Seminar Schedule in Google Calendar
AERL 120 (2202 Main Mall)
Mon 20th June 2016
Guillaume Blanchet, Postdoctoral Fellow, Department of Mathematics and Statistics and Department of Biology, McMaster University
A flexible statistical framework to link a variety of data in community ecology
In community ecology, building model is often a complex task for a few reasons. First, the multivariate nature of community data is technically challenging to handle, resulting in difficulties in making inferences and predictions. Also, obtaining reliable inferences when constructing species-specific models is a difficult task because most species in a community are rare. Lastly, to better understand the complexity of nature, ecologists are using an increasing diversity of data (e.g. habitat characteristics or species traits); linking these different data types in an ecologically meaningful way require technical developments beyond that of traditional statistics. In this presentation, I will present a flexible and comprehensive statistical framework that can be used to model species association by estimating the positive and negative correlations among species within a specious community and that also accounts for species traits and habitat characteristics (both as fixed and random effects). Through this framework it is also possible to make species- and community-level predictions. This framework relies on a Bayesian hierarchical modelling. I will illustrate the potential of this modelling approach by applying it to fisheries stock data gathered from 1950 to 2010 in the Gulf of Alaska Large Marine Ecosystem, where for each species traits information were gathered with FishBase and SeaLifeBase. As this is a 60 years time series, I will use asymmetric eigenvector maps (an eigenfunction-based method developed to model the effect of directional processes) to account for temporal autocorrelation. With these data, I will show how this statistical framework can be used to approach different ecological questions on marine harvesting in a community ecology context.
Room 202, MacLeod Bldg (2356 Main Mall)
Thu 16th June 2016
James Thorson, Operations Research Analyst, National Marine Fisheries Service; Instructor, University of Washington
Fish community dynamics and interactions: An illustration of multivariate spatio-temporal models
Fisheries science has traditionally concerned itself with the interplay of fish population abundance, fishing effort, and fishery catch. However, fisheries managers must increasingly cope with changes over time in fish productivity (e.g., changing individual growth and juvenile survival rates). One hypothesis for changing productivity is that interactions among species will be modified by climate change and fishing impacts, and that changes in these interactions cause the parameters of single-species models to be nonstationary. Estimating community dynamics and species interactions has historically been difficult using time-series data. However, recent research suggests that spatio-temporal analyses have greater statistical efficiency than previous time-series approaches because they use spatial variation as a form of replication.
In this talk, I discuss ongoing collaborations to estimate community dynamics and interactions using multivariate spatio-temporal point process models. I start with a global meta-analysis of a classic hypothesis for nonstationary catch rates, i.e., that fish populations collapse to a core habitat during declines in population size. Using bottom trawl data for 120 populations worldwide, colleagues and I estimate a 0.6% decrease in “effective area” for every 10% decline in abundance, but also show that this relationship varies widely among populations and regions. I then use “spatial dynamic factor analysis” to summarize community dynamics for the Eastern Bering Sea. This case study captures the decline and recovery of cod-like species in the mid-2000s, and shows that species with similar evolutionary history have more similar dynamics than unrelated species. Finally, colleagues and I propose a new procedure for estimating the matrix of pairwise species interactions, where this approach bridges between unregulated (“neutral”) and highly-regulated (“niche”) approaches to community ecology. Using the marine community in the Gulf of St. Lawrence as case study, we show a mixture of regulated and unregulated dynamics, where the unregulated component is associated with a recovering grey seal population that is negatively impacting productivity for three prey species of fish.
I conclude by outlining opportunities for future research in statistical ecology. Throughout, I stress that continuing progress will likely combine methodological improvements (e.g., Riemann MCMC) with increased biological realism in models (e.g., advective-diffusive movement in community models). Given the increasing role of statistics in ecological theory (e.g., neutral and maximum entropy theories), I hypothesize that this two-pronged approach will yield improvements in both the theory and practice of fisheries science.
MSL 102 (2185 E Mall)
Mon 13th June 2016
Marie Auger-Méthé, Postdoctoral Fellow, Ocean Tracking Network, Dalhousie University
From footsteps to foraging: using movement models to understand animal behaviour
Predicting the impacts of environmental change on species requires a mechanistic understanding of biological processes such as foraging, migration, and reproduction. However, the continuous behavioural data needed to assess how these processes change through time is often impossible to gather, particularly for Arctic and marine species. Thus, ecologists increasingly rely on animal telemetry to monitor activity patterns. In this talk, I will demonstrate how emerging statistical methods and movement data can be used to model the behaviour of a range of species (e.g. polar bear, rhinoceros auklet), and discuss how the information provided by movement models can help us answer fundamental ecological questions and solve conservation problems.
Room 4192, Earth Sciences Building (2207 Main Mall)
Thu 9th June 2016
Kalman filter, anti-coagulant therapy and life in general
I will present a patented application of a statistical model for time series in connection with monitoring of warfarin treatment.
Anticoagulant monitoring is done by measurements of the International Normalized Ratio (INR) in bloodsamples in order to maintain INR within a certain terapeutic interval.
A noticeable between patients variation in response to warfarin entails individual dosing and frequent monitoring of the INR.
We have developed a monitoring algorithm based on a non-linear state space model using the Kalman filter to provide individualized dose suggestions. The model handles between-patients variations in dosage and in sensitivity to changes in dose, and also variations over time in dosage within-patient.
In my talk I will discuss the model and our retrospective and prospective validation of safety and the impact of the model on treatment quality. Also I will discuss my personal motivation to be involved in this research. Throughout the talk I will try to relate this project to the work I did, when I was so fortunate to visit UBC.
As this visit was in 1993/94 it can be expected that I will illustrate the talk with nearly 25 years old photos.
Room 4192, Earth Sciences Building
Tue 7th June 2016
Pseudo-observations for interval censored survival data using parametric estimates of the marginal survival function
In event history analysis, periodic examinations may lead to event times which are known only to lie within a certain time interval. This can occur when a patient group is followed by routine controls or when a screening for a disease, for example cancer screenings, is performed in a population. In such cases, event times are said to be interval censored. Although a common phenomenon, interval censoring can be notoriously difficult to deal with analytically and is often unjustly ignored in applications.
Pseudo-observations have been proposed by Andersen, Klein and Rosthøj1
and can be used to formulate regression models using a non-parametric estimator of the marginal survival function, or cumulative incidence function in the presence of competing risks, by applying a generalized linear model to the derived pseudo-observations. Pseudo-observations enable routine regression analysis of clinically relevant effect measures, including risk ratios, risk differences, and the restricted mean survival time. The theory behind pseudo-observation methods does not, however, cover existing non-parametric estimators of the survival function based on interval censored data.
We aimed to construct a simple method for generating pseudo-observations in the context of interval censored event times. To this end, we used the approach of Royston and Parmar2
as a preliminary step to constructing a flexible spline-based parametric estimator of the marginal survival function.
1) Andersen, Biometrika 2003; 90:15–27
2) Royston, Stat Med. 2002; 21(15):2175–2197
MSL 102 (2185 E Mall)
Thu 2nd June 2016
Kristin Broms, Postdoctoral Fellow, Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University
Dynamic occupancy models for explicit colonization processes
The occupancy model has become increasingly popular in ecology as a means to account for imperfect detection of a species when predicting where it is likely to occur. The dynamic, multi-season occupancy model extends the framework to account for open populations with occupancies that change over time through local colonizations and extinctions. However, few versions of the model relate these probabilities to the occupancies of neighboring sites or patches. I will present a version that does incorporate this information, where a site is more likely to be colonized if more of its neighbors were previously occupied and if it provides more appealing environmental characteristics than its neighboring sites. Additionally, a site without occupied neighbors may become colonized through long-distance dispersal. In my presentation, I will describe the concept and mathematics of the occupancy model, how we incorporate the spatial and temporal processes, and use the model to obtain inference for the ongoing Common Myna (Acridotheres tristis) invasion in South Africa. The results suggest that the Common Myna continues to enlarge its distribution and its spread via short-distance movement, rather than long-distance dispersal. Overall, the new modeling framework provides a powerful tool for managers examining the drivers of colonization, including short- vs. long-distance dispersal, habitat quality, and distance from source populations.