Environmental factors influencing detection of ring‐necked pheasant broods during August roadside surveys

Abstract The August roadside survey is a population index used to monitor trends in productivity and population status of ring‐necked pheasant ( Phasianus colchicus ) in several states of the United States. Inter‐annual population changes from roadside surveys have occasionally implied biologically implausible outcomes, hinting at survey bias and constrained utility of the index under certain environmental conditions. Research has shown correlations between environmental conditions and ring‐necked pheasant detections but range‐wide rigorous assessment of factors influencing detection probabilities has never been considered. We sought to evaluate environmental factors influencing detection probability of ring‐necked pheasant broods across a large geographic scale, where August roadside surveys are an important monitoring tool. State wildlife resource agencies conducted 1,000 August roadside surveys on 174 unique route‐by‐year combinations in 11 states during 2019–2021. We used a single‐species N‐mixture model in a Bayesian framework to examine factors influencing detection probability of broods. Wind speed and cloud cover negatively influenced detection probability of pheasant broods. Dewpoint depression, a proxy for morning dew conditions where higher values indicate less dew, had a significant negative effect on detection probability for pheasant broods. Soil moisture had a positive effect on the detection probability of pheasant broods. Observed variation in conditions across routes, among the years in our study, and across the geographic scale covered could constrain direct comparisons of uncorrected counts. Survey methodology can be adjusted to target mornings with few clouds, low winds, and favorable dew conditions to increase detection probability and consistency among surveys. However, soil moisture may be difficult to methodologically control for over larger scales of space and time. Posterior estimates from our model may be used to gauge intra‐ and inter‐seasonal variation in conditions for detecting pheasant broods, which could improve inference from long‐term population monitoring, especially across large spatial and temporal scales.