Toolunterstützte Datenmodellierung

Vetter, Max

doi:10.1007/978-3-663-05742-0_9

Cited by 1 publication

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“…A less maintained data alternative, biodiversity records from GBIF, are free of charge but with limitations in data quality due to several known and unknown errors. Expert and GBIF data form the data layer (Bakshi, 2012 ; Vetter, 1990 ). However, the critical difference between expert data and GBIF data is that the expert data may be used unprocessed as input to the data modeling workflow as there are no data errors to be expected.…”

Section: Discussionmentioning

confidence: 99%

Influence of different data cleaning solutions of point‐occurrence records on downstream macroecological diversity models

Fuehrding-Potschkat

Kreft

Ickert‐Bond

2022

Ecology and Evolution

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Digital point‐occurrence records from the Global Biodiversity Information Facility (GBIF) and other data providers enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time‐consuming and often unfeasible, given that the databases may contain thousands or millions of records. Automated data cleaning pipelines are therefore of high importance. Taking North American Ephedra as a model, we examined how different data cleaning pipelines (using, e.g., the GBIF web application, and four different R packages) affect downstream species distribution models (SDMs). We also assessed how data differed from expert data. From 13,889 North American Ephedra observations in GBIF, the pipelines removed 31.7% to 62.7% false positives, invalid coordinates, and duplicates, leading to datasets between 9484 (GBIF application) and 5196 records (manual‐guided filtering). The expert data consisted of 704 records, comparable to data from field studies. Although differences in the absolute numbers of records were relatively large, species richness models based on stacked SDMs (S‐SDM) from pipeline and expert data were strongly correlated (mean Pearson's r across the pipelines: .9986, vs. the expert data: .9173). Our results suggest that all R package‐based pipelines reliably identified invalid coordinates. In contrast, the GBIF‐filtered data still contained both spatial and taxonomic errors. Major drawbacks emerge from the fact that no pipeline fully discovered misidentified specimens without the assistance of taxonomic expert knowledge. We conclude that application‐filtered GBIF data will still need additional review to achieve higher spatial data quality. Achieving high‐quality taxonomic data will require extra effort, probably by thoroughly analyzing the data for misidentified taxa, supported by experts.

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Section: Discussionmentioning

confidence: 99%