Greg Interpreting and visualising outputs 2020 SCIENCE.

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Presentation transcript:

Greg Interpreting and visualising outputs 2020 SCIENCE

1. Visualisation DO WE SPEND TOO MUCH TIME EXHIBITING OUR WORK?

Exhibit“Wow, X & Y looks amazing, I need to find out more!” DATAENCODINGDECODING Explore“I wonder how x relates to y” Explain“X does y” X1, Y1, x2, y2 … Goals in data visualisation

Exhibit“Wow, X & Y looks amazing, I need to find out more!” DATAENCODINGDECODING Explore“I wonder how x relates to y” Explain“X does y” X1, Y1, x2, y2 … Goals in data visualisation

Hof, C. et al Nature 480, 516–519 McInerny, G J, et al. (in review). TREE. Elith, J. & Leathwick, J.R. (2009) Annual Review of Ecology, Evolution and Systematics, 40, 677– 697. (1) Recode (2) Hope Thuiller, W. et al. (2005) GEB. 14, 347–357. “we observed that 83% of articles studies focused exclusively on model output (i.e. maps) without providing readers with any means to critically examine modelled relationships” Yackulic, C. B. et al. (2012) MEE. 3,

(3) Summarise Hof, C. et al Nature 480, 516–519 5,041 pixels of information “the results reveal an intriguing pattern” McInerny, G J, et al. (in review). TREE. Average Model Variable Response Araujo, M.B. & New, M TREE. 22, 42–47. Individual models Average model Hof, C. et al Nature 480, 516–519 ns/other_publishers/OCR/ne_2001_iverson001.pdf ?! (4) Cram it in

Exhibit“Wow, X & Y looks amazing, I need to find out more!” DATAENCODINGDECODING Explore“I wonder how x relates to y” Explain“X does y” X1, Y1, x2, y2 … Explain (2)“… because of A & B, X does y” ? Lets try ‘model visualisation’…

2. Interpretation DO WE RECOGNISE WHY WE DISAGREE?

What are these? Geographic distribution Potential distribution Abiotic env. response Habitat suitability Env. / Eco. niche Fund. / Real niche Climate affinity Bio-climate envelope Multivariate env. space Functional response Species’ Env. response Env. Correlates Interpolated Pattern Soberon Huntley Austin Elith Kearney Franklin Thuiller Araujo Thomas O’Hara Nogues-Bravo Peterson Theory Statistical method Variable Response function Model tuning Model selection Application DataTerminology Audience Who is right?

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 Deductive Reasoning (agreements are clear)

Reason (abstract) Describe (concrete) Encode (concrete) Understand (abstract) model output code/ formula graph numbers data goals M AX E NT, R, B IO M OD, O PEN M ODELLER, M OD E CO, GARP, B IO M APPER, C ANOCO, W INBUGS, O PEN B UGS, D OMAIN, S PECIES, H YPER N ICHE, HYKL, D ISMO … ANN, A QUA M APS, B IO C LIM, BRT, CSM, CTA, ENFA, E NVELOPE S CORE, E NV D ISTANCE, BUGS, GA, GAM, GBM, GLM, GLS, M AHALANOBIS D ISTANCE, MARS, M AX E NT, M OD E CO, R ANDOM F ORESTS, 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 Inductive Modelling (understand the pitfalls)

1. Visualisation DO WE SPEND TOO MUCH TIME EXHIBITING OUR WORK? 2. Interpretation DO WE RECOGNISE WHY WE DISAGREE?