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Title: Treating ecological data structures as thinned point processes Authors:  Janine Illian - University of St Andrews (United Kingdom) [presenting]
Abstract: In statistical ecology specific data structures resulting from common survey methods, motivate statistical methodology and software development. The specific survey method as well as the statistical methodology adapt to the practicality of data collection but do not directly reflect the underlying ecological process of interest. This results in highly specialised modelling approaches and little exchange among developers of different strands of methodology. Thinking in terms of the processes that we would like to model rather than in terms of the survey method can yield a flexible class of models. Specifically, ecological processes of interest are structures formed by individuals in space and time, reflecting interaction among individuals and with the environment. Classical point process methodology assumes the entire spatial area of interest has been surveyed and that individuals have been detected uniformly in space. Treating ecological data structures as a thinning of a spatial point process allows us to use methodology developed for spatial point processes. As a result, general and computationally efficient model fitting software such as R-INLA, based on computationally efficient integrated nested Laplace approximation become relevant. The package inlabru has been designed to fit thinned point process models, based on R-INLA. It accommodates a wide range of ecological data structures and has also contributed to widening the class of models that may be fitted with R-INLA