Helmut Lorek

Modelling and simulation software to support individual-based ecological modelling

Abstract Individual-based modelling is a reductionistic technique for describing ecological systems. In contrast to more traditional mathematical models, an individual-based model is not described ‘top-down’, like a black box, but ‘bottom-up’ by the structure and dynamics of the discrete entities of the system considered important for the issue under study. The majority of individual-based models are hand-coded software programs, designed and implemented by ecologists. In truth individual-based models are currently synonymous with their implementation on computer—for generally speaking no complete description of a model other than the executable program itself is available. This leads to a number of fundamental and technical problems, which may partly be overcome by the use of appropriate software tools. But although software tools are available for simulation studies in many other fields of application, the number of tools which assist individual-based modelling and simulation is limited. In this paper we examine different strategies for the software support of individual-based modelling. We identify three different layers of software support. Firstly, we discuss software frameworks being implemented on top of general purpose programming languages. Although software frameworks support the programming of any individual-based model, they can only be used by an ecologist able to develop computer programs. Secondly, we look at interactive modelling tools being used by ecologists who build models for specific fields of application. Thirdly, we investigate simulators which support users who do not want to build their own model but would rather use a predefined existing model. For each of these categories we briefly describe a tool that has been or is currently being developed at the OFFIS institute. Attention is mainly focused on the conception of a modelling tool ( wesp-tool ) designed to support the modelling and analysis of individual-based metapopulation models.

Object-oriented support for modelling and simulation of individual-oriented ecological models

Abstract Opposed to traditional mathematical methods, the technique of individual-oriented modelling chooses distinguishable individuals as the basic entities of description. An ecosystem is described by all static and dynamic properties of the individuals involved in the system as well as time varying properties of the environment. Individuals change their state over time or due to internal and external events. Using the individual-oriented approach, programming skills are indispensable. Coding individual-oriented models is a complex, tedious and error prone task, which leads to a long list of problems. Many, although not all, problems may be solved using object-oriented software libraries. E co S im is a C++-class library especially designed to support individual-oriented modelling and simulation of ecological systems. E co S im brings together new advances in object-oriented discrete-event simulation and ecology. The process of implementing individual-oriented models is facilitated by providing classes for those parts, that are common to all such models. This covers among others the specification of static and dynamic properties of ‘individuals’, the specification of dynamically changing environments as well as support for ‘on the fly’ analysis and animation of generated data. Using E co S im ecologists may therefore concentrate on the unique parts of their models.

META-X: Generic Software for Metapopulation Viability Analysis

The major tools used to make population viability analyses (PVA) quantitative are stochastic models of population dynamics. Since a specially tailored model cannot be developed for every threatened population, generic models have been designed which can be parameterised and analysed by non-modellers. These generic models compromise on detail so that they can be used for a wide range of species. However, generic models have been criticised because they can be employed without the user being fully aware of the concepts, methods, potentials, and limitations of PVA. Here, we present the conception of a new generic software package for metapopulation viability analysis, META-X. This conception is based on three elements, which take into account the criticism of earlier generic PVA models: (1) comparative simulation experiments; (2) an occupancy-type model structure which ignores details of local population dynamics (these details are integrated in external submodels); and (3) a unifying currency to quantify persistence and viability, the ‘intrinsic mean time to extinction’. The rationale behind these three elements is explained and demonstrated by exemplary applications of META-X in the three fields for which META-X has been designed: teaching, risk assessment in the field, and planning. The conception of META-X is based on the notion that PVA is a tool to deal with rather than to overcome uncertainty. The purpose of PVA is to produce relative, not absolute, assessments of extinction risk which support, but do not supplant, management decisions.

Scenarios and Experiments

You will spend most of your time working with META-X specifying scenarios and experiments, i.e. with translating your real-world or theoretical problems into parameter sets for the generic metapopulation model which is implemented in META-X. Most elements of how to specify scenarios and experiments are described in the Guided Tour and will not be repeated here in detail; in particular, the Screenshots are not repeated. Instead, this chapter gives an overview of: The structure and elements of the Experiment Wizard. The structure and elements of the Scenario Wizard. The specification and purpose of ‘homogeneous’ parameters. The hierarchy of model parameters in META-X which allows you to create ‘user-defined’ scenarios.

The Landscape Editor

The right-hand part of the META-X window contains the Landscape Editor which is designed to visualize the landscape of a scenario, to modify existing scenarios via a graphical interface, and to create new scenarios with this graphical interface. Once a scenario has been selected in the Project Tree by a double-click or by choosing EditorpLoad Scenario in the menu, the Landscape Editor will indicate: 1. Whether the scenario is completely specified or some parameters regarding patches, dispersal range or correlation length are still missing. 2. The position of the patches and if a patch is occupied by a subpopulation. 3. The scales of the scenario, i.e. the basic scale of the map, dispersal range and correlation length. 4. If a so-called ‘Local Aspect’ is chosen, different aspects of the patches, i.e. the mean time to extinction, number of emigrants produced or number of immigrants needed to establish a new subpopulations (with 50% probability). 5. The connections between patches, i.e. all pairs of patches which can recolonize each other via dispersal.

The Project Tree

The Project Tree in the left half of the META-X screen is designed to organize your projects: It hierarchically lists the experiments of a project, the scenarios of an experiment and the parameters and evaluations of experiments and scenarios. The most important procedures of META-X (scenario, experiment and simulation wizard) can be started directly from the Project Tree. Scenarios may be selected to be displayed in the Landscape Editor. You can copy (or cut) and paste existing experiments and scenarios. Experiments and scenarios can be deleted or renamed. You can modify the sequence of scenarios in the experiment.

Parameterizing META-X

One main task when working with META-X is to translate questions regarding hypothetical or real metapopulations into parameterizations of the META-X model. To be able to do this, you need to know the model and exactly what its parameters mean (Chap. 14). The next step is to compile all the relevant empirical information available and to extract the model parameters. To give you an idea of how to do this, we briefly describe some general concepts of parameterizing META-X (or any other PVA model) in this chapter. However, this is not the place for a complete introduction into the problem of parameterizing (meta-)population models. There is a whole body of literature on, for example, obtaining demographic or dispersal data from mark/recapture studies (Moilanen et al. 1998, Henle et al 1999, Moilanen 1999, Hanski et al. 2000). In general, if you want to learn how to parameterize PVA models, you will need to scan the PVA literature (see suggested readings at the end of Chap. 13) and assemble your own tool chest.

Import, Export and Report

META-X has four ways to communicate with ‘the rest of the (computer) world’: Import of model parameters. This is useful if you want to import model parameters from other programs, for example programs which calculate ‘Patch characteristics’ (mean time to extinction, etc.). The format of the import files is thus an interface between all kinds of sources which produce model parameters for all kinds of species and landscapes, and META-X. Export of simulation results. Raw data from the simulations may be exported and analyzed with other programs. Export of graphs. Some of the diagrams of META-X and the entire window of the Landscape Editor can be exported to other programs via the clipboard, or sent directly to the printer. Reports. You can generate reports of scenarios and whole experiments which contain all the information and parameters specified by you, and all evaluations. Reports are HTML format and can thus easily be imported into wordprocessing programs, printed and modified.

Goals, Methods, and Concepts of PVA

META-X has been developed as a tool for education and for practical use by specialists such as field ecologists, conservation biologists, managers of natural resources, and environmental decision-makers. Working with META-X requires no programming and — at least at the level of the metapopulation — no modelling, and is therefore easy to use by non-experts. However, as with any tool, even nonexperts have to know the purpose of the tool and its basic function if they want to use it appropriately. Moreover, META-X is not a fully ‘canned’ PVA software because external sub-models, for example of local population dynamics, are needed to parameterize META-X for actual species and landscapes. Therefore, to work with META-X you need to be familiar with the basics of PVA. In this chapter we briefly introduce the goals, methods and concepts of population viability analysis (PVA). In particular, we explain in detail how to quantify the persistence and viability of populations (and metapopulations), since this is usually not explained in PVA literature. For those needing a full introduction into PVA, we strongly recommend consulting the literature listed at the end of this chapter.

Simulation and Evaluation

As soon as you have specified scenarios and experiments, the time has come for the computer to do its job: to simulate metapopulation dynamics for a certain number of years, e.g. 300 years. ‘Simulation’ means starting with a certain state of the model metapopulation (i.e. a certain pattern of patch occupancy) and then using the model parameters to calculate the state of the metapopulation after one time step. This new state is then used to calculate the state after the next time step, and so on.