Quantifying the value of user-level data cleaning for big data: A case study using mammal distribution models

Gueta, Tomer; Carmel, Yohay

doi:10.1016/j.ecoinf.2016.06.001

Cited by 60 publications

(47 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy of the automated conservation assessment was in the same range as found by previous studies (Nic Lughadha et al 2019; Zizka, Azevedo, et al 2019). The similar accuracy of the raw and filtered dataset for the automated conservation assessment was surprising, in particular given the EOO and AOO reduction observed in the filtered dataset (Table 4) and the impact of errors on spatial analyses observed in previous studies (Gueta and Carmel 2016). The robustness of the automated assessment was likely due to the fact that the EOO for most species was large, even after the considerable reduction caused by filtering.…”

Section: Discussionmentioning

confidence: 78%

No one-size-fits-all solution to clean GBIF

Zizka

Carvalho

Calvente

et al. 2020

Preprint

View full text Add to dashboard Cite

28Species occurrence records provide the basis for many biodiversity studies. They derive from geo-referenced specimens deposited in natural history collections and visual observations, such as those obtained through various mobile applications. Given the rapid increase in availability of such data, the control of quality and accuracy constitutes a particular concern. Automatic flagging and filtering are a scalable and reproducible means to identify potentially problematic records in datasets from public databases such as the Global Biodiversity Information Facility (GBIF; www.gbif.org). However, it is unclear how much data may be lost by filtering, whether the same tests should be applied across all taxonomic groups, and what is the effect of filtering for common downstream analyses. Here, we evaluate the effect of 13 recently proposed filters on the inference of species richness patterns and automated conservation assessments for 18 Neotropical taxa including animals, fungi, and plants, terrestrial and marine, downloaded from GBIF. We find that 29-90% of the records are potentially erroneous, with large variation across taxonomic groups. Tests for duplicated information, collection year, basis of record as well as urban areas and coordinates for terrestrial taxa in the sea or marine taxa on land have the greatest effect. While many flagged records might not be de facto erroneous, they could be overly imprecise and increase uncertainty in downstream analyses. Automated flagging can help in identifying problematic records, but requires customization of which tests and thresholds should be applied to the taxonomic group and geographic area under focus. Our results stress the importance of thorough exploration of the meta-data associated with species records for biodiversity research. 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44Publicly available species distribution data have become a crucial resource in biodiversity research, including studies in 46 ecology, biogeography, systematics and conservation biology. In particular, the availability of digitized collections from 47 museums and herbaria, and citizen science observations has increased drastically over the last few years. As of today, 48 the largest public aggregator for geo-referenced species occurrences data, the Global Biodiversity Information Facility 49 (www.gbif.org), provides access to more than 1.3 billion geo-referenced occurrence records for species from across the 50 globe and the tree of life. 51A central challenge to the use of these publicly available species occurrence data in research are erroneous geographic 52 coordinates (Anderson et al. 2016). Errors mostly arise because public databases integrate records collected with 53 different methodologies in different places, at different times; often without centralized curation and only rudimentary 54 meta-data. For instance, erroneous coordinates caused by data-entry errors or automated geo-referencing from vague 55 locality descriptions are common (Maldonado et al. 2015; Yesson et al. 2007)...

show abstract

Section: Discussionmentioning

confidence: 78%

No one-size-fits-all solution to clean GBIF

Zizka

Carvalho

Calvente

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…To model the paleoclimatic distribution of Triplostegia, we used three global We corrected all the occurrence data to reduce the negative effect of sampling bias on the performance of the SDMs. For records from GBIF, CVH, and SDNPT, we excluded the duplicated entries within a 2.5 arc-minute pixel (4.3 km at the equator) and omitted entries with implausible geographical coordinates using ArcGIS 10.2 (ESRI Inc., Redlands, CA, USA; http://www.esri.com/software/arcgis/arcgis-for-desktop) and Google Earth (http://earth.google.com/; Gueta & Carmel, 2016;Maldonado et al, 2015). We also checked voucher photos for records from CVH and SDNPT to make sure that the species are correctly identified.…”

Section: Species Distribution Modelingmentioning

confidence: 99%

Long‐distance dispersal or postglacial contraction? Insights into disjunction between Himalaya–Hengduan Mountains and Taiwan in a cold‐adapted herbaceous genus, Triplostegia

Niu

Zhang³

et al. 2017

Ecology and Evolution

View full text Add to dashboard Cite

Current disjunct patterns can result from long‐distance dispersal or postglacial contraction. We herein investigate the evolutionary history of Triplostegia to elucidate the disjunction between the Himalaya–Hengduan Mountain region (HHM) and Taiwan (TW). Genetic structure of Triplostegia was investigated for 48 populations using sequences from five chloroplast loci and the ribosomal nuclear internal transcribed spacer. Divergence time estimation, ancestral area reconstruction, and species distribution modeling (SDM) were employed to examine the biogeographic history of Triplostegia. Substantial genetic differentiation among populations from southwestern China (SW), Central China (CC), and TW was detected. Triplostegia was inferred to have originated in SW, and diversification began during the late Miocene; CC was colonized in the mid‐Pliocene, and TW was finally colonized in the early Pleistocene. SDM suggested an expansion of climatically suitable areas during the Last Glacial Maximum and range contraction during the Last interglacial in Triplostegia. Disjunction between HHM and TW in Triplostegia is most likely the consequence of topographic isolation and postglacial contraction. The potential climatic suitability areas for Triplostegia by 2070s (2061–2080) are predicted to slightly shrink and move northward. With continued global warming and human‐induced deforestation, extinction risk may increase for the cold‐adapted species, and appropriate strategies should be employed for ecosystem conservation.

show abstract

“…The distribution data for Kūmarahou ( n = 406) and Kuta ( n = 3559) were derived from databases, herbarium specimens and field observations (Table S3). To improve species distribution model (SDM) performance, occurrence records were cleaned; points were removed if they were obviously inaccurate (e.g., points in the ocean), missing coordinates, located exactly at the centre of the country (which suggests incorrect georeferencing) or had fewer than three decimal digits in the latitude or longitude (which suggests insufficient spatial accuracy; Gueta & Carmel, ). True absences were not available for these species; to account for climatic bias of presence data, pseudo‐absences were randomly distributed within a buffer region 200 km from presence points (Figures S2 and S3) (Barbet‐Massin, Jiguet, Albert, & Thuiller, ).…”

Section: Methodsmentioning

confidence: 99%

“…The distribution data for Kūmarahou (n = 406) and Kuta (n = 3559) were derived from databases, herbarium specimens and field observations (Table S3). To improve species distribution model (SDM) performance, occurrence records were cleaned; points were removed if they were obviously inaccurate (e.g., points in the ocean), missing coordinates, located exactly at the centre of the country (which suggests incorrect georeferencing) or had fewer than three decimal digits in the latitude or longitude (which suggests insufficient spatial accuracy; Gueta & Carmel, 2016 (VanDerWal, Shoo, Graham, & Williams, 2009) was 200 km. To reduce spatial sampling bias, counter residual spatial autocorrelation and improve SDM performance (Boria, Olson, Goodman, & Anderson, 2014;de Oliveira, Rangel, Lima-Ribeiro, Terribile, & Diniz-Filho, 2014;Hijmans, 2012), occurrence and pseudo-absence points were spatially thinned by randomly removing a single point from within a 10 km radius using the spThin package (Aiello-Lammens, Boria, Radosavljevic, Vilela, & Anderson, 2015).…”

Section: Species Distribution Modellingmentioning

confidence: 99%

Effects of climatically shifting species distributions on biocultural relationships

et al. 2019

View full text Add to dashboard Cite

Local and Indigenous Peoples play critical roles in safeguarding global biological and cultural diversity. However, species distribution modelling has yet to incorporate perspectives that assess threats to the linked biological and cultural systems of local and Indigenous Peoples. Here, we provide the first example of integrating species distribution modelling with benefit‐relevant indicators. This novel approach assesses how human access to culturally important species may change over time. Focusing on two culturally significant species used by the Indigenous Māori people of New Zealand, we first identified predictor variables relevant to the habitat of each species. We used species distribution models (SDMs) to estimate the recent (1961–1990) potential distribution for each species based on occurrence records and predictor variables, then generated future climate suitability maps. Our models show that future suitability for one species shifts to the south, in line with changes in temperature and precipitation, while the second species range expands into higher latitudes, driven primarily by increased temperature. When we combined these models with knowledge of tribal boundaries and cultural practices, results indicated that these distributions might decrease access to culturally important plants. Future suitability for one species shifted substantially from where it is most valued for weaving, while the second species range expanded to include more of its primary medicinal users. Climate change‐mediated shifts in the ranges of these species are likely to affect intergenerational human–environment relationships, sense of place, cultural identity and knowledge on a regional scale, as well as cultural identity and social cohesion on a national scale. By interpreting SDMs within a socioecological framework, this research illustrates a new approach to assessment of vulnerabilities to climate change and identifies strategies for adaptation. A plain language summary is available for this article.

show abstract

Quantifying the value of user-level data cleaning for big data: A case study using mammal distribution models

Cited by 60 publications

References 49 publications

No one-size-fits-all solution to clean GBIF

No one-size-fits-all solution to clean GBIF

Long‐distance dispersal or postglacial contraction? Insights into disjunction between Himalaya–Hengduan Mountains and Taiwan in a cold‐adapted herbaceous genus, Triplostegia

Effects of climatically shifting species distributions on biocultural relationships

Contact Info

Product

Resources

About