*36C3 preroll music*
Angel: Right now I'd like to welcome our first speaker on stage. The talk will be
about protecting the wild and I'll hand over to her. Please give her a warm round
of applause.
*Applause*
Jutta Buschbom: Thank you very much for the introduction. My name is Jutta
Buschbom, I'm an evolutionary biologist. That is my background. I did do my PHD at
the University of Chicago working on little fungees that live in symbiosis with
algae and form colorful rocks, colorful crust on rocks. I then did a Postdoc in
bioinformatics and after that moved back into organismal biology, working in forest
genetics. And the ten years I worked in forest genetics for the first time I
encountered questions that were with regard to application, and I found out
that actually moving from research to application is not trivial. So what I'm
going to present is a high tech way using genomic data to protect biodiversity in a
way that you can actually reach application and use conservation genomic
tools. So this summer the draft of the report of the Intergovernmental Science
Policy Panel for Biodiversity and Ecosystem Services came out and its
results were quite warning. It stated that around a million animal and plant species
are currently stated and of those...half of those species are already dead species
walking. So because due to the destruction of the habitats or habitat deterioration,
they are not able to reproduce in a sustainable way anymore. A third of the
total species extinction rate risk to date has arisen in the last 25 years. And just
to give you an idea about the relation we are talking about...currently the rate of
extinction risk is already at least ten to hundreds times higher than it has averaged
over the past 10 million years. And within these 10 million years there were the Ice
Ages, for example. And most of the extinction risk is due to the fact of land
and sea use change. The report also talks, even talks about that we already seem to
have transgressed a proposed precautionary planetary boundary, which means within the
boundary we have a stable biological system. But having transgressed it, we
might already be in a transition to a new state that we have no way to find out how
this state is going to look like. So all of these facts that the report is stating
are actually pretty negative. And I was quite happy to read that they also present
that there are actually people who do better than most of us. And they point out
that many practices of indigenous people and local communities actually conserve
and sustain wild and domesticated biodiversity quite well. Today, a higher
proportion of the remaining terrestrial biodiversity lies in areas managed and
held by indigenous people. And these ecosystems are more intact and less
declining, less rapidly declining. So we have examples of lifestyles that actually
do better than most of us. And I know the solutions won't be simple and it won't be
easy to get there but we can look to what these people do better than we do. All of
this sounds...it's a global report and it sounds kind of like far away, like
probably somewhere in the tropics, but actually threats to biodiversity happen
also directly in front of our own front doors. This summer a paper came out from
two colleagues from the University of Greifswald, who had analyzed the long term
data set about leaf beetles. And they were asking if we already have a decline of
leaf beetles in Central Europe. So they compiled long term data sets of leaf
beetle observations for Central Europe, starting from 1900 now to 2017, so
spanning a hundred and twenty years. And what they find is that systematic reports
on leaf beetles and leaf beetle observations are increasing during this
time interval, time span. But despite the fact that we have...like in the last two
decades, we had very high numbers of reports and observations for leaf beetles,
the number of species, the orange line, is declining. It's slightly declining. But
the question is, is this real or not? And what was most worrisome to the authors is
that in the data set, the number of species here in orange that were having
more reports was declining, while the number of species that showed less reports
than before is expanding. So this kind of long term datasets are very hard to
interpret and many factors can contribute to those patterns. And it's not clear if
this pattern is statistically significant. But if you take a step back and consider
your background knowledge, your prior knowledge about the state of the world, do
you say, like, how does the current state look like? Does it look good or rather
worrisome? And then with that knowledge, tell me that these results are an
artifact or a bias. I'm worried that once we have statistical significant signal in
this dataset, it will be already too late. So right now, I've been talking about leaf
beetles and beetles are the largest group within insects with about 400.000 species.
Leaf beetles are a large family of about 50.000 species which are worldwide
distributed. And here in Germany, we have over 470 leaf beetle species. So how do we
actually know how many species there are and who actually counted all these
species? And is that just a task of taxonomists. Taxonomy is the science of
naming and defining, including circumscribing and classifying groups of
biological organisms on the basis of shared characters. So one could have the
picture of some woman with a funny hat running over a meadow catching like
butterflies or some guy mushroom hunter crawling through the forest trying to find
mushrooms. And it's true, as biodiversity scientists we spent a lot of time outdoors
and yeah...on the other hand, biotaxonomy is a high-tech science today. So
taxonomists actually take up new technological tools and developments to
help them identify and describe, understand the species. So taxonomists
actually are often experts in, for example, microscopy, mathematics,
biochemistry, even proteomics and genomics. So throughout the talk, I'm
going to compile this list of people and experts we're going to need to protect
biodiversity if we want to do this on the basis of genetic data. Right now, the list
is quite empty. The first entry is a taxonomists, but that will change quickly
and taxonomists are a subgroup of evolutionary biologists mostly. So I told
you as taxonomists and biodiversity scientists take up technology and...so as
soon as computers came about and the internet started people started to use
that to compile information about species, and today we have several global resources
available at the species level and above the species level. So we biodiversity
scientists were among the first who defined biodiversity information
standards. We have a global catalog of life. A list of all named species. The
Global Biodiversity Information Facility has an aim to bring together information
from different sources and they are compiling, producing this wonderful map.
This is leaf beetles, all the records about leaf beetles that we have in the
world. And it looks like as if leaf beetles are highly associated with third
world economics. However that clearly is an artifact and it just shows that we need
many more taxonomists and biodiversity scientists all over the world to find and
identify leaf beetles. So we also need biodiversity informaticians to help us
compile global lists and distribute knowledge. So far I have been talking
about species which is a simplification. The question is what is...what are species
actually? And so we need to talk about genetic diversity within and between
species. And I'm going to do so using gulls, which most of us might know. Here
in Europe, we have two large gulls of the genus Larus. One is in the front, the
lighter gray is our Silbermöwe. And in the back is our Heringsmöwe, the dark one. And
I'm going to use German names because the English names go crosswise and that's
completely confusing. So I will stick with the German names. Here in Europe these two
species seem to be really fine species because they barely interbreed, so they
don't hybridize. However, if you take a step back and look at the genus in
general, you see that the species of the genus are distributed kind of ringwise
around the Arctic. And so the idea is that, say during the Ice Age, all of this
area was glaciated and the gulls retreated to a refuge here near the Caspian Sea. And
then after the ice retreated, the gulls moved back north. One branch moved into
Europe forming our Heringsmöwe and another branch then moved counterclockwise
around the Arctic, producing different morphotypes, different species across the
Bering Strait and then into North America. There the dark blue one is...I'm
simplifying, the equivalent of our European Silbermöwe, the American
Silbermöwe. Then the idea is that some individuals crossed back to Europe and
formed our European Silbermöwe. And while all of these species here are
interbreeding, so they hybridize. Only when this ring is closed those two species
don't interbreed anymore. And the big question is, are we actually dealing with
one single species or are we dealing with different species that just happened to
hybridize more or less? The question is not trivial because it has consequences
for protection. If we are dealing with one single species, all the gulls in Eurasia
could go extinct and it wouldn't matter because we still would have the gulls in
North America. However, if we have different species in all of these areas,
we would need to protect individuals or the species on a regional level and
protect all of these different species. So to investigate this question about: Do we
have different species? And what were the evolutionary processes and histories that
brought about the species? A group of scientists investigated that using DNA
sequences. And on the left, you have the model, the theoretical model of the ring
species. And here on the right you have reality. And the scientists found that the
reality is always much more complex. So, for example, they found two refuges or
they proposed two refuges. But what they found was that genetic diversity was
correlated with those species or morphotypes. So what that also means is
that genetic diversity is cultivated with geographic origin. What we learn from this
type of analysis is we learn about evolutionary processes and history, about
variability and differentiation of our gene flow and migration, about speciation
processes. That we all need to understand our species, which will allow us to
protect them. So we need evolutionary biologists who do follow genetics and
population genetics. So once we found out that one can use genetic diversity, to
infer geographic origin because genetic diversity is correlated with geography,
people immediately said: 'Okay, we can use it for conservation applications.'. And
it's also...we learned that we...often it is unclear what is a species, species
boundaries are unclear and some species have huge distribution ranges with
different clusters of viability within this huge range. So we know that we need
to protect within species genetic diversity, which means that we need to
understand within species population structure and we need to build useful and
reliable models of population structure. These models are actually required for all
of our applications. They are required for monitoring, for example, for conservation
strategies, for functional adaptation and adaptability, questions of productability
of different provenances, its impact on management regimes, breeding strategies,
and also for enforcement applications. From the studies I showed you before with
the gulls we also know that we need to approach the question of a population
structure on a distribution range wide scale. So here's the map produced by
EUFORGENE, the European Network for forest reproductive material for one of our
native oaks, the sessil oak. And the dots are the sites for genetic conservation
units. And so that is one strategy how to represent within species genetic diversity
and how to sample it. And you can see this is a hypothetical example, but we likely
will see a gradient from west to east or might see one at this scale. Then once we
have these kind of global data sets, we can go to the fine scale and maybe, for
example, do a national genetic monitoring. And we will find much finer scale
gradients. We also will find especially for first trace outliers, so for stands
that don't fit the usual pattern. And that is because the first reproductive material
has been moved around a lot. And so these lighter or darker dots is material that
was moved to Germany from the outside. And we only will identify these outliers if we
have the whole reference dataset. If we don't have the whole reference dataset, we
might not identify these outliers - stands with a different history. Or in a worst
case, these outliers might actually bias our gradients. And we are always talking
about very slight gradients. So it's easy to bias these gradiants, dilute them, so
we actually won't get the results we need. To compile these kinds of reference
datasets that's huge collaborative efforts because people need to go out into the
field and collect the reference samples and that might be scientists, that might
be people from local communities, citizen scientists, managers, owners, government
officials who provide background information, maps, distribution
information and also in many parts of the world might protect the people who are
actually collecting the samples. And it might be conservation activists and NGOs.
So once the samples have been collected they need to be stored somewhere for the
long term and the information needs to be databased. And that is the work of
scientific connections, which are mostly at natural history museums and there the
samples are processed. They're organized in ways that you can find them again. All
the metadata is entered, which curators do, collection managers, preparators,
technical staff at the scientific collections. So once we have these kind of
data sets, large scale data sets, what are we actually doing with them? So the
foundation for all of our applications is population structure and there
specifically population assignment. So the process is set first. We decide on a
question and design our project accordingly that we can answer the
question. Then we need to infer the population structure model and optimize
it. In the next step we need to check if a model actually is good enough for
application because we might have found the best model, but it might still not be
good enough for application. So we need to test that. And that is the step of
population assignment or predictive assignment. And then in the end, we want
to test our hypothesis. Are the two stands different or does an individual come from
stand A or from stand B? And here we identify error rates and accuracy. So this
whole process is very statistical. And so the analysis of these reference data they
need to be accompanied by biostatisticians who can tell us how to analyze our data.
So what is the state-of-the-art right now? What kind of geographic resolution do we
actually get of this non model specie currently? And I'm going to present the
example of an African timber tree species, which is a very valuable timber.
It's one example but basically all results for species who have large distribution
ranges and are continuously distributed and are also long-lived, are very similar.
So this kind of results seem to be species independent. So the species are Milica
regia and excelsa, African teak, which cannot be grown in plantations for timber
quality. So it is harvested unsustainably from natural forests. It's distributed in
West, Central and East Africa. Here's a black rectangle. And a group of a dozen
scientists got together and they actually sampled a reference dataset for these two
species. It's about over 400 samples, they analyzed four marker systems, resulting in
a total of something like 100 markers, genetic markers, and then they optimized
the population model and used different parameter settings. And we're going to
concentrate here on the best solution that they found. And basically this rectangle
here is the black one over here. So the resolution is... they found population
structure with clear clusters. So the populations and the species from West
Africa can be distinguished from those populations in Central Africa. And the
ones in East Africa can be differentiated. So that is really good. So we have
population structure. We know their signal. The problem is still that our
resolution is much lower than we would need to have it because we basically need
resolution at least on a country level, because most of the laws are national. So
it might be legal to harvest a tree in one country, but not in another country. So we
need to get our resolution down to country level or even to regional level. If you
want to distinguish, was the tree harvested in a national park in a
protected area or outside in a managed forest. And when as biodiversity
scientists, we don't know how to continue, one thing is to look for what people do
with model organisms and specifically what people do in human population genomics
because there thousands of populations geneticists are working and there is a
completely different funding background due to the interest of the medical and the
pharma industry. So they are always advanced. What we can learn from there,
from the human populations genomics is that we need two features. One is we
already know that we need distribution wide sampling, which provides a spatial
context. The second feature is that we need genome wide sequencing, preferably
genome sequencing, which provides us steps in time because our genomes are archives
of our evolutionary history. They are records of all the processes and events
and these steps in time then translate also into resolution. Once we have these
two features, actually these reference datasets open Pandora's box. Suddently we
can ask all kinds of questions and objectives, even those that we still don't
know. We can develop all kinds of applications which is done for humans.
Currently, there are at least four global datasets on human diversity. These are
very widely reused and these big datasets - so they are big data with regard to the
number of samples and also the genomes or the genome representations and this
results in very information rich data which initiates analytical development so
people continuously are developing new statistical methods. And right now, a new
wave is coming in of these methods. So once you have these global datasets,
people start in human populations genomics, started to do these intense
regional samplings. And this is the example of the United Kingdom Biobank.
It's a project with 500.000 volunteers, they are all UK citizens from all over the
islands. And each individual was genotyped in a vet lab for 820.000 markers. That's
completely I mean, that's a different number than the 100 or 1000...in
biodiversity scientists we normally analyse a maximum of a couple of 10.000
markers. So that's a completely different number. But then statistical geneticists
come. They do some weird and wonderful voodoo and they derive 96 million markers
per genome that is per individual from these 820.000 markers that were produced
in the lab. So that's a hundred fold increase. And once you have this kind of
dataset for a genome, you suddenly or you finally become country level and within
country level resolution. So these panels are examples. So the first panel shows
individuals who were born in Edinburgh and the question was "Where were people born
who had a similar ancestral background, genetic background?". And what they found
was that was all over Scotland and Northern Ireland. Northern Yorkshire was
even more local. So people from Yorkshire don't seem to get around a lot. For London
the situation is completely different. That is what we would expect because
London is a people magnet. People move there all the time. They meet there, they
get children and the kids born in London, their genetic ancestry has nothing to do
with London. It's from all over the place, from the British Isles and the world. So
that's why the colors are strongly dissolved. So this study came out also
this summer. And it's the first time that I have seen that we actually really can
achieve regional resolution. And I find this possibility for biodiversity science
really exciting. So it was made possible by very sophisticated statistical
approaches which are able to analyze genetic data from highly complex
evolutionary and ecological systems. And at the same time these analyses are able
to handle big data. We we're talking about gigabytes and terabytes of data and
results. So a statistical geneticist are developing new methods of data
representation to handle this amount of data. And then we are able to sufficiently
extract the signal for a very specific question from data which are very low
signal to noise ratio. So to get there, we need many experts and specialists. So we
need statistical geneticists, big data experts who also might contribute machine
learning expertise. We need molecular biologists who know how to sequence
complex genomes. We now need bioinformatics with an expertise in
genomics for assembly, annotation and alignment of genomic sequences. The result
is actually this: This is the author list for the thousands genomes project
reference data set, and I don't expect you to be able to read it, but the bold type
is of interest because it shows all the different tasks that are necessary to
produce a standardized and highly cleaned reverence dataset. So the whole author
list is something like 1.5 pages long and even considering that some authors will
have contributed to several tasks. The publications for reference datasets mostly
have author lists that are far over 50 people. So they are huge collaborative
efforts. Now we take the step into biodiversity science. Here these are eight
gastrotrichs, they are little worm like... organisms who live in the sediments of
freshwater lakes and marine sediment. They are in general a couple of hundreds micro
meters large. And I don't have any numbers, but my guess would be that maybe
worldwide, a hundred to a thousand people actually work on these species. There are
800 species of gastrotrichs. So let's say there's one, two, maybe three experts per
species for these organisms. So how are these three people going to manage all
these tasks to produce a reference dataset? You might say, well, it's
gastrotrichs, I mean, have never heard about them. Maybe they are not so
important. Maybe you don't need a reference data sets, but actually some of
those species are bioindicators for water quality. So what we observe right now is a
gap for biodiversity conservation. In model organisms, we have Pandora's Box
open. We have all the statistical analyses at our hands to analyze our data sets.
However, in none model organisms, we are still stuck with summary statistics that
don't provide us the resolution that we need. And we know that to close this gap,
even for a single species, it's a huge effort. But at the same time, we have over
35.000 species listed by scientists which need already now effective protection. So
we need to find a way to close this gap and actually move in this direction. And
the good thing is, so all of this... in biodiversity science, in academia, and we
need to make the transition over the conservational genomic gap into the big
loop of real world conservation tasks. And the good thing is we already know what we
have to do. So we need to have reference data sets, distribution range wide. We
need to have statistics. And it's going to be big data. So we need collection
management, data management and an analysis environment. So looking at
different ingredients or different steps the first we need is a general data
infrastructure for global diversity of reference data sets that actually can be
used across species for preferably as many species as possible and provide a working
environment for biodiversity scientists and experts. It should be user friendly so
it can be used by scientists, but also that people from local communities and
citizen scientists can add their observation data and their data into this
data infrastructure. I have listed quite a lot of features that these kind of
infrastructures should have. And I'm going to argue that these features are not some
nice to have, but actually some must have. Because our goal is always application. So
we need developers, managers and curators for data infrastructures. Since our goal
is application, our main features are quality control and error reduction. These
are the basis. So that our conservation tools can be robustly and reliably applied
under real world operating conditions. And the way to achieve quality and error
reduction is through chains of custody. So it means that from project of sign, from
the questions through all the steps that are necessary to produce a reference data
set and then...so from sample collection, genomic statistical analysis down to
application. These steps need to be documented and standardized. They need to
be, each one of them needs to be validated and reproducible. They should be modular
so they can be user friendly. And the whole chain of custody needs to be
scalable. So if our chains of custody have these characteristics, we actually will
have tools that will work in everyday life. So we need professional developers
and programmers who are able to produce these very collaborative softwares. We
need free and open source experts. So we always can ensure that our code and that
our infrastructures are still integer and we can check them. And I'm a biologist, I
don't have any background in hardware, but I've heard a couple of talks here in the
conference about Green IT. And I have the feeling we should have people who know
hardware and software and know how to develop these high tech tools in a way
sustainable so that by developing these tools, we don't use more resources than we
are trying to protect. So I've shown all these features and characteristics that
the software should have. And I'm arguing that these features are necessary because
of the reality we find us in. It is one of rising over-exploitation and destruction
of nature. So the extent of environmental crimes is up in the billions. All
environmental crime together, the green bubbles are only second to drug associated
crimes. They are up there with counterfeiting or human trafficing. So
these are multi-billion enterprises. They are often transnational and industries
with huge profits. So if there's some crime, some mafia boss, some criminal
manager who just bribed a government official somewhere in the neck in the
woods, it just would make sense that that person would not wait or not take the
risks to be discovered just because some customs officer pulls out a container
somewhere in the harbor, for example, opens it and says "This looks kind of
weird. Let's take a sample, send it to a lab." and then a population geneticist
comes back and says "Oh, yes, this sample is not from area A as documented, but
actually it's from area B and it was illegally logged." If we have reference
data sets, information rich reference data sets, they become highly valuable and they
need protection themselves against manipulation and destruction. So we will
need to think about IT security from the beginning. Also, these data sets are often
very politically sensitive because if it is shown that in a certain country there
is the illegal logging repeatedly, that country might not be too excited about
this information. So we need to think about IT security experts. So my hope is
that these kind of very high tech digital conservation tools can actually contribute
to the U.N. Sustainable Development Goals by empowering indigenous people, local
communities and also us to protect and force and sustainably use our lands and
our biodiversity by providing some management and law enforcement tools. So
we need people from around the world, users from around the world who use these
tools and help to develop them further and to maintain them. And finally here, these
high tech tools will just another technological fix. If we don't manage to
get our back down, our way of life down to sustainable levels. So what we need is to
today...this year, the Earth Overshoot Day was at the end of July. So at the end of
July, we had used all the resources that we had available for the whole year. And
we need to get this back to the end of the year so that our resources actually
sustain us for the whole year. The graphic here for Germany suggests that we are on a
good way. We are reducing our resource consumption and maybe even our biocapacity
moves up a little bit. So actually it seems that our personal lifestyles and
choices make a difference and we just need to close this gap here much quicker. So
protecting biodiversity needs all of us to achieve that. And with that, thank you
very much.
*Applause*
Angel: So thank you Jutta for this very interesting talk and the very valuable
work you're doing. We have three mics here. Please line up at the microphones if
you have any questions or suggestions or want to participate and work together with
Jutta. We have one question from the Internet, so please Signal-Angel start.
Signal-Angel: Why do wild plant species within a genus are further apart than wild
animal species within a genus? Angel: Could you repeat it, please?
Signal-Angel: Why do wild plant species within a genus are further apart than wild
animal species within a genus? Jutta: I'm not sure I understand the
background for the question. Mic 1: Because animals move and plants
don't move. Jutta: Oh, okay. If that is the idea
behind the question. Plants actually move, too. They don't move as individuals, but
they move their genetic material through pollen or fragments. So actually diversity
in plants and in animals can be quite similar. So the idea is that plants are
just stuck and should have a completely different population structure does not
hold because plants move around their genetic material through seeds, through
pollen, through vegetative propagules. Angel: So thank you microphone 1 for
helping out. Please ask your question. Mic 1: So my question is about the success
factor of it. If you think of this, whatever database being set up there and I
think it's gonna be a huge database...I downloaded my own genome on the Internet.
It was about 150 megabytes. And if we multiply that, I think the genetic
variation from one person to another is about 1 percent only. So we can compress
that to 4 megabytes per person. If we sequence all the humans in the world, that
would be 32 petabytes, that would cost approximately 15 billion dollars. And
that's only for the storage. Now comes the entire management. Of course, we don't
want to digitize all the human genome, but rather the plants and animal species
genome. So it's a huge data program. And what would be for you the success factors
for this thing to really fly? And did you talk to organizations like WikiData or
others or where would it ideally be hosted? At a university or an
international nonprofit or who would be running the thing?
Jutta: Yeah, I mean, it's just really big data. I think our first goal is not to
think about having all predicted 5 to 10 million species be sequenced on a
population level. I think we need to think about the next step. And there it would
make sense to start with species that are actually highly exploited, like many
timber species and also many marine fishes. I think that's where we should
start. And to host this kind of data I think it should be in political
independent hands. So it should be with an NGO or with the U.N., some organization
that is independent. Mic 1: Are you the first to think about
this or are there existing initiatives? Jutta: There are actually existing
initiatives. I have been in contact with the Forest Stewardship Council and they
are actually starting to sample their concessions and initiated to build up the
samples, they work together with Kew Botanical Gardens and the U.S. Forest
Service. And right now they're analyzing the samples, using isotopes which is
another method which is very powerful and can also produce geographic information.
And so, yeah, so people are moving in this way. So, yeah, I think the idea is out
there, just we have to start and we have to really do it and provide one
infrastructure so that we can combine, for example, morphological data, isotope data
and genomic data into one dataset, which will increase our resolution and our
reliability. Angel: Okay. Microphone number two,
please. Mic 2: Thank you for your valuable talk.
My question would be you'd start your talk with the possible decrease of leaf beetles
in the data set you showed on slide number six there was an increase in leaf beetle
population until the 70s, something about that. Is there a possible explanation for
that? Jutta: Yeah, I believe it is, because
people started to much more systematically observe leaf beetles. So it's a sample
effort. And also at that time the people - so it's a multi-people collaboration who
actually has assembled this dataset so the people who are part of this collaboration
they edit their own private data sets. And that's why you have an increase I think.
While the people from the nineteen hundreds, nineteen hundred ten you only
can use the data that is available in publications and samples in museums or in
scientific collections. I think that is the reason why you have the sharp
increase. Mic 2: Thank you.
Angel: So we have another question of microphone number two.
Mic 2: Thank you for your fine talking. Excuse me. Maybe my question is a bit off
topic. Do you think the methods and roles that you identified in your talk could be
transferred to the assessment of raw materials? I'm thinking about metals?
Jutta: Maybe the data infrastructure, like if you wanted to collect raw metals or
materials from all over the world and...a sampleized scientific collection and to
have kind of a reference dataset that might work, actually. But the genomics
obviously won't. So that part of what you would need to use different methods from
physics, obviously. But actually the infrastructure, certain parts will be
quite similar. I think so, yes. Angel: So we have one more question from
the Internet. Signal-Angel: Who does contract a
freelance evolutionary biologist? Can you give an example of this kind of work you
proposed? Jutta: So I see this gap between science
and applications, that we need these applications and there's a huge potential
for these applications. We know that illegal logging and that is my background,
but doesn't seem to be much different, for example, in marine fisheries. We know that
there is this huge amount of illegal logging and timber trade going on. And we
need to have some assets actually that have the power to detect illegally traded
timber. So I think there is a huge need for these kind of methods and
organizations who are interested in these kind of methods. Our governments, their
companies, NGOs, customs, Interpol. So, yeah.
Angel: Do we have any other questions? So thank you again Jutta for your talk and
the valuable work you're doing. Please give a warm round of applause to Jutta.
*Applause*
*36c3 postrol music*
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