Life-history strategies of perennial grassland plants : bet-hedging in time and metapopulation viability in fragmented landscapes

Gerstenlauer, Jakob Ludwig Karl GND

Landscape fragmentation, intensification of land use, and climate change are going to strongly impact future plant populations. Currently, plant ecologists are unable to assess the interactions between changes in landscape structure, local disturbance regime, and plant traits. Thus, new models and conceptual frameworks are needed to predict the viability of plant populations in fragmented landscapes based on plant traits, landscape properties, and the environmental stochasticity of local habitats. Methodologically, this thesis is based on non-spatial and spatially-explicit matrix population models. Non-spatial matrix models assume homogeneity in space, spatially-explicit models describe different subpopulations inhabiting patches of variable habitat quality and the exchange of individuals in space, i.e. seed dispersal. In the first chapter, I used non-spatial matrix models to find optimal life-history strategies for grassland plants inhabiting stochastic environments differing in predictability and harshness. I investigated the interplay between key life-history traits such as clonality, seed dormancy, and adult longevity. In the second chapter, I proposed guidelines for the setup and evaluation of spatially-explicit models of plant population dynamics. I tested the spatially-explicit model against the non-spatial equivalent to clarify the deficiencies of the latter. In the last chapter, I used the methodology developed in the second chapter to investigate interactions between plant traits and landscape properties. I proposed a simple trait based formula which can be used to predict metapopulation survival ability for perennial grassland species given a specific landscape setting.


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Gerstenlauer, Jakob Ludwig Karl: Life-history strategies of perennial grassland plants. bet-hedging in time and metapopulation viability in fragmented landscapes. 2012.

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