WEBVTT

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<i>36C3 preroll music</i>

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Angel: Right now I'd like to welcome our
first speaker on stage. The talk will be

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about protecting the wild and I'll hand
over to her. Please give her a warm round

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of applause.

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<i>Applause</i>

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Jutta Buschbom: Thank you very much for
the introduction. My name is Jutta

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Buschbom, I'm an evolutionary biologist.
That is my background. I did do my PHD at

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the University of Chicago working on
little fungees that live in symbiosis with

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algae and form colorful rocks, colorful
crust on rocks. I then did a Postdoc in

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bioinformatics and after that moved back
into organismal biology, working in forest

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genetics. And the ten years I worked in
forest genetics for the first time I

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encountered questions that were with
regard to application, and I found out

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that actually moving from research to
application is not trivial. So what I'm

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going to present is a high tech way using
genomic data to protect biodiversity in a

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way that you can actually reach
application and use conservation genomic

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tools. So this summer the draft of the
report of the Intergovernmental Science

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Policy Panel for Biodiversity and
Ecosystem Services came out and its

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results were quite warning. It stated that
around a million animal and plant species

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are currently stated and of those...half
of those species are already dead species

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walking. So because due to the destruction
of the habitats or habitat deterioration,

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they are not able to reproduce in a
sustainable way anymore. A third of the

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total species extinction rate risk to date
has arisen in the last 25 years. And just

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to give you an idea about the relation we
are talking about...currently the rate of

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extinction risk is already at least ten to
hundreds times higher than it has averaged

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over the past 10 million years. And within
these 10 million years there were the Ice

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Ages, for example. And most of the
extinction risk is due to the fact of land

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and sea use change. The report also talks,
even talks about that we already seem to

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have transgressed a proposed precautionary
planetary boundary, which means within the

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boundary we have a stable biological
system. But having transgressed it, we

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might already be in a transition to a new
state that we have no way to find out how

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this state is going to look like. So all
of these facts that the report is stating

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are actually pretty negative. And I was
quite happy to read that they also present

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that there are actually people who do
better than most of us. And they point out

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that many practices of indigenous people
and local communities actually conserve

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and sustain wild and domesticated
biodiversity quite well. Today, a higher

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proportion of the remaining terrestrial
biodiversity lies in areas managed and

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held by indigenous people. And these
ecosystems are more intact and less

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declining, less rapidly declining. So we
have examples of lifestyles that actually

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do better than most of us. And I know the
solutions won't be simple and it won't be

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easy to get there but we can look to what
these people do better than we do. All of

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this sounds...it's a global report and it
sounds kind of like far away, like

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probably somewhere in the tropics, but
actually threats to biodiversity happen

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also directly in front of our own front
doors. This summer a paper came out from

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two colleagues from the University of
Greifswald, who had analyzed the long term

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data set about leaf beetles. And they were
asking if we already have a decline of

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leaf beetles in Central Europe. So they
compiled long term data sets of leaf

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beetle observations for Central Europe,
starting from 1900 now to 2017, so

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spanning a hundred and twenty years. And
what they find is that systematic reports

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on leaf beetles and leaf beetle
observations are increasing during this

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time interval, time span. But despite the
fact that we have...like in the last two

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decades, we had very high numbers of
reports and observations for leaf beetles,

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the number of species, the orange line, is
declining. It's slightly declining. But

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the question is, is this real or not? And
what was most worrisome to the authors is

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that in the data set, the number of
species here in orange that were having

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more reports was declining, while the
number of species that showed less reports

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than before is expanding. So this kind of
long term datasets are very hard to

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interpret and many factors can contribute
to those patterns. And it's not clear if

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this pattern is statistically significant.
But if you take a step back and consider

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your background knowledge, your prior
knowledge about the state of the world, do

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you say, like, how does the current state
look like? Does it look good or rather

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worrisome? And then with that knowledge,
tell me that these results are an

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artifact or a bias. I'm worried that once
we have statistical significant signal in

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this dataset, it will be already too late.
So right now, I've been talking about leaf

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beetles and beetles are the largest group
within insects with about 400.000 species.

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Leaf beetles are a large family of about
50.000 species which are worldwide

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distributed. And here in Germany, we have
over 470 leaf beetle species. So how do we

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actually know how many species there are
and who actually counted all these

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species? And is that just a task of
taxonomists. Taxonomy is the science of

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naming and defining, including
circumscribing and classifying groups of

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biological organisms on the basis of
shared characters. So one could have the

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picture of some woman with a funny hat
running over a meadow catching like

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butterflies or some guy mushroom hunter
crawling through the forest trying to find

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mushrooms. And it's true, as biodiversity
scientists we spent a lot of time outdoors

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and yeah...on the other hand, biotaxonomy
is a high-tech science today. So

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taxonomists actually take up new
technological tools and developments to

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help them identify and describe,
understand the species. So taxonomists

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actually are often experts in, for
example, microscopy, mathematics,

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biochemistry, even proteomics and
genomics. So throughout the talk, I'm

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going to compile this list of people and
experts we're going to need to protect

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biodiversity if we want to do this on the
basis of genetic data. Right now, the list

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is quite empty. The first entry is a
taxonomists, but that will change quickly

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and taxonomists are a subgroup of
evolutionary biologists mostly. So I told

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you as taxonomists and biodiversity
scientists take up technology and...so as

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soon as computers came about and the
internet started people started to use

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that to compile information about species,
and today we have several global resources

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available at the species level and above
the species level. So we biodiversity

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scientists were among the first who
defined biodiversity information

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standards. We have a global catalog of
life. A list of all named species. The

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Global Biodiversity Information Facility
has an aim to bring together information

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from different sources and they are
compiling, producing this wonderful map.

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This is leaf beetles, all the records
about leaf beetles that we have in the

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world. And it looks like as if leaf
beetles are highly associated with third

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world economics. However that clearly is
an artifact and it just shows that we need

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many more taxonomists and biodiversity
scientists all over the world to find and

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identify leaf beetles. So we also need
biodiversity informaticians to help us

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compile global lists and distribute
knowledge. So far I have been talking

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about species which is a simplification.
The question is what is...what are species

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actually? And so we need to talk about
genetic diversity within and between

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species. And I'm going to do so using
gulls, which most of us might know. Here

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in Europe, we have two large gulls of the
genus Larus. One is in the front, the

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lighter gray is our Silbermöwe. And in the
back is our Heringsmöwe, the dark one. And

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I'm going to use German names because the
English names go crosswise and that's

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completely confusing. So I will stick with
the German names. Here in Europe these two

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species seem to be really fine species
because they barely interbreed, so they

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don't hybridize. However, if you take a
step back and look at the genus in

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general, you see that the species of the
genus are distributed kind of ringwise

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around the Arctic. And so the idea is
that, say during the Ice Age, all of this

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area was glaciated and the gulls retreated
to a refuge here near the Caspian Sea. And

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then after the ice retreated, the gulls
moved back north. One branch moved into

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Europe forming our Heringsmöwe and
another branch then moved counterclockwise

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around the Arctic, producing different
morphotypes, different species across the

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Bering Strait and then into North America.
There the dark blue one is...I'm

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simplifying, the equivalent of our
European Silbermöwe, the American

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Silbermöwe. Then the idea is that some
individuals crossed back to Europe and

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formed our European Silbermöwe. And while
all of these species here are

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interbreeding, so they hybridize. Only
when this ring is closed those two species

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don't interbreed anymore. And the big
question is, are we actually dealing with

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one single species or are we dealing with
different species that just happened to

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hybridize more or less? The question is
not trivial because it has consequences

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for protection. If we are dealing with one
single species, all the gulls in Eurasia

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could go extinct and it wouldn't matter
because we still would have the gulls in

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North America. However, if we have
different species in all of these areas,

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we would need to protect individuals or
the species on a regional level and

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protect all of these different species. So
to investigate this question about: Do we

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have different species? And what were the
evolutionary processes and histories that

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brought about the species? A group of
scientists investigated that using DNA

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sequences. And on the left, you have the
model, the theoretical model of the ring

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species. And here on the right you have
reality. And the scientists found that the

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reality is always much more complex. So,
for example, they found two refuges or

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they proposed two refuges. But what they
found was that genetic diversity was

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correlated with those species or
morphotypes. So what that also means is

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that genetic diversity is cultivated with
geographic origin. What we learn from this

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type of analysis is we learn about
evolutionary processes and history, about

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variability and differentiation of our
gene flow and migration, about speciation

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processes. That we all need to understand
our species, which will allow us to

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protect them. So we need evolutionary
biologists who do follow genetics and

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population genetics. So once we found out
that one can use genetic diversity, to

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infer geographic origin because genetic
diversity is correlated with geography,

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people immediately said: 'Okay, we can use
it for conservation applications.'. And

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it's also...we learned that we...often it
is unclear what is a species, species

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boundaries are unclear and some species
have huge distribution ranges with

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different clusters of viability within
this huge range. So we know that we need

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to protect within species genetic
diversity, which means that we need to

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understand within species population
structure and we need to build useful and

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reliable models of population structure.
These models are actually required for all

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of our applications. They are required for
monitoring, for example, for conservation

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strategies, for functional adaptation and
adaptability, questions of productability

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of different provenances, its impact on
management regimes, breeding strategies,

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and also for enforcement applications.
From the studies I showed you before with

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the gulls we also know that we need to
approach the question of a population

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structure on a distribution range wide
scale. So here's the map produced by

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EUFORGENE, the European Network for forest
reproductive material for one of our

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native oaks, the sessil oak. And the dots
are the sites for genetic conservation

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units. And so that is one strategy how to
represent within species genetic diversity

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and how to sample it. And you can see this
is a hypothetical example, but we likely

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will see a gradient from west to east or
might see one at this scale. Then once we

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have these kind of global data sets, we
can go to the fine scale and maybe, for

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example, do a national genetic monitoring.
And we will find much finer scale

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gradients. We also will find especially
for first trace outliers, so for stands

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that don't fit the usual pattern. And that
is because the first reproductive material

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has been moved around a lot. And so these
lighter or darker dots is material that

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was moved to Germany from the outside. And
we only will identify these outliers if we

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have the whole reference dataset. If we
don't have the whole reference dataset, we

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might not identify these outliers - stands
with a different history. Or in a worst

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case, these outliers might actually bias
our gradients. And we are always talking

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about very slight gradients. So it's easy
to bias these gradiants, dilute them, so

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we actually won't get the results we need.
To compile these kinds of reference

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datasets that's huge collaborative efforts
because people need to go out into the

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field and collect the reference samples
and that might be scientists, that might

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be people from local communities, citizen
scientists, managers, owners, government

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officials who provide background
information, maps, distribution

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information and also in many parts of the
world might protect the people who are

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actually collecting the samples. And it
might be conservation activists and NGOs.

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So once the samples have been collected
they need to be stored somewhere for the

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long term and the information needs to be
databased. And that is the work of

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scientific connections, which are mostly
at natural history museums and there the

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samples are processed. They're organized
in ways that you can find them again. All

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the metadata is entered, which curators
do, collection managers, preparators,

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technical staff at the scientific
collections. So once we have these kind of

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data sets, large scale data sets, what are
we actually doing with them? So the

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foundation for all of our applications is
population structure and there

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specifically population assignment. So the
process is set first. We decide on a

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question and design our project
accordingly that we can answer the

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question. Then we need to infer the
population structure model and optimize

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it. In the next step we need to check if a
model actually is good enough for

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application because we might have found
the best model, but it might still not be

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good enough for application. So we need to
test that. And that is the step of

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population assignment or predictive
assignment. And then in the end, we want

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to test our hypothesis. Are the two stands
different or does an individual come from

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stand A or from stand B? And here we
identify error rates and accuracy. So this

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whole process is very statistical. And so
the analysis of these reference data they

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need to be accompanied by biostatisticians
who can tell us how to analyze our data.

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So what is the state-of-the-art right now?
What kind of geographic resolution do we

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actually get of this non model specie
currently? And I'm going to present the

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example of an African timber tree
species, which is a very valuable timber.

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It's one example but basically all results
for species who have large distribution

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ranges and are continuously distributed
and are also long-lived, are very similar.

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So this kind of results seem to be species
independent. So the species are Milica

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regia and excelsa, African teak, which
cannot be grown in plantations for timber

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quality. So it is harvested unsustainably
from natural forests. It's distributed in

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West, Central and East Africa. Here's a
black rectangle. And a group of a dozen

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scientists got together and they actually
sampled a reference dataset for these two

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species. It's about over 400 samples, they
analyzed four marker systems, resulting in

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a total of something like 100 markers,
genetic markers, and then they optimized

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the population model and used different
parameter settings. And we're going to

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concentrate here on the best solution that
they found. And basically this rectangle

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here is the black one over here. So the
resolution is... they found population

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structure with clear clusters. So the
populations and the species from West

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Africa can be distinguished from those
populations in Central Africa. And the

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ones in East Africa can be differentiated.
So that is really good. So we have

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population structure. We know their
signal. The problem is still that our

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resolution is much lower than we would
need to have it because we basically need

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resolution at least on a country level,
because most of the laws are national. So

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it might be legal to harvest a tree in one
country, but not in another country. So we

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need to get our resolution down to country
level or even to regional level. If you

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want to distinguish, was the tree
harvested in a national park in a

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protected area or outside in a managed
forest. And when as biodiversity

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scientists, we don't know how to continue,
one thing is to look for what people do

00:28:10.740 --> 00:28:17.179
with model organisms and specifically what
people do in human population genomics

00:28:17.179 --> 00:28:24.179
because there thousands of populations
geneticists are working and there is a

00:28:24.179 --> 00:28:28.210
completely different funding background
due to the interest of the medical and the

00:28:28.210 --> 00:28:39.119
pharma industry. So they are always
advanced. What we can learn from there,

00:28:39.119 --> 00:28:46.660
from the human populations genomics is
that we need two features. One is we

00:28:46.660 --> 00:28:53.570
already know that we need distribution
wide sampling, which provides a spatial

00:28:53.570 --> 00:28:59.950
context. The second feature is that we
need genome wide sequencing, preferably

00:28:59.950 --> 00:29:09.210
genome sequencing, which provides us steps
in time because our genomes are archives

00:29:09.210 --> 00:29:14.710
of our evolutionary history. They are
records of all the processes and events

00:29:14.710 --> 00:29:21.429
and these steps in time then translate
also into resolution. Once we have these

00:29:21.429 --> 00:29:30.150
two features, actually these reference
datasets open Pandora's box. Suddently we

00:29:30.150 --> 00:29:36.390
can ask all kinds of questions and
objectives, even those that we still don't

00:29:36.390 --> 00:29:47.010
know. We can develop all kinds of
applications which is done for humans.

00:29:47.010 --> 00:29:59.400
Currently, there are at least four global
datasets on human diversity. These are

00:29:59.400 --> 00:30:08.860
very widely reused and these big datasets
- so they are big data with regard to the

00:30:08.860 --> 00:30:18.850
number of samples and also the genomes or
the genome representations and this

00:30:18.850 --> 00:30:26.470
results in very information rich data
which initiates analytical development so

00:30:26.470 --> 00:30:33.799
people continuously are developing new
statistical methods. And right now, a new

00:30:33.799 --> 00:30:42.330
wave is coming in of these methods. So
once you have these global datasets,

00:30:42.330 --> 00:30:47.500
people start in human populations
genomics, started to do these intense

00:30:47.500 --> 00:30:56.299
regional samplings. And this is the
example of the United Kingdom Biobank.

00:30:56.299 --> 00:31:02.789
It's a project with 500.000 volunteers,
they are all UK citizens from all over the

00:31:02.789 --> 00:31:13.982
islands. And each individual was genotyped
in a vet lab for 820.000 markers. That's

00:31:13.982 --> 00:31:19.620
completely I mean, that's a different
number than the 100 or 1000...in

00:31:19.620 --> 00:31:26.409
biodiversity scientists we normally
analyse a maximum of a couple of 10.000

00:31:26.409 --> 00:31:36.220
markers. So that's a completely different
number. But then statistical geneticists

00:31:36.220 --> 00:31:47.140
come. They do some weird and wonderful
voodoo and they derive 96 million markers

00:31:47.140 --> 00:31:53.460
per genome that is per individual from
these 820.000 markers that were produced

00:31:53.460 --> 00:32:00.630
in the lab. So that's a hundred fold
increase. And once you have this kind of

00:32:00.630 --> 00:32:07.510
dataset for a genome, you suddenly or you
finally become country level and within

00:32:07.510 --> 00:32:18.970
country level resolution. So these panels
are examples. So the first panel shows

00:32:18.970 --> 00:32:25.980
individuals who were born in Edinburgh and
the question was "Where were people born

00:32:25.980 --> 00:32:32.419
who had a similar ancestral background,
genetic background?". And what they found

00:32:32.419 --> 00:32:41.980
was that was all over Scotland and
Northern Ireland. Northern Yorkshire was

00:32:41.980 --> 00:32:50.250
even more local. So people from Yorkshire
don't seem to get around a lot. For London

00:32:50.250 --> 00:32:54.090
the situation is completely different.
That is what we would expect because

00:32:54.090 --> 00:32:59.580
London is a people magnet. People move
there all the time. They meet there, they

00:32:59.580 --> 00:33:05.700
get children and the kids born in London,
their genetic ancestry has nothing to do

00:33:05.700 --> 00:33:12.760
with London. It's from all over the place,
from the British Isles and the world. So

00:33:12.760 --> 00:33:21.600
that's why the colors are strongly
dissolved. So this study came out also

00:33:21.600 --> 00:33:26.100
this summer. And it's the first time that
I have seen that we actually really can

00:33:26.100 --> 00:33:36.580
achieve regional resolution. And I find
this possibility for biodiversity science

00:33:36.580 --> 00:33:46.820
really exciting. So it was made possible
by very sophisticated statistical

00:33:46.820 --> 00:33:51.890
approaches which are able to analyze
genetic data from highly complex

00:33:51.890 --> 00:33:59.450
evolutionary and ecological systems. And
at the same time these analyses are able

00:33:59.450 --> 00:34:04.910
to handle big data. We we're talking about
gigabytes and terabytes of data and

00:34:04.910 --> 00:34:13.810
results. So a statistical geneticist are
developing new methods of data

00:34:13.810 --> 00:34:20.309
representation to handle this amount of
data. And then we are able to sufficiently

00:34:20.309 --> 00:34:25.520
extract the signal for a very specific
question from data which are very low

00:34:25.520 --> 00:34:36.919
signal to noise ratio. So to get there, we
need many experts and specialists. So we

00:34:36.919 --> 00:34:41.659
need statistical geneticists, big data
experts who also might contribute machine

00:34:41.659 --> 00:34:49.299
learning expertise. We need molecular
biologists who know how to sequence

00:34:49.299 --> 00:34:54.259
complex genomes. We now need
bioinformatics with an expertise in

00:34:54.259 --> 00:35:05.010
genomics for assembly, annotation and
alignment of genomic sequences. The result

00:35:05.010 --> 00:35:12.569
is actually this: This is the author list
for the thousands genomes project

00:35:12.569 --> 00:35:20.380
reference data set, and I don't expect you
to be able to read it, but the bold type

00:35:20.380 --> 00:35:25.539
is of interest because it shows all the
different tasks that are necessary to

00:35:25.539 --> 00:35:36.140
produce a standardized and highly cleaned
reverence dataset. So the whole author

00:35:36.140 --> 00:35:41.880
list is something like 1.5 pages long and
even considering that some authors will

00:35:41.880 --> 00:35:51.130
have contributed to several tasks. The
publications for reference datasets mostly

00:35:51.130 --> 00:35:57.079
have author lists that are far over 50
people. So they are huge collaborative

00:35:57.079 --> 00:36:05.219
efforts. Now we take the step into
biodiversity science. Here these are eight

00:36:05.219 --> 00:36:13.440
gastrotrichs, they are little worm like...
organisms who live in the sediments of

00:36:13.440 --> 00:36:23.069
freshwater lakes and marine sediment. They
are in general a couple of hundreds micro

00:36:23.069 --> 00:36:29.569
meters large. And I don't have any
numbers, but my guess would be that maybe

00:36:29.569 --> 00:36:38.640
worldwide, a hundred to a thousand people
actually work on these species. There are

00:36:38.640 --> 00:36:44.829
800 species of gastrotrichs. So let's say
there's one, two, maybe three experts per

00:36:44.829 --> 00:36:52.240
species for these organisms. So how are
these three people going to manage all

00:36:52.240 --> 00:37:01.420
these tasks to produce a reference
dataset? You might say, well, it's

00:37:01.420 --> 00:37:05.209
gastrotrichs, I mean, have never heard
about them. Maybe they are not so

00:37:05.209 --> 00:37:08.349
important. Maybe you don't need a
reference data sets, but actually some of

00:37:08.349 --> 00:37:17.579
those species are bioindicators for water
quality. So what we observe right now is a

00:37:17.579 --> 00:37:27.510
gap for biodiversity conservation. In
model organisms, we have Pandora's Box

00:37:27.510 --> 00:37:34.630
open. We have all the statistical analyses
at our hands to analyze our data sets.

00:37:34.630 --> 00:37:39.709
However, in none model organisms, we are
still stuck with summary statistics that

00:37:39.709 --> 00:37:46.839
don't provide us the resolution that we
need. And we know that to close this gap,

00:37:46.839 --> 00:37:52.599
even for a single species, it's a huge
effort. But at the same time, we have over

00:37:52.599 --> 00:38:03.560
35.000 species listed by scientists which
need already now effective protection. So

00:38:03.560 --> 00:38:10.008
we need to find a way to close this gap
and actually move in this direction. And

00:38:10.008 --> 00:38:19.940
the good thing is, so all of this... in
biodiversity science, in academia, and we

00:38:19.940 --> 00:38:24.890
need to make the transition over the
conservational genomic gap into the big

00:38:24.890 --> 00:38:32.130
loop of real world conservation tasks. And
the good thing is we already know what we

00:38:32.130 --> 00:38:37.940
have to do. So we need to have reference
data sets, distribution range wide. We

00:38:37.940 --> 00:38:43.959
need to have statistics. And it's going to
be big data. So we need collection

00:38:43.959 --> 00:38:54.140
management, data management and an
analysis environment. So looking at

00:38:54.140 --> 00:38:59.880
different ingredients or different steps
the first we need is a general data

00:38:59.880 --> 00:39:05.269
infrastructure for global diversity of
reference data sets that actually can be

00:39:05.269 --> 00:39:11.779
used across species for preferably as many
species as possible and provide a working

00:39:11.779 --> 00:39:19.749
environment for biodiversity scientists
and experts. It should be user friendly so

00:39:19.749 --> 00:39:25.759
it can be used by scientists, but also
that people from local communities and

00:39:25.759 --> 00:39:33.489
citizen scientists can add their
observation data and their data into this

00:39:33.489 --> 00:39:41.339
data infrastructure. I have listed quite a
lot of features that these kind of

00:39:41.339 --> 00:39:48.400
infrastructures should have. And I'm going
to argue that these features are not some

00:39:48.400 --> 00:40:02.609
nice to have, but actually some must have.
Because our goal is always application. So

00:40:02.609 --> 00:40:13.279
we need developers, managers and curators
for data infrastructures. Since our goal

00:40:13.279 --> 00:40:30.900
is application, our main features are
quality control and error reduction. These

00:40:30.900 --> 00:40:38.880
are the basis. So that our conservation
tools can be robustly and reliably applied

00:40:38.880 --> 00:40:46.459
under real world operating conditions. And
the way to achieve quality and error

00:40:46.459 --> 00:40:52.759
reduction is through chains of custody. So
it means that from project of sign, from

00:40:52.759 --> 00:40:58.299
the questions through all the steps that
are necessary to produce a reference data

00:40:58.299 --> 00:41:08.219
set and then...so from sample collection,
genomic statistical analysis down to

00:41:08.219 --> 00:41:15.599
application. These steps need to be
documented and standardized. They need to

00:41:15.599 --> 00:41:22.239
be, each one of them needs to be validated
and reproducible. They should be modular

00:41:22.239 --> 00:41:28.999
so they can be user friendly. And the
whole chain of custody needs to be

00:41:28.999 --> 00:41:40.690
scalable. So if our chains of custody have
these characteristics, we actually will

00:41:40.690 --> 00:41:51.390
have tools that will work in everyday
life. So we need professional developers

00:41:51.390 --> 00:41:59.519
and programmers who are able to produce
these very collaborative softwares. We

00:41:59.519 --> 00:42:06.130
need free and open source experts. So we
always can ensure that our code and that

00:42:06.130 --> 00:42:13.859
our infrastructures are still integer and
we can check them. And I'm a biologist, I

00:42:13.859 --> 00:42:19.390
don't have any background in hardware, but
I've heard a couple of talks here in the

00:42:19.390 --> 00:42:26.099
conference about Green IT. And I have
the feeling we should have people who know

00:42:26.099 --> 00:42:33.849
hardware and software and know how to
develop these high tech tools in a way

00:42:33.849 --> 00:42:38.450
sustainable so that by developing these
tools, we don't use more resources than we

00:42:38.450 --> 00:42:48.940
are trying to protect. So I've shown all
these features and characteristics that

00:42:48.940 --> 00:42:57.459
the software should have. And I'm arguing
that these features are necessary because

00:42:57.459 --> 00:43:04.819
of the reality we find us in. It is one of
rising over-exploitation and destruction

00:43:04.819 --> 00:43:19.799
of nature. So the extent of environmental
crimes is up in the billions. All

00:43:19.799 --> 00:43:29.029
environmental crime together, the green
bubbles are only second to drug associated

00:43:29.029 --> 00:43:35.489
crimes. They are up there with
counterfeiting or human trafficing. So

00:43:35.489 --> 00:43:45.479
these are multi-billion enterprises. They
are often transnational and industries

00:43:45.479 --> 00:44:02.019
with huge profits. So if there's some
crime, some mafia boss, some criminal

00:44:02.019 --> 00:44:09.539
manager who just bribed a government
official somewhere in the neck in the

00:44:09.539 --> 00:44:17.859
woods, it just would make sense that that
person would not wait or not take the

00:44:17.859 --> 00:44:23.809
risks to be discovered just because some
customs officer pulls out a container

00:44:23.809 --> 00:44:29.170
somewhere in the harbor, for example,
opens it and says "This looks kind of

00:44:29.170 --> 00:44:37.380
weird. Let's take a sample, send it to a
lab." and then a population geneticist

00:44:37.380 --> 00:44:44.171
comes back and says "Oh, yes, this sample
is not from area A as documented, but

00:44:44.171 --> 00:44:52.449
actually it's from area B and it was
illegally logged." If we have reference

00:44:52.449 --> 00:44:58.660
data sets, information rich reference data
sets, they become highly valuable and they

00:44:58.660 --> 00:45:08.430
need protection themselves against
manipulation and destruction. So we will

00:45:08.430 --> 00:45:14.739
need to think about IT security from the
beginning. Also, these data sets are often

00:45:14.739 --> 00:45:20.069
very politically sensitive because if it
is shown that in a certain country there

00:45:20.069 --> 00:45:25.680
is the illegal logging repeatedly, that
country might not be too excited about

00:45:25.680 --> 00:45:41.380
this information. So we need to think
about IT security experts. So my hope is

00:45:41.380 --> 00:45:48.599
that these kind of very high tech digital
conservation tools can actually contribute

00:45:48.599 --> 00:45:55.690
to the U.N. Sustainable Development Goals
by empowering indigenous people, local

00:45:55.690 --> 00:46:02.810
communities and also us to protect and
force and sustainably use our lands and

00:46:02.810 --> 00:46:10.139
our biodiversity by providing some
management and law enforcement tools. So

00:46:10.139 --> 00:46:14.059
we need people from around the world,
users from around the world who use these

00:46:14.059 --> 00:46:25.789
tools and help to develop them further and
to maintain them. And finally here, these

00:46:25.789 --> 00:46:33.910
high tech tools will just another
technological fix. If we don't manage to

00:46:33.910 --> 00:46:45.770
get our back down, our way of life down to
sustainable levels. So what we need is to

00:46:45.770 --> 00:46:53.759
today...this year, the Earth Overshoot Day
was at the end of July. So at the end of

00:46:53.759 --> 00:47:01.639
July, we had used all the resources that
we had available for the whole year. And

00:47:01.639 --> 00:47:09.400
we need to get this back to the end of the
year so that our resources actually

00:47:09.400 --> 00:47:22.910
sustain us for the whole year. The graphic
here for Germany suggests that we are on a

00:47:22.910 --> 00:47:29.819
good way. We are reducing our resource
consumption and maybe even our biocapacity

00:47:29.819 --> 00:47:38.099
moves up a little bit. So actually it
seems that our personal lifestyles and

00:47:38.099 --> 00:47:46.329
choices make a difference and we just need
to close this gap here much quicker. So

00:47:46.329 --> 00:47:53.689
protecting biodiversity needs all of us to
achieve that. And with that, thank you

00:47:53.689 --> 00:47:57.770
very much.

00:47:57.770 --> 00:48:08.020
<i>Applause</i>

00:48:08.020 --> 00:48:12.680
Angel: So thank you Jutta for this very
interesting talk and the very valuable

00:48:12.680 --> 00:48:16.609
work you're doing. We have three mics
here. Please line up at the microphones if

00:48:16.609 --> 00:48:22.809
you have any questions or suggestions or
want to participate and work together with

00:48:22.809 --> 00:48:29.660
Jutta. We have one question from the
Internet, so please Signal-Angel start.

00:48:29.660 --> 00:48:34.749
Signal-Angel: Why do wild plant species
within a genus are further apart than wild

00:48:34.749 --> 00:48:42.509
animal species within a genus?
Angel: Could you repeat it, please?

00:48:42.509 --> 00:48:49.069
Signal-Angel: Why do wild plant species
within a genus are further apart than wild

00:48:49.069 --> 00:48:55.910
animal species within a genus?
Jutta: I'm not sure I understand the

00:48:55.910 --> 00:49:01.180
background for the question.
Mic 1: Because animals move and plants

00:49:01.180 --> 00:49:06.449
don't move.
Jutta: Oh, okay. If that is the idea

00:49:06.449 --> 00:49:12.299
behind the question. Plants actually move,
too. They don't move as individuals, but

00:49:12.299 --> 00:49:24.289
they move their genetic material through
pollen or fragments. So actually diversity

00:49:24.289 --> 00:49:30.760
in plants and in animals can be quite
similar. So the idea is that plants are

00:49:30.760 --> 00:49:36.459
just stuck and should have a completely
different population structure does not

00:49:36.459 --> 00:49:43.130
hold because plants move around their
genetic material through seeds, through

00:49:43.130 --> 00:49:49.610
pollen, through vegetative propagules.
Angel: So thank you microphone 1 for

00:49:49.610 --> 00:49:55.999
helping out. Please ask your question. Mic
1: So my question is about the success

00:49:55.999 --> 00:50:00.939
factor of it. If you think of this,
whatever database being set up there and I

00:50:00.939 --> 00:50:07.430
think it's gonna be a huge database...I
downloaded my own genome on the Internet.

00:50:07.430 --> 00:50:12.989
It was about 150 megabytes. And if we
multiply that, I think the genetic

00:50:12.989 --> 00:50:17.539
variation from one person to another is
about 1 percent only. So we can compress

00:50:17.539 --> 00:50:25.009
that to 4 megabytes per person. If we
sequence all the humans in the world, that

00:50:25.009 --> 00:50:32.689
would be 32 petabytes, that would cost
approximately 15 billion dollars. And

00:50:32.689 --> 00:50:36.890
that's only for the storage. Now comes the
entire management. Of course, we don't

00:50:36.890 --> 00:50:41.470
want to digitize all the human genome, but
rather the plants and animal species

00:50:41.470 --> 00:50:46.309
genome. So it's a huge data program. And
what would be for you the success factors

00:50:46.309 --> 00:50:51.229
for this thing to really fly? And did you
talk to organizations like WikiData or

00:50:51.229 --> 00:50:56.469
others or where would it ideally be
hosted? At a university or an

00:50:56.469 --> 00:51:02.170
international nonprofit or who would be
running the thing?

00:51:02.170 --> 00:51:14.519
Jutta: Yeah, I mean, it's just really big
data. I think our first goal is not to

00:51:14.519 --> 00:51:23.670
think about having all predicted 5 to 10
million species be sequenced on a

00:51:23.670 --> 00:51:30.239
population level. I think we need to think
about the next step. And there it would

00:51:30.239 --> 00:51:35.530
make sense to start with species that are
actually highly exploited, like many

00:51:35.530 --> 00:51:40.579
timber species and also many marine
fishes. I think that's where we should

00:51:40.579 --> 00:51:48.039
start. And to host this kind of data I
think it should be in political

00:51:48.039 --> 00:51:56.410
independent hands. So it should be with an
NGO or with the U.N., some organization

00:51:56.410 --> 00:52:02.449
that is independent.
Mic 1: Are you the first to think about

00:52:02.449 --> 00:52:06.509
this or are there existing initiatives?
Jutta: There are actually existing

00:52:06.509 --> 00:52:14.219
initiatives. I have been in contact with
the Forest Stewardship Council and they

00:52:14.219 --> 00:52:23.219
are actually starting to sample their
concessions and initiated to build up the

00:52:23.219 --> 00:52:28.730
samples, they work together with Kew
Botanical Gardens and the U.S. Forest

00:52:28.730 --> 00:52:37.589
Service. And right now they're analyzing
the samples, using isotopes which is

00:52:37.589 --> 00:52:45.579
another method which is very powerful and
can also produce geographic information.

00:52:45.579 --> 00:53:00.710
And so, yeah, so people are moving in this
way. So, yeah, I think the idea is out

00:53:00.710 --> 00:53:05.839
there, just we have to start and we have
to really do it and provide one

00:53:05.839 --> 00:53:13.210
infrastructure so that we can combine, for
example, morphological data, isotope data

00:53:13.210 --> 00:53:18.329
and genomic data into one dataset, which
will increase our resolution and our

00:53:18.329 --> 00:53:23.980
reliability.
Angel: Okay. Microphone number two,

00:53:23.980 --> 00:53:27.069
please.
Mic 2: Thank you for your valuable talk.

00:53:27.069 --> 00:53:32.660
My question would be you'd start your talk
with the possible decrease of leaf beetles

00:53:32.660 --> 00:53:37.100
in the data set you showed on slide number
six there was an increase in leaf beetle

00:53:37.100 --> 00:53:41.930
population until the 70s, something about
that. Is there a possible explanation for

00:53:41.930 --> 00:53:49.869
that?
Jutta: Yeah, I believe it is, because

00:53:49.869 --> 00:53:55.359
people started to much more systematically
observe leaf beetles. So it's a sample

00:53:55.359 --> 00:54:05.869
effort. And also at that time the people -
so it's a multi-people collaboration who

00:54:05.869 --> 00:54:12.369
actually has assembled this dataset so the
people who are part of this collaboration

00:54:12.369 --> 00:54:16.949
they edit their own private data sets. And
that's why you have an increase I think.

00:54:16.949 --> 00:54:23.509
While the people from the nineteen
hundreds, nineteen hundred ten you only

00:54:23.509 --> 00:54:29.009
can use the data that is available in
publications and samples in museums or in

00:54:29.009 --> 00:54:33.289
scientific collections. I think that is
the reason why you have the sharp

00:54:33.289 --> 00:54:35.589
increase.
Mic 2: Thank you.

00:54:35.589 --> 00:54:38.750
Angel: So we have another question of
microphone number two.

00:54:38.750 --> 00:54:44.459
Mic 2: Thank you for your fine talking.
Excuse me. Maybe my question is a bit off

00:54:44.459 --> 00:54:51.730
topic. Do you think the methods and roles
that you identified in your talk could be

00:54:51.730 --> 00:54:59.880
transferred to the assessment of raw
materials? I'm thinking about metals?

00:54:59.880 --> 00:55:09.349
Jutta: Maybe the data infrastructure, like
if you wanted to collect raw metals or

00:55:09.349 --> 00:55:16.471
materials from all over the world and...a
sampleized scientific collection and to

00:55:16.471 --> 00:55:22.390
have kind of a reference dataset that
might work, actually. But the genomics

00:55:22.390 --> 00:55:29.170
obviously won't. So that part of what you
would need to use different methods from

00:55:29.170 --> 00:55:36.010
physics, obviously. But actually the
infrastructure, certain parts will be

00:55:36.010 --> 00:55:40.249
quite similar. I think so, yes.
Angel: So we have one more question from

00:55:40.249 --> 00:55:43.420
the Internet.
Signal-Angel: Who does contract a

00:55:43.420 --> 00:55:51.619
freelance evolutionary biologist? Can you
give an example of this kind of work you

00:55:51.619 --> 00:56:01.429
proposed?
Jutta: So I see this gap between science

00:56:01.429 --> 00:56:07.739
and applications, that we need these
applications and there's a huge potential

00:56:07.739 --> 00:56:18.150
for these applications. We know that
illegal logging and that is my background,

00:56:18.150 --> 00:56:23.769
but doesn't seem to be much different, for
example, in marine fisheries. We know that

00:56:23.769 --> 00:56:29.730
there is this huge amount of illegal
logging and timber trade going on. And we

00:56:29.730 --> 00:56:39.670
need to have some assets actually that
have the power to detect illegally traded

00:56:39.670 --> 00:56:49.789
timber. So I think there is a huge need
for these kind of methods and

00:56:49.789 --> 00:57:00.869
organizations who are interested in these
kind of methods. Our governments, their

00:57:00.869 --> 00:57:12.719
companies, NGOs, customs, Interpol. So,
yeah.

00:57:12.719 --> 00:57:19.700
Angel: Do we have any other questions? So
thank you again Jutta for your talk and

00:57:19.700 --> 00:57:23.739
the valuable work you're doing. Please
give a warm round of applause to Jutta.

00:57:23.739 --> 00:57:29.009
<i>Applause</i>

00:57:29.009 --> 00:57:33.599
<i>36c3 postrol music</i>

00:57:33.599 --> 00:57:56.000
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