Phaedra E. Budy Richard A. Wilkison Michael Mills David Speas Peter D. Mackinnon Mark C. Mckinstry Mary M. Conner 2020 <p><span>The inclusion of passive interrogation antenna (PIA) detection data has promise to increase precision of population abundance estimates (</span><span id="ieq1"><span class="inline-graphic"><img src="https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq1.gif" alt="" data-mce-src="https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq1.gif"></span></span><span>). However, encounter probabilities are often higher for PIAs than for physical capture. If the difference is not accounted for,&nbsp;</span><span id="ieq2"><span class="inline-graphic"><img src="https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq2.gif" alt="" data-mce-src="https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq2.gif"></span></span><span>&nbsp;may be biased. Using simulations, we estimated the magnitude of bias resulting from mixed capture and detection probabilities and evaluated potential solutions for removing the bias for closed capture models. Mixing physical capture and PIA detections (</span><i>p</i>_det<span>) resulted in negative biases in&nbsp;</span><span id="ieq3"><span class="inline-graphic"><img src="https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq3.gif" alt="" data-mce-src="https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq3.gif"></span></span><span>. However, using an individual covariate to model differences removed bias and improved precision. From a case study of fish making spawning migrations across a stream-wide PIA (</span><i>p</i>_det<span>&nbsp;≤ 0.9), the coefficient of variation (CV) of&nbsp;</span><span id="ieq4"><span class="inline-graphic"><img src="https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq4.gif" alt="" data-mce-src="https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq4.gif"></span></span><span>&nbsp;declined 39%–82% when PIA data were included, and there was a dramatic reduction in time to detect a significant change in&nbsp;</span><span id="ieq5"><span class="inline-graphic"><img src="https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq5.gif" alt="" data-mce-src="https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq5.gif"></span></span><span>. For a second case study, with modest&nbsp;</span><i>p</i>_det<span>&nbsp;(≤0.2) using smaller PIAs, CV (</span><span id="ieq6"><span class="inline-graphic"><img src="https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq6.gif" alt="" data-mce-src="https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq6.gif"></span></span><span>) declined 4%–18%. Our method is applicable for estimating abundance for any situation where data are collected with methods having different capture–detection probabilities.</span></p> application/pdf 10.1139/cjfas-2019-0326 en Canadian Science Publishing Estimating population abundance with a mixture of physical capture and passive PIT tag antenna detection data article