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References

Published online by Cambridge University Press:  09 December 2009

Derek A. Roff
Derek A. Roff
Affiliation:
University of California, Riverside
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Introduction to Computer-Intensive Methods of Data Analysis in Biology , pp. 233 - 241
Publisher: Cambridge University Press
Print publication year: 2006

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References

Alatalo, R. V. (1982). Bird species distributions in the Galapagos and other archipelagoes: competition or chance?Ecology, 63, 881–7.CrossRefGoogle Scholar
Anderson, M. J. and Braak, C. J. F. (2003). Permutation tests for multi-factorial analysis of variance. Journal of Statistical Computation and Simulation, 73, 85–113.CrossRefGoogle Scholar
Anderson, T. W. (1958). An Introduction to Multivariate Statistical Analysis. New York: Wiley.Google Scholar
Arditi, R. (1989). Avoiding fallacious significance tests in stepwise regression; a Monte Carlo method applied to a meteorological theory for the Canadian lynx cycle. International Journal of Biometeorology, 33, 24–6.CrossRefGoogle Scholar
Armbruster, W. S. (1986). Reproductive interactions between sympatric Dalechampia species: are natural assemblages “random” or organized?Ecology, 67, 522–33.CrossRefGoogle Scholar
Armbruster, W. S., Edwards, M. E. and Debevec, E. M. (1994). Floral character displacement generates assemblage structure of Western Australian triggerplants (Stylidium). Ecology, 75, 315–29.CrossRefGoogle Scholar
Arvesen, J. N. and Schmitz, T. H. (1970). Robust procedures for variance component problems using the jackknife. Biometrics, 26, 677–86.CrossRefGoogle Scholar
Avise, J. C., Reeb, C. A. and Sanders, N. C. (1987). Geographic population and species differences in mitochondrial DNA of mouthbrooding catfishes (Ariidae) and dmersal spawning toadfishes (Batrachoididae). Evolution, 41, 991–1002.CrossRefGoogle Scholar
Bartolucci, F., Mira, A. and Scaccia, L. (2004). Answering two biological questions with latent class model via MCMC applied to capture-recapture data. In M. Di Bacco, G. D'Amore, and F. Scalfari, eds., Applied Bayesian Statistical Studies in Biology and Medicine, pp. 7–24. Boston: Kluwer Academic Publishers.Google Scholar
Begin, M. and Roff, D. A. (2004). The effect of temperature and wing morphology on quantitative genetic variation in the cricket, Gryllus firmus, with an appendix examining the statistical properties of the Jackknife-MANOVA method of matrix comparison. Journal of Evolutionary Biology, 17, 1255--67.CrossRef
Bentzen, P., Leggett, W. C. and Brown, C. G. (1988). Length and restriction site heteroplasmy in the mitochondrial DNA of American shad (Alosa sapidissima). Genetics, 118, 509–18.Google Scholar
Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis. New York: Springer-Verlag.CrossRefGoogle Scholar
Besag, J. and Clifford, P. (1989). Generalized Monte Carlo significance Tests. Biometrika, 76, 633–42.CrossRefGoogle Scholar
Besag, J. and Clifford, P. (1991). Sequential Monte Carlo p-Values. Biometrika, 78, 301–4.CrossRefGoogle Scholar
Blau, G. E.Neely, W. B. (1975). Mathematical model building with an application to determine the distribution of Dursban insecticide added to a simulated ecosystem. In Macfadyen, A., ed., Advances in Ecological Research, vol. 9, pp. 133--63. London: Academic Press.Google Scholar
Bliss, C. I. (1935). The calculation of the dosage-mortality curve. Annals of Applied Biology, 22, 220–33.CrossRefGoogle Scholar
Bowers, M. A. and Brown, J. H. (1982). Body size and coexistence in desert rodents: chance or community structure?Ecology, 63, 391–400.CrossRefGoogle Scholar
Brandl, R. and Topp, W. (1985). Size structure of Pterostichus spp. (Carabidae): aspects of competition. Oikos, 44, 234–8.CrossRefGoogle Scholar
Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. G. (1984). Classification and Regression Trees. Belmont, California: Wadsworth International Group.Google Scholar
Buonaccorsi, J. P. and Liebhold, A. M. (1988). Statistical methods for estimating ratios and products in ecological studies. Environmental Entomology, 17, 572–80.CrossRefGoogle Scholar
Capone, T. A. and Kushlan, J. A. (1991). Fish community structure in dry-season stream pools. Ecology, 72, 983–92.CrossRefGoogle Scholar
Carpenter, J. R. (1999). Test inversion bootstrap confidence intervals. Journal of the Royal Statistical Society, B, 61, 159–172.CrossRefGoogle Scholar
Case, T. J., Faaborg, J. and Sidell, R. (1983). The role of body size in the assembly of West Indian bird communities. Evolution, 37, 1062–74.CrossRefGoogle ScholarPubMed
Castledine, B. J. (1981). A Bayesian analysis of multiple-recapture sampling for a closed population. Biometrika, 67, 197–210.Google Scholar
Chambers, J. M. and Hastie, T. J. (1992). Statistical Models. New York: S. Chapman 8 Hall/CRC.Google Scholar
Chemini, C., Rizzoli, A., Merler, S., Furlanello, C. and Genchi, C. (1997). Ixodes ricinus (Acari: Ixodidae) infestation on roe deer (Capreolus capreolus). Trentino, Italian Alps. Parassitologia, 39, 59–63.Google ScholarPubMed
Cleveland, W. S., Grosse, E.Shyu, W. M. (1992). Local regression models. In Chambers, J. M., Hastie, T. J., eds., Statistical Models in S. pp. 309–76 London: Chapman and Hall.Google Scholar
Cochran, W. G. (1954). Some methods for strengthening the common χ2 tests. Biometrics, 10, 417–51.CrossRefGoogle Scholar
Cole, B. J. (1981). Overlap, regularity, and flowering phenologies. American Naturalist, 117, 993–7.CrossRefGoogle Scholar
Connor, E. F. and Simberloff, D. (1979). The assembly of species communities: chance or competition?Ecology, 60, 1132–40.CrossRefGoogle Scholar
Connor, E. F. and Simberloff, D. (1986). Competition, scientific method, and null models in ecology. American Scientist, 74, 155–62.Google Scholar
Conover, W. J., Johnson, M. E. and Johnson, M. M. (1981). A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics, 23, 351–61.CrossRefGoogle Scholar
Cordell, H. J. and Carpenter, J. R. (2000). Bootstrap confidence intervals for relative risk parameters in affected-sib-pair data. Genetic Epidemiology, 18, 157–72.3.0.CO;2-W>CrossRefGoogle ScholarPubMed
Cordell, H. J. and , J. M. (1997). Confidence intervals for relative risk estimates obtained using affected-sib-pair data. Genetic Epidemiology, 14, 593–98.3.0.CO;2-0>CrossRefGoogle ScholarPubMed
Couteron, P., Seghieri, J. and Chadoeuf, J. (2003). A test for spatial relationships between neighbouring plants in plots of heterogeneous plant density. Journal of Vegetation Science, 14, 163–72.CrossRefGoogle Scholar
Cox, D. R. and Hinkley, D. V. (1974). Theoretical Statistics. London: Chapman and Hall.CrossRefGoogle Scholar
Cox, D. R. and Snell, E. J. (1989). Analysis of Binary Data. London: Chapman and Hall.Google Scholar
Crowley, P. H. (1992). Resampling methods for computation-intensive data analysis in ecology and evolution. Annual Review of Ecology and Systematics, 23, 405–48.CrossRefGoogle Scholar
Dalaka, A., Kompare, B., Robnik-Sikonja, M. and Sgardelis, S. P. (2000). Modelling the effects of environmental conditions on apparent photosynthesis of Stipa bromoides by machine learning tools. Ecological Modelling, 129, 245–57.CrossRefGoogle Scholar
Damgaard, C. and Weiner, J. (2000). Describing inequality in plant size or fecundity. Ecology, 81, 1139–42.CrossRefGoogle Scholar
Davison, A. C. and Hinkley, D. V. (1999). Bootstrap Methods and their Applications. Cambridge: Cambridge University Press.Google Scholar
De'ath, G. and Fabricius, K. E. (2000). Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81, 3178–92.CrossRefGoogle Scholar
Deely, J. (2004). Comparing two groups or treatments - a Bayesian approach. In Di Bacco, M., D'Amore, G., Scalfari, F., eds., Applied Bayesian Statistical Studies in Biology and Medicine. pp. 89–107. Boston: Kluwer Academic Publishers.Google Scholar
Diamond, J. M. and Gilpin, M. E. (1982). Examination of the “null” model of Connor and Simberloff for species co-occurrences on islands. Oecologia, 52, 64–74.CrossRefGoogle ScholarPubMed
Dietz, E. J. (1983). Permutation tests for association between two distance measures. Systematic Zoologist, 32, 21–26.CrossRefGoogle Scholar
Dillon, R. T. J. (1981). Patterns in the morphology and distribution of gastropods in Oneida lake, New York, detected using computer-generated null hypotheses. American Naturalist, 118, 83–101.CrossRefGoogle Scholar
Dixon, P. M., Weiner, J., Mitchell-Olds, T. and Woodley, R. (1987). Bootstrapping the Gini coefficient of inequality. Ecology, 68, 1548–51.CrossRefGoogle Scholar
Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.CrossRefGoogle Scholar
Draper, N. R. and Smith, H. (1981). Applied Regression Analysis. New York: John Wiley 8 Sons.Google Scholar
Dzeroski, S., and Drumm, D. (2003). Using regression trees to identify the habitat preference of the sea cucumber (Holothuria leucospilota) on Rarontonga, Cook Islands. Ecological Modelling, 170, 219–26.CrossRefGoogle Scholar
Edgington, E. S. (1987). Randomization Tests. New York: Marcel Dekker, Inc.Google Scholar
Efron, B. (1979). Computers and the theory of statistics: thinking the unthinkable. Siam Review, 2, 460–80.CrossRefGoogle Scholar
Efron, B. (1981). Nonparametric standard errors and confidence intervals. The Canadian Journal of Statistics, 9, 139–72.CrossRefGoogle Scholar
Efron, B. (1982). The Jackknife, the Bootstrap and Other Resampling Plans. Philadelphia: Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American Statistical Association, 82, 171–200.CrossRefGoogle Scholar
Efron, B., Halloran, E. and Holmes, S. (1996). Bootstrap confidence levels for phylogenetic trees. Proceedings of the National Academy of Science USA 93, 13429–34.CrossRefGoogle ScholarPubMed
Efron, B. and Morris, C. (1973). Stein's estimation rule and its competitors – an empirical Bayes approach. Journal of the American Statistical Association, 68, 117–30.Google Scholar
Efron, B. and Morris, C. (1977). Stein's paradox in statistics. Scientific American, 119–28.CrossRef
Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. New York: Chapman and Hall.CrossRefGoogle Scholar
Eliason, S. R. (1993). Maximum likelihood estimation. Newbury Park: Sage Publications.CrossRefGoogle Scholar
Felsenstein, J. (1985). Confidence limits on phylogenies: an approach using the bootstrap. Evolution, 39, 783–91.CrossRefGoogle ScholarPubMed
Felsenstein, J. and Kishino, H. (1993). Is there something wrong with the bootstrap on phylogenies? A reply to Hillis and Bull. Systematic Biology, 42, 193–200.CrossRefGoogle Scholar
Flury, B. (1988). Common Principal Components and Related Multivariate Models. New York: Wiley.Google Scholar
Fong, D. W. (1989). Morphological evolution of the amphipod Gammarus minus in caves: quantitative genetic analysis. American Midland Naturalist, 121, 361–78.CrossRefGoogle Scholar
Garthwaite, P. H. (1996). Confidence intervals from randomization tests. Biometrics, 52, 1387–93.CrossRefGoogle Scholar
Gazey, W. J. and Staley, M. J. (1986). Population estimation from mark-recapture experiments using a sequential Bayes algorithm. Ecology, 67, 941–51.CrossRefGoogle Scholar
Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (1995). Bayesian Data Analysis. London: Chapman and Hall.Google Scholar
George, E. I. and Robert, C. P. (1992). Capture-recapture estimation via Gibbs sampling. Biometrika, 79, 677–83.Google Scholar
Gilpen, M. E. and Diamond, J. M. (1982). Factors contributing to non-randomness in species co-occurrences on islands. Oecologia, 52, 75–84.CrossRefGoogle Scholar
Gonzalez, L. and Manly, B. F. J. (1998). Analysis of variance by randomization with small data sets. Environmetrics, 9, 53–65.3.0.CO;2-#>CrossRefGoogle Scholar
Gotelli, N. J. (2000). Null model analysis of species co-occurrence patterns. Ecology, 81, 2606–21.CrossRefGoogle Scholar
Gotelli, N. J. and Ellison, A. M. (2004). A Primer of Ecological Genetics. Sunderland: Sinauer Associates, Inc.Google Scholar
Hanski, I. (1982). Structure in bumblebee communities. Annales Zoologici Fennici, 19, 319–26.Google Scholar
Harvey, P. H., Colwell, R. K., Silvertown, J. W. and May, R. M. (1983). Null models in ecology. Annual Reviews of Ecology and Systematics, 14, 189–211.CrossRefGoogle Scholar
Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models. London: Chapman and Hall.Google Scholar
Hellmann, J. J. and Fowler, G. W. (1999). Bias, precision, and accuracy of four measures of species richness. Ecological Applications, 9, 824–34.CrossRefGoogle Scholar
Hendrickson, J. A. J. (1981). Community-wide character displacement reexamined. Evolution, 35, 794–810.CrossRefGoogle ScholarPubMed
Hillis, D. M. and Bull, J. J. (1993). An empirical test of bootstrapping as a method for assessing confidence in phylogenetic analysis. Systematic Biology, 42, 182–92.CrossRefGoogle Scholar
Hizer, S. E., Wright, T. M. and Garcia, D. K. (2004). Genetic markers applied in regression tree prediction models. Animal Genetics, 35, 50–2.CrossRefGoogle ScholarPubMed
Holyoak, M. (1993). The frequency of detection of density dependence in insect orders. Ecological Entomology, 18, 339–47.CrossRefGoogle Scholar
Jackson, D. A., Somers, K. M. and Harvey, H. H. (1992). Null models and fish communities evidence of nonrandom patterns. American Naturalist, 139, 930–51.CrossRefGoogle Scholar
Jacobsen, N. O. (1984). Estimates of pup production, age at first parturition and natural mortality for hooded seals in the west ice. Fiskeridirektoratet. Skrifter. Serie Havundersoekelser, 17, 483–98.Google Scholar
Jernigan, R. W., Culver, D. C. and Fong, D. W. (1994). The dual role of selection and evolutionary history as reflected in genetic correlations. Evolution, 48, 587–96.CrossRefGoogle ScholarPubMed
Joern, A. and Lawlor, L. R. (1980). Food and microhabitat utilization by grasshoppers from arid grasslands: comparisons with neutral models. Ecology, 61, 591–9.CrossRefGoogle Scholar
Kendall, M. G. and Buckland, W. R. (1982). A Dictionary of Statistical Terms. London: Longman Group Ltd.Google Scholar
Kennedy, P. E.Cade, B. S. (1996). Randomization tests for multiple regression. Communications in Statistics, Simulation and Computing, 25, 923–36.CrossRefGoogle Scholar
Kimura, D. K. (1980). Likelihood methods for the von Bertalanffy growth curve. Fishery Bulletin, 77, 765–76.Google Scholar
Knapp, S. J., Bridges, J. W. C. and Yang, M. (1989). Nonparametric confidence estimators for heritability and expected selection response. Genetics, 121, 891–8.Google ScholarPubMed
Kochmer, J. P. and Handel, S. N. (1986). Constraints and competition in the evolution of flowering phenology. Ecological Monographs, 56, 303–25.CrossRefGoogle Scholar
Krause, A. and Olson, M. (1997). The Basics of S and S-PLUS. New York: Springer.CrossRefGoogle Scholar
Lawlor, L. R. (1980). Overlap, similarity, and competition coefficients. Ecology, 6, 245–51.CrossRefGoogle Scholar
LeBlanc, M. and Crowley, J. (1992). Relative risk trees for censored survival data. Biometrics, 48, 411–25.CrossRefGoogle ScholarPubMed
Leonard, T. and Hsu, J. S. J. (2001). Bayesian Methods. Cambridge: Cambridge University Press.Google Scholar
Link, W. A. and Hahn, D. C. (1996). Empirical Bayes estimation of proportions with application to cowbird parasitism rates. Ecology, 77, 2528–37.CrossRefGoogle Scholar
Losos, J. B., Naeem, S. and Colwell, R. K. (1989). Hutchinsonian Ratios and Statistical Power. Evolution, 43, 1820–6.CrossRefGoogle ScholarPubMed
Lynch, M. and Walsh, B. (1998). Genetics and Analysis of Quantitative Traits. Sunderland, MA: Sinauer Associates.Google Scholar
Madigan, D. and York, J. C. (1997). Bayesian methods for estimation of the size of a closed population. Biometrika, 84, 19–31.CrossRefGoogle Scholar
Magnuson, J. J., Tonn, W. M., Banerjee, A., Toivonen, J., Sanchez, O. and Rask, M. (1998). Isolation vs. extinction in the assembly of fishes in small northern lakes. Ecology, 79, 2941–56.CrossRefGoogle Scholar
Manly, B. F. J. (1991). Randomization and Monte Carlo Methods in Biology. London: Chapman and Hall.CrossRefGoogle Scholar
Manly, B. F. J. (1993). A review of computer-intensive multivariate methods in ecology. In Patil, G. P., Rao, C. R., eds., Multivariate Environmental Statistics. pp. 307–46. Amsterdam: Elsevier Science Publishers.Google Scholar
Manly, B. F. J. (1995). A note on the analysis of species co-occurrences. Ecology, 76, 1109–15.CrossRefGoogle Scholar
Manly, B. F. J. (1997). Randomization, Bootstrap and Monte Carlo Methods in Biology. New York: Chapman and Hall.Google Scholar
Marshall, R. J. (2001). The use of classification and regression trees in clinical epidemiology. Journal of Clinical Epidemiology, 54, 603–9.CrossRefGoogle ScholarPubMed
Meyer, J. S., Ingersoll, C. G., McDonald, L. L. and Boyce, M. S. (1986). Estimating uncertainty in population growth rates: jackknife vs. bootstrap techniques. Ecology, 67, 1156–66.CrossRefGoogle Scholar
Miller, R. G. (1974). The jackknife – a review. Biometrika, 61, 1–15.Google Scholar
Mingoti, S. A. and Meeden, G. (1992). Estimating the total number of distinct species using presence and absence data. Biometrics, 48, 863–75.CrossRefGoogle Scholar
Mooney, C. Z. and Duval, R. D. (1993). Bootstrapping: A nonparametric approach to statistical inference. Newbury Park: Sage Publications.CrossRefGoogle Scholar
Mueller, L. D. (1979). A comparison of two methods for making statistical inferences on Nei's measure of genetic distance. Biometrics, 35, 757–63.CrossRefGoogle ScholarPubMed
Mueller, L. D. and Altenberg, L. (1985). Statistical inference on measures of niche overlap. Ecology, 66, 1204–10.CrossRefGoogle Scholar
Negron, J. F. (1998). Probability of infestation and extent of mortality associated with the Douglas-fir beetle in the Colorado Front Range. Forest Ecology and Management, 107, 71–85.CrossRefGoogle Scholar
Phillips, P. C. and Arnold, S. J. (1999). Hierarchical comparison of genetic variance-covariance matrices. I. Using the Flury hierarchy. Evolution, 53, 1506–15.CrossRefGoogle ScholarPubMed
Pleasants, J. M. (1990). Null-model tests for competitive displacement: the fallacy of not focusing on the whole community. Ecology, 71, 1078–84.CrossRefGoogle Scholar
Pollard, E. and Lakhani, K. H. (1987). The detection of density-dependence from a series of annual censuses. Ecology, 58, 2046–55.CrossRefGoogle Scholar
Potvin, C. and Roff, D. (1996). Permutation tests in ecology: A statistical panacea?Bulletin of the Ecological Society of America, 77, 359Google Scholar
Press, S. J. (1989). Bayesian Statistics: Principles, Models, and Applications. New York: John Wiley 8 Sons.Google Scholar
Quenouille, M. (1949). Approximate tests of correlation in time series. Journal of the Royal Statistical Society, Series B, 11, 18–84.Google Scholar
Ranta, E. (1982a). Animal communities in rock pools. Annales Zoologi Fennici, 19, 337–47.Google Scholar
Ranta, E. (1982b). Species structure of North European bumblebee communities. Oikos, 38, 202–9.CrossRefGoogle Scholar
Reichard, S. H. and Hamilton, C. W. (1997). Predicting invasions of woody plants introduced into North America. Conservation Biology, 11, 193–203.CrossRefGoogle Scholar
Rejwan, C., Collins, N. C., Brunner, L. J., Shuter, B. J. and Ridgway, M. S. (1999). Tree regression analysis on the nesting habitat of smallmouth bass. Ecology, 80, 341–8.CrossRefGoogle Scholar
Ricklefs, R. E., Cochran, D. and Pianka, E. R. (1981). A morphological analysis of the structure of communities of lizards in desert habitats. Ecology, 62, 1474–83.CrossRefGoogle Scholar
Roff, D. A. (1997). Evolutionary Quantitative Genetics. New York: Chapman and Hall.CrossRefGoogle Scholar
Roff, D. A. (2000). The evolution of the G matrix: selection or drift?Heredity, 84, 135–42.CrossRefGoogle ScholarPubMed
Roff, D. A. (2002). Comparing G matrices: a MANOVA method. Evolution, 56, 1286–91.Google Scholar
Roff, D. A. and Bentzen, P. (1989). The statistical analysis of mitochondrial DNA polymorphisms: χ2 and the problem of small samples. Molecular Biological Evolution, 6, 539–45.Google Scholar
Roff, D. A.Bradford, M. J. (1996). Quantitative genetics of the trade-off between fecundity and wing dimorphism in the cricketAllonemobius socius. Heredity, 76, 178–85.CrossRefGoogle Scholar
Roff, D. A. and Preziosi, R. (1994). The estimation of the genetic correlation: the use of the jackknife. Heredity, 73, 544–8.CrossRefGoogle Scholar
Roff, D. A. and Roff, R. J. (2003). Of rats and Maoris: a novel method for the analysis of patterns of extinction in the New Zealand avifauna prior to European contact. Evolutionary Ecology Research, 5, 1–21.Google Scholar
Roff, D. A., Mousseau, T. A. and Howard, D. J. (1999). Variation in genetic architecture of calling song among populations of Allonemobius socius, A. fasciatus and a hybrid population: drift or selection?Evolution, 53, 216–24.Google ScholarPubMed
Roff, D. A., Mousseau, T., Møller, A. P., Lope, F. D. and Saino, N. (2004). Geographic variation in the G matrices of wild populations of the barn swallow. Heredity, 93, 8--14.CrossRef
Sahai, H. and Ageel, M. I. (2000). The Analysis of Variance. Boston: Birkhauser.CrossRefGoogle Scholar
Saitoh, T., Bjornstad, O. N. and Stenseth, N. C. (1999). Density dependence in voles and mice: a comparative study. Ecology, 80, 638–50.CrossRefGoogle Scholar
Schluter, D. (1988). Estimating the form of natural selection on a quantitative trait. Evolution, 42, 849–61.CrossRefGoogle ScholarPubMed
Schluter, D. and Nychka, D. (1994). Exploring fitness surfaces. American Naturalist, 143, 597–616.CrossRefGoogle Scholar
Schoener, T. W. (1984). Size differences among sympatric, bird-eating hawks: a worldwide survey. In Strong, D. R., Simberloff, D., Abele, and A. B. Thistle L. G., eds., Ecological Communities: Conceptual Issues and the Evidence, pp. 245–81. Princeton, NJ: Princeton University Press.Google Scholar
Segal, M. R. and Bloch, D. A. (1989). A comparison of estimated proportional hazards models and regression trees. Statistics in Medicine, 8, 539–50.CrossRefGoogle ScholarPubMed
Shackell, N. L., Lemon, R. E. and Roff, D. A. (1988). Song similarity between neighbouring American redstarts (Setophaga ruticilla): a statistical analysis. The Auk, 105, 609–15.Google Scholar
Shaw, R. G. (1991). The comparison of quantitative genetic parameters between populations. Evolution, 45, 143–51.CrossRefGoogle ScholarPubMed
Shaw, R. G. and Mitchell-Olds, T. (1993). ANOVA for unbalanced data: an overview. Ecology, 74, 1638–45.CrossRefGoogle Scholar
Silvertown, J. and Wilson, J. B. (1994). Community structure in a desert perennial community. Ecology, 75, 409–17.CrossRefGoogle Scholar
Simons, A. M. and Roff, D. A. (1994). The effect of environmental variability on the heritabilities of traits of a field cricket. Evolution, 48, 1637–49.CrossRefGoogle ScholarPubMed
Skov, F. (1997). Stand and neighbourhood parameters as determinants of plant species richness in a managed forest. Journal of Vegetation Science, 8, 573–8.CrossRefGoogle Scholar
Solow, A. R. (1993). Inferring extinction from sighting data. Ecology, 74, 962–4.CrossRefGoogle Scholar
Soltis, P. S. and Soltis, D. E. (2003). Applying the bootstrap in phylogeny reconstruction. Statistical Science, 18, 256–67.CrossRefGoogle Scholar
Stratoudakis, Y., Gallego, A. and Morrison, J. A. (1998). Spatial distribution of developmental egg ages within a herring Clupea harengus spawning ground. Marine Ecology-Progress Series, 174, 27–32.CrossRefGoogle Scholar
Strong, D. R. J. (1979). Tests of community-wide character displacement against null hypotheses. Evolution, 33, 897–913.Google ScholarPubMed
Strong, D. R. J. (1982). Null hypotheses in ecology. In E. Saarinen, ed., Conceptual Issues in Ecology, Dordrecht: D. Reidel, pp. 245–59.Google Scholar
Strong, D. R. J. and Simberloff, D. S. (1981). Straining at gnats and swallowing ratios character displacement. Evolution, 35, 810–12.CrossRefGoogle ScholarPubMed
Strong, D. R., Simberloff, D., Abele, L. G. and Thistle, A. B. (1984). Ecological communities: conceptual issues and the evidence. Princeton, N.J: Princeton University Press.CrossRefGoogle Scholar
Stuart, A., Ord, K. and Arnold, S. (1999). Kendall's Advanced Theory of Statistics. In Classical Inference and the Linear Model. Vol. 2A. London: Arnold.Google Scholar
Braak, C. J. F. (1992). Permutation versus bootstrap significance tests in multiple regression and ANOVA. In G. R. K. –H. Jöckel, W. Sendler, eds., Bootstrapping and related techniques: proceedings of an International Conference held in Trier, Germany, June 4--8, 1990, pp. 79–86. New York: Springer-Verlag.Google Scholar
Tibshirani, R. J. (1988). Variance stabilization and the bootstrap. Biometrika, 75, 433–44.CrossRefGoogle Scholar
Tokeshi, M. (1986). Resource utilization, overlap and temporal community dynamics: a null model analysis of an epiphytic chironomid community. The Journal of Animal Ecology, 55, 491–506.CrossRefGoogle Scholar
Tukey, J. W. (1958). Bias and confidence in not quite large samples. Annals of Mathematical Statistics, 29, 614.Google Scholar
Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. New York: Springer.CrossRefGoogle Scholar
Vitt, L. J., Sartorius, S. S., Avila-Pires, T. C. S., Esposito, M. C. and Miles, D. B. (2000). Niche segregation among sympatric Amazonian teiid lizards. Oecologia, 122, 410–20.CrossRefGoogle ScholarPubMed
Watters, G. and Deriso, R. (2000). Catches per unit of effort of bigeye tuna: a new analysis with regression trees and simulated annealing. Inter-American Tropical Tuna Commission Bulletin, 21, 531–71.Google Scholar
Willis, J. H., Coyne, J. A. and Kirkpatrick, M. (1991). Can one predict the evolution of quantitative characters without genetics?Evolution, 45, 441–4.CrossRefGoogle ScholarPubMed
Wilson, J. B. (1987). Methods for detecting non-randomness in species co-occurrences: a contribution. Oecologia, 73, 579–82.CrossRefGoogle ScholarPubMed
Zhou, X.-H., Gao, S. and Hui Siu, L. (1997). Methods for comparing the means of two independent log-normal samples. Biometrics, 53, 1129–35.CrossRefGoogle ScholarPubMed
Alatalo, R. V. (1982). Bird species distributions in the Galapagos and other archipelagoes: competition or chance?Ecology, 63, 881–7.CrossRefGoogle Scholar
Anderson, M. J. and Braak, C. J. F. (2003). Permutation tests for multi-factorial analysis of variance. Journal of Statistical Computation and Simulation, 73, 85–113.CrossRefGoogle Scholar
Anderson, T. W. (1958). An Introduction to Multivariate Statistical Analysis. New York: Wiley.Google Scholar
Arditi, R. (1989). Avoiding fallacious significance tests in stepwise regression; a Monte Carlo method applied to a meteorological theory for the Canadian lynx cycle. International Journal of Biometeorology, 33, 24–6.CrossRefGoogle Scholar
Armbruster, W. S. (1986). Reproductive interactions between sympatric Dalechampia species: are natural assemblages “random” or organized?Ecology, 67, 522–33.CrossRefGoogle Scholar
Armbruster, W. S., Edwards, M. E. and Debevec, E. M. (1994). Floral character displacement generates assemblage structure of Western Australian triggerplants (Stylidium). Ecology, 75, 315–29.CrossRefGoogle Scholar
Arvesen, J. N. and Schmitz, T. H. (1970). Robust procedures for variance component problems using the jackknife. Biometrics, 26, 677–86.CrossRefGoogle Scholar
Avise, J. C., Reeb, C. A. and Sanders, N. C. (1987). Geographic population and species differences in mitochondrial DNA of mouthbrooding catfishes (Ariidae) and dmersal spawning toadfishes (Batrachoididae). Evolution, 41, 991–1002.CrossRefGoogle Scholar
Bartolucci, F., Mira, A. and Scaccia, L. (2004). Answering two biological questions with latent class model via MCMC applied to capture-recapture data. In M. Di Bacco, G. D'Amore, and F. Scalfari, eds., Applied Bayesian Statistical Studies in Biology and Medicine, pp. 7–24. Boston: Kluwer Academic Publishers.Google Scholar
Begin, M. and Roff, D. A. (2004). The effect of temperature and wing morphology on quantitative genetic variation in the cricket, Gryllus firmus, with an appendix examining the statistical properties of the Jackknife-MANOVA method of matrix comparison. Journal of Evolutionary Biology, 17, 1255--67.CrossRef
Bentzen, P., Leggett, W. C. and Brown, C. G. (1988). Length and restriction site heteroplasmy in the mitochondrial DNA of American shad (Alosa sapidissima). Genetics, 118, 509–18.Google Scholar
Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis. New York: Springer-Verlag.CrossRefGoogle Scholar
Besag, J. and Clifford, P. (1989). Generalized Monte Carlo significance Tests. Biometrika, 76, 633–42.CrossRefGoogle Scholar
Besag, J. and Clifford, P. (1991). Sequential Monte Carlo p-Values. Biometrika, 78, 301–4.CrossRefGoogle Scholar
Blau, G. E.Neely, W. B. (1975). Mathematical model building with an application to determine the distribution of Dursban insecticide added to a simulated ecosystem. In Macfadyen, A., ed., Advances in Ecological Research, vol. 9, pp. 133--63. London: Academic Press.Google Scholar
Bliss, C. I. (1935). The calculation of the dosage-mortality curve. Annals of Applied Biology, 22, 220–33.CrossRefGoogle Scholar
Bowers, M. A. and Brown, J. H. (1982). Body size and coexistence in desert rodents: chance or community structure?Ecology, 63, 391–400.CrossRefGoogle Scholar
Brandl, R. and Topp, W. (1985). Size structure of Pterostichus spp. (Carabidae): aspects of competition. Oikos, 44, 234–8.CrossRefGoogle Scholar
Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. G. (1984). Classification and Regression Trees. Belmont, California: Wadsworth International Group.Google Scholar
Buonaccorsi, J. P. and Liebhold, A. M. (1988). Statistical methods for estimating ratios and products in ecological studies. Environmental Entomology, 17, 572–80.CrossRefGoogle Scholar
Capone, T. A. and Kushlan, J. A. (1991). Fish community structure in dry-season stream pools. Ecology, 72, 983–92.CrossRefGoogle Scholar
Carpenter, J. R. (1999). Test inversion bootstrap confidence intervals. Journal of the Royal Statistical Society, B, 61, 159–172.CrossRefGoogle Scholar
Case, T. J., Faaborg, J. and Sidell, R. (1983). The role of body size in the assembly of West Indian bird communities. Evolution, 37, 1062–74.CrossRefGoogle ScholarPubMed
Castledine, B. J. (1981). A Bayesian analysis of multiple-recapture sampling for a closed population. Biometrika, 67, 197–210.Google Scholar
Chambers, J. M. and Hastie, T. J. (1992). Statistical Models. New York: S. Chapman 8 Hall/CRC.Google Scholar
Chemini, C., Rizzoli, A., Merler, S., Furlanello, C. and Genchi, C. (1997). Ixodes ricinus (Acari: Ixodidae) infestation on roe deer (Capreolus capreolus). Trentino, Italian Alps. Parassitologia, 39, 59–63.Google ScholarPubMed
Cleveland, W. S., Grosse, E.Shyu, W. M. (1992). Local regression models. In Chambers, J. M., Hastie, T. J., eds., Statistical Models in S. pp. 309–76 London: Chapman and Hall.Google Scholar
Cochran, W. G. (1954). Some methods for strengthening the common χ2 tests. Biometrics, 10, 417–51.CrossRefGoogle Scholar
Cole, B. J. (1981). Overlap, regularity, and flowering phenologies. American Naturalist, 117, 993–7.CrossRefGoogle Scholar
Connor, E. F. and Simberloff, D. (1979). The assembly of species communities: chance or competition?Ecology, 60, 1132–40.CrossRefGoogle Scholar
Connor, E. F. and Simberloff, D. (1986). Competition, scientific method, and null models in ecology. American Scientist, 74, 155–62.Google Scholar
Conover, W. J., Johnson, M. E. and Johnson, M. M. (1981). A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics, 23, 351–61.CrossRefGoogle Scholar
Cordell, H. J. and Carpenter, J. R. (2000). Bootstrap confidence intervals for relative risk parameters in affected-sib-pair data. Genetic Epidemiology, 18, 157–72.3.0.CO;2-W>CrossRefGoogle ScholarPubMed
Cordell, H. J. and , J. M. (1997). Confidence intervals for relative risk estimates obtained using affected-sib-pair data. Genetic Epidemiology, 14, 593–98.3.0.CO;2-0>CrossRefGoogle ScholarPubMed
Couteron, P., Seghieri, J. and Chadoeuf, J. (2003). A test for spatial relationships between neighbouring plants in plots of heterogeneous plant density. Journal of Vegetation Science, 14, 163–72.CrossRefGoogle Scholar
Cox, D. R. and Hinkley, D. V. (1974). Theoretical Statistics. London: Chapman and Hall.CrossRefGoogle Scholar
Cox, D. R. and Snell, E. J. (1989). Analysis of Binary Data. London: Chapman and Hall.Google Scholar
Crowley, P. H. (1992). Resampling methods for computation-intensive data analysis in ecology and evolution. Annual Review of Ecology and Systematics, 23, 405–48.CrossRefGoogle Scholar
Dalaka, A., Kompare, B., Robnik-Sikonja, M. and Sgardelis, S. P. (2000). Modelling the effects of environmental conditions on apparent photosynthesis of Stipa bromoides by machine learning tools. Ecological Modelling, 129, 245–57.CrossRefGoogle Scholar
Damgaard, C. and Weiner, J. (2000). Describing inequality in plant size or fecundity. Ecology, 81, 1139–42.CrossRefGoogle Scholar
Davison, A. C. and Hinkley, D. V. (1999). Bootstrap Methods and their Applications. Cambridge: Cambridge University Press.Google Scholar
De'ath, G. and Fabricius, K. E. (2000). Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81, 3178–92.CrossRefGoogle Scholar
Deely, J. (2004). Comparing two groups or treatments - a Bayesian approach. In Di Bacco, M., D'Amore, G., Scalfari, F., eds., Applied Bayesian Statistical Studies in Biology and Medicine. pp. 89–107. Boston: Kluwer Academic Publishers.Google Scholar
Diamond, J. M. and Gilpin, M. E. (1982). Examination of the “null” model of Connor and Simberloff for species co-occurrences on islands. Oecologia, 52, 64–74.CrossRefGoogle ScholarPubMed
Dietz, E. J. (1983). Permutation tests for association between two distance measures. Systematic Zoologist, 32, 21–26.CrossRefGoogle Scholar
Dillon, R. T. J. (1981). Patterns in the morphology and distribution of gastropods in Oneida lake, New York, detected using computer-generated null hypotheses. American Naturalist, 118, 83–101.CrossRefGoogle Scholar
Dixon, P. M., Weiner, J., Mitchell-Olds, T. and Woodley, R. (1987). Bootstrapping the Gini coefficient of inequality. Ecology, 68, 1548–51.CrossRefGoogle Scholar
Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.CrossRefGoogle Scholar
Draper, N. R. and Smith, H. (1981). Applied Regression Analysis. New York: John Wiley 8 Sons.Google Scholar
Dzeroski, S., and Drumm, D. (2003). Using regression trees to identify the habitat preference of the sea cucumber (Holothuria leucospilota) on Rarontonga, Cook Islands. Ecological Modelling, 170, 219–26.CrossRefGoogle Scholar
Edgington, E. S. (1987). Randomization Tests. New York: Marcel Dekker, Inc.Google Scholar
Efron, B. (1979). Computers and the theory of statistics: thinking the unthinkable. Siam Review, 2, 460–80.CrossRefGoogle Scholar
Efron, B. (1981). Nonparametric standard errors and confidence intervals. The Canadian Journal of Statistics, 9, 139–72.CrossRefGoogle Scholar
Efron, B. (1982). The Jackknife, the Bootstrap and Other Resampling Plans. Philadelphia: Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American Statistical Association, 82, 171–200.CrossRefGoogle Scholar
Efron, B., Halloran, E. and Holmes, S. (1996). Bootstrap confidence levels for phylogenetic trees. Proceedings of the National Academy of Science USA 93, 13429–34.CrossRefGoogle ScholarPubMed
Efron, B. and Morris, C. (1973). Stein's estimation rule and its competitors – an empirical Bayes approach. Journal of the American Statistical Association, 68, 117–30.Google Scholar
Efron, B. and Morris, C. (1977). Stein's paradox in statistics. Scientific American, 119–28.CrossRef
Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. New York: Chapman and Hall.CrossRefGoogle Scholar
Eliason, S. R. (1993). Maximum likelihood estimation. Newbury Park: Sage Publications.CrossRefGoogle Scholar
Felsenstein, J. (1985). Confidence limits on phylogenies: an approach using the bootstrap. Evolution, 39, 783–91.CrossRefGoogle ScholarPubMed
Felsenstein, J. and Kishino, H. (1993). Is there something wrong with the bootstrap on phylogenies? A reply to Hillis and Bull. Systematic Biology, 42, 193–200.CrossRefGoogle Scholar
Flury, B. (1988). Common Principal Components and Related Multivariate Models. New York: Wiley.Google Scholar
Fong, D. W. (1989). Morphological evolution of the amphipod Gammarus minus in caves: quantitative genetic analysis. American Midland Naturalist, 121, 361–78.CrossRefGoogle Scholar
Garthwaite, P. H. (1996). Confidence intervals from randomization tests. Biometrics, 52, 1387–93.CrossRefGoogle Scholar
Gazey, W. J. and Staley, M. J. (1986). Population estimation from mark-recapture experiments using a sequential Bayes algorithm. Ecology, 67, 941–51.CrossRefGoogle Scholar
Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (1995). Bayesian Data Analysis. London: Chapman and Hall.Google Scholar
George, E. I. and Robert, C. P. (1992). Capture-recapture estimation via Gibbs sampling. Biometrika, 79, 677–83.Google Scholar
Gilpen, M. E. and Diamond, J. M. (1982). Factors contributing to non-randomness in species co-occurrences on islands. Oecologia, 52, 75–84.CrossRefGoogle Scholar
Gonzalez, L. and Manly, B. F. J. (1998). Analysis of variance by randomization with small data sets. Environmetrics, 9, 53–65.3.0.CO;2-#>CrossRefGoogle Scholar
Gotelli, N. J. (2000). Null model analysis of species co-occurrence patterns. Ecology, 81, 2606–21.CrossRefGoogle Scholar
Gotelli, N. J. and Ellison, A. M. (2004). A Primer of Ecological Genetics. Sunderland: Sinauer Associates, Inc.Google Scholar
Hanski, I. (1982). Structure in bumblebee communities. Annales Zoologici Fennici, 19, 319–26.Google Scholar
Harvey, P. H., Colwell, R. K., Silvertown, J. W. and May, R. M. (1983). Null models in ecology. Annual Reviews of Ecology and Systematics, 14, 189–211.CrossRefGoogle Scholar
Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models. London: Chapman and Hall.Google Scholar
Hellmann, J. J. and Fowler, G. W. (1999). Bias, precision, and accuracy of four measures of species richness. Ecological Applications, 9, 824–34.CrossRefGoogle Scholar
Hendrickson, J. A. J. (1981). Community-wide character displacement reexamined. Evolution, 35, 794–810.CrossRefGoogle ScholarPubMed
Hillis, D. M. and Bull, J. J. (1993). An empirical test of bootstrapping as a method for assessing confidence in phylogenetic analysis. Systematic Biology, 42, 182–92.CrossRefGoogle Scholar
Hizer, S. E., Wright, T. M. and Garcia, D. K. (2004). Genetic markers applied in regression tree prediction models. Animal Genetics, 35, 50–2.CrossRefGoogle ScholarPubMed
Holyoak, M. (1993). The frequency of detection of density dependence in insect orders. Ecological Entomology, 18, 339–47.CrossRefGoogle Scholar
Jackson, D. A., Somers, K. M. and Harvey, H. H. (1992). Null models and fish communities evidence of nonrandom patterns. American Naturalist, 139, 930–51.CrossRefGoogle Scholar
Jacobsen, N. O. (1984). Estimates of pup production, age at first parturition and natural mortality for hooded seals in the west ice. Fiskeridirektoratet. Skrifter. Serie Havundersoekelser, 17, 483–98.Google Scholar
Jernigan, R. W., Culver, D. C. and Fong, D. W. (1994). The dual role of selection and evolutionary history as reflected in genetic correlations. Evolution, 48, 587–96.CrossRefGoogle ScholarPubMed
Joern, A. and Lawlor, L. R. (1980). Food and microhabitat utilization by grasshoppers from arid grasslands: comparisons with neutral models. Ecology, 61, 591–9.CrossRefGoogle Scholar
Kendall, M. G. and Buckland, W. R. (1982). A Dictionary of Statistical Terms. London: Longman Group Ltd.Google Scholar
Kennedy, P. E.Cade, B. S. (1996). Randomization tests for multiple regression. Communications in Statistics, Simulation and Computing, 25, 923–36.CrossRefGoogle Scholar
Kimura, D. K. (1980). Likelihood methods for the von Bertalanffy growth curve. Fishery Bulletin, 77, 765–76.Google Scholar
Knapp, S. J., Bridges, J. W. C. and Yang, M. (1989). Nonparametric confidence estimators for heritability and expected selection response. Genetics, 121, 891–8.Google ScholarPubMed
Kochmer, J. P. and Handel, S. N. (1986). Constraints and competition in the evolution of flowering phenology. Ecological Monographs, 56, 303–25.CrossRefGoogle Scholar
Krause, A. and Olson, M. (1997). The Basics of S and S-PLUS. New York: Springer.CrossRefGoogle Scholar
Lawlor, L. R. (1980). Overlap, similarity, and competition coefficients. Ecology, 6, 245–51.CrossRefGoogle Scholar
LeBlanc, M. and Crowley, J. (1992). Relative risk trees for censored survival data. Biometrics, 48, 411–25.CrossRefGoogle ScholarPubMed
Leonard, T. and Hsu, J. S. J. (2001). Bayesian Methods. Cambridge: Cambridge University Press.Google Scholar
Link, W. A. and Hahn, D. C. (1996). Empirical Bayes estimation of proportions with application to cowbird parasitism rates. Ecology, 77, 2528–37.CrossRefGoogle Scholar
Losos, J. B., Naeem, S. and Colwell, R. K. (1989). Hutchinsonian Ratios and Statistical Power. Evolution, 43, 1820–6.CrossRefGoogle ScholarPubMed
Lynch, M. and Walsh, B. (1998). Genetics and Analysis of Quantitative Traits. Sunderland, MA: Sinauer Associates.Google Scholar
Madigan, D. and York, J. C. (1997). Bayesian methods for estimation of the size of a closed population. Biometrika, 84, 19–31.CrossRefGoogle Scholar
Magnuson, J. J., Tonn, W. M., Banerjee, A., Toivonen, J., Sanchez, O. and Rask, M. (1998). Isolation vs. extinction in the assembly of fishes in small northern lakes. Ecology, 79, 2941–56.CrossRefGoogle Scholar
Manly, B. F. J. (1991). Randomization and Monte Carlo Methods in Biology. London: Chapman and Hall.CrossRefGoogle Scholar
Manly, B. F. J. (1993). A review of computer-intensive multivariate methods in ecology. In Patil, G. P., Rao, C. R., eds., Multivariate Environmental Statistics. pp. 307–46. Amsterdam: Elsevier Science Publishers.Google Scholar
Manly, B. F. J. (1995). A note on the analysis of species co-occurrences. Ecology, 76, 1109–15.CrossRefGoogle Scholar
Manly, B. F. J. (1997). Randomization, Bootstrap and Monte Carlo Methods in Biology. New York: Chapman and Hall.Google Scholar
Marshall, R. J. (2001). The use of classification and regression trees in clinical epidemiology. Journal of Clinical Epidemiology, 54, 603–9.CrossRefGoogle ScholarPubMed
Meyer, J. S., Ingersoll, C. G., McDonald, L. L. and Boyce, M. S. (1986). Estimating uncertainty in population growth rates: jackknife vs. bootstrap techniques. Ecology, 67, 1156–66.CrossRefGoogle Scholar
Miller, R. G. (1974). The jackknife – a review. Biometrika, 61, 1–15.Google Scholar
Mingoti, S. A. and Meeden, G. (1992). Estimating the total number of distinct species using presence and absence data. Biometrics, 48, 863–75.CrossRefGoogle Scholar
Mooney, C. Z. and Duval, R. D. (1993). Bootstrapping: A nonparametric approach to statistical inference. Newbury Park: Sage Publications.CrossRefGoogle Scholar
Mueller, L. D. (1979). A comparison of two methods for making statistical inferences on Nei's measure of genetic distance. Biometrics, 35, 757–63.CrossRefGoogle ScholarPubMed
Mueller, L. D. and Altenberg, L. (1985). Statistical inference on measures of niche overlap. Ecology, 66, 1204–10.CrossRefGoogle Scholar
Negron, J. F. (1998). Probability of infestation and extent of mortality associated with the Douglas-fir beetle in the Colorado Front Range. Forest Ecology and Management, 107, 71–85.CrossRefGoogle Scholar
Phillips, P. C. and Arnold, S. J. (1999). Hierarchical comparison of genetic variance-covariance matrices. I. Using the Flury hierarchy. Evolution, 53, 1506–15.CrossRefGoogle ScholarPubMed
Pleasants, J. M. (1990). Null-model tests for competitive displacement: the fallacy of not focusing on the whole community. Ecology, 71, 1078–84.CrossRefGoogle Scholar
Pollard, E. and Lakhani, K. H. (1987). The detection of density-dependence from a series of annual censuses. Ecology, 58, 2046–55.CrossRefGoogle Scholar
Potvin, C. and Roff, D. (1996). Permutation tests in ecology: A statistical panacea?Bulletin of the Ecological Society of America, 77, 359Google Scholar
Press, S. J. (1989). Bayesian Statistics: Principles, Models, and Applications. New York: John Wiley 8 Sons.Google Scholar
Quenouille, M. (1949). Approximate tests of correlation in time series. Journal of the Royal Statistical Society, Series B, 11, 18–84.Google Scholar
Ranta, E. (1982a). Animal communities in rock pools. Annales Zoologi Fennici, 19, 337–47.Google Scholar
Ranta, E. (1982b). Species structure of North European bumblebee communities. Oikos, 38, 202–9.CrossRefGoogle Scholar
Reichard, S. H. and Hamilton, C. W. (1997). Predicting invasions of woody plants introduced into North America. Conservation Biology, 11, 193–203.CrossRefGoogle Scholar
Rejwan, C., Collins, N. C., Brunner, L. J., Shuter, B. J. and Ridgway, M. S. (1999). Tree regression analysis on the nesting habitat of smallmouth bass. Ecology, 80, 341–8.CrossRefGoogle Scholar
Ricklefs, R. E., Cochran, D. and Pianka, E. R. (1981). A morphological analysis of the structure of communities of lizards in desert habitats. Ecology, 62, 1474–83.CrossRefGoogle Scholar
Roff, D. A. (1997). Evolutionary Quantitative Genetics. New York: Chapman and Hall.CrossRefGoogle Scholar
Roff, D. A. (2000). The evolution of the G matrix: selection or drift?Heredity, 84, 135–42.CrossRefGoogle ScholarPubMed
Roff, D. A. (2002). Comparing G matrices: a MANOVA method. Evolution, 56, 1286–91.Google Scholar
Roff, D. A. and Bentzen, P. (1989). The statistical analysis of mitochondrial DNA polymorphisms: χ2 and the problem of small samples. Molecular Biological Evolution, 6, 539–45.Google Scholar
Roff, D. A.Bradford, M. J. (1996). Quantitative genetics of the trade-off between fecundity and wing dimorphism in the cricketAllonemobius socius. Heredity, 76, 178–85.CrossRefGoogle Scholar
Roff, D. A. and Preziosi, R. (1994). The estimation of the genetic correlation: the use of the jackknife. Heredity, 73, 544–8.CrossRefGoogle Scholar
Roff, D. A. and Roff, R. J. (2003). Of rats and Maoris: a novel method for the analysis of patterns of extinction in the New Zealand avifauna prior to European contact. Evolutionary Ecology Research, 5, 1–21.Google Scholar
Roff, D. A., Mousseau, T. A. and Howard, D. J. (1999). Variation in genetic architecture of calling song among populations of Allonemobius socius, A. fasciatus and a hybrid population: drift or selection?Evolution, 53, 216–24.Google ScholarPubMed
Roff, D. A., Mousseau, T., Møller, A. P., Lope, F. D. and Saino, N. (2004). Geographic variation in the G matrices of wild populations of the barn swallow. Heredity, 93, 8--14.CrossRef
Sahai, H. and Ageel, M. I. (2000). The Analysis of Variance. Boston: Birkhauser.CrossRefGoogle Scholar
Saitoh, T., Bjornstad, O. N. and Stenseth, N. C. (1999). Density dependence in voles and mice: a comparative study. Ecology, 80, 638–50.CrossRefGoogle Scholar
Schluter, D. (1988). Estimating the form of natural selection on a quantitative trait. Evolution, 42, 849–61.CrossRefGoogle ScholarPubMed
Schluter, D. and Nychka, D. (1994). Exploring fitness surfaces. American Naturalist, 143, 597–616.CrossRefGoogle Scholar
Schoener, T. W. (1984). Size differences among sympatric, bird-eating hawks: a worldwide survey. In Strong, D. R., Simberloff, D., Abele, and A. B. Thistle L. G., eds., Ecological Communities: Conceptual Issues and the Evidence, pp. 245–81. Princeton, NJ: Princeton University Press.Google Scholar
Segal, M. R. and Bloch, D. A. (1989). A comparison of estimated proportional hazards models and regression trees. Statistics in Medicine, 8, 539–50.CrossRefGoogle ScholarPubMed
Shackell, N. L., Lemon, R. E. and Roff, D. A. (1988). Song similarity between neighbouring American redstarts (Setophaga ruticilla): a statistical analysis. The Auk, 105, 609–15.Google Scholar
Shaw, R. G. (1991). The comparison of quantitative genetic parameters between populations. Evolution, 45, 143–51.CrossRefGoogle ScholarPubMed
Shaw, R. G. and Mitchell-Olds, T. (1993). ANOVA for unbalanced data: an overview. Ecology, 74, 1638–45.CrossRefGoogle Scholar
Silvertown, J. and Wilson, J. B. (1994). Community structure in a desert perennial community. Ecology, 75, 409–17.CrossRefGoogle Scholar
Simons, A. M. and Roff, D. A. (1994). The effect of environmental variability on the heritabilities of traits of a field cricket. Evolution, 48, 1637–49.CrossRefGoogle ScholarPubMed
Skov, F. (1997). Stand and neighbourhood parameters as determinants of plant species richness in a managed forest. Journal of Vegetation Science, 8, 573–8.CrossRefGoogle Scholar
Solow, A. R. (1993). Inferring extinction from sighting data. Ecology, 74, 962–4.CrossRefGoogle Scholar
Soltis, P. S. and Soltis, D. E. (2003). Applying the bootstrap in phylogeny reconstruction. Statistical Science, 18, 256–67.CrossRefGoogle Scholar
Stratoudakis, Y., Gallego, A. and Morrison, J. A. (1998). Spatial distribution of developmental egg ages within a herring Clupea harengus spawning ground. Marine Ecology-Progress Series, 174, 27–32.CrossRefGoogle Scholar
Strong, D. R. J. (1979). Tests of community-wide character displacement against null hypotheses. Evolution, 33, 897–913.Google ScholarPubMed
Strong, D. R. J. (1982). Null hypotheses in ecology. In E. Saarinen, ed., Conceptual Issues in Ecology, Dordrecht: D. Reidel, pp. 245–59.Google Scholar
Strong, D. R. J. and Simberloff, D. S. (1981). Straining at gnats and swallowing ratios character displacement. Evolution, 35, 810–12.CrossRefGoogle ScholarPubMed
Strong, D. R., Simberloff, D., Abele, L. G. and Thistle, A. B. (1984). Ecological communities: conceptual issues and the evidence. Princeton, N.J: Princeton University Press.CrossRefGoogle Scholar
Stuart, A., Ord, K. and Arnold, S. (1999). Kendall's Advanced Theory of Statistics. In Classical Inference and the Linear Model. Vol. 2A. London: Arnold.Google Scholar
Braak, C. J. F. (1992). Permutation versus bootstrap significance tests in multiple regression and ANOVA. In G. R. K. –H. Jöckel, W. Sendler, eds., Bootstrapping and related techniques: proceedings of an International Conference held in Trier, Germany, June 4--8, 1990, pp. 79–86. New York: Springer-Verlag.Google Scholar
Tibshirani, R. J. (1988). Variance stabilization and the bootstrap. Biometrika, 75, 433–44.CrossRefGoogle Scholar
Tokeshi, M. (1986). Resource utilization, overlap and temporal community dynamics: a null model analysis of an epiphytic chironomid community. The Journal of Animal Ecology, 55, 491–506.CrossRefGoogle Scholar
Tukey, J. W. (1958). Bias and confidence in not quite large samples. Annals of Mathematical Statistics, 29, 614.Google Scholar
Venables, W. N. and Ripley, B. D. (2002). Modern Applied Statistics with S. New York: Springer.CrossRefGoogle Scholar
Vitt, L. J., Sartorius, S. S., Avila-Pires, T. C. S., Esposito, M. C. and Miles, D. B. (2000). Niche segregation among sympatric Amazonian teiid lizards. Oecologia, 122, 410–20.CrossRefGoogle ScholarPubMed
Watters, G. and Deriso, R. (2000). Catches per unit of effort of bigeye tuna: a new analysis with regression trees and simulated annealing. Inter-American Tropical Tuna Commission Bulletin, 21, 531–71.Google Scholar
Willis, J. H., Coyne, J. A. and Kirkpatrick, M. (1991). Can one predict the evolution of quantitative characters without genetics?Evolution, 45, 441–4.CrossRefGoogle ScholarPubMed
Wilson, J. B. (1987). Methods for detecting non-randomness in species co-occurrences: a contribution. Oecologia, 73, 579–82.CrossRefGoogle ScholarPubMed
Zhou, X.-H., Gao, S. and Hui Siu, L. (1997). Methods for comparing the means of two independent log-normal samples. Biometrics, 53, 1129–35.CrossRefGoogle ScholarPubMed

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  • References
  • Derek A. Roff, University of California, Riverside
  • Book: Introduction to Computer-Intensive Methods of Data Analysis in Biology
  • Online publication: 09 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511616785.009
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  • Derek A. Roff, University of California, Riverside
  • Book: Introduction to Computer-Intensive Methods of Data Analysis in Biology
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  • References
  • Derek A. Roff, University of California, Riverside
  • Book: Introduction to Computer-Intensive Methods of Data Analysis in Biology
  • Online publication: 09 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511616785.009
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