crestr An R package to perform probabilistic palaeoclimate reconstructions from palaeoecological datasets
crestr produces probabilistic reconstructions of past climate change
from fossil assemblage data (Chevalier,
2022). crestr works
by analysing how certain biological indicators (like plant or animal
remains) respond to climate factors, using statistical methods to
estimate these relationships. These relationships are mdelled as
probability density functions (PDFs; see Chevalier et al.
(2014) and Chevalier
(2019)). The
theory underpinning this package is explained in section A bit of
theory
and is illustrated with an application based on pseudo-data in section
Get
Started.
The different vignettes present different aspects of the structure of
the package and the data it contains, along with applications based on
real data.
Why choose crestr? Unlike traditional methods, crestr uses
probabilistic techniques to provide more accurate and flexible climate
reconstructions. Its focus on accessibility means you don’t need to be
an expert coder to get meaningful results.
NOTE: While active development of crestr has concluded, its robust features will continue to provide valuable insights for palaeoclimate research. The available documentation and resources will remain accessible for independent use. In addition, I am committed to maintaining this bug-free. As such, please reach out at paleo@manuelchevalier.com if you encounter technical issues.
Ready to explore the climate history hidden in your data? Install
crestr now and leverage its robust tools for your research.
The package is available from GitHub and can be installed as follow:
if(!require(devtools)) install.packages("devtools")
devtools::install_github("mchevalier2/crestr")The package is fully documented and the help of each function can be
accessed with ?function. More detailed information as well as
documented examples are available from
https://www.manuelchevalier.com/crestr/.
The following example illustrates the basics of crestr using
pseudo-data (i.e. randomly generated data).
library(crestr)
## loading example data
data(crest_ex)
data(crest_ex_pse)
data(crest_ex_selection)Let’s first have a look at the data. The dataset is composed of 20 fossil samples from which 7 taxa have been identified. The data are expressed in percentages.
## the first 6 samples
head(crest_ex)
#> Age Taxon1 Taxon2 Taxon3 Taxon4 Taxon5 Taxon6 Taxon7
#> Sample_1 1 0 0 45 1 22 32 1
#> Sample_2 2 0 0 50 0 23 27 0
#> Sample_3 3 0 0 49 0 25 26 0
#> Sample_4 4 0 0 37 0 27 36 0
#> Sample_5 5 0 3 36 3 18 40 0
#> Sample_6 6 2 2 25 0 21 50 0
##
## the structure of the data frame
str(crest_ex)
#> 'data.frame': 20 obs. of 8 variables:
#> $ Age : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ Taxon1: int 0 0 0 0 0 2 3 5 10 15 ...
#> $ Taxon2: int 0 0 0 0 3 2 5 5 12 8 ...
#> $ Taxon3: int 45 50 49 37 36 25 18 17 10 12 ...
#> $ Taxon4: int 1 0 0 0 3 0 0 6 15 14 ...
#> $ Taxon5: int 22 23 25 27 18 21 21 20 16 13 ...
#> $ Taxon6: int 32 27 26 36 40 50 53 47 37 38 ...
#> $ Taxon7: int 1 0 0 0 0 0 0 0 0 0 ...For each reconstruction, a proxy-species equivalency (‘pse’) table must be provided. Here, with the 7 pseudo-taxa, it looks like:
crest_ex_pse
#> Level Family Genus Species ProxyName
#> 1 3 Randomaceae Randomus Taxon1 Taxon1
#> 2 3 Randomaceae Randomus Taxon2 Taxon2
#> 3 3 Randomaceae Randomus Taxon3 Taxon3
#> 4 3 Randomaceae Randomus Taxon4 Taxon4
#> 5 3 Randomaceae Randomus Taxon5 Taxon5
#> 6 3 Randomaceae Randomus Taxon6 Taxon6
#> 7 3 Randomaceae Randomus Taxon7 Taxon7Finally, unique sets of taxa can be specified to reconstruct each climate variable. In the example, bio1 (mean annual temperature) and bio12 (annual precipitation) will be reconstructed. The dataset has been designed so that Taxa 1, 2, 3 and 7 are sensitive to bio1 while Taxa 1, 4, 5 and 7 are sensitive to bio12. Check https://www.manuelchevalier.com/crestr/articles/get-started.html for more details on this selection.
crest_ex_selection
#> bio1 bio12
#> Taxon1 1 1
#> Taxon2 1 0
#> Taxon3 1 0
#> Taxon4 0 1
#> Taxon5 0 1
#> Taxon6 0 0
#> Taxon7 1 1These pseudo-data can be provided to the crest function and provided some parameters (see the full vignettes for a detail of these parameters), the reconstructions will be processed.
recons <- crest(
df = crest_ex, pse = crest_ex_pse, taxaType = 0,
site_info = c(7.5, 7.5), site_name = 'crest_example',
climate = c("bio1", "bio12"), bin_width = c(2, 50),
shape = c("normal", "lognormal"),
selectedTaxa = crest_ex_selection, dbname = "crest_example"
)
#> Warning in climate_from_xy(crest$misc$site_info[["long"]],
#> crest$misc$site_info[["lat"]], : No climate associated with these coordinates.
#> Warning in crest.get_modern_data(pse = pse, taxaType = taxaType, climate =
#> climate, : The classification of one or more taxa into species was not
#> successful. Use PSE_log() with the output of this function for details.A specific print function was created to summarise the crestObj.
recons
#> *
#> * Summary of the crestObj named ``:
#> * x Calibration data formatted .. TRUE
#> * x PDFs fitted ................. TRUE
#> * x Climate reconstructed ....... TRUE
#> * x Leave-One-Out analysis ...... FALSE
#> *
#> * The dataset to be reconstructed (`df`) is composed of 20 samples with 7 taxa.
#> * Variables to analyse: bio1, bio12
#> *
#> * The calibration dataset was defined using the following set of parameters:
#> * x Proxy type ............ Example dataset
#> * x Longitude ............. [0 - 15]
#> * x Latitude .............. [0 - 15]
#> *
#> * The PDFs were fitted using the following set of parameters:
#> * x Minimum distinct of distinct occurences .. 20
#> * x Weighted occurence data .................. FALSE
#> * x Number of points to fit the PDFs ......... 500
#> * x Geographical weighting ................... TRUE
#> * Using bins of width .................... bio1: 2
#> * ________________________________________ bio12: 50
#> * x Weighting of the climate space ........... TRUE
#> * Using a linear correction
#> * x Restriction to climate with observations . FALSE
#> * x Shape of the PDFs ........................ bio1: normal
#> * __________________________________________ bio12: lognormal
#> *
#> * Of the 7 taxa provided in `df` and `PSE`, 1 cannot be analysed.
#> * (This may be expected, but check `$misc$taxa_notes` for additional details.)
#> *
#> * The reconstructions were performed with the following set of parameters:
#> * x Minimum presence value .................. 0
#> * x Weighting of the taxa ................... normalisation
#> * x Calculated uncertainties ................ 0.5, 0.95
#> * x Number of taxa selected to reconstruct .. bio1: 3
#> * ----------------------------------------- bio12: 3
#> *The climate sampled by the data can be graphically represented for a quick assessment of the calibration dataset.
plot_climateSpace(recons)
Additional graphical tools can be used to assess which taxa should/could be used for each variable. On the following example, it is clear that Taxon2 has a much stronger correlation with bio1 than to bio12, hence its selection for bio1 only.
plot_taxaCharacteristics(recons, taxanames='Taxon2')
The results can be quickly visualised using the plot function and the
reconstructed climate values can be accessed from the nested recons
object:
names(recons)
#> [1] "inputs" "parameters" "modelling" "reconstructions"
#> [5] "misc"
lapply(recons$reconstructions, names)
#> $bio1
#> [1] "likelihood" "uncertainties" "optima"
#>
#> $bio12
#> [1] "likelihood" "uncertainties" "optima"head(recons$reconstructions$bio1$optima)
#> Age optima mean
#> 1 1 15.71142 15.69949
#> 2 2 15.71142 15.69949
#> 3 3 15.71142 15.69949
#> 4 4 15.71142 15.69949
#> 5 5 17.31463 17.29054
#> 6 6 18.11623 18.12464
str(recons$reconstructions$bio1$optima)
#> 'data.frame': 20 obs. of 3 variables:
#> $ Age : num 1 2 3 4 5 6 7 8 9 10 ...
#> $ optima: num 15.7 15.7 15.7 15.7 17.3 ...
#> $ mean : num 15.7 15.7 15.7 15.7 17.3 ...signif(recons$reconstructions$bio1$likelihood[1:6, 1:6], 3)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0.00e+00 8.02e-02 1.60e-01 2.40e-01 3.21e-01 4.01e-01
#> [2,] 8.41e-15 1.15e-14 1.57e-14 2.14e-14 2.92e-14 3.97e-14
#> [3,] 8.41e-15 1.15e-14 1.57e-14 2.14e-14 2.92e-14 3.97e-14
#> [4,] 8.41e-15 1.15e-14 1.57e-14 2.14e-14 2.92e-14 3.97e-14
#> [5,] 8.41e-15 1.15e-14 1.57e-14 2.14e-14 2.92e-14 3.97e-14
#> [6,] 1.45e-18 2.09e-18 3.01e-18 4.32e-18 6.19e-18 8.86e-18
str(recons$reconstructions$bio1$likelihood)
#> num [1:21, 1:500] 0.00 8.41e-15 8.41e-15 8.41e-15 8.41e-15 ...plot(recons, climate = 'bio1')
plot(recons, climate = 'bio12', simplify=TRUE, uncertainties=c(0.4, 0.6, 0.8))
If satisfying, the results can be directly exported from the R environment in unique spreadsheets for each variables (or csv files) and the crest object is exported as an RData file to enable easy reuse in the future.
export(recons, loc=getwd(), dataname='crest-test')
list.files(file.path(getwd(), 'crest-test'))
#> [1] "bio1" "bio12" "crest-test.RData"- Chevalier, M., Cheddadi, R., Chase, B.M., 2014. CREST (Climate REconstruction SofTware): a probability density function (PDF)-based quantitative climate reconstruction method. Clim. Past 10, 2081–2098. 10.5194/cp-10-2081-2014
- Chevalier, M., 2019. Enabling possibilities to quantify past climate from fossil assemblages at a global scale. Glob. Planet. Change 175, 27–35. 10.1016/j.gloplacha.2019.01.016
- Chevalier, M., 2022. crestr an R package to perform probabilistic climate reconstructions from palaeoecological datasets. Clim. Past doi:10.5194/cp-18-821-2022