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<br /> <br /> <br />1 <br />1 <br /> <br /> <br />J <br />r <br />1 <br />1 <br />1 <br /> <br />f <br /> <br /> <br /> <br />INTRODUCTION <br />Mathematical models can be useful tools for the conservation of threatened or <br />endangered species (Wootton and Bell 1992). Models fulfill 4 important functions for <br />these kinds of activities: 1) clarifying the critical data required to monitor the health of a <br />population; 2) determining the current status of data collection and analysis, and of the <br />population dynamics themselves; 3) aid in projecting the outcome of current <br />management strategies; and 4) providing insight into the most effective management <br />strategies given alternatives (Mertz 1971, Crouse et al. 1987, Lande 1988, Doak 1989, <br />Getz and Haight 1989, Menges 1990, Wootton and Bell 1992). <br />Historically, population models took relatively simplistic forms such as Lefkovitch or <br />Leslie Matrix models (sensu Ryel and Valdez 1994) in which transition probabilities <br />from one life stage to the next were used to determine how populations were <br />responding through time and how changes in the transition values might effect those <br />dynamics. It is now increasingly apparent that the spatial structure of populations <br />often has important effects on population dynamics (Holt 1985; Kareiva 1986, 1987; <br />Pulliam 1988). Incorporating population substructure into models and population <br />monitoring schemes may be critical to predict future population dynamics accurately <br />which may allow either different management strategies altogether (Lande 1988, <br />Pulliam 1988, Doak 1989) or at least a quantitative method for prioritizing where efforts <br />are to take place for population enhancement. <br />1 <br /> <br />