Explorative Data Analysis for Prediction? Ecological Statistics between Anything Goes and the Statistical Cutting Edge
von 11:15 bis 13:00
|Wo||Eckerstr. 1, Raum 404|
Ecological data are a mess: environmental states are difficult to measure, extremely variable, governed by processes at various spatial and temporal scales and describing highly adaptive systems. Ecologists are rarely trained well enough in statistics to even recognise the problems they are facing. At the same time, environmental questions are high on the political agenda and ecologists desire to support policy with their knowledge. A typical example is the attempt to predict the ``whereabouts'' of species under climate change. Large data bases are currently being filled with geographical locations of where species currently are, analysed statistically and the predicted to climate change scenarios. In this talk I will present some statistical challenges that our discipline is facing and the strategies it has developed. Specifically, I will touch on spatial autocorrelation, multicollinearity and typical modelling approaches. I would like to dwell a bit on prediction uncertainty and on the unrelatedness of two fundamental developments in the trade, Bayesian statistics (focussing on embracing detection probabilities) and machine learning (focussing on flexible relationships between predictors and the response). In the end I hope to have given the audience an overview of the many challenges ecological statistics are stubbornly trying to address.