estimate_sensitivity: Estimate climatic sensitivity for all data.

In LiamDBailey/baileyetal2021: Bird populations most exposed to climate change are less sensitive to climatic variation

Description

For every species/population combination this function will: - Format the laying date data using format_data. - Use this format data to run climwin and detect the 'best window' where temperature affects laying date. Using the function run_climwin - Run randwin to determine the distribution of deltaAICc values that would be expected from the same population with no climate signal. Using the function run_climwin - Run an SEM to detrend data. Using the function run_SEM

Usage

estimate_sensitivity(
  input_data,
  first_clutch_method = "calc",
  temp_data,
  randwin = FALSE,
  repeats,
  SEM = FALSE,
  i = 1000,
  dummy_test = FALSE
)

Arguments

input_data	Dataframe. Full dataset containing brood information of all birds from all populations.
first_clutch_method	Logical. Whether first clutch is determined by data owner or >30 day rule
temp_data	A stacked raster. Mean daily temperature data. Stored as 'tg_0.25deg_reg_v17.0' in extdata.
randwin	Logical. Should randwin be run with climwin?
repeats	Numeric. If randwin is TRUE, how many repeats should be run.
SEM	Logical. Should SEM analysis also be run.
i	Numeric. If SEM is TRUE, the number of bootstrap iterations.
dummy_test	Logical. If TRUE, will only use the first two rows of data. Used to test code.

Details

Important decisions that were made as part of the process: - We only include years where there were at least 2 values of laying date recorded. - We only include populations where there were at least 9 years of data recorded that met this criteria.

Value

A list with climwin and (if requested) SEM results.

Examples

## Not run: 
### EXAMPLE FOR HOGE VELUWE ###

#Load mean temperature data
#Temperature data file can be requested from https://www.ecad.eu/ archive
temp_data  <- raster::stack(here::here("data/unshared_files/tg_0.25deg_reg_v17.0.nc"))

#Load Brood_data.csv
input_data <- read.csv(here::here("data/unshared_files/Brood_info.csv"),
stringsAsFactors = FALSE, header = T, sep = ",")

#Create a subset of data for just Hoge Veluwe
input_data_test <- dplyr::filter(input_data, Pop_ID %in% "HOG")

#Run climwin and randwin
HOG_output <- estimate_sensitivity(input_data = input_data_test,
temp_data = temp_data,
randwin = TRUE, repeats = 100)

## End(Not run)

LiamDBailey/baileyetal2021 documentation built on Feb. 10, 2022, 12:34 a.m.