Project module 'Spatial Ecology and Modeling' (MSc)
Module code
BIO.07666.03
Semester of first implementation
Faculty/Institute
Institut für Biologie
Module used in courses of study / semesters
Biodiversity Sciences (MA120 LP) (Master) > Biologie BiodiversityMA120, Version of accreditation valid from SS 2021 > Project modules offered by the Institute of Biology (Nat Sci I)
Biologie (MA120 LP) (Master) > Biologie BiologieMA120, Version of accreditation valid from WS 2010/11 > Wahlpflichtmodule
Responsible person for this module
Further responsible persons
Prof. Dr. H. M. Pereira, Prof. Dr. T. M. Knight, Prof. Dr. S. Harpole, Prof. Dr. I. Kühn
Prerequisites
Skills to be acquired in this module
Develop a basic understanding of the different types of models used in ecology, including differential , individual based models and grid simulations, statistical models, and particularly species distribution models. Apply this knowledge to ecological questions and determine the appropriate type of model needed for a given scenario.
Develop the ability to create and parameterize models in order to simulate ecological systems. Understand the importance of evaluating uncertainty in model results and apply appropriate techniques to assess and communicate this uncertainty.
Gain proficiency in comparing model results with empirical data, to interpret model results, interpreting model outputs, and assessing the quality and relevance of the models. Develop critical thinking skills to identify limitations and assumptions in ecological models and evaluate their implications.
Acquire a basic command of the R programming language, including the ability to write simple programs for data manipulation, analysis, and visualization. Understand how to apply R for ecological modeling and simulation.
Develop the ability to read and analyse research articles with a strong theoretical or modeling component. Use this skill to critically evaluate the approaches, methods and results presented in the literature and identify gaps or areas for further research.
Module contents
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; Modelling animal movement and plant dispersal as a random-walk. Monitoring theory: bayesian modelling of site occupancy, species-area relationships and species-abundance distributions. - Social-ecological models: coupling social models of decision-making with ecological models; introduction to regime shifts and scenario modelling.
Part II: Population Ecology: Introduction to modeling the dynamics of populations using mathematical models (difference equations and individual based models). - Focus on developing and interpreting models, including generating questions, deciding on the appropriate modelling approach, creating the model, parameterizing the model, creating population projections using the model, conducting sensitivity analyses of model parameters, and interpreting and presenting the results. -Models will focus on conservation application. -Models will increase in complexity, from simple exponential growth models, to incorporating various complexities that are common in ecological systems, such as environmental stochasticity, density dependence, and stage, age or size structure.
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.
Share of module grade on the course of study's final grade
1
Reference text
Maximum number of students (with focus ecology): 16; 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.
The project modules require physical presence. In case of inability to attend (due to illness or other reasons) the lecturer must be notified promptly. To earn course credits, students must not exceed a 10% absence, equivalent to missing three days in a six-week block module. In case of a longer absence there might be the possibility to compensate for missed days by additional tasks.
