EcoSim

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EcoSim is an individual-based predator-prey ecosystem simulation in which agents can evolve. It has been designed to investigate several broad ecological questions, as well as long-term evolutionary patterns and processes such as speciation and macroevolution. [1] [2] [3] [4] EcoSim has been designed by Robin Gras at the University of Windsor in 2009 and it is still currently [ when? ] used for research in his Bioinformatics and Ecosystem Simulation Lab.

Contents

Main concepts

The agents have a behavior model which allows the evolutionary process to modify the behaviors of the predators and prey. Furthermore, there is a speciation mechanism which allows to study global patterns as well as species-specific patterns. In EcoSim, an individual's genomic data codes for its behavioral model and is represented by a fuzzy cognitive map (FCM). The FCM contains sensory concepts such as foodClose or predatorClose, internal states such as fear or hunger, and motor concepts such as escape or reproduce. The FCM is represented as an array of floating-point values which represent the extent to which one concept influences another. For example, it would be expected that the sensory concept predatorClose would positively affect the internal concept fear, which would then positively affect the escape motor concept. These relationships among concepts evolve over time, sometimes giving a new meaning to a concept. Furthermore, the FCM is heritable, meaning that a new agent is given an FCM which is a combination of that of its parents with possible mutations.

EcoSim subscribes to the “genotypic cluster” definition of a species. [5] Speciation has been implemented using a 2-means clustering algorithm technique designed to allow the splitting of an existing species into two species, by clustering the individuals that initially belonged to the first species into one of the new two species, each one of them containing the agents that are mutually the most similar. Since EcoSim has the capacity to allow speciation events to occur, it is possible to track speciation events throughout a run of the simulation and construct the actual phylogenetic tree. [6]

Each agent also possesses several physical characteristics such as: maximum and current ages, minimum age for mating, maximum and current speeds, vision distance, maximum and current levels of energy, and the amount of energy transmitted to the offspring. Energy is provided to individuals by the resources (grass or meat) they find in their environment. An agent consumes some energy each time it performs an action and proportionally to the complexity (number of edges) of its FCM. If an individual uses all its energy, it dies.

A typical run lasts several tens of thousands of time steps. Each time step involves the time needed for each agent to perceive its environment, use its behavioral model to make a decision, perform its action as well as the time to update the species membership, including speciation events and all the world parameters. In a typical run, more than one billion of agents can be born and several thousands of species can be generated, which allows new behaviors to emerge and agents to adapt to a constantly changing environment. In addition, a food chain consisting of three levels, primary producers, predators and preys, has been implemented allowing complex interactions between agents and co-evolution to occur. All events, the mental state and action of every agent, are saved for every time step of every run. This allows a deep statistical analysis of the whole system using several dedicated tools that we have conceived to extract, measure and correlate any possible facts that could be useful to understand the underlying and emerging properties of the system. [7]

Research publications

Several studies have already been done using EcoSim. For example, Devaurs and Gras [8] have analyzed the species abundance patterns observed in the communities generated by EcoSim, based on Fisher's log series. This study shows that the communities of species generated by the simulation follow the same lognormal law as natural communities and that EcoSim can help to evaluate the overall level of diversity of a given community. In other studies, the chaotic behavior of the system with multi-fractal properties has been proven in [9] as it also has been observed for real ecosystems. Mashayekhi and Gras [10] investigated the effect of spatial distribution and spatiotemporal information on speciation. In more recent research, Golestani et al. [11] investigated how small, randomly distributed physical obstacles influence the distribution of populations and species, the level of population connectivity (e.g., gene flow), as well as the mode and tempo of speciation.

Related Research Articles

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<span class="mw-page-title-main">Herbivore</span> Organism that eats mostly or exclusively plant material

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<span class="mw-page-title-main">Predation</span> Biological interaction where a predator kills and eats a prey organism

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<span class="mw-page-title-main">Food web</span> Natural interconnection of food chains

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<span class="mw-page-title-main">Keystone species</span> Species with a large effect on its environment

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<span class="mw-page-title-main">Three-spined stickleback</span> Species of fish

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<span class="mw-page-title-main">Apex predator</span> Predator at the top of a food chain

An apex predator, also known as a top predator, is a predator at the top of a food chain, without natural predators of its own.

<span class="mw-page-title-main">Aposematism</span> Honest signalling of an animals powerful defences

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<span class="mw-page-title-main">Optimal foraging theory</span> Behavioral ecology model

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<span class="mw-page-title-main">AgentSheets</span>

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<span class="mw-page-title-main">Ecosystem model</span> A typically mathematical representation of an ecological system

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<span class="mw-page-title-main">Pursuit predation</span> Hunting strategy by some predators

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<span class="mw-page-title-main">Reinforcement (speciation)</span> Process of increasing reproductive isolation

Reinforcement is a process of speciation where natural selection increases the reproductive isolation between two populations of species. This occurs as a result of selection acting against the production of hybrid individuals of low fitness. The idea was originally developed by Alfred Russel Wallace and is sometimes referred to as the Wallace effect. The modern concept of reinforcement originates from Theodosius Dobzhansky. He envisioned a species separated allopatrically, where during secondary contact the two populations mate, producing hybrids with lower fitness. Natural selection results from the hybrid's inability to produce viable offspring; thus members of one species who do not mate with members of the other have greater reproductive success. This favors the evolution of greater prezygotic isolation. Reinforcement is one of the few cases in which selection can favor an increase in prezygotic isolation, influencing the process of speciation directly. This aspect has been particularly appealing among evolutionary biologists.

<span class="mw-page-title-main">Marine food web</span> Marine consumer-resource system

Compared to terrestrial environments, marine environments have biomass pyramids which are inverted at the base. In particular, the biomass of consumers is larger than the biomass of primary producers. This happens because the ocean's primary producers are tiny phytoplankton which grow and reproduce rapidly, so a small mass can have a fast rate of primary production. In contrast, many significant terrestrial primary producers, such as mature forests, grow and reproduce slowly, so a much larger mass is needed to achieve the same rate of primary production.

<span class="mw-page-title-main">Ecology of fear</span> Psychological impact induced by predators

The ecology of fear is a conceptual framework describing the psychological impact that predator-induced stress experienced by animals has on populations and ecosystems. Within ecology, the impact of predators has been traditionally viewed as limited to the animals that they directly kill, while the ecology of fear advances evidence that predators may have a far more substantial impact on the individuals that they predate, reducing fecundity, survival and population sizes. To avoid being killed, animals that are preyed upon will employ anti-predator defenses which aid survival but may carry substantial costs.

Eco-evolutionary dynamics refers to the reciprocal effects that ecology and evolution have on each other. The effects of ecology on evolutionary processes are commonly observed in studies, but the realization that evolutionary changes can be rapid led to the emergence of eco-evolutionary dynamics. The idea that evolutionary processes can occur quickly and on one timescale with ecological processes led scientists to begin studying the influence evolution has on ecology along with the affects ecology has on evolution. Recent studies have documented eco-evolutionary dynamics and feedback, which is the cyclic interaction between evolution and ecology, in natural and laboratory systems at different levels of biological organization, such as populations, communities, and ecosystems.

References

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  6. Scott, Ryan; Gras R. (2012). "Comparing Distance-Based Phylogenetic Tree Construction Methods Using an Individual-Based Ecosystem Simulation, EcoSim". Artificial Life 13. pp. 105–110. CiteSeerX   10.1.1.401.5208 . doi:10.7551/978-0-262-31050-5-ch015. ISBN   9780262310505.{{cite book}}: |journal= ignored (help)
  7. Stephen, Fields (August 2, 2011). "New resources speed up ecosystem evolution simulations for computer scientist". Archived from the original on April 4, 2015. Retrieved July 17, 2012.
  8. Devaurs, D.; Gras R. (2010). "Species abundance patterns in an ecosystem simulation studied through Fisher's logseries". Simulation Modelling Practice and Theory. 18: 100–123. CiteSeerX   10.1.1.739.5030 . doi:10.1016/j.simpat.2009.09.012. S2CID   3230946.
  9. Golestani, A.; Gras R. (2010). "Regularity analysis of an individual-based ecosystem simulation". Chaos: An Interdisciplinary Journal of Nonlinear Science. 20: 043120 (4): 043120. Bibcode:2010Chaos..20d3120G. doi:10.1063/1.3514011. PMID   21198090.
  10. Mashayekhi, M.; Gras R. (2012). "Investigating the Effect of Spatial Distribution and Spatiotemporal Information on Speciation using Individual-Based Ecosystem Simulation". Journal of Computing. 2: 98–103.
  11. Golestani, A.; Gras R.; Cristescu M. (August 2012). "Speciation with gene flow in a heterogeneous virtual world: can physical obstacles accelerate speciation?". Proceedings of the Royal Society B: Biological Sciences. 279 (1740): 3055–3064. doi:10.1098/rspb.2012.0466. PMC   3385488 . PMID   22513856.