Complete dataset for: Ambient temperature outperforms buffered microclimate as a correlate of reproductive phenology

Background: This dataset contains all reproductive and microclimate data used in the study: Ambient temperature outperforms buffered microclimate as a correlate of reproductive phenology. The data were collected from five caves in central Ghana between January 2011 and July 2012. Study Design: The study used a natural gradient in microclimate buffering across five caves (Abutia, Boten, Dwamerawa, Mframabuom, Mpirisi). Temperature and relative humidity were recorded synchronously inside each roost and outside the cave entrance at 30-minute intervals using iButton data loggers. Bats were captured at bimonthly intervals using mist nets; each individual was sexed, measured (body mass, forearm length), and assigned a reproductive status. Contents: The RDS file contains 13 components: Component Type Description repro_data_raw Data frame (7,152 × 16) Raw reproductive records from all captures microclimate_data_raw Data frame (138,501 × 8) Raw hourly microclimate data repro_processed Data frame (7,152 × 17) Cleaned reproductive data with derived variables microclimate_processed Data frame (138,501 × 12) Cleaned microclimate data with temporal variables daily_microclimate Data frame (1,767 × 20) Daily aggregates of temperature and humidity combined_data Data frame (7,152 × 38) Merged reproductive + microclimate data adult_data_with_lags Data frame (2,323 × 44) Adult bats with 7/14/30-day temperature lags correlation_data Data frame (2,295 × 45) For microclimate buffering analysis dlnm_data_roost Data frame (2,274 × 47) Roost temperature DLNM analysis input dlnm_data_ambient Data frame (2,289 × 47) Ambient temperature DLNM analysis input matched_data Data frame (2,268 × 47) Matched subset for sensitivity analysis bci_model lm object Body Condition Index model (mass ~ forearm length) metadata List (10 elements) Description, authors, sampling info, R version How to Load the Data in R: The dataset is provided as an R binary file (.rds). Load it in R using:  # Load the complete datasetdata <- readRDS("Nkrumah_et_al_complete_dataset.rds")