Theoretical Models of Obligate Mutualism to Link Micro- with Macro-Coevolutionary Dynamics
Diaz Eaton, C.; Moore, C. M.
Show abstract
There is a need to link micro- and macro-coevolution to bridge mechanistic theory and observations of micro-coevolutionary change with observations of macro-coevolutionary patterns. This need is particularly conspicuous in theoretical models of obligate mutualism, where phylogenetic matching is the predicted outcome. However, these theoretical models of obligate mutualism create a mismatch with empirical studies of obligate mutualism, which experience extensive phylogenetic discordance. Although environmental variation on geographic scales is often invoked, there are other, non-mutually exclusive mechanisms that can possibly explain genetic diversity and co-phylogenetic patterns in mutualistic communities. In this study, we use a genetic-explicit mathematical model of obligate mutualism that explain host-switching outcomes and, consequently, discordance in cophylogenies. We then explore the role of temporal variation in maintenance of genetic variation (i.e., phenology), which can further account for phylogenetic discordance. These insights are possible due to the focus on initial conditions and short-term behavior of model results. This work ultimately supports the continued importance of theoretical work which expands its analysis of outcomes beyond asymptotic behavior.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Selection on sporulation strategies in a metapopulation can lead to coexistence 97%
- Feedback Between Coevolution and Epidemiology Can Help or Hinder the Maintenance of Genetic Variation in Host-Parasite Models 97%
- During environmental change, cooperation can promote rescue or lead to evolutionary suicide 97%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.