Gulf Sea Turtles (Southeast Blueprint Indicator) [U.S. Fish and Wildlife Service] Full Details

Full Details

Title:

Gulf Sea Turtles (Southeast Blueprint Indicator) [U.S. Fish and Wildlife Service]

Description:

Reason for Selection As a keystone species, even at diminished population levels, sea turtles play an important role in ocean ecosystems by maintaining healthy seagrass beds and coral reefs, providing key habitat for other marine life, helping to balance marine food webs, and facilitating nutrient cycling from water to land (Wilson 2010). Sea turtles use large areas of the ocean for feeding and reproduction, making them a good indicator of ocean productivity and overall ocean health. For example, Kemp's ridley sea turtles nesting in southern Texas consistently forage in areas near the Yucatán Peninsula, the Gulf coast of Florida, the Mississippi River Delta, and the Texas-Louisiana shelf (Gredzens and Shaver 2020). Input Data Gulf of Mexico Marine Assessment Program for Protected Species (GoMMAPPS) -GoMMAPPS sea turtle spatial density model outputs (version 2.2) Based on ship-based and aerial line-transect surveys conducted in the U.S. waters of the Gulf of Mexico between 2003 and 2019, the NOAA Southeast Fisheries Science Center developed spatial density models (SDMs) for cetacean and sea turtle species for the entire Gulf of Mexico. SDMs were developed using a generalized additive modeling framework to determine the relationship between species abundance and environmental variables (monthly averaged oceanographic conditions during 2015-2019). Southeast Blueprint 2023 subregions- marine (combined Atlantic & Gulf of Mexico) Southeast Blueprint 2023 extent 2019 National Land Cover Database(NLCD) Mapping Steps Replace all values of -9999 with 0. Convert to monthly rasters for each species using the following fields: "Jan_n", "Feb_n", "Mar_n", "Apr_n", "May_n", "Jun_n", "Jul_n", "Aug_n", "Sep_n", "Oct_n", "Nov_n", and "Dec_n". Use the marine subregion for pixel size, snap, and extent. Use the loggerhead sea turtle data and the NLCD to create a mask to define the extent of the Zonation analysis. The loggerhead data represents the full sample area for the other species in GoMMAPPS. The area covered by the sea turtle models overlaps with land in a few areas. This mask removes from the analysis all landcover classes that are not open water (not a value of 11 in the NLCD) within the extent of NLCD. The resulting Zonation mask covers open water areas where there is both modeled data for sea turtles and NLCD data to remove land. To identify important areas for each species, use the core area algorithm (CAZMAX) in Zonation 5. Include each monthly density layer as a separate input and weight them equally. Reproject the Zonation results data to Albers Equal Area. Convert from a floating point raster with a range of 0-1 to an integer raster ranging from 0-100. Reclassify to produce the indicator values seen below so that 0-65 is 1, 66-70 is 2, 71-80 is 3, 81-90 is 4, and 91-100 is 5. The variation in values from Zonation below 65 was less helpful than the other higher classes so we classified all values from 65 and below as 1. Use the NLCD and the modeling extent of the source data to identify areas of land not used in the analysis and assign those pixels a value of 0, since they are outside the scope of this marine indicator. As a final step, clip to the spatial extent of Southeast Blueprint 2023. Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code. Final indicator values Indicator values are assigned as follows: 5 = >90th percentile of importance for sea turtle index species (across larger analysis area) 4 = >80th-90th percentile of importance 3 = >70th-80th percentile of importance 2 = >65th-70th percentile of importance 1 = ≤65th percentile of importance 0 = Land Known Issues While this layer has a 30 m resolution, the source data was coarser than that. We downsampled hexagons with an area of 40 km2 to 30 m pixels. Other Things to Keep in Mind We ran the Zonation analysis across open water areas where there were both sea turtle models and NLCD data present to discriminate between land and water. We did this for multiple reasons. We didn't run Zonation across the full area covered by the GoMMAPPS data because the full files were very large and required long processing times. We also anticipated that Zonation would not have been able to computationally handle the full area. We extended the Zonation run beyond U.S. waters to try to account for areas of high mammal density just south of the Blueprint's Gulf of Mexico subregion. As a result, the various classes within the indicator do not cover equal areas within the indicator's extent, as you might expect with a percentile-based indicator—they cover equal areas within the full analysis area, and then are clipped down to produce the indicator. Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (emailhilary_morris@fws.gov). Literature Cited Gredzens C, Shaver DJ. 2020. Satellite tracking can inform population-level dispersal to foraging grounds of post-nesting Kemp's ridley sea turtles. Frontiers in Marine Science, section Marine Megafauna, Special Theme Issue Research Topic: Advances in Understanding Sea Turtle Use of the Gulf of Mexico. [https://doi.org/10.3389/fmars.2020.00559]. Litz J, Aichinger Dias L, Rappucci G, Martinez A, Soldevilla M, Garrison L, Mullin K, Barry K, Foster M. 2022. Cetacean and sea turtle spatial density model outputs from visual observations using line-transect survey methods aboard NOAA vessel and aircraft platforms in the Gulf of Mexico from 2003-06-12 to 2019-07-31 (NCEI Accession 0256800). NOAA National Centers for Environmental Information. Dataset. [https://doi.org/10.25921/efv4-9z56]. Moilanen A, Lehtinen P, Kohonen I, Virtanen E, Jalkanen J, Kujala H. 2022.Novel methods for spatial prioritization with applications in conservation, land use planning and ecological impact avoidance. Methods in Ecology and Evolution. [https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13819]. Wilson, E.G., Miller, K.L., Allison, D. and Magliocca, M. 2010. Why Healthy Oceans Need Sea Turtles: The Importance of Sea Turtles to Marine Ecosystems. [https://oceana.org/wp-content/uploads/sites/18/Why_Healthy_Oceans_Need_Sea_Turtles_0.pdf].

Creator:

Department of the Interior

Provider:

U.S. Fish and Wildlife Service Open Data

Resource Class:

Imagery and Web services

Resource Type:

Raster data

Temporal Coverage:

Last modified 2024-10-09

Date Issued:

2024-07-16

Place:

United States

Rights:

The United States Fish and Wildlife Service (Service) shall not be held liable for improper or incorrect use of the data described and/or contained herein. While the Service makes every reasonable effort to ensure the accuracy and completeness of data provided for distribution, it may not have the necessary accuracy or completeness required for every possible intended use. The Service recommends that data users consult the associated metadata record to understand the quality and possible limitations of the data. The Service creates metadata records in accordance with the standards endorsed by the Federal Geographic Data Committee. As a result of the above considerations, the Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of the data. It is the responsibility of the data user to use the data in a manner consistent with the limitations of geospatial data in general and these data in particular. Although these data have been processed successfully on a computer system at the Service, no warranty, expressed or implied, is made regarding the utility of the data on another system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This applies to the use of the data both alone and in aggregate with other data and information.

Access Rights:

Public

Format:

Imagery

Language:

English

Date Added:

2024-10-26