Evolution – Page 5

The space for species: how spatial aspects influence speciation

November 1, 2018November 1, 2018Sean Buckley2 Comments

Spatial and temporal factors of speciation

The processes driving genetic differentiation, and the progressive development of populations along the speciation continuum, are complex in nature and influenced by a number of factors. Generally, on The G-CAT we have considered the temporal aspects of these factors: how time much time is needed for genetic differentiation, how this might not be consistent across different populations or taxa, and how a history of environmental changes affect the evolution of populations and species. We’ve also touched on the spatial aspects of speciation and genetic differentiation before, but in significantly less detail.

To expand on this, we’re going to look at a few different models of how the spatial distribution of populations influences their divergence, and particularly how these factor into different processes of speciation.

What comes first, ecological or genetic divergence?

One key paradigm in understanding speciation is somewhat an analogy to the “chicken and the egg scenario”, albeit with ecological vs. genetic divergence. This concept is based on the idea that two aspects are key for determining the formation of new species: genetic differentiation of the populations in question, and ecological (or adaptive) changes that provide new ecological niches for species to inhabit. Without both, we might have new morphotypes or ecotypes of a singular species (in the case of ecological divergence without strong genetic divergence) or cryptic species (genetically distinct but ecologically identical species).

The order of these two processes have been in debate for some time, and different aspects of species and the environment can influence how (or if) these processes occur.

Different spatial models of speciation

Generally, when we consider the spatial models for speciation we divide these into distinct categories based on the physical distance of populations from one another. Although there is naturally a lot of grey area (as there is with almost everything in biological science), these broad concepts help us to define and determine how speciation is occurring in the wild.

Allopatric speciation

The simplest model is one we have described before called “allopatry”. In allopatry, populations are distributed distantly from one another, so that there are separated and isolated. A common way to imagine this is islands of populations separated by ocean of unsuitable habitat.

Allopatric speciation is considered one of the simplest and oldest models of speciation as the process is relatively straightforward. Geographic isolation of populations separates them from one another, meaning that gene flow is completely stopped and each population can evolve independently. Small changes in the genes of each population over time (e.g. due to different natural selection pressures) cause these populations to gradually diverge: eventually, this divergence will reach a point where the two populations would not be compatible (i.e. are reproductively isolated) and thus considered separate species.

Allopatry_example — The standard model of allopatric speciation, following an island model. 1) We start with a single population occupying a single island. 2) A rare dispersal event pushes some individuals onto a new island, forming a second population. Note that this doesn’t happen often enough to allow for consistent gene flow (i.e. the island was only colonised once). 3) Over time, these populations may accumulate independent genetic and ecological changes due to both natural selection and drift, and when they become so different that they are reproductively isolated they can be considered separate species.

Although relatively straightforward, one complex issue of allopatric speciation is providing evidence that hybridisation couldn’t happen if they reconnected, or if populations could be considered separate species if they could hybridise, but only under forced conditions (i.e. it is highly unlikely that the two ‘species’ would interact outside of experimental conditions).

Parapatric and peripatric speciation

A step closer in bringing populations geographically together in speciation is “parapatry” and “peripatry”. Parapatric populations are often geographically close together but not overlapping: generally, the edges of their distributions are touching but do not overlap one another. A good analogy would be to think of countries that share a common border. Parapatry can occur when a species is distributed across a broad area, but some form of narrow barrier cleaves the distribution in two: this can be the case across particular environmental gradients where two extremes are preferred over the middle.

The main difference between paraptry and allopatry is the allowance of a ‘hybrid zone’. This is the region between the two populations which may not be a complete isolating barrier (unlike the space between allopatric populations). The strength of the barrier (and thus the amount of hybridisation and gene flow across the two populations) is often determined by the strength of the selective pressure (e.g. how unfit hybrids are). Paraptry is expected to reduce the rate and likelihood of speciation occurring as some (even if reduced) gene flow across populations is reduces the amount of genetic differentiation between those populations: however, speciation can still occur.

Parapatric speciation across a thermocline.jpg — An example of parapatric species across an environment gradient (in this case, a temperature gradient along the ocean coastline). **Left:** We have two main species (red and green fish) which are adapted to either hotter or colder temperatures (red and green in the gradient), respectively. A small zone of overlap exists where hybrid fish (yellow) occur due to intermediate temperature. **Right:** How the temperature varies across the system, forming a steep gradient between hot and cold waters.

Related to this are peripatric populations. This differs from parapatry only slightly in that one population is an original ‘source’ population and the other is a ‘peripheral’ population. This can happen from a new population becoming founded from the source by a rare dispersal event, generating a new (but isolated) population which may diverge independently of the source. Alternatively, peripatric populations can be formed when the broad, original distribution of the species is reduced during a population contraction, and a remnant piece of the distribution becomes fragmented and ‘left behind’ in the process, isolated from the main body. Speciation can occur following similar processes of allopatric speciation if gene flow is entirely interrupted or paraptric if it is significantly reduced but still present.

Peripatric distributions.jpg — The two main ways peripatric species can form. **Left:** The dispersal method. In this example, there is a central ‘source’ population (orange birds on the main island), which holds most of the distribution. However, occasionally (more frequently than in the allopatric example above) birds can disperse over to the smaller island, forming a (mostly) independent secondary population. If the gene flow between this population and the central population doesn’t overwhelm the divergence between the two populations (due to selection and drift), then a new species (blue birds) can form despite the gene flow. **Right:** The range contraction method. In this example, we start with a single widespread population (blue lizards) which has a rapid reduction in its range. However, during this contraction one population is separated from the main body (i.e. as a refugia), which may also be a precursor of peripatric speciation.

Sympatric (ecological) speciation

On the other end of the distribution spectrum, the two diverging populations undergoing speciation may actually have completely overlapping distributions. In this case, we refer to these populations as “sympatric”, and the possibility of sympatric speciation has been a highly debated topic in evolutionary biology for some time. One central argument rears its head against the possibility of sympatric speciation, in that if populations are co-occurring but not yet independent species, then gene flow should (theoretically) occur across the populations and prevent divergence.

It is in sympatric speciation that we see the opposite order of ecological and genetic divergence happen. Because of this, the process is often referred to as “ecological speciation”, where individual populations adapt to different niches within the same area, isolating themselves from one another by limiting their occurrence and tolerances. As the two populations are restricted from one another by some kind of ecological constraint, they genetically diverge over time and speciation can occur.

This can be tricky to visualise, so let’s invent an example. Say we have a tropical island, which is occupied by one bird species. This bird prefers to eat the large native fruit of the island, although there is another fruit tree which produces smaller fruits. However, there’s only so much space and eventually there are too many birds for the number of large fruit trees available. So, some birds are pushed to eat the smaller fruit, and adapt to a different diet, changing physiology over time to better acquire their new food and obtain nutrients. This shift in ecological niche causes the two populations to become genetically separated as small-fruit-eating-birds interact more with other small-fruit-eating-birds than large-fruit-eating-birds. Over time, these divergences in genetics and ecology causes the two populations to form reproductively isolated species despite occupying the same island.

Ecological sympatric speciation — A diagram of the ecological speciation example given above. Note that ecological divergence occurs first, with some birds of the original species shifting to the new food source (‘ecological niche’) which then leads to speciation. An important requirement for this is that gene flow is somehow (even if not totally) impeded by the ecological divergence: this could be due to birds preferring to mate exclusively with other birds that share the same food type; different breeding seasons associated with food resources; or other isolating mechanisms.

Although this might sound like a simplified example (and it is, no doubt) of sympatric speciation, it’s a basic summary of how we ended up with so many species of Darwin’s finches (and why they are a great model for the process of evolution by natural selection).

The complexity of speciation

As you can see, the processes and context driving speciation are complex to unravel and many factors play a role in the transition from population to species. Understanding the factors that drive the formation of new species is critical to understanding not just how evolution works, but also in how new diversity is generated and maintained across the globe (and how that might change in the future).

What’s the (allele) frequency, Kenneth?

October 24, 2018October 24, 2018Sean Buckley5 Comments

Allele frequency

A number of times before on The G-CAT, we’ve discussed the idea of using the frequency of different genetic variants (alleles) within a particular population or species to test a number of different questions about evolution, ecology and conservation. These are all based on the central notion that certain forces of nature will alter the distribution and frequency of alleles within and across populations, and that these patterns are somewhat predictable in how they change.

One particular distinction we need to make early here is the difference between allele frequency and allele identity. In these analyses, often we are working with the same alleles (i.e. particular variants) across our populations, it’s just that each of these populations may possess these particular alleles in different frequencies. For example, one population may have an allele (let’s call it Allele A) very rarely – maybe only 10% of individuals in that population possess it – but in another population it’s very common and perhaps 80% of individuals have it. This is a different level of differentiation than comparing how different alleles mutate (as in the coalescent) or how these mutations accumulate over time (like in many phylogenetic-based analyses).

Allele freq vs identity figure.jpg — An example of the difference between allele frequency and identity. In this example (and many of the figures that follow in this post), the circle denote different populations, within which there are individuals which possess either an A gene (blue) or a B gene. **Left:** If we compared Populations 1 and 2, we can see that they both have A and B alleles. However, these alleles vary in their frequency within each population, with an equal balance of A and B in Pop 1 and a much higher frequency of B in Pop 2. **Right:** However, when we compared Pop 3 and 4, we can see that not only do they vary in frequencies, they vary in the presence of alleles, with one allele in each population but not the other.

Non-adaptive (neutral) uses

Testing neutral structure

Arguably one of the most standard uses of allele frequency data is the determination of population structure, one which more avid The G-CAT readers will be familiar with. This is based on the idea that populations that are isolated from one another are less likely to share alleles (and thus have similar frequencies of those alleles) than populations that are connected. This is because gene flow across two populations helps to homogenise the frequency of alleles within those populations, by either diluting common alleles or spreading rarer ones (in general). There are a number of programs that use allele frequency data to assess population structure, but one of the most common ones is STRUCTURE.

Gene flow homogeneity figure — An example of how gene flow across populations homogenises allele frequencies. We start with two initial populations (1 and 2 from above), which have very different allele frequencies. Hybridising individuals across the two populations means some alleles move from Pop 1 and Pop 2 into the hybrid population: which alleles moves is random (the smaller circles). Because of this, the resultant hybrid population has an allele frequency somewhere in between the two source populations: think of like mixing red and blue cordial and getting a purple drink.

Simple YPP structure figure.jpg — An example of a Structure plot which long-term *The G-CAT* readers may be familiar with. This is taken from Brauer *et al.* (2013), where the authors studied the population structure of the Yarra pygmy perch. Each small column represents a single individual, with the colours representing how well the alleles of that individual fit a particular genetic population (each population has one colour). The numbers and broader columns refer to different ‘localities’ (different from populations) where individuals were sourced. This shows clear strong population structure across the 4 main groups, except for in Locality 6 where there is a mixture of Eastern and Merri/Curdies alleles.

Determining genetic bottlenecks and demographic change

Other neutral aspects of population identity and history can be studied using allele frequency data. One big component of understanding population history in particular is determining how the population size has changed over time, and relating this to bottleneck events or expansion periods. Although there are a number of different approaches to this, which span many types of analyses (e.g. also coalescent methods), allele frequency data is particularly suited to determining changes in the recent past (hundreds of generations, as opposed to thousands of generations ago). This is because we expect that, during a bottleneck event, it is statistically more likely for rare alleles (i.e. those with low frequency) in the population to be lost due to strong genetic drift: because of this, the population coming out of the bottleneck event should have an excess of more frequent alleles compared to a non-bottlenecked population. We can determine if this is the case with tests such as the heterozygosity excess, M-ratio or mode shift tests.

Genetic drift and allele freq figure — A diagram of how allele frequencies change in genetic bottlenecks due to genetic drift. **Left:** Large circles again denote a population (although across different sequential times), with smaller circle denoting which alleles survive into the next generation (indicated by the coloured arrows). We start with an initial ‘large’ population of 8, which is reduced down to 4 and 2 in respective future times. Each time the population contracts, only a select number of alleles (or individuals) ‘survive’: assuming no natural selection is in process, this is totally random from the available gene pool. **Right:** We can see that over time, the frequencies of alleles A and B shift dramatically, leading to the ‘extinction’ of Allele B due to genetic drift. This is because it is the less frequent allele of the two, and in the smaller population size has much less chance of randomly ‘surviving’ the purge of the genetic bottleneck.

Adaptive (selective) uses

Testing different types of selection

We’ve also discussed previously about how different types of natural selection can alter the distribution of allele frequency within a population. There are a number of different predictions we can make based on the selective force and the overall population. For understanding particular alleles that are under strong selective pressure (i.e. are either strongly adaptive or maladaptive), we often test for alleles which have a frequency that strongly deviates from the ‘neutral’ background pattern of the population. These are called ‘outlier loci’, and the fact that their frequency is much more different from the average across the genome is attributed to natural selection placing strong pressure on either maintaining or removing that allele.

Other selective tests are based on the idea of correlating the frequency of alleles with a particular selective environmental pressure, such as temperature or precipitation. In this case, we expect that alleles under selection will vary in relation to the environmental variable. For example, if a particular allele confers a selective benefit under hotter temperatures, we would expect that allele to be more common in populations that occur in hotter climates and rarer in populations that occur in colder climates. This is referred to as a ‘genotype-environment association test’ and is a good way to detect polymorphic selection (i.e. when multiple alleles contribute to a change in a single phenotypic trait).

Genotype by environment figure.jpg — An example of how the frequency of alleles might vary under natural selection in correlation to the environment. In this example, the blue allele A is adaptive and under positive selection in the more intense environment, and thus increases in frequency at higher values. Contrastingly, the red allele B is maladaptive in these environments and decreases in frequency. For comparison, the black allele shows how the frequency of a neutral (non-adaptive or maladaptive) allele doesn’t vary with the environment, as it plays no role in natural selection.

Taxonomic (species identity) uses

At one end of the spectrum of allele frequencies, we can also test for what we call ‘fixed differences’ between populations. An allele is considered ‘fixed’ it is the only allele for that locus in the population (i.e. has a frequency of 1), whilst the alternative allele (which may exist in other populations) has a frequency of 0. Expanding on this, ‘fixed differences’ occur when one population has Allele A fixed and another population has Allele B fixed: thus, the two populations have as different allele frequencies (for that one locus, anyway) as possible.

Fixed differences are sometimes used as a type of diagnostic trait for species. This means that each ‘species’ has genetic variants that are not shared at all with its closest relative species, and that these variants are so strongly under selection that there is no diversity at those loci. Often, fixed differences are considered a level above populations that differ by allelic frequency only as these alleles are considered ‘diagnostic’ for each species.

Fixed differences figure.jpg — An example of the difference between fixed differences and allelic frequency differences. In this example, we have 5 cats from 3 different species, sequencing a particular target gene. Within this gene, there are three possible alleles: **T, A** or G respectively. You’ll quickly notice that the T allele is both unique to Species A and is present in all cats of that species (i.e. is fixed). This is a fixed difference between Species A and the other two. Alleles A and G, however, are present in both Species B and C, and thus are not fixed differences even if they have different frequencies.

Intrapopulation (relatedness) uses

Allele frequency-based methods are even used in determining relatedness between individuals. While it might seem intuitive to just check whether individuals share the same alleles (and are thus related), it can be hard to distinguish between whether they are genetically similar due to direct inheritance or whether the entire population is just ‘naturally’ similar, especially at a particular locus. This is the distinction between ‘identical-by-descent’, where alleles that are similar across individuals have recently been inherited from a similar ancestor (e.g. a parent or grandparent) or ‘identical-by-state’, where alleles are similar just by chance. The latter doesn’t contribute or determine relatedness as all individuals (whether they are directly related or not) within a population may be similar.

To distinguish between the two, we often use the overall frequency of alleles in a population as a basis for determining how likely two individuals share an allele by random chance. If alleles which are relatively rare in the overall population are shared by two individuals, we expect that this similarity is due to family structure rather than population history. By factoring this into our relatedness estimates we can get a more accurate overview of how likely two individuals are to be related using genetic information.

The wild world of allele frequency

Despite appearances, this is just a brief foray into the many applications of allele frequency data in evolution, ecology and conservation studies. There are a plethora of different programs and methods that can utilise this information to address a variety of scientific questions and refine our investigations.

You’re perfect, you’re beautiful, you look like a model (species)

October 11, 2018Sean Buckley1 Comment

What is a ‘model’?

There are quite literally millions of species on Earth, ranging from the smallest of microbes to the largest of mammals. In fact, there are so many that we don’t actually have a good count on the sheer number of species and can only estimate it based on the species we actually know about. Unsurprisingly, then, the number of species vastly outweighs the number of people that research them, especially considering the sheer volumes of different aspects of species, evolution, conservation and their changes we could possibly study.

Species on Earth estimate figure — Some estimations on the number of eukaryotic species (i.e. not including things like bacteria), with the number of known species in blue and the predicted number of total species on Earth in purple. Source: Census of Marine Life.

This is partly where the concept of a ‘model’ comes into it: it’s much easier to pick a particular species to study as a target, and use the information from it to apply to other scenarios. Most people would be familiar with the concept based on medical research: the ‘lab rat’ (or mouse). The common house mouse (Mus musculus) and the brown rat (Rattus norvegicus) are some of the most widely used models for understanding the impact of particular biochemical compounds on physiology and are often used as the testing phase of medical developments before human trials.

So, why are mice used as a ‘model’? What actually constitutes a ‘model’, rather than just a ‘relatively-well-research-species’? Well, there are a number of traits that might make certain species ideal subjects for understanding key concepts in evolution, biology, medicine and ecology. For example, mice are often used in medical research given their (relative) similar genetic, physiological and behavioural characteristics to humans. They’re also relatively short-lived and readily breed, making them ideal to observe the more long-term effects of medical drugs or intergenerational impacts. Other species used as models primarily in medicine include nematodes (Caenorhabditis elegans), pigs (Sus scrofa domesticus), and guinea pigs (Cavia porcellus).

The diversity of models

There are a wide variety and number of different model species, based on the type of research most relevant to them (and how well it can be applied to other species). Even with evolution and conservation-based research, which can often focus on more obscure or cryptic species, there are several key species that have widely been applied as models for our understanding of the evolutionary process. Let’s take a look at a few examples for evolution and conservation.

Drosophila

It would be remiss of me to not mention one of the most significant contributors to our understanding of the genetic underpinning of adaptation and speciation, the humble fruit fly (Drosophila melanogaster, among other species). The ability to rapidly produce new generations (with large numbers of offspring with very short generation time), small fully-sequenced genome, and physiological variation means that observing both phenotypic and genotypic changes over generations due to ‘natural’ (or ‘experimental’) selection are possible. In fact, Drosphilia spp. were key in demonstrating the formation of a new species under laboratory conditions, providing empirical evidence for the process of natural selection leading to speciation (despite some creationist claims that this has never happened).

Darwin’s finches

The original model of evolution could be argued to be Darwin’s finches, as the formed part of the empirical basis of Charles Darwin’s work on the theory of evolution by natural selection. This is because the different species demonstrate very distinct and obvious changes in morphology related to a particular diet (e.g. the physiological consequences of natural selection), spread across an archipelago in a clear demonstration of a natural experiment. Thus, they remain the original example of adaptive radiation and are fundamental components of the theory of evolution by natural selection. However, surprisingly, Darwin’s finches are somewhat overshadowed in modern research by other species in terms of the amount of available data.

Zebra finches

Even as far as birds go, one species clearly outshines the rest in terms of research. The zebra finch is one of the most highly researched vertebrate species, particularly as a model of song learning and behaviour in birds but also as a genetic model. The full genome of the zebra finch was the second bird to ever be sequenced (the first being a chicken), and remains one of the more detailed and annotated genomes in birds. Because of this, the zebra finch genome is often used as a reference for other studies on the genetics of bird species, especially when trying to understand the function of genetic changes or genes under selection.

Fishes

Fish are (perhaps surprisingly) also relatively well research in terms of evolutionary studies, largely due to their ancient origins and highly diverse nature, with many different species across the globe. They also often demonstrate very rapid and strong bouts of divergence, such as the cichlid fish species of African lakes which demonstrate how new species can rapidly form when introduced to new and variable environments. The cichlids have become the poster child of adaptive radiation in fishes much in the same way that Darwin’s finches highlighted this trend in birds. Another group of fish species used as a model for similar aspects of speciation, adaptive divergence and rapid evolutionary change are the three-spine and nine-spine stickleback species, which inhabit a variety of marine, estuarine and freshwater environments. Thus, studies on the genetic changes across these different morphotypes is a key in understanding how adaptation to new environments occur in nature (particularly the relatively common transition into different water types in fishes).

cichlid diversity figure — The sheer diversity of species and form makes African cichlids an ideal model for testing hypotheses and theories about the process of evolution and adaptive radiation. Figure sourced from Brawand *et al.* (2014) in Nature.

Zebra fish

More similar to the medical context of lab rats is the zebrafish (ironically, zebra themselves are not considered a model species). Zebrafish are often used as models for understanding embryology and the development of the body in early formation given the rapid speed at which embryonic development occurs and the transparent body of embryos (which makes it easier to detect morphological changes during embryogenesis).

Using information from model species for non-models

While the relevance of information collected from model species to other non-model species depends on the similarity in traits of the two species, our understanding of broad concepts such as evolutionary process, biochemical pathways and physiological developments have significantly improved due to model species. Applying theories and concepts from better understood organisms to less researched ones allows us to produce better research much faster by cutting out some of the initial investigative work on the underlying processes. Thus, model species remain fundamental to medical advancement and evolutionary theory.

That said, in an ideal world all species would have the same level of research and resources as our model species. In this sense, we must continue to strive to understand and research the diversity of life on Earth, to better understand the world in which we live. Full genomes are progressively being sequenced for more and more species, and there are a number of excellent projects that are aiming to sequence at least one genome for all species of different taxonomic groups (e.g. birds, bats, fish). As the data improves for our non-model species, our understanding of evolution, conservation management and medical research will similarly improve.

Lost in a forest of (gene) trees

October 4, 2018November 9, 2023Sean Buckley3 Comments

Using genetics to understand species history

The idea of using the genetic sequences of living organisms to understand the evolutionary history of species is a concept much repeated on The G-CAT. And it’s a fundamental one in phylogenetics, taxonomy and evolutionary biology. Often, we try to analyse the genetic differences between individuals, populations and species in a tree-like manner, with close tips being similar and more distantly separated branches being more divergent. However, this runs on one very key assumption; that the patterns we observe in our study genes matches the overall patterns of species evolution. But this isn’t always true, and before we can delve into that we have to understand the difference between a ‘gene tree’ and a ‘species tree’.

A gene tree or a species tree?

Our typical view of a phylogenetic tree is actually one of a ‘gene tree’, where we analyse how a particular gene (or set of genes) have changed over time between different individuals (within and across populations or species) based on our understanding of mutation and common ancestry.

However, a phylogenetic tree based on a single gene only demonstrates the history of that gene. What we assume in most cases is that the history of that gene matches the history of the species: that branches in the genetic tree mirror when different splits in species occurred throughout history.

The easiest way to conceptualise gene trees and species trees is to think of individual gene trees that are nested within an overarching species tree. In this sense, individual gene trees can vary from one another (substantially, even) but by looking at the overall trends of many genes we can see how the genome of the species have changed over time.

Gene tree incongruence

Different genes may have different patterns for a number of reasons. Changes in the genetic sequences of organisms over time don’t happen equally across the entire genome, and very specific parts of the genome can evolve in entirely different directions, or at entirely different rates, than the rest of the genome. Let’s take a look at a few ways we could have conflicting gene trees in our studies.

Incomplete lineage sorting

One of the most prolific, but more complicated, ways gene trees can vary from their overarching species tree is due to what we call ‘incomplete lineage sorting’. This is based on the idea that species and the genes that define them are constantly evolving over time, and that because of this different genes are at different stages of divergence between population and species. If we imagine a set of three related populations which have all descended from a single ancestral population, we can start to see how incomplete lineage sorting could occur. Our ancestral population likely has some genetic diversity, containing multiple alleles of the same locus. In a true phylogenetic tree, we would expect these different alleles to ‘sort’ into the different descendent populations, such that one population might have one of the alleles, a second the other, and so on, without them sharing the different alleles between them.

If this separation into new populations has been recent, or if gene flow has occurred between the populations since this event, then we might find that each descendent population has a mixture of the different alleles, and that not enough time has passed to clearly separate the populations. For this to occur, sufficient time for new mutations to occur and genetic drift to push different populations to differently frequent alleles needs to happen: if this is too recent, then it can be hard to accurately distinguish between populations. This can be difficult to interpret (see below figure for a visualisation of this), but there’s a great description of incomplete lineage sorting here.

ILS_adaptedfigure — A demonstration of incomplete lineage sorting, generously adapted from a talk by fellow MELFU postdocs Dr Yuma (Jonathon) Sandoval-Castillo and Dr Catherine Attard. On the left is a depiction of a single gene coalescent tree over time: circles represent a single individual at a particular point in time (row) with the colours representing different alleles of that same gene. The tree shows how new mutations occur (colour changes along the branches) and spread throughout the descendent populations. In this example, we have three recently separated species, with a good number of different alleles. However, when we study these alleles in tree form (the phylogeny on the right), we see that the branches themselves don’t correlate well with the boundaries of the species. For example, the teal allele found within Species C is actually more similar to Species B alleles (purple and blue) than any other Species B alleles, based on the order and patterns of these mutations.

Hybridisation and horizontal transfer

Another way individual genes may become incongruent with other genes is through another phenomenon we’ve discussed before: hybridisation (or more specifically, introgression). When two individuals from different species breed together to form a ‘hybrid’, they join together what was once two separate gene pools. Thus, the hybrid offspring has (if it’s a first generation hybrid, anyway) 50% of genes from Species A and 50% of genes from Species B. In terms of our phylogenetic analysis, if we picked one gene randomly from the hybrid, we have 50% of picking a gene that reflects the evolutionary history of Species A, and 50% chance of picking a gene that reflects the evolutionary history of Species B. This would change how our outputs look significantly: if we pick a Species A gene, our ‘hybrid’ will look (genetically) very, very similar to Species A. If we pick a Species B gene, our ‘hybrid’ will look like a Species B individual instead. Naturally, this can really stuff up our interpretations of species boundaries, distributions and identities.

Hybridisation_figure — An example of hybridisation leading to gene tree incongruence with our favourite colourful fish. A) We have a hybridisation event between a red fish (Species A) and a green fish (Species B), resulting in a hybrid species (‘Species’ H). The red fish genome is indicated by the yellow DNA, the green fish genomes by the blue DNA, and the hybrid orange fish has a mixture of these two. B) If we sampled one set of genes in the hybrid, we might select a gene that originated from the red fish, showing that the hybrid is identical (or very similar) the Species A. D) Conversely, if we sampled a gene originating from the green fish, the resultant phylogeny might show that the hybrid is the same as Species B. C) If we consider these two patterns in combination, which see the true pattern of species formation, which is not a clear dichotomous tree and rather a mixture of the two sets of trees.

Paralogous genes

More confusingly, we can even have events where a single gene duplicates within a genome. This is relatively rare, although it can have huge effects: for example, salmon have massive genomes as the entire thing was duplicated! Each version of the gene can take on very different forms, functions, and evolve in entirely different ways. We call these duplicated variants paralogous genes: genes that look the same (in terms of sequence), but are totally different genes.

This can have a profound impact as paralogous genes are difficult to detect: if there has been a gene duplication early in the evolutionary history of our phylogenetic tree, then many (or all) of our study samples will have two copies of said gene. Since they look similar in sequence, there’s all possibility that we pick Variant 1 in some species and Variant 2 in other species. Being unable to tell them apart, we can have some very weird and abstract results within our tree. Most importantly, different samples with the same duplicated variant will seem similar to one another (e.g. have evolved from a common ancestor more recently) than it will to any sample of the other variant (even if they came from the exact same species)!

An example of how paralogous genes can confound species tree. We start with a single (purple) gene: at a particular point in time, this gene duplicates into a red and a blue form. Each of these genes then evolve and spread into four separate descendent species (A, B, C and D) but not in entirely the same way. However, since both the red and blue genetic sequences are similar, if we took a single gene from each species we might (somewhat randomly) sequence either the red or the blue copy. The different phylogenetic trees on the right demonstrate how different combinations of red and blue genes give very different patterns, since all blue copies will be more related to other blue genes than to the red gene of the same species. E.g. a blue A and a blue C are more similar than a blue A and a red A.

Overcoming incongruence with genomics

Although a tricky conundrum in phylogenetics and evolutionary genetics broadly, gene tree incongruence can largely be overcome with using more loci. As the random changes of any one locus has a smaller effect of the larger total set of loci, the general and broad patterns of evolutionary history can become clearer. Indeed, understanding how many loci are affected by what kind of process can itself become informative: large numbers of introgressed loci can indicate whether hybridisation was recent, strong, or biased towards one species over another, for example. As with many things, the genomic era appears poised to address the many analytical issues and complexities of working with genetic data.

Hotter and colder: how historic glacial cycles have shaped modern diversity

September 21, 2018Sean Buckley4 Comments

A tale as old as time

Since evolution is a constant process, occurring over both temporal and spatial scales, the impact of evolutionary history for current and future species cannot be overstated. The various forces of evolution through natural selection have strong, lasting impacts on the evolution of organisms, which is exemplified within the genetic make-up of all species. Phylogeography is the domain of research which intrinsically links this genetic information to historical selective environment (and changes) to understand historic distributions, evolutionary history, and even identify biodiversity hotspots.

The Ice Age(s)

Although there are a huge number of both historic and contemporary climatic factors that have influenced the evolution of species, one particularly important time period is referred to as the Pleistocene glacial cycles. The Pleistocene epoch spans from ~2 million years ago until ~100,000 years ago, and is a time of significant changes in the evolution of many species still around today (particularly for vertebrates). This is because the Pleistocene largely consisted of several successive glacial periods: at times, the climate was significantly cooler, glaciers were more widespread and sea-levels were lower (due to the deeper freezing of water around the poles). These periods were then followed by ‘interglacial periods’, where much of the globe warmed, ice caps melted and sea-levels rose. Sometimes, this natural pattern is argued as explaining 100% of recent climate change: don’t be fooled, however, as Pleistocene cycles were never as dramatic or irreversible as modern, anthropogenically-driven climate change.

Annotated glacial cycles.jpg — The general pattern of glacial and interglacial periods over the last 1 million years, adapted from Oceanbites.

The glacial cycles of the Pleistocene had a number of impacts on a plethora of species on Earth. For many of these species, these glacial-interglacial periods resulted in what we call ‘glacial refugia’ and ‘interglacial expansion’: at the peak of glacial periods, many species’ distributions contracted to small patches of suitable habitat, like tiny islands in a freezing ocean. As the globe warmed during interglacial periods, these habitats started to spread and with them the inhabiting species. While it’s expected that this likely happened many times throughout the Pleistocene, the most clearly observed cycle would be the most recent one: referred to as the Last Glacial Maximum (LGM), at ~21,000 years ago. Thus, a quick dive into the literature shows that it is rife with phylogeographic examples of expansions and contractions related to the LGM.

glacial refugia example figure.jpg — An example of how phylogeographic analysis can find glacial refugia in species, in this case the montane caddisfly *Thremma gallicum* from Macher *et al.* (2017). The colours refer to the two datasets they used (blue = ddRADseq; red = mtDNA) and the arrows demonstrate migration pathways in the interglacial period following the LGM.

The glacial impact on genetic diversity

Why does any of this matter? Didn’t it all happen in the past? Well, that leads us back to the original point in this post: forces of evolution leave distinct impacts on the genetic architecture of species. In regards to glacial refugia, a clear pattern is often observed: populations occurring approximately in line with the refugia have maintained greater genetic diversity over time, whilst those in more unstable or unsuitable regions show much more reduced genetic diversity. And this makes sense: many of those populations likely went extinct during glaciation, and only within the last 20,000 or so years have been recolonised from nearby refugia. Accounting for genetic drift due to founder effect, it’s easy to see how this would cause genetic diversity to plummet.

Case study: the charismatic cheetah

And this loss of genetic diversity isn’t just a hypothetical, or an interesting note in evolution. It can have dire impacts for the survivability of species. Take for example, the very charismatic cheetah. Like many large, apex predator species, the cheetah in the modern day is endangered and at risk of extinction to a variety of threats, and although many of these are linked to modern activity (such as being killed to protect farms or habitat clearing), some of these go back much further in history.

Believe it not, the cheetah as a species actually originated from an ancestor in the Americas: they’re closely related to other American big cats such as the puma/cougar. During the Miocene (5 – 8 million years ago), however, the ancestor of the modern cheetah migrated a very long way to Africa, diverging from its shared ancestor with jaguarandi and cougars. Subsequent migrations into Africa and Asia (where only the Iranian subspecies remains) during the Pleistocene, dated at ~100,000 and ~12,000 years ago, have been shown through whole genome analysis to have resulted in significant reductions in the genetic diversity of the cheetah. This timing correlates with the extinction of the cheetah and puma within North America, and the worldwide extinction of many large mammals including mammoths, dire wolves and sabre-tooth tigers.

cheetah bottleneck.jpg — The demographic history of the African cheetah population, based on whole genomes in Dobrynin *et al.* (2015). In this figure, ‘Eastern’ refers to a Tanzanian population whilst ‘southern’ refers to a Namibian population (and as such doesn’t depict bottlenecks elsewhere in the cheetah e.g. Iran). The initial population underwent a severe genetic bottleneck ~12,000 years ago, likely due to glaciation.

What does this mean for the cheetah? Well, the cheetah has one of the lowest amounts of genetic variation for any living mammal. It’s even lower than the Tasmanian Devil, a species with such notoriously low genetic diversity that a rampant face cancer (Devil Facial Tumour Disease) is transmissible simply because their immune system can’t recognise the transferred cancer cells as being different to the host animal. Similarly, for the cheetah, it’s possible to do reciprocal skin transplants without the likelihood of organ rejection simply because their immune system is incapable of determining the difference between foreign and host tissue cells.

cheetah diversity 2.jpg — Examples of the incredibly low genetic diversity in cheetah, both from Dobrynin *et al.* (2015). A) shows the relative level of genetic diversity in cheetah compared to many other species, being lower than Tasmanian Devils and significantly lower than humans and domestic cats. D) shows the overall variation across the genome of a domestic cat (top), the inbred Abyssinian cat (middle) and the cheetah (bottom). Highly variable regions are indicated in red, whilst low variability regions are indicated in green. As you can see, the entirety of the cheetah genome has incredibly low genetic variation, even compared to another cat species considered to have low genetic variation (the Abyssinian).

Inference for the future

Understanding the impact of the historic environment on the evolution and genetic diversity of living species is not just important for understanding how species became what they are today. It also helps us understand how species might change in the future, by providing the natural experimental evidence of evolution in a changing climate.