetic distances (Fst). Data are for the 19 spatially isolated locations with large sample sizes. Only those collections with large sample sizes were included in the analysis (>19).Distance data KM above FST below.xlsxInput file used to construct neighbor joining tree using PHYLIP.Input file used to construct neighbor joining tree using PHYLIP. Population IDs, 1-36, defined in companion file uploaded with this data package.infile.txtBootstrap consensus tree from neighbor joining analysis using PHYLIP.Bootstrap consensus tree from neighbor joining analysis using PHYLIP. Population IDs, 1-36, defined in companion file uploaded with this data package.outtree.consense.txtProject/parameter input file for Bayesian cluster analysis using STRUCTURE.Project input file containing parameters for Bayesian cluster analysis using STRUCTURE. Site IDs, 1-47, are defined in companion file with this data package.GobyFull.spjGenotypic data (input file) for Bayesian cluster analysis using STRUCTURE.Project input file containing genotypic data for Bayesian cluster analysis using STRUCTURE. Site IDs, 1-47, are defined in companion file with this data package.project_dataSite location information file, including site IDs, date of collection, and geographic coordinates.Site, collection year, sample ID, and within population microsatellite DNA genetic diversity [sample size (N), observed heterozygosity (Ho), Hardy-Weinberg expected heterozygosity (He), allelic richness (A), rarified allelic richness (Ar), rarified number of private alleles (Ap), inbreeding effective size (Nei) and 95% confidence interval (CI)] for northern California tidewater goby populations (listed from north to south). Also included are site reference numbers for analysis using PHYLIP (1-36) and STRUCTURE (1-47).Site IDs References STRUCTURE.xlsx Extinction and colonization dynamics are critical to understanding the evolution and conservation of metapopulations. However, traditional field studies of extinction–colonization are potentially fraught with detection bias and have rarely been validated. Here, we provide a comparison of molecular and field-based approaches for assessment of the extinction–colonization dynamics of tidewater goby (Eucyclogobius newberryi) in northern California. Our analysis of temporal genetic variation across 14 northern California tidewater goby populations failed to recover genetic change expected with extinction–colonization cycles. Similarly, analysis of site occupancy data from field studies (94 sites) indicated that extinction and colonization are very infrequent for our study populations. Comparison of the approaches indicated field data were subject to imperfect detection, and falsely implied extinction–colonization cycles in several instances. For northern California populations of tidewater goby, we interpret the strong genetic differentiation between populations and high degree of within-site temporal stability as consistent with a model of drift in the absence of migration, at least over the past 20–30 years. Our findings show that tidewater goby exhibit different population structures across their geographic range (extinction–colonization dynamics in the south vs. drift in isolation in the north). For northern populations, natural dispersal is too infrequent to be considered a viable approach for recolonizing extirpated populations, suggesting that species recovery will likely depend on artificial translocation in this region. More broadly, this work illustrates that temporal genetic analysis can be used in combination with field data to strengthen inference of extinction–colonization dynamics or as a stand-alone tool when field data are lacking.