Wildlife managers are often tasked with making management decisions with limited information. When research can be conducted to inform management decisions, analysis of the data collected can be daunting. Here we detail the use of an R package, `EcoCountHelper`, and an associated analytical pipeline aimed at making GLMM-based analysis of wildlife count data more accessible. To demonstrate the utility of this approach, we use our package to model acoustic bat activity data relative to multiple landscape characteristics in a protected area threatened by encroaching disease - Grand Teton National Park. Our package uses a series of easy to use functions that can accept both wide- and long-form multi-taxa count data without the need for programming experience. In our case study in the Tetons, we found that an increased prevalence of porous buildings increases activity levels of Eptesicus fuscus and Myotis volans; Myotis lucifugus activity decreases as distance to water increases; and Myotis volans activity increases with the amount of forested area. By using GLMMs in tandem with `EcoCountHelper`, managers can assess the effects of landscape characteristics on wildlife in a statistically-robust framework.