run_climwin: Run climwin analysis on data for a single population/species...

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

Description

Run slidingwin and (optionally) randwin on data from a given bird population. This is generally used inside function estimate_sensitivity.

Usage

run_climwin(
  input_data,
  temp_data,
  randwin = FALSE,
  repeats = NA,
  dummy_test = FALSE
)

Arguments

input_data	A data frame with laying date data for a given population and species.
temp_data	Raster stack of temperature data across Europe.
randwin	Logical (TRUE/FALSE). Should randwin be run as well as slidingwin?
repeats	If randwin is TRUE, the number of times that data will be randomised.
dummy_test	To test code. If TRUE, will only analyse the first two years of data. Used for testing code.

Details

As part of this approach we:

• Calculate the annual mean laying date for the focal population.

• Calculate standard error of the laying date mean that will be used for weighting in models.

• Exclude years with only 1 nest (where no SE could be calculated).

• Extract mean daily temperature data using extract_temp_data.

• For Vlieland and Sicily, remove records where climatic data is unavailable.

• Run climwin with a baseline model: laying date ~ year, weight = 1/SE.

• Our climate windows test for a linear relationship between mean temperature and annual mean laying date up to 365 days before June 1st. Any missing climatic data is interpolated by taking the mean value of temperature on that date in all other years.

• Run randwin using the same setup as climwin.

Value

A list containing the output of climwin and (if conducted) randwin.

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