Systems with mixed ecological interactions include systems with multiple types of interactions, interactions where selection acts on multiple loci or traits, or interactions between more than two species or populations. Empirically, these are the most challenging systems to investigate because of the numerous factors involved. As a result, this is an area in which mathematical modeling has proved extremely beneficial. Theoreticians at the junction of ecology and evolution have attempted to create models to help researchers understand the dynamics and potential coevolutionary consequences of interacting populations under mixed ecological interactions. Of particular recent interest, is the effect of mixed ecological interactions on speciation rate. Emerson and Kolm highlighted this interest in an article in Nature in 2005 entitled 'Species diversity can drive speciation', and found a relationship between the number of species on islands and the speciation rate. This is consistent with the idea that high species diversity allows for more ecological interactions which can in turn lead to
John Thompson has passionately advocated the view that spatial structure can play a major role in coevolutionary dynamics. As seen with the lodgepole pine and red crossbills above, the environment in which the interaction is occurring can play a role in the evolutionary dynamics and therefore affect outcomes. In the case of lodgepole pine, these differing interactions due to an environment with red squirrels and an environment without red squirrels has led to allopatric differentiation and perhaps even to allopatric speciation. The situation is even more complex when populations are not strictly allopatric, that is, there is gene flow between the populations causing a metapopulation structure. Within each subpopulation, there may be different strengths of interactions, different types of ecological interactions, or different degrees of local adaptation to the environment. All combinations of these variables and their resulting evolutionary dynamics lead to what Thompson terms 'the Geographic Mosaic of Coevolution'. For a comprehensive review on this view and coevolution in general, see John Thompson's books, one of which is listed in the 'Further reading' section.
Mutualisms can be vulnerable to exploitation. Both the fig and yucca systems have seen the evolution of cheater insect species that use the plant resources, but do not pollinate. Under cheater over-exploitation, a mutualistic relationship becomes antagonistic. Consider a metapopulation of a coevolved plant and pollinator mutualistic pair of species where there are other less-well suited pollinators for the plant available. If in one subpopulation there emerges a cheater variant of the primary and most-beneficial pollinator, then in that subpopulation alone the interaction is antagonistic, while the same interaction is mutualistic in the other subpopulations. In this case, there is spatial variation with respect to the type of ecological interaction, that is, it is a variable mutualism. Since in a metapopulation, there is migration and mating between subpopulations, the cheating can affect the ability of the interaction to persist. The plant subpopulation with the nonpollinating cheaters would be expected to re-allocate its genetic evolution to optimize the benefits of other pollinators, thus disfavoring trait matching as a strategy for the metapopulation as a whole. This is not always the case; Gomulkiewicz, Nuismer, and Thompson show in their mathematical models of these types of mixed ecological interactions that allele frequency cycling and trait matching can also occur under certain conditions.
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