Evaluation of and insights from ALFISH: a spatially explicit landscape-level simulation of fish populations in the Everglades

We present an evaluation of a spatially explicit, age-structured model created to assess fish density dynamics in the Florida Everglades area. This model, ALFISH, has been used to compare alternative management scenarios for the Florida Everglades region. This area is characterized by periodic dry downs and refloodings. ALFISH uses spatially explicit water depth data to predict patterns of fish density. Here we present a method for calibration of ALFISH, based on information concerning fish movement, pond locations and other field data. With the current information, the greatest coefficient of determination achieved from regressions of ALFISH output to field data is 0.35 for fish density and 0.88 for water depth. The poor predictability of fish density mirrors the empirical findings that hydrology, which is the main driver of the model, only accounts for 20–40% of the variance of fish densities across the Everglades landscape. Sensitivity analyses indicate that fish in this system are very sensitive to frequency, size and location of permanent ponds as well as availability of prey.