September 2018 – The G-cat

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

September 21, 2018thegcatblog4 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.

The MolEcol Toolbox: Species Distribution Modelling

September 12, 2018thegcatblog3 Comments

Where on Earth are species?

Understanding the spatial distribution of species is a critical component for many different aspects of biological studies. Particularly for conservation, the biogeography of regions is a determinant factor for designating and managing biodiversity hotspots and management units. Or understanding the biogeographical mechanisms that have shaped modern biodiversity may allow us to understand how species will change under future climate change scenarios, and how their distributions will (and have) shift(ed).

Typically, the maximum distribution of species is based on their ecological tolerances: that is, the most extreme environments they can tolerate and proliferate within. Of course, there are a huge number of other factors on top of just natural environment which can shape species distributions, particularly related to human-induced environmental changes (or introducing new species as invasive pests, which we seem to be good at). But exactly where species are and why they occur there are intrinsically linked to the adaptive characteristics of species relative to their environment.

Species distribution modelling

The connection of a species distribution with innate environmental tolerances is the background for a type of analysis we call species distribution modelling (SDM) or environmental niche modelling (ENM). Species distribution modelling seeks to correlate the locations where a species occurs with the local environment around those sites to predict where the species should occur. This is an effective tool for trying to understand the distribution of species that might be tricky to study so thoroughly in the wild; either because they are hard to catch, live in very remote areas, or because they are highly threatened. There are a number of different algorithms and data types that will work with SDM, and there is always ongoing debate about ‘best practices’ in modelling techniques.

SDM method.jpg — The generalised pipeline of SDM, taken from Svenning *et al.* (2011). By correlating species occurrence data (bottom left) with environmental data (top left), we can develop a model that describes how the species is distributed based on environmental limitations (top right). From here, we can choose to validate the model with other methods (top and bottom centre) or see how the distribution might change with different environmental changes (e.g. bottom right).

A basic how-to on running SDM

The first major component that is needed for SDM is the occurrence data. Some methods will work with presence-only data: that is, a map of GPS coordinates which describes where that species has been found. Others work with presence-absence data, which may require including sites of known non-occurrence. This is an important aspect as the non-occurring sites defines the environment beyond the tolerance threshold of the species: however, it’s very likely that we haven’t sampled every location where they occur, and there will be some GPS co-ordinates that appear to be absent of our species where they actually occur. There are some different analytical techniques which can account for uneven sampling across the real distribution of the species, but they can get very technical.

An example of species (occurrence only) locality data (with >72,000 records) for the koala (*Phascolarctos cinereus*) across Australia, taken from the Atlas of Living Australia. Carefully checking the locality data is important, as visual inspection clearly shows records where koalas are not native: they might have been recorded from an introduced individual, given incorrect GPS coordinates or incorrectly identified (**red circles**).

The second major component is our environmental data. Typically, we want to include environmental data for the types of variables that are likely to constrain the distribution of our species: often temperature and precipitation variables are included, as these two largely predict habitat types. However, it can also be important to include non-climatic variables such as topography (e.g. elevation, slope) in our model to help constrain our predictions to a more reasonable area. It is also important to test for correlation between our variables, as using many variables which are highly correlated may ‘overfit’ the model and underestimate the range of the distribution by placing an unrealistic number of restrictions on the model.

An example of some of the environmental data/maps we might choose to include in a species distribution model, obtained from the Atlas of Living Australia. A) Mean annual temperature. B) Mean annual precipitation. C) Elevation. D) Weighted distance to nearest waterbody (e.g. rivers, lakes, streams).

Our SDM analysis of choice (e.g. MaxEnt) will then use various algorithms to build a model which best correlates where the species occurs with the environmental variables at those sites. The model tries to create a set of environmental conditions that best encapsulate the occurrence sites whilst excluding the non-occurrence sites from the prediction. From the final model, we can evaluate how strong the effect of each of our variables is on the distribution of the species, and also how well our overall model predicts the locality data.

Projecting our SDM into the past and the future

One reason to use SDM is the ability to project distributions onto alternative environments based on the correlative model. For example, if we have historic data (say, from the last glacial maximum, 21,000 years ago), we can use our predictions of how the species responds to climatic variables and compare that to the environment back then to see how the distribution would have shifted. Similarly, if we have predictions for future climates based on climate change models, we can try and predict how species distributions may shift in the future (an important part of conservation management, naturally).

Correct LGM projection example.png — An example of projecting a species distribution model back in time (in this case, to the Last Glacial Maximum 21,000 years ago), taken from Pelletier *et al.* (2016). On the left is the contemporary distribution of each species; on the right the historic projection. The study focused on three different species of American salamanders and how they had evolved and responded to historic climate change. This figure clearly shows how the distribution of the species have changed over time, particularly how the top two species have significantly reduced in distribution in modern times.

Species distribution modelling continues to be a useful tool for conservation and evolution studies, and improvements in analytical algorithms, available environmental data and increased sampling of species will similarly improve SDM. Particularly, improvements in environmental projections from both the distant past and future will improve our ability to understand and predict how species will change, and have changed, with climatic changes

Rescuing the damselfish in distress: rescue or depression?

September 6, 2018September 6, 2018thegcatblog7 Comments

Conservation management

Managing and conserving threatened and endangered species in the wild is a difficult process. There are a large number of possible threats, outcomes, and it’s often not clear which of these (or how many of these) are at play at any one given time. Thankfully, there are also a large number of possible conservation tools that we might be able to use to protect, bolster and restore species at risk.

Using genetics in conservation

Naturally, we’re going to take a look at the more genetics-orientated aspects of conservation management. We’ve discussed many times the various angles and approaches we can take using large-scale genetic data, some of which include:
• studying the evolutionary history and adaptive potential of species
• developing breeding programs using estimates of relatedness to increase genetic diversity
• identifying and describing new species for government legislation
• identifying biodiversity hotspots and focus areas for conservation
• identifying population boundaries for effective management/translocations

Genetics flowchart.jpg — An example of just some of the conservation applications of genetics research that we’ve talked about previously on *The G-CAT.*

This last point is a particularly interesting one, and an area of conservation research where genetics is used very often. Most definitions of a ‘population’ within a species rely on using genetic data and analysis (such as Fst) to provide a statistical value of how different groups of organisms are within said species. Ignoring some of the philosophical issues with the concept of a population versus a species due to the ‘speciation continuum’ (read more about that here), populations are often interpreted as a way to cluster the range of a species into separate units for conservation management. In fact, the most commonly referred to terms for population structure and levels are evolutionarily-significant units (ESUs), which are defined as a single genetically connected group of organisms that share an evolutionary history that is distinct from other populations; and management units (MUs), which may not have the same degree of separation but are still definably different with enough genetic data.

Hierarchy of structure.jpg — A diagram of the hierarchy of structure within a species. Remember that ESUs, by definition, should be evolutionary different from one another (i.e. adaptively divergent) whilst MUs are not necessarily divergent to the same degree.

This can lead to a particular paradigm of conservation management: keeping everything separate and pure is ‘best practice’. The logic is that, as these different groups have evolved slightly differently from one another (although there is often a lot of grey area about ‘differently enough’), mixing these groups together is a bad idea. Particularly, this is relevant when we consider translocations (“it’s never acceptable to move an organism from one ESU into another”) and captive breeding programs (“it’s never acceptable to breed two organisms together from different ESUs”). So, why not? Why does it matter if they’re a little different?

Outbreeding depression

Well, the classic reasoning is based on a concept called ‘outbreeding depression’. We’ve mentioned outbreeding depression before, and it is a key concept kept in mind when developing conservation programs. The simplest explanation for outbreeding depression is that evolution, through the strict process of natural selection, has pushed particularly populations to evolve certain genetic variants for a certain selective pressure. These can vary across populations, and it may mean that populations are locally adapted to a specific set of environmental conditions, with the specific set of genetic variants that best allow them to do this.

However, when you mix in the genetic variants that have evolved in a different population, by introducing a foreign individual and allowing them to breed, you essentially ‘tarnish’ the ‘pure’ gene pool of that population with what could be very bad (maladaptive) genes. The hybrid offspring of ‘native’ and this foreign individual will be less adaptive than their ‘pure native’ counterparts, and the overall adaptiveness of the population will decrease as those new variants spread (depending on the number introduced, and how negative those variants are).

Outbreeding depression example figure.jpg — An example of how outbreeding depression can affect a species. The original red fish population is not doing well- it is of conservation concern, and has very little genetic diversity (only the blue gene in this example). So, we decide to introduce new genetic diversity by adding in green fish, which have the orange gene. However, the mixture of the two genes and the maladaptive nature of the orange gene actually makes the situation worse, with the offspring showing less fitness than their preceding generations.

You might be familiar with inbreeding depression, which is based on the loss of genetic diversity from having too similar individuals breeding together to produce very genetically ‘weak’ offspring through inbreeding. Outbreeding depression could be thought of as the opposite extreme; breeding too different individuals introduced too many ‘bad’ alleles into the population, diluting the ‘good’ alleles.

Inbreeding vs outbreeding figure.jpg — An overly simplistic representation of how inbreeding and outbreeding depression can reduce overall fitness of a species. In inbreeding depression, the lack of genetic diversity due to related individuals breeding with one another makes them at risk of being unable to adapt to new pressures. Contrastingly, adding in new genes from external populations which aren’t fit for the target population can also reduce overall fitness by ‘diluting’ natural, adaptive allele frequencies in the population.

Genetic rescue

It might sound awfully purist to only preserve the local genetic diversity, and to assume that any new variants could be bad and tarnish the gene pool. And, surprisingly enough, this is an area of great debate within conservation genetics.

The counterpart to the outbreeding depression concerns is the idea of genetic rescue. For populations with already severely depleted gene pools, lacking the genetic variation to be able to adapt to new pressures (such as contemporary climate change), the situation seems incredibly dire. One way to introduce new variation, which might be the basis of new adaptation, bringing in individuals from another population of the same species can provide the necessary genetic diversity to help that population bounce back.

Genetic rescue example figure.jpg — An example of genetic rescue. This circumstance is identical to the one above, with the key difference being in the fitness of the introduced gene. The orange gene in this example is actually beneficial to the target population: by providing a new, adaptive allele for natural selection to act upon, overall fitness is increased for the red fish population.

The balance

So, what’s the balance between the two? Is introducing new genetic variation a bad idea, and going to lead to outbreeding depression; or a good idea, and lead to genetic rescue? Of course, many of the details surrounding the translocation of new genetic material is important: how different are the populations? How different are the environments (i.e. natural selection) between them? How well will the target population take up new individuals and genes?

Overall, however, the more recent and well-supported conclusion is that fears regarding outbreeding depression are often strongly exaggerated. Bad alleles that have been introduced into a population can be rapidly purged by natural selection, and the likelihood of a strongly maladaptive allele spreading throughout the population is unlikely. Secondly, given the lack of genetic diversity in the target population, most that need the genetic rescue are so badly maladaptive as it is (due to genetic drift and lack of available adaptive alleles) that introducing new variants is unlikely to make the situation much worse.

Purging and genetic rescue figure.jpg — An example of how introducing maladaptive alleles might not necessarily lead to decreased fitness. In this example, we again start with our low diversity red fish population, with only one allele (AA). To help boost genetic diversity, we introduce orange fish (with the TT allele) and green fish (with the GG allele) into the population. However, the TT allele is not very adaptive in this new environment, and individuals with the TT gene quickly die out (i.e. be ‘purged’). Individual with the GG gene, however, do well, and continue to integrate into the red population. Over time, these two variants will mix together as the two populations hybridise and overall fitness will increase for the population.

That said, outbreeding depression is not an entirely trivial concept and there are always limitations in genetic rescue procedures. For example, it would be considered a bad idea to mix two different species together and make hybrids, since the difference between two species, compared to two populations, can be a lot stronger and not necessarily a very ‘natural’ process (whereas populations can mix and disjoin relatively regularly).

The reality of conservation management

Conservation science is, at its core, a crisis discipline. It exists solely as an emergency response to the rapid extinction of species and loss of biodiversity across the globe. The time spent trying to evaluate the risk of outbreeding depression – instead of immediately developing genetic rescue programs – can cause species to tick over to the afterlife before we get a clear answer. Although careful consideration and analysis is a requirement of any good conservation program, preventing action due to almost paranoid fear is not a luxury endangered species can afford.

Origination of adaptation: the old and the new (genes)

September 1, 2018thegcatblog3 Comments

Adaptation is arguably the most critical biological process in the evolution of species. The process of evolution by natural selection is the cornerstone of evolutionary biology (and indeed, all of contemporary biology!) and adaptation remains fundamental to the process. We know that adaptation is based on the idea that some genetic variants are ‘better’ adapted than others, and thus are unequally shared across a population. But where does this genetic variation come from?

The accumulation of new genetic variation

The classic way for new genetic variants to appear is often thought of as mutation: changes in a single base in the DNA are caused by various external processes such as chemical, physical or environmental influences (such as the sci-fi classics like UV rays or toxic chemicals). Although these forms of mutations happen very rarely and certainly don’t have the same effects comic books would leave you to believe, new mutations can occur relatively rapidly depending on the characteristics of the species. However, the most common way for new mutations to occur is actually part of the DNA replication process: copying DNA is not always perfect and even though the relevant proteins essentially run a spellcheck, sometimes the copy is not 100% perfect and new mutations occur.

Adaptation of mutation figure — An example of how adaptation can occur from a new mutation. In this example, we have one gene (TTXTT), with initial only one allele (variant), TTATT. In the second generation (row), a mutation occurs in one individual which creates a new, second allele: TTGTT. This allele is favoured over the TTATT allele, and in the next generation it’s frequency increases as the alternative allele frequency decreases (the pattern is shown in the frequency values on the right side).

It is important to remember that only mutations that are present in the reproductive cells (sperm and eggs) can be inherited and passed on, and thus be a source for adaptation. Mutations in other tissues of the body, such as within the skin, are not spread across the entire body of the subject and thus aren’t passed on to offspring.

Standing genetic variation

Alternatively, genetic variation might already be present within a species or population. This is more likely if population sizes are large and populations are well connected and interbreeding. We refer to this diverse initial gene pool as ‘standing genetic variation’: that is, the amount of genetic variation within the population or species before the selective pressure requiring adaptation. Standing genetic variation can be thought of as the ‘diversity of choices’ for natural selection to act upon: the variants are readily available, and if a good choice exists it will be favoured by natural selection and become more widespread within the population or species (i.e. evolve).

Adaptation of standing variation figure.jpg — A slightly more complex example of how adaptation can occur from standing variation, this time with two different genes. One codes for fur colour, with two different alleles: GCATA codes for orange fur, and GCGTA codes for grey fur. The other gene codes for ear tufts, with TTCCT coding for tufts and TCCCT coding for no tufts. Natural selection favours both orange fur and tufted ears, and cats with these traits reproduce more frequently than those without (see graph below). These cats probably look familiar.

Graph of standing variation.jpg — The frequency of all four alleles (i.e. either allele for both genes) over the generations in the above figure. Clearly, we can see how adaptation rapidly favours orange fur and tufted ears over grey fur and non-tufted ears with the shifts in frequencies over the different alleles.

We’ve discussed standing genetic variation before on The G-CAT, but often in a different light (and phrasing). For example, when we’ve talked about founder effect: that is, when a population is formed from only a few different individuals which causes it to be very genetically depauperate. In populations under strong founder effect, there is very little standing genetic variation for natural selection to act upon. This has long been an enigma for many pest species: how have they managed to proliferate so widely when they often originate from so few individuals and lack genetic diversity?

Adaptive variation

Adaptation may not require new genetic variants to be generated from mutation. If there are a large number of alleles within the gene pool to start with, then natural selection may favour one of those variants over others and allow adaptation to start immediately. Compared to the rate at which new mutations occur, are potentially corrected for in DNA repair, are potentially erased by genetic drift, and then put under selective pressure, adaptation from standing genetic variation can occur very quickly.

Rate of adaptation figure.jpg — A rough example of the speed of adaptation depending on how the adaptive allele originated: whether it was already present (in the form of standing variation), or whether it was created by a new mutation. As one would expect, there is a significant lag delay in adaptation in the mutation scenario, based on the time it takes for said adaptive mutation to be created through relatively random processes. Thus, a positively selected allele from standing variation can allow a species to adapt much faster than waiting for a positive mutation to occur.

Conserving genetic variation

Given the adaptive potential provided by maintaining a good amount of standing genetic variation, it is imperative to conserve genetic diversity within populations in conservation efforts. This is why we often equate genetic diversity with ‘adaptive potential’ of species, although the exact amount of genetic diversity required for adaptive potential depends on a large number of other factors. Clearly, in some instances species show the ability to adapt to new pressures or novel environments even without a large amount of standing genetic variation.

It is important to remember that standing genetic variation consists of two types: neutral genetic diversity, which is not necessarily under selection at the time, and adaptive genetic diversity, which is directly under selection (although this can be either for or against the given variant). However, currently neutral genetic variants may become adaptive variants in the future if selective pressures change: although those different variants aren’t necessarily beneficial or detrimental at the moment, that may change in the future. Thus, conserving both types of genetic diversity is important for the survivability and longevity of populations under conservation programs.

Other types of adaptation

Although genetic diversity is clearly critically important for adaptive potential, alternative mechanisms for adaptation also exist. One of these relies less on the actual genetic variants being different, but rather how individual genes are used. This can happen in a few different ways, but mostly commonly this is through alternative splicing: when a gene is being ‘read’ and a protein is produced, different parts of the gene can be used (and in different order) to make a completely different protein.