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Greg Interpreting and visualising outputs 2020 SCIENCE.

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Presentation on theme: "Greg Interpreting and visualising outputs 2020 SCIENCE."— Presentation transcript:

1 Greg Interpreting and visualising outputs 2020 SCIENCE

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

3 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

4 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

5 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,

6 (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

7 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’…

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9 2. Interpretation DO WE RECOGNISE WHY WE DISAGREE?

10 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?

11 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)

12 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...

13 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)

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


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