G r e g M c I n e r n y@ G r e g M c I

w w w . 2 0 2 0 s c i e n c e . n e t / p e o p l e / g r e g - m c i n e r n y

g m c i n e r n y @ h o t m a i l . c o m

I n t e r p r e ti n g a n d v i s u a l i s i n g o u t p u t s

2 0 2 0 S C I E N C E

1 . V i s u a l i s a ti o nDO WE SPEND TOO MUCH TIME EXHIBITING OUR WORK?

E x h i b i t “ W o w, X & Y l o o k s a m a z i n g ,I n e e d t o fi n d o u t m o r e ! ”

D A T A E N C O D I N G D E C O D I N G

E x p l o r e “ I w o n d e r h o w x r e l a t e s t o y ”

E x p l a i n “ X d o e s y ”

X1, Y1, x2, y2 …

G o a l s i n d a t a v i s u a l i s a ti o n

E x h i b i t “ W o w, X & Y l o o k s a m a z i n g ,I n e e d t o fi n d o u t m o r e ! ”

D A T A E N C O D I N G D E C O D I N G

E x p l o r e “ I w o n d e r h o w x r e l a t e s t o y ”

E x p l a i n “ X d o e s y ”

X1, Y1, x2, y2 …

G o a l s i n d a t a v i s u a l i s a ti o n

E l i t h , J . & L e a t h w i c k , J . R . ( 2 0 0 9 ) A n n u a l R e v i e w o f E c o l o g y , E v o l u ti o n a n d S y s t e m a ti c s , 4 0 , 6 7 7 – 6 9 7 .

( 1 ) R e c o d e

( 2 ) H o p e

T h u i l l e r , W . e t a l . ( 2 0 0 5 ) G E B . 1 4 , 3 4 7 – 3 5 7 .

“ w e o b s e r v e d t h a t 8 3 % o f a r ti c l e s s t u d i e s f o c u s e d e x c l u s i v e l y o n m o d e lo u t p u t ( i . e . m a p s ) w i t h o u t p r o v i d i n g r e a d e r s w i t h a n y m e a n s t o c r i ti c a l l y e x a m i n e m o d e l l e d r e l a ti o n s h i p s ”

Y a c k u l i c , C . B . e t a l . ( 2 0 1 2 ) M E E . 3 , 5 4 5 - 5 5 4

( 3 ) S u m m a r i s e

5 , 0 4 1 p i x e l s o fi n f o r m a ti o n

“ t h e r e s u l t s r e v e a l a n i n t r i g u i n g p a tt e r n ”

A r a u j o , M . B . & N e w , M . 2 0 0 7 . T R E E . 2 2 , 4 2 – 4 7 .

I n d i v i d u a l m o d e l s

A v e r a g e m o d e l

H o f , C . e t a l . 2 0 1 1 . N a t u r e 4 8 0 , 5 1 6 – 5 1 9

h tt p : / / w w w . f s . f e d . u s / n e / n e w t o w n _ s q u a r e /p u b l i c a ti o n s / o t h e r _ p u b l i s h e r s / O C R /n e _ 2 0 0 1 _ i v e r s o n 0 0 1 . p d f

? !

( 4 ) C r a m i t i n

E x h i b i t “ W o w, X & Y l o o k s a m a z i n g ,I n e e d t o fi n d o u t m o r e ! ”

D A T A E N C O D I N G D E C O D I N G

E x p l o r e “ I w o n d e r h o w x r e l a t e s t o y ”

E x p l a i n “ X d o e s y ”

X1, Y1, x2, y2 …

E x p l a i n ( 2 ) “ … b e c a u s e o f A & B ,X d o e s y ”?

L e t s t r y ‘ m o d e l v i s u a l i s a ti o n ’ …

h tt p : / / x k c d . c o m / 1 1 3 8 /

2 . I n t e r p r e t a ti o nDO WE RECOGNISEWHY WE DISAGREE?

W h a t a r e t h e s e ?

G e o g r a p h i c d i s t r i b u ti o n

P o t e n ti a l d i s t r i b u ti o n

A b i o ti c e n v . r e s p o n s e

H a b i t a t s u i t a b i l i t y

E n v . / E c o . n i c h e

F u n d . / R e a l n i c h e

C l i m a t e a ffi n i t y

B i o - c l i m a t e e n v e l o p e

M u l ti v a r i a t e e n v . s p a c e

F u n c ti o n a l r e s p o n s e

S p e c i e s ’ E n v . r e s p o n s e

E n v . C o r r e l a t e s

I n t e r p o l a t e dP a tt e r n

S o b e r o n

H u n t l e y

A u s ti nE l i t h

K e a r n e y

F r a n k l i n

T h u i l l e rA r a u j oT h o m a sO ’ H a r a

N o g u e s - B r a v o

P e t e r s o n T h e o r y S t a ti s ti c a l m e t h o d

V a r i a b l e

R e s p o n s e f u n c ti o n

M o d e l t u n i n g

M o d e l s e l e c ti o n

A p p l i c a ti o n

D a t aT e r m i n o l o g y

A u d i e n c e

W h o i s r i g h t ?

Reason(abstract)

idea/concept

Describe(concrete)

model

output

assumption

definition

code/formula

graph

numbers

words

words/algorithm/

formula

data

Encode(concrete)

Understand(abstract)

idea/concept

goals

Deducti ve Reasoning(agreements are c lear)

Reason(abstract)

Describe(concrete)

Encode(concrete)

Understand(abstract)

model

output

code/formula

graph

numbers

data

goals

MAXENT, R, BIOMOD, OPENMODELLER, MODECO, GARP, BIOMAPPER, CANOCO, WINBUGS, OPENBUGS, DOMAIN, SPECIES, HYPERNICHE, HYKL, DISMO… ANN, AQUAMAPS, BIOCLIM, BRT, CSM, CTA, ENFA, ENVELOPE SCORE, ENV DISTANCE, BUGS, GA, GAM, GBM, GLM, GLS, MAHALANOBIS DISTANCE, MARS, MAXENT, MODECO, RANDOM FORESTS, SRE, SVM ...

goals

Reason(abstract)

idea/concept

Describe(concrete)

model

output

assumption

definition

code/formula

graph

numbers

words

words/algorithm/

formula

data

Encode(concrete)

Understand(abstract)

idea/concept

Inducti ve Model l ing(understand the pitf al ls)

1 . V i s u a l i s a ti o nDO WE SPEND TOO MUCH TIME EXHIBITING OUR WORK?

2 . I n t e r p r e t a ti o nDO WE RECOGNISE WHY WE DISAGREE?