Corresponding dataset for effectiveness of canine-assisted surveillance and human searches for early detection of invasive spotted lanternfly

This study experimentally tested whether utilizing trained detector dogs could improve the probability of detecting SLF in both agricultural and forest settings. This dataset includes the data from spotted lanternflys (SLF) surveys in 20 vineyards in Pennsylvania and New Jersey, USA using both human and trained detection dogs as observers. We used a multi-scale occupancy model to estimate detection probability of human observers and detection dogs as a function of SLF infestation level, weather, and habitat covariates. We modeled transect-level occupancy of SLF as a function of infestation level, habitat type, topographic position index, and distance to forests. The dataset includes six csv files with the data from the project, including the human surveys, dog surveys, detection data for each, and associated covariate data. It also includes the scripts used to process the data for analysis and the modeling scripts including: 1) Build occupancy dataset.R: formats vine and forest detection data along with transect covariates for occupancy analysis. 2) Run Lanternfly Occupancy Landsape Covs FiniteMix with detection.R: runs the multiscale occupancy analysis. 3) Run search times.R: does the search time analysis. 4) Process Posterior.R: takes the output of (2) and (3) and produces posterior point and interval estimates along with plots.