Generalised additive models to investigate environmental drivers of Antarctic minke whale ( Balaenoptera bonaerensis ) spatial density in austral summer
There is a need to characterise the physical environment associated with Antarctic minke whale density in order to understand long-term changes in minke whale distribution and density in open waters of the Southern Ocean during austral summer months. To investigate environmental drivers of Antarctic...
| Main Authors: | , , , , , |
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| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | https://hdl.handle.net/11370/2bb84021-7eb0-4869-a728-3c7984dd983c https://research.rug.nl/en/publications/2bb84021-7eb0-4869-a728-3c7984dd983c https://pure.rug.nl/ws/files/14498770/2010JCetaceanResManageBeekmans.pdf |
| Summary: | There is a need to characterise the physical environment associated with Antarctic minke whale density in order to understand long-term changes in minke whale distribution and density in open waters of the Southern Ocean during austral summer months. To investigate environmental drivers of Antarctic minke whale density, generalised additive models (GAMs) were developed, based on line transect data collected for the International Decade of Cetacean Research (IDCR) and Southern Ocean Whale Ecosystem Research (SOWER) programmes. The GAMs were fitted independently by survey year. Explained deviances ranged from 14.9% to 35.1%. Most models included covariates related to transition zones, such as distances to the continental shelf break and sea ice edge, both of which showed a predominantly negative relationship with whale density. This study suggests high variability in the relationships between Antarctic minke whale density and the environment. None of the selected covariates had a consistent qualitative relationship with density at either the circumantarctic or the regional scale. This in part may be explained by the changing ice-related boundaries of the surveys between years and hence differences in survey region. Another possible reason is that in absence of better data, most of the covariates considered were derived from remote sensing data. More localised surveys with comparable survey area conducted across the Southern Ocean, where whale sightings data are collected simultaneously with in situ non-biotic and prey data, are likely to provide a better assessment of the environmental determinants of whale density. |
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