Estimating population abundance from field surveys is often complicated by interference between multiple survey protocols. In this paper, we propose a two-stage estimation framework for abundance models in which detection processes interact, leading to both missed detections and sample loss caused by survey procedures. Our approach separates the calibration of sample loss from the estimation of detection probability, thereby avoiding the feedback and weak identifiability that can arise in fully joint hierarchical models. We further derive a sandwich-type robust variance estimator that propagates first-stage uncertainty into the second stage and remains valid under certain forms of model misspecification. Simulation studies demonstrated that the proposed method provides more reliable uncertainty quantification than a Bayesian hierarchical joint model, which tends to underestimate uncertainty even under correct specification. We illustrate the practical utility of the method using ectoparasite abundance data from the invasive Pallas's squirrel \textit{Callosciurus erythraeus}.

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