Introduction to the Special Volume on "Ecology and Ecological Modeling in R"

The third special volume in the "Foometrics in R" series of the Journal of Statistical Software collects a number of contributions describing statistical methodology and corresponding implementations related to ecology and ecological modelling. The scope of the papers ranges from theoretical ecology and ecological modelling to statistical methodology relevant for data analyses in ecological applications.

Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME

Mathematical simulation models are commonly applied to analyze experimental or environmental data and eventually to acquire predictive capabilities. Typically these models depend on poorly defined, unmeasurable parameters that need to be given a value. Fitting a model to data, so-called inverse modelling, is often the sole way of finding reasonable values for these parameters. There are many challenges involved in inverse model applications, e.g., the existence of non-identifiable parameters, the estimation of parameter uncertainties and the quantification of the implications of these uncertainties on model predictions. The R package FME is a modeling package designed to confront a mathematical model with data. It includes algorithms for sensitivity and Monte Carlo analysis, parameter identifiability, model fitting and provides a Markov-chain based method to estimate parameter confidence intervals. Although its main focus is on mathematical systems that consist of differential equations, FME can deal with other types of models. In this paper, FME is applied to a model describing the dynamics of the HIV virus.

simecol: An Object-Oriented Framework for Ecological Modeling in R

The simecol package provides an open structure to implement, simulate and share ecological models. A generalized object-oriented architecture improves readability and potential code re-use of models and makes simecol-models freely extendable and simple to use. The simecol package was implemented in the S4 class system of the programming language R. Reference applications, e.g. predator-prey models or grid models are provided which can be used as a starting point for own developments. Compact example applications and the complete code of an individual-based model of the water flea Daphnia document the efficient usage of simecol for various purposes in ecological modeling, e.g. scenario analysis, stochastic simulations and individual based population dynamics. Ecologists are encouraged to exploit the abilities of simecol to structure their work and to use R and object-oriented programming as a suitable medium for the distribution and share of ecological modeling code.

Introduction to the Special Volume on "Ecology and Ecological Modeling in R"

The third special volume in the "Foometrics in R" series of the Journal of Statistical Software collects a number of contributions describing statistical methodology and corresponding implementations related to ecology and ecological modelling. The scope of the papers ranges from theoretical ecology and ecological modelling to statistical methodology relevant for data analyses in ecological applications

Solving Differential Equations in R: Package deSolve

In this paper we present the R package deSolve to solve initial value problems (IVP) written as ordinary differential equations (ODE), differential algebraic equations (DAE) of index 0 or 1 and partial differential equations (PDE), the latter solved using the method of lines approach. The differential equations can be represented in R code or as compiled code. In the latter case, R is used as a tool to trigger the integration and post-process the results, which facilitates model development and application, whilst the compiled code significantly increases simulation speed. The methods implemented are efficient, robust, and well documented public-domain Fortran routines. They include four integrators from the ODEPACK package (LSODE, LSODES, LSODA, LSODAR), DVODE and DASPK2.0. In addition, a suite of Runge-Kutta integrators and special-purpose solvers to efficiently integrate 1-, 2- and 3-dimensional partial differential equations are available. The routines solve both stiff and non-stiff systems, and include many options, e.g., to deal in an efficient way with the sparsity of the Jacobian matrix, or finding the root of equations. In this article, our objectives are threefold: (1) to demonstrate the potential of using R for dynamic modeling, (2) to highlight typical uses of the different methods implemented and (3) to compare the performance of models specified in R code and in compiled code for a number of test cases. These comparisons demonstrate that, if the use of loops is avoided, R code can efficiently integrate problems comprising several thousands of state variables. Nevertheless, the same problem may be solved from 2 to more than 50 times faster by using compiled code compared to an implementation using only R code. Still, amongst the benefits of R are a more flexible and interactive implementation, better readability of the code, and access to RâÂÂs high-level procedures. deSolve is the successor of package odesolve which will be deprecated in the future; it is free software and distributed under the GNU General Public License, as part of the R software project.

Statistical Methods for the Qualitative Assessment of Dynamic Models with Time Delay (R Package qualV)

Results of ecological models differ, to some extent, more from measured data than from empirical knowledge. Existing techniques for validation based on quantitative assessments sometimes cause an underestimation of the performance of models due to time shifts, accelerations and delays or systematic differences between measurement and simulation. However, for the application of such models it is often more important to reproduce essential patterns instead of seemingly exact numerical values. This paper presents techniques to identify patterns and numerical methods to measure the consistency of patterns between observations and model results. An orthogonal set of deviance measures for absolute, relative and ordinal scale was compiled to provide informations about the type of difference. Furthermore, two different approaches accounting for time shifts were presented. The first one transforms the time to take time delays and speed differences into account. The second one describes known qualitative criteria dividing time series into interval units in accordance to their main features. The methods differ in their basic concepts and in the form of the resulting criteria. Both approaches and the deviance measures discussed are implemented in an R package. All methods are demonstrated by means of water quality measurements and simulation data. The proposed quality criteria allow to recognize systematic differences and time shifts between time series and to conclude about the quantitative and qualitative similarity of patterns.

The Development of a Cognitive Skills Training to Support Driver Education: Experimental Validation of Theoretical Underpinnings

Crash numbers of novice drivers are, despite best efforts of all involved institutions, alarmingly high. One central explanation refers to deficits in cognitive skills such as hazard perception, which have a tremendous influence on accident involvement of younger drivers. Conventional forms of driver training have largely failed to build up skills that go beyond a rather descriptive knowledge of how to drive. Computer based trainings (CBTs) are assumed to provide new ways of tackling this problem. There are already CBTs available that address relevant issues and are presumed to be effective. However, their evaluations lack evidence for the superiority of the specific features of multimedia based interventions over other forms of training. This shortcoming, in addition to the fact that all available relevant CBTs have been developed within contexts that differs significantly from European conditions in terms of the “average” driving environment as well as the respective educational schemes, has prompted us to develop a new CBT that is intended to complement the existing driver training program by addressing critical cognitive skills. In a first step, we tested the CBTs theoretical validity by comparing the performance in the training itself between learner drivers and experienced drivers. The results show that experienced drivers achieve higher scores in the CBT. We conclude that our application does indeed address relevant cognitive skills that are associated with driving experience

The Development of a Cognitive Skills Training to Support Driver Education – Comparing Performance of Experienced and Trained Learner Drivers

Deficits in cognitive skills such as hazard perception are considered one of the major factors explaining the high numbers of crashes for novice drivers. Computer based trainings (CBTs) have been identified as a potential measure to improve such skills. Several CBTs have been developed since. Some of them have been evaluated, however, only by comparing a treatment group and a control group. While results show that the evaluated CBTs are somewhat effective, it is unclear how an experienced driver would have performed in the test scenarios. We developed our own CBT, and in a first step, evaluated it following the same known strategy (treatment and control group, adding a “paper based training group). Results provided evidence for the assumption that the CBT had a positive effect on learner drivers’ glance behaviour in simulated driving (Petzoldt et al., 2013). However, after we confirmed the effectiveness, we tested a group of experienced drivers on exactly the same simulator scenarios. The comparison between treatment, control and experienced driver group is presented in this paper. Results show comparable patterns of glance behaviour for the treatment group and the experienced drivers, superior to that of the control group. Driving performance rated by experts was mostly appropriate for all groups, with notable exceptions for some scenarios

Statistical Methods for the Qualitative Assessment of Dynamic Models with Time Delay (R Package qualV)

Results of ecological models differ, to some extent, more from measured data than from empirical knowledge. Existing techniques for validation based on quantitative assessments sometimes cause an underestimation of the performance of models due to time shifts, accelerations and delays or systematic differences between measurement and simulation. However, for the application of such models it is often more important to reproduce essential patterns instead of seemingly exact numerical values. This paper presents techniques to identify patterns and numerical methods to measure the consistency of patterns between observations and model results. An orthogonal set of deviance measures for absolute, relative and ordinal scale was compiled to provide informations about the type of difference. Furthermore, two different approaches accounting for time shifts were presented. The first one transforms the time to take time delays and speed differences into account. The second one describes known qualitative criteria dividing time series into interval units in accordance to their main features. The methods differ in their basic concepts and in the form of the resulting criteria. Both approaches and the deviance measures discussed are implemented in an R package. All methods are demonstrated by means of water quality measurements and simulation data. The proposed quality criteria allow to recognize systematic differences and time shifts between time series and to conclude about the quantitative and qualitative similarity of patterns

Der Einfluss von Klimavariabilität auf aquatische Nahrungsnetze: Der Einfluss von Klimavariabilität auf aquatische Nahrungsnetze

In den gemäßigten Breiten zeigte sich die allgemeine Erwärmung der letzten Jahrzehnte insbesondere im Winter und im zeitigen Frühjahr. Dementsprechend traten Veränderungen in der Phänologie, dem zeitlichen Verlauf von Populations- und Entwicklungsprozessen von Organismen (z. B. Zeitpunkt der Knospung bei Pflanzen oder der Laichperiode bei Fischen), vor allem im Frühjahr auf. Obwohl generell eine frühere und beschleunigte Entwicklung als Reaktion auf die Erwärmung beobachtet wurde, zeigten sich doch Unterschiede in der Sensitivität von Organismen. Dadurch kann es in Nahrungsnetzen zu Match- oder Mismatch- Situationen in Räuber-Beute Beziehungen kommen. Am Beispiel der komplexen Interaktionen im Nahrungsnetz der Talsperre Saidenbach wird der Einfluss verschiedener Erwärmungsszenarien auf Schlüsselorganismen und deren Interaktionen im Nahrungsnetz und letztlich auf die Wassergüte in dieser Trinkwassertalsperre im Rahmen des DFG-Schwerpunktprogramms AQUASHIFT analysiert.In temperate regions, the warming trends of the last decades have been observed primarily in winter and early spring. Accordingly, changes in the phenology of individual species, e.g. sprouting in plants or spawning of fish, occurred mainly in spring. Although the general pattern is earlier and faster development in response to warming, differences in sensitivity have been apparent between species, potentially giving rise to match or mismatch scenarios in predator-prey relations. The impact of warming scenarios on key species, their interactions and ultimately on the water quality is studied at Saidenbach Reservoir within the framework of the DFG priority program AQUASHIFT