For those wanting to embark on learning computational methods, this blog post outlining “Why you should learn R first for data science” does a nice job of explaining why it is a good idea to start off by just learning one language. Although I don’t necessarily agree with the prescriptive attitude, it is fairly persuasive on the use of R and there are links to plenty of R resources to get you started.
On the subject of R, Francisco Rodriguez-Sanchez has posted a tutorial “Spatial data in R: using R as a GIS”. The tutorial provides a nice introduction, and has a focus on species’ distribution modelling and other ecological applications.
Moving from the computational to the quantitative, Johnson et al. have investigated the issue of power analysis for GLMMs, finding traditional methods are inadequate and recommend the use of simulation-based power analysis.
Courses Distance sampling workshops at St Andrews in August: these sound like a great introduction to designing and analysing data from distance sampling surveys e.g. point and line transects. Bayesian population analysis in WinBUGS courses coming up in France (April) and South Africa (October).
Jobs PhD opportunity on informing climate change policy through data analysis at University of East Anglia: sounds like there’s a really broad scope to this PhD, with one of the potential pathways being to research biodiversity impacts. Research fellow in honey bee foraging ecology at University of Sussex: mix of fieldwork, GIS and statistical analysis. Fully-funded internships at Microsoft’s Ecology group for 3 months over the summer: open to undergraduate, masters and PhD students.