Abstract
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. 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.