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Research methodologies and statistical approaches for multitactic systems

Published online by Cambridge University Press:  20 January 2017

Edward C. Luschei  and
Randall D. Jackson
Randall D. Jackson
Affiliation:
Department of Agronomy, University of Wisconsin, Madison, WI 53706
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Abstract

Scientific understanding of multitactic weed management systems (MTS) is complicated by (1) the large number of potential combinations among tactics, (2) potentially noisy and complex system behavior because of individually more moderate mortality events, and (3) possible transient system behavior of unknown duration. Therefore, decomposing the relative performance of MTS components is much more difficult than it is for single-tactic strategies (STS). Attempting to accommodate the increased complexity of system behavior while maintaining the generality of results requires analytical methods capable of accomplishing these tasks. We provide two examples of statistical procedures that may help gain understanding of MTS systems using previously published weed demographic time-series data. First, we demonstrate the use of mixed-effects models capable of representing and removing factors contributing uncontrolled variation to system behavior. Model selection criteria are used to highlight the importance of the increased flexibility the mixed-model framework provides. Second, by explicitly modeling the probabilistic process presumed to be generating the data, we demonstrate how different components of the MTS can be compared and how the methodology can facilitate integration of such information into a decision-making application.

Keywords

Bayesiandecision makingintegrated weed managementlikelihoodmixed-effects modelspopulation dynamicsseedbank
Type
Symposium
Information
Weed Science , Volume 53 , Issue 3 , June 2005 , pp. 393 - 403
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Barnett, V. 1999. Comparative Statistical Inference. 3rd ed. New York: J. Wiley. 381 p.Google Scholar
Bartolome, J. W., Fehmi, J. S., Jackson, R. D., and Allen-Diaz, B. 2004. Disturbance effects on a native perennial grass stand in coast range Grassland of California. Restor. Ecol 12:279–289.Google Scholar
Beck, M. W. 1997. Inference and generality in ecology: current problems and an experimental solution. Oikos 78:265–273.Google Scholar
Buckley, Y., Briese, D. T., and Rees, M. 2003. Demography and management of the invasive plant species Hypericum perforatum. I. Using multi-level mixed-effects models for characterizing growth, survival and fecundity in a long-term data set. J. Appl. Ecol 40:481–493.Google Scholar
Buhler, D. D. 2002. Challenges and opportunities for integrated weed management. Weed Sci 50:273–280.Google Scholar
Burnham, K. P. and Anderson, D. R. 1998. Model Selection and Inference: A Practical Information—Theoretic Approach. New York: Springer-Verlag. 353 p.Google Scholar
Carpenter, S. R., Cottingham, K. L., and Stow, C. A. 1994. Fitting predator-prey models to time series with observation errors. Ecology 75:1254–1264.Google Scholar
Clark, J. S., Carpenter, S. R., and Barber, M. et al. 2001. Ecological forecasts: an emerging imperative. Science 293:657–660.Google Scholar
Clutton-Brock, M. 1967. Likelihood distributions for estimating functions when both variables are subject to error. Technometrics 9:261–269.Google Scholar
De Valpine, P. and Hastings, A. 2002. Fitting population models incorporating process noise and observation error. Ecol. Monogr 72:57–76.Google Scholar
Dwyer, G. 2000. On the use of mathematical models in ecological research: example from studies of insect-baculovirus interactions. Pages 37–40 in Professional Societies and Ecologically Based Pest Management: Proceedings of a Workshop. Board of Agriculture and Natural Resources. Washington, DC: National Academy Press.Google Scholar
Edwards, A. F. W. 1992. Likelihood. The Johns. London: Hopkins University Press. 275 p.Google Scholar
Edwards, D. 1996. Comment: the first data analysis should be journalistic. Ecol. Appl 6/4:1090–1094.Google Scholar
Freckleton, R. P. and Watkinson, A. R. 2001. Nonmanipulative determination of plant community dynamics. Trends Ecol. Evol 16/6:301–307.Google Scholar
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. 2004. Bayesian Data Analysis. 2nd ed. New York: Chapman & Hall/CRC. 668 p.Google Scholar
Hilborn, R. and Mangel, M. 1997. The Ecological Detective: Confronting Models with Data. Princeton, NJ: Princeton University Press. 315 p.Google Scholar
Hilborn, R. and Walters, C. J. 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty. New York: Chapman and Hall.Google Scholar
Liebman, M. and Davis, A. S. 2000. Integration of soil, crop and weed management in low-external-input farming systems. Weed Res 40:27–47.Google Scholar
Liebman, M. and Dyck, E. 1993. Weed management: a need to develop ecological approaches. Ecol. Appl 3:39–41.Google Scholar
Liebman, M. and Gallandt, E. 1997. Many little hammers: ecological approaches for management of crop-weed interactions. Pages 291–343 in Jackson, L. E. ed. Ecology in Agriculture. San Diego, CA: Academic.Google Scholar
Littel, R. C., Milliken, G. A., Stroup, W. W., and Wolfinger, R. D. 1996. SAS System for Mixed Models. 1st ed. Cary, NC: Statistical Analysis Systems Institute. 633 p.Google Scholar
Loehle Enterprises. 2004. Global Optimatization. Version 5.0. Naperville, IL.Google Scholar
Maxwell, B. 1999. My view. Weed Sci 47:129.Google Scholar
Mortensen, D., Bastiaans, L., and Sattin, M. 2000. The role of ecology in the development of weed management systems: an outlook. Weed Res 40:49–62.Google Scholar
Mulugeta, D. and Stoltenberg, D. E. 1997. Weed and seedbank management with integrated methods as influenced by tillage. Weed Sci 45:706–715.Google Scholar
Neeser, C., Dille, J. A., Krishnan, G., Mortensen, D. A., Rawlinson, J. T., Martin, A. R., and Bills, L. B. 2004. WeedSOFT®: a weed management decision support system. Weed Sci 52:115–122.Google Scholar
Nielsen, O. K., Ritz, C., and Streibig, J. C. 2004. Nonlinear mixed-model regression to analyze herbicide dose-response relationships. Weed Technol 18:30–37.Google Scholar
Pascual, M. A. and Kareiva, P. 1996. Predicting the outcome of competition using experimental data: maximum likelihood and Bayesian approaches. Ecology 77:337–349.Google Scholar
Pinheiro, J. C. and Bates, D. M. 2000. Mixed-Effects Models in S and S-PLUS. New York: Springer. 528 p.Google Scholar
Robert, C. P. and Casella, G. 1999. Monte Carlo Statistical Methods. New York: Springer. 507 p.Google Scholar
Rossi, R. E., Borth, P. W., and Tollefson, J. J. 1993. Stochastic simulation for characterizing ecological spatial patterns and appraising risk. Ecol. Appl 3:719–735.Google Scholar
[SAS] Statistical Analysis Systems. 2003. SAS Procedures Guide. Version 9.1. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Steel, R. G. D., Torrie, J. H., and Dickey, D. A. 1997. Principles and Procedures of Statistics: A Biometrical Approach. New York: McGraw-Hill. 666 p.Google Scholar
Ver Hoef, J. M. 1996. Parametric empirical bayes methods for ecological applications. Ecol. Appl 6:1047–1055.Google Scholar
Wallinga, J., Grasman, J., Groeneveld, R. M. W., Kropff, M. J., and Lotz, L. A. P. 1999. Prediction of weed density: the increase of error with prediction interval, and the use of long-term prediction for weed management. J. Appl. Ecol 36:307–316.Google Scholar
Walters, C. J. and Ludwig, D. 1981. Effects of measurement errors on the assessment of stock-recruitment relationship. Can. J. Fish. Aquat. Sci 38:704–710.Google Scholar
Walters, C. J. and Ludwig, D. 1994. Calculation of bayes posterior probability distributions for key population parameters. Can. J. Fish. Aquat. Sci 51:713–722.Google Scholar
Weiner, J. 1995. On the practice of ecology. J. Ecol 83:153–158.Google Scholar
Wolfram Research. 2003. Mathematica. Version 5.0. Champaign, IL: Wolfram Research.Google Scholar