Climate change coupled with land-use change will likely alter habitats and affect state parameters of the animal populations that dwell in them. Affected parameters are anticipated to include site occupancy and abundance, population range, and phenophase cycles (e.g., arrival dates on breeding grounds for migrant bird species). Detecting these changes will require monitoring many sites for many years, a process that is well suited for an automated system. We developed and tested monitoR, an R package that is designed for long-term, multi-taxa automated passive acoustic monitoring programs. monitoR correctly identified presence for black-throated green warbler and ovenbird in 64% and 72% of the 52 surveys using binary point matching, respectively, and 73% and 72% of the 52 surveys using spectrogram cross-correlation, respectively. Of individual black-throated green warbler song events, 73% of 166 black-throated green warbler songs and 69% of 502 ovenbird songs were identified by binary point matching. Spectrogram cross correlation identified 64% of 166 black-throated green warbler songs and 64% of 502 ovenbird songs. False positive rates were We describe a method to identify the probability of survey presence in a template-based automated detection system using known false positive rates for each template. True and false positive detection rates were observed in 146 training surveys. These probabilities were used in a Bayesian approach that discriminates between detections in occupied surveys and unoccupied surveys. We evaluated this approach in 146 test surveys. A total of 1142 Black-throated green warbler (Setophaga virens) songs were observed in the training surveys and test surveys, which we attempted to locate with 3 different binary point matching templates. When only posterior probabilities greater than 0.5 were considered detections, the average ratio of accurate identifications of survey presence to false positive identifications in 500 bootstrapped samples improved from 1.2:1 using a standard score cutoff approach to 2.8:1 using all 3 templates and a likelihood-based discriminator. With the selected score cutoffs the average true positive and false positive rates for the combined three templates were 0.18 and 0.002, respectively. Automated detection methods are increasingly being used for identification and monitoring of landscape-scale responses to climate change and land-use change. Skepticism of automated acoustic monitoring software is largely due to higher false positive and negative error rates than those in traditional human surveys, but the false positive multiple method occupancy model is capable of estimating detection parameters and occupancy state when one method has occasional false positive detections. We test the accuracy of the model when automated detection of black-throated green warbler is mixed with human detection in 4 recorded surveys at 60 sites. Precision and accuracy are evaluated by simulation, and we use the results to optimize future sampling. In simulation, parameter estimates by the multiple method occupancy model are close to those we computed manually when two surveys are manually analyzed. Our results support the use of the multiple method false positive occupancy model to track detection rates in automated monitoring programs.