Abstract:
The dataset contains: 1) Atlantis parameterization and output files for Atlantis-ICHTHYOP simulations, 2) Atlantis parameterization and output files for seagrass carrying capacity simulations, 3) harmful algal bloom (HAB) forcing data developed for Atlantis, 4) red tide-turtle interaction data including an animation, and 5) turtle particle transport data from ICTHYOP. The Atlantis-ICTHYOP simulations force turtle numbers in Atlantis using particle density data from the individual based model ICHTHYOP by LGL (Nathan Putman, nathan.putman@gmail.com) and evaluated three assumptions on larval survivorship (250, 817, 950). Atlantis seagrass carrying capacity experiments vary the abundance of seagrass to 60%, 70%, 80%, 90%, 100%, 150%, 200% and 300% of baseline.
Data Parameters and Units:
NetCDF are self-describing file formats and contain many types of data. The following NetCDF files in this data set describe the time history of turtle numbers (e,g. Ichthyop_250.nc and CM_GOM_Seasonal_Simulated_Abundance_1996-2017.nc), Atlantis state and rate variables in various units specific to Atlantis tracers (e.g., SG0,7_GOM_OUT.nc and i250.GOM_OUT.nc), a (unitless) index of relative consumption (e.g., i817_juv_TotalQ.gif), unitless scalers on recruitment (e.g. RecruitmentForcing0,6.nc) and seagrass biomass [mgN*m^3] (e.g., SeagrassForcing0,6.nc).
The Atlantis-ICHTHYOP model parameters are divided into three groups: physical, tracer, and epibenthos. The physical parameters include bottom coverage types, bioturbation and oxygen descriptive depths, and hydrodynamic sources, sinks, and exchanges. The tracer parameters include salinity; light adaptation; ammonia [mg N/m^3]; nitrate [mg/m^3]; dissolved organic nitrogen [mg N/m^3]; micronutrients [mg N/m^3]; dissolved oxygen [mg O2/m^3]; dissolved and detritral silica [mg Si/m^3]; surface light intensity; temperature [deg C]; dentrification; nitrification; chlorophyll; surface stress [N/m^2]; nitrogen in the modeled species [mg N/m^3]; silica in the modeled species [mg Si/m^3]; individual structural N for modeled species cohorts [mg N]; individual reserve N for modeled species cohorts [mg N]; and numbers within individual modeled species cohorts. The epibenthic parameters include boundary layer flag, nitrogen contained in modeled benthic species [mgN/m^2], and percent cover by benthic organisms (bivalves, oysters, macroalgae, stony coral).
Methods:
Dataset: Atlantis-ICTHYOP
\GRIIDC\USF\Atlantis_Ichthyop\input
Files [7]:
1. Ichthyop_250.nc
2. Ichthyop_817.nc
3. Ichtyop_940.nc
4. gom_prm_2021_sg.prm
5. at_force_TURandKMP250.prm
6. at_force_TURandKMP817.prm
7. at_force_TURandKMP940.prm
Description:
This folder contains 7 input files for Atlantis turtle consumption simulations generated using particle densities output from ICTHYOP. NetCDF Files 1-3 contain forcing information for turtle numbers based on ICTHYOP particle densities. Three different scenarios (250, 817, 940) feature different assumptions on larval survivorship. File 4 “gom_prm_2021_sg.prm” is the biology dynamic conditions file (“biol.prm”) used for these simulations showing rate parameters. Files 5-7 are at_force.prm files which show what forcing files are active.
\GRIIDC\USF\Atlantis_Ichthyop\output
Files [6]:
1. i817_juv_TotalQ.gif
2. i817_juv_InvertQ.gif
3. i817_juv_FishQ.gif
4. i250.GOM_OUT.nc
5. i817.GOM_OUT.nc
6. i940.GOM_OUT.nc
Description:
Files 1-3 demonstrate products from Atlantis, “i817_juv_TotalQ.gif” shows an animation of invertebrate consumption (InvertQ), fish consumption (FishQ), and total consumption (TotalQ) by turtles. This folder contains NetCDF output (out.nc) from Atlantis ICHTHYOP simulations showing all state variable conditions in the model. Files 4-5 represent 3 different assumptions on larval survivorship.
\GRIIDC\USF\Atlantis_Seagrass\input
Files [17]:
1. RecruitmentForcing0,6.nc
2. RecruitmentForcing0,7.nc
3. RecruitmentForcing0,8.nc
4. RecruitmentForcing0,9.nc
5. RecruitmentForcing1.nc
6. RecruitmentForcing1,5.nc
7. RecruitmentForcing2.nc
8. RecruitmentForcing3.nc
9. SeagrassForcing0,6.nc
10. SeagrassForcing0,7.nc
11. SeagrassForcing0,8.nc
12. SeagrassForcing0,9.nc
13. SeagrassForcing1.nc
14. SeagrassForcing1,5.nc
15. SeagrassForcing2.nc
16. SeagrassForcing3.nc
17. at_force_SG_CarryingCapacity.prm
Description:
Files 1-8 contain spatial recruitment forcing data representing seagrass density effects from 60% to 300% of baseline (RecruitmentForcing0,6.nc to RecruitmentForcing3.nc, respectively). Files 9-16 represent the seagrass GRS tracer forcing data from 60% tot 300% of baseline (SeagrassForcing0,6.nc to SeagrassForcing0,7.nc). File 17 is the at_force.prm file which shows what forcing files are active in the carrying capacity simulations.
\GRIIDC\USF\Atlantis_Seagrass\output
Files [8]:
SG0,6_GOM_OUT.nc
1. SG0,7_GOM_OUT.nc
2. SG0,8_GOM_OUT.nc
3. SG0,9_GOM_OUT.nc
4. SG1,0_GOM_OUT.nc
5. SG1,5_GOM_OUT.nc
6. SG2_GOM_OUT.nc
7. SG3_GOM_OUT.nc
Description:
Files 1-8 contain NetCDF output from Atlantis seagrass simulations showing state variable conditions under different seagrass simulations. SG0,6_GOM_OUT.nc represents seagrass at 60% of baseline, SG1,0_GOM_OUT.nc represents seagrass at baseline density, and SG3_GOM_OUT.nc represents 300% of baseline density.
\GRIIDC\USF\HAB_forcing
Files [1]:
1. HABforcing.nc
Description:
This NetCDF file contains spatial mortality forcing data representing red tide blooms. Bloom intensity was calculated over the West Florida Shelf from January 1, 2015 to December 31, 2019. These data contained a mean Red Band Difference product for all pixels with a Karina brevis presence during that month. This was based on exceeding a predefined threshold of 1.5 x 105 cells/L. Pixels were sorted into their corresponding Atlantis polygons and all the pixels occurring within a polygon were averaged to generate a bloom intensity value for that polygon. We developed a monthly time series for each polygon for the 60-month period. The scale of the effect was adjusted for each functional group using data borrowed from Gray and Ainsworth (2018).
\GRIIDC\USF\Redtide_Turtle
Files [3]:
1. MODIS_red_tide_2003_2017.nc
2. Turtle_habs_overlay_stats_month.csv
3. Turtle_habs_overlay_stats_year.csv
Description:
Turtle_habs_overlay_stats_month.csv contains spatial data that compares ICHTHYOP turtle particles positions to plankton bloom data. The same data are provided at yearly resolution in the Turtle_habs_overlay_stats_year.csv. Plankton blooms are determined by daily red band difference (RBD) (Amin et al., 2009) images generated from daily Aqua MODIS data. The red tide patches are generated from MODIS RBD data, but not all high value RBD patches are caused by red tide blooms. So, the field red tide cell concentration data was used as a reference when manually delineating RBD high value patches. Each MODIS RBD image was overlaid with K. brevis cell concentration data (location and concentration) collected within ±7 days of the image acquisition date. Only high RBD value patches that have high field cell concentrations (100,000 cells/L) are regarded as red tide bloom patches. If there was a high RBD value patch co-located with FWC field data (https://myfwc.com/research/redtide/statewide/) showing a high Karenia brevis concentration in surface water (RBD threshold of > 0.015 mW cm-2 µm-1 sr-1), then the bloom patch was regarded as a K. brevis bloom (red tide bloom) (Hu et al., 2022).
Headings are:
Timestep, Year-month, CM_overlay_number, CM_overlay_area, CM_total_number, LK_overlay ,LK_overlay_area ,LK_total_number, red tide.
In corresponding order, these represent: timestep of ICHTHYOP data, year in decimal in format, number of green turtle (Chelonia mydas) particles interacting with HABs, total area of interaction between C. mydas and HABs in km2, total number of C. mydas particles, number of Kemp’s ridley turtle (Lepidochelys kempii) particles interacting with HABs, total area of interaction between L. kempii and HABs in km2, total number of L. kempii particles, is red tide present true or false. The NetCDF MODIS_red_tide_2003_2017.nc file containing monthly red tide data (1=present, 0=not present) from 2003-2017. Spatial resolution is 0.1o (same with turtle data).
\GRIIDC\USF\Redtide_Turtle\animation
Files [16]:
1. turtle_w_habs_wsf_animation.mp4
2. turtle_w_redtide_2003.png
3. turtle_w_redtide_2004.png
4. turtle_w_redtide_2005.png
5. turtle_w_redtide_2006.png
6. turtle_w_redtide_2007.png
7. turtle_w_redtide_2008.png
8. turtle_w_redtide_2009.png
9. turtle_w_redtide_2010.png
10. turtle_w_redtide_2011.png
11. turtle_w_redtide_2012.png
12. turtle_w_redtide_2013.png
13. turtle_w_redtide_2014.png
14. turtle_w_redtide_2015.png
15. turtle_w_redtide_2016.png
16. turtle_w_redtide_2017.png
Description:
The .mp4 file shows an animation of the green turtle and Kemps ridley turtle data from MODIS_red_tide_2003_2017.nc based on CM_overlay_area and LK_overlay_area. The .png files show the same data.
\GRIIDC\LGL
Files [2]:
1. CM_GOM_Seasonal_Simulated_Abundance_1996-2017.nc
2. LK_GOM_Seasonal_Simulated_Abundance_1996-2017.nc
Description:
NetCDF files contains C. mydas and L. kempii densities from ICTHYOP. We modeled changes in the distribution and densities of the oceanic-stage of Kemp’s ridley and green turtles following the methods of Putman et al. (2020). Model predictions were made from 1993-2017 for indices of annual hatchling production. These estimates were used to weight transport predictions from a Lagrangian model from respective spawning regions based on observed nest counts and survival rates (Putman et al. 2015). Nesting counts were used to estimate the abundance of turtles of each sea turtle cohort. These values were multiplied by annual survival rates for juvenile turtles obtained from the literature to determine turtle abundance through time. To bracket the considerable uncertainty associated with annual survival in sea turtles we used the median (81.7%), maximum (94%), and minimum (25%) values from a comprehensive review of the literature (Putman et al. 2015).
The movement of young sea turtles during their oceanic stage was simulated using Global HYCOM daily snapshots of surface velocity at 0.08° resolution (Chassignet et al. 2009). HYCOM ocean currents are based on forcing fields and data assimilation that depict ocean conditions at specific times in the past. Dispersal was modeled for years 1993–2017 (HYCOM experiments 19.0, 19.1, 90.9, 91.0, 91.1, 91.2) by ICHTHYOP (ver. 2.2.1) particle-tracking software (Lett et al. 2008). For each nesting region, 350 virtual particles were released daily, just offshore of the primary nesting sites during each of the 60 d of peak hatchling emergence (Table 1). This resulted in 21,000 particles released per region annually for 25 turtle cohorts. ICHTHYOP implemented a Runge–Kutta fourth-order time-stepping method whereby particle position was calculated each half-hour as they moved through the HYCOM velocity fields. Virtual particles were tracked for up to 3.5 yr for green turtles and 2.5 yr for Kemp's ridley to account for the period of the oceanic stage when movement is most dominated by surface currents (Putman et al. 2013, 2015, Naro-Maciel et al. 2017). These drift times are representative of the entire oceanic-stage for Kemp's ridley (~100% of the oceanic stage) and many green turtles (~70–100%). No swimming behavior was simulated as our aim was to produce a simple model of sea turtle movement and distribution.
The density of oceanic-stage sea turtles was determined for the years 1996 through 2017, as 1996 is the first year when all modeled age classes were represented, i.e. virtual turtles hatched in 1993 (3.5 yr old), 1994 (2.5 yr old), 1995 (1.5 yr old) and 1996 (0.5 yr old). Data were formatted as snapshot of total juvenile turtle abundance at 0.25 year intervals for the years 1996.0 through 2017.75 and at a spatial resolution of 0.08° latitude by 0.08° longitude. The spatial extent spanned from 15°N to 32°N and from 100°W to 78°W.
Provenance and Historical References:
Amin, R., J. Zhou, A. Gilerson, B. Gross, F. Moshary, and S. Ahmed. (2009). Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery. Optics Express, 17(11), 9126. https://doi.org/10.1364/OE.17.009126.
Gray, A.M. and C.H. Ainsworth. 2019. Effects of Karenia brevis harmful algal blooms on fish community structure on the West Florida Shelf. Ecological Modeling, 392: 250-267. https://doi.org/10.1016/j.ecolmodel.2018.11.022.
Hu, C., Y. Yao, J.P. Cannizzaro, M. Garrett, M. Harper, L. Markley, C. Villac, and K. Hubbard. (2022). Karenia brevis bloom patterns on the west Florida shelf between 2003 and 2019: Integration of field and satellite observations. Harmful Algae, 117, 102289. https://doi.org/10.1016/j.hal.2022.102289.
Putman, N.F., F.A. Abreu-Grobois, I. Iturbe-Darkistade, E.M. Putman, P.M. Richards, and P. Verley, 2015. Deepwater Horizon oil spill impacts on sea turtles could span the Atlantic. Biology letters, 11(12), p.20150596. https://doi.org/10.1098/rsbl.2015.0596
Putman, N.F., E.E. Seney, P. Verley, D.J. Shaver, M.C. López‐Castro, M. Cook, V. Guzmán, B. Brost, S.A. Ceriani, R.D.J.G.D. Mirón, L.J. Peña, et al. 2020. Predicted distributions and abundances of the sea turtle ‘lost years’ in the western North Atlantic Ocean. Ecography, 43(4), pp.506-517. https://doi.org/10.1111/ecog.04929.
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