Landbird trends in protected areas using time‐to‐event occupancy models

Abstract

AbstractGlobal populations of wildlife are affected by human activity, land cover change, and climate change. Long‐term monitoring programs across large spatial scales are required to understand how these and other factors affect wildlife populations. Occupancy models are frequently used to monitor changes in species distribution while accounting for imperfect detection. Occupancy surveys can be expensive because they typically require multiple surveys to estimate the probability of detection. Time‐to‐detection models provide a promising approach for estimating occupancy because they require just one visit; however, few studies have tested or applied these models to wildlife data. We ran a simulation study to assess biases of time‐to‐event occupancy models for standardized avian point‐count surveys and then applied the models to 10 yr of data. Time to first detection occupancy models had minimal bias and almost nominal coverage for species with a mean time to first detection <8 min on surveys with 10 min of sampling. Biases and root mean squared error increased with increasing time to first detection. We applied a single species, multi‐year occupancy model to 34,665 detections of 77 landbird species collected across 500 km of latitude in five protected areas along the Rocky Mountains. Models from 64 species converged and had mean times to first detection <8 min. Average time to first detections was 3.2 min, which reflected a cumulative probability of detection of 0.96. Occupancy rates increased, decreased, and remained unchanged for 53%, 9%, and 38% of species, respectively. Overall, occupancy rates increased in 2015 and 2016 for short‐ and long‐distance migrants and decreased slightly for winter residents. Average decadal temperature and precipitation were important predictors for almost half of the species, while annual changes in spring temperature and precipitation affected 23% of species. Our studies demonstrate that time to first event occupancy models provide an efficient method for monitoring changes in distribution so long as encounter rates are much shorter than the survey duration. Our stable to increasing trends and strong responses to spring temperature and precipitation highlight the value of long‐term monitoring for understanding how changing climatic conditions affect wildlife.