EFI and the ESA Statistical Ecology Section are hosting this virtual seminar series that will demonstrate a variety of quantitative methods applied within Ecology and Environmental Science in the R programming language. Attendees will gain valuable insight into methods that they may or may not be familiar with from experts on a given topic.

**Target audience:** Quantitative environmental scientists and ecologists either in-training (graduate students and postdocs) or working professionals in academia, government agencies, or non-governmental organizations. Attendees are expected to be proficient in R.

**Webinar structure:** Each seminar will be 1 hour in length and be led by a different invited speaker with expertise on a given topic or statistical method. Speakers will spend the first 20 min of the webinar presenting a project where they used the method, followed by 40 minutes where they will share R code for how they answered their question. The presenter’s shared R code is expected to be made available online. Presenters will walk through the code, taking time to describe common pitfalls or stumbling blocks for performing the method and visualizing results. R code will be available on this GitHub repository. Recordings from the webinars are available in the EFI YouTube Statistical Methods Webinar Series Playlist.

**Dates/Times:** We will have monthly webinars typically on the first Monday of each month at noon US Eastern unless. Note there are a few additional webinars at other dates. Registration for each call is below.

**Recordings and R Resources from Previous Seminars**:

Genealized Additive Models (GAMs) by Gavin Simpson*January 3, 2022:*Species Archetype Models and Regions of Common Profile Models by Skip Woolley*December 6, 2021:*Mixed Models by Ben Bolker*November 1, 2021:*

## January Seminars

*Generalized Joint Attribute Modeling (GJAM)*

January 24, 2022 at noon US EST/ 5pm UTC. *Register to join the call **HERE**.*

A recording of the webinar will be made available by January 31.

The Generalized Joint Attribute Model (GJAM) is a probabilistic framework that allows combinations of presence-absence, ordinal, continuous, discrete, composition, zero-inflated, and censored data. The gjam R package provides inference on sensitivity to input variables, correlations between responses, model selection, prediction of responses, inverse prediction of predictors, and community classification by response to predictors. This model is useful for creating probabilistic forecasts of species distribution and abundance that incorporate a wide range of ecological data and can accommodate massive zeros by relying on censoring.

Tong Qiu is a Postdoc Associate at Duke University. Dr. Qiu’s research aims to understand how the function and structure of the terrestrial ecosystem respond to global environmental changes at regional to global scales. He uses a data-model synthesis approach that integrates satellite and airborne remote sensing, monitoring networks, and forest inventory with Bayesian hierarchical models. Dr. Qiu uses GJAM to model responses of 1) forest trees and 2) ground beetles to climate habitat interactions.

## February Seminar

*Movement Ecology*

February 7, 2022 at noon US ET / 5 pm UTC. *Register to join the call HERE.*

Recent developments in tracking technology have made it possible to collect high volumes of data on animal movement and behaviour, e.g., animal trajectories using GPS tags, or detailed activity profiles with accelerometers. Increasingly sophisticated statistical methods are required to obtain ecological inferences from these complex data (which often include autocorrelation, and can reach millions of observations). This webinar will provide a very brief overview of existing frameworks, and will then focus on one main theme: using location (long-lat) data to learn about animals’ behaviour. In particular, we will discuss how hidden Markov models (HMMs) can be used to draw inferences about the behavioural state process underlying observed movement patterns. The outcomes of an HMM analysis include movement parameters (such as mean step length) for each behavioural state, as well as an estimated state for each time of observation. It is also possible to estimate the effect of covariates (e.g., temperature, bathymetry) on the behavioural dynamics of the animal, which is often of great ecological interest. We will illustrate the application of this method with the R package momentuHMM, and discuss common practical challenges with model fitting. A secondary theme of this webinar will be the filtering and regularisation of animal tracking data. HMMs assume that animal locations are observed at regular time intervals and with no error. When this assumption is not satisfied, a two-stage approach is typically applied, and we will demonstrate this using the R packages foieGras and crawl.

Théo Michelot is a postdoctoral researcher in statistics at the Centre for Research into Ecological and Environmental Modelling (CREEM) at the University of St. Andrews. Dr. Michelot is developing flexible stochastic differential equation models, and using them as continuous-time models of animal movement and behaviour. Additional research interests include hidden Markov models and applications in ecology and statistical software development.

## March Seminar

*Integrated Step-Selection Analysis*

March 7, 2022 at noon US ET / 5pm UTC. *Register to join the call HERE.*

Brian Smith is a PhD student, co-advised by Tal Avgar and Dan MacNulty, studying the space-use ecology of northern Yellowstone elk and the feedbacks between space-use and demography. Brian is particularly interested in how density-dependent habitat selection interacts with predation risk and how animals balance this tradeoff between “many mouths to feed” and “safety in numbers”. His goal is to find insights from individual behavior that scale up to population- and community-level patterns.

Tal Avgar is an Assistant Professor of Movement Ecology in the Department of Wildland Resource and Ecology Center at Utah State University. Dr. Avgar’s research focuses on the ecological and evolutionary causes and consequences of animal movement behaviour. The premise behind Dr. Avgar’s research is that quantitative understanding of the processes underlying animal movement behaviours is essential, not only as means to identifying ecological needs and interactions at the individual level, but as a mechanistic key to emerging population and community patterns.

## April Seminar

*Multi-Species (Species Interactions) Occupancy Modeling*

April 4, 2022 at noon US ET / 4 pm UTC. *Register to join the call HERE. *

Christopher Rota is an Assistant Professor of Wildlife & Fisheries Resources at West Virginia University. Dr. Rota’s research addresses diverse questions in applied vertebrate ecology working with birds, mammals, reptiles, and amphibians. He is interested in understanding factors that shape the spatial distribution of species, and the dynamic interplay between space use and demography. A common link throughout his research is the application and development of modern statistical techniques that capture many of the myriad processes giving rise to ecological data sets.

## May Seminar

*Hidden Markov Models in Ecology*

May 2, 2022 at noon US ET / 4pm UTC. *Register to join the call HERE.*

Vianey Leos Barajas is an Assistant Professor in the Department of Statistical Sciences and the School of the Environment at the University of Toronto and leads the Bayesian Ecological and Environmental Statistics (B.E.E.S.) research group. B.E.E.S. is dedicated to the development of statistical methodology to answer pressing ecological and environmental questions. Dr. Leos Barajas’ work focuses on the analysis of sensor data collected from animals and the environment over time and space but also includes collaborations in health and other areas.

## Recordings and R Resources from Previous Seminars

*January 3, 2022: *Generalized Additive Models (GAMs).

- A recording of the presentation and Q&A are here: https://youtu.be/Ukfvd8akfco
- Gavin’s walk-through of the R code starts at time 33:49 and the Q&A starts at time 1:19:10.

- R code, slides, resources, and answers to the questions we didn’t get to in the Q&A and the R code shared by Skip are available on GitHub HERE. A quick link to the presentation slides are HERE.

Generalized Additive Models were introduced as an extension to linear and generalized linear models, where the relationships between the response and covariates are not specified up-front by the analyst but are learned from the data themselves. This learning is achieved by viewing the effect of a covariate on the response as a smooth function, rather than following a fixed form (linear, quadratic, etc). The smooth functions are represented in the GAM using penalized splines, in which a penalty against fitting overly-complex functions is employed. GAMs are most useful when the relationships between covariates and response are non linear, and GAMs have found particular use for modelling inter alia spatiotemporal data.

The presentation will briefly explain what a GAM is and how penalized splines work before focusing on the practical aspects of fitting GAMs to data using the mgcv R package, and will be most useful to ecologists who already have some familiarity with linear and generalized linear models.

Gavin Simpson is an Assistant Professor in the Department of Animal Science at Aarhus University. Dr. Simpson’s research uses approaches to modelling large regional to global spatio-temporal data sets using generalized additive models (GAMs) and functional statistical methods to examine broad ecosystem responses to environmental change. He is an active member of the R and Data Science communities and was a lead developer on the vegan package for multivariate data analysis and wrote the permute package for restricted permutation tests that allow multi-species data analyses from complex experimental designs. Dr. Simpson is currently developing a package, gratia, to work with GAMs fitted in R.

** December 6, 2021: **Species Archetype Models and Regions of Common Profile Models by Skip Woolley

**A recording of the presentation and Q&A here: https://youtu.be/ukx7ZFX-71A****Skip’s walk-through of the R code for SAMs starts at time 21:54 and the R code for RCPs starts at time 58:26.**

**Resources and R code shared by Skip are available on GitHub HERE.**

Dr. Woolley will present two types of finite mixture models, that extend GLMs by allowing for multiple components. Specifically, he will present on Species Archetype Models (SAM; Dunstan et al. 2011) and the Region of Common Profile models (RCP; Foster et al. 2013, 2017). Together, these approaches cover inferential situations where understanding joint responses of species are of primary importance (SAMs) or when managing groups of sites are of primary importance (RCPs). Species Archetype Models (SAMs) are a “Mixture-of-regressions”, and describe how a homogeneous group of species varies with the environment. The environmental gradients are represented by covariates in the model. Regions of Common Profile (RCP) models are a type of ‘Mixture-of-Experts Models’ and try to describe how groups of sites vary with the environment. The sites are grouped based on the profile of biological content at the sites, with sites that have relatively similar observed assemblages are grouped together. The RCPs are defined by estimating how these groups vary with environment.

Skip Woolley is a research fellow at the University of Melbourne working on Integrated Environmental Assessment Modelling and he is a visiting

scientist at CSIRO. His research focuses on the development, implementation and interpretation of statistical modelling for integrated environmental risk assessment. Dr. Woolley’s research also focuses on understanding how biodiversity interacts with economic, social and environmental drivers of human activities and pressures, to better protect and reduce the risk of biodiversity loss into the future.

** November 1, 2021: **Mixed Models by Ben Bolker

“Mixed models” refers to a broad class of statistical models that extend linear and generalized linear models to handle data where observations are measured within discrete groups such as field sites; years or other temporal blocks; individuals that are observed multiple times; genotypes; species; etc. They can be thought of (equivalently)

as (1) accounting for the correlation among observations from the same group; (2) estimating the variability among groups, or (3) parsimoniously estimating the effects of groups. They are most useful when the experimental or observational design includes a large number of groups with varying numbers of observations per group.

This presentation will be most useful to ecologists who already have some familiarity with linear and generalized linear models.

**A recording of the presentation and Q&A here: https://youtu.be/iFikMDuNeVM****Resources and R code shared by Ben are available on GitHub HERE.**

Ben Bolker is the Director of the School for Computational Science and Engineering and Acting Associate Chair for Mathematics at McMaster University. His interests include spatial, theoretical, mathematical, computational and statistical ecology, evolution and epidemiology, plant community, ecosystem, and epidemic dynamics. He has two books, including Ecological Models and Data in R, and is the co-author of a Very Short Introduction to Infectious Disease with Marta Wayne. Dr. Bolker maintains a popular GLMM FAQ, and keeps miscellaneous mixed models resources here.