James Patrick Cronin
Leah L. Dale
Virginia L. Brink
Blair E. Tirpak
John M. Tirpak
20210120
Bayesian network model that predicts the probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon at a 30-m pixel scale in Apalachicola Bay, FL
Netica conditional probability tables
Reston, VA
U.S. Geological Survey
https://doi.org/10.5066/P9KNSKMT
Leah L. Dale
James Patrick Cronin
Virginia L. Brink
Blair E. Tirpak
John M. Tirpak
William E. Pine
20210323
Identifying Information Gaps in Predicting Winter Foraging Habitat for Juvenile Gulf Sturgeon
publication
n/a
Wiley
https://doi.org/10.1002/tafs.10288
The Gulf Sturgeon is a federally listed, anadromous species, inhabiting Gulf Coast rivers, estuaries, and coastal waters from Louisiana to Florida. The U.S. Geological Survey partnered with the U.S. Fish and Wildlife Service (USFWS), U.S. Army Corps of Engineers, University of Georgia, and their conservation partners to support adaptive management of Gulf Sturgeon (Acipenser oxyrinchus desotoi) by developing a quantitative, spatial model. The model is a Bayesian network that predicts the probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon at a 30-m pixel scale in estuarine critical habitat. The model predicts habitat availability (days) for 75 alternative physiological and habitat scenarios, which were the unique combination of river discharge, winter month, and month of arrival to the estuary. The probability of habitat availability (days) is predicted from habitat characteristics that could be influenced by management actions. The model's structure was defined by empirical data, expert elicitation, and simplifying assumptions.
Data were collected to create a spatially explicit Bayesian network model for Gulf Sturgeon that predicts habitat availability (days) for 75 alternative physiological and habitat scenarios, which were the unique combination of river discharge, winter month, and month of arrival to the estuary. The tabular data associated with this metadata were a combination of empirical data available in the literature, expert elicitation, and simplifying assumptions. These tabular data were used to populate conditional probability tables for predicted variables. Of the 75 possible model outputs, geospatial datasets could not be created for 28 scenarios due to missing data and 12 scenarios were excluded due to incongruous regression results. When entering evidence directly into the Bayesian network, the 40 omitted scenarios have equal prior probabilities for each acceptable water condition (days) class.
The findings and conclusions in this dataset are those of the author(s) and do not necessarily represent the views of the U.S. Fish and Wildlife Service.
Author ORCIDs: Cronin, J.P(0000-0001-6791-5828);Dale, L.L.(0000-0002-3480-9954);Brink, V.L.(0000-0001-7575-6759);Tirpak, B.E.(0000-0002-2679-8378);Tirpak, J.M.(0000-0003-1937-9754)
2019
publication date
As needed
-85.263684009375
-84.702303475953
29.820224339057
29.591217918316
Apalachicola Bay, FL
ISO 19115 Topic Category
environment
farming
NASA Global Change Master Directory
Sturgeons/Paddlefishes
estuaries
custom
recovery objectives
habitat objectives
spatial models
Bayesian network
decision support tools
strategic habitat conservation
adaptive management
landscape conservation
water condition
USGS Metadata Identifier
USGS:5ab94893e4b081f61ab9ca2a
Common geographic areas
Apalachicola Bay
Gulf coast
None.
Acknowledgement of the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data. Sharing new data layers developed directly from the data would be appreciated by the WARC staff. Users should be aware that comparison with other datasets for the same area from other time periods may be inaccurate because of inconsistencies resulting from changes in mapping conventions, data collection procedures, and computer processes over time. These data have been approved for release by the USGS. Although these data have been subjected to rigorous review and are substantially complete, the USGS reserves the right to revise the data pursuant to further analysis and review. Furthermore, these data are released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.
James Patrick Cronin
U.S. Geological Survey, Southeast Region
Ecologist
Mailing and Physical
700 Cajundome Blvd
Lafayette
LA
70506
United States
337-266-8589
jcronin@usgs.gov
This work was supported with funding from the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative, Gulf Coast Prairie Landscape Conservation Cooperative, the U.S. Fish and Wildlife Service and the U.S. Geological Survey. Additional support was provided by U.S. Army Corps of Engineers and the University of Georgia.
Netica 5.12 (Norsys Software Corporation, Vancouver, British Columbia)
Blair E. Tirpak
James P. Cronin
Leah L. Dale
Virginia L. Brink
John M. Tirpak
2017
Biological planning units and aquatic extensions for the Gulf Coast
dataset
https://www.sciencebase.gov
U.S. Geological Survey
https://doi.org/10.5066/f7tt4p5c
USGS Biocomplexity Thesaurus
Fishes
Integrated Taxonomic Information System (ITIS)
2018
Integrated Taxonomic Information System (ITIS)
Netica conditional probability tables
Washington, D.C.
Integrated Taxonomic Information System (ITIS)
http://itis.gov
NA
Species were not collected.
Gulf Sturgeon, Acipenser oxyrinchus desotoi, is a subspecies of the Atlantic Sturgeon, A. Oxyrinchus. Direct observations of Gulf Sturgeon were not collected or analyzed for model creation or parameterization.
Kingdom
Animalia
Subkingdom
Bilateria
Infrakingdom
Deuterostomia
Phylum
Chordata
Subphylum
Vertebrata
Infraphylum
Gnathostomata
Superclass
Actinopterygii
Class
Chondrostei
Order
Acipenseriformes
Suborder
Acipenseroidei
Family
Acipenseridae
Subfamily
Acipenserinae
Genus
Acipenser
Species
Acipenser oxyrinchus
Subspecies
Acipenser oxyrinchus desotoi
Gulf Sturgeon
Bayesian networks are discrete probabilistic models. Netica® is a platform that enables users to create and view Bayesian network models.
https://www.norsys.com/index.html
Downloads and instructions are available at the Norsys Software Corp. website
Norsys Software Corp.
mailing and physical
3512 West 23rd Ave.
Vancouver
BC
V6S 1K5
Canada
604-221-2223
info@norsys.com
Norsys Software Corp.
1995
Netica
Tools Software
No formal attribute accuracy tests were conducted
No formal logical accuracy tests were conducted
Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details.
No formal positional accuracy tests were conducted
No formal positional accuracy tests were conducted
National Hurricane Center
2010
Estimated return period in years for major hurricanes passing within 50 nautical miles of various locations on the U.S. Coast
map image
Miami, Florida
National Hurricane Center
http://www.nhc.noaa.gov/climo/#returns
Digital and/or Hardcopy Resources
2010
publication date
Storm Return
These data were used to develop the Major Storm Return Time dataset.
Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS)
1998
Apalachicola Bay, FL (G100) Bathymetric Digital Elevation Model (30-m resolution) Derived from Source Hydrographic Survey Soundings Collected by NOAA
Raster Digital Data Set
Silver Spring, Maryland
Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS)
https://data.noaa.gov/dataset/dataset/estuarine-bathymetric-digital-elevation-models-30-meter-resolution-derived-from-source-hydrogra
Digital and/or Hardcopy Resources
1998
publication date
30-m Bathymetry
These data were used to delineate the spatial extent of the model, and to develop the Bathymetry (m) and Meters to Intertidal datasets.
NOAA National Centers for Environmental Information
2001
U.S. Coastal Relief Model
Raster Digital Data Set
Boulder, Colorado
NOAA National Centers for Environmental Information
http://www.ngdc.noaa.gov/mgg/coastal/crm.html
Digital and/or Hardcopy Resources
2015
publication date
90-m Bathymetry
These data were used to delineate the spatial extent of the model, and to develop the Bathymetry (m) and Meters to Intertidal datasets.
U.S. Geological Survey
2013
USGS NED 1 arc-second 2013 1 x 1 degree ArcGrid
Raster Digital Data Set
Reston, Virginia
U.S. Geological Survey
https://nationalmap.gov/elevation.html
Digital and/or Hardcopy Resources
2013
publication date
Elevation
These data were used to delineate the spatial extent of the model, and to develop the Bathymetry (m) and Meters to Intertidal datasets.
Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Coastal Services Center (CSC)
2012
NOAA Coastal Services Center Sea Level Rise Data: Current Mean Higher High Water Inundation Extent
Raster Digital Data Set
Charleston, South Carolina
Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Coastal Services Center (CSC)
https://coast.noaa.gov/slr/
Digital and/or Hardcopy Resources
2012
publication date
Mean Higher High Water
These data were used to delineate the spatial extent of the model, and to develop the Meters to Intertidal dataset.
Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), National Centers for Coastal Ocean Science (NCCOS), Center for Coastal Monitoring and Assessment (CCMA)
2012
Gulf of Mexico 5-Zone Seasonal Dynamic Salinity Digital Geography (revised)
Vector Digital Data Set
Stennis Space Center, Mississippi
Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), National Centers for Coastal Ocean Science (NCCOS), Center for Coastal Monitoring and Assessment (CCMA)
http://coastalscience.noaa.gov/projects/detail?key=107
Digital and/or Hardcopy Resources
2012
publication date
Salinity
These data were used to delineate the spatial extent of the model and to develop the Acceptable Water Condition (Days) datasets.
Arnold, W. S
2011
GOM_Oysters_DigitalAtlas2011
Vector Digital Data Set
NA
NOAA National Marine Fisheries Service Southeast Regional Office
http://gulfatlas.noaa.gov/
Digital and/or Hardcopy Resources
2011
publication date
Oyster Reef
These data were used to develop the Landcover dataset.
U.S. Army Corp of Engineers
2016
The National Channel Framework
Vector Digital Data Set
NA
U.S. Army Corp of Engineers
http://navigation.usace.army.mil/Survey/Framework
Digital and/or Hardcopy Resources
2016
publication date
Dredged Channel
These data were used to develop the Landcover and Meters to Dredged Channel datasets.
Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Coastal Services Center (CSC)
2004
Benthic grab data from October 1999 in Apalachicola Bay, Florida
tabular digital data
Charleston, South Carolina
Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Coastal Services Center (CSC)
https://data.noaa.gov/dataset/dataset/benthic-grab-data-from-october-1999-in-apalachicola-bay-florida
Digital and/or Hardcopy Resources
2004
publication date
Macroinvertebrates
These data were used to develop the Macroinvertebrate Biomass dataset.
US Geological Survey
2009
River discharge from US Geological Survey gauge data
tabular digital data
National Water Information System data
http://waterdata.usgs.gov/nwis/
Digital and/or Hardcopy
20070101
20161231
ground condition
River Discharge
These data identified years that were representative of long-term average, above long-term average, and below long-term average river discharge scenarios.
NOAA National Estuarine Research Reserve System
2016
System-wide Monitoring Program
tabular digital data
http://www.nerrsdata.org/
Digital and/or Hardcopy
20070101
20161231
ground condition
Water Condition
These data provided water condition data from three monitoring stations that were used to develop the Acceptable Water Condition (Days) dataset.
Huff, J. A.
1975
Life history of Gulf of Mexico Sturgeon, Acipenser oxyrhynchus desotoi, in the Suwannee River, Florida
text and tables
Florida Marine Resources Publ. No. 16, St. Petersburg, FL, USA.
Digital and/or Hardcopy
1975
publication date
Huff 1975
These data identified macroinvertebrate taxonomic groups that are potential food sources for juvenile Gulf Sturgeon for the Macroinvertebrate Biomass dataset.
Mason, W. T., and J. P. Clugston
1993
Foods of the Gulf Sturgeon in the Suwannee River, Florida
text and tables
Transactions of the American Fisheries Society 122:378–385.
Digital and/or Hardcopy
1993
publication date
Mason and Clugston 1993
These data identified macroinvertebrate taxonomic groups that are potential food sources for juvenile Gulf Sturgeon for the Macroinvertebrate Biomass dataset.
Brooks, R. A., and K. J. Sulak
2005
Quantitative assessment of benthic food resources for juvenile Gulf Sturgeon, Acipenser oxyrinchus desotoi in the Suwannee River Estuary, Florida, USA
text and tables
Estuaries 28(5):767–775.
Digital and/or Hardcopy
2005
publication date
Brooks and Sulak 2005
These data identified macroinvertebrate taxonomic groups that are potential food sources for juvenile Gulf Sturgeon for the Macroinvertebrate Biomass dataset.
The spatial extent of the model is the estuary to the mean higher high water (MHHW) line, cut at the mouth of the tributaries and the eastern extent of the salinity data.
Mean Higher High Water
Salinity
20161110
The Bathymetry (m) dataset was created using the best available data for Apalachicola Bay, which included 30-m bathymetry data supplemented with 90-m bathymetry, which was resampled to 30-m using bilinear interpolation or the value of their nearest neighbor. To address dredge spoil islands created after the collection of the bathymetry data, we incorporated the 30-m National Elevation Dataset (NED). All elevation values were converted to mean lower low water (MLLW) using observations collected from the NOAA Apalachicola, FL tide gauge (station ID: 8728690). All values above MLLW were converted to 1 and represent intertidal.
30-m Bathymetry
90-m Bathymetry
Elevation
20161110
Macroinvertebrate biomass was characterized using ash-free dry biomass and count data on macroinvertebrate taxonomic groups collected at 136 sampling locations in Apalachicola Bay. Taxonomic groups as known or potential prey for juvenile Gulf Sturgeon were identified from available literature. The geospatial dataset was created by summing each sampling site’s biomass and interpolating these data using Empirical Bayesian Kriging.
Macroinvertebrates
Huff 1975
Mason and Clugston 1993
Brooks and Sulak 2005
20161110
The meters to dredged channel dataset was created using Euclidean Distance to estimate the distance of each pixel to the nearest dredged channel.
Dredged Channel
20161110
The Landcover dataset was classified into intertidal and 3 subtidal classes. Intertidal was identified as the area between MHHW and MLLW. Subtidal was subdivided into oyster reefs, dredged channels, and other.
Mean Higher High Water
Oyster Reef
Dredged Channel
20161110
The meters to the intertidal dataset was created using Euclidean Distance to estimate the distance of each pixel to the nearest intertidal habitat.
Mean Higher High Water
20161110
Euclidean Distance was used to determine the distance to the mouth of the East River for each pixel in our Apalachicola Bay study area to create the meters to river mouth dataset.
20161110
We characterized acceptable water condition for each of five salinity zones within Apalachicola Bay. We developed the acceptable water condition datasets using Apalachicola River discharge data, 15-minute incremental water condition data from three water monitoring stations, and NOAA’s four salinity shapefiles. Discharge data identified years in which that month’s average daily discharge was significantly (p<0.05) below (i.e., less than) the long-term average, or above (i.e., greater than) the long-term average daily discharge. The four salinity shapefiles (i.e., high salinity, decreasing salinity, low salinity, and increasing salinity) were used to characterize estuary salinity for winter months under three discharge scenarios (i.e., below the long-term average, long-term average, and above the long-term average). Experts then hypothesized physiological tolerances as lower and upper thresholds for each month of arrival class (Table B.1) and the cumulative number of days per month that three monitoring stations’ water condition data were within these thresholds was calculated. Acceptable water condition was then characterized for each salinity zone using linear fixed-intercept regression models (R stats package: lm()).
River Discharge
Water Condition
Salinity
20161110
Prey biomass data that were collected within one year following a major storm were not available. Therefore, we created a macroinvertebrate biomass summary variable that equaled the interpolated macroinvertebrate biomass classes when there is no major storm and increased the uncertainty about macroinvertebrate summary biomass when there is a major storm. Conditional Probability Table values were elicited from experts.
Macroinvertebrates
20161110
Protective Structures was predicted. The conditional probability table values for protective structures were elicited from experts.
20161110
A major storm return time raster (value = 31) was created from the National Hurricane Center's major storm return time for Apalachicola Bay (i.e., the frequency at which a major storm will pass within 58 miles of the estuary).
Storm Return
20161110
A major storm variable was created to predict the annual probability of a major storm, where the conditional probabilities for major storm were calculated in R (R Core Team, Vienna, Austria) as the annual binomial probability of a major storm, given the major storm return time.
Storm Return
20161110
Abiotic suitability was predicted. The conditional probability table values for abiotic suitability were elicited from experts.
20161110
Habitat availability was modeled as the total time (in days) that young of year (age-0) Gulf Sturgeon can access and use 30-m pixels (n = 498,244) of an estuary. The conditional probability table values for habitat availability (days) were elicited from experts.
20161110
Lab
1. Modelers and species experts developed a Bayesian network model, where the model's structure (i.e., discrete classes and conditional probability table values) was defined using a combination of empirical data available in the literature, expert elicitation, and simplifying assumptions.
2. The spatial extent of the model was defined for Apalachicola Bay, FL.
3. Available data were used to create geospatial model inputs. Development of these GIS datasets was supported by the following:
a. River Discharge data were used to determine which years represent above, at, and below long-term average discharge scenarios for Apalachicola Bay, FL.
b. Water condition data were used in conjunction with expert elicited thresholds for juvenile physiological tolerances to calculate Acceptable Water Condition (Days).
4. Spatial outputs were created for 35 physiological and habitat scenarios, which were unique combinations of river discharge, winter month, and month of age-0 arrival to the estuary.
Bayesian network model
Discrete, probabilistic model that predicts young of year (age-0) habitat availability (days) per winter month (October-April) in estuarine critical habitat under alternative river discharge and timing of arrival scenarios.
Producer defined
Month of Arrival
The month that age-0 Gulf Sturgeon arrive to the estuary. This variable, along with Month and River Discharge, is used to determine which of the Acceptable Water Condition (Days) datasets are used as an input to the model. Values are the percent probability for each month of arrival.
Producer defined
October
The percent probability that age-0 Gulf Sturgeon arrive in the estuary in October
Producer defined
November
The percent probability that age-0 Gulf Sturgeon arrive in the estuary in November
Producer defined
December
The percent probability that age-0 Gulf Sturgeon arrive in the estuary in December
Producer defined
January
The percent probability that age-0 Gulf Sturgeon arrive in the estuary in January
Producer defined
February
The percent probability that age-0 Gulf Sturgeon arrive in the estuary in February
Producer defined
Month
The month that the prediction is being made for. This variable, along with Month of Arrival and River Discharge, is used to determine which of the Acceptable Water Condition (Days) datasets are used as an input to the model. Values are the percent probability for each month.
Producer defined
October
The percent probability that the prediction is being made for the month of October
Producer defined
November
The percent probability that the prediction is being made for the month of November
Producer defined
December
The percent probability that the prediction is being made for the month of December.
Producer defined
January
The percent probability that the prediction is being made for the month of January
Producer defined
February
The percent probability that the prediction is being made for the month of February
Producer defined
March
The percent probability that the prediction is being made for the month of March
Producer defined
April
The percent probability that the prediction is being made for the month of April
Producer defined
River Discharge
The relative magnitude of freshwater inflow into the estuarine critical habitat. This variable, along with Month of Arrival and Month, is used to determine which of the Acceptable Water Condition (Days) datasets are used as an input to the model. Values are the percent probability of each River Discharge class.
Producer defined
> Long-Term Average
The percent probability that river discharge is significantly greater than the long-term average.
Producer defined
Long-Term Average
The percent probability that river discharge does not significantly differ from the long-term average.
Producer defined
< Long-Term Average
The percent probability that river discharge is significantly less than the long-term average.
Producer defined
Input: Acceptable Water Condition (Days)
The number of days that salinity, temperature, and dissolved oxygen are within the tolerance thresholds of age-0 Gulf Sturgeon at specific Month of Arrival, Month, and River Discharge combinations. Values are the percent probability of each Acceptable Water Condition (Days) class.
Producer defined
0
The percent probability that salinity, temperature, and dissolved oxygen are not within the tolerance thresholds of age-0 Gulf Sturgeon.
Producer defined
0 to 3
The percent probability that salinity, temperature, and dissolved oxygen are within the tolerance thresholds of age-0 Gulf Sturgeon for more than 0, but less than 3 days.
Producer defined
3 to 6
The percent probability that salinity, temperature, and dissolved oxygen are within the tolerance thresholds of age-0 Gulf Sturgeon for more than 3, but less than 6 days.
Producer defined
6 to 9
The percent probability that salinity, temperature, and dissolved oxygen are within the tolerance thresholds of age-0 Gulf Sturgeon for more than 6, but less than 9 days.
Producer defined
9 to 12
The percent probability that salinity, temperature, and dissolved oxygen are within the tolerance thresholds of age-0 Gulf Sturgeon for more than 9, but less than 12 days.
Producer defined
12 to 15
The percent probability that salinity, temperature, and dissolved oxygen are within the tolerance thresholds of age-0 Gulf Sturgeon for more than 12, but less than 15 days.
Producer defined
15 to 18
The percent probability that salinity, temperature, and dissolved oxygen are within the tolerance thresholds of age-0 Gulf Sturgeon for more than 15, but less than 18 days.
Producer defined
18 to 21
The percent probability that salinity, temperature, and dissolved oxygen are within the tolerance thresholds of age-0 Gulf Sturgeon for more than 18, but less than 21 days.
Producer defined
21 to 24
The percent probability that salinity, temperature, and dissolved oxygen are within the tolerance thresholds of age-0 Gulf Sturgeon for more than 21, but less than 24 days.
Producer defined
24 to 27
The percent probability that salinity, temperature, and dissolved oxygen are within the tolerance thresholds of age-0 Gulf Sturgeon for more than 24, but less than 27 days.
Producer defined
27 to 31
The percent probability that salinity, temperature, and dissolved oxygen are within the tolerance thresholds of age-0 Gulf Sturgeon for more than 27 days, but not exceeding the number of days within the month
Producer defined
Input: Major Storm Return Time (Yrs)
The frequency at which a tropical cyclone with maximum sustained winds of at least 111 mph (96 knots) can be expected within 58 miles of Apalachicola Bay, FL. Values are the percent probability for each Major Storm Return Time class.
Producer defined
22 to 23
The percent probability that a major storm return time is 22 years.
Producer defined
23 to 24
The percent probability that a major storm return time is 23 years.
Producer defined
24 to 25
The percent probability that a major storm return time is 24 years.
Producer defined
25 to 26
The percent probability that a major storm return time is 25 years.
Producer defined
26 to 27
The percent probability that a major storm return time is 26 years.
Producer defined
27 to 28
The percent probability that a major storm return time is 27 years.
Producer defined
28 to 29
The percent probability that a major storm return time is 28 years.
Producer defined
29 to 30
The percent probability that a major storm return time is 29 years.
Producer defined
30 to 31
The percent probability that a major storm return time is 30 years.
Producer defined
31 to 32
The percent probability that a major storm return time is 31 years.
Producer defined
32 to 33
The percent probability that a major storm return time is 32 years.
Producer defined
33 to 34
The percent probability that a major storm return time is 33 years.
Producer defined
34 to 35
The percent probability that a major storm return time is 34 years.
Producer defined
35 to 36
The percent probability that a major storm return time is 35 years.
Producer defined
36 to 37
The percent probability that a major storm return time is 36 years.
Producer defined
37 to 38
The percent probability that a major storm return time is 37 years.
Producer defined
38 to 39
The percent probability that a major storm return time is 38 years
Producer defined
39 to 40
The percent probability that a major storm return time is 39 years
Producer defined
40 to 41
The percent probability that a major storm return time is 40 years.
Producer defined
41 to 42
The percent probability that a major storm return time is 41 years
Producer defined
42 to 43
The percent probability that a major storm return time is 42 years
Producer defined
43 to 44
The percent probability that a major storm return time is 43 years.
Producer defined
44 to 45
The percent probability that a major storm return time is 44 years.
Producer defined
45 to 46
The percent probability that a major storm return time is 45 years.
Producer defined
Major Storm
The annual predicted probability that a tropical cyclone corresponding to a Category 3, 4, or 5 on the Saffir-Simpson Hurricane Wind Scale will pass within 58 miles of Apalachicola Bay. Values are the percent probability for each Major Storm class.
Producer defined
No
The annual percent probability that a major storm will not pass within 58 miles of the estuary.
Producer defined
Yes
The annual percent probability that a major storm will pass within 58 miles of the estuary.
Producer defined
Input: Macroinvertebrate Biomass (g)
Interpolation of ash-free dry biomass of macroinvertebrates that have been identified through literature as potential prey of age-0 Gulf Sturgeon. Values are the percent probability of each Macroinvertebrate Biomass class.
Producer defined
0
The percent probability that the interpolated macroinvertebrate ash-free dry biomass was 0 g.
Producer defined
0 to 0.0133
The percent probability that the interpolated macroinvertebrate ash-free dry biomass was greater than 0 g, but less than 0.0133 g.
Producer defined
0.0133 to 0.0214
The percent probability that the interpolated macroinvertebrate ash-free dry biomass was greater than 0.0133 g, but less than 0.02134 g.
Producer defined
0.02138 to 0.071
The percent probability that the interpolated macroinvertebrate ash-free dry biomass was greater than 0.02134 g, but did not exceed 0.071 g.
Producer defined
Macroinvertebrate Biomass Summary (g)
The predicted macroinvertebrate biomass, given Macroinvertebrate Biomass and the predicted probability of Major Storm. Values are the percent probability of each Macroinvertebrate Biomass Summary class.
Producer defined
0
The percent probability that the predicted macroinvertebrate biomass is 0 g.
Producer defined
0 to 0.0133
The percent probability that the predicted macroinvertebrate biomass is greater than 0 g, but less than 0.0133 g.
Producer defined
0.0133 to 0.0214
The percent probability that the predicted macroinvertebrate biomass is greater than 0.0133 g, but less than 0.0214 g.
Producer defined
0.0214 to 0.071
The percent probability macroinvertebrate biomass is greater than 0.0214 g, but does not exceed 0.071 g.
Producer defined
Input: Meters to Dredged Channel
The linear distance (m) from nearest dredged channel. Values are the percent probability for each Meters to Dredged Channel class.
Producer defined
0 to 250
The percent probability of being less than 250 m from the nearest dredged channel.
Producer defined
> 250
The percent probability of being greater than, or equal to, 250 m from the nearest dredged channel.
Producer defined
Input: Meters to Intertidal
The linear distance (m) to nearest intertidal, where intertidal is located between the Mean Higher High Water and Mean Lower Low Water lines. Values are the percent probability for each Meters to Intertidal class.
Producer defined
0
The percent probability of being 0 m from the nearest intertidal.
Producer defined
0 to 200
The percent probability of being greater than 0 m, but less than 200 m from the nearest intertidal.
Producer defined
200 to 6000
The percent probability of being greater than 200 m, but less than 6000 m from the nearest intertidal.
Producer defined
Input: Landcover
Landcover represents the major features of the estuary. Values are the percent probability for each Landcover class.
Producer defined
Intertidal
The percent probability of being intertidal (i.e., located between the Mean Higher High Water and Mean Lower Low Water lines).
Producer defined
Subtidal: Oyster Reef
The percent probability of being seaward of the Mean Lower Low Water line and being an oyster reef.
Producer defined
Subtidal: Dredged Channel
The percent probability of being seaward of the Mean Lower Low Water line and being a dredged channel.
Producer defined
Subtidal: Other
The percent probability of bring seaward of the Mean Lower Low Water line and being neither an oyster reef nor a dredged channel.
Producer defined
Protective Structures
The predicted probability that protective structures (e.g., large woody debris) are present. Values are the percent probability for each Protective Structures class.
Producer defined
Absent
The percent probability that protective structures are absent.
Producer defined
Present
The percent probability that protective structures are present.
Producer defined
Input: Bathymetry (m)
Bathymetry (i.e., submarine topography) is water depth (m) relative to the Mean Lower Low Water line. Values are the percent probability for each Bathymetry class.
NOAA
0 to 1
The percent probability that the water depth relative to the Mean Lower Low Water line is > 0 m
Producer defined
-1 to 0
The percent probability that the water depth relative to the Mean Lower Low Water line is between 0 and -1.
Producer defined
-2 to -1
The percent probability that the water depth relative to the Mean Lower Low Water line is between -1 and -2.
Producer-defined
-3 to -2
The percent probability that the water depth relative to the Mean Lower Low Water line is between -2 and -3.
Producer defined
-4 to -3
The percent probability that the water depth relative to the Mean Lower Low Water line is between -3 and -4.
Producer defined
-5 to -4
The percent probability that the water depth relative to the Mean Lower Low Water line is between -4 and -5.
Producer defined
-6 to -5
The percent probability that the water depth relative to the Mean Lower Low Water line is between -5 and -6.
Producer defined
-7 to -6
The percent probability that the water depth relative to the Mean Lower Low Water line is between -6 and -7.
Producer defined
-8 to -7
The percent probability that the water depth relative to the Mean Lower Low Water line is between -7 and -8.
Producer defined
-9 to -8
The percent probability that the water depth relative to the Mean Lower Low Water line is between -8 and -9.
Producer defined
-10 to -9
The percent probability that the water depth relative to the Mean Lower Low Water line is between -9 and -10.
Producer defined
-11 to -10
The percent probability that the water depth relative to the Mean Lower Low Water line is between -10 and -11.
Producer defined
-12 to -11
The percent probability that the water depth relative to the Mean Lower Low Water line is between -11 and -12.
Producer defined
-13 to -12
The percent probability that the water depth relative to the Mean Lower Low Water line is between -12 and -13.
Producer defined
-14 to -13
The percent probability that the water depth relative to the Mean Lower Low Water line is between -13 and -14.
Producer defined
-15 to -14
The percent probability that the water depth relative to the Mean Lower Low Water line is between -14 and -15.
Producer defined
-16 to -15
The percent probability that the water depth relative to the Mean Lower Low Water line is between -15 and -16.
Producer defined
Abiotic Suitability
The predicted abiotic suitability. Values are the percent probability for each Abiotic Suitability class.
Producer defined
No
The percent probability that the area is not suitable for age-0 Gulf Sturgeon.
Producer defined
Yes
The percent probability that the area is suitable for age-0 Gulf Sturgeon.
Producer defined
Input: Meters to River Mouth
The linear distance (m) to the river mouth (i.e., the point from which age-0 Gulf Sturgeon disperse into the estuary from the river). Values are the percent probability for each Meters to River Mouth class.
Producer defined
0
The percent probability that the linear distance (m) to the river mouth is 0 m (i.e., the river mouth).
Producer defined
0 to 5000
The percent probability that the linear distance (m) to the river mouth is greater than 0 m, but less than 5000 m.
Producer defined
5000 to 10000
The percent probability that the linear distance (m) to the river mouth is greater than 5000 m, but less than 10000 m.
Producer defined
10000 to 15000
The percent probability that the linear distance (m) to the river mouth is greater than 10000 m, but less than 15000 m.
Producer defined
15000 to 20000
The percent probability that the linear distance (m) to the river mouth is greater than 15000 m, but less than 20000 m.
Producer defined
20000 to 25000
The percent probability that the linear distance (m) to the river mouth is greater than 20000 m, but less than 25000 m.
Producer defined
25000 to 30000
The percent probability that the linear distance (m) to the river mouth is greater than 25000 m, but less than 30000 m.
Producer defined
30000 to 35000
The percent probability that the linear distance (m) to the river mouth is greater than 30000 m, but less than 35000 m.
Producer defined
Habitat Availability (Days)
The predicted probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon in estuarine critical habitat. Values are the percent probability for each Habitat Availability (Days) class.
Producer defined
0
The percent probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon is 0.
Producer defined
0 to 3
The percent probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon is between 0 and 3.
Producer defined
3 to 6
The percent probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon is between 3 and 6.
Producer defined
6 to 9
The percent probability of habitat availability (days) per winter month for age-0Gulf Sturgeon is between 6 and 9.
Producer defined
9 to 12
The percent probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon is between 9 and 12.
Producer defined
12 to 15
The percent probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon is between 12 and 15.
Producer defined
15 to 18
The percent probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon is between 15 and 18.
Producer defined
18 to 21
The percent probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon is between 18 and 21.
Producer defined
21 to 24
The percent probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon is between 21 and 24.
Producer defined
24 to 27
The percent probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon is between 24 and 27.
Producer defined
27 to 31
The percent probability of habitat availability (days) per winter month for age-0 Gulf Sturgeon is between 27 and 31.
Producer defined
U.S. Geological Survey - ScienceBase
mailing and physical
Denver Federal Center, Building 810, Mail Stop 302
Denver
CO
80225
USA
1-888-275-8747
sciencebase@usgs.gov
Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.
Digital Data
https://doi.org/10.5066/P9KNSKMT
None. No fees are applicable for obtaining the data set.
20210120
James Patrick Cronin
U.S. Geological Survey, Southeast Region
Ecologist
Mailing and Physical
700 Cajundome Blvd
Lafayette
LA
70506
United States
337-266-8589
jcronin@usgs.gov
FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata
FGDC-STD-001.1-1999