Prof. Dr. H. M. Pereira, Prof. Dr. T. Knight, Prof. Dr. S. Harpole, Prof. Dr. I. Kühn
Teilnahmevoraussetzungen
Kompetenzziele
Have a basic understanding of the models used in ecology including: differential and difference equations; individual based models and grid simulations; statistical models, particularly species distribution models.
Ability to create and parameterize models, and to evaluate uncertainty in model results.
Ability to determine the type of model needed for the ecological question of interest.
Ability to compare models results with empirical data, to interpret model results, and to assess the quality and relevance of the models.
Have a basic command of the R language, including the ability to write simple programs.
Ability to read a research article with a strong theoretical or modeling component.
Modulinhalte
Part I: Theoretical Ecology and Modeling: Introduction to programming in R: scripts and the command line, variables, data structures (vectors and matrices); numerical operations; matrix operations; plots; logical expressions and conditional operations, functions. - Basic statistical operations with R: descriptive statistics and histograms, regression, and hypothesis testing. - Ecophysiology: a model of thermoregulation and the concept of climate space; modeling the impacts of climate change using ecophysiological models. - Behavioral ecology: introduction to economic analysis of behavior; models for optimal foraging; game theory and evolutionary stable strategies; habitat selection and the ideal free distribution; affinity of species to natural and human-dominated habitats. - Social-ecological models: coupling social models of decision-making with ecological models; introduction to regime shifts and multiple stable states.
Part II: Introduction to Population Ecology: Introduction to modeling the dynamics of populations using mathematical models (difference equations and individual based models). - Focus on conservation application of modeling for preserving natural populations and for ecological restoration. - Incorporating environmental stochasticity and density dependence into the exponential growth model. - Stage-structured population growth using matrix population models. - Integral projection models. - Metapopulation models and individual-based population models.
Part III: Community Ecology (Theory, reading and modeling in R): Competition and coexistence (phenomenological). - Competition and coexistence (mechanistic). - Other coexistence mechanisms (predation). - Coexistence in space. - Niche, neutral and stochasticity.
Part IV: Analyzing Spatial data with R: Specifics of spatial data in statistical analyses; data preparation and transformations; assumptions of and conditions for spatial analyses of ecological data. - Visualizing spatial data in R. - Reviving Generalized Linear Models; calibration, validation, prediction and projection; accounting for spatial autocorrelation. - Introduction to Species Distribution Models; overview on different algorithms (e.g. Generalized Additive Models, Boosted Regression Trees) and available R packages.
Lehrveranstaltungsformen
Vorlesung (1 SWS)
Vorlesung
Vorlesung
Vorlesung
Praktikum (8 SWS)
Kursus
Kursus
Faktor der Modulnote für die Endnote des Studiengangs
1
Hinweise
Maximum number of students (with focus ecology): 16; Calendar: The four parts take place in Halle (Institute for Biology - Geobotany and Botanical Garden, MLU, Halle and/or Helmholtz Centre for Environmental Research, UFZ, Halle) and in Leipzig (German Center for Integrative Biodiversity Research - iDiv), respectively.
Pre- and post-lecture self-study and literature work
0
Workload modulbezogen
450
450
Workload Modul insgesamt
450
Prüfung
Prüfungsvorleistung
Prüfungsform
LV 1
LV 2
LV 3
LV 4
LV 5
LV 6
LV 7
Gesamtmodul
Practical course reports 'Plant chorology/macroecology', Literature review 'Current research in spatial ecology', Mini-paper 'Spatial modeling', Computational lab assignment reports (8 reports), Development, report and presentation of an ecological model