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Introducing model-data fusion to graduate students in ecology

Introducing model-data fusion to graduate students in ecology. Topics of discussion: The impact of NEON on ecology What are the desired outcomes from a basic curriculum? Content of a 1-2 semester course. manipulative observational. heterogeneity embraced. heterogeneity minimized.

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Introducing model-data fusion to graduate students in ecology

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  1. Introducing model-data fusion to graduate students in ecology • Topics of discussion: • The impact of NEON on ecology • What are the desired outcomes from a basic curriculum? • Content of a 1-2 semester course

  2. manipulative observational heterogeneity embraced heterogeneity minimized plot scale continental scale quantitative training optional quantitative training essential ANOVA, regression, multivariates ? data poor data rich few, isolated effects and interactions multiple effects, composite forces, contingencies

  3. 1 = 1) C 1 time time 2 D between between + h V bites bites max 2) I = CS S = 3) I 1 Intake Rate Intake Rate + h (g/min) (g/min) 2 DV max Bite Density (m ) - 2 Plant Density (m Plant Density (m ) ) - - 2 2 Outcomes of a new curriculum 1) The ability to represent ecological processes as mathematical models.

  4. Observations = y i Measured population size Year SE 1965 510 104 1966 521 103 1967 502 105 Statements about 1968 382 117 1969 677 91 Hypothesis Probability hypothesis supported 1970 502 105 1971 591 97 (process model) model by observations 1972 688 90 1973 467 109 q q y = f( , x ) P(y | , xi) 1974 608 96 i 1975 538 102 1976 988 80 1977 580 98 1978 932 80 1979 826 83 1980 852 82 1981 918 80 1982 797 84 1983 1562 119 1984 929 80 1985 1149 85 1986 864 81 1987 896 81 1988 978 80 1989 812 83 1990 868 81 Outcomes of a new curriculum 2) A an understanding of the use of process models, observations, and probability models as routes to insight.

  5. Outcomes of a new curriculum 4) Understanding how inferences may be influenced by temporal and spatial scale. Fridley, J. D et al. 2007. The invasion paradox: Reconciling pattern and process in species invasions. Ecology 88:3-17.

  6. Outcomes of a new curriculum 3) The ability to represent “hidden processes” including all sources of stochasticity. data model process model

  7. Outcomes of a new curriculum 5) Facility in using multiple sources of data to parameterize and evaluate models. Data sources: Census: 15 years Sex / age ratios 22 years Survival: 3 years Annual harvest and culling Annual weather records Literature estimates of survival, fertility Response to perturbation

  8. Outcomes of a new curriculum 6) The ability to collaborate with statisticians and mathematicians in a way that is mutually beneficial. PRIMES PRogram for Interdisciplinary Mathematics, Ecology, and Statistics “Plug and play” is good news and bad news….

  9. Outcomes of a new curriculum 7) Quantitative confidence needed to support a lifetime of self-teaching. Hobbs, N. T., S. Twombly, and D. S. Schimel. 2006. Deepening ecological insights using contemporary statistics. Ecological Applications 16:3-4.

  10. Resources Books Clark, J. M. 2007. Models for Ecological Data. Princeton University Press., Princeton, N. J. Bolker, B. 2008. Ecological Models and Data in R. Princeton University Press, Princeton N. J. Hilborn, R., and M. Mangel. 1997. The Ecological Detective: Confronting Models with Data. Princeton University Press, Princeton, N. J. Software R, WinBugs Courses Univeristy of Washington, Duke, Colorado State University, University of Florida, Cornell

  11. Syllabus: NR 575, Systems Ecology • Deterministic models in ecology • Mathematical basis for dynamic models in discrete and continuous time • A modeler’s toolbox of useful functions • Composing models to represent mechanisms • Basic probability and probability distributions • Stochastic models and data simulation • Likelihood • Support, strength of evidence • Likelihood ratios • Likelihood profiles, profile confidence intervals • Prior information • Multiple sources of data • Information theoretics • Kullback-Leilbler information discrepancy • AIC and its allies • Akaike weights • Multimodal inference • More sources of stochasticity: Process variance, observation error, random effects • Introduction to Bayesian methods • Relationship between likelihood and Bayes • Monte Carlo Markov Chain • Hierarchical, state-space models • Bayesian model selection and model averaging Laboratory: Programming in R and WinBugs Examples from organismal, population, community, ecosystem ecology

  12. Statistical Analyses Used in Journals of the Ecological Society of America chi-square analysis of variance linear regression t - test maximum likelihood model selection Bayesian

  13. Karieva, P., and M. Anderson. 1988. Spatial aspects of species interactions: the wedding of models and experiments. Pages 35-50 in A. Hastings, editor. Community Ecology. Springer-Verlag, New York. 97 papers 40 issues

  14. Update of Karieva and Anderson: Each point is take from a paper in Ecology published between January 2000-December 2006. 229 papers 80 issues

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