This is partly where the concept of a ‘model’ comes into it: it’s much easier to pick a particular species to study as a target, and use the information from it to apply to other scenarios. Most people would be familiar with the concept based on medical research: the ‘lab rat’ (or mouse). The common house mouse (Mus musculus) and the brown rat (Rattus norvegicus) are some of the most widely used models for understanding the impact of particular biochemical compounds on physiology and are often used as the testing phase of medical developments before human trials.
The idea of using the genetic sequences of living organisms to understand the evolutionary history of species is a concept much repeated on The G-CAT. And it’s a fundamental one in phylogenetics, taxonomy and evolutionary biology. Often, we try to analyse the genetic differences between individuals, populations and species in a tree-like manner, with close tips being similar and more distantly separated branches being more divergent. However, this runs on one very key assumption; that the patterns we observe in our study genes matches the overall patterns of species evolution. But this isn’t always true, and before we can delve into that we have to understand the difference between a ‘gene tree’ and a ‘species tree’.
However, a phylogenetic tree based on a single gene only demonstrates the history of that gene. What we assume in most cases is that the history of that gene matches the history of the species: that branches in the genetic tree mirror when different splits in species occurred throughout history.
The easiest way to conceptualise gene trees and species trees is to think of individual gene trees that are nested within an overarching species tree. In this sense, individual gene trees can vary from one another (substantially, even) but by looking at the overall trends of many genes we can see how the genome of the species have changed over time.
One of the most prolific, but more complicated, ways gene trees can vary from their overarching species tree is due to what we call ‘incomplete lineage sorting’. This is based on the idea that species and the genes that define them are constantly evolving over time, and that because of this different genes are at different stages of divergence between population and species. If we imagine a set of three related populations which have all descended from a single ancestral population, we can start to see how incomplete lineage sorting could occur. Our ancestral population likely has some genetic diversity, containing multiple alleles of the same locus. In a true phylogenetic tree, we would expect these different alleles to ‘sort’ into the different descendent populations, such that one population might have one of the alleles, a second the other, and so on, without them sharing the different alleles between them.
If this separation into new populations has been recent, or if gene flow has occurred between the populations since this event, then we might find that each descendent population has a mixture of the different alleles, and that not enough time has passed to clearly separate the populations. For this to occur, sufficient time for new mutations to occur and genetic drift to push different populations to differently frequent alleles needs to happen: if this is too recent, then it can be hard to accurately distinguish between populations. This can be difficult to interpret (see below figure for a visualisation of this), but there’s a great description of incomplete lineage sorting here.
Hybridisation and horizontal transfer
Another way individual genes may become incongruent with other genes is through another phenomenon we’ve discussed before: hybridisation (or more specifically, introgression). When two individuals from different species breed together to form a ‘hybrid’, they join together what was once two separate gene pools. Thus, the hybrid offspring has (if it’s a first generation hybrid, anyway) 50% of genes from Species A and 50% of genes from Species B. In terms of our phylogenetic analysis, if we picked one gene randomly from the hybrid, we have 50% of picking a gene that reflects the evolutionary history of Species A, and 50% chance of picking a gene that reflects the evolutionary history of Species B. This would change how our outputs look significantly: if we pick a Species A gene, our ‘hybrid’ will look (genetically) very, very similar to Species A. If we pick a Species B gene, our ‘hybrid’ will look like a Species B individual instead. Naturally, this can really stuff up our interpretations of species boundaries, distributions and identities.
This can have a profound impact as paralogous genes are difficult to detect: if there has been a gene duplication early in the evolutionary history of our phylogenetic tree, then many (or all) of our study samples will have two copies of said gene. Since they look similar in sequence, there’s all possibility that we pick Variant 1 in some species and Variant 2 in other species. Being unable to tell them apart, we can have some very weird and abstract results within our tree. Most importantly, different samples with the same duplicated variant will seem similar to one another (e.g. have evolved from a common ancestor more recently) than it will to any sample of the other variant (even if they came from the exact same species)!
Overcoming incongruence with genomics
Although a tricky conundrum in phylogenetics and evolutionary genetics broadly, gene tree incongruence can largely be overcome with using more loci. As the random changes of any one locus has a smaller effect of the larger total set of loci, the general and broad patterns of evolutionary history can become clearer. Indeed, understanding how many loci are affected by what kind of process can itself become informative: large numbers of introgressed loci can indicate whether hybridisation was recent, strong, or biased towards one species over another, for example. As with many things, the genomic era appears poised to address the many analytical issues and complexities of working with genetic data.
There are a massive number of potential traits we could focus on, each of which could have a large number of different (and interacting) impacts on evolution. One that is often considered, and highly relevant for genetic studies, is the influence of dispersal capability.
Dispersal is essentially the process of an organism migrating to a new habitat, to the point of the two being used almost interchangeably. Often, however, we regard dispersal as a migration event that actually has genetic consequences; particularly, if new populations are formed or if organisms move from one population to another. This can differ from straight migration in that animals that migrate might not necessarily breed (and thus pass on genes) into a new region during their migration; thus, evidence of those organisms will not genetically proliferate into the future through offspring.
As these individuals occupy large ranges, localised impacts are unlikely to critically affect their full distribution. Individual organisms that are occupying an unpleasant space can easily move to a more favourable habitat (provided that one exists). Furthermore, with a large population (which is more likely with highly dispersive species), genetic drift is substantially weaker and natural selection (generally) has a higher amount of genetic diversity to work with. This is, of course, assuming that dispersal leads to a large overall population, which might not be the case for species that are critically endangered (such as the cheetah).
A large number of species, however, are likely to occupy a more intermediate range of dispersal ability. These species might be able to migrate to neighbouring populations, or across a large proportion of their geographic range, but individuals from one end of the range are still somewhat isolated from individuals at the other end.
Species with low dispersal capabilities are often at risk of local extinction and are unable to easily recolonise these habitats after the event has ended. Their movement is often restricted to rare environmental events such as flooding that carry individuals long distances despite their physiological limitations. Because of this, low dispersal species are often at greater risk of total extinction and extinction vertices than their higher dispersing counterparts.
Accounting for dispersal in population genetics
Incorporating biological and physiological aspects of our study taxa is important for interpreting the evolutionary context of species. Dispersal ability is but one of many characteristics that can influence the ability of species to respond to selective pressures, and the context in which this natural selection occurs. Thus, understanding all aspects of an organism is important in building the full picture of their evolution and future prospects.
This is the fourth (and final) part of the miniseries on the genetics and process of speciation. To start from Part One, click here.
In last week’s post, we looked at how we can use genetic tools to understand and study the process of speciation, and particularly the transition from populations to species along the speciation continuum. Following on from that, the question of “how many species do I have?” can be further examined using genetic data. Sometimes, it’s entirely necessary to look at this question using genetics (and genomics).
Genetic tools to study species: the ‘Barcode of Life’
A classically employed method that uses DNA to detect and determine species is referred to as the ‘Barcode of Life’. This uses a very specific fragment of DNA from the mitochondria of the cell: the cytochrome c oxidase I gene, CO1. This gene is made of 648 base pairs and is found pretty well universally: this and the fact that CO1 evolves very slowly make it an ideal candidate for easily testing the identity of new species. Additionally, mitochondrial DNA tends to be a bit more resilient than its nuclear counterpart; thus, small or degraded tissue samples can still be sequenced for CO1, making it amenable to wildlife forensics cases. Generally, two sequences will be considered as belonging to different species if they are certain percentage different from one another.
Despite the apparent benefits of CO1, there are of course a few drawbacks. Most of these revolve around the mitochondrial genome itself. Because mitochondria are passed on from mother to offspring (and not at all from the father), it reflects the genetic history of only one sex of the species. Secondly, the actual cut-off for species using CO1 barcoding is highly contentious and possibly not as universal as previously suggested. Levels of sequence divergence of CO1 between species that have been previously determined to be separate (through other means) have varied from anywhere between 2% to 12%. The actual translation of CO1 sequence divergence and species identity is not all that clear.
Gene tree – species tree incongruences
One particularly confounding aspect of defining species based on a single gene, and with using phylogenetic-based methods, is that the history of that gene might not actually be reflective of the history of the species. This can be a little confusing to think about but essentially leads to what we call “gene tree – species tree incongruence”. Different evolutionary events cause different effects on the underlying genetic diversity of a species (or group of species): while these may be predictable from the genetic sequence, different parts of the genome might not be as equally affected by the same exact process.
A classic example of this is hybridisation. If we have two initial species, which then hybridise with one another, we expect our resultant hybrids to be approximately made of 50% Species A DNA and 50% Species B DNA (if this is the first generation of hybrids formed; it gets a little more complicated further down the track). This means that, within the DNA sequence of the hybrid, 50% of it will reflect the history of Species A and the other 50% will reflect the history of Species B, which could differ dramatically. If we randomly sample a single gene in the hybrid, we will have no idea if that gene belongs to the genealogy of Species A or Species B, and thus we might make incorrect inferences about the history of the hybrid species.
There are a number of other processes that could similarly alter our interpretations of evolutionary history based on analysing the genetic make-up of the species. The best way to handle this is simply to sample more genes: this way, the effect of variation of evolutionary history in individual genes is likely to be overpowered by the average over the entire gene pool. We interpret this as a set of individual gene trees contained within a species tree: although one gene might vary from another, the overall picture is clearer when considering all genes together.
In earlier posts on The G-CAT, I’ve discussed the biogeographical patterns unveiled by my Honours research. Another key component of that paper involved using statistical modelling to determine whether cryptic species were present within the pygmy perches. I didn’t exactly elaborate on that in that section (mostly for simplicity), but this type of analysis is referred to as ‘species delimitation’. To try and simplify complicated analyses, species delimitation methods evaluate possible numbers and combinations of species within a particular dataset and provides a statistical value for which configuration of species is most supported. One program that employs species delimitation is Bayesian Phylogenetics and Phylogeography(BPP): to do this, it uses a plethora of information from the genetics of the individuals within the dataset. These include how long ago the different populations/species separated; which populations/species are most related to one another; and a pre-set minimum number of species (BPP will try to combine these in estimations, but not split them due to computational restraints). This all sounds very complex (and to a degree it is), but this allows the program to give you a statistical value for what is a species and what isn’t based on the genetics and statistical modelling.
The end result of a BPP run is usually reported as a species tree (e.g. a phylogenetic tree describing species relationships) and statistical support for the delimitation of species (0-1 for each species). Because of the way the statistical component of BPP works, it has been found to give extremely high support for species identities. This has been criticised as BPP can, at time, provide high statistical support for genetically isolated lineages (i.e. divergent populations) which are not actually species.
Improving species identities with integrative taxonomy
Due to this particular drawback, and the often complex nature of species identity, using solely genetic information such as species delimitation to define species is extremely rare. Instead, we use a combination of different analytical techniques which can include genetic-based evaluations to more robustly assign and describe species. In my own paper example, we suggested that up to three ‘species’ of N. vittata that were determined as cryptic species by BPP could potentially exist pending on further analyses. We did not describe or name any of the species, as this would require a deeper delve into the exact nature and identity of these species.
As genetic data and analytical techniques improve into the future, it seems likely that our ability to detect and determine species boundaries will also improve. However, the additional supported provided by alternative aspects such as ecology, behaviour and morphology will undoubtedly be useful in the progress of taxonomy.