SpiderATLAS: A database of spider traits and distributions in the Brazilian Atlantic Forest
Data files
Nov 03, 2025 version files 812.92 KB
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README.md
1.85 KB
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SpiderATLAS_code.R
12.12 KB
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SpiderATLAS_metadata.csv
4.21 KB
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SpiderATLAS_occurrence_data.csv
459.32 KB
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SpiderATLAS_trait_data.csv
335.42 KB
Abstract
Aim
Biodiversity shortfalls related to limited knowledge about geographic distribution (Wallacean) and species traits (Raunkiæran) are extremely common in many animal groups, and perhaps more evident in invertebrate groups such as spiders. This lack of knowledge could present challenges for researchers investigating the response and effects of spiders along large spatial scales, particularly in the Global South. The aim of the SpiderATLAS is to facilitate research that links macroecology, biogeography, trait-based ecology, and global change biology.
Location
Brazilian Atlantic Forest, spanning virtually the entire eastern coastline of Brazil, encompassing 1,62 million km², 30 degrees of latitude (3° 42’ 36” S to 33° 31’ 12” S), and 22 degrees of longitude (34° 50’ 24” W to 56° 45’ 0” W).
Taxon
Spiders (Class Araneae), including 1,648 species from 63 families.
Methods
We compiled the spatial distribution (i.e., occurrence data) and data on morphological characteristics and derived traits for spider species from species descriptions, taxonomic revisions, and personal collection. Species occurrences were extracted from several data sources spanning from the 1880s to 2023. Data on the morphological traits of species were compiled from taxonomic and diversity inventory papers published between 1833 and 2024.
Results
We provide 9,369 georeferenced point locations and six morphological characteristics (body length, prosoma length, prosoma width, prosoma height, femur I length, and patella I length) and three derived traits (body size, body volume, and leg length) for 1,648 spider species. Most data are available for females and males. The dataset and code are available to download from Dryad, World Spider Trait database, and the ZooTraits app.
Main Conclusions
The SpiderATLAS can be useful to access information on distribution and traits of spiders, contributing to development of macroecological, biogeographical, and trait-based ecology. This dataset can also facilitate the biodiversity conservation of one of the most abundant and important arthropod predators on the planet, in a biodiversity hotspot.
README file for SpiderATLAS: A Database of Spider Traits and Distributions in the Brazilian Atlantic Forest
Access this dataset on Dryad: https://doi.org/10.5061/dryad.2v6wwq01j
The SpiderATLAS dataset provides information on the distribution and traits of 1,648 spider species, contributing to macroecological, biogeographical, and trait-based ecology. This dataset can also support biodiversity conservation efforts for one of the most abundant and ecologically important arthropod predators in a biodiversity hotspot.
Description of the data and file structure
- SpiderATLAS_metadata.csv
Provides a detailed description of the columns provided in the dataset, including units and specificities of spider morphological measurements. - SpiderATLAS_trait_data.csv
Contains trait data for 1,648 spider species, including: species name, family name, localities of species description, morphological characteristics/traits (i.e., body size, prosoma length, width, height, femur and patella length) of females and males, reference (article) of species description, year of description, and World Spider Catalog link for species description. - SpiderATLAS_occurrence_data.csv
Contains occurrence data for spider specimens, including: species name, family name, longitude and latitude coordinates (decimal degrees). - SpiderATLAS_code.R
Contains an R script (with usage notes) for merging trait and occurrence data, creating maps of spider distributions and traits, and performing some descriptive analyses. R is required to run the SpiderATLAS_code. The script was created using version 4.5.1. Annotations are provided throughout the script, for example: 1) library loading, 2) dataset loading and preparation, 3) figure creation, and 4) descriptive analyses.
