Kernel Density Estimates of Linear Home Ranges for Stream Fishes: Advantages and Data Requirements

Abstract Kernel density estimates of linear home range can increase the information content of the home range estimate. Particularly in lotic systems, univariate kernel density estimates have desirable properties relative to simple reporting of linear ranges. Kernel density estimates are calculated from a set of relocation points (i.e., radiotelemetry contacts) that can be interpreted as a utilization distribution (UD). The UD estimates the amount of time spent at a given point within the home range. The amount of time that elapses between relocations (sampling interval), total number of relocations (sample size), and bandwidth (smoothing applied to the estimate) are important considerations when using kernel density estimates. Monte Carlo simulations using unimodal and bimodal distributions sampled randomly at sample sizes of 10-100 and three automated bandwidth selection procedures (simple normal reference, Silverman's rule of thumb, and Sheather-Jones plug-in) suggested that at sample sizes of 30 or mo...