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ANNUAL PLANTS UNDER CYCLIC DISTURBANCE REGIME: BETTER UNDERSTANDING THROUGH MODEL AGGREGATION
Author(s) -
Pagel Jörn,
Fritzsch Katrin,
Biedermann Robert,
Schröder Boris
Publication year - 2008
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/07-1305.1
Subject(s) - ecology , population viability analysis , population , ibm , vital rates , computer science , biology , population growth , physics , endangered species , habitat , demography , sociology , optics
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.