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Cloud-Based Exploration of Complex Ecosystems for Science, Education and Entertainment Ilmi Yoon1,2, Sangyuk Yoon2, Gary Ng1, Hunvil Rodrigues1, Sonal Mahajan1, and Neo D. Martinez2 1 Computer Science Department, San Francisco State University, San Francisco, California, USA 2 Pacific Ecoinformatics and Computational Ecology Lab, 1604 McGee Avenue, Berkeley, California, USA Abstract Recent computational advancement has opened up a new field of computational analyses of complex ecological networks where the nonlinear dynamics of many interacting species can be more realistically modeled and understood. Cloud computing supports ecological network research to extend to more complex and realistic modeling of ecosystems. Considering that simpler models underpinning much natural resource extraction policy leads to less biomass, and biodiversity than predicted by those simple models, more realistic models are desirable for sustaining extraction of biomass (e.g., fish, forests, fiber) from ecosystems that account for ecological complexity and computing for maximizing economic profit, employment and carbon sequestration by ecosystems. These realistic ecology computation models in Clouds support not only the scientific computation, but also support Multiplayer online game, βWorld of Balanceβ for players to entertain the experience of nurturing, managing and sustaining ecosystems. The game intends to educate players about the significant factors about ecosystems β non linear impact of one species over others within ecosystem. Clouds computing makes it feasible to supports the multifaceted usages of ecological network computational models for science, education and entertainment seamlessly; it will effectively circulate the meaningful data created from game play (simulation) to scientific research, and from research data to more realistic ecosystem managing experience to game players. Ecology Research The destruction of biodiversity and ecological productivity continues to degrade ecosystems' abilities to sustain human and non-human life (Millennium Ecosystem Assessment 2005). Ecosystems that provide such services are complex systems comprised by networks of diverse interdependent interacting species. These networks or "food webs" depict interconnected food chains within habitats such as lakes or forests (Dunne 2009). Ecologists need to better understand ecosystems in order to help manage threats to them. Only recently has this understanding progressed to the point that realistically complex ecosystems can be computationally modeled. This advance emerged from computational studies of ecosystems that uncovered how such nonlinear high dimensional systems may dynamically persist despite their mathematical improbability. This improbability, described four decades ago, directly contradicted the dominant paradigm at the time that held that diversity and complexity stabilize ecosystems. The instability was based on some of the earliest computational studies that found that large random networks did not persist because increasing their complexity by increasing the number of nodes and links decreases the probability that the network would return to equilibrium following a small disturbance (Gardner & Ashby 1970). This led to a famously coined challenge for supporters of the paradigm to "elucidate the devious strategies that make for stability in enduring natural systems" (May 1973). Over thirty years later, computational research addressing this challenge found that regularities in food web structure (Martinez et al. 2006), predator-prey body-size ratios (Otto et al. 2007), and feeding behavior (Williams & Martinez 2000) put ecosystems in a highly non random parameter space where diversity begets stability (Brose et al. 2006). Whereas before, realistically complex models failed to persist, the new insights enabled such systems modeled as nonlinear, high dimensional, coupled ordinary differential equations to characterize the bioenergetic feeding and biomass dynamics of complex networks of persistently interacting species (Berlow et al. 2009, Gross et al. 2009). Such advances led to a resurgence of basic research on ecological stability and initiated new and highly active computational research focused on the ecological effects of species loss and pollution (Brose et al. 2005, Berlow et al. 2009). The seriousness of the many stressors on ecosystems, which provide services critical for human life on Earth, is recognized by an increasingly large number of people across the world. Climate change and habitat degradation due to human activity are chief among these challenges (Millennium Ecosystem Assessment 2005). Some immediate and dramatic results of such perturbations are the loss of species native to ecosystems (Hughes et al. 1997), the invasion of ecosystems by species alien to them (Williamson 1996) and degradation of ocean productivity due to overfishing (Worm et al. 2009). Interacting species within ecosystems including humans form highly complex, nonlinear, dynamically coupled systems, but scientists are only beginning to understand how this interdependence impacts the fundamental structure, dynamics, function, and stability of complex ecological systems (Carpenter et al. 2009). Computational analyses of ecological systems that ignore humans have illuminated powerful structural regularities in the consumer-resource networks that comprise food webs. Knowledge of this structure in turn helped illuminate network dynamics that describe how species' abundances and feeding rates vary over time and the dynamic consequences of species loss, invasion, and environmental change for ecosystems. For example, computational studies of species loss can now quantitatively predict the effects on the abundance of other species in field experiments (Berlow et al. 2009) and suggest which additional species to eliminate to prevent extinction cascades resulting from the initial loss (Sahastrabudhe and Motter 2011). The urgency of environmental problems and complexity involved in solving them require new advances to computational approaches to these problems. More usable approaches are needed to enable ecologists and other noncomputational experts to conduct computational research. More powerful approaches are needed to explore more and larger networks of increased complexity that reflect more of the variability, interactions, and environmental problems found in nature. Computational approaches are helping to continue such research by modeling specific habitats (e.g., coral reefs, lakes, forests, etc.), and we describe advances in these approaches that improve the power and usability of modeling, data management, and visualization (Fig. 1). The Niche Model (William & Martinez 2000, 2008) generates the initial structure of the food webs and has two input parameters: the number of species S and connectance πΆ (Martinez 1992) where πΆ = πΏ/π ! and L is the number of trophic links. The model assigns a uniformly random "niche value" (0 β€ π! β€ 1) to each of S species. Consumer i eats only species whose niche values are contained within a range π! with a center of π! < π! . π! is randomly chosen from a uniform distribution between π! /2 and πππ(π! , 1 β π! /2). π! = π₯π! , where x is a random variable defined on [0,1] with a beta-distributed probability density function π π₯ = π½ 1 β π₯ !!! with π½ = 1/(2πΆ) β 1. We use 17 network properties to describe food-web structure (William & Martinez 2000, 2008, Dunne et al. 2002, 2008): Top, Int, Bas are the proportions of species that are respectively without predators (top), with both predators and preys (intermediate), and without preys (basal); Can, Herb, Omn and Loop are the fractions of species that are cannibals, herbivore (only basal preys), omnivores (i.e. feeding on multiple trophic levels) and involved in loops (apart from cannibalism); ChLen, ChSD and ChNum, the mean length, standard deviation of length and log number of the food chains; TL, the mean short-weighted trophic level of species (Williams and Martinez 2004); MaxSim, the mean of the maximum trophic similarity of each species; VulSD, GenSD and LinkSD are the normalized standard deviations of vulnerability (number of predators), generality (number of preys) and total links; Path is the mean shortest food-chain length between two species and Clust is the clustering coefficient (Watts & Strogatz 1998). Fig. 1 β Network3D provides user friendly browser based interface and visualization and analysis tools for computa-β tional ecology models The population dynamics within the food webs were simulated using (Berlow et al. 2009): π΅! = π! (1 β !β!"#$#%$&!! π΅! )π΅ β πΎ ! π΅! = βπ₯! π΅! + π₯! π¦!" π΅! !β!"#$%&'($ π₯! π¦!" π΅! πΉ!" β !β!"#$%!&"# β πΉ!" (1) π!" π₯! π¦!" π΅! !β!"#$%&'($ πΉ!" π!" π! πΈ!" π΅! (2) !β!"#$% Eq. 1 and 2 describe the changes in the biomass densities of, respectively, an autotroph and a heterotroph species. In these equations, π! is intrinsic growth-rate of basal species i, K is the carrying capacity shared by all the basal species, π₯! is iβs metabolic rate (π₯!"#"$ = 0, Brose et al. 2006), π¦!" is the maximum consumption rate of i eating j, π!" is iβs assimilation efficiency when consuming j. Current implementation of this population dynamics has 8 system parameters, 8 node parameters and 15 link parameters. As number of node and links increases rapidly and each parameter has large parameter spaces, so endless variations of simulation are possible and computationally it becomes heavily complicated. With the price of the complexity, extraction of biomass from realistically complex ecological systems can result in profoundly different effects that extraction from more simplistically modeled systems. The high dimensional, nonlinear, and nonrandom nature of these networks largely prohibit more analytical approaches from shedding much light on their behavior. Cloud computing that provides as-needed access to computing with the appearance of unlimited resources makes it feasible. However, while cloud computing has found widespread application in business environments, it has yet to demonstrate many scientific accomplishments (Fox 2010). This makes the harnessing of cloud-based approaches to scientific problems a novel computer science challenge deserving of innovative research efforts. Gamification Approach Multiplayer online game is a new multifaceted medium of communication to reach the masses (players) effectively that fosters healthy interaction and team cohesion. Recent multiplayer online game, FoldIT enabled ordinary game players to play for leisure while their intuitive and collaborative efforts has lead to unlocking of the structure of an AIDS-related enzyme that scientific community had been unable to unlock for a decade (Cooper et al., 2010, Firas Khatib et al., 2011). These approaches make use of social interaction and competition tendencies to engage massive players to work together to achieve the intended objectives. βWorld of Balanceβ is an entertaining educational multiplayer online game promoting the concept of ecosystem nurturing inspired by βFarmvilleβ, farm nurturing game that are leisurely played by over 100 million players (Babcock, C., 2011). Inside World of Balance game, players are given an empty land within the Serengeti eco environment of Africa (first level eco environment), develop ecosystems by adding plants, animal, and insect species one by one up to 95 real representative species from the latest paper (S. Visser et al., 2011, Dobson 2009). Playersβ level increases as they unlock new species while maintaining the balance of the ecosystem. New player starts as an apprentice of one of three types of mastery of special power (Weather, Animal Reproduction, or Plant Growth Rate) and increases the influence over ecosystems. Inside World of Balance, players can build and nurture complex balanced ecosystems by collaborating with each other; termed as βPlayer vs. Environment (PvE)β mode β or battle out to destroy and unbalance the ecosystems created by other players; termed as βPlayer vs. Player (PvP)β mode. In both modes players experience the process of ecosystem creation from producers (plants) to herbivores and carnivores, and the complex prey-predator relationship that exists between them; along with the inter-dependence essential to strike a balance in the ecosystem (Fig. 2). The game also facilitates players to share their progress in the game with their friends by posting it on their Facebook wall. This game opens a mutually educational communication channel between ecologists and masses (players). Players benefit by learning important aspects of ecosystem development and food-web stability while producing prolific and significant amount of scientific data useful for ecologists to analyze population dynamics model which is unfeasible for ecologists to produce on their own merits. Game handles biological environment consisting of biotic (living) components (organisms) and abiotic (non-living) components (eg. air, soil, water, sunlight). Players are managing both components within a game while playing with features like creating a new food web, adding new species to the food-web (invasion), adding more species of the same type (proliferation), decreasing number of species of the same type (exploit), removing species from the foodweb (removal), modifying parameters such as carrying capacity, growth rate and metabolic rate; viewing userβs manipulations and networks; updating biomass of nodes (species) in the network etc. Fig. 2 β screen capture from World of Balance game. Population dynamics of computational models deal with effect of birth and death rates, immigration and emigration, population decline etc. on the existing population. There are 3 different types of parameters, system parameter, node parameter and link parameter. And the current population dynamics engine supports 8 system parameters, 8 node parameters and 15 link parameters. In case of Serengeti web, there are 95 nodes and 547 links, then there are 8 system parameters, 760 node parameters and 8205 parameters that are all interdependent. For a human scientist or computational method alone, the parameter space is too huge to be manipulated individually. Using the game interface, the huge parameter space can be intuitively tuned and guided by players. Human players can manipulate parameters based on the knowledge and intuition such as carrying capacity, metabolism rate, and growth rate. Also distinctions among plants, invertebrates, endotherms and ecotherms vertebrates according to their body size to predict both metabolic and maximum assimilation rates of organisms, changing feeding rate of a predator in response to variations in prey density, predator divides its time searching for prey or processing captured prey, attack rate as a function of density of the prey, searching time in interacting with members of their own species are parameters to be tuned to simulate more realistic and balanced ecosystem. Simulation results (for example, reduction in biomass of species) on each time step (equivalent to single game day) are feed to game engine to map them into meaningful game activities (species are hunted down for the amount of reduction if predator exist or die with other reasons) that user can experience and interact further. World of Balance game uses same game engine to support both collaboratively nurturing world (inspired by Farmville) and competitively battling world (inspired by Starcraft) by mainly altering the game day scale. And the game engine uses the same cloud computing infrastructure to predict the ecosystem for each players, resulting fun and educational experience to players and scientific simulation data carefully driven by human players. This may be just the first of a series of ecosystem management games that more realistically depict exploitation of forests, lakes, grasslands and oceans. Such games may contribute to ecology and conservation biology much of what Foldit has contributed to molecular biology. That is the widespread inclusion of the complementary talents of players and computers in solving important research questions (Khatib et al. 2011). Clouds Computing as Solution NIST has defined Cloud Computing as follows: "Cloud Computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.β This means Cloud computing creates perfect environment to support computational ecology models for science, education and entertainment approach we have designed. (1) On-demand resource: usage pattern of ecology research is not predictable as researchers from world wide intensely use up memory and computation cycle during active simulation period, but sparsely during other data collection period. Also game is about to be deployed without having good estimation of near-future user community size. Each player runs one or more population simulation engines for each ecosystem player owns. cloud computing can also store the times series from the simulations allowing analyses to focus on the data rather than rerunning simulations to output subsets of simulation data thought to be able to help answer research questions. On-demand re- source reduces burden of purchasing expensive computational resource to support the whole infrastructure. (2) Each simulation engine is independent to each other and the large parameter sweeps to be conducted in short times using vast computational resources in parallel makes our analyses perfect application gaining the great potential of cloud computing approaches. (3) Minimal management effort or service provider interaction enables all the effort to focus on the problems to solve, not the system maintenance. (4) Researcher or game players from all over the world will have convenient access to the resource. These benefits could greatly accelerate scientific progress on critically important problems. Fig. 2-β Diagram of cyberinfrastructure for ecological network analysis. A tool called Network3D is used to simulate the com-β plex non-βlinear systems that characterize these problems (i.e., Equations 1-β3). We ported Network3D to Azure. The Network3D engine uses Windows Workflow Foundation to implement long-β running processes as workflows. Given requests which contain a number of manipulations, each manipulation is delivered to a worker instance to execute. The result of each manipulation is saved to SQL Azure. A web role provides the user with an inter-β face where the user can initiate, monitor and manage their ma-β nipulations as well as web services for other sites and visualiza-β tion clients. Once the request is submitted through the web role interface, the manipulation workflow starts the task and a ma-β nipulation is assigned to an available worker to process. The Network3D visualization client communicates through web ser-β vices and visualizes the ecological network and population dy-β namics results. Here we briefly discuss the process to port the existing codes (Network3D Windows application) to Microsoft Window Azure Cloud Computing environment. Population dynamics engine of Network3D has been imported to Windows Azure Cloud Computing as core processing unit. User Interface of Network3D has been transformed to Network3D Silverlight Web application as web role of Windows Azure. Network3D Rest Web Services (web role) serve as core management unit and manages Network3D tasks. User request which contains a number of manipulations sent from Network3D Silverlight web application to Network3D web services is delivered to worker instance (worker role) to execute. Manipulations are executed by Network3D Population Dynamics module and results of each manipulation are being saved to SQL Azure during the process. N3D web services access SQL Azure to perform network or manipulation query or retrieve user authentication. N3D population dynamics access SQL Azure to perform network or manipulation related operation. Windows Azure Tables(Windows Azure Storage) which is only used at Windows Azure is not used for compatibility issue when N3D needs to be ported to regular windows operating system. And the completed structure that supports Network3D browser interface and World of Balance multiplayer social game is shown in figure 3. Performance has been radically improved on Clouds. Batch process for running population dynamics on nine different food webs shown at table 1 on 3000 time steps on windows application on PC vs. Clouds computing. The table 1 shows the performance gain. 90000 PC 80000 .)c se ( e m ti n io ta t u p m o C Windows A zure 70000 60000 50000 40000 Results such as those presented here have helped motivate significant developments of cyber infrastructure to address complex ecological networks. The cloud computing provides invaluable resources for infrastructure to support Complex Ecosystems Computational Models for Science, Education and Entertainment References Babcock, C., "Lessons From FarmVille: How Zynga Uses The Cloud". InformationWeek (UMB): 29β34, 57. (2011). Berlow, E.L. et al. Simple prediction of interaction strengths in complex food webs. Proceedings of the National Academy of Sciences of the USA, 106: 187-β191. (2009) Brose, U. et al. Scaling up keystone effects from simple modules to complex ecological networks. Ecology Letters, 8: 1317-β1325. (2005) Brose, U. et al. Allometric scaling enhances stability in complex food webs. Ecology Letters, 9: 1228-β1236. (2006) Carpenter, S. et al. 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(2009) 30000 20000 10000 0 0 1 2 3 4 5 6 7 8 9 10 Index No. Index No. 1 2 3 4 5 6 7 8 9 πͺπππππππππ Food web St.Martin Island Coachella Glass Bridge Brook Lake Flack Hawkins Seregenti Everglades Elverde Node No. 44 30 75 75 48 87 86 65 156 Link No. 218 290 113 553 702 126 547 652 1510 Table 1. β food webs in different complexity were used to com-β pare the performance of Clouds computing vs. earlier Windows application on single PC. Gardner, M.R. & Ashby, W.R. Connectance of large dynamic (cy-β bernetic) systems: critical values for stability. Nature 228, 784 (1970). Hughes, J.B. Et al. Population diversity: its extent and extinction. Science, 278: 689-β692. (1997) Millennium Ecosystem Assessment. Ecosystems and Human Well-βBeing: Current State and Trends: Findings of the Condition and Trends Working Group. Island Press. (2005) Sahasrabudhe, S. & Motter, A.E. Rescuing ecosystems from ex-β tinction cascades through compensatory perturbations. Nature communications, 2, 170. (2011) S. 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