Survival of Juvenile greater sage-grouse in Wyoming

This dataset contains information to evaluate juvenile sage-grouse survival in central and south-central Wyoming. The study objectives were to evaluate the effects of intrinsic factors including sex and body condition, and extrinsic factors including temperature and precipitation on juvenile survival to better understand factors contributing to variation in mortality risk.

Radio-marked juveniles were monitored monthly from September to March using fixed-wing aircraft (2017-2018, 2018-2019, and 2019-2020 in central Wyoming; 2020-2021 and 2021-2022 in south-central Wyoming). We used mixed Cox proportional hazards regression models (Cox 1972) with the counting process (Anderson and Gill 1982) to assess monthly mortality risk of juvenile sage-grouse. Models were developed separately for each study region. Individual was used as a random intercept term in our models. We considered a model containing sex as the base model and compared it to models including sex and all combinations of body condition index, precipitation, and temperature covariates (described in data description below). We used Akaike's Information Criterion adjusted for small sample size (AICc) to identify the most parsimonious models but interpreted all models within 4 AICc of the top model. We considered regression coefficients from each competitive model to be informative if 85% confidence intervals surrounding estimates did not overlap zero (Arnold 2010). Analyses were performed in R version 4.1.3 (R Core Team 2022).

Literature Cited:

Anderson, P.K., and Gill, R.D. 1982. Cox's regression model for counting process: a large sample study. –Ann. Stat. 1100-1120.

Arnold, T.W. 2010. Uninformative parameters and model selection using Akaike's Information Criterion.–J. Wild. Manage. -74:1175-1178

Cox, D.R. 1972. Regression models and life-tables. –J.R. Stat. Soc. B. Met. 34:187-220.

R Core Team. 2022. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available online: https://www.R-project.org/

The following attributions are contained within the dataset to evaluate juvenile sage-grouse survival:

BirdID: BirdID is a unique identifier to indentify each individual juvenile sage-grouse.

Sex: Indicates the sex (male, female) of each individual sage-grouse.

Study Area: This column identifies the study area. Note that survival was assessed separately for each study area. Unique study areas = Central and South-Central Wyoming

Event: This indicates whether the individual survived (Event=0) or died (Event=1) during the suvival time interval.

Start: This column indicates the start of the survival time interval (days).

Stop: This column indicates the end of the survival time interval (days).

AdjWeight: AdjWeight represents the body condition metric. We followed Blomberg et al. (2014) to develop a body condition index, for each study area and sex separately, where we used the first principal component from a principal component analysis (PCA) of tarsus and wing chord lengths. We used general linear models to regress individual mass on the size index generated from the PCA. Models included Julian date of capture to standardize body condition among individuals captured on different dates (Blomberg et al. 2014). We used model residuals to assign body condition values to each individual, with values greater than zero signifying individuals in above average condition, and values less than zero reflecting individuals in below average condition.

Blomberg, E. J., Sedinger, J. S., Gibson, D., Coates, P. S., and Casazza, M. L. 2014. Carryover effects and climate conditions influence the postfledging survival of greater sage-grouse. – Ecol. Evol. 4:4488–4499.

MayAugPrecip: Preciptation (total) from 1 May to 31 August, obtained from PRISM (PRISM Climate Group 2023).

PRISM Climate Group. 2023. United States Annual Total Precipitation, 2023 (4 km; BIL). Oregon State University http://prism.oregon.state.edu. Accessed 23 February 2023.

MayAugTemp: Temperature (average monthly maximum) obtained from PRISM (PRISM Climate Group 2023).

PRISM Climate Group. 2023. United States Annual Total Precipitation, 2023 (4 km; BIL). Oregon State University http://prism.oregon.state.edu. Accessed 23 February 2023.