Abstract
A stochastic, spatially explicit simulation model for clonal growth is presented which relates growth patterns to the pattern of resource availability in the environment in both space and time. The effects of two simple growth rules were examined which affect the length of spacers depending on the local environmental conditions. According to one of the rules, shorter spacers were developed in resource-rich microsites than in resource-poor microsites (growth rule G-). If the other rule acted, the spacers were lengthened in resource-rich sites (growth rule G+). The neutral reference, G0, represented a plant of rigid growth form.
A wide range of habitat types was used in the tests and characterized by an information theory model. It was found that the effectiveness of resource capture in most habitat types can be explained by spatio-temporal predictability of the environment, measured on the scale of spacer length. Shortening the spacers in resource-rich microsites, as hypothesized by “foraging theory”, reduced the proportion of misplaced ramets. Lengthening the spacers never reduced this proportion. However, the degree of intraclonal competition was significantly reduced by both shortening and lengthening the spacers in response to site quality. There were certain types of environment where plastic modification of spacers had no effect on the efficiency of resource capture when compared to the reference random (non-environment-dependent) search pattern. Such habitats can be identified exactly on the basis of the information content of habitat pattern, measured here by spatio-temporal predictability.
This study emphasizes that a wide range of environmental types should be taken into consideration when examining the adaptive nature of a certain growth pattern. Generalizing from experimental results gained in temporally constant environments may strongly bias our view on morphological adaptation.
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Oborny, B. Spacer length in clonal plants and the efficiency of resource capture in heterogeneous environments: A Monte Carlo simulation. Folia Geobot 29, 139–158 (1994). https://doi.org/10.1007/BF02803791
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DOI: https://doi.org/10.1007/BF02803791