Indeed, in depth analysis of the nutrient specialization of very distinct small and large colony morphs of three populations provided strong support for resource specialization [12]. While both colony types exhibited diauxic growth, during which they first utilize glucose through glycolysis before using acetate through the TCA cycle, the small and large colony morphs differed in their ability to switch between the use of glucose and acetate, with the small colonies being able to switch much faster.
Similarly, Tyerman et al. The resource specialists are most likely maintained by frequency dependence our data and [12] , [15]. We observed strong negative frequency dependence in three of the microcosms. This variation in the strength of frequency dependence could be due to the fact that we only assayed the two phenotypically most divergent morphs. More extensive sampling of different colony morphs in different pair-wise combinations, or competition of the different colony morphs against the source population at different initial frequencies, may have led to a better support of negative frequency dependence.
Both frequency dependence, a measure of the strength of niche specificity, and colony size, a correlated trait without direct benefits and cost, indicated that ecological diversity within microcosms evolved over the course of the selection experiment. We can compare both measurements of ecological diversity — colony size diversity and frequency-dependent selection — by comparing the coefficients of variation for both traits over the eleven microcosms assayed for frequency dependence. The corrected coefficient of variation for frequency dependence was determined using the variance in selection coefficients at high and low densities, over the average selective coefficient, corrected for the small sample size.
Although the average coefficient of variation for frequency dependence is larger than for colony size This suggests that colony morphology is a good indicator of ecological diversity. In addition, small colony morphs of different microcosms have been shown to have similar ecological functions [15]. When we mixed small colonies of one microcosm with large colonies of another microcosm, the two colony morphs reached equilibrium frequencies similar to the frequencies reached by co-evolved small and large colony morphs, as long as the microcosms evolved in the same selective environment [15].
Our approach of using mean colony size as a measure of the evolved ecological diversity results in an imperfect estimate of the evolved genetic diversity, but it allowed us to capture the evolved ecological diversity, as different colony morphologies are indicative of resource specialization and ecological function.
However, we are very likely to underestimate genetic diversity, as evidence suggests that different genotypes result in the same phenotype [16]. We observed parallel evolution for all traits, except for frequency dependence.
Different factors can affect parallelism and divergence. At low ecological complexity, the fitness landscape is a useful metaphor [32]. If there are no epistatic incompatibilities among beneficial mutations, and if there is only one most fit genotype out of all possible genotypes, parallel evolution is likely to occur [22] , [23].
Epistatic interactions, increased environmental complexity [33] , and ecological interactions can contribute to a more rugged landscape. The more rugged a landscape, the less likely it becomes that independent populations evolve in parallel. Divergence on the fitness landscape is driven by the effects of chance and contingency, and is possible even for initially isogenic populations under the same selective conditions [34].
Chance affects the occurrence and fixation of beneficial mutations, as even beneficial mutations can be lost by drift when they first appear, and are very rare [35]. Contingency can promote divergence if a fitness landscape is complex, with multiple peaks and flat areas where chance can lead to sustained divergence among populations, since subsequent adaptation is contingent on the location of each population on the landscape.
The observed parallel evolution suggests relatively simple landscapes, although the divergence among microcosms suggests that there are several peaks or perhaps an adaptive ridge [36]. Even so, we cannot exclude the possibility that selection would ultimately lead to complete phenotypic convergence given sufficient time [23] , [37].
Parallel evolution among independent populations has previously been observed for traits such as fitness and cell size in less complex environments [24] , [27] , [38]. Similar to those studies, we observed very strong parallel evolution for fitness [24] , [27] , despite the more complex selective environment.
The predominance of parallelism in our experiment is likely due to the presence of the two nutrients used in our study, glucose and acetate, which differ in transport into cells, catabolism, and regulation. These metabolic limitations can cause trade-offs for resource use [17] , a requirement for the evolution of diversity through adaptive radiations [1] , [21] , [39]. These strong trade-offs very likely contributed to the parallel evolution among the microcosms.
Relaxing the trade-offs associated with resource specialization would likely reduce parallelism for correlated phenotypic characters, as the constraints on their evolution would also be relaxed. MacLean and Bell [30] observed more divergence for correlated traits when they assessed the growth of Pseudomonas fluorescens populations evolved in an environment with one limiting resource, and subsequently tested the correlated response in almost alternate environments. The observed divergence suggests that many of these alternate environments did not involve strong trade-offs.
We observed parallel evolution for the two correlated phenotypic traits, colony size and colony size diversity. However, both correlated traits are affected by nutrient specific adaptation [12] , [13] , [17] , [29].
Our expectation is that the degree of parallelism would decrease for traits less tightly associated with fitness [27] , [40] because they would experience relaxed selection and therefore accumulate random mutations. Alternatively, pleiotropic effects of mutations can lead to increased diversification of non-essential traits [41]. Therefore, if we were to extend our analysis to traits not associated with nutrient metabolism and growth, we would most likely observe less parallel evolution.
The observed parallel evolution at the phenotypic level could be due to parallel evolution at the genetic level, or due to a different combination of mutations that result in similar phenotypes. Investigation of the underlying genetic changes associated with acetate specialization indicated that the overall phenotypic similarity was not maintained at the genetic level. In one of the microcosms with strong frequency-dependent selection, Spencer et al. A transposon insertion in the iclR gene causes de-repression of the aceBAK operon leading to its expression during growth on glucose, when the operon is typically repressed.
As a result, genotypes with this mutation express a gene critical for acetate metabolism, malate synthase A, even during growth on glucose when this gene is normally down regulated.
Genotypes with this mutation are superior competitors on acetate, but are inferior to derived glucose specialists when only glucose is available.
This mutation was not found in any of the other microcosms that showed strong frequency dependence. Across microcosms, ecologically equivalent specialists arise by different mutations having the same or similar physiological impact. The equivalent specialists among microcosms remain divergent at the genetic level since there is no additional fitness benefit to fixing additional mutations having the same consequences, as they are already ecologically equivalent [42].
This pattern of overall phenotypic similarity, but divergent underlying mechanisms, is consistent with diminishing returns epistasis [6] , [43].
In sexually reproducing species, this mode of divergence could lead to Dobzhansky-Muller postzygotic reproductive incompatibilities [44]. While repeated evolution of phenotypic diversity has previously been observed in more complex environments, the repeatability of the evolution of diversity in this system has not been addressed in a systematic way under the same selective conditions.
For example, the rapid evolution of phenotypically distinct ecotypes in static Pseudomonas fluorescens microcosms has been observed repeatedly in independent experiments [45] , [46] , [47]. Genetic analysis showed that mutations in one of three regulatory modules can readily lead to the evolution of one of these ecotypes [48] , suggesting that chance may not have a big effect on the evolution of this ecotype since other mutational pathways are far less likely.
MacLean and Bell [30] , [49] tested the ability of P. In our study, we quantify the parallelism during adaptive radiation in a two-resource environment by measuring multiple traits and focusing on the interactions among diverging ecological specialists.
By allowing diversity to evolve within a microcosm, we select for the ability of a specialist to grow better on a particular resource, while interacting with other resource specialists during the adaptive process. These interactions can be crucial for the maintenance of the evolved diversity as shown in a different study, where diversity declined rapidly when the imposition of spatial structure interrupted established interactions among coevolved specialists [13].
Although those populations evolved in a slightly different selective environment, the observed negative frequency-dependent selection suggests that the populations also evolved important interactions in a two-resource environment. Furthermore, the experimental replication allowed us to directly track the evolution of genetic variation and to compare the degree of parallelism for different traits. Parallel adaptive radiations are thought to be the result of similar selection pressures [3] , [9] , [10].
Our microcosms were exposed to identical environments. Therefore the high degree of phenotypic parallelism among our populations may not be surprising. Despite the identical selective environments and the parallelism observed for almost all traits examined we observed divergence among the populations. The partial genetic results [16] indicated larger underlying genetic variation, suggesting that not all populations evolved similarly. While the experimentally supplied resources are identical across microcosms, the evolution of resource specialists critically depends on the genetic and ecological diversity present in the population.
For example, the order with which mutations arise in independent microcosms can potentially affect the fixation of subsequent mutations [50]. Our observation of diversity in frequency-dependent selection across microcosms suggests that different interactions among the ecologically diverged phenotypes evolved despite the observed parallelism at the phenotypic level.
This is the first study that systematically tests the repeatability of the evolution of diversity through adaptive radiations in an environment containing two distinct resources. We observed very parallel evolution for different phenotypic traits despite divergence among independent microcosms. This suggests strong selection for certain phenotypes despite the evolution of ecological diversity and complexity.
As in other studies, all diversity evolved de novo. However, strong trade-offs associated with nutrient specialization and a partial analysis of the underlying genetic mutations [16] suggest less parallel evolution at the genetic level and the potential for a greater number of beneficial mutational pathways than observed in other systems [48]. These results suggest that the evolution of ecological diversity through adaptive radiations can be both robust and yet surprisingly subtle in their effects.
We initiated and propagated the microcosms as described previously [12]. Every second day, we sampled each microcosm onto tetrazolium-arabinose TA agar plates, to check for cross-contamination, census each microcosm, and assess colony morphology.
We isolated single genotypes from frozen microcosm samples by plating stationary phase cultures onto agar plates, and randomly selecting colonies that varied in colony size or morphology. Competitors were of two types, either single genotypes or large population samples from microcosms.
We estimated relative fitness of the evolved populations or single genotypes in competition with either a conspecific having a distinct colony phenotype or the reciprocally marked ancestor.
Experiments with large population samples from microcosms allow estimation of the mean fitness and fitness variation that takes into account the genetic diversity within microcosms. We determined the relative fitness of the two competitors as the ratio of the number of doublings for two competitors over one day of growth [22].
A fitness of 1 indicates that both competitors are equally fit. Fitness diversity within microcosms after generations of selection was assessed with two to six isolates obtained from each microcosm, corresponding to all the readily differentiable colony phenotypes within each microcosm, for a total of 42 derived isolates.
Each isolate was competed against the alternate marker variant of the common ancestor. For each microcosm, we determined the genetic variation for fitness among the single colony isolates as the variance in fitness among all single isolates from one microcosm. With three-fold replication, a total of 39 measures of diversity were obtained, 36 for the derived microcosms and 3 for the ancestral marker variants.
Microcosm 25 was excluded from the analyses, as we were unable to consistently distinguish the genotypes based on colony morphology. For each microcosm and block, we calculated the selection rate coefficient per day s of the small colony morph as the linear regression of the natural logarithm of the ratio of both competitors over time [53].
The final selection coefficient per population was calculated as the average of the two blocks. We used two different measures of colony size. During propagation, we monitored the evolution of colony size diversity within microcosms by assessing the colony sizes of different genotypes from each microcosm every second day.
Colony size classes were determined relative to other phenotypes from the same microcosm at each sample time and recorded as small, medium or large. We used the frequencies of these different colony morphologies in a microcosm to calculate Shannon's Index of Diversity H'.
To obtain a more accurate measure of mean colony size and diversity within a microcosm at generation and , we took digital measurements of colony sizes. We sampled each derived microcosm and ancestral genotype onto TA agar plates, for 42 estimates for each trait at each time point. Before using colony morphology as a trait for resource specialization, we tested the heritability of colony morphology by isolating colonies with different colony morphologies and measuring their colony size on plates before and after growth in liquid medium.
The correlation coefficient between the first and second samplings of colony size of 41 isolates was 0. This high coefficient, observed despite the single cell bottleneck and the approximately seven generations of growth in liquid medium between the two measurements, is indicative of a heritable, genetic basis for colony size. To assess the changes in fitness, fitness diversity, mean colony size and diversity within microcosms between the common ancestor and the derived microcosms, we performed a one-factor ANOVA with the difference between derived and ancestral values as a planned comparison.
This method is equivalent to a fixed-effects ANOVA assessing experimental treatments relative to a control and is valid given that the control is the ancestral genotype and not a single, randomly collected individual. Since relative fitness measurements are made by direct competition of the derived and ancestral competitors, a t -test would be an appropriate statistical test.
To maintain a consistent presentation, the results can be equivalently analyzed as an ANOVA with one degree of freedom in the numerator and were presented as such. To test for a marker effect, we performed a planned comparison between the ancestor and the derived population for each marker individually. Changes in the rate of adaptation were measured by comparing the rate of evolutionary change during the first generations of selection to that during the second generations.
For the first generations, rates of evolutionary change were obtained for each microcosm by calculating the slope between 0 and generation values. The rates for the second generations were obtained by substraction of the first generation rate from twice the rate for the entire generations [22] , [26].
Rates over all generations were calculated by determining the least squares best fit straight line anchored at the 0 generation value and through the and generation values.
To assess divergence among microcosms, we calculated the genetic variation among microcosms as the added variance component [24] by performing a two-way ANOVA, with microcosms and block as fixed factors. Genetic variation, V G was calculated as: [52]. Significance of genetic variation was determined by the significance of the microcosm effect.
Confidence intervals for genetic variation were calculated following the Moriguti-Bulmer procedure [52]. Marker effects among the populations were calculated by a nested ANOVA with marker and microcosms nested within marker as fixed factors for each time point and trait individually.
To test for the relative contributions of chance and adaptation, we assessed the effect of chance as the square root of the genetic variation so that the effects of chance and adaptation would be of the same units. Confidence intervals were calculated as the square root of the upper and lower limits of the added variance components [52].
We measured adaptation as the difference in a trait between the ancestral value and the average value in this trait among all the derived populations. A value of smaller than one indicates parallel evolution among initially isogenic populations, while a value larger than one indicates divergent evolution. The confidence intervals were calculated by bootstrapping the data 10 6 times.
We are grateful to many people that have supported us during this project. We thank R. Azevedo, C. Burch, B. Cole, T. Cooper, V. Cooper, D. Greig, E. Ostrowski, S. Otto, E. Peters, J. Quance, M. Quance, R. Redfield, D. Schluter, J. Strassmann, D. Wiernasz, an anonymous reviewer, and Rees Kassen for fruitful discussions and comments. Performed the experiments: GS. Analyzed the data: GS MT. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field.
Abstract Adaptive radiations occur when a species diversifies into different ecological specialists due to competition for resources and trade-offs associated with the specialization. Introduction Adaptive radiations are a major source of biological diversity [1]. Results Diversity in colony size evolved in all twelve microcosms propagated in medium containing a combination of acetate and glucose as the limiting resources Figure 1A. Download: PPT. Figure 1.
Diverse colony morphologies evolved in all twelve microcosms. Relative fitness and diversity increased, while colony size decreased To assess the trait evolution within microcosms, we assessed relative fitness, mean colony size, and colony size diversity in the ancestor and the derived populations, and performed a single factor ANOVA with a planned comparison between the ancestor and the derived populations analyses are summarized in Table 1.
Table 1. Figure 2. Fitness and colony size diversity increased and mean colony size decreased over the course of the experiment. The rate of evolutionary change declined We compared the change in a trait during the first generations and during the second generations [22]. Table 2. Analysis of the Rate of Change of the Microcosms over Generations. Ecological diversity within microcosms is likely maintained by frequency-dependence Competition experiments between pairs of the most divergent colony morphs from each microcosm indicated that frequency-dependent selection was a likely mechanism maintaining diversity within microcosms.
Figure 3. Diversity is most likely maintained by frequency-dependent selection. Microcosms did not diverge in fitness, but diverged in fitness variance, frequency dependence, mean colony size and colony size diversity To assess whether the microcosms diverged for fitness, fitness variance, mean colony size, colony size diversity, and frequency dependence we performed an ANOVA and extracted the added variance component which is equivalent to the genetic variation V G among microcosms.
Table 3. Adaptation contributed more to the evolutionary process than chance, which is reflected in parallel evolution of independent microcosms To assess the relative contributions of chance and adaptation to the evolutionary process, we calculated their relative contributions.
Figure 4. Relative contributions of adaptation were larger than chance. Figure 5. Fitness, mean colony size and colony size diversity evolved in parallel. Discussion We performed a selection experiment in a two-resource environment to assess the repeatability of different instances of adaptive radiations and the relative contributions of chance and adaptation to the evolution of fitness, mean colony size and colony size diversity.
The evolution of ecological diversity The underlying causes of adaptive radiations are competition for limited resources that lead to resource specialization, and trade-offs associated with resource specialization [1] , [4] , [21].
Parallel evolution of adaptive radiations We observed parallel evolution for all traits, except for frequency dependence. Conclusions This is the first study that systematically tests the repeatability of the evolution of diversity through adaptive radiations in an environment containing two distinct resources.
Materials and Methods Selection Experiment We initiated and propagated the microcosms as described previously [12]. Colony Size and Diversity We used two different measures of colony size. Statistical considerations for within microcosm evolution To assess the changes in fitness, fitness diversity, mean colony size and diversity within microcosms between the common ancestor and the derived microcosms, we performed a one-factor ANOVA with the difference between derived and ancestral values as a planned comparison.
There are four circumstances where open niches would be available for an organism, all of which were first proposed by Simpson : 1 origin of newly available resources, 2 invasion of a novel landmass, 3 extinction of predators or competitors, and 4 evolution of a key innovation. Additionally, these sources could act together to produce an adaptive radiation. In the first source of EO, the diversification of one group of organisms can in turn create ecological opportunity for another unrelated group of species — in that the process of the former's radiation may yield an untapped niche for the latter Losos This is illustrated in recent work on the coevolution of weevils and the flowering plants McKenna et al.
The authors sought to clarify higher-level relationships and divergence times within weevils to examine the evolutionary history of weevil-angiosperm associations. A time-calibrated phylogeny was created using fossil and molecular sequence data.
Divergence times of major weevil clades were plotted against previous angiosperm dominance over the time period of the Cretaceous. Their results suggest that a major increase in angiosperm and associated weevil diversity arose approximately simultaneously throughout the mid-Cretaceous McKenna et al. The invasion of a novel habitat depauperate in competing taxa and predators provides yet another, and perhaps one of the most common sources, of EO.
Colonization of remote islands and subsequent diversification on these vacant landmasses, such as in Galapagos finches and the anoles of the Greater Antilles, are classic examples of EO via invasion of novel habitats. The study of EO at continental scales can be a daunting task, yet it may be the source of much of the diversity of life, as these landmasses provide the largest area and usually the most diverse habitats.
Returning to our example in the beginning of this article, the diverse New World ratsnakes have recently been used to address hypotheses of ecological opportunity. This group diversified, both in terms of species numbers and morphological specialization, early in their history, following their colonization of North America from an Asian ancestor approximately 24 million years ago ma. These elevated, early diversification rates slowed drastically through time.
This event provided the ecological opportunity for mammals to diversify into many ecological roles and increase in body size Smith et al. Recently researchers analyzed data from the fossil record to examine the maximum size for land mammals across continents, lineages, and ecological guilds through time.
Results show that maximum size leveled off after 40 million years of diversification and remained constant thereafter.
The authors indicated that a multitude of niches became available and were filled by the diversification of mammals following the extinction of non-avian dinosaurs. Furthermore, the authors noted that the radiation of mammals resulted in convergence in both maximum body size and in ecologies, because similar niches were filled by different phylogenetic lineages at different times and in different areas Smith et al.
The evolution of a key innovation has also been proposed as a mechanism for organisms to exploit new resources, thus gaining access to ecological opportunity Schluter A key innovation can be defined as a newly evolved trait that allows a taxon to interact with the environment in a novel way without a specific change in the external environment Losos , Yoder et al.
The hypothesis that viviparity was a key innovation to exploit temperate habitats featuring drastically contrasting seasons was tested using phylogenetic methods to infer diversification rates across clades of viviparous and oviparous groups of vipers Lynch It was found that viviparous clades diversified at a constant rate through time, whereas the diversification of oviparous groups declined at the onset of the cooler, Oligocene epoch. This global decrease in temperature was directly responsible for the decreased rate of diversification of oviparous clades of vipers Lynch The results suggest that viviparity offered a buffer for live-bearing species against the potentially negative effects of global cooling and therefore was a key innovation that promoted the diversification of live-bearing vipers in cooler climates Lynch Adaptive radiation through the process of ecological opportunity accounts for much of the diversity of life, including both extinct and extant taxa.
The idea of ecological opportunity acting as the trigger of adaptive radiation has been demonstrated in numerous examples using both living and extinct organisms. Ecological opportunity permits a group to experience rapid diversification in species number and morphological attributes. Additionally, morphological disparity in subclades is expected to be reduced early in the history of a group as ecological space is partitioned among these subclades.
Unfortunately, tests of diversification have been carried out in a rather limited number of taxonomic groups. Future research will likely sample more taxa as well as more genes i. Therefore studying the time course of diversification requires greater integration of molecular phylogenies with fossil data to gain a better understanding of the tempo and mode of diversification.
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