arXiv subjects

Ethan Lawler

Publications and source records attributed to Ethan Lawler.

3 recordsLinked to original sources

arXiv2021

Species distribution modelling with spatio-temporal nearest neighbour Gaussian processes

1.) Spatio-temporal datasets that are difficult to analyze are common in ecological surveys. There are software packages available to analyze these datasets, but many of them require advanced coding skills. There is a growing need for easy to use packages that researchers can use to analyze common ecological datasets 2.) We develop a particular generalized linear mixed model for spatio-temporal point-referenced data that is flexible enough to accommodate data from most ecological surveys while being structured enough to facilitate analyses without advanced coding. Our implementation in the staRVe package uses a computationally efficient version of a nearest neighbour Gaussian process enabling analysis of relatively large datasets. 3.) A brief simulation study shows our model produces accurate predictions and forecasts, while a tutorial analysis of a Carolina wren survey suggests a recommended workflow for analyses. We also analyze a more complicated scientific survey of haddock to showcase the capabilities of our model. 4.) Our model and package are tools that can easily be added to researchers' workflow to help make sense of data from ecological surveys. We emphasize the ability of our model to create useful visualisations of data which can then lead to identification of important trends in species distributions.

stat.ME↗

arXiv2020

Constructing symplectomorphisms between symplectic torus quotients

We identify a family of torus representations such that the corresponding singular symplectic quotients at the $0$-level of the moment map are graded regularly symplectomorphic to symplectic quotients associated to representations of the circle. For a subfamily of these torus representations, we give an explicit description of each symplectic quotient as a Poisson differential space with global chart as well as a complete classification of the graded regular diffeomorphism and symplectomorphism classes. Finally, we give explicit examples to indicate that symplectic quotients in this class may have graded isomorphic algebras of real regular functions and graded Poisson isomorphic complex symplectic quotients yet not be graded regularly diffeomorphic nor graded regularly symplectomorphic.

math.SG↗

arXiv2019

The conditionally autoregressive hidden Markov model (CarHMM): Inferring behavioural states from animal tracking data exhibiting conditional autocorrelation

One of the central interests of animal movement ecology is relating movement characteristics to behavioural characteristics. The traditional discrete-time statistical tool for inferring unobserved behaviours from movement data is the hidden Markov model (HMM). While the HMM is an important and powerful tool, sometimes it is not flexible enough to appropriately fit the data. Data for marine animals often exhibit conditional autocorrelation, self-dependence of the step length process which cannot be explained solely by the behavioural state, which violates one of the main assumptions of the HMM. Using a grey seal track as an example, along with multiple simulation scenarios, we motivate and develop the conditionally autoregressive hidden Markov model (CarHMM), which is a generalization of the HMM designed specifically to handle conditional autocorrelation. In addition to introducing and examining the new CarHMM, we provide guidelines for all stages of an analysis using either an HMM or CarHMM. These include guidelines for pre-processing location data to obtain deflection angles and step lengths, model selection, and model checking. In addition to these practical guidelines, we link estimated model parameters to biologically meaningful quantities such as activity budget and residency time. We also provide interpretations of traditional "foraging" and "transiting" behaviours in the context of the new CarHMM parameters.

stat.AP↗