Axel H. Jul 31, Explanation: Consider two species in a case of coo evolution ,cooperation like polinators and flowered plants or parasitism with and more effectifs parasit and an host which evolved to limits it's action. Related questions How do I determine the molecular shape of a molecule? What is the lewis structure for co2? What is the lewis structure for hcn? One of the best examples of this comes from the interaction between the pollinating parasitic moth, G. The moth passively pollinates the plant as it oviposits into the flower through the corolla with pollen that has adhered to the abdomen of the female.
The developing larvae eat a small fraction of the developing seeds and, as a result, there is always a cost associated with Greya oviposition. However, in populations where there are few other co-pollinators, the interaction outcome is shown to be highly mutualistic as Lithophragma depends on Greya for pollination.
By contrast, in other populations where co-pollinators are abundant, the interaction is antagonistic to the extent that flowers containing Greya eggs are selectively aborted in some years. In yet other populations the presence of Greya eggs in the flowers does not affect the number of developing seeds.
Hence, ecological conditions may render the same interaction highly mutualistic in some populations, commensalistic in yet others, and antagonistic in others over distances of some hundreds of kilometres Fig. These geographically different dynamics are shown to be temporally persistent Thompson and Fernandez, Competitors are identified as causal agents of divergent coevolutionary selection in several other studies as well.
Coevolution between crossbills and conifers a seed predator—host tree interaction is altered in some regions by red squirrels Tamiasciurus hudsonicus and Abert's squirrels Sciurus alberti , seed consumers that outcompete crossbills. As a result, reciprocal selection is stronger between conifers and crossbills in areas lacking squirrels Benkman, ; Parchman et al.
Accounting for interactions at the community level, Rey et al. Trophic complexity in the form of predators or parasites may alter the intensity of coevolving interactions. Differences in local densities of birds and parasitic wasp Mordellistena convicta created geographic variation in selection on coevolving traits in the interaction between fly Eurosta solidaginis and its parasitic wasp, Eurytomea gigantea , between the prairie and forest biomes Craig et al.
A parasitic green alga Cephaleuros that clogs extrafloral nectaries of wild cotton may generate spatial variation in selection in the protection mutualism between the wild cotton and ants that guard wild cotton against herbivores in exchange for the extrafloral Rudgers and Strauss, Further spatial divergence in selection trajectories may be generated by the local ant community that varied significantly among sites Rudgers and Strauss, Alternative host plants are also shown to be important in generating selection mosaics Antonovics et al.
In the interaction between webworms and wild parsnip, the webworms are capable of exerting selective impact on host plant chemistry. Both in the mid-western United States where parsnip has been introduced, as well as in its indigenous area, Europe, in populations where webworms are rare the parsnip produces lower levels of chemical defence compounds.
In Europe, attack rates were lower due to the presence of an alternative host plant. While this host plant was associated with higher levels of webworm parasitism, it was the preferred host over parsnip, most likely because of the lower furanocoumarin content of Heracleum sphondylium Berenbaum and Zangerl, Anderson and Johnson suggested that variability in the coevolutionary process between pollinating long-tongued fly P.
By contrast, simpler communities, lacking these short-tubed nectar plants may allow escalatory coevolution between fly proboscis and flower depth Anderson and Johnson, Eight of the 29 studies suggest the physical environment as a causal agent of a selection mosaic. Variability in a coevolving interaction could be generated by abiotic factors that constrain how far the coevolutionary process may proceed.
For example, in the interaction between pollinating long-tongued fly P. It is well established that temperature is among the strongest and most ubiquitous sources of environmental variation affecting the biochemical, physiological and ecological properties of species Burdon, ; Thomas and Blanford, Plant pathogens with a free transmission stage are considered particularly vulnerable to variation in temperature Burdon, ; Truscott and Gilligan, In general, temperature has been shown to affect parasite ability to establish or maintain infection, its latency as well as its severity Burdon, ; Thomas et al.
In the interaction between the host plant, P. The differences observed in the experiment were linked to natural temperature regimes of the populations the strains for the experiment were sampled from, and hence, the results demonstrate how tightly coupled the trajectories of host—parasite coevolution may be with adaptation to the abiotic habitat Laine, For the same host—pathogen interactions it was demonstrated that a selection mosaic may be formed even within host populations through an interaction with microclimate Laine, Microclimate coupled with limited dispersal distances generated highly asymmetric encounter rates between host and pathogen even within host populations, generating hotspot areas where selection for resistance was higher than in the coldspot areas where infected individuals were rarely observed Laine, The process was accelerated during a severe drought year when susceptible individuals suffered higher mortality and seedling recruitment was unsuccessful Laine, Variation in climatic conditions is also suggested to generate geographical variation in the interaction between wild flax L.
This variation is linked with host life-history as in the hot, dry areas hosts go through regular seasonal declines with significant levels of outcrossing, while in the cool areas host tissue is available year-round and the hosts are predominantly selfing Burdon et al. Interactions aside from those between hosts and their pathogens show sensitivity to temperature.
Pericarp thickness and weevil rostrum length vary remarkably even over some kilometres, and while armament size of the sympatric counterpart best explained this variation, pericarp thickness significantly decreased in cool temperatures, suggesting how climate may contribute to the spatial structuring of the interactions Toju and Sota, c ; Toju, Metapopulation dynamics are further suggested to shape the geographic structure of the interaction Toju and Sota, c ; Toju, In the interaction between pollinating long-tongued fly P.
The results of this review support one of the key hypotheses of the Geographic Mosaic Theory of Coevolution—divergent natural coevolutionary selection produces genetic differentiation among populations. Indeed, divergent natural coevolutionary selection may be an overwhelmingly important mechanism generating diversity in nature given how different types of interactions show divergence, and how variable the putative causes generating such divergence are.
One of the most striking results of this review is the spatial scale over which we may find divergent coevolutionary trajectories. We need not compare species interactions across continents to detect variation, comparisons of populations separated by some hundreds of kilometres are typically found to follow their own unique coevolutionary paths resulting in spatial variation of the coevolving traits.
At one extreme we may even find divergent selection within a single host population at the scale of some metres where selective mortality caused by microclimatic differences results in divergent evolutionary selection imposed by an obligate parasite on its host Laine, While this may represent an extreme case of fine-scale coevolutionary divergence, the example does highlight the potential for the environment to create geographically variable selection trajectories.
The methodological implication of these results is that it is of the utmost importance to study coevolving interactions across several populations. Studying reciprocal evolutionary dynamics of two species simultaneously is demanding in itself, doing so over geographical distance is quite another. The scale at which the studies are carried out will depend on system-specific properties.
What the relevant distances are at which we may expect to find divergence will depend on the gene flow of the interacting species, and what the distances are over which the habitat properties diverge to the extent that it may affect the reciprocal selection trajectories. As genomic tools, such as cDNA microarrays, are becoming readily available for ecological studies and non-model organisms, we should expect to see an increase in their use in the future.
They may, at least partially, solve some of the difficulties of studying multiple populations across large spatial scales over time. Gomulkiewicz et al. Second, researchers should gather data that could be used to test for reciprocal selection between coevolving partners. Third, researchers could either measure the spatial genetic structures of the interacting species or look for cold spots and selection mosaics. Finally, researchers should determine whether trait remixing and selection are effective at comparable spatial scales.
If conditions for a selection mosaic are not filled at any of these steps, the Geographic Mosaic Theory of Coevolution should be rejected for that study system Gomulkiewicz et al.
In order to guarantee biodiversity persistence there is a need to adopt a habitat approach at the metapopulation level in our conservation efforts. Ongoing coevolution among species also permeates many applied fields such as agriculture and forestry. Breeding for disease resistance in crops and guaranteeing ecosystem services such as pollination are just some examples of processes that depend on coevolutionary selection that may diverge across landscapes.
Sami Ojanen is acknowledged for compiling Tables 1 and 2. Google Scholar. Google Preview. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Sign In. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. Literature search.
How is coevolution measured? Abiotic environment. Role of coevolution in generating biological diversity: spatially divergent selection trajectories.
Oxford Academic. Revision received:. Select Format Select format. Permissions Icon Permissions. Abstract The Geographic Mosaic Theory of Coevolution predicts that divergent coevolutionary selection produces genetic differentiation across populations. Geographic Mosaic Theory of Coevolution , selection mosaic , spatial scale , species interactions. From Wright's shifting balance theory to spatially realistic metapopulations.
Open in new tab Download slide. Table 1. Reference Type Species Number of populations Smallest scale km Largest scale km Spatial variation in selection Causal agent of divergent selection Anderson and Johnson, Host plant—pollinator Zaluzianskya microsiphon host plant , Prosoeca ganglbaueri long-tongued fly 16 9.
Open in new tab. Table 2. Reference Measure of coevolution Local adaptation Anderson and Johnson, Fly proboscis and flower's corolla tube length Yes Antonovics et al. Analysis of Rhizobium etli and of its symbiosis with wild Phaseolus vulgaris supports coevolution in centers of host diversification.
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Temperature-mediated patterns of local adaptation in a natural plant—pathogen metapopulation. Constraints on the coevolution of bacteria and virulent phage: a model, some experiments, and predictions for natural communities. Migration, virulence, and the geographic mosaic of adaptation by parasites. Symbiosis as a mechanism of evolution: status of the cell symbiosis theory. Google Scholar PubMed. Population subdivision and genetic diversity in two narrow endemics of Antirrhinum L.
Host—parasite and genotype-by-environment interactions: temperature modifies potential for selection by a sterilizing pathogen. The effect of migration on local adaptation in a coevolving host—parasite system. The top x -axis represents the climatic effect on the intrinsic rate of increase of species 1, b 1 E.
This occurs because an increase in the density of species 1 with environmental change leads to a decrease in the density of species 2. Because selection pressure is positively correlated with the density of the other species, species 1 experiences relatively less selection pressure from competition with species 2 compared to the selection pressure on species 2 from species 1.
When competition is conflicting Figure 2A,C , the decreased selection on species 1 is beneficial to species 2, which acts to limit the decline of the population of species 2 and hence the decline of its effect on species 1.
Also, the increased selection on species 2 increases its per capita competitive effect on species 1. These two sources of selective pressures combine to help species 2 and, in turn, are detrimental to species 1.
When competition is nonconflicting Figure 2B,D , the converse occurs; the decreased selection on species 1 caused by low densities of species 2 increases the effect of competition on species 2, and the increased selection on species 2 decreases its per capita competition effect on species 1.
This selective pressure benefits species 1, further increasing its density. Equilibrium population densities A, B and trait values C, D for two competing species at different climatic conditions. The intrinsic rate of increase of species 1 solid lines increases linearly with climate E , while the intrinsic rate of increase of species 2 dashed lines is unaffected.
The trait value shown on the y -axis of C, D for species i dictates the strength of competition felt by species i per capita of species j. The x -axis represents the climatic effect on the intrinsic rate of increase of species 1, b 1 E. In summary, conflicting competition sets up coevolution as a negative feedback, because selection on one species to reduce competition increases its competitive effect on the other species. In contrast, nonconflicting competition sets up coevolution as a positive feedback, because selection to reduce the impact of competition on one species also reduces the impact of competition on the other Box 1.
As with competition, the effects of coevolution on mutualists depended on the type of coevolution. This effect occurs because the increase in the density of species 1 due to the environmental change increases selection pressure on species 2 for investment in the mutualism. When the mutualism is conflicting, this change is detrimental to species 1 and limits its increase, because the benefits of mutualism decrease with the investment of species 2 in the interaction.
In contrast, in the case of nonconflicting mutualism, increased investment by species 2 is beneficial to species 1, further increasing the density of species 1.
In summary, conflicting mutualism sets up coevolution as a negative feedback, whereas nonconflicting mutualism sets up a positive feedback Box 1. The intrinsic rate of increase of species 1 solid lines increases linearly with the climate variable E , while the intrinsic rate of increase of species 2 dashed lines is unaffected.
The trait value for species i shown on the y -axis of C, D dictates the benefits of mutualism accrued by species i per capita of species j. When the intrinsic rate of increase of species 1 is high enough and species are allowed to coevolve, there is no equilibrium in the nonconflicting mutualism model B, D , as the growth of each species is unbounded. For competition and mutualism, interacting species might have either conflicting or nonconflicting coevolutionary feedbacks.
In contrast, predator and prey interactions are generally expected to exhibit conflicting coevolution and hence generate negative coevolutionary feedbacks: prey coevolution of defenses that reduce predation will be detrimental to the predator, and predator coevolution to increase the predation rate will be detrimental to prey. To verify this expectation, we analyzed both the case in which climate change increases the prey intrinsic rate of increase and the case in which climate change increases the predation rate and hence the predator population growth rate.
When climate change enhances the prey intrinsic rate of increase, the resulting increase in prey density leads to increased predator density, and in the absence of coevolution the equilibrium predator density increases dramatically Figure 4A. In contrast, the equilibrium predator density increases more slowly when predator and prey coevolve Figure 4A.
As with conflicting competition and mutualism, higher predator density strengthens selection pressure for prey investment in the coevolutionary arms race Figure 4C. With increased prey investment, the predator density cannot increase as much due to heightened prey defense Figure 4A,C. Equilibrium prey solid lines and predator dashed lines densities A, B and trait values C, D for different climatic conditions. A, C The prey intrinsic rate of increase increases linearly with climate E , while the predation rate is unaffected.
B, D The predation rate increases linearly with E , while the prey intrinsic rate of increase is unaffected. The x -axis for panels A and C represents the climatic effect on the intrinsic rate of increase of prey, b n E , and the x -axis for panels B and D represents the climatic effect on the predation rate, b p E. Increases in either prey or predator trait values reduce per capita predation rate.
When climate change increases the predation rate but has no effect on the prey intrinsic rate of increase, the predator density increases rapidly in the absence of coevolution, but this increase is slowed by coevolution Figure 4B,D. Because prey selection pressure is positively correlated with predator density, prey evolve higher defensive trait values in the presence of higher predation rates, which in turn lowers the predation rate, increases prey density, and decreases predator density.
Thus, coevolution sets up a negative feedback loop that reduces the decline in prey density and increase in predator density Figure 4B. While these results pertain to specialist predators that have no other prey species, we found that coevolution also reduces the ecological effects of climate change in a model for generalist predators Figure S1.
We have shown, using simple models, that coevolution may increase or decrease the effect of environmental change, depending on the form that coevolution takes between species. In cases where species have conflicting interests, coevolution reduces the effects of environmental change on densities, because coevolution acts as a negative feedback to the effects of environmental change.
Conversely, when species have nonconflicting interests, coevolution sets up a positive feedback that increases the effects of environmental change on densities. Given these contrasts, is coevolution in nature likely to involve conflicting or nonconflicting interests of interacting species? Competitors and mutualists, in particular, have the potential to coevolve along either conflicting or nonconflicting pathways.
Thus, determining the predominant type of coevolution will be critical to identifying the long-term effects of climate change on species. Below, we first give brief discussions of classical studies and show that cases of both conflicting and nonconflicting coevolution are common.
Therefore, no a priori prediction can be made for their relative importance when anticipating the effects of climate change. We then turn to coevolutionary studies that directly address climate change, using these to show how evidence can be obtained to make and test predictions about the coevolutionary effects on specific systems facing climate change.
It has long been recognized that coevolution can lead to increased asymmetries in competitive abilities [15] , which is the hallmark of conflicting coevolution. But the idea that competition drives partitioning of food sources is even older [53] — [55] , and this is the hallmark of nonconflicting coevolution. The effects of climate change for specific competitors hinge on which type of coevolution occurs.
Evidence suggests that both are common. Laboratory experiments that evaluate the effect of competitive interactions on trait evolution for each species have documented both conflicting coevolution in flies [15] and nonconflicting coevolution in E.
Furthermore, conflicting and nonconflicting coevolution are not mutually exclusive; Colpoda protozoans with initially weak competitive abilities have been shown to evolve along both pathways [57]. While these types of experimental studies have the advantage of documenting coevolution as it happens, they are limited by the range of species and time scales that are amenable to experiments, and the magnitude of environmental heterogeneity that may affect coevolution [58] , [59].
Alternatively, field studies can be used to infer the prevalence of conflicting versus nonconflicting coevolution. Research focusing on character displacement in natural populations attempts to identify the effects of coevolutionary processes based on species' phenotypes in solitary and sympatric populations [60]. This approach has documented both conflicting [61] , [62] and nonconflicting coevolution [63] — [65].
There is a rich theory describing the evolution of mutualisms [66] , [67]. Theoretical predictions often suggest that mutualistic interactions have the potential to break down into parasitic interactions [47] , [68] , [69] ; this is an extreme form of conflicting interests between species.
If mutualism breakdown into parasitism is common, then conflicting coevolution is likely, and this will likely diminish the effects of climate change. Nonetheless, if mutualistic partners can enforce good behavior of their partners [68] , then nonconflicting coevolution is expected. For example, the plant Medicago truncatula discriminately rewards the most beneficial mycorrhizal partners with more carbohydrates, and mycorrhizal partners form partnerships only with the roots that provide the most carbohydrates [7].
Thus, each partner constrains the selection pressure of the other to allow only nonconflicting coevolution. If nonconflicting coevolution is frequently imposed by mutualists, our results suggest that coevolution between mutualistic species will exaggerate, rather than diminish, the effects of climate change on species densities.
Conflicting coevolution is expected for most types of predator-prey or consumer-resource interactions, because increases in prey defenses will decrease benefits to predators, and increases in predator effectiveness will be detrimental to prey. Nonetheless, evolution of parasite virulence could be different [70] , [71]. The conventional wisdom is that parasites should evolve to be less virulent, because this will increase their transmission among hosts; parasites are not transmitted by dead hosts, at least not for long [72].
Nonetheless, this ignores, among other things, the relationship between the production of large numbers of propagules that generally harms the host and transmission rates, and more-detailed analyses generally predict evolution of parasite virulence to represent a balance between higher virulence caused by selection for production of propagules and lower virulence caused by selection for lengthening the transmission period [73]. Therefore, evolution of the parasite may be nonconflicting with the host, even at the same time evolution of the host to limit infection is conflicting with the parasite.
In models describing this interaction results not shown , we found that when climatic changes directly affect the parasite, coevolution in the host fuels a negative feedback loop that mitigates the effects of climate change.
In contrast, in some cases when climatic changes directly affect the host, coevolution can lead to a positive feedback loop that exaggerates the effects of climate change on the host density. Thus, when there are both conflicting and nonconflicting coevolution, the ultimate outcome will be determined by whether the host or parasite experiences greater evolutionary change. Given the widespread occurrences of both conflicting and nonconflicting coevolution in competition and mutualism, and to a lesser extent in predator-prey interactions, systems will have to be studied on a case-by-case basis to predict and test the role of coevolution in modifying the effects of climate change.
This could be done either using experimental studies or taking advantage of naturally occurring environmental gradients. An example of an experimental study is given by Lopez-Pascua and Buckling [74] , who performed an environmental manipulation of bacterial productivity by altering nutrient concentrations in the growth media. They showed that increasing bacterial productivity increases the rate of coevolution between bacteria and phages. They proposed that this is due, in part, to increased selection pressure on the bacterial population in environments with high productivity high intrinsic rates of bacterial increase.
This increased selection stems from increased encounters with phages, as phages numerically respond to increased bacterial density. The phages then evolve greater infectivity in response to bacterial evolution. This explanation is consistent with our theoretical expectations for conflicting evolution of prey and predators; increasing the prey intrinsic rate of increase leads to evolution of stronger prey defenses against the predator Figure 4C.
In addition to experimental manipulations of environmental factors, it is possible to take advantage of natural environmental gradients similar to classical studies of character displacement. For example, in a field experiment, Toju et al.
Female beetles use their snout to pierce the camellia fruit pericarp and oviposit eggs into seeds, with oviposition success determined by the length of the beetle's snout and ovipositor relative to the pericarp thickness.
Thus, plant defense is determined by pericarp thickness, and beetle snout and ovipositor lengths determine beetle ability to overcome this defense. The authors measured beetle and plant traits along a latitudinal gradient, and previous work had showed that plants exhibit faster potential for growth at lower latitudes [76]. Thus, in the camellia-beetle arms race we expect that coevolution will favor plants more at lower latitudes. The authors indeed found this to be the case; plants in high latitude populations that experienced endemic predation by beetles had pericarp thicknesses similar to populations that did not experience beetles.
In contrast, at lower latitudes plant populations that experienced beetle predation had thicker pericarps than populations that did not.
There was thus an increase in plant defense along the environmental gradient. Furthermore, this plant defense increased with decreasing latitude at a greater rate than weevil ovipositor length, suggesting that plants exhibited a larger coevolutionary advantage in environmental conditions with increased prey growth [75].
These results support our theoretical predictions that higher prey intrinsic rates of increase should lead to a coevolutionary advantage to prey, thereby buffering the changes in predator densities driven by climate change. The majority of coevolutionary studies involving environmental manipulations or environmental gradients have been conducted on predator-prey or herbivore-plant systems where conflicting coevolution is likely.
Similar experiments that document changes in traits and density might help build a better understanding of coevolution in competitive and mutualistic relationships.
Laboratory studies have suggested that coevolution can lead to a reversal of competitive hierarchy in just 24 generations [15] , and can occur fast enough to drive population dynamics [16]. Therefore, experimental competition studies in which environmental factors are manipulated are possible for some types of organisms. Environmental gradient, rather than experimental, studies will be more practical for larger organisms with longer lifespans that operate at larger spatial scales.
Using character displacement to infer conflicting versus nonconflicting coevolution is necessarily correlative, although it opens up the study of coevolution in the context of climate change to a much wider range of species under natural spatial and temporal scales. Studies that evaluate coevolution over environmental gradients fit within the broader conceptual paradigm of geographic mosaic theory [77] in which differences in coevolutionary selection among spatially separated populations are analyzed as genotype by genotype by environment interactions.
A key feature of geographic mosaic theory is that some local populations experience environmental conditions under which coevolutionary pressures are strong. This pair of equations has the same general structure as that we have used for Equations 1 — 4. Thus, our results address the possible character of evolution within coevolutionary hotspots, and how coevolutionary outcomes might differ under different environmental regimes. We have only considered local populations, explicitly ignoring gene flow among populations.
Thus, we have ignored the large body of theoretical and empirical studies evaluating gene flow among populations under different selective forces [77] — [79]. For example, Nuismer et al. When interaction types vary spatially, however, both dynamic and equilibrium clines occur, and the presence of each depends on the levels of selection and gene flow across the landscape [80].
In an experimental bacteria-bacteriophage community, bacteriophages became locally maladapted in the absence of gene flow, but became locally adapted when gene flow occurred between bacteriophage populations [81].
The importance of gene flow in both theoretical and empirical studies gives a caution to our recommendation that natural environmental gradients be used to assess the character of coevolution—conflicting versus nonconflicting—and whether coevolution sets up positive or negative feedback loops to environmental changes.
Gene flow and a geographic mosaic of selective pressure may dampen or otherwise modify the effects of local selection on coevolutionary traits. While it is recognized that evolution will play a role in determining how climatic changes directly affect species [18] , the interactions among species force us to also consider coevolution between species.
Our models suggest that the effects of coevolution on population densities depend on the presence of conflicting versus nonconflicting coevolutionary interests. While we encourage future studies that experimentally manipulate both coevolution and environmental change, we acknowledge that experiments are likely to be difficult logistically for most study systems. It may be possible, however, to use character displacement across environmental gradients to distinguish whether conflicting versus nonconflicting coevolution is more likely, even when directly measuring coevolution is impossible.
Experimental [15] and environmental gradient [60] approaches to infer the nature of coevolution are both five decades old, and we hope that our theoretical results provide new impetus for these types of studies. They give needed information to anticipate whether coevolution will increase or decrease the effects of climate change on the densities of interacting species. Generalist predator equilibrium densities and traits. Equilibrium values of prey and generalist predator population densities A, B and traits C, D for different climatic conditions.
A, C The prey intrinsic rate of increase rose linearly with climate E , while the predation rate was unaffected. B, D The predation rate increased linearly with climate E , while prey growth was unaffected. Analytical approximation for changes in species abundances with coevolution.
Rosenheim, B. Barton, H. Fan, C.
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