R Python, and Ruby clients for GBIF species occurrence data

Chamberlain, Scott; Boettiger, Carl

doi:10.7287/peerj.preprints.3304

Cited by 66 publications

(57 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A list of fish species recorded from the marine and freshwater environments of the British Isles was compiled from three sources: (a) the Global Biodiversity Information Facility (https://www.gbif.org; rgbif v1.1.0 ; Chamberlain & Boettiger, ); (b) FishBase (https://www.fishbase.org); and (c) the European Water Framework Directive United Kingdom Technical Advisory Group list of transitional fish species (https://www.wfduk.org/resources/transitional-waters-fish; Annex 1). These species were then cross‐referenced for all synonyms using rfishbase v3.0.0 (Boettiger, Lang, & Wainwright, ).…”

Section: Methodsmentioning

confidence: 99%

Non‐specific amplification compromises environmental DNA metabarcoding with COI

et al. 2019

View full text Add to dashboard Cite

Metabarcoding extra‐organismal DNA from environmental samples is now a key technique in aquatic biomonitoring and ecosystem health assessment. Of critical consideration when designing experiments, and especially so when developing community standards and legislative frameworks, is the choice of genetic marker and primer set. Mitochondrial cytochrome c oxidase subunit I (COI), the standard DNA barcode marker for animals, with its extensive reference library, taxonomic discriminatory power and predictable sequence variation, is the natural choice for many metabarcoding applications. However, for targeting specific taxonomic groups in environmental samples, the utility of COI has yet to be fully scrutinized. Here, by using a case study of marine and freshwater fishes from the British Isles, we quantify the in silico performance of twelve primer pairs from four mitochondrial loci – COI, cytochrome b, 12S and 16S – in terms of reference library coverage, taxonomic discriminatory power and primer universality. We subsequently test in vitro four primer pairs – three COI and one 12S – for their specificity, reproducibility, and congruence with independent datasets derived from traditional survey methods at five estuarine and coastal sites around the English Channel and North Sea. Our results show that for aqueous extra‐organismal DNA at low template concentrations, both metazoan‐targeted and fish‐targeted COI primers perform poorly in comparison to 12S, exhibiting low levels of reproducibility due to non‐specific amplification of prokaryotic and non‐target eukaryotic DNAs. An ideal metabarcode would have an extensive reference library upon which custom primers could be designed, either for broad assessments of biodiversity, or taxon specific surveys. Such a database is available for COI, but low primer specificity hinders practical application, while conversely, 12S primers offer high specificity, but lack adequate references. The latter, however, can be mitigated by expanding the concept of DNA barcodes to include whole mitochondrial genomes generated by genome‐skimming existing tissue collections.

show abstract

Section: Methodsmentioning

confidence: 99%

Non‐specific amplification compromises environmental DNA metabarcoding with COI

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Georeferenced records for Pooideae and outgroup taxa were downloaded from the Global Biodiversity Information Facility (GBIF.org, , ) using the rgbif package in R (Chamberlain & Boettiger, ). To exclude unreliable records, we discarded coordinates with fewer than three decimals and used additional filtering implemented in the SpeciesGeoCoder package (Töpel et al, ) in R. In short, we discarded records where coordinates: (a) were not valid (not part of the coordinate system, or marine coordinates); (b) were exactly or close to zero (threshold 0.5); (c) were the same for latitude and longitude; (d) had the same values as the capital of the country; (e) lay outside the polygon of the country; or (f) had the same value as the GBIF institutions.…”

Section: Methodsmentioning

confidence: 99%

The grass subfamily Pooideae: Cretaceous–Palaeocene origin and climate‐driven Cenozoic diversification

Schubert

Marcussen

Meseguer

et al. 2019

Global Ecol Biogeogr

View full text Add to dashboard Cite

Aim Frost is among the most dramatic stresses a plant can experience, and complex physiological adaptations are needed to endure long periods of sub‐zero temperatures. Owing to the need to evolve these complex adaptations, transitioning from tropical to temperate climates is regarded as difficult. Here, we study the transition from tropical to temperate climates in the grass subfamily Pooideae, which dominates cool temperate, continental and Arctic regions. We produce a dated phylogeny and investigate the role of climate cooling in diversification. Location Global, temperate regions. Time period Cretaceous–Cenozoic. Major taxa Pooideae. Methods Using newly available fossils and methods, we dated a comprehensive Pooideae phylogeny and tested for the impact of palaeoclimates on diversification rates. Using ancestral state reconstruction, we investigated whether Pooideae ancestors experienced frost and winter. To locate the ancestral distribution area of Pooideae, we performed biogeographical analyses. Results We estimated a Late Cretaceous/early Palaeocene origin of the Pooideae (61–77 Ma), with all major clades already having diversified at the Eocene–Oligocene climate cooling (34 Ma). Climate cooling was a probable driving force of Pooideae diversification. Pooideae probably evolved in a temperate niche experiencing frost, but not long winters. Main conclusion Pooideae probably originated in a temperate niche and experienced cold temperatures and frost long before expansion of temperate biomes after the Eocene–Oligocene transition. This suggests that the Pooideae ancestor had adaptations to temperate climate and that certain responses to low‐temperature stress are shared in extant Pooideae grasses. Throughout the Cenozoic, falling temperatures and expansion of temperate biomes were associated with an increase in diversification. However, complex mechanisms for enduring strongly seasonal climate with long, cold winters most probably evolved independently in daughter lineages. Our findings provide insight into how adaptations to historical changes in chill and frost exposure influence the distribution of plant diversity today.

show abstract

“…As an alternative, automated flagging methods to identify potentially problematic records have been proposed as a scalable option, as they are able to deal with datasets containing up to millions of records and many different taxa. Those methods are usually based on geographic gazetteers (e.g., Chamberlain 2016; Zizka, Silvestro, et al 2019; Jin and Yang 2020) or on additional data, such as environmental variables (Robertson, Visser, and Hui 2016). Additionally, filtering procedures based on record meta-data, such as collection year, record type, and coordinate precisions have been proposed to improve the suitability of publicly available occurrence records for biodiversity research (Zizka, Silvestro, et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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

R Python, and Ruby clients for GBIF species occurrence data

Cited by 66 publications

References 8 publications

Non‐specific amplification compromises environmental DNA metabarcoding with COI

Non‐specific amplification compromises environmental DNA metabarcoding with COI

The grass subfamily Pooideae: Cretaceous–Palaeocene origin and climate‐driven Cenozoic diversification

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

Contact Info

Product

Resources

About