PhD in Pure and Applied Mathematics

Process-based modeling for biodiversity and eco-evolutionary dynamics

Process-based modeling for biodiversity and eco-evolutionary dynamics

Academic year 2019/2020

Sommario del corso

• Course objectives
• Learning assessment methods
• Program
• Suggested textbooks and readings
• Notes
• Teaching tools

Course objectives

The participants will get introduced to different ecological theories and frameworks as well as to eco-evolutionary mechanistic simulation models for biodiversity dynamics. Disclaimer: this is not about statistics, but mechanistic simulation models (i.e. agent- or individual-based models). Ecological modeling has gained importance in the ecological and environmental literature due to progress in computational power and necessity of understanding and of forecasting inherently complex biodiversity dynamics in a changing world. However, mechanistic modeling remains a rare discipline in environmental studies worldwide. Therefore, the course will promote the development of modeling and eco-evolutionary skills in the next generation of environmental researchers. The course also aims at promoting potential collaborative work between the CCTB and Politecnico di Torino. The focus will be largely concentrated on the integration of multiple theories in mechanistic simulation models via different ecological (physiological, demographic, dispersal, interactions), evolutionary (mutation, speciation) and environmental (e.g. landuse and climate change) processes. During the days, the participants will get acquainted with different ecological theories, agent-based modeling (in R language), get introduced to modeling workflow and experiment with models with gradual complexity, from individuals, over populations to evolving communities. The participants will work on existing models to tackle eco-evolutionary questions, simulate experiments and present their findings.

Learning assessment methods

1) presence and discussion about modeling experiments during the class;
2) presentation of the results from the modeling experiments performed during class;
3) handling protocol of activities: report with background of the study question, hypotheses, simulation experiments, results, interpretation, and codes.

More references will be provided during the course

Program

Teacher: Juliano Sarmento Cabral, head of Ecosystem Modeling – Center for Computational and Theoretical Biology (CCTB), University of Würzburg, Germany

Preliminary schedule: 7 h/day (3 hours in the morning [e.g. 9-12]; 4 hours in the afternoon [e.g. 13-17])
Marco Morandotti’s proposal: morning 10-13, afternoon 14:30-18:30 (this is in line with poliTO’s scheduling)
The exercise classes could be trimmed down to two hours and then Friday Dec 6th could be used to stock 4 hours of exercises; similarly, Wednesday Dec 11th.
[Dec 5th] Day 1 – Introduction in mechanistic ecological modeling, R and first models
Morning (lectures): Explaining the Course (content, structure), background of lecturer and participants. Introduction to ecological theories and modeling: types of ecological models (e.g. verbal, phenomenological, mechanistic), pros and cons. Ecological theories as source for implementing into computer simulation models the processes necessary for biodiversity dynamics. Metabolic, neutral, niche and coexistence theories. Methods in process-based/mechanistic simulation modeling: study questions, gathering of causal relationships (literature search), integrating ecological theories, causal diagram, flow chart, experimental design, model properties, forward vs. inverse modeling, observation model, virtual ecologist, validation, model presentation.
Afternoon (exercises): 1) Identifying pairs of students with common interests and introduction to agent-based programing with R. 2) Playing around with coding functions and simulations: simple individual dispersal (e.g. random walk, Lévy walk), boundary conditions, population dynamics (deterministic and stochastic Ricker and Beverton-Holt equations, Allee effects). 3) Thinking about own experiment: study question, potential processes, causal diagram.
[Dec 6th] Day 2 – Spatially-explicit mechanistic models
Morning (lectures): Spatially-explicit models, with examples at different spatiotemporal scales and level of ecological organization, cross-scale and integrative models. Influence of spatial components on intra- and interspecific interactions. Increase in model complexity by including intraspecific processes (e.g. density-dependent transition rates, dispersal) and interspecific biotic interactions (e.g. competition). Metapopulation (demographically-explicit and -implicit incidence function models) and metacommunity theories. Emergent vs. imposed processes; model complexity vs. generality.
Afternoon (exercises): 1) Coding functions and playing with simulations: meta-population and metacommunity dynamics. 2) Thinking about own experiment: experimental design.
[Dec 9th] Day 3 – Evolving metacommunities and environmental change
Morning (lectures): Macroevolution and eco-evolutionary models; complex models for biodiversity dynamics, spatial heterogeneity and long-distance dispersal in a biogeographical context. Environmental dynamics and human-induced environmental change. Island biogeography theories (equilibrium theory of island biogeography and the general dynamic model of island biogeography).
Afternoon (exercises): keep playing around with the model to adapt functions depending on own study questions. Perform simulation experiment and summarize results.
[Dec 10th] Day 4 – Experiments and presentation
Morning (lecture and exercises): Recapitulating integrative eco-evolutionary mechanisms, differences in mechanistic vs. phenomenological (i.e. statistical) thinking. Preparing model description and experiment for publication. In group: quickly presenting own ideas about study question and relevant processes (15 min max. per pair). Potential ideas: i) applying game theory to species niche differentiation (different species strategies), species coexistence and biodiversity gradients; ii) developing null models for species-area relationships based on the mid-domain effect; iii) scaling of seemingly equilibrium dynamics (e.g. propagation vs. buffering of stochasticity from low to high level of ecological organization); iv) biodiversity flow under environmental non-equilibrium.
Afternoon: Discussing missing components in the model depending on the groups’ ideas and final/explorative simulation experiments. Presenting results.

Cabral, J.S., Schurr, F.M. (2010) Estimating demographic models for the range dynamics of plant species. Global Ecology and Biogeography 19: 85-97.
Cabral, J.S., Kreft, H. (2012) Linking ecological niche, community ecology and biogeography: insights from a mechanistic niche model. Journal of Biogeography 39: 2212-2224.
Cabral, J.S., Valente, L., Hartig, F. (2017) Mechanistic models in macroecology and biogeography: state-of-art and prospects. Ecography 40: 267-280.
Cabral, J.S., Wiegand, K., Kreft, H. (2019) Interactions between ecological, evolutionary and environmental processes unveil complex dynamics of island biodiversity. Journal of Biogeography. https ://doi.org/10.1111/jbi.13606
Cobben, M.M., Verboom, J., Opdam, P.F., Hoekstra, R.F., Jochem, R., Smulders, M.J. (2012) Wrong place, wrong time: climate change‐induced range shift across fragmented habitat causes maladaptation and declined population size in a modelled bird species. Global Change Biology 18: 2419-2428.
Dormann, C.F., Schymanski, S.J., Cabral, J., Chuine, I., Graham, C., Hartig, F., Kearney, M., Morin, X., Römermann, C., Schröder, B., Singer, A. (2012) Correlation and process in species distribution models: bridging a dichotomy. Journal of Biogeography 39: 2119-2131.
Kubisch, A., Holt, R.D., Poethke, H.J., Fronhofer, E.A. (2014) Where am I and why? Synthesizing range biology and the eco‐evolutionary dynamics of dispersal. Oikos 123: 5-22.
Lawson, D., Jensen, H.J. (2006) The species–area relationship and evolution. Journal of Theoretical Biology 241: 590-600.
Lawson, D., Jensen, H.J., Kaneko, K. (2006) Diversity as a product of inter-specific interactions. Journal of Theoretical Biology 243: 299-307.
Pontarp, M., Bunnefeld, L., Cabral, J. S., Etienne, R. S., Fritz, S. A., Gillespie, R., Graham, C. H., Hagen, O., Hartig, F., Huang, S., Jansson, R., Maliet, O., Münkemüller, T., Pellissier, L., Rangel, T. F., Storch, D., Wiegand, T., Hurlbert, A. H. (2018) The latitudinal diversity gradient - novel understanding through mechanistic eco-evolutionary models. Trends in Ecology and Evolution 34: 211-223.
Sarmento Cabral, J., Jeltsch F., Midgley G.F., Higgins S.I., Phillips S.I., Rebelo A.G., Rouget M., Thuiller W., Schurr F.M. (2013) Impacts of past habitat loss and future climate change on the range dynamics of South African Proteaceae. Diversity and Distributions 19: 363-376.
Travis, J.M., Mustin, K., Bartoń, K.A., Benton, T.G., Clobert, J., Delgado, M.M., Dytham, C., Hovestadt, T., Palmer, S.C., Van Dyck, H., Bonte, D. (2012) Modelling dispersal: an eco‐evolutionary framework incorporating emigration, movement, settlement behaviour and the multiple costs involved. Methods in Ecology and Evolution 3: 628-641.
Zurell, D., Thuiller, W., Pagel, J., Cabral, J.S., Muenkemueller, T., Gravel, D., Dullinger, S., Normand, S., Schiffers, K., Moore, K.A., Zimmerman, N. (2016) Benchmarking novel approaches for modelling species range dynamics. Global Change Biology 22: 2651-2664.

Note

Class schedule:

Thursday 5th December (Aula Seminari): 10-13 (Intro and Theory); 14:30-18:30 (Exercises)

Friday 6th December (Aula Seminari): 10-13 (Intro and Theory); 14:30-18:30 (Exercises)

Monday 9th December (Aula Buzano): 10-13 (Intro and Theory); 14:30-18:30 (Exercises)

Tuesday 10th December (Aula Buzano): 10-13 (Exercises and Experiments); 14:30-18:30 (Experiments and Presentations)

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