Hello.
My
name
is
Caleb
King.
I'm
a
senior
developer
in
the
design
of
Experiments
and
Reliability
group
here
at
JMP
statistical
Discovery.
T
oday
I
have
the
privilege
of
telling
you
about
a
very
interesting
project
that
I
was
able
to
be
a
part
of
concerning
classification
of
species
and
sex
within
a
small
mammal
group
called
Fishers
using
the
Footprint
Identification
Technique.
F
ishers,
I'll
give
you
a
quick
image
here,
so
here's
an
example
of
a
fisher.
To
me
it
looks
like
a
bit
of
a
weasel
or
ferret-
type
animal.
I
know
that's
definitely
not
the
same
species,
but
they're
a
small
mammal,
and
we're
particularly
interested
in
fishers
located
in
the
Sierra
Nevada,
as
those
are
a
federally
endangered
species.
Specifically,
we'd
like
to
be
able
to
identify
the
presence
of
females,
as
the
larger
number
of
females
indicates
a
very
healthy
population.
They're
also
vital
to
helping
develop
effective
conservation
strategies.
Now,
the
way
we
intend
to
do
that
is
use
what's
called
the
Footprint
Identification
Technique
or
FIT.
This
has
been
made
popular
through
wild
track,
is
a
non-i
nvasive
method
for
identifying
individuals
based
on
images
of
their
tracks.
This
is
especially
helpful
since
you
may
not
be
able
to
actually
see
a
fisher
in
the
wild
or
capture
them,
but
their
tracks
are
everywhere
so
that
should
be
helpful
to
identify
them.
U
sing
JMP,
we
were
able
to
create
a
technique
to
distinguish
fishers
from
a
nearby
species
known
as
Pacific
martens,
as
well
as
distinguish
sexes
within
species.
T
he
way
this
works
is
we
started
with
a
data
set
of
around
160
something
martens
and
well
over
300
fishers
consisting
of
about
34
males
and
27
females.
What
they
would
then
do
is
then,
as
you
can
see
here
on
the
track
image,
they
would
identify
seven
landmark
points,
is
what
we
call
them,
and
then
from
those,
we
could
then
compute
well
over
120
something
features
consisting
of
lengths,
distances,
angles,
and
areas.
What
we
would
then
do,
is
then
using
those
features,
we
would
then
feed
that
into
a
linear
discrimination
analysis,
which
we
could
then
use
to
discriminate
among
species
and
then
sex
ID
within
species.
To
help
assess
that
fit,
we
split
the
data
into
50%
training,
and
for
the
remaining
50%,
we
evenly
split
roughly
between
validation
and
testing.
Prior
to
the
modeling,
we
also
tried
to
look
at
the
effect
of
track
orientation,
so
we
would
flip
the
left
tracks
horizontally
to
match
the
right,
and
then
also
any
potential
bias
from
the
observers.
These
are
people
identifying
landmark
points,
so
we
wanted
to
check
and
make
sure
that
any
variation
there
did
not
affect
our
outcomes.
T
hankfully,
both
the
orientation
and
the
observer
bias
did
not
have
a
significant
effect
on
our
outcomes.
W
hat
brought
myself
and
my
colleague
Ryan
into
the
project
was they had...
I
noticed
that
some
of
the
tracks
as
they
were
classified,
seemed
a
little
to
have
a
little
bit
too
much
spread
in
them
to
the
point
that
maybe
there
was
actually
multiple
individuals.
T
he
way
they
would
collect
this
data
is
there
would
be
a
little
cage
area
out
in
the
woods.
Fishers
could
easily
go
in
and
out,
and
there
was
a
track
plate
in
the
bottom
that
would
capture
their
footprints,
and
there
was
also
little
spurs
that
would
capture
a
bit
of
their
hair.
It
didn't
hurt
the
animal.
They
had
no
idea
what
was
going
on.
W
hat
they
would
then
do
is
take
some
samples
of
those
hairs
and
send
them
out
for
genetic
testing,
which
was
a
bit
of
a
long
and
expensive
process.
Now,
because
of
the
way
things
were
sampled,
you
might
have
a
sampled
hair
that
would
identify
the
animals
as
potentially,
say,
male,
but
what
could
have
happened
was
a
male
and
a
female
might
have
gone
in,
and
you
only
cut
hair
from
one
of
them,
so
the
tracks
might
indicate
potentially
multiple
individuals,
whereas
the
genetics
said
there
was
only
one.
W
hat
they
wanted
was
a
method
to
be
able
to,
a
more
data-
driven
method,
if
you
will,
to
identify
potentially
misclassified
multiple
individuals
that
we
could
then
exclude
from
our
analysis
so
that
it
wouldn't
bias
the
results.
B
efore
we
actually
got
into
that
procedure,
one
of
the
things
that
we
would
do
is
use
JMP's
Predictor
screen
tool
to
identify,
for
each
response
of
interest,
what
were
some
of
the
top
predictors?
Notice
for
species
and
sex
ID
here?
There's
actually
a
lot
of
common
features
that
I'll be
able
to
distinguish
between
the
two
or
at
least
have
a
strong
ability
to
help
distinguish
between
the
two.
Much
more
so
with
the
species
than
the
sex.
We've
shown
you
what
these
variables
look
like
over
here,
so
area
one
is
the
complete
shaded
region.
We've
got
some
distances,
V
16,
V
15.
Y
ou'll
notice
a
lot
of
them
have
to
do
essentially
with
the
size
of
the
track.
We've
got
some
big
distances
in
there.
I'll
get
back
to
these
in
a
second,
but
using
those
top
features
,
let
me
get
back
to
a
full
screen
of
that.
Us
ing
some
of
those
top
features,
we
would
then
make
a
plot
that
looks
like
this.
T
his
is
just
plotting
it
by
the
individuals.
All
the
red
ones
here
are
females.
All
of
these
are
males,
so
already
visually,
you
can
tell
why
these
are
some
of
the
top
predictors.
Just
visually,
you
can
see
those
groupings,
clear
groupings
between
the
sex
ID.
What
we've
identified
with
these
arrows
is
you'll
notice
a
big
spread.
You've
got
a
cluster
here
and
here.
Got
a
little
bit
here
and
there,
especially
here
and
there.
This
is
what
they
were
interested
in,
especially
with
the
males,
because
what
this
could
be
is
we
could
have...
It
could
be
the
same
male,
just
a
lot
of
spread,
that's
a
bit
unlikely.
We
could
have
a
male
and
a
young
male,
or
we
could
have
a
male,
and
that's
actually
more
of
a
female,
but
we
don't
really
know.
T
hey
wanted
a
more
data-
driven
method
to
say,
is
this
something
we
should
be
concerned
about?
Is
that
spread
too
much?
W
hat
we
did
is,
we
used
a
control
chart,
which
is
from
industrial
statistics.
We
thought
that
was
actually
ideally
suited
because
control
charts
are
built
for
identifying
parts
that
are
out
of
spec,
and
so
what
we
did
is
created
a
control
chart
for,
here's
females
and
males,
and
notice
they
each
have
their
own
limits,
this
is
because
there
are
potentially
multiple
tracks
for
each
individual,
so
we
could
get
a
sense
of
their
spread
on
an
individual
by
individual
basis.
Y
ou'll
see,
we
flagged
some
individuals
that
might
have
too
much
spread.
This
is
an
S-
chart
that
stands
for
sigma.
W
e're
looking
at
the
spread,
if
you
will.
W
e've
got
a
couple
of
individuals
that
maybe
there's
a
bit
too
much
spread
in
there,
so
that
could
potentially
mean
that
there
might
actually
be
multiple
individuals.
O
n
that
basis,
we
then
excluded
those
individuals
from
the
final
analysis,
and
speaking
of
the
final
analysis,
once
we
ran
everything
through
the
linear
discriminant
analysis,
what
we
found
was,
for
distinguishing
between
species,
we
only
needed
one
feature,
that
is
this
V
16
right
here.
I
call
it
the
distance
between
the
thumb
and
maybe
the
middle
finger
or
something.
Those
are
not
formal
biological
terms.
Please
don't
quote
me
on
that.
But
just
visually,
that's
what
I
see,
so
that's
a
big
distance
measure.
Using
just
that,
we
were
able
to
successfully
distinguish
between
species
with
99%
classification,
successful
classification
rate,
we
missed
only
four
out
of
500
tracks,
so
that
is
an
incredible
result.
For
the
sex
ID
within
fishers.
Using
just
these
two
features,
v
15
and
V6
,
which
is
a
distance
between
what
I
call
the
thumb
and
the
upper
palm.
Again,
not
formal,
biological
terms.
By
using
those
two,
we
got
a
successful
classification
rate
of
around
90%,
and
most
of
the
individuals
that
we
misclassified,
were
actually
males
misclassified
as
females.
In
our
interpretation,
what
that
might
mean
is
they
could
have
been
actual
females,
or
maybe
they
could
have
also
been
young
males.
In
either
case,
both
are
strong
indicators
of
family
units
and
thus
potentially
healthy
growing
populations.
T
hat
was
our
contribution
to
this
project.
We
hope
it
goes
on
to
provide
a
significant
impact
in
conservation
of
the
species.
If
you
have
any
other
questions,
I'll
be
around
and
meet
the
experts
and
also
the
poster
presentation
session.
I'd
be
happy
to
answer
them
there.
Enjoy
the
rest
of
the
summit.
