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

Grant Richard Woodrow Humphries

doi:10.1371/journal.pone.0137241

Grant Richard Woodrow Humphries

Open Access

https://doi.org/10.1371/journal.pone.0137241

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Journal: PloS one	Publication Date: Sep 2, 2015
Citations: 5	License type: CC BY 4.0

Affiliation: University of California, Davis, University of Otago

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.

Highlights

In the last two decades, technological advances have led to increased efficiency and lower costs of GPS units which allow scientists to track species for varying lengths of time in order to identify regions of importance [1,2,3]
Advances in GPS tracking technologies have allowed for rapid assessment of important oceanographic regions for seabirds
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

Summary

Introduction

In the last two decades, technological advances have led to increased efficiency and lower costs of GPS units which allow scientists to track species for varying lengths of time in order to identify regions of importance [1,2,3]. These data are often used for predicting distributions which are used in conservation management. GPS data are sparse, covering only a limited temporal and spatial.

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