Species distribution modelling uses records of where a species is known to occur to model how it relates to a suite of environmental variables.
This may be done to better understand the ecology of the target species, to predict its response to environmental change, or to assist conservation efforts. The modelling process involves many challenging steps, and we are currently exploring the following problems:
Previously we unified different methods for analysing presence-only data: pseudo-absences, point-process models and MAXENT (Ian Renner and Leah Shepherd) and investigated model-baesd control of observer bias in presence only anlaysis (Ian Renner with Dan Ramp).
Models including latent variables are invaluable in ecology for a number of applications - not just for models with surveys with multiple sampling units, but recently we have been interested in their application for visualising and modelling multivariate data, and for accounting for errors in predictor variables. How can we fit such models more efficiently? We will explore a number of options centered around extensions of the Monte Carlo EM algorithm and efficient Laplace and variation approximation algorithms.
Often predictor variables (in ecology and elsewhere) are measured with error, and failing to take this into account biases estimates of the fitted model, and often, subsequent predictions. We have been developing easy-to-use algorithms for modelling such data, having initially focussed on generalised linear models for data with measurement error that is independent across observations. Important extensions include: how to extend to spatially correlated measurement error? How to generalise to handle general predictive models (beyond GLM)?