rworldmap is a relatively new package available on CRAN for the mapping and visualisation of global data. The vision is to make the display of global data easier, to facilitate understanding and communication. The initial focus is on data referenced by country or grid due to the frequency of use of such data in global assessments. Tools to link data referenced by country (either name or code) to a map, and then to display the map are provided as are functions to map global gridded data. Country and gridded functions accept the same arguments to specify the nature of categories and colour and how legends are formatted. This package builds on the functionality of existing packages, particularly sp, maptools and fields. Example code is provided to produce maps, to link with the packages classInt, RColorBrewer and ncdf, and to plot examples of publicly available country and gridded data.

Summary1. Kernel-density estimation (KDE) is one of the most widely used home-range estimators in ecology. The recommended implementation uses least squares crossvalidation (LSCV) to calculate the smoothing factor ( h ) which has a considerable influence on the home-range estimate. 2. We tested the performance of least squares cross-validated kernel-density estimation (LSCV KDE) using data from global positioning system (GPS)-collared lions subsampled to simulate the effects of hypothetical radio-tracking strategies. 3. LSCV produced variable results and a 7% failure rate for fewer than 100 locations ( n = 2069) and a 61% failure rate above 100 points ( n = 1220). Patterns of failure and variation were not consistent among lions, reflecting different individual space use patterns. 4. Intensive use of core areas and site fidelity by animals caused LSCV to fail more often than anticipated from studies that used computer-simulated data. 5. LSCV failures at large sample sizes and variation at small sample sizes, limits the applicability of LSCV KDE to fewer situations than the literature suggests, and casts doubts over the method's reliability and comparability as a home-range estimator.

Lee, J., South, A. B., and Jennings, S. 2010. Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data. – ICES Journal of Marine Science, 67: 1260–1271. Vessel monitoring systems (VMS) are used primarily for fisheries enforcement purposes, but also provide information on the spatial and temporal distribution of fishing activity for use in fisheries and environmental assessment and management. A reliable, repeatable, and accessible method using readily available software for estimating fishing effort from unprocessed VMS data is developed, tested, and applied. Caveats associated with the method are identified, and the biases introduced by our assumptions are quantified. Application of the method provides a high-resolution description of gear-specific fishing activity by UK vessels. An index is developed to describe variation in the spatial pattern of fishing effort generated by different gears. The proposed method for VMS analysis involves removing duplicate VMS records and records close to ports, calculating the time interval between successive records to identify periods of activity, linking each record to a vessel and gear type, differentiating fishing and non-fishing activity, and summing fishing records in time and space to estimate fishing effort. The approach is a step towards the development of standardized methods to facilitate wider exchange and use of European VMS data. A clear audit trail for the methods of VMS analysis already used to inform management needs to be documented.

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