1. This Must Be the Place: The Abundance and Distribution of Microbes using Maximum Entropy Will Shoemaker

2. Outline Microbial Abundance & Distribution Entropy MaxEnt Theory – General principle – Maximum Entropy Theory of Ecology Previous Usage for Macrobes Preliminary Trials on Microbes Future Directions

3. Microbial Abundance & Distribution ~ 10 30 bacteria & archaea (Whitman et al., 1998) Microbes play crucial roles in ecological functioning and human health Ability to infer community composition increases Still little known about patterns of abundance relative to macrobes

4. Current Studies Conditionally Rare Taxa (CRT) contribute to microbial diversity – Low abundance – Presence temporally variable – Most difficult taxa to detect Emphasizes importance of having a null model for abundance data (Shade & Gilbert, 2015)

5. Some Issues with Models Models can make assumptions – ex. trade-offs, life-history traits, etc. Models can allow for parameter manipulation – ex. UNTB What about looking at patterns in a model based off of what we know?

6. What Do We know? Good idea to start from the data – How does the data constrain our inference? Large amounts of open-access microbial sequence data – ex. JGI, MG-RAST, NCBI – Metadata often poor quality Some constraints are easily inferred – N = Number of individuals – S = Number of species Basis for calculating our uncertainty in a distribution – i.e. our entropy

7. What is Entropy? Let’s reframe. Q: what is information? – A: Reduction in uncertainty – i.e. a reduction in entropy – Entropy = uncertainty But how is entropy calculated? Quantifying information content relies on the frequency of events in a distribution Jaynes, 1982

8. Entropy & Fair Coins What is the entropy of a fair coin? Let’s do the math But, entropy depends on what data you have! What’s my entropy vs. yours?

9. So, what did we just do? We both calculated the maximum entropy for one coin toss With incomplete information you predicted the most uniform distribution

10. Why maximum entropy (MaxEnt) ? We just learned that information is useful Maximize entropy = minimize commitment Model all you know and nothing more – What you’re modeling is a set of constraints that must hold Then choose the most uniform distribution – i.e. maximum entropy

11. MaxEnt Example

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14. What MaxEnt is not It’s not “creating uncertainty” – The uncertainty is already in the data The results are not “arbitrary” – It’s constrained by the data It’s not about reducing biology – “MaxEnt is most useful …where the observed frequencies do not agree with the maximum entropy predictions” - E. T. Jaynes (Jaynes, 2003)

15. Maximum Entropy Theory of Ecology (METE) & the Species Abundance Distribution (SAD) One soft constraint: – Average abundance (N 0/ S 0 ) Based on Shannon’s information entropy Uses two LaGrangian multipliers to solve for the maximum entropy solution to a SAD Fisher’s log-series distribution

16. Predicted Abundance Observed Abundance Mammal Community Database (MCDB), R 2 = 0.83 North American Butterfly Count (NABC), R 2 = 0.93 Breeding Bird Survey (BBS), R 2 = 0.91Christmas Bird Count (CBC), R 2 = 0.90 White et al., 2012

17. Limitations Current METE can’t handle very large N 0 N 0 > ~1xE6 A rapidly changing system may not be well- described by state variables Likely to fail in systems with heterogeneous habitats over large spatial scales

18. Preliminary Trials Can METE explain OTU abundance? – i.e. not using metadata to infer distribution One well-maintained dataset – Human Microbiome Project (Barbara et al., 2012) – 16s regions V3-V5 GI tract & skin microbiomes Compare to another MaxEnt distribution – Geometric series – Hard constrained for N 0 & S 0 http://www.hmpdacc.org/resources/data_browser.php

19. Work Environment / Methods METE package from Weecology Lab https://github.com/weecology/METE – Used for METE distribution & geometric series code – Estimated fit using custom IPython notebooks – Markdown documentation & visualization in line with Python code – Store in GitHub repo – Will make public once analysis complete (Xiao et al., 2013)

20. Raw data: r 2 = 0.260 Log- transformed: r 2 = 0.588 Abundant taxa skewing the fit SAD- GI Tract METE

21. SAD– GI Tract Geometric Series Raw data: r 2 = 0.0379 Log- transformed: r 2 = -0.0268 Low abundant taxa skewing the fit

22. SAD–Skin METE Raw data: r 2 = -0.231 Log- transformed: r 2 = 0.397 Abundant taxa skewing the fit

23. SAD–Skin Geometric Series Raw data: r 2 = 0.0955 Log- transformed: r 2 = -0.0246 Low abundant taxa skewing the fit

24. Summary METE & the geometric series both fail to predict the SAD – Surprising giving METE’s prior success with Macrobes Over predicted for the most taxa across sites Geometric series under predicted the few abundant taxa.

25. What Use is METE to Microbial Ecology? Plenty of use as a null model N 0 / S 0 might constrain abundance at a higher or lower scales Potential use to compare OTU clustering methods – Large scale microbiome / microbial comm. sequencing efforts – The most widely used algorithm is most sensitive to any change in parameters: UCLUST Schmidt et al., 2014

26. Future Direction Apply towards traits and effect of S 0 – Microbial trait-based modeling using METE KEGG annotated Global Ocean Sampling Expedition metagenomes (~80) – Examine microbial patterns using sub-OTU clustering Minimum Entropy Decomposition Work on research with the evolutionary ecology of quorum sensing in Janthinobacterium

27. Questions?

28. Works Cited The Human Microbiome Project Consortium, Barbara A.; Nelson, Karen E.; Pop, Mihai; Creasy, Heather H.; Giglio, Michelle G.; Huttenhower, Curtis; Gevers, Dirk; Petrosino, Joseph F. et al. (2012). "A framework for human microbiome research". Nature 486 (7402): 215–221. Gilbert JA, Steele JA, Caporaso JG, et al. Defining seasonal marine microbial community dynamics. The ISME Journal 2012;6(2):298-308. Harte, P. H. et al. Maximum Entropy and Ecology. Schmidt, T. S. B., Matias Rodrigues, J. F. & von Mering, C. Limits to Robustness and Reproducibility in the Demarcation of Operational Taxonomic Units. Environ. Microbiol. (2014). Shade, A. & Gilbert, J. a. Temporal patterns of rarity provide a more complete view of microbial diversity. Trends Microbiol. 1–6 (2015). Whitman, W. B., Coleman, D. C. & Wiebe, W. J. Prokaryotes: the unseen majority. Proc. Natl. Acad. Sci. U. S. A. 95, 6578–6583 (1998). Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model. Ecology. 93, 1772–1778 (2012). Xiao, X., McGlinn, D. J. & White, E. P. A strong test of the Maximum Entropy Theory of Ecology. arXiv Prepr. arXiv1308.0731 25 (2013).

29. Given data with any constraint on the problem, the probability distribution which maximized the entropy is identical with the frequency distribution which can be realized the greatest number of ways MaxEnt tells us which predictions are most likely given our information

30. Discuss strong test of MaxEnt?

31.   Species Sites

32. How Does METE Calculate Abundance? 1.Calculate β based off of N 0 and S 0 1.Done for the range of 1 to S 0 2. Calculate

33. Fairly Good Fit Harte, P. H. et al. Maximum Entropy and Ecology.

34. Fairly Poor Fit Harte, P. H. et al. Maximum Entropy and Ecology.