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MANAGING PLANT POPULATION SPREAD: PREDICTION AND ANALYSIS USING A SIMPLE MODEL
Author(s) -
Bullock James M.,
Pywell Richard F.,
Coulson-Phillips Sarah J.
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 - 10.1890/07-1128.1
Subject(s) - biological dispersal , population , ecology , seed dispersal , population model , hay , vital rates , population size , biology , statistics , geography , population growth , demography , mathematics , agronomy , sociology
Models can be used to direct the management of population spread for the control of invasives or to encourage species of conservation value. Analytical models are attractive because of their theoretical basis and limited data requirements, but there is concern that their simplicity may limit their practical utility. We address the applied use of simple models in a study of a declining annual herb, Rhinanthus minor. We parameterized a population‐spread model using field data on demography and dispersal for four management systems: grazed only (GR), hay‐cut once (H1), hay‐cut twice (H2), and hay‐cut with autumn grazing (HG). Within a replicated experiment we measured spread rates of introduced R. minor populations over eight years. The modeled and measured spread rates were very similar in terms of both patterns of management effects and absolute values, so that in both cases HG > H2, H1 > GR. The treatments affected both dispersal and demography (establishment and survival) and so we used decomposition approaches to analyze the major causes of differences in population spread. Increased dispersal under hay‐cutting was more important than demographic changes and accounted for ∼70% of the differences in spread rate between the hay‐cut and grazed‐only treatments. Furthermore, management effects on the tail of the dispersal curve were by far the most critical in governing spread. This study suggests that simple models can be used to inform practical conservation management, and we demonstrate straightforward uses of our model to predict the impacts of different management strategies. While simple models can give accurate projections, we emphasize that they must be parameterized with high‐quality data gathered at the appropriate spatial scale.