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Greg McInerny @ GregMcI 2020science/people/greg-mcinerny gmcinerny@hotmail

Interpreting and visualising outputs. Greg McInerny @ GregMcI www.2020science.net/people/greg-mcinerny gmcinerny@hotmail.com. 2020 Scienc e. 1. Visualisation. do we spend too much time exhibiting our work?. Goals in data visualisation. X1, Y1, x2, y2 …. data. Encoding. Decoding.

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Greg McInerny @ GregMcI 2020science/people/greg-mcinerny gmcinerny@hotmail

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  1. Interpreting and visualising outputs Greg McInerny @GregMcI www.2020science.net/people/greg-mcinerny gmcinerny@hotmail.com 2020 Science

  2. 1. Visualisation do we spend too much time exhibiting our work?

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

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

  5. (1) Recode Elith, J. & Leathwick, J.R. (2009) Annual Review of Ecology, Evolution and Systematics, 40, 677– 697. (2) Hope “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” McInerny, G J, et al. (in review). TREE. Yackulic, C. B. et al. (2012) MEE. 3, 545-554 Thuiller, W. et al. (2005) GEB. 14, 347–357. Hof, C. et al. 2011. Nature 480, 516–519

  6. (3) Summarise Individual models Average model Araujo, M.B. & New, M.2007. TREE. 22, 42–47. (4) Cram it in McInerny, G J, et al. (in review). TREE. ?! 5,041 pixels of information Average Model Hof, C. et al. 2011. Nature 480, 516–519 “the results reveal an intriguing pattern” http://www.fs.fed.us/ne/newtown_square/publications/other_publishers/OCR/ne_2001_iverson001.pdf Response Variable Hof, C. et al. 2011. Nature 480, 516–519

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

  8. http://xkcd.com/1138/

  9. 2. Interpretation do we recognise why we disagree?

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

  11. Reason (abstract) Describe (concrete) Encode (concrete) Understand (abstract) idea/ concept definition words words/ algorithm/ formula assumption code/ formula model goals idea/ concept data Deductive Reasoning (agreements are clear) output graph numbers

  12. Reason (abstract) Describe (concrete) Encode (concrete) Understand (abstract) 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 ... code/ formula goals model data output graph numbers

  13. Reason (abstract) Describe (concrete) Encode (concrete) Understand (abstract) idea/ concept definition words words/ algorithm/ formula assumption code/ formula model goals idea/ concept data Inductive Modelling (understand the pitfalls) output graph numbers

  14. 1. Visualisation do we spend too much time exhibiting our work? 2. Interpretation do we recognise why we disagree?

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