@article{WangWhiteGrimmetal.2018, author = {Wang, Ming and White, Neil and Grimm, Volker and Hofman, Helen and Doley, David and Thorp, Grant and Cribb, Bronwen and Wherritt, Ella and Han, Liqi and Wilkie, John and Hanan, Jim}, title = {Pattern-oriented modelling as a novel way to verify and validate functional-structural plant models}, series = {Annals of botany}, volume = {121}, journal = {Annals of botany}, number = {5}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {0305-7364}, doi = {10.1093/aob/mcx187}, pages = {941 -- 959}, year = {2018}, abstract = {Background and Aims Functional-structural plant (FSP) models have been widely used to understand the complex interactions between plant architecture and underlying developmental mechanisms. However, to obtain evidence that a model captures these mechanisms correctly, a clear distinction must be made between model outputs used for calibration and thus verification, and outputs used for validation. In pattern-oriented modelling (POM), multiple verification patterns are used as filters for rejecting unrealistic model structures and parameter combinations, while a second, independent set of patterns is used for validation. Key Results After calibration, our model simultaneously reproduced multiple observed architectural patterns. The model then successfully predicted, without further calibration, the validation patterns. The model supports the hypothesis that carbon allocation can be modelled as being dependent on current organ biomass and sink strength of each organ type, and also predicted the observed developmental timing of the leaf sink-source transition stage.}, language = {en} } @article{GrimmBergerBastiansenetal.2006, author = {Grimm, Volker and Berger, Uta and Bastiansen, Finn and Eliassen, Sigrunn and Ginot, Vincent and Giske, Jarl and Goss-Custard, John and Grand, Tamara and Heinz, Simone K. and Huse, Geir and Huth, Andreas and Jepsen, Jane U. and Jorgensen, Christian and Mooij, Wolf M. and Mueller, Birgit and Piou, Cyril and Railsback, Steven Floyd and Robbins, Andrew M. and Robbins, Martha M. and Rossmanith, Eva and Rueger, Nadja and Strand, Espen and Souissi, Sami and Stillman, Richard A. and Vabo, Rune and Visser, Ute and DeAngelis, Donald L.}, title = {A standard protocol for describing individual-based and agent-based models}, series = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, volume = {198}, journal = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0304-3800}, doi = {10.1016/j.ecolmodel.2006.04.023}, pages = {115 -- 126}, year = {2006}, abstract = {Simulation models that describe autonomous individual organisms (individual based models, IBM) or agents (agent-based models, ABM) have become a widely used tool, not only in ecology, but also in many other disciplines dealing with complex systems made up of autonomous entities. However, there is no standard protocol for describing such simulation models, which can make them difficult to understand and to duplicate. This paper presents a proposed standard protocol, ODD, for describing IBMs and ABMs, developed and tested by 28 modellers who cover a wide range of fields within ecology. This protocol consists of three blocks (Overview, Design concepts, and Details), which are subdivided into seven elements: Purpose, State variables and scales, Process overview and scheduling, Design concepts, Initialization, Input, and Submodels. We explain which aspects of a model should be described in each element, and we present an example to illustrate the protocol in use. In addition, 19 examples are available in an Online Appendix. We consider ODD as a first step for establishing a more detailed common format of the description of IBMs and ABMs. Once initiated, the protocol will hopefully evolve as it becomes used by a sufficiently large proportion of modellers. (c) 2006 Elsevier B.V. All rights reserved.}, language = {en} } @phdthesis{Scherer2019, author = {Scherer, Philipp C{\´e}dric}, title = {Infection on the move}, school = {Universit{\"a}t Potsdam}, pages = {x, 107, XXXVIII}, year = {2019}, abstract = {Movement plays a major role in shaping population densities and contact rates among individuals, two factors that are particularly relevant for disease outbreaks. Although any differences in movement behaviour due to individual characteristics of the host and heterogeneity in landscape structure are likely to have considerable consequences for disease dynamics, these mechanisms are neglected in most epidemiological studies. Therefore, developing a general understanding how the interaction of movement behaviour and spatial heterogeneity shapes host densities, contact rates and ultimately pathogen spread is a key question in ecological and epidemiological research. In my thesis, I address this gap using both theoretical and empirical modelling approaches. In the theoretical part of my thesis, I investigated bottom-up effects of individual movement behaviour and landscape structure on host density, contact rates, and ultimately disease dynamics. I extended an established agent-based model that simulates ecological and epidemiological key processes to incorporate explicit movement of host individuals and landscape complexity. Neutral landscape models are a powerful basis for spatially-explicit modelling studies to imitate the complex characteristics of natural landscapes. In chapter 2, the first study of my thesis, I introduce two complementary R packages, NLMR and landscapetools, that I have co-developed to simplify the workflow of simulation and customization of such landscapes. To demonstrate the use of the packages I present a case study using the spatially explicit eco-epidemiological model and show that landscape complexity per se increases the probability of disease persistence. By using simple rules to simulate explicit host movement, I highlight in chapter 3 how disease dynamics are affected by population-level properties emerging from different movement rules leading to differences in the realized movement distance, spatiotemporal host density, and heterogeneity in transmission rates. As a consequence, mechanistic movement decisions based on the underlying landscape or conspecific competition led to considerably higher probabilities than phenomenological random walk approaches due directed movement leading to spatiotemporal differences in host densities. The results of these two chapters highlight the need to explicitly consider spatial heterogeneity and host movement behaviour when theoretical approaches are used to assess control measures to prevent outbreaks or eradicate diseases. In the empirical part of my thesis (chapter 4), I focus on the spatiotemporal dynamics of Classical Swine Fever in a wild boar population by analysing epidemiological data that was collected during an outbreak in Northern Germany persisting for eight years. I show that infection risk exhibits different seasonal patterns on the individual and the regional level. These patterns on the one hand show a higher infection risk in autumn and winter that may arise due to onset of mating behaviour and hunting intensity, which result in increased movement ranges. On the other hand, the increased infection risk of piglets, especially during the birth season, indicates the importance of new susceptible host individuals for local pathogen spread. The findings of this chapter underline the importance of different spatial and temporal scales to understand different components of pathogen spread that can have important implications for disease management. Taken together, the complementary use of theoretical and empirical modelling in my thesis highlights that our inferences about disease dynamics depend heavily on the spatial and temporal resolution used and how the inclusion of explicit mechanisms underlying hosts movement are modelled. My findings are an important step towards the incorporation of spatial heterogeneity and a mechanism-based perspective in eco-epidemiological approaches. This will ultimately lead to an enhanced understanding of the feedbacks of contact rates on pathogen spread and disease persistence that are of paramount importance to improve predictive models at the interface of ecology and epidemiology.}, language = {en} }