Using random forest algorithm to model cold-stunning events in sea turtles in North Carolina

Niemuth, JN

Ransom, CC

Finn, SA

Godfrey, MH

Nelson, SAC

Stoskopf, MK

2021-04-01T14:06:15Z

2021-04-01T14:06:15Z

2020-12-01

2021-04-01T14:06:14Z

© 2020 U.S. Fish and Wildlife Service. All rights reserved. Sea turtle strandings due to cold-stunning are seen when turtles are exposed to ocean temperatures that acutely and persistently drop below approximately 128C. In North Carolina, this syndrome affects imperiled loggerhead Caretta caretta, green Chelonia mydas, and Kemp’s ridley Lepidochelys kempii sea turtle species. Based on oceanic and meteorological patterns of cold-stunning in sea turtles, we hypothesized that we could predict the daily size of cold-stunning events in North Carolina using random forest models. We used cold-stunning data from the North Carolina Sea Turtle Stranding and Salvage Network from 2010 to 2015 and oceanic and meteorological data from the National Data Buoy Center from 2009 to 2015 to create a random forest model that explained 99% of the variance. We explored additional models using the 10 and 20 most important variables or only oceanic and meteorological variables. These models explained similar percentages of variance. The variables most frequently found to be important were related to air temperature, atmospheric pressure, wind direction, and wind speed. Surprisingly, variables associated with water temperature, which is critical from a biological perspective, were not among the most important variables identified. We also included variables for the mean change in these metrics daily from 4 d before the day of stranding. These variables were among the most important in several of our models, especially the change in mean air temperature from 4 d before stranding to the day of stranding. The importance of specific variables from our random forest models can be used to guide the selection of future model predictors to estimate daily size of cold-stunning events. We plan to apply the results of this study to a predictive model that can serve as a warning system and to a downscaled climate projection to determine the potential impact of climate change on cold-stunning event size in the future.

1944-687X

1944-687X

https://hdl.handle.net/10161/22478

en

U.S. Fish and Wildlife Service

Journal of Fish and Wildlife Management

10.3996/052019-JFWM-043

Using random forest algorithm to model cold-stunning events in sea turtles in North Carolina

Journal article

531

541

2

Nicholas School of the Environment

Marine Science and Conservation

Duke

Published

11