Abstract
When studying the impacts of climate change, there is a tendency to select climate data from a small set of arbitrary time periods or climate windows (e.g., spring temperature). However, these arbitrary windows may not encompass the strongest periods of climatic sensitivity and may lead to erroneous biological interpretations. Therefore, there is a need to consider a wider range of climate windows to better predict the impacts of future climate change. We introduce the R package climwin that provides a number of methods to test the effect of different climate windows on a chosen response variable and compare these windows to identify potential climate signals. climwin extracts the relevant data for each possible climate window and uses this data to fit a statistical model, the structure of which is chosen by the user. Models are then compared using an information criteria approach. This allows users to determine how well each window explains variation in the response variable and compare model support between windows. climwin also contains methods to detect type I and II errors, which are often a problem with this type of exploratory analysis. This article presents the statistical framework and technical details behind the climwin package and demonstrates the applicability of the method with a number of worked examples.
Highlights
With the growing importance of climate change there are an increasing number of studies seeking to understand the impact of climate on biological systems (e.g., [1,2,3,4,5])
We introduce the R package climwin that provides a number of methods to test the effect of different climate windows on a chosen response variable and compare these windows to identify potential climate signals. climwin extracts the relevant data for each possible climate window and uses this data to fit a statistical model, the structure of which is chosen by the user
In many study systems the impacts of climate are likely to be different at different times of the year (e.g., [4,5,6]) making it necessary for researchers to subset their climate data to encompass a particular period of interest, here termed the climate window
Summary
With the growing importance of climate change there are an increasing number of studies seeking to understand the impact of climate on biological systems (e.g., [1,2,3,4,5]). In many study systems the impacts of climate are likely to be different at different times of the year (e.g., [4,5,6]) making it necessary for researchers to subset their climate data to encompass a particular period of interest, here termed the climate window (e.g., spring temperature, winter precipitation) This subsetting decision is often made with little a priori knowledge on the relationship between climate and the biological response, leading to the arbitrary selection of one, or few, climate windows [7]. One can determine a weighted climate mean using a single fitted weight distribution, allowing each climate record to take any weight value between 1 and 0 This allows for more biologically realistic relationships between climate and the biological response.
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