occTest: An integrated approach for quality control of species occurrence data

Josep M. Serra-Diaz; Jeremy Borderieux; Brian Maitner; Coline C.F. Boonman; Daniel Park; Wen Yong Guo; Arnaud Callebaut; Brian J. Enquist; Jens C. Svenning; Cory Merow

doi:10.1111/geb.13847

occTest: An integrated approach for quality control of species occurrence data

Josep M. Serra-Diaz^*, Jeremy Borderieux, Brian Maitner, Coline C.F. Boonman, Daniel Park, Wen Yong Guo, Arnaud Callebaut, Brian J. Enquist, Jens C. Svenning, Cory Merow

^*Corresponding author for this work

Department of Biology - Center for Ecological Dynamics in a Novel Biosphere (ECONOVO)

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Abstract

Aim: Species occurrence data are valuable information that enables one to estimate geographical distributions, characterize niches and their evolution, and guide spatial conservation planning. Rapid increases in species occurrence data stem from increasing digitization and aggregation efforts, and citizen science initiatives. However, persistent quality issues in occurrence data can impact the accuracy of scientific findings, underscoring the importance of filtering erroneous occurrence records in biodiversity analyses. Innovation: We introduce an R package, occTest, that synthesizes a growing open-source ecosystem of biodiversity cleaning workflows to prepare occurrence data for different modelling applications. It offers a structured set of algorithms to identify potential problems with species occurrence records by employing a hierarchical organization of multiple tests. The workflow has a hierarchical structure organized in testPhases (i.e. cleaning vs. testing) that encompass different testBlocks grouping different testTypes (e.g. environmental outlier detection), which may use different testMethods (e.g. Rosner test, jacknife,etc.). Four different testBlocks characterize potential problems in geographic, environmental, human influence and temporal dimensions. Filtering and plotting functions are incorporated to facilitate the interpretation of tests. We provide examples with different data sources, with default and user-defined parameters. Compared to other available tools and workflows, occTest offers a comprehensive suite of integrated tests, and allows multiple methods associated with each test to explore consensus among data cleaning methods. It uniquely incorporates both coordinate accuracy analysis and environmental analysis of occurrence records. Furthermore, it provides a hierarchical structure to incorporate future tests yet to be developed. Main conclusions: occTest will help users understand the quality and quantity of data available before the start of data analysis, while also enabling users to filter data using either predefined rules or custom-built rules. As a result, occTest can better assess each record's appropriateness for its intended application.

Original language	English
Article number	e13847
Journal	Global Ecology and Biogeography
Volume	33
Issue	7
ISSN	1466-822X
DOIs	https://doi.org/10.1111/geb.13847
Publication status	Published - Jul 2024

Keywords

data cleaning
outlier
quality
R
species occurrence

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10.1111/geb.13847

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@article{64ebef292ea04067883a044b317c7a85,

title = "occTest: An integrated approach for quality control of species occurrence data",

abstract = "Aim: Species occurrence data are valuable information that enables one to estimate geographical distributions, characterize niches and their evolution, and guide spatial conservation planning. Rapid increases in species occurrence data stem from increasing digitization and aggregation efforts, and citizen science initiatives. However, persistent quality issues in occurrence data can impact the accuracy of scientific findings, underscoring the importance of filtering erroneous occurrence records in biodiversity analyses. Innovation: We introduce an R package, occTest, that synthesizes a growing open-source ecosystem of biodiversity cleaning workflows to prepare occurrence data for different modelling applications. It offers a structured set of algorithms to identify potential problems with species occurrence records by employing a hierarchical organization of multiple tests. The workflow has a hierarchical structure organized in testPhases (i.e. cleaning vs. testing) that encompass different testBlocks grouping different testTypes (e.g. environmental outlier detection), which may use different testMethods (e.g. Rosner test, jacknife,etc.). Four different testBlocks characterize potential problems in geographic, environmental, human influence and temporal dimensions. Filtering and plotting functions are incorporated to facilitate the interpretation of tests. We provide examples with different data sources, with default and user-defined parameters. Compared to other available tools and workflows, occTest offers a comprehensive suite of integrated tests, and allows multiple methods associated with each test to explore consensus among data cleaning methods. It uniquely incorporates both coordinate accuracy analysis and environmental analysis of occurrence records. Furthermore, it provides a hierarchical structure to incorporate future tests yet to be developed. Main conclusions: occTest will help users understand the quality and quantity of data available before the start of data analysis, while also enabling users to filter data using either predefined rules or custom-built rules. As a result, occTest can better assess each record's appropriateness for its intended application.",

keywords = "data cleaning, outlier, quality, R, species occurrence",

author = "Serra-Diaz, {Josep M.} and Jeremy Borderieux and Brian Maitner and Boonman, {Coline C.F.} and Daniel Park and Guo, {Wen Yong} and Arnaud Callebaut and Enquist, {Brian J.} and Svenning, {Jens C.} and Cory Merow",

note = "Publisher Copyright: {\textcopyright} 2024 John Wiley & Sons Ltd.",

year = "2024",

month = jul,

doi = "10.1111/geb.13847",

language = "English",

volume = "33",

journal = "Global Ecology and Biogeography",

issn = "1466-822X",

publisher = "Wiley-Blackwell Publishing Ltd.",

number = "7",

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TY - JOUR

T1 - occTest

T2 - An integrated approach for quality control of species occurrence data

AU - Serra-Diaz, Josep M.

AU - Borderieux, Jeremy

AU - Maitner, Brian

AU - Boonman, Coline C.F.

AU - Park, Daniel

AU - Guo, Wen Yong

AU - Callebaut, Arnaud

AU - Enquist, Brian J.

AU - Svenning, Jens C.

AU - Merow, Cory

PY - 2024/7

Y1 - 2024/7

N2 - Aim: Species occurrence data are valuable information that enables one to estimate geographical distributions, characterize niches and their evolution, and guide spatial conservation planning. Rapid increases in species occurrence data stem from increasing digitization and aggregation efforts, and citizen science initiatives. However, persistent quality issues in occurrence data can impact the accuracy of scientific findings, underscoring the importance of filtering erroneous occurrence records in biodiversity analyses. Innovation: We introduce an R package, occTest, that synthesizes a growing open-source ecosystem of biodiversity cleaning workflows to prepare occurrence data for different modelling applications. It offers a structured set of algorithms to identify potential problems with species occurrence records by employing a hierarchical organization of multiple tests. The workflow has a hierarchical structure organized in testPhases (i.e. cleaning vs. testing) that encompass different testBlocks grouping different testTypes (e.g. environmental outlier detection), which may use different testMethods (e.g. Rosner test, jacknife,etc.). Four different testBlocks characterize potential problems in geographic, environmental, human influence and temporal dimensions. Filtering and plotting functions are incorporated to facilitate the interpretation of tests. We provide examples with different data sources, with default and user-defined parameters. Compared to other available tools and workflows, occTest offers a comprehensive suite of integrated tests, and allows multiple methods associated with each test to explore consensus among data cleaning methods. It uniquely incorporates both coordinate accuracy analysis and environmental analysis of occurrence records. Furthermore, it provides a hierarchical structure to incorporate future tests yet to be developed. Main conclusions: occTest will help users understand the quality and quantity of data available before the start of data analysis, while also enabling users to filter data using either predefined rules or custom-built rules. As a result, occTest can better assess each record's appropriateness for its intended application.

AB - Aim: Species occurrence data are valuable information that enables one to estimate geographical distributions, characterize niches and their evolution, and guide spatial conservation planning. Rapid increases in species occurrence data stem from increasing digitization and aggregation efforts, and citizen science initiatives. However, persistent quality issues in occurrence data can impact the accuracy of scientific findings, underscoring the importance of filtering erroneous occurrence records in biodiversity analyses. Innovation: We introduce an R package, occTest, that synthesizes a growing open-source ecosystem of biodiversity cleaning workflows to prepare occurrence data for different modelling applications. It offers a structured set of algorithms to identify potential problems with species occurrence records by employing a hierarchical organization of multiple tests. The workflow has a hierarchical structure organized in testPhases (i.e. cleaning vs. testing) that encompass different testBlocks grouping different testTypes (e.g. environmental outlier detection), which may use different testMethods (e.g. Rosner test, jacknife,etc.). Four different testBlocks characterize potential problems in geographic, environmental, human influence and temporal dimensions. Filtering and plotting functions are incorporated to facilitate the interpretation of tests. We provide examples with different data sources, with default and user-defined parameters. Compared to other available tools and workflows, occTest offers a comprehensive suite of integrated tests, and allows multiple methods associated with each test to explore consensus among data cleaning methods. It uniquely incorporates both coordinate accuracy analysis and environmental analysis of occurrence records. Furthermore, it provides a hierarchical structure to incorporate future tests yet to be developed. Main conclusions: occTest will help users understand the quality and quantity of data available before the start of data analysis, while also enabling users to filter data using either predefined rules or custom-built rules. As a result, occTest can better assess each record's appropriateness for its intended application.

KW - data cleaning

KW - outlier

KW - quality

KW - R

KW - species occurrence

UR - http://www.scopus.com/inward/record.url?scp=85190790056&partnerID=8YFLogxK

U2 - 10.1111/geb.13847

DO - 10.1111/geb.13847

M3 - Journal article

AN - SCOPUS:85190790056

SN - 1466-822X

VL - 33

JO - Global Ecology and Biogeography

JF - Global Ecology and Biogeography

IS - 7

M1 - e13847

ER -

occTest: An integrated approach for quality control of species occurrence data

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