Species Distribution Modelling

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:

  • MARGE - multivariate adaptive regression splines for correlated non-normal data (Jakub Stoklosa).
  • Spatial confounding in point process models (Wesley Brooks).
  • Fast methods to fit point process models to clustered data (Wesley Brooks, Elliot Dovers).
  • Point process models for clustered data along stream networks (Wesley Brooks, with Jay verHoef, Erin Peterson).
  • Point process models for multi-species point event data (Elliot Dovers, Wesley Brooks).
  • Accounting for uncertainty in predictor variables (Jakub Stoklosa and Firouzeh Noghrehchi, with Chris Daly and Scott Foster).
  • The use of shrinkage approaches for model selection and to improve predictive performance (Ian Renner, Francis Hui with Scott Foster).
  • Using field measurements of climate variables to improve predictive performance (Eve Slavich).
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).

Contact us

Phone: +61 2 9385-7031
Fax: +61 2 9385-7123
E-mail: David.Warton(at)unsw.edu.au

Red Centre room 2052
(David's office)
Red Centre room 6103
(Eco-Stats office)

School of Mathematics and Statistics
UNSW Sydney

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