More than just a metaphor, Wright’s Adaptive Landscape provides inspiration

[Originally appeared on Nothing in Biology Makes Sense]

Review of The Adaptive Landscape in Evolutionary Biology edited by Erik Svensson and Ryan Calsbeek

Have you ever wished you could go back in time to be present at a particular historical event? The 1932 International Congress of Genetics sounds perfect, right? There R. A. Fisher, J. B. S. Haldane, and Sewall Wright all presented papers of their recent research. If you’re a student of population genetics, you probably recognize these names as some of the founders of the field. At this meeting, Wright was asked to condense some of his more technical mathematical framework into a form that was more widely accessible to the audience of biologists. The result was his conceptualization of the Adaptive Landscape where an analogy is made between the fitness of an individual or population and the varied topographic landscape (pictured on the cover of the book). Wright used this metaphor to describe aspects evolutionary dynamics of populations.

The editors of a recent book, The Adaptive Landscape in Evolutionary Biology, gathered together contributions from evolutionary biologists, ecologists, and philosophers to demonstrate the impact that the Adaptive Landscape has had on the field of biology. This book embraces an 80 year old metaphor created by one of the founders of the modern synthesis to explore the breadth and depth of research generated in evolutionary biology. Unlike a recent book addressing aspects of the modern synthesis, Evolution: The Extendend Synthesis (Pigliucci and Müller, 2010) which called for a revolution, Svensson and Calsbeek have assembled authors that explore the innovations and contributions that build upon the fundamental ideas of population genetics and seek to grow the field. Early in this book, Pigliucci asks about the utility of the Adaptive Landscape metaphors, even titling his chapter with the question, “what are they good for?” I think the rest of the book provides a more than sufficient answer to his question.

Living at the edge, range expansion is a losing battle with mutations

[I originally posted this at the blog Nothing in Biology Makes Sense]

Environments can vary substantially in habitat quality, local population abundance, or carrying capacity. Under some climate change scenarios, new, higher quality habitats become available along the margin of a species’ range (e.g. higher latitudes or altitudes) (Thomas et al 2001). These new habitats may be able to support larger population sizes. Factors of demography, evolution, and qualities of the abiotic and biotic communities all interact to determine where a species is found and may influence the ability of a species to expand its range. New research is building genetically explicit models in order to understand how the interplay of these different factors influence evolutionary changes,

The authors of a recent study focus on how the interaction of the demographic process of range expansion changes the way that natural selection favors beneficial and deleterious mutations (Peischl et al 2013). Using both computer simulations as well as mathematical approximations, the authors find that at the range margins, individuals carry a substantial load of deleterious mutations.

Selection mosaics and the GMTC

This past week in Coevolvers, we dropped back into the empirical world and ready a paper from Piculell et al (2008) on evidence of selection mosaics. Selection mosaics describe a case where the fitness function of the interacting players varies across space (Gomulkiewicz et al 2007; Thompson 1999, 2005), sometimes described as GxGxE interactions (G: genetic; E: environment). What does this mean more generally? Simply put, the fitness of a plant may change from one population to the next because the nature of the interaction with a mutualist is affected by the environment. This can occur even if the genotypes that make up those populations are exactly the same.

The experimental design was certainly setting up the case for a maximum chance of detection of interaction effects. With only levels of each factor, (e.g. two genotypes of the host) the authors had less power to detect any main effects, but that clearly wasn't the objective. They wanted to find evidence of significant GxGxE. Essentially this experiment had 4 environmental treatments, so they maximized the chance of an interaction. The authors of this paper were very upfront that they were not intending to measure a selection mosaic in the natural setting. Their objective was to demonstrate the possibility and they certainly obtained that goal. With that limitation in mind, how general are these results? Measuring the potential for a selection mosaic is one thing, but for this to really have an impact in generating or maintaining diversity as imagined in the Geographic Mosaic Theory of Coevolution (Thompson 1999, 2005) then it must hold for a broad sample of the populations under investigation. The authors are on a good track though to discovering more about this system. Perhaps they plan on taking the methodology outlined in Nuismer and Gandon (2008) on reciprocal-transplant designs. Picking a larger sample of the genetic variation found in nature for at least one of the players would extend their results from the possible into the probable.

References

Gomulkiewicz, R., D. M. Drown, M. F. Dybdahl, W. Godsoe, S. L. Nuismer, K. M. Pepin, B. J. Ridenhour, C. I. Smith, and J. B. Yoder. 2007. Dos and don'ts of testing the geographic mosaic theory of coevolution. Heredity 98:249-258.

Nuismer, S. L., and S. Gandon. 2008. Moving beyond Common-Garden and Transplant Designs: Insight into the Causes of Local Adaptation in Species Interactions. American Naturalist 171:658-668.

Piculell, B., J. Hoeksema, and J. Thompson. 2008. Interactions of biotic and abiotic environmental factors in an ectomycorrhizal symbiosis, and the potential for selection mosaics. Bmc Biol 6:23.

Thompson, J. N. 1999. Specific hypotheses on the geographic mosaic of coevolution. American Naturalist 153:S1-S14.

Thompson, J. N. 2005.
The Geographic Mosaic of Coevolution. University of Chicago Press, Chicago.

Paper Read

Piculell, B., Hoeksema, J., & Thompson, J. (2008). Interactions of biotic and abiotic environmental factors on an ectomycorrhizal symbiosis, and the potential for selection mosaics BMC Biology, 6 (1) DOI: 10.1186/1741-7007-6-23

Universal understanding of host-parasite adaptation

We recently read a theory paper by Gandon and Day (2009). In this paper they describe a valuable method for dissecting how interactions between a host and parasite alter mean fitness. Their method uses an understanding built from Fisher's fundamental theorem. They partition changes in mean fitness based on three different factors: natural selection, environmental change, and mutation. We know that the rate of adaptation is going to result from the amount of genetic variance in the focal organism (Fisher's theorem), but what about the impact of an interacting species that evolves as well (i.e. a coevolving parasite? Here is the real beauty of their analysis because the coevolving species becomes the environment. By separating the changes in a population mean fitness into changes driven by different forces, the authors provide not only a mathematically useful model, but also a useful intuition for understanding how hosts and parasites coevolve.

There are several ways that theoreticians often describe a host-parasite interaction (e.g. gene-for-gene, matching alleles) and these describe natural systems to some degree of accuracy. The authors use their method to analyze some recent empirical evidence (Buckling and Rainey 2002; Decaestecker et al 2007). They use the time series data on the interaction to test hypotheses of the nature of the interaction. These empirical studies compare the fitness of parasites against hosts from the past that they have coevolved with and those from the future (hosts that evolve later in the study). By making these comparisons, they have the ability to hold other factors constant (the genetic variance of the parasite population) and vary the environment (the hosts). Their model makes different predictions that should be evident from empirical evidence about how parasite mean fitness should change when the environment is varied.

The authors very elegant proposed method of looking at changes over time works well for systems where archives of past populations are possible as in experimental evolution systems (Buckling and Rainey 2002) or clever natural systems (Decaestecker et al 2007), but what about the rest of us? Addressed in at the very end, but only briefly, is a comparison of spatial patterns of coevolution when temporal data is missing. I think this issue of substituting space for time is potentially very powerful, but also somewhat more complicated. Temporal samples of a coevolutionary system could be predicted to have a certain amount of autocorrelation, but does this hold for spatially distributed systems. It certainly would nice to assume that there is a relationship between distance and time and this will of course depend on gene flow. How would selection mosaics (Gomulkiewicz et al 2007; Thompson 1999, 2005) impact this potential relationship? I look forward to future research as it provides some answers.

References

Buckling, A., and P. B. Rainey. 2002. Antagonistic coevolution between a bacterium and a bacteriophage. P Roy Soc Lond B Bio 269:931-936.

Decaestecker, E., S. Gaba, J. A. M. Raeymaekers, R. Stoks, L. Van Kerckhoven, D. Ebert, and L. De Meester. 2007. Host-parasite 'Red Queen' dynamics archived in pond sediment. Nature 450:870-873.

Gandon, S., and T. Day. 2009. Evolutionary epidemiology and the dynamics of adaptation. Evolution 63:826-838.

Gomulkiewicz, R., D. M. Drown, M. F. Dybdahl, W. Godsoe, S. L. Nuismer, K. M. Pepin, B. J. Ridenhour, C. I. Smith, and J. B. Yoder. 2007. Dos and don'ts of testing the geographic mosaic theory of coevolution. Heredity 98:249-258.

Thompson, J. N. 1999. Specific hypotheses on the geographic mosaic of coevolution. American Naturalist 153:S1-S14.

Thompson, J. N. 2005.
The Geographic Mosaic of Coevolution. University of Chicago Press, Chicago.

Paper read

Gandon, S., & Day, T. (2009). EVOLUTIONARY EPIDEMIOLOGY AND THE DYNAMICS OF ADAPTATION Evolution, 63 (4), 826-838 DOI: 10.1111/j.1558-5646.2009.00609.x