Comparison of Pollination Graphs

From the agent-based, correlated random walk model presented, we observe the effects of varying the maximum turning angle, δ max , tree density, ω , and pollen carryover, κ max , on the distribution of pollen within a tree population by examining pollination graphs. Varying maximum turning angle and pollen carryover alters the dispersal of pollen, which affects many measures of connectivity of the pollination graph. Among these measures the cluster- ing coefficient of fathers is largest when δ max is between 60 and 90 ∘ . The greatest effect of varying ω is not on the clustering coefficient of fathers, but on the other measures of genetic diversity. In particular when comparing simulations with randomly placed trees with that of actual tree placement of C. florida at the VCU Rice Center, it is clear that having specific tree locations is crucial in determining the properties of a pollination graph. agents determine the ability of the plant population to maintain population genetic structure and determine relative reproductive output for individual trees. The movement of pollinating agents across the landscape is defined as a correlated random walk parameterized by inertia and speed. Across model runs, the aggregate movements of pollinating agents define a de facto pollination network whose characteristics are used to infer the robustness of the overall mating network and provide insights into population genetic stabil-ity. Across replicate runs, we extract parameters describing pollinator-movement dynamics parameters (average and maximum dispersal distances), pollen network robustness (pollen donor connectance and spatial clustering), and future population genetic structure (pollen donor density and diversity).