- Good morning, everyone.
And welcome to Alumni Day on our 43rd Darwin Festival.
We have a talk momentarily beginning,
and then we have another one this afternoon.
Before I introduce one of our alumni, Mr. Peter Shearstone,
I just wanted to rewind those in the audience
who happen to be alumni or old friends of biology
to consider joining us for a short, separate Zoom at 12:30.
If you just email me, I can send you that Zoom link.
In addition, any students who happen to be in Meier Hall
after the second talk this afternoon,
if you wanted to find your way down to Meier Hall 414,
one of the labs, I have some surprises for you.
Without further ado,
I'd like to introduce one of our alumni,
Mr. Peter Shearstone.
Peter joined Thermo Fisher Scientific in 2018
as Vice President for Global Quality and Regulatory Affairs,
and is responsible for leading
the company's global QARA team,
which consists of over 8,000 employees.
The company is headquartered in Waltham, Mass,
and is the world leader in serving science,
their mission being to enable their customers
to make the world a healthier, cleaner, and safer place.
Peter holds a BS in Biology from Salem State,
our very own department, having graduated in 1989.
And his company Thermo Fisher Scientific
is honored to support the 2022 Darwin Festival,
and for the next four years as well,
bringing the importance of science to Salem State
and as well as the wider community
on the North Shore of Massachusetts.
Peter will introduce our guest speaker this morning,
Dr. Tara Pelletier, who is also an alumna
of the biology department.
So, over to you, Peter.
- Thanks, Dr. Fisher.
Good to be here, honored to.
I've been part of Darwin Festivals years ago as a student,
and of course today
as your host for the Alumni Day.
I'm very honored to introduce Dr. Tara Pelletier,
who is an assistant professor of biology
at Radford University in Radford, Virginia.
Radford is a public university founded at 1910,
and is one of eight doctorate granting universities
in the Virginia educational system.
She is a 2002 graduate of Salem State University.
Tara is broadly interested
in the eco-evolutionary processes
that have shaped current biodiversity patterns.
Her lab works to integrate various types of data,
but that being genetic, or geographic, environmental,
historical, morphological
to understand these complex processes
and there are several large data sets that she's aggregated
over the last few years that lend themselves
to research in computational biology.
Her team conducts local field work
with the Aquatic Ecology Laboratory
to sample water and soil samples.
They also sequence DNA in targeted insect
and salamander species to estimate genetic diversity
and dispersal for conservation purposes.
And I encourage all of you to check out researchgate.net,
her papers there are quite inspiring
and very, very interesting as a scientist.
So, without further ado,
please join me welcoming Dr. Pelletier
to the 43rd annual Darwin Festival.
Her talk today is entitled Big Data for Biodiversity.
Dr. Pelletier.
- I'm very excited to be here
and share my research with you.
So, like Peter said, I'm an evolutionary biologist
and my main interest lie
in understanding how biodiversity is created and maintained.
But before I get started with my research,
as an alumni I thought it would make sense to share my path
to how I got to where I am now,
it's kind of a windy long path.
So, I started at Salem State
back when it was Salem State College, not University.
I wasn't even a biology major when I started,
but I had to take a science class,
so I took biology 101 and 102
with Doctors Buttner and Cuevas Ingle.
And that's where I just fell in love with biology,
more specifically, just science in general.
And I often like to share,
particularly with undergraduate students,
that I did really, really poorly
on my first biology exam ever.
So, if that's you, don't fret,
you just kind of have to figure out
how to be a college student.
And so, I figured that out
and I worked for several years
at the Cat Cove Marine Laboratory
while I was a student there,
and have lots of fond memories
of being out on the water and cleaning fish tanks.
I also did some undergraduate research with Dr. Paul Kelly.
So, this image here is plethodon cinereus,
this is the eastern red-backed salamander.
And the goal of the project that we conducted
was to compare cover boards.
So, these are just these small squares of wood
that we would put on the forest floor,
and see how they compared to natural cover,
things like rocks and logs,
where we would find salamanders
as a way to monitor salamander numbers over time.
And I didn't really know it at the time,
but getting to know these plethodon salamanders
would play a big role in the career path
that I ended up taking.
But after I graduated, I had some odd jobs,
I worked at Eastern Mountain Sports,
I worked at a ski resort,
but I spent a couple summers
working for the Northeast Mosquito Control.
This was with Dr. Cuevas Ingle from Salem State.
And we would go out and collect mosquitoes,
identify them, and test them for West Nile virus.
And I love this job
because I got to spend a lot of time outside.
And at the time,
back then, I still thought I was gonna be an ecologist
or a wildlife biologist 'cause I loved animals even insects,
but I needed a more steady income
so I took a job at Charles River Laboratory,
which is a company that supports pharmaceutical research
and realized that definitely wasn't for me.
Then I spent some time working as a vet tech
and thought about vet school for a brief period of time,
and also realized that that wasn't for me.
So, I finally decided I should go to grad school
and I was living in Portland, Oregon at the time
so I went to Portland State University
to talk to some biology professors there
and figure out how to go to grad school
and what that even meant.
And eventually, a couple professors agreed to co-advise me.
I think they realized that I was just gonna keep showing up
and so they might as well let me in their master's program.
And one of my co-advisors
asked me to take his field hepatology class.
It was the three-week course in the Southwest,
Desert Southwest before the semester started.
And so, before this trip
I started reading a bunch of scientific papers
because I don't know, I wanted to be able to impress him.
And so, I was focusing mostly on papers
on plethodon salamanders
'cause it was something that I kind of felt familiar with.
So, we're on the trip
and he asked me what I wanted to study
and I have no idea why this came out of my mouth,
but I said the population genetics of plethodon salamanders.
And I'm not sure I even really knew
what it meant at the time,
but I didn't wanna say, "I don't know".
And then that's what I ended up doing,
so more specifically I studied
the phylogeography of plethodon vehiculum.
So, that's in this image down here.
This is the western red-back salamander.
They're not actually that closely related
to the eastern red-back salamander,
they're about 40 million years divergent from one another,
but they both do have wide distributions,
one on the East Coast of the United States
and the one that I study
is on the West Coast of the United States.
So, I studied the phylogeography of this species,
what is phylogeography?
It is the study
of the geographic distribution of genetic variation.
And we can examine patterns in genetic variation
and that will tell us a lot of information
about how and why biodiversity is shaped the way that it is.
So, it's a little bit deeper in time
than population genetics, but the ideas are sort of similar.
For example, genetic data has been used
to test the out of Africa hypothesis in humans.
And what we can expect
is that in the ancestral range of the species,
we have high levels of genetic diversity.
As individuals migrate out of that ancestral range,
they take small portions
of that genetic diversity with them.
And what we see is lower levels of genetic diversity
at the leading edge of a range expansion.
And so, we can use this information,
how the geographic or how the genetic variation
is distributed across geographic space
in phylogeography to identify unknown species,
make informed conservation decisions,
and just better understand the processes of evolution.
So, things like migration,
genetic drift, and natural selection.
So, I thought that this was just super cool
and I should go get a PhD
and continue studying phylogeography.
So, I started my PhD work
at Louisiana State University in Baton Rouge.
I was working with Brian Carsons
who was a prominent phylogeographer
doing work in the Pacific Northwest,
which is where plethodon vehiculum is found,
the species I studied for my master's degree.
So, here I studied five different species of plethodon.
These images here, this is plethodon dunni
and plethodon vehiculum.
Dunni might be my favorite,
although they all have cool features.
I also got a field assistant, this is my dog Shasta,
who I never was actually able to train
to sniff out salamanders
so she was more of a companion than an assistant,
but she was great.
But during this work,
I started using more sophisticated techniques
to analyze my data.
So, analyzing genetic data can be really complicated,
and in order to test hypotheses
rather than just describe
how the genetic variation is distributed,
you have to be able to simulate data
and develop models to test those hypotheses.
And in order to do that,
you need to learn how to code,
so I learned how to code.
This is an image here
of one of the first scripts I ever wrote,
and it's modeling genetic drift in a population.
This image here is an image
from the first chapter of my dissertation
where I was examining demographic models
in plethodon idahoensis.
So, after several years, I...
Oh, I forgot to mention,
I have this Ohio State here.
About halfway through my dissertation work,
my advisor switched positions
and took a job at Ohio State University
so the lab moved up to Ohio State with him,
and that's where I ended up finishing my PhD at OSU.
And I like to joke that that was the year
I officially became an adult
because I finally finished going to college
and I got married and had a baby
all within this six-month period.
But I spent a few more years there teaching
and just doing some more phylogeographic research
before I ended up in the position that I'm in now.
So, I'm an Assistant Professor of Biology
at Radford University.
So, when I did my masters,
I studied the phylogeography
of one species of plethodon salamander.
Then when I did my PhD work,
I studied several species of plethodon salamander.
And one of the things that I learned about
about this was that when you study multiple species
and you could compare them and contrast them,
you can actually learn a little bit more
about the evolutionary processes.
So, if two species have similar demographic history,
similar patterns in their genetic data,
but maybe one has a much larger distribution,
you can start thinking about the physiology
and the specific ecological niches
that are shaping those biodiversity patterns.
So, me and some colleagues thought
what if we did this even bigger, right?
What if we asked questions like this
in thousands of species on a global scale?
Which really is kind of a ridiculous undertaking,
but we did it anyway.
And so, what I'm gonna share with you today,
the first thing I'm gonna talk about
is the data that we need.
So, we aggregated a database that we called phylogatR
that puts together a lot of different
type of phylogeographic data,
and I'm gonna walk you through those steps.
Then I'm gonna describe
a machine learning predictive modeling technique
that we've been using.
When we have lots of lots of data for thousands of species,
it can be a little bit more challenging to analyze,
so we have to use these machine learning techniques
to take into account data that is numerous,
and messy, and potentially correlated.
And then I'll share with you two examples
about how we use these data
and this machine learning approach
to explore hidden diversity in mammals
and prioritize conservation efforts in plants.
So first, the data.
We live in a very data-rich age,
we're producing data all the time as humans, right?
You Google something and that information is being recorded.
We all know that our social media platforms
are sharing our data with other companies
and our Google Maps is always keeping track of where we are.
But if we think about like our Google Map data, for example,
our own individual Google Map data is kind of boring.
But once we put that together in a large volume,
it's actually really informative.
If you see that red line on your Google Map,
you know that you don't wanna take that route
because it's super busy.
It's the same thing in biology, right?
If we have one data point where we know a species is found
in a particular geographic location,
it doesn't really give us a lot of information.
But when we start putting data together for lots of species
and lots of geographic locations,
we can get a lot of information.
And biologists are out there collecting data all the time,
there are probably people in the field right now
collecting biodiversity data.
And then they put those data on these open source databases
that people can use.
So, this one here,
this is the Global Biodiversity information Facility,
GBIF for short,
and it has almost 2 billion occurrence records.
And an occurrence record is basically just information
that says this species was in,
this was observed in this geographic location on this day.
And once we have that geographic information,
we can get other information about the species.
So, we can take a GPS coordinate
and we can extract information from data layers.
So, we can know what the elevation is,
we can know what the average rainfall is,
what the range in rainfall is, what the landscape is like.
Is it a grassland, a city?
So on and so forth.
So, we can get a lot of information
about where that species is found.
Additionally, there are genetic databases.
So, this one down here, BOLD,
it's the Barcode of Life Database
and they have DNA sequence data housed on this database.
And a lot of them have GPS coordinates
associated with those DNA sequences.
There's also, so the NCBI GenBank,
which has over 200 million DNA sequences.
So, as phylogeographers, we are interested
in this DNA sequence data,
but we need to know where geographically
that DNA sequence data came from.
So, the problem
is just putting all this information together.
So, BOLD usually does have GPS coordinates
associated with their DNA sequences,
but GenBank most of the time does not.
However GBIF, this database that has the occurrence records,
often has IDs that allow you to go on GenBank
and pull a DNA sequence
associated with an individual from that occurrence record.
So, we have a pipeline that puts together all these data.
Right, the first step are primary researchers
going out in the field or sequencing DNA in the lab
and collecting the original data,
putting it on these databases.
So, our step was to pull the data that has,
or pull the sequence data that has geographic coordinates
associated with it and merge it all together into one place.
The next steps are to organize it in a way that makes sense
so that we can analyze it.
And one of the first things we have to do
is check species names, right?
A lot of these data are messy,
sometimes there are typos in species names,
sometimes species names change,
so we have to standardize the taxonomic information
to the best of our ability.
The next thing we need to do is group data together
based on like where it comes from or what it is.
So, when we sequence DNA,
we're usually sequencing several hundred base pairs
or nucleotides or several thousand base pairs,
nucleotides from one specific region of a genome.
And so, we need to group all the DNA sequences
that come from the same region of the genome together,
and then group all the DNA sequences
from the same species together,
and we put those in what we call a DNA sequence alignment.
And so, it's sort of like a data frame
that you might have seen before
where on every single row we have an individual species
or an individual specimen,
and then every single column is a different nucleotide
or position in the genome,
and this is the format in which we can analyze genetic data.
There are also many other steps we have to take
to clean the data.
So, like I said, these data can be messy,
sometimes they're duplicates, and record changes,
and just other things that look suspicious.
I'm not gonna walk through the details
because it's a little tedious and not that exciting,
but in this paper that we have describing our database,
we do have two other flow charts
that walkthrough all these steps that we made.
So, even though I'm not gonna go in the details,
it's important to know
that these data cleaning steps are important.
Maybe some of you have heard the term
garbage in, garbage out.
And basically that's saying
if your data is bad, your results are gonna be crap, right?
So, we have to have good data.
So, we take all these steps to clean it,
and organize it, and put it in a format that we can analyze.
And then what we end up it with is our final data set,
and so we have this organized in a way that we know
what data is where and what's in there.
So, what we ended up with was over 2 million DNA sequences
that have GPS coordinates associated with them
for over 87,000 species.
And you might recall that this is actually only a fraction
of the data that we started with, right?
There were hundreds of millions of DNA sequences available,
and I'm gonna bring this up again at the end of the talk.
But regardless of that,
this is still the biggest data set of its kind
that exists right now.
It's so big, you could kind of imagine
if you had an Excel spreadsheet
with over 2.6 million rows
and thousands and thousands of columns, right?
Even Excel couldn't even open a fraction of that data,
it would just crash on you, right?
So, we need to have special tools
to be able to analyze these data.
So, that brings me to the methods
that we're using to analyze these data.
So, genetic data is complicated to analyze,
and I haven't even talked yet
about how we bring in the geographic and environmental data,
but we've been using these predictive modeling techniques
to handle these really large data sets.
So, the first step,
if you were to go Google predictive modeling,
it often comes up as like in an industry
let's make money perspective,
but we can use these same tools in biology.
So, the first thing is to define our problem.
In our case broadly,
we wanna explain biodiversity patterns
and hopefully protect biodiversity.
The next steps are to collect data
and clean the data, which we already talked about.
Then we need to analyze our data
and develop these predictive models.
And these two things kind of go hand in hand
where we're often developing models
that have good predictive power,
exploring the variables that we have
to make these predictions and kind of going back and forth
until we develop a useful model.
And then we make our predictions.
The final step would be to see
if those predictions hold true, right?
Did our model do a good job?
And we're not quite there yet
in the research that I'm doing,
but that is something we could keep in mind
in years down the road
like are these predictive modeling techniques with big data
actually doing the thing that we think that they're doing?
But in short, the way that I like to describe
predictive modeling is that we are predicting the response
for an outcome that we don't know
based on the response of past outcomes that we do know,
and I'm gonna walk you through briefly how this might look.
So, let's say we have a data frame,
and this case, every single row is a different movie,
and this second column here is my score of the movie.
So, I've seen the movie, I have a response to it,
and I scored it from one through 10.
So, you can see that I really like "Star Wars"
and "Wonder Woman",
not as big a fan as some of these Disney movies, right?
But we have all these different occurrences with a response.
Every single one of these movies has a set of variables,
which we're gonna call predictor variables
that describe those movies.
So, who is the director?
What is the genre of the movie?
What's the plot?
What are some other scores from other organizations?
How do they rate those movies?
So, we have all this information that describes
each of the movies that I've rated.
And we can ask a question
like how would I score the movie "Eternals",
which I have haven't seen,
based on this set of predictor variables?
And so, we have all the same
predictor variables for "Eternals",
the only thing we don't have is my score,
a response variable,
but we can predict how I might score it.
This is the kind of thing that Netflix does, right?
Whenever you like movies,
it takes all the information about those movies
and tries to make recommendations for you
for movies that you might wanna see.
So, we've been using this random forest technique,
it's a machine learning approach
that uses decision trees to predict a response.
So, the way this works,
this is a decision tree here is that on every node
in this tree is one of our predictor variables.
So, in this case, is it an action movie?
And everything that is an action movie
will go to one side of the tree
and everything that isn't will go to another side
of the tree.
Then we add in another predictor variable
is the IMDB score greater than five?
All those movies will go here.
If it's less than .05, all those movies will go here.
And then we go through all the predictor variables
that we have until we get to the tips of the tree,
and on the tips of the trees would be my scores.
So, what we would do is take the unknown response,
so in this case, the movie "Eternals",
and we just walk it through the tree, right?
Is it in action movie?
What's the IMDB score?
And go through all those variables,
and wherever it ends up,
whatever tip it ends up in the tree,
that is our prediction for my score.
However, one decision tree by itself
is not always a good predictor,
and that's because depending on the order of the variables
that we put in the tree,
the "Eternals" movie might take a slightly different path.
So, we make a forest of trees
where the input order of the variables is random
and different in every single tree.
So, we put the movie "Eternals",
it follows a different path in all the different trees.
And if it consistently came out
with a score of eight or nine, or eight or nine,
right, our prediction would end up being something like 8.5.
So, this method is great because it's relatively quick,
it's easy to assess model performance
so we can see if our model
is actually making accurate predictions,
and it allows to see which predictor variables
are the most important.
So, for example, if movies that I tend to score high
always have like action and magic,
we can pull that out of the data
and say that that's an important predictor variable.
If my score doesn't generally match up with the IMDB score,
you know, we can say
that that's not an important predictor variable.
So, how can we use these methods to understand biodiversity?
So, one of the most basic areas of research in biology
is documenting biodiversity.
So, the Catalogue of Life is the most comprehensive list
of species that exist on planet Earth,
right now at how just over 2 million species listed.
And I really love their mission statement
so I'm not gonna try to put something in my own words,
but they say, "Without accurate documentation of species,
we cannot sustainably use, explore, monitor,
manage, and protect biodiversity resources."
Right, and so managing and protecting biodiversity
is the main mission of biologists.
And, you know, we could argue whether biodiversity
has innate value in and of itself.
I would argue that it does,
there are studies that show that people
who spend time in nature are happier.
But there are also a lot of monetary benefits
and benefits to human health, right?
We wouldn't be sequencing human genomes,
we wouldn't be creating the drugs that we have
if we didn't find those biomolecules
that we base that stuff off in nature first, right?
So, we wanna document biodiversity and hopefully protect it.
There's this really funny xkcd comic I came across recently,
and the joke here is that chemistry
is a hundred percent complete
with the discovery of the last molecule,
physics is 98% complete, and biology is 93% complete.
And the funniest part about this comic
is that if you're on the xkcd website and you hover over it,
a little window pops up and it says,
"Biology is really struggling,
they're only at 93% and they keep finding more ants."
And this is so true, right?
We're not even close to documenting
all biodiversity that exists.
Right now, depending on what estimates you look at,
we've really only described one to 10%
of all the biodiversity that exists on our planet.
And so, we often think about documenting
or discovering new biodiversity
by going out to these remote locations
that haven't really been explored
and just finding brand new species.
And that is a big part of documenting biodiversity,
but we also have this problem of cryptic species.
So, cryptic species are organisms that are appear identical,
but are actually distinct species.
I'm gonna just give you a little more details
about what this might look like.
So, plethodontid salamanders
are often reproductive isolated,
so they represent different species,
even when no obvious morphological
or ecological differences are present.
And this is true, not just for plethodontid salamanders,
but for basically all organisms that exist on the planet.
So, this group here,
this is seven different species of plethodon salamander
that you can find in the Southern Appalachians,
this represents all their distributions here.
But for many years they were considered one species,
plethodon jordoni.
In some cases, you can see some markings
that might indicate that there are different species,
but I can guarantee that color markings on salamanders
are not always reliable indicators
as to whether there are different species or not.
There's a lot of variation as to whether this orange patch
shows up in this species.
But with the technological advances
that we've had in sequencing DNA,
we've been able to find these cryptic species.
And oftentimes it's not even the goal of a project,
but you might sequence a bunch of DNA from this species,
plethodon jordoni, and then what you end up seeing
are these discreet genetic units within one species,
and it turns out they actually are more than one species.
So, what we wanted to do was develop a predictive model
to see if we could find shared characteristics
of mammalian clades
that are likely to contain cryptic species.
So, the first thing we need to do
is get a bunch of genetic data
and estimate cryptic species, right?
So, we have a named species
and we wanna know are there cryptic species present
in what we think is just one species?
And then see if we can make predictions
about where we might find those.
First, I have a little note here just to remind myself
to tell you that I'm using the term cryptic species,
I might also say hidden diversity,
but I'm meaning the same thing, right?
We're talking about potentially,
you know, cryptic species in a single named species
that we've identified with genetic data.
So, the first steps here
are what we've already talked about, right?
People are going out in field and collecting data,
putting it on these databases, we're mining these data,
aligning it, and putting it in a format that we can analyze.
So, what we've ended up with here
is data for over 4,000 species of mammals,
and this represents about 70% of named mammal species.
We focused on two genes that are commonly sequenced
in phylogeographic studies,
the cytochrome b gene and the cytochrome oxidase 1 gene,
but this allowed us to compare
the highest number of species
'cause there's a lot of data for these two genes.
So, if we look at this plot right here,
this is carnivora what we're seeing here, this dark bar,
is showing that we have cytochrome oxidase 1 sequence data
for almost all species of carnivora.
And then we have cytochrome b data
for about half the species of carnivora.
All the orders of mammals are represented in our data set.
We also see here,
this is just the proportion of sequences
and species diversity in the dataset.
So, our data set consists mostly of rodents,
which isn't really surprising
because there are more species of rodents
than any other mammals followed by bats,
ungulates, the shrews and moles,
carnivores, primates, the bunnies, and the opossums.
And then this shows
just the distribution of the data that we have.
So, you might notice that we have a lot more data in Europe,
North America, and Southeast Australia.
And this is a common pattern in biodiversity data,
these are just areas that have been well researched.
So, we could use more of those sort of expeditions
where we find more species in other parts of the world,
but we do still have pretty good global sampling
in our data set.
So then, the next step in the project
was to estimate the hidden species
or cryptic species that we have.
So, we use methods of molecular species limitation,
and this is basically using genetic data
to identify species level biological diversity.
So, what we're seeing here is the phylogenetic tree
of three species of aliatypus spiders.
On the tips of the tree are individuals,
so all the individuals here
belong to this species thompsoni.
All the individuals here belong to the species starretti,
and all the individuals here belong to our roxxiae species.
So, we can take DNA sequences from all these individuals
across the three species and run them through
our molecular species delimitation analyses.
In this case, we used two different methods.
We often use multiple methods
because every method that we use
has some set of assumptions,
so we like to use multiple methods.
What we see here in this first species,
the species delimitation analyses match
the current species description
so we don't have cryptic species present.
That's also true for this one here, right?
The species description
matches what we see in the genetic data.
Alternatively, in our third species over here,
the genetic data suggests
that there are actually four species
within our currently recognized one species.
So, when we're running our random forest analysis,
we are coding our species
as either having cryptic species or not.
So both of these,
we say no, we don't have any cryptic species,
but in this named species we do.
So, this is gonna be our response variable
in our random forest analysis.
But first, I'm just gonna summarize the data
from the molecular species delimitation analysis
that we did.
So, this is a phylogenetic tree
that represents all the orders of mammals.
And what we're seeing here is the shaded region
is the number of species estimated using the genetic data
relative to the number of named species,
which is the solid figure for all the different orders.
And there's some variation here,
so we have a lot of cryptic species
in the opossums, rodents, bunnies, the hyraxes.
There are not a lot of cryptic species,
or we didn't detect any cryptic species in the sea cows,
very few in the armadillos.
But overall, there are a lot of cryptic species
present in mammals.
So then, the next thing was to collect
our predictor variables,
so we have environmental, geographic, climatic information
that we're gonna use to see if we can predict
the presence of cryptic species.
So, we use GPS coordinates to extract things like elevation,
average temperature, range in temperature.
What is the relative day to night temperature
compared to the seasonal variation,
all kinds of climatic variables.
The nice thing about studying mammals
is that there's this really large database
that houses life history characteristics
for much of mammals,
so things like clutch size, age of first reproduction,
body mass, and several others.
And then we also collected geographic information,
so what is the size of the range?
Is it large, small?
How closes it to the equator, right?
So, these are our predictor variables.
So, in our random forest analysis,
all the things that are in the nodes are all those climatic,
environmental, geographic variables.
And at our tips are all the named species,
and there are either no cryptic species present,
or there are cryptic species present
where we detected more than one species
with the genetic data.
So, our goal here was to see
if we could predict hidden diversity.
So, the first thing that we did was pull out which variables
were the most important in predicting hidden diversity.
So, our model performed well,
we were able to make those predictions.
So, what we have here on our x-axis
is our variable importance.
So, the larger the bars here,
the more important the variables are,
and we have them ordered.
Our top two predictor variables
that were the most important in making these predictions
were body mass and range area.
So, on average, species that had hidden diversity
or cryptic species within them
had overall smaller body size
than those that did not contain cryptic species.
Alternatively, species that had hidden diversity
or cryptic species present
had larger ranges than those that did not.
These variables were not normally distributed
so we conducted a nonparametric test
and the body size and range area
are significantly different for species
who do or do not contain cryptic species.
So, to summarize this,
there are hundreds of prescribed mammal species,
and this wasn't super surprising for us.
There was a study done in 2007 that looked at the literature
and made some conclusions about cryptic species reports.
So, what we're seeing on the x-axis here
is just the number of species present
in all these different metazoa and taxa.
On the y-axis here, we have cryptic species reports.
So basically, the higher number of species present
in any of these taxa
was correlated with the number of cryptic species reports.
So, the more species that exist existed within a group,
the more cryptic species reports.
So, it's a common practice that we see, right?
Cryptic species are prevalent
across all these different taxa.
Mammals do stand out a little bit,
they seem to have a higher number of cryptic species reports
per number of species in the group
compared to some of these other metazoa and taxa,
but this is likely due to the fact that mammals
are a really well studied group.
But in addition into sort of being aware
that there are hundreds of undescribed mammal species,
we now know where to look for them, right?
So, they're most likely to be found in small body taxa
with large ranges.
Within these large ranges,
there also tends to be high variability
in temperature and precipitation.
And both of these things make sense if we think about it
because small taxa are harder to find,
and they're also gonna be harder to find
defining morphological characteristics
to describe species with.
And having large ranges with high variability
leaves more room for multiple ecological niches
and local adaptation to happen.
So, we really see this as a starting point
for where to focus efforts
because going out in the field and collecting all the data
that's necessary to describe
and formally name a species can...
It's a lot of effort, right?
But now we know where to look
and so we can make this process a little more efficient.
We can also use this predictive model for species
in which genetic data don't exist,
so sequencing DNA a is very expensive.
So, we might have a lot of species
that we have our predictor variables for,
we can know a lot about the environmental
and geographic information just by knowing
where the species is.
And then we can predict
whether there might be cryptic species in there
without even having to have the genetic data.
And then we can go collect that secondarily
after we know that that might be a species of interest.
So, the second example I'm gonna share using this approach,
we attempted to estimate the probability
of being at-risk for land plants.
So, the International Union for Conservation of Nature
has a Red List that categorizes species
as being threatened or not threatened.
And that this is the most comprehensive list that exists
for putting species into these categories.
So, species can be labeled as critically endangered,
endangered, vulnerable, near threatened, or least concern.
However, only about 10% of currently recognized species
are Red Listed.
And again, this is a method
that it's just time consuming and expensive.
We need a lot of information
to accurately categorize any species.
So, there's a only about 10% of species listed,
and this is even lower for land plants.
And there has been more of a push
to focus on the conservation of plants.
And this makes sense
because they're often at the base of the food chain,
they provide habitat for a lot of other organisms.
And so, if we're protecting plant species
we're secondary are gonna protect
a lot of other species as well.
So, since not a lot of species are listed,
we wanted to see if we could estimate
the probability of being at-risk
for things that aren't actually already on the Red List.
So, we downloaded GPS coordinates
for all land plants from GBIF,
this is the database that has all the occurrence records.
And this was one of the first studies that we did
using this data science approach,
so we didn't actually use any genetic data for this study,
we just used the GPS coordinates
and the information from the Red List.
So, we used the GPS coordinates to extract environmental
and geographic information
the same way that I talked about previously.
So, for our random forest model,
our response was being threatened or non-threatened, right?
So, species belong to any one of these categories,
so that's a response variable.
And then our predictors are things like range size,
maximum latitude, average temperature,
range in temperature, isothermality, right?
Things that describe the distribution,
the geographic distribution of the species.
This map here represents our geographic sampling,
so the numbers are just the number of GPS coordinates
that we have in any particular location.
And we ended up with over 12,000 species
that we had the right kind of data for
that are on the Red List.
So, they are categorized as being threatened
or not threatened at some level,
and we used those to build the predictive model.
And then we had data for over 150,000 species
that aren't listed, but we could estimate their probability
of being listed as endangered.
So, we broke up the data by continent and on each continent
we had tens of thousands of species
that we were making predictions for
that didn't exist on the Red List.
We also had had a global data set
where we included species that were found
on more than one continent.
And what we ended up seeing was on every continent,
there were over a thousand plant species
that had a high probability of being threatened.
So, had a probability of belonging
to either the critically endangered, endangered,
or vulnerable group with a probability greater than .8.
This number is even higher if we lower that threshold.
And so, this can be really useful for people
who are sort of on the front lines of conservation efforts.
You know, people who work for the National Forest Service
or USGS, and I'm sure that they will tell you
that they are very time and money limited,
and so this can help focus efforts in a more efficient way.
So now, we have a database of over 150,000 species
with a probability of being listed
in any one of these categories.
So, trillium species are native to the United States,
they are, a lot of them are listed as endangered.
And they're a very sensitive species,
so in some parts of the country,
it's illegal to pick these
because even if you just take a little piece off of a plant,
it will often die, so it's a very sensitive species.
However, you can see by this list,
a lot of trillium species are not on the IUCN Red List.
So, rather than having to go through
and assess every single one of these species,
which can be a difficult task,
you could scan through and say, "Okay,
this one species in particular has a pretty high probability
of being critically endangered
so we should focus our efforts
on that one particular species."
In addition to that, we average the probability
of being at risk and plotted these on a global map.
So, this gives us hotspots
of places in need of conservation, right?
So, these areas that have the red and orange colors
are areas that have a lot of species
with a high probability of being at risk.
And this can help us focus not just on species,
but geographic areas that are in high need, right?
So, the coast of North America, Southeast United States,
this western coast of Africa, and this Indo-Pacific region.
Right, so if we're sort of trying to figure out
where to allocate funds for protecting biodiversity,
we might say something like, "Well,
Everglades National Park is definitely an area
that need that needs some protection
since we have a lot
of potentially endangered species there."
So, this work did get a little bit of a press attention.
And I bring that up because one,
I wanna convince you that people care about biodiversity,
but also the subtitle here,
when researchers used machine learning
to evaluate 150,000 plant species,
they found that 10% were likely to qualify
for the IUCN Red List."
And I think that subtitles like this
make things sound really fancy, right?
Machine learning, and it's hard to understand
what that means, but I hope that I broke it down enough
for you to show you that these methods aren't as fancy
as they sound, right?
Computational biology is really just the act
of taking big problems and breaking them into small pieces
so that we can answer big questions.
And, you know, if someone told me 10 years ago
or when I was at Salem State
that I would be spending the majority of my time
coding in front of a computer to understand about diversity,
I would've told them that they were absolutely insane
and didn't know anything about me.
And I bring that up because I think it's important
for the undergraduates in the audience
to sort of be open to things
and know that it's okay to change your mind
about what you wanna do
and what you end up doing might not look like
what you think it's gonna look like,
and that's totally fine.
So, this is where my path took me, right?
Using data science approaches to understand biodiversity.
And the big goal is to understand
the processes of evolution,
why about diversity patterns look the way they do,
and then also to produce data
that can be useful in targeting and documenting species
in order to protect biodiversity.
The second goal is to increase the back and forth
of people who are in the field collecting the data,
putting the data on databases,
aggregating the data, and then using the data.
Right, so I mentioned that the data that we're using
is a very, very small proportion of the data that exists.
And so, increasing data standards
for putting data on databases
could really help us improve these databases
and have a lot more data that we can use.
And then the last goal,
and this is the one that I'm kind of the most excited about
right now is to increase the accessibility
of these large databases for researchers and instructors
without the resources or skills to do so.
So, our database is gonna be free and open to the public
in the next couple of months.
And with that, we have tutorials or teaching modules
that you can use that use the data,
and all of these have been used in the classroom.
Right now, they're based on R, so if you have,
you might need a little bit of R experience
to use these as a researcher or a teacher,
but we are also developing Shiny R apps
where you can use these data in the classroom
without any coding experience
and you can just click on buttons to use those data.
And they make really good research projects too,
one of my undergraduate research students
used this genetic diversity R tutorial
to conduct a research project.
So, I'm gonna wrap up by showing you this,
this map of biodiversity hotspots in the United States.
I'm very lucky to be located
here in the southwestern part of Virginia.
You guys are in Massachusetts here
so your biodiversity isn't quite as high,
but it's not all gray.
So, I went on our phylogatR database
and just to see what we had there.
And there's DNA sequenced data
with GPS coordinates for over 1,100 species.
This is the distribution of the data,
so the number of species,
and all these different phyla that exists on the database.
And so, if anything that I said
sounds sort of interesting to you
and you wanted to use it in a classroom,
or you were an undergraduate researcher
and wanted to understand biodiversity,
these data will be available.
And now you have a contact
for someone who has directly put this database together
and you can always reach out to us.
So with that, I'm just gonna acknowledge
the people who contributed to the database
and the two projects I discussed with you today.
All this work was funded by NSF,
the Ohio Supercomputer houses the database.
I put both my parents on here who might be in the audience.
My dad helped me a lot in the field
when I was working on my dissertation.
And my mom helped me when I first started to learn to code,
she works in the IT department at Salem State.
And with that, I will take any questions.
- Dr. Pelletier, Tara, I can't...
I am so excited and that was such a wonderful talk,
and thank you, thank you, thank you.
And I wanted to sort of go
into the what kind of connections you have
because you mentioned that this how wonderful
this would be for groups
such as the Forest Service and so forth,
have they actually shown an interest in this?
And have you been able to make some connections?
And where is that kind of avenue headed?
- Yeah, so this is such an important question.
I actually have a really long answer,
so if I go too off the rails, you can stop me.
But so, I actually did have the Northwest Forest Service
reach out to me recently to ask for some of my data layers,
which I found really encouraging
because I find that phylogeography
can be a very sort of theoretical field,
and we get really caught up
in just how do we answer these questions?
And don't always think about how to apply
the answers to those questions.
And I see it in these
molecular species delimitation analyses all the time
where people will say like, "Oh,
there's cryptic species present here,"
but they don't always take it to the next step
to formally describe those species.
And a lot of times we just don't have the skills,
or time, or whatever to do it.
So, I am hoping that some of this work
will bridge those gaps a little bit more
and make the results that we're seeing
more applicable to like those people
who are describing species
and people who are doing conservation work.
So, I think so, like I said,
the Northwest Forest Service did reach out to me.
So far, that's all I've gotten interest from,
but the Hidden Diversity Project
is actually in revision right now,
we're just kind of wrapping it up.
So, it hasn't been published yet,
but it probably will be in the next couple months.
And the database will be available
in the next couple months too.
- I would, so as somebody who's older
and some of the people in these groups might be older,
the fact that you're developing in a button
kind of format and that kind of thing,
yes, it's great for teachers,
but it's also great for people who are not coders,
and so that could help. - Yeah, you know,
it depends too like what are your learning objectives
for a course, right?
Like I like to teach coding and R
because I think it teaches some like organizational skills,
and paying attention to detail, and things like that.
But sometimes you're more concerned about the content,
right, and you don't wanna get...
And it will take a long time getting everyone up to speed
on getting R working on your computer, right?
That's a whole process,
and so sometimes those clicky buttons can be really helpful.
- Dr. Popolizio.
- Thank you so much, Tara.
That was great.
I have if I could share a comment from our Q&A
from a colleague, Dr. David Tapley.
It's not a question, but he would like to share
that he thinks it's one of the best Darwin talks ever,
and so well done. (laughs) (Tara laughing)
- Thank you.
- And then I have a question.
I actually, I'm a biodiversity scientist myself,
but on a much smaller scale,
I study specific group of organisms, red algae mostly,
seaweeds in general,
but mostly tropical red algae
and use a lot of phylogenetics in my work.
And so, I was really curious
about the species delimitation analysis
that you mentioned and wondering how those particular models
that you choose help you decide
whether there's cryptic diversity or not,
if there's a specific kind of cutoff for genetic variation.
And since you're using,
since you're looking at so many different,
you know, categories of organisms and taxa,
and that molecular evolution differs
so greatly between and among those taxa,
how those models kind of account for that.
- Yeah, so the approach that we took
specifically for that project,
we used two different methods of species delimitation
that are both designed for single-locus data.
The ABGD is the Automatic Barcode Gap Detection,
and that just uses, that's a distance-based method,
and so it just looks for genetic variation
within and between species
and kind of finds that biggest gap
and calls those two different species.
We also use the GMYC, the General Mixed Yule Coalescent,
which is a tree-based method.
And so, you have to estimate a phylogenetic tree first,
and it looks for changes in the branching patterns
from within population to between species patterns.
So, that sort of addresses your question
about having different rates of molecular evolution,
we're kind of using like these different methods.
And the GMYC tends to overestimate species
and the ABGD tends to underestimate.
And so, when we did this analysis, we only considered,
we only called things...
Actually, forgot to mention this,
I was gonna mention it on the one slide.
We only considered things cryptic
when the two methods matched.
And so, in a lot of cases in the one figure I showed
with like the shadow
and the silhouettes of the different organisms,
there were a couple that had lines through it.
And those were in those groups,
we didn't have consistent results
and we didn't include them in the random forest.
But I have seen in a lot of species delimitation
where you do get mixed results
because all the different methods
have different assumptions.
And like you said, different taxa
have different rates of evolution.
So, we really kind of feel like this is a first pass, right?
There's probably cryptic species
within that recognized species,
but now it's time to go back and reexamine that species.
Which is why we use the word hidden diversity sometimes
because calling them cryptic species
makes certain groups of people mad.
(Thea laughing)
Thank you. - Mm-hmm.
- Dr. Pelletier, if I could ask a question
from the virtual room we're sitting in
on this side of the webinar.
You showed us the different orders of mammalia
and the proportion that you guys consider hidden species.
And one that struck,
the one that had the greatest difference were the hyraxes,
the rock rabbits.
In the next slide, you gave some general ideas
why there might be the difference
between described versus cryptic species.
And I know you're a salamander person,
but any thoughts as to why the rock hyraxes
are so potentially different
in terms of their hidden versus current described diversity?
- You know, I was so afraid
someone was gonna ask me this question. (laughs)
I don't know.
I don't know enough about hyraxes to answer.
There are some emologists that were a part of this project,
and I feel like I need to go ask them
specifically about that group
because I had the same thought
when I was looking through those slides.
And I was like, "Shoot,
I don't really know a lot about this group."
That one group didn't have a high number of species
in it to start with.
That's all I can say about it.
- The reason I ask is I grew up in Southern Africa
and we would see them daily.
Funny little animals. - Yeah.
You know, it might just be a lack of effort,
not because people aren't trying, right?
But maybe there's just fewer funds,
and resources, and that kind of stuff
to go out in the field there.
- Great, thanks very much.
- We have another question from the audience
from Dr. Buttner, who was mentioned earlier.
He says, "Hello and welcome back.
Is the Mississippi River being examined
as a conduit for species diversification?
Lots of subtropical species make northern incursions."
- Yeah, I actually have had some colleagues
spend quite a bit of time.
There are consistent phylogeographic breaks
across the Mississippi River.
That's, I mean, I don't think
I can get into much more details than that.
I think, did I answer the question?
But I would say yes, it is a common break
that would likely lead to speciation.
- I think actually another,
I'm coming at this from my brother
used to live in a Illinois, Southern Illinois.
And there were lots of species of plants
that were in Southern Illinois
that shouldn't be there 'cause it's too cold,
but that's right along the Mississippi River.
- Hmm. - And so, could there be...
Are people looking at how that particular
large body of water could be a conduit-
- Oh. - for allowing species
to migrate in different places?
- Yeah, that's a good question.
I think we should explore that.
- Okay, cool, cool. (laughs) (Tara laughing)
- Do we have time for another question?
- Sure. - Just (indistinct).
Is it possible to give a sense of among your data
that you're working with kind of comparing
how much information you have for terrestrial
versus marine ecosystems,
and then for microbial versus macroscopic life?
- Yeah, so it is, it's dominated by animals.
There are a few proteus in there,
we don't have any data
for like microorganisms
like bacteria and that kind of stuff.
We've sort of, I mean, I don't even know how to,
I can barely describe what a species is in a salamander.
You know, so doing that in microbes
kind of blows my mind a little bit.
So, it's dominated by animals.
There are also a lot of plants,
like I said, there are a few proteus,
but that's really all we have now.
And it is mostly arthropods,
which I guess probably isn't that surprising.
- I think with that, we'll have to call it an end.
Thank you so much again, Dr. Pelletier.