On Wednesday 20th January 2021, 13:30 GMT
Speaker: Vianey Leos Barajas (University of Toronto).
Title: Learning through failing with hidden Markov models
The hidden Markov model (HMM) is a ubiquitous modeling framework in statistical ecology. In this talk, I will go back to the basics of applying HMMs to ecological data by taking a journey through the computational and inferential challenges of fitting mis-specified HMMs in practice. I will also detail the practical consequences that arises from these challenges in terms of model building and model interpretation. By taking a closer look at an established modeling framework in statistical ecology, we will find that there’s always more to learn and discover!
Wednesday 16th December 2020, 13.00
Stylianos Taxidis (Data scientist)
Title: The data scientists process and how can data science help in the field of climate change.
Data science and AI have lately become commonly used words to describe the progress of the field in analysing and creating new value of our digital world. How can we ensure a successful and scientifically rigorous process while remaining goal and time focused to resolved the critical problems of our time. What can data science offer to help with the resolution of environmental issues and more specifically climate change. What are the techniques we can employ from NLP, classical machine learning and deep learning to measure, monitor, quantify the impact of climate change but also suggest solutions becoming an action aggregator and accelerator. This will be a presentation of a holistic review on techniques that have been utilised in other industries and can provide value in the data to information to environmental action lifecycle. The audience will learn about the stages of a data science process and how they can be applied in the environmental domain. The presentation also aims to close highlighting both risks and opportunities of this application
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 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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