Biodiversity Data Journal 7: 47369 (o @)
doi: 10.3897/BDJ.7.e47369 ora face
Research Article

Current GBIF occurrence data demonstrates both
promise and limitations for potential red listing of

spiders

Vaughn Shirey*S:!, Sini Seppala*, Vasco Veiga Brancot, Pedro Cardoso+

+ Laboratory for Integrative Biodiversity Research (LIBRe), Finnish Museum of Natural History, University of Helsinki, Helsinki,
Finland

§ Georgetown University, Washington, DC, United States of America

| The Academy of Natural Sciences of Drexel University, Philadelphia, United States of America

Corresponding author: Vaughn Shirey (vmshirey@qmail.com), Pedro Cardoso (pedro.cardoso@helsinki.fi)
Academic editor: Jeremy Miller

Received: 16 Oct 2019 | Accepted: 16 Dec 2019 | Published: 19 Dec 2019

Citation: Shirey V, Seppala S, Branco VV, Cardoso P (2019) Current GBIF occurrence data demonstrates both
promise and limitations for potential red listing of spiders. Biodiversity Data Journal 7: e47369.
https://doi.org/10.3897/BDJ.7.e47369

Abstract

Conservation assessments of hyperdiverse groups of organisms are often challenging and
limited by the availability of occurrence data needed to calculate assessment metrics such
as extent of occurrence (EOO). Spiders represent one such diverse group and have
historically been assessed using primary literature with retrospective georeferencing. Here
we demonstrate the differences in estimations of EOO and hypothetical IUCN Red List
classifications for two extensive spider datasets comprising 479 species in total. The EOO
were estimated and compared using literature-based assessments, Global Biodiversity
Information Facility (GBIF)-based assessments and combined data assessments. We
found that although few changes to hypothetical IUCN Red List classifications occurred
with the addition of GBIF data, some species (3.3%) which could previously not be
classified could now be assessed with the addition of GBIF data. In addition, the
hypothetical classification changed for others (1.5%). On the other hand, GBIF data alone
did not provide enough data for 88.7% of species. These results demonstrate the potential
of GBIF data to serve as an additional source of information for conservation assessments,

© Shirey V et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY
4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are
credited.

2 Shirey V et al

complementing literature data, but not particularly useful on its own as it stands right now
for spiders.

Keywords

Araneae, arthropoda, conservation, extent of occurrence, IUCN

Introduction

The mobilisation of biodiversity data through aggregating platforms such as the Global
Biodiversity Information Facility (GBIF) has generated excitement about the potential for
applying such publicly available data towards filling gaps in biological knowledge (Edwards
2004). To this end, the ability to predict species distributions more accurately using
aggregated occurrence data may have broad implications for land management,
environmental policy, ecosystem monitoring and conservation. Examples of such potential
include the utilisation of GBIF occurrence data towards prioritising areas and species for
conservation purposes (Alburquerque and Beier 2016, Milicic et al. 2017).

For many taxa, conservation assessments are conducted through the International Union
for Conservation of Nature’s (IUCN) Red List framework, which provides information about
species threat levels. The Red List also aims to monitor global trends in biodiversity and
inform policy-makers on the conservation of nature (IUCN 2012). Given the scarcity of data
on population numbers and trends for megadiverse taxa, these assessments are mostly
based on the geographical range of a taxon and how that range has changed over time.
Thus, a comprehensive, or at least representative, set of georeferenced occurrence data is
needed to assess the potential threat to a species.

Araneae represent one group of largely understudied and under-sampled organisms, still
lagging other taxa in terms of representative data in GBIF (Troudet et al. 2017). In addition
to this lack of occurrence data, additional knowledge gaps in the ability to conserve spiders
and other arthropods are apparent, including taxon-specific deficiencies in ecological and
biogeographic knowledge (Cardoso et al. 2011). With acknowledgement of this deficiency
in spider data, assessing the utility of our current knowledge base is essential for
promoting further digitisation and discovery of species, their natural history and eventual
conservation. In this study, we aim to test the utility of current GBIF occurrence data in the
extinction risk assessment of spiders using two large-scale assessments as examples.

Material and methods

Two extensive datasets were used to assess the applicability of GBIF occurrence data in
threat assessments. The first consists of a random selection of 200 species from the World
Spider Catalog (Natural History Museum Bern 2017), a global database of all recognised
species names for spiders. These were chosen for another study concerning a baseline
spider Sampled Red List Index - SRLI (Seppala et al. 2018a, Seppala et al. 2018b,

Current GBIF occurrence data demonstrates both promise and limitations ... 3

Seppala et al. 2018c, Seppala et al. 2018d). The SRLI is a methodological approach to
quantify global trends towards extinction of speciose taxa (Baillie et al. 2008, Lewis and
Senior 2011) and these data will contribute to its inception for spiders. Occurrence data for
these 200 species were collected from the taxonomic bibliography available at the same
database until the end of 2017. Data from publications found on Google Scholar and
several other online sources were also included in this dataset and detailed in Cardoso et
al. (2019). The use of literature as the baseline for these assessments follows established
IUCN procedures for Red List methodologies (IUCN 2012).

The second dataset was compiled for all 279 endemic spider species of the Iberian
Peninsula (Continental Portugal, Spain, Andorra and Gibraltar, plus the Balearic Islands),
collected from a bibliographic database on species occurrences in the region (Branco et al.
2019). This database includes all published citations until the end of 2018.

Geographic coordinates were obtained for each locality across both datasets using
literature sources and georeferenced locality data. To these data, we added ll
georeferenced records from GBIF of the same 200 (see original data references to GBIF in
Seppala et al. 2018a, Seppdla et al. 2018b, Seppala et al. 2018c, Seppala et al. 2018d)
plus 279 species (GBIF.org 2019). Non-georeferenced data were removed from the
analysis as they cannot be utilised in our spatial metric calculation, species lacking any
georeferenced data being designated as Data Deficient (DD). Our goal was to use GBIF
data without any further modification and/or annotation and, therefore, we did not
georeference records lacking coordinates. Coordinates obtained from GBIF were reviewed
alongside known species distributions to determine if dubious localities existed (e.g.
records of Iberian endemics occurring outside of their known ranges). We found no records
that warranted deletion from dubious localities.

Our analysis consisted of comparing IUCN classifications assigned to each species by
using the GBIF, literature and combined literature and GBIF datasets in an Extent of
Occurrence (EOO) calculation. EOO is defined as the area contained within the shortest
continuous imaginary boundary that can be drawn to encompass all records (IUCN 2012).
Note that to build a minimum convex polygon, at least three data points are needed,
otherwise the species was classified as Data Deficient. Particular EOO thresholds must be
met in order for a species to be considered Critically Endangered (CR, < 100 km?%),
Endangered (EN, < 5,000 km?), Vulnerable (VU, < 20,000 km?) or Near Threatened (NT, <
30,000 km?). Species with no calculated area are classified as Data Deficient (DD).
Although other criteria must be met for a full IUCN assessment, we did not consider them
here in the context of spatial occurrence data. EOO was calculated by using the R-
package “red” (v.1.4.0) (Cardoso 2018) in R version 3.6.0 (R Core Team 2019).

R scripts used for data retrieval and processing are available on GitHub (hittps://
github.com/vmshirey/spiders) where the dated version of this repository that corresponds
to this publication is December 2019. The literature datasets were contributed to GBIF and
consisted of 2,378 records for the global list and 30,141 records for all the Iberian taxa
(Cezon and Cardoso 2019, Cardoso et al. 2019).

4 Shirey V et al

Results
Global Spider Taxa

Using GBIF data alone, 17.5% of species from our global taxon list could be classified into
a hypothetical IUCN category. A total of 40.0% could be classified using literature data
alone and 45.5% could be classified using the combined GBIF and literature datasets
(Table 1). With the addition of GBIF data to the literature dataset, 6.5% of species shifted
their classification. A few species, in particular, suffered considerable downgrades in their
hypothetical classification, namely Myrmarachne bicolor (L. Koch, 1879) (VU to LC). This
change was due to an addition of 16 GBIF records to the literature dataset of 7 records.

Table 1.
Hypothetical IUCN Red List classifications for the global spider list.

Literature GBIF Combined
DD 120 165 109
CR 3 2 6
EN 10 3 10
VU 4 0 6
NT 3 0 3
Lc 60 30 66

Iberian Endemic Spider Taxa

Using GBIF data alone, 6.8% of lberian endemic species could be classified into a
hypothetical IUCN category. A total of 58.1% could be classified using literature data alone
and 59.9% could be classified using the combined GBIF and literature datasets (Table 2).
With the addition of GBIF data to the literature dataset, 4.7% of records shifted
Classification. A few species, in particular, suffered considerable downgrades in their
hypothetical classification, including Micrommata aragonensis Urones, 2004 and WM. aljibica
Urones, 2004 (both EN to LC). These changes were due to the addition of 1 and 2 GBIF
records to the literature dataset of 3 and 4 records, respectively.

Overall Summary

Overall, we found that, although few changes to hypothetical IUCN Red List classifications
occurred with the addition of GBIF data, some species (3.3%), which could previously not
be classified, could now be assessed with the addition of GBIF data. In addition, the
hypothetical classification changed for others (1.5%). On the other hand, GBIF data alone
did not provide enough data for 88.7% of species.

Current GBIF occurrence data demonstrates both promise and limitations ... 5

Table 2.
Hypothetical IUCN Red List classifications for Iberian endemics by data source.

Literature GBIF Combined
DD 117 260 112
CR 17 4 16
EN 53 7 55
VU 29 3 28
NT 5 0 7
LC 58 5 61

Discussion

The status of current GBIF data for extinction risk assessment of spiders shows both
promise and limitations. These results largely fall in line with prior exploration of GBIF data
in species conservation assessments, including the need for experts in taxonomy to review
the validity of records and taxonomic determinations (Hjarding et al. 2014). Recent
analyses of museum datasets have suggested that researchers take a critical lens to using
museum occurrence data, as taxonomic misidentification and spatial biases are known to
occur (Nekola et al. 2019). In addition, particular research disciplines may focus on
collecting and digitising specimens related to taxonomic work that could influence over-
and undersampling of particular species. These pitfalls are difficult to mitigate when
utilising online data without validation of species taxonomy or correct label transcriptions.
Thus, results presented using such data (and in particular, results in which a few records
drastically change results) should not be taken as absolute fact. Yet, in a few cases, GBIF
data might contribute more records without expanding species occurrence ranges if the
new records fall inside the polygon encompassed by the old ones (Beck et al. 2013).

Despite this, promising results in our study include the change of hypothetical EOO-based
classification amongst species listed as threatened across both species lists. Moreover,
any change of risk assessment classifications from Data Deficient (DD) is notable. These
changes provide initial assessments to previously DD taxa, which may add up to very large
proportions of assessments on many hyperdiverse groups, including spiders (Seppadla et
al. 2018d). Additionally, other researchers have focused on using GBIF data to partly
automate the process of Red Listing, including the calculation of spatial metrics (Bachman

et al. 2011, https://spbachman.shinyapps.io/rapidLC/).

Although such advancements should be noted, it is worth realising that just 6.5% and 4.7%
of the taxa in the global and Iberian datasets, respectively, change their hypothetical IUCN
Classifications. The low rates of observed classification shift could be an artifact of the
aforementioned data pitfalls for spiders in GBIF, which strengthens the argument for more

6 Shirey V etal

collection, observation and/or digitisation of data. Retrospective georeferencing of locality
data within GBIF will also serve to further enhance these metrics. Currently (as of
December 2019), 93% of GBIF records are georeferenced; however, coordinates are less
often available for certain groups, such as Araneae (88%).

Despite current limitations, we believe that there is potential for the use of GBIF occurrence
data in Red List assessments. Additional data sourced from GBIF will help refine IUCN
spatial metrics, in particular EOO, even when considering the currently identified pitfalls of
GBIF data. While these metrics should, in general, not be calculated with GBIF data alone,
it is important to consider GBIF as a source of additional information. Moreover, the
addition of more data from collections and community-based observations improves the
potential applicability of GBIF data in Red List classification assessments.

Acknowledgements

We thank Sergio Henriques, Mike Draney, Stefan Foord, Alastair Gibbons, Luz Gomez,
Sarah Kariko, Jagoba Malumbres-Olarte, Marc Milne and Cor Vink for providing data and
conducting the SRLI assessments that were the basis for the global study.

Funding program

VS was supported by the Fulbright Finland Foundation U.S. Student Program 2017-2018.

References

° Alburquerque F, Beier P (2016) Identifying and prioritizing sites for conservation planning.
Ecology and Evolution 6 (22): 8107-8114.

° Bachman S, Moat J, Hill A, de la Torre J, Scott B (2011) Supporting Red List threat
assessments with GeoCAT: geospatial conservation assessment tool. ZooKeys 150:
117-126. https://doi.org/10.3897/zookeys. 150.2109

° Baillie JM, Collen B, Amin R, Akcakaya HR, Butchart SM, Brummitt N, Meagher T, Ram M,
Hilton-Taylor C, Mace G (2008) Toward monitoring global biodiversity. Conservation Letters
1 (1): 18-26. https://doi.org/10.1111/j.1755-263x.2008.00009.x

° Beck J, Ballesteros-Mejia L, Nagel P, Kitching | (2013) Online solutions and the ‘Wallacean
shortfall’: wnat does GBIF contribute to our knowledge of species' ranges? Diversity and
Distributions 19 (8): 1043-1050. httos://doi.org/10.1111/ddi.12083

° Branco VV, Morano E, Cardoso P (2019) An update to the Iberian spider checklist
(Araneae). Zootaxa 4614 (2): 201-254. https://doi.org/10.11646/zootaxa.4614.2.1

° Cardoso P, Erwin T, Borges PV, New T (2011) The seven impediments in invertebrate
conservation and how to overcome them. Biological Conservation 144 (11): 2647-2655.

https://doi.org/10.1016/j.biocon.2011.07.024
° Cardoso P (2018) red: IUCN Redlisting Tools. 1.4.0. URL: httos://CRAN.R-project.org/

package=red

Current GBIF occurrence data demonstrates both promise and limitations ... 7

Cardoso P, Shirey V, Seppala S, Henriques S, Draney ML, Foord S, Gibbons AT, Gomez
LA, Kariko S, Malumbres-Olarte J, Milne M, Vink CJ (2019) Globally distributed
occurrences utilised in 200 spider species conservation profiles (Arachnida, Araneae).
Biodiversity Data Journal 7: €33264. https://doi.org/10.3897/BDJ.7.e33264

Cezon K, Cardoso P (2019) Morano and Cardoso: Aralb. Base de datos de aranas
ibéricas. Sociedad Entomoldgica Aragonesa. URL: https://doi.org/10.15468/d4unx4
Edwards J (2004) Research and Societal Benefits of the Global Biodiversity Information
Facility. BioScience 54 (6). https://doi.org/10.1641/0006-3568(2004)054

[0486:rasbot]2.0.co;2
GBIF.org (2019) GBIF Occurrence Download. https://doi.org/10.15468/dl.6fsft1. Accessed

on: 2019-12-04.

Hjarding A, Tolley K, Burgess N (2014) Red List assessments of East African chameleons:
a case study of why we need experts. Oryx 49 (4): 652-658. https://doi.org/10.1017/
$0030605313001427

IUCN (2012) IUCN Red List Categories And Criteria: Version 3.1. Second Edition. Zenodo
https://doi.org/10.5281/ZENODO.15039

Lewis OT, Senior MJM (2011) Assessing conservation status and trends for the world’s
butterflies: the Sampled Red List Index approach. In: Dover J, Warren M, Shreeve T (Eds)
Lepidoptera Conservation in a Changing World. Springer, 67-74 pp. https://
doi.org/10.1007/978-94-007-1442-7 8

Milici¢ M, Vuji¢ A, Jurca T, Cardoso P (2017) Designating conservation priorities for
Southeast European hoverflies (Diptera: Syrphidae) based on species distribution models
and species vulnerability. Insect Conservation and Diversity 10 (4): 354-366. https://
doi.org/10.1111/icad.12232

Natural History Museum Bern (2017) World Spider Catalog. http://wsc.nmbe.ch. Accessed
on: 2017-12-12.

Nekola J, Hutchins B, Schofield A, Najev B, Perez K (2019) Caveat consumptor notitia
museo: Let the museum data user beware. Global Ecology and Biogeography 28 (12):
1722-1734. https://doi.org/10.1111/geb.12995

R Core Team (2019) R: A language and environment for statistical computing. 3.6.0. R
Foundation for Statistical Computing. URL: httos:/Awww.R-project.org/

Seppala S, Henriques S, Draney M, Foord S, Gibbons A, Gomez L, Kariko S, Malumbres-
Olarte J, Milne M, Vink C, Cardoso P (2018a) Species conservation profiles of a random
sample of world spiders I: Agelenidae to Filistatidae . Biodiversity Data Journal 6 https://
doi.org/10.3897/bd)j.6.e23555

Seppala S, Henriques S, Draney ML, Foord S, Gibbons AT, Gomez LA, Kariko S,
Malumbres-Olarte J, Milne M, Vink CJ, Cardoso P (2018b) Species conservation profiles of
a random sample of world spiders II: Gnaphosidae to Nemesiidae. Biodiversity data journal
6: €26203. https://doi.org/10.3897/BDJ.6.e26203

Seppala S, Henriques S, Draney ML, Foord S, Gibbons AT, Gomez LA, Kariko S,
Malumbres-Olarte J, Milne M, Vink CJ, Cardoso P (2018c) Species conservation profiles of
a random sample of world spiders III: Oecobiidae to Salticidae. Biodiversity Data Journal 6:
e27004. https://doi.org/10.3897/BDJ.6.e27004

Seppala S, Henriques S, Draney ML, Foord S, Gibbons AT, Gomez LA, Kariko S,
Malumbres-Olarte J, Milne M, Vink CJ, Cardoso P (2018d) Species conservation profiles of
a random sample of world spiders IV: Scytodidae to Zoropsidae. Biodiversity Data Journal

6: e30842. https://doi.org/10.3897/BDJ.6.e30842

Shirey V etal

° Troudet J, Grandcolas P, Blin A, Vignes-Lebbe R, Legendre F (2017) Taxonomic bias in
biodiversity data and societal preferences. Scientific Reports 7 (1). httos://doi.org/10.1038/
$41598-017-09084-6