It shouldn’t come as a surprise to anyone with a basic understanding of evolution that it is a temporal (and also spatial concept). Time is a fundamental aspect of the process of evolution by natural selection, and without it evolution wouldn’t exist. But time is also a fickle thing, and although it remains constant (let’s not delve into that issue here) not all things experience it in the same way.
It should come as no surprise to any reader of The G-CAT that I’m a firm believer against the false dichotomy (and yes, I really do love that phrase) of “nature versus nurture.” Primarily, this is because the phrase gives the impression of some kind of counteracting balance between intrinsic (i.e. usually genetic) and extrinsic (i.e. usually environmental) factors and how they play a role in behaviour, ecology and evolution. While both are undoubtedly critical for adaptation by natural selection, posing this as a black-and-white split removes the possibility of interactive traits.
Despite how important the underlying genes are for the formation of proteins and definition of physiology, they are not omnipotent in that regard. In fact, many other factors can influence how genetic traits relate to phenotypic traits: we’ve discussed a number of these in minor detail previously. An example includes interactions across different genes: these can be due to physiological traits encoded by the cumulative presence and nature of many loci (as in quantitative trait loci and polygenic adaptation). Alternatively, one gene may translate to multiple different physiological characters if it shows pleiotropy.
From an evolutionary standpoint again, epigenetics can similarly influence the ‘bang for a buck’ of particular genes. Being able to translate a single gene into many different forms, and for this to be linked to environmental conditions, allows organisms to adapt to a variety of new circumstances without the need for specific adaptive genes to be available. Following this logic, epigenetic variation might be critically important for species with naturally (or unnaturally) low genetic diversity to adapt into the future and survive in an ever-changing world. Thus, epigenetic information might paint a more optimistic outlook for the future: although genetic variation is, without a doubt, one of the most fundamental aspects of adaptability, even horrendously genetically depleted populations and species might still be able to be saved with the right epigenetic diversity.
To expand on this, we’re going to look at a few different models of how the spatial distribution of populations influences their divergence, and particularly how these factor into different processes of speciation.
What comes first, ecological or genetic divergence?
The order of these two processes have been in debate for some time, and different aspects of species and the environment can influence how (or if) these processes occur.
Different spatial models of speciation
Generally, when we consider the spatial models for speciation we divide these into distinct categories based on the physical distance of populations from one another. Although there is naturally a lot of grey area (as there is with almost everything in biological science), these broad concepts help us to define and determine how speciation is occurring in the wild.
A step closer in bringing populations geographically together in speciation is “parapatry” and “peripatry”. Parapatric populations are often geographically close together but not overlapping: generally, the edges of their distributions are touching but do not overlap one another. A good analogy would be to think of countries that share a common border. Parapatry can occur when a species is distributed across a broad area, but some form of narrow barrier cleaves the distribution in two: this can be the case across particular environmental gradients where two extremes are preferred over the middle.
This can be tricky to visualise, so let’s invent an example. Say we have a tropical island, which is occupied by one bird species. This bird prefers to eat the large native fruit of the island, although there is another fruit tree which produces smaller fruits. However, there’s only so much space and eventually there are too many birds for the number of large fruit trees available. So, some birds are pushed to eat the smaller fruit, and adapt to a different diet, changing physiology over time to better acquire their new food and obtain nutrients. This shift in ecological niche causes the two populations to become genetically separated as small-fruit-eating-birds interact more with other small-fruit-eating-birds than large-fruit-eating-birds. Over time, these divergences in genetics and ecology causes the two populations to form reproductively isolated species despite occupying the same island.
As you can see, the processes and context driving speciation are complex to unravel and many factors play a role in the transition from population to species. Understanding the factors that drive the formation of new species is critical to understanding not just how evolution works, but also in how new diversity is generated and maintained across the globe (and how that might change in the future).
One particular distinction we need to make early here is the difference between allele frequency and allele identity. In these analyses, often we are working with the same alleles (i.e. particular variants) across our populations, it’s just that each of these populations may possess these particular alleles in different frequencies. For example, one population may have an allele (let’s call it Allele A) very rarely – maybe only 10% of individuals in that population possess it – but in another population it’s very common and perhaps 80% of individuals have it. This is a different level of differentiation than comparing how different alleles mutate (as in the coalescent) or how these mutations accumulate over time (like in many phylogenetic-based analyses).
Fixed differences are sometimes used as a type of diagnostic trait for species. This means that each ‘species’ has genetic variants that are not shared at all with its closest relative species, and that these variants are so strongly under selection that there is no diversity at those loci. Often, fixed differences are considered a level above populations that differ by allelic frequency only as these alleles are considered ‘diagnostic’ for each species.
To distinguish between the two, we often use the overall frequency of alleles in a population as a basis for determining how likely two individuals share an allele by random chance. If alleles which are relatively rare in the overall population are shared by two individuals, we expect that this similarity is due to family structure rather than population history. By factoring this into our relatedness estimates we can get a more accurate overview of how likely two individuals are to be related using genetic information.
The wild world of allele frequency
Despite appearances, this is just a brief foray into the many applications of allele frequency data in evolution, ecology and conservation studies. There are a plethora of different programs and methods that can utilise this information to address a variety of scientific questions and refine our investigations.
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 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.
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.
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
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.
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?
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).
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.
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.
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 rapidlypurged 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.
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.
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?
One of the most obvious ways the evolution of two different species can interact is in predator and prey relationships. Naturally, prey species evolve to be able to defend themselves from predators in various ways, such as crypsis (e.g. camouflage), toxicity or behavioural changes (such as nocturnalism or group herding). Contrastingly, predators will evolve new and improved methods for detecting and hunting prey, such as enhanced senses, venom and stealth (through soft-padded feet, for example).
The pine marten is a species in the mustelid family, along with otters, weasels, stoats, and wolverines. Like many mustelids, they are carnivorous mammals which feed on a variety of different prey items like rodents, small birds and insects. One of the more abundant species that they prey upon are squirrels: both red squirrels and grey squirrels are potential food for the cute yet savage pine marten.
In a similar vein to predator and prey coevolution, pathogenic species and their unfortunate hosts also undergo a sort of ‘arms race’. Parasites must keep evolving new ways to infect and transmit to hosts as the hosts evolve new methods of resisting and avoiding the infecting species. This spiralling battle of evolutionary forces is dubbed as the ‘Red Queen hypothesis’, formulated in 1973 by Leigh Van Valen and used to describe many other forms of coevolution. The name comes from Lewis Carroll’s Through the Looking Glass, and one quote in particular:
‘Now, here, you see, it takes all the running you can do, to keep in the same place’.
Plenty of other strange and unique mechanisms of coevolution exist within nature. One of them is mimicry, the process by which one species attempts to look like another to protect itself. The most iconic group known for this is butterflies: many species, although they may be evolutionarily very different, share similar colouration patterns and body shapes as mimics. Depending on the nature of the copy, mimicry can be classified into two broad categories. In either case, the initial ‘reference’ species is toxic or unpalatable to predators and uses a type of colour signal to communicate this: think of the bright yellow colours of bees and wasps or the red of ladybirds. Where the two categories change is in the nature of the ‘mimic’ species.
Coevolution of species and the importance of species interactions
There are countless of other species interactions which could drive coevolutionary relationships in nature. These can include various forms of symbiosis, or the response of different species to ecosystem engineers: that is, species that can change and shape the environment around them (such as corals in reef systems). Understanding how a species evolves within its environment thus needs to consider how many other local species are also evolving and responding in their own ways.