From mutation to speciation: the genetics of species formation

The genetics of speciation

Given the strong influence of genetic identity on the process and outcomes of the speciation process, it seems a natural connection to use genetic information to study speciation and species identities. There is a plethora of genetics-based tools we can use to investigate how speciation occurs (both the evolutionary processes and the external influences that drive it). One clear way to test whether two populations of a particular species are actually two different species is to investigate genes related to reproductive isolation: if the genetic differences demonstrate reproductive incompatibilities across the two populations, then there is strong evidence that they are separate species (at least under the Biological Species Concept; see Part One for why!). But this type of analysis requires several tools: 1) knowledge of the specific genes related to reproduction (e.g. formation of sperm and eggs, genital morphology, etc.), 2) the complete and annotated genome of the species (to be able to find and analyse the right genes properly) and 3) a good amount of data for the populations in question. As you can imagine, for people working on non-model species (i.e. ones that haven’t had the same history and detail of research as, say, humans and mice), this can be problematic. So, instead, we can use other genetic information to investigate and suggest patterns and processes related to the formation of new species.

Is reproductive isolation naturally selected for or just a consequence?

A fundamental aspect of studies of speciation is a “chicken or the egg”-type paradigm: does natural selection directly select for rapid reproductive isolation, preventing interbreeding; or as a secondary consequence of general adaptive differences, over a long history of evolution? This might be a confusing distinction, so we’ll dive into it a little more.

Of the two proposed models of speciation, the by-product of natural selection (the second model) has been the more favoured. Simply put, this expands on Darwin’s theory of evolution that describes two populations of a single species evolving independently of one another. As these become more and more different, both in physical (‘phenotype’) and genetic (‘genotype’) characteristics, there comes a turning point where they are so different that an individual from one population could not reasonably breed with an individual from the other to form a fertile offspring. This could be due to genetic incompatibilities (such as different chromosome numbers), physiological differences (such as changes in genital morphology), or behavioural conflicts (such as solitary vs. group living).

Certainly, this process makes sense, although it is debatable how fast reproductive isolation would occur in a given species (or whether it is predictable just based on the level of differentiation between two populations). Another model suggests that reproductive isolation actually might arise very quickly if natural selection favours maintaining particular combinations of traits together. This can happen if hybrids between two populations are not particularly well adapted (fit), causing natural selection to favour populations to breed within each group rather than across groups (leading to reproductive isolation). Typically, this is referred to as ‘reinforcement’ and predominantly involves isolating mechanisms that prevent individuals across populations from breeding in the first place (since this would be wasted energy and resources producing unfit offspring). The main difference between these two models is the sequence of events: do populations ecologically diverge, and because of that then become reproductively isolated, or do populations selectively breed (enforcing reproductive isolation) and thus then evolve independently?

Reinforcement figure.jpg
An example of reinforcement leading to speciation. A) We start with two populations of a single species (a red fish population and a green fish population), which can interbreed (the arrows). B) Because these two groups can breed, hybrids of the two populations can be formed. However, due to the poor combination of red and green fish genes within a hybrid, they are not overly fit (the red cross). C) Since natural selection doesn’t favour forming hybrids, populations then adapt to selectively breed only with similar fish, reducing the amount of interbreeding that occurs. D) With the two populations effectively isolated from one another, different adaptations specific to each population (spines in red fish, purple stripes in green fish) can evolve, causing them to further differentiate. E) At some point in the differentiation process, hybrids move from being just selectively unfit (as in B)) to entirely impossible, thus making the two populations formal species. In this example, evolution has directly selected against hybrids first, thus then allowing ecological differences to occur (as opposed to the other way around).

Reproductive isolation through DMIs

The reproductive incompatibility of two populations (thus making them species) is often intrinsically linked to the genetic make-up of those two species. Some conflicts in the genetics of Population 1 and Population 2 may mean that a hybrid having half Population 1 genes and half Population 2 genes will have serious fitness problems (such as sterility or developmental problems). Dramatic genetic differences, particularly a difference in the number of chromosomes between the two sources, is a significant component of reproductive isolation and is usually to blame for sterile hybrids such as ligers, zorse and mules.

However, subtler genetic differences can also have a strong effect: for example, the unique combination of Population 1 and Population 2 genes within a hybrid might interact with one another negatively and cause serious detrimental effects. These are referred to as “Dobzhansky-Müller Incompatibilities” (DMIs) and are expected to accumulate as the two populations become more genetically differentiated from one another. This can be a little complicated to imagine (and is based upon mathematical models), but the basis of the concept is that some combinations of gene variants have never, over evolutionary history, been tested together as the two populations diverge. Hybridisation of these two populations suddenly makes brand new combinations of genes, some of which may be have profound physiological impacts (including on reproduction).

DMI figure
An example of how Dobzhansky-Müller Incompatibilities arise, adapted from Coyne & Orr (2004). We start with an initial population (center top), which splits into two separate populations. In this example, we’ll look at how 5 genes (each letter = one gene) change over time in the separate populations, with the original allele of the gene (lowercase) occasionally mutating into a new allele (upper case). These mutations happen at random times and in random genes in each population (the red letters), such that the two become very different over time. After a while, these two populations might form hybrids; however, given the number of changes in each population, this hybrid might have some combinations of alleles that are ‘untested’ in their evolutionary history (see below). These untested combinations may cause the hybrid to be infertile or unviable, making the two populations isolated species.

DMI table
The list of ‘untested’ genetic combinations from the above example. This table shows the different combinations of each gene that could be made in a hybrid if these two populations interbred. The red cells indicate combinations that have never been ‘tested’ together; that is, at no point in the evolutionary history of these two populations were those two particular alleles together in the same individual. Green cells indicate ones that were together at some point, and thus are expected to be viable combinations (since the resultant populations are obviously alive and breeding).

How can we look at speciation in action?

We can study the process of speciation in the natural world without focussing on the ‘reproductive isolation’ element of species identity as well. For many species, we are unlikely to have the detail (such as an annotated genome and known functions of genes related to reproduction) required to study speciation at this level in any case. Instead, we might choose to focus on the different factors that are currently influencing the process of speciation, such as how the environmental, demographic or adaptive contexts of populations plays a role in the formation of new species. Many of these questions fall within the domain of phylogeography; particularly, how the historical environment has shaped the diversity of populations and species today.

Phylogeo of speciation
An example of the interplay between speciation and phylogeography, taken from Reyes-Velasco et al. (2018). They investigated the phylogeographic history of several different groups of species within the frog genus Ptychadena; in this figure, we can see how the different species (indicated by the colours and tree on the left) relate to the geography of their habitat (right).

A variety of different analytical techniques can be used to build a picture of the speciation process for closely related or incipient species. A good starting point for any speciation study is to look at how the different study populations are adapting; is there evidence that natural selection is pushing these populations towards different genotypes or ecological niches? If so, then this might be a precursor for speciation, and we can build on this inference with other complementary analyses.

For example, estimating divergence times between populations can help us suggest whether there has been sufficient time for speciation to occur (although this isn’t always clear cut). Additionally, we could estimate the levels of genetic hybridisation (‘introgression’) between two populations to suggest whether they are reasonably isolated and divergent enough to be considered functional species.

The future of speciation genomics

Although these can help answer some questions related to speciation, new tools are constantly needed to provide a clearer picture of the process. Understanding how and why new species are formed is a critical aspect of understanding the world’s biodiversity. How can we predict if a population will speciate at some point? What environmental factors are most important for driving the formation of new species? How stable are species identities, really? These questions (and many more) remain elusive for a wide variety of life on Earth.

 

Of birds and bees: where do species come from?

This is Part 2 of a four part miniseries on the process of speciation: how we get new species, how we can see this in action, and the end results of the process. This week we’re taking a look at how new species are formed from natural selection. For Part 1, on the identity and concept of the species, click here.

The Origin of Species

Despite Darwin’s scientifically ground-breaking revelations over 150 years ago, the truth of the origin of species has remained a puzzling and complex question in biology. While the fundamental concepts of Darwin’s theory remain heavily supported – that groups which become separated from one another and undergo differing evolutionary pathways through natural selection may over time form new species – the mechanisms leading to this are mysterious. Even though the heritable component of evolution (DNA) was not uncovered for a hundred years after publishing ‘On the Origin of Species’, Darwin’s theory can largely explain many patterns of the formation of species on Earth.

The population-speciation continuum

The understanding that groups that are separated progress into species through differential adaptation leads to a phenomenon as the ‘speciation continuum’: all populations exist at some point on the continuum, with those that are most differentiated (i.e. most progressed) are distinct species, whereas those least differentiated are closely related or the same population. Whether or not populations progress along this continuum, and how fast this progression happens, depends on the difference in selective pressure and speed of evolution in the populations. Even if two populations are physically separated, they might not necessarily form new species if the separation is too short-term or if they do not evolve in different ways. Even if they do differentially evolve, whether or not they develop reproductive isolation is not always consistent.

Speciation continuum figure
A vague diagram of the population-speciation continuum. In this figure, we have two different organisms (Taxa 1 and Taxa 2) and we’re comparing their genetic similarity/differences (the grey arrow). At the bottom left of the chart, there are very few genetic differences between the two, likely indicated that they are from the same population (or closely related e.g. siblings). As we progress towards the upper left, the two start to diverge from one another, first to different populations of the same species, different subspecies of the same overarching species, and eventually becoming so different that they must be new species (i.e. are genetically incompatible and thus reproductively isolated). Exactly where this cut-off is a bit of a grey area (the species boundary) and is unlikely to be consistent across species.

Furthermore, how these populations are changing may affect the rate or success of speciation: if the traits that evolve differently across the population also cause them to be unable to breed, then they may quickly become reproductively isolated and thus new species. For example, Momigliano et al. (2017) demonstrated the fastest known rate of speciation (within 3000 generations) in a marine vertebrate in a species of flounders. Flounders that adapted to a higher salinity environment became reproductively isolated from their sister population as their sperm could not tolerate the high salinity conditions (directly preventing breeding and causing reproductive isolation).  This strong and rapid selection to an environment, and its subsequent selection on reproductive ability, was cutely described as a “magic trait”.

Modes of speciation

Darwin’s model of speciation describes what is called “allopatric speciation”, whereby physical separation of populations by some form of barrier (often attributed to changes such as climatic shifts, mountain range formations or island separation) isolates populations which then independently evolve until they reach a point of differentiation where they can no longer interbreed. Thus, they are now separate species (based on the Biological Species Concept, anyway). Allopatric speciation has traditionally believed to be the most common process of speciation, and is consistently used as the model for teaching and understanding speciation.

While this physical separation is the strongest and most immediately obvious method of speciation, other forms without geographic barriers have been documented. “Sympatric speciation” involves speciation events where there are no apparent geographical barriers that separate populations: instead, other factors may be driving their divergence from one another. This can relate to different microenvironments within the same area, where one population migrates and adapts to an environment which excludes the other population. This is referred to as “ecological speciation” and has been particularly noted within lake fish radiating into different habitats. There are a number of other mechanisms by which sympatric speciation could also occur, however, including temporal isolation (e.g. different flowering times in plants), sexual selection (e.g. a mutation leads to a new physiology that is more attractive to others with that physiology) or polyploidy (e.g. a ‘mutation’ causes an organism to have multiple copies of its genome, making it effectively reproductively isolated from its neighbours due to incompatible sex cells).

Allopatric vs sympatric speciation
Representations of allopatric and sympatric speciation using our friends the fruit-eating catsA) An example of allopatric speciation. Similar to how we’ve seen it before, a geographic barrier (the dashed green line) separates the ancestral species in two; each of these groups then evolve in different directions based on the different environmental pressures of each zone. After enough divergence, these two groups become reproductively isolated from one another and thus are different species. B) An example of sympatric speciation. We start with a single species of red apple eating cats, which form one contiguous group. A mutation within the group produces a new type of fruit-eating cat; one that feeds on green apples (grey cats). Because these feed on a different food source, they move into a different part of the environment, associating with other green apple-eating cats and less with red apple-eating cats. Over time, and with strong enough selection for apple preferences, these two types may become different species.

Sympatric speciation has received a great deal of controversy, due to the fact that some levels of gene flow could occur across the two populations with relative ease (compared to allopatric populations). This gene flow should cause the two populations to reconnect and prevent each population from evolving differently from one another (as changes in one population’s gene pool will be introduced into the other). Speciation with gene flow has been shown for some species, based on the idea that the pressure of natural selection (i.e. being adapted to the right habitat) is much stronger than the level of gene flow (i.e. the introduction of non-adapted genes from the other population), so the two populations still diverge genetically.

Gene flow across populations (through hybridisation) will balance out the different allele frequencies of the two gene pools, preventing adaptive alleles from moving towards fixation as per the standard natural selection process. While the effect of gene flow might slow the process, taking longer for the populations to diverge to the species level, speciation can still be achieved. Thus, the balance of gene flow and adaptive divergence is critical in determining whether ecological speciation is possible.

Sympatric speciation figure
A slightly more convoluted example of sympatric speciation. A) We start with a single species of small orange cats (top row), which can share readily share genes with one another. A mutation within the species creates a new type of cat; one that is much larger and has tufted ears. Although there are somewhat morphologically distinct from one another, they’re still genetically similar enough to continue to breed and share genes across the two types. However, with the big size comes a new ecological niche and these big cats differentially evolve to be grey (to hide better from their new bigger prey, perhaps) whilst the non-mutated group stays the same size and colour. Because large grey cats will preferentially breed with other large grey cats and not with small orange cats, this group genetically diverges from the ancestor to form a new species. B) A representation of the genetic changes between the two groups over time. The figure shows the genome (the grey bar) of the cat; the y-axis is the level of genetic differentiation between the two (measured as Fst). The different coloured sections represent specific genes within the genome, whilst the dashed line represents the average Fst across the whole genome. At initial divergence (top), there is little difference between the two. However, as the new big cats form and evolve, we can see the average Fst increase, with strong peaks around particular genes (blue and green; those related to the changes in physiology). As the two groups continue to diverge, this average raises even higher until genetic changes cause the reproduction-related genes (red and yellow) to become too different to allow for hybridisation, making the two species reproductively isolated (the red X in A)).

The reality of species

While the distinction between divergent populations and species might be a complex one, development in genomic technologies and greater understanding of evolutionary patterns is helping us uncover the real origin of species. And while species might not be as concrete a concept as one might expect, understanding the processes that generate new species and diversity is critical for understanding the diversity within nature that we see today, and also the potential diversity for the future (and why protecting said diversity is important!).

What is a species, anyway?

This is Part 1 of a four part miniseries on the process of speciation; how we get new species, how we can see this in action, and the end results of the process. This week, we’ll start with a seemingly obvious question: what is a species?

The definition of a ‘species’

‘Species’ are a human definition of the diversity of life. When we talk about the diversity of life, and the myriad of creatures and plants on Earth, we often talk about species diversity. This might seem glaringly obvious, but there’s one key issue: what is a species, anyway? While we might like to think of them as discrete and obvious groups (a dog is definitely not the same species as a cat, for example), the concept of a singular “species” is actually the result of human categorisation.

In reality, the diversity of life is spread across a huge spectrum of differentiation: from things which are closely related but still different to us (like chimps), to more different again (other mammals), to hardly relatable at all (bacteria and plants). So, what is the cut-off for calling something a species, and not a different genus, family, or kingdom? Or alternatively, at what point do we call a specific sub-group of a species as a sub-species, or another species entirely?

This might seem like a simple question: we look at two things, and they look different, so they must be different species, right? Well, of course, nature is never simple, and the line between “different” and “not different” is very blurry. Here’s an example: consider that you knew nothing about the history, behaviour or genetics of dogs. If you simply looked at all the different breeds of dogs on Earth, you might suggest that there are hundreds of species of domestic dogs. That seems a little excessive though, right? In fact, the domestic dog, Eurasian wolf, and the Australian dingo are all the same species (but different subspecies, along with about 38 others…but that’s another issue altogether).

Dogs
Morphology can be misleading for identifying species. In this example, we have A) a dog, B) also a dog, C) still a dog, D) yet another dog, and E) not a dog. For the record, A-D are all Canis lupus of some variety; and are domestic dogs (Canis lupus familiaris), C is a dingo (Canis lupus dingo) and is a grey wolf (Canis lupus lupus). E, however, is the Ethiopian wolf, Canis simensis.

How do we describe species?

This method of describing species based on how they look (their morphology) is the very traditional approach to taxonomy. And for a long time, it seemed to work…until we get to more complex scenarios like the domestic dog. Or scenarios where two species look fairly similar, but in reality have evolved entirely differently for a very, very long time. Or groups which look close to more than one other species. So how do we describe them instead?

Cats and foxes
A), a fox. B), a cat. C), a foxy cat? A catty fox? A cat-fox hybrid? Something unrelated to cat or a fox?

 

Believe it or not, there are dozens of ways of deciding what is a species and what isn’t. In Speciation (2004), Coyne & Orr count at least 25 different reported Species Concepts that had been suggested within science, based on different requirements such as evolutionary history, genetic identity, or ecological traits. These different concepts can often contradict one another about where to draw the line between species…so what do we use?

The Biological Species Concept (BSC)

The most commonly used species concept is called the Biological Species Concept (BSC), which denotes that “species are groups of interbreeding natural populations that are reproductively isolated from other such groups” (Mayr, 1942). In short, a population is considered a different species to another population if an individual from one cannot reliably breed to form fertile, viable offspring with an individual from the other. We often refer to this as “reproductive isolation.” It’s important to note that reproductive isolation doesn’t mean they can’t breed at all: just that the hybrid offspring will not live a healthy life and produce its own healthy offspring.

For example, a horse and zebra can breed to produce a zorse, however zorse are fundamentally infertile (due to the different number of chromosomes between a horse and a zebra) and thus a horse is a different species to a zebra. However, a German Shepherd and a chihuahua can breed and make a hybrid mutt, so they are the same species.

zorse
A zorse, which shows its hybrid nature through zebra stripes and horse colouring. These two are still separate species since zorses are infertile, and thus are not a singular stable entity.

You might naturally ask why reproductive isolation is apparently so important for deciding species. Most directly, this means that groups don’t share gene pools at all (since genetic information is introduced and maintained over time through breeding events), which causes them to be genetically independent of one another. Thus, changes in the genetic make-up of one species shouldn’t (theoretically) transfer into the gene pool of another species through hybrids. This is an important concept as the gene pool of a species is the basis upon which natural selection and evolution act: thus, reproductively isolated species may evolve in very different manners over time.

RI example
An example of how reproductive isolation maintains genetic and evolutionary independence of species. In A), our cat groups are robust species, reproductively isolated from one another (as shown by the black box). When each species undergoes natural selection and their genetic variation changes (colour changes on the cats and DNA), these changes are kept within each lineage. This contrasts to B), where genetic changes can be transferred between species. Without reproductive isolation, evolution in the orange lineage and the blue lineage can combine within hybrids, sharing the evolutionary pathways of both ancestral species.

Pitfalls of the BSC

Just because the BSC is the most used concept doesn’t make it infallible, however. Many species on Earth don’t easily demonstrate reproductive isolation from one another, nor does the concept even make sense for asexually reproducing species. If an individual reproduced solely asexually (like many bacteria, or even some lizards), then by the BSC definition every individual is an entirely different species…which seems a little excessive. Even in sexually reproducing organisms, it can be hard to establish reproductive isolation, possibly because the species never come into contact physically.

This raises the debate of whether two species could, let alone will, hybridise in nature, which can be difficult to determine. And if two species do produce hybrid offspring, assessing their fertility or viability can be difficult to detect without many generations of breeding and measurements of fitness (hybrids may not be sustainable in nature if they are not well adapted to their environment and thus the two species are maintained as separate identities).

Hybrid birds
An example of unfit hybrids causing effective reproductive isolation. In this example, we have two different bird species adapted to very different habitats; a smaller, long-tailed bird (left) adapted to moving through dense forest, and a large, longer-legged bird (right) adapted to traversing arid deserts. When (or if) these two species hybridised, the resultant offspring would be middle of the road, possessing too few traits to be adaptive in either the forest or the desert and no fitting intermediate environment available. Measuring exactly how unfit this hybrid would be is a difficult task in establishing species boundaries.

 

Integrative taxonomy

To try and account for the issues with the BSC, taxonomists try to push for the usage of “integrative taxonomy”. This means that species should be defined by multiple different agreeing concepts, such as reproductive isolation, genetic differentiation, behavioural differences, and/or ecological traits. The more traits that can separate the two, the greater support there is for the species to be separated: if they disagree, then more information is needed to determine exactly whether or not that should be called different species. Debates about taxonomy are ongoing and are likely going to be relevant for years to come, but form critical components of understanding biodiversity, patterns of evolution, and creating effective conservation legislation to protect endangered or threatened species (for whichever groups we decide are species).

 

How did pygmy perch swim across the desert?

“Pygmy perch swam across the desert”

As regular readers of The G-CAT are likely aware, my first ever scientific paper was published this week. The paper is largely the results of my Honours research (with some extra analysis tacked on) on the phylogenomics (the same as phylogenetics, but with genomic data) and biogeographic history of a group of small, endemic freshwater fishes known as the pygmy perch. There are a number of different messages in the paper related to biogeography, taxonomy and conservation, and I am really quite proud of the work.

Southern_pygmy_perch 1 MHammer
A male southern pygmy perch, which usually measures 6-8 cm long.

To my honest surprise, the paper has received a decent amount of media attention following its release. Nearly all of these have focused on the biogeographic results and interpretations of the paper, which is arguably the largest component of the paper. In these media releases, the articles are often opened with “…despite the odds, new research has shown how a tiny fish managed to find its way across the arid Australian continent – more than once.” So how did they manage it? These are tiny fish, and there’s a very large desert area right in the middle of Australia, so how did they make it all the way across? And more than once?!

 The Great (southern) Southern Land

To understand the results, we first have to take a look at the context for the research question. There are seven officially named species of pygmy perches (‘named’ is an important characteristic here…but we’ll go into the details of that in another post), which are found in the temperate parts of Australia. Of these, three are found with southwest Western Australia, in Australia’s only globally recognised biodiversity hotspot, and the remaining four are found throughout eastern Australia (ranging from eastern South Australia to Tasmania and up to lower Queensland). These two regions are separated by arid desert regions, including the large expanse of the Nullarbor Plain.

Pygmyperch_distributionmap
The distributions of pygmy perch species across Australia. The dots and labels refer to different sampling sites used in the study. A: the distribution of western pygmy perches, and essentially the extent of the southwest WA biodiversity hotspot region. B: the distribution of eastern pygmy perches, excluding N. oxleyana which occurs in upper NSW/lower QLD (indicated in C). C: the distributions relative to the map of Australia. The black region in the middle indicates the Nullarbor Plain. 

 

The Nullarbor Plain is a remarkable place. It’s dead flat, has no trees, and most importantly for pygmy perches, it also has no standing water or rivers. The plain was formed from a large limestone block that was pushed up from beneath the Earth approximately 15 million years ago; with the progressive aridification of the continent, this region rapidly lost any standing water drainages that would have connected the east to the west. The remains of water systems from before (dubbed ‘paleodrainages’) can be seen below the surface.

Nullarbor Plain photo
See? Nothing here. Photo taken near Watson, South Australia. Credit: Benjamin Rimmer.

Biogeography of southern Australia

As one might expect, the formation of the Nullarbor Plain was a huge barrier for many species, especially those that depend on regular accessible water for survival. In many species of both plants and animals, we see in their phylogenetic history a clear separation of eastern and western groups around this time; once widely distributed species become fragmented by the plain and diverged from one another. We would most certainly expect this to be true of pygmy perch.

But our questions focus on what happened before the Nullarbor Plain arrived in the picture. More than 15 million years ago, southern Australia was a massively different place. The climate was much colder and wetter, even in central Australia, and we even have records of tropical rainforest habitats spreading all the way down to Victoria. Water-dependent animals would have been able to cross the southern part of the continent relatively freely.

Biogeography of the enigmatic pygmy perches

This is where the real difference between everything else and pygmy perch happens. For most species, we see only one east and west split in their phylogenetic tree, associated with the Nullarbor Plain; before that, their ancestors were likely distributed across the entire southern continent and were one continuous unit.

Not for pygmy perch, though. Our phylogenetic patterns show that there were multiple splits between eastern and western ancestral pygmy perch. We can see this visually within the phylogenetic tree; some western species of pygmy perches are more closely related, from an evolutionary perspective, to eastern species of pygmy perches than they are to other western species. This could imply a couple different things; either some species came about by migration from east to west (or vice versa), and that this happened at least twice, or that two different ancestral pygmy perches were distributed across all of southern Australia and each split east-west at some point in time. These two hypotheses are called “multiple invasion” and “geographic paralogy”, respectively.

MCC_geographylabelled
The phylogeny of pygmy perches produced by this study, containing 45 different individuals across all species of pygmy perch. Species are labelled in the tree in brackets, and their geographic location (east or west) is denoted by the colour on the right. This tree clearly shows more than one E/W separation, as not all eastern species are within the same clade. For example, despite being an eastern species, N. variegata is more closely related to Nth. balstoni or N. vittata than to the other eastern species (N. australisN. obscuraN. oxleyana and N. ‘flindersi’.

So, which is it? We delved deeper into this using a type of analysis called ‘ancestral clade reconstruction’. This tries to guess the likely distributions of species ancestors using different models and statistical analysis. Our results found that the earliest east-west split was due to the fragmentation of a widespread ancestor ~20 million years ago, and a migration event facilitated by changing waterways from the Nullarbor Plain pushing some eastern pygmy perches to the west to form the second group of western species. We argue for more than one migration across Australia since the initial ancestor of pygmy perches must have expanded from some point (either east or west) to encompass the entirety of southern Australia.

BGB_figure
The ancestral area reconstruction of pygmy perches, estimated using the R package BioGeoBEARS. The different pie charts denote the relative probability of the possible distributions for the species or ancestor at that particular time; colours denote exactly where the distribution is (following the legend). As you can see, the oldest E/W split at 21 million years ago likely resulted from a single widespread ancestor, with it’s range split into an east and west group. The second E/W event, at 15 million years ago, most likely reflects a migration from east to west, resulting in the formation of the N. vittata species group. This coincides with the Nullarbor Plain, so it’s likely that changes in waterway patterns allowed some eastern pygmy perch to move westward as the area became more arid.

So why do we see this for pygmy perch and no other species? Well, that’s the real mystery; out of all of the aquatic species found in southeast and southwest Australia, pygmy perch are one of the worst at migrating. They’re very picky about habitat, small, and don’t often migrate far unless pushed (by, say, a flood). It is possible that unrecorded extinct species of pygmy perch might help to clarify this a little, but the chances of finding a preserved fish fossil (let alone for a fish less than 8cm in size!) is extremely unlikely. We can really only theorise about how they managed to migrate.

Pygmy perch biogeo history
A diagram of the distribution of pygmy perch species over time, as suggested by the ancestral area reconstruction. A: the initial ancestor of pygmy perches was likely found throughout southern Australia. B: an unknown event splits the ancestor into an eastern and western group; the sole extant species of the W group is Nth. balstoniC: the ancestor of the eastern pygmy perches spreads towards the west, entering part of the pre-Nullarbor region. D: due to changes in the hydrology of the area, some eastern pygmy perches (the maroon colour in C) are pushed towards the west; these form N. vittata species and N. pygmaea. The Nullarbor Plain forms and effectively cuts off the two groups from one another, isolating them.

What does this mean for pygmy perches?

Nearly all species of pygmy perch are threatened or worse in the conservation legislation; there have been many conservation efforts to try and save the worst-off species from extinction. Pygmy perches provide a unique insight to the history of the Australian climate and may be a key in unlocking some of the mysteries of what our land was like so long ago. Every species is important for conservation and even those small, hard-to-notice creatures that we might forget about play a role in our environmental history.

The direction of evolution: divergence vs. convergence

Direction of evolution

We’ve talked previously on The G-CAT about how the genetic underpinning of certain evolutionary traits can change in different directions depending on the selective pressure it is under. Particularly, we can see how the frequency of different alleles might change in one direction or another, or stabilise somewhere in the middle, depending on its encoded trait. But thinking bigger picture than just the genetics of one trait, we can actually see that evolution as an entire process works rather similarly.

Divergent evolution

The classic view of the direction of evolution is based on divergent evolution. This is simply the idea that a particular species possess some ancestral trait. The species (or population) then splits into two (for one reason or another), and each one of these resultant species and populations evolves in a different way to the other. Over time, this means that their traits are changing in different directions, but ultimately originate from the same ancestral source.

Evidence for divergent evolution is rife throughout nature, and is a fundamental component of all of our understanding of evolution. Divergent evolution means that, by comparing similar traits in two species (called homologous traits), we can trace back species histories to common ancestors. Some impressive examples of this exist in nature, such as the number of bones in most mammalian species. Humans have the same number of neck bones as giraffes; thus, we can suggest that the ancestor of both species (and all mammals) probably had a similar number of neck bones. It’s just that the giraffe lineage evolved longer bones whereas other lineages did not.

Homology figure
A diagrammatic example of homologous structures in ‘hand’ bones. The coloured bones demonstrate how the same original bone structures have diverged into different forms. Source: BiologyWise.

Convergent evolution

But of course, evolution never works as simply as you want it to, and sometimes we can get the direct opposite pattern. This is called convergent evolution, and occurs when two completely different species independently evolve very similar (sometimes practically identical) traits. This is often caused by a limitation of the environment; some extreme demand of the environment requires a particular physiological solution, and thus all species must develop that trait in order to survive. An example of this would be the physiology of carnivorous marsupials like Tasmanian devils or thylacines: despite being in another Class, their body shapes closely resemble something more canid. Likely, the carnivorous diet places some constraints on physiology, particularly jaw structure and strength.

Convergent evol intelligence
A surprising example of convergent evolution is cognitive ability in apes and some bird groups (e.g. corvids). There’s plenty of other animal groups more related to each of these that don’t demonstrate the same level of cognitive reasoning (based on the traits listed in the centre): thus, we can conclude that cognition has evolved twice in very, very different lineages. Source: Emery & Clayton, 2004.

A more dramatic (and potentially obvious) example of convergent evolution would be wings and the power of flight. Despite the fact that butterflies, bees, birds and bats all have wings and can fly, most of them are pretty unrelated to one another. It seems much more likely that flight evolved independently multiple times, rather than the other 99% of species that shared the same ancestor lost the capacity of flight.

Parallel evolution

Sometimes convergent evolution can work between two species that are pretty closely related, but still evolved independently of one another. This is distinguished from other categories of evolution as parallel evolution: the main difference is that while both species may have shared the same start and end point, evolution has acted on each one independent of the other. This can make it very difficult to diagnose from convergent evolution, and is usually determined by the exact history of the trait in question.

Parallel evolution is an interesting field of research for a few reasons. Firstly, it provides a scenario in which we can more rigorously test expectations and outcomes of evolution in a particular environment. For example, if we find traits that are parallel in a whole bunch of fish species in a particular region, we can start to look at how that particular environment drives evolution across all fish species, as opposed to one species case studies.

Marsupial handedness.jpg
Here’s another weird example; different populations of marsupials (particularly kangaroos and wallabies) show preferential handedness depending on where the population is. That is, different populations of different species of marsupials shows parallel evolution of handedness, since they’re related to one another but have evolved it independently of the other species. Source: Giljov et al. (2015).

Following from that logic, it is then important to question the mechanisms of parallelism. From a genetic point of view, do these various species use the same genes (and genetic variants) to produce the same identical trait? Or are there many solutions to the selective question in nature? While these questions are rather complicated, and there has been plenty of evidence both for and against parallel genetic underpinning of parallel traits, it seems surprisingly often that many different genetic combinations can be used to get the same result. This gives interesting insight into how complex genetic coding of traits can be, and how creative and diverse evolution can be in the real world.

Where is evolution going?

Cat phylogeny
An example of all three types of evolutionary trajectory in a single phylogeny of cats (you know how we do it here at The G-CAT). This phylogeny consists of two distinct genera; one with one species (P. aliquam) and another of three species (the red box indicates their distance). Our species have three main physical traits: coat colour, ear tufts and tail shape. At the ancestral nodes of the tree, we can see what the ancestor of these species looked like for these three traits. Each of these traits has undergone a different type of evolution. The tufts on the ears are the result of divergent evolution, since F. tuftus evolved the trait differently to its nearest relative, F. griseo. Contrastingly, the orange coat colour of F. tuftus and P. aliquam are the result of convergent evolution: neither of these species are very closely related (remembering the red box) and evolved orange coats independently of one another (since their ancestors are grey). And finally, the fluffy tails of F. hispida and F. griseo can be considered parallel evolution, since they’re similar evolutionarily (same genus) but still each evolved tail fluff independently (not in the ancestor). This example is a little convoluted, but if you trace the history of each trait in the phylogeny you can more easily see these different patterns.

So, where is evolution going for nature? Well, the answer is probably all over the place, but steered by the current environmental circumstances. Predicting the evolutionary impacts of particular environmental change (e.g. climate change) is exceedingly difficult but a critical component of understanding the process of evolution and the future of species. Evolution continually surprises us with creative solution to complex problems and I have no doubt new mysteries will continue to be thrown at us as we delve deeper.

All the world in the palm of your hand: whole genome sequencing for evolution and conservation

Building an entire genome

If bigger is better, then biggest is best. Having the genome of a particular study species fully sequenced allows us to potentially look at all of the genetic variation in the entire gene pool: but how do we sequence the entirety of the genome? And what are the benefits of having a whole genome to refer to?

Whole genome assembly
A very, very simplified overview of whole genome sequencing. Similar to other genomic technologies, we start by fragmenting the genome into much smaller, easier to sequence parts (reads). We then use a computer algorithm which pieces these reads together into a consecutive sequence based on overlapping DNA sequence (like building a chain out of Lego blocks). From this assembled genome, we can then attach annotations using information from other species’ genomes or genetic studies, which can correlate a particular sequence to a gene, a function of that gene, and the resultant protein from these gene (although not always are all of these aspects included).

Well, assembling the whole genome of an organism for the first time is a very tricky process. It involves taking DNA sequence from only a few individuals, breaking them down into smaller fragments and multiplying these fragments into the billions (moreorless the same process used in other genomics technologies: the real difference is that we need the full breadth of the genome so that we don’t miss any spaces). From these fragments, we use a complex computer algorithm which builds up a consensus sequence like a Lego tower; by finding parts of sequences which overlap, the software figures out which pieces connect to one another. Hopefully, we eventually end up with one very long continuous sequence; the genome! Sometimes, we might end with a few very large blocks (called contigs), but this is also useful for analyses (correlated with how many/big blocks there are). With this full genome, we use information from other more completed genomes (such as those from model species like humans, mice or even worms) to figure out which sections of the genome relate to specific genes. We can then annotate these sections by labelling them as clear genes, complete with start and end point, and attach a particular physical function of that gene.

The benefits of whole genomes

Having an entire genome as a reference is an extremely helpful tool in conservation and evolutionary studies. The first, and perhaps most obvious benefit, is the sheer scale of the data we can use. By having the entirety of the genome available, we can use potentially billions of base pairs of sequence in our genetic analyses (for reference, the human genome is >3 billion base pairs long). Even if we don’t sequence the full genome for all of our samples, having a reference genome as basis for assembly our reduced datasets significantly improves the quantity and quality of sequences we can use.

Another very important benefit is the ability to prescribe function in our studies. Many of our processes for obtaining data, even for genomic technologies, use random and anonymous fragments of the genome. Although this is a cost-effective way to obtain a very large amount of data, it unfortunately means that we often have no idea which part of the genome our sequences came from. This means that we don’t know which sequences relate to specific genes, and even if we did we would have no idea what those genes are or do! But with an annotated genome, we can take even our fragmented sequence and check it against the genome and find out what genes are present.

Understanding adaptation

Based on that, it seems pretty obvious about exactly how having an annotated genome can help us in studies of adaptation. Knowing the functional aspect of our genetic data allows us to more directly determine how evolution is happening in nature; instead of only being able to say that two species are evolving differently from one another, for example, we can explicitly look at how they are evolving. Is one evolving tolerance to hotter temperatures? Are they evolving different genes to handle different diets? Are they evolving in response to an external influence, like a viral outbreak or changing climate? What are the physiological consequences of these changes? These questions are critical in understanding past and future evolution, and full genome analysis allows us to delve into them much deeper.

Manhattan plot example
A (slightly edited) figure of full genome comparisons between domestic dogs and wild wolves by Axelsson et al. (2013), with the aim of understanding the evolutionary changes associated with domestication. For avid readers, this figure probably looks familiar. This figure compares the genetic differentiation across the entire genome between dogs and wolves, with some sections of the genome (circled) showing clear differences. As there is an annotated dog genome, the authors then delved into these genes to understand the functional differences between the two. By comparing their genetic differences to functional genes, the authors can more explicitly suggest mechanisms or changes associated with the domestication process (such as adaptation to a starch-heavy and human-influenced diet).

 

 

This includes allowing us to better understand how adaptation actually works in nature. As we’ve discussed before, more traditional studies often assumed that single, or very few, genes were responsible for allowing a species to adapt and change, and that these genes had very strong effects on their physiology. But what we see far more often is polygenic adaptation; small changes in a very large number of genes which, combined together, allow the species to adapt and evolve. By having the entirety of the genome available, we are much more likely to capture all of the genes that are under natural selection in a particular population or species, painting a clearer picture of their evolutionary trajectory.

Understanding demography

The much larger dataset of full genomes is also important for understanding the non-adaptive parts of evolution; the demographic history. Given that selectively neutral impacts (e.g. reductions in population size) are likely to impact all of the genes in the gene pool somewhat equally, having a full genome allows us to more accurately infer the demographic state and historical patterns of species.

For both adaptive and non-adaptive variation, it is also important to consider what we call linkage disequilibrium. Genetic sequences that are physically close to each other in the genome will often be inherited together due to the imprecision of recombination (a fairly technical process, so I won’t delve into this): what this can mean is that if a gene is under very strong selection, then sequences around this gene will also look like they’re under selection too. This can give falsely positive adaptive genes (i.e. sequences that look like genes under selection but are just linked to a gene that is) or can interfere with demographic analyses (since they often assume no selection, or linkage to selection, on the sequences used). With a whole genome, we can actually estimate how far away a base pair has to be before it’s not linked anymore; we call these linkage blocks, and they’re very useful additions to analyses.

Linkage_example
An example of linkage as a process. We start with a particular sequence (top); during recombination, this sequence may randomly break and rearrange into different parts. In this example, I’ve simulated four different ‘breaks’ (dashed coloured lines) due to recombination. Each of these breaks leads to two separate blocks of fragments; for example, the break at the blue line results in the second two sequence blocks (middle). If we focus on one target base pair in the sequence (golden A), then we can see in some fragments it remains with certain bases, but sometimes it gets separated by the break. If we compare how often the golden A is in the same block (i.e. is co-inherited) as each of the other bases, across all 4 breaks, then we see that the bases that are closest to it (the golden A is represented by the golden bar) are almost always in the same block. This makes sense: the further away a base is from our target, the more likely that there will be a break between it. This is shown in the frequency distributions at the bottom: the left figure shows the actual frequencies of co-inheritance (i.e. linkage) using the top example and those 4 breaks. The right figure shows a more realistic depiction of how linkage looks in the genome; it rapidly decays as we move away from the target (although the width and rate of this can vary).

Improving conservation management

In a similar fashion to demography, full genome datasets can improve our estimates of relatedness and pedigrees in captive breeding programs. The massive scale of whole genomes allows us to more easily trace the genealogical history of individuals, allowing us to assign parents more accurately. This also helps with our estimations of genetic relatedness, arguably the most critical aspect of genetic-based breeding programs. This is particularly helpful for species with tricky mating patterns, such as polyamory, brood spawning or difficult to track organisms.

Pedigrees
An example of how whole genomes can improve our estimation of pedigrees. Say we have a random individual (star), and we want to know how they fit into a particular family tree (pedigree). With only a few genes, we might struggle to pick where in the family it fits based on limited genetic information. With a larger genetic dataset (such as reduced-representation genomics), we might be able to cross off a few potential candidate spots but still have some trouble with a few places (due to unknown parents, polygamy or issues with genetic analysis). With whole genomes, we should be able to much better clarify the whole pedigree and find exactly where our star individual fits in the tree (red circle). It is thanks to whole genomes, we can do those ancestry analyses that have gone viral lately!

The way forwards

While many non-model species are still lacking in the available genomic information, whole genomes are progressively being sequenced for more and more species. As this astronomical dataset grows, our ability to investigate, discover and test theories about evolution, natural selection and conservation will also improve. Many projects already exist which aim specifically to increase the number of whole genomes available for certain taxonomic groups such as birds and bats: these will no doubt prove to be invaluable resources for future studies.

Why we should always pander to diversity

Diversity in the natural world

‘Diversity’ is a term that gets used a lot these days, albeit usually in reference to social changes and structures. However, diversity is not merely a human construct and reflects an extremely important aspect of the natural world at a variety of levels. From the smallest genes to the biggest ecosystems, diversity is a trait that confers a massive range of benefits to individuals, populations, species and even the entire globe. Let’s dissect this diversity down at different scales and see how beneficial it can be.

Hierarchy of diversity
The generalised hierarchy at life, with diversity being an important component of each tier. At the smallest tier, genes underpin all life. The collection of genetic diversity is often summarised into a population (as a single cohesive genetic unit). Several populations can be pooled together into a single (usually) cohesive speciesDifferent species are then components of a larger community (which in turn are components of a broader ecosystem).

Genetic diversity

At the smallest scale in the hierarchy of genetic differentiation, we have the genes themselves. It is a well-established concept that having a diversity of genetic variants (alleles) within a population or species is critical to their future adaptation, evolution and persistance. This is because different alleles will have different benefits (or costs) depending on the environmental pressure that influences them; natural selection might favour one allele over another at one time, but a different one as the pressure changes. Having a higher number of alleles within the population or species means that there is a greater chance at least a few individuals will possess an adaptive gene with the changing environment (which we know can be quite rapid and very, very strong). The diversity serves as a ‘buffer’ against extinction; evolution by natural selection functions best when there are many options to choose from.

Without this diversity, species run the risk of having no adaptive genes at the ready to deal with a selective pressure. Either a new adaptive gene must mutate (or come about in other ways, such as through gene flow from another population or species) or the population/species will suffer and potentially go extinct. As strong selection causes the species to dwindle, it enters what is referred to as the ‘extinction vortex’. Without genetic diversity, they can’t adapt: thus, more individuals die off, causing more genetic diversity to be lost from the population. This pattern is a vicious cycle which can inevitably destroy species (without serious intervention).

Extinction vortex
A very dramatic representation of the extinction vortex.

For this reason, captive breeding programs aim to maintain as much of the genetic diversity of the original population as possible. This reduces the probability of entering a downward extinction spiral from inbreeding depression and helps to maintain populations into the future (both the captive one and the wild population when we reintroduce individuals into the wild).

“Population”  diversity

Because genetic diversity is critically important for species survival, we must also try to preserve the diversity of the entire gene pool of a species. This means conserving highly genetically differentiated populations within a species as a priority, as they may be the only ones that possess the necessary adaptive genes to save the rest of the species. This adaptive genetic variation can then be introduced into other populations in genetic rescue programs and serve as a means to semi-naturally allow the species to evolve. Evolutionarily-significant units (ESUs) are one measure of the invaluable nature of genetically unique populations.

Although many more traditional conservationists strongly believe that ESUs should be managed entirely independently of one another (to preserve their evolutionary ‘pedigree’ and prevent the risk of outbreeding depression), it has been suggested that the benefit of genetic rescue in many cases significantly outweighs this risk of outbreeding depression. For some species, this really is an act of rescue: they are at the edge of extinction, and if we do nothing we condemn them to die out.

Introducing genetic material across populations (or even species!) can generate new functional genes that allow the recipient species to adapt to selective pressures. This might sound very strange, and could be extremely rare, but examples of adaptive genetic material in one species originating from another species through hybridisation do exist in nature. For example, the black coat of wolves is a highly adaptive trait in some populations and is encoded for by the Melanocortin 1 receptor (Mc1r) gene. However, the specific mutation in Mc1r gene that generates the black coat colour actually first originated in domestic dogs; when wild wolves and domestic dogs interbred, this mutation was transferred into the wolf gene pool. Natural selection strongly favoured this new variant, and it very rapidly underwent strong positive selection. Thus, the adaptiveness of black wolves is thanks to a domestic dog mutation!

Species diversity

At a higher level of the hierarchy, the diversity of species within a particular community or ecosystem has been shown to be important for the health and stability of said community. Every species, however small or seemingly unimpressive, plays a role in the greater ecosystem balance, through interactions with other species (e.g. as predator, as prey, as competitor) and the abiotic environment. While some species are known to have very strong impacts on the immediate ecosystem (often dubbed ‘keystone species’, such as apex predators), all species have some influence on the world around them (we’re especially good at it).

Species interactions flowchart

The overall health and stability of an ecosystem, as well as the benefits it can provide to all living things (including humans) is largely determined by the diversity of species. For example, ‘habitat engineers’ are types of species that, by altering the physical environment around them (such as to build a home), directly provide new habitat for other species. They are a fundamental underpinning of many incredibly vibrant ecosystems; think of what a reef system would look like if there were no corals in it. There’d be no anemones growing colourfully; no fish to live in them; no sharks to feed on these non-existent fish. This is just one example of a complex ecosystem that truly relies on its inhabiting species to function.

Ecosystem jenga
Much like Jenga, taking out one block (a species) could cause the entire stack (the ecosystem) to collapse in on itself. Even if it stands up, however, the system will still be weaker without the full diversity to support it.

Protecting our diversity

Diversity is not just a social construct and is an important phenomenon in nature, at a variety of different levels. Preserving the full diversity of life, from genetic diversity within populations and species to full species diversity within ecosystems, is critical to maintaining healthy and robust natural systems. The more diversity we have at each level of this hierarchy, the greater robustness and security we will have in the future.

The history of histories: philosophy in biogeography

Biogeography of the globe

The distribution of organisms across the Earth, both over time and across space, is a fundamental aspect of the field of biogeography. But our understanding of the mechanisms by which organisms are distributed across the globe, and how this affects their evolution, can be at times highly enigmatic. Why are Australia and the Americas the only two places that have marsupials? How did lemurs get all the way to Madagascar, and why are they the only primate that has made the trip? How did Darwin’s famous finches get over to the Galápagos, and why are there so many species of them there now?

All of these questions can be addressed with a combination of genetic, environmental and ecological information across a variety of timescales. However, the overall field of biogeography (and phylogeography as a derivative of it) has traditionally been largely rooted on a strong yet changing theoretical basis. The earliest discussions and discoveries related to biogeography as a field of science date back to the 18th Century, and to Carl Linnaeus (to whom we owe our binomial classification system) and Alexander von Humboldt. These scientists (and undoubtedly many others of that era) were among the first to notice how organisms in similar climates (e.g. Australia, South Africa and South America) showed similar physical characteristics despite being so distantly separated (both in their groups and geographic distance). The communities of these regions also appeared to be highly similar. So how could this be possible over such huge distances?

Arctic and fennec final
A pretty unreasonable mechanism (and example) of dispersal in foxes. And yes, all tourists wear sunglasses and Hawaiian shirts, even arctic fox ones.

 

Dispersal or vicariance?

Two main explanations for these patterns are possible; dispersal and vicariance. As one might expect, dispersal denotes that an ancestral species was distributed in one of these places (referred to as the ‘centre of origin’) before it migrated and inhabited the other places. Contrastingly, vicariance suggests that the ancestral species was distributed everywhere originally, covering all contemporary ranges within it. However, changes in geography, climate or the formation of other barriers caused the range of the ancestor to fragment, with each fragmented group evolving into its own distinct species (or group of species).

Dispersal vs vicariance islands
An example of dispersal vs. vicariance patterns of biogeography in an island bird (pale blue). In the top example, the sequential separation of parts of the island also cause parts of the distribution of the original bird species to become fragmented. These fragments each evolve independently of their ancestor and form new species (red, and then blue). In the bottom example, the island geography doesn’t change but in rare events a bird disperses from the main island onto a new island. The new selective pressures of that island cause the dispersed birds to evolve into new species (red and blue). In both examples, islands that were recently connected or are easy to disperse across do not generate new species (in the sandy island in the bottom right). You’ll notice that both processes result in the same biogeographic distribution of species.

In initial biogeographic science, dispersal was the most heavily favoured explanation. At the time, there was no clear mechanism by which organisms could be present all over the globe without some form of dispersal: it was generally believed that the world was a static, unmoving system. Dispersal was well supported by some biological evidence such as the diversification of Darwin’s finches across the Galápagos archipelago. Thus, this concept was supported through the proposals of a number of prominent scientists such as Charles Darwin and A.R. Wallace. For others, however, the distance required for dispersal (such as across entire oceans) seemed implausible and biologically unrealistic.

 

A paradigm shift in biogeography

Two particular developments in theory are credited with a paradigm shift in the field; cladistics and plate tectonics. Cladistics simply involved using shared biological characteristics to reconstruct the evolutionary relationships of species (think like phylogenetics, but using physical traits instead of genetic sequence). Just as importantly, however, was plate tectonic theory, which provided a clear way for organisms to spread across the planet. By understanding that, deep in the past, all continents had been directly connected to one another provides a convenient explanation for how species groups spread. Instead of requiring for species to travel across entire oceans, continental drift meant that one widespread and ancient ancestor on the historic supercontinent (Pangaea; or subsequently Gondwana and Laurasia) could become fragmented. It only required that groups were very old, but not necessarily very dispersive.

Lemur dispersal
Surf’s up, dudes! Although continental drift was no doubt an important factor in the distribution and dispersal of many organisms on Earth, it actually probably wasn’t the reason lemurs got to Madagascar. Sorry for the mislead.

From these advances in theory, cladistic vicariance biogeography was born. The field rapidly overtook dispersal as the most likely explanation for biogeographic patterns across the globe by not only providing a clear mechanism to explain these but also an analytical framework to test questions relating to these patterns. Further developments into the analytical backbone of cladistic vicariance allowed for more nuanced questions of biogeography to be asked, although still fundamentally ignored the role of potential dispersals in explaining species’ distributions.

Modern philosophy of biogeography

So, what is the current state of the field? Well, the more we research biogeographic patterns with better data (such as with genomics) the more we realise just how complicated the history of life on Earth can be. Complex modelling (such as Bayesian methods) allow us to more explicitly test the impact of Earth history events on our study species, and can provide more detailed overview of the evolutionary history of the species (such as by directly estimating times of divergence, amount of dispersal, extent of range shifts).

From a theoretical perspective, the consistency of patterns of groups is always in question and exactly what determines what species occurs where is still somewhat debatable. However, the greater number of types of data we can now include (such as geological, paleontological, climatic, hydrological, genetic…the list goes on!) allows us to paint a better picture of life on Earth. By combining information about what we know happened on Earth, with what we know has happened to species, we can start to make links between Earth history and species history to better understand how (or if) these events have shaped evolution.

Age and dating with phylogenetics

Timing the phylogeny

Understanding the evolutionary history of species can be a complicated matter, both from theoretical and analytical perspectives. Although phylogenetics addresses many questions about evolutionary history, there are a number of limitations we need to consider in our interpretations.

One of these limitations we often want to explore in better detail is the estimation of the divergence times within the phylogeny; we want to know exactly when two evolutionary lineages (be they genera, species or populations) separated from one another. This is particularly important if we want to relate these divergences to Earth history and environmental factors to better understand the driving forces behind evolution and speciation. A traditional phylogenetic tree, however, won’t show this: the tree is scaled in terms of the genetic differences between the different samples in the tree. The rate of genetic differentiation is not always a linear relationship with time and definitely doesn’t appear to be universal.

 

Anatomy of phylogenies.jpg
The general anatomy of a phylogenetic tree. A phylogeny describes the relationships of tips (i.e. which are more closely related than others; referred to as the topology), how different these tips are (the length of the branches) and the order they separated in time (separations shown by the nodes). Different trees can share some traits but not others: the red box shows two phylogenetic trees with similar branch lengths (all of the branches are roughly the same) but different topology (the tips connect differently: A and B are together on the left but not on the right, for example). Conversely, two trees can have the same topology, but show differing lengths in the branches of the same tree (blue box). Note that the tips are all in the same positions in these two trees. Typically, it’s easier to read a tree from right to left: the two tips who have branches that meet first are most similar genetically; the longer it takes for two tips to meet along the branches, the less similar they are genetically.

How do we do it?

The parameters

There are a number of parameters that are required for estimating divergence times from a phylogenetic tree. These can be summarised into two distinct categories: the tree model and the substitution model.

The first one of these is relatively easy to explain; it describes the exact relationship of the different samples in our dataset (i.e. the phylogenetic tree). Naturally, this includes the topology of the tree (which determines which divergences times can be estimated for in the first place). However, there is another very important factor in the process: the lengths of the branches within the phylogenetic tree. Branch lengths are related to the amount of genetic differentiation between the different tips of the tree. The longer the branch, the more genetic differentiation that must have accumulated (and usually also meaning that longer time has occurred from one end of the branch to the other). Even two phylogenetic trees with identical topology can give very different results if they vary in their branch lengths (see the above Figure).

The second category determines how likely mutations are between one particular type of nucleotide and another. While the details of this can get very convoluted, it essentially determines how quickly we expect certain mutations to accumulate over time, which will inevitably alter our predictions of how much time has passed along any given branch of the tree.

Calibrating the tree

However, at least one another important component is necessary to turn divergence time estimates into absolute, objective times. An external factor with an attached date is needed to calibrate the relative branch divergences; this can be in the form of the determined mutation rate for all of the branches of the tree or by dating at least one node in the tree using additional information. These help to anchor either the mutation rate along the branches or the absolute date of at least one node in the tree (with the rest estimated relative to this point). The second method often involves placing a time constraint on a particular node of the tree based on prior information about the biogeography of the species (for example, we might know one species likely diverged from another after a mountain range formed: the age of the mountain range would be our constraints). Alternatively, we might include a fossil in the phylogeny which has been radiocarbon dated and place an absolute age on that instead.

Ammonite comic.jpg
Don’t you know it’s rude to ask an ammomite her age?

In regards to the former method, mutation rates describe how fast genetic differentiation accumulates as evolution occurs along the branch. Although mutations gradually accumulate over time, the rate at which they occur can depend on a variety of factors (even including the environment of the organism). Even within the genome of a single organism, there can be variation in the mutation rate: genes, for example, often gain mutations slower than non-coding region.

Although mutation rates (generally in the form of a ‘molecular clock’) have been traditionally used in smaller datasets (e.g. for mitochondrial DNA), there are inherent issues with its assumptions. One is that this rate will apply to all branches in a tree equally, when different branches may have different rates between them. Second, different parts of the genome (even within the same individual) will have different evolutionary rates (like genes vs. non-coding regions). Thus, we tend to prefer using calibrations from fossil data or based on biogeographic patterns (such as the time a barrier likely split two branches based on geological or climatic data).

The analytical framework

All of these components are combined into various analytical frameworks or programs, each of which handle the data in different ways. Many of these are Bayesian model-based analysis, which in short generates hypothetical models of evolutionary history and divergence times for the phylogeny and tests how well it fits the data provided (i.e. the phylogenetic tree). The algorithm then alters some aspect(s) of the model and tests whether this fits the data better than the previous model and repeats this for potentially millions of simulations to get the best model. Although models are typically a simplification of reality, they are a much more tractable approach to estimating divergence times (as well as a number of other types of evolutionary genetics analyses which incorporating modelling).

Molecular dating pipeline
A (believe it or not, simplified) pipeline for estimating divergence times from a phylogeny. 1) We obtain our DNA sequences for our samples: in this example, we’ll see each Sample (A-E) is a representative of a single species. We align these together to make sure we’re comparing the same part of the genome across all of them. 2) We estimate the phylogenetic tree for our samples/species. In a Bayesian framework, this means creating simulation models containing a certain substitution model and a given tree model (containing certain topology and branch lengths). Together, these two models form the likelihood model: we then test how well this model explains our data (i.e. the likelihood of getting the patterns in our data if this model was true). We repeat these simulations potentially hundreds of thousands of times until we pinpoint the most likely model we can get. 3) Using our resulting phylogeny, we then calibrate some parts of it based on external information. This could either be by including a carbon-dated fossil (F) within the phylogeny, or constraining the age of one node based on biogeographic information (the red circle and cross). 4) Using these calibrations as a reference, we then estimated the most likely ages of all the splits in the tree, getting our final dated phylogeny.

Despite the developments in the analytical basis of estimating divergence times in the last few decades, there are still a number of limitations inherent in the process. Many of these relate to the assumptions of the underlying model (such as the correct and accurate phylogenetic tree and the correct estimations of evolutionary rate) used to build the analysis and generate simulations. In the case of calibrations, it is also critical that they are correctly dated based on independent methods: inaccurate radiocarbon dating of a fossil, for example, could throw out all of the estimations in the entire tree. That said, these factors are intrinsic to any phylogenetic analysis and regularly considered by evolutionary biologists in the interpretations and discussions of results (such as by including confidence intervals of estimations to demonstrate accuracy).

Understanding the temporal aspects of evolution and being able to relate them to a real estimate of age is a difficult affair, but an important component of many evolutionary studies. Obtaining good estimates of the timing of divergence of populations and species through molecular dating is but one aspect in building the picture of the history of all organisms, including (and especially) humans.

The many genetic faces of adaptation

The transition from genotype to phenotype

While evolutionary genetics studies often focus on the underlying genetic architecture of species and populations to understand their evolution, we know that natural selection acts directly on physical characteristics. We call these the phenotype; by studying changes in the genes that determine these traits (the genotype), we can take a nuanced approach at studying adaptation. However, our ability to look at genetic changes and relate these to a clear phenotypic trait, and how and why that trait is under natural selection, can be a difficult task.

One gene for one trait

The simplest (and most widely used) models of understanding the genetic basis of adaptation assume that a single genotype codes for a single phenotypic trait. This means that changes in a single gene (such as outliers that we have identified in our analyses) create changes in a particular physical trait that is under a selective pressure in the environment. This is a useful model because it is statistically tractable to be able to identify few specific genes of very large effect within our genomic datasets and directly relate these to a trait: adding more complexity exponentially increases the difficulty in detecting patterns (at both the genotypic and phenotypic level).

Single locus figure
An example of a single gene coding for a single phenotypic trait. In this example, the different combination of alleles of the one gene determines the colour of the cat.

Many genes for one trait: polygenic adaptation

Unfortunately, nature is not always convenient and recent findings suggest that the overwhelming majority of the genetics of adaptation operate under what is called ‘polygenic adaptation’. As the name suggestions, under this scenario changes (even very small ones) in many different genes combine together to have a large effect on a particular phenotypic trait. Given the often very small magnitude of the genetic changes, it can be extremely difficult to separate adaptive changes in genes from neutral changes due to genetic drift. Likewise, trying to understand how these different genes all combine into a single functional trait is almost impossible, especially for non-model species.

Polygenic adaptation is often seen for traits which are clearly heritable, but don’t show a single underlying gene responsible. Previously, we’ve covered this with the heritability of height: this is one of many examples of ‘quantitative trait loci’ (QTLs). Changes in one QTL (a single gene) causes a small quantitative change in a particular trait; the combined effect of different QTLs together can ‘add up’ (or counteract one another) to result in the final phenotype value.

Height QTL
An example of polygenic quantitative trait loci. In this example, height is partially coded for by a total of ten different genes: the dominant form of each gene (Capitals, green) provides more height whereas the recessive form (lowercase, red) doesn’t. The cumulative total of these components determines how tall the person is: the person on the far right was very unlucky and got 0/10 height bonuses and so is the shortest. Progressively from left to right, some genes are contributing to the taller height of the people, with the far right person standing tall with the ultimate 10/10 pro-height genes. For reference, height is actually likely to be coded for by thousands of genes, not 10.

The mechanisms which underlie polygenic adaptation can be more complex than simple addition, too. Individual genes might cause phenotypic changes which interact with other phenotypes (and their underlying genotypes) to create a network of changes. We call these interactions ‘epistasis’, where changes in one gene can cause a flow-on effect of changes in other genes based on how their resultant phenotypes interact. We can see this in metabolic pathways: given that a series of proteins are often used in succession within pathways, a change in any single protein in the process could affect every other protein in the pathway. Of course, knowing the exact proteins coded for every gene, including their physical structure, and how each of those proteins could interact with other proteins is an immense task. Similar to QTLs, this is usually limited to model species which have a large history of research on these specific areas to back up the study. However, some molecular ecology studies are starting to dive into this area by identifying pathways that are under selection instead of individual genes, to give a broader picture of the overall traits that are underlying adaptation.

Labrador epistasis figure
My favourite example of epistasis on coat colour in labradors. Two genes together determine the colour of the coat, with strong interactions between them. The first gene (E/e) determines whether or not the underlying coat gene (B/b) is masked or not: two recessive alleles of the first gene (ee) completely blocks Gene 2 and causes the coat to become golden regardless of the second gene genotype (much like my beloved late childhood pet pictured, Sunny). If the first gene has at least one dominant allele, then the second gene is allowed to express itself. Possessing a dominant allele (BB or Bb) leads to a black lab; possessing two recessive alleles (bb) makes a choc lab!
Labrador epistasis table
The possible combinations of genotypes for the two above genes and the resultant coat colour (indicated by the box colour).

One gene for many traits: pleiotropy and differential gene expression

In contrast to polygenic traits, changes in a single gene can also potentially alter multiple phenotypic traits simultaneously. This is referred to as ‘pleiotropy’ and can happen if a gene has multiple different functions within an organism; one particular protein might be a component of several different systems depending on where it is found or how it is arranged. A clear example of pleiotropy is in albino animals: the most common form of albinism is the result of possessing two recessive alleles of a single gene (TYR). The result of this is the absence of the enzyme tyrosinase in the organism, a critical component in the production of melanin. The flow-on phenotypic effects from the recessive gene most obviously cause a lack of pigmentation of the skin (whitening) and eyes (which appear pink), but also other physiological changes such as light sensitivity or total blindness (due to changes in the iris). Albinism has even been attributed to behavioural changes in wild field mice.

Albinism pleiotropy
A very simplified diagram of how one genotype (the albino version of the TYR gene) can lead to a large number of phenotypic changes via pleiotropy (although many are naturally physiologically connected).

Because pleiotropic genes code for several different phenotypic traits, natural selection can be a little more complicated. If some resultant traits are selected against, but others are selected for, it can be difficult for evolution to ‘resolve’ the balance between the two. The overall fitness of the gene is thus dependent on the balance of positive and negative fitness of the different traits, which will determine whether the gene is positively or negatively selected (much like a cost-benefit scenario). Alternatively, some traits which are selectively neutral (i.e. don’t directly provide fitness benefits) may be indirectly selected for if another phenotype of the same underlying gene is selected for.

Multiple phenotypes from a single ‘gene’ can also arise by alternate splicing: when a gene is transcribed from the DNA sequence into the protein, the non-coding intron sections within the gene are removed. However, exactly which introns are removed and how the different coding exons are arranged in the final protein sequence can give rise to multiple different protein structures, each with potentially different functions. Thus, a single overarching gene can lead to many different functional proteins. The role of alternate splicing in adaptation and evolution is a rarely explored area of research and its importance is relatively unknown.

Non-genes for traits: epigenetics

This gets more complicated if we consider ‘non-genetic’ aspects underlying the phenotype in what we call ‘epigenetics’. The phrase literally translates as ‘on top of genes’ and refers to chemical attachments to the DNA which control the expression of genes by allowing or resisting the transcription process. Epigenetics is a relatively new area of research, although studies have started to delve into the role of epigenetic changes in facilitating adaptation and evolution. Although epigenetics is still a relatively new research topic, future research into the relationship between epigenetic changes and adaptive potential might provide more detailed insight into how adaptation occurs in the wild (and might provide a mechanism for adaptation for species with low genetic diversity)!

 

The different interactions between genotypes, phenotypes and fitness, as well as their complex potential outcomes, inevitably complicates any study of evolution. However, these are important aspects of the adaptation process and to discard them as irrelevant will not doubt reduce our ability to examine and determine evolutionary processes in the wild.