Hi everybody, thanks for tuning in.
My name is Anderson Mayfield,
and I'm a core reef scientist working in South Florida.
Over the next 45 minutes or so, I'm going to talk to you
about some exciting research I've been doing entirely
in the JMP Pro suite on attempting to enable coral reef triage
with machine learning.
So, to give you an outline of what I'm going to be discussing today,
I'm going to give you a brief overview
of some problems facing coral reefs, the ecosystem I study.
I'm going to give you a little bit of a recap
of the talk I gave in the JMP Discovery 2021 summit
as well as the 2020 summit.
And this is what I'll refer to as the coral Veterinarians approach,
in which I was trying to make predictions
about the fates of individual coral colonies.
About halfway through the talk, I'm going to segue
to what I've been working on more recently,
which is attempting to find the resilient reefs,
which reefs out there are going to be the ones
that can weather the storm with respect to climate change
and are going to be around in future millennia.
And this approach I'll refer to as the poor epidemiologist approach.
So I think most of you probably already
are aware of the motivation or the need for this research.
Coral reefs are in bad shape.
The reason is because, the simple coral animals
that build these amazing structures have a delicate intricate association
with dinoflagellates of the family symbiodyacia.
This allows them to build these massive structures
that can be seen from space.
The problem is, as seawater temperatures get warmer and warmer,
the symbiosis breaks down, the algae,
the dinoflagellates are no longer able to photosynthesize,
and they leave the coral or digested or actually just cease to photosynthesize.
What this means is the corals slowly begin starving to death and they perish.
Certainly we're worried about other stressors as well,
things like seawater pollution,
disease, eutrophication, over development of coastal regions.
But on a truly global scale,
climate change is what we're most concerned with,
particularly these rising seawater temperatures.
But for sure, certain corals fare better than others.
There's harder species,
there's more resistant genotypes within a species.
You might even have clone mates that are in close proximity to one another,
one of which might die due to high temperatures,
the other will maintain resilience.
So what drives this resilience in these more robust corals
has been something I've been working on for about 20 years now.
What I've been trying to do more recently
is not just explain what makes corals resilient,
but try to predict which corals that we haven't studied yet
will be the ones that might inherit the Earth.
So I'm going to give a kind of brief overview
of my former approach.
I don't want to make it seem like I've completely abandoned
this line of research,
but as you'll see there are some issues with it in terms of its cost.
The goal today is to show you kind of the old way I was doing it
and then this transition to this newer, cheaper, potentially more global way.
So what I was doing before, I'm a molecular biologist by training,
I was using molecular and physiological data from corals
nearly exclusively to make predictive models
that would then give me a prediction about the fate of the longevity,
the lifespan of the coral.
And this is what I call the coral veterinarians approach,
because I was basically doing what your own physician would do.
I would check in on my patients every now and then,
I would take biopsies,
I would profile them using molecular stress tests
that I've developed over the years,
and then I would attempt to make predictions about
whether or not these corals would bleach as temperatures became warmer.
I think it's important to note that, the molecular components of this
are particularly important because subcellular biology
is going to reflect aberrant behavior or stress indicative behavior
before you observe the changes with the naked eye.
I don't want to wait for the corals to bleach
or become diseased or start to slough off their tissues.
I want to look at sublethal indications of stress that happened weeks or months
before these catastrophic manifestations.
Analogously, this is why we have our annual physicals.
You want to know, for instance if your cholesterol levels are high
before you have a heart attack,
because if you know you have high cholesterol
you might be able to change your diet, take medication, change your lifestyle.
You might be able to thwart these kind of more severe signs
of health decline, like a cardiac arrest.
It's the same idea with coral.
We want to look at something at sublethal scales
that we can do something proactive.
So if we know a coral is stressed based on its molecular signatures,
we might be able to mitigate something at the local scale.
We may not be able to slow the rate of climate change
for the sake of that coral, but we could do something
at the local scale that would give it a chance.
What I was doing a few years ago
at a project I carried out at Noah's Marine Lab in Miami, AOML ,
is I was building thousands of neural networks in JMP Pro 16,
in which I was taking laboratory corals and field corals.
I was taking data from their protein levels.
This is a proteomic project.
Then we had our field test samples
where there were these corals out in the field in the Florida Keys,
where we didn't know if they were going to bleach
or become diseased or perish.
But we would routinely take biopsies
and then enter the proteomic or the protein data
into these neural network models I made in JMP Pro,
and then the models will spit out a prediction.
Then the beauty of working with adult corals
is they don't move this is actually also a bad thing for them
because it means they can't just move away when conditions deteriorate,
but it means I know where to find them
and then I can go out there and see,
if the neural networks predictions were correct.
They actually worked really well.
This was one particular species we did this kind of proof of concept with
was called Orbicella feveolata.
It looks like this.
With these neural network models
that were trained with lab and field protein data,
their accuracy is about 92%.
So 92%, this is about 11 out of 12.
So 90, 95% of the time, I can use the protein data exclusively
tell you whether or not a coral colony will bleach
as temperatures get really warm.
Typically in South Florida, we see our highest seawater temperatures
in August or September.
In 2019, I took some samples from different reefs throughout the Keys.
For instance, we have this sample here, 6745 from Crocker Reef.
We basically entered the proteomic data from that sample
months in advance of bleaching, so I think sometime in the winter.
The neural network from JMP Pro 16 is flagged as being bleaching sensitive.
We went out there as temperatures reached 32 or 33 C,
which is very stressful for corals,
and we saw the colony appearing like this.
This is bad news.
It might recover from this, but it probably hasn't.
There was another coral from a site we know
is typically more resilient.
I mean, this is a huge, ancient, several hundred year old Orbicella colony.
Based on its protein biomarkers
input into the neural network from JMP Pro it was deemed bleaching resistant.
Low and behold, we went out there
during the high temperature event that was killing other corals,
it looks pretty good.
You don't see any signs of hailing or bleaching.
Similarly, we have another site that's also known for having
more resilient corals called the Rocks.
It's protein biomarker signatures were input into the neural network model
and it was also deemed bleaching resistant,
and this indeed appeared to be the case.
This is kind of a map of the Florida Keys,
our marine labs up in Miami, so not too far away.
This is something I've wanted to do for a long time,
using molecular signatures to assign a level of health or stress,
the case may be
because this could enable coral reef triage
in which we could prioritize our conservation efforts.
Maybe this example reef down here that I gave an A plus,
lots of resilient corals that don't seem in jeopardy of bleaching or disease.
Maybe we will let that reef be for now
and focus our efforts on reef that was given a grade of C.
Maybe the one that we gave a grade of F, maybe it's too far gone
it's not even worth our efforts to try to save it.
But I think these kinds of triage data are going to be important
for prioritizing management decisions
and I was really excited about this project.
But there's a huge issue, it's really expensive and it's slow.
That's one coral species in a relatively small area of the Earth
took three years of my time, working 80 hours a week,
quarter of a million dollars to basically build
those neural network models.
Most of the world's coral reefs are in the Indo Pacific.
The most beautiful one are found in this region
that I've highlighted in the bottom, known as the Coral Triangle.
These are areas that do not fund coral reef research
to any great extent,
they simply don't have the human power or the funding.
Even if they did, there are hundreds, up to six or 700 coral species
you can find on these reefs.
I will have passed away long before
I could do this sort of analysis with all these corals,
even if I had a couple of helpers.
It's too expensive and it's too slow.
Is there something else we could do that would help us to know something
about the resilience, the longevity,
the stress loads of these reefs, without having to do these fancy,
expensive molecular analyses that require well trained personnel.
That's what I'm going to be talking about the rest of the time.
This is what I call
kind of transitioning from a coral veterinarian
who's got a handful of patients that I know their health in great detail,
to thinking of myself more as an epidemiologist.
I'm trying to look for more global trends in coral health
that I could use to make models about their future persistence
on the Earth as temperatures warm.
If you remember before,
I only used the physiological data to make a predictive model.
Now what I'm going to do is I'm going to try to integrate
three disparate data types into making a predictive model.
We're going to look at environmental data, and by that I mean things like,
seawater quality, the type of reef,
whether the reef is exposed to the elements,
the shape of the reef,
those kinds of physical properties, ecological data,
this is essentially what's living on the reef.
The corals present, how much algae there is,
how many fish live on the reef.
These are all things that could be important for reef health,
and then also the physiological data from the corals themselves.
This actually has never been done before.
Most people monitor the health of reefs based on only two properties,
temperature and the abundance of coral, which is a good start.
But as I'll show you,
I think these models that are more comprehensive and holistic
are going to give you much
higher predictive power.
So in this case,
we're not simply trying to predict the resilience,
individual coral colonies,
we're looking at a more habitat or entire ecosystem level scale,
that's what we're trying to predict.
So as a proof of concept for this, I've got a nice data set.
I've been playing with from the Solomon Islands
it's in the southeastern part of this Coral Triangle
I mentioned that this is where you see the most biodiverse reefs,
the reefs with the most amount of coral and in my subjective opinion,
this is where you see the most beautiful reefs on the planet.
And I had an amazing opportunity to dive all over this region and beyond
with Khaled bin Sultan Living Oceans Foundation.
A couple of years back,
they carried out what was known as the Global Reef Expedition,
it was the largest coral reef survey ever undertaken
So we had a whole team of scientists monitoring the reef
from the satellite level, from space all the way down into the molecules
of the organisms residing on these reefs.
So it's a really rich data set.
We have nice reef maps we've been developing, we have scuba surveys,
divers collecting information about what's living on the reefs.
We're looking at our environmental data, our seawater quality
this is obviously going to be important for coral health
and then my role, as you can see in this image here
was in sampling corals, just taking tiny little biopsies
to profile with some molecular assays I've developed over the last 20 years.
And we used a different species from the Caribbean.
We use this coral called, Pocillopora acuta.
It's kind of intermediately sensitive,
so it's kind of in the middle, it's kind of a typical coral
but more importantly, it's the model coral for research.
So this is the coral that we know the most about its physiology.
So I would encourage you to either check out my personal website,
coralreefdagnostics.com,
to really see how incredible a location, Solomon Islands
and other places we visited were for people that are more interested
in the data.
Living Oceans Foundation has this interactive map web server
that's loaded with high resolution maps and all manner of data we collected,
it's all open access, it's a really nice resource
and I was really happy to have been a part.
So finally, 15 minutes in, let's start doing something in JMP.
So I mentioned we have all these different data types.
We've got stuff living on the bentos,
we've got the ecological data, we've got the coral health data.
If I talked to my marine biologist friend,
the first thing they're going to want to know is,
what's the coral cover on the reefs?
Ecologists are admittedly a little bit too focused on abundance
as you may see later in the talk, depending on how the models run.
Coral cover alone or coral abundance
is not actually a good predictor of poor resilience.
A reef with tons of coral doesn't actually do any better
than a reef with a few coral.
One of the reasons that might be is,
a reef that's been decimated that may only have a few corals left.
Those stragglers inherently adapted or acclimatized
to whatever killed off their brethren so they actually are more resilient.
The reef might be gross and ugly and no tourists may want to go there,
but it's not actually a lower resilience.
So for me, I'm more interested in what's going on with corals.
Most people in the field are more obsessed with coral cover,
which is still important, even if it's not a good metric for resilience,
you still want to know, where do I find the reefs with the most coral?
Maybe that's where you want to start [inaudible 00:16:11] .
How would you go about doing this in JMP Pro?
With this demo, I'm actually going to do it in JMP Pro 17,
a beta version that I've been demoing for a few months
but you could just as easily do this analysis in JMP Pro.
Just to familiarize you with what the data set looks like,
the rows, there's 272,
these are what we call transects.
These are swaths of the reef that we surveyed.
You can see we looked at different depths.
These are the environmental data I mentioned before.
We've got spatial data such as coordinates,
the type of the reef, seawater quality.
And you don't need to worry too much about these abbreviations,
but these are just the abbreviations
for the genera of organisms that were living on the reef.
We basically bend them into 54 different coral bins,
six algae bins, barren substrate, so this is where nothing is living,
this is going to be important to remember.
Then other invertebrates.
These are the main things that occupy the reef environment.
I've excluded the fish data because
I didn't have a nicely curated data set at the moment,
but I definitely want to factor that in later.
But let's look at this live coral cover.
This is all the different coral genera, sum together.
This is a simple univariate analysis.
I want to know, in the Solomon Islands
what's contributing most to the variation in coral cover.
And I think a really good way to get at this really simply as a first go,
is to predict your screen.
In this analysis, the Y is going to be my live coral cover,
and I want to look at these eleven environmental parameters that I think
might influence coral cover in the Solomon Islands.
I'm going to put them here as my X.
Right off the bat, you can see depth.
It's contributing to about 40% of the variation in the coral cover.
To a marine biologist or a coral biologist,
this is not going to be a surprising finding,
we know different parts of the world, corals prefer different depths.
Most of the most lush coral reefs you're going to see
are from about 2 meters down to about 30 meters.
Let's see where we find the most corals in the Solomon Islands.
With this selected, I don't even have to go back to my columns.
I can just go directly into fit Y by X, move the live coral cover into the Y.
Let's just do a simple ANOVA.
I actually have my depth as bins, although I've got the continuous data somewhere.
We see from doing this analysis of variance
a really strong effect across these four depth bins,
and we're seeing significantly higher coral cover
in the eight to twelve meter window.
We can actually look at these two Keys post hoc test
and we see that eight to 12 have over 50% coral coverage.
A healthy reef can range from 20, 40,
50% is astonishingly impressive coral cover,
you're not going to see this kind of coral cover in much of the world.
But for now it's important to know that,
in the Solomon Islands eight to 12 meters is where you find the most coral.
But to me that might be good for a publication,
but that's not really that interesting.
So if I've got colleagues or marine park managers
who are working in the Solomon Islands and they say,
We can't go out there and survey all these reefs.
I mean, this is a huge area. What we surveyed was a drop in the bucket.
We want to make predictions about reefs we didn't visit
that might also have a lot of coral, that might be important for conservation.
High coral cover reefs also where you see more fish and other invertebrates.
This might be important for people that want to bio prospects, for instance.
Now what I'm going to do is I'm going to do something similar,
but rather than just do a simple predictor screen of coral cover,
I'm going to do a model screen,
which I try to build a simple predictive model of coral cover.
Let's go back into JMP Pro 17.
This was a newly- available feature in JMP Pro 16, I believe,
and is arguably my favorite feature in the entire package.
What you're going to see here,
I'm going to set this up exactly the same way I did before.
Live coral cover is my Y,
and then we've got our 11 environmental potential predictors here.
I had JMP make me ahead of time a validation column
because it's going to be important to validate this.
You see down here a list of all the different predictive models you can test.
I want to include all of them.
I want to look at two- way interactions as well as quadratics.
I'm not going to do k-fold cross validation
because I have a validation column.
Let's let this run.
It's going to be looking at this fairly large dataset.
It's not huge. I think many of you working in industry,
this will actually be a pretty puny data set, but it's going to test it
with all these different modeling types and it's going to give me
this nice summary output.
I can see right here who won this particular battle.
A generalized regression with forward selection using a pretty advanced
it's looking at quadratics, it's looking at factorial combinations.
It considered a lot of different parameters in the 68 samples
that were flagged as validation.
We don't actually even have to go into fit model now and try to rerun this.
We can run it right out of the model screen.
There's a lot of data, we're not going to sift through
all of this because, to be honest,
this was something I did on the fly by design.
I've never run this particular model before, just because I think that really
emphasizes how easy it is to dive in and start interpreting.
There's other ways to get at this, but I'm lazy,
so I want to see what are the most important predictors
that this generalized regression model found.
Depth. We're not surprised to see depth there
because we just saw from the predictor screen that is important
in driving trends in coral cover on the Solomon Islands.
Reef type times latitude interaction, that's maybe a little bit harder
to wrap our heads around.
But let's go into the profiler and see what we can learn
in more detail about this.
The profiler is here.
Let me close some of these things so we get a little bit more room.
Enlarge this. The profiler is not showing me
the reef type times latitude interaction on the same plot per se.
But watch what so if you just look at reef type in isolation,
we have barrier reefs, fringing reefs, patch reefs,
and these other which tend to be these pinnacles
that come up out of the ocean depths.
We don't see much difference in coral cover,
but look how the latitude line shifts.
This is emphasizing that latitude times reef type interaction.
Over here, we're seeing a very similar plot
as when we did the [inaudible 00:23:59] in the fit Y by X.
We're seeing 8- 12 meters as being the sweet spot
for finding the most coral.
But what I think is cool is to go one step further
and do this desirability analysis.
What I'm going to do, I think it's probably going to remember
my presets, but let's just start it from scratch.
I want to tell JMP to give me the scenario
that would result in the highest live coral cover,
because this is what a marine biologist is going to want to know.
Right here, my response goal is to maximize live coral cover,
so I want to have high desirability values for my high coral cover levels.
I hit okay, then I go back in here and I say Maximize Desirability.
Unsurprisingly, they stay the same, 8-12 meters is where we want to
hone in on our search.
But this might be more interesting
to people that are embarking on a field trip.
"Hey, we've got a week in the country,
we want to find rich high coral cover reefs
where should we go?" Well, I think you should go
to this farther flung islands out and farther away from the equator.
As you'll see later, these are the more
remote, sparsely- populated parts of the country,
which is probably where you expect to find more coral.
A lthough it's very similar to the barrier reef,
you'd probably want to focus on these other types of reefs and barrier reefs,
if you have the choice, versus fringing reefs and patch reefs.
I think doing this kind of analysis
could be important for conservation and for planning field trips.
But arguably, this is a little bit of an aside,
and we have not yet reached the actual goal at this time. That's coming up.
All right, we've done these two demos,
let's go back into PowerPoint.
I really wish I had more time for this, but I just know I don't
and I feel so bad for all the developers and people that work so hard on this,
but I take full advantage of the multivariate platform
and this is going to be really important because even though in this past demos,
I just looked at live coral cover, singular Y.
In reality, that's completely belittling the complexity of these ecosystems.
There's hundreds of things living on the sea floor.
You really need to do a multivariate analysis
where you've got multiple Ys, multiple Xs.
We're talking about things like principal components analysis,
multi- dimensional scaling, doing these daily in JMP Pro.
Really like discriminant analysis.
For instance, right here, this took me 1 minute.
I can quickly see that reefs of Tinakula in a multivariate scale
are very different from those of the rest of the country.
If you were to go to the Solomon Islands, you would know,
this is because these are reefs growing at the base of an active volcano.
They look very different, they behave very differently.
The multivariate benthic data corroborate this.
Similarly, we see this nice effect. I've color- coded the reef sites
by exposure, whether they were sheltered or exposed to the waves or intermediate.
And you can see pretty nice parsing by exposure in this discriminant analysis.
I'm a big fan of these algorithms and partially squares in particular,
and I've got some hidden slides and some scripts in the data table
that I'll make publicly available. So if you want to get more detail
about the multiv ariate analysis, you're definitely welcome to download.
But what I want to spend the rest of the talk on
is the health of the corals themselves.
T hat was looking at the bentos, the reef as a whole.
I'm a physiologist, I want to know what's going on in the corals,
and I measured so many different things in these corals over the years
that I recently created what I call the Coral Health Index for the tree.
This is basically an amalgamation of a bunch of different response variables
that I know from my past research scale with coral resilience.
What I've done is tried to simplify things to where if your Coral Health Index
score is zero, this means you're about to kick the bucket.
Five means you're immortal.
Trivia is [inaudible 00:28:25] like corals and jellyfish technically are immortal
if left their own devices and no stress, they can continue to regenerate forever
but of course, in reality, there's always going to be some limitation.
They're going to reach the surface, the water is going to get too cold,
but they can actually live forever.
Anyway, we're not going to see any corals their fives.
This basically follows a bell curve so we're going to find most of our corals,
their health indices are in this 2-3 window.
With the help of John Powell, he made these really nice customized pie graphs.
I adapted this from some... They're called these really
great coral reef report cards. They're developed by an NGO called AGRA.
I said, I love that visual.
I want to adapt it, but focus on coral scale.
What this is is each of these outer four widgets,
which you can see the details here,
the interior is basically showing you the average of the four widgets.
A s you can see, we're seeing values as low as 1.5.
Corals and Nono Lagoon seem to be the least resilient.
Most of the people in the Solomon Islands live close to the capital of Honiara.
We probably would expect this kind of west- east gradient.
We tend to see higher Coral Health Index values
over here in the provinces and the Reef Islands and Monte Carlo.
This is not surprising. This map was made with Graph Builder.
Let's see. I think I have enough time.
I'm not going to try to reproduce this map
because I think this map,
even though I love it, I think it's still too complicated for a manager.
They don't want to see all these pie widgets.
They want a single number.
I want to show you a really cool trick.
There's great webinars about how to plot data onto a map on the JMP website
but I'm going to do something that was new to me
and it might actually be useful to a lot of you.
It's taking it one step further.
We're going to do it in JMP Pro 16
because I want to be able to publish this online.
That's not yet a feature in JMP Pro 17 because it's still the beta version.
I want to plot the Coral Health Index on a map.
This is going to be shockingly easy in a Graph Builder.
Just going to drag my latitude and longitude over JMP nose
to treat these as such.
I don't want this line.
Right now it's just showing me essentially the location of my dive sites.
I want to add a background map.
This is the detailed Earth. Let's make it bigger.
We see the Solomon Islands now.
Getting closer. I want to overlay my Coral H ealth Index
its color.
Still not there yet. I want to convert this to a heat map,
but I want a finer scale of resolution
and this is the trick that I learned that I think is going to be really useful
because I was actually doing this ArcGIS before, which is a PC- only program.
I'm on a Mac, cost thousands of dollars.
I said, why can't I do this in JMP? And it turns out that I can.
What I want to do is I want to force a smaller grid onto this map
because I want these cells to be much smaller.
I want them to be 0.5 by 0.5 degrees.
As long as you turn the grid lines on, it's going to give you an average
of the Coral Health Index in each of these 0.5 by 0.5 decimal degree boxes.
That's what I want.
I actually prefer to use a green to red, and the default is to have red be high.
If you remember the image of the Coral Health Index,
I actually have green as the high value, so I'm going to switch it as such.
I actually want it to span the entire range,
even though I don't have many zeros or fives.
I'm going to do this, drag this here,
and now I think it's looking good, but it's still too busy.
I'm going to turn the grid lines back off.
It will keep the cell shapes that I want.
Voila, in my opinion, this is exactly
how I want to see these Coral Health data portrayed.
But I'm going to take it yet another step further.
I'm going to say, "Hey, look, my friends that have never seen
these data, they may want to play around
with the different environmental variables and see how these change
depending on the type of reef, the temperature and whatnot.
I'm going to add this local data filter.
Going to give this a name.
Still not done yet, though.
I want to actually share this with my friends.
What I'm going to do, I'm going to publish to JMP Public.
This may take a minute because I may not be logged in,
but let's just see. I'm going to create a new post.
I want to share it with everyone.
I can add an image if I want.
I'm just going to leave all these defaults as is for now, and we'll publish it.
It's going to take a few seconds.
Hopefully it works well.
It's going to migrate me over to the website,
and I'll show you, as it's working, what you can then do once it publishes.
All right, here we go.
Let's go ahead and check it out online first.
This is what I can share with my friends so they can say,
"Hey, look, I only care about reefs over ... I'm only going to be able to go
to the western part of the country for my field trip.
I don't care about those reefs in the east.
So let me just turn them off.
Then it's going to refresh.
Then you can hone in your search here.
You could look at the different reef types.
Another thing you can do,
which I do all the time, is you can actually take the embed code
or the embed card, copy it, and put it in your personal website.
Because of the way my website's set up,
I have so much padding here, it's not actually going to show the map very well.
It's better for me to simply do what they call a card
where I've got a schematic of it here, and then if people want more details,
they can click on it and then go back to JMP public.
This is a super cool feature that I think people with access to JMP
should be taking advantage of.
This is just showing you how you can
basically even embed it within your website, within a presentation.
But I don't think we need to go into that.
A gain, that's another aside, we're finally getting to the good stuff.
This is what I've been wanting to do.
This is the goal of this whole analysis. So we're almost at the finish line.
This is using the JMP Pro suite to try to find the climate- resilient corals
that we haven't stumbled upon yet.
We usually find climate resilient corals
either through experiments, through surveys.
We've lost this time window. We don't have time
to do all these experiments, we don't have the money.
Coral reefs are in bad shape.
We need a way to speed up the search for the resilient corals
that we may want to use for restoration.
The ones we may want to protect, buy or preserve.
What we're going to do is we're going to make a predictive model
of the Coral Health Index we factor in all the different survey data we've collected.
It sounds daunting, but I think you'll see this is actually
something that could be done relatively quickly.
In this case, I'm going to go to another data table
that's got my coral physiological data
and that is somewhere here.
This is 110 rows. Instead of dive sites now,
these are coral samples.
This is the ecological data. The Coral Health Index is here.
We're going to go over to my beloved model screen again.
I probably could use recall,
but just to be safe, we're going to take 50 benthic categories.
These are the bins of things that live on the reef.
Move them here.
World Health Index is what we want to predict.
We're going to take this validation column here.
We're going to use the same settings as last time.
It looks a little bit different because I'm now doing this in JMP Pro,
but it's working very similarly.
I want to do the additional methods with quadratics.
I think this will run fairly quickly, and indeed it did.
In this case, a neural network that was boosted rose to the top.
Validation R squared of about 0. 49,
it's not bad, let's run it.
It's going to be different because of the way neural networks work.
They can vary actually quite dramatically
from run to run, especially when you have relatively smaller data sizes like mine.
But we're still in the ballpark, 0.52.
But if you know about neural networks, you know, there's tons of different
modeling parameters that you can tinker and tweak.
That's why this really brilliant ad- in from Dietrich Schmidt
has been an absolute game changer for my research.
He created a nice GUI that's going to let me look at potentially thousands
of different factorial combinations of modeling parameters.
But today, for the interest of time, I'm just going to do four.
I input the model exactly like I did in Model Screen,
but now you'll see these options that are specific to the neural network platform.
I want to just look at, you know what, I'm going to explain this
while it's running because it might take a second to run
and we're running low on time.
I'm going to have to build four models for me.
I think everything's in there like I want.
All right, now let me explain this while it's running.
I think I input something wrong.
Apologize for that.
Let's see, let me restart this input this year.
This is all correct. I want these to vary.
I think maybe this was too low.
Let's try it again.
It's basically going to start running these models.
It's going to use the JMP default.
I've heard, basically he leveraged the power of design of experiments
to basically have the number of sigmoidal, linear,
and radial activation nodes span 0-4.
We can have up to 20 boost. I'm allowing the covariance to either be
transformed or untransformed, either with or without a robust fit.
Because I want to go with the minimum number of potential factors,
I want to use a weight decay algorithm.
It gives me this nice output.
Let's see if the R squared of the validation models
did any better than the JMP default.
Most of the time they do.
In this case, it's not way too much different.
About 0.55
We can run it, it will ask me to save the output
and in the meantime it's going to run this model
which may end up actually being very similar to the JMP default one.
But then once it spits it out,
we're actually going to, whatever gives us, we're going to go with it.
I'm going to show you,
assuming it was our square
or another modeling benchmark, that you're happy with
what you could then do with the analysis.
That's going to be going back into the Desirability analysis.
If you just bear with me another few seconds, it should finish.
What we're going to do is
we're basically going to go into the Profiler,
and I'm going to tell the Profiler,
hey, I want to find the conditions, the environmental conditions,
and the benthic conditions that lead to the highest Coral Health Index scores.
Because that's where I might want to focus my efforts for conservation,
for trying to find Brazilian corals.
You can see in this case we got a fair bit higher our squared.
Let's go into the Profiler.
It's probably going to remember my settings, just safety,
let's go, set Desirability.
I want to maximize the Coral Health Index, so it remembered it.
Now I want to maximize Desirability.
It's going to tell me the conditions
in which I'm going to find the corals with the highest Coral Health Index scores
We don't have time to go into all these, but this is going to be super useful
for people that are embarking on field trips, and to managers.
They're going to say, look, if I want to find
the most resilient corals in the Solomon Islands,
I'm best sticking to intermediately exposed fringing reefs, within the lagoon,
submerged reef types.
Some of these may not make as much sense,
the time of day, temperature, you may not have that luxury.
Things like depth, you want to focus on shallow corals, in this example.
These are going to be super useful data that are going to allow us
to find resilient corals on a much faster time scale.
The important thing to note here is
one thing to note is these aren't necessarily the conditions
in which you find the most corals,
because, remember, more is not necessarily healthier,
but these are things that are cheap to measure.
Latitude, longitude, you just need a smartphone.
Temperature, you need a thermometer.
You don't need to do these fancy, expensive molecular analysis
by PhD scientists.
You can train a high school student to go out there and collect these data
that are going to be really informative for coral health.
My idea is I have all these similar data sets from all over the world.
I can start building what I'm calling this Coral Health Atlas.
I can use Graph Builder to make these nice plots
where I'm showing people where resilient corals are likely to be found.
This is going to help us,
in concert with these temperature based models from Noah,
envision what the future reefs are going to look like,
where we're going to find corals in the future,
which corals are going to live there.
Since we're running out of time,
don't worry, I'm not going to read off this list.
But this was not completely done in isolation.
I did obviously benefited greatly from the JMP Pro software itself,
but a lot of these people behind the scenes lended their support.
Some of you won't be surprised to see your name there,
some of you might be surprised,
and that's it was probably because you gave a webinar or you wrote a blog
or something that was really inspiring to me.
I hope you're happy to see your name up there.
I really want to give a shout out to Diedrich Schmidt if he's on,
for developing that really excellent auto- tuning add- in
that's greatly benefited my research.
I also want to give a shout out to John Powell,
not just for helping me make those figures,
but because he was the person that really convinced me
that JMP is more than just a software package.
You've got this network of really talented individuals behind the scenes
that are willing and able to help you along the way.
I really appreciate John and everybody else's support.
So with that, I'll end my talk
and I'm probably over here furiously answering questions.
If we are to any time left, I'm happy to field more.
Alright, thanks a lot.
