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.
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 December 2015.
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#10,482,974
of 32,726,043 outputs
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#90,541
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#94,880
of 294,009 outputs
Outputs of similar age from PLOS ONE
#2,387
of 6,004 outputs
Altmetric has tracked 32,726,043 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 217,920 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.1. This one has gotten more attention than average, scoring higher than 57% of its peers.
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We're also able to compare this research output to 6,004 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.
