Abstract
The Chesapeake Bay is one of the mostproductive systems in the world. It is theNation's largest estuary (64,000 square miles)and is home to about 13 million people. Itsupports a variety of aquatic resources offlora and fauna. However, for the past 350years and especially in the last two to threedecades, there has been substantialdeterioration of the natural resources. Manyspecies of submerged aquatic vegetation andbenthic invertebrates have been diminished orbecome extinct. Commercial harvests of fish,crab and shell fish have also declined.
In 1983, a Chesapeake Bay Agreement was signedby Pennsylvania, Maryland, the District ofColumbia, Virginia and the Bay Commission. Itwas subsequently amended in 1987 and 1992. TheAgreement identified the improvement andmaintenance of water quality as the mostcritical elements in the overall restorationand protection of the Chesapeake Bay. In orderto restore the Bay area and to conserve thefish resources, the causal relationshipsbetween the environmental stressors and thecomposition and health of the fish communitiesmust be understood.
Multivariate ordination techniques are usefulexploratory tools to help elucidate latentenvironmental relationships, define specificbiocriteria and to generate hypotheses. Geographical information systems (GIS) is ananalytical technique for identifying spatialrelationships. In this project, an integratedmethodology involving the use of multivariateordination, statistical, and GIS techniques wasadopted. A non-metric multi-dimensionalscaling (NMDS) ordination technique wasemployed in conjunction with other statisticaltechniques (such as correlation analysis) andArcView GIS to analyze a huge data set from theMaryland Biological Stream Survey (MBSS). Theobjectives were to elucidate the intricaterelationships between a suite of environmentalfactors and fish conditions in the riverinesystem in the Chesapeake Bay and to evaluatethe effectiveness of this approach inexploratory analyses.
The results showed that landuse issignificantly related to nutrient loading. Toa large extent, landuse and nitrates are alsoaffecting the composition and health of thefish communities in some subwatersheds in theChesapeake Bay. It was also found that theapproach adopted in this study is flexible,requiring few model assumptions. But it iscomprehensive and reliable, capable ofrevealing the impacts of environmentalstressors on the ecology, structure,composition and health of the fishcommunities.
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References
Austin, M. P., 1979. Current approaches to the non-linearity problem in vegetation analysis. In: G. P. Patil & M. L. Rosenzweig (eds), Contemporary Quantitative Ecology and Related Econometrics. International Co-operative Publishing House, Fairland, MD: 197-210.
Attrill, M. J., S. D. Rundle & R. M. Thomas, 1996. The influence of drought-induced low freshwater flow on an upper-estuarine macroinvertebrate community. Wat. Res. 30(2): 261-268.
Boward, D., P. Kazyak, S. Stranko, M. Hurd & A. Prochaska, 1999. From the Mountains to the Sea: The State of Maryland's Freshwater Streams. U.S.E.P.A., Washington, DC.
Cao, Y., A. W. Bark & W. P. Williams, 1996. Measuring the responses of macroinvertebrate communities to water pollution: A comparison of multivariate approaches, biotic and diversity indices. Hydrobiologia 341: 1-9.
Chesapeake Bay and Watershed Programs, 1997. Chester River Basin. Environmental Assessment of Stream Conditions. Maryland Department of Natural Resources. CBWP-MANTA-EA-96-4.
Chesapeake Bay and Watershed Programs, 1998a. Choptank River Basin. Environmental Assessment of Stream Conditions. Maryland Department of Natural Resources. CBWP-MANTA-EA-98-6.
Chesapeake Bay and Watershed Programs, 1998b. Nanticoke/ Wicomico River Basin. Environmental Assessment of Stream Conditions. Maryland Department of Natural Resources. CBWPMANTA-EA-96-6.
Chesapeake Bay and Watershed Programs, 1998c. Middle Potomac River Basin. Environmental Assessment of Stream Conditions. Maryland Department of Natural Resources. CBWP-MANTAEA-98-5.
Chesapeake Bay and Watershed Programs, 1998d. Lower Potomac River Basin. Environmental Assessment of Stream Conditions. Maryland Department of Natural Resources. CBWP-MANTAEA-96-5.
Chesapeake Bay Executive Council, 1998. Population Growth and Development in the Chesapeake Bay Watershed to the Year 2020. The Report of the Year 2020 Panel to the Chesapeake Executive Council. Annapolis, MD.
Clarke, K. R., 1993. Non-parametric multivariate analysis of changes in community structure. Aust. J. Ecol. 18: 117-143.
Clarke, K. R., 1999. Nonmetric multivariate analysis in community-level ecotoxicology. Environ. Toxicol. Chem. 18(2): 118-127.
Clarke, K. R. & M. Ainsworth, 1993. A method of linking multivariate community structure to environmental variables. Mar. Ecol. Progr. Ser. 92: 205-219.
Clarke, K. R. & R. G. Warwick, 1997. Change in Marine Communities: An Approach to Statistical Analysis and Interpretation. Marine Laboratory, Plymouth, U.K.
Correll, D. L., 1987. Nutrients in Chesapeake Bay. In: S. K. Majumdar, L. W. Jr. Hall, & H. M. Austin (eds), Contaminant Problems and Management of Living Chesapeake Bay Resources. The Pennsylvania Academy Science: 298-319.
Cox, T. F. & G. Ferry, 1993. Discriminant analysis using non-metric multidimensional scaling. Pattern Recogn. 26(1): 145-154
Dargie, T. C. D., 1984. On the integrated interpretation of indirect site ordinations: A case study using semi-arid vegetation in southeastern Spain. Vegetatio 55: 37-55.
Davidson, M. L., 1983. Multidimensional Scaling. John Wiley and Sons, New York.
De'ath, G., 1999. Extended dissimilarity: A method of robust estimation of ecological distances from high beta diversity data. Plant Ecol. 144: 191-199.
deFur, P. L., 1997. The Chesapeake Bay Program: An example of ecological assessment. Am. Zool. 37(6): 641-649.
Diaz, R. J., 1987. Benthic resources of the Chesapeake Bay estuarine system. In: S. K. Majumdar, L. W. Jr. Hall & H. M. Austin (eds), Contaminant Problems and Management of Living Chesapeake Bay Resources. The Pennsylvania Academy Science: 158-164.
Digby, P. G. N. & R. A. Kempton, 1987. Multivariate Analysis of Ecological Communities. Chapman & Hall, London.
Fasham, Jr. M., 1977. A comparison of non-metric multidimensional scaling, principal component analysis and reciprocal averaging for the ordination of simulated coenoclines and coenoplanes. Ecology 58: 551-561.
Fisher, D. C. & M. Oppenheimer, 1991. Atmospheric nitrogen deposition and the Chesapeake Bay Estuary. Ambio 20: 102-108.
Gauch, H. G., 1982a. Multivariate Analysis in Community Ecology. Cambridge University Press, Cambridge.
Gauch, H. G., 1982b. Noise reduction by eigenvector ordinations. Ecology. 63: 1643-1649.
Goh, B. P. L. & L. M. Chou, 1997. Heavy metal level in marine sediments of Singapore. Environ. Monit. Assess 44: 67-80.
Golledge, R. G. & G. Rushton, 1972. Multi-dimensional Scaling: Review and Geographical Application. A.A.G. Geographical Technical Paper Series Commission on College Geography Technical Paper: 10.
Goodall, D. W., 1954. Objective methods for the classification of vegetation III: An essay on the use of factor analysis. Aust. J. Bot. 2: 304-324.
Grace, J. B., L. Allain & C. Allen, 2000. Vegetation associations in a rare community type-coastal tallgrass prairie. Plant Ecol. 147: 105-115.
Green, P. E. & F. J. Carmone, 1972. Multi-dimensional Scaling and Related Techniques in Marketing Analysis. Allyn and Bacon, Boston.
Hall, L. W. Jr, M. C. Scott, W. D. Jr. Killen, & R. D. Anderson, 1996. The effects of land use characteristics and acid sensitivity on the ecological status of Maryland Coastal Plain streams. Environ. Toxicol. Chem. 15(3): 384-394.
Hamilton, P. A. & R. J. Shedlock, 1992. Are Fertilizers and Pesticides in the Ground Water? A Case Study of the Delmarva Peninsula. Geological Survey Circular (U.S.): 1080.
Hershner, C. & R. L. Wetzel, 1987. Submerged and emergent aquatic vegetation of the Chesapeake Bay. In: S. K. Majumdar, L. W. Jr. Hall & M. Austin (eds), Contaminant Problems and Management of Living Chesapeake Bay Resources. The Pennsylvania Academy of Science: 116-133.
Hojo, H., 1993. A new nonmetric multidimensional scaling method for sorting data. Jap. Psy. Res. 35(3): 129-139.
Jaworski, N. A., P. M. Groffman, A. A. Keller & J. C. Prager, 1992. A watershed nitrogen and phosphorus balance: The Upper Potomac River Basin. Estuaries 15: 83-95.
Johnston, J., 1972. Econometric Methods. McGraw Hill, New York.
Karr, J. R., 1991. Biological integrity: A long-neglected aspect of water resource management. Ecol. Appl. 1: 66-84.
Kemp, W. M., R. R. Twilley, J. C. Stevenson, W. R. Boynton & J. C. Means, 1983. The decline of submerged vascular plants in Upper Chesapeake Bay: Summary of results concerning possible causes. MTS J. 17(2): 78-89.
Kenkel, N. C. & L. Orlóci, 1986. Applying metric and nonmetric multidimensional scaling to ecological studies: Some new results. Ecology 67: 919-928.
Kruskal, J. B., 1964. Non-metric multi-dimensional scaling: A numerical method. Psychometri. 29: 115-129.
Kruskal, J. B., 1971. Multidimensional scaling in archaeology: Time is not the only dimension. In: F. R. Hodson, D. G. Kendall & P. Tautu (eds), Mathematics in the Archaeological and Historical Sciences. Edinburgh University Press, Edinburgh: 119-132.
Kruskal, J. B., 1977. Multidimensional scaling and other methods for discovering structure. In: K. Enslein, A. Ralston & H. S. Wilf (eds), Statistical Methods for Digital Computers. Wiley, New York: 296-339.
Kruskal, J. B. & F. Carmone, 1969. How to Use MDSCAL and Other Useful Information. Bell Telephone Laboratories, New Jersey.
Kruskal, J. B. & M. Wish, 1978. Multidimensional Scaling. Sage, Newbury Park, CA.
Lang, D. J., 1982. Water Quality of the Three Major Tributaries to the Chesapeake Bay, the Susquehanna, Potomac and James Rivers, Jan 1979 to April 1981. U.S.G.S. Water Resource Investigation: 82-32.
Lowrance, R. R., 1983. Waterborne nutrient budgets for the riparian zone of an agricultural watershed. AEENDO 10: 371-384.
Macknis, J., M. E. Gillelan & C. E. Glotfelty, 1983. Land use methodology and data. Appendix B, Section 2. In: Chesapeake Bay: A Framework for Action. U.S.E.P.A. Chesapeake Bay Program, Washington, DC.
Marshall, S. & M. Elliot, 1998. Environmental influences on the fish assemblage of the Humber Estuary, U.K. Estuarine Coastal Shelf Sci. 46(2): 175-184.
Maryland Department of Natural Resources, 1997a. Maryland Biological Stream Survey: Ecological Status of Non-tidal Streams in Six Basins Sampled in 1995. Chesapeake Bay and Watershed Programs, Monitoring and Non-tidal Assessment.
Maryland Department of Natural Resources, 1997b. Guide to Using 1995 Maryland Biological Stream Survey Data. Chesapeake Bay and Watershed Programs, Monitoring and Non-tidal Assessment Division.
Maryland Department of Natural Resources, 1999. An Eye on Maryland Streams. MBSS Newsletter, 4(1).
Maryland Department of Natural Resources, 2000. Maryland Biological Stream Survey. Sampling Manual. Monitoring and Nontidal Assessment Division.
McRae, G., D. K. Camp, W. G. Lyons & T. L. Dix, 1998. Relating benthic infaunal community structure to environmental variables in estuaries using nonmetric multidimensional scaling and similarity analysis. Environ. Monit. Assess. 51: 233-246.
Miller, J. E., R. N. Shepard & J. J. Chang, 1964. An analytical approach to the interpretation of multidimensional scaling solutions. Am. Psychol. 19: 579-580.
Minchin, P. R., 19878a. Simulation of multidimensional community pattern: Toward a comprehensive model. Vegetatio 71: 145-156.
Minchin, P. R., 1987b. An evaluation of the relative robustness of techniques for ecological ordination. Vegetatio 69: 89-107.
Ohio E. P. A., 1987. Biological Criteria for the Protection of Aquatic Life, Vol. I-III. Division of Water Quality Monitoring and Assessment, Surface Water Section, Columbus, Ohio.
Plafkin, J. L., M. T. Barbour, K. D. Porter, S. K. Gross & R. M. Hughes, 1989. Rapid Bioassessment Protocols for Use in Streams and Rivers: Benthic Macroinvertebrates and Fish. U.S.E.P.A., Office of Water, Washington, DC.
Pielou, E. C., 1984. The Interpretation of Ecological Data: A primer on Classification and Ordination. Wiley, New York.
Pitakänen, S., 2000. Classification of vegetation diversity in managed boreal forests in eastern Finland. Plant Ecol. 146: 11-28.
Preikshot, D., E. Nsiku, T. Pitcher & D. Pauly, 1998. An interdisciplinary evaluation of the status and health of African lake fisheries using a rapid appraisal technique. J. Fish Biol. 53(A): 381-393.
Price, K. S., D. A. Flemer, J. L. Taft, G. B. Mackiernan, W. Nehlsen & R. B. Biggs, 1985. Nutrient enrichment of Chesapeake Bay and its impact on the habitat of striped bass: A speculative hypotheses. Trans. Am. Fisheries Soc. 114: 97-106.
Rankin, E. T., 1989. The Qualitative Habitat Evaluation Index (QHEI): Rationale, Methods and Application. Ohio E.P.A., Columbus, Ohio.
Rundle, S. D., M. J. Attrill & A. Arshad, 1998. Seasonality in macroinvertebrate community composition across a neglected ecological boundary, the fresh water-estuarine transition zone. Aquat Ecol. 32: 211-216.
Sellner, K. G., 1987. Phytoplankton in Chesapeake Bay: Role in carbon, oxygen and nutrient dynamics. In: S.K. Majumdar, L.W. Jr. Hall & H.M. Austin (eds), Contaminant Problems and Management of Living Chesapeake Bay Resources. The Pennsylvania Academy Science: 134-157.
Shepard, R. N., 1980. Multidimensional scaling, trends in ecology and evolution-fitting, and clustering. Science 210: 390-398.
Sheppard, C. R. C., 1995. Species and community changes along environmental and pollution gradients. Marine Pollut. Bull. 30(8): 504-514.
Sparks, T. H., W. A. Scott & R. T. Clarke, 1999. Traditional multivariate techniques: Potential for use in ecotoxicology. Environ. Toxicol. Chem. 18(2): 128-137.
Stalans, L. J., 1995. Multidimensional scaling. In: L. G. Grimm & P. R. Yarnold (eds), Reading and Understanding Multivariate Statistics. American Psychological Association, Washington: 131-168.
Stenson, H. H. & R. C. Knoll, 1969. Goodness of fit for random rankings in Kruskal's nonmetric scaling procedure. Psychol. Bull. 71: 122-126.
ter Braak, C. J. F., 1985. Correspondence analysis of incidence and abundance data: Properties in terms of a unimodal response model. Biometrics 41: 859-873.
Tong, S. T. Y., 1989. On non-metric multidimensional scaling ordination and interpretation of the matorral vegetation in lowland Murcia. Vegetatio 79: 65-74.
Tong, S. T. Y., 1992. The use of non-metric multidimensional scaling as an ordination technique in resource survey and evaluation: A case study from southeast Spain. Appl. Geog. 12: 243-260.
Trosset, M. W., 1998. A new formulation of the nonmetric strain problem in multidimensional scaling. J. Classification. 15: 15-35.
U.S.E.P.A., 1983. Chesapeake Bay: A Framework for Action.
U.S.E.P.A., Chesapeake Bay Program, Washington, DC.
U.S.E.P.A., 1985. National primary drinking water regulations: Synthetic organic chemicals, inorganic chemicals, and microorganisms. Proposed Rule. Federal Registrar 50: 46935-47022.
U.S.D.A., 1991. Nitrate Occurrence in U.S. Waters. U.S.D.A., Washington, DC.
Warwick, R. M., M. R. Carr, K. R. Clarke, J. M. Gee & R. H. Green, 1988. A mesocosm experiment on the effects of hydrocarbon and copper pollution on a sublittoral soft-sediment meiobenthic community. Mar. Ecol. Progr. Ser. 46: 181-191.
Wish, M. & J. D. Carroll, 1982. Multidimensional scaling and its application. In: P. R. Krishnaiah & L. N. Kanal (eds), Handbook of Statistics. North-Holland Publishing. Amsterdam: (2) 317-345.
Young, M. P., J. W. Scannell & C. Blakemore, 1995. Non-metric multidimensional scaling in the analysis of neuroanatomical connection data and the organization of the primate cortical visual system. Phil. Trans. R. Soc. 345(1325): 281-308.
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Tong, S.T. An integrated exploratory approach to examining the relationships of environmental stressors and fish responses. Journal of Aquatic Ecosystem Stress and Recovery 9, 1–19 (2001). https://doi.org/10.1023/A:1013184311165
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DOI: https://doi.org/10.1023/A:1013184311165