Migration Corridors and Threats in the Gulf of Mexico and Florida Straits for Loggerhead Sea Turtles

Abstract

Along migration corridors, animals can face natural and anthropogenic threats that differ from those in breeding and non-breeding residence areas. Satellite telemetry can aid in describing the timing and location of these migrations. We use this tool with switching state-space modeling and line kernel density estimates to identify migration corridors of post-nesting adult female loggerhead sea turtles (Caretta caretta, n = 89 tracks) that nested at five beaches in the Gulf of Mexico. Turtles migrated in both neritic and oceanic areas of the Gulf of Mexico with some exiting the Gulf. High-use migration corridors were found in neritic areas to the west of Florida and also in the Florida Straits. Repeat tracking of post-nesting migrations for eight turtles showed variability in track overlap, ranging from ∼13 to 82% of tracks within 10 km of each other. Migration primarily occurred in July and August. We document the longest known post-nesting migration to-date of a wild adult female loggerhead of >4,300 km, along with an apparent stopover of about 1 month. Migration corridors overlaid on three spatially explicit anthropogenic threats (shipping density, commercial line fishing, and shrimp trawling) showed hotspots in the Florida Straits, off the northwest Florida coast and off the coast of Tampa Bay. Identifying where and at what intensity multiple human activities and natural processes most likely occur is a key goal of Cumulative Effects Assessments. Our results provide the scientific information needed for designing management strategies for this threatened species. Information about this loggerhead migration corridor can also be used to inform adaptive management as threats shift over time.

Inter-Nesting Movements, Migratory Pathways, and Resident Foraging Areas of Green Sea Turtles (Chelonia mydas) Satellite-Tagged in Southwest Florida

“…We used switching state space modeling (SSM) to characterize the movements of tagged turtles. The model was previously described in Jonsen et al (2005) and has been applied to model movement of many marine animals (Bailey et al, 2009;Shillinger et al, 2010;Maxwell et al, 2011;Shaver et al, 2016;Iverson et al, 2020). Specifically, we applied a Bayesian hierarchical movement model with behavioral-state switching in the R package "bsam" (Jonsen et al, 2005(Jonsen et al, , 2017Jonsen, 2016;R Core Team, 2020) by calling JAGS (package rjags; Plummer, 2019) to run the Markov Chain Monte Carlo (MCMC) algorithm.…”

Modeling protected species distributions and habitats to inform siting and management of pioneering ocean industries: A case study for Gulf of Mexico aquaculture

“…The southern portion of the Atlas study area, near the Florida Keys, was identified as a high-use migratory corridor for Loggerheads, Greens, and Hawksbills based on a review of previous satellite telemetry studies (Foley et al 2013; Shaver et al 2016; Iverson et al 2020; Hart et al 2021).…”

Modeling protected species distributions and habitats to inform siting and management of pioneering ocean industries: A case study for Gulf of Mexico aquaculture

“…Insufficient data were available on hawksbill turtles to identify HUAs using these methods. The southern portion of the Atlas study area, near the Florida Keys, was identified as a high-use migratory corridor for loggerheads, greens, and hawksbills (Fig 5) based on a review of previous satellite telemetry studies [37,[53][54][55]. The union of HUA and migratory corridor layers for leatherback, Kemp's ridley, hawksbill, loggerhead, and green sea turtles were scored at 0.1, 0.2, 0.2, 0.4, and 0.5, respectively.…”