Climate Window Analysis

Contains functions to detect and visualise periods of climate sensitivity (climate windows) for a given biological response.


climwin is designed for those interested in understanding the impacts of climate, particularly focussed on biological systems. When seeking to understand the effects of climate it is neccesary to select a sampling period over which climate is recorded, a climate window. Often this choice is made arbitrarily, with many studies using seasonal values (e.g. spring temperature, winter precipitation). However, these climate windows may not be the most relevant for the biological system in question. If we fail to find a relationship between climate and the biological response it can be difficult to determine whether this is due to climate insensitivity in the biological response or if the choice of climate window is flawed.

Rather than being required to make a single arbitrary choice of climate window, climwin allows users to test the effectiveness of a wide range of possible climate windows with the aim of identifying the most appropriate climate window for further use. climwin gives users the ability to visualise the results of their climate window analysis, using ggplot2, as a means to best interpret and understand the climate window results.

To install:

  • latest released version: install.packages("climwin")

How to use climwin:

Examples for both climate window analysis and plotting are provided in the package help documentation. See library(help = "climwin") once installed for more detail.

For more detailed insight on how to use climwin to investigate climate data see our introductory vignette using vignette("climwin", package = "climwin"), or our more advanced vignette with vignette("advanced_climwin", package = "climwin").

To access the current beta version please visit our github repository (LiamDBailey/climwin).

News

climwin 1.2.0

Our newest version includes a number of important tweaks and bug fixes, as well as an updated help vignette. To report errors and bugs or ask questions please visit the climwin google group.

Major changes

Functionality with 'nlme'

Older versions of climwin allowed the use of mixed effects models with the package lme4; however, there have been a number of requests for nlme compatability as an alternative. This has been made possible in v1.2.0, with the option to include constant and exponential variance functions for model weighting (varIdent and varExp respectively). Note, however, that k-fold cross validation is currently not available with nlme models.

N.B. Please see minor changes below for other updates relating to mixed effects models.

K-fold cross validation included with weightwin

Along with the use of randwin, k-fold cross validation is an important tool for overcoming potential issues of over-fitting in climwin. K-fold cross validation was previously only functional in slidingwin, but has now been included with weightwin, using the argument k.

Using climate thresholds on weekly/monthly data

Users may run climwin with cinterval == "week"/"month" in cases where only weekly/monthly data is available but also to reduce computational time when using daily data (see news for v1.1.0). This raises potential concerns when using climatic thresholds (e.g. arguments upper, lower, binary). When daily data is available, users may wish to apply thresholds before data is grouped by week/month (e.g. for each week, what is the mean number of days that temperature > x). Alternatively, users may wish to apply thresholds after week/month grouping has occurred (e.g. is mean weekly temperature > x). v1.2.0 now includes a prompt allowing users to select between these two possibilities.

Changes to plots in weightwin

While running weightwin returns a number of plots to help the user track the optimisation process. We have replaced the bottom left plot to show the plotted relationship between the weighted mean of climate and the response variable specified in the baseline model. This allows users to spot any outliers that may be driving patterns in the data.

N.B. This plot does not account for any additional variables included in the baseline model. It should be used as a tool for identifying outliers rather than an accurate reflection of the statistical relationship between the variables.

Minor changes

Use of maximum likelihood for mixed effects models

When using mixed effects models in climwin it is necessary to use a maximum likelihood [ML] approach rather than restricted maximum likelihood [REML]. In v1.2.0 this has now been made mandatory. Any models fitted with REML will be refitted with ML (along with an accompanying message).

N.B. This means that the best model output will also be fitted with ML. Users can manually refit the best model with REML if desired.

Changes to the use of weekly data

The method use to group data at a weekly scale has been changed slightly. This may lead to slight changes in results when using the argument cinterval = "week" compared to previous versions; however, changes should be minimal and the overall interpretation of results should remain the same.

Changes to warnings and messages

Previously, climwin printed any messages and warnings immediately during model fitting; however, this disrupted the progress bar making it difficult for users to follow the progress of their analysis. All warning messages are now displayed at the end of the process. Messages associated with model fitting (such as rank-deficiency when using lmer) are now suppressed. These messages are expected when fitting very small climate windows and so should not be a concern for users.

Bug fixes

Slope statistic

Fixed a bug where the 'slope' statistic was calculated with the wrong sign.


climwin 1.1.0

Our newest version adds a number of useful features to climwin as well as a few bug fixes. In addition, we have now created a climwin google group for users to ask questions about the package. Please report any errors or bugs on this forum.

Major changes

Custom model structure

In older versions of climwin, climate data was extracted from the fitted climate window and included as a fixed effect in the specified baseline model. This precluded the use of more complex model structures, such as interactions between climate and other fixed effects or mixed effects models with random climate slopes. We now include a more versatile method for specifying model structure in climwin. This involves the inclusion of a dummy 'climate' variable in the baseline model (N.B. the variable must be called 'climate'). This dummy variable will then be replaced with climate data from each fitted climate window, maintaining the original model structure. See an example of baseline model structure below:

baseline = lm(response ~ climate*sex)

Using this more versatile model construction makes the argument func redundant (i.e. the argument used to specify quadratic, cubic etc. terms). Users can now structure their model manually to test for these effects.

Note: We have maintained functionality for the original baseline model design. If no variable called 'climate' is provided the original method will be used.

Dealing with missing data

In previous verisons, the argument cmissing could be designated as either TRUE or FALSE. When FALSE, the presence of missing values in any tested climate window would return an error. When TRUE, all climate windows containing missing values would be removed from our analysis. On further consideration, we felt it was unwise to remove data from our analysis. As an alternative, we now provide two methods to estimate the value of NA records. "method1" will replace the value of a missing cell with the mean of the two preceding and following records. "method2" will replace the value of a missing cell with the mean of all other records on the same date. For more detial on dealing with missing data please read our FAQ.

Multiple iterations with weightwin

As weightwin uses an optimisation function, there is the possibility that the function may fail to converge or will converge on a local optima. Because of these issues, a single run of weightwin may not provide an accurate measure of the climate landscape. Users should instead run multiple iterations of weightwin with varying starting parameters to help find the global optima. The argument n in weightwin allows users to specify the number of iterations to run, with starting parameters randomly assigned in each run. weightwin will then return a summary table of results from all iterations.

Minor changes

Non-daily data

The original design of climwin required users to provide their climate data at a daily resolution, even if users had only a single recorded value across each month/week. This caused some confusion with users. climwin can now deal with climate data at a monthly or weekly resolution (i.e. one record for each month/week). Running climate window analysis at a monthly or weekly scale also provides a method for dealing with missing climate data.


climwin 1.0.0

Major changes

climwin version 1.0.0 includes a number of major changes to coincide with the release of our corresponding paper [1]. We have tried to make most of our changes backwards compatible, but any major issues should be reported to the package maintainer ([email protected]).

New function slidingwin:

climwin aims to distinguish between two separate methods for testing climate windows. The commonly used sliding window approach [1] and less common weighted window approach [2]. To ensure the distinction between these two methods is clear, the function climatewin has been made redundant and been replaced with the function slidingwin. Parameters used in slidingwin and climatewin are identical. Users can now conduct a sliding window analysis using slidingwin and a weighted window analysis using weightwin.

randwin for weighted window analysis:

The function randwin can now also be used to conduct randomisations with a weighted window approach (i.e. using the function weightwin). Users must now define whether randomisations are to be conducted using a sliding window ("sliding") or weighted window ("weighted") approach with the parameter 'window'. Note that all parameters from weightwin will be required to run randwin using a weighted window approach (e.g. 'par', 'weightfunc').

Cohort parameter:

When a group of biological measurements covers two years (e.g. Southern hemisphere species which breed between November - February [2]) use of 'absolute' climate windows will cause these measurements to be split across different calendar years. To overcome this issue, we include a 'cohort' parameter to our functions.

The cohort variable will determine which biological measurements should be grouped together (e.g. measurements from the same breeding season), and ensure that these measurements share the same reference day. The cohort variable should come from the same dataset as the 'bdate' parameter (i.e. variables should have equal lengths).

See our advanced vignette for more details:

vignette("advanced_climwin", package = "climwin")

Spatial parameter:

Climate window analysis often requires large amounts of data to effectively determine periods of climate sensitivity. Often this is achieved through temporal replication (collecting many years of data on the same population) but can also be achieved through spatial replication (collecting data on multiple populations), or, ideally, a combination of the two. The new parameter 'spatial' allows users to carry out a climate window analysis with data from multiple populations by linking each set of biological measurements to a corresponding set of climate data.

Users can include data from multiple study sites/populations in their climate dataset (i.e. the dataset used for parameters 'cdate' and 'xvar'), with a new site ID variable included to distinguish between different sites. Similarly, the user can add a new site ID variable to the biological dataset that can be used to link biological measurements to the corresponding climate data. When carrying out analyses the user can then include the parameter 'spatial', a list item, containing the biological site ID variable and climate site ID variable respectively. During model fitting, the climate window analysis will extract different climate data for each biological record based on the provided site ID.

N.B. Spatial replication in climate window analysis works on the assumption that all populations share the same period of climate sensitivity. If this is NOT the case, populations should be analysed separately.

See our advanced vignette for more details:

vignette("advanced_climwin", package = "climwin")

Cox proportional hazard models:

Proportional hazard models may often be useful for climate window analyses on phenological data. We have included the ability for users to fit proportional hazard models for the parameter 'baseline' using the function coxph. For more detail on understanding the use of proportional hazard models for phenology analysis see [2].

Function pvalue:

In previous versions of climwin climate windows have been compared visually using a number of methods (e.g. deltaAICc distribution, model weights). In this newest version, we have included two metrics that allow for a standard method of distinguishing real periods of climate sensitivity in biological data.

These two metrics, $P_{C}$ and $P_{\Delta AICc}$, determine the likelihood that a given climate window would occur by chance, given the results of a randwin analysis on the same data. They can be calculated using the new function pvalue. For more information on the effectiveness of the new metrics, please see [1].

'exclude' parameter:

Although climwin helps us move away from the selection of arbitrary climate windows the method is inherently exploratory, raising concerns about overfitting. Climate data from short duration time windows are particularly likely to show spurious relationships in climate window analysis [1]. The inclusion of the function pvalue (above) reduces the chance that these short duration windows will be mistaken as 'real' climate signals; however, these spurious windows can still cause problems when conducting multi-model inferencing as the short duration windows may be distinctly different from other top models. As a solution, we include the parameter 'exclude' in the functions slidingwin and randwin. This allows users to exclude windows of a specific duration and lag to prevent these small windows from interfering with analyses.

Minor changes

Parameter changes:

  • 'furthest' and 'closest' are now combined into a single parameter 'range'.

  • 'cutoff.day' and 'cutoff.month' are now combined into a single parameter 'refday'.

  • 'cvk' parameter is now redundant, replaced with parameter 'k'.

  • 'thresh' parameter is now redundant, replaced with parameter 'binary'.

  • 'type' now accepts possible arguments 'absolute' and 'relative' (rather than 'fixed' and 'variable').


climwin 0.1.2

Fixed bug which caused convergence issues using cross-validation.


climwin 0.1.1

Fixed serious bug causing an error in 'plotwin' and 'plotall'. Naming mismatch in the 'closest' column in climatewin$Dataset. Column name changes from Closest to closest.


climwin 0.1.0

Our newest release aims to speed up the functions and provide greater versatility to users. In addition, we have produced a vignette providing a detailed introduction on how to use the climwin package. See vignette("climwin") for more.

We have made some changes to the names and levels or parameters that should be checked in the help documentation.

Major changes

  • Changes to parameter names and levels (e.g. Cinterval levels are now in the format "day", "week", "month" rather than "D", "W", "M"). Please check the help documentation of each function to familiarise yourself with the new function wording.

  • Function climatewin (and corresponding functions) now contains parameter CVK. This provides the ability to run k-fold cross validation during climate window analysis. This provides a further check against finding strong climate signals by chance.

  • Function climatewin (and corresponding functions) now contains parameters upper, lower and thresh. These allow users to adapt their climate data to consider climatic thresholds. This will be useful for those interested in investigating topics like growing degree or chill days.

  • Function climatewin (and corresponding functions) now contains parameter centre. This allows users to carry out within-group centreing with their climate data. Note that within-group centreing will only test linear relationships.

  • Function climatewin now includes functionality to test multiple parameter combinations (e.g. func = c("lin", "quad")) in succession. Please see the vignette vignette("climwin") for more detail.

  • Function crosswin now includes parameter stat2. This allows users to test the correlation between two climate variables using different aggregate statistics (e.g. Mean temperature and minimum rainfall).

Minor changes

  • The parameter Xvar must now be a list item (e.g. Xvar = list(MassClimate$Temp))

climwin 0.0.1

  • Initial package release. Forthcoming changes will be noted.

Reference manual

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install.packages("climwin")

1.2.0 by Liam D. Bailey, 7 months ago


https://github.com/LiamDBailey/climwin


Report a bug at https://github.com/LiamDBailey/climwin/issues


Browse source code at https://github.com/cran/climwin


Authors: Liam D. Bailey and Martijn van de Pol


Documentation:   PDF Manual  


GPL-2 license


Imports evd, lubridate, lme4, MuMIn, reshape, plyr, numDeriv, RcppRoll, nlme

Depends on ggplot2, gridExtra, Matrix

Suggests testthat, knitr, rmarkdown


See at CRAN