Highly seasonal environments can increase competition among herbivores for nutrients, leading to consequences affecting rates of reproduction and survival. There is concern about the impacts of non-native ungulates on Greater Sage-grouse in the Great Basin of North America. We estimated nesting propensity, the annual proportion of females attempting a nest, for Greater Sage-grouse in relation to the abundance of ungulates using 7 years of data from the northwestern Great Basin, USA. We focused on nesting because it is the necessary first major investment required for the production of new recruits. We used a Bayesian multi-stratum model to investigate the effects of weather and sympatric non-native herbivores, free-roaming horses and domestic cattle, on reproductive rates and female survival of adult and yearling sage-grouse. Adults nested at a higher rate (0.931, 95% CI, 0.904 – 0.953) than yearlings (0.867, 95% CI, 0.802 – 0.922) under average conditions of all other covariates. If the first nest failed, renesting rates were similar between adults (0.349, 95% CI, 0.292 – 0.410) and yearlings (0.353, 95% CI, 0.217 – 0.507). Females in better body condition at the start of the season nested at higher rates, and moderately snowy winters led to the highest nesting propensity during the following spring. Drier conditions led to low rates of nesting, particularly in areas with dense cattle grazing. Female survival was lower for nesting females, indicating a survival cost of reproduction. Areas with abundant free-roaming horses had slightly higher nesting propensity, though other research suggests negative impacts later in the breeding cycle. Sage-grouse face life-history trade-offs that may be shifting due to changing climatic conditions. Our work suggests that the effects of competition with non-native ungulates on sage-grouse life-histories may be exacerbated by adverse weather.

Greater Sage-grouse data was collected by trapping and radio collaring female birds. Birds were monitored using ground telemetry. Other variables were collected from Bureau of Land Management reports and PRISM climate data.

Code was originally run with R version 4.0.3 using R Studio. Required packages are noted in each code file and the README.