- David L Miller – Centre for Research into Ecological and Environmental Modelling – University of St. Andrews – Scotland
- FCUL – Campo Grande – Bloco C6 Piso 4 – Sala: 6.4.30 – 14:30h
- Quinta-feira, 11 de Junho de 2015
Our understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates. Such models can be used to investigate the relationships between distribution and environmental covariates as well as reliably estimate abundances and create maps of animal and plant distributions.
Here I’ll give an overview of “density surface models”, which consist of a spatial model of the abundance which has been corrected for uncertain detection via distance sampling methods. The spatial model consists of a generalised additive (mixed) model, which can include many varied components, such as smooth terms and random effects. In particular, I’ll
highlight: flexible detection functions, quantification of uncertainty in a two-stage model, correction for availability bias, alternative/unusual response distributions, autocorrelation and smoothing in areas with complex boundaries. Through examples of seabirds in Rhode Island and black bears in Alaska, I’ll show how such models are easily constructed, fitted, checked and compared using the R packages dsm and Distance.