1b:I[42426,["/_next/static/immutable/chunks/1xctgzqre-wsy.js","/_next/static/immutable/chunks/06_-wz6pdtp2b.js","/_next/static/immutable/chunks/30e52g49hg40p.js","/_next/static/immutable/chunks/02yq6tul2z-oh.js","/_next/static/immutable/chunks/10bx_ec2vj7n_.js","/_next/static/immutable/chunks/33bfn03fo8xnt.js","/_next/static/immutable/chunks/0tfjf78scu61b.js","/_next/static/immutable/chunks/31xnvej4n1bi9.js"],"default"] 1c:T412,

Data were collected on day-trips aboard a 21-foot powerboat in St. Joseph Bay, Florida during 5 survey events in 2016 (June 27-28; July 8-9; July 11-14; July 22-23; July 25-28), 1 survey event in 2017 (May 8-9), and two survey events in 2019 (August 2-3; September 9-10).

Methods are described in detail in Rodriguez et al. 2021. Criteria for sampling were that conditions had to be mostly sunny with winds no higher than 15 miles per hour (mph). A DJI Phantom 3 unmanned aerial vehicle (UAV) was remotely controlled with a handheld unit from a small boat, using a Samsung Nexus 7 tablet with the DJI Go app to maximize the display area. The parallel transects were 1 kilometer (km) and 0.5 km long, and were always flown east to west or west to east to minimize the likelihood of encountering an individual turtle more than once in a survey. The UAV flew at an altitude of approximately 15 meters in 1.5 km intervals across a 2000 hectare (ha) area in the southern portion of the bay at a speed of 5 to 10 kilometers per hour.

1d:T6ec,

As described by Fuentes et al. (2015), there are several biases inherent when performing aerial surveys on animals that only spend a small amount of their time at the surface of the water. One is availability bias, or when animals in a transect are not detected due to environmental conditions (water depth, turbidity, glare, cloud cover, habitat, etc.) and animal characteristics (body size, body color, diving patterns, etc.), and the second is perception bias, or when observers miss animals available for detection. Because transects were recorded and the footage was viewed twice, perception bias was minimized. To account for availability bias, I used the amount of time turtles spent at visible depth, reported by Fuentes et al. (2015) (18% of a green turtle's time spent between depths 0 to 2.5 meters) to more accurately calculate density. Each transect was also corrected for sun glitter, which is a subjective estimate of the percent area of the video affected by sun glitter (0-25%, 25-50%, 50-75%, and >75%) and used in estimating the final abundances (Hodgson et al. 2013). Density for each flight was estimated as individuals per hectare using the equation below:

Turtle density (individuals ha-1) = n (# of turtles) / area covered (ha)

n was calculated three ways: (1) the raw number of turtles seen in a transect, (2) the number of turtles in a transect as determined after using the sun glitter correction factor for each transect, and (3) the previous value divided by 0.18 as suggested by Fuentes et al. (2015) to account for availability bias. Green turtle bay-wide abundance was calculated for each density by multiplying the density by the area (2,000 ha).

1e:Tbdb,NSF Award Abstract: This project builds on an ongoing project that studies the tropicalization of seagrass beds in the northern Gulf of Mexico (NSF award OCE-1737144, https://www.bco-dmo.org/project/750843) where native species are prevented from moving north as temperature rises because of the continental land mass. Hurricane Michael opened a new pass from the Gulf of Mexico to St Joseph Bay, enabling elevated immigration of tropical species (e.g. parrotfish), and the associated winds and storm surge likely decimated green turtle populations. This project takes advantage of the hurricane's passage to study the interplay of turtles and parrotfish as consumers of the dominant seagrass and will support new and complemental sampling to evaluate the storm's effect on the ongoing tropicalization of St Joseph Bay. Seagrass communities provide major ecosystem services and their resilience to changing climate has consequences for coastal communities. This project will expand on the NSF-funded network to capture critical environmental information during this hurricane-induced natural experiment of increased species access. Training of two female early career scientists (one from an underserved group) will take place in addition to mentoring through collaborations with the partners of the network. The recent passage of Major Hurricane Michael directly over the northernmost site (St. Joe Bay, FL) of the NSF collaborative project "Collaborative Research: The tropicalization of Western Atlantic seagrass beds" has raised additional questions regarding the trajectory and speed of the influx of tropical grazers along the northern Gulf of Mexico. Hurricane Michael produced numerous overwash areas along Cape San Blas and opened a new pass from the Gulf of Mexico to St. Joseph Bay. This will likely alter salinities and water temperatures and bring additional larval and adult recruits to the Bay. Hurricanes have been documented to move species large distances from their low-latitude home ranges and while these can be only short-lived range shifts, there is potential for enhanced establishment in locations where tropicalization is already occurring owing to the decreasing frequency of cold winter temperatures. This study investigates to what extent these newly formed passes allow elevated immigration of tropically-associated species, such as the seagrass consuming emerald parrotfish into St. Joe Bay. Supporting new and complementary field activities, and leveraging the 12 year record of fish abundance and species composition in St. Joe Bay and the 2017 population estimate of green turtle abundance in the Bay are used to evaluate the storm's effects on the on-going tropicalization of St. Joe Bay that could dramatically affect the overall conclusions of our collaborators in other locations of the NSF funded network of sites. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.6:["$","$L1b",null,{"dataset":{"_id":"986917","description":"","problem":"","acquisition":"$1c","processing":"$1d","bcodmoProcessing":"- Imported original file \"SJB_Turtle_surveys_drone.txt\" into the BCO-DMO system.\r\n- Marked \"NA\" as a missing data value (missing data are empty/blank in the final CSV file).\r\n- Renamed fields to comply with BCO-DMO naming conventions.\r\n- Converted dates to YYYY-MM-DD format.\r\n- Saved the final file as \"986917_v1_green_turtle_density_uav.csv\".","title":"Green turtle density in St. Joseph Bay, Florida, USA estimated by performing aerial surveys in 2016, 2017, and 2019","nickname":"St. Joseph Bay UAV Urchin survey","dataUrl":"","currentState":"Final no updates expected","currentStateId":"7","validated":true,"restricted":false,"version":"1","version_date":"2025-12-18T05:00:00.000Z","abstract":"Green turtle (Chelonia mydas) density was estimated by performing aerial surveys with a DJI Phantom 3 Professional unmanned aerial vehicle (UAV). UAV systems have been found to provide an effective method for monitoring abundance when conducting daytime surveys of large marine organisms in coastal waters. Aerial surveys are an effective survey method for estimating sea turtle abundance because the method allows coverage of their extensive range. Because primary C. mydas foraging times are during the early morning and late afternoon throughout most of its range, transects were flown in the morning to enhance the reliability of estimates. Efforts were focused on the dense turtlegrass beds in the southern portion of the bay, as acoustic telemetry in St. Joseph Bay suggests that green turtles spend most of their time in this area. Aerial surveys were conducted over two surveys in 2019 during August and September and compared to surveys conducted previously in 2016 and 2017. ","citation":"Rodriguez, A., Byron, D., Heck, K. (2026) Green turtle density in St. Joseph Bay, Florida, USA estimated by performing aerial surveys in 2016, 2017, and 2019. Biological and Chemical Oceanography Data Management Office (BCO-DMO). 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Also referred to as a drone.","type_acronym":"UAV"}],"parameters":[{"_id":"990789","name":"Flight_Date","format":"%Y-%m-%d","frictionlessType":"date","description":"

Date transect was flown by drone

","units":{"_id":"","name":"unitless"},"type":{"_id":"1070","name":"date","description":"

date; generally reported in GMT as YYYYMMDD (year; month; day); also as MMDD (month; day); EqPac dates are local Hawaii time. ISO_Date format is YYYY-MM-DD (http://www.iso.org/iso/home/standards/iso8601.htm)

"}},{"_id":"990790","name":"Survey","format":"","frictionlessType":"integer","description":"

Density survey number

","units":{"_id":"","name":"unitless"},"type":{"_id":"960","name":"sample","description":"

unique sample identification or number; any combination of alpha numeric characters; precise definition is file dependent

"}},{"_id":"990791","name":"prevpost","format":"","frictionlessType":"string","description":"

Whether this flight was before (pre) or after (post) Hurricane Michael.

","units":{"_id":"","name":"unitless"},"type":{"_id":"1741","name":"sample_descrip","description":"

text description or other traits of the sample collected

"}},{"_id":"990792","name":"flight","format":"","frictionlessType":"integer","description":"

Flight number of the day, starting sequentially each day. 99 means these results are an averaged because transect was flown multiple times

","units":{"_id":"","name":"unitless"},"type":{"_id":"960","name":"sample","description":"

unique sample identification or number; any combination of alpha numeric characters; precise definition is file dependent

"}},{"_id":"990793","name":"Waypoint","format":"","frictionlessType":"string","description":"

Start point location. Begins with "DF"

","units":{"_id":"","name":"unitless"},"type":{"_id":"1124","name":"site","description":"

Sampling site identification.

"}},{"_id":"990794","name":"SG_transect1","format":"","frictionlessType":"integer","description":"

Sun glitter rank, or the estimated area of Transect 1 affected by sun glitter. 1=0-25%, 2=25-50% 3=50-75%, 4=75-100%

","units":{"_id":"","name":"unitless"},"type":{"_id":"1741","name":"sample_descrip","description":"

text description or other traits of the sample collected

"}},{"_id":"990795","name":"SG_transect3","format":"","frictionlessType":"integer","description":"

Sun glitter rank, or the estimated area of Transect 3 affected by sun glitter. 1=0-25%, 2=25-50% 3=50-75%, 4=75-100%

","units":{"_id":"","name":"unitless"},"type":{"_id":"1741","name":"sample_descrip","description":"

text description or other traits of the sample collected

"}},{"_id":"990796","name":"perc_vis_trans1","format":"","frictionlessType":"number","description":"

Average % visibility of the screen affected by sun glitter in Transect 1, based on the average of assigned sun glitter rank in Column D. 12.5%, 37.5%, 62.5%, or 87.5%

","units":{"_id":"","name":"unitless"},"type":{"_id":"1741","name":"sample_descrip","description":"

text description or other traits of the sample collected

"}},{"_id":"990797","name":"perc_vis_trans3","format":"","frictionlessType":"number","description":"

Average % visibility of the screen affected by sun glitter in Transect 1, based on the average of assigned sun glitter rank in Column E. 12.5%, 37.5%, 62.5%, or 87.5%

","units":{"_id":"","name":"unitless"},"type":{"_id":"1741","name":"sample_descrip","description":"

text description or other traits of the sample collected

"}},{"_id":"990798","name":"n_turtles_trans1","format":"","frictionlessType":"integer","description":"

number of green turtles observed in transect 1

","units":{"_id":"","name":"unitless"},"type":{"_id":"903","name":"number","description":"

number of organisms counted/observed, a generic term, specific to the data set in which it appears

"}},{"_id":"990799","name":"n_turtles_trans3","format":"","frictionlessType":"integer","description":"

number of green turtles observed in transect 3

","units":{"_id":"","name":"unitless"},"type":{"_id":"903","name":"number","description":"

number of organisms counted/observed, a generic term, specific to the data set in which it appears

"}},{"_id":"990800","name":"n","format":"","frictionlessType":"integer","description":"

number of green turtles observed in whole waypoint flight (sum of transects 1 & 3)

","units":{"_id":"","name":"unitless"},"type":{"_id":"903","name":"number","description":"

number of organisms counted/observed, a generic term, specific to the data set in which it appears

"}},{"_id":"990801","name":"n_SG","format":"","frictionlessType":"number","description":"

number of green turtles in whole waypoint flight after correcting for sun glitter

","units":{"_id":"","name":"unitless"},"type":{"_id":"903","name":"number","description":"

number of organisms counted/observed, a generic term, specific to the data set in which it appears

"}},{"_id":"990802","name":"n_SG_AB","format":"","frictionlessType":"number","description":"

number of green turtles in whole waypoint flight after correcting for sun glitter and availability bias (i.e. amount of time green turtles spend at visible depth [18%] as suggested by Fuentes et al 2015)

","units":{"_id":"","name":"unitless"},"type":{"_id":"903","name":"number","description":"

number of organisms counted/observed, a generic term, specific to the data set in which it appears

"}},{"_id":"990803","name":"km_trans1","format":"","frictionlessType":"number","description":"

length flown in Transect 1

","units":{"_id":"","name":"kilometers"},"type":{"_id":"744809","name":"len_track","description":"

The linear distance along which a sample was collected or an observation was made, such as a beach transect length or the distance a net was towed through the water.

"}},{"_id":"990804","name":"km_trans3","format":"","frictionlessType":"number","description":"

length flown in Transect 3

","units":{"_id":"","name":"kilometers"},"type":{"_id":"744809","name":"len_track","description":"

The linear distance along which a sample was collected or an observation was made, such as a beach transect length or the distance a net was towed through the water.

"}},{"_id":"990805","name":"total_km","format":"","frictionlessType":"number","description":"

total length flown in whole waypoint flight

","units":{"_id":"","name":"kilometers"},"type":{"_id":"1097","name":"length","description":"

Length is a one-dimensional extent equal to the distance between two points.

"}},{"_id":"990806","name":"km2_flight","format":"","frictionlessType":"number","description":"

visible area flown in whole waypoint flight (taking camera field of view into account - FOV = 14.6m x 20.8m)

","units":{"_id":"","name":"square kilometers"},"type":{"_id":"906445","name":"area","description":"

Area is the size of a two-dimensional plane or a flat surface.

"}},{"_id":"990807","name":"Area_covered","format":"","frictionlessType":"number","description":"

visible area flown in whole waypoint flight (taking camera field of view into account) converted to hectares

","units":{"_id":"","name":"hectares"},"type":{"_id":"906445","name":"area","description":"

Area is the size of a two-dimensional plane or a flat surface.

"}},{"_id":"990808","name":"density","format":"","frictionlessType":"number","description":"

Number of green turtles per hectare