DOI: 10.5061/dryad.jm63xsjbs
title: Tropical South America Diatom Database (TSADB)

description: Determining the mechanisms of community assembly forms the foundation of
 biogeography and community ecology. Studies of the biodiversity and
 distribution of Neotropical macro-organisms have revealed the roles of
 environmental, spatial, and historical factors in structuring communities
 at different spatial and temporal scales, but the role of these factors
 play for species and communities on microorganisms are still poorly known.
 Diatoms are a very species-rich group of algae, disperse widely, and
 sensitive to environmental variation due to their position in the base of
 aquatic food webs. Here, we present the Tropical South American Diatom
 Database (TSADB) which contains geographical and ecological information of
 species across lentic and lotic environments, including predictors that
 describe local (limnological) and regional (geo-climatic) factors. The
 database can be used to tackle fundamental questions in macroecology,
 including metacommunity ecology and biogeography theories that form the
 foundation for better understanding the rapid environmental change the
 tropical regions are experiencing. The TSADB includes diatom taxa from 437
 samples in 326 sites containing distributed across 26 regions (0 to 5,070
 m a.s.l, and between 8°N–35°S; 58–90°W). In addition, long-term,
 diatom-based paleolimnological records are presented as a complementary
 tool for identifying regions with available modern and paleo-datasets for
 a long-term limnological change observatory in the tropical Americas. We
 describe the TSADB structure and functionality, and R codes for data
 manipulation and visualization. Each of the 26 study regions is
 represented by 3 data matrices: sampling site information, environmental
 variables (limnology, climate, and landscape), and diatom community data
 (relative abundance or presence/absence). Access to data and future
 additions is through publicly available repositories and a guide to
 contributors, respectively, providing opportunities for complementing
 existing databases on diatoms and allowing optimal usage of TSADB by
 scientist including diatomists, limnologists, aquatic ecologists, and
 natural resource managers.
description: The Tropical South America Diatom Database (TSADB) includes 326
 sites containing 437 samples distributed across 26 regions (0 to 5,070 m
 a.s.l, and between 8°N–35°S; 58–90°W). The database comprises published
 and unpublished studies from lentic and lotic environments sampled by
 different authors for different purposes (e.g. paleoclimatic
 reconstructions, taxonomy, biodiversity). Diatom samples correspond to
 multiple habitats (e.g. sediment surface, periphyton, and plankton) and
 cover the period 1978–2017, including predictors that describe local
 (limnology) and regional (geo-climatic) factors. 
description: The TSADB is hosted
 in two different data repositories (Dryad for Excel spreadsheets, and
 Zenodo for R files) via two different formats to provide users alternative
 ways to access and use the data. 
 Excel spreadsheets: 26 excel files (one for each study
 region). Depending on the original information, each file has three sheets
 containing the three data matrices (site information, environmental
 variables, and diatom community data). A metadata sheet describing sources
 and descriptions of environmental variables is common in all excel files.
 A guide on how to share datasets for the TSADB is also
 included. R folder: files
 consisting in the TSADB’s region datasets as a R list object
 (“TSABD.RData”), three R scripts for data processing, plotting (Figs. 1-4)
 and updating taxon nomenclature, and the R shiny apps. The “TSADB.Rdata”
 is a list of objects, in which each element refers to a data matrix
 ($<b>diatoms,</b> $<b>sites</b>,
 $<b>environment</b>) that itself contains a list of study
 regions. The R script “<i>1-cleaning and
 formatting.R</i>” allows for data preparation, checking
 the correct format (i.e., numeric, text, ordered factors, region name
 spelling), and merging the original datasets by unique IDs into regions.
 Database manipulation, plotting and generating metadata is available via
 the R script “<i>2-visualization.R</i>”.
 Finally, the R script
 “<i>3-taxonomic-harmonisation.R</i>” run
 steps for nomenclature synchronization and authority harmonization
 (<i>see below</i>). A guide on how to share datasets for the
 TSADB is also included. The R code routines generate output files and
 plots that can be turned on/off by commenting/uncommenting lines. All
 steps were conducted using several R packages, including
 <i>tidyverse</i> (Wickham et al. 2019),
 <i>ggplot2</i> (Wilke et al. 2019), <i>cowplot</i>
 (Wilke et al. 2019), and <i>shiny</i> (Winston et al.
 2021).