DOI: 10.5068/d1m100
title: Data from: Stress responsiveness in a wild primate predicts survival across an extreme El Niño drought

description: We know more about the costs of chronic stress than the benefits of the
 acute stress response – an adaptive response that buffers organisms from
 life-threatening challenges. Yet, no primate study has empirically
 identified how the stress response adaptively impacts evolutionary
 fitness. Here, we take advantage of a natural experiment – an El Niño
 drought – that produced unprecedented mortality for wild white-faced
 capuchins. Using a reaction norm approach, we provide evidence from
 primates that a more robust stress response to a stressor, measured using
 fecal glucocorticoids, predicts a greater likelihood of survival. We show
 that individuals with greater stress responsiveness to previous droughts
 later had higher survival across a severe El Niño drought. Evolutionary
 models need empirical data on how stress responsivity varies in adaptive
 ways. While we cannot buffer subjects from catastrophic events, we can use
 them to understand which aspects of the stress response help animals to
 “weather the storm”.
description: These are observational data: Demographic data and noninvasively
 obtained fecal samples were collected from a wild population of
 white-faced capuchin monkeys that are subjects of a long-term study at
 Lomas Barbudal Biological Reserve, Costa Rica. The rawest form of the
 demographic data and information about fecal samples are housed in a MySQL
 database at UCLA, which was queried to produce the data used in this
 study. Fecal samples were dried in the Lomas Barbudal Monkey Project field
 lab, and then extracted and assayed in a University of Michigan-Ann Arbor
 core facility lab.
description: We used the following R packages:  brms
 (versions 2.20.4 and 2.21.0) sjPlot (version
 2.8.14) standaRdized (version 1.0)
 tidyverse (version 2.0.0) 
description: # Data from: Stress responsiveness in a wild primate predicts survival
 across an extreme El Niño drought Included here are all data and code
 necessary to replicate findings from “Stress responsiveness in a wild
 primate predicts survival across an extreme El Niño drought”. Demographic
 and fecal sample information came from the Lomas Barbudal Monkey Project
 database. #### Description of the data and file structure THE FOLLOWING
 DATASETS ARE PROVIDED AS .CSV FILES: Date formats should be yyyy-mm-dd
 “data_droughtIndices.csv” - drought indices used in main analyses Indices:
 std_don_pverde, std_don_chelsa, SPI_Don_PVERDE, SPI_Don_CHELSA
 std_don_pverde: drought index based on Palo Verde estimates (unitless)
 std_don_chelsa: alternative drought index based on CHELSA estimates
 (unitless) SPI_Don_PVERDE: alternative drought index based on Palo Verde
 estimates, using SPI (unitless) SPI_Don_CHELSA: alternative drought index
 based on CHELSA estimates, using SPI (unitless)
 “data_glucocorticoids3.csv” - capuchin data used in main analyses Outcome
 variables: Survived (0: No, 1: Yes), LN_GCS (log glucocorticoids ng/g)
 Fixed effects: ctime (collection time, minutes into day), season
 (categorical: wet/dry), Pregnancy (categorical: other, Early pregnancy,
 Late pregnancy), group_size (continuous, # of capuchins in group), age
 (continuous, years), rank01 (proportion dominated) Random effects: ID
 (identity of subject), groupid (group of residence)
 "data_rainfall.csv" - expected rainfall columns day_index:
 numeric value for day of year Month: Month of year Day: Day of year MD:
 Month-Day (e.g., 01/Jan for January 1st) mu30: daily average precipitation
 estimate over 30-day window (mm) sd30: standard deviation of mu30 value
 data: categorical, dataset from which precipitation value comes from
 (PVERDE: Palo Verde; CHELSA) mm_rain_std: standardised value for daily mm
 average precipitation estimate over 30-day window (unitless)
 "data_additional3.csv" - age and rank at start of El Nino
 columns ID: categorical, identity of subject age_ElNino: age (years) of
 subject at start of El Niño drought rank_ELNino: rank (proportion of group
 dominated) of subject at start of El Niño File used to examine mortality
 due to El Niño: “data_adultFemaleMortality.csv” - data on female mortality
 Outcome variable: n died (number of females that died) Fixed effects:
 exposure (offset) (deathrate is ndied divided by exposure; exposure takes
 into account the fraction of the year that females were both adults and
 still alive) Files used to generate the drought indices found in
 “data_droughtIndices.csv”: “data_precip_CHELSA*1979-2016.csv” - CHELSA
 *daily precipitation measures.* columns Date: precipitation estimate date
 YEAR: precipitation estimate year MONTH: precipitation estiamte month DAY:
 precipitation estimate day MD: precipitation estimate day-month (e.g.,
 "01/Jan" for January 1st) YDAY: index for day of year
 rain_mean_mm: precipitation estimate for day rain_30daysSUm_CHELSA: sum
 for precipitation estimate for 30-day rolling window. The first 29 rows of
 data for the last column (which represents the sum of the past 30
 days' values) are "NA" due to the fact that 30 days of
 previous data would be required to calculate this sum, and such data do
 not exist. “data_precip_PaloVerde_19962020.csv” - Palo Verde daily
 precipitation measures Additional precipitation file for correlations
 between Lomas and Palo Verde date: rainfall estimate data final_rain:
 rainfall measure from Palo Verde OTS station NA for dates without rainfall
 measure year: rainfall estimate year month: rainfall estimate month day:
 rainfall estimate day mean_daily_30rain: mean daily rainfall for 30-days
 surrounding date, without filling missing data. This is the 15-days prior
 to date and the 15-days after date (does not include date). NA for dates
 without sufficient rainfall measures n_missing_daily: missing days of data
 in "final_rain" for calculating "mean_daily_30rain" NA
 for dates where "mean_daily_30rain" was not calculated (n=15,
 first 15 days in data set) dayrain: copy of "final_rain", but
 with missing values filled in, if possible, by values in
 "main_daily_30rain" NA for dates without updated rainfall
 measure pv_tot_30rain: 30-day rolling sum of rainfall, based on column
 "dayrain" NA for dates without rainfall measure n_missing_total:
 missing days of data estimate of "pv_tot_30rain" NA for dates
 where "pv_tot_30rain" was not calculated (n=29, first 29 days in
 data set) est_dayrain: copy of "dayrain", but with NA values for
 dates where there is excess missing data (i.e., more than 15 days of
 missing values) “data_precip_reallomas.csv” - Lomas Barbudal precipitation
 measures date: date of rainfall measure Year: year of rainfall measure
 Month: month of rainfall measure Day: day of rainfall measure (range:
 1-31) rainfall: rainfall measure (mm) l_tot_rain: 30-day rolling sum of
 rainfall (mm) Additional precipitation file for the average monthly
 rainfall totals at Palo Verde "total_monthly_rain_pv.csv" - Palo
 Verde monthly rainfall totals (1996-2016) Files used for post-hoc foraging
 analyses: "data_foraging1.csv" - proportion of time foraging
 insects or fuit Y: calendar year M: calendar month D: calendar day n:
 number of group scans class: categorical, foraging insects (FI) or
 foraging fruit (FF) count: number of observations of foraging date: date
 of observation std_don_pverde: drought index based on Palo Verde estimates
 (unitless) day_index: numeric value for day of year MD: day-month (e.g.,
 "01/Jan" for January 1st) mm_rain_std: standardised value for
 mu30 (unitless) RESPONSE AND PREDICTOR VARIABLE DESCRIPTIONS FOR MAIN
 MODELS Columns used later as response variables: LN_GCS: log
 glucocorticoids ng/g Lived: whether a female survived (1) or not (0)
 during the El Niño drought Columns used later as predictor variables:
 std_don_pverde: STD drought risk index based on Palo Verde rainfall
 estimates (unitless) SPI_Don_PVERDE: SPI drought risk index based on Palo
 Verde rainfall estimates (unitless) std_don_chelsa: STD drought risk index
 based on CHELSA rainfall estimates (unitless) SPI_Don_CHELSA: SPI drought
 risk index based on CHELSA rainfall estimates (unitless) rain_mm_std:
 standardized measure of expected rainfall (rain seasonality) (unitless)
 Survived: whether a female survived (1) or not (0) the El Niño drought
 age: age of female (in years) at time of fecal sample collection
 age_ElNino: age of female (in years) at the start of the El Niño-related
 drought rank01: rank of female (proportion of group dominated) at the time
 of sample collection rank_ElNino: rank of female (proportion of group
 dominated) at the start of the El Niño-related drought group_size: number
 of capuchins in group Pregnancy: categorical female reproductive state at
 time of sample collection (’Other’, ‘Early pregnancy’, ‘Late pregnancy’)
 cdate: date of sample collection ctime: time of sample collection Columns
 used later for random effects: ID: identity of female groupid: group
 female was resident in at time of sample collection #### Sharing/Access
 information Precipitation estimates were derived from the following
 sources: * CHELSA estimates were accessed via:
 [[https://chelsa-climate.org](https://chelsa-climate.org)]. See the
 following reference for more information about the data set: Dirk N.
 Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Niklaus E.
 Zimmermann (2021):* CHELSA-W5E5 v1.1: W5E5 v1.0 downscaled with CHELSA
 v2.0*. **ISIMIP
 Repository. **[https://doi.org/10.48364/ISIMIP.836809.1](https://doi.org/10.48364/ISIMIP.836809.1 "https://doi.org/10.48364/ISIMIP.836809.1") * Raw Palo Verde station data can be accessed via the Organization for Tropical Studies (OTS): [[https://tropicalstudies.org/portfolio/palo-verde-research-station/](https://tropicalstudies.org/portfolio/palo-verde-research-station/)] * Meteorological data can be assessed through: [[https://bixa.tropicalstudies.org/meteoro/default.php](https://bixa.tropicalstudies.org/meteoro/default.php)] #### Code/Software You will need to have R installed. Analyses were primarily conducted using R version 4.3.2. Download all files into one folder and set it as your working directory. You can create a subfolder ‘objects’ to save model objects in (all .rds files). We used the following R packages: brms (versions 2.20.4 and 2.21.0) sjPlot (version 2.8.14) standaRdized (version 1.0) tidyverse (version 2.0.0) Code used to run the main models are in the following .Rmd files. The code is written assuming at least 4 cores for running models in parallel. “1-drought-indices.Rmd” \- requires the use of the following data files: \- “data_precip_CHELSA_1979-2016.csv” \- “data_precip_PaloVerde_19962020.csv” “2_Model_fGC.Rmd” \- requires the use of the following data files: \- “data_glucocorticoids3.csv” \- “data_droughtIndices.csv” \- "data_rainfall.csv" Files for visualization and model checks "1a-drought-visualization.Rmd" \- can use file "data_droughtIndices.csv" \- can use file "analysis_std_spi_chelsa_PV.csv" “2a_Model-check-and-visualization.Rmd” \- can use file "plotmodelsdata.csv" Model outputs (objects) have also been saved as .rds files Preliminary models: prelim_pverde.rds prelim_chelsa.rds Reaction-norm-extraction model outputs (to generate reaction norm BLUPs): fGC_2x_spi_chelsa.rds fGC_2x_spi_pverde.rds fGC_2x_std_chelsa.rds fGC_2x_std_pverde.rds Glucocorticoid-predicting-survival model outputs: surv_all_spi_chelsa2.rds surv_all_spi_pverde2.rds surv_all_std_chelsa2.rds surv_all_std_pverde2.rds Survival-predicting-glucocorticoids model outputs (3-way interaction model): fGC_3x_spi_chelsa.rds fGC_3x_pverde.rds fGC_3x_std_chelsa.rds fGC_3x_std_pverde.rds for convenience, reaction norm BLUPs have also been saved as .csv files BLUP_std_pverde.csv BLUP_spi_pverde.csv BLUP_std_chelsa.csv BLUP_spi_chelsa.csv columns used in downstream analyses ID: categorical, identity of subject intDry_mu: BLUP value for dry season intercept intWet_mu: BLUP value for wet season intercept slpDry_mu: BLUP value for dry season slope slpWet_mu: BLUP value for wet season slope index: categorical, drought index used age_ElNino: age at start of el Niño drought rank_ElNino: rank (proportion of group dominated) at start of El Niño Survived: categorical, whether subject survived the drought (No: "0", Yes: "1") Lived: integer, whether subject survived the drought (No: "0", Yes: "1") SUPPLEMENTARY FILES Code for supplementary analyses are provided in the following .Rmd files. “S1 Mortality-analysis.Rmd” \- requires the use of the following data file: \- “data_adultFemaleMortality.csv” “S2 Compare-lomas-pv.Rmd” \- requires the use of the following data files: \- “data_precip_reallomas.csv” \- “data_precip_PaloVerde_19962020.csv” "SI_models_fGC_subset_rainy.Rmd" \- same analyses as in main text, but using only wet season data \- requires the use of the following data files: \- “data_glucocorticoids3.csv” \- “data_droughtIndices.csv” \- "data_rainfall.csv" \- "data_additional3.csv" "SI_models_foraging.Rmd" \- requires the use of the following data files: \- “data_foraging1.csv” \- “data_droughtIndices.csv” \- "data_rainfall.csv"