- Welcome, everyone, to Wednesday Nite @ the Lab.
I'm Tom Zinnen,
I work here at the UW Madison Biotechnology Center.
I also work for UW Extension Cooperative Extension,
and on behalf of those folks and our other co-organizers,
Wisconsin Public Television,
the Wisconsin Alumni Association,
and the UW Madison Science Alliance,
thanks again for coming to Wednesday Nite @ the Lab.
We do this every Wednesday night, 50 times a year.
Tonight, it's my pleasure
to introduce to you Steve Carpenter.
He's a professor in the Center for Limnology
in the Department of Zoology.
He was born in Kansas City, Missouri.
On the Missouri side.
He went to high school in Bethesda, Maryland,
where he went to Walter Johnson High School.
And, as he pointed out to me,
that's probably the only high school that he knows of
that's named for a major league pitcher.
He got his undergraduate degree at Amherst College,
and then came here for his master's
and PhD in botany, here at UW Madison,
then he spent 10 years at that university
called Notre Dame du Lac,
otherwise known to most of us as Notre Dame.
But since he's a limnologist,
you gotta throw in there that du Lac.
In 1989, he came back to UW Madison,
where he's been ever since.
Tonight, he's gonna be talking with us
about early warnings of big changes in complex systems.
Please join me in welcoming Steve Carpenter
to Wednesday Nite @ the Lab.
[audience applauding]
- Well, thanks, everyone,
for coming out on a night that looks stormy,
and thank you, Tom, for that nice introduction.
I welcome the chance to talk with you today
about some research that I've been involved in
for about the past 15 years
about very large changes in ecosystems.
An ecosystem is a place on Earth,
and all the animals and plants that live there,
along with their abiotic environment.
And, as you'll see in a minute,
sometimes those change in a very big way fairly quickly,
and we've been trying to figure out how that works
and if there are ever warnings of it.
So, let's start with a familiar example.
In 2011, Lake Erie experienced
the largest bloom of toxic algae in history.
The limnologists who were studying Lake Erie said,
you know, this is a harbinger of things to come,
and they were right.
In 2014, an even larger algae bloom,
again, of toxic cyanobacteria,
more about those in a few minutes,
emerged on Lake Erie,
and shut down the water supply for 300,000 people.
It made national and international news.
The Toledo Blade did a terrific job of covering it,
and if you go their website,
you see a lot of articles about it.
It was a major environmental problem,
and ultimately, a very expensive one for the city of Toledo.
So, that's an example of a big change in an ecosystem,
in this case, the ecosystem being Lake Erie.
Lakes do this all the time, and in southern Wisconsin,
we see big algae blooms frequently.
They usually occur in July, August, maybe early September,
so we'll start to see them in the news any day now.
Lakes can also be in a very clear state,
where you could lower a dinner plate into the water
a long, long way and still see it.
An example of big alternative configurations
of that ecosystem.
Coral reefs are another example
that's been in the news recently.
We think of coral reefs as this tourist attraction
with beautiful fish and ornate corals,
but they can suffer massive mortality events.
Sometimes those are due to nutrient input,
sometimes those are due to invading species,
and sometimes those are due to warm water.
Just in the last month,
the Great Barrier Reef in Australia
has suffered the largest bleaching event in history,
and a massive amount of corals in Australia have died,
particularly in the northern part of the reef,
which is the northern stretch of it.
And the scientists there think
we're looking at decades to hundreds of years
for recovery of that major tourist attraction for Australia.
And they're also known from rangelands very well.
Rangelands have two configurations.
One is fairly grassy, shown at the top,
and that's a really good situation
if you're trying to raise sheep or cattle.
They can also be covered with shrubs,
as in the picture at the bottom,
and that's a really bad setup
if you're trying to raise sheep and cattle,
because sheep and cattle don't get much out of that forage.
Goats maybe would, but not sheep and cattle.
What goes on is,
if you overgraze the rangeland in a dry year,
you eliminate the grass and it can no longer carry fire,
and fire is needed to kill off the shrubs
and keep them from getting a toehold.
So, overgrazing combined with a drought,
even a short drought,
can switch the ecosystem from grassy to shrubby,
and its economic value is down the drain.
The only way to reverse it at that point
is with a D9 Caterpillar, and that can be costly.
Scientists have developed many mathematical models
for how these sorts of large changes in ecosystems occur.
And, of course, the details of those models
are intricate, and highly dependent
on the ecosystem that is under study,
but they all have some common features,
and I'm just gonna show you
some of the common features right now.
We think about the state of the system.
So, for example, is the water turbid or clear,
or is the grassland grassy or shrubby,
or is the coral reef in good shape or has it died off?
We think about driving conditions,
such as the nutrient input to a lake,
or the grazing intensity on the grassland,
or whether there are lots of algae overgrowing the coral.
And in the models, you can calculate
something called a potential surface,
and I'm not gonna go into how you do the calculation,
but I've shown five examples of them here in gray.
So, in this upper potential surface,
we've really only got one valley,
and the ecosystem represented by the ball
is sitting in that one valley.
And as we move to this second potential surface,
changing the conditions down on this axis here,
now, all of a sudden, we have two valleys,
and the ecosystem can be in either one.
We call that a critical transition
because a different set of stable points has appeared.
So, we now have two stable points where we used to have one.
And the ecosystem, as it turns out,
will spend most of its time in the deepest of the valleys.
Then, in the, I'm having a little trouble, there we go.
Waking up my pointer here, it's sleeping.
In the third one, now the new valley has deepened, actually,
so the ecosystem will spend even more time over there.
In the fourth frame, it's deepened even more,
and, actually, the situation in the original valley
is getting quite precarious.
And in this last panel,
the original valley has completely disappeared,
and the ecosystem has moved over here.
So, if this is, say, the shrubby state,
and we started in the grassy state,
or if this is the turbid lake,
and we started in the clear lake,
we've now undergone a very massive change in the ecosystem.
And this general pattern of changing valleys
and the ecosystem moving from valley to valley
in a potential surface
occurs in all the models of this type of process.
Another important word
that I'm gonna use again and again is resilience.
Resilience is a measure of how deep the valley is.
So, a very deep valley is a resilient one.
The ecosystem is not gonna leave there very easily.
A low resilience valley is a very shallow one,
where a little push could knock the ecosystem
out of the valley.
I'm hovering, now, over a very low resilience ecosystem.
So, if we want to predict one of these changes,
the key lies in figuring out a way
to measure the resilience of the valley
where the ecosystem sits,
and determine if that valley is losing its resilience.
So, I'm gonna talk about measuring resilience
in the slides to come.
Are you all with me?
Okay.
I've only got a couple of really technical diagrams.
This is a very important one.
Okay, so, the question we started to ask
in the early 2000s is,
are there generic indicators of declining resilience?
Ecosystem managers all over the world
worry about these alternate states
that ecosystems can be in,
because up until about 15 years ago,
we thought there were no good ways
of predicting the transition between states,
so they generally came as surprises,
and often, they were very expensive surprises.
We're now beginning to get a clue
that there are various ways of predicting these transitions.
I'm not gonna talk very much about specific indicators,
but I think those are fairly straightforward to understand.
The idea is to monitor the drivers and conditions
that lead to big changes,
and point out that the ecosystem is at risk.
For example, I don't really need
a very sophisticated indicator
of algae blooms in Lake Mendota,
because there are 40,000 cows out there,
and those cows have been dumping manure on the ground
and in the streams all winter long,
and I'm pretty sure
that as soon as we get some big thunderstorms,
that manure is going into Lake Mendota,
and it's gonna turn green.
That's a very specific indicator.
I can't generalize that to a rangeland or a coral reef,
and maybe I can't even generalize it to another lake,
but I've kinda got Lake Mendota nailed,
because I know what's causing that green water out there.
By a generic indicator,
I mean a statistical pattern that emerges
before big changes in many kinds of complex systems.
So, if you understood a generic indicator,
you could go to a rangeland, a coral reef, a forest,
a lake, an ocean, and you could estimate its resilience.
So, I'm asking, are there generic things
that we could measure?
I got started on this work
with the folks at the bottom of the slide.
Martin Scheffer is one of the world's
most famous theoretical ecologists,
and he is deservedly famous for the terrific work he's done
on alternate states in a wide range of ecosystems
over about 30 years.
Buz Brock is a mathematical economist here at UW Madison,
he's one of the top nonlinear econometricians in the world,
and he got interested in this problem
and was a key member of the group.
Egbert van Nes is a physicist in Holland.
Vasilis Dakos is a theoretical ecologist from Greece
who now lives in Switzerland.
And the group of us have worked off and on,
usually meeting in a nice place for a few days
on this problem since the early 2000s.
So, to focus our thinking
about generic indicators a little bit,
let me give you another example,
and a very dramatic example
of alternate states in ecosystems.
The Sahara Desert right now is a vast area of sand
with the occasional camel, an oasis here and there,
and a few migratory people, Bedouins,
but it's a pretty dry place.
A few thousand years ago, it was a vast wetland
with rivers and lakes all over it, hippopotamus,
a very moist area,
dramatically different from the way it was today.
And what went on there?
We know a lot about the Ahara,
because we can measure the dust
that fell into the Atlantic Ocean
for thousands and thousands of years.
So, the left-hand panel there shows the outline of Africa.
This is a satellite image,
and you can see a billowing dust storm out here
of dust blowing out from northern Africa over the Atlantic.
It sinks in the ocean, some of it sinks in the ocean.
And you can go out and core the ocean
and reconstruct the dust deposition rate
for thousands of years.
And between about 9,000 and 5,700 years ago,
it was a pretty low dust deposition rate.
And that's one of the reasons that we know
we were dealing with a very wet environment in the Sahara,
because if it was dry,
there would be a lot of dust moving around.
Now, if you were this hippopotamus sitting there,
and you were a scientifically inclined hippopotamus,
you might look at that data
and think, this ecosystem is staying wet.
There is not much trend there in the dust deposition,
and if I had to predict the dust level
for the next thousand years,
I'd say it's gonna be between 40 and 50,
which is a low number.
So I'm feeling pretty good, if I'm the hippopotamus.
But here's what actually happened.
Between about 5,000 and 5,700 years ago,
there was a marked increase in dust deposition,
and we now know from many paleoecological studies
that that period of a few hundred years
marked a rapid transition
in the moisture regime of the Sahara region.
So, it moved from relatively wet to relatively dry
in a fairly short period of time.
The point I wanna make
is that simply by looking at that time series
from 9,000 to 5,700 years ago,
you wouldn't really have guessed
that this was gonna happen,
and that this would become the new normal.
This is the kind of situation
we'd like to have a generic indicator for.
This is my second fairly technical slide
that you have to pay attention to.
There are some other data slides you can ignore,
but this is another one you have to pay attention to,
and it has to do with the behavior of the ecosystem
in its valley, in cases where the valley is highly resilient
or not very resilient.
In the case where the valley is highly resilient,
on the left, the valley's pretty deep.
If we perturb the ecosystem a lot,
really whack it and drive it up the side of the valley,
it's gonna snap back pretty fast to where it was.
It's not gonna take its time to come back.
And it turns out that if you expose the ball
to small shocks for a long period of time,
you get a very constrained variability,
like I'm hovering over now.
It also turns out
that if you try to predict next year from this year,
so, you try to predict the state in year T plus one
from the state at year T,
you don't get a very good prediction.
All the data sort of cluster in a ball here,
so, knowing conditions this year
doesn't really help you very much
in knowing conditions next year.
You know you're somewhere in the ball, but that's about it.
Contrast that with the situation
in this low resilience ecosystem over here.
So, now, the valley is pretty flat.
And, in fact, we know the ecosystem is pretty precarious.
It could fairly easily be tipped over
into the more resilient valley.
But let's think about resilience
in this low resilience valley.
Now, if we perturb the ecosystem
by driving it a long way up the slope,
it's gonna take much longer to come back.
Its recovery time will be longer.
If we look at the variability over time,
we see these wide swings, and it's sort of sticky.
So, it swings up somewhere, gets stuck for a little while,
then it swings somewhere else, gets stuck for a while.
Very different pattern over here
than we saw over here in the variability.
And we can measure statistics
such as the variance or the standard deviation,
which I'll use later in the talk.
Those are just ways of measuring how variable it is.
We can measure that variability.
Down here, we see that if we want to predict next year
from this year, we can do pretty well, actually.
We've got a pretty tight relationship
between this year and next year.
That's called autocorrelation.
That means the system has memory, or it's sticky,
as I mentioned a few moments ago.
It doesn't change very quickly.
So, if you know the state at a given time,
you know quite a lot
about what the state of the system will be the next time.
For example, if the system is right here,
I know it's gonna be right about there next time.
So, it's relatively predictable.
That can be measured, too.
This phenomenon, this second one that I mentioned,
is called autocorrelation,
there's a simple statistic for measuring that.
So, we can measure the variability and the autocorrelation,
and if those are going up, resilience is going--
- [Audience] Down.
- Down, you got it, right.
If those are going down,
if autocorrelation's going down,
and variance is going down, resilience is going--
- Up. - Up, right.
So, we've got a tool now.
We've got something we can measure
to work with resilience in these complex systems.
This looks complicated,
but I'm gonna give you the bottom line very quickly.
These are two laboratory experiments,
highly replicated,
very carefully conducted laboratory experiments
that test the theory that I just showed you.
In the experiment on the left,
cyanobacteria, the toxic algae
that I was talking about a minute ago,
are exposed to little shocks in the nutrient level
that they receive at different levels of light extinction.
And, as these cyanobacteria are very sensitive to light,
so when the light intensity goes up, they do not do as well.
And the experiment measured the recovery rate,
that's how long it takes to come back from a perturbation,
as a function of light intensity, and it went down.
And here, at point six,
this point right here, they collapsed.
They didn't come back at all.
So, this experiment shows the decrease in recovery rate
that I talked about just a few minutes ago
in a very carefully controlled laboratory experiment.
The right-hand experiment is one with yeast,
and these yeast, in this case,
actually have two stable states.
One is a high concentration of yeast,
and the other is when they're extinct.
So, they're gone there.
That's a really bad alternate state
that we all should try to stay out of.
But nonetheless,
the researchers were able to measure resilience indicators
as they moved along this curve.
And they knew a lot about the yeast,
so they knew a lot about the growth.
And they measured the autocorrelation,
and dilution factor is the stress.
So, as dilution factor goes up,
eventually, the yeast collapse to the extinct stable state.
And the autocorrelation time,
which is just a exponential transform
of the autocorrelation, goes up.
So, autocorrelation goes up
as we approach the collapse point,
exactly as we said it would.
Resilience got low, autocorrelation goes up.
And standard deviation, which is a measure of variability,
standard deviation is just the square root of the variance.
It goes up.
So, this experiment also corroborates the theory.
It says that as autocorrelation and variance go up,
resilience goes down, even to the point
of causing this yeast population to disappear.
So, it's just experimental evidence
for the theory I gave you a minute ago.
Okay?
Okay.
All right, now I'm finally back
to the kinds of ecosystems I like to work on,
which is big ones.
And so, if this theory is gonna be any good,
we have to be able to use it in real world ecosystems
to measure resilience, and to do that,
a research team of lake collaborators
helped me out in designing an experiment to test the theory,
and some of these people are my very oldest friends.
John Cole and I were undergraduates together,
we worked in the same lab when we were sophomores.
Jim Kitchell was on my PhD committee.
Mike Pace and Jim Hodgson are long-term friends.
Mike is at University of Virginia;
Hodgson, at St. Norbert College in De Pere.
Jim Kitchell is retired from UW Madison.
John Cole is retired from the ecosystem center in New York.
We worked on some lakes here in northern Wisconsin,
there's our crew there in that team picture.
The basic idea was to use something
called the trophic cascade as a test case.
Kitchell and I discovered the trophic cascade
in the early 1980s,
and published a number of papers about it,
and we have figured out how it works pretty well,
and we know it's a critical transition.
And the two states of the lake
are shown in the diagram here.
In one case, you have a lot of large, big,
veracious predatory fish, that's the bottom case.
And I'm trying to, there we go.
So we've got a lot of these big guys.
They eat, or in some cases terrorize, the little guys,
and so, they leave.
The little guys normally would eat
these crustacean zooplankton, particularly Daphnia.
Those are little animals about this big
that live in the lake.
But if they're terrified or absent,
they can't eat the Daphnia, and the Daphnia eat the algae,
and when there's a lot of Daphnia,
there are not very many algae.
That's called a trophic cascade,
'cause it goes bang, bang, bang, bang,
down the trophic levels of the lake.
The opposite state is a state
where heavy fishing pressure has driven out the big fish.
So, the big fish numbers are reduced to very low,
or maybe they're absent.
In that case, you get a lot of the little fish.
They quickly wipe out the Daphnia
and other big zooplankton, and you get lots of algae.
We know this is a critical transition,
and we thought we could use it to test the theory in a lake.
The first thing we do before we do any of these experiments
is we do a lot of computer modeling,
and in the case of the trophic cascade,
we had a really good computer model
that'd we'd been honing for more than 20 years
at the time we did the experiments.
And what I've shown diagrammatically here
is the transition in the fish from big fish to little fish.
And the point of it is
that the standard deviation and the autocorrelation
measured in the phytoplankton,
so, the phytoplankton are the little green guys
floating in the water.
You can actually measure changes
in their autocorrelation and their standard deviation,
in the model at least, as you get this transition
from big fish to little fish
or from little fish to big fish,
and that's important, because measuring fish populations
is really, really hard work.
You would think it would be fun because it's fishing,
but you have to catch zillions and zillions
and zillions of fish, and it gets tedious after a while,
and the statistics are not very good.
We can just get robots and put them on buoys
and stick them in the lake,
and they measure the phytoplankton every five minutes,
and we don't really have to do anything
except go download the data every once in a while.
So it's really great that the phytoplankton
can be the sentinel for us.
We did the experiment in this set of experimental lakes,
I'm gonna talk mainly about Peter and Paul.
Paul is a reference lake, or control.
We call it a reference lake
because it's like a reference electrode in electrochemistry,
if you ever took electrochemistry.
For our purposes,
you can remember Paul is small and rhymes with control.
Sort of.
[audience laughing]
Peter is the manipulated lake,
and Peter started out with a lot of little fish,
and we gradually added big fish.
Just a few at a time.
We didn't just chuck a bunch of them in there.
We'd get a couple, put them in, see what happened,
get a couple, put them in, see what happened,
and monitor the behavior of the little fish
to see if they're getting scared.
This summarizes the experimental design
in a little more detail.
We went from less than 20 adults of large mouth bass
in Peter Lake to more than a hundred.
CPUE is catch per unit effort.
That means we were getting 15 little fish
per minnow trap per hour,
and that dropped to essentially zero.
It first dropped because they were terrorized,
and then it dropped 'cause the bass ate them all.
So, they really had a good reason to be scared.
[audience laughing]
The zooplankton went from pretty little,
0.4 millimeters to 1 millimeter.
And it's really size of the zooplankton-- its body size,
more than biomass-- that determines how much grazing they do.
When we started the experiment,
the chlorophyll, which is a measure of phytoplankton,
was much greater in the manipulated lake
than the reference lake.
At the end of year four they were about the same.
So, the trophic cascade worked in this experiment,
and the indicators worked too.
So, here I've shown you, this is actually the four years,
so, A is 2008, B is 2009, C is 2010, D is 2011.
And what I've plotted here is the variance of chlorophyll.
This is the phytoplankton variance in Paul,
the reference lake, and Peter, the manipulated lake.
You can see that it began to go up pretty consistently
here in the second year.
And in the third year, it was just screamingly high.
So, we had an early warning
of the regime shift.
By day 220 of the third year,
essentially, the minnows were either gone
or completely terrorized clear up in the bog mat.
The zooplankton had switched
and the phytoplankton had switched.
So, the regime shift was over by day 220 of the third year,
but we were getting these warnings
more than a year in advance.
So, if we had only been measuring variance, and, by the way,
auto correlation had essentially the same pattern,
we would have known that the shift was coming.
And, of course, this is an experiment,
we were making the shift, so we weren't surprised.
The point wasn't that we could make a shift,
we knew we could make a shift.
The point was, would we see the indicators
in the manipulated lake and not in the reference lake?
And we did.
So, that's pretty cool.
So it worked.
The next thing you might say is, so what?
I mean, suppose you can measure these indicators.
What if you were managing the lake
and you changed the way you managed it
when you detected the indicators?
And would the lake recover?
Would it go back the way it was before,
or is it committed to collapse
by the time you detect the indicators?
Maybe the thing is going off the cliff anyway
by the time you can discern the change.
And in that case,
the indicators aren't really very worthwhile.
So, that was the next question we asked
is could we actually recover a lake
once the indicators fired off?
So, if you could imagine a situation
where you're measuring water quality,
water quality is doing just fine.
You're measuring an indicator,
and the indicator is starting to squeal,
and that indicator is related to the resilience
or the distance to a threshold for collapse,
the depth of the valley.
So the valley is getting smaller,
the indicator is firing off,
water quality looks fine so far.
Could you intervene?
Could you go in and do something about it
and maintain the water quality,
or is the ecosystem committed to falling off the cliff?
That's what we wanted to find out.
We switched to algae blooms for this experiment
because they're a lot easier to deal with than fish.
With fish, in the trophic cascade,
you're keeping track of four trophic levels.
I already mentioned it's difficult to census fish.
And we had discovered that the phytoplankton worked so well,
we thought, let's just focus on the phytoplankton.
And we knew we could make algae blooms with nutrients.
We can skip the cows.
Unlike Lake Mendota, we don't even need a cow,
we can just buy nitrogen and phosphorus and put it in there.
This is the research team.
My PI collaborators were Mike Pace and John Cole again,
by then, Jim Hodgson and Jim Kitchell had retired,
and there we are.
You can see we're a lot better coordinated
for this second experiment.
We have matching T-shirts, and it really looks like a team,
whereas, we were pretty slovenly looking the first time.
As in the previous case with the trophic cascades,
the first thing we did was we ran ecosystem models
to find out if this was gonna work.
In the case of algae blooms, there are a bunch of models,
and they all suggest that the early warnings should work.
So, we had a lot to choose from.
It's really important to do this kind of modeling study.
In this case, it took a graduate student
about six months to do the modeling study.
The experiment took eight people several years
and probably cost a million dollars.
So, it's definitely worth it to do this in silico
before you go to a real lake.
As before, we worked with the sensors.
We had a little bit more sophisticated system this time.
We downloaded the data from the sensors daily,
updated the time series,
and we also updated the indicator statistics.
For this experiment, we needed an alarm.
We needed to know when the indicators were so strong
that we should reverse the manipulation.
And so, we used a method called quickest detection,
which was developed during the Cold War
to see when missiles are coming in.
And it is an extremely sensitive statistical method
for detecting a change in an indicator.
So, every day we calculated variance and autocorrelation
of everything that our little robot measured,
and we also calculated the quickest detection statistic
to see if an alarm went off.
And then the computer, if an alarm went off,
the computer immediately texted us and sent us emails.
So your phone would go off, uh-oh, I got a text.
My buoy is calling,
and my buoy said there's something happening,
look at the website,
and the website is updated every day with all the data.
So, we had really quick feedback,
and even though we had people all over the continent,
you know, the PIs were on the east coast, I was here,
we had some students up there.
Everybody on the team got the warning,
got the information at the same time.
We could get on Skype or get on the phone
and figure out what we're gonna do.
So first, let me show you the results of an experiment
to test the indicators for algae bloom.
Again, we're gonna use Paul as the reference lake,
Peter as the manipulated lake.
This is a pretty simple experiment.
We're gonna very slowly add nutrients,
just a little bit a day,
'cause we don't want to go into the bloom too fast.
Measure for warnings, check to see if we have warnings,
continue adding nutrients, and see if we get a bloom.
So, this basically tests whether the warnings work at all
for the algae bloom case.
And it turns out they do.
So, this is the data for 2014.
Paul is in blue, Peter is in red,
and there's another experimental lake
that I'm not gonna talk about
due to time tonight in black.
Let's focus on the red one.
And here are the statistics
for standard deviation and autocorrelation.
And they start firing off.
The ball is an alarm.
So, that's the quickest detection statistics
as a missile is coming, but, in this case,
it's not a missile, it's a blue-green algae bloom.
So, we started to get alarms around day 180 or so,
which is late June, about now.
And you can see that we had a big algae bloom,
this is large numbers, by about day 200, 220,
something like that.
So, the warnings went off 20 or maybe 40 days
before the algae bloom,
and then here I have some Erlenmeyer flasks.
The clear water is from Paul,
the turbid water is from Peter,
so, yep, that's an algae bloom.
So, basically, the indicators worked again
for this situation.
Now, let's see if we can halt the manipulation
and get an effect.
So, in 2015 now,
this is the same experimental design diagram
you saw a minute ago,
but now, we're gonna change a couple of things.
So, if we're gonna halt the manipulation
when we get a warning, so, when the warning occurs,
we're gonna halt,
and see if we get the harmful algae bloom or not.
HAB stands for harmful algae bloom.
Sorry for that.
And so, the intervention decision
depends on getting alarms in both phycocyanin,
which is a pigment of cyanobacteria,
it's unique to cyanobacteria,
and chlorophyll, which is a generic pigment,
the one that I talked about a few minutes ago.
So, we actually wrote down a priori
the conditions for an alarm,
and programmed that into the computer
that was monitoring the data
so we couldn't fudge it ourselves later.
You know, the computer was telling us what we wrote down
months before the experiment occurred.
So, here's what happened in the first part of 2015
up to about day 180.
Again, we're looking at the red line.
So, that's Peter Lake.
And you can see phycocyanin,
which is the indicator of cyanobacteria,
is going up at a pretty good clip.
Chlorophyll was too.
And the standard deviation, the measure of variability,
is going up,
and the autocorrelation is pretty high.
It went up quickly and stayed pretty high.
So we're getting some indicators here,
and we're getting some alarms,
and we had them in chlorophyll too.
So, we had basically met the criteria for pulling the plug,
and we decided to end the nutrient addition on day 180,
which is the 29th of June in that year.
The air photo just shows
what the lakes are looking like then,
and you can see Peter's beginning to green up.
This is Peter over here,
it's beginning to green up pretty well.
So, what happened?
You could imagine, you know,
we'd already added a bunch of nutrients.
These things are growing exponentially.
Maybe they just kept going up.
On the other hand, you could think,
well, we added all the nutrients we were gonna add,
'cause we stopped on day 180.
Maybe they're just gonna flat line,
and they'll just hang in there,
'cause we did add those nutrients and they'll use them.
Or maybe, they're gonna go down.
Maybe they have not crossed a threshold
and the lake is gonna recover.
How many of you think they went up?
Okay, one, all right.
Oh, two, all right.
How many think they flat lined?
That's what I thought.
Okay, a few.
How many think they went down?
Well, there's a lot of people out there that didn't vote.
[audience laughing]
You know, in Australia, you can be fined for not voting,
so we're gonna be taking names.
They went down.
So, the green box there
is the period of time we were adding nutrients,
and literally the day after we stopped adding nutrients,
they went down.
So, what happened here is we got these warnings,
most of them around starting at day 160,
and, actually, the warnings were complete by day 176.
On day 180, we pulled the plug on the nutrients,
and the water quality improved immediately.
So, the early warning came soon enough
to prevent the transition to the turbid state,
the permanently turbid state.
So, this is pretty encouraging, actually.
I mean, maybe this could really work
to do something in the world.
I think we have a couple of minutes,
so I'm gonna tell you about another really cool development
that's very, very recent.
I've been talking about variability in time,
but what about variability in space?
It would be really useful if variability in space
could give us an early warning of a change,
of a big change, in an ecosystem.
The reason is that it's very, very laborious
to measure everything, every day, all the time.
It takes a big crew, it takes a lot of gadgets,
and the gadgets have to be completely reliable.
They've got to function every five minutes
for hundreds of days at a time,
and that's a difficult thing to do in the real world.
But if you could do it with space,
maybe you could measure the spatial pattern
just every once in a while.
Maybe every couple of weeks or something like that
which would be much less work.
That just means you have to make your machine work
once every two weeks,
and you have the other 13 days to practice with it,
you know, to hope that it works.
Or maybe you could even measure it from a satellite
or from an aircraft.
I think that would be really, really great,
although, we didn't do that in this experiment.
Turns out that Emily Stanley,
who's another professor in the Center for Limnology,
and her team have devised a gadget
that can map the pigments in a lake in about an hour.
And so here you see Anders Uppgard,
who did an undergrad thesis on this, driving a boat around,
and Anders can just do this squiggly path in the lake
and get a pretty good map of pigments in the lake,
and we can calculate all sorts of variability statistics
from those maps.
And Vince Butitta, who's a master's student with Emily,
is the guy who did these calculations.
So, it's potentially a really neat tool.
Once again, we have models that tell us
how the pattern should change.
What this diagram shows
is results of a model at increasing nutrients.
So, top left is low nutrients, then they go up a little.
Top middle, up a little more, top right, a little more,
bottom left, it's just like reading in a western language,
a little more, bottom center, and the most, bottom right.
And so it starts out with pretty much no variability
in space, and, actually, not much autocorrelation.
Then it begins to develop more and more variability,
and also more autocorrelation,
until, actually, the variability and the correlation
start to disappear.
And over here, in the green state,
there's really low variability and autocorrelation.
So, actually, the variability and autocorrelation
in space in this model change much the way they did in time,
but in this case,
we only sampled it six times over in the model.
Only six times over a very long period
instead of every five minutes.
But we had to do it in space.
So, what happens when we do it in the real lakes?
So now, Anders goes out, he drives the boats around,
makes the squiggly lines.
Vince takes the data,
runs them through a bunch of statistics.
So, what you're seeing here in this panel,
the Peter phycocyanin is the pink polygon.
The Paul phycocyanin is a pale blue polygon
that you can barely see,
so Paul had hardly any in it.
And you can see this bloom that you saw
a couple of minutes ago at just before day 180.
These are actually the spatial data,
not the middle of the lake data,
so they're slightly displaced.
The red is the standard deviation,
a measure of variability, now in space,
and the blue is the standard deviation in Paul Lake.
So, you can see that the standard deviation went up
and got pretty high,
and then came down after we turned off the nutrients.
So, we could have used the variability in space
as the early warning indicator for this experiment.
We didn't do it with space, but we could've
if we'd known we could've done it in space.
And I've got some other statistics here,
but in the interest of time,
I'm just gonna skip over them,
but we can go back to them later
if somebody's fascinated by them,
because I think I should wrap up.
So, I've told you a lot today,
and I've bounced around from rangelands
to coral reefs to lakes.
I could have talked about oceans, but I didn't,
and I could have talked about forests, but I didn't either.
But the same phenomena are known from them.
So, big changes can occur,
and they can occur reasonably quickly by human standards,
fairly quickly on a human time scale,
in resources that we might care a lot about.
So, we might need that lake for drinking water,
for fishing, for recreation.
Coral reefs are the basis of huge recreational industries
in the countries where they occur,
and many of those are poor countries
where that recreational industry
is a key part of the economy.
Rangelands are obviously importantly economically
for food production,
forests for wood and fiber production, and so on.
And these things can undergo really big changes.
They're rare, the big changes are very rare,
but when they happen, they're important,
and they're hard to predict in advance.
I went through a theory that explains
that ecosystems become more variable and auto correlated
as stress builds before a big change.
And even though the underlying math
may seem a little complicated,
the rule of thumb is pretty easy.
The variability and autocorrelation go up,
that means the resilience is getting low,
and that means a big change is gonna happen.
And by the way, that theory has been tested in systems
other than ecosystems.
So, for example, there are studies using it
to predict depressive episodes in patients
where the patient is pinged with a question
at random times about five times a day, simple question.
They answer the question,
and statistics based on the variability of their answers
are used to predict whether they are losing their resilience
to a depressive episode.
And in the papers that I've seen, it appears to work.
I'm not a psychologist, so I can't comment further.
There are also physiological cases with heart attacks
and with seizures, with epileptic seizures.
So, this seems to be a hallmark of complex systems,
not merely ecosystems or lakes.
I showed you experiments that support the theory,
I showed you some lab experiments from other people's work,
and I showed you the whole lake experiments
that my group has done.
I pointed out that maps might make this a lot easier,
and so Emily Stanley's work
mapping the patterns of the pigments
so we can calculate the variability
from the spatial patterns of the pigments
I think is really an important frontier here,
and I'm looking forward to working on that more.
Grant proposals are in the mill.
The experiments that I showed you
occurred in near ideal field conditions, right?
We know these lakes very well.
We have big teams, ton of equipment.
We're measuring everything,
much of it at five-minute intervals,
the rest of it almost every day,
and we have a reference lake,
we have a control to protect us
from certain kinds of spurious results.
In the real world, you may not have any of those things.
So, that's a caution
about the potential application of these methods.
We don't know if they're gonna work in the real world,
because even though we had real lakes,
the real world is much messier
than our experimental lake situation.
For example, we completely control the watersheds
and we control the fishing,
which you don't in the real world.
So, actually, the smarter thing to do
would be to build resilience of things you like, right?
Try to make the valleys, the things you want,
as deep as you can.
Deepen the valleys for ecosystems that you want.
Or for your own health, for example.
And building resilience
means you don't have to worry
about the resilience indicators so much,
because even though they work,
what they tell you is you've lost a lot of resilience,
and it would be better to just not lose the resilience
in the first place.
So with that, I will stop.
I don't know if you all take questions,
I'd be glad to take them, if that's your custom.
[audience applauding]