DOS AND DON’TS OF TESTING THE GEOGRAPHIC MOSAIC 2 THEORY OF COEVOLUTION 4 Richard Gomulkiewicz*†‡, Devin M. Drown*, Mark F. Dybdahl*, William 6 Godsoe§, Scott L. Nuismer§, Kim M. Pepin§#, Benjamin J. Ridenhour§, Christopher Irwin Smith§, and Jeremy B. Yoder§ 8 10 *School of Biological Sciences, Washington State University §Department of Biology, University of Idaho 12 †Department of Mathematics, Washington State University #Current address: Department of Biology, New Mexico State University 14 ‡Corresponding Author. PO Box 644236, School of Biological Sciences, Washington State University, Pullman, WA 99164, phone: +1 (509) 335- 16 2527, fax: +1 (509) 335-3184, email: [email protected] 18 Key words: hot spot, cold spot, selection mosaic, trait remixing, local maladaptation 20 Running Head: Testing the geographic mosaic theory Main Text Word Count: 4814 1 ABSTRACT 2 The geographic mosaic theory of coevolution is stimulating much new research on interspecific interactions. We provide a guide to the fundamental 4 components of the theory, its processes, and main predictions. Our primary objectives are to clarify misconceptions regarding the geographic mosaic theory 6 of coevolution and to describe how empiricists can test the theory rigorously. In particular, we explain why confirming the three main predicted empirical 8 patterns (spatial variation in traits mediating interactions among species, trait mismatching among interacting species, and few species-level coevolved traits) 10 does not provide unequivocal support for the theory. We suggest that strong empirical tests of the geographic mosaic theory of coevolution should focus on 12 its underlying processes: coevolutionary hot and cold spots, selection mosaics, and trait remixing. We describe these processes and discuss potential ways 14 each can be tested. 16 2 INTRODUCTION 2 Coevolution among species and the impacts of geography on evolution have always been major research areas within evolutionary biology. However, 4 interest in studying coevolution in a geographic context has recently escalated. A search of the ISI Web of Science shows a dramatic increase over the last 6 fifteen years in the number of publications considering both coevolution and geography (Figure 1). Moreover, a growing portion of these papers cite the 8 publications of John N. Thompson, whose book “The Geographic Mosaic of Coevolution” (Thompson 2005) is the latest treatise on this emerging sub- Figure 1 10 discipline (Figure 1). Thompson’s book and papers provide a compendium of about here the empirical literature on interacting species whose ranges span broad and 12 variable landscapes. They also advance the geographic mosaic theory of coevolution, which is a particular thesis regarding the role of evolutionary and 14 ecological processes in determining patterns of coevolutionary outcome across geographical localities. 16 The geographic mosaic theory of coevolution (“GMTC”) hypothesizes that three processes are the primary drivers of coevolutionary dynamics: 18 intermingled coevolutionary hot and cold spots, selection mosaics, and trait remixing. For a pair of interacting species, a “hot spot” is a location where the 20 fitness of both species is affected by the distribution of traits in the other species. In contrast, in a “cold spot” the fitness of at least one of the species is 22 unaffected by the other. The term “selection mosaic” refers to variability in the functions that describe reciprocal fitness interactions across space. Finally, 3 “trait remixing” includes gene flow across landscapes, random genetic drift 2 within populations, extinction and recolonization of local populations, and mutation. The GMTC predicts these three processes lead to three observable 4 patterns: spatial variation in the traits mediating an inter-specific interaction, trait mismatching among interacting species, and few species-level coevolved 6 traits (Thompson 1999, 2005). Here, we aim to explain these hypothesized processes and predicted 8 patterns clearly, and highlight empirical approaches to use—and to avoid— when testing the geographic mosaic theory of coevolution. Note that this brief 10 review does not consider the historical context or development of the GMTC; interested readers should consult Thompson (2005). 12 VERIFYING PREDICTED PATTERNS IS NOT ENOUGH 14 The geographic mosaic theory of coevolution predicts that hot and cold spots, selection mosaics, and trait remixing should result in the three major 16 patterns of coevolution described above. Although mathematical models confirm that these predicted patterns follow from the assumptions of the GMTC 18 (e.g., Gomulkiewicz et al. 2000), identical patterns could be generated without invoking selection mosaics, trait remixing, or intermingled coevolutionary hot 20 and cold spots. We illustrate this point using the example of an antagonistic, cyclical interaction, although similar arguments can be readily developed for all 22 major forms of ecological interactions. 4 Spatially variable traits. The GMTC predicts that traits mediating 2 species interactions should be spatially variable, yet this pattern will almost inevitably be produced by antagonistic coevolution even in the absence of 4 selection mosaics, trait remixing or occasional cold spots. For example, take coevolution between a host and parasite mediated by quantitative traits in each 6 species (Figure 2). In the absence of gene flow, coevolutionary dynamics are likely to be out of phase among patches because of slight variations in initial 8 phenotype frequencies (Morand et al. 1996). Consequently, traits mediating the Figure 2 about here interaction are likely to vary across space (Figure 2). 10 Sensitivity to historical genotype frequencies also occurs in mutualistic and competitive interactions (Parker 1999; Nuismer et al. 2000; Thompson et al. 12 2004). Documenting spatial variation in traits mediating an interaction therefore does not by itself provide clear support for the GMTC over alternative 14 explanations. However, such observations may be critical in identifying traits of importance to an interaction, and may warrant further study in the context of 16 the GMTC. Trait mismatches. A second major pattern predicted by the GMTC is 18 that the traits mediating an interaction will be well matched in some localities but mismatched in others. This prediction has been evaluated in multiple 20 empirical studies by comparing the extent to which traits mediating an interaction mismatch across multiple locales (Brodie et al. 2002; Zangerl and 22 Berenbaum 2003; Siepielski and Benkman 2004). Evaluating whether the results of these studies support the GMTC, however, is made difficult by the 5 absence of any formal definition for a “match” or “mismatch” (Thompson 2 1994, 1999, 2005). An intuitive definition of matching can be made in terms of functional 4 relationships among coevolving traits. For example, pollinators and the flowers they pollinate must emerge at about the same; the functional relationship 6 between pollinator emergence time and plant flowering time thus defines trait matching. Such relationships have primarily been determined via regression 8 techniques (e.g., Benkman 1999; Brodie et al. 2002; Brodie and Ridenhour 2003; Toju and Sota 2006); the coefficient of determination (R2) then indicates 10 how well matched a pair of traits are across a geographic mosaic. Techniques used in determining allometric scaling relationships (such as reduced major- 12 axis regression; Sokal and Rohlf 1995) may be particularly useful in determining functional trait matching. Note, however, that in some cases trait 14 matching is not expected. For example, trait values should continually increase when "bigger is better" in the absence of fitness trade-offs (i.e. costs) and thus 16 trait matching should not be evident. An alternative, indirect approach to evaluating trait mismatching, is to 18 quantify levels of local maladaptation in interacting species (Nuismer 2006). Local maladaptation has been defined both verbally and mathematically, and 20 has been subject to extensive empirical and theoretical investigation (Gandon et al. 1996; Lively and Jokela 1996; Morand et al. 1996; Kaltz and Shykoff 1998; 22 Kaltz et al. 1999; Oppliger et al. 1999; Gandon and Michalakis 2002; Thrall et al. 2002; Dybdahl and Storfer 2003; Kawecki and Ebert 2004; Lively et al. 6 2004; Nuismer 2006). Local adaptation has been characterized theoretically 2 within two broad frameworks whose appropriateness is under debate: home versus away and local versus foreign (Kawecki and Ebert 2004). In contrast, 4 empirical approaches to determining local adaptation involve either reciprocal cross-infection or reciprocal transplant experimental designs. These designs 6 involve measuring and comparing the fitness of individuals within their natal and non-natal environments. Several studies have used such an approach to 8 document local maladaptation in at least one member of an interacting species pair (e.g., Morand et al. 1996; Kaltz and Shykoff 1998; Kaltz et al. 1999; 10 Oppliger et al. 1999). Neither trait mismatching nor local maladaptation provide a rigorous test of 12 the GMTC since both can evolve in the absence of selection mosaics, coevolutionary cold spots, or trait remixing. For example, Figure 2 shows there 14 can be strong temporal and spatial variation in the degree of trait mismatching despite lacking the central evolutionary processes invoked by the GMTC. The 16 same is true for local maladaptation which has been demonstrated