Abstract
I sought to evolve plant species richness patterns on 22 Galápagos Islands, Ecuador, as an exploration of the utility of evolutionary computation and an agent-based approach in biogeography research. The simulation was spatially explicit, where agents were plant monocultures defined by three niche dimensions, lava (yes or no), elevation, and slope. Niches were represented as standard normal curves subjected to selection pressure, where neighboring plants bred if their niches overlapped sufficiently, and were considered the same species, otherwise they were different species. Plants that bred produced seeds with mutated niches. Seeds dispersed locally and longer distances, and established if the habitat was appropriate given the seed's niche. From a single species colonizing a random location, hundreds of species evolved to fill the islands. Evolved plant species richness agreed very well with observed plant species richness. I review potential uses of an agent-based representation of evolving niches in biogeography research.
Highlights
In biogeographical analyses of island species richness, the number of species on each island is typically compared with island area, the distance between island centroids, and other variables using regression
Plants were represented by three niche dimensions: two were unimodal based on elevation and slope, and a third was whether the land was soil or unsuitable lava [17]
Plants spread quickly from the single pair that colonized the Galapagos Islands, and secondary colonies appeared on neighboring islands through direct and stepping-stone dispersal
Summary
In biogeographical analyses of island species richness, the number of species on each island is typically compared with island area, the distance between island centroids, and other variables using regression. Colonization, speciation, extinction, and dispersal are subsumed in the analytical models (i.e., mathematical models with simple closed-form solutions or approximations [1]). They are straightforward and provide precise and rapid results, but the analytical solutions may not hold for more complex or less stylized situations. Formulation of a simulation model can be complex, multiple simulations are required to improve precision and interpretation can be difficult, and simulations can be computer intensive. A main advantage of simulation modeling is its flexibility. Variation between population members is included in simulations. Advancements in computational capacities and speed, and in the flexibility of modeling platforms and programming languages, have sped development and simulations [9]
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