36C3 - Protecting the Wild

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

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