Theory 5. The Maximum Likelihood Method 5. Assessing Model Assumptions: Residual Analysis 6. Model Assumptions 6. Independent and Identically Distributed 6. The QQ Plot 6. Temporal Autocorrelation 6. Spatial Autocorrelation 6. Heteroscedasticity Chapter 7.
Resource Ecology and Ecosystem Modeling - Models
Linear Mixed Effects Models 7. Background 7. Fitting a Linear Mixed Model in R 7. Restricted Maximum Likelihood Estimation 7. Assessing Model Assumptions 7.
- Data and Models in Ecology and Evolution | Université Paris Saclay!
- Ecological modelling.
- Models and tools for water quality improvement.
Drawing Conclusions 7. Frequentist Results 7.
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Random Intercept and Random Slope 7. Nested and Crossed Random Effects 7. Model Selection in Mixed Models Chapter 8. Generalized Linear Models 8. Background 8. Binomial Model 8. Fitting a Binary Logistic Regression in R 8. Poisson Model Chapter 9. Generalized Linear Mixed Models 9. Binomial Mixed Model 9. Poisson Mixed Model Chapter Posterior Predictive Model Checking Measures of Explained Variance Chapter Model Selection and Multimodel Inference Multimodel Inference Markov Chain Monte Carlo Simulation Background Modeling Assumptions Explicit Modeling of Spatial Autocorrelation Chapter Advanced Ecological Models Prior Influence and Parameter Estimability Wednesday, June 5, Measuring and improving the fit of spatial-temporal aquatic ecosystem models.
The workshop brought together experts across different ecological modeling and spatial-temporal statistical approaches to discuss the state of the art and plan a way forward to improve spatial-temporal models for policy advice. See details here:. There was a great participation of students of which some are doing their master thesis in collaboration with the SMEG group. The course described some of the most prominent SDMs methods currently in use both from a frequentist and a Bayesian approach. We are seeking a motivated MS student to join a large international project funded by the Human Frontiers Science Program HFSP aiming at understanding how seabirds navigate and the role of infrasound in their movement .
Applications are encouraged for both profiles, but only one student will be selected. Applicants should have a background in mathematics or statistics, including at least a course in mathematical statistics and not just applied statistics and strong programming skills; fluency in R and a genuine interest in ecological applications are highly desirable. Previous experience, e.
Ecological Modelling. University of British Columbia. Fisheries Management and Ecology. Feroz; Desai, V. Environmental Biology of Fishes. Feroz Environment, Power, and Society. Wiley-Interscience New York, N. A practical guide to ecological modelling: using R as a simulation platform. An introduction to ecological modelling: putting practice into theory. In Jopp, Fred; et al.
Modeling Complex Ecological Dynamics. African Invertebrates.
Theory vs. models in ecology (UPDATED) | Dynamic Ecology
Population parameters: estimation for ecological models. Ecosystem approaches to landscape management in Central Europe. Encyclopedia of environmental science. In Hunsaker, Carolyn T. Spatial uncertainty in ecology: implications for remote sensing and GIS applications. Fundamentals of ecological modelling. Gulf Professional Publishing. In Millspaugh, Joshua J. Systems Science and Modeling for Ecological Economics. Assumptions, Validity Range and Documentation".
The Elements of Physical Biology. Ecology: Individuals, Populations and Communities. Blackwell Scientific Publications Inc. Journal of Theoretical Biology. Ecology, the Ascendent Perspective. Columbia University Press. Ecology : Modelling ecosystems : Trophic components. Chemoorganoheterotrophy Decomposition Detritivores Detritus. Archaea Bacteriophage Environmental microbiology Lithoautotroph Lithotrophy Microbial cooperation Microbial ecology Microbial food web Microbial intelligence Microbial loop Microbial mat Microbial metabolism Phage ecology.