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RSS FeedsRemote Sensing, Vol. 14, Pages 3887: One Shell of a Problem: Cumulative Threat Analysis of Male Sea Turtles Indicates High Anthropogenic Threat for Migratory Individuals and Gulf of Mexico Residents (Remote Sensing)

 
 

11 august 2022 11:31:07

 
Remote Sensing, Vol. 14, Pages 3887: One Shell of a Problem: Cumulative Threat Analysis of Male Sea Turtles Indicates High Anthropogenic Threat for Migratory Individuals and Gulf of Mexico Residents (Remote Sensing)
 


Human use of oceans has dramatically increased in the 21st century. Sea turtles are vulnerable to anthropogenic stressors in the marine environment because of lengthy migrations between foraging and breeding sites, often along coastal migration corridors. Little is known about how movement and threat interact specifically for male sea turtles. To better understand male sea turtle movement and the threats they encounter, we satellite-tagged 40 adult male sea turtles of four different species. We calculated movement patterns using state-space modeling (SSM), and quantified threats in seven unique categories; shipping, fishing, light pollution, oil rigs, proximity to coast, marine protected area (MPA) status, and location within or outside of the U.S. Exclusive Economic Zone (EEZ). We found significantly higher threat severity in northern and southern latitudes for green turtles (Chelonia mydas) and Kemp’s ridleys (Lepidochelys kempii) in our study area. Those threats were pervasive, with only 35.9% of SSM points encountering no high threat exposure, of which 47% belong to just two individuals. Kemp’s ridleys were most exposed to high threats among tested species. Lastly, turtles within MPA boundaries face significantly lower threat exposure, indicating MPAs could be a useful conservation tool.


 
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Remote Sensing, Vol. 14, Pages 3886: Simulating the Changes of Invasive Phragmites australis in a Pristine Wetland Complex with a Grey System Coupled System Dynamic Model: A Remote Sensing Practice (Remote Sensing)
Remote Sensing, Vol. 14, Pages 3889: Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data (Remote Sensing)
 
 
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