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Ecosystem Responses to Hurricanes across North America, the Caribbean, and Taiwan; 1985 to 2018

General Information
Data Package:
Local Identifier:edi.493.1
Title:Ecosystem Responses to Hurricanes across North America, the Caribbean, and Taiwan; 1985 to 2018
Alternate Identifier:DOI PLACE HOLDER
Abstract:

Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems. These data measure a variety of different parameters to understand ecosystem response such as biogeochemical, hydrological and physical measurements as well as the abundance of mobile and sedentary biota.       

Meteorological data characterizing tropical cyclones are also presented and are derived from:

IBTrACS: International Best Track Archive for Climate Stewardship
Global storm track data set for all recorded low pressure systems and tropical cyclones dating back to 1842. Data includes timestamped spatial information on storm center location as well as meteorological readings of wind speed, wind direction, and barometric pressure. Most importantly for the purposes of this data set, wind speeds at various distances from storm center are provided, which allows for the development of a model of wind speed with direction for different category storms. 


GRIDMET: University of Idaho Gridded Surface Meteorological Dataset
The Gridded Surface Meteorological dataset provides high spatial resolution (~4-km) daily surface fields of temperature, precipitation, winds, humidity and radiation across the contiguous United States from 1979. The dataset blends the high resolution spatial data from PRISM with the high temporal resolution data from the National Land Data Assimilation System (NLDAS) to produce spatially and temporally continuous fields that lend themselves to additional land surface modeling.
This dataset contains provisional products that are replaced with updated versions when the complete source data become available. Products can be distinguished by the value of the status property. At first, assets are ingested with status=early. After several days, they are replaced by assets with status=provisional. After about 2 months, they are replaced by the final assets with status=permanent.

Daymet V3: Daily Surface Weather and Climatological Summaries
Daymet V3 provides gridded estimates of daily weather parameters for United States, Mexico, Canada, Hawaii, and Puerto Rico. It is derived from selected meteorological station data and various supporting data sources.
Compared to the previous version, Daymet V3 uses an entirely new suite of inputs including:
•	NASA SRTM DEM version 2.1.
•	Land/Water Mask: MODIS 250 MOD44W_v2.NASA_ORNL_
•	Horizon files derived from the SRTM DEM.
•	Ground station weather inputs from several sources with QA/QC.

PERSIANN-CDR: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record
PERSIANN-CDR is a daily quasi-global precipitation product that spans the period from 1983-01-01 to present. The data is produced quarterly, with a typical lag of three months. The product is developed by the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine (UC-IRVINE/CHRS) using Gridded Satellite (GridSat-B1) IR data that are derived from merging ISCCP B1 IR data, along with GPCP version 2.2.


 CHIRPS Daily: Climate Hazards Group InfraRed Precipitation with Station Data (version 2.0 final)
Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.

Publication Date:2020-03-18
Language:English

Time Period
Begin:
1985-07-25
End:
2018-09-13

People and Organizations
Contact:Leon, Miguel (University of New Hampshire) 
Creator:Leon, Miguel (University of New Hampshire)
Creator:Patrick, Christopher (College of William and Mary, Biology Department, Virginia Institute of Marine Science)
Creator:Branoff, Benjamin (University of Tennessee Knoxville, National Institute for Mathematical and Biological Synthesis)
Creator:Kominoski, John (FIU, Department of Biological Sciences)
Creator:Armitage, Anna (Texas A&M University at Galveston, Department of Marine Biology)
Creator:Campos-Cerqueira, Marconi (Sieve Analytics, San Juan, PR)
Creator:Chapela Lara, María (University of New Hampshire)
Creator:Congdon, Victoria (University of Texas at Austin, Department of Marine Science)
Creator:Crowl, Todd (Florida International University, Institute of Water and Environment)
Creator:Devlin, Donna (Texas A&M University Corpus Christi, Department of Life Sciences)
Creator:Douglas, Sarah (University of Texas at Austin, Marine Sciences Institute)
Creator:Erisman, Brad (University of Texas at Austin, Marine Sciences Institute)
Creator:Feagin, Russell (Texas A&M University System, Department of Ecosystem Science and Management)
Creator:Fisher, Mark (Texas Parks and Wildlife)
Creator:Geist, Simon (Texas A&M University Corpus Christi, Department of Life Sciences)
Creator:Hall, Nathan (University of North Carolina at Chapel Hill Institute of Marine Sciences)
Creator:Hardison, Amber (University of Texas at Austin, Department of Marine Science)
Creator:Hogan, Aaron 
Creator:Lin, Teng-Chiu (National Taiwan Normal University, Department of Life Sciences)
Creator:Liu, Xianbin (The Institute for Tropical Ecosystem Studies (ITES), University of Puerto Rico)
Creator:Lu, Kaijun (University of Texas at Austin, Marine Sciences Institute)
Creator:Montagna, Paul (Florida International University, Institute of Water and Environment)
Creator:O'Connell, Christine (University of California-Berkeley)
Creator:Pennings, Steven (Department of Biology and Biochemistry, University of Houston)
Creator:Proffitt, C (Texas A&M University Corpus Christi, Department of Life Sciences)
Creator:Rehage, Jennifer (Florida International University)
Creator:Reustle, Joseph (Texas A&M University Corpus Christi, Department of Life Sciences)
Creator:Robinson, Kelly (University of Louisiana at Lafayette, Department of Biology)
Creator:Rush, Scott (Mississippi State University, Department of Wildlife, Fisheries, and Aquaculture)
Creator:Santos, Rolando (University of Miami, Rosenstiel School of Marine and Atmospheric Science)
Creator:Smith, Rachel (University of Georgia, Odum School of Ecology)
Creator:Starr, Gregory (University of Alabama, Department of Biological Sciences)
Creator:Strazisar, Theresa (Florida Atlantic University)
Creator:Strickland, Bradley (Florida International University, Department of Biological Sciences)
Creator:Wetz, Michael (Harte Research Institute for Gulf of Mexico Studies, Texas A&M University-Corpus Christi)
Creator:Kelly, Stephen (University of Louisiana at Lafayette, Department of Biology)
Creator:Wilson, Sara (Florida International University, Department of Biological Sciences)
Creator:Xinping, Hu (University of Texas at Austin, Marine Sciences Institute)
Creator:Xue, Jianhong (University of Texas at Austin, Marine Sciences Institute)
Creator:Yeager, Lauren (University of Texas at Austin, Marine Sciences Institute)
Creator:Zou, Xiaoming (University of Puerto Rico, Rio Piedras Campus)
Creator:McDowell, William (University of New Hampshire)

Data Entities
Data Table Name:
Ecosystem responses to hurricanes
Description:
Ecosystem response to hurricanes data were collected from 42 different researchers across 142 monitored ecosystem sites where 289 different observation types were inspected for responses to hurricanes that effected the sites. These ecosystem responses span North America, the Caribbean, and Taiwan. Data were reorganized and reclassified to be more consistent between entries and to achieve the ecosystem groupings we are looking for. Variables were divided into groups of Biogeochemistry, Physical, Sedentary Biota, Mobile Biota and Microfauna. Ecosystems were classified into groups of Forest, Lotic, Estuarine/In-Shore waters, Estuarine marshes, Riparian, and Off-Shore. Data was quality checked and site locations where verified.
Detailed Metadata

Data Entities

Data Table

Data:
 Inline Data
Name:Ecosystem responses to hurricanes
Description:Ecosystem response to hurricanes data were collected from 42 different researchers across 142 monitored ecosystem sites where 289 different observation types were inspected for responses to hurricanes that effected the sites. These ecosystem responses span North America, the Caribbean, and Taiwan. Data were reorganized and reclassified to be more consistent between entries and to achieve the ecosystem groupings we are looking for. Variables were divided into groups of Biogeochemistry, Physical, Sedentary Biota, Mobile Biota and Microfauna. Ecosystems were classified into groups of Forest, Lotic, Estuarine/In-Shore waters, Estuarine marshes, Riparian, and Off-Shore. Data was quality checked and site locations where verified.
Number of Records:29658
Number of Columns:39

Table Structure
Object Name:HurricaneWorkshopData_L0.csv
Size:13388836
Text Format:
Number of Header Lines:1
Record Delimiter:\r\n
Line Delimiter:\r\n
Orientation:column
Simple Delimited:
Field Delimiter:,
Quote Character:"

Table Column Descriptions
 IDContributor First NameContributor Last NameSource of FundingAward Number (if applicable)Variable CategorySystem NameSystem TypeVariableUnitsLatitudeLongitudeEvent YearEvent MonthEvent DayEventPre-Storm Sampling Interval (Days)Post-Storm Sampling Interval (Days)Pre-Storm Sampling Series Length (Days)Post-Storm Sampling Series Length (Days)Max/Min Value Post-HurricaneSD Max/Min (NaN if no replication)Baseline ValueSD Baseline Value (NaN if no replication)Observed ChangeProportional Change %Return Time (Days)Adjusted Return Time (Days)LogResponseRatioLong-Term Average Max/Min (NaN if none available)Long-Term SD Max/Min - (NaN if none available or no replication)Historic Period Length (years) - NaN if none availableHistorical Observed ChangeHistorical Proportional Change %Historical LogResponseRatioNotesNotes 2System MajorSystem Minor
Column Name:ID  
Contributor First Name  
Contributor Last Name  
Source of Funding  
Award Number (if applicable)  
Variable Category  
System Name  
System Type  
Variable  
Units  
Latitude  
Longitude  
Event Year  
Event Month  
Event Day  
Event  
Pre-Storm Sampling Interval (Days)  
Post-Storm Sampling Interval (Days)  
Pre-Storm Sampling Series Length (Days)  
Post-Storm Sampling Series Length (Days)  
Max/Min Value Post-Hurricane  
SD Max/Min (NaN if no replication)  
Baseline Value  
SD Baseline Value (NaN if no replication)  
Observed Change  
Proportional Change %  
Return Time (Days)  
Adjusted Return Time (Days)  
LogResponseRatio  
Long-Term Average Max/Min (NaN if none available)  
Long-Term SD Max/Min - (NaN if none available or no replication)  
Historic Period Length (years) - NaN if none available  
Historical Observed Change  
Historical Proportional Change %  
Historical LogResponseRatio  
Notes  
Notes 2  
System Major  
System Minor  
Definition:IDContributor First NameContributor Last NameSource of FundingAward Number (if applicable)The category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.Name of the ecosystem where samples were collected.The category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.The name of the response variable.The units of the response variable (if applicable).The latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762The longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762Year the event took placeMonth the event took place (numeric), e.g. if October, then enter "10"Day (numeric) the event took place, e.g. if October 22nd, then enter "22"Name of the event (e.g. Hurricane Maria)Sampling frequency for pre-storm data collection. If high frequency, e.g. 15 minute, then use fractional values such as 0.01041Sampling frequency for post-storm data collection. If high frequency, e.g. 15 minute, then use fractional values such as 0.01041Sampling period for pre-storm data collection. E.g. if 6 months of pre-storm data then report 180 daysSampling period for post-storm data collection. E.g. if 12 months of post-storm data then report 365 daysThe minimum or maximum value of the response variable after the hurricane. In the case of multiple peaks and valleys following the storm (no clear trend), the first peak or valley post storm was used (immediate response).If the minimum or maximum response was calculated from replicate samples, the standard deviation of those samples is reported here.The value of the response variable before the event. In the case of daily or weekly data, the mean value for month immediately before the event should be used account for natural variation.The standard deviation of the baseline value. In the case of temporal data, this is the standard devation for the month,week, etc for the period preceding the event (whichever is most appropriate or available). In the case of spatially replicated data taken before the storm this is the standard deviation of the replicates.The difference between the post-storm max/min and pre-storm baseline.The difference between the post-storm max/min and pre-storm baseline divided by the pre-storm baseline.The number of days post storm for values to return the baseline.In the case of variables that had not returned to baseline at end of sampling, assign the maximum post-event observation period in days. If return to baseline has been observed, then simply re-enter the value in the adjacent "Return Time (Days)" cellThe natural log of the min/max post hurricane value divided by the baseline value.The average value of the response variable, historically, at the time of year of first post storm sampling. For example, if monthly data was used to estimate the change in response between August 2017 (pre-storm for Hurricane Harvey) and September 2017 (post-storm for Hurricane Harvey), then this cell would contain the average value for September based on historical data.The standard deviation of the response variable, historically, at the time of year of first post storm sampling. For example, if monthly data was used to estimate the change in response between August 2017 (pre-storm for Hurricane Harvey) and September 2017 (post-storm for Hurricane Harvey), then this cell would contain the average value for September based on historical data.Number of years of data used to calculate the long term average valuesTypical change in response variable between the time of year when the storm occurred and the time of year when first post storm sampling occurredTypical percent change in response variable between the time of year when the storm occurred and the time of year when first post storm sampling occurredThe natural log of the long-term average min/max value divided by the baseline value.Any notes about the data set or values entered that the investigator believe are important. These may include caveates or concerns about estimate precision, moving baseline, etc, etcadditional notes fieldEither terrestrial or aquaticminor system type is wetland, estuarine, freshwater, marine, or terrestrial
Storage Type:string  
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Measurement Type:nominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominalnominal
Measurement Values Domain:
DefinitionID
DefinitionContributor First Name
DefinitionContributor Last Name
DefinitionSource of Funding
DefinitionAward Number (if applicable)
DefinitionThe category of the response. Classifications include biogeochemistry, hydrography, hydrology, mobile biota, sedentary biota, and phyiscal.
DefinitionName of the ecosystem where samples were collected.
DefinitionThe category of the ecosystems. Classifications include off-shore coastal, in-shore coastal, estuary, river, barrier island, riparian, terrestrial.
DefinitionThe name of the response variable.
DefinitionThe units of the response variable (if applicable).
DefinitionThe latitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionThe longitude of the data collection in the case of repeated fixed station sampling. Use a central spatial location if values were aggregated from randomly selected sampling locations within an ecosystem (e.g. embayment) whose precise location changed through time. Format is Signed Degrees Format (DDD.dddd), e.g. -120.9762
DefinitionYear the event took place
DefinitionMonth the event took place (numeric), e.g. if October, then enter "10"
DefinitionDay (numeric) the event took place, e.g. if October 22nd, then enter "22"
DefinitionName of the event (e.g. Hurricane Maria)
DefinitionSampling frequency for pre-storm data collection. If high frequency, e.g. 15 minute, then use fractional values such as 0.01041
DefinitionSampling frequency for post-storm data collection. If high frequency, e.g. 15 minute, then use fractional values such as 0.01041
DefinitionSampling period for pre-storm data collection. E.g. if 6 months of pre-storm data then report 180 days
DefinitionSampling period for post-storm data collection. E.g. if 12 months of post-storm data then report 365 days
DefinitionThe minimum or maximum value of the response variable after the hurricane. In the case of multiple peaks and valleys following the storm (no clear trend), the first peak or valley post storm was used (immediate response).
DefinitionIf the minimum or maximum response was calculated from replicate samples, the standard deviation of those samples is reported here.
DefinitionThe value of the response variable before the event. In the case of daily or weekly data, the mean value for month immediately before the event should be used account for natural variation.
DefinitionThe standard deviation of the baseline value. In the case of temporal data, this is the standard devation for the month,week, etc for the period preceding the event (whichever is most appropriate or available). In the case of spatially replicated data taken before the storm this is the standard deviation of the replicates.
DefinitionThe difference between the post-storm max/min and pre-storm baseline.
DefinitionThe difference between the post-storm max/min and pre-storm baseline divided by the pre-storm baseline.
DefinitionThe number of days post storm for values to return the baseline.
DefinitionIn the case of variables that had not returned to baseline at end of sampling, assign the maximum post-event observation period in days. If return to baseline has been observed, then simply re-enter the value in the adjacent "Return Time (Days)" cell
DefinitionThe natural log of the min/max post hurricane value divided by the baseline value.
DefinitionThe average value of the response variable, historically, at the time of year of first post storm sampling. For example, if monthly data was used to estimate the change in response between August 2017 (pre-storm for Hurricane Harvey) and September 2017 (post-storm for Hurricane Harvey), then this cell would contain the average value for September based on historical data.
DefinitionThe standard deviation of the response variable, historically, at the time of year of first post storm sampling. For example, if monthly data was used to estimate the change in response between August 2017 (pre-storm for Hurricane Harvey) and September 2017 (post-storm for Hurricane Harvey), then this cell would contain the average value for September based on historical data.
DefinitionNumber of years of data used to calculate the long term average values
DefinitionTypical change in response variable between the time of year when the storm occurred and the time of year when first post storm sampling occurred
DefinitionTypical percent change in response variable between the time of year when the storm occurred and the time of year when first post storm sampling occurred
DefinitionThe natural log of the long-term average min/max value divided by the baseline value.
DefinitionAny notes about the data set or values entered that the investigator believe are important. These may include caveates or concerns about estimate precision, moving baseline, etc, etc
Definitionadditional notes field
DefinitionEither terrestrial or aquatic
Definitionminor system type is wetland, estuarine, freshwater, marine, or terrestrial
Missing Value Code:                                                                              
Accuracy Report:                                                                              
Accuracy Assessment:                                                                              
Coverage:                                                                              
Methods:                                                                              

Data Package Usage Rights

Data Policies

            This data package is released to the “public domain” under Creative Commons CC0 1.0 “No Rights Reserved” (see: https://creativecommons.org/publicdomain/zero/1.0/). It is considered professional etiquette to provide attribution of the original work if this data package is shared in whole or by individual components. A generic citation is provided for this data package on the website https://portal.edirepository.org (herein “website”) in the summary metadata page. Communication (and collaboration) with the creators of this data package is recommended to prevent duplicate research or publication. This data package (and its components) is made available “as is” and with no warranty of accuracy or fitness for use. The creators of this data package and the website shall not be liable for any damages resulting from misinterpretation or misuse of the data package or its components. Periodic updates of this data package may be available from the website. Thank you.

Keywords

By Thesaurus:
Core Areasdisturbance
LTER Controlled Vocabularyecosystem ecology, productivity

Methods and Protocols

These methods, instrumentation and/or protocols apply to all data in this dataset:

Methods and protocols used in the collection of this data package
Description:

Ecosystem response to hurricanes data were collected from 42 different researchers across 142 monitored ecosystem sites where 289 different observation types were inspected for responses to hurricanes that effected the sites. These ecosystem responses span North America, the Caribbean, and Taiwan. Data were reorganized and reclassified to be more consistent between entries and to achieve the ecosystem groupings we are looking for. Variables were divided into groups of Biogeochemistry, Physical, Sedentary Biota, Mobile Biota and Microfauna.  Ecosystems were classified into groups of Forest, Lotic, Estuarine/In-Shore waters, Estuarine marshes, Riparian, and Off-Shore. Data was quality checked and site locations where verified.

People and Organizations

Creators:
Individual: Miguel Leon
Organization:University of New Hampshire
Individual: Christopher Patrick
Organization:College of William and Mary, Biology Department, Virginia Institute of Marine Science
Individual: Benjamin Branoff
Organization:University of Tennessee Knoxville, National Institute for Mathematical and Biological Synthesis
Individual: John Kominoski
Organization:FIU, Department of Biological Sciences
Individual: Anna Armitage
Organization:Texas A&M University at Galveston, Department of Marine Biology
Individual: Marconi Campos-Cerqueira
Organization:Sieve Analytics, San Juan, PR
Individual: María Chapela Lara
Organization:University of New Hampshire
Individual: Victoria Congdon
Organization:University of Texas at Austin, Department of Marine Science
Individual: Todd Crowl
Organization:Florida International University, Institute of Water and Environment
Individual: Donna Devlin
Organization:Texas A&M University Corpus Christi, Department of Life Sciences
Individual: Sarah Douglas
Organization:University of Texas at Austin, Marine Sciences Institute
Individual: Brad Erisman
Organization:University of Texas at Austin, Marine Sciences Institute
Individual: Russell Feagin
Organization:Texas A&M University System, Department of Ecosystem Science and Management
Individual: Mark Fisher
Organization:Texas Parks and Wildlife
Individual: Simon Geist
Organization:Texas A&M University Corpus Christi, Department of Life Sciences
Individual: Nathan Hall
Organization:University of North Carolina at Chapel Hill Institute of Marine Sciences
Individual: Amber Hardison
Organization:University of Texas at Austin, Department of Marine Science
Individual: Aaron Hogan
Individual: Teng-Chiu Lin
Organization:National Taiwan Normal University, Department of Life Sciences
Individual: Xianbin Liu
Organization:The Institute for Tropical Ecosystem Studies (ITES), University of Puerto Rico
Individual: Kaijun Lu
Organization:University of Texas at Austin, Marine Sciences Institute
Individual: Paul Montagna
Organization:Florida International University, Institute of Water and Environment
Individual: Christine O'Connell
Organization:University of California-Berkeley
Individual: Steven Pennings
Organization:Department of Biology and Biochemistry, University of Houston
Individual: C Proffitt
Organization:Texas A&M University Corpus Christi, Department of Life Sciences
Individual: Jennifer Rehage
Organization:Florida International University
Individual: Joseph Reustle
Organization:Texas A&M University Corpus Christi, Department of Life Sciences
Individual: Kelly Robinson
Organization:University of Louisiana at Lafayette, Department of Biology
Individual: Scott Rush
Organization:Mississippi State University, Department of Wildlife, Fisheries, and Aquaculture
Individual: Rolando Santos
Organization:University of Miami, Rosenstiel School of Marine and Atmospheric Science
Individual: Rachel Smith
Organization:University of Georgia, Odum School of Ecology
Individual: Gregory Starr
Organization:University of Alabama, Department of Biological Sciences
Individual: Theresa Strazisar
Organization:Florida Atlantic University
Individual: Bradley Strickland
Organization:Florida International University, Department of Biological Sciences
Individual: Michael Wetz
Organization:Harte Research Institute for Gulf of Mexico Studies, Texas A&M University-Corpus Christi
Individual: Stephen Kelly
Organization:University of Louisiana at Lafayette, Department of Biology
Individual: Sara Wilson
Organization:Florida International University, Department of Biological Sciences
Individual: Hu Xinping
Organization:University of Texas at Austin, Marine Sciences Institute
Individual: Jianhong Xue
Organization:University of Texas at Austin, Marine Sciences Institute
Individual: Lauren Yeager
Organization:University of Texas at Austin, Marine Sciences Institute
Individual: Xiaoming Zou
Organization:University of Puerto Rico, Rio Piedras Campus
Address:
University of Puerto Rico at Rio Piedras, Ponce de Leon Ave.,,
San Juan, PR 00931 US
Phone:
(787) 764-0000 x2868 (voice)
Phone:
(787) 772-1481 (facsimile)
Email Address:
xzou2011@gmail.com
Individual: William McDowell
Organization:University of New Hampshire
Contacts:
Individual: Miguel Leon
Organization:University of New Hampshire

Temporal, Geographic and Taxonomic Coverage

Temporal, Geographic and/or Taxonomic information that applies to all data in this dataset:

Time Period
Begin:
1985-07-25
End:
2018-09-13
Geographic Region:
Description:Data here represent the responses of ecosystems to hurricanes from across North America, the Caribbean and Taiwan. Data across a variety of ecosystems are represented here including wetlands, estuaries, marine, terrestrial and fresh water systems.
Bounding Coordinates:
Northern:  -90Southern:  90
Western:  -180Eastern:  180

Project

Parent Project Information:

Title: Ecosystem Responses to Hurricanes Synthesis Workshop
Personnel:
Individual:Dr. Christopher Patrick
Address:
William and Mary, Virginia Institue of Marine Science,
1370 Greate Rd.,
Gloucester Point, Virginia 23062 United States
Email Address:
miller@msi.ucsb.edu
Role:Principal Investigator
Abstract:

hree major hurricanes (Harvey, Irma, and Maria) made landfall in the United States during the fall of 2017. Catastrophic human, economic, and ecological effects occurred from the storm surge, saltwater intrusion, wind damage, and flooding. The number of storms, their intensity, the number that made landfall, and the catastrophic damage was remarkable. Global models predict hurricanes will increase and effected areas will change over the next century. Thus, synthesizing responses to severe tropical storm disturbances is needed to understand general patterns of impact and recovery. This award will provide workshop funds for researchers, experts, managers, and graduate students to develop a synthetic understanding of hurricane impacts on freshwater, estuarine, and terrestrial ecosystems. The proposed workshop will provide important opportunities for career advancement and training of underrepresented and early career scientists. The unprecedented landfall of multiple major hurricanes in the United States presents a rare opportunity to document generalizable patterns in ecosystem response to extreme disturbance. The workshop will leverage the significant investment of the National Science Foundation in Hurricane Research following the devastating impacts of storms Harvey, Irma, and Maria in 2017 to generate a novel cross-system disturbance ecology synthesis. The synthesis will result from three major activities including: recruitment and workflow design, a three-day workshop, and post-workshop analysis and synthesis. The workshop will bring together research teams studying multiple ecosystem types (estuarine, freshwater, terrestrial) and ecosystem responses (physical, biogeochemical, organismal ? mobile vs sedentary, microbial, animal, and plant) to these hurricanes. The participants will merge diverse datasets into a common statistical framework and conduct synthetic analyses to identify shared and unique responses to different types of hurricane stressors. Both workshop and post workshop activities will yield an understanding of how ecosystems respond to severe disturbance and contribute a generalizable conceptual framework that is applicable across ecosystems and ecosystem components.

Funding:

NSF Award DEB-1903760

Maintenance

Maintenance:
Description:
Frequency:
Other Metadata

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