Annual plants under cyclic disturbance regime : better understanding through model aggregation
(2008)
In their application for conservation ecology, 'classical' analytical models and individual-based simulation models (IBMs) both entail their specific strengths and weaknesses, either in providing a detailed and realistic representation of processes or in regard to a comprehensive model analysis. This well-known dilemma may be resolved by the combination of both approaches when tackling certain problems of conservation ecology. Following this idea, we present the complementary use of both an IBM and a matrix population model in a case study on grassland conservation management. First, we develop a spatially explicit IBM to simulate the long-term response of the annual plant Thlaspi perfoliatum (Brassicaceae), claspleaf pennycress, to different management schemes (annual mowing vs. infrequent rototilling) based on field experiments. In order to complement the simulation results by further analyses, we aggregate the IBM to a spatially nonexplicit deterministic matrix population model. Within the periodic environment created by management regimes, population dynamics are described by periodic products of annual transition matrices. Such periodic matrix products provide a very conclusive framework to study the responses of species to different management return intervals. Thus, using tools of matrix model analysis (e.g., loop analysis), we can both identify dormancy within the age-structured seed bank as the pivotal strategy for persistence under cyclic disturbance regimes and reveal crucial thresholds in some less certain parameters. Results of matrix model analyses are therefore successfully tested by comparing their results to the respective IBM simulations. Their implications for an enhanced scientific basis for management decisions are discussed as well as some general benefits and limitations of the use of aggregating modeling approaches in conservation.
Das Teilprojekt Landschafts- und Autökologie (LÖK) hat den Schwerpunkt auf die Erarbeitung einer e- Learning-Einheit zur Habitatmodellierung im allgemeinen und dem Verfahren der logistischen Regression im speziellen gelegt. In den sechs Lernmodulen der Lerneinheit werden alle für eine erfolgreiche Modellierung der Habitateignung erforderlichen Arbeitsschritte sequentiell behandelt. Die wesentlichen Schritte werden mit interaktiven Aufgaben vertieft, in welchen an entscheidenden Stellen WebGIS eingesetzt wird. Der räumliche Bezug wird in der Regel über WebGIS- Anwendungen zu einer virtuellen Landschaft hergestellt, die in das GIMOLUS-System integriert ist. Die erforderlichen Datensätze für die Analyse von Art-Habitat- Beziehungen werden bereitgestellt oder können interaktiv aus der virtuellen Landschaft erzeugt werden.