Proposal

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###Ecostations Data Access Monitor (EDAM) Proposal

####Pitch As large biodiversity collections and environmental data are accessible online, global research communities have an unprecedented access to datasets. Now that methods are within reach to combine and process biodiversity data at global scales, institutions can start to re-examine existing data to coordinate data collection efforts, evolve data sharing strategies and discover methods to efficiently sustain ecosystems. A first step toward integrating the data is to provide a side-by-side comparison of existing data associated with active ecostation communities to stimulate knowledge sharing and collaboration.

####Summary Systems biology is revolutionizing medicine leading to a Predictive, Preventative, Personalized, and Participatory (P4) approach. Yet human wellbeing is also inherently linked to healthy societies and environments (sustainability). Our ultimate goal is to simulate social and ecological systems by creating the Island Digital Ecosystem Avatars (IDEA) Consortium. IDEA Consortium is an open science initiative in systems ecology. Its primary mission is to conduct the basic scientific research needed to build use-oriented simulations (avatars) of entire social-ecological systems. In order to develop IDEA, our first step is to build a tool to enable scientists to compare ecological data side by side. The tool we are developing is called the Ecostations Data Access Monitor (EDAM). Islands are the most scientifically tractable places for this and we begin with one of the best known: Moorea, French Polynesia. The Moorea IDEA will be a sustainability simulator modeling links and feedbacks between climate, environment, biodiversity, and human activities across a coupled marine-terrestrial landscape.

Ecostation biodiversity data summaries are derived from openly available biodiversity data repositories (e.g. GBIF, iDigBio, GloBI). Initially only species lists and associated food webs are compiled for participating ecostations using automated data processing algorithms. For each ecostation, the completeness of the lists and webs are estimated. Also, the similarity of the lists and webs are calculated across the spatially separated island ecosystems to highlight ecological likeness. With EDAM, spatially and institutionally disjointed projects now have a data-driven method to see how much ecological data is available for specific spatio-taxonomic spaces. We hope that comparing available ecological data across ecostations will help stimulate collaboration between scientists, technologists, educators, local governments and research foundations to help better understand and sustain ecosystems around us.

####Needs Statement High-throughput data collection techniques and large-scale computing are transforming our understanding of ecosystems, making convergent scientific frameworks a research priority [1]. As human activities increasingly impact ecosystem processes, we need “a new kind of ecology” that focuses on how whole communities of organisms interact with people and the physical environment at the scale of landscapes or catchments [2]. This requires an e-infrastructure for data intensive science that enables the integration of computational physics, chemistry, biology, ecology, and social science. Such an advance would allow researchers to (1) characterize the multidisciplinary functional attributes of social-ecological systems; (2) quantify the relationships between those functional attributes under historic and current conditions; and (3) model the trajectories of goods and services under a range of policy-driven scenarios and environmental conditions. The resulting knowledge would improve our ability to predict human and natural change at scales relevant to policy decisions and management/conservation actions.

Information on species distribution in space is essential for effective management of biodiversity and ecosystems and in addressing ecological and evolutionary questions. Recent studies have shown that gaps in digital accessible information (DAI) on species distributions hinders the efforts of ecosystem and biodiversity services to protect and conserve endangered species [1]. Outside a few regions, DAI on point occurrences provide a very limited and biased inventory of species. Achieving international targets on biodiversity knowledge requires that information gaps be identified. Multi-model inference shows that completeness is limited by distance to researchers, locally available research funding and the ability and willingness to participate in data sharing networks. The EDAM project aims to build a platform that compresses data, enabling data sharing to enable a wholesome, side-by-side data comparison of ecosystems. These aims would be achieved by automating data processing algorithms to compile species lists and associated food webs for participating ecostations, estimating the completeness of the lists and webs, calculating similarity of the lists and webs and creating a web accessible visualization tool that allows comparison.

####Team Bios

• Jonathan Wang - Jonathan is a junior undergraduate student studying computer science and statistics. He has experience with data analysis and developing web apps. In the scope of this project, Jonathan is helping to clean and model the data as well as integrating our research into a web app to present our findings.

• Nisreen Hejab

• Carlo Liquido - Carlo is a graduate student at the School of Information focusing on data science and information visualization. He is focusing his efforts on the UI/UX and front-end side of the project.

• Vedant Saran

• Jong-kai Yang

• Tong Zhang - Tong is a junior undergraduate student studying industrial engineering and operations research. She has experience with data visualization and analysis in optimization area. She is also helping to design the UI/UX of the project.

• Jorrit Poelen - Jorrit is a freelance software engineer based in Oakland, California with 15+ years of experience in academia, industry and startup environments. He was awarded an Encyclopedia of Life Rubenstein Fellowship, created Global Biotic Interactions, published a paper in Ecological Informatics, and has ongoing collaborations spanning many non-profit and academic organizations (e.g. Manylabs, Encyclopedia of Life, rOpenSci, iNaturalist, Texas A&M). Jorrit is a mentor/advisor for the EDAM project.

####References

1. National Research Council: Convergence : Facilitating Transdisciplinary Integration of Life Sciences,Physical Sciences , Engineering , and Beyond. 2014.
2. Mace G: Ecology must evolve. Nature 2013, 503:191–2.
3. Karr JR, Sanghvi JC, Macklin DN, Gutschow M V, Jacobs JM, Bolival B, Assad-Garcia N, Glass JI, Covert MW: A whole-cell computational model predicts phenotype from genotype. Cell 2012, 150:389–401.
4. Meyer, C. et al., 2015. Global priorities for an effective information basis of biodiversity distributions. Nat Comms, 6, p.8221
5. Butchart, S. H. M.et al.Global biodiversity: indicators of recent declines.Science328,1164–1168(2010).
6. Boitani, L.et al.What spatial data do we need to develop global mammal conservation strategies?Philos. Trans. R. Soc. Lond. B. Biol. Sci366, 2623–2632(2011).
7. Global Biodiversity Information Facility (http://gbif.org)
8. Matthews, T.J. et al., 2014. Thresholds and the species-area relationship: a synthetic analysis of habitat island datasets K. C. Burns, ed. J. Biogeogr., 41(5), pp.1018–1028.
9. The Theory of Island Biogeography by Robert H. MacArthur & Edward O. Wilson