This dataset provides a comprehensive overview of global population trends, historical data, and future projections. It includes detailed information for various countries and regions, encompassing key demographic indicators such as population size, growth rates, and density.

The dataset covers a broad time span, from 1980 to 2050, allowing for analysis of long-term population dynamics. It incorporates data from reputable sources like the United Nations Population Division and World Population Review, ensuring data accuracy and reliability.

About this dataset

Context

The dataset tabulates the population of Science Hill by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Science Hill. The dataset can be utilized to understand the population distribution of Science Hill by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Science Hill. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Science Hill.

Key observations

Largest age group (population): Male # 20-24 years (41) | Female # 35-39 years (54). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Science Hill population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Science Hill is shown in the following column.
• Population (Female): The female population in the Science Hill is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Science Hill for each age group.

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 Science Hill Population by Gender. You can refer the same here

About this dataset

Context

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

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Science Hill, KY, is 35.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Science Hill, KY, is 27.4.
• Total dependency ratio for Science Hill, KY is 62.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Science Hill, KY is 3.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Science Hill Population by Age. You can refer the same here

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 EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Cedar Creek Ecosystem Science Reserve (CDR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

In 2014, the USGS Lake Erie Biological Station participated in the Coordinated Science and Monitoring Initiative (CMSI) program, a program founded by the U.S. Environmental Protection Agency (EPA) and Environment Canada in the 1990s as a means to focus collaborative research attention on one of the five Great Lakes each year (on a rotating schedule) as a means to increase scientific knowledge for Great Lakes restoration. The Lake Erie survey examined the food web across a nearshore to offshore gradient, matching the sampling design the preceding USGS studies of the other four Great Lakes (2010-2013). We sampled all trophic levels in all three lake basins across multiple seasons in order to determine nutrient availability and trophic energy transfers from nearshore to offshore across the lakes west-east production gradient. In each basin two transects, each consisting of replicate nearshore, mid, and offshore sites were sampled. The lower trophic food web (water nutrients, zooplankton, and benthos) was sampled monthly, and the fish community (via bottom trawl and hydroacoutics) was sampled bi-monthly (May, July, and September). By examining the trophic interactions and energy transfer in all three basins, this data may be of interest to anyone interested in examining some of Lake Eries principal environmental and ecological issues such as sedimentation and nutrient loading (western basin), seasonal hypoxia (central basin), and strong nearshore to offshore production gradients (eastern basin).

Table 1. Annual Estimates of the Resident Population for the United States, Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2017 (NST-EST2017-01)

Source: U.S. Census Bureau, Population Division

Release Date: December 2017

Data reformatted from wide to long format.

Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. See Geographic Terms and Definitions at http://www.census.gov/programs-surveys/popest/guidance-geographies/terms-and-definitions.html for a list of the states that are included in each region. All geographic boundaries for the 2017 population estimates series except statistical area delineations are as of January 1, 2017. For population estimates methodology statements, see http://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html.

Data include population information of eastern screech owl (Megascops asio, formerly Otus asio) populations in four counties of northern Ohio. Researchers recorded adult and chick color phases, eggs laid, brood size and location.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Cedar Creek Ecosystem Science Reserve (CDR) contains human population (total) measurements in number units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Cedar Creek Ecosystem Science Reserve (CDR) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

The "Forest Proximate People" (FPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The FPP dataset provides an estimate of the number of people living in or within 5 kilometers of forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level.

For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L. Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: A new methodology and global estimates. Background Paper to The State of the World’s Forests 2022 report. Rome, FAO.

Contact points:

Maintainer: Leticia Pina

Maintainer: Sarah E., Castle

Data lineage:

The FPP data are generated using Google Earth Engine. Forests are defined by the Copernicus Global Land Cover (CGLC) (Buchhorn et al. 2020) classification system’s definition of forests: tree cover ranging from 15-100%, with or without understory of shrubs and grassland, and including both open and closed forests. Any area classified as forest sized ≥ 1 ha in 2019 was included in this definition. Population density was defined by the WorldPop global population data for 2019 (WorldPop 2018). High density urban populations were excluded from the analysis. High density urban areas were defined as any contiguous area with a total population (using 2019 WorldPop data for population) of at least 50,000 people and comprised of pixels all of which met at least one of two criteria: either the pixel a) had at least 1,500 people per square km, or b) was classified as “built-up” land use by the CGLC dataset (where “built-up” was defined as land covered by buildings and other manmade structures) (Dijkstra et al. 2020). Using these datasets, any rural people living in or within 5 kilometers of forests in 2019 were classified as forest proximate people. Euclidean distance was used as the measure to create a 5-kilometer buffer zone around each forest cover pixel. The scripts for generating the forest-proximate people and the rural-urban datasets using different parameters or for different years are published and available to users. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022. Rome, FAO.

References:

Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3 epoch 2019. Globe.

Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M. and Schiavina, M., 2020. Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics, p.103312.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University, 2018. Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

Online resources:

GEE asset for "Forest proximate people - 5km cutoff distance"

Seabirds are affected by changes in the marine ecosystem. The influence of climatic factors on marine food webs can be reflected in long-term seabird population changes. We modelled the survival and recruitment of the Mediterranean storm petrel (Hydrobates pelagicus melitensis) using a 21-year mark-recapture dataset involving almost 5000 birds. We demonstrated a strong influence of prebreeding climatic conditions on recruitment age and of rainfall and breeding period conditions on juvenile survival. The results suggest that the juvenile survival rate of the Mediterranean subspecies may not be negatively affected by the predicted features of climate change, i.e., warmer summers and lower rainfall. Based on considerations of winter conditions in different parts of the Mediterranean, we were able to draw inferences about the wintering areas of the species for the first time.

Information was obtained from the ANARE Health Register. See Metadata record entitled ANARE Health Register.

INDICATOR DEFINITION Human population in stations and ships expressed in person-days.

TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system.

This indicator is one of: PRESSURE

RATIONALE FOR INDICATOR SELECTION It is generally accepted that the potential impact on the natural environment is proportional to the human population. This is the 'human footprint'. Human activities can cause disruption in physical, chemical and biological systems. As stated by the Australian Bureau of Statistics (1996): 'To understand the human impact on the Australian environment, it is necessary to know how many people live here, and how they are distributed across the continent.'

This indicator reveals where the greatest direct pressures related to size of the human population (e.g. fuel usage, sewerage and other waste generation etc) occur.

DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial scale: Antarctic and sub-Antarctic stations and ANARE ships travelling to and from these stations.

Frequency: Monthly figures reported annually.

Measurement technique: The Polar Medicine Branch collects data on all expeditioner movements. These data are entered into the Health Register and updated as personnel arrive on or leave a station.

RESEARCH ISSUES Now that this figure is available, research is required to ascertain the quantitive relationships of station and ship population to other indicators such as fuel usage and waste generation. This measure may be able to deliver a quantitative estimate of human pressure on the Antarctic environment.

LINKS TO OTHER INDICATORS SOE Indicator 47 - Number and nature of incidents resulting in environmental impact SOE Indicator 49 - Medical consultations per 1000 person years SOE Indicator 50 - Effluent monitoring - Volume of coastal discharge from Australian stations SOE Indicator 51 - Effluent monitoring - Biological oxygen demand SOE Indicator 52 - Effluent monitoring - Suspended solids content SOE Indicator 53 - Recycled and quarantine waste returned to Australia SOE Indicator 54 - Amount of waste incinerated at Australian Stations SOE Indicator 56 - Monthly fuel usage of the generator sets and boilers SOE Indicator 57 - Monthly total of fuel used by station incinerators SOE Indicator 58 - Monthly total of fuel used by station vehicles SOE Indicator 59 - Monthly electricity usage SOE Indicator 60 - Total helicopter hours SOE Indicator 61 - Total potable water consumption

The fields in this dataset are: Location Date Population (person-days) Illness Rate (per 1000 person years) Injury Rate (per 1000 person years)

---- Public Summary from Project ---- Leopard seals are usually seen in the pack-ice where they pup on the ice and where they must first face life at sea. However at Macquarie Island, well to the north of the ice, for 50 years now there has been the odd phenomenon of 'Leopard seal years'. At seemingly semi-regular periods (~3-4 years) considerable numbers (can be greater than 100) of leopard seals arrive at the island; and then virtually none are seen for some more years. The periodicity of these arrivals has been striking.

Thus it seems that young leopard seals (which is the group arriving in poor condition on Macquarie Island) suffer acute food shortages in the pack-ice zone every 3-4 years. This project will continue to record these events and tag and weigh the seals which come ashore. This will allow the long-term dataset to continue and give some more information about the seals which arrive. It is also planned to glue some satellite recorders to the seals so that their journeys after M.I. can be known.

Data are collected when seals are seen on beach. Since the 1980s few seals have been seen so data are sparse but significant.

Currently the dataset contains the number of leopard seals sighted at Macquarie Island each year and a record of sightings of Leopard Seals from 1948 till 2002 (some years are omitted due to unavailability of data, see quality information). Details on the sightings include date and location of sighting and condition of the seal.

The fields in the dataset for the number of seals sighted each year at Macquarie Island are:

Year Number of seals.

The fields in the dataset detailing the sightings of Leopard Seals on Macquarie Island from 1948 till 2002 include the following:

Seal ID: Each seal has been allocated a unique ID number. This acts as a means of tracking the seal if a tag is replaced or removed.

Tag #1 and Tag #2: Tag numbers include plastic tags attached to the seals flippers and substitute tag numbers allocated to those seals marked with paint in 1959 and those seals resighted by length and/or a distinguishing feature or injury.

Information on plastic tags:

-All tags used from 1976-1981 were yellow plastic - except 50 (30/9/76) which is red plastic diamond shaped, and 90a which is metal.

-Tag numbers followed by a in 1976 are coffin shaped (note: a prefix of 0 was used in original tag rather than an a following the number).

-Tag numbers followed by a in 1977 are combinations of shovel and coffin shaped parts (note: a prefix of 0 was used in original tag rather than an a following the number).

-Tag numbers not followed by a in 1977 are shovel-shaped.

-Tags used by 1986 were the 'Jumbo Rototag' which are smaller and made of less flexible plastic than the 'Allflex' tags originally used.

-See references below for further information on tags and methods of tagging used.

Information on substitute or'S' tags

-Tags prefixed with S are substitute tags. Seals with a tag prefixed by S were not physically tagged with a plastic or metal tag. This 'tag number' was allocated when collating data from years when plastic tagging were not used and resights of seals were determined by either coloured markings painted on the seals (as in 1959) or by a combination of length, sex, distinguishing features or injuries.

-S Tag numbers were allocated in date order of the original or 'New' sighting. Hence 'tag' S1 was allocated to the first seal sighted and then resighted in 1949.

-Note: There are some instances where the original recorder of the sightings did not note any distinguishing features or paint markings on the seal but later recorded that the seal had been resighted. When this occurred the 'word' of the recorder was taken and an S tag allocated.

Date: Date of sighting whether initial sighting or a resighting of the same seal.

Location Codes: This field notes the location code for the area on Macquarie Island where the seal was sighted. The code corresponds to a grid reference on Macquarie Island that was originally used for locating Elephant Seal sightings.

A listing of these reference codes is also attached to this dataset. The fields in the location code dataset are: Location Name, Location ID, Latitude and Longitude.

Within the original records a number of locations were noted using outdated or informal names. These locations were renamed with the reference code now used for that location. A listing of the informal names and the location codes they respond to has been included in the Location Codes worksheet for reference.

Sex: the sex of the seal is noted in this column as either: M = Male or F = Female.

Length: The nose to tail length of the seal is noted in centimetres.

Condition: This field details the general condition of the Leopard Seal. The coding is as follows: G = Good, F = Fair, P = Poor, T = Thin, E = Emancipated, D = Dead and K = Killed.

Comments on Condition: This field is used to note any additional details regarding the conditio...

Evaluating the status of threatened and endangered salmonid populations requires information on the current status of the threats (e.g., habitat, hatcheries, hydropower, and invasives) and the risk of extinction (e.g., status and trend in the Viable Salmonid Population criteria). For salmonids in the Pacific Northwest, threats generally result in changes to physical and biological characteristi...

This dataset contains information on the breeding outcome, breeding site occupancy, and breeding site quality for a sample of common guillemots breeding on the Isle of May, Scotland. Data is available for all attributes from 1981-2018. These data are part of the Isle of May long-term study to assess population trends of seabirds under environmental change (IMLOTS https://www.ceh.ac.uk/our-science/projects/isle-may-long-term-study). Full details about this dataset can be found at https://doi.org/10.5285/33b42f0a-12a5-47fe-aaaf-25f4ee5e13a5

The French Southern Territories (TAAF), with 4 permanent bases constitute a unique setting of breeding sites for top predators such as seabirds and seals because they are part of 3 marine biomes, subtropical, subAntarctic and Antarctic waters. Long term studies have been started in the late 1970s, with continuous at-sea monitoring survey and individual tracking since then. All these information are today centralised in a long term data base on the at-sea distribution of 17 species of top predators based at the CEB Chizé laboratory.

The CEB Chizé (CNRS-UPR 1934) is in charge of archiving the long term data on at-sea distribution of marine top predators (seabirds and sea mammals). It gives access to these data for french scientific community (through collaborative ANR Project-REMIGE) and international (through the Global Procellariiform Tracking Database-Birdlife International).

Dreissenid mussels (including the zebra mussel Dreissena polymorpha and the quagga mussel D. rostriformis) are among the world's most notorious invasive species, with large and widespread ecological and economic effects. However, their long‐term population dynamics are poorly known, even though these dynamics are critical to determining impacts and effective management. We gathered and analyzed 67 long‐term (>10 yr) data sets on dreissenid populations from lakes and rivers across Europe and North America. We addressed five questions: (1) How do Dreissena populations change through time? (2) Specifically, do Dreissena populations decline substantially after an initial outbreak phase? (3) Do different measures of population performance (biomass or density of settled animals, veliger density, recruitment of young) follow the same patterns through time? (4) How do the numbers or biomass of zebra mussels or of both species combined change after the quagga mussel arrives? (5) How does body size change over time? We also considered whether current data on long‐term dynamics of Dreissena populations are adequate for science and management. Individual Dreissena populations showed a wide range of temporal dynamics, but we could detect only two general patterns that applied across many populations: (1) Populations of both species increased rapidly in the first 1–2 yr after appearance, and (2) quagga mussels appeared later than zebra mussels and usually quickly caused large declines in zebra mussel populations. We found little evidence that combined Dreissena populations declined over the long term. Different measures of population performance were not congruent; the temporal dynamics of one life stage or population attribute cannot generally be accurately inferred from the dynamics of another. We found no consistent patterns in the long‐term dynamics of body size. The long‐term dynamics of Dreissena populations probably are driven by the ecological characteristics (e.g., predation, nutrient inputs, water temperature) and their temporal changes at individual sites rather than following a generalized time course that applies across many sites. Existing long‐term data sets on dreissenid populations, although clearly valuable, are inadequate to meet research and management needs. Data sets could be improved by standardizing sampling designs and methods, routinely collecting more variables, and increasing support.

This dataset contains the results from studies of the Elephant Seal (Mirounga leonina) at Macquarie Island. Results from branding surveys and photographs from 1985 onwards are reported. Numbers, life stage, sex, moult stage and migration patterns have been reported. Currently some 2000 pups a year are branded and the dataset includes birth dates, weights at birth and weaning and at 6, 12 and 18 months.

This work was completed as part of ASAC (AAS) project 2265 (ASAC_2265).

Objectives:

1. To prepare research papers, from the extensive southern elephant seal dataset, that deal with key demographic parameters of the population such as size, age specific survivorship, fecundity, recruitment into the breeding population, age specific growth rates, and intrinsic rate of change of the population. In addition, later papers will investigate interannual variability in these parameters, how these relate to changing environmental conditions, and the effects of this on long term population fluctuations.

2. To analyse and compare stable isotope ratios in the facial vibrissae of the seals and the hard parts of their prey to determine the geographical positions of the major foraging grounds of the seals. The isotope values will also allow the food webs, that support the seals, to be better defined.

3. To measure the growth rates of elephant seal vibrissae so that changing isotope values, related to the prey and foraging areas, can be referred to particular foraging periods. Elephant seals characteristically have two separate periods of foraging: one in summer and one in winter. The positions of these episodes on a vibrissa can be identified once the growth rates of vibrissae are known.

Taken from the progress report for the 2009-2010 season:

Progress against objectives: 1. One paper published from the elephant seal dataset. Two papers also published during 2009/10 using data collected opportunistically during the life of this project.

1. PhD student Andrea Walters continues to analyse the results of the whisker analyses. She has presented some of her results at the AMSA 2009 marine connectivity conference in Adelaide.

An honours student has been engaged (start date March 2010) to analyse the squid component of the seals' diet.

1. John van den Hoff spent the early summer at Macquarie Island finalising the collection of the demographic data. 2154 tag/brand resights were recorded. Collection of the data has continued on the island by Chris Oosthuizen, Ben Arthur and Iain Field since John returned to Australia. When those field workers return data collection will cease.

Environmental data from long-term monitoring sites in St. John, USVI. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Methodology can be found in paper (Gross, K. and Edmunds, P. J. 2015). awards_0_award_nid=55191 awards_0_award_number=DEB-0841441 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=0841441&HistoricalAwards=false awards_0_funder_name=National Science Foundation awards_0_funding_acronym=NSF awards_0_funding_source_nid=350 awards_0_program_manager=Saran Twombly awards_0_program_manager_nid=51702 awards_1_award_nid=562593 awards_1_award_number=DEB-1350146 awards_1_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=1350146 awards_1_funder_name=NSF Division of Environmental Biology awards_1_funding_acronym=NSF DEB awards_1_funding_source_nid=550432 awards_1_program_manager=Betsy Von Holle awards_1_program_manager_nid=701685 cdm_data_type=Other comment=Environmental data P. Edmunds, PI Version 14 September 2018 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.735088.1 infoUrl=https://www.bco-dmo.org/dataset/735088 institution=BCO-DMO metadata_source=https://www.bco-dmo.org/api/dataset/735088 param_mapping={'735088': {}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/735088/parameters people_0_affiliation=California State University Northridge people_0_affiliation_acronym=CSU-Northridge people_0_person_name=Peter J. Edmunds people_0_person_nid=51536 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=North Carolina State University people_1_affiliation_acronym=NCSU people_1_person_name=Kevin Gross people_1_person_nid=535324 people_1_role=Contact people_1_role_type=related people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI BCO-DMO people_2_person_name=Hannah Ake people_2_person_nid=650173 people_2_role=BCO-DMO Data Manager people_2_role_type=related project=St. John LTREB,RUI-LTREB projects_0_acronym=St. John LTREB projects_0_description=Long Term Research in Environmental Biology (LTREB) in US Virgin Islands: From the NSF award abstract: In an era of growing human pressures on natural resources, there is a critical need to understand how major ecosystems will respond, the extent to which resource management can lessen the implications of these responses, and the likely state of these ecosystems in the future. Time-series analyses of community structure provide a vital tool in meeting these needs and promise a profound understanding of community change. This study focuses on coral reef ecosystems; an existing time-series analysis of the coral community structure on the reefs of St. John, US Virgin Islands, will be expanded to 27 years of continuous data in annual increments. Expansion of the core time-series data will be used to address five questions: (1) To what extent is the ecology at a small spatial scale (1-2 km) representative of regional scale events (10's of km)? (2) What are the effects of declining coral cover in modifying the genetic population structure of the coral host and its algal symbionts? (3) What are the roles of pre- versus post-settlement events in determining the population dynamics of small corals? (4) What role do physical forcing agents (other than temperature) play in driving the population dynamics of juvenile corals? and (5) How are populations of other, non-coral invertebrates responding to decadal-scale declines in coral cover? Ecological methods identical to those used over the last two decades will be supplemented by molecular genetic tools to understand the extent to which declining coral cover is affecting the genetic diversity of the corals remaining. An information management program will be implemented to create broad access by the scientific community to the entire data set. The importance of this study lies in the extreme longevity of the data describing coral reefs in a unique ecological context, and the immense potential that these data possess for understanding both the patterns of comprehensive community change (i.e., involving corals, other invertebrates, and genetic diversity), and the processes driving them. Importantly, as this project is closely integrated with resource management within the VI National Park, as well as larger efforts to study coral reefs in the US through the NSF Moorea Coral Reef LTER, it has a strong potential to have scientific and management implications that extend further than the location of the study. The following publications and data resulted from this project: 2015 Edmunds PJ, Tsounis G, Lasker HR (2015) Differential distribution of octocorals and scleractinians around St. John and St. Thomas, US Virgin Islands. Hydrobiologia. doi: 10.1007/s10750-015-2555-zoctocoral - sp. abundance and distributionDownload complete data for this publication (Excel file) 2015 Lenz EA, Bramanti L, Lasker HR, Edmunds PJ. Long-term variation of octocoral populations in St. John, US Virgin Islands. Coral Reefs DOI 10.1007/s00338-015-1315-xoctocoral survey - densitiesoctocoral counts - photoquadrats vs. insitu surveyoctocoral literature reviewDownload complete data for this publication (Excel file) 2015 Privitera-Johnson, K., et al., Density-associated recruitment in octocoral communities in St. John, US Virgin Islands, J.Exp. Mar. Biol. Ecol. DOI 10.1016/j.jembe.2015.08.006octocoral recruitmentDownload complete data for this publication (Excel file) 2014 Edmunds PJ. Landscape-scale variation in coral reef community structure in the United States Virgin Islands. Marine Ecology Progress Series 509: 137–152. DOI 10.3354/meps10891. Data at MCR-VINP. Download complete data for this publication (Excel file) 2014 Edmunds PJ, Nozawa Y, Villanueva RD. Refuges modulate coral recruitment in the Caribbean and Pacific. Journal of Experimental Marine Biology and Ecology 454: 78-84. DOI: 10.1016/j.jembe.2014.02.00 Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Edmunds PJ, Gray SC. The effects of storms, heavy rain, and sedimentation on the shallow coral reefs of St. John, US Virgin Islands. Hydrobiologia 734(1):143-148. Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Levitan, D, Edmunds PJ, Levitan K. What makes a species common? No evidence of density-dependent recruitment or mortality of the sea urchin Diadema antillarum after the 1983-1984 mass mortality. Oecologia. DOI 10.1007/s00442-013-2871-9. Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Lenz EA, Brown D, Didden C, Arnold A, Edmunds PJ. The distribution of hermit crabs and their gastropod shells on shallow reefs in St. John, US Virgin Islands. Bulletin of Marine Science 90(2):681-692. https://dx.doi.org/10.5343/bms.2013.1049 Data at MCR-VINP.Download complete data for this publication (Excel file) 2013 Edmunds PJ. Decadal-scale changes in the community structure of coral reefs in St. John, US Virgin Islands. Marine Ecology Progress Series 489: 107-123. Data at MCR-VINP.Download complete data for this publication (zipped Excel files) 2013 Brown D, Edmunds PJ. Long-term changes in the population dynamics of the Caribbean hydrocoral Millepora spp. J. Exp Mar Biol Ecol 441: 62-70. doi: 10.1016/j.jembe.2013.01.013Millepora colony sizeMillepora cover - temps - storms 1992-2008Millepora cover 1992-2008seawater temperature USVI 1992-2008storms USVI 1992-2008Download complete data for this publication (Excel file) 2012 Brown D, Edmunds PJ. The hermit crab Calcinus tibicen lives commensally on Millepora spp. in St. John, United States Virgin Islands. Coral Reefs 32: 127-135. doi: 10.1007/s00338-012-0948-2crab abundance and coral sizecrab displacement behaviorcrab nocturnal surveyscrab predator avoidanceDownload complete data for this publication (Excel file) 2011 Green DH, Edmunds PJ. Spatio-temporal variability of coral recruitment on shallow reefs in St. John, US Virgin Islands. Journal of Experimenal Marine Biology and Ecology 397: 220-229. Data at MCR-VINP.Download complete data for this publication (Excel file) 2011 Colvard NB, Edmunds PJ. (2011) Decadal-scale changes in invertebrate abundances on a Caribbean coral reef. Journal of Experimental Marine Biology and Ecology. 397(2): 153-160. doi: 10.1016/j.jembe.2010.11.015benthic invert codesinverts - Tektite and Yawzi Ptinverts - pooledDownload complete data for this publication (Excel file) projects_0_end_date=2014-04 projects_0_geolocation=St. John, U.S. Virgin Islands; California State University Northridge projects_0_name=LTREB Long-term coral reef community dynamics in St. John, USVI: 1987-2019 projects_0_project_nid=2272 projects_0_project_website=http://coralreefs.csun.edu/ projects_0_start_date=2009-05 projects_1_acronym=RUI-LTREB projects_1_description=Describing how ecosystems like coral reefs are changing is at the forefront of efforts to evaluate the biological consequences of global climate change and ocean acidification. Coral reefs have become the poster child of these efforts. Amid concern that they could become ecologically extinct within a century, describing what has been lost, what is left, and what is at risk, is of paramount importance. This project exploits an unrivalled legacy of information beginning in 1987 to evaluate the form in which reefs will persist, and the extent to which they will be able to resist further onslaughts of environmental challenges. This long-term project continues a 27-year study of Caribbean coral reefs. The diverse data collected will allow the investigators to determine the roles of local and global disturbances in reef degradation. The data will also reveal the structure and function of reefs in a future with more human disturbances, when corals may no longer dominate tropical reefs. The broad societal impacts of this