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Additive Mixed Modelling Applied on Deep-Sea Pelagic Bioluminescent Organisms

Part of the Statistics for Biology and Health book series (SBH)

Abstract

The oceans, with a mean depth of 3,729 m and extending to a maximum depth of 11 km comprise the largest habitat on earth. The distribution of living organisms in this vast environment is far from uniform and description of this variation in space and time is challenging, both from the point of view of sampling and of statistical analysis. Most life in the oceans is dependent on primary production in the surface layers, generally in the epipelagic zone down to a depth of 200 m, where there is sufficient solar radiation to sustain photosynthesis. Microscopic algae or phytoplankton containing the pigment chlorophyll intercept solar light and use the energy to combine CO2 and water to produce simple sugars polysaccharides, oils, proteins, and all the other constituents of the living organism. The algae and phytoplankton are either consumed by planktonic animals or dies loses buoyancy and becomes part of the downward stream of particulate organic matter (POC) falling towards the sea floor. The primary consumers themselves produce faecal pellets that enhance the POC flux and also form the basis of the food chain in the surface layers of the oceans. Predators living at greater depths also ascend at night to feed on the surface riches and then descend during the day digesting and excreting as they go. Thus, surface-derived production is exported downwards by passive and active processes sustaining life throughout the water column down to the abyssal sea floor.

Keywords

  • Depth Gradient
  • Smoothing Curve
  • Depth Relationship
  • Spatial Correlation Structure
  • Additive Mixed Model

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.

    We used R version 2.6 and mgcv version 1.3–27. More recent versions of R and mgcv require a small modification to the code; see the book website (www.highstat.com) for updated code.

Acknowledgments

We thank Dr Emma Gillibrand for permission to use data from her PhD thesis.

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© 2009 Springer Science+Business Media, LLC

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Zuur, A., Priede, I., Ieno, E., Smith, G., Saveliev, A., Walker, N. (2009). Additive Mixed Modelling Applied on Deep-Sea Pelagic Bioluminescent Organisms. In: Mixed effects models and extensions in ecology with R. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-87458-6_17

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