<i>bdc</i>: A toolkit for standardizing, integrating and cleaning biodiversity data

Ribeiro, Bruno R.; Velazco, Santiago José Elías; Guidoni‐Martins, Karlo Gregório; Tessarolo, Geiziane; Jardim, Lucas; Bachman, Steven P.; Loyola, Rafael

doi:10.1111/2041-210x.13868

Cited by 32 publications

(28 citation statements)

References 28 publications

(40 reference statements)

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“…The BeeBDC package and script relied heavily on several R- packages. Much of the workflow script used and built upon the bdc (biodiversity data cleaner) package 13 , tidyverse packages — particularly dplyr 65 , magrittr 66 , tibble 67 , stringr 68 , tidyselect 69 , ggplot2 70 , tidyr 71 , rlang 72 , xml2 73 , readr 74 , and lubridate 75 , — and CoordinateCleaner 76 . Additionally, we used R.utils 77 , galah 63 , emld 78 , openxlsx 79 , rnaturalearth 80 , rnaturalearthdata 81 , countrycode 82 , hexbin 83 , cowplot 84 , ggspatial 85 , renv 86 , chorddiag 87 , igraph 88 , sf 89 , and terra 90 .…”

Section: Methodsmentioning

confidence: 99%

A globally synthesised and flagged bee occurrence dataset and cleaning workflow

Dorey,

Fischer,

Chesshire

et al. 2023

Preprint

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Species occurrence data are foundational for research, conservation, and science communication. But the limited availability and accessibility of reliable data represents a major obstacle, particularly for insects, which face mounting pressures. We present BeeDC, a new R package, and a global bee occurrence dataset to address this issue. We combined >17.7 million bee occurrence records from multiple public repositories (GBIF, SCAN, iDigBio, USGS, ALA) and smaller datasets, then standardised, flagged, deduplicated, and cleaned the data using the reproducible BeeDC R-workflow. Specifically, we harmonised species names following established global taxonomy, country, and collection date and we added record-level flags for a series of potential quality issues. These data are provided in two formats, "completely-cleaned" and "flagged-but-uncleaned". Our data cleaning process is open and documented for transparency and reproducibility. The BeeDC package and R Markdown are provided, and will be improved and updated regularly. By publishing reproducible R workflows and globally cleaned datasets we can increase the accessibility and reliability of downstream analyses. This workflow can be implemented for other taxa to support research and conservation.

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Section: Methodsmentioning

confidence: 99%

A globally synthesised and flagged bee occurrence dataset and cleaning workflow

Dorey,

Fischer,

Chesshire

et al. 2023

Preprint

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“…While strides have been made to refine SDM methods that are robust to very small sample sizes, there has also been tremendous growth in the volume of biodiversity data federated in, for example, the Global Biodiversity Information Facility (GBIF) and other databases, addressing the ‘Wallacean shortfall’ (Lomolino, 2004) and providing larger samples for SDM. There are also improved tools to access, screen and edit georeferenced biodiversity data from multiple sources for SDM (Ribeiro et al, 2022).…”

Section: Challengesmentioning

confidence: 99%

Species distribution modelling supports the study of past, present and future biogeographies

Franklin

2023

Journal of Biogeography

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Species distribution modelling (SDM), also called environmental or ecological niche modelling, has developed over the last 30 years as a widely used tool used in core areas of biogeography including historical biogeography, studies of diversity patterns, studies of species ranges, ecoregional classification, conservation assessment and projecting future global change impacts. In the 50th anniversary year of Journal of Biogeography, I reflect on developments in species distribution modelling, illustrate how embedded the methodology has become in all areas of biogeography and speculate on future directions in the field. Challenges to species distribution modelling raised in this journal in 2006 have been addressed to a significant degree. Those challenges are clarification of the niche concept; improved sample design for species occurrence data; model parameterization; predictor selection; assessing model performance and transferability; and integrating correlative and process models of species distributions. SDM is used, often in conjunction with other evidence, to understand past species range dynamics, identify patterns and drivers of biological diversity, identify drivers of species range limits, define and delineate ecoregions, estimate the distributions of biodiversity elements in relation to protected status and to prioritize conservation action, and to forecast species range shifts in response to climate change and other global change scenarios. Areas of progress in SDM that may become more widely accessible and useful tools in biogeography include genetically informed models and community distribution models.

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“…In practice, a first step is always to build species geographic extents of occurrence, which is now usually done based on local occurrence data (e.g., as available from GBIF). Dealing with such datasets, in turn, require procedures to evaluate data consistency and taxonomic resolution (e.g., Ribeiro et al 2022), which may be challenging under the potential links between the three shortfalls. Additionally, we can use several methods to deal with this issue of occurrence data uncertainty and explore Wallacean shortfall, and a promising approach would be to use the 'maps of biogeographical ignorance' (MOBIs) of each currently known species (Tessarolo et al 2021), or to use MOBIs created for entire groups altogether.…”

Section: Geographical Components Of Taxonomic Uncertainty and The Wal...mentioning

confidence: 99%