1. Webs on the Web: Ecoinformatic Approaches to Synthetic Food-Web Research from Cambrian to Contemporary Ecosystems Jennifer Dunne, Neo Martinez, Rich Williams Pacific Ecoinformatics and Computational Ecology Lab (PEaCE Lab) Rocky Mountain Biological Laboratory (RMBL) Santa Fe Institute (SFI) San Francisco State University (SFSU) www.foodwebs.org

2. Early food-web researchers introduced sharable catalogs of ecological datasets: -1978: First published catalog included 30 food webs (14 “community webs”, 16 “sink webs”) -1986: Expanded to 113 food webs -1989: EcOWEB, the first “machine-readable data base of food webs,” which now includes over 200 webs. These efforts by Cohen, colleagues, and other researchers created a culture of data sharing among most trophic ecologists Cohen JE (1978) Food Webs and Niche Space. Princeton University Press, NJ Cohen JE, Briand F, Newman CM (1986) A stochastic theory of community food webs: III. Predicted and observed length of food chains. Proc R Soc Lond B 228:317-353 Cohen JE (1989) Ecologists Co-operative Web Bank (ECOWeB). Version 1.0. Machine Readable Data Base of Food Webs. Rockefeller University, NY

3. Currently, researchers who want food-web data acquire it in basically primitive ways: -Requesting EcOWEB floppy disc from Cohen/Rockefeller -Hand-mining individual datasets from literature -Contacting researchers individually -Emailing me (I maintain a zoo of recent datasets) A recent improvement: Interaction Web Database -As of 2004, several dozen ecological network datasets, primarily focused on pollination, dispersal, and parasitism, are available online through NCEAS http://www.nceas.ucsb.edu/interactionweb/html/datasets.html

4. Interaction Web Database Home Data Membership Contribute data Who we are Contact us Lafferty et al. (in press) General information The study was conducted in Carpinteria Salt Marsh Reserve, Carpinteria, Santa Barbara County, California. The purpose of the study was to investigate the effect of parasites on food-web topology. The results are reported in detail in Lafferty et al (in press) with other publications forthcoming which examine connectance, chain length, vulnerability, etc. This publication also details how taxa and links were selected for inclusion and provides additional information on the species lists. Data type The matrix breaks down into 4 subwebs: predator-prey, parasite-host, predator-parasite and parasite-parasite. Links are binary (presence or absence of interspecific interactions), but coded by type of trophic interaction and certainty. The links are from a combination of published reports, direct observations, and logical, but presumed interactions. A key is included as a text box in the matrix. The web is being updated regularly as new information is obtained. Source Lafferty, K. D., R. F. Hechinger, J. C. Shaw, K. L. Whitney and A. M. Kuris (in press) Food webs and parasites in a salt marsh ecosystem. Disease ecology: community structure and pathogen dynamics (eds S. Collinge and C. Ray). Oxford University Press, Oxford. Data files Text format: interaction matrix (no species names)interaction matrix Excel format: interaction matrix (includes species lists)interaction matrix Explanation of data format: readme filereadme file

5. Recent controversies in food-web research: Conclusions altered by more extensive data Question 1: Do food webs display small-world, scale-free network structure? Answer 1: Yes, they are small-world and scale-free (3 webs, Montoya and Solé 2002) Answer 2: No, they are not small-world nor scale-free (7 webs, Camacho et al. 2002). Answer 3: No, they are typically not small-world or scale free, but sometimes they are depending on web diversity and connectance (16 webs, Dunne et al. 2002b). Question 2: Does food-web theory work for marine ecosystems? Answer 1: No, marine food-web structure differs from that seen in other aquatic and terrestrial ecosystems (1 marine web, Link 2002). Answer 2: Yes, marine food-web structure is similar to that of other food webs, once diversity and connectance are accounted for (3 marine webs, Dunne et al. 2004). Question 3: Do food webs display universal scaling relations? Answer 1: Yes, the branching properties of food-web minimal spanning trees display universal scaling exponents (7 webs, Garlaschelli et al. 2003). Answer 2: No, scaling exponents vary across webs (17 webs, Camacho and Arenas 2005).

6. Webs on the Web (WoW) 1)Knowledge Base -100s to 1000s of food webs and other ecological networks, flexible data format -10Ks to 100Ks + instances of feeding interactions -Species info (taxonomy, phylogeny, biomass, body size, abundance, metabolic rates) -Quantitative link info (frequency, flow, preference, etc.) -Additional info (geographic, provenance, citations, versions, etc.) -Downloading, uploading, annotation capabilities

7. Webs on the Web (WoW) 1)Knowledge Base -100s to 1000s of food webs and other ecological networks, flexible data format -10Ks to 100Ks + instances of feeding interactions -Species info (taxonomy, phylogeny, biomass, body size, metabolic rates, etc.) -Quantitative link info (frequency, flow, preference, etc.) -Additional info (geographic, provenance, versions, citations, geographic, etc.) -Downloading, uploading, annotation capabilities 2)Analysis Tools -Calculation of dozens of network structure metrics/properties -Modeling (network structure, nonlinear bioenergetic dynamics) -In silico experiments (biodiversity loss, invasions, etc.) -Link to other software (Pajek, EcoPath/EcoSim, etc.) -Trophic inference (phylogenetic, morphological) -”Pipeline architecture” allows users to plug in their own algorithms

8. Webs on the Web (WoW) 1)Knowledge Base -100s to 1000s of food webs and other ecological networks, flexible data format -10Ks to 100Ks + instances of feeding interactions -Species info (taxonomy, phylogeny, biomass, body size, metabolic rates, etc.) -Quantitative link info (frequency, flow, preference, etc.) -Additional info (geographic, provenance, versions, citations, geographic, etc.) -Downloading, uploading, annotation capabilities 2)Analysis Tools -Calculation of dozens of network structure properties -Modeling (network structure, nonlinear bioenergetic dynamics) -In silico experiments (biodiversity loss, invasions, etc.) -Link to other software (Pajek, Mage, EcoPath/EcoSim, etc.) -Trophic inference (phylogenetic, morphological) -Pipeline architecture allows users to plug in their own algorithms 3)Visualization Tools -Highly interactive and customizable 3D visualizations of ecological networks -Animations of dynamics, and graphical output of simulations -Images & movies of species & interactions (www.arkive.com - Images of Life on Earth)www.arkive.com -Useful for research (scientific illustration, hypothesis generation) and education

13. Challenges -Beyond prototype: usability and utility -Long-term maintenance -Quality control -Integrating dispersed, diverse WWW-based resources SPiRE: Semantic Prototypes in Research Ecoinformatics

14. Opportunities Creating a central repository for species’ interaction data to: -Facilitate the next generation of empirical and theoretical research on the structure, function, dynamics, and evolution of ecological networks -Support conservation and policy goals -Create modules for effective biocomplexity and ecological interdependence teaching & learning Building the World Wide Food Web via: -Data mining literature for trophic information -Creating spatially explicit global species interaction grid -MoaM- Meal of a Meal- trophic inference

15. Opportunities Creating a central repository for species’ interaction data to facilitate the next generation of empirical and theoretical research on the structure, function, dynamics, and evolution of ecological networks 1)Does food-web structure vary by habitat/ecosystem? 2)Does food-web structure vary through deep time, in response to major extinction events, or in response to major morphological or ecological innovations?

16. Burgess Shale Biota Wiwaxia Waptia Marella Anomalocaris Hallucigenia Opabinia Ollenoides Pikaia Ottoia

17. Burgess Shale Food Web Marine Middle Cambrian Burgess Shale in British Columbia (505 Ma) Original Taxa = 154Trophic Species = 53 Links = 261 C = 9% L/S = 4.9 Mean TL = 2.0 Max TL = 3.0 Links = 832 C = 4% L/S = 5.4 Mean TL = 1.9 Max TL = 3.0 21% basal taxa 57% herbivores 3% cannibals 21% omnivores 17% basal taxa 49% herbivores 8% cannibals 32% omnivores

18. Evolution of Food-Web Structure Through Deep Time Paleo Webs Burgess Shale Ames Lime/Shale Modern Webs Benguela Caribbean Reef NE US Shelf percent S C Burgess 41 0.10 Ames 53 0.09 Benguela 29 0.27 Reef 50 0.22 Shelf 79 0.23 Paleo vs. Recent Marine Food Webs

19. Paleo vs. Modern Marine Webs: Niche Model   2 model SD  > +2 model SD (model overestimates)  < -2 model SD (model underestimates) Good Fit Poor Fit Model fit is considered “good” when the model mean, based on 1000 model versions of an empirical web, falls within  2 model SD (normalized error) of empirical value

20. Webs on the Web Public Debut Fall 2005! www.foodwebs.org Acknowledgements: NSF DBI-0234980; Prof. Ilmi Yoon; Paul Yoon All people who collect/compile trophic information & share it!

21. Ailuravus Amphiperca fish and crocodile coprolites Messel Shale Biota Laurophyllum leaf mining damselfly eggs Buprestidae pollen in gut Macrocranion jumping hedgehog stomach contents Propalaeotherium leaf cuticle grape seeds predator prey fur Schaal, S., and Ziegler, W., (eds.). 1992. Messel: An Insight into the History of Life and of the Earth teeth (hard seeds)