Information

More extreme phenotypes when parents are from diverged populations


In quantitative traits we have many loci affecting a trait. If we took a bunch of parents where all mothers and fathers were of a certain value at the upper end of a trait range, and then looked at the offspring would we expect offspring from pairs of parents from more divergent populations to be even more extreme variance in their traits?

For example: if we look at 2 men from East Africa, both 190 cm tall. They each have children with women who are 180 cm, one of whom is from East Africa and the other from Canada. We could assume that the Canadian woman is probably more unrelated to her male than the East African woman. I would therefore expect that it is quite likely that the East Africans in the example are tall because of "tall-alleles" in many of the same loci, whereas there is a greater probability that the Canadian female is tall thanks to mutations at different loci. Therefore would it be reasonable to expect greater variation in the offspring of the Canadian woman?

Is there any evidence of this phenomenon?

Note: Ignore environmental and gene * environment interaction variance, just go with a simple additive model, and assume populations have had some barriers to migration for some time


Yes, it would be reasonable to expect greater variation in the offspring of the Canadian woman.

Human genetic variation is the genetic differences among populations. Populations have access a pool of genes and over-time polymorphisms may arise to create greater variation.

Human populations within a species are defined by, divergence times and rates of gene flow - From mitochondrial, X and Y chromosome re-sequencing data.

Genetic Distance is the genetic divergence between species or between populations within a species (as in your example above). Populations with many similar alleles have small genetic distances. This indicates that they are closely related and have a recent common ancestor.

E.g. Height as you use for your example is a polygene. The inheritance of many genes alter phenotypic height of offspring.

Thus, it would be reasonable to expect a difference in allelic variations pertaining to height from the Canadian woman. The offspring may have a different height profile of alleles, and thus a different phenotypical height.

I should mention that:

"individuals from different populations can be genetically more similar than individuals from the same population"

But the above example - should not be expected.


Icefish Genome Reveals Adaptations to Extreme Antarctic Environments

A multinational team of researchers has successfully sequenced the genome of the Antarctic blackfin icefish (Chaenocephalus aceratus), opening a genetic window on fish that evolved over the last 77 million years to survive in extreme Antarctic temperatures.

The Antarctic blackfin icefish (Chaenocephalus aceratus). Image credit: Thomas Desvignes.

Antarctic icefishes are members of the family Channichthyidae.

These ‘white blooded’ creatures inhabit the Earth’s coldest marine environment and are the only vertebrates that lack functional red blood cells and functional hemoglobin genes.

Icefish blood carries oxygen solely in physical solution, resulting in an oxygen-carrying capacity per unit of blood volume of less than 10% of that in closely related red-blooded Antarctic notothenioid fishes.

The blackfin icefish, along with 5 other species among the 16 recognized species of icefishes, also lack oxygen-binding proteins called myoglobins.

Icefishes evolved mechanisms that appear to compensate for loss of these oxygen-binding proteins, including enormous hearts with increased stroke volume relative to body size, enhanced vascular systems, and changes in mitochondrial density and morphology.

“In a human, such traits would normally signal disease. These adaptations, however, help the fish survive,” said University of Oregon’s Professor John Postlethwait, co-lead author of the study.

“The icefishes are examples of what Charles Darwin called the ‘wrecks of ancient life.’ They diverged from the ancestors of stickleback, losing many of the features common to their ancestral forms as they evolved. Among genes that disappeared amid the months of night and months of sunlight in the polar region were those tied to circadian rhythms.”

“Ice fish populations first appeared at the end of the Pliocene after Antarctica’s surface temperatures dropped by 2.5 degrees Celsius. About 77 million years ago, they had diverged from the line of their common ancestors with the stickleback — and then developed phenotypes that were better adapted to the cold,” said co-author Professor Manfred Schartl, a researcher at the Julius Maximilians University of Würzburg and Texas A&M University.

To help investigate the genomic basis for these extreme evolutionary adaptations, Professor Postlethwait, Professor Schartl and their colleagues sequenced the genome of the blackfin icefish.

They collected blackfin icefish, which average about 12 inches (30.5 cm) in length, from various depths near King Sejong Station and the Western Bransfield Strait along the Antarctic Peninsula.

Along with genomic DNA taken from the female, RNA was extracted from 12 tissues — brain, eye, gill, heart, intestine, kidney, liver, muscle, ovary, skin, spleen and stomach — to help understand what genes each organ uses.

The scientists mapped 30,773 protein-coding genes and how they localize along chromosomes.

“Icefish and other notothenioid fishes experienced gene changes that produced antifreeze proteins to help them survive — an adaptation discovered in the 1970s. The completed mapping helps place this discovery into a genomic context,” they said.

The genome assembly and linkage map reveal remarkable stability of contents of the 24 chromosomes among bony fish, including medaka (Japanese rice fish), European sea bass and blackfin icefish spanning 110 million years, especially when compared with chromosome changes in mammals over the same time period.

The biggest divergence involved the genes of icefish and sea bass, suggesting changes in the cold.

“Our results show that the number of genes involved in the protection against ice damage, including the genes coding for anti-freeze glycoproteins, is strongly expanded in the icefish genome,” Professor Schartl said.

“Icefish evolved from fish that lived on the bottom and had no swim bladder, an organ that develops like our lungs but then loses the attachment to the pharynx and fills with gas. Most fish, except for bottom feeders, have one and it helps them maintain position in the water column,” Professor Postlethwait said.

“When most fish species became extinct around Antarctica as the waters cooled, icefishes evolved to occupy the Southern Ocean water column. One of the biggest challenges they faced was getting off the bottom without a swim bladder.”

“They likely limited the mineralization of their bones, the most dense part of our bodies, and accumulated lipids, which are lighter than water — think of olive oil that floats on the top of the water in a pan about to cook spaghetti.”

The results appear in the journal Nature Ecology & Evolution.

Bo-Mi Kim et al. 2019. Antarctic blackfin icefish genome reveals adaptations to extreme environments. Nature Ecology & Evolution 3: 469-478 doi: 10.1038/s41559-019-0812-7


Genetic isolation between two recently diverged populations of a symbiotic fungus

Fungi are an omnipresent and highly diverse group of organisms, making up a significant part of eukaryotic diversity. Little is currently known about the drivers of fungal population differentiation and subsequent divergence of species, particularly in symbiotic, mycorrhizal fungi. Here, we investigate the population structure and environmental adaptation in Suillus brevipes (Peck) Kuntze, a wind-dispersed soil fungus that is symbiotic with pine trees. We assembled and annotated the reference genome for Su. brevipes and resequenced the whole genomes of 28 individuals from coastal and montane sites in California. We detected two clearly delineated coast and mountain populations with very low divergence. Genomic divergence was restricted to few regions, including a region of extreme divergence containing a gene encoding for a membrane Na + /H + exchanger known for enhancing salt tolerance in plants and yeast. Our results are consistent with a very recent split between the montane and coastal Su. brevipes populations, with few small genomic regions under positive selection and a pattern of dispersal and/or establishment limitation. Furthermore, we identify a putatively adaptive gene that motivates further functional analyses to link genotypes and phenotypes and shed light on the genetic basis of adaptive traits.

Fig. S1 Observed and estimated site frequency spectra under the isolation model from coastal and montane Suillus brevipes populations.

Table S1Suillus brevipes isolate location, habitat, pine host, coordinates and elevation, number of raw and high quality reads, % alignment rate, genome coverage, and Short Read Archive accession nos.

Table S2 Properties of the Suillus brevipes reference genome assembly and annotation.

Table S3 5-kb window Dxy gene 1% outliers, with localization in genome and protein function and description.

Table S4 Genes under positive selection as inferred by McDonald–Kreitman tests on genes using S. luteus as the outgroup.

Table S5 Selective sweep analysis top five H value peaks for coastal and mountain Suillus brevipes populations.

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.


Forces of Evolution

The factors that cause allele frequencies to change are called the forces of evolution. There are four such forces: mutation, gene flow, genetic drift, and natural selection.

Genetic Drift

Genetic drift is a random change in allele frequencies that occurs in a small population. When a small number of parents produce just a few offspring, allele frequencies in the offspring may differ, just by chance, from allele frequencies in the parents. This is like tossing a coin. If you toss a coin just a few times, you may, by chance, get more or less than the expected 50 percent heads and 50 percent tails. Due to such chance variations in small populations, allele frequencies drift over time.

There are two special conditions under which genetic drift occurs. They are called the bottleneck effect and founder effect.

  1. The bottleneck effect occurs when a population suddenly gets much smaller. This might happen because of a natural disaster such as a forest fire or disease epidemic. By chance, allele frequencies of the survivors may be different from those of the original population.
  2. The founder effect occurs when a few individuals start or found a new population. By chance, allele frequencies of the founders may be different from allele frequencies of the population they left. An example of the founder effect occurred in the Amish, as described in figure (PageIndex<2>).

Mutation

Mutation creates new genetic variation in a gene pool. It is how all new alleles first arise. In sexually reproducing species, the mutations that matter for evolution are those that occur in gametes. Only these mutations can be passed to offspring. For any given gene, the chance of a mutation occurring in a given gamete is very low. Thus, mutations alone do not have much effect on allele frequencies. However, mutations provide the genetic variation needed for other forces of evolution to act.

Gene Flow

Gene flow occurs when individuals move into or out of a population. If the rate of migration is high, this can have a significant effect on allele frequencies. Allele frequencies may change in the population the migrants leave as well as in the population the migrants enter. An example of gene flow occurred during the Vietnam War in the 1960s and 1970s. Many young American servicemen had children with Vietnamese women. Most of the servicemen returned to the United States after the war. However, they left copies of their genes behind in their offspring. In this way, they changed the allele frequencies in the Vietnamese gene pool. Do you think the gene pool of the U.S. was also affected? Why or why not?

Natural Selection

Natural selection occurs when there are differences in fitness among members of a population. As a result, some individuals pass more genes to the next generation than do other members of the population. This causes allele frequencies to change over time. The example of sickle cell anemia, which is shown in the following table and described below, shows how natural selection can keep even a harmful allele in a gene pool.

Table (PageIndex<1>): Sickle Cell Anemia and Natural Selection
Genotype Phenotype Fitness
AA 100% normal hemoglobin Somewhat reduced fitness because of no resistance to malaria
AS Enough normal hemoglobin to prevent sickle-cell anemia Highest fitness because of resistance to malaria
SS 100% abnormal hemoglobin, causing sickle-cell anemia Greatly reduced fitness because of sickle-cell anemia

The allele (S) for sickle cell anemia is a harmful, autosomal recessive allele. It is caused by a mutation in the normal allele (A) for hemoglobin (the oxygen-carrying protein on red blood cells). Malaria is a deadly tropical disease that is common in many African populations. Heterozygotes (AS) with the sickle cell allele are resistant to malaria. Therefore, they are more likely to survive and reproduce. This keeps the Sallele in the gene pool.

The sickle cell example shows that fitness depends on phenotypes and also on the environment. What do you think might happen if malaria were to be eliminated in an African population with a relatively high frequency of the S allele? How might the fitness of the different genotypes change? How might this affect the frequency of the S allele?

The sickle cell trait is controlled by a single gene. Natural selection for polygenic traits, which are controlled by multiple genes, is more complex, although it is less complicated if you consider just phenotypes for polygenic traits rather than genotypes. There are three major ways that natural selection can affect the distribution of phenotypes for a polygenic trait. The three ways are shown in the graphs in Figure (PageIndex<3>).

  1. Disruptive selection occurs when phenotypes in the middle of the range are selected against. This results in two overlapping phenotypes, one at each end of the distribution. An example is a sexual dimorphism. This refers to differences between the phenotypes of males and females of the same species. In humans, for example, males and females have different average heights and body shapes.
  2. Stabilizing selection occurs when phenotypes at both extremes of the phenotypic distribution are selected against. This narrows the range of variation. An example is human birth weight. Babies that are very large or very small at birth are less likely to survive, and this keeps birth weight within a relatively narrow range.
  3. Directional selection occurs when one of two extreme phenotypes is selected for. This shifts the distribution toward that extreme. This is the type of natural selection that the Grants observed in the beak size of Galápagos finches. Larger beaks were selected for during drought, so beak size increased over time.

Feature: Human Biology in the News

Recently reported research may help solve one of the most important and long-lasting mysteries of human biology. The mystery is why people with the AS genotype for sickle cell hemoglobin are protected from malaria. As you read above, their sickle cell hemoglobin gives them higher fitness in malaria areas than normal homozygotes (AA) who have only normal hemoglobin.

The malaria parasite and its mosquito vector were discovered in the late 1800s. The genetic basis of sickle cell hemoglobin anemia and the resistance to malaria it confers were discovered around 1950. Since then, scientists have assumed, and some evidence has suggested, that the few sickle-shaped red blood cells of heterozygotes make them less hospitable hosts for the malaria parasite than the completely normal red blood cells of AA homozygotes. This seems like a reasonable hypothesis, but is it the correct one? The new research suggests a different hypothesis.

Working with genetically engineered mice as model organisms, researchers in Portugal discovered that an enzyme that produces the gas carbon monoxide is expressed at much higher levels in the presence of sickle cell hemoglobin than normal hemoglobin. Furthermore, the gas seems to protect the infected host from developing the lesions and symptoms of malaria, even though it does not seem to interfere with the life cycle of the malaria parasite in red blood cells. These findings may lead to new therapies for treating malaria, which is still one of the most serious public health problems in the world. The findings may also shed light on other abnormal hemoglobin variants that are known to protect against malaria.


Results

Male song

Pulse rate means and variances for parental and hybrid males are given in Table 1. Hybrid males sang at intermediate pulse rates compared to parental males (Fig. 1a,b). On average, F1 hybrid males with L. cerasina mothers sang significantly slower than those with L. eukolea mothers (type 3 test of fixed effect, F7,24 = 12.36, P = 0.010) though we observed no significant heterogeneity among F1 families within each cross direction (log-likelihood-ratio test, χ 2 (1 d.f.) = 0.2, P = 0.65). This difference suggests an X chromosome effect equal to 7.59% of the phenotypic difference between L. cerasina and L. eukolea. Therefore, in all subsequent analyses, the mean and variance for F1 pulse rate were calculated as the average of the values calculated within reciprocal crosses, weighted by the number of individuals in each cross direction. Unweighted F1 mean and variance (i.e. arithmetic mean of within cross mean and variance) were nearly identical to weighted values and were therefore not included in subsequent analyses. Pulse rates for L. cerasina and L. eukolea males differed significantly from one another and in relation to both hybrid generations (F3,70 = 171.2, P < 0.001 P < 0.05 for pairwise comparisons, Tukey–Kramer test), whereas F1 and F2 males sang at similar pulse rates (P > 0.05, Tukey–Kramer). Variance was greater among F2 compared to F1 males (F1,203 = 12.68, P < 0.001, Levene’s test), which is consistent with expectations for a polygenic trait segregating in the second hybrid generation. However, there was no significant difference in pulse rate between F2 males descended from reciprocal parental crosses (F1,143 = 0.07, P = 0.79), suggesting that the observed X chromosome effect is unlikely to be confounded by maternal cytoplasmic inheritance.

Generation Trait
Male song Female acoustic preference
N Mean pulse rate (pps) Variance N Mean Pulse rate (pps) Variance
P (L. cerasina) 24 2.33 0.54 × 10 −2 16 2.50 1.33 × 10 −2
P (L. eukolea) 16 3.99 1.47 × 10 −2 11 3.86 3.69 × 10 −2
F1 (L. cerasina dam) 14 3.06 0.95 × 10 −2 10 2.93 0.46 × 10 −2
F1 (L. eukolea dam) 19 3.18 0.68 × 10 −2 9 2.92 1.69 × 10 −2
F1 (combined) 33 3.13 * 0.79 × 10 −2* 19 2.93 0.98 × 10 −2
F1 (cross means) 2 3.12 0.81 × 10 −2
F2 172 3.24 7.44 × 10 −2 54 2.95 6.29 × 10 −2

Distributions of male song pulse rate (a, b) and female acoustic preference (c, d) in parental (grey bars), F1 and F2 hybrid (diagonally striped open bars) generations. Distribution of F1 song pulse rate values (a) from reciprocal parental crosses are shown combined (diagonally striped open bars) and separated by cross direction (inset histogram: grey bars, Laupala cerasina dam cross-hatched open bars, L. eukolea dam).

Female acoustic preference

Parental females exhibited completely assortative acoustic preferences with all individuals preferring song stimuli with conspecific (vs. heterospecific) pulse rates (Fig. 2a,c χ 2 (1, n = 12) = 12.0, P = 0.001, Fisher’s exact test). Additionally, F1 hybrid females preferred songs with intermediate pulse rates compared to parental values (Fig. 2b L. cerasina vs. F1: Z(19) = 4.36, P < 0.001 L. eukolea vs. F1: Z(19) = 3.90, P < 0.001).

Proportion of (a) Laupala cerasina (n = 6) and (c) L. eukolea (n = 6) females responding to synthetic stimuli with conspecific and heterospecific pulse rates in two-choice phonotaxis trials. (b) Each F1 hybrid female (n = 19) was used in two separate trials in which individuals were presented stimuli with F1-typical and either L. cerasina (upper graph) or L. eukolea (lower graph) typical pulse rates (see Materials and Methods for values). Error bars are 95% confidence intervals, Lc = L. cerasina and Le = L. eukolea.

Overall, average response functions from parental and hybrid female populations were consistent with a unimodal acoustic preference (Fig. 2, Shaw & Herlihy, 2000 ). Accordingly, each individual female’s preference was estimated as the midpoint of the pulse rate choice between the trials in which the female’s phonotactic response switched from the faster to the slower pulse rate stimulus. Across all populations, the majority of females (74%) were completely consistent in their phonotactic preferences, as evidenced by response profiles with single inflection points. For the proportion that showed some degree of inconsistent response, acoustic preference was estimated from the first (in considering responses from slower to faster pulse rate) identifiable inflection point, as this was consistent with the overall population-level pattern as well as those females that were perfectly consistent in their responses (Fig. 3). Preference data for L. cerasina were published previously ( Grace & Shaw, 2011 ) but reanalysed here to conform with this methodology.

Proportion of females responding to stimulus with the faster pulse rate across six-two-choice phonotaxis trials (midpoint of two song stimuli shown along abscissa, see Materials and Methods for values) (a) Laupala cerasina (n = 16), (b) F1 hybrids (reciprocal directions combined, n = 19) and (c) L. eukolea (n = 11). Error bars are 95% binomial confidence intervals trials in which intervals exclude 50% response (dashed lined) indicate significant preference at the population level.

Analysis of phonotactic responses across the study indicated significant differences in mean preference between parental species and in comparison with hybrids (Table 1, F3,83 = 47.68, P < 0.001 P < 0.05, Tukey–Kramer test), although preference did not differ between F1 (Fig. 1c) and F2 (Fig. 1d) females (Tukey–Kramer, P > 0.05). As with male song, variance was greater in the F2 compared to F1 population, although the difference only approached statistical significance (F1,63 = 3.79, P = 0.056 Levene’s test). As expected for hybrids with homogametic sex chromosomes, F1 females from reciprocal crosses had similar acoustic preferences (F1,17 = 0.03, P = 0.87) and were thus combined for subsequent analysis.

Joint-scaling tests for means and variances

Parameter estimates obtained from weighted least-squares regression on means and variances of male song and female acoustic preference are shown in Table 2. Fitting of simple additive models revealed significant additive genetic parameter estimates and no departure between expected and observed means for parental and hybrid generations (Fig. 4a,c song pulse rate: χ 2 (2, n = 245) = 0.357, P = 0.83 acoustic preference: χ 2 (2, n = 100) = 3.26, P = 0.20). The inclusion of dominance terms did not significantly improve model fit for either male song pulse rate (Λ = 0.042, P = 0.84) or female acoustic preference (Λ = 2.70, P = 0.10).

Trait
Male song pulse rate Female acoustic preference
(a) Line means
Additive model
μ0 ± SE 3.175 (±0.011)† 2.997 ± 0.016†
α c ± SE 0.841 (±0.015)* 0.569 ± 0.028*
χ 2 (d.f.) 0.357 (2) NS 3.26 (2) NS
Additive ± dominance model
μ0 ± SE 3.173 (±0.012)† 3.028 ± 0.017†
α c ± SE 0.845 (±0.017)* 0.659 ± 0.031*
δ c ± SE 0.000‡ 0.000‡
χ 2 (d.f.) 0.315 (1) NS 0.556 (1) NS
(b) Line variances
Additive model
Var(L. eukolea) ± SE 0.0127 ± 0.003 0.0209 ± 0.008
Var(L. cerasina) ± SE 0.0052 ± 0.001 0.0114 ± 0.004
Var(S) ± SE 0.0655 ± 0.009 0.0468 ± 0.012
χ 2 (d.f.) 0.473 (1) NS 5.193 (1)*
  • Var(S), segregational variance.
  • Parameter estimates: μ0– modelled mean, α c – composite additive effects, δ c – composite dominance effects. Statistical significance of parameter estimate (t-test) and model fit (χ 2 ): NS, P > 0.05 *P < 0.05 †P < 0.01.
  • ‡Negative variance component equated to zero.

Observed (closed circles) means and variances of male song pulse rate (a, b) and female acoustic preference (c, d) in relation to the proportion of L. cerasina genes in parental, F1 and F2 hybrid generations. Lines and open circles are maximum-likelihood predictions of models including only additive genetic effects ( Lynch & Walsh, 1998 ). Mean and variance for F1 males’ song pulse rate were weighted by the number of individuals in each reciprocal cross. Error bars represent twice the standard errors of observed values. Parameter estimates for best fit models from each joint-scaling test shown in upper right corner: μ0, modelled mean α c , composite additive effect Var(Le) and Var(Lc), modelled phenotypic variance in L. eukolea and L. cerasina, respectively Var(S), segregational variance.

Observed variances of parental and hybrid male song pulse rate were consistent with predicted values under a model of additive gene action (Fig. 4b, χ 2 (1, n = 245) = 0.473, P = 0.49). Variances in female preference, however, showed significant departure from expectations of additivity (Fig. 4d, χ 2 (1, n = 100) = 5.19, P = 0.02).

Minimum number of genetic factors

Biometrical analysis of variation among parental and hybrid males resulted in an estimate of nE = 5.28 (± 0.70 SE) genetic factors contributing to differences in pulse rate between L. cerasina and L. eukolea. Analysis of female acoustic preference yielded a similar value of nE = 5.01 (± 1.51 SE). In our experimental design, females in the parental generation were subjected to trials with pulse rates that varied incrementally by 0.2 pps, while hybrid females were tested with trials that varied by 0.1 pps. To assess how this difference affected our estimate of nE, we recalculated preference values for hybrid females after removing every other trial from the data set, thereby effectively producing a series of phonotaxis trials that varied by 0.2 pps, equivalent to those presented to females in the parental generation. This resulted in a value of nE = 4.11 (± 1.15 SE), reducing the estimate by 0.87 (ca. 18%). Because this does not qualitatively change our interpretation, we report the value using the full data set.


Results and Discussion

Assessing Patterns of Morphological Evolution in Central American Boas

In this study, we extended previous analyses of the evolution of body size and craniofacial morphology across island and mainland boa populations in Belize and Honduras. Based on at least six samples per population, we found that body size is reduced across all island populations in comparison with mainland populations from Belize ( fig. 1D). Although some mainland samples do exhibit small body sizes similar to that observed on islands, and overall greater variation in body size, island populations appear to have a restricted upper size limit compared with mainland populations ( fig. 1D). Sexual dimorphism is evident in mainland Belize and in the Cayos Cochinos populations, as the variance in body size is higher in females than males, especially on the mainland ( fig. 1D). Moreover, in many cases males on islands appear to have a more reduced body size than that on the mainland, though island female body sizes are more likely to overlap the range of female body sizes observed on the mainland ( fig. 1D). Previous studies have documented this same pattern of body size evolution and sexual dimorphism ( Boback 2006 Reed et al. 2007).

Previous analyses of craniofacial morphology in island and mainland populations from Belize indicated that craniofacial morphology varies between the island and mainland populations and also across island populations ( Boback 2006). Our analysis based on an expanded data set shows a similar pattern, with all island populations differing from the mainland Belize populations along the first linear discrimination (LD1) axis of variation and the False Cay and Lagoon Cay populations also varying noticeably from the mainland Belize populations along the LD1 axis (all Belize island populations have a similar position along the second linear discrimination [LD2] axis fig. 1E). The LD1 axis corresponds with head length while the LD2 axis corresponds with head width ( supplementary fig. S2 , Supplementary Material online). Craniofacial morphology does vary between the two mainland populations by an amount similar to what is observed between individual island populations ( fig. 1E). Importantly, craniofacial morphology in the populations from Cayo Cochino Menor, which has not been previously assessed, also varies from mainland Belize populations along LD1 ( fig. 1E). Overall, our findings mirror previous studies, and add new insight into the distinct craniofacial morphology in the Cayo Cochino Menor population and a more nuanced view of craniofacial morphology across the mainland.

Annotation of a Boa Reference Genome

We annotated an existing genome assembly (contig and scaffold N50 values of 29.3 kb and 4.5 Mb, respectively) for B. constrictor ( Bradnam et al. 2013). Our annotation inferred 31.61% of the genome as repetitive, with transposable elements and simple sequence repeats (microsatellites) composing 29.6% and 2.4% of the assembly, respectively. LINE elements (12.8%), DNA transposons (5.2%), LTR elements (2.3%), non-LTR elements (1.1%), and Penelope-like elements (1.0%) comprised significant portions of the genome ( supplementary fig. S3 and table S8 , Supplementary Material online). We identified 19,178 gene models and were able to confidently assign functional information (i.e., gene IDs) for 96.7% of annotated genes based on homology searches, including 93.18% of genes that were matched with human gene orthologs. Details on the results of several annotation-derived summaries are described in the supplementary results, Supplementary Material online. We have made this genome annotation publicly available (see http://darencard.github.io/boaCon last accessed October 22, 2019 and Figshare doi: 10.6084/m9.figshare.9793013) and use it as the basis for our inferences of functionally relevant genomic differences among island and mainland populations inferred from our sampling of these populations.

Independent Origins of Island Dwarf Boa Populations Support Convergent Phenotypic Evolution

Boas have colonized at least 43 islands across Central America, but the exact number of independent island colonization events is unknown. In a previous study, we demonstrated that island populations from Belize (Lagoon and West Snake Cays) and Honduras (Cayos Cochinos) cluster into distinct Central American clades with significant divergence (4–5 Myr Card et al. 2016), yet it has remained unclear whether different islands in either Belize or Honduras represent distinct populations and thus independent origins of dwarfism. Our demographic analyses suggest independent colonization and subsequent evolution of dwarfism in the two Belize island populations, as well as the Honduran Cayos Cochinos population, where we found evidence of ongoing gene flow between the Cayo Cochino Menor and Cayo Cochino Mayor (also known as Grande fig. 3). Our SNAPP analysis of dense RADseq sampling yielded a consensus population-level tree for which all nodes were resolved with 100% posterior support, suggesting that the two Belizean island populations (Lagoon and West Snake Cays) represent two independent colonizations from their respective mainland population ( fig. 3A). Demographic model tests of Lagoon Cay and West Snake Cay using RADseq variants inferred a best-fit model consisting of population divergence without gene flow, further indicating that these two populations have evolved independent of one another ( fig. 3B). Importantly, our demographic analysis only tests for the presence of gene flow following divergence between Lagoon Cay and West Snake Cay, assuming that dwarfism evolved independently in each island population. Our analysis thus ignores two possibilities: 1) that dwarfism evolved once in an island population and a subset of that island population recolonized the mainland, forming a paraphyletic pattern of island dwarfism and 2) that dwarfism evolved once after isolation from the mainland but before the two islands became isolated from one another. The first possible confounding scenario is highly unlikely, since any buildup on genetic divergence on an early island would almost certainly be overwhelmed by gene flow with larger, existing mainland populations upon recolonization and would not persist as a clearly defined population cluster. Moreover, because selection for reduced body size is only apparent on small islands with limited, canopy-dwelling prey species, it is unlikely that dwarfism evolved once in an ancestral population inhabiting an early insular landmass. The distance between Lagoon Cay and West Snake Cay is relatively large (∼62 km), and this distance dictates that the landmass would have been quite large and likely capable of supporting prey communities like those found on the mainland, making the second possible confounding demographic scenario improbable. Additionally, such a demographic scenario implies a monophyletic relationship between Lagoon Cay and West Snake Cay, which is not supported by our phylogenetic inference in SNAPP. In contrast, the two Honduran islands (Cayo Cochino Menor and Mayor) were found to be sister to one another in the SNAPP tree ( fig. 3A), and the best-fit model identified by δaδi consisted of secondary contact with asymmetric gene flow between these two populations (higher migration rates from Mayor to Menor fig. 3C), suggesting that these two islands represent a single dwarf lineage. In this circumstance, it is harder to rule out the possibility that dwarfism evolved before or after the two islands became geographically isolated from one another, as Cayo Cochino Menor and Major are quite close together (∼2.5 km) and any previous landmass uniting the two likely would have been relatively small and ecologically similar to the modern islands.

—Demographic analysis of island populations establishes three independent instances of the evolution of dwarfism on islands. (A) DensiTree showing posterior topologies estimated from our RADseq data using SNAPP, with the consensus population phylogeny highlighted in orange. (B) Results of δaδi 2D SFS analysis of the RADseq data comparing plausible demographic relationships between Lagoon and West Snake Cays in Belize, which supports a model of divergence with no subsequent migration. (C) Results of δaδi 2D SFS analysis comparing plausible demographic relationships between the two Cayos Cochinos populations, which results in a best-supported model of ongoing gene flow between islands. In panels (B) and (C), site frequency spectra heatmaps are shown for the empirical data and simulated data based on the best-fit demographic model (top left and right, respectively). The residual heatmaps depict where the allele frequency spectra of the empirical and simulated differ (bottom left). A diagram depicting the best-supported, parameterized demographic scenario is sown at the bottom right. The inferred parameters T and nu reflect the timing of the demographic event in coalescent units (2N generations) and the effective population size in coalescent units (2N individuals), respectively.

—Demographic analysis of island populations establishes three independent instances of the evolution of dwarfism on islands. (A) DensiTree showing posterior topologies estimated from our RADseq data using SNAPP, with the consensus population phylogeny highlighted in orange. (B) Results of δaδi 2D SFS analysis of the RADseq data comparing plausible demographic relationships between Lagoon and West Snake Cays in Belize, which supports a model of divergence with no subsequent migration. (C) Results of δaδi 2D SFS analysis comparing plausible demographic relationships between the two Cayos Cochinos populations, which results in a best-supported model of ongoing gene flow between islands. In panels (B) and (C), site frequency spectra heatmaps are shown for the empirical data and simulated data based on the best-fit demographic model (top left and right, respectively). The residual heatmaps depict where the allele frequency spectra of the empirical and simulated differ (bottom left). A diagram depicting the best-supported, parameterized demographic scenario is sown at the bottom right. The inferred parameters T and nu reflect the timing of the demographic event in coalescent units (2N generations) and the effective population size in coalescent units (2N individuals), respectively.

These findings bring the confirmed number of independent dwarf boa populations to three: Lagoon Cay, West Snake Cay, and Cayos Cochinos. Considering the existence of additional island dwarf populations not sampled here in Belize ( Boback 2005, 2006 Boback and Carpenter 2007) and elsewhere across Central America ( Henderson et al. 1995 Porras 1999), three independent origins of island dwarf populations is likely the lower bound of the number of independently evolved island dwarf populations. Evidence for multiple independently evolved boa island populations with similar dwarfed phenotypes makes this system a particularly rich model to investigate the genetic basis of complex traits (e.g., body size and craniofacial morphology)—our analyses of genomic variation among island and mainland populations leverage these features to investigate links between molecular and phenotypic evolution that may explain the repeated evolution of similar island phenotypes.

Roles of Drift and Selection in Shaping the Evolution of Island Dwarf Populations

On islands, drift may have a particularly strong influence on population genetic variation due to the smaller population sizes typical of these populations, which is reflected in our estimated effective population sizes inferred using SNAPP ( fig. 3). We estimated that the average allelic differentiation based on our RADseq data between each island and mainland population pair was variable across islands, with Cayos Cochinos being the most differentiated (median FST = 0.19), followed by Lagoon Cay (median FST = 0.03) and West Snake Cay (median FST = 0 fig. 4). Allelic differentiation between the mainland Belize (including both Belize populations) and Honduras populations was similar to that between island–mainland population pairs (median FST = 0.04), yet relatively small considering the geographic distance and divergence time between mainland populations compared with island–mainland population pairs. Elevated measures of allelic differentiation observed between most island–mainland population pairs are consistent with the expected heightened effects of drift on these small populations compared with the mainland.

—Evidence for genomic diversity stemming from natural selection versus neutral genetic drift in island populations. Panels present the distributions of FST values inferred from RADseq data from pairwise comparisons between island and mainland population pairs (A–C) and between the two mainland populations (mainland Belize samples combined D). Sample sizes for each population in the comparison are indicated above the plots. Left-most panel provides the full FST distributions while the right panel focuses on FST values >0.5, which represent the most differentiated regions of the genome in each pairwise comparison. The black line and points represent the mean FST and the grey ribbons represent the 95% confidence interval that resulted from ten GppFst PPS runs. The blue line and points represent the empirical frequency of FST across bins. Statistically significant excess frequencies were observed in the bins with high FST values in comparisons between island and mainland population pairs (A–C), while the same threshold did not yield excess frequencies in the comparison between mainland populations (D). These findings indicate that natural selection, on top of drift, has impacted allelic differentiation between island and mainland populations, but not between the two mainland populations.

—Evidence for genomic diversity stemming from natural selection versus neutral genetic drift in island populations. Panels present the distributions of FST values inferred from RADseq data from pairwise comparisons between island and mainland population pairs (A–C) and between the two mainland populations (mainland Belize samples combined D). Sample sizes for each population in the comparison are indicated above the plots. Left-most panel provides the full FST distributions while the right panel focuses on FST values >0.5, which represent the most differentiated regions of the genome in each pairwise comparison. The black line and points represent the mean FST and the grey ribbons represent the 95% confidence interval that resulted from ten GppFst PPS runs. The blue line and points represent the empirical frequency of FST across bins. Statistically significant excess frequencies were observed in the bins with high FST values in comparisons between island and mainland population pairs (A–C), while the same threshold did not yield excess frequencies in the comparison between mainland populations (D). These findings indicate that natural selection, on top of drift, has impacted allelic differentiation between island and mainland populations, but not between the two mainland populations.

Considering evidence for the independent evolution of multiple island populations with convergent phenotypes, and the expectation that drift may be strong in island populations, we used our RADseq data to test for evidence that allelic differentiation of island Boa populations is due to natural selection, in addition to genetic drift. We conducted a simulation based on demographic information inferred from our high-resolution RADseq data set to understand neutral expectations of allelic differentiation that could be compared with our empirical results. Posterior predictive simulations (PPS), based on the neutral coalescent model using GppFst, suggested that genetic drift alone was capable of producing measures of FST as high as 0.75–1.0, depending on the specific island–mainland comparison. However, these extreme values were quite rare (i.e., <5% of PPS loci had FST >0.5 fig. 4). The 97.5% quantile threshold for empirical FST values varied from 0.35 to 0.75 among island–mainland comparisons ( fig. 4 and supplementary table S9 , Supplementary Material online). In each of these comparisons, the top 2.5% tail of empirical FST values in our RADseq data contained significantly more loci than expected given the simulated distributions of FST (P < 0.05 in all island–mainland comparisons). Using the simulated distributions of neutral FST values, we estimated that 16–52% of highly differentiated variants (FST > 0.50) in our empirical data could be explained by drift alone, suggesting the poor explanatory power of a strictly neutral model of divergence in generating high FST variants between island and mainland population pairs ( fig. 4A–C). In contrast, we found no significant excess of variants in the top 2.5% tail of the FST distribution in the comparison of the two mainland populations (FST measured in Belize vs. Honduras mainland populations P = 0.42), with the number of expected values due to drift almost exactly matching the number of observed loci ( fig. 4D). Taken together, these results provide strong support for drift being a substantial driver of genetic divergence in island Boa populations, and yet drift alone does not appear to explain an appreciable fraction of highly differentiated regions of the genome between island and mainland populations. Instead, these results argue for combined roles of genetic drift and other processes (i.e., natural selection) in shaping patterns of genomic divergence in all three island populations of dwarf boas. These findings also suggest that even in relatively small island populations, where selection is expected to be less effective due to the dominating impact of drift, selection is also an appreciable force driving allelic differentiation—presumably due to the strength of selection on particular loci that may function in increasing the fitness of island boas to their similar island environments.

Considering that 16–52% of highly differentiated variants (FST > 0.50) in our empirical data could be explained by drift alone, many highly differentiated variants are likely false positives for evolving under the influence of natural selection. Previous studies have demonstrated that multivariate measures show increased power for detecting signals of selection ( Lotterhos et al. 2017). In an effort to further distinguish true and false positive signals of selection, we used the software package MINOTAUR ( Verity et al. 2017) to estimate a multivariate Mahalanobis distance measure based on measures of FST and three other univariate measures of nucleotide evolution between island and mainland populations: the absolute differences of the change in nucleotide diversity (π), Tajima’s D, and observed heterozygosity, taking covariation in these nonindependent measures into account. We found that outlier measures of Mahalanobis distance (top 2.5% tail of each island–mainland comparison distribution) largely overlap with FST outliers, though unique loci are also identified among Mahalanobis outliers ( supplementary fig. S4 , Supplementary Material online). These results indicate that our FST-based selection scans are detecting true signals of selection in many cases, as least based on secondary evidence from multivariate measures. Considering these results and that FST is a direct measure of allelic differentiation between populations, we focused primarily on the results of our FST in downstream analyses. Moreover, we focused on shared patterns of high allelic differentiation across all three replicated island lineages, where false signals of selection are unlikely to persist.

Identifying Shared Patterns of Molecular Evolution across Island Populations

To begin to identify regions of the genome potentially linked to convergent island phenotypes, we searched for genomic regions with extreme allelic divergence between island and mainland populations and examined whether these regions overlapped in multiple island populations. Our RAD variant data showed some evidence for shared patterns of high allelic differentiation among islands, which only occurred in the comparison between Lagoon and West Snake Cays ( supplementary fig. S5 A, Supplementary Material online). In our empirical comparison, 11 highly differentiated loci (3.3% of highly differentiated island loci) were identified in both Lagoon and West Snake Cay, which greatly exceeds what is expected by random chance ( supplementary fig. S5 B, Supplementary Material online). These results suggest that drift alone is unlikely to adequately explain such a high degree of overlap in loci with extreme allele fluctuation between multiple island populations in Belize. These 11 highly differentiated loci were spread across 9 genomic scaffolds. Scaffold 1273 contained two highly differentiated loci separated by ∼146 kb while scaffold 3122 contained two highly differentiated loci separated by ∼2.8 Mb. About 31 genes were located within 100 kb of these 11 highly differentiated loci, of which 29 were confidently annotated with human gene IDs ( supplementary file S10 , Supplementary Material online). About 53 mouse phenotypes showed enrichment (FDR-corrected P value <0.05) based on these 29 genes, with several enriched phenotypes linked to craniofacial morphology ( supplementary file S11 , Supplementary Material online).

To further explore potential convergence across populations, we used our WGS data set derived from 20 individuals sampled from island and mainland populations. Similar to our approach with the RADseq data analysis, we used a genome-wide windowed approach to identify 10 kb regions of the genome with extreme allele frequency fluctuations (maximum is >0.90) between island and mainland populations. With the higher resolution of our WGS data, we found 4,278, 3,848, and 6,887 10-kb genomic regions with extreme allelic fluctuations (≥0.90) in the Lagoon Cay, West Snake Cay, and Cayos Cochinos populations, respectively, and 6,678 such regions between the two mainland populations. We found 238 shared regions between Lagoon and West Snake Cays, 285 between Lagoon Cay and Cayos Cochinos, and 259 between West Snake Cay and Cayos Cochinos ( fig. 5A). For all interisland comparisons, the degree of overlap in genomic windows was significantly higher than expected based on randomly permutated data sets ( fig. 5B), indicating that these genomic windows occurred at the same places in the genome more frequently than expected by chance among island populations.

—About 10-kb WGS windows with extreme fluctuations in allele frequencies in island populations are shared between islands. (A) Venn diagram summarizing the overlap of 10 kb genomic windows with extreme (≥0.90) allele frequency fluctuation between an island and its associated mainland population (labeled by island name) or between the two mainland populations (labeled “Mainland”) based on WGS data. (B) Permutation analyses indicate that the empirical number of windows with extreme allelic fluctuation in pairwise or all three island populations is higher than expected by chance. In each panel, the permutation density distribution of the Jaccard index is shown in orange and the empirical Jaccard index is depicted with a blue vertical line.

—About 10-kb WGS windows with extreme fluctuations in allele frequencies in island populations are shared between islands. (A) Venn diagram summarizing the overlap of 10 kb genomic windows with extreme (≥0.90) allele frequency fluctuation between an island and its associated mainland population (labeled by island name) or between the two mainland populations (labeled “Mainland”) based on WGS data. (B) Permutation analyses indicate that the empirical number of windows with extreme allelic fluctuation in pairwise or all three island populations is higher than expected by chance. In each panel, the permutation density distribution of the Jaccard index is shown in orange and the empirical Jaccard index is depicted with a blue vertical line.

Candidate Genes and Protein-Coding Variants Related to Convergent Island Phenotypes

Our analysis of highly differentiated genomic regions from our WGS data revealed evidence for extreme island–mainland allele frequency differentiation in 20 genomic regions (i.e., 10 kb windows) shared between all three island populations. These 20 genomic regions were located across 14 genomic scaffolds. In two cases, multiple regions were located in close proximity: five regions were located in an 80-kb area on scaffold 509 and two regions were located in a 50-kb area on scaffold 739. In one instance (scaffold 2231), two genomic regions were located distantly on the same scaffold (1.65 Mb separating the regions). About 47 genes were located within 100 kb of these genomic regions, including 42 that were confidently annotated with human gene IDs ( supplementary file S12 , Supplementary Material online). About 11 mouse phenotypes showed enrichment (FDR-corrected P value <0.05) based on these 47 genes ( supplementary file S13 , Supplementary Material online), though only one (MP: 0030384: short facial bone) showed an obvious link to one of the phenotypes examined in this study (craniofacial morphology). The paucity of genes enriched for mouse phenotypes that are linked to observed island phenotypes could be due to the small number of genes available for the enrichment analysis.

We surveyed our WGS data for potentially phenotypically relevant protein-coding variation in island–mainland comparisons within and around these 20 genomic regions as a means of identifying candidate genes with evidence of penetrant coding variation with relatively high likelihoods of being phenotypically relevant in an interpretable way. We identified four annotated genes within these regions with nonsynonymous allelic variants in island populations. Three of these genes contained coding variants with extreme (0.75 or greater) allele frequency fluctuations in at least one population and deleterious functional impacts (as assessed by VEP and PROVEAN): protein tyrosine phosphatase, receptor type S (PTPRS), myosin regulatory light chain interacting protein (MYLIP), and dimethylglycine dehydrogenase (DMGDH). PTPRS and DMGDH each contain a variant with high allele frequency fluctuation in West Snake Cay, and we found a variant in MYLIP with high allele frequency fluctuation in West Snake Cay and Cayos Cochinos (with modest variation in the mainland). A fourth gene, arylsulfatase B (ARSB) is the single instance of an allele exhibiting high-frequency fluctuations across all island populations that are absent in the mainland–mainland comparison (i.e., nonreference alleles are fluctuating at high frequency in island versus mainland populations). In some cases, this fluctuation was less extreme, but was still >0.5 allele frequency change between island and mainland populations. These four genes are excellent candidates for explaining key phenotypic traits that differentiate island boa populations, including their unique dwarf phenotypes, craniofacial morphology, and slender body form. Below, we describe the characteristics of each of these genes and their links to key island phenotypes, while integrating these findings with existing knowledge of genes and pathways impacting body size and craniofacial morphology.

Links between Regulation of the IGF-1/GH Pathway to Dwarfism in Island Boa Populations

Among the four identified candidate genes, both PTPRS and DMGDH play a role in regulating the Insulin-like Growth Factor/Growth Hormone (IGF-1/GH) pathway, an important regulator of vertebrate growth ( Baker et al. 1993). The knockout of PTPRS in mice causes a significant reduction in circulating levels of insulin-like growth factor 1 (IGF-1) and growth hormone (GH Elchebly et al. 1999 Batt et al. 2002). Accordingly, mice PTPRS null mutants exhibit reduced body size and weight, general retardation of growth, and decreased litter size ( Elchebly et al. 1999). In the West Snake Cay population, PTPRS contains an indel segregating at high frequency that results in a frameshift mutation at protein residue 222, which was not observed in any other island or mainland population ( figs. 6A and ( 7A) this frameshift variant was classified as high-impact by VEP. A second moderate-impact variant was observed at protein residue 434 and results in an Alanine to Valine substitution, which is at high frequency in the Lagoon Cay population and segregates at a frequency of 0.1 in the mainland Belize population ( figs. 6A and 7A supplementary table S10 , Supplementary Material online). Although classified as moderate-impact based on VEP, this second variant had a nondeleterious PROVEAN score of −0.012. The region of the genome containing PTPRS also contains a RAD locus located ∼840 kb downstream from PTPRS with an exceptionally high FST that is in the top 2.5% of FST values identified as statistically significant in the Cayos Cochinos population based on our PPS analyses ( figs. 6A and 7A). The genomic region around PTPRS also shows particularly low relative heterozygosity in both the West Snake and Cayos Cochinos populations—2 Mb regions surrounding PTPRS average 54% and 63% of the genome-wide average heterozygosity, respectively ( figs. 6A and 7A). Together, our results suggest that distinct island-specific alleles in or adjacent to PTPRS may contribute to altering the function of this gene in island populations and driving the observed island dwarf phenotypes.

—Genomic variation surrounding genes putatively underlying island traits. Each column represents the genomic region surrounding (A) PTPRS, (B) DMGDH and ARSB, and (C) MYLIP. The first row for each region depicts allelic differentiation in island–mainland population pairs and between mainland populations in Belize and Honduras based on RADseq data, with FST measurements indicative of selection (i.e., above the 97.5% quantile) indicated as triangles. Colored lines depict loess-smoothed trendlines (span = 1) showing comparison-specific trends across the entire genomic scaffold. The second row shows scaffold-wide observed heterozygosity in individual populations from WGS variants at loci across scaffolds with functionally relevant genes. The trendlines are based on a generalized additive model with the formula ys(x, bs=“cs”), and the grey areas represent the 95% confidence interval for each trendline. The third row depicts the allele frequency fluctuation of coding sequence variation between island–mainland population pairs. Tracks at the top of each coding region panel summarize the genomic scale of the local regions and the gene models for the four focal genes. Relevant protein substitutions discussed in the main text are indicated using standard notation.

—Genomic variation surrounding genes putatively underlying island traits. Each column represents the genomic region surrounding (A) PTPRS, (B) DMGDH and ARSB, and (C) MYLIP. The first row for each region depicts allelic differentiation in island–mainland population pairs and between mainland populations in Belize and Honduras based on RADseq data, with FST measurements indicative of selection (i.e., above the 97.5% quantile) indicated as triangles. Colored lines depict loess-smoothed trendlines (span = 1) showing comparison-specific trends across the entire genomic scaffold. The second row shows scaffold-wide observed heterozygosity in individual populations from WGS variants at loci across scaffolds with functionally relevant genes. The trendlines are based on a generalized additive model with the formula ys(x, bs=“cs”), and the grey areas represent the 95% confidence interval for each trendline. The third row depicts the allele frequency fluctuation of coding sequence variation between island–mainland population pairs. Tracks at the top of each coding region panel summarize the genomic scale of the local regions and the gene models for the four focal genes. Relevant protein substitutions discussed in the main text are indicated using standard notation.

—Summary of evidence for signatures of selection in phenotypically relevant gene regions and the broader functional context tying these genes to island phenotypes. As with figure 6, each column represents the genomic region surrounding (A) PTPRS, (B) DMGDH and ARSB, and (C) MYLIP. The top tracks indicate the genomic scale of the local regions and gene models for the four focal genes (see also fig. 6). For each gene, tables summarize amino acid or frameshift (fs) substitutions coding by nonsynonymous variants on islands (WSC, West Snake Cay LC, Lagoon Cay CC, Cayos Cochinos). Additional columns and green checkmarks in each table indicate whether these protein-coding regions had high allelic differentiation (i.e., RADseq FST values above the 97.5% quantile) or low heterozygosity based on the WGS data set. Stars beneath substitutions indicate that the substitution was found and varied greatly in allele frequency in two or more island populations. (D) Schematics of the functional context and interactions of phenotypically relevant genes in developmental signaling pathways with demonstrated importance in craniofacial morphology, body size, and fat/triglyceride regulation.

—Summary of evidence for signatures of selection in phenotypically relevant gene regions and the broader functional context tying these genes to island phenotypes. As with figure 6, each column represents the genomic region surrounding (A) PTPRS, (B) DMGDH and ARSB, and (C) MYLIP. The top tracks indicate the genomic scale of the local regions and gene models for the four focal genes (see also fig. 6). For each gene, tables summarize amino acid or frameshift (fs) substitutions coding by nonsynonymous variants on islands (WSC, West Snake Cay LC, Lagoon Cay CC, Cayos Cochinos). Additional columns and green checkmarks in each table indicate whether these protein-coding regions had high allelic differentiation (i.e., RADseq FST values above the 97.5% quantile) or low heterozygosity based on the WGS data set. Stars beneath substitutions indicate that the substitution was found and varied greatly in allele frequency in two or more island populations. (D) Schematics of the functional context and interactions of phenotypically relevant genes in developmental signaling pathways with demonstrated importance in craniofacial morphology, body size, and fat/triglyceride regulation.

In addition to PTPRS, alleles of DMGDH demonstrate patterns of variation that may have functional, and potentially synergistic, impacts on development and growth of island boas. DMGDH functions in the catabolism of choline, and a loss of function mutation in this gene in mice leads to decreased circulating thyroxine ( Smith et al. 2018), resulting in depressed GH secretion, suppressed growth, and reduced body weight ( Root et al. 1986 Amit et al. 1991 Choi et al. 2018). We found three nonsynonymous coding variants in DMGDH (protein residues 271, 585, and 667), although only one (protein residue 271) has a particularly deleterious PROVEAN score of −4.628 ( figs. 6B and 7B). This variant results in a Histidine to Aspartic Acid substitution, which shows very high allelic differentiation in the West Snake Cay population (the nonreference allele is at high frequency on West Snake Cay, yet segregates at 0.083 in mainland Belize), but very low allelic differentiation in the other island–mainland comparisons ( figs. 6B and 7B supplementary table S10 , Supplementary Material online). Although only the West Snake Cay population shows high allelic frequency shifts for the nonsynonymous DMGDH allele, the genomic region surrounding DMGDH contains a high density of putatively selected RAD-based variants (i.e., variants with FST values in the upper 2.5% quantiles) in all three island populations, and this region is characterized by particularly low heterozygosity in both Lagoon Cay and Cayos Cochinos populations (21% and 71% of genome-wide average heterozygosity, respectively figs. 6B and 7B). Similar to PTPRS, these results suggest that different island populations have experienced selection for different DMGDH alleles, some of which are highly penetrant coding variants while others are not.

Selection on IGF-1 alleles is known to impact body size in dogs ( Sutter et al. 2007) and humans ( Becker et al. 2013), and modulation of the function of this pathway appears to represents a recurrent target for selection in vertebrates. Our finding that multiple distinct alleles for PTPRS and DMGDH are associated with dwarf island populations, together with evidence that selection may play a role at these loci in multiple island populations, provides an example of independent allelic solutions that may result in convergent signaling outcomes (i.e., modulation of IGF-1/GH pathway) leading to convergent dwarfism phenotypes. Neither PTPRS nor DMGDH are currently known to be associated with sex-biased phenotypes and thus it remains an open question whether either of these contributes to the patterns of sexual dimorphism evident in island populations. Collectively, our results supplement existing work on the genetics underlying body size that indicates many genes of large effect have some regulatory role in the IGF-1/GH pathway ( Sutter et al. 2007 Becker et al. 2013).

The Potential Role of Wnt Signaling in Craniofacial Morphology of Island Boa Populations

Island boas possess unique snout attenuation, head width, and eye size compared with mainland populations ( fig. 1E). These phenotypic traits are likely linked to the unique arboreal habits and hunting behavior of these island populations ( Shine 1983 Lillywhite and Henderson 2002). Wnt signaling has been implicated in craniofacial development in many systems ( Schmidt and Patel 2005 Brugmann et al. 2007, 2010 Kurosaka et al. 2014), and two genes identified in our analysis, PTPRS and ARSB, are known to have impacts on the Wnt pathway. In addition to the roles PTPRS can have on growth (discussed above), loss of PTPRS function in mice also causes alterations to BMP and Wnt signaling pathways, resulting in improper maxillary and mandibular development and changes to craniofacial morphology ( Stewart et al. 2013). Accordingly, nonsynonymous variants observed in PTPRS in the two Belize island populations (Lagoon and West Snake), and evidence for selection in the Cayos Cochinos and West Snake Cay populations, may also be linked to phenotypic effects on craniofacial morphology through genomic variation in PTPRS via its interaction with Wnt signaling ( figs. 6A and 7A).

A second gene, ARSB, is also involved in Wnt signaling related to craniofacial phenotypes, as well as in cell signaling that effects body size and mass. ARSB is associated with abnormal caudal vertebrae morphology, head and nose morphology, fat/triglyceride levels, and decreased birth and adult body size in mice ( Smith et al. 2018), and is genetically linked to another of our four candidate genes, DMGDH, in most vertebrates these two genes are located in close proximity to one another in the boa genome (∼20 kb figs. 6B and 7B). Reduced expression of ARSB has been linked to downstream increases in Wnt/β-catenin signaling ( Bhattacharyya et al. 2017) through a proposed interaction with LDL-receptor-related protein 5/6 ( Kawano et al. 2006 Veeck and Dahl 2012 Ueno et al. 2013). ARSB is the causative gene for the human disorder mucopolysaccharidosis type VI (Maroteaux Lamy Syndrome), which is associated with short stature and with facial dysmorphism ( Azevedo et al. 2004). Similar phenotypes caused by mutations to an orthologous gene have also been noted in dogs ( Wang et al. 2018).

Our results suggest that convergent molecular evolution at the amino acid level in and around ARSB may underlie some aspects of phenotypic convergent evolution across all three island populations. All island population-specific ARSB alleles contain a nonsynonymous Glycine to Serine substitution classified as moderate-impact by VEP, but with a nondeleterious PROVEAN score of 0.467. The Serine residue appears nearly at high frequency in all three island populations but segregates at 0.25–0.30 in both mainland populations ( figs. 6B and 7B supplementary table S10 , Supplementary Material online). Given the close genomic proximity of ARSB and DMGDH, the region encompassing these two genes shares characteristics of genetic variation (discussed above) and evidence of selection acting on this region in multiple island populations, including a high density of putatively selected variants in all three island populations and low heterozygosity in both the Lagoon Cay and Cayos Cochinos populations (17% and 71% of genome-wide average heterozygosity in each population, respectively figs. 6B and 7B).

Taken together, evidence for molecular convergence at the amino acid level and regional genomic patterns consistent with selection in all three island lineages implicate ARSB as a likely driver of convergent island phenotypes. Island-specific genomic variation patterns associated with both ARSB and DMGDH also suggest that Wnt signaling may represent an important nexus for molecular and pathway-level convergence mediating adaptation and phenotypic evolution of island populations. This conclusion is also consistent with previous studies that have found that Wnt signaling underlies adaptive craniofacial variation in the rapid evolution of cichlid fish in Lake Malawi ( Parsons et al. 2014). Craniofacial variation in the African lake cichlid adaptive radiation is hypothesized to be driven by trophic adaptation, representing a key example of how evolutionary variation in Wnt signaling may underlie trophic adaptation. Similarly, craniofacial shifts in island boas appear to be driven by the unique, arboreal feeding ecology of snakes in these populations ( Lillywhite and Henderson 2002 Boback 2005), and thus further highlight the broad potential for evolutionary variation in Wnt signaling to drive rapid trophic adaptation in vertebrates.

Links between Lipid Metabolism and Reduced Body Mass in Island Boas

The rarity and seasonality of prey, together with the less massive, slender phenotypes of island boas suggest that substantial differences in metabolism and fat storage may be a shared feature of island populations. The fourth candidate gene identified by our WGS analysis, MYLIP, plays a role in regulating lipid metabolism and body mass. Human GWAS studies have identified MYLIP in screens for low-density lipoprotein cholesterol and total cholesterol ( Weissglas-Volkov et al. 2011 Global Lipids Genetics Consortium et al. 2013 Surakka et al. 2015), and mice with null mutations in MYLIP show a number of phenotypes, including those linked to cholesterol levels, lipid regulation, and body fat mass ( Smith et al. 2018).

Our comparisons of island and mainland boa populations identified a nonsynonymous coding variant (protein residue 360) in MYLIP with relatively high shifts in allele frequency on West Snake Cay (0.75 allele frequency shift from mainland Belize) and Cayos Cochinos (0.625 allele frequency fluctuation versus mainland Honduras figs. 6C and 7C supplementary table S10 , Supplementary Material online). This nonsynonymous variant is classified as moderate-impact by VEP and has a deleterious PROVEAN score of −3.269. The scaffold containing MYLIP did not contain any putatively selected variants in our RAD data set in any island populations, although heterozygosity in the Cayos Cochinos population is moderately reduced in this region (2 Mb region surrounding MYLIP has an average heterozygosity that is 42% of the genome-wide average figs. 6C and 7C). Our finding that a nonsynonymous MYLIP allelic variant with inferred deleterious impacts has convergent, high allele frequency fluctuation in both the West Snake Cay and Cayos Cochinos populations suggests that MYLIP may also be relevant in mediating island-specific phenotypes, such as body mass, fat storage, or fat metabolism. However, the lack of strong evidence for selection acting on this region in island populations raises the question of whether drift may have driven the elevated frequency of this variant in island populations, or if we instead failed to detect selection due to a lack of power (due to limited sampling or the age or strength of selection).

Genetically and Functionally Linked Gene Sets May Evolutionary Tune Island Phenotypes

The genomic and functional characteristics of our candidate genes, and associated variants, highlight the potential role of genetic linkage and overlapping functional interactions in driving rapid phenotypic convergence through modulation of relatively few genes. Two candidate genes for island phenotypes, DMGDH and ARSB, are found in close proximity in vertebrate genomes, including that of boas ( fig. 6B), and this genomic region also contains two betaine—homocysteine S-methyltransferase genes adjacent to DMGDH: BHMT and BHMT2 ( figs. 6B and 7B). The DMGDH, BHMT, and BHMT2 complex is associated with modulation of plasma betaine levels in humans ( Hartiala et al. 2016). Betaine and choline also regulate insulin sensitivity, fat deposition, and energy metabolism ( Millard et al. 2018). This group of three genes therefore plays an important role in physiological processes that impact body growth and fat deposition, two key traits that differentiate island and mainland boas. The fourth gene in this region, ARSB, plays no apparent role in betaine metabolism, but does impact body size and craniofacial morphology through alternative mechanisms. However, variation in the nearby BHMT gene is associated with differential methylation of ARSB, which can modulate ARSB expression ( Lupu et al. 2017). The functional interplay between genes in this region and the collective ability of these genes to potentially alter a broad spectrum of distinctive traits that characterize island boas—body size, craniofacial morphology, and fat metabolism—suggests that this linked gene cluster may be an important conserved target of rapid adaptation to modulate island-specific traits and phenotypes broadly. Indeed, the strong signatures of selection in this region based on both RADseq and WGS further support the conclusion that variation in this genomic region plays a role in convergent island phenotypes in boas. Such genomic regions that contain functionally interrelated genes—referred to as “supergenes” in certain contexts ( Thompson and Jiggins 2014)—have been shown to be important for other adaptive traits including self-incompatibility in plants ( Takayama and Isogai 2005), assortative mating in white-throated sparrows ( Thomas et al. 2008 Tuttle et al. 2016), and mimicry in butterflies ( Joron et al. 2011 Kunte et al. 2014). Our results suggest that the DMGDHBHMTARSB region could also be included in this emerging list of functionally dense targets for rapid phenotypic adaptation in vertebrates. Further work is needed to more definitely link genetic variation in this region to phenotypic differences between island and mainland populations, and follow-up functional assays would be valuable to determine if this region may be capable of modulating a broad spectrum of phenotypes with only a small number of mutations.

Experimental Considerations for Future Research on Convergent Island Phenotypes in Boas

Our results provide an exciting, yet preliminary, perspective on the potential connections between genotypic and phenotypic evolution across distinct island boa populations, and highlight the value of this island boa system for studying the genetic basis of complex traits and the propensity for molecular convergence. To more fully leverage this system, future studies would benefit from expanded sampling to increase power to detect and understand genetic shifts occurring across one or more island populations. In our case, the use of RADseq data facilitated the economy to sample more individuals to better quantify the relative contributions of drift and selection to the evolution of island populations but lacked power to identify causal genetic variation. Moreover, WGS provided some power to dissect underlying causal genetic variation, but at a cost that limited our ability to sample many individuals. Thus, expanding WGS sampling to include greater numbers of individuals from an increased number of populations would provide substantially increased power to uncover across-island and island-specific evolutionary patterns that contribute to both convergent and divergent island phenotypes. In particular, additional sampling of other dwarf island populations not included in this study would further leverage the power of the natural replication of this system ( Henderson et al. 1995 Boback 2005).

In this study, we primarily focused on the role of protein-coding variation in shaping patterns of convergence and divergence among mainland–island boa populations. Thus, a clear limitation of our study is a lack of insight into noncoding, regulatory variation that may play a fundamental role in shaping phenotypes. Previous studies indicate that changes in cis-regulatory elements (i.e., enhancers) are important for producing new gene expression patterns that can impact phenotypes (reviewed in Carroll 2008 and Wray 2007). However, identifying enhancers and associating them with gene expression is difficult due to the fact that these regions are relatively small and can be located up to 1 Mb away from the transcription start sites of genes that they regulate. Moreover, there is currently remarkably little knowledge about regulatory elements in reptiles, which led us to forego more focused analyses of regulatory evolution that very likely serves as a target for selection in island boa populations. Similarly, previous studies have documented instances of TE proliferation seeding regulatory switches important for the evolution of new, complex traits ( Wagner and Lynch 2010 Sundaram et al. 2014 Chuong et al. 2017). Patterns of TE abundance and evolution may therefore be a nontrivial mechanism for rapid phenotypic evolution in small island populations, where the efficacy of purifying selection is reduced and could result in proliferation of active TE families. Indeed, analysis of copy-number changes of TE families across island populations in relation to relevant mainland populations in putatively selected versus neutrally evolving regions found significantly higher numbers of Maverick DNA transposons in the Lagoon Cay population and TcMar-Tigger DNA transposons in the West Snake Cay population (Bonferroni-corrected P < 0.05 supplementary table S11 , Supplementary Material online). However, while this general pattern supports the possibility that TEs could play a role in island evolution, determining the penetrance (i.e., impact on phenotype) of TE insertions, and mutations in general, linked to regulatory regions is far less straight-forward than it is in protein-coding regions. Expanding our understanding of the presence and evolutionary dynamics of regulatory regions in nonavian reptiles, and especially boas, would be an exciting step forward that would enable more thorough interrogation of the role of regulatory elements, and genomic change overall, has played in the evolution of convergent and divergent phenotypic changes across islands.


Genes in Populations

Individuals do not evolve because their genes do not change over time. Instead, evolution occurs at the level of the population. A population consists of organisms of the same species that live in the same area. In terms of evolution, the population is assumed to be a relatively closed group. This means that most mating takes place within the population. Evolutionary change that occurs over relatively short periods of time within populations is called microevolution. The science that focuses on evolution within populations is population genetics. It is a combination of evolutionary theory and Mendelian genetics.

The Gene Pool

The genetic makeup of an individual is the individual&rsquos genotype. A population consists of many individuals and therefore many genotypes. All the genotypes together make up the population&rsquos gene pool. The gene pool consists of all the genes of all the members of the population. For each gene, the gene pool includes all the different alleles of the gene that exist in the population. An allele is referred to as a version of a gene. For a given gene, the population is characterized by the frequency of the different alleles in the gene pool. Allele frequency is how often an allele occurs in a gene pool relative to the other alleles for the same gene.


Mendel's Monohybrid Cross

Creating pure lines

Cross pollinating pure lines.

Mendel chose to study inheritance of the pea plant. While the pea is a fast-growing species (which makes it a good experimental subject), its most important characteristic is the pea can be self-fertilized. Self-fertilizing a plant is the process in which the sperm (pollen) from one plant is used to fertilize the eggs (ovules) of the same plant. Self fertilization creates pure lines , in which all offspring are exact copies ( clones ) of the self-fertilized plant.

P generation

From his controlled self-pollinations, Mendel germinated and grew the “pure line” seeds of plants with several different phenotypes: seed shape, seed color, pod shape, pod color, flower color and stem length. He collectively called pure line plants the P generation , the parent generation. The P generation served as the starting point for his inheritance experiments

F1 generation

Mendel mated peas representing two extreme “pure line” phenotypes from the P generation. The resulting offspring are the first filial (or F1 ) generation. We will focus on his experiment with different flower colors: purple and white.

Results of the F1 generation

Mendel’s results for all of his physical traits did not support the blended inheritance hypothesis. Rather, he found that one of the extreme traits appeared in a cross of different pure lines. He called these expressed phenotypes dominant , meaning that if there is a mix of two pure lines this phenotype will be expressed. For flower color, purple dominated over white, meaning if a pure-line, purple-flowered plant is mated with a pure-line, white-flowered plant, all of the resulting offspring have purple flowers. In contrast, the phenotype that is masked is known as the recessive phenotype. White flowers are recessive to purple flowers in pea plants.

F2 generation

Mendel pondered, “If one phenotype dominates over another, how can the recessive phenotype even exist in a population?.” This led him to conduct another controlled cross, this time between plants of the F1 generation. While the P generation was composed of pure line plants, he knew that the F1 generation was composed of half the genetic information from each plant in the P generation. What happens if the hybrids are crossed? The resulting generation is the F2 generation (hybrids of hybrids), and the results awaiting him were another surprise to M endel.

Results of the F2 generation from Mendel's monohybrid cross.

For all the different phenotypes Mendel analyzed, the recessive characteristics reemerged in the F2 generation! And they did so with a predictable regularity. The ratio of dominant to recessive phenotypic ratio of all of the characteristics Mendel analyzed were all very close to 3 dominant: 1 recessive. In other words, in the F2 generation ¾ of the pea plants expressed the dominant phenotype, while ¼ expressed the recessive phenotype.


Population divergence in fish elemental phenotypes associated with trophic phenotypes and lake trophic state

Studies of ecological stoichiometry typically emphasize the role of interspecific variation in body elemental content and the effects of species or family identity. Recent work suggests substantial variation in body stoichiometry can also exist within species. The importance of this variation will depend on insights into its origins and consequences at various ecological scales, including the distribution of elemental phenotypes across landscapes and their role in nutrient recycling. We investigated whether trophic divergence can produce predictable patterns of elemental phenotypes among populations of an invasive fish, the white perch (Morone americana), and whether elemental phenotypes predict nutrient excretion. White perch populations exhibited a gradient of trophic phenotypes associated with landscape-scale variation in lake trophic state. Perch body chemistry varied considerably among lakes (from 0.09 for % C to 0.31-fold for % P) casting doubt on the assumption of homogenous elemental phenotypes. This variation was correlated with divergence in fish body shape and other trophic traits. Elemental phenotypes covaried (r 2 up to 0.84) with lake trophic state. This covariation likely arose in contemporary time since many of these perch populations were introduced in the last century and the trophic state in many of the lakes has changed in the past few decades. Nutrient excretion varied extensively among populations, but was not readily related to fish body chemistry or lake trophic state. This suggests that predictable patterns of fish body composition can arise quickly through trophic specialization to lake conditions, but such elemental phenotypes may not translate to altered nutrient recycling by fish.

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Synthesis and discussion

While never fully woven into the fabric of the Neo-Darwinian synthesis, phenotypic plasticity has had a long history of study across a wide range of biological disciplines, most notably developmental biology, ecological genetics, behavioural and evolutionary ecology (reviewed in West-Eberhard 2003 ). Here we have argued that this legacy supports the argument that phenotypic plasticity in response to new environments does not preclude evolutionary change, however, the route and speed by which plasticity can lead to adaptive genetic differentiation depends in part on the type of plasticity being considered. Distinguishing between different types of plasticity is an important first step in understanding the consequences of environmentally induced variation in evolutionary change. Here we: (i) attempt to synthesize different views on plasticity and its contribution to adaptive evolution on ecological time-scales, (ii) show how adaptive plasticity may arise from initially non-adaptive responses to these environments, and (iii) provide a conceptual framework for future research examining the role plasticity might play in contemporary adaptation.

Plasticity and adaptation on ecological time-scales

The first hurdle of adapting to new environments is the ability to persist in the face of directional selection, followed by the second hurdle of exhibiting an adaptive evolutionary response to selection. Phenotypic plasticity encompasses a wide range of adaptive and non-adaptive responses to heterogeneous environments, yet too often the term plasticity is used in a general context that obscures different kinds of environmentally induced variation, with different consequences for the likelihood of persistence and adaptation to new environments. We distinguish between four types of plasticity that are likely to have very different consequences for evolution on ecological time-scales and can be summarized in a two-dimensional phenotypic landscape (Fig. 2). What interests us here is whether or not it can be argued that any of these forms of plasticity enhance an organism's probability of surviving an episode of directional selection and facilitate adaptation at the population level. We distinguish between two types of adaptive plasticity that differ in the degree to which the mean phenotype tracks the environment. First, when adaptive plasticity produces a mean phenotype that is a close match to what is favoured by selection in the new environment, the derived population is most likely to persist, but unlikely to evolve because the population will be subjected to stabilizing, as opposed to directional selection (Fig. 2 response A). Second, when adaptive plasticity produces a mean phenotype that is closer to the optimum favoured by selection, but incomplete (i.e. still short of the optimal response), the derived population will likely persist, but still be far enough away from the favoured optimum to be subjected to directional selection (Fig. 2 response B). This second, and perhaps more common, form of adaptive plasticity is likely to result in the most rapid adaptive genetic differentiation between populations because of the reduced likelihood of extinction in combination with moderate directional selection on extreme phenotypes (see review in Price et al. 2003 ).

The role of plasticity in allowing colonization and subsequent adaptation to new habitats can be illustrated using Fisher's geometric model of adaptation. In this figure the thick outer circle represents the n-dimensional phenotype of the ancestral population, X. The optimal phenotype for the new environment is at the centre of the circle. Shown are three types of phenotypic plasticity. A, represents a genotype with perfect adaptive plasticity, the new environment causes individual phenotypes to change in exactly the correct vector in N-dimensional phenotypic space. B represents a genotype in which the plasticity is also adaptive, but incomplete, placing individuals outside the optimum. In genotype C, the plasticity is non-adaptive and moves individuals further away from optimal phenotype. Finally, in the case of extreme stress, increased variance may produce responses in all directions (random vectors not shown), most of which are expected to be maladaptive but some by chance may be adaptive in the new environment.

We also distinguish between two types of non-adaptive plasticity that differ primarily in how the environment alters the mean vs the variance of a trait. First, when plasticity results in the mean phenotype being further away from the new optimum relative to the ancestral phenotype, the derived population is less likely to persist in the new environment and plasticity becomes an impediment that selection must overcome (Fig. 2 response C). Here, the combination of plasticity that is not beneficial in increasing the likelihood of persistence in the new environment and strong directional selection are in theory most likely to result in extinction. However, at least one empirical study suggests that adaptive differentiation between populations has occurred relatively rapidly in the face of initially non-adaptive plasticity (e.g. Carroll et al. 1997 ). Second, when environmental stress increases the variance around the mean phenotype via the expression of cryptic genetic variation, the beneficial effects of plasticity in facilitating the establishment of a new population or the opportunity for adaptation to the new environment is dependent on the chance occurrence of an adaptive variant appearing (Fig. 2). Successful establishment and subsequent adaptation under this scenario is completely dependent on the probability that somewhere among the genetic variation normally suppressed in a population resides a beneficial mutation that is captured by selection.

Our distinction between different types of plasticity suggests that no single conceptual framework can easily be applied to encompass these diverse forms of environmentally induced variation. However, in the context of adaptation to new environments it is clear that adaptive plasticity is most likely to reduce the probability of extinction by facilitating the move from one adaptive peak to another ( Robinson & Dukas 1999 Pigliucci & Murrern 2003 Price et al. 2003 West-Eberhard 2003 Schlichting 2004 Amarillo-Saurez & Fox 2006 ). This may be especially important in cases where an invading population is comprised of a small number of individuals that have undergone a severe genetic bottleneck, and are dependent on adaptive plasticity to survive during the initial phases of invasion ( Sexton et al. 2002 Lambrinos 2004 Dybdahl & Kane 2005 Richards et al. 2006 Strauss et al. 2006 ). In this sense, plasticity is not in itself an evolutionary mechanism on a par with natural selection ( de Jong 2005 ), but rather provides the first step in the adaptive walk otherwise dependent on new mutation, as described in the geometric models of Fisher (1930 ) and Orr (1998 ). Instead of waiting for a rare, non-deleterious mutation along the correct n-dimensional vector of selection (e.g. Fisher 1930 Kimura 1983 Orr 1998 ), plasticity can allow a lineage to cross an adaptive valley, and move closer to the optimum phenotype in the new environment.

Empirical studies of adaptive evolution reveal that adaptations to new environments rarely involve single traits, but rather suites of traits that respond to diverse selection pressures ( Reznick & Ghalambor 2001 ). At the whole organism level, new environments are likely to result in a combination of adaptive and non-adaptive plasticity in a suite of traits, but the consequences of such responses for evolution on ecological time-scales remains largely unexplored territory. To date, empirical studies looking at multivariate phenotypes suggest the potential for integrative ( Parsons & Robinson 2006 ) and non-integrative responses ( Carroll et al. 1997 ) to play some role in plasticity leading to adaptive evolution. We feel that identifying different types of plasticity and viewing individuals as being made up of a mosaic of traits is an important starting point in reconciling different viewpoints on the relative importance of plasticity to adaptive evolution.

The ghost of selection past and adaptive plasticity in new environments

We have argued that adaptive plasticity enhances the probability of persistence in a new environment and can facilitate adaptive genetic differentiation when directional selection acts on extreme phenotypes ( Price et al. 2003 ), but it is less obvious what the origins of this plasticity are and why variation in plasticity persists. The most parsimonious explanation is that past selection shapes the reaction norm, and as long as fitness costs to maintaining a plastic response are not large, adaptive plasticity should persist in a population (e.g. Sultan 1995 ). This was empirically demonstrated by Cook & Johnson (1968 ) in their study of leaf development in populations of Ranunculus flammula that experience both aquatic and terrestrial conditions. Populations that experience persistent aquatic or terrestrial conditions are more specialized and exhibit less adaptive plasticity in leaf development when reared in the opposite environment, whereas populations that regularly experience both aquatic and terrestrial conditions exhibit the greatest adaptive plasticity in leaf development ( Cook & Johnson 1968 ). Thus, an important attribute distinguishing adaptive from non-adaptive plasticity is that it is an adaptation to past and/or current selection, rather than being a serendipitous response to environmental variation.

One conceptual framework for understanding the origins of adaptive reaction norms is to visualize how natural selection acts on neutral genetic variation in the reaction norm. Populations appear to have abundant genetic variation for phenotypic plasticity, although it is only under certain environments that this cryptic genetic variation is expressed ( Rutherford 2000 ). In other words, the reason that stressful environments generate a greater variation in phenotypes is because outside the range of ‘non-stressful’ ancestral environments there is no opportunity for selection to act on the reaction norm, which in turn allows for the accumulation of genetic variation that is effectively neutral ( Rutherford 2000 ). If we think of the reaction norm metaphorically as a piece of string, selection should act to keep the string taught and at an angle or shape that is adaptive across current and historical environments (Fig. 3). In contrast, new environments that fall outside the range of current and past selection result in regions of the reaction norm that have never or rarely experienced the effects of stabilizing selection, thus releasing tension on the string and allowing it to move more freely (Fig. 3). This release of cryptic genetic variation should be manifest as a significant G × E effect only in the stressful environments, whereas only environmental effects will be significant in non-stressful environments (Fig. 3). Adaptive change in the reaction norm across a wider range of environments will therefore occur when an adaptive variant is captured by the process of natural selection and the ‘tension’ on the string is extended into the new environment (Fig. 3). A comparison of reaction norms of derived populations living in extreme environments relative to ancestral ones may provide insight into the prospect that such events have commonly occurred in the past (e.g. Haugen & Vøllestad 2000 ).

Depicts three genotypes that have the same reaction norms within the range of environments they experience. All three genotypes coexist in the same population, and experience the same variable environment, for example, temperature. Selection maintains the ability of these three genotypes to respond appropriately (adaptive plasticity) to the range of environments that they experience. However, since these genotypes have never been exposed to the novel environments on the high and low end, this part of the reaction norm evolves neutrally and accumulates cryptic genetic variation. This part of the reaction norm is not adaptive or even relevant for the current environment, but since the genotypes differ in this part of the reaction norm, by chance one of them could be pre-adapted for a novel environment.

Future research ideas

We have reviewed and outlined different routes by which adaptive and non-adaptive plasticity may facilitate evolution on ecological time-scales, however, no study to date has actually provided empirical evidence for a major role of plasticity in facilitating adaptive evolution in natural populations. A lack of evidence may reflect a failure in past research programmes to specifically design studies that evaluate processes such as genetic assimilation (e.g. Pigliucci & Murren 2003 ) or simply suggests that plasticity is unimportant (e.g. de Jong 2005 ). We argue that in theory both adaptive and non-adaptive plasticity can facilitate adaptive differentiation of populations, albeit through different means. Recognizing the different means by which plasticity can contribute to adaptive evolution is a critical starting point for designing empirical studies that explicitly test for these processes. We envision two general approaches to testing the role of plasticity in adaptation: (i) selection/introduction experiments in nature, and (ii) comparisons of contemporary reaction norms in ancestral vs derived population sets. For the experimental method, the more direct approach is to conduct selection experiments in nature and follow populations over time ( Reznick & Ghalambor 2005 ). A straightforward design would be to replicate planned introductions of individuals into new environments. Such selection experiments in nature have the benefit of providing an opportunity to measure the patterns of plasticity and the rate at which populations become genetically differentiated from each other ( Reznick & Ghalambor 2005 ). In addition, by conducting these experiments in nature, plasticity can be evaluated in a context where the fitness trade-offs associated with plasticity can be realized. To date, the only study that has used such an approach to explicitly study plasticity and evolution is work carried out by Losos and colleagues (1997, 2000, 2001, 2004). For example, Losos et al. (2000 ) have found that plasticity in hindlimb length in response to different substrates leads to the production of beneficial phenotypes appropriate to particular environments adaptive plasticity in this case foreshadows adaptive changes that evolve over longer periods of time. Similarly, behavioural changes (a type of adaptive plasticity) in response to predatory lizard introductions, appear to not only bring lizard populations within the realm of a new adaptive peak, but also appear to facilitate evolutionary change in the direction expected based on patterns of habitat use and co-existence observed in lizard communities on other islands ( Losos et al. 2004 ). Whether these initial patterns of plasticity will be observed to evolve over a contemporary time-scale remain to be seen, however, at least the conditions for future investigation have been established.

A second approach to studying plasticity and evolution is to compare the reaction norms of known ancestral and derived populations that occupy different environments (as described in Carroll et al. 1997 , 1998 Parsons & Robinson 2006 ). Reciprocal transplant experiments that measure plasticity in the native and introduced environments can provide insights into the initial patterns of plasticity (ancestral type reared in novel environment) and how that plasticity has evolved (derived type in native environment). Introduced species are good candidates for this approach because in many cases the ancestral and derived populations are known and the rate of adaptation can be inferred if the approximate time of establishment is known. While, a more indirect measure, such comparisons are potentially readily available for a wide range of species.

Under both approaches, it is important that suites of fitness related traits be measured, and attention be paid to the subset of individuals that persist and flourish in the new environments ( Carroll et al. 1997 ). If an identifiable subset of individuals that possess a particularly favourable combination of plastic traits are found to be the successful colonizers of new environments, such evidence could show an important role of plasticity in facilitating adaptation. One area where such an approach can be applied and has practical application is in understanding the mechanisms that result in the spread of invasive species. For example, many introduced species persist as small populations for various periods of time before undergoing rapid population growth and range expansion (e.g. Lambrinos 2004 ). Despite scepticism regarding the role of plasticity in invasions ( Lee 2002 ), it would be interesting to know whether the period of persistence is made possible by plasticity, and that evolutionary changes in the reaction norm allow for adaptation and expansion. At the very least, integrating an explicit role for plasticity in studies of invasive species has the advantage of bringing ecological and evolutionary processes into a common framework ( Lambrinos 2004 Richards et al. 2006 ).


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