AICME II abstracts
Individual-Based Spatial Simulations of Ecological Systems
Projecting Future Population Dynamics of the Florida Snail Kite in Relation to Hydrology by Means of a Suite of Models Wolf M. Mooij1 and Donald L. DeAngelis2 . The Florida snail kite is an endangered raptor that occurs as a closed population of about 2000 birds in the wetlands of Southern and Central Florida. Its demography is severely affected by the hydrology of these wetlands (Beissinger 1995). The present study aims at projecting the development of the population under future hydrological scenarios. The basic information for any model that makes such projections should be good empirical studies. A large number of empirical studies have been done on the Florida snail kite (e.g. Bennetts and Kitchens 1997a, 1997b). These studies provide basic information on the biology of the species. They also provide the correlative relationships between specific aspects of the snail kite life-history and behavior with the hydrology of the system. These relationships form the building blocks of any hydrology driven population-dynamical kite model. Opinions differ on whether the best approach to modeling the life history of a population should be by means of a system-wide deterministic matrix model or, alternatively, a spatially-explicit stochastic individual-based model (Caswell 2001). We argue that rather than choosing among these two approaches, it is better to implement both concurrently. Next to the system-wide deterministic matrix model and the spatially-explicit stochastic individual-based model (Mooij et al. 2002) two other versions were implemented: a system-wide stochastic matrix model and a spatially-explicit deterministic individual-based model. With these four tools in hand we approached the challenge of making reliable projections of future population development of the snail kite under various hydrological scenarios. Next to having a rigorous and transparent model structure, two issues are central to getting a reliable kite model: how to parameterize the model and how to set ranges of uncertainty to its output. The preferred statistical framework to
Individual-Based Spatial Simulations of Ecological Systems
solve both issues would be maximum likelihood estimation (Mooij and DeAngelis 2003). In principle, the maximum likelihood approach provides a formal and rigorous approach to deal with the four sources of uncertainty in the projections: structural uncertainty, uncertainty in the hydrological input, uncertainty in the biological parameters and uncertainty due to demographic stochasticity.
References [1] Beissinger, S.R., 1995. Modeling extinction in periodic environments: Everglades water levels and snail kite population viability. Ecological Applications 5, 618-631. [2] Bennetts, R.E. and W.M. Kitchens, 1997a. The demography and movements of snail kites in Florida. USGS/Biological Resources Division and Florida Cooperative Fish and Wildlife Research Unit, Technical Report Number 56. [3] Bennetts, R.E. and W.M. Kitchens, 1997b. Population dynamics and conservation of snail kites in Florida: the importance of spatial and temporal scale. Colonial Waterbirds 20: 324-329. [4] Caswell, H., 2001. Matrix Population Models. Sinauer Associates, Sunderland, MA. [5] Mooij, W.M., R.E. Bennetts, W.M. Kitchens and D.L. DeAngelis, 2002. Ezploring the effects of drought extend and interval on the Florida snail kite: interplay between spatial and temporal scales. Ecological Modeling 149: 25-39. [6] Mooij, W.M. and D.L. DeAngelis, 2003. Uncertainty in spatially explicit animal dispersal models. Ecological Applications, in press.
1 Netherlands Institute of Ecology, Centre for Limnology, Nieuwersluis, The Netherlands (e-mail:
[email protected]). 2 US Geological Survey, Centre for Water and Restoration Studies, University of Miami, Coral Gables, FL (e-mail:
[email protected]).
09-Moo-a
AICME II abstracts
09-Moo-b
