The data provided were used in a mark-recapture analysis conducted in 2022. Environmental covariates included water quality and Upper Klamath Lake surface elevation. Raw data were downloaded from the USGS Oregon Water Science Center for multiple sampling sites (422622122004000_MDNL_2021-1-14.csv, 422622122004003_MDNU_2021-1-14.csv, 422042121513100_RPT_2021-1-14.csv, 422719121571400_WMR_2021-1-14.csv, MDNL_WQ2_Ammonia_2021-2-3.csv, WMR_WQ2_Ammonia_2021-2-3.csv, Elevation_2021-1-19.csv) and were formatted for use in mark-recapture models. A Program R script (Covariate_Cleaning.R) formats data and then writes multiple csv files with annual environmental covariates for use in subsequent mark-recapture models. Annual survival was estimated using a Cormack-Jolly-Seber (CJS) model for three adult sucker spawning populations: Lost River sucker lake (lrss) and river spawners (lrsr) and Shortnose suckers (sns). The recapture data for each population from 1999-2021 are found in the files lrsrboth.inp, lrssboth.inp, and snsboth.inp. R script ran CJS models using a maximum likelihood approach (LRSS_CJS_ProgramMARK.R, LRSR_CJS_ProgramMARK.R, and SNS_CJS_ProgramMARK.R) that compared multiple competing environmental survival models. Using a Bayesian framework, the top covariate survival models from the maximum likelihood approach for each population were run by calling JAGS (Just Another Gibbs Samplers) through R using the following scripts: LRSS_BayesianCJS_TopCovariateModel.R, LRSR_BayesianCJS_TopCovariateModel.R, SNS_BayesianCJS_TopCovariateModel.R. A sucker population was simulated (SuckerSimulation.R) based on survival from the lake spawning Lost River Sucker population to understand what environmental effect sizes are required to significantly affect sucker survival.