Improving the Accuracy of Self -reported Waterfowl Harvest Estimates

Imprecision in respondent recall can cause response heaping or spikes in frequency data for particular values (e.g., 5, 10, 15). In human di mensions research, heaping can occur for variables such as days of participation (e.g., hunting, fishing), or animals / fish harvested. Distributions with heaps can bias population estimates because the means and totals can be inflated or deflated. Because bias can result in poor management decisions, determining if the bias is large enough to matter is important. This presentation introduces the logic and flow of a deheaping program that estimates bias in means and totals when people use approximate respon ses (i.e., prototypes). The program can make estimates even when spikes occur due to bag limits. The program is available online, and smooths heaps at multiples of 5 (numbers ending in 5 and 0) and 7 (e.g., 7, 14, 21), and produces standard deviations in estimates. The program is illustrated using 25 years of waterfowl harvest estimates from Illinois. Discussion focuses on improving the accuracy of harvest estimates for adaptive harvest management.