Complete dataset for: Ambient temperature outperforms buffered microclimate as a correlate of reproductive phenology
Domaine:
environment and energyType de record:
dataset
Créateur:
Bal
BalÉditeur:
Hôte:
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")
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