Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data

PLOS_ONEPLOS One, Published 02 September 2015

By Grant Richard Woodrow Humphries

“Advances in GPS tracking technologies have allowed for rapid assessment of important oceanographic regions for seabirds. This allows us to understand seabird distributions, and the characteristics which determine the success of populations. In many cases, quality GPS tracking data may not be available; however, long term population monitoring data may exist. In this study, a method to infer important oceanographic regions for seabirds will be presented using breeding sooty shearwaters as a case study. This method combines a popular machine learning algorithm (generalized boosted regression modeling), geographic information systems, long-term ecological data and open access oceanographic datasets. Time series of chick size and harvest index data derived from a long term dataset of Maori ‘muttonbirder’ diaries were obtained and used as response variables in a gridded spatial model.

Map showing GLS data from Shaffer et al. (2006) for GLS birds tracked from Whenua Hou/Codfish Island (starred on the map) from January 2005 to March 2006.

Map showing GLS data from Shaffer et al. (2006) for GLS birds tracked from Whenua Hou/Codfish Island (starred on the map) from January 2005 to March 2006.

“It was found that areas of the sub-Antarctic water region best capture the variation in the chick size data. Oceanographic features including wind speed and charnock (a derived variable representing ocean surface roughness) came out as top predictor variables in these models. Previously collected GPS data demonstrates that these regions are used as “flyways” by sooty shearwaters during the breeding season. It is therefore likely that wind speeds in these flyways affect the ability of sooty shearwaters to provision for their chicks due to changes in flight dynamics. This approach was designed to utilize machine learning methodology but can also be implemented with other statistical algorithms. Furthermore, these methods can be applied to any long term time series of population data to identify important regions for a species of interest.”

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