ABSTRACT
The unintentional transport of invasive species (i.e., non-native and harmful species that adversely affect habitats and native species) through the Global Shipping Network (GSN) causes substantial losses to social and economic welfare (e.g., annual losses due to ship-borne invasions in the Laurentian Great Lakes is estimated to be as high as USD 800 million). Despite the huge negative impacts, management of such invasions remains challenging because of the complex processes that lead to species transport and establishment. Numerous difficulties associated with quantitative risk assessments (e.g., inadequate characterizations of invasion processes, lack of crucial data, large uncertainties associated with available data, etc.) have hampered the usefulness of such estimates in the task of supporting the authorities who are battling to manage invasions with limited resources. We present here an approach for addressing the problem at hand via creative use of computational techniques and multiple data sources, thus illustrating how data mining can be used for solving crucial, yet very complex problems towards social good. By modeling implicit species exchanges as a network that we refer to as the Species Flow Network (SFN), large-scale species flow dynamics are studied via a graph clustering approach that decomposes the SFN into clusters of ports and inter-cluster connections. We then exploit this decomposition to discover crucial knowledge on how patterns in GSN affect aquatic invasions, and then illustrate how such knowledge can be used to devise effective and economical invasive species management strategies. By experimenting on actual GSN traffic data for years 1997-2006, we have discovered crucial knowledge that can significantly aid the management authorities.
Supplemental Material
- R. Abell, M. L. Thieme, C. Revenga, M. Bryer, M. Kottelat, N. Bogutskaya, B. Coad, N. Mandrak, S. C. Balderas, W. Bussing, M. L. J. Stiassny, P. Skelton, G. R. Allen, P. Unmack, A. Naseka, R. Ng, N. Sindorf, J. Robertson, E. Armijo, J. V. Higgins, T. J. Heibel, E. Wikramanayake, D. Olson, H. L. Lopez, R. E. Reis, J. G. Lundberg, M. H. Sabaj Perez, and P. Petry. Freshwater ecoregions of the world: A new map of biogeographic units for freshwater biodiversity conservation. BioScience, 58(5):403{414, May 2008.Google ScholarCross Ref
- J. I. Antonov, D. Seidov, T. P. Boyer, R. A. Locarnini, A. V. Mishonov, H. E. Garcia, O. K. Baranova, M. M. Zweng, and D. R. Johnson. World Ocean Atlas 2009, Volume S: Salinity. In S. Levitus, editor, NOAA Atlas NESDIS, volume 69, page 184. U.S. Government Printing Office, Washington, D.C., 2010.Google Scholar
- A.-L. Barab--asi, R. Albert, and H. Jeong. Scale-free characteristics of random networks: the topology of the world-wide web. Physica A: Statistical Mechanics and its Applications, 281(1--4):69{77, June 2000.Google Scholar
- K. Bohmann, A. Evans, M. T. P. Gilbert, G. R. Carvalho, S. Creer, M. Knapp, D. W. Yu, and M. de Bruyn. Environmental DNA for wildlife biology and biodiversity monitoring. Trends in Ecology & Evolution, 29:358{367, May 2014.Google ScholarCross Ref
- A. Clauset, C. R. Shalizi, and M. E. J. Newman. Power-law distributions in empirical data. SIAM Review, 51:661{703, Apr 2009. Google ScholarDigital Library
- N. N. I. S. Council. 2008--2012 national invasive species management plan, 2008.Google Scholar
- S. Devin and J.-N. Beisel. Biological and ecological characteristics of invasive species: a gammarid study. Biological Invasions, 9(1):13{24, 2007.Google Scholar
- J. M. Drake and D. M. Lodge. Global hot spots of biological invasions: Evaluating options for ballast-water management. Proceedings: Biological Sciences, 271(1539):575{580, Mar. 2004.Google Scholar
- O. Floerl, G. Rickard, G. Inglis, and H. Roulston. Predicted effects of climate change on potential sources of non-indigenous marine species. Diversity and Distributions, 19(3):257{267, 2013.Google ScholarCross Ref
- M. Girvan and M. E. J. Newman. Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12):7821{7826, 2002.Google ScholarCross Ref
- B. Goodwin, A. McAllister, and L. Fahrig. Predicting invasiveness of plant species based on biological information. Conservation Biology, 13:422{426, 1999.Google ScholarCross Ref
- R. Guimera and L. A. Amaral. Functional cartography of complex metabolic networks. Nature, 433:895{900, Feb. 2005.Google ScholarCross Ref
- B. S. Halpern, S. Walbridge, K. A. Selkoe, C. V. Kappel, F. Micheli, C. D'Agrosa, J. F. Bruno, K. S. Casey, C. Ebert, H. E. Fox, R. Fujita, D. Heinemann, H. S. Lenihan, E. M. P. Madin, M. T. Perry, E. R. Selig, M. Spalding, R. Steneck, and R. Watson. A global map of human impact on marine ecosystems. Science, 319(5865):948{952, Feb. 2008.Google ScholarCross Ref
- R. P. Keller, J. M. Drake, M. B. Drew, and D. M. Lodge. Linking environmental conditions and ship movements to estimate invasive species transport across the global shipping network. Diversity and Distributions, 17(1):93{102, 2011.Google ScholarCross Ref
- R. P. Keller, D. M. Lodge, M. A. Lewis, and J. F. Shogren. Bioeconomics of Invasive Species : Integrating Ecology, Economics, Policy, and Management: Integrating Ecology, Economics, Policy, and Management. Oxford University Press, Apr. 2009.Google Scholar
- R. A. Locarnini, A. V. Mishonov, J. I. Antonov, T. P. Boyer, H. E. Garcia, O. K. Baranova, M. M. Zweng, and D. R. Johnson. World ocean atlas 2009, volume 1: Temperature. In S. Levitus, editor, NOAA Atlas NESDIS, volume 68, page 184. U.S. Government Printing Office, Washington, D.C., 2010.Google Scholar
- J. L. Molnar, R. L. Gamboa, C. Revenga, and M. D. Spalding. Assessing the global threat of invasive species to marine biodiversity. Frontiers in Ecology and the Environment, 6(9):485{492, Feb. 2008.Google ScholarCross Ref
- G. Palla, I. Der--enyi, I. Farkas, and T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043):814{818, June 2005.Google ScholarCross Ref
- D. Pimentel, R. Zuniga, and D. Morrison. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecological Economics, 52(3):273{288, Feb 2005.Google ScholarCross Ref
- J. Richard, S. A. Morley, M. A. S. Thorne, and L. S. Peck. Estimating long-term survival temperatures at the assemblage level in the marine environment: Towards macrophysiology. PLoS ONE, 7(4):e34655, Apr. 2012.Google ScholarCross Ref
- M. Rosvall and C. T. Bergstrom. Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4):1118{1123, 2008.Google ScholarCross Ref
- J. Rothlisberger, D. Finnoff, R. Cooke, and D. Lodge. Ship-borne nonindigenous species diminish great lakes ecosystem services. Ecosystems, 15(3):1{15, 2012.Google ScholarCross Ref
- M. Sales-Pardo, R. Guimera, A. Moreira, and L. Amaral. Extracting the hierarchical organization of complex systems. Proc. National Academy of Sciences of the United States of America, 104:15224{15229, Sept. 2007.Google ScholarCross Ref
- H. Seebens, M. T. Gastner, and B. Blasius. The risk of marine bioinvasion caused by global shipping. Ecology Letters, Apr. 2013.Google ScholarCross Ref
- C. E. Shannon and W. Weaver. A Mathematical Theory of Communication. University of Illinois Press, Champaign, IL, USA, 1963. Google ScholarDigital Library
- M. D. Spalding, H. E. Fox, G. R. Allen, N. Davidson, Z. A. F. na, M. Finlayson, B. S. Halpern, K. D. Martin, E. Mcmanus, J. Molnar, C. A. Recchia, and J. Robertson. Marine ecoregions of the world: A bioregionalization of coastal and shelf areas. BioScience, 57(7):573{583, July 2007.Google ScholarCross Ref
- E. Tufte. Beautiful Evidence. Graphics Press, 2006. Google ScholarDigital Library
- M. Wonham, J. Byers, E. D. Grosholz, and B. Leung. Modeling the relationship between propagule pressure and invasion risk to inform policy and management. Ecological Applications, Mar. 2013.Google ScholarCross Ref
Index Terms
- Improving management of aquatic invasions by integrating shipping network, ecological, and environmental data: data mining for social good
Recommendations
Mining fuzzy specific rare itemsets for education data
Association rule mining is an important data analysis method for the discovery of associations within data. There have been many studies focused on finding fuzzy association rules from transaction databases. Unfortunately, in the real world, one may ...
Mining uncertain data for constrained frequent sets
IDEAS '09: Proceedings of the 2009 International Database Engineering & Applications SymposiumData mining aims to search for implicit, previously unknown, and potentially useful pieces of information---such as sets of items that are frequently co-occurring together---that are embedded in data. The mined frequent sets can be used in the discovery ...
Exploring Disease Association from the NHANES Data: Data Mining, Pattern Summarization, and Visual Analytics
Finding associations among different diseases is an important task in medical data mining. The NHANES data is a valuable source in exploring disease associations. However, existing studies analyzing the NHANES data focus on using statistical techniques ...
Comments