Exceptions to the rule: Relative roles of time, diversification rates and regional energy in shaping the inverse latitudinal diversity gradient
Cite this dataset
Cerezer, Felipe; Machac, Antonin; Rangel, Thiago; Dambros, Cristian (2022). Exceptions to the rule: Relative roles of time, diversification rates and regional energy in shaping the inverse latitudinal diversity gradient [Dataset]. Dryad. https://doi.org/10.5061/dryad.z08kprrd4
Aim: Inverse latitudinal diversity gradients (i-LDG), whereby regional richness peaks outside the tropics, have rarely been investigated and their causes remain unclear. Here, we investigate three prominent explanations, postulating that species-rich regions have had (1) longer time to accumulate species, (2) faster diversification, and (3) more energy to support more diverse communities. These mechanisms have been shown to explain the tropical megadiversity, and we examine whether they can also explain i-LDG.
Time period: Contemporary
Major taxa studied: Amphibians, birds, mammals
Methods: We estimated the time for species accumulation, regional diversification rates, and regional energy for six tetrapod taxa (≈ 800 species). Then, we quantified the relative effects and interactions among these three classes of variables, using variance partitioning, and confirmed the results across alternative metrics for time (community phylometrics and BioGeoBEARS), diversification rates (BAMM and DR), and regional energy (past and current temperature, productivity).
Results: While regional richness across each of the six taxa peaked in the temperate region, it varied markedly across hemispheres and continents. The effects of time, diversification rates, and regional energy varied greatly from one taxon to another, but high diversification rates generally emerged as the best predictor of high regional richness. The effects of time and regional energy were limited, with the exception of salamanders and cetaceans.
Main conclusions: Together, our results indicate that the causes of i-LDG are highly taxon-specific. Consequently, large-scale richness gradients might not have a universal explanation and different causal pathways might converge on similar gradients. Moreover, regional diversification rates might vary dramatically between similar environments and, depending on the taxon, regional richness might or might not depend on the time for species accumulation. Together, these results underscore the complexity behind the formation of richness gradients, which might involve a symphony of variations on the interplay of time, diversification rates, and regional energy.
This release includes the code and data that support the findings as described in the paper "Cerezer et al. (2022) Exceptions to the rule: Relative roles of time, diversification rates, and regional energy in shaping the inverse latitudinal diversity gradient" published in Global Ecology and Biogeography. The detailed methods are in the main text of the associated article.
Scripts and documentation are provided in different folders based on their primary functionalities in the analyses. Below is a brief description of the data structure in each folder (in italics). Please refer to README file.
Presence-absence matrix based on species range maps ("Pres_Abs_RawData"; section 2.1 of the main text):
- Spatial_Distribution.R = This R script computes the presence-absence on each 2 x 2 degree grid-cells from species range maps.
- Shapefiles = These are some species range maps used as an illustrative proposal.
- WorldShape = Shapefile with the World Administrative boundaries.
- RawDataExtracted = Presence-absence matrix calculated for each of the six taxa.
- Phylogenetic hypotheses considered in the analyses ("Phylogenies"; section 2.2 of the main text):
- These are the phylogenies obtained for the six taxa and used in all downstream analyses (see section 2.2 of the main text for more details).
Time for species accumulation ("Evolutionary_time"; section 2.3 of the main text):
- MPD = The R script "MPD_Codes.R" quantifies the mean pairwise distance (MPD) between species co-occurring in a community.
- MBL = The R script "MBL_Codes.R" calculates the maximum branch length (MBL) among species co-occurring in a community.
- Species_tip_age = The R script "Species_tip_age_codes.R" calculates the species tip age through the age of its closest internal node.
- Geographic_Reconstructions = The R script "Geographic.R" measures an index of geographic tropicality by verifying the percentage in which the species' distribution is within the Tropics of Cancer and Capricorn (23.436°N and 23.436°S). The phylogenetic signal on geographic tropicality is also calculated.
- Climatic_Reconstructions = The R script "Climatic.R" calculates the mean temperature across the species' distribution and checks the phylogenetic signal of climatic preferences.
Estimates of diversification rates ("Diversification_rates"; section 2.4 of the main text):
- BAMM = This folder contains the Bayesian analysis of macroevolutionary mixtures (BAMM - "Outputs.zip") outputs, which include estimates of diversification rates for 100 tree samples in each taxon.
- DR = The R script "DR.codes.R" computes the DR statistic (Jetz et al., 2012) across 100 tree samples for each taxon.
Regional energy ("Regional_energy"; section 2.5 of the main text):
- Regional_energy.R = This R script extracts a multitude of proxies associated with regional energy for terrestrial and marine environments.
- AtlasBiosphere = This folder provides actual evapotranspiration (AET) and net primary productivity (NPP) data taken from the Atlas of the Biosphere.
- RawDataExtracted = These are the regional energy measurements extracted for the six taxa.
Statistical and sensitivity analyses ("Analyses"; sections 2.6 and 2.7 of the main text):
- Analyses_codes.R = The R script "Analysis_codes.R" is used to test the influence of evolutionary time, diversification rates, and regional energy on species richness (e.g. variance partitioning and gam analyses). This script also compares the main findings against a series of sensitivity analyses.
- All_Merged_Data = This folder presents the combined data used to perform the main analyses and figures of the manuscript.
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