• home

Join Zoom Meeting: https://uvic.zoom.us/s/84818303141

Moving-habitat models provide insight into the mechanisms that support local persistence of species within their suitable habitats which shift polewards or upwards in altitude as the planet warms. A species’ suitable habitat is described through the species’ net growth; where the specie’s net growth is positive, the habitat is suitable. Beyond temperature, topographical changes in space present obstacles and barriers that change the shape of the suitable habitat. With conservation in mind, we ask, when does a species persist and can we identify when a species is at high risk of extinction. Reaction-diffusion equations provide one framework to study how the density of a population changes in space and time over these climate-driven moving-habitats. In their simplest form, they provide fundamental insight into persistence ability, studied through asymptotic analysis assuming a constant shift of the suitable habitat. To capture the temporal and spatial complexities of these moving habitats, we introduce two numerical methods, the first for spatially one-dimensional systems, the second for spatially two-dimensional systems. Our methods can capture complex movements of the suitable habitat, like deformation, accelerating shifting speeds, and jumps in density across the interface of suitable and unsuitable habitat resulting from habitat dependent dispersal rates and habitat bias. We analyse our numerical methods and demonstrate their strength in application, particularly focusing on time variable suitable-habitat sizes.

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.