• home

Join Zoom Meeting https://uvic.zoom.us/j/89487222886

Ecological populations experience booms and busts, with abundances changing by orders of magnitude within just a few short years. Some of these fluctuations may be explained by predator-prey cycles or deterministic chaos, two phenomena that hold immense practical and historical significance in the field of ecology. Here, I estimate the prevalence of cycles and chaos, using classic time series models, new tools from nonlinear systems analysis, and a giant database of ecological time series. Previous assessments find a 6% incidence of cyclical patterns, whereas my robust model-based procedure indicates that 8-25% of populations are cyclical. Chaos appears to be rare (with a likely prevalence of 0%), yet uncertainty remains high (0-18%). In an attempt to control false positive rates, I devised a surrogate data hypothesis test; however, the test suffers from low power, indicating that even exceptionally long time series cannot be reliably classified. Through a sensitivity analysis, I show that high noise levels, moderate observation errors, and weak density dependence are the primary obstacles to detecting ecological chaos.

Zoom link: https://uvic.zoom.us/j/89487222886.

The majority of attempts to enumerate the homeless population rely on point-in-time or shelter counts, which can be costly and inaccurate. As an alternative, we use electronic health data from the Vancouver Island Health Authority, British Columbia, Canada from 2013 to 2022 to identify adults contending with homelessness based on their self-reported housing status. We estimate the annual population size of this population using a flexible open-population capture-recapture model that takes into account 1) the age and gender structure of the population, including aging across detection occasions, 2) annual recruitment into the population, 3) behavioural-response, and 4) apparent survival in the population, including emigration and incorporating known deaths. With this model, we demonstrate how to perform model selection for the inclusion of covariates. We then compare our estimates of annual population size with reported point-in-time counts of homeless populations on Vancouver Island over the same time period, and find that using data extracts from electronic health records gives comparable estimates. We find similarly comparable results using only a subset of interaction data, when using only ER interactions, suggesting that even if cross-continuum data is not available, reasonable estimates of population size can still be found using our method.

Join Zoom Meeting https://uvic.zoom.us/j/89487222886.

In the Arctic, specific microbial communities thrive in and around sea ice, leading to blooms, within, beneath, and at the edge of sea ice. The Arctic Ocean is strongly impacted by climate change, with important modifications of the sea ice environment set to continue during the 21st century, and as a result ice algae are poised to undergo significant changes. Conversely, these primary producers play a role in biogeochemical cycles and the Arctic climate. They produce sulfur compounds, such as dimethylsulfide (DMS) which once emitted to the atmosphere is an aerosol precursor, thereby impacting cloud formation. To examine the future of sea ice algae and the production of DMS at the Arctic scale, a regional ocean and sea ice biogeochemical model has been developed. The complexity of such models raises the issue of analyzing model simulations and understanding the impact of modeling choices. In this talk, I will present diagnostics that help to understand model output, both for the analysis of future projections but also to guide model development. To explore the climate-ocean interactions in the Arctic, diagnostics have been developed that allow to link environmental conditions to the growth and phenology of ice algae as well as the production of DMS. These diagnostics also reveal how the mathematical representation of biogeochemical processes impacts model projections.

Join Zoom Meeting.

Intraguild predation (IGP) consists of two (or more) consumers of the same shared resource exhibiting a predator-prey relation among themselves, and is a very present phenomena in terrestrial, freshwater and marine ecological systems. Theoretical works show that IGP allows for coexistence between two consumers of the same guild, as long as IG prey is a more effective consumer than IG predator, revealing an important mechanism for consumer coexistence in food chains. Here we explore biological invasions forming IGP communities, by either introducing IG prey or IG predator to established (single) Consumer-Resource populations in homogeneous and heterogeneous landscapes. We use reaction-diffusion equations as our modeling framework, and explore them through numerical simulations and homogenization techniques. In homogeneous landscapes, we find that asymptotic spreading speeds are linearly determinate and also that the formation of traveling wave solutions and dynamical stabilization regimes are possible. On heterogeneous landscapes, we find that coexistence regimes in highly heterogeneous landscapes can occur regardless of IG-Prey being the least effective consumer, or be hindered even when IG-Prey remains as the dominant competitor, depending on habitat preferences of each of the species involved. We provide some conclusions of the work and venues of future research.

Bayesian hierarchical models have gained popularity for analyzing complex ecological data gathered through contemporary animal tracking technologies, including acoustic telemetry. Their widespread use is attributed to several advantages such as remarkable flexibility, the capacity to integrate prior knowledge, and enhanced precision. Acoustic telemetry systems are increasingly employed to investigate fish movement patterns, habitat utilization, and demographic parameter estimation. Nonetheless, the data produced by omnidirectional acoustic telemetry studies are intricate, characterized by multiple sources of variability. In this presentation, we explore several applications of Bayesian hierarchical modeling for acoustic telemetry data, focusing on estimating individual-level Walleye movement paths in Lake Winnipeg and examining survival probability variations of Arctic Char in the Cambridge Bay area. Additionally, we confront the practical challenges encountered in modeling such data and underscore the benefits of the Bayesian approach compared to traditional methods.

Zoom link.

We consider the SARS-CoV-2 pandemic, between March 2020 and February 2021, in three Canadian regions: Greater Vancouver, Greater Toronto, and Calgary. This period was dominated by the wild-type strain of SARS-CoV-2 and occurred prior to widespread vaccination roll-out. To assess the effectiveness of age-specific control measures on viral transmission, we construct a Susceptible-Exposed-Infectious-Recovered (SEIR) deterministic model with two age groups: youth (0-19 years), and adults (20+ years). We fit this model to SARS-CoV-2 case data over sequential time frames that capture the changes in public health control measures in each region. The age-specific control parameters of the fitted model are then adjusted to simulate alternative policy scenarios and assess their relative potential effectiveness in reducing the Fall/Winter epidemic peaks.

Zoom link.

Areas in which B cells, T cells and macrophages assemble in close spatial proximity to each other, known as lympho-myeloid aggregates (LMAs), are prognostic for patients with High Grade Serous Ovarian Cancer (HGSC), yet there is still much to be discovered about their vital role in anti-tumour immunity. This includes quantitative descriptors of their cellular makeup; the specifics of the interactions and collaborations within these communities; and the immunomodulatory and immunostimulatory factors that promote and prevent their formation. We are investigating spatial distributions of the immune cells that comprise LMAs, their gene expression profiles, and their receptor (ie. T-Cell receptor - TCR, and B-Cell receptor - BCR) sequences via modification of the 10X genomics Visium technology for this purpose. We are identifying LMAs, their subtypes, and the specific role these subtypes pose in anti-tumour immunity using kernel density analysis, clustering, and spatial point process statistics. We are also leveraging our data to identify pairs of collaborating T and B cells. Specifically, we used (i) cross-K functions and simulation envelopes to evaluate spatial dependence, and (ii) number of pixels shared to flag putative sites of direct contact between B and T cells. Overall, we aim to understand how location, phenotype and antigen specificity of tumor-infiltrating T-Cells and B-Cells contribute to antitumor immunity. This information is essential for understanding the clinical benefits of immune cells and LMAs, and maximizing their functionality for therapeutic purposes.

Join Zoom Meeting

Few statistical measures receive as much attention (implicitly or explicitly) as the arithmetic mean. The history for this particular measure is rooted in the historic development of quantitative science and the personalities that influenced it. Today, environmental measures, predictions, and evaluations of environmental and sustainable development interventions and policy are routinely done on mean responses, even when the "average" may not be the best way to understand a system or inform a decision. This talk will be a broad survey of ecological, evolutionary, and sustainable development contexts where measuring the "average" may mislead more than enlighten, and will serve as an exploration and an invitation to openly question and consider alternatives to the kinds of technical measures we take for granted when doing environmental research.