Original provider: Jaime Bolaños-Jiménez

Dataset credits: Jaime Bolaños-Jiménez

Abstract: Since 2001, the non governmental organization Sea Vida has been promoting responsible dolphin watching in the State of Aragua, on the basis of previous research efforts by the Venezuelan Ministry of Environment and Sea Vida. Attempts have been made to gather baseline information on the bio-ecological and population aspects of cetaceans in the region. Opportunistic and systematic surveys have confirmed the presence of the Atlantic spotted (Stenella frontalis) and bottlenose (Tursiops truncatus) dolphins. Encounter rate with dolphin groups is about 70%. Preliminary results indicate that at least a proportion of the population is resident to the area, with recaptures ranging from a few days to at least 12 years.

Photo-identification data to evaluate the population status of dolphins inhabiting the coast off the State of Aragua, Venezuela.

About this dataset

Context

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

Key observations

The largest age group in Central Valley, UT was for the group of age 45 to 49 years years with a population of 89 (12.97%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Central Valley, UT was the 50 to 54 years years with a population of 3 (0.44%). 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 Central Valley is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Central Valley 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 Central Valley Population by Age. You can refer the same here

Transport for NSW provides projections of population and dwellings at the small area (Travel Zone or TZ) level for NSW. The latest version is Travel Zone Projections 2024 (TZP24), released in January 2025.

TZP24 replaces the previously published TZP22.

The projections are developed to support a strategic view of NSW and are aligned with the NSW Government Common Planning Assumptions.

The TZP24 Population & Dwellings Projections dataset covers the following variables:

• Estimated Resident Population

• Structural Private Dwellings (Regional NSW only)

• Population in Occupied Private Dwellings, by 5-year Age categories & by Sex

• Population in Non-Private Dwellings

The projections in this release, TZP24, are presented annually from 2021 to 2031 and 5-yearly from 2031 to 2066, and are in TZ21 geography.

Please note, TZP24 is based on best available data as at early 2024, and the projections incorporate results of the National Census conducted by the ABS in August 2021.

Key Data Inputs used in TZP24:

• 2024 NSW Population Projections – NSW Department of Planning, Housing & Infrastructure

• 2021 Census data - Australian Bureau of Statistics (including dwellings by occupancy, total dwellings by Mesh Block, household sizes, private dwellings by occupancy, population age and gender, persons by place of usual residence)

For a summary of the TZP24 projection method please refer to the TZP24 Factsheet.

For more detail on the projection process please refer to the TZP24 Technical Guide.

Additional land use information for workforce and employment as well as Travel Zone 2021 boundaries for NSW (TZ21) and concordance files are also available for download on the Open Data Hub.

Visualisations of the population projections are available on the Transport for NSW Website under Data and research/Reference Information.

Cautions

The TZP24 dataset represents one view of the future aligned with the NSW Government Common Planning Assumptions and population and employment projections.

The projections are not based on specific assumptions about future new transport infrastructure but do take into account known land-use developments underway or planned, and strategic plans.

• TZP24 is a strategic state-wide dataset and caution should be exercised when considering results at detailed breakdowns.

• The TZP24 outputs represent a point in time set of projections (as at early 2024).

• The projections are not government targets.

• Travel Zone (TZ) level outputs are projections only and should be used as a guide. As with all small area data, aggregating of travel zone projections to higher geographies leads to more robust results.

• As a general rule, TZ-level projections are illustrative of a possible future only.

• More specific advice about data reliability for the specific variables projected is provided in the “Read Me” page of the Excel format summary spreadsheets on the TfNSW Open Data Hub.

• Caution is advised when comparing TZP24 with the previous set of projections (TZP22) due to addition of new data sources for the most recent years, and adjustments to methodology.

Further cautions and notes can be found in the TZP24 Technical Guide

Important note:

The Department of Planning, Housing & Infrastructure (DPHI) published the 2024 NSW Population Projections in November 2024. As per DPHI’s published projections, the following variables are excluded from the published TZP24 Population and Dwellings Projections:

• Structural Private Dwellings for Travel Zones in 43 councils across Greater Sydney, Illawarra-Shoalhaven, Central Coast, Lower Hunter and Greater Newcastle

• Occupied Private Dwellings for Travel Zones in NSW.

Furthermore, in TZP24, the Structural Private Dwellings variable aligns with the 2024 Implied Dwelling projections while the Occupied Private Dwellings variable aligns with the 2024 Households projections at SA2 level prepared by DPHI.

The above variables are available upon request by contacting model.selection@transport.nsw.gov.au - Attention Place Forecasting.

Projection data for New South Wales are available to the year 2041; and for Regional NSW, Sydney, Illawarra, Lower Hunter & Central Coast and all Local Government Areas (LGA) to the year 2031.

Individual file tabs contain summary population projection data for New South Wales, projection regions and all LGAs. Individual file tabs are also available for population projections by five-year age group and sex for New South Wales and the projection regions. Five year age group data are available for LGAs with populations greater than 3,000 in 2011. For smaller LGAs, age group data are provided for four age groups: 0-14, 15-44, 45-64, 65+.

For more information, including reports, frequently asked questions and an information brochure, please see http://www.planning.nsw.gov.au/Research-and-Demography/Demography/Population-Projections

Demographic data for introduced crab from multiple bays along the Central California coast, shallow subtidal (<3 m depth), in 2015. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=We conducted monthly trappings of invasive European green crabs to gather demographic data from several bays in northern California: Bodega Harbor, Tomales Bay, Bolinas Lagoon, San Francisco Bay, and Elkhorn Slough. All sites were accessed by foot via shore entry. At each of four sites within each bay, we placed 5 baited traps (folding Fukui fish traps) and 5 baited minnow traps in shallow intertidal areas. Traps arrays were set with fish and minnow traps alternating and with each 20 m apart. Traps were retrieved 24 hours later and traps were rebaited and collected again the following day.\u00a0Trapping was continued for three consecutive days with traps removed on the final day.\u00a0Each day, data for crab species, size, sex, reproductive condition, and injuries were collected for all crabs in the field. Following data collection, all crabs were returned to the lab, and frozen overnight prior to disposal.\u00a0

See Turner et al. (2016)\u00a0Biological Invasions\u00a018: 533-548 for additional methodological details:
Turner, B.C., de Rivera, C.E., Grosholz, E.D., & Ruiz, G.M. 2016. Assessing population increase as a possible outcome to management of invasive species. Biological Invasions, 18(2), pp 533\u2013548. doi:10.1007/s10530-015-1026-9 awards_0_award_nid=699764 awards_0_award_number=OCE-1514893 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1514893 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Demographic data for introduced crab from multiple bays in 2015 PI: Edwin Grosholz (UC Davis) Co-PI: Catherine de Rivera & Gregory Ruiz (Portland State University)
Version: 15 June 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.701751.1 Easternmost_Easting=-121.738422 geospatial_lat_max=38.316968 geospatial_lat_min=36.823953 geospatial_lat_units=degrees_north geospatial_lon_max=-121.738422 geospatial_lon_min=-123.058725 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/701751 institution=BCO-DMO instruments_0_dataset_instrument_description=At each of four sites within each bay, we placed 5 baited traps (folding Fukui fish traps) and 5 baited minnow traps in shallow intertidal areas. instruments_0_dataset_instrument_nid=701774 instruments_0_description=Fukui produces multi-species, multi-purpose collapsible or stackable fish traps, available in different sizes. instruments_0_instrument_name=Fukui fish trap instruments_0_instrument_nid=701772 instruments_0_supplied_name=folding Fukui fish traps metadata_source=https://www.bco-dmo.org/api/dataset/701751 Northernmost_Northing=38.316968 param_mapping={'701751': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/701751/parameters people_0_affiliation=University of California-Davis people_0_affiliation_acronym=UC Davis people_0_person_name=Edwin Grosholz people_0_person_nid=699768 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Portland State University people_1_affiliation_acronym=PSU people_1_person_name=Catherine de Rivera people_1_person_nid=699771 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Portland State University people_2_affiliation_acronym=PSU people_2_person_name=Gregory Ruiz people_2_person_nid=471603 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Shannon Rauch people_3_person_nid=51498 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Invasive_predator_harvest projects_0_acronym=Invasive_predator_harvest projects_0_description=The usual expectation is that when populations of plants and animals experience repeated losses to predators or human harvest, they would decline over time. If instead these populations rebound to numbers exceeding their initial levels, this would seem counter-intuitive or even paradoxical. However, for several decades mathematical models of population processes have shown that this unexpected response, formally known as overcompensation, is not only possible, but even expected under some circumstances. In what may be the first example of overcompensation in a marine system, a dramatic increase in a population of the non-native European green crab was recently observed following an intensive removal program. This RAPID project will use field surveys and laboratory experiments to verify that this population explosion results from overcompensation. Data will be fed into population models to understand to what degree populations processes such as cannibalism by adult crabs on juvenile crabs and changes in maturity rate of reproductive females are contributing to or modifying overcompensation. The work will provide important insights into the fundamental population dynamics that can produce overcompensation in both natural and managed populations. Broader Impacts include mentoring graduate trainees and undergraduate interns in the design and execution of field experiments as well as in laboratory culture and feeding experiments. The project will also involve a network of citizen scientists who are involved with restoration activities in this region and results will be posted on the European Green Crab Project website. This project aims to establish the first example of overcompensation in marine systems. Overcompensation refers to the paradoxical process where reduction of a population due to natural or human causes results in a greater equilibrium population than before the reduction. A population explosion of green crabs has been recently documented in a coastal lagoon and there are strong indications that this may be the result of overcompensation. Accelerated maturation of females, which can accompany and modify the expression of overcompensation has been observed. This RAPID project will collect field data from this unusual recruitment class and conduct targeted mesocosm experiments. These will include population surveys and mark-recapture studies to measure demographic rates across study sites. Laboratory mesocosm studies using this recruitment class will determine size specific mortality. Outcomes will be used in population dynamics models to determine to what degree overcompensation has created this dramatic population increase. The project will seek answers to the following questions: 1) what are the rates of cannibalism by adult green crabs and large juveniles on different sizes of juvenile green crabs, 2) what are the consequences of smaller size at first reproduction for population dynamics and for overcompensation and 3) how quickly will the green crab population return to the levels observed prior to the eradication program five years earlier? projects_0_end_date=2016-11 projects_0_geolocation=Europe projects_0_name=RAPID: A rare opportunity to examine overcompensation resulting from intensive harvest of an introduced predator projects_0_project_nid=699765 projects_0_start_date=2014-12 sourceUrl=(local files) Southernmost_Northing=36.823953 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-123.058725 xml_source=osprey2erddap.update_xml() v1.3

VITAL SIGNS INDICATOR Migration (EQ4)

FULL MEASURE NAME Migration flows

LAST UPDATED December 2018

DESCRIPTION Migration refers to the movement of people from one location to another, typically crossing a county or regional boundary. Migration captures both voluntary relocation – for example, moving to another region for a better job or lower home prices – and involuntary relocation as a result of displacement. The dataset includes metropolitan area, regional, and county tables.

DATA SOURCE American Community Survey County-to-County Migration Flows 2012-2015 5-year rolling average http://www.census.gov/topics/population/migration/data/tables.All.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Data for migration comes from the American Community Survey; county-to-county flow datasets experience a longer lag time than other standard datasets available in FactFinder. 5-year rolling average data was used for migration for all geographies, as the Census Bureau does not release 1-year annual data. Data is not available at any geography below the county level; note that flows that are relatively small on the county level are often within the margin of error. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area, in addition to the primary MSAs for the nine other major metropolitan areas, by aggregating county data based on current metropolitan area boundaries. Data prior to 2011 is not available on Vital Signs due to inconsistent Census formats and a lack of net migration statistics for prior years. Only counties with a non-negligible flow are shown in the data; all other pairs can be assumed to have zero migration.

Given that the vast majority of migration out of the region was to other counties in California, California counties were bundled into the following regions for simplicity: Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma Central Coast: Monterey, San Benito, San Luis Obispo, Santa Barbara, Santa Cruz Central Valley: Fresno, Kern, Kings, Madera, Merced, Tulare Los Angeles + Inland Empire: Imperial, Los Angeles, Orange, Riverside, San Bernardino, Ventura Sacramento: El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba San Diego: San Diego San Joaquin Valley: San Joaquin, Stanislaus Rural: all other counties (23)

One key limitation of the American Community Survey migration data is that it is not able to track emigration (movement of current U.S. residents to other countries). This is despite the fact that it is able to quantify immigration (movement of foreign residents to the U.S.), generally by continent of origin. Thus the Vital Signs analysis focuses primarily on net domestic migration, while still specifically citing in-migration flows from countries abroad based on data availability.

U.S. Census Bureau QuickFacts statistics for Helena Valley West Central CDP, Montana. 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.

Protected areas are one of the most widespread and accepted conservation interventions, yet their population trends are rarely compared to regional trends to gain insight into their effectiveness. Here, we leverage two long-term community science datasets to demonstrate mixed effects of protected areas on long-term bird population trends. We analyzed 31 years of bird transect data recorded by community volunteers across all major habitats of Stanford University’s Jasper Ridge Biological Preserve to determine the population trends for a sample of 66 species. We found that nearly a third of species experienced long-term declines, and on average, all species declined by 12%. Further, we averaged species trends by conservation status and key life history attributes to identify correlates and possible drivers of these trends. Observed increases in some cavity-nesters and declines of scrub-associated species suggest that long-term fire suppression may be a key driver, reshaping bird communities through changes in forest and chaparral structure and composition. Additionally, we compared our results to those of the North American Breeding Bird Survey’s Central California Coast region (n = 55 species) to place Jasper Ridge in a broader context. Most species experienced similar directional population trends inside vs. outside of the preserve, and only eight species (14.5%) did better inside this small, protected area. Therefore, we must identify relevant management strategies for declining populations and explicitly consider how existing protected areas target and manage each species. Further, this analysis underscores the importance of local and national community science for revealing nuanced long-term bird population trends. Methods

From 1989 to 2020, volunteer observers conducted monthly surveys of six sectors within Stanford University's Jasper Ridge Biological Preserve (JRBP). Each survey consisted of a trail-based transect in which a group of observers walked the trail in the morning and counted all birds detected over roughly 3 hours. Observers recorded the number of each species seen or heard along the route, regardless of the distance to the bird. Over 31 years of surveys, 192 observers conducted 2,055 transects and recorded a total of 473,401 observations of 184 species (91% of JRBP’s documented avian richness). We used these data to estimate long-term avian population trends at JRBP. Prior to analy- sis, we performed extensive data cleaning, including the standardization of species names and observer identity. Unlikely species without notes or supporting information were removed from the analysis. All transects with fewer than seven species (n = 30) were considered incidental and removed. These transects were often performed during suboptimal conditions (e.g. wind or rain) and/or were of abnormally short duration. Finally, we limited our analysis to the 100 most consistently detected species (those detected in the greatest number of transects).

The GIS layer "Census_sum_15" provides a standardized tool for examining spatial patterns in abundance and demographic trends of the southern sea otter (Enhydra lutris nereis), based on data collected during the spring 2015 range-wide census. The USGS range-wide sea otter census has been undertaken twice a year since 1982, once in May and once in October, using consistent methodology involving both ground-based and aerial-based counts. The spring census is considered more accurate than the fall count, and provides the primary basis for gauging population trends by State and Federal management agencies. This Shape file includes a series of summary statistics derived from the raw census data, including sea otter density (otters per square km of habitat), linear density (otters per km of coastline), relative pup abundance (ratio of pups to independent animals) and 5-year population trend (calculated as exponential rate of change). All statistics are calculated and plotted for small sections of habitat in order to illustrate local variation in these statistics across the entire mainland distribution of sea otters in California (as of 2015). Sea otter habitat is considered to extend offshore from the mean low tide line and out to the 60m isobath: this depth range includes over 99% of sea otter feeding dives, based on dive-depth data from radio tagged sea otters (Tinker et al 2006, 2007). Sea otter distribution in California (the mainland range) is considered to comprise this band of potential habitat stretching along the coast of California, and bounded to the north and south by range limits defined as "the points farthest from the range center at which 5 or more otters are counted within a 10km contiguous stretch of coastline (as measured along the 10m bathymetric contour) during the two most recent spring censuses, or at which these same criteria were met in the previous year". The polygon corresponding to the range definition was then sub-divided into onshore/offshore strips roughly 500 meters in width. The boundaries between these strips correspond to ATOS (As-The-Otter-Swims) points, which are arbitrary locations established approximately every 500 meters along a smoothed 5 fathom bathymetric contour (line) offshore of the State of California.

Tethering data for introduced crab for 2015. Experiments were conducted in several bays along Central California coast, shallow subtidal (<3 m depth). access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=We conducted tethering experiments in several northern California bays: Bodega Harbor, Tomales Bay, Bolinas Lagoon, and Seadrift Lagoon. All sites were accessed by foot via shore entry.\u00a0At each of four sites within each bay, we placed 10 small European green crabs (collected locally) in parallel arrays near the 0.0 tide level. Tethers were retrieved 24 hours later data and scored for presence/absence of crab including missing appendages and or condition of remaining tether line.

See Turner et al. (2016) Biological Invasions 18: 533-548 for additional methodological details:
Turner, B.C., de Rivera, C.E., Grosholz, E.D., & Ruiz, G.M. 2016. Assessing population increase as a possible outcome to management of invasive species. Biological Invasions, 18(2), pp 533\u2013548. doi:10.1007/s10530-015-1026-9 awards_0_award_nid=699764 awards_0_award_number=OCE-1514893 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1514893 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Tethering data for introduced crab for 2015 PI: Edwin Grosholz (UC Davis) Co-PI: Catherine de Rivera & Gregory Ruiz (Portland State University)
Version: 15 June 2017 Note that all Seadrift sites are very close together and thus one lat/lon pair are used to represent all sites within Seadrift. Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.701726.1 Easternmost_Easting=-122.653096 geospatial_lat_max=38.316968 geospatial_lat_min=37.906503 geospatial_lat_units=degrees_north geospatial_lon_max=-122.653096 geospatial_lon_min=-123.058725 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/701726 institution=BCO-DMO metadata_source=https://www.bco-dmo.org/api/dataset/701726 Northernmost_Northing=38.316968 param_mapping={'701726': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/701726/parameters people_0_affiliation=University of California-Davis people_0_affiliation_acronym=UC Davis people_0_person_name=Edwin Grosholz people_0_person_nid=699768 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Portland State University people_1_affiliation_acronym=PSU people_1_person_name=Catherine de Rivera people_1_person_nid=699771 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Portland State University people_2_affiliation_acronym=PSU people_2_person_name=Gregory Ruiz people_2_person_nid=471603 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Shannon Rauch people_3_person_nid=51498 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Invasive_predator_harvest projects_0_acronym=Invasive_predator_harvest projects_0_description=The usual expectation is that when populations of plants and animals experience repeated losses to predators or human harvest, they would decline over time. If instead these populations rebound to numbers exceeding their initial levels, this would seem counter-intuitive or even paradoxical. However, for several decades mathematical models of population processes have shown that this unexpected response, formally known as overcompensation, is not only possible, but even expected under some circumstances. In what may be the first example of overcompensation in a marine system, a dramatic increase in a population of the non-native European green crab was recently observed following an intensive removal program. This RAPID project will use field surveys and laboratory experiments to verify that this population explosion results from overcompensation. Data will be fed into population models to understand to what degree populations processes such as cannibalism by adult crabs on juvenile crabs and changes in maturity rate of reproductive females are contributing to or modifying overcompensation. The work will provide important insights into the fundamental population dynamics that can produce overcompensation in both natural and managed populations. Broader Impacts include mentoring graduate trainees and undergraduate interns in the design and execution of field experiments as well as in laboratory culture and feeding experiments. The project will also involve a network of citizen scientists who are involved with restoration activities in this region and results will be posted on the European Green Crab Project website. This project aims to establish the first example of overcompensation in marine systems. Overcompensation refers to the paradoxical process where reduction of a population due to natural or human causes results in a greater equilibrium population than before the reduction. A population explosion of green crabs has been recently documented in a coastal lagoon and there are strong indications that this may be the result of overcompensation. Accelerated maturation of females, which can accompany and modify the expression of overcompensation has been observed. This RAPID project will collect field data from this unusual recruitment class and conduct targeted mesocosm experiments. These will include population surveys and mark-recapture studies to measure demographic rates across study sites. Laboratory mesocosm studies using this recruitment class will determine size specific mortality. Outcomes will be used in population dynamics models to determine to what degree overcompensation has created this dramatic population increase. The project will seek answers to the following questions: 1) what are the rates of cannibalism by adult green crabs and large juveniles on different sizes of juvenile green crabs, 2) what are the consequences of smaller size at first reproduction for population dynamics and for overcompensation and 3) how quickly will the green crab population return to the levels observed prior to the eradication program five years earlier? projects_0_end_date=2016-11 projects_0_geolocation=Europe projects_0_name=RAPID: A rare opportunity to examine overcompensation resulting from intensive harvest of an introduced predator projects_0_project_nid=699765 projects_0_start_date=2014-12 sourceUrl=(local files) Southernmost_Northing=37.906503 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-123.058725 xml_source=osprey2erddap.update_xml() v1.3

The GIS shapefile "Census summary of southern sea otter 2017" provides a standardized tool for examining spatial patterns in abundance and demographic trends of the southern sea otter (Enhydra lutris nereis), based on data collected during the spring 2017 range-wide census. The USGS range-wide sea otter census has been undertaken twice a year since 1982, once in May and once in October, using consistent methodology involving both ground-based and aerial-based counts. The spring census is considered more accurate than the fall count, and provides the primary basis for gauging population trends by State and Federal management agencies. This Shape file includes a series of summary statistics derived from the raw census data, including sea otter density (otters per square km of habitat), linear density (otters per km of coastline), relative pup abundance (ratio of pups to independent animals) and 5-year population trend (calculated as exponential rate of change). All statistics are calculated and plotted for small sections of habitat in order to illustrate local variation in these statistics across the entire mainland distribution of sea otters in California (as of 2017). Sea otter habitat is considered to extend offshore from the mean low tide line and out to the 60m isobath: this depth range includes over 99% of sea otter feeding dives, based on dive-depth data from radio tagged sea otters (Tinker et al 2006, 2007). Sea otter distribution in California (the mainland range) is considered to comprise this band of potential habitat stretching along the coast of California, and bounded to the north and south by range limits defined as "the points farthest from the range center at which 5 or more otters are counted within a 10km contiguous stretch of coastline (as measured along the 10m bathymetric contour) during the two most recent spring censuses, or at which these same criteria were met in the previous year". The polygon corresponding to the range definition was then sub-divided into onshore/offshore strips roughly 500 meters in width. The boundaries between these strips correspond to ATOS (As-The-Otter-Swims) points, which are arbitrary locations established approximately every 500 meters along a smoothed 5 fathom bathymetric contour (line) offshore of the State of California. References: Tinker, M. T., Doak, D. F., Estes, J. A., Hatfield, B. B., Staedler, M. M. and Bodkin, J. L. (2006), INCORPORATING DIVERSE DATA AND REALISTIC COMPLEXITY INTO DEMOGRAPHIC ESTIMATION PROCEDURES FOR SEA OTTERS. Ecological Applications, 16: 2293–2312, https://doi.org/10.1890/1051-0761(2006)016[2293:IDDARC]2.0.CO;2 Tinker, M. T. , D. P. Costa , J. A. Estes , and N. Wieringa . 2007. Individual dietary specialization and dive behaviour in the California sea otter: using archival time–depth data to detect alternative foraging strategies. Deep Sea Research II 54: 330–342, https://doi.org/10.1016/j.dsr2.2006.11.012

The GIS shapefile "Census summary of southern sea otter 2016" provides a standardized tool for examining spatial patterns in abundance and demographic trends of the southern sea otter (Enhydra lutris nereis), based on data collected during the spring 2016 range-wide census. The USGS range-wide sea otter census has been undertaken twice a year since 1982, once in May and once in October, using consistent methodology involving both ground-based and aerial-based counts. The spring census is considered more accurate than the fall count, and provides the primary basis for gauging population trends by State and Federal management agencies. This Shape file includes a series of summary statistics derived from the raw census data, including sea otter density (otters per square km of habitat), linear density (otters per km of coastline), relative pup abundance (ratio of pups to independent animals) and 5-year population trend (calculated as exponential rate of change). All statistics are calculated and plotted for small sections of habitat in order to illustrate local variation in these statistics across the entire mainland distribution of sea otters in California (as of 2016). Sea otter habitat is considered to extend offshore from the mean low tide line and out to the 60m isobath: this depth range includes over 99% of sea otter feeding dives, based on dive-depth data from radio tagged sea otters (Tinker et al 2006, 2007). Sea otter distribution in California (the mainland range) is considered to comprise this band of potential habitat stretching along the coast of California, and bounded to the north and south by range limits defined as "the points farthest from the range center at which 5 or more otters are counted within a 10km contiguous stretch of coastline (as measured along the 10m bathymetric contour) during the two most recent spring censuses, or at which these same criteria were met in the previous year". The polygon corresponding to the range definition was then sub-divided into onshore/offshore strips roughly 500 meters in width. The boundaries between these strips correspond to ATOS (As-The-Otter-Swims) points, which are arbitrary locations established approximately every 500 meters along a smoothed 5 fathom bathymetric contour (line) offshore of the State of California.

In 2023, approximately 127.1 million people lived in Guangdong province in China. That same year, only about 3.65 million people lived in the sparsely populated highlands of Tibet. Regional differences in China China is the world’s most populous country, with an exceptional economic growth momentum. The country can be roughly divided into three regions: Western, Eastern, and Central China. Western China covers the most remote regions from the sea. It also has the highest proportion of minority population and the lowest levels of economic output. Eastern China, on the other hand, enjoys a high level of economic development and international corporations. Central China lags behind in comparison to the booming coastal regions. In order to accelerate the economic development of Western and Central Chinese regions, the PRC government has ramped up several incentive plans such as ‘Rise of Central China’ and ‘China Western Development’. Economic power of different provinces When observed individually, some provinces could stand an international comparison. Jiangxi province, for example, a medium-sized Chinese province, had a population size comparable to Argentina or Spain in 2023. That year, the GDP of Zhejiang, an eastern coastal province, even exceeded the economic output of the Netherlands. In terms of per capita annual income, the municipality of Shanghai reached a level close to that of the Czech Republik. Nevertheless, as shown by the Gini Index, China’s economic spur leaves millions of people in dust. Among the various kinds of economic inequality in China, regional or the so-called coast-inland disparity is one of the most significant. Posing as evidence for the rather large income gap in China, the poorest province Heilongjiang had a per capita income similar to that of Sri Lanka that year.

Coastal and freshwater cetaceans are particularly vulnerable due to their proximity to human activity, localized distributions and small home ranges. These species include Sotalia guianensis, found in the Atlantic and Caribbean coastal areas of central and South America, and Sotalia fluviatilis, distributed in the Amazon River and tributaries. We investigated the population structure and genetic diversity of these two species by analyses of mtDNA control region and 8-10 microsatellite loci. MtDNA analyses revealed strong regional structuring for S. guianensis (i.e. Colombian Caribbean vs. Brazilian Coast, FST= 0.807, ΦST = 0.878, P <0.001) especially north and south of the Amazon River mouth. For S. fluviatilis, population structuring was detected between the western and eastern Amazon (i.e. Colombian Amazon vs. Brazilian Amazon, FST= 0.085, ΦST = 0.277, P <0.001). Haplotype and nucleotide diversity were higher for S. fluviatilis. Population differentiation was supported by analysis of the microsatellite loci (S. guianensis, northern South America vs. southern South America FST= 0.275, Jost´s D = 0.476, P<0.001; S. fluviatilis, western and eastern Amazon FST= 0.197, Jost´s D = 0.364, P<0.001). Most estimated migration rates in both species overlapped with zero, suggesting no measurable migration between most of the sampling locations. However, for S. guianensis, there was measurable migration in neighboring sampling locations. These results indicate that the small home ranges of these species may act to restrict gene flow between populations separated by relatively short distances, increasing the risk of extirpation of some localized populations in the future if existing threats are not minimized.

We develop a Bayesian model-based approach to finite population estimation accounting for spatial dependence. Our innovation here is a framework that achieves inference for finite population quantities in spatial process settings. A key distinction from the small area estimation setting is that we analyze finite populations referenced by their geographic coordinates (point-referenced data). Specifically, we consider a two-stage sampling design in which the primary units are geographic regions, the secondary units are point-referenced locations, and the measured values are assumed to be a partial realization of a spatial process. Traditional geostatistical models do not account for variation attributable to finite population sampling designs, which can impair inferential performance. On the other hand, design-based estimates will ignore the spatial dependence in the finite population. This motivates the introduction of geostatistical processes that will enable inference at arbitrary locations in our domain of interest.We demonstrate using simulation experiments that process-based finite population sampling models considerably improve model fit and inference over models that fail to account for spatial correlation. Furthermore, the process based models offer richer inference with spatially interpolated maps over the entire region. We reinforce these improvements and demonstrate scalable inference for groundwater Nitrate levels in the population of California Central Valley wells by offering estimates of mean Nitrate levels and their spatially interpolated maps.