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Antlion Foraging: Tracking Prey Across Space And Time
Ecology, Oct, 1999 by Philip H. Crowley, Mary C. Linton
A specific goal of the present study was to clarify why larval antlions in the Sleeping Bear system relocated their pits after several weeks rather than, for example, every few days (as in the laboratory study) or every few months. To accomplish this, we evaluated [greater than]5000 composite foraging strategies, using Monte Carlo simulations to estimate the expected net daily energy gain rate associated with each strategy, based on spatiotemporal prey availabilities measured in the field data. We expected to find that the foraging strategy yielding the highest gain rate, and other high-gaining strategies, would generate pit relocations about as frequently as observed in the field population. The key assumption underlying this expectation is that these larval antlions are capable of adopting a foraging strategy that maximizes (or nearly maximizes) net gain rate. Because antlion growth rate appears to be strongly food limited at Sleeping Bear Dunes National Lakeshore (Linton 1995) and at other sites studied to date (e.g., Griffiths 1980b, 1991, Furunishi and Masaki 1983, Lucas 1985, Gotelli 1993), maximally efficient foraging should increase larval growth rate. Because life cycle length is apparently flexible (Wheeler 1930, Furunishi and Masaki 1982), faster larval growth may increase fitness by reducing generation time or by reducing exposure to sources of larval mortality.
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A general goal of this study is to present and use a conceptual framework concerning how trap-building predators might be expected to respond to spatial and temporal patterns of prey availability, a fluctuating spatiotemporal mosaic (Forman and Godron 1986). The patchiness of prey distributions over space and time is critically important in determining the best foraging strategies. When current site-specific gain rates provide information about the near future at the site or about nearby sites, then gain rate data are autocorrelated (see Chatfield 1996). Except in unusually long data sequences, autocorrelations are difficult to detect statistically, but even subtle correlative linkages may provide an edge to the forager whose strategy makes effective use of them.
In the following sections, we explain how prey availabilities were derived from pitfall trap data, describe the strategies to be evaluated, consider some implications of autocorrelation for optimal strategies, and indicate the way in which the simulations were parameterized and conducted. Then we present the simulation results, focusing mainly on the highest gain strategies and the features that characterize them, comparing against field results and predictions implied by the autocorrelation structure of the data. By contrasting results for two sites and by examining the implications of altering movement costs, we test predictions on the effects of prey availability and clarify the determinants of relocation frequency. Finally, we consider implications of the results and emphasize follow-up work and new directions that should further our understanding of how animals deal with spatiotemporal variation.
