Molecular systematics of the Cactaceae

Bayesian, maximum-likelihood, and maximum-parsimony phylogenies, constructed using nucleotide sequences from the plastid gene region trnK-matK, are employed to investigate relationships within the Cactaceae. These phylogenies sample 666 plants representing 532 of the 1438 species recognized in the family. All four subfamilies, all nine tribes, and 69% of currently recognized genera of Cactaceae are sampled. We found strong support for three of the four currently recognized subfamilies, although relationships between subfamilies were not well defined. Major clades recovered within the largest subfamilies, Opuntioideae and Cactoideae, are reviewed; only three of the nine currently accepted tribes delimited within these subfamilies, the Cacteae, Rhipsalideae, and Opuntieae, are monophyletic, although the Opuntieae were recovered in only the Bayesian and maximum-likelihood analyses, not in the maximum-parsimony analysis, and more data are needed to reveal the status of the Cylindropuntieae, which may yet be monophyletic. Of the 42 genera with more than one exemplar in our study, only 17 were monophyletic; 14 of these genera were from subfamily Cactoideae and three from subfamily Opuntioideae. We present a synopsis of the status of the currently recognized genera

How accurate are volcanic ash simulations of the 2010 Eyjafjallajökull eruption?

In the event of a volcanic eruption the decision to close airspace is based on forecast ash maps, produced using volcanic ash transport and dispersion models. In this paper we quantitatively evaluate the spatial skill of volcanic ash simulations using satellite retrievals of ash from the Eyja allajökull eruption during the period from 7 to 16 May 2010. We find that at the start of this period, 7–10 May, the model (FLEXible PARTicle) has excellent skill and can predict the spatial distribution of the satellite-retrieved ash to within 0.5∘ × 0.5∘ latitude/longitude. However, on 10 May there is a decrease in the spatial accuracy of the model to 2.5∘× 2.5∘ latitude/longitude, and between 11 and 12 May the simulated ash location errors grow rapidly. On 11 May ash is located close to a bifurcation point in the atmosphere, resulting in a rapid divergence in the modeled and satellite ash locations. In general, the model skill reduces as the residence time of ash increases. However, the error growth is not always steady. Rapid increases in error growth are linked to key points in the ash trajectories. Ensemble modeling using perturbed meteorological data would help to represent this uncertainty, and assimilation of satellite ash data would help to reduce uncertainty in volcanic ash forecasts.