PREDICTION OF IMMINENT SPECIES’ EXTINCTION IN EcoSim

Meisam Hosseini Sedehi, Robin Gras, Md Sina

2012

Abstract

The process of evolution involves the emergence and disappearance of species. Many factors affect on the survival of species. Real study of factors’ influence is particularly difficult due to the complex interaction between them. An individual-based model (IBM) can assist in the analysis of effective factors. In this study, using an IBM called EcoSim, we have examined the impact of some factors on the prediction of the imminent extinction. By applying some machine learning’s techniques for feature selection and classification, we have shown that demographic and genetic factors have a critical role for the prediction. Especially, paying attention to both factors can highly improve the accuracy of the species’ prediction.

References

  1. Aspinall, A., & Gras, R. (2010). K-Means Clustering as a Speciation Mechanism within an Individual-Based Evolving Predator-Prey Ecosystem Simulation. Active Media Technology, LNCS6335, 318-329.
  2. DeAngelis, D. L., & Mooij, W. M. (2005). IndividualBased Modeling of Ecological and Evolutionary Processes 1. Annual Review of Ecology, Evolution, and Systematics, 36(1), 147-168.
  3. Devaurs, D., & Gras, R. (2010). Species abundance patterns in an ecosystem simulation studied through Fisher's logseries. Simulation Modelling Practice and Theory, 18(1), 100-123. Elsevier B.V.
  4. Drake, J. M., & Griffen, B. D. (2010). Early warning signals of extinction in deteriorating environments. Nature, 467(7314), 456-9. Nature Publishing Group.
  5. Drake, J. M., & Lodge, D. M. (2004). Effects of environmental variation on extinction and establishment. Ecology Letters, 7(1), 26-30.
  6. Drake, J. M., Shapiro, J., & Griffen, B. D. (2011). Experimental demonstration of a two-phase population extinction hazard. Journal of the Royal Society, Interface / the Royal Society, 8(63), 1472-9.
  7. Gilman, R. T., & Behm, J. E. (2011). Hybridization, Species Collapse, and Species Reemergence After Disturbance To Premating Mechanisms of Reproductive Isolation. Evolution, no-no.
  8. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional.
  9. Gras, R., Devaurs, D., Wozniak, A., & Aspinall, A. (2009). An individual-based evolving predator-prey ecosystem simulation using a fuzzy cognitive map as the behavior model. Artificial life, 15(4), 423-63.
  10. Griffen, B. D., & Drake, J. M. (2008). A review of extinction in experimental populations. The Journal of animal ecology, 77(6), 1274-87.
  11. Hovel, K. a, & Regan, H. M. (2007). Using an individualbased model to examine the roles of habitat fragmentation and behavior on predator-prey relationships in seagrass landscapes. Landscape Ecology, 23(Sep1), 75-89.
  12. Jammalamadaka, S. R., & Sengupta, A. (2001). Topics in circular statistics (Vol. 5). World Scientific Pub.
  13. Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65-75. Elsevier.
  14. Mallet, J. (1995). A species definition for the modern synthesis. Trends in Ecology & Evolution, 10(7), 294- 299. Elsevier.
  15. Ovaskainen, O., & Meerson, B. (2010). Stochastic models of population extinction. Trends in ecology & evolution, 25(11), 643-652. Elsevier Ltd.
  16. Patten, M. A., Wolfe, D. H., Shochat, E., & Sherrod, S. K. (2007). Habitat fragmentation, rapid evolution and population persistence. Evolutionary Ecology, 7, 235- 249.
  17. Quinlan, J. R. (1993). C4. 5: programs for machine learning. Morgan Kaufmann.
  18. Reed, D. H., Lowe, E. H., Briscoe, D. A., & Frankham, R. (2003). Inbreeding and extinction: Effects of rate of inbreeding. Conservation Genetics, 4(3), 405-410.
  19. Schueller, A. M., & Hayes, D. B. (2011). Minimum viable population size for lake sturgeon (Acipenser fulvescens) using an individual-based model of demographics and genetics. Canadian Journal of Fisheries and Aquatic Sciences, 68(1), 62-73.
  20. Sherwin, W. B. (2010). Entropy and Information Approaches to Genetic Diversity and its Expression: Genomic Geography. Entropy, 12(7), 1765-1798.
  21. Walters, J. R., Crowder, L. B., & Priddy, J. A. (2002). Population Viability Analysis for Red-Cockaded Woodpeckers Using an Individual-Based Model. Ecological Applications, 12(1), 249-260.
  22. WEKA, V3.6.4, http://www.cs.waikato.ac.nz/ml/weka/
Download


Paper Citation


in Harvard Style

Hosseini Sedehi M., Gras R. and Sina M. (2012). PREDICTION OF IMMINENT SPECIES’ EXTINCTION IN EcoSim . In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-95-9, pages 318-323. DOI: 10.5220/0003737703180323


in Bibtex Style

@conference{icaart12,
author={Meisam Hosseini Sedehi and Robin Gras and Md Sina},
title={PREDICTION OF IMMINENT SPECIES’ EXTINCTION IN EcoSim},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2012},
pages={318-323},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003737703180323},
isbn={978-989-8425-95-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - PREDICTION OF IMMINENT SPECIES’ EXTINCTION IN EcoSim
SN - 978-989-8425-95-9
AU - Hosseini Sedehi M.
AU - Gras R.
AU - Sina M.
PY - 2012
SP - 318
EP - 323
DO - 10.5220/0003737703180323