On Wednesday 25th November 2020, 13:00
Speaker: Megan Laxton (University of Glasgow).
Title: Taking the Observation Process into Account: Modelling Species Distributions and Abundance in Space using R-inlabru
Often in ecology, it is impossible to fully observe the study domain, and the uniformity of sampling methodologies is limited by real-world constraints. The spatial distribution of resulting data is therefore produced by a combination of the underlying ecological process and the method of observation. In species distribution modelling, failing to take the observation process into account could lead to spurious significance in the interpretation of covariate effects, and a generally poorer understanding of population spatial structure. The R-package inlabru was created for realistically complex ecological data, so is equipped to handle spatially varying detection probabilities; helping to prevent misled inference and inaccurate predictions. In this talk, I will discuss some ways in which complex observation processes can be accounted for within inlabru, to more accurately represent input data in ecological modelling. This will include examples such as orangutan nest distribution in Borneo, Eurasian crane population spread in the UK, and estimating abundance of wildebeest in the Serengeti.
On Wednesday 20th January 2021
Speaker: Vianey Leos Barajas (Toronto).
More information will be available shortly.
On Wednesday 14. October at 13:00
Speaker: Emiko Dupont (University of Bath)
Title: Spatial+: a novel approach to spatial confounding
In spatial regression models, collinearity between covariates and spatial effects can lead to significant bias in effect estimates. We encounter this problem, known as spatial confounding, modelling forestry data to assess the effect of temperature on tree health. Reliable inference is difficult as results depend on whether or not we include spatial effects in the model. The mechanism behind spatial confounding is poorly understood and methods for dealing with it are limited. We propose a novel method, spatial+, a modified version of the spatial model. Using a thin plate spline model formulation, we are able to analyse the asymptotic behaviour of effect estimates to provide a theoretical explanation for why spatial confounding happens and why spatial+ works. These results are also demonstrated in a simulation study. Spatial+ is straight-forward to implement using existing software and standard model selection criteria can be used for comparing models. Another advantage of the method is that it extends to models with non-Gaussian response distributions. Finally, while our results are derived in a thin plate spline setting, the spatial+ methodology transfers naturally to other spatial model formulations.
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