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The relationship between temperature and nitrate in the upper 200 m of the central and eastern tropical Pacific was investigated using regression techniques, and the slope of this relationship was compared along spatial and temporal dimensions. In the open‐ocean waters of the eastern and central Pacific, variation in the slope of the temperature‐nitrate (TN) relationship was primarily north‐south, with the steepness of the slope decreasing to the south. In the more coastal waters of the southeastern Pacific, however, the slope showed strong gradients in the east‐west direction, with the slope steepening from west to east. Seasonal trends in the slope of the TN relationship were examined along the equator. During March–May the relatively flat slope of the TN relationship that is found year‐round in the central Pacific is found to extend further east, associated with the intensification and shoaling of the Equatorial Undercurrent during this same period. Regressions predicting nitrate from temperature were tested on new data and errors of prediction were low for open‐ocean locations (1.4 μM); however, errors increased (to 3.2–4.3 μM) with proximity to coastal waters, reflecting the increasing and variable contribution of nitrate uptake by phytoplankton.

This paper describes treeClust, an R package that produces dissimilarities useful for clustering. These dissimilarities arise from a set of classification or regression trees, one with each variable in the data acting in turn as a the response, and all others as predictors. This use of trees produces dissimilarities that are insensitive to scaling, benefit from automatic variable selection, and appear to perform well. The software allows a number of options to be set, affecting the set of objects returned in the call; the user can also specify a clustering algorithm and, optionally, return only the clustering vector. The package can also generate a numeric data set whose inter-point distances relate to the treeClust ones; such a numeric data set can be much smaller than the vector of inter-point dissimilarities, a useful feature in big data sets.

There is a need to consider moderator variables when examining mental health in healthcare populations to avoid drawing overly simplistic conclusions. Interns in Ireland reported particularly high levels of psychological distress compared to other studies of mental health among healthcare populations.

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