Satellite Images Show the Movement of Floating _Sargassum_ in the Gulf of Mexico and Atlantic Ocean

The question of the origin, distribution and fate of the floating seaweed _Sargassum_ has fascinated sailors and scientists from the time of Columbus. Observations from ships are hampered by the large and variable area over which _Sargassum_ is dispersed. Here we use satellite imagery to present the first mapping of the full distribution and movement of the population of _Sargassum_ in the Gulf of Mexico and western Atlantic in the years 2002 to 2008. For the first time, we show a seasonal pattern in which _Sargassum_ originates in the northwest Gulf of Mexico in spring of each year, is advected into the Atlantic in about July, appearing east of Cape Hatteras as a "Sargassum jet", and ending northeast of the Bahamas in February of the following year. This pattern appears consistent with historical surveys. Future satellite observations will show whether this pattern repeats in all or most years

Visualising interactions in bi- and triadditive models for three-way tables

This paper concerns the visualisation of interaction in three-way arrays. It extends some standard ways of visualising biadditive modelling for two-way data to the case of three-way data. Three-way interaction is modelled by the Parafac method as applied to interaction arrays that have main effects and biadditive terms removed. These interactions are visualised in three and two dimensions. We introduce some ideas to reduce visual overload that can occur when the data array has many entries. Details are given on the interpretation of a novel way of representing rank-three interactions accurately in two dimensions. The discussion has implications regarding interpreting the concept of interaction in three-way arrays

Predictive nonlinear biplots: maps and trajectories

When the difference between samples is measured using a Euclidean embeddable dissimilarity function, observations and the associated variables can be displayed on a nonlinear biplot. Furthermore, a nonlinear biplot is predictive if information on variables is added in such a way that it allows the values of the variables to be estimated for points in the biplot. In this paper an r dimensional biplot which maps the predicted value of a variable for every point in the plot, is introduced. Using such maps it is shown that even with continuous data, predicted values do not always vary continuously across the biplot plane. Prediction trajectories that appropriate for summarising such non-continuous prediction maps are also introduced. These prediction trajectories allow information about two or more variables to be estimated even when the underlying predicted values do not vary continuously