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References

Published online by Cambridge University Press:  04 December 2009

James B. Grace
Affiliation:
USGS National Wetlands Research Center, Louisiana
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Print publication year: 2006

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References

Aarssen, L. W. & Schamp, B. S. (2002). Predicting distributions of species richness and species size in regional floras: Applying the species pool hypothesis to the habitat templet model. Perspectives in Plant Ecology, Evolution and Systematics, 5, 3–12CrossRefGoogle Scholar
Abelson, R. P. (1995). Statistics as Principled Argument. Hillsdale, NJ: Lawrence Erlbaum PublishersGoogle Scholar
Abrams, P. A. (1995). Monotonic or unimodal diversity – productivity gradients: what does competition theory predict? Ecology, 76, 2019–2027CrossRefGoogle Scholar
Agrawal, R.,Imielienski, T., & Swami, A. (1993). In: Mining association rules between sets of items in large databases. Proceedings of Conference on Management of Data. New York:ACM PressGoogle Scholar
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control AC, 19, 716–723Google Scholar
Al-Mufti, M. M., Sydes, C. L., Furness, S. B., Grime, J. P., & Band, S. R. (1977). A quantitative analysis of shoot phenology and dominance in herbaceous vegetation. Journal of Ecology, 65, 759–791CrossRefGoogle Scholar
Andersen, J. A. (1995). An Introduction to Neural Networks. Cambridge, MA: MIT PressGoogle Scholar
Anderson, D. R., Burnham, K. P., & Thompson, W. L. (2000). Null hypothesis testing: problems, prevalence, and an alternative. Journal of Wildlife Management, 64, 912–923CrossRefGoogle Scholar
Bacon, F. (1620). Novum Organum. London: Bonham Norton and John BillGoogle Scholar
Baldwin, H. Q. (2005). Effects of fire on home range size, site fidelity, and habitat associations of grassland birds overwintering in southeast Texas. M. S. thesis, Louisiana State University, Baton Rouge
Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418CrossRefGoogle Scholar
Bezdek, J. C. & Pal, N. (1992). Fuzzy Models for Pattern Recognition. New York: IEEE PressGoogle Scholar
Blalock, H. M. (1964). Causal Inferences in Nonexperimental Research. Chapel Hill, NC: University of North Carolina PressGoogle Scholar
Bollen, K. A. (1984). Multiple indicators: internal consistency or no necessary relationship. Quality and Quantity, 18, 377–385CrossRefGoogle Scholar
Bollen, K. A. (1989). Structural Equations with Latent Variables. New York: John Wiley & SonsCrossRefGoogle Scholar
Bollen, K. A. (1996). An alternative 2SLS estimator for latent variable models. Psychometrika, 61, 109–121CrossRefGoogle Scholar
Bollen, K. A. (1998). Path analysis. pp. 3280–3284. In: Encyclopedia of Biostatistics. Armitage, P. and Colton, T. (eds.). New York: John Wiley & SonsGoogle Scholar
Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53, 605–634CrossRef
Bollen, K. A. & Lennox, R. (1991). Conventional wisdom on measurement: a structural equation perspective. Psychological Bulletin, 110, 305–314CrossRefGoogle Scholar
Bollen, K. A. & Long, J. S. (eds.) (1993). TestingStructural Equation Models. Newbury Park, CA: Sage PublicationsGoogle Scholar
Bollen, K. A. & Stine, R. (1992). Bootstrapping goodness of fit measures in structural equation models. Sociological Methods and Research, 21, 205–229CrossRefGoogle Scholar
Bollen, K. A. & Ting, K. (2000). A tetrad test for causal indicators. Psychological Methods, 5, 3–22CrossRefGoogle ScholarPubMed
Borgelt, C. & Kruse, R. (2002). Graphical Models. New York: John Wiley & SonsGoogle Scholar
Bozdogan, H. (1987). Model selection and Akaike's Information Criterion (AIC). Psychometrika, 52, 345–370CrossRefGoogle Scholar
Brewer, J. S. & Grace, J. B. (1990). Vegetation structure of an oligohaline tidal marsh. Vegetatio, 90, 93–107CrossRefGoogle Scholar
Browne, M. W. & Cudeck, R. (1989). Single sample cross-validation indices for covariance structures. Multivariate Behavioral Research, 24, 445–455CrossRefGoogle ScholarPubMed
Burnham, K. P. & Anderson, D. R. (2002). Model Selection and Multimodel Inference. Second Edition. New York: Springer VerlagGoogle Scholar
Byrne, B. M. (1994). Structural Equation Modeling EQS and EQS/Windows. Thousand Oaks, CA: Sage PublicationsGoogle Scholar
Byrne, B. M. (1998). Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS. Mahway, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Byrne, B. M. (2001). Structural Equation Modeling with AMOS. Mahway, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Campbell, D. R., Waser, N. M., Price, M. V., Lynch, E. A., & Mitchell, R. J. (1991). A mechanistic analysis of phenotypic selection: pollen export and corolla width in Ipomopsis aggregata. Evolution, 43, 1444–1455Google Scholar
Casella, B. (1992). Explaining the Gibbs sampler. The American Statistician, 46, 167–174Google Scholar
Congdon, P. (2001). Bayesian Statistical Modeling. Chichester: Wiley PublishersGoogle Scholar
Congdon, P. (2003). Applied Bayesian Modeling. Chichester: Wiley PublishersCrossRefGoogle Scholar
Cottingham, K. L., Lennon, J. T., & Brown, B. L. (2005). Knowing when to draw the line: designing more informative ecological experiments. Frontiers in Ecology, 3, 145–152CrossRefGoogle Scholar
Cudeck, R., Toit, Du S. H. C., & Sörbom, D. (eds.) (2001). Structural Equation Modeling: Present and Future. Lincolnwood, IL: SSI Scientific Software InternationalGoogle Scholar
Dasarathy, B. V. (1990). Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. Los Alamitos, CA: IEEE Computer Science PressGoogle Scholar
Diamantopoulous, A. & Winklhofer, H. M. (2001). Index construction with formative indicators: an alternative to scale development. Journal of Marketing Research, 38, 269–277CrossRefGoogle Scholar
Duncan, T. E., Duncan, S. C., Strycker, L. A., Li, F., & Alpert, A. (1999). An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications. Mahwah, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Edwards, J. R. (2001). Multidimensional constructs in organizational behavior research: an integrative analytical framework. Organizational Research Methods, 4, 144–192CrossRefGoogle Scholar
Fan, X., Thompson, B., & Wang, L. (1999). Effects of sample size, estimation methods and model specification on structural equation modeling fit indexes. Structural Equation Modeling, 6, 56–83CrossRef
Fisher, R. A. (1956). Statistical Methods and Scientific Inference. Edinburgh, UK: Oliver and BoydGoogle Scholar
Fornell, C., ed. (1982). A Second Generation of Multivariate Analyses: Volumes 1 and II. New York: Praeger Publishers.Google Scholar
Gadgil, M. & Solbrig, O. T. (1972). The concept of r- and K-selection: evidence from wild flowers and some theoretical considerations. American Naturalist, 106, 14–31CrossRefGoogle Scholar
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian Data Analysis. Boca Raton: Chapman & HallGoogle Scholar
Glymour, B., Scheines, R., Spirtes, R., & Kelly, K. (1987). Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling. Orlando, FL: Academic PressGoogle Scholar
Goldberger, A. S. & Duncan, O. D. (1973). Structural Equation Models in the Social Sciences. New York: Seminar PressGoogle Scholar
Good, I. J. (1983). Good Thinking. Minneapolis: University of Minnesota PressGoogle Scholar
Gough, L. & Grace, J. B. (1999). Predicting effects of environmental change on plant species density: experimental evaluations in a coastal wetland. Ecology, 80, 882–890CrossRefGoogle Scholar
Gough, L., Grace, J. B., & Taylor, K. L. (1994). The relationship between species richness and community biomass: the importance of environmental variables. Oikos, 70, 271–279CrossRefGoogle Scholar
Grace, J. B. (1991). A clarification of the debate between Grime and Tilman. Functional Ecology, 5, 503–507CrossRefGoogle Scholar
Grace, J. B. (1999). The factors controlling species density in herbaceous plant communities: an assessment. Perspectives in Plant Ecology, Evolution and Systematics, 2, 1–28CrossRefGoogle Scholar
Grace, J. B. (2001). The roles of community biomass and species pools in the regulation of plant diversity. Oikos, 92, 191–207CrossRefGoogle Scholar
Grace, J. B. (2003a). Comparing groups using structural equations. chapter 11, pp. 281– 296. In: Pugesek, B. H., Tomer, A., & Eye, A. (eds.). Structural Equation Modeling. Cambridge: Cambridge University PressGoogle Scholar
Grace, J. B. (2003b). Examining the relationship between environmental variables and ordination axes using latent variables and structural equation modeling. chapter 7, pp. 171–193. In: Pugesek, B. H., Tomer, A., & Eye, A. (eds.). Structural Equation Modeling. Cambridge: Cambridge University PressGoogle Scholar
Grace, J. B. & Bollen, K. A. (2005). Interpreting the results from multiple regression and structural equation models. Bulletin of the Ecological Society of America, 86, 283–295CrossRef
Grace, J. B. & Guntenspergen, G. R. (1999). The effects of landscape position on plant species density: evidence of past environmental effects in a coastal wetland. Ecoscience, 6, 381–391CrossRefGoogle Scholar
Grace, J. B. & Jutila, H. (1999). The relationship between species density and community biomass in grazed and ungrazed coastal meadows. Oikos, 85, 398–408CrossRefGoogle Scholar
Grace, J. B. & Keeley, J. E. (2006). A structural equation model analysis of postfire plant diversity in California shrublands. Ecological Applications, 16, 503–514CrossRef
Grace, J. B. & Pugesek, B. (1997). A structural equation model of plant species richness and its application to a coastal wetland. American Naturalist, 149, 436–460CrossRefGoogle Scholar
Grace, J. B. & Pugesek, B. H. (1998). On the use of path analysis and related procedures for the investigation of ecological problems. American Naturalist 152, 151–159CrossRefGoogle ScholarPubMed
Grace, J. B., Allain, L., & Allen, C. (2000). Factors associated with plant species richness in a coastal tall-grass prairie. Journal of Vegetation Science, 11, 443–452CrossRefGoogle Scholar
Grime, J. P. (1973). Competitive exclusion in herbaceous vegetation. Nature, 242, 344–347CrossRefGoogle Scholar
Grime, J. P. (1977). Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. American Naturalist, 111, 1169–1194CrossRefGoogle Scholar
Grime, J. P. (1979). Plant Strategies and Vegetation Processes. London: John Wiley & SonsGoogle Scholar
Grime, J. P. (2001). Plant Strategies, Vegetation Processes, and Ecosystem Properties. London: John Wiley & SonsGoogle Scholar
Grime, J. P. (2002). Declining plant diversity: empty niches or functional shifts? Journal of Vegetation Science, 13, 457–460CrossRefGoogle Scholar
Grimm, V. (1994). Mathematical models and understanding in ecology. Ecological Modelling, 74, 641–651CrossRefGoogle Scholar
Gross, K. L., Willig, M. R., & Gough, L. (2000). Patterns of species density and productivity at different spatial scales in herbaceous plant communities. Oikos, 89, 417–427CrossRefGoogle Scholar
Grubb, P. J. (1998). A reassessment of the strategies of plants which cope with shortages of resources. Perspectives in Plant Ecology, Evolution and Systematics, 1, 3–31CrossRefGoogle Scholar
Hägglund, G. (2001). Milestones in the history of factor analysis. pp. 11–38. In: Cudeck, R., Toit, S. H. C. Du, & Sörbom, D. (eds.). Structural Equation Modeling: Present and Future. Lincolnwood, IL: SSI Scientific Software InternationalGoogle Scholar
Hair, J. F., Anderson, R. E.Jr., Tatham, R. L., & Black, W. C. (1995). Multivariate Data Analysis. Fourth Edition. Englewood Cliffs, NJ: Prentice HallGoogle Scholar
Hannon, B. & Ruth, M. (1997). Modeling Dynamic Biological Systems. New York: SpringerGoogle Scholar
Hargens, L. L. (1976). A note on standardized coefficients as structural parameters. Sociological Methods & Research, 5, 247–256CrossRefGoogle Scholar
Harrison, S., Safford, H. D., Grace, J. B., Viers, J. H., & Davies, K. F. (2006). Regional and local species richness in an insular environment: serpentine plants in California. Ecological Monographs, 76, 41–56CrossRef
Hastings, W. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97–106CrossRefGoogle Scholar
Hayduk, L. A. (1987). Structural Equation Modeling with LISREL. Baltimore, MD: Johns Hopkins University PressGoogle Scholar
Hayduk, L. A. (1996). LISREL Issues, Debates, and Strategies. Baltimore, MD: Johns Hopkins University PressGoogle Scholar
Heise, D. R. (1972). Employing nominal variables, induced variables, and block variables in path analyses. Sociological Methods & Research, 1, 147–173CrossRefGoogle Scholar
Hodson, J. G., Thompson, K., Wilson, P. J., & Bogaard, A. (1998). Does biodiversity determine ecosystem function? The ecotron experiment reconsidered. Functional Ecology, 12, 843–848Google Scholar
Hox, J. (2002). Multilevel Analysis. Mahway, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Hoyle, R. H. (ed.) (1999). Statistical Strategies for Small Sample Research. Thousand Oaks, CA: Sage PublicationsGoogle Scholar
Huston, M. A. (1979). A general hypothesis of species diversity. American Naturalist, 113, 81–101CrossRefGoogle Scholar
Huston, M. A. (1980). Soil nutrients and tree species richness in Costa Rican forests. Journal of Biogeography, 7, 147–157CrossRefGoogle Scholar
Huston, M. A. (1994). Biological Diversity. Cambridge: Cambridge University PressGoogle Scholar
Huston, M. A. (1997). Hidden treatments in ecological experiments: Re-evaluating the ecosystem function of biodiversity. Oecologia, 110, 449–460CrossRefGoogle ScholarPubMed
Huston, M. A. (1999). Local processes and regional patterns: appropriate scales for understanding variation in the diversity of plants and animals. Oikos, 86, 393–401CrossRefGoogle Scholar
Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30, 199–218CrossRefGoogle Scholar
Jedidi, K. & Ansari, A. (2001). Bayesian structural equation models for multilevel data. pp. 129–158. In: Marcoulides, B. A. & Schumacker, R. E. (eds.), New Developments and Techniques in Structural Equation Modeling. Mahway, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Jensen, F. V. (2001). Bayesian Networks and Decision Graphs. New York: Springer VerlagCrossRefGoogle Scholar
Johnson, M. L., Huggins, D. G., & Noyelles, F. Jr. (1991). Ecosystem modeling with LISREL: a new approach for measuring direct and indirect effects. Ecological Applications, 1, 383–398CrossRefGoogle ScholarPubMed
Johnson, J. B. (2002). Divergent life histories among populations of the fish Brachyrhaphis rhabdophora: detecting putative agents of selection by candidate model analysis. Oikos, 96, 82–91CrossRefGoogle Scholar
Jöreskog, K. G. (1973). A general method for estimating a linear structural equation system. pp. 85–112. In: Goldberger, A. S. & Duncan, O. D. (eds.). Structural Equation Models in the Social Sciences. New York: Seminar PressGoogle Scholar
Jöreskog, K. G. & Sörbom, D. (1996). LISREL 8: User's Reference Guide. Chicago: Scientific Software InternationalGoogle Scholar
Jutila, H. & Grace, J. B. (2002). Effects of disturbance and competitive release on germination and seedling establishment in a coastal prairie grassland. Journal of Ecology, 90, 291–302CrossRefGoogle Scholar
Kaplan, D. (2000). Structural Equation Modeling: Foundations and Extensions. Thousand Oaks, CA: Sage Publishers.Google Scholar
Kaplan, D., Harik, P., & Hotchkiss, L. (2001). Cross-sectional estimation of dynamic structural equation models in disequilibrium. pp. 315–339. In: Cudeck, R., Toit, S. H. C. Du & Sörbom, D. (eds.). Structural Equation Modeling: Present and Future. Lincolnwood, IL: SSI Scientific Software InternationalCrossRefGoogle Scholar
Keddy, P. A. (1990). Competitive hierarchies and centrifugal organization in plant communities. pp. 265–289. In: Grace, J. B. & Tilman, D. (eds.). Perspectives on Plant Competition, New York: Academic PressGoogle Scholar
Keesling, J. W. (1972). Maximum Likelihood Approaches to Causal Flow Analysis. Ph.D. Dissertation, Department of Education, University of ChicagoGoogle Scholar
Kelloway, E. K. (1998). Using LISREL for Structural Equation Modeling. Thousand Oaks, CA: Sage PublicationsGoogle Scholar
Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling. 2nd Edition. New York: The Guilford PressGoogle Scholar
Langley, P., Iba, W., & Thompson, K. (1992). An analysis of Bayesian classifiers. In: Proceedings of the 10th National Conference on Artificial Intelligence. pp. 223–228. Cambridge, MA: MIT PressGoogle Scholar
Laplace, P. S. (1774). Mémoire sur la probabilité des causes par les événements. Mémoires de l'Academie de Science de Paris, 6, 621–656Google Scholar
Larson, D. L. & , Grace J. B. (2004). Temporal dynamics of leafy spurge (Euphorbia esula) and two species of flea beetles (Aphthona spp.) used as biological control agents. Biological Control, 29, 207–214CrossRefGoogle Scholar
Lawton, J. H., Naeem, S., Thompson, L. J., Hector, A., & Crawley, J. J. (1998). Biodiversity and ecosystem function: getting the ecotron experiment in its correct context. Functional Ecology, 12, 848–852Google Scholar
Lee, S. Y., & Bentler, P. M. (1980). Some asymptotic properties of constrained generalized least squares estimation in covariance structure models. South African Statistical Journal, 14, 121–136Google Scholar
Legendre, P. (1993). Spatial autocorrelation: Trouble or a new paradigm. Ecology, 74, 659–673CrossRefGoogle Scholar
Levins, R. (1968). Evolution in Changing Environments. Princeton, NJ: Princeton University PressGoogle Scholar
Li, C. C. (1975). Path Analysis – A primer. Pacific Grove, CA: Boxwood PressGoogle Scholar
Little, T. D., Schnabel, K. U., & Baumert, J. (eds.) (2000). Modeling Longitudinal and Multilevel Data. Mahway, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Loehle, C. (1987). Hypothesis testing in ecology: psychological aspects and the importance of theory maturation. The Quarterly Review of Biology, 62, 397–409CrossRefGoogle ScholarPubMed
Loehle, C. (1988). Problems with the triangular model for representing plant strategies. Ecology, 69, 284–286CrossRefGoogle Scholar
Loehlin, J. C. (1998). Latent Variable Models. Third Edition. Mahway, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Loreau, M., Naeem, S., & Inchausti, P. (2002). Biodiversity and Ecosystem Functioning. Oxford: Oxford University PressGoogle Scholar
MacArthur, R. H. & Wilson, E. O. (1967). The Theory of Island Biogeography. Princeton, NJ: Princeton University PressGoogle Scholar
MacCallum, R. C. & Browne, M. W. (1993). The use of causal indicators in covariance structure models: some practical issues. Psychological Bulletin, 114, 533–541CrossRefGoogle ScholarPubMed
Mancera, J. E., Meche, G. C., Cardona-Olarte, P. P.et al. (2005). Fine-scale environmental control of spatial variation in species richness in a wetland community. Plant Ecology, 178, 39–50CrossRefGoogle Scholar
Marcoulides, G. A. & Schumacker, R. E. (eds.) (1996). Advanced Structural Equation Modeling: Issues and Techniques. Mahway, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Marcoulides, G. A. & Schumacker, R. E. (eds.) (2001). New Developments and Techniques in Structural Equation Modeling. Mahway, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Marrs, R., Grace, J. B., & Gough, L. (1996). On the relationship between plant species diversity and biomass: a comment on a paper by Gough, Grace, and Taylor. Oikos, 75, 323–326CrossRefGoogle Scholar
Marsh, H. W., Balla, J. R., & Hau, K.-T. (1996). An evaluation of incremental fit indices: a clarification of mathematical and empirical properties. pp. 315–353. In: Marcoulides, B. A. & Schumacker, R. E. (eds.). Advanced Structural Equation Modeling.Mahway, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: comment on hypothesis testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler's findings. Structural Equation Modeling, 11, 320–341CrossRefGoogle Scholar
Maruyama, G. M. (1998). Basics of Structural Equation Modeling. Thousand Oaks, CA: Sage PublicationsCrossRefGoogle Scholar
McCune, B. & Grace, J. B. (2002). Analysis of Ecological Communities. Gleneden Beach, Oregon: MJMGoogle Scholar
Meziane, D. & Shipley, B. (2001). Direct and indirect relationships between specific leaf area, leaf nitrogen and leaf gas exchange. Effects of irradiance and nutrient supply. Annals of Botany, 88, 915–927CrossRefGoogle Scholar
Mikola, J., Salonen, V., & Setälä, H (2002). Studying the effects of plant species richness on ecosystem functioning: does the choice of experimental design matter? Oecologia, 133, 594–598CrossRefGoogle ScholarPubMed
Mitchell, R. J. (1992). Testing evolutionary and ecological hypotheses using path analysis and structural equation modelling. Functional Ecology, 6, 123–129CrossRefGoogle Scholar
Mitchell, R. J. (1994). Effects of floral traits, pollinator visitation, and plant size on Ipomopsis aggregata fruit production. The American Naturalist, 143, 870–889CrossRefGoogle Scholar
Mittelbach, G. G., Steiner, C. F., Scheiner, S. M.et al. (2001). Ecology, 82, 2381–2396CrossRef
Moore, D. R. J. & Keddy, P. A. (1989). The relationship between species richness and standing crop in wetlands: the importance of scale. Vegetatio, 79, 99–106CrossRefGoogle Scholar
Muggleton, S. (ed.) (1992). Inductive Logic Programming. San Diego, CA: Academic PressGoogle Scholar
Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49, 115–132CrossRefGoogle Scholar
Muthén, L. K. & Muthén, B. O. (2004). Mplus User's Guide. Third Edition. Los Angeles, CA: Muthén and MuthénGoogle Scholar
Naeem, S. (2002). Ecosystem consequences of biodiversity loss: the evolution of a paradigm. Ecology, 83, 1537–1552CrossRefGoogle Scholar
Neapolitan, R. E. (2004). Learning Bayesian Networks. Upper Saddle River, NJ: Prentice Hall PublishersGoogle Scholar
Oakes, M. (1990). Statistical Inference. Chestnut Hill, MA: Epidemiology Resources IncGoogle Scholar
Palmer, M. W. (1994). Variation in species richness: towards a unification of hypotheses. Folia Geobot. Phytotax. Praha, 29, 511–530CrossRefGoogle Scholar
Pankratz, A. (1991). Forecasting with Dynamic Regression Models. New York: John Wiley & SonsCrossRefGoogle Scholar
Pearl, J. (1992). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan KaufmannGoogle Scholar
Pearl, J. (2000). Causality. Cambridge: Cambridge University PressGoogle Scholar
Pedhazur, E. J. (1997). Multiple Regression in Behavioral Research, 3rd edition. Toronto: Wadsworth PressGoogle Scholar
Peters, R. H. (1991). A Critique for Ecology. Cambridge: Cambridge University PressGoogle Scholar
Pianka, E. R. (1970). On r- and K-selection. American Naturalist, 104, 592–597CrossRefGoogle Scholar
Popper, K. R. (1959). The Logic of Scientific Discovery. London: HutchinsonGoogle Scholar
Pugesek, B. H. (2003). Modeling means in latent variable models of natural selection. pp. 297–311. In: Pugesek, B. H., Tomer, A., & Eye, A. (eds.). Structural Equation Modeling. Cambridge: Cambridge University PressCrossRefGoogle Scholar
Pugesek, B. H. & Tomer, A. (1996). The Bumpus house sparrow data: a reanalysis using structural equation models. Evolutionary Ecology, 10, 387–404CrossRefGoogle Scholar
Pugesek, B. H., Tomer, A., & Eye, A. (2003). Structural Equation Modeling. Cambridge: Cambridge University PressCrossRefGoogle Scholar
Raftery, A. E. (1993). Bayesian model selection in structural equation models. pp. 163–180. In: Bollen, K. A. & Long, J. S. (eds.). Testing Structural Equation Models. Newbury Park, CA: Sage PublishersGoogle Scholar
Raykov, T. & Marcoulides, G. A. (2000). A First Course in Structural Equation Modeling. Mahway, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Raykov, T. & Penev, S. (1999). On structural equation model equivalence. Multivariate Behavioral Research, 34, 199–244CrossRefGoogle ScholarPubMed
Reich, P. B., Ellsworth, D. S., Walters, M. B.et al. (1999). Generality of leaf trait relationships: a test across six biomes. Ecology, 80, 1955–1969CrossRefGoogle Scholar
Reyment, R. A. & Jöreskog, K. G. (1996). Applied Factor Analysis in the Natural Sciences. Cambridge: Cambridge University PressGoogle Scholar
Rosenzweig, M. L. & Abramsky, Z. (1993). How are diversity and productivity related? pp. 52–64. In: Ricklefs, R. E. & Schluter, D. (eds.). Species Diversity in Ecological Communities. Chicago: University of Chicago PressGoogle Scholar
Rupp, A. A., Dey, D. K., & Zumbo, B. D. (2004). To Bayes or not to Bayes, from whether to when: Applications of Bayesian methodology to modeling. Structural Equation Modeling, 11, 424–451CrossRefGoogle Scholar
Salsburg, D. (2001). The Lady Tasting Tea. New York: Henry Holt & CompanyGoogle Scholar
Satorra, A. & Bentler, P. M. (1988). Scaling corrections for chi-square statistics in covariance structure analysis. pp. 308–313. In: Proceedings of the American Statistical AssociationGoogle Scholar
Satorra, A. & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. pp. 399–419. In: Eye, A. & Clogg, C. C. (eds.). Latent Variables Analysis: Applications for Developmental Research. Thousand Oaks, CA: Sage PublishersGoogle Scholar
Scheiner, S. M., Mitchell, R. J., & Callahan, H. S. (2000). Using path analysis to measure natural selection. Journal of Evolutionary Biology, 13, 423–433CrossRefGoogle Scholar
Scheines, R., Hoijtink, R., & Boomsma, A. (1999). Bayesian estimation and testing of structural equation models. Psychometrika, 64, 37–52CrossRefGoogle Scholar
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Test of significance and descriptive goodness-of-fit measures. Methods of Psychological Research – Online, 8, 23–74Google Scholar
Schumacker, R. E. & Lomax, R. G. (eds.) (1996). A Beginner's Guide to Structural Equation Modeling. Mahway, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Schumacker, R. E. & Marcoulides, G. A. (eds.) (1998). Interaction and Nonlinear Effects in Structural Equation Modeling. Mahway, NJ: Lawrence Erlbaum AssociatesGoogle Scholar
Shipley, B. (2000). Cause and Correlation in Biology. Cambridge: Cambridge University PressCrossRefGoogle Scholar
Shipley, B. & Lechowicz, M. J. (2000). The functional coordination of leaf morphology and gas exchange in 40 wetland plant species. Ecoscience, 7, 183–194CrossRefGoogle Scholar
Shipley, B., Keddy, P. A., Gaudet, C., & Moore, D. R. J. (1991). A model of species density in shoreline vegetation. Ecology, 72, 1658–1667CrossRefGoogle Scholar
Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search. Cambridge: MIT PressGoogle Scholar
Stamp, N. (2003). Theory of plant defense level: example of process and pitfalls in development of ecological theory. Oikos, 102, 672–678CrossRefGoogle Scholar
Stearns, S. C. (1977). The evolution of life history traits: a critique of the theory and a review of the data. Annual Review of Ecology and Systematics, 8, 145–171CrossRefGoogle Scholar
Steiger, J. H. (1990). Structural model evaluation and modification: an interval estimation approach. Multivariate Behavioral Research, 25, 173–180CrossRefGoogle ScholarPubMed
Symstad, J. J, Chapin, F. W., Wall, D. H.et al. (2003). Long-term and large-scale perspectives on the relationship between biodiversity and ecosystem functioning. Bioscience, 53, 89–98CrossRefGoogle Scholar
Taper, M. L. & Lele, S. R. (2004). The Nature of Scientific Evidence. Chicago, Illinois: University of Chicago PressCrossRefGoogle Scholar
Taylor, D. R., Aarssen, L. W., & Loehle, C. (1990). On the relationship between r/K selection and environmental carry capacity: a new habitat templet for plant life history strategies. Oikos, 58, 239–250CrossRefGoogle Scholar
Tilman, D. (1982). Resource competition and community structure. Princeton, NJ: Princeton University PressGoogle ScholarPubMed
Tilman, D. (1986). Resources, competition and the dynamics of plant communities. pp. 51–75. In: Crawley, M. J. (ed.). Plant Ecology. London: Blackwell Scientific PublicationsGoogle Scholar
Tilman, D. (1987). On the meaning of competition and the mechanisms of competitive superiority. Functional Ecology, 1, 304–315CrossRefGoogle Scholar
Tilman, D. (1988). Plant Strategies and the Dynamics and Structure of Plant Communities. Princeton, New Jersey: Princeton University PressGoogle Scholar
Tilman, D. (1997). Mechanisms of plant competition. chapter. In: Crawley, M. J. (ed.). Plant Ecology, 2nd edn. Malden, MA: Blackwell Scientific PublicationsGoogle Scholar
Tilman, D., Wedin, D., & Knops, J. (1996). Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature, 379, 718–720CrossRefGoogle Scholar
Tomer, A. (2003). A short history of structural equation models. pp. 85–124. In: Pugesek, B. H., , A.Tomer, , & Eye, A. (eds.). Structural Equation Modeling. Cambridge: Cambridge University PressGoogle Scholar
Tukey, J. W. (1954). Causation, regression, and path analysis. pp. 35–66. In: Kempthorne, O., Bancroft, T. A.Gowen, J. W., & Lush, J. D. (eds.). Statistics and Mathematics in Biology. Ames, IA: Iowa State College PressGoogle Scholar
Turner, M. E. & Stevens, C. D. (1959). The regression analysis of causal paths. Biometrics, 15, 236–258CrossRefGoogle Scholar
Verheyen, K., Guntenspergen, G. R., Biesbrouck, B., & Hermy, M. (2003). An integrated analysis of the effects of past land use on forest herb colonization at the landscape scale. Journal of Ecology, 91, 731–742CrossRefGoogle Scholar
Mises, R. (1919). Grundlagen der Wahrscheinlichkeitsrechnung. Mathematische Zeitschrift, Vol. 5. (referenced in Neapolitan (2004))CrossRefGoogle Scholar
Waide, R. B., Willig, M. R., Steiner, C. F.et al. (1999). The relationship between productivity and species richness. Annual Reviews in Ecology and Systematics, 30, 257–300CrossRefGoogle Scholar
Wardle, D. A. (1999). Is “sampling effect” a problem for experiments investigating biodiversity–ecosystem function relationships? Oikos, 87, 403–407CrossRefGoogle Scholar
Weiher, E., Forbes, S., Schauwecker, T., & Grace, J. B. (2004). Multivariate control of plant species richness in a blackland prairie. Oikos, 106, 151–157CrossRefGoogle Scholar
Wheeler, B. D. & Giller, K. E. (1982). Species richness of herbaceous fen vegetation in Broadland, Norfolk in relation to the quantity of above-ground plant material. Journal of Ecology, 70, 179–200CrossRefGoogle Scholar
Wheeler, B. D. & Shaw, S. C. (1991). Above-ground crop mass and species richness of the principal types of herbaceous rich-fen vegetation of lowland England and Wales. Journal of Ecology, 79, 285–302CrossRefGoogle Scholar
Wiley, D. E. (1973). The identification problem for structural equation models with unmeasured variables. In: Goldberger, A. S. & Duncan, O. D. (eds.). Structural Equation Models in the Social Sciences. New York: Seminar Press A. S.Google Scholar
Williams, L. J., Edwards, J. R., & Vandenberg, R. J. (2003). Recent advances in causal modeling methods for organizational and management research. Journal of Management, 29, 903–936CrossRefGoogle Scholar
Wilson, J. B. & Lee, W. G. (2000). C-S-R triangle theory: community-level predictions, tests, evaluation of criticisms, and relation to other theories. Oikos, 91, 77–96CrossRefGoogle Scholar
Wisheu, I. C. & Keddy, P. A. (1989). Species richness-standing crop relationships along four lakeshore gradients: constraints on the general model. Canadian Journal of Botany, 67, 1609–1617CrossRefGoogle Scholar
Wootton, J. T. (1994). Predicting direct and indirect effects: an integrated approach using experiments and path analysis. Ecology, 75, 151–165CrossRefGoogle Scholar
Wootton, J. T. (2002). Indirect effects in complex ecosystems: recent progress and future challenges. Journal of Sea Research, 48, 157–172CrossRefGoogle Scholar
Wright, S. (1918). On the nature of size factors. Genetics, 3, 367–374Google ScholarPubMed
Wright, S. (1920). The relative importance of heredity and environment in determining the piebald pattern of guinea pigs. Proceedings of the National Academy of Sciences, 6, 320–332CrossRefGoogle ScholarPubMed
Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 10, 557–585Google Scholar
Wright, S. (1932). General, group, and special size factors. Genetics, 17, 603–619Google ScholarPubMed
Wright, S. (1934). The method of path coefficients. Annals of Mathematical Statistics, 5, 161–215CrossRefGoogle Scholar
Wright, S. (1960). Path coefficients and path regressions: alternative or complementary concepts? Biometrics, 16, 189–202CrossRefGoogle Scholar
Wright, S. (1968). Evolution and the Genetics of Populations, Vol. 1: Genetic and Biometric Foundations. Chicago: University of Chicago PressGoogle Scholar
Wright, S. (1984). Diverse uses of path analysis. pp. 1–34. In: Chakravarti, A. (ed.). Human Population Genetics. New York: Van Nostrand ReinholdGoogle Scholar

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  • References
  • James B. Grace
  • Book: Structural Equation Modeling and Natural Systems
  • Online publication: 04 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511617799.016
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  • References
  • James B. Grace
  • Book: Structural Equation Modeling and Natural Systems
  • Online publication: 04 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511617799.016
Available formats
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  • References
  • James B. Grace
  • Book: Structural Equation Modeling and Natural Systems
  • Online publication: 04 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511617799.016
Available formats
×