Choose Language Hide Translation Bar
abmayfield
Level VI

Enabling “Coral Reef Triage” Via Machine Learning in JMP® Pro (2022-US-45MP-1110)

Anderson Mayfield, Dr. (assistant scientist), Coral Reef Diagnostics

 

You don't need an elegant predictive model to tell you what's going on with coral reefs these days; as seawater temperatures continue to rise as a result of climate change, thermo-sensitive corals continue to decline in abundance across the globe. Where data science comes in handy, however, is in what I refer to as "coral reef triage;" by that I mean, in the likely event that we can't save everything, which corals and coral reefs do we prioritize for preservation? Where are the refugia characterized by the unusually hardy corals that may have a chance of weathering the storm? Historically, we answered these questions by randomly stumbling upon corals and coral reefs that, for whatever reason (e.g., environmental factors or unique adaptations of the corals themselves), were unusually robust. Given the incredibly small percentage of coral reefs characterized to date (<0.0001%), however, simply hoping to come upon climate change-resistant corals by chance alone is neither a time- nor cost-effective conservation strategy.

 

In this presentation, I use JMP Pro to demonstrate how we can instead leverage data from laboratory experiments and coral reef surveys to predict where we will find the most stress-prone corals, as well as those that display a marked capacity for resilience. By triaging coral reefs across a spectrum of climate resilience, we can not only make more informed management decisions, but we can actually use machine learning and other predictive modeling tools to dictate the optimal mitigation and/or bioremediation ("coral rescue") approach(es) for particular reefs.

 

 

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

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

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.

Comments
abmayfield

Dominica.jpg

Georg

Thanks for this excellent and inspiring talk!

wendytseng

Is the Model Tuning add-in available for public use?  I could not locate it in the Community.

Article Tags