Modeling complex systems: understanding biodiversity-disease relationships
Using data from approximately five decades of field research, I parameterized a spatially explicitly individual-based model that simulates tick-host-pathogen interactions. This model, the first to explicitly represent the tick vector and wildlife hosts as individual agents, mimicking the interactions between various tick life stages and hosts to reveal the significance of host grooming and tick molting rates to the maintenance of tick populations.
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Ticks...Pathogen...wildlife hosts: A quantitative review
utilize long-term datasets, I collated approximately five decades of data gleaned from the literature to quantify and synthesize patterns of I. scapularis and B. burgdorferi prevalence in relation to wildlife hosts. This quantitative review identified imbalances in data collection from wildlife hosts with a bias toward Peromyscus leucopus and Odocoileus virginianus5. In addition, this synthesis provided further evidence that additional wildlife hosts may contribute more to Lyme disease risk than previously thought.
This study has been published in Ticks and Tick-Borne Diseases
This study has been published in Ticks and Tick-Borne Diseases
Modeling complex systems
Using data from approximately five decades of field research, I parameterized a spatially explicitly individual-based model that simulates tick-host-pathogen interactions6. This model, the first to explicitly represent the tick vector and wildlife hosts as individual agents, mimicking the interactions between various tick life stages and hosts to reveal the significance of host grooming and tick molting rates to the maintenance of tick populations.
This study has been published in Ecological Modelling.
This study has been published in Ecological Modelling.
understanding biodiversity-disease relationships
Plants inhabit spatially and temporally heterogeneous habitat with various landscape characteristics influencing growth, survival and reproduction. Utilizing microhabitat variables such as slope, elevation and aspect can allow for a spatially explicit approach to understand the important ecological drivers of population persistence. By applying knowledge about individual plant demographics and their response to microhabitat variables, inferences into how the entire population responds over time are made possible. We used a spatially-explicit individual based modelling (SEIBM) approach to model the population demographics and distribution of a restored population of Cirsium pitcheri in Illinois. Using regression analysis, we estimated model parameters for survival, growth and reproduction which were subsequently chosen by comparing observed and projected abundances. Projected population abundances followed the same trajectory as the observed abundances for our chosen model. Using that model, 100-year projections revealed that this Illinois Beach population has a median time to extinction (MTE) of 20 years, presenting a slightly more optimistic outlook for C. pitcheri as compared to traditional matrix modelling approaches. We then analyzed how landscape characteristics influenced plant occupancy via hotspot analysis to determine optimum locations. Optimum plant habitat include low, east-facing slopes with elevation having limited influence. This approach presents a formal modeling exercise for using spatially explicit, individual-based models to conduct population viability analysis. By comparing this SEIBM approach to matrix modelling methods, we affirm that SEIBM are a valid tool for population viability analysis while also having the ability to include information that is spatially explicit to the habitat upon which C. pitcheri occupies. This study has been published in Ecological Modelling.