1. A computational phylogenetic approach to interaction analysis Cynthia Sims Parr University of Maryland College Park Ecological Society of America Montreal, Canada August 9, 2005

2. Predicting Ecological Interactions ?

3. Terminology & Outline Describe computational framework for predicting links Propose general algorithms and discuss implications Preliminary results Simple model using large database and evolutionary trees does a surprisingly good job. web nodelink

4. Evolutionary trees Family Genus Species

5. Computational framework Database Interaction Web Database ADW DB and Graph Vis tools Algorithms Field Test Predictions Explore for patterns Phylogenies Classifications Note: More than one way to do it!

6. Predicting Links: parameterized functions Step 1. Select functions that might predict links using characteristics of taxa. For example, size or stoichiometry. Step 2. Determine parameters using known links among all taxa across whole or partial database. For taxon A and taxon B with known link status: LinkStatus AB LinkStatus AB = ƒ(α, size A, size B ) + ƒ(β, stoich A, stoich B ) Step 3. Use parameterized equation to estimate LinkStatus between target taxa C and D.

7. Implications: parameterized functions Requires good data for target species Can incrementally add natural history functions to get better estimate, try different functions from literature or use genetic algorithms Parameterizing functions: multivariate statistics, machine learning, fuzzy inference Could use evolutionary info if you localize parameter estimates to clades or taxonomic subsets LinkPredicted CD = ƒ(α, size C,size D ) + ƒ(β, stoich C,stoich D )

8. Predicting Links: neighbor distance weighting E.g. for taxa X and Y, where X has nearest neighbor A and Y has nearest neighbor B, where LinkStatus between A,B is known N LinkPredicted XY = 1 (LinkStatus AB ) 1 + distance XA + distance YB  Step 1. Provide distance threshold or number of neighbors N to use. Step 2. Find nearest neighbors to your target nodes in evolutionary or trait space with known link status. Step 3. Combine LinkStatus weighted by distances:

9. Implications: Neighbor distance weighting Evolutionary Uses phylogeny or classification or combination of these Distance could be branch length or # steps Does not explicitly take advantage of natural history Trait space e.g. Euclidean distance in N-space Uses richest possible natural history data Could include evolutionary distance as a term

10. Missing data avoid it avoid comparisons with nodes without complete data substitute value of relative otherwise closest in trait space “Ancestral” Node Reconstruction e.g. Phylogenetic Mixed Model (Houseworth et al. 2001) Nodes that do not map to taxa e.g. detritus, suspended organic matter Treat as if they are a phylogenetic unit all in one polytomy Can create a “phylogeny” of neighbors. For example, “detritus” may be part of a reasonable heirarchy of organic material. Nodes that are not resolved to species Doesn’t matter for these algorithms Problems and suggested solutions

11. Picture of tree from TaxonTree overview Take advantage of all information as needed

12. Whole web solutions Some links affect others use a priori prediction of strongest links to run first, allow status of these links to enter link predictions. Webs should be realistic Vary parameters (e.g. scale of parameterization, thresholds) and rerun analyses until criterion met for the whole web Criteria: “natural” values for connectedness, stability, chain length, trophic level ratios, etc. Methodology: parsimony or likelihood analysis Computational demands will be high S 2 possible links, simultaneous multivariate equations by all variants of runs. May need heuristics.

13. Summary of approaches Link prediction Parameterized functions Weighted distances Evolutionary Trait space Total community solution Parsimony or likelihood solution Include other links as terms and run prioritized, stepwise analysis

14. Data needed Wide range of well-identified taxa Cross section of habitats Natural history data

15. Database status 4214 unique taxa Evolutionary tree as in Parr et al. 2004. Bioinformatics.

16. LinkPredictor preliminary results Data 43% of nodes mapped to species level 16% nodes have no evolutionary information at all. Using only presence or absence of links Procedure Pulling out one food web at a time and predicting its links based on the rest of the data Up to 4 steps up and down the evolutionary tree, no weighting yet for distance Results On average, 49% of actual links are correctly predicted 38% of predicted links are false positives Take home: Our DB and evolutionary approach does surprisingly well at predicting food links …With SPIRE at UMBC

17. More questions What about predicting links among taxa from big studies outside the current database? How much improvement comes from adding links to the DB? How robust are results to differing degrees of phylogenetic resolution or taxon sampling? How robust are results to missing data? How to handle data quality issues? Error estimates?

18. Future work with SPIRE Role in ELVIS – LinkEP (Evidence Provider) Integrate into platform that takes location as input generates list of taxa gives evidence for interaction among taxa models change due to invasive species Pull data from semantic web rather than local database

19. Acknowledgements NSF IDM/ITR 0219492 (PI Bederson) Bongshin Lee NBII Joel Sachs and Andrey Parafiynyk Bill Fagan and lab members Michael Kantor EcoWeb (Joel Cohen) NCEAS Interaction Web Database (Diego Vázquez) WoW (J. Dunne and N. Martinez) http://www.cs.umd.edu/hcil/biodiversity http://spire.umbc.edu/linkpredictor/