In 2024, there were 42 hurricanes registered worldwide, up from 45 hurricanes a year earlier. This was nevertheless below the average of 47 hurricanes per year registered from 1990 to 2022. The years of 1992 and 2018 tied as the most active in the indicated period, each with 59 hurricanes recorded. The Pacific Northwest basin recorded the largest number of hurricanes in 2024. Most exposed countries to hurricanes With the Pacific Northwest basin being one of the most active for hurricanes in the world, there is perhaps no surprise that Japan and the Philippines were two of the countries most exposed to tropical cyclones in 2024, both West Pacific nations. Meanwhile, the Dominican Republic was the most exposed country in the Atlantic Ocean and ranked first as the most exposed country worldwide during the same year. Effects of tropical cyclones From 1970 to 2019, almost 800,000 deaths due to tropical cyclones have been reported worldwide. In the past decade, the number of such casualties stood at some 19,600, the lowest decadal figure in the last half-century. In contrast to the lower number of deaths, economic losses caused by tropical cyclones have continuously grown since 1970, reaching a record high of more than 700 billion U.S. dollars from 2010 to 2019.

Between 2011 and 2020, 19 hurricanes made landfall in the United States, the same figure reported in the previous decade. This is the highest number recorded for a 10-year timespan since the 1940s, which holds the current record for most landfalls, with 24 hurricanes. In 2023, only hurricane Ian made landfall in the U.S.

Between 1851 and 2023, there were 304 hurricane direct hits in the United States, of which 40 percent were category 1 hurricanes. In the same period, 97 major hurricanes (with a category 3 or higher) made landfall in the country. Hurricane Michael, in 2018, was the latest category 5 hurricane to hit the North American country. Florida was the state most commonly hit by hurricanes.

The State of the Climate is a collection of periodic summaries recapping climate-related occurrences on both a global and national scale. The State of the Climate Monthly Overview - Hurricanes & Tropical Storms report focuses primarily on storms and conditions that affect the U.S. and its territories, in Atlantic and Pacific basins. The report places each basin's tropical cyclone activity in a climate-scale context. Key statistics (dates, strengths, landfall, energy, etc.) for major cyclone activity in other basins is occasionally presented. Reports began in June 2002. The primary Atlantic hurricane season (June-November) is covered each year; other months are included as storm events warrant. An annual summary is available from 2002. These reports are not updated in real time.

In 2021, there were 68 fatalities due to hurricanes reported in the United States. Since the beginning of the century, the highest number of fatalities was recorded in 2005, when four major hurricanes – including Hurricane Katrina – resulted in 1,518 deaths.

The worst hurricanes in U.S. history
Hurricane Katrina, which made landfall in August 2005, ranked as the third deadliest hurricane in the U.S. since records began. Affecting mainly the city of New Orleans and its surroundings, the category 3 hurricane caused an estimated 1,500 fatalities. Katrina was also the costliest tropical cyclone to hit the U.S. in the past seven decades, with damages amounting to roughly 186 billion U.S. dollars. Hurricanes Harvey and Maria, both of which made landfall in 2017, ranked second and third, resulting in damage costs of 149 and 107 billion dollars, respectively.

How are hurricanes classified?
According to the Saffir-Simpson scale, hurricanes can be classified into five categories, depending on their maximum sustained wind speed. Most of the hurricanes that have made landfall in the U.S. since 1851 are category 1, the mildest of the five. Hurricanes rated category 3 or above are considered major hurricanes and can cause devastating damage. In 2021, there were 38 hurricanes recorded across the globe, of which 17 were major hurricanes.

In 2024, there were four hurricanes tracked in the Atlantic basin, up from seven recorded a year earlier. 2020 had recorded the second most active hurricane season in the displayed period. It only ranked behind 2005, when 15 hurricanes were recorded in the region. Between 1990 and 2021, there were on average seven hurricanes tracked per year in the Atlantic. In the same period, 54 hurricanes made landfall in the U.S.

This web map includes the track points, track lines, and hazard layers for historical Hurricane Georges, AL071998.DATA OVERVIEWKinetic Analysis's Tropical Cyclone datasets use best-track data for the requested storm as is available from IBTRaCS (or, for recent storms where there is no best-track, we use ATCF a-deck data provided by the U.S. National Hurricane Center, Joint Typhoon Warning Center, or Central Pacific Hurricane Center) to drive in-house, advanced numerical modeling that computes the spatial distribution of maximum wind speedwinds by Saffir-Simpson categorieswave heightsstorm surge inundationcumulative rainfallUSE CASESWhile this data may be used in a variety of ways, the most common ways we see it in action is by insurance, emergency management, disaster relief, supply chain, and governmental agencies/organization in making decisions about actions to take before, during, and after a tropical cyclone. A collection of historical tropical cyclone data can provide information on the probability and trends that can be expected for a given location affected by tropical cyclones in the future. Claims officers, for example, can use this information to determine the vulnerability and exposure level of a given area or property. Government agencies can use impact data to determine where to focus on building climate resilience safeguards and resources next.DATA SOURCEHazard footprints are based on observed storm track, intensity and wind radii provided by the designated expert-reviewed sources U.S. NHC (National Hurricane Center), JTWC (Joint Typhoon Warning Center), CPHC (Central Pacific Hurricane Center) - collectively termed OFCL (Official). UPDATE FREQUENCYSince these are historical/past storms, as long as the storm's path was recorded and publicly available, the resulting hazards and impacts can be modeled by Kinetic Analysis at any time upon request.SCALE/RESOLUTIONThis post-event data is provided at a 30 arcsecond (~1 km) resolution. AREA COVEREDWorldINTERESTED IN MORE?Our full ArcGIS Marketplace listing grants you access to the Kinetic Analysis Corporation's proprietary tropical storm hazard data for a past/historical tropical cyclone of your choice per purchase, to be custom-generated for you upon purchase request. Different price options are available for those who wish to purchase to purchase footprints for multiple historical storms, bundle with our real-time data, or make other custom requests.Customized resolutions, best track data source, and data units (default is SI) are available upon request to sales@kinanco.com. Learn more on the Kinetic Analysis website.GLOSSARY/DATA FIELDSTrack Points - These points indicate the locations of a storm over time. They are generated by forecast agencies and numerical model guidance.Track Line - This is the line formed by connecting all the track points. It depicts a continuous path for the storm by interpolating between any two track points.ATCF ID - Unique ID associated with a tropical cyclone, defined using the Automated Tropical Cyclone Forecasting (ATCF) system. The format is usually a two-letter abbreviation of the ocean basin (see "Storm Basin" below for list) in which the storm can be found, the annual cyclone number starting from 1 for the first storm in each basin per year, and the 4-digit year. For example, AL112017 (Hurricane Irma) refers to AL (Atlantic basin), 11th storm of the year in that basin, in the year 2017.Storm Name - The World Meteorological Organization (WMO) tropical cyclone name, such as Irma, Katrina, and Rai.Storm Basin - Ocean basin in which the storm is taking place. These include AL (North Atlantic), WP (Western North Pacific), CP (Central North Pacific), EP (Eastern North Pacific), IO (North Indian Ocean), SH (South-West Indian Ocean, Australian region, and South Pacific Ocean), and LS (Southern Atlantic).Storm Age - Number of days the storm has been active at time of forecastCategory Description - How the selected layer would be categorized against similar data. For example, data in a wind layer may be categorized into groups of 5 mph each, such as 100-105 mph for one group and 105-110 mph for another group. In such a case, the category description field displays which grouping the selected location belongs to. This is a variable/field separate from the name of each map layer.Latitude & Longitude - Geographic indicators of a storm's past, current, or forecast location derived from dividing the Earth into grids measured in degrees.Wind Speed - Maximum wind speed of the storm at that location. The units are knots for track points and track line layers and miles per hour (mph) for the wind speed hazard layer. These represent terrain-adjusted, 2-minute sustained winds at 10-meter elevation and are consistent with wind speeds reported by Automated Surface Observing Stations (ASOS weather stations). They can differ from wind speed forecast by different agencies because, in contrast with winds forecast by agencies such as the NHC, Kinetic Analysis-generated winds account for the effects of surface roughness and topography. In addition, different agencies can report winds based on different averaging times. For example, the NHC and JTWC report 1-minute sustained winds while the World Meteorological Organization (WMO) standard is 10-minute sustained winds.Minimum Sea Level Pressure - The lowest sea level pressure at that storm location. Measured in millibars.Radius of Max Winds - The distance between the storm's center, where the central pressure is lowest, and the maximum winds of a storm. Measured in nautical miles. Forward Speed - How fast a storm is moving at the selected location. Measured in meters per second (m/s).Storm Direction - The direction toward which a storm is moving at the selected location. Measured with a 360-degree system where North is represented by 0 degrees and East by 90 degrees.Forecast Time - Time at which an agency (such as OFCL) released its newest update of storm track data. This is the set of data used to simulate the model results displayed. Simulation Time - Time at which Kinetic Analysis's models processed the current data.Model in Simulation - The forecast agency, or model that generated the inputs for the Kinetic Analysis-simulated storm hazard data.NOTE: This map and its data are provided for informational purposes only. Due to limitations in modern modeling technology, this data may not reflect the ultimate path, hazards, and/or impacts of a storm with 100% accuracy. Usage of this map and its data voids Kinetic Analysis of any responsibilities for consequences that may arise from using it to make personal or business decisions.

About this dataset

Context

The dataset tabulates the Hurricane population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Hurricane across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Hurricane was 6,806, a 0.61% decrease year-by-year from 2022. Previously, in 2022, Hurricane population was 6,848, a decline of 1.05% compared to a population of 6,921 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Hurricane increased by 719. In this period, the peak population was 6,961 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Hurricane is shown in this column.
• Year on Year Change: This column displays the change in Hurricane population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Hurricane Population by Year. You can refer the same here

In 2023, hurricane Idalia – a hurricane of category 3 – made landfall in the United States. Just three years earlier, the North American country had seen the record number of hurricanes to hit the nation in one year. Despite the lower number of hurricanes, 2021 tied with 2002 for the highest number of tropical storms to hit the U.S. in one year, with six occurrences. Tropical storms are cyclones with a wind speed surpassing 39 miles per hour but below the hurricane threshold of 74 miles per hour.

Nearly 40 percent of all hurricanes that made landfall in the United States between 1851 and 2022 hit Florida. The state was hit by 120 hurricanes in the period, of which 37 were major hurricanes (category 3 or higher). Texas and Louisiana were the second and third most hit states in the country, with 64 and 63 hurricanes, respectively.

The Hurricane Intensity product contains information about tropical cyclones along their trajectories from the time they are identified to the current time. Tropical cyclone information provided in the product includes its identity, _location, maximum wind speed, Dvorak tropical cyclone current intensity number, detailed wind shear, cloud, and eye characteristics, strengthening and weakening state information, and the start, midpoint, and end observation time of the source ABI product image. Data quality information is not included in the product. A hurricane intensity product file is produced for each tropical cyclone. The units of measure for the maximum sustained wind speed value is meters per second. The advanced Dvorak technique tropical cyclone current and tropical intensity numbers are dimensionless quantities. The Hurricane Intensity product is produced using ABI Full Disk coverage region observations. Product data is produced when a tropical cyclone is in the ABI's field of regard for both daytime and nighttime conditions.

This project used a combination of historical research and computer modeling to study the impacts of hurricanes in New England since 1620. For details on methods and results, please see the published paper (Boose, E. R., K. E. Chamberlin and D. R. Foster. 2001. Landscape and regional impacts of hurricanes in New England. Ecological Monographs 71: 27-48). The Abstract from the paper is reproduced below. "Hurricanes are a major factor controlling ecosystem structure, function and dynamics in many coastal forests and yet their ecological role can be understood only by assessing impacts in space and time over a period of centuries. We present a new method for reconstructing hurricane disturbance regimes using a combination of historical research and computer modeling. Historical data on wind damage for each hurricane in the selected region are quantified using the Fujita scale to produce regional maps of actual damage. A simple meteorological model (HURRECON), parameterized and tested for selected recent hurricanes, provides regional estimates of wind speed, direction, and damage for each storm. Individual reconstructions are compiled to analyze spatial and temporal patterns of hurricane impacts. Long-term effects of topography on a landscape scale are then examined with a simple topographic exposure model (EXPOS). "We applied this method to New England, USA, examining hurricanes since European settlement in 1620. Results showed strong regional gradients in hurricane frequency and intensity from southeast to northwest: average return intervals for F0 damage on the Fujita scale (loss of leaves and branches) ranged from 5 to 85 years, average return intervals for F1 damage (scattered blowdowns, small gaps) ranged from 10 to more than 200 years, and average return intervals for F2 damage (extensive blowdowns, large gaps) ranged from 85 to more than 380 years. On a landscape scale, average return intervals for F2 damage in the town of Petersham MA ranged from 125 years across most sites to more than 380 years on scattered lee slopes. Actual forest damage was strongly dependent on land-use and natural disturbance history. Annual and decadal timing of hurricanes varied widely. There was no clear century-scale trend in the number of major hurricanes. "The historical-modeling approach is applicable to any region with good historical records and will enable ecologists and land managers to incorporate insights on hurricane disturbance regimes into the interpretation and conservation of forests at landscape to regional scales."

Storm Data is provided by the National Weather Service (NWS) and contain statistics on personal injuries and damage estimates. Storm Data covers the United States of America. The data began as early as 1950 through to the present, updated monthly with up to a 120 day delay possible. NCDC Storm Event database allows users to find various types of storms recorded by county, or use other selection criteria as desired. The data contain a chronological listing, by state, of hurricanes, tornadoes, thunderstorms, hail, floods, drought conditions, lightning, high winds, snow, temperature extremes and other weather phenomena.

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons.

Fields available:
SID: A unique storm identifier (SID) assigned by IBTrACS algorithm.
ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss)
BASIN: Basin of the current storm position
SUBBASIN: Sub-basin of the current storm position
NATURE: Type of storm (a combination of the various types from the available sources)
NUMBER: Number of the storm for the year (restarts at 1 for each year
LAT: Mean position - latitude (a combination of the available positions)
LON: Mean position - longitude (a combination of the available positions)
WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency
WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

The National Weather Service (NWS) maintains historical Atlantic Ocean hurricane weather data in a format that can be exploited by Graphic Information System (GIS) software. Using KML/KMZ formats, it takes the numbers and words from the rows and columns in databases and spreadsheets and puts them on a map. This data file contains information about named and unnamed Altantic tropical storms and hurricanes from 1851 to 2006. Once downloaded, the file can be decompiled by decade and by year in a a KML/KMZ GIS viewer.

About this dataset

Context

The dataset tabulates the data for the Hurricane, UT population pyramid, which represents the Hurricane 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 Hurricane, UT, is 29.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Hurricane, UT, is 40.1.
• Total dependency ratio for Hurricane, UT is 69.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Hurricane, UT is 2.5.

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 Hurricane population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Hurricane for the selected age group is shown in the following column.
• Population (Female): The female population in the Hurricane for the selected age group is shown in the following column.
• Total Population: The total population of the Hurricane 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 Hurricane Population by Age. You can refer the same here

Historical Dataset of Hurricane Middle School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1987-2023),Total Classroom Teachers Trends Over Years (1987-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1987-2023),Asian Student Percentage Comparison Over Years (2002-2023),Hispanic Student Percentage Comparison Over Years (2009-2023),Black Student Percentage Comparison Over Years (1999-2023),White Student Percentage Comparison Over Years (1993-2023),Two or More Races Student Percentage Comparison Over Years (2014-2023),Diversity Score Comparison Over Years (1993-2023),Free Lunch Eligibility Comparison Over Years (1993-2023),Reduced-Price Lunch Eligibility Comparison Over Years (1999-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2022),Math Proficiency Comparison Over Years (2010-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2010-2022)

The National Weather Service (NWS) maintains historical Eastern Pacific Ocean hurricane weather data in a format that can be exploited by Graphic Information System (GIS) software. Using KML/KMZ formats, it takes the numbers and words from the rows and columns in databases and spreadsheets and puts them on a map. This data file contains information about named and unnamed Eastern Pacific Ocean tropical storms and hurricanes from 1949 to 2006. Once downloaded, the file can be decompiled by decade and by year in a a KML/KMZ GIS viewer.

The 2020 Atlantic hurricane season ranked amongst the top six most powerful seasons in the satellite era, based on Accumulated Cyclone Energy (ACE). That year, the 30 named storms (including 14 hurricanes, of which seven major hurricanes) in the basin registered an ACE of 180. All of the top ten most extreme Atlantic hurricane seasons recorded in the satellite era happened since 1990, with 2005 leading the ranking with an ACE of 250.

Names and descriptions of hurricanes near St. John USVI. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Based on Tsounis and Edmunds (In press), Ecosphere:\u00a0

Physical environmental conditions were characterized using three features that are well-known to affect coral reef community dynamics (described in Glynn 1993, Rogers 1993, Fabricius et al. 2005): seawater temperature, rainfall, and hurricane intensity. Together, these were used to generate seven dependent variables describing physical environmental features. Seawater temperature was recorded at each site every 15-30 min using a variety of logging sensors (see Edmunds 2006 for detailed information on the temperature measurement regime). Seawater temperature was characterized using five dependent variables calculated for each calendar year: mean temperature, maximum temperature, and minimum temperature (all averaged by day and month for each year), as well as the number of days hotter than 29.3 deg C (\u201chot days\u201d), and the number of days with temperatures greater than or equal to 26.0 deg C (\u201ccold days\u201d). The temperature defining "hot days" was determined by the coral bleaching threshold for St. John ("%5C%22http://www.coral.noaa.gov/research/climate-change/coral-%0Ableaching.html%5C%22">http://www.coral.noaa.gov/research/climate-change/coral- bleaching.html), and the temperature defining "cold days" was taken as 26.0 deg C which marks the lower 12th percentile of all daily temperatures between 1989 and 2005 (Edmunds, 2006). The upper temperature limit was defined by the local bleaching threshold, and the lower limit defined the 12th\u00a0percentile of local seawater temperature records (see Edmunds 2006 for details). Rainfall was measured at various locations around St. John (see http://www.sercc.com) but often on the north shore (courtesy of R.\u00a0Boulon) (see Edmunds and Gray 2014). To assess the influence of hurricanes, a categorical index of local hurricane impact was employed, with the index based on qualitative estimates of wave impacts in Great Lameshur Bay as a function of wind speed, wind direction, and distance of the nearest approach of each hurricane to the study area (see Gross and Edmunds 2015). Index values of 0 were assigned to years with no hurricanes, 0.5 to hurricanes with low impacts, and 1 for hurricanes with high impacts, and years were characterized by the sum of their hurricane index values. 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=562085 awards_1_award_number=OCE-1332915 awards_1_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1332915 awards_1_funder_name=NSF Division of Ocean Sciences awards_1_funding_acronym=NSF OCE awards_1_funding_source_nid=355 awards_1_program_manager=David L. Garrison awards_1_program_manager_nid=50534 awards_2_award_nid=562593 awards_2_award_number=DEB-1350146 awards_2_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=1350146 awards_2_funder_name=NSF Division of Environmental Biology awards_2_funding_acronym=NSF DEB awards_2_funding_source_nid=550432 awards_2_program_manager=Betsy Von Holle awards_2_program_manager_nid=701685 cdm_data_type=Other comment=Hurricane Data G. Tsounis and P. Edmunds, PIs Version 10 November 2016 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.664760 infoUrl=https://www.bco-dmo.org/dataset/664267 institution=BCO-DMO metadata_source=https://www.bco-dmo.org/api/dataset/664267 param_mapping={'664267': {}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/664267/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=California State University Northridge people_1_affiliation_acronym=CSU-Northridge people_1_person_name=Dr Georgios Tsounis people_1_person_nid=565353 people_1_role=Co-Principal Investigator people_1_role_type=originator 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,VI Octocorals 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