Using random forest algorithm to model cold-stunning events in sea turtles in North Carolina
Abstract
© 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.
Type
Journal articlePermalink
https://hdl.handle.net/10161/22478Published Version (Please cite this version)
10.3996/052019-JFWM-043Publication Info
Niemuth, JN; Ransom, CC; Finn, SA; Godfrey, MH; Nelson, SAC; & Stoskopf, MK (2020). Using random forest algorithm to model cold-stunning events in sea turtles in North
Carolina. Journal of Fish and Wildlife Management, 11(2). pp. 531-541. 10.3996/052019-JFWM-043. Retrieved from https://hdl.handle.net/10161/22478.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Matthew H. Godfrey
Adjunct Associate Professor

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