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Trophic metacommunities : lessons from studying bromeliad and teaching programming in biostatistics

University of British Columbia

Trophic metacommunity ecology brings together the spatial thinking of metacommunity ecology and the complexity of food web ecology. While theoretical development in this field has been bountiful, empirical development has been slower. Using a diverse methodology, I bring together three different empirical approaches to understand trophic metacommunities as exemplified by bromeliads macro-invertebrates. First, I used Markov network analysis to study the effect of regional environmental gradients on community composition and trophic interactions. I found that a gradient in precipitation underlies the spatial turnover of some species and that the interactions of certain predators differed due to differences in bromeliad water volume. Second, I combined experimental feeding trials and a food web model to study the effect of body size diversity at the local scale on food web dynamics. I found that predator persistence was maximized when the minimum prey size in the community was intermediate, but as prey diversity increased the minimum body size could take a broader range of values due to Jensen’s inequality. Third, I used population genetics to estimate dispersal kernels of a predator and a prey. I then used these empirical estimates of dispersal kernels and feeding rates to parameterize a trophic metacommunity model, to study the effect of differences in dispersal between a predator and a prey on persistence. From the empirical dispersal kernel estimates, I found that the prey dispersed up to 25 km whereas the predator dispersed up to 200 m. From the trophic metacommunity model, I found that differences in dispersal rates were sufficient to generate differences in occupancy of our modelled landscape, without requiring variation in the abiotic niche. None of this work would have been possible without strong programming skills and a good understanding of statistics. In my final chapter, I studied the effect of using cognitive load theory to design R programming assignments for undergraduate biostatistics courses. I found that students that learned R through our assignments rated their programming ability higher and were more likely to put the usage of R as a skill in their CVs than control students.

Text

2019-04-23

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/69856

Zoology

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/