I got very close to our speakers and to our episodes, so yes, I’m a bit nostalgic already. Yeah, we got to know many different people from our communities. What do you want to talk about today? We went through all of the specifics and technical stuff that happens in biodiversity genomics, but then how do people really use these tools and these methods and this data in real life? So I would say we should talk about applications of biodiversity genomics.

Yeah, I definitely agree. I think this is a bit of a black box for many of us, but honestly how society uses the information that we provide is a central point also for the discipline in the future. Especially nowadays where biodiversity genomics is getting more and more used by NGOs, by governments, or also by the managers of a natural park.

So, let’s start our discussion. Who would you pick in your community? I would call Brent Emerson, who is the work package leader of barcode application within BG, who leads the research group on ecology and evolution on islands, an initiative within the Institute of Natural Products and Agrobiology and the Spanish National Research Council based in Tenerife. His research uses genetic and genomic tools to understand what structures diversity and especially focusses on the use of DNA barcodes to characterise species assemblages.

And you? I go for José Melo Ferreira, the leader of the Genomics of Evolutionary Change research group at CBO Biopolis, based at CBO in Bio, the research centre on biodiversity and genetic resources of Portugal. He is also an assistant professor at the University of Porto. His team uses genomics tools to understand fundamental evolutionary processes.

And José is particularly interested in using genomics for the conservation and management of biodiversity and to understand the impact of anthropogenic change on the adaptive potential of species. He is also the leader of BG genome application work package. Brent, José, welcome.

OK, welcome, everybody. Let’s try to warm up in one sentence each. So what’s the single most useful thing your data deliver? Eyeball clearly with barcodes and erga with reference genomes and genomic data.

OK, I guess the most useful thing we deliver is taxonomic data that’s in a universal format that can be accessed anywhere, anytime in the world. So taxonomy is a very specialised skill to identify material, especially in sick material. It’s a very complicated process.

So we can actually make all the taxonomic information available without the difficulty of being a taxonomist. So really, it’s like making the most of the limited taxonomic resources we have. And so the DNA sequence associated with the species is a universal language, if you like, or a piece of information that even if you’re not, it doesn’t matter what language you might speak.

As soon as you access a sequence with the name associated with it, you can use it. Yeah, that’s brilliant. And you, José, from the erga side? Yeah, well, when we have access to complete information about the genomes of a given species, we have access to its history, its evolutionary history.

And so everything is very useful for conservation and management, like estimates of genetic diversity, population connectivity in breeding. But I can mention in the single sentence you requested, the thing that fascinates me, and it’s also very important for conservation management, which is to have access to adaptive genetic variation, meaning that we can, analysing the genomes of individuals of a certain species, we can identify local adaptations, so understand what are the genes or the portions of the genome that are important for local adaptation of a certain population. And sometimes that’s not very evident only by looking at morphology.

And so we can have this access to history that gives us the makeup of a biological species. Yeah, thank you so much. So the entire picture.

So basically, eyeball and erga, you can have the entire picture of a species with morphology, taxonomy, and then reference genomes. Let’s say that eyeball, you’re the neighbourhood watching who’s here now, while erga, you’re the forensic lab and understanding how it’s doing and why. Can you give us an example where that partnership shifted an actual decision on the ground? So I think that’s a great image of what the power of eyeball perspective with barcodes and erga perspective with genomics, where with barcodes, you can identify what exists in a certain place.

And then with the genomes, we can understand why it’s there, when it went there, and all the history that is involved in the establishment of the species in a certain place. And so these two dimensions are very complementary and actually work together to deliver very meaningful information. There are plenty of examples that certainly Brent will mention a few also later, but I can mention, for example, the cases of invasive mosquitoes that are vectors of important diseases that affect humans, like dengue virus or the West Nile virus, and barcode-based approaches like, for example, using eDNA, which is the possibility of analysing the DNA that is present in a certain, for example, water sample.

We can identify the presence of a certain mosquito, for example. We can identify changes in the ranges of this species. And then with genomics, we can understand why this, for example, range changes is occurring.

If there is any genetic basis, for example, the Asian tiger mosquito is now invading north in Europe. And genomics can understand exactly why, if it’s being, if it’s adapting to these northernmost regions in terms of overwintering strategies. Also, an important thing that genomics can do is identify the basis of resistance to insecticides, for example, some types of insecticides.

And this has been used actually already for monitoring these variants that confer insecticide resistance and that locally can be used to change the strategy for the control, for example, change the type of insecticide, knowing that there is a certain resistance to a certain product that has been developing in a certain population. So that’s a simple example where these two perspectives of barcoding and genomics can work together for actual management decisions on the ground. Yeah, I could probably add another practical example here.

So like DNA barcoding, use it to distinguish different species. And that can be a really useful tool when we have legal harvesting of materials, for example, wood. Now, this tree can be harvested, that tree can’t.

OK, we can easily follow up and understand whether it’s been harvested, belongs to the right or the wrong species. But sometimes species are so closely related that becomes a very difficult or impossible task with standard DNA barcoding. So, for example, with rosewood trees in Madagascar.

Now, with a whole genome of any of the rosewood species, we can then use genome skimming to identify those species that we can’t distinguish with a standard barcode. Now, it might sound like you’re smashing a walnut with a sledgehammer using whole genome skim data, but it’s actually not. It turns out to be probably more cost effective and quicker than many other approaches.

And when we’re dealing with little bits of wood that have been processed, you know, it has to be a DNA based approach. So, I mean, so this is kind of this coming together of the two approaches, barcoding and genomics, is something that we’re trying to catalyse now in the Biodiversity Genomics Europe project. And so these examples, hopefully they will be really one of many examples in five years time rather than highlights right now.

You were talking about monitoring and monitoring at scale can feel like counting the raindrops when it’s raining. So could you name one policy or management decision that only happened because barcodes turned an unclear, shady picture into something decision ready? Yeah, sure. I could probably give you a real world example.

It’s not where a policy decision has been effected yet. It’s where it’s in process, if you like. So here in the Canary Islands, you know, we have a very rich fauna of invertebrates in terms of endemic species.

And so the other ones that we value, the natives and the endemics, and the introduced species are the ones we’re more worried about. Now, we have a great database that lists all species and their status as introduced or native, and that helps us with management strategy. Now, with our barcoding work here, we’re finding that many of the species that have been believed to be natives are actually introduced.

So we have to shift them from this bucket of species we’re interested in protecting to this bucket, which is species we should be watching for. So that’s been a fairly straightforward exercise. Get a specimen here, sequence it, and then look to see what’s similar to that sequence globally.

And if you find that that same sequence or something very similar is also found in Germany, in Poland and the UK, it’s very clear that it’s been here a very little amount of time and is a human introduced. So that reshapes the management landscape here for invertebrates. And on the erga side, conservation in practise.

When did our reference genome, together with population genomic data, move a species from we’re concerned to this is the plan? Which metric actually defined the genetic diversity in breeding adaptive variants? Yeah, fortunately, I think that we’re at a stage in the development of the field of genomics where this example starts accumulating and that’s I think it’s an excellent sign for the strategies for conservation. There’s, for example, the example of the kakapo, which is a flightless parrot in New Zealand, that is actually the world’s only flightless parrot and the heaviest parrot that exists. And this species is critically endangered so the total population living is a couple hundred individuals.

And actually with chromosome level reference genome plus resequencing data, meaning adding genomic data at the population level, it has been used for strategies to restore and recover this population. For example, using metrics like runs of homozygosity, which is a metric of inbreeding of if the population is very little, has very little genetic diversity, then this metric gives us an idea of this little diversity. And this can anchor translocation strategies and things like that, making pair recommendations.

It can be used to analyse, for example, immune gene diversity, which is really important to understand the potential for the populations to persist. I could talk about the example of the California condor, but I want actually to touch on an example that is here from the Iberian Peninsula, which is the Iberian lynx. And so the Iberian lynx was on the brink of extinction early this century and there was a major and important recovery programme for the recovery and conservation of the Iberian lynx.

And so this started early in the century and essentially accompanied all the development of genomics and I think it’s an excellent example of how the progress that the technological analytical capacities that we’ve been developing in genomics has been uptaken by this programme along the way. And now towards the existence of first the draft and our high quality reference genome. And this has been used, I can give several examples first to understand the evolutionary history of the, of the, of the species, and importantly the demographic history meaning in the past what was the effective population size the sizes of the populations that were able to maintain diversity to create to understand the baselines of this natural diversity for for conservation, but then also to understand and quantify genomic erosion, erosion we call it when when diversity is being lost, and this leads also in very small populations to the accumulation of variants of mutations that are deleterious meaning bad that cause problems.

And so to quantify this, this fitness implications of this genomic erosion, and then in also to, to, to guide the captive breeding programme to select individuals that are more diverse that are more suitable to be reintroduced in nature to guide the translocations and introductions and, and also to to, there has been also a change in a way that these populations can be then monitored by shifting from the use of a small amount of markers in the genome to do this monitoring to larger sets of markers that are much more informative about the provenance diversity of the individuals. So I think this is a, an example that where the progress of genomics and now with reference genomes with the greatest power to to do this inferences that has really helped the to bring this species back and from a critically endangered situation to a vulnerable situation which is an important progress, but this, this can also be used so these situations where we have this is a problem, and this is how we solve it. Often this is the problem is really critical is something that is disappearing and we want to, to, to restore it and conserve it.

But this can also be using situations where, where we can identify. This is not a problem yet but this will be a problem for example if we identify situations of adaptations that will be impacted or are being impacted by climate change. We can predict the trajectories of these, these populations take into account this adaptive variation.

And so actually be preemptive, meaning, before we have a problem, we already know that we may have a problem and we can target measures to devise target measures to solve it in the future. Okay, let’s try now to make a budget test. So if a random authority hands you 100k, 100,000 euro.

Eyeball, do you spend it more on barcoding sites and for Erga on fever but deeper genomes plus population genomic data. These are just two ideas but you can give us your shopping list. I’ll start, I’ll spend it on neither of those two things I would, I would use it.

So what we lack I mean more data. We need to understand the data we have before we get to more data and what we find is a bottleneck is understanding the sequences you obtain and what species they belong to. So I would I would definitely invest that money in forging taxonomic collaborations so that we can have better curated reference libraries for the Canary Islands.

I would use it to to apply techniques that have been developed within the biodiversity genomics Europe project to obtain sequence data from museum material so we can use, we can basically take advantage of the taxonomic history that we have, and the hard work that many taxonomics have invested. And we can, we can make that universal and so I’d be wanting to obtain material well identified from the Canary Islands from different collections across Europe and obtaining barcode reference sequences and that way we can clearly identify what we have and where it is right now, maybe 30 or 40% of our sample material we can identify taxonomically there’s a vast amount that needs. We need to know what that species belongs to so that’s that’s pretty much where I spend that money, and it would be a wise investment for sure.

What about you, Joseph. Well, it really depends on what already exists. For example, if, if we identify a problem that we need to solve for for a certain species.

Sometimes genomics is incremental so there are several studies that produce genomic data, sometimes a reference genome already exists and so this changes the strategy that I would follow. If nothing exists at all. I would definitely start by generating a high quality reference genome to have a baseline for the follow up genomic analysis that that we can we can do.

And this is actually the strategy that we’re doing in the biodiversity genomics Europe project, where we have case studies for applications where we, the different types of projects that are involved there, where we generate population level sequencing data to anchor analysis on a reference genome, so that the inferences that that we can we can make are more complete and then this can be crafted to the different objectives of each problem. If it’s a problem of low effective population size and understanding of connectivity. If it’s a problem where understanding adaptation can be can be important.

If we want to use all genomes to, for example, monitor genetic diversity through time. It really depends on the on the specifics, but I would definitely in terms of strategy for genomics use generate the first high quality reference genome, and then population level data of all genomes to to to tackle in depth is is this problem or and find the best solution and management and conservation strategies, depending on the objective of the of the study. What if we talked about bioeconomy.

So, for Ergem, one way that genomes and genomic data make industries smarter, you can think of resilience, breeding, innovation, we already talked a bit about some of those. Yeah, for for genomics there are also numerous examples from, I think the one obvious is livestock breeding right where genomic profiling can help select animals that had better disease resistance faster growth depending on the production that that is being done. But I think one one very interesting example is for fish stock management.

And that’s actually covered also by a case study in the biodiversity genomics your project. And so, the, the fisheries is an important activity in the world and sustainable fishery is is important to to develop and genomics and can really help to understand because sometimes neutral genetic variation or genetic variation that can be identified with with a few markers show very little structure in the end of this fish populations, but with genomics and and anchored on reference genomes shallow structure can be identified sometimes related with with some adaptive variants that exists in some in some regions, and this has actually been used to develop a snapshot so essays that can be used to to monitor the origin of the of the fish that are fish and understand in this way, manage the, the, the way that that fish are being harvested.

(Transcribed by TurboScribe.ai. Go Unlimited to remove this message.)

and devise this continuous monitoring and management strategies to control and ensure the sustainability of this important economic activity. And for the AIVOL side, so Brent, can you tell us one way that barcodes clean up supply chains like traceability, fraud or miscandling? Yeah, sure. I mean, there’s a lot of examples.

I think we’re going to go back to what one of the first ones that ever came out. This was before it was even called barcoding, and this was a study that came out in 1994. It was based on the fact that we knew, or the world knew, that there’s a lot of illegal harvesting of whales for commercial purposes, particularly in Japan.

So this group from New Zealand, at least one of the authors was from New Zealand, they took small laboratory equipment with them into Japan, set up little PCR labs in hotel rooms, went around restaurants, harvested what was allegedly legally harvested minke whale meat, and then PCR amplified it and sequenced it and referenced that against known species sequences. And they found it just was a Pandora’s box. There was minke whales that were harvested outside of harvestable areas.

There’s also humpback whales and thin whales. And even some of the meat was porpoise and dolphin. And I think, but I’m not 100% sure, some of the meat was not even terrestrial, was not even marine mammal, was terrestrial mammal.

And so this really demonstrates how with new sequences to regulate the supply chain, to detect fraud, and to detect illegal harvesting. And so fraud in terms of selling something that isn’t what it says on the label, and illegal harvesting, things that just shouldn’t be on the plate anyway. And so this is now quite widely applied, and DNA sequences used by various authorities in the United States and in Europe, particularly for this reason.

So it’s super powerful and easily applicable. Right. So one day when we will buy a tuna can, we will be 100% sure that it’s only tuna inside.

Thank you very much for your answer. But now let’s talk a bit about data, about files. So what’s your messiest data headache? Like mixed samples, shaky vouchers, biassed references? And the workaround you use so the application does what it says on the tin? So Brent, you can start.

Okay. Messiest data headache is just real data and the concept of what a species is. So we have a, you know, when we have a lot of anonymous sequence data, or allegedly belongs to one species, we find it’s very divergent with regard to its barcode sequences.

You know, the inevitable question is, was this one species or two? And that becomes quite important for policy and decision making. Particularly if it’s more, if it’s, we have multiple species, but it’s been classically labelled as one. Well, we need to, I mean, that needs to be described.

It doesn’t exist unless it has a taxonomic name in administrative and policymaking circles. Making those decisions is not straightforward. And so this is an area that, where I think there needs to be more work in terms of fully integrating species concepts into the interpretation of barcode sequence data and allowing for the nuances of speciation, that it is not a black and white process.

It’s a, there is not a single number of mutations beyond which you can say, oh, now we have two species. So being as a headache is also super exciting and a lot of fun to work with. And it’s again, an area where genomics comes in and helps.

So quite often we’re applying genomic tools. We have these two divergent, or in some cases, nine divergent lineages that are labelled as one species and we find them in sympatry. So we can then use genomic data to ask the question, are they behaving as biological species in sympatry? It’s an instant answer with genomic data.

It’s an instant answer. So there’s a lot of scope there. And this is another area where these two fields in taxonomy can really come together and be super helpful, I think, especially given that the logistics of generating the kind of data we’re talking about is becoming more and more achievable or less problematic.

Yes. So in our day-to-day work in genomics, we often find all sorts of difficult samples or difficult data. Yet, I think that sometimes what makes that sample or data set difficult is often an opportunity to extract information that was previously inaccessible for researchers or to derive extra amounts of information that can enrich the knowledge that can be derived from them.

And I can give a couple of examples. One are museum samples, meaning that these are biological specimens deposited in natural history museums, sometimes for over a century, very often. And the DNA that can be extracted from these samples is degraded and is present in low amounts naturally.

And so this makes genomic analysis challenging, but possible. And this possibility really opens a new window of opportunity to sort of unlock the vault and making use of these remarkable natural history collections. And it also reduces the impact of new sampling.

So if we have all these collections deposited in museums, we can reuse it using cutting-edge genomic techniques to analyse them. Of course, if we have really short pieces of DNA and with low amounts, that requires some care in terms of laboratory work to extract correctly and produce the libraries that are required and to sequence. And then in the analysis, we also need to apply some crafted approaches to take into account, for example, mutations that may arise that are not really original, that are physical damages given the time that has passed since the collection.

Since we have very little DNA, often we need to pull individuals from the same populations to increase the amount of diversity that we can analyse and analyse it at the population level. So this strategy allows us to really analyse and apply this cutting-edge, high-throughput genomics to understand things without new sampling, just simply going to the museums. And that’s, I think, a fascinating field.

And then I can give a second example, which are samples with mixed DNAs from several species. And this can be, for example, non-invasive samples, scats that have DNA from the host, but also from the preys, for example, hosts, cobiants, parasites. And when we extract the full DNA that is present in a certain sample and then sequence everything, part will be from the host.

And often, if we’re interested in studying the host, we just get rid of all the rest of the DNA sequences that are there. But these actually have all this information about, I don’t know, the diet of individuals, about plants that a pollinator visits, about parasites that exist in certain hosts. And so this sort of mixed and difficult samples actually present an opportunity to provide a more integrative view on the biology of species.

What advancement has the highest chance to change your field trajectory? And I would start with Joseph. I think that one foreseeable advance that we have on the field is to move from this strategy that we’ve been talking about, so generating a single reference genome and then additional re-sequencing data from individuals from that population to a pan-genome approach. So, meaning that we can generate high-quality genomes from every single individual that we analyse in a certain population.

Because the approach that we have right now, we essentially can identify a variation that is present in the common part of the genome that is common to all individuals we analyse. And this is the majority of the genome, if we’re talking about a single species, and has been very, very informative for all the things that we’ve been discussing. But we’re leaving this perhaps tinier piece of the genome where there is variation among individuals and that we currently cannot analyse with this strategy of reference genome and then re-sequencing data.

And so having access to that, we can, for example, analyse genes that are present in some individuals and not in others. The expansion of gene families, for example, in the immune system, in which sometimes the number of copies of genes is important for the ability of individuals to resist to diseases. Structural variations or changes in the way the genome is organised that can be important also for local adaptation.

And so I think this is an advance that is foreseeable, that we’re moving in that direction. And I think that will change also the way our inference power in the field, because we will have access to much more information, much more information than we have now. But I think at this stage, I can also see an advance that will change also the application power, which is a sort of a consolidation stage where we have all the technological ability to generate reference genomes and whole genome data.

We have all the analytical pipelines developed. And so in terms of fundamental science, we are able to do all sorts of analysis. And now I think our work and our job as a genomicist and scientist is also to make this more and more accessible to non-specialists so that they can use this routinely in their activities.

Managers that can then influence policy decisions. This already occurs, but making this analysis of genomes from the lab to the analytical part accessible for non-specialists, I think that that will change and increase a lot the way these techniques can be really applied in the field. Yeah, that’s very, very promising.

And Brent? I think it’s a kind of similar response to Jose, that the developments that are letting us overcome historical barriers to working at scale, like the pan-genome approach is now kind of tangible, and that’s super exciting. And I think in the barcoding world, just as an example, when I was doing my PhD, which was a long time ago, just to get one sequence was such a painful process. It involved weeks of work and radioactivity.

Then you put your X-ray in the freezer and you pull it out a week later and you open it up and there’s nothing there. You cry and you go back again. But now just generating like a thousand sequences just doesn’t seem like that big a deal.

It’s so achievable and now becoming more affordable. So the idea of research groups that were previously excluded from this kind of research because of cost limitations, that’s becoming less and less. So the idea of having in your lab everything you need, all the resources, including the sequencer, the sequencing unit to generate barcode data at scale, is every year it’s becoming more affordable for more labs.

And so in the Biodiversity Genomics Europe Research Project, there’s been a lot of focus on training for these newly available technologies for implementation. We need more of that. And so barcoding, its most basic application is monitoring an inventory.

And some of the most exciting places to do that are the places that have been sort of left out or have been left out because of cost restrictions. So I think one of the biggest advances and there’s groups working on reducing the cost further for the peripheries that are needed, the equipment that’s needed. So as that continues, it has the strongest chance of us having globally generated data from some of the most exciting parts of the world.

So yeah, I think that’s the strongest advance is this overcoming of scale issues. Yeah, that also democratise, let’s say, access to technologies. And yeah, it’s so beautiful.

So I foresee the next discussion coming at higher level. How many individuals do you need to create a reference PanGenome to make it so? That’s a good question. Do you need 10 or 100? All of them.

So let’s proceed with our last question. I would like to start with Brent again. So if you have 24 hours, a backpack and your kit, all of things that you are necessary for you, for your job, where do you go and what do you sample? Your dream? Okay, first of all, I need more than 48 hours because I live in the Canary Islands.

It takes a long time to get anywhere from here. I think there’s two places I go. The first would be to revisit the Trans-Mexican Volcanic Belt.

I had a PhD student working there. She did some sort of preliminary work on arthropods and it just turned out to be super interesting. So to be able to go back there and sort of scale up more sites, a broader section of the vertebrate community, not just sampling on top of the Sky Islands but in the lowlands below, just have a really detailed understanding of what’s driving diversification there because we saw some really cool signatures and it’d just be great to follow up on that.

So that would be a pretty exciting thing to do. The second thing I’d do is if I had a rucksack and a full kit, I would travel around the museums of Europe trying to get collaborations for sequencing relevant material for the former of the Canary Islands. It sounds like a bit of a boring, pretty boring to do the first one but it’s equally as exciting as far as I’m concerned.

Both are exciting, yeah. Joseph? Yeah, I would probably also need more than 48 hours but so one thing that has always fascinated me is biodiversity in general, meaning the diversity of species of animals and plants and all types of species that exist and it strikes me how little we still know about this diversity. And so if I could do whatever I wanted with infinite money and perhaps a little bit more of time, I will definitely, and also provided that all the ethical and legal aspects of sampling are covered and agreed with the local authorities and communities, I would definitely go to a biodiversity-rich locality in the world like in Africa or in South America, do a bioblitz and then sequence everything.

As a second one, I share this brand’s desire to go around the museums in the world because this has been a really fascinating aspect of my research. It is really, really amazing the level of diversity, of biodiversity that we can find in the drawers in natural history museums, things that are not shown to the public. It’s really resources that are amazing and that need to be used to increase the knowledge that we have about biodiversity.

Sounds like a good plan for 48 hours, we stick to that. And that was the last question, Brent and Josette, thank you so much for joining us. And thank you to all our listeners, this was the last episode of Genomic Connections, we hope you enjoyed it and thank you.

This podcast is brought together by Biodiversity Genomics Europe, a project funded by the European Union, the Swiss Confederation and the United Kingdom.