Amphioxus Evx genes: implications for the evolution of the Midbrain-Hindbrain Boundary and the chordate tailbud.

Evx genes are widely used in animal development. In vertebrates they are crucial in gastrulation, neurogenesis, appendage development and tailbud formation, whilst in protostomes they are involved in gastrulation and neurogenesis, as well as segmentation at least in Drosophila. We have cloned the Evx genes of amphioxus (Branchiostoma floridae), and analysed their expression to understand how the functions of Evx have evolved between invertebrates and vertebrates, and in particular at the origin of chordates and during their subsequent evolution. Amphioxus has two Evx genes (AmphiEvxA and AmphiEvxB) which are genomically linked. AmphiEvxA is prototypical to the vertebrate Evx1 and Evx2 genes with respect to its sequence and expression, whilst AmphiEvxB is very divergent. Mapping the expression of AmphiEvxA onto a phylogeny shows that a role in gastrulation, dorsal-ventral patterning and neurogenesis is probably retained throughout bilaterian animals. AmphiEvxA expression during tailbud development implies a role for Evx throughout the chordates in this process, whilst lack of expression at the homologous region to the vertebrate Midbrain-Hindbrain Boundary (MHB) is consistent with the elaboration of the full organiser properties of this region being a vertebrate innovation.     

Novel methods improve prediction of species' distributions from occurrence data

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.