Muskellunge Spawning Site Selection in Northern Wisconsin Lakes and a GIS‐Based Predictive Habitat Model

Abstract

AbstractSpawning habitat degradation has been linked to declines in naturally reproducing Muskellunge Esox masquinongy populations, and managers require efficient methods to identify and protect these habitats. We collected spawning habitat data from 28 lakes in northern Wisconsin to determine Muskellunge spawning habitat selection and to create a GIS‐based model for predicting the locations of spawning sites. Spawning site selection by Muskellunge may be more complex than previously thought. Muskellunge showed selection for spawning in habitats with a sheltered effective fetch and east‐facing shorelines. The strongest selection was for habitats with a combination of moderate slope, small flats, and concave bathymetric curvature. Muskellunge selected against steeply sloping shorelines; very large areas of shallow flats; developed shorelines; herbaceous wetlands; and complex‐leafed submersed aquatic vegetation. Lake trophic status appears to interact with other habit variables to determine spawning site selection; sites without submersed aquatic vegetation were more strongly selected in eutrophic lakes than in other lake types. A GIS model of spawning site selection was created using the machine learning program MaxEnt (Maximum Entropy Modeling). The model predicted that Muskellunge would spawn in areas with moderately sheltered effective fetches, moderate to small areas of shallow flats, away from outflowing streams, and (to a lesser extent) along shorelines facing east or west. The model was tested on novel lakes using area‐under‐the‐curve (AUC) analysis, in which values ranged from 0.5 (predictions no better than random) to 1.0 (perfect assignment). The mean AUCtest value (i.e., the expectation of model performance for a novel lake) was 0.637 (SD = 0.12). When the model was used to designate the best 20% of available spawning habitat area for Muskellunge in each lake (based on the relative probability of spawning), that area contained 32% of the spawning sites. The model provides an efficient method for management agencies and conservation groups to use in designating spawning habitat for conservation and in communicating with the public through spawning habitat maps.Received January 26, 2014; accepted October 7, 2014