Previous seminars

University of St Andrews

Title: Efficient Methods for Fitting Nonlinear Non-Gaussian State Space Models of Wildlife Population Dynamics

Abstract: State-space models (SSMs) are a popular and flexible framework for modelling time series due to their ability to separate changes in the underlying state of a system from the noisy observations made on these states. However, fitting these models can sometimes be challenging. In my thesis, I explored methods for fitting these models in non-linear and non-Gaussian Bayesian SSMs, using a case study of the UK grey seal population. Here, I give an introduction to these methods and summarise the findings of my thesis. These include a comparison of sequential Monte Carlo methods with the much faster Kalman filter which uses a linear and normalized approximation, and how estimating likelihood components separately can lead to a 5-fold increase in speed.

Professor David Paterson

MASTS (Marine Alliance for Science and Technology for Scotland)

Title: MASTS Update Seminar

Abstract: Prof David Paterson (MASTS Executive Director) will talk about what the Marine Alliance for Science and Technology for Scotland (MASTS) has achieved, what it means for MASTS partners and opportunities for the future. They will also talk about cooperation, advice to government, graduate school and the impact of marine research. Everyone is welcome. If you are already involved with MASTS, then please come along for an update on activities. If you are not yet involved but have any marine interests, please come along to discover how you might get involved and benefit from MASTS. There will be time for some general discussion and networking.

University of Glasgow

Title: Defining, estimating and understanding the fundamental niches of complex animals in heterogeneous environments

Abstract: During the past century, the fundamental niche, the complete set of environments that allow an individual, population, or species to persist, has shaped ecological thinking. It is a crucial concept connecting population dynamics, spatial ecology and evolutionary theory, and a prerequisite for predictive ecological models at a time of rapid environmental change. Yet, its properties have eluded quantification, particularly for mobile, cognitively complex organisms. These difficulties are mainly a result of the separation between niche theory and field data, and the dichotomy between environmental and geographical spaces. Here, I combine recent mathematical and statistical results linking habitats to population growth, to achieve a quantitative and intuitive understanding of the fundamental niches of animals. I trace the development of niche ideas from the early steps of ecology to their use in modern statistical and conservation practice. I examine, in particular, how animal mobility and behaviour may blur the division between geographical and environmental space. I discuss how the fundamental models of population and spatial ecology lead to a concise mathematical equation for the fundamental niche of animals and demonstrate how fitness parameters can be understood and directly estimated by fitting this model simultaneously to field data on population growth and spatial distributions. I illustrate these concepts and methods using both simulation and real animals and, in this way, confirm ideas that had been anticipated in the historical niche literature. Specifically, within traditionally defined environmental spaces, habitat heterogeneity and behavioural plasticity make the fundamental niche more complex and malleable than was historically envisaged. However, once examined in higher-dimensional spaces, the niche is more predictable, than recently suspected. This re-evaluation quantifies how organisms might buffer themselves from change by bending the boundaries of viable environmental space, and offers a framework for designing optimal habitat interventions to protect biodiversity or obstruct invasive species. It therefore promotes the fundamental niche as a key theoretical tool for understanding animal responses to changing environments and a central tool for environmental management. To this end, ecological mechanism (dispersal, density dependence, community effects and individual variation), integrated inference, and ecosystem optimization are the key future areas of development.

University of St Andrews

Title: Hierarchical GAMs for studying the irruptive migration of crossbill species in northern Europe

Abstract: Irruptions by seed-eating birds are assumed to be driven by the production of seeds and fruits, whose crops are highly variable between years. Using data from Sweden, Finland and UK, we tested a variety of assumptions about synchrony in coniferous seed crops and whether irruptions of crossbills Loxia sp. were correlated with seed production further afield. In a second set of analyses, we developed hierarchical generalised additive models to study when irruptions into northern Europe took place. The models indicate that the incidental co-occurrence of low seed production of Norway spruce and Scots pine in a given year, after a year of high seed production, may result in an irruption. The seed production of Norway spruce and Scots pine in Sweden was correlated with production by the same species in Finland, indicating widespread synchrony of cropping across northern Europe.

University of St Andrews

Title: Do identification guides hold the key to species misclassification by citizen scientists?

Abstract: Citizen science data often contain high levels of species misclassification that can bias inference and conservation decisions. Current approaches to address mislabelling rely on expert taxonomists validating every record. This approach makes intensive use of a scarce resource and reduces the role of the citizen scientist. 2. Species, however, are not confused at random. If two species appear more similar, it is probable they will be more easily confused than two highly distinctive species. Identification guides are intended to use these patterns to aid correct classification, but misclassifications still occur due to user-error and imperfect guidebook design. Statistical models should be able to exploit this non-randomness to learn confusion patterns from small validation data-sets provided by expert taxonomists, yielding a much-needed reduction in expert workload. Here, we use a variety of Bayesian hierarchical models to probabilistically classify species based on the species-label provided by the citizen scientist. We also explore the utility of guidebooks provided by the citizen science schemes as a prior for species similarity, and hence draw conclusions for their future improvement. 3. We find that the species-label assigned to a record by a citizen scientist, even when incorrect, contains useful information about the true species-identity. The citizen scientists correctly identify the species in around 58% of records. Using models trained on only 10% of these records (validated by experts), we can correctly predict species-identity for 69 (90%CI: 64-73)% of records when the guidebook is used, vs 64 (58-69)% for models that do not use the guidebook. The fact that misclassifications can be predicted systematically indicates that improvements could be made to the guidebook to reduce misclassification.4. By using Bayesian, hierarchical models we can greatly reduce the workload for experts by providing a probabilistic correction to citizen science records, rather than requiring manual review. This is increasingly important as the number of citizen science schemes grows and the relative number of taxonomists shrinks. By learning confusion patterns statistically, we open up future avenues of research to identify what causes these confusions and how to better address them

University of Auckland, New Zealand

Title: Introduced rodent management on islands

Abstract: This talk will be incredibly applied in content and light on analytical details providing an overview of a lifetime drawing upon diverse analytical approaches to addressing the impacts and management of introduced rodents on islands. The results presented will include those that have utilised population differential equation modelling, spatially explicit capture recapture analyses, proof-of-absence (false absence) modelling, probabilistic genetic assignment of individuals, individual agent-based models, survival analyses and gradient-boosted decision trees. Overall, the talk will present an example of how a broad training in ecology and statistics can empower a practitioner to wield an incredibly diverse analytical tool kit in pursuit of high impact conservation outcomes. References to all material will be provided for those who wish to dive-deeper into particular elements, or meet with the speaker who is on sabbatical at the University of Aberdeen until Christmas.

University of St Andrews

Title: Why impulsiveness of sound matters

Abstract: Pile-driving of foundations of windfarms generates high-amplitude impulsive underwater noise into the marine environment, which can result in physical or auditory injury to marine mammals. Impulsive sounds are more damaging to the mammalian ear than exposure to non-impulsive sound, as impulsive sound increases the hearing threshold faster, i.e., less sound energy is needed to induce a temporary or permanent shift in the hearing threshold (TTS or PTS) for an impulsive sound, than for a non-impulsive sound. The signal of impulsive sound sources, however, loses its impulsive characteristics as a function of distance from the source (due to propagation effects) and could potentially be characterised as non-impulsive beyond a certain distance. In this talk I would like to present the results from SMRUC led project called RADIN: Range-dependent nature of impulsive noise and discuss:

• What characterise impulsiveness of the sound and how does it change with various parameters
• Should we consider sound transition from impulsive to non-impulsive in environmental impact assessments
• What are the main drivers of animals receiving TTS/PTS.

Apologies in advance that this talk may not be very stat heavy but I promise to add at least one equation.

BTO

Title: Counting birds and other hard problems

Abstract: The British Trust for Ornithology (BTO) is one of the world’s leading research organisations specialising in knowledge about birds. BTO organises and conducts a broad spectrum of ecological monitoring projects and is a major custodian of wildlife data, with datasets that are both extensive in space and time.

Analysing these data is not always straightforward, as non-random sampling, measurement errors and other challenging features are prevalent in many of them. I will present examples of recent BTO work on addressing these challenges in the context of estimating population trends from multiple data sources, estimating three-dimensional densities of birds around renewable energy infrastructure, and estimating the timing of birds’ annual cycles.

Speaker: Dr Chrissy Fell, University of St Andrews

Title: Examples of deep learning applied to medical and ecological images.

Abstract: In this talk I will discuss three projects I have been working on applying deep learning to images. The first project is creating an automated classifier for images of endometrial and cervical biopsies that allows prioritisation of pathology workloads. The second project I will talk about is automatically detecting animals in aerial images. Finally I will explain my recent work on using brain MRI scans from the UK Biobank to classify if someone is at high or low genetic risk of mental health condition.

Speaker: Dr Ben Baur, University of St Andrews

Title: Some problems I’m working on

Abstract: The talk has three parts in which an overview of an estimation framework I now commonly use is sandwiched by some problems I’m working on. In the first part, a problem involving a barely identified discrete parameter in a highly structured model is presented. After, the failure of various Bayes estimators with non-informative priors is briefly described. In the second part, some aspects of data coarsening and semi- and non-parametric efficiency theory are explained alongside examples from causal inference and survival analysis. In the third part, several ongoing projects involving coarsening or efficiency theory are presented with a frame or two per project. The audience is encouraged to frequently stop me with comments and questions.

Speaker: Brandon Edwards, Carleton University, Canada

Abstract: Since the 1970s, North America has lost approximately 2.9 billion birds, despite decades of targeted conservation efforts. In a world where biodiversity monitoring data is increasing year after year, but biodiversity itself continues to decrease, methods and expertise from the world of Big Data are now needed to properly synthesize the thousands of bird monitoring datasets into one cohesive story. Using hundreds of open datasets across North America, the NA-POPS project has derived detection probabilities for nearly 75% of North America’s landbirds. Here, I will present the results of this broad-scale effort to estimate accurate detection probabilities for North American landbirds. Additionally, I will present some preliminary results from two additional projects that have stemmed off this effort: 1) a modelling exercise to predict detection probabilities for rare or undersampled birds using a hierarchical Bayesian model, and 2) a framework to estimate distance to singing birds (i.e., detection distance) using autonomous recording units. Finally, I will talk about my proposed project that I am undertaking at University of St Andrews in conjunction with CREEM to apply these detection probabilities as statistical offsets in an integrated trend modelling framework, to improve data coverage and (hopefully) trend estimates of the North American Breeding Bird Survey.