Detecting, estimating, and correcting for biases in harvest data. Issue 7 (14th July 2015)

• Record Type: Journal Article
• Authors: Schmidt, Jennifer I.; Kellie, Kalin A.; Chapin, F. Stuart
• Is Part Of: Journal of wildlife management. Volume 79:Issue 7(2015)
• Page Start: 1152
• Page End: 1162
• Publication Date: 2015-07-14
• Subjects: Wildlife management -- Periodicals; Zoology -- Periodicals; 333.954
• DOI: 10.1002/jwmg.928
• Languages: English
• ISSNs: 0022-541X

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>ABSTRACT</title> <sec id="jwmg928-sec-0001" sec-type="section"> <p>Hunting is important to many people because it provides food, recreation, and cultural identity, so proper management of wildlife is necessary. Wildlife agencies and researchers often rely on harvest data supplied by hunters, but interpretation of these data can be misleading when biases are not acknowledged, assessed, and corrected. We use harvest information collected by the Alaska Department of Fish and Game (ADF&G) from moose (<italic>Alces alces gigas</italic>) hunters to examine and correct 3 common biases in harvest data: heaping in responses of estimated effort (i.e., rounding), changes in report design, and nonreporting. We found that bias due to heaping was limited (2.8%). A large increase in special permits in 2004 (6.1% in 2003 and 40.1% in 2004) corresponded with increases in individuals with multiple permits (8.6% and 17.3%), which biased estimates of hunt participation calculated from permit data. Failure to correct for multiple reports per hunter also resulted in an artificial decline in success over time. Road access influenced reporting rates; rural Alaska residents without a road had the lowest reporting rate (67%) and rural with a road the greatest (82%). A statewide trend of 663 additional hunters per year calculated from raw permit data was eliminated once data were corrected for both multiple permits and nonreporting. Late reporters were also less likely to hunt (11.8%) than all reporters. Our research shows that survey data bias can significantly influence data interpretation, and wildlife managers must balance information needs, time constraints, and financial resources when determining which biases to correct. © 2015 The Wildlife Society.</p> </sec> </abstract>