Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data
Humphries, Grant Richard Woodrow
Cite this item:
Humphries, G. R. W. (2015). Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data. PLOS One, 10(9). doi:10.1371/journal.pone.0137241
Permanent link to OUR Archive version:
http://hdl.handle.net/10523/7116
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.
Date:
2015-09-02
Publisher:
PLOS One
Rights Statement:
Copyright: © 2015 Grant Richard Woodrow
Humphries. This is an open access article distributed
under the terms of the Creative Commons Attribution
License, which permits unrestricted use, distribution,
and reproduction in any medium, provided the
original author and source are credited.
Data Availability Statement: The raw data used to
create the harvest indices, and chick size index used
in this manuscript are available upon request with
legal restrictions. Interested researchers may submit
requests to the author ( humphries.grant@gmail.com)
or Prof. Henrik Moller ( ecosyst@ihug.co.nz). A
reduced version of the three mean harvest indices
which does not include identifying information can be
downloaded at: http://www.humphriesresearch.com/
work.html. The indices were published in Humphries
et al. [15], in Ecological Applications. The ECMWF
data used for modeling are already available open
access on the web at www.ECMWF.int. Third-party
GLS tracking data are available upon request from Dr. Scott Shaffer ( scott.shaffer@sjsu.edu), or Dr.
David Thompson ( David.Thompson@niwa.co.nz).
Keywords:
A-Spatial Data, seabird tracking, sooty shearwater
Research Type:
Journal Article
Languages:
English
Collections
- Journal Article [785]
- Zoology collection [315]
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