pastclim 1.2: an R package to easily access and use paleoclimatic reconstructions

Abstract

The recent development of continuous paleoclimatic reconstructions covering hundreds of thousands of years paved the way for a large number of studies from disciplines ranging from paleoecology to archaeology, conservation to population genetics, macroevolution to anthropology and human evolution to linguistics. Unfortunately, (paleo)climatic data can be challenging to extract and analyze for scholars unfamiliar with such specific file formats.Here we present pastclim, an R package facilitating the access and use of paleoclimatic reconstructions. It currently includes two of such datasets, covering respectively the last 120 000 and 800 000 years, and a vignette provides instructions on how to include additional datasets.The package contains a set of functions to quickly and easily recover the climate for time periods of interest either for the whole world or specific areas, extract data from locations scattered in space and/or time, retrieve time series from individual sites, and manage the ice or land coverage, offering a handy platform to include the climate of the past into existing or new analyses and pipelines.

Current sika deer effective population size is near to reaching its historically highest level in the Japanese archipelago by release from hunting rather than climate change and top predator extinction

“…Among various climate conditions, temperature, and precipitation are expected to affect the historical demography of sika deer because temperature affect the amount of snow that are suggested to affect deer survival (Ohashi et al, 2016; Takatsuki, 1992) and precipitation affect the amount of vegetation. We obtained the annual mean temperature (Bio01) and annual precipitation (Bio12) from Beyer et al (2020) by using R package pastclim (Leonardi et al, 2023).…”

Niche conservatism in a generalist felid: low differentiation of the climatic niche among subspecies of the leopard(Panthera pardus)

“…We used the R package ‘pastclim’ (Leonardi et al, 2023) to retrieve the present-day bioclimatic variables from the dataset constructed by Beyer et al (2020). We considered BioClim variables 1, 4-14, and 16-19 (O’Donnell & Ignizio, 2012), also described by WorldClim, https://www.worldclim.org); Net Primary Productivity (npp), Leaf Area Index (lai), and rugosity (ruggedness of terrain) (Supplementary Table 1).…”

“…100 independent repeats of this sampling process were performed and the dataset containing the maximum number of points was selected as the final cleaned dataset. Climate variables for the location of each leopard data point were generated using 'pastclim' (Leonardi et al, 2023) to form a dataset with occurrences for leopards and their associated climate.…”

Phylogenomics reveals an island as a genetic reservoir of a continental population

“…We generated a “zero-filled” dataset following the instruction of an R package auk [51], and we used presence and pseudo-absence points of this species per observation event and site as occurrence and background inputs, respectively. We used an R package pastclim v. 1.2 [52] to extract time slices of 22 variables [53]. We selected one variable per group of variables correlated >0.7, resulting in seven covariates for modeling.…”

“…We generated a ‘zero-filled’ dataset following the instruction of an R package ‘auk’ (Strimas-Mackey et al, 2018), and we used presence and ‘pseudo-absence’ points of this species per observation event and site as occurrence and background inputs, respectively. We used an R package ‘pastclim’ v. 1.2 (Leonardi et al, 2023) to extract time slices of 22 variables (Krapp et al, 2021). We selected one with the best explanatory power to the response variable per pair of variables correlated >0.7, by using the ‘corSelect’ function in an R package fuzzySim v. 4.9.2 (Barbosa, 2015), resulting in seven covariates for modeling.…”