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Data from Beyond MAP: A guide to dimensions of rainfall variability for tropical ecology Schwartz, Naomi; Lintner, Benjamin R.; Feng, Xue; Powers, Jennifer S.

Description

<b>Abstract</b><br/>

Tropical ecologists have long recognized rainfall as the key climate filter shaping tropical ecosystem structure and function across space and time. Still, tropical ecologists have historically had a limited toolkit for characterizing rainfall, largely relying on simple metrics like mean annual precipitation (MAP) and dry season length to characterize rainfall regimes that vary along many more dimensions. Here, we review methods for quantifying dimensions of rainfall variability on multiple time scales, with a focus on ecological applications of these methods. We also discuss key considerations for tropical ecologists looking to use rainfall metrics that better align with hypothesized biological or ecological mechanisms or that more effectively describe rainfall variability in the systems we study, and provide a toolkit (R scripts and gridded datasets) to do so. We argue that incorporating more sophisticated approaches to quantify rainfall variability into study design and statistical analyses will enhance our understanding of past, ongoing, and future changes in tropical ecosystems.  </p>; <b>Methods</b><br />

Dataset contains four gridded datasets of dimensions of rainfall variation as described in Schwartz et al. All variables were derived from monthly rainfall time series, derived from TRMM data from 1997-2016 and are presented at 0.25 degree resolution. Each .tif file has two bands. Band 1 is the mean value for the DRV and band 2 is its standard deviation over the 20-year time period. </p>

Also included are R functions and example scripts for calculating DRVs. </p>; <b>Usage notes</b><br />

Necessary information is in the readme files. </p>

Item Metadata

Title
Data from Beyond MAP: A guide to dimensions of rainfall variability for tropical ecology
Creator
Schwartz, Naomi; Lintner, Benjamin R.; Feng, Xue; Powers, Jennifer S.
Date Issued
2021-05-19
Description
<b>Abstract</b><br/>

Tropical ecologists have long recognized rainfall as the key climate filter shaping tropical ecosystem structure and function across space and time. Still, tropical ecologists have historically had a limited toolkit for characterizing rainfall, largely relying on simple metrics like mean annual precipitation (MAP) and dry season length to characterize rainfall regimes that vary along many more dimensions. Here, we review methods for quantifying dimensions of rainfall variability on multiple time scales, with a focus on ecological applications of these methods. We also discuss key considerations for tropical ecologists looking to use rainfall metrics that better align with hypothesized biological or ecological mechanisms or that more effectively describe rainfall variability in the systems we study, and provide a toolkit (R scripts and gridded datasets) to do so. We argue that incorporating more sophisticated approaches to quantify rainfall variability into study design and statistical analyses will enhance our understanding of past, ongoing, and future changes in tropical ecosystems.  </p>; <b>Methods</b><br />

Dataset contains four gridded datasets of dimensions of rainfall variation as described in Schwartz et al. All variables were derived from monthly rainfall time series, derived from TRMM data from 1997-2016 and are presented at 0.25 degree resolution. Each .tif file has two bands. Band 1 is the mean value for the DRV and band 2 is its standard deviation over the 20-year time period. </p>

Also included are R functions and example scripts for calculating DRVs. </p>; <b>Usage notes</b><br />

Necessary information is in the readme files. </p>

Subject
Other
Type
Dataset
Notes

Dryad version number: 3</p>

Version status: submitted</p>

Dryad curation status: Published</p>

Sharing link: https://datadryad.org/stash/share/JSx2Lnv1ZXNAxJ11VGOOImJy3kDOM0zQlfMPuPIizpk</p>

Storage size: 3957440</p>

Visibility: public</p>

Date Available
2020-08-17
Provider
University of British Columbia Library
License
CC0 1.0
DOI
10.14288/1.0397688
URI
https://doi.org/10.5683/SP2/FPL9F0
Publisher DOI
https://doi.org/10.5683/SP2/FPL9F0; https://doi.org/10.5061/dryad.f7m0cfxsq
Grant Funding Agency
National Science Foundation; U.S. Department of Energy
Rights URI
http://creativecommons.org/publicdomain/zero/1.0
Aggregated Source Repository
Dataverse

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Licence

CC0 1.0