Jan C. Thiele

Software, Data and Modelling News: NetLogo meets R: Linking agent-based models with a toolbox for their analysis

NetLogo is a software platform for agent-based modelling that is increasingly used in ecological and environmental modelling. So far, for comprehensive analyses of agent-based models (ABMs) implemented in NetLogo, results needed to be written to files and evaluated by using external software, for example R. Ideally, however, it would be possible to call any R function from within a NetLogo program. This would allow sophisticated interactive statistical analysis of model structure and dynamics, using R functions and packages for generating certain statistical distributions and experimental design, and for implementing complex descriptive submodels within ABMs. Here we present an R extension of NetLogo. It consists of only nine new NetLogo primitives for sending data between NetLogo and R and for calling R functions (six additional primitives for debugging). We demonstrate the usage of the R extension with three short examples.

Increasing bioenergy production on arable land: Does the regional and local climate respond? Germany as a case study

The extent and magnitude of land cover change effect on local and regional future climate during the vegetation period due to different forms of bioenergy plants are quantified for extreme temperatures and energy fluxes. Furthermore, we vary the spatial extent of plant allocation on arable land and simulate alternative availability of transpiration water to mimic both rainfed agriculture and irrigation. We perform climate simulations down to 1 km scale for 1970-1975 C20 and 2070-2075 A1B over Germany with Consortium for Small-Scale Modeling in Climate Mode. Here an impact analysis indicates a strong local influence due to land cover changes. The regional effect is decreased by two thirds of the magnitude of the local-scale impact. The changes are largest locally for irrigated poplar with decreasing maximum temperatures by 1 ◦ C in summer months and increasing specific humidity by 0.15 g kg −1 . The increased evapotranspiration may result in more precipitation. The increase of surface radiative fluxes Rnet due to changes in latent and sensible heat is estimated by 5 W m −2 locally. Moreover, increases in the surface latent heat flux cause strong local evaporative cooling in the summer months, whereas the associated regional cooling effect is pronounced by increases in cloud cover. The changes on a regional scale are marginal and not significant. Increasing bioenergy production on arable land may result in local temperature changes but not in substantial regional climate change in Germany. We show the effect of agricultural practices during climate transitions in spring and fall.

Challenges in Climate-Driven Decision Support Systems in Forestry

The history of Decision Support Systems in forestry is quite long as well as the list of created systems and reviews summarizing their merits and flaws. It is generally recognized that a modern decision support system (DSS) should address simultaneously as many economical and ecological issues as possible without becoming overly complex and still remain understandable for users (Reynolds et al., 2008). The ongoing global change including the climate change sets new boundary conditions for decision makers in the forestry sector. The changing growth conditions (Albert & Schmidt, 2010) and expected increasing number of weather extremes like storms force forest owners to make decisions on how to replace the damaged stands and/or how to mitigate the damages. This decision making process requires adequate information on the future climate as well as on complex climate-forest interactions which could be provided by an appropriate climate-driven decision support tool. Both the damage factors and the forest management (e.g. harvesting) result in changes of the structure of forest stands. The structural changes result in immediate changes of albedo and roughness of land surface as well as of microclimatological conditions within the stand and on the soil surface. The consequences are manifold. The changed stand density and leaf area index trigger energy and water balance changes which in turn increase or decrease the vulnerability of the remaining stand to abiotic and biotic damage factors like droughts or insect attacks. A change of the microclimatic conditions might strengthen the forest against drought, but at the same time reduce its resistance to windthrow. The sign and extent of vulnerability changes depend on complex interactions of the effective climatic agents, aboveand belowground forest structure, and soil. There are many DSS that are capable of assessing one or several risk factors; however there are few that are able to assess the additional increase or decrease of risks triggered by modification of forest structure resulting from previous damage or forest management activities. Disregarding these effects will inevitably lead user to either underor overestimation of the potential damages. The question arises whether these additional risks are significant enough to be considered in a DSS. In this chapter we present a new DSS developed according to the above mentioned requirements and capable to provide decision support taking into account economical and ecological considerations under the conditions of changing climate the Decision Support

Fluorescence lifetime correlation spectroscopy: Basics and applications

This chapter presents a concise introduction into the method of Fluorescence Lifetime Correlation Spectroscopy (FLCS). This is an extension of Fluorescence Correlation Spectroscopy (FCS) that analyses fluorescence intensity fluctuations from small detection volumes in samples of ultra-low concentration. FCS has been widely used for investigating diffusion, conformational changes, molecular binding/unbinding equilibria, or chemical reaction kinetics, at single molecule sensitivity. In FCS, this is done by calculating intensity correlation curves for the measured intensity fluctuations. FLCS extends this idea by calculating fluorescence-lifetime specific intensity correlation curves. Thus, FLCS is the method of choice for all studies where a parameter of interest (conformational state, spatial position, molecular environmental condition) is connected with a change in the fluorescence lifetime. After presenting the theoretical and experimental basis of FLCS, the chapter gives an overview of its various applications.