Content assessment of the primary biodiversity data published through GBIF network: Status, Challenges and Potentials


Biodiversity Informatics, 8, 2013, pp. 94-172 

 

CONTENT ASSESSMENT OF THE PRIMARY BIODIVERSITY DATA 

PUBLISHED THROUGH GBIF NETWORK: STATUS, CHALLENGES AND 

POTENTIALS 

 

SAMY GAIJI 
(1)

*, VISHWAS CHAVAN 
(1)

, ARTURO H. ARIÑO 
(2)

, JAVIER OTEGUI 
(2)

, DONALD 

HOBERN 
(1)

, RAJESH SOOD 
(1), 

ESTRELLA ROBLES 
(2)

 

(1) Global Biodiversity Information Facility Secretariat, Universitetsparken 15, DK-2100, 

Copenhagen, Denmark 

(2) University of Navarra, Pamplona, Spain 

*Corresponding author, Email: sgaiji@gbif.org 

 

Abstract —With the establishment of the Global Biodiversity Information Facility (GBIF) in 2001 as an 

inter-governmental coordinating body, concerted efforts have been made during the past decade to establish a 

global research infrastructure to facilitate the publishing, discovery, and access to primary biodiversity data. 

The participants in GBIF have enabled the access to over 377 million records of such data as of August 2012. 

This is a remarkable achievement involving efforts at national, regional and global levels in multiple areas 

such as data digitization, standardization and exchange protocols. However concerns about the quality and 

‘fitness for use’ of the data mobilized in particular for the scientific communities have grown over the years 

and must now be carefully considered in future developments. This paper is the first comprehensive 

assessment of the content mobilised so far through GBIF, as well as a reflexion on possible strategies to 

improve its ‘fitness for use’. The methodology builds on complementary approaches adopted by the GBIF 

Secretariat and the University of Navarra for the development of comprehensive content assessment 

methodologies. The outcome of this collaborative research demonstrates the immense value of the GBIF 

mobilized data and its potential for the scientific communities. Recommendations are provided to the GBIF 

community to improve the quality of the data published as well as priorities for future data mobilization. 

Keywords— Primary Biodiversity Data, Content Assessment, and Gap Analysis. 

INTRODUCTION 

Free and open access to primary biodiversity 

data is essential both to enable effective decision-

making and to empower those concerned with the 

conservation of biodiversity and the natural world 

(Bisby, 2000; Gaikwad and Chavan, 2005; GBIF, 

2008). However, the history of publishing of 

primary biodiversity data is very recent. With the 

establishment of the Global Biodiversity 

Information Facility (GBIF) in 2001, concerted 

efforts to publish primary biodiversity data using 

community driven and agreed standards and tools 

gained momentum. GBIF was created to facilitate 

free and open access to biodiversity data 

worldwide, via the Internet, to underpin scientific 

research, conservation and sustainable 

development. The GBIF network, through its data 

portal (http://data.gbif.org), already facilitates 

access to over 377 million records from more than 

400 data publishers
1
. The progress achieved in 

GBIF’s first decade indicates that the development 

of a global informatics infrastructure, facilitating 

free and open access to biodiversity data, is indeed 

a realistic aspiration. One of the key future 

challenges for GBIF is now to ensure that such 

volume of knowledge about biodiversity on earth 

is indeed of high relevance for the scientific 

communities. 

                                                      
1
 As of August 2012. 



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95 

Why assess the content of GBIF-mobilised data? 

Despite GBIF’s achievements, questions are 

frequently raised about whether it can yet be 

considered a global facility (Yesson et al., 2007), 

and about the usefulness of the data mobilised. 

GBIF has been criticised for the taxonomic, 

thematic, geospatial as well as temporal biases in 

the data mobilised by its network of data 

publishers (Johnson, 2007). There have been 

isolated studies to assess gaps, quality and fitness 

for use of GBIF-mobilised data (e.g. Guralnick et 

al., 2007; Collen et al., 2008; GBIF, 2010a). In 

2010, an initial overview of the data published 

through the GBIF network (GBIF, 2010b) 

provided a first set of indicators on the content 

mobilized so far as well as major bias such as in 

the taxonomy and temporal areas. Recognising 

this, the GBIF-constituted Content Needs 

Assessment Task Group (CNATG) recommended 

that assessment of GBIF-mobilised content at 

various levels (global, regional, national and 

thematic) is crucial for determining the demand-

driven approach for data mobilisation (Faith et. al., 

2013, 2013). In 2011, in response to these 

recommendations, a series of improvements to the 

GBIF infrastructure were made such as the rework 

of the GBIF ‘backbone taxonomy’ with up-to-date 

checklists and taxonomic catalogues such as the 

Catalogue of Life 2011
2
. Other improvements such 

as the automated interpretation of the coordinates, 

country location and scientific names used in 

published records have been improved to screen 

out inaccuracies – for example, ensuring that 

records identified as coming from a particular 

country are shown as occurring within the borders 

and territorial waters of that country. The current 

study attempts to assess the gaps and fitness for 

use of the GBIF-mobilised data. It aims to provide 

a comprehensive overview of the ‘state of the 

                                                      
2
 Ruggiero M., Gordon D., Bailly N., Kirk P., Nicolson D. (2009). 

The Catalogue of Life Taxonomic Classification, Edition 2, Part A. In: 

Species 2000 & ITIS Catalogue of Life, 3rd February 2012 (Bisby 
F.A., Roskov Y.R., Culham A., Orrell T.M., Nicolson D., Paglinawan 

L.E., Bailly N., Appeltans W., Kirk P.M., Bourgoin T., Baillargeon 

G., Ouvrard D., eds). DVD; Species 2000: Reading, UK. 

network’ for data published through the GBIF 

network in 2012. Such assessment is aimed at 

demonstrating the value of the content mobilised 

and how it can contribute to our improved 

understanding of biodiversity in particular by the 

scientific community. 

To achieve this objective and taking into 

account the large volume of information to be 

analysed, the authors of this study have adopted 

two complementary methodologies. One approach 

led by the GBIF Secretariat (GBIFS) focused on 

two temporal complete studies (December 2010 

and February 2012) while the Department of 

Zoology and Ecology at the University of Navarra 

(UNZYEC) focused on processing random 

samples of the full content. The research outputs of 

these two studies were compared and 

complemented each other.  

The outcomes of these two complementary 

exercises are presented in three categories: (a) data 

quality assessment, (b) trends/patterns assessment, 

and (c) fitness-for-use assessment. 

Data flow of the GBIF network 

As of August 2012, the GBIF network is 

comprised of 419 data publishers from 44 

countries and 15 international organisations. 

Together they publish through GBIF 10,028 

occurrence based data resources (or datasets). 

Figure 1 depicts the typical flow of the data 

publishing processes through the GBIF network. 

Data publishers can use a variety of tools and 

protocols (e.g. DiGIR
3
, BioCASE

4
, Tapir

5
, GBIF 

Integrated Publishing Toolkit
6
) and data standards 

(e.g. DwC
7
 and ABCD

8
) in order to publish 

primary occurrence records to GBIF. After 

successful registration of their resources through 

the central registry, GBIF centrally indexes a 

limited but essential number of core data elements 

                                                      
3 http://www.digir.net/ 
4 http://www.biocase.org/ 
5 http://wiki.tdwg.org/TAPIR/ 
6 http://www.gbif.org/orc/?doc_id=2935 
7 http://rs.tdwg.org/dwc/index.htm 
8 http://www.tdwg.org/standards/115/ 



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96 

detailing the ‘what’ (species), ‘when’ (date/time), 

‘where’ (location), “with what evidence” (basis of 

record) and ‘by whom’ (collector/observer) of the 

primary biodiversity data published by the GBIF 

network (also called GBIF-mediated data). The list 

of core data elements (Table 1) follows a common 

data standard: the Darwin Core standard
9
. This 

data standard has been used for the discovery of 

the vast majority of specimen occurrence and 

observational records published through the GBIF 

network. The Darwin Core standard was originally 

conceived to facilitate the discovery, retrieval, and 

integration of information about modern biological 

specimens, their spatio-temporal occurrence, and 

their supporting evidence housed in collections 

(physical or digital). These elements are compiled 

into a central database (also called GBIF Index) 

and their discovery and access is enabled through 

the GBIF data portal (http://data.gbif.org) as well 

as through web services 

(http://data.gbif.org/tutorial/services). Such a 

global discovery system is aimed at promoting 

access to the original information sources owned 

by each single publisher participating in the GBIF 

network, where more information can be found 

(e.g. media, richer data etc.). 

While all data publishers are expected to 

follow common standards (e.g. DwC), their data 

resources discoverable through the GBIF 

infrastructure have varying precision and quality. 

This could be explained by incomplete information 

at the publisher level, errors during the publishing 

processes (e.g. formatting of date information) as 

well as errors during the central GBIF harvesting 

and indexing procedures.  

In order to assess the content mobilised 

through the GBIF network, this study will focus on 

using the content of the GBIF Index as a proxy to 

the information published by the contributing 

publishers.  

                                                      
9 http://rs.tdwg.org/dwc/index.htm 

CONTENT ASSESSMENT OF GBIF-MOBILISED 

DATA 

Methodology 

In the last two decades, the informatics field 

has evolved to a stage where the handling of very 

large volume of data is becoming the central 

component of data discovery
10

. The capacity to 

store, manage and analyse a large volume of data is 

becoming a fundamental requirements in the field 

of Biodiversity Informatics and in particular for 

infrastructures like GBIF
11

. Today, technologies 

like Hadoop
12

 and Hive
13

 offer the ability to 

process such huge volumes of information on 

certain kinds of distributable problems using a 

large number of computers. 

The assessment carried out by GBIFS used this 

new technology to process and analyse the full 

GBIF Index is depicted in Figure 2. The full GBIF 

Index was extracted in the form of Hive tables in 

December 2010 and February 2012. All outputs of 

the data-mining processes were stored in MySQL 

tables for easy processing and visualisation. The 

results of these analyses were kept so that in the 

future similar experiments could be repeated and 

compared temporally.  

The Hadoop/Hive technology allowed the 

processing and analysis of the full GBIF Index in a 

reasonable amount of time compared to 

conventional technologies like relational database 

using known database management systems like 

MySQL. However such methodology requires a 

dedicated infrastructure with sufficient IT expertise 

and understanding of the processes involved in 

manipulating such large volume of information at 

once.  

UNZYEC used two separate approaches in 

their assessment (Figure 3). In one, a random 

sample of the GBIF Index was obtained by issuing 

                                                      
10 Jiawei Han and Jing Gao, “Research Challenges for Data Mining in 
Science and Engineering", in H. Kargupta, et al., (eds.), Next 

Generation of Data Mining, Chapman & Hall/CRC, 2009, pp. 3-28. 
11 http://www.gbif.org/communications/news-and-
events/showsingle/article/important-quality-boost-for-gbif-data-portal/ 
12 http://hadoop.apache.org/ 
13 http://hive.apache.org/ 



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97 

an automated set of queries through the portal’s 

web services
14

. This approach mimics an 

ecological sampling where a vast amount of data is 

represented by a subset, thus greatly reducing the 

data processing requirements. In another approach, 

mirrors of both the GBIF Index and the raw data 

harvested from the participants were queried using 

standard SQL statements and scripts. Although 

much more taxing in terms of resources, this 

approach enabled the authors to finely track the 

flow of information (not just data) from the 

publishers to the index. In this way, gaps caused by 

the data processing flow can be detected. 

The UNZYEC team made queries and 

samplings during a three-year period, over ten 

versions of the GBIF Index. However, for the 

purpose of this assessment, analyses were made 

mostly on the November, 2010-released mirror, in 

order to provide an independent comparison of 

GBIFS-obtained results. 

Limitations of the methodologies 

The methodology used in this article enables 

the fast data mining of the GBIF data index but 

does not address issues such as: 

- The level of accuracy of the data (e.g. 

precision in geospatial coordinates). 

- The risk of misidentification of taxa. 

- Duplicate records that can arise from: 

i. Datasets being unwittingly published 

repeatedly, 

ii. Duplicate records within and between 

datasets, 

iii. Multiple digital records derived from the 

same physical specimen, such as a 

specimen being physically split and 

stored in multiple museums. 

-  Computing interpretation errors in the data 

harvesting and indexing routines. 

                                                      
14 http://data.gbif.org/tutorial/services 

For example, depending on the data schema 

used (Darwin Core or ABCD) and their versions, 

an occurrence date may be represented as a date-

time stamp, an ISO-formatted date, a simple text 

string in varying formats, or composed of 

individual fields (day, month, year). The mapping 

of the data by the publisher may therefore 

introduce additional error or ambiguity, if for 

example month and day are swapped. In order to 

overcome this difficulty, we assumed the level of 

error of the year within a malformed date-time 

stamp as sufficiently low to be considered as a 

good proxy to assess the temporal dimension.  

With regards to the conversion and validation 

of taxonomical information (e.g. genus, species, 

scientific names) the challenges are more complex. 

During the harvesting and indexing procedures, the 

taxonomical information is checked against the 

most up-to-date GBIF taxonomical backbone. 

Until end 2011, GBIF used the Catalogue of life 

(CoL) 2007 as its core taxonomical backbone and 

when unmatched names were identified during the 

harvesting/indexing procedures they were simply 

added to the backbone. In November 2011, GBIF 

has entirely refreshed its taxonomical backbone 

and uses now primarily the latest version of the 

Catalogue of life in addition to other resources 

(Table 2). Today, unmatched names are not added 

to the core backbone and whenever possible, 

expert taxonomists are consulted. Therefore the 

study undertaken in terms of taxonomical 

comparison (in 2010 and 2012) should be 

undertaken taking into account this particular bias 

due to the improvement of the GBIF taxonomical 

backbone and resolution services.  

Material 

For the purpose of this study, elements 

covering three dimensions (“what”, “where” and 

“when”) were extracted from the GBIF Index by 

GBIFS and UNZYEC in December 2010, and also 

from raw data as supplied by the providers by 

UNZYEC for some specific analysis. Further 

analyses using the February version of the GBIF 

Index were undertaken by GBIFS.  



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98 

The elements covered in these analyses are: 

Source of the data: The assessment has taken 

into account the identifiers of the data publisher 

and data resources. However, due to 

incompleteness and lack of accuracy of entries in 

the institution ID, collection ID and catalogue 

fields in the GBIF Index, we have decided to 

exclude these fields from the analysis.  

Taxonomic data: Taxonomic ranks such as 

Kingdom, Phylum, Class, Family, Genus and 

Species are included. The assessments have also 

taken into account the synonyms as recorded in the 

GBIF Index, in order to provide the most accurate 

estimate of the number of species. Data from 

multiple synonyms get merged during the 

harvesting and indexing routines.  

Geospatial data: Latitude and longitude 

information was used when available. However, 

due to scarce information provided by data 

publishers, it was not possible to consider 

precision. This is a serious limitation that will need 

to be addressed in future analysis. 

Temporal data:  Limited to the field year of 

observation/collection. The assessments ignored 

the day and month recorded in the date field, 

except for analysing possible causes of year mis-

assignment. 

Other data: The basis of records, a descriptive 

term indicating whether the record represents an 

object or observation, was included in the analysis. 

The basis of record actually contains useful 

information such as the level of evidence and other 

categories that may be considered enhanced 

subclasses of information. 

Results of the content assessment of the GBIF-

mobilised data 

We present the salient outcomes of these two 

independent exercises in four categories, namely: 

(a) data quality, (b) trends/patterns and (c) fitness-

for-use assessments. In most cases, both exercises 

reached similar conclusions and therefore validate 

each other. In some instances, significant 

differences arose and were assessed. 

A. Data Quality Assessment: 

Taxonomy: 

Until November 2011, the processing of 

taxonomical references was made against some 

taxonomical references such as the checklist of 

Catalogue of Life 2007 

(http://www.catalogueoflife.org/annual-

checklist/2007/) or the International Plant Names 

Index (http://www.ipni.org). During the discovery 

of unmatched taxonomical references against the 

accumulated GBIF taxonomical backbone, these 

are automatically added. Therefore, the 2010 GBIF 

taxonomical backbone contained accepted names 

(e.g. from CoL 2007) and new names discovered 

during the indexing process. This also means that 

in our December 2010 assessment, we had limited 

capacity to distinguish between authoritative 

names (e.g. referring to Catalogue of Life 2007 

version) and added names, which had no validation 

against any taxonomical reference. In November 

2011, the GBIF taxonomical backbone was rebuilt 

using primarily the latest version of the Catalogue 

of Life as well as many new taxonomical 

authoritative references (Table 2).  Therefore the 

February 2012 assessment on taxonomical names 

can be considered as much more accurate.  

Matching against the Catalogue of Life 

Using a less advanced interpretation 

techniques developed in 2006 by the GBIFS, the 

backbone taxonomy that covers the occurrence 

records has 1,946,429 concepts at species or lower 

ranks, of which 458,716 (24%) is provided by the 

Catalogue of Life 2007 Annual Checklist
15

. A 

more recent study made in December 2010
16

 

showed that 52 per cent of the distinct canonical 

names found in the GBIF Index matched to a name 

in the CoL 2010 using straight, case insensitive 

matches. This can be slightly increased to 54% if a 

‘fuzzy’ matching with a maximum difference of 

10% in characters is used. In February 2012, a 

                                                      
15 GBIFS personal communication (March 2011) 
16 http://code.google.com/p/gbif-

occurrencestore/wiki/TaxonomicIntegration 

http://www.catalogueoflife.org/annual-checklist/2007/
http://www.catalogueoflife.org/annual-checklist/2007/


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99 

similar study (Table 3) showed than 53.47% of 

names were straight, case insensitive matched of 

the canonical names in the Catalogue of Life 2011 

Annual Checklist.  

Completeness of the taxonomical classification 

In order to study the completeness of 

taxonomical classification in the GBIF Index, we 

assessed for each rank (kingdom, phylum, class, 

order, family, genus and species) the valid 

references generated after the harvesting and 

indexing routines. The level of completeness is 

therefore based on valid taxonomical references 

within the GBIF taxonomical backbone. In cases 

where for example a family name wasn’t mapped 

correctly, a ‘null’ value is assigned to this field in 

the published occurrence record. For each rank, we 

evaluated the number of occurrences and species 

(or lower taxa) having incomplete or unknown 

taxonomical status – or ‘null’ values (e.g. counting 

all occurrences having an `unknown` status for the 

kingdom rank).  Table 4.a provides a summary of 

our findings in December 2010 and Table 4.b the 

summary for February 2012.  

In 2010, a total of 114,721 species or lower 

taxa corresponding to 15 million occurrences 

representing 5.6% of the GBIF Index were not 

‘mapped’ against the GBIF taxonomical backbone 

at the kingdom level. Similar trends are observed 

for other taxonomical ranks with somehow a 

variation in amplitude of incompleteness (e.g. 

14.5% for species and lower taxa at the family 

level and 7.4% at the species level). This analysis 

confirmed similar results obtained in 2008 and 

2010 (GBIF, 2010b and Ariño and Otegui, 2008). 

However some of the correctly matched names 

against the GBIF taxonomy backbone may not be 

valid names if referred to authoritative references 

such as Catalogue of Life. The reasons being that 

some of these names if not matched to the existing 

GBIF taxonomy backbone during the harvesting 

and indexing processes were simply added as valid 

references. The mixing of valid taxonomical 

references with new unverified references with 

limited capacity to track such changes over time 

caused serious difficulties to our study. The 

assessment summarized in Table 4.a provides 

therefore more a status of incompleteness of the 

taxonomical backbone rather than a real 

comparison to any authoritative taxonomical 

references.  

In December 2010, our preliminary findings 

suggested the need for an urgent review of the 

GBIF taxonomical backbone in particular against 

the most critical taxonomical authorities such as 

the annual checklist Catalogue of Life 2010 

(http://www.catalogueoflife.org/) and other sources 

such as the Interim Register of Marine and 

Nonmarine Genera (IRMNG). The decision not to 

mix unverified names with existing authoritative 

names was critical. In November 2011, GBIFS 

successfully upgraded its taxonomical backbone 

against the latest version of the Catalogue of Life 

(2011) and other authoritative references.  

This resulted in our February 2012 study in a 

more accurate assessment of the taxonomical gaps 

within the GBIF Index. The results of this analysis 

are presented in Table 4.b. The percentages of 

incompleteness observed in 2012 were 

significantly lower (i.e. 0,35%, 1.81%, 2.82%, 

2.17% respectively at the Kingdom, Class, Family 

and Genus levels) than the once observed in 

December 2010 (i.e. 7.0%, 14,5%, 14,5% and 

4.7% respectively at the Kingdom, Class, Family 

and Genus levels) with the exception of the species 

rank. Similar trends are observed taking into 

account occurrences. Therefore a high number of 

unmapped taxonomical ranks from Kingdom to 

Genus levels were resolved using the upgraded 

GBIF taxonomical backbone. The higher number 

of taxonomical references used to construct the 

GBIF taxonomic backbone largely explains this. 

The observed percentages of unresolved names at 

the species level represents 9.15% in 2012 while in 

2010 this percentage was of 7.4%. However these 

numbers can’t be compared because of the changes 

in the taxonomical backbone between these dates.  

Taking into account these improvements in 

taxonomical name resolution, we have tried to 

assess the additional data quality improvements 



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100 

that could be undertaken. To achieve this, we have 

looked at the  top 10 possible misidentifications (at 

the kingdom level) by number of occurrences 

(Table 5). The three species within the genus 

Zonotrichia listed as within the plantae kingdom 

are wrongly assigned. These species belong to the 

American sparrows group of the family 

Emberizidae
17

. This misidentification is due to the 

generic homonym Zonotrichia being both present 

in the Plantae and Animalia Kingdom. This 

misidentification is being resolved in the GBIF 

taxonomical backbone and these obvious 

misidentifications progressively corrected
18

. For 

the other cases listed in Table 5, the discrepancy 

with CoL 2011 version is resolved in the latest 

version of the CoL (February 2012) or other 

taxonomical authorities (i.e. Marine Species 

Identification Portal). Once these changes are 

implemented we estimate that 1,808,488 

occurrences would be correctly mapped and the 

total of occurrences with ‘unknown’ status at the 

species level would decrease from 25,343,834 to 

23,535,346. This shows that while the GBIF Index 

has grown from 267 to 324 million occurrences 

(+21.3%) from December 2010 to February 2012, 

corrections on the top 10 species misidentifications 

in February 2012 would have resolved a 

substantive volume of the GBIF Index: the growth 

in occurrences with ‘unknown’ status at the species 

rank would have grown of only 2.3% (from 

23,015,905 to 23,535,346).  

It is therefore reasonable to extrapolate that: a 

large portion of the gaps identified in Table 4.b 

will in the future be resolved with newest versions 

of the taxonomical authorities used to build the 

GBIF taxonomic backbone. The rate of resolved 

names should in principle directly be correlated 

with the growth in volume of the taxonomic 

authoritative references used by GBIF.  

Table 6.a provides a summary of the 

taxonomical misidentification at the Kingdom 

level and an indication of the total number of 

associated occurrences affected. For example, 

                                                      
17 http://en.wikipedia.org/wiki/Zonotrichia 
18 http://dev.gbif.org/issues/browse/CLB-119 

correcting the wrong assignment of 90 species 

from the Kingdom Plantae to Animalia will impact 

more than 1.3 million occurrences within the GBIF 

Index as of February 2012. On the other hand 

correction of the wrong assignments to Animalia 

of 26 species will only affect 1,536 occurrences. 

Similar breakdowns are provided for Phylum 

(Table 6.b) and Class (Table 6.c). This table shows 

that the effort in correcting misidentifications at a 

high taxonomical rank (e.g. Kingdom) will impact 

a limited number of occurrences (1.3 million 

representing less than 0.5% of the GBIF Index)  

Only 9.15% of the discovered scientific names 

in the GBIF network have not been mapped to a 

taxonomic reference at the species level. Such 

volume of unknown references includes for 

example species not yet endorsed by existing 

authoritative references used to construct the GBIF 

taxonomy backbone, as well as misidentified or 

wrongly spelled names. This represents 7.82% of 

the GBIF Index in terms of volume of occurrences 

(i.e. 25.3 million occurrences). We have also 

demonstrated that this volume of unmapped 

scientific names has grown less than the growth of 

the GBIF Index: +9.9% (25.3 million in 2012 

against 23 million in 2010) while the GBIF Index 

has grown in the same period of +21% (267 

million occurrences in 2010 and 323 million in 

2012). The study also demonstrated that compared 

to the largest authoritative reference - the 

Catalogue of Life (CoL) – only half (53.47%) of 

the species names known to GBIF would have 

been recognized. The other half are mostly names 

known to other taxonomical references but 

unknown to CoL.  

Geospatial: 

During the harvesting and indexing routines, 

these geo-referenced occurrences are checked in 

particular for wrong assignments (e.g. when the 

latitude and longitude information is not 

corresponding to the country where the occurrence 

was observed/collected). In the context of this 

study, we considered geo-referenced occurrences 

as a record in the GBIF Index with the latitude and 



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101 

longitude within the earth-bounding box (i.e. -

90<=latitude<=90 and -180<=longitude<=180). 

This amounted to 99.67% of all occurrence records 

where geo-spatial information was provided in the 

GBIF Index in December 2010- the remainder 

being ‘extra-terrestrial’ (Otegui et al., 2009). This 

includes a substantial number of records being 

reported as 0.0N, 0.0E and therefore suspicious. 

This could happen for example when the publisher 

maps a zero value to the latitude or longitude fields 

instead of a ‘null’ value. In order to solve such 

problems, publishers should in addition to ensuring 

that the mapping of the fields is appropriate, 

provide for example the country in which the 

observation/collection has occurred. This would 

greatly facilitate the validation of geo-referenced 

occurrences during the harvesting and indexing 

routines.  

In end of 2010, 18.45% of the mobilised data 

were not geo-referenced. This percentage was 

lower (14.1%) in our assessment of February 2012. 

As shown in Figure 4, the rate of geo-referenced 

records is increasing over time. Such rate is higher 

for recent years of collection/observation (e.g. 

from 1973 the rate is constantly greater than 80% 

for the February 2012 assessment). For older 

occurrences the rate of geo-referencing is 

decreasing substantially. For example, before 

1930, the rate of geo-referencing was largely lower 

than 50% and this can be explained by technology 

limitations (e.g. absence of GPS), absence or 

limited data collection standards covering geo-

location (e.g. latitude/longitude, or location fields 

in collection forms) or simply due to the absence 

of such information in the collection forms. 

During the harvesting and indexing 

procedures, a series of verifications on geospatial 

fields (e.g. latitude, longitude, country boundaries 

etc.) are performed enabling for example 

identification of potential latitude/longitude 

incorrect assignments. This can be the case for 

occurrences where longitude and latitude values 

were swapped; or simply when the longitude value 

was incorrectly assigned causing for example 

occurrences originally collected/observed in North 

America to appear on the Asian continent. In 

February 2012, we estimated that less than 3.6% of 

the total georeferenced occurrences are falling in 

this category. In addition we estimated that 

occurrences without latitude and longitude 

information but with information for the locality 

represent 11.1% of the total GBIF Index. Taking 

into consideration that GBIFS is not mandated to 

apply corrections to the original published 

occurrence records, these records with possibly 

wrong coordinates are therefore only flagged 

during the harvesting and indexing routines. These 

occurrences aren’t displayed on maps through the 

GBIF data portal but the original occurrences 

records are kept intact. Users of the GBIF Index 

(e.g. scientists) should be aware of this limitation 

and ensure that they consult the ‘geospatial issue’ 

flag provided by the GBIFS. While this addresses 

partly the problem, it is important to note that the 

verification and correction of the original 

occurrences records lies with the publishers. The 

availability of better guidelines
19

 and practices in 

recording biodiversity observations/specimens 

should support publishers in this effort. In addition, 

the use of tools like BioGeomancer and 

GEOLocate should be recommended and more 

widely promoted. Such tools parse place name 

descriptions in multiple languages and provide in 

return a set of longitude/latitude coordinates 

associated with that description. The data curators 

can therefore enrich their database content, 

increase the quality and accuracy of the content 

mobilised through GBIF and thus makes it suitable 

for wider uses.    

The high percentage of georeferenced records 

within the GBIF Index as well as the observed 

positive improvements in our two assessments is 

an important quality stamp of the GBIF mobilised 

data.  

The study showed that the rate of geo-

referencing in the GBIF-Index is increasing over 

time due mostly to better data quality checking 

                                                      
19 Principles of Data Quality - Arthur Chapman 

http://www.gbif.org/orc/?doc_id=1229 



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102 

activities both at the publisher and central levels 

(Figure 4). The improvements are observed for all 

decades since 1900. However, the variability is 

very high and older occurrences are expected to 

have a lower probability of valid geospatial 

information (e.g. prior to 1930: <50%, 1930 – 

1960: <70%, 1980-2010: >80%). More 

importantly, our study shows that the percentage of 

potential wrong geo-spatial records is very low 

(<3.6%). In most cases, such situation can be 

explained by wrong latitude or longitude sign 

assignments and these can be easily resolved by 

swapping coordinates.  

Temporal: 

As detailed in Table 7, 30.8% of the GBIF 

Index contains records with null or not valid year 

in the date time stamp field. The UNZYEC 

analysis (Table 8) estimated a similar percentage 

(31%) although it distinguished invalid years (i.e. 

before 1750 or in the future). The breakdown 

provided in this analysis shows that 4.3% had not 

valid date stamp data and 26.7% had no data or 

null values. However, the comparison between raw 

data and processed data uncovered some issues on 

date processing, such as mismatches between the 

published and interpreted date stamp. For example, 

8.6% of the records with a value in the date stamp 

field were nullified during the harvesting and 

indexing procedures. In addition, 5.0% of the null 

values in the publisher data were converted to valid 

date stamp values after harvesting and indexing. 

More details about this mismatch can be found in 

Otegui & al., 2013 (this volume). Thus, according 

to the UNZYEC study (Table 8), 36.1% of the 

records would be either undated or doubtfully 

dated at the year level. These analyses show that a 

large volume of date stamp information does not 

convert to a valid date stamp, or at least lacks 

information about the year of 

collection/observation. In 2011, these preliminary 

findings were taken into account by the GBIFS and 

existing processes to interpret date stamp at the 

publisher level were reviewed and improved. Table 

7 shows also the comparison between the 

assessment made in December 2010 and February 

2012. While during this period of time, the GBIF 

Index has grown by 21% in total (respectively 267 

in December 2010 to 324 million occurrences in 

February 2012), the total number of occurrences 

with no year provided in the final GBIF Index has 

decreased by 47.9%. This amounted to 13.3% of 

the total GBIF Index compared to 30.8% in 

December 2010. Most of these improvements 

relate to improved interpretation of malformed data 

stamp information in the published resources 

during the harvesting and indexing routines.  

Temporal information is useful for two classes 

of questions: 1) biogeographic changes over time 

and 2) phenological. The year information is the 

most important element within temporal date 

stamp information to note long-term changes. 

However, the month and day elements provide 

additional accuracy in particular when looking at 

migratory species moving for example from 

feeding to reproduction areas during the same year. 

Partial date, as found on many older specimens 

may be useful for one or the other of these 

purposes even if they cannot serve all needs. Such 

gaps in the temporal attributes are a limitation for 

certain types of analysis, such as population cycles 

or changes in migration patterns related to climate 

change.  

Alone, the low percentage of occurrence 

records without temporal information (13.2%) is 

not considered as a major limitation. However, 

combined with other parameters like geo-

referencing, it could become a serious limitation 

for scientists in particular when dealing with 

analyses requiring the combination of these (e.g. 

ecological niche modelling). As shown in Table 9, 

if we consider only presence of valid temporal and 

geospatial information as determinants of ‘fitness 

for use’ in the context of ecological niche 

modelling analysis, 78.8% of the GBIF Index is 

meeting these criteria. This total represents 

484,963 (48.6%) species from the total identified 

in the full GBIF Index of 995.974 species as of 

February 2012. But this also indicates that 51.4% 

of the species recorded in the GBIF Index don’t 



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103 

have a single information on the 

temporal*geospatial dimensions.  

Background ‘noise’: 

In December 2010, we estimated that 121.7 

million records had missing, doubtful or wrong 

information in at least one of the three key 

attributes (i.e. taxonomy, georeferencing and 

temporal). This represented 45.6% of the GBIF-

mobilised data records (267 million records). 

Although this was an improved figure compared to 

the 50.1% calculated in May 2008, it calls for 

concerted efforts firstly to sensitise data publishers 

of the need to ensure that all available temporal, 

taxonomical and geospatial information are 

correctly mapped during the publishing process to 

GBIF. In 2011, GBIFS had greatly improved its 

harvesting and indexing processes in order to 

optimize its ability to interpret as accurately as 

possible the information of publishers. In February 

2012, the taxonomical backbone was greatly 

improved and the indexing processes fine-tuned. 

This has led to a lower percentage (21.3%) of the 

GBIF Index with absence of information in at least 

one of the three variables: temporal, taxonomical 

and geospatial. While these data quality trends are 

promising (Figure 5), they are mostly due to 

technical improvements in the GBIF IT 

infrastructure and much more efforts are required 

at the level of the data publishers within the GBIF 

community.  

Collection curators should be encouraged to 

explore ways to improve the quality of the 

published information in particular for three 

dimensions, namely: taxonomical, temporal and 

geospatial. Many tools are aimed at helping 

curators to identify possible errors and to 

standardise data in accordance with authoritative 

references. Some key examples are: 

 SpeciesLink developed by CRIA (Centro de 

Referência em Informação Ambiental) 

available at: http://splink.cria.org.br/ 

 BioGeomancer coordinated by the 

University of California at Berkeley  

(http://www.biogeomancer.org) 

 DIVA-GIS developed by Robert Hijmans 

(http://www.diva-gis.org/) 

 BIDDSAT developed at UNZYEC (Otegui 

& Ariño, 2012) available at: 

http://www.unav.es/unzyec/mzna/biddsat/ 

Duplicates 

Concerns about the amount of record 

duplicates in the GBIF Index were also raised over 

recent years (Hobern, 2003; Page, 2012). Such 

situation could happen for example when the same 

dataset is published more than one time through 

GBIF. Comparing datasets on criteria like 

taxonomy, temporal and geospatial information 

can easily identify these cases. To assess these 

cases, we assumed that a duplicate record would be 

identified when the values respectively for 

taxonomical (species id), temporal (timestamp date 

e.g. YYYYMMDD) and geospatial (latitude and 

longitude) are identical. Based on this assumption, 

we calculated in February 2012 the total amount of 

potential duplicates between resources. The results 

are summarized in Table 10. We have identified 42 

combinations of datasets with at least 100,000 

potential duplicate occurrences representing a total 

of more than 30 million occurrences. This 

represents more than 9.5% of the GBIF Index.  The 

top 20 potential duplicate combinations are listed 

in Table 11. In all cases (e.g. INBio, 

CNIN/Lepidoptera, Pelagic Fish Observations 

1968-1999, Birds (KIEE-BI)) it appeared that the 

resources were republished twice to GBIF but with 

a different name (e.g. ‘Pelagic Fish Observations 

1968-1999’ and ‘Pelagic Fish Observations 1968-

1999 (Australian Antarctic Data Centre)’). What 

appears very surprising is that most of these 

potentially duplicated resources were registered 

with very similar names (e.g. ‘CNIM/Lepidoptera’ 

and ‘Colección de Referencia de Lepidópteros 

Diurnos Mexicanos de la CNIN’). When a new 

resource is registered, a simple text comparison 

between the title of the new resource with existing 

published ones would have enabled rapid 

http://www.cria.org.br/
http://www.cria.org.br/
http://splink.cria.org.br/
http://www.biogeomancer.org/
http://www.des.ucdavis.edu/FacultyInfo.aspx?ID_Number=83
http://www.diva-gis.org/


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104 

identification of obvious possible duplication. This 

has never been implemented up to now in GBIF 

but efforts are underway to automate this process 

as well as to resolve the already identified potential 

duplicates in close communication with the 

respective GBIF publishers.  

An improved monitoring of the resource at the 

time of registration is indeed an immediate 

solution but ultimately the adoption of persistent 

identifiers for each resource published (e.g. DOI), 

with proper metadata, would have been a much 

more robust solution.  

B. Trends and patterns assessment 

Taxonomy: 

In December 2010, of the 267 million 

occurrences records accessible through the GBIF 

network 62% belonged to Kingdom Animalia, 

followed by Kingdom Plantae (23%), Fungi 

(1.55%), Protozoa (0.67%), and Bacteria (0.59%) 

(Figure 6.a). A similar assessment in February 

2012 (Figure 6.b) showed that the major variation 

was the increase for the Plantae from 23% to 30%. 

Between these two assessments the GBIF 

taxonomical backbone was reviewed with the latest 

version of the Catalogue of Life. Monitoring of the 

taxonomical name resolution during the GBIF 

harvesting and indexing procedures has shown that 

a large proportion of names previously classified 

as ‘Unranked/Unknown’ were now reclassified in 

particular within the Kingdom Plantae (GBIF, 

personal communication).  

In December 2010, as depicted in Figure 6.a, 

52% of the occurrences belonged to Phylum 

Chordata (Kingdom Animalia) followed by 17.7% 

belonging to Phylum Magnoliophyta (Kingdom: 

Plantae), and 9.8% to Phylum Arthropoda 

(Kingdom Animalia). A breakdown at the Class 

rank (Figure 8.a, Figure 9) shows that the largest 

Class in the GBIF mobilised data is Aves (43%). 

This is mostly due to field observation from the 

ornithological community as depicted in Figure 10. 

In December 2010, the Bird Observation Checklist 

database represented 42.21 million occurrences or 

15.7% of the total GBIF Index at that time. Within 

this top 5, four resources are related to bird 

watching activities (e.g. Bird Observation 

Checklist database, Project FeederWatch, Great 

Backyard Bird Count, Southern African Bird Atlas 

Project). While these figures clearly indicate the 

dominance of bird observations among the data 

accessible through the GBIF network, it also 

demonstrate the effectiveness of a given 

specialized network to leverage on the existence of 

GBIF to enable the publishing, discovery and 

access to such type of biodiversity observations.   

These figures also show that the spread of 

occurrences across various taxonomical levels is 

also rather heterogeneous (Figure 7.a). Some phyla 

are extremely underrepresented, while specific 

classes such as Aves dominate, or even orders 

within the class Hexapoda (insects) (Figure 8.a and 

b, Figure 9). The hierarchy of the most represented 

groups (irrespective of taxonomic level) shows 

Classes Aves, Actinopterygii (bony fishes), Poales 

(grasses), Mammals and Asterales as the largest 

groups, followed by Order Lepidoptera within the 

Class Hexapoda. On the other extreme, for 

example Phyla Zygomycota, Nemata or 

Platyhelminthes, or Kingdom Bacteria, have 

marginal occurrence despite their natural 

abundance. However it is important to note that 

many of these apparently overrepresented taxa are 

species-rich, and have greater biomass and greater 

visibility, but also a higher number of competent 

specialists and observers. Having such large 

amount of data for a relatively small number of 

taxa should also be considered as a positive asset 

in particular when looking at temporal species 

distribution, provided that these taxa are 

ecologically diverse as well as representative. The 

availability of such high-density information for 

fewer taxa should not be under-estimated.  

The analysis of the temporal spread of GBIF-

mediated data for the two dominant Kingdoms 

(Animalia and Plantae, Figure 11) shows that the 

exponential increase observed from 1960 is mostly 

explained by the abundance of occurrences for the 

Kingdom Animalia. This increase of bird 



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105 

observation data records exceeds the mobilised 

data from all other classes from year 2000 

onwards. In the same period (1960-2010), we also 

observed that beside a peak in 1999, the trend for 

Plantae is stable varying from 1.5 to 2.1 million 

occurrences observed/collected per year. As shown 

in Figure 13, the exponential increase of data 

records in the GBIF Index in recent years is largely 

explained by the growth of occurrences in the class 

Aves. Figure 14 provides a breakdown of 

occurrence records by basis of record within the 

class Aves. Since 1960, bird observation data have 

been growing almost exponentially while the trend 

remains stable for specimen and other types of 

data. If these trends are confirmed in upcoming 

years, it is expected that the growth of data records 

in Aves will be the main driver behind the growth 

of GBIF Index in terms of volume.  

This phenomenon is even more revealing when 

listing the top 15 species by the number of data 

records. Tables 12.a and 12.b show that all of the 

top 15 species are birds, mostly published through 

networks like ‘eBird Bird Observation Checklist’, 

or other similar resources (e.g. Project 

FeederWatch, Great Backyard Bird Count).  

In order to demonstrate the difference between 

the Kingdom Animalia and Plantae, we have 

generated two sub-indexes for each Kingdom from 

the February, 2012 version of the GBIF Index. 

Each sub-index was subdivided in new subsets 

based on the range of occurrence numbers for each 

species. Table 13 provides the summary of the 

results. For example, from the total of 457,340 

Animalia species in the GBIF Index, 400,088 are 

species with less than 100 occurrences each, and 

represent 2.4% of the total number of occurrences 

in the Kingdom Animalia. The breakdown of 

species by occurrences did not show any major 

differences between the two Kingdoms except for 

20 species in the Kingdom Animalia holding more 

than 1 million occurrences each, while no species 

had as many occurrences within the Kingdom 

Plantae. However, occurrences themselves 

diverged between Kingdoms. The set of 20 species 

having more than 1 million occurrences each 

identified in the Animalia Kingdom accounted for 

15.9% of all Animalia occurrences (zero for 

Plantae), and for the species in the range 100,000-1 

million occurrences a higher percentage was also 

observed for Animalia (39.8%). Plantae 

occurrences concentrated around species 

represented each by less than 100,000 occurrences. 

We conclude that the abundance of occurrences 

records in the GBIF Index for a few Animalia 

species is representing a significant portion of the 

full GBIF Index. These records are mostly 

represented by bird observation data. However this 

trend shouldn’t under-estimate the amount of 

species from all Kingdoms having less than 1 

million and more than 1,000 occurrences, since 

these do represent a large portion of the GBIF 

Index (74.5% of Animalia and 78.7% of Plantae).  

We have compared the distributions of the year 

of collection/observation of occurrences for both 

Plantae and Animalia Kingdoms (see Figure 12.a. 

and 12.b) taking into consideration the December 

2010 and February 2012 versions of the GBIF 

Index. Both figures show that over time the rate of 

data mobilised per year tend to increase in both 

cases. However, for Plantae (Figure 12.a) we 

observed that, aside a few artefacts (e.g. year 1999) 

the rate of mobilisation is increasing at a slower 

rate to even stagnate from year 2000 compared to 

the Animalia Kingdom (Figure 12.b). On the other 

hand, we observed that the evolution for the 

Animalia Kingdom was approximately exponential 

in both versions of the GBIF Index. As indicated 

previously, this is attributed to the increased 

proportion of bird data in the GBIF Index in 

particular in the last decade, as shown in Figure 13. 

This confirms the fact that the rapid growth of the 

volume of occurrences in the GBIF Index is mostly 

driven by the bird observation data. The spread of 

other large publishers is perhaps wider, the main 

difference being the concentration of bird data 

towards recent years and few publishers (see 

Otegui & Ariño, 2013).  

The value of observational data in comparison 

to voucher specimen in museums or accessions 

stored in genebanks is a subject for another 



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106 

discussion. However this study (Figure 14) 

demonstrates the over-representation of 

observational occurrences in the GBIF Index. The 

ratio between observation and specimen was very 

close to 1:1 until 1975. Thenceforth, the amount of 

observation occurrences has grown exponentially 

while the trend for specimen data was very much 

stagnating until 2000, where we observed a 

decline. In the last decade, the proportion of 

observation occurrences represented more than 

90% of the yearly collected/observed occurrences.  

The dominance of bird observational data in 

the last decades, as well as the drop for data 

mobilized in recent years for other classes during 

the last decades, is cause of concern. While on one 

hand the availability of such large volume of bird 

data will enable advanced research in temporal 

trends of bird populations, it also reveal the 

difficulty to undertake such valuable research in 

other classes. Part of this can be explained by rapid 

data mobilisation of the “low-hanging fruits” (or 

relatively easy to digitise and publish) vouchered 

specimen data (Berendsohn et. al., 2010). Many of 

the large natural history museums have digitised 

their main historical records (Ariño, 2010) and 

published them through GBIF. It is therefore 

expected if this situation of dichotomy between 

bird observation data and the other classes will 

increase in the next years.  

Taking into consideration the existing major 

threats to biodiversity, the GBIF community needs 

to greatly strengthen its capacity to assess trends 

also for all non-bird biodiversity records. For 

example, GBIF could evaluate the opportunity to 

develop a list of priority species based on known 

references, for example the IUCN Red List; gather 

information about their distribution, and evaluate 

for each the availability of rich yet still undigitized 

or electronically unavailable occurrence data in the 

GBIF community. This approach would lead to a 

series of strategic data mobilisation strategies for 

each priority species.  

 

Geospatial: 

In the December 2010 assessment we observed 

(Table 14) that the majority of the occurrences 

present in the GBIF Index were located in 

Northern America (28.19%), Northern (30.06%) 

and Western (11.48%) Europe. This represents a 

total of 69.73% of the GBIF Index. In February 

2012, we observed the same trend where these 

three regions represented 70.8% of the GBIF 

Index, with minor variations in the order (e.g. 

Northern America was classified as the second 

region in 2010 while it became first in the 2012 

assessment). There are multiple reasons that can 

explain this distribution. 

The comparison between existing financial 

contributions to the GBIF Secretariat (Table 15, as 

of year 2010) on a regional basis shows that the 

sum of the contributions of these three regions 

equals 64.9% of the total GBIF operational budget, 

which is very similar to the percentage of 

occurrences collected/observed in these regions 

(69.73%). The major discrepancy observed in this 

table is the financial contribution of Eastern Asia 

countries (22.1%) for only 2% of the occurrences 

in the GBIF Index. This can be explained by the 

contribution of Japan within a region where the 

rate of data mobilisation is still low.  

In 1999, The OECD Biodiversity Informatics 

Subgroup in its Working Group on Biological 

Informatics report made major recommendations 

for the establishment of GBIF. It is therefore not 

surprising today to observe (Table 16) that the 

majority of the occurrences in the GBIF Index are 

located in OECD countries (84.45%). Taking into 

consideration megadiverse countries, large 

countries like Brazil, China, Democratic Republic 

of Congo, Indonesia, Malaysia, Papua New Guinea 

or Venezuela are not yet members of GBIF in 2012 

making it difficult for information from these 

countries to be published through GBIF. Thus, 

GBIF mobilised data are very much biased towards 

it original founders, mostly OECD countries. A 

clear example can be found in Otegui et al. (2009), 

where the geospatially-explicit provenance of data 



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107 

contributed by European publishers in the 2008 

sample nicely matches the publisher’ country 

(Figure 19). 

As shown on Table 17, not surprisingly the 

majority (85%) of the occurrences were located in 

high-income countries, 11.7% in upper-middle 

income countries and less than 4% in lower-middle 

and low income countries.  

The distribution of occurrences along latitude 

(Figure 16) confirmed also the large proportion of 

occurrences located in northern hemisphere, where 

the three regions contributing most records are 

located (Table 14).  The peak observed in the 

southern hemisphere is mostly explained by the 

recent publication of a large volume of occurrences 

from South Africa, Australia, and in particular 

through the Atlas of Living Australia. However, 

the species richness, as measured by density of 

species per half-degree of latitude (Figure 17), 

showed a slightly different trend. We did not 

observe the large dichotomy between the two 

hemispheres that appeared in the density of 

occurrences, and the species richness ranged from 

10,000 to 40,000 species per half-degree. Figure 18 

provides a justification for these trends. The 

average number of occurrences in the southern 

hemisphere did not exceed 35 occurrences per 

species at that latitude range resolution, while this 

rate exceeded 50 for much of the latitudes north of 

50ºN, and even reached peaks higher than 160 

occurrences per species per half-degree. We 

therefore conclude that despite a bias of 

occurrences towards northern hemisphere, the 

species richness observed is equally distributed 

between hemispheres. We also conclude that 

species in northern hemisphere had a higher rate of 

occurrences/species than in southern hemisphere. 

This can suggest a wider distribution of temporal 

data for these species in the northern hemisphere, 

and therefore the availability of information more 

suitable for studying the temporal trends of species 

distribution in these regions. For the southern 

hemisphere, we also conclude that many species 

may not have sufficient occurrences to perform 

such analysis.  

Temporal: 

The temporal evolution in the GBIF Index is 

summarized in Figure 20. With the exception of a 

few artefacts (1950, and 1987 for the December 

2010 curve), we observed that the availability of 

occurrence data over time grew almost 

exponentially. A striking feature in this trend was 

the presence of large peaks in specific years. These 

peaks seemed to respond to a combination of a 

provider effect and a possible mismatch between 

published data and indexed data arising from the 

date processing algorithms, that is explained in 

detail in Otegui et al., 2013 (this volume). The 

drop observed in the last period (between 2007 and 

2010) for the December 2010 assessment can be 

attributed to the lag time required between the data 

collection/observation, digitization and publishing 

through GBIF. The same lag time (3 years) was 

later confirmed for the February assessment. We 

conclude that the amount of biodiversity data 

collected or observed tends to be greater for more 

recent years than for any older period (e.g. prior to 

1970-1980). We also analysed the evolution of 

such trend by comparing the December 2010 and 

February 2012 assessments (Figure 21). The two 

horizontal lines represent the average growth in the 

GBIF Index for all occurrences and for 

occurrences having temporal information. The 

difference can be explained by two factors: (1) the 

improvement of the GBIF indexing processes in 

2011, which enabled greater recovery of 

malformed date-stamp fields; and (2) the greater 

percentage of well-formed temporal fields (e.g. 

date of collection/observation) in the recently 

published data. The graph shows that for more 

recent decades (e.g. 1971-1980 onward) the growth 

of data in the GBIF Index is faster than for older 

data. More remarkably, we observe that for the 

latest decade (2000-2010) the variation is of 

89.6%, which is the highest growth rate ever 

observed. The exponential growth of recent data in 

the GBIF network content is particularly driven by 

the availability of bird observational data during 

the last decade; this growth in recent content is 

sometimes termed a 'data deluge'. 



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108 

The trends for the number of species 

collected/observed every year since 1900 (Figure 

22) for both Plantae and Animalia were very 

similar. We observed an increase until the 1990’s, 

and then stagnation followed by a drop from year 

200 (with the exception of few artefacts). The 

drops observed in 1914 to 1917 as well as from 

1939 to 1942 can be easily explained by the effect 

of the two world wars. What is troublesome, 

however, is the drop in both curves from 2000 

onwards. The drop for the Animalia is even more 

severe than for Plantae. While the volume of 

occurrences mobilized every year is increasing 

until 2009, we note that at the same time these 

occurrences belonged to fewer species across both 

Kingdoms. One possible explanation could have 

been related to a lower number of data resources 

publishing since year 2000 (Figure 23), but it 

should be noted that the decline in species richness 

started more than one decade earlier. We have also 

calculated for each year the rate of geospatial 

occupancy in a grid with a resolution of half-

degree (Figure 24). We observed that in all cases 

the grid occupancy for Animalia species was 

higher than for Plants. From 1963 to 1993, grid 

occupancy for Animalia was stable followed by a 

peak in 2000. For Plantae, grid occupancy was 

stable from 1970 until 2000. We also noted that in 

both cases grid occupancy started to decrease in 

2000.  

 

A detailed analysis of these trends will be 

further presented in a separate study.  

C- ‘Fitness-for-use’ assessment: 

Assessing the value of the GBIF mobilised 

data for a variety of usages is challenging. In this 

study, we decided to focus on the most common 

uses for GBIF-mobilised data reported in the 

scientific literature: ecological niche modelling 

(ENM) (Grinnell, 1917; Fernández et al., 2009; 

Peterson and Vieglais, 2001) and related analyses. 

The compilation of scientific literature using or 

citing GBIF is available since 2011 on-line at: 

http://www.mendeley.com/groups/1068301/gbif-

public-library/. Such modelling techniques (e.g. 

using Maxent) required occurrence records with 

proper temporal attributes, correct geo-referencing 

attributes as well as sufficient volume of well-

distributed data-points. The minimum number of 

distinct data-points for a niche modelling analysis 

is in the range of 10 to 20 (Pearson et al., 2007; 

Grantham et al. 
 
2008). Recent studies on GBIF-

mediated data using more than 19,000 plant 

species showed that a preferred threshold of 20 to 

40 points is recommended (Jarvis, Personal 

communication). Maxent models generated for 

species meeting these criteria have an area under 

the curve (AUC) greater than 0.75 in more than 

95% of the cases.  

If time series are part of the models, then the 

requirements on number of data points can be an 

order of magnitude higher. For example, Ariño and 

Pimm (1995) showed that successful modelling of 

the evolution of population extremes require a 

minimum of 15 distinct time-dependent population 

estimates. In terms of ENM, it could be argued that 

if using cell frequencies in the ENM as an 

indicator of potential population estimates, at least 

15 independent models, each time-constrained, 

should be needed to adequately characterize any 

time-dependent changes in the model. This may 

hold for both terrestrial and marine models, despite 

their intrinsic differences (Warner et al., 1995). 

In this study we have decided to use the 

threshold of “presence in at least 20 distinct cells 

in a 1/10 degree grid” to define whether a species 

has sufficient occurrences in the GBIF Index to be 

used for ecological niche modelling. We used this 

threshold for temporal/spatial requirements to 

assess the number of species suitable for such 

ecological niche modelling analysis, but make no 

attempt yet to assess whether each selected species 

can be adequately modelled over time.  

Tables 18.a, b and c provide a distribution of 

the number of species falling in various categories 

of grid occupancy. Our analysis was based on the 

February 2012 version of the GBIF Index due to 

the improved accuracy of the taxonomical 

matching, the greater resolution of date stamp as 



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109 

well as for geo-referencing attributes. For the full 

GBIF Index, more than 995,975 species (Table 

18.a) were recorded in the GBIF Index (with at 

least one occurrence record). However only 

603,532 species had at least one occurrence present 

in the GBIF Index with at least one presence in a 

distinct 1/10-degree grid. This means that 39.4% of 

the species recorded had no geo-referenced 

attributes. 747,988 species had at least one 

occurrence with a valid temporal attribute. This 

number dropped to 485,105 species if we added 

the condition of at least one geo-referenced 

attribute within a 1/10-degree grid. If we consider 

the ENM threshold of 20 presences in 1/10-degree 

grid with valid temporal attributes, the total 

number of species that were suitable for ENM 

analysis fell to 81,057. This represents 8.1% of the 

species recorded in the GBIF Index. While this 

percentage could be interpreted as a low 

percentage, the number of species falling in this 

category is already very high for many scientists 

and researchers interested in estimating the actual 

species distribution as well a projections in the 

future taking into account future climatic scenario. 

The use of such information is extremely valuable 

already for advanced scientific research and in 

particular in support of global biodiversity 

assessments such as the Strategic Plan on 

Biodiversity of the MEAs (also called Aichi 

Targets). Taking into account the constant growth 

of the GBIF Index with the addition of new 

datasets for example, it is logical to expect that this 

amount of ‘eligible’ species will increase over 

time.  

What was also remarkable was the number of 

species with a presence in at least 100 1/10-degree 

cells with valid temporal attribute: 14,041. This 

rich reservoir of species with high quality 

occurrences is already an important message to the 

research community seeking to assess the species 

distribution evolution over time as well as future 

predictions. As shown in Table 18.b and c, this 

number is somehow equally distributed between 

species within the two dominant Kingdoms: 

Plantae (6,100) and Animalia (6,756). Figure 24 

also shows the temporal trends between these two 

Kingdoms. Even if the grid occupancy is 

constantly higher on a yearly basis for Animalia, 

the trends between these two Kingdoms are very 

similar. The same trends are also observed for a 

low threshold of 20 1/10-degree grid presences. 

We also noted no major differences between 

Kingdoms in the breakdown assessment of the 

Animalia (Table 18.b) and Plantae (Table 18.c): 

36,462 Animalia species were suitable for ENM 

(7.9% of the total number of Animalia species 

recorded in the GBIF Index) against 37,730 

Plantae species (8.7%). Therefore the concerns 

about the over-representation of bird observation 

data within the Kingdom Animalia are contradicted 

here in terms of ‘fitness for use’ since a large 

number of plant species were already meeting the 

ENM suitability criteria. Figure 25 proves how 

much the two Kingdoms can’t be distinguished 

when looking at a presence higher than 20 in 1/10-

degree grids. 

In this study, we have also tried to assess the 

grid occupancy at Class (Table 19), and Family 

(Table 20) levels. Our objective was to assess the 

percentage of species within each rank suitable for 

ENM. For example, within the class Aves 45.2% 

of the recorded species in the GBIF Index were 

already suitable for ENM studies. In this particular 

case, we can conclude that the GBIF Index as of 

today can be used to estimate the biodiversity of 

most species within the class Aves. Taking into 

account the actual trend in terms of bird 

observation data, it is therefore expected that this 

percentage will grow in the future. Such volume of 

information can now open new opportunities such 

as studies on the over-sampled areas (e.g. North 

America or Western Europe) and 

recommendations for new areas where collection 

of new specimen/observation is required. Studies 

on the estimated number of species (Chao2) at a 

regional and global level could now be performed. 

Table 19 also shows that other classes are eligible 

for such analysis: cartilaginous fish 

(Elasmobranchii, Holocephali), bryophyte plants 

(Marchantiophyta). For many, the limited number 



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110 

of species within that Class can explain this. 

However, for some larger classes like 

Actinopterygii (ray-finned fishes), Elasmobranchii 

(cartilaginous fish) or Pinophyta (conifers) the 

GBIF Index holds sufficient information for more 

advanced ENM or other biodiversity assessment 

analysis. Figure 26 provides a visual representation 

of these trends. With the exception of the Class 

Aves, the other Classes shared a similar trend.  

Such analysis may thus include a bias: the 

number of species representing each taxon group at 

a given level. Large families or classes, e.g. 

Hexapoda (insects), may not be listed in the Top 

10 or 20 lists. Figure 27 shows the distribution of 

Families taking into account the number of species 

within each Family. While we observed that 

classes Aves and Actinopterygii were listed as the 

ones with the highest suitability for large-scale 

ENM, families within other classes, such as some 

insects (e.g. Cryptophagidae - beetles), or 

mammals (Phyllostomidae  - New World leaf-

nosed bats) are also to be considered. For even 

larger families (e.g. with a number of species 

greater than 1,000) it was not surprising to observe 

that the percentage of species suitable for ENM 

was lower. However for such large families, the 

suitability for ENM of 5-10% of their known 

species is probably a good proxy to initiate an 

assessment of the full class. In this category, in 

addition to families of insects we observe some 

large families of reptiles (e.g. Scincidae – lizards, 

Colubridae – snakes). For plants (Figure 28), the 

distribution of families is somehow distorted due 

to the high to very high number of species found 

within each class.  

 

Discussion 

The idea that birthed GBIF ten years ago 

remains as simple and powerful now as it was 

then: to make the world’s biodiversity information 

freely and universally available for science, society 

and a sustainable future (OECD, 1999). After 10 

years of existence, the GBIF network represents 

the largest resource of primary biodiversity data 

that is freely accessible to all. With over 377 

million occurrence records about nearly one 

million species (as of August 2012), the GBIF 

mobilised data provides a data-driven window to 

the state of the world’s biodiversity.  Access to 

such large volume of data opens for example new 

research avenues from assessing the state of 

biodiversity, identifying the potential threats up to 

monitoring trends and predicting future evolution 

and composition of biodiversity and ecosystems 

(Rödder & Lötters, 2010; Ramírez-Villegas et al., 

2010; Ready et al.,2010). Since 2008 till June 

2012, over 600 scientific peer reviewed papers 

have been published which are based on analysis 

and interpretations of GBIF mediated data (GBIF, 

2012a).   

To become such a truly ‘global biodiversity 

information facility’, GBIF needs now to take into 

consideration the primary applications it originally 

intended to offer to the public such as in policy 

formulation, economic development, 

environmental protection, education, and scientific 

research. In order to ensure its relevance for such 

applications, the GBIF community needs to 

warrant that the information it delivers is of 

relevance to address the major science, societal and 

policy challenges.  

While these needs are very diverse and difficult 

to categorize they do have in common essential 

pre-requisites that can be summarized as follows: 

 “Can I trust the information provided?” 

 “Is the information representative of 

biodiversity on earth?” 

 “Can I use the data to model biodiversity 

over time?” 

The present study was therefore aimed at 

assessing the data quality, bias and ‘fitness-for-

use’ of the GBIF mobilized content. These 

challenging questions were addressed by tasking 

two separate teams to evaluate the content using 

different methodologies. The results from the 

GBIFS and UNZYEC teams were similar and they 

both demonstrated the validity of the conclusions 

presented hereby.   



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111 

Are the GBIF-mediated data scientifically 

credible/reliable? 

GBIF mobilised data is often criticized for 

errors (Yesson et al., 2007; Otegui et al., 2009). 

However, these errors are reflections of the data as 

collected, collated, and published by the 

heterogeneous data publishers across the globe. 

The role of GBIF is to provide a discovery window 

on the published data. Such a role requires 

reconciling, interpreting and publishing the 

essential key attributes: taxonomic, temporal and 

geospatial. In assessing the state of data quality in 

the GBIF Index over time, inevitably such study 

will combine data quality improvements at the 

level of the data publishers as well as at the central 

discovery point.   

The recent improvements made by GBIF in the 

re-building of its taxonomical backbone and data 

quality checking routines have positively impacted 

on the level of data quality in the GBIF Index. 

However these improvements are explained by the 

improvements of the informatics infrastructure and 

processing algorithms, but these are not addressing 

the most critical underlying causes of poor data 

quality: accuracy and gaps. Informatics routines 

alone can eventually spot but cannot recover 

missing attributes,(if anything, perhaps hint or 

guess), in particular when these attributes were not 

mapped correctly at the publisher level or if they 

weren’t even digitized from the original voucher 

specimen. 

Taxonomy: 

A majority of the scientific names published by 

the GBIF network are now recognized as valid 

references against a collection of authoritative 

taxonomic catalogues. The current GBIF taxonomy 

backbone provides an appropriate resolution 

service to the large majority of the scientific names 

discovered by GBIF. Given the fact that GBIF 

taxonomic backbone is a combination of multiple 

authoritative taxonomic catalogues (e.g. CoL, 

WORMS, IPNI, NCBI, and ITIS etc.), it has 

potential to serve larger systematicians 

communities than any specific taxonomic group 

alone. While doing so, informatics approaches are 

proven to be effective; however, questions about 

future improvements can be raised. Linkages with 

more authoritative taxonomic catalogues 

(Recommendation 4 in Faith et al., 2013) and 

involvement of taxonomic expertise will soon be 

required to resolve taxonomic discrepancies.  

In order to continuously assess the effectiveness 

of its taxonomic backbone, GBIF Secretariat 

should perform regular estimation of completeness 

at all taxonomic ranks as described in Table 4.b. 

Such an analysis should in particular assess the 

amount of mis-identifications (e.g. species within 

genus Zonotrichia). GBIF should also improve its 

reporting services to the original publishers so that 

potential taxonomic mis-identifications are 

reported (Recommendation 6 in Faith et al., 2013). 

GBIF should also monitor over time the taxonomic 

data quality improvements made in the GBIF 

Index (e.g. indicators of taxonomic completeness 

at the class, order or family levels). In addition, 

GBIF should provide means to assess the 

effectiveness of its taxonomic names resolution 

services used during the harvesting and indexing 

processes. All taxa mis-identifications should be 

documented and calls to expert groups (e.g. marine 

biologists, crop wild relatives experts) should be 

considered in order to tap into taxonomist expertise 

and increase their engagements (Chavan et al., 

2005) in improving the quality of such valuable 

global resource.  

Temporal and Geospatial: 

Setting an ideal target for the rate of geo-

referenced occurrences within the GBIF index is a 

difficult task. While the ideal scenario would be 

that all occurrences are georeferenced, the reality is 

that in many cases the original records for example 

specimens in zoological or botanical collections 

itself won’t have such information. Some voucher 

specimens (especially older ones) have in general a 

lower percentage of geo-referenced records 

compared to recent field observation records. 

There is a high variability between data resources 

within the GBIF Index. However and as shown in 



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112 

Figure 4, the average percentage of georeferenced 

records has increased between 1-5% in average 

during the period 1990-2010 and is consistently 

higher in February 2012 than in December 2010. 

The data publisher community is therefore 

addressing this challenge in particular for recent 

records. While GBIF’s role is to enable the 

discovery of primary biodiversity data from a 

network of publishers (Recommendation 14, Faith 

et al., 2013), it is not mandated to undertake or 

correct the content published. However, this can be 

questioned in particular when a simple correction, 

such as a sign correction on a longitude or latitude 

field, could be undertaken within the GBIF Index 

and therefore immediately improve the quality of 

data published. Taking into consideration the 

growing difficulties in communicating with a large 

network of publishers, such option may be 

considered for the most obvious data corrections. 

While this can be seen as a limitation, one way 

forward would be to set targets by periods where 

we observe low variation of the geo-referencing 

average (e.g. 1900-1930, 1930-1960, 1960-1990 

and 1990-today). Within each period, a 

georeferencing target could be set based on a 

subset of data resources (e.g. comparing all 

datasets publishing insect occurrences against the 

top 10% best georeferenced datasets). However, 

any decision on such baselines would need to be 

discussed and agreed with the community of 

publishers. Experts could investigate datasets 

falling well under these baselines and reports with 

recommendations on possible corrections should 

be sent to the original publishers. However, this 

approach would require engagement from expert 

groups as well as willingness and availability of 

data owners to undertake more accurate 

verifications such as getting back to the original 

voucher specimen (Recommendation 3, Faith et 

al., 2013).  

As shown in Figure 5, we demonstrated that in 

December 2010 approximately 50% of the 

occurrences in GBIF Index had at least one of the 

taxonomic, temporal and/or geospatial attributes 

missing; this percentage dropped to less than 22% 

by February 2012. This means that occurrence 

records with essential attributes represent now 

more than three quarters of the GBIF Index. 

Taking into consideration that more recent 

occurrences tend to be of such quality, it is 

expected that over time this percentage will 

continue to increase.  

The most critical priority for the GBIF network 

in this field is now to engage the data publisher 

community at large (including data curators and 

original collectors) to be (1) aware of the 

importance of data quality and accuracy; (2) 

alerted of the possible data gaps and/or quality 

issues identified centrally; and (3) investigate and 

fix these whenever possible (e.g. by checking the 

data publishing process up to involving the original 

curators and specimen) (Recommendation 6, Faith 

et al., 2013).  

To achieve this, a distributed annotation service 

will be required whereby reports on possible data 

quality issues are communicated to the original 

publishers. However such service would in turn 

require the promotion of effective identification of 

data objects such as persistent identifiers and 

sustainable resolution services (Recommendation 

13, Faith et al., 2013). GBIF should therefore place 

the use and re-use of persistent identifiers as a high 

priority activity and possibly as mandatory for all 

datasets (GBIF, 2009).  

Are the GBIF-mediated data increasingly 

representative of “some” biodiversity on earth? 

The GBIF Index has recorded information 

about 995,974 species, which is a remarkable 

amount compared for example with the Catalogue 

of Life, which contains, as of June 2012, more than 

1.3 million species. However, the GBIF Index is 

facing a bias toward the Kingdom Animalia and to 

a less extent towards Plants. Other Kingdoms like 

Fungi, Protozoa and even Bacteria are under-

represented within the data mobilized so far. 

Therefore, the GBIF Index is not representative of 

all Kingdoms and can’t be used yet as a proxy to 

all biodiversity on earth (Recommendations 1 & 2, 

Faith et al., 2013).  



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113 

The over-representation of bird observation 

data should not be considered as problematic, as 

the “over-“ bit means just by comparison to other 

groups. Our study shows that the bird observation 

community has managed in particular over the last 

two decades to mobilize a vast amount of 

information on a number of species. Such volume 

is remarkable and of great value to understand not 

only the distribution of species at a given time but 

also on a temporal basis. This is, for example, of 

immense value when dealing with monitoring 

potentially invasive alien species or reaction to 

climate change over time (Sullivan et al., 2009). 

Citizen scientists are using such bird observation 

network (e.g. eBird) data to monitor the biological 

patterns and the environmental and anthropogenic 

factors that influence them. These networks are 

providing today a near real-time observational 

network, a model to be followed by many other 

networks. 

However, what is of greater concern to the 

authors is the flat data mobilization rate since the 

1990’s for classes other than Aves (Figure 13), a 

phenomenon masked by the approximately 

exponential growth of bird observation data, or 

even more critically (Figure 14)  by the 

exponential growth of the observation/specimen 

ratio. Taking into consideration the greater 

intrinsic value of specimen versus observation data 

(e.g. accuracy, taxonomic validation, validation by 

experts, and availability of voucher specimen for 

verification), the stagnation of such valuable 

resources over the last 2 decades is a priority that 

needs to be addressed by GBIF (Recommendation 

3, Faith et al., 2013). Instead of focusing on a 

volume target, such as the one-  and two-billion-

records  goals as set by the GBIF Governing Board 

in 2007 and 2009 respectively GBIF (GBIF, 2008, 

2012b), GBIF should instead focus on an optimal 

distribution of such volume across Kingdoms, 

Classes, Orders, Families and Genera 

(Recommendations 1, 2 & 8, Faith et al., 2013). It 

is unrealistic to hope that the GBIF network will 

manage within the next decade to mobilize as 

many data for all classes as what has been 

mobilized so far for birds.  

The volume of records has always been an easy 

and tempting target. We argue here that this is not 

an appropriate indicator of the success of GBIF as 

being a window on earth biodiversity. We 

demonstrated in our study that even though the 

volume of occurrences has a clear bias towards the 

northern hemisphere (Figure 18: 70% for Northern 

America, Western and Northern Europe - Table 

15), in terms of species richness (Figures 17) we 

do not observe such bias. Therefore, even with 

fewer occurrences per species, the southern 

hemisphere shows to have a similar amount of 

species richness in the GBIF Index than the 

northern hemisphere. This can be explained by the 

recent addition of species-rich datasets from South 

Africa (SANBI), Australia (Atlas of Living 

Australia), and Costa Rica (InBIO).  

Therefore if GBIF needs to become a window 

on earth biodiversity, and taking into account the 

stagnation of specimen records and the exponential 

growth of observational data, one way forward 

would be to engage non-bird observation networks 

(e.g. flowering plants, snails, fish, butterflies etc.) 

to participate as actively as the bird observation 

community. Because, the additions of biodiversity-

rich datasets with fewer records per species can 

make a large difference in the representativeness of 

GBIF Index of the earth biodiversity distribution 

(Recommendations 1, 2, 3 & 8, Faith et al., 2013].  

Another complementary strategy would be to 

focus on priority species derived from key 

scientific-policy priorities (e.g. reducing threats 

caused by invasive alien species, reducing the loss 

of threatened species etc.) and priority 

regions/areas (e.g. biodiversity hotspots, protected 

areas, high biodiversity regions) (Recommendation 

8, Faith et al., 2013).  

Therefore we conclude that if GBIF wants to 

become the main window on earth biodiversity it 

needs to articulate data mobilization strategies 

engaging the full GBIF network on a list of priority 

species and regions (Recommendation 2, Faith et 

al., 2013). To decide on these two major priorities, 

GBIF needs to undertake a more advanced data 



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114 

gap analysis looking at what biodiversity needs to 

be monitored and in which locations 

(Recommendations 1 & 10, Faith et al., 2013). 

This would be somehow a radical shift from the 

former ‘opportunistic’ to a more pragmatic 

demand-driven approach (Berents et al., 2010). 

This shift in strategy may however have some 

serious financial implications since most of the 

high biodiversity regions in the world (e.g. 

Amazonia, Tropical Africa, etc.) are difficult of 

access or even dangerous (e.g. war zones or 

unstable regions).  

Are the GBIF-mediated data opening new 

opportunities for the scientific communities to 

assess the state of biodiversity as well as the 

pressure it faces and its response? 

Our study on the ‘fitness-for-use’ of GBIF-

mediated data can be summarized in Figure 29. 

From a large volume of 323 million occurrences 

and 995 thousand species, the GBIF Index can be 

synthetized to a smaller volume of information 

(71.4 million occurrences) covering less than 50% 

of the known species in GBIF. This first filter of 

the GBIF Index is based primarily on the 

availability of valid taxonomical references, 

temporal and geospatial elements.  

Less than 200,000 species have sufficient 

occurrences (requiring a minimum presence in at 

least 10 distinct 1/10 degree grids) to be used to 

assess their distribution through ecological niche 

modelling (ENM) analysis. Still, this represents a 

large volume of valuable information that can be 

immediately used to assess for example the status 

of biodiversity for a group of species within a 

given ecosystem. Our study also showed that many 

classes and families already have many species 

meeting these ENM requirements.  The growth of 

scientific literature using GBIF mediated data in 

recent years is also an additional indicator that the 

GBIF mediated data is a valuable resource.  

Therefore we concluded that the GBIF Index is 

a valuable resource that can already be used by the 

scientific community to assess the status of 

biodiversity at least for major groups such as for 

birds, fish, plants and insects (Rödder & Lötters, 

2010;  Ramírez-Villegas et al., 2010; Ready et al.,  

2010). Enhancing the fitness-for-use and 

trustworthiness of GBIF mobilised data is a natural 

course of action in this direction, that needs be 

attained urgently at all levels of data management 

chain (Recommendation 5 & 6, Faith et al., 2013). 

We further opine that GBIF as a community needs 

to proactively advocate the use of GBIF mediated 

data in scientific analysis, which may result into 

sound decision making and effective conservation 

and sustainable uses of biological resources 

(Recommendation 9, Faith et al., 2013). In order to 

encourage the cross-sectional scientific and 

naturalist communities in publishing primary 

biodiversity data through the GBIF network, a 

comprehensive ‘data publishing framework’ 

(Chavan & Ingwersen, 2009; Moritz et al., 2011) 

needs to be promoted and implemented 

(Recommendation 11, Faith et al., 2013).  

CONCLUSION 

The Third Global Biodiversity Outlook (GBO3) 

published by the CBD in 2010 concluded that the 

target agreed by the world’s governments in 2002, 

“to achieve by 2010 a significant reduction of the 

current rate of biodiversity loss at the global, 

regional and national level as a contribution to 

poverty alleviation and to the benefit of all life on 

Earth”, was not met (SCBD, 2010). The loss of 

biodiversity is an issue of profound concern for its 

own sake, but biodiversity also underpins the 

functioning of ecosystems, which provide a wide 

range of services to human societies (SCBD, 

2010). Its continued loss, therefore, has major 

implications for current and future human well-

being. 

The lack of a consistent baseline data and 

ongoing monitoring of biodiversity has been often 

cited as a major obstacle towards improving the 

scientific evidence of the consequences of 

biodiversity loss. GBIF through its mission is 

providing a mean to achieve greater improvements 

in this evidence base in particular through its 

global network and the discovery, access and use 



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115 

of the largest resources of primary biodiversity 

data. 

This study has clearly demonstrated the great 

value of the GBIF mediated data in various aspects 

from improved data quality and accuracy, 

progressive reduction of gaps in the content and 

increased fitness-for-use. More importantly, the 

GBIF mediated data is offering today opportunities 

to undertake scientific research that has never been 

possible before. Access to three fundamental and 

essential biodiversity variables (i.e. taxonomy, 

geospatial and temporal) opens opportunities for 

example in assessing today’s distribution of 

species as well as predicting their future 

distribution. Taking ecological niche modelling as 

the model application of GBIF-mediated data 

already opens a myriad of research opportunities, 

such as assessing the state of biodiversity (e.g. 

threatened species assessments, genetic diversity, 

etc.) or the pressures (e.g. invasive alien species, 

effect of climate change or land cover change) as 

well as the responses. 

GBIF is therefore uniquely positioned today to 

become the ‘data to science’ interface in support of 

major scientific research trends such as in support 

of the Strategic Plan on Biodiversity as agreed in 

Nagoya in 2010 (SCBD, 2012).  GBIF must 

therefore take concrete steps toward effective 

monitoring of current trends in science and policy, 

such that it is maximally responsive and effective 

as a mega-science data infrastructure. 

This is how GBIF’s focus can remain on being 

the single most important infrastructure for 

primary biodiversity data at the organism level, 

accompanied by strong, effective, and targeted 

links to data at the genetic, genomic, and 

ecosystem levels.  

ACKNOWLEDGEMENTS 

Authors are grateful to their colleagues in the 

Global Biodiversity Information Facility 

Secretariat, and University of Navarra for their 

support in this study and providing access to the 

GBIF Index.  

 

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http://www.gbif.es/ficheros/GBIFBiodiversityInfor

maticsIntroduction-v1.0-final.pdf  

IUCN and UNEP (2009). The World Database of 

Protected Areas (WDPA). 2010 BIP Indicator: 

Coverage of Protected Areas. UNEP-WCMC. 

Cambridge, UK. 

http://www.wdpa.org/Statistics.aspx 

Johnson N.F. (2007). Biodiversity informatics. Annual 

Review of Entomology, 52: 421-438. 

OECD (1999). Final report of the OECD Megascience 

Forum Working Group on Biological Informatics, 

January 1999, pp. 74, accessible at 

http://www.oecd.org/dataoecd/24/32/2105199.pdf.  

Otegui J., Ariño A.H. (2013). On the time series of 

records mobilised through GBIF 

Otegui J, Ariño A.H. (2012b). BIDDSAT: visualizing 

the content of biodiversity data publishers in the 

GBIF network. Bioinformatics, 2012: doi: 

10.1093/bioinformatics/bts359. 

Otegui J., Robles E., Ariño A.H. (2009). Noise in 

Biodiversity Data. Poster presented at e-Biosphere 

09. e-Biosphere Conference 2009, Conference 

Abstracts, London; McLeod N. & J. Edwards, eds. 

P. 190. http://www.e-biosphere09.org/assets/files/e-

Biosphere%20Abstracts%20Volume%20-

%20FINAL.pdf 

Mittermeier R.A., Robles-Gil P., Hoffmann M., Pilgrim 

J., Brooks T., Mittermeier C.G., Lamoreux J., da 

http://www2.gbif.org/WP2009-10.pdf
http://www.gbif.org/
http://www.gbif.org/BestPracticeGuide-final.pdf
http://www.gbif.org/communications/resources/monthly-statistics/
http://www.gbif.org/communications/resources/monthly-statistics/
http://www.gbif.org/communications/press/history-of-gbif/
http://www.gbif.org/communications/press/history-of-gbif/
http://www.oecd.org/dataoecd/24/32/2105199.pdf


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Fonseca G.A.B. (2004). Hotspots revisited. 

CEMEX, Mexico. 

Page R. (2012). How many specimens does GBIF really 

have? Retrieved August 2, 2012, from 

http://iphylo.blogspot.com.es/2012/02/how-many-

specimens-does-gbif-really.html 

Pearson R.G., Raxworthy C.J., Nakamura M., 

Townsend Peterson A. (2007). Predicting species 

distributions from small numbers of occurrence 

records: A test case using cryptic geckos in 

Madagascar. Journal of Biogeography, 34, 102-117.  

Peterson A., Vieglais D. (2001). Predicting species 

invasions using ecological niche modelling: new 

approaches from bioinformatics attsack a pressing 

problem. Bioscience 51: 363-371. 

Pino-del-Carpio A., Villarroya A., Ariño A.H., Puig J., 

Miranda R. (2011).  

Communication gaps of knowledge of freshwater fish 

biodiversity: implications for the management and 

conservation of Mexican Biosphere Reserves. 

Journal of Fish Biology, 79(6): 1563-1591. 

Ramírez-Villegas J., Khoury C., Jarvis A., Debouck D. 

G., Guarino L. (2010). A Gap Analysis 

Methodology for Collecting Crop Genepools: A 

Case Study with Phaseolus Beans. PLoS ONE, 

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from 

http://dx.doi.org/10.1371/journal.pone.0013497 

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Rödder D., Lötters S. (2010). Potential distribution of 

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Secretariat of the Convention on Biological Diversity 

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(2012). Strategic Plan for Biodiversity 2011-2020, 

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http://www.cbd.int/sp/ 

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D., Kelling S. (2009). eBird: A citizen-based bird 

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doi:DOI: 10.1016/j.biocon.2009.05.006 

Warner S., Linburg K., Ariño A.H., Dushoff J., Dodd 

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Ecological Time Series. Chapman & Hall, New 

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Pahwa J.S., et al. (2007) How Global Is the Global 

Biodiversity Information Facility? PLoS ONE 

2(11): e1124. doi:10.1371/journal.pone.0001124

 

 

LIST OF TABLES 
 

 

Table 1. Essential core data elements (in the GBIF-Index occurrence table). 

Table 2. Resources currently available through GBIF ChecklistBank used to build the GBIF taxonomical 

backbone. 

Table 3. Taxonomical rank matching with Catalogue of Life 2011 (February 2012) 

Table 4.a. Scientific names and occurrences summary for each ‘unknown’ taxonomic rank (as of 

December 2010). 

Table 4.b. Scientific names and occurrences summary for each ‘unknown’ taxonomic rank (as of 

February 2012). 

Table 5: Potential misidentification at the Kingdom rank and tentative resolution through CoL 2011 and 

more recent version (February 2012) 

http://iphylo.blogspot.com.es/2012/02/how-many-specimens-does-gbif-really.html
http://iphylo.blogspot.com.es/2012/02/how-many-specimens-does-gbif-really.html
http://dx.doi.org/10.1371/journal.pone.0013497


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Table 6.a: Estimation of the taxonomical misidentification at the Kingdom level (February 2012) 

Table 6.b: Estimation of the taxonomical misidentification at the Phylum level (February 2012) 

Table 6.c: Estimation of the taxonomical misidentification at the Class level (February 2012) 

Table 7. Percentage (%) temporal quality of the GBIF mobilised data records according to GBIFS 

methodology. 

Table 8. Breakdown of the year provided in the GBIF mobilised data records.  

Table 9. Breakdown of the temporal and geospatial data availability. 

Table 10. Summary of potential resource duplicates.  

Table 11. Top 20 potential resources duplicates.  

Table 12.a: Top 15 species with highest number of data records (December 2010). 

Table 12.b: Top 15 species with highest number of data records (February 2012). 

Table 13: Breakdown of the species and occurrences richness for Kingdom Animalia and Plantae 

(February 2012). 

Table 14. Breakdown of occurrences by continents.  

Table 15. Proportion of financial contribution to GBIF (2010) by continents.  

Table 16. Distribution of occurrences per members to the Organisation for Economic Co-operation and 

Development (OECD).  

Table 17. Distribution of occurrences per country income status.  

Table 18.a Grid (1/10 degree) occupancy (February 2012). 

Table 18.b Grid (1/10 degree) occupancy for Animalia.  

Table 18.c Grid (1/10 degree) occupancy for Plantae.  

Table 19. Grid (1/10 degree) occupancy by Classes (Animalia and Plantae).  

Table 20. Grid (1/10 degree) occupancy by Families (Animalia and Plantae).  

 

 

LIST OF FIGURES 
 

Figure 1. Typical flow of data discovered and published through the GBIF network. 

Figure 2. Data mining methodology employed during content assessment exercise carried out by the 

GBIF Secretariat. 

Figure 3. Data mining methodologies employed during content assessment exercise carried out by the 

University of Navarra. 

Figure 4. Evolution of the percentage of geo-referenced records (1800-2010). 

Figure 5. Evolution of incomplete records (taxonomical*temporal*geospatial) in the GBIF-Index  

Figure 6.a. Data records by Kingdom (December 2010). 

Figure 6.b. Data records by Kingdom (February 2012). 

Figure 7.a. Data records by Phylum (December 2010). 

Figure 7.b. Data records by Phylum (February 2012). 

Figure 8.a. Data records by Class (December 2010). 

Figure 8.b. Data records by Class (February 2012). 

Figure 9. Treemap of occurrences according to taxon group, down to Class. Cell surface proportional to 

number of occurrences. Blue: Invertebrates; purple: Vertebrates; green: Higher plants; yellow: Algae and 

Ferns; brown: Fungi; red: unicellular organisms. 

Figure 10. Top 10 data resources publishing maximum number of data records (December 2010). 



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Figure 11. Kingdom wise distribution of data records by Year. 

Figure 12.a. Plantae Kingdom wise distribution of occurrences by year (February 2012).  

Figure 12.b. Animalia Kingdom wise distribution of occurrences by year (February 2012). 

Figure 13. Aves wise distribution of occurrences by year (February 2012) 

Figure 14. Basis of records distribution of occurrences by year (February 2012) 

Figure 15. Distribution of voting and associate country participants (February 2012) 

Figure 16. Distribution of occurrences by latitude (February 2012) 

Figure 17. Distribution of species richness by latitude (February 2012) 

Figure 18. Distribution of the average number of occurrences per species by latitude (February 2012) 

Figure 19. Georeferenced records (dots) published by the largest data providers in Europe. Colors 

represent distinct providers. (From Otegui et al., 2009). 

Figure 20. Records available in the GBIF-Index by date 

Figure 21. Variation of the records available in the GBIF-Index by date (December 2010 – February 

2012) 

Figure 22. Distribution of distinct species collected/observed over time (February 2012) 

Figure 23. Distribution of distinct resources contributing to the GBIF-Index over time (February 2012) 

Figure 24. Grid occupancy (1/2 degree grid) of the GBIF-Index over time (February 2012) 

Figure 25. Grid occupancy (1/10 degree grid) of the GBIF-Index by Kingdom (February 2012) 

Figure 26. Grid occupancy (1/10 degree grid) of the GBIF-Index by Classes (February 2012) 

Figure 27. Distribution of families for FFU-ENM – Kingdom Animalia (February 2012) 

Figure 28. Distribution of families for FFU-ENM – Kingdom Plantae (February 2012) 

Figure 29. Selection of fit-for-use records in the GBIF Index for ecological niche modelling (ENM). 

 



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Table 1. Essential core data elements (in the GBIF-Index occurrence table). 

 

Title Description 

Publisher Publisher of the resource/dataset 

Dataset Resource/Dataset 

Institution 
The name (or acronym) in use by the institution having custody of the object(s) or information referred to in the 

record. 

Collection The name, acronym, code, or initials identifying the collection or data set from which the record was derived. 

Catalogue number An identifier (preferably unique) for the record within the data set or collection. 

Scientific name 
The full scientific name, with authorship and date information if known. When forming part of identification, this 

should be the name in lowest level taxonomic rank that can be determined. 

Taxon author The authorship information for the Scientific name. 

Taxon rank 
The taxonomic rank of the most specific name in the Scientific name. Recommended best practice is to use a 

controlled vocabulary. 

Kingdom The full scientific name of the kingdom in which the taxon is classified. 

Phylum The full scientific name of the phylum or division in which the taxon is classified. 

Class The full scientific name of the class in which the taxon is classified. 

Order The full scientific name of the order in which the taxon is classified. 

Family The full scientific name of the family in which the taxon is classified. 

Genus The full scientific name of the genus in which the taxon is classified. 

Species epithet The name of the first or species epithet of the Scientific name. 



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Infraspecific 

epithet 
The name of the lowest or terminal infraspecific epithet of the Scientific name, excluding any rank designation. 

Latitude 
The geographic latitude (in decimal degrees) of the geographic center of a Location. Positive values are north of 

the Equator; negative values are south of it. Legal values lie between -90 and 90, inclusive. 

Longitude 
The geographic longitude (in decimal degrees) of the geographic center of a Location. Positive values are east of 

the Greenwich Meridian, negative values are west of it. Legal values lie between -180 and 180, inclusive. 

Coordinate 

precision 
A decimal representation of the precision of the coordinates given in the Latitude and Longitude. 

Maximum altitude The upper limit of the range of elevation (altitude, usually above sea level), in meters. 

Minimum altitude The lower limit of the range of elevation (altitude, usually above sea level), in meters. 

Altitude precision A decimal representation of the precision of the altitude. 

Minimum depth The lesser depth of a range of depth below the local surface, in meters. 

Maximum depth The lesser depth of a range of depth below the local surface, in meters. 

Depth precision A decimal representation of the precision of the depth. 

Continent or ocean 

The name of the continent in which the Location occurs. Recommended best practice is to use a controlled 

vocabulary such as the Getty Thesaurus of Geographic Names or the ISO 3166 Continent code. Recommended 

best practice is to use a controlled vocabulary such as the Getty Thesaurus of Geographic Names. 

Country 
The name of the country or major administrative unit in which the Location occurs. Recommended best practice is 

to use a controlled vocabulary such as the Getty Thesaurus of Geographic Names. 

State or province 
The name of the next smaller administrative region than country (state, province, canton, department, region, etc.) 

in which the Location occurs. 

County 
The full, unabbreviated name of the next smaller administrative region than State or Province (county, shire, 

department, etc.) in which the location occurs. 



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Name of 

collector/observer 

A list (concatenated and separated) of names of people, groups, or organizations responsible for recording the 

original occurrence.  

Locality 

The specific description of the place. Less specific geographic information can be provided in other geographic 

terms. This term may contain information modified from the original to correct perceived errors or standardize the 

description. 

Year of collection The four-digit year in which the collection or observation event occurred, according to the Common Era Calendar. 

Month of 

collection 
The ordinal month in which the collection or observation event occurred. 

Day of collection The integer day of the month on which the collection or observation event occurred. 

Basis of record 
The specific nature of the data record. Recommended best practice is to use a controlled vocabulary such as the 

Darwin Core Type Vocabulary (http://rs.tdwg.org/dwc/terms/type-vocabulary/index.htm). 

Name of identifier 
A list (concatenated and separated) of names of people, groups, or organizations that assigned the taxon to the 

subject. 

Identification date 
The date on which the subject was identified as representing the taxon. Recommended best practice is to use an 

encoding scheme, such as ISO 8601:2004(E). 

date of creation Timestamp of creation of this raw occurrence record in the index. 

date of 

modification 
Timestamp of last update of this raw occurrence record in the index. 

date of deletion Timestamp of deletion of this raw occurrence record in the index (obsolete). 

 

 

http://rs.tdwg.org/dwc/terms/type-vocabulary/index.htm


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Table 2. Top 10 resources currently available through GBIF ‘ChecklistBank’ used to build the GBIF 

taxonomical backbone. 

 

Title Version Families Genera Species 

The Catalogue of Life 2012-01-14 8,149 129,461 1,379,178 

Register of Marine and Nonmarine Genera 

(IRMNG) 
2012-01-13 34,119 790,025 1,017,851 

International Plant Names Index 2011-07-13 791 59,766 1,317,317 

NCBI Taxonomy 2012-01-13 7,223 59,404 668,915 

The Integrated Taxonomic Information 

System (ITIS) 
2012-01-14 6,972 45,531 306,358 

World Register of Marine Species 2012-05-02 6,370 41,293 233,811 

Index Fungorum 2011-07-13 2,926 10,569 267,553 

Fauna Europaea 2011-07-13 - 37,214 131,671 

Wikipedia Species Pages - English 2011-09-04 - - - 

GRIN Taxonomy for Plants 2012-01-14 492 12,909 58,773 

 

A full up-to-date list can be accessed at: http://ecat-dev.gbif.org/ 

 

 



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Table 3. Taxonomical rank matching with Catalogue of Life 2011 (February 2012) 

 

Taxonomical rank matching with 

Catalogue of Life 2011 

Percentage of 

the GBIF-

Index 

Percentage of the 

total number of 

species 

K
in

g
d

o
m

 

P
h

y
lu

m
 

C
la

ss
 

O
rd

er
 

F
am

il
y
 

G
en

u
s 

S
p

ec
ie

s (324,247,283 

occurrences) 

(995,974 species 

in total) 

       0.05% 0.27% 

✔       1.38% 0.29% 

✔ ✔      0.53% 0.89% 

✔ ✔ ✔     0.77% 1.35% 

✔ ✔ ✔ ✔    0.76% 2.40% 

✔ ✔ ✔ ✔ ✔   4.54% 13.36% 

✔ ✔ ✔ ✔ ✔ ✔  9.13% 27.98% 

✔ ✔ ✔ ✔ ✔ ✔ ✔ 82.83% 53.47% 

       100.00% 100.00% 

 

Table 4.a. Scientific names and occurrences summary for each ‘unknown’ taxonomic rank (as of 

December 2010). 

 

Taxonomy Scientific name with 

‘unknown’ status 

% of total 

species 

recorded in 

GBIF 

Occurrences with 

‘unknown’ status 

% of total 

occurrences 

recorded in 

GBIF Index 

Kingdom 114,721  7.0% 15,030,014 5.6% 

Phylum 223,433  13.8% 22,180,639 8.3% 

Class 235,857  14.5% 23,071,180 8.6% 

Order 261,706  16.1% 24,605,925 9.2% 

Family 235,089  14.5% 21,508,688 8.1% 

Genus   76,416  4.7%   8,665,178 3.2% 

Species 120,362  7.4% 23,015,905 8.6% 

 



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125 

Table 4.b. Scientific names and occurrences summary for each ‘unknown’ taxonomic rank (as of 

February 2012). 

 

Taxonomy Scientific name with 

‘unknown’ status 

% of total 

species 

recorded in 

GBIF 

Occurrences with 

‘unknown’ status 

% of total 

occurrences 

recorded in 

GBIF Index 

Kingdom 5,153 0.35% 167,208 0.05% 

Phylum 11,305 0.77% 4,640,252 1.43% 

Class 26,266 1.81% 3,963,750 1.22% 

Order 52,007 3.58% 6,304,444 1.94% 

Family 41,932 2.82% 6,015,636 1.86% 

Genus 31,565  2.17%   8,959,016 2.76% 

Species 133,086  9.15% 25,343,834 7.82% 

 

Table 5: Potential misidentification at the Kingdom rank and tentative resolution through CoL 2011 

and more recent version (February 2012) 

 

Kingdom Species Occurrences CoL 2011 

Plantae Zonotrichia albicollis 775,671 Accepted name in CoL 2012 in 

Animalia kingdom 

Plantae Zonotrichia leucophrys 362,767 Accepted name in CoL 2012 in 

Animalia kingdom 

Protozoa Neogloboquadrina pachyderma 141,720 Not in CoL 2011 

Accepted in CoL 2012 

Plantae Zonotrichia atricapilla 106,804 Accepted name in CoL 2012 in 

Animalia kingdom 

Protozoa Globigerinoides ruber 86,563 Not in CoL 2011 

Accepted name in CoL 2012 

Protozoa Globigerina bulloides 82,643 Not in CoL 2011 

Accepted name in CoL 2012 

Protozoa Globigerinita glutinata 74,617 Not in CoL 2011 

Accepted in CoL 2012 



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Protozoa Globorotalia truncatulinoides 64,707 Not in CoL 2011 

Not CoL 2012 

Identified in Marine Species 

Identification Portal (as of Feb 

2012)
20

 

Protozoa Globorotalia inflata 57,706 Not in CoL 2011 

Not in CoL 2012 

Identified in Marine Species 

Identification Portal (as of Feb 

2012)
21

 

Protozoa Orbulina universa 55,290 Not in CoL 2011 

Not in CoL 2012 

Identification Portal (as of Feb 

2012)
22

 

 

Table 6.a: Estimation of the taxonomical misidentification at the Kingdom level (February 2012) 

 

Incorrect Kingdom 

assignment 

Correct Kingdom in CoL 

2011 
Occurrences Species 

Plantae Animalia 1,308,111 90 

Animalia Plantae 1,536 26 

Chromista Animalia 1,504 1 

Chromista Plantae 310 3 

Animalia Fungi 190 23 

Fungi Animalia 186 10 

Protozoa Chromista 100 8 

Plantae Fungi 98 11 

Plantae Protozoa 61 2 

Plantae Chromista 43 6 

                                                      
20 http://species-identification.org/species.php?species_group=zsao&id=1387 
21 http://species-identification.org/species.php?species_group=zsao&id=1384 
22 http://species-identification.org/species.php?species_group=zsao&id=1397 



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Fungi Plantae 41 2 

Animalia Chromista 26 3 

Bacteria Protozoa 22 1 

Plantae Bacteria 13 5 

Protozoa Plantae 9 5 

Protozoa Fungi 6 1 

Fungi Protozoa 2 1 

 Total 1,312,258 198 

 

Table 6.b: Estimation of the taxonomical misidentification at the Phylum level (February 2012) 

 

Incorrect Phylum 

assignment 

Correct Phylum in CoL 

2011 
Occurrences Species 

Bryophyta Magnoliophyta 17,488 24 

Magnoliophyta Arthropoda 2,788 36 

Cnidaria Chordata 2,213 12 

Ochrophyta Arthropoda 1,504 1 

Chordata Magnoliophyta 833 5 

Cyanobacteria Proteobacteria 312 5 

Ochrophyta Rhodophyta 309 2 

Arthropoda Magnoliophyta 297 10 

Arthropoda Chlorophyta 244 1 

Magnoliophyta Chordata 201 5 

Magnoliophyta Cnidaria 176 2 

Labyrinthista Sarcomastigophora 116 4 

Ascomycota Chordata 115 1 

Marchantiophyta Bryozoa 114 1 

Annelida Tardigrada 111 1 

Arthropoda Ascomycota 93 6 

Arthropoda Rhodophyta 82 2 

Magnoliophyta Ascomycota 80 2 



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Mollusca Ascomycota 48 10 

Bryozoa Magnoliophyta 47 4 

Sarcomastigophora Ochrophyta 46 4 

Ascomycota Magnoliophyta 40 1 

Ascomycota Arthropoda 39 3 

Chlorophyta Magnoliophyta 34 2 

Brachiopoda Ascomycota 32 2 

Mollusca Arthropoda 30 2 

Arthropoda Nematoda 26 1 

Arthropoda Ochrophyta 25 2 

Annelida Magnoliophyta 24 1 

Pinophyta Arthropoda 23 1 

Platyhelminthes Arthropoda 22 9 

Rhodophyta Arthropoda 21 1 

Basidiomycota Arthropoda 19 2 

Arthropoda Bacillariophyta 17 5 

Chlorophyta Rhodophyta 16 2 

Chlorophyta Cyanobacteria 13 5 

Echinodermata Arthropoda 12 1 

Bacteroidetes Proteobacteria 11 3 

Magnoliophyta Ochrophyta 8 5 

Bryophyta Rhodophyta 8 1 

Ascomycota Bryozoa 7 2 

Echinodermata Cnidaria 6 1 

Magnoliophyta Rotifera 6 1 

Ciliophora Chlorophyta 4 1 

Arthropoda Mollusca 4 1 

Arthropoda Pinophyta 4 1 

Ascomycota Bacillariophyta 3 2 

Euglenozoa Rhodophyta 3 2 



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Annelida Bacillariophyta 3 1 

Ascomycota Cnidaria 3 1 

Ascomycota Echinodermata 3 1 

Chlorophyta Arthropoda 2 1 

Platyhelminthes Bacillariophyta 2 1 

Magnoliophyta Bacillariophyta 2 1 

Platyhelminthes Acanthocephala 1 1 

Pteridophyta Arthropoda 1 1 

Ascomycota Chlorophyta 1 1 

Cnidaria Ochrophyta 1 1 

Arthropoda Platyhelminthes 1 1 

Dinophyta Rhodophyta 1 1 

    

 Total 27,695 210 

 

Table 6.c: Estimation of the taxonomical misidentification at the Class level (February 2012) 

 

Incorrect Class assignment Correct Class in CoL 2011 Occurrences Species 

Bryopsida Andreaeopsida 21,966 82 

Bryopsida Liliopsida 17,488 24 

Magnoliopsida Insecta 2,429 12 

Hydrozoa Arachnida 2,213 12 

Phaeophyceae Insecta 1,504 1 

Actinopterygii Magnoliopsida 808 2 

Insecta Malacostraca 664 11 

Malacostraca Insecta 359 1 

Phaeophyceae Florideophyceae 309 2 

Insecta Trebouxiophyceae 244 1 

Liliopsida Insecta 239 15 

Lecanoromycetes Dothideomycetes 220 1 



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Magnoliopsida Hydrozoa 176 2 

Liliopsida Andreaeopsida 175 1 

Insecta Magnoliopsida 167 3 

Insecta Liliopsida 116 6 

Labyrinthulea Polycystina 116 4 

Jungermanniopsida Gymnolaemata 114 1 

Polychaeta Eutardigrada 111 1 

Insecta Florideophyceae 82 2 

Magnoliopsida Lecanoromycetes 78 1 

Liliopsida Maxillopoda 59 1 

Insecta Lecanoromycetes 58 1 

Magnoliopsida Arachnida 57 7 

Lobosa Coscinodiscophyceae 54 4 

Stenolaemata Magnoliopsida 47 4 

Zoomastigophora Craspedophyceae 45 3 

Lecanoromycetes Magnoliopsida 40 1 

Lecanoromycetes Insecta 35 1 

Insecta Leotiomycetes 35 5 

Chlorophyceae Liliopsida 34 2 

Rhynchonellata Lecanoromycetes 32 2 

Ostracoda Secernentea 26 1 

Actinopterygii Liliopsida 24 2 

Polychaeta Liliopsida 24 1 

Pinopsida Insecta 23 1 

Turbellaria Arachnida 22 9 

Florideophyceae Insecta 21 1 

Agaricomycetes Insecta 19 2 

Magnoliopsida Actinopterygii 18 2 

Insecta Agaricomycetes 17 5 

Chlorophyceae Florideophyceae 16 2 



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Maxillopoda Liliopsida 14 1 

Asteroidea Entognatha 12 1 

Sphingobacteria Alphaproteobacteria 8 2 

Bryopsida Florideophyceae 8 1 

Insecta Phaeophyceae 8 1 

Lecanoromycetes Gymnolaemata 7 2 

Magnoliopsida Eurotatoria 6 1 

Granuloreticulosea Lecanoromycetes 6 1 

Magnoliopsida Reptilia 6 1 

Magnoliopsida Coscinodiscophyceae 5 3 

Ciliatea Chlorophyceae 4 1 

Magnoliopsida Entognatha 4 1 

Ostracoda Gastropoda 4 1 

Dothideomycetes Insecta 4 2 

Magnoliopsida Liliopsida 4 2 

Insecta Pinopsida 4 1 

Rhabditophora Turbellaria 4 1 

Dothideomycetes Asteroidea 3 1 

Polychaeta Bacillariophyceae 3 1 

Dothideomycetes Eurotiomycetes 3 1 

Euglenida Florideophyceae 3 2 

Flavobacteria Gammaproteobacteria 3 1 

Leotiomycetes Hydrozoa 3 1 

Liliopsida Actinopterygii 2 1 

Magnoliopsida Agaricomycetes 2 1 

Turbellaria Bacillariophyceae 2 1 

Lecanoromycetes Granuloreticulosea 2 1 

Ulvophyceae Insecta 2 1 

Pezizomycetes Leotiomycetes 2 2 

Magnoliopsida Phaeophyceae 2 1 



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Liliopsida Coscinodiscophyceae 1 1 

Eurotiomycetes Dothideomycetes 1 1 

Dinophyceae Florideophyceae 1 1 

Filicopsida Insecta 1 1 

Appendicularia Liliopsida 1 1 

Gastropoda Orbiliomycetes 1 1 

Neoophora Palaeacanthocephala 1 1 

Anthozoa Phaeophyceae 1 1 

Zoomastigophora Synurophyceae 1 1 

Arachnida Turbellaria 1 1 

  50,434 290 

 

Table 7. Percentage (%) temporal quality of the GBIF mobilised data records according to GBIFS 

methodology. 

 December 2010 February 2012 Difference 

Occurrences with no year 

provided 

82,300,746 42,890,654 -47.9% 

Percentage  of the GBIF 

Index 

30.8 % 13,2%  

 

 

 



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Table 8. Breakdown of the year provided in the GBIF mobilised data records.  

RAW refers to records as supplied by the publisher, whereas OCC indicates records available through 

the portal after processing. According to UNZYEC methodology. “Not valid” year includes years 

supplied as <1750 (including explicit zero) or in the future. “Null” includes records with year provided 

as null value but do not include years explicitly stated as a numerical zero value. “Matching/not 

matching” indicates whether the value for year in a record matches between the raw data collected from 

providers (RAW) and the processed data made available through the portal (OCC).  

   Valid OCC Not valid OCC Null value % 

RAW 

Valid 
Matching 63,9%  -- -- 63,9%  

Not matching -- 0,1%  8,6%  8,7%  

Not valid 
Matching -- 0,2%  -- 0,2%  

Not matching 0,1%  -- 0,4%  0,5%  

Null 
Matching -- -- 17,7%  17,7%  

Not matching 5,0%  3,9%  -- 8,9%  

  Total 69,1%  4,3%  26,7%  100,0%  

    31%  

 

Table 9. Breakdown of the temporal and geospatial data availability 

 

 With year Without year Total 

Georeferenced 
255,4  

(78.8%) 

24.4 

(7.5%) 

279.8 

(86.3%) 

Not georeferenced 
25.9 

(8%) 

18.5 

(5.7%) 

44.4 

(13.7%) 

Total 
281.3 

(86.8%) 

42.9 

(13.2%) 

324.4 

(100%) 

(in million occurrences) 

 

Table 10. Summary of potential resource duplicates  

 

Estimated potential  

duplicates 

Number of  

resources combinations 

Total number of  

‘potential’ duplicates’ 

>100.000 42 30.905.772 

10.000 – 100.000 215 5.460.179 



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1.000 – 10.000 560 1.989.482 

100 – 1.000 820 309.663 

 1.637 38.665.096 

 

Table 11. Top 20 potential resources duplicates  

 

Resource Name (1) Resource Name (2) 
'Potential' 

duplicates 

Biodiversidad de Costa Rica Especímenes INBio 12,993,467 

CNIN/Lepidoptera Colección de Referencia de 

Lepidópteros Diurnos Mexicanos de la 

CNIN (Lepidoptera: Papilionoidea) 

(IBUNAM) 

1,779,872 

(Appendix 1) Planktonic foraminifera 

abundances in ODP Site 181-1123 

(Appendix 1) Census data of planktic 

foraminiferal faunas together with 

estimates of mean annual SST for ODP 

Site 181-1123 

1,620,028 

Planktic foraminifera counts of sediment 

core MD95-2040 

Planktonic foraminifera, stable isotope 

record and temperature reconstruction 

of sediment core MD95-2040 

1,514,214 

Pelagic Fish Observations 1968-1999 Pelagic Fish Observations 1968-1999 

(Australian Antarctic Data Centre) 

1,288,625 

(Fig. 2) Abundance of Neogloboquadrina 

pachyderma sinistral in sediment core 

MD95-2040 

Planktic foraminifera counts of 

sediment core MD95-2040 

1,132,425 

(Appendix B5) Distribution of planktic 

foraminifera in DSDP Site 90-594 east of 

New Zealand 

(Appendix 1) Census data of planktic 

foraminiferal faunas together with 

estimates of mean annual SST for 

DSDP Site 90-594 

770,149 

Planktic foraminifera counts of sediment 

core MD95-2040 

(Appendix 4) Stable oxygen isotope 

record of Globigerina bulloides and 

abundances of Neogloboquadrina 

pachyderma and ice-rafted debris in 

sediment core MD95-2040 

761,421 

Birds (KIEE-BI) Birds (MNHM-BI) 593,674 



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Benthic foraminifera abundance in counts of 

Hole PRAD1-2 

Benthic foraminifera abundance in per 

cent of Hole PRAD1-2 

585,766 

(Table 3) Distribution and abundance of 

selected planktonic foraminifera of the 

Pliocene DSDP Hole 41-366A 

(Table 4) Distribution and abundance of 

selected planktonic foraminifera of the 

Pliocene DSDP Hole 41-366A 

515,705 

(Appendix 3) Assemblage of benthic 

foraminifera in sediment core M5/2_KL15 

Relative abundance of benthic 

foraminifera in sediment core 

M5/2_KL15 

480,479 

Birds (UWEP-BI) Birds (KIEE-BI) 479,080 

Planktic foraminifera counts of sediment 

core MD95-2040 

(Fig. 8g-h, 11) Abundance of planktonic 

foraminifera and estimation of sea 

surface temperature and export 

production of sediment core MD95-

2040 

420,615 

Planktic foraminifera abundance in counts of 

Hole PRAD1-2 

Planktic foraminifera abundance in per 

cent of Hole PRAD1-2 

395,733 

(Fig. 2) Abundance of Neogloboquadrina 

pachyderma sinistral in sediment core 

MD95-2040 

Planktonic foraminifera, stable isotope 

record and temperature reconstruction 

of sediment core MD95-2040 

368,550 

(Table 3) Occurrences of planktonic 

foraminifers in samples from ODP Hole 

105-647A 

(Table 2) Occurrences of planktonic 

foraminifers in samples from ODP Hole 

105-647A 

321,750 

Planktic foraminifera abundance of Hole 41-

369A 

(Table 2) Distribution and abundance of 

selected planktonic foraminifera of the 

Pliocene DSDP Hole 41-369A 

286,390 

Hatikka Observation Data Gateway Tiira information service 281,119 

(Appendix A) Stable carbon and oxygen 

isotopes and paleoproductivity 

reconstructions for the last 550 kyr of ODP 

Hole 130-807A from the Ontong Java 

Plateau, Pacific Ocean 

(Appendix A) Benthic foraminiferal 

assemblages in sediments of the last 550 

kyr of ODP Hole 130-807A from the 

Ontong Java Plateau, Pacific Ocean 

249,300 

 

 

 



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Table 12.a: Top 15 species with highest number of data records (December 2010). 

 

 
Occurrences (% of total) Georeferenced 

(%) 

Zenaida macroura 2,163,341 0.8% 99.8% 

Cardinalis cardinalis 1,953,522 0.7% 99.8% 

Passer domesticus 1,892,301 0.7% 94.9% 

Sturnus vulgaris 1,852,357 0.7% 98.0% 

Junco hyemalis 1,735,767 0.6% 99.4% 

Cyanocitta cristata 1,697,922 0.6% 99.8% 

Picoides pubescens 1,673,374 0.6% 99.8% 

Carduelis tristis 1,671,365 0.6% 99.8% 

Carpodacus mexicanus 1,611,847 0.6% 99.7% 

Poecile atricapillus 1,518,874 0.6% 99.9% 

Corvus brachyrhynchos 1,369,418 0.5% 99.8% 

Baeolophus bicolor 1,259,308 0.5% 99.9% 

Sitta carolinensis 1,244,000 0.5% 99.8% 

Turdus migratorius 1,220,210 0.5% 99.5% 

Anas platyrhynchos 1,202,864 0.4% 99.0% 

    

 24,066,470 9.0%  

 

 



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Table 12.b: Top 15 species with highest number of data records (February 2012). 

 

 Occurrences 

(difference with 

2010) 

(% of total) Georeferenced (%) 

Zenaida macroura 2,270,891 

(+4,9%) 

0.7% 99.9% 

Sturnus vulgaris 2,171,136 

(+17,2%) 

0.7% 99.7% 

Passer domesticus 2,029,427 

(+7.2%) 

0.6% 99.0% 

Cardinalis cardinalis 1,779,316  

(-8.9%) 

0.5% 99.9% 

Picoides pubescens 1,776,269 

(+6.2%) 

0.5% 99.9% 

Junco hyemalis 1,731,413 

(-0.2%) 

0.5% 99.3% 

Cyanocitta cristata 1,695,019 

(-0.1%) 

0.5% 99.8% 

Carduelis tristis 1,666,477 

(-0.3%) 

0.5% 99.8% 

Poecile atricapillus 1,612,214 

(+6.2%) 

0.5% 99.9% 

Carpodacus mexicanus 1,609,953 

(-0.1%) 

0.5% 99.8% 

 

 



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Table 13: Breakdown of the species and occurrences richness for Kingdom Animalia and Plantae 

(February 2012) 

 

 Species Occurrences 

Number of 

occurrences/species 

Animalia Plantae Animalia Plantae 

<100 400.088 
(87.5%) 

374.524 

(86.5%) 

4.608.908 
(2.4%) 

 

5.655.807 
(6%) 

100-1.000 46.264 

(10.1%) 

49.643 

(11.5%) 

14.226.733 
(7.4%) 

14.455.647 
(15.3%) 

1.000-10.000 9.178 

(2%) 

7.428 

(1%) 

25.228.569 
(13.1%) 

18.437.768 
(19.5%) 

10.000-100.000 1.483 
(<0.1%) 

1.126 

(<1%) 

41.730.120 
(21.6%) 

33.901.539 
(35.9%) 

100.000-1.000.000 306 

(<0.1%) 

129 

(<0.1%) 

76.851.930 
(39.8%) 

22.025.055 
(23.3%) 

>1.000.000 20 

(<0.01%) 

- 30.641.798 
(15.9%) 

- 

Total 457.340 
(100%) 

432.851 
(100%) 

193.288.058 
(100%) 

99.475.816 
(100%) 



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Table 14. Breakdown of occurrences by continents  

 

 % of GBIF Index 

Region (December 2010) (February 2012) 

Northern America 32.26% 28.19% 

Northern Europe 30.24% 30.06% 

Western Europe 8.34% 11.48% 

Southern Africa 4.12% 3.75% 

Central America 3.61% 4.43% 

South America 2.72% 2.63% 

Southern Europe 2.35% 2.21% 

Australia and New Zealand 2.09% 7.03% 

Eastern Asia 1.72% 2.00% 

Eastern Africa 1.00% 0.66% 

Eastern Europe 0.87% 0.90% 

South-Eastern Asia 0.70% 0.64% 

Caribbean 0.47% 0.44% 

Melanesia 0.47% 0.42% 

Antartica 0.37% 0.30% 

Western Africa 0.31% 0.25% 

Western Asia 0.31% 0.30% 

Middle Africa 0.30% 0.28% 

Southern Asia 0.30% 0.33% 

Northern Africa 0.14% 0.15% 

Micronesia 0.06% 0.06% 

Polynesia 0.05% 0.05% 

Central Asia 0.04% 0.04% 

   

Unknown 7.16% 1.08% 

Total 100% 100% 



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Table 15. Proportion of financial contribution to GBIF (2010) by continents  

   

Region 

% of contribution 

(2011)  

OECD 

countries 

% of GBIF 

Index  

(February 2012) 

Northern America 23.0% 2 28.19% 

Northern Europe 17.8% 8 30.06% 

Western Europe 24.1% 7 11.48% 

Southern Africa 1.0% - 3.75% 

Central America 1.4% 1 4.43% 

South America 0.5% 1 2.63% 

Southern Europe 6.3% 5 2.21% 

Australia and New Zealand 3.6% 2 7.03% 

Eastern Asia 22.1% 2 2.00% 

Eastern Africa <0.1% - 0.66% 

Eastern Europe <0.1% 3 0.90% 

South-Eastern Asia <0.1% - 0.64% 

Caribbean <0.1% - 0.44% 

Melanesia <0.1% - 0.42% 

Antartica <0.1% - 0.30% 

Western Africa <0.1% - 0.25% 

Western Asia <0.1% 2 0.30% 

Middle Africa <0.1% - 0.28% 

Southern Asia <0.1% - 0.33% 

Northern Africa <0.1% - 0.15% 

Micronesia <0.1% - 0.06% 

Polynesia <0.1% - 0.05% 

Central Asia <0.1% - 0.04% 

    

Unknown -  1.08% 

Total 100%  100% 

 

 



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Table 16. Distribution of occurrences per members to the Organisation for Economic Co-operation 

and Development (OECD).  

 

 Occurrences %  

OECD countries 261,377,957 84.45% 

Non-OECD countries 48,126,509 15.55% 

(based on occurrences where geospatial information is provided) 

 

 

Table 17. Distribution of occurrences per country income status. 

 

Country  

Income status Occurrences %  

High 263,073,917 85.00% 

Upper middle 36,197,975 11.70% 

Lower middle 7,292,802 2.36% 

Low 2,939,563 0.94% 

 

 

Table 18.a Grid (1/10 degree) occupancy (February 2012) 

 

Presence in grid All  With valid  

date 

% 

In at least 1 grid 603,532 (60.6%) 485,105 (48.7%) 

In at least 10 grids 150,771 (15.1%) 127,408 (17.0%) 

In at least 20 grids 95,783 (9.6%) 81,057 (10.8%) 

In at least 50 grids 38,520 (3.9%) 31,596 (3.2%) 

In at least 100 grids 18,072 (1.8%) 14,041 (4.2%) 

Total number of species 995,975 (100%) 747,988 (100%) 

[75.1%] 

 

 



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Table 18.b Grid (1/10 degree) occupancy for Animalia.  

 

Presence in grid All  With valid  

date 

% 

In at least 1 grid 307,871 (67.3%) 213,498 (72.3%) 

In at least 10 grids 68,475 (15.0%) 55,561 (18.8%) 

In at least 20 grids 43,761 (9.6%) 36,462 (12.3%) 

In at least 50 grids 17,747 (3.9%) 15,071 (5.1%) 

In at least 100 grids 7,893 (1.7%) 6,756 (2.3%) 

Total number of species 457,600 (100%) 295,380 (100%) 

[64.6%] 

 

Table 18.c Grid (1/10 degree) occupancy for Plantae.  

 

Presence in grid All  With valid  

date 

% 

In at least 1 grid 253,477 (58.5%) 233,720 (62.5%) 

In at least 10 grids 71,332 (16.5%) 61,771 (16.5%) 

In at least 20 grids 44,572 (10.3%) 37,730 (10.1%) 

In at least 50 grids 17,325 (4.0%) 13,454 (3.6%) 

In at least 100 grids 8,690 (2.0%) 6,100 (1.6%) 

Total number of species 433,174 (100%) 373,885 (100%)  

[86%] 

 

Table 19. Grid (1/10 degree) occupancy by Classes (Animalia and Plantae).  

 

Class Total number  

of species  

in GBIF-Index 

Number of 

species 

with sufficient 

grid  

presence (>=20) 

% 

Aves 12,065 5,452 45.2% 

Holocephali 54 19 35.2% 



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Marchantiopsida 156 54 34.6% 

Cephalaspidomorphi 47 14 29.8% 

Elasmobranchii 1,275 286 22.4% 

Sphagnopsida 293 64 21.8% 

Jungermanniopsida 1,750 381 21.8% 

Actinopterygii 26,417 5,047 19.1% 

Pinopsida 1,064 199 18.7% 

Phascolosomatidea 43 8 18.6% 

Sipunculidea 95 16 16.8% 

Nuda 7 1 14.3% 

Bryopsidophyceae 431 52 12.1% 

Asteroidea 1,664 199 12.0% 

Ulvophyceae 716 69 9.6% 

Liliopsida 65,196 6,237 9.6% 

Thaliacea 75 7 9.3% 

Bangiophyceae 108 9 8.3% 

Holothuroidea 1,178 98 8.3% 

Echinoidea 1,290 106 8.2% 

Appendicularia 51 4 7.8% 

Hydrozoa 2,704 210 7.8% 

Amphibia 5,665 409 7.2% 

Haplomitriopsida 14 1 7.1% 

Magnoliopsida 324,251 22,677 7.0% 

Filicopsida 21,260 1,477 6.9% 

Anthocerotopsida 58 4 6.9% 

Bivalvia 11,956 801 6.7% 

Cephalopoda 2,871 174 6.1% 

Insecta 223,933 13,146 5.9% 

Malacostraca 23,167 1,203 5.2% 

Gastropoda 33,760 1,701 5.0% 

Myxini 64 3 4.7% 



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Crinoidea 552 25 4.5% 

Maxillopoda 7,864 345 4.4% 

Cubozoa 25 1 4.0% 

Anthozoa 7,747 278 3.6% 

NULL 5,548 159 2.9% 

Aplacophora 223 4 1.8% 

Stenolaemata 859 14 1.6% 

Demospongiae 4,589 63 1.4% 

Neoophora 201 2 1.0% 

Rhynchonellata 2,033 12 0.6% 

Cycadopsida 352 2 0.6% 

Hexactinellida 504 1 0.2% 

Leiosporocerotopsida 1 - - 

Sarcopterygii 36 - - 

Remipedia 17 - - 

Cephalocarida 9 - - 

Somasteroidea 2 - - 

Eucycliophora 2 - - 

Lingulata 163 - - 

Pleurastrophyceae 7 - - 

Pedinophyceae 2 - - 

Pararotatoria 2 - - 

Cestoda 150 - - 

Pauropoda 185 - - 

Eoacanthocephala 25 - - 

Phylactolaemata 32 - - 

Myxosporea 72 - - 

Eutardigrada 107 - - 

Collembola 1 - - 

Archoophora 5 - - 

 



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Table 20. Grid (1/10 degree) occupancy by Families Top 10 (Animalia and Plantae).  

 

Taxonomical ranks  

(Kingdom –Phylum – Class – Order) 

Families with sufficient grid  

presence (>=20) for a  

number of species greater  

than 100 

Number 

of 

species 

% 

of total 

Total 

number  

of species 

in the family 

Animalia - Chordata - Aves - Anseriformes Anatidae  133 (73.1%) 182 

Animalia - Chordata - Actinopterygii - Myctophiformes Myctophidae  173 (68.4%) 253 

Animalia - Chordata - Actinopterygii - Perciformes Carangidae  103 (66.9%) 154 

Animalia - Chordata - Aves - Ciconiiformes Laridae  102 (66.2%) 154 

Animalia - Chordata - Aves - Passeriformes Thraupidae  139 (48.9%) 284 

Animalia - Chordata - Aves - Passeriformes Tyrannidae  212 (48.0%) 442 

Animalia - Chordata - Aves - Passeriformes Emberizidae  159 (46.8%) 340 

Animalia - Chordata - Aves - Piciformes Picidae  115 (46.4%) 248 

Animalia - Chordata - Aves - Ciconiiformes Accipitridae  132 (43.7%) 302 

Animalia - Chordata - Aves - Passeriformes Furnariidae  128 (40.3%) 318 

 

 

 



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Figure 1. Typical flow of data discovered and published through the GBIF network. 

 



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Figure 2. Data mining methodology employed during content assessment exercise carried out by the 

GBIF Secretariat. 

 



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Figure 3. Data mining methodologies employed during content assessment exercise carried out by the 

University of Navarra. 

 



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Figure 4. Evolution of the percentage of geo-referenced records (1800-2010). 

 

 

 



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Figure 5. Evolution of incomplete records (taxonomical*temporal*geospatial) in the GBIF-Index 

 

 



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Figure 6.a. Data records by Kingdom (Dec 2010). 

 

 

Figure 6.b. Data records by Kingdom (February 2012). 



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Figure 7.a. Data records by Phylum (December 2010). 

 

 

Figure 7.a. Data records by Phylum (February 2012). 



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Figure 8.a. Data records by Class (December 2010). 

 

 

Figure 8.b. Data records by Class (February 2012). 



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Figure 9. Treemap of occurrences according to taxon group, down to Class. Cell surface proportional to 

number of occurrences. Blue: Invertebrates; purple: Vertebrates; green: Higher plants; yellow: Algae and 

Ferns; brown: Fungi; red: unicellular organisms. 

 



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Figure 10. Top 10 data resources publishing maximum number of data records (December 2010). 

 



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Figure 11. Kingdom wise distribution of data records by Year (Feb 2012). 



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Figure 12.a. Plantae kingdom wise distribution of occurrences by year. 

 



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Figure 12.b. Animalia kingdom wise distribution of occurrences by year (February 2012). 

 



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Figure 13. Aves wise distribution of occurrences by year (February 2012) 

(red: Aves, blue: other classes, black: all classes) 

 



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Figure 14. Basis of records distribution of occurrences by year (February 2012) 

(red: observation, blue: specimen, black: all types) 

 



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Figure 15. Distribution of voting and associate country participants (February 2012) 

 



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Figure 16. Distribution of occurrences by latitude (February 2012) 

 

Figure 17. Distribution of species richness by latitude (February 2012) 



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Figure 18. Distribution of the average number of occurrences per species by latitude (February 2012) 

 



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Figure 19. Georeferenced records (dots) published by the largest data providers in Europe. Colors 

represent distinct providers. (From Otegui et al., 2009). 

 



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Figure 20. Records available in the GBIF-Index by date 

 

 

 

 

Figure 21. Variation of the records available in the GBIF-Index by date (December 2010 – February 



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2012) 

 

 

 

Figure 22. Distribution of distinct species collected/observed over time (February 2012) 

(red: Plantae, green: Animalia, black: both) 

 

 

 

Figure 23. Distribution of distinct resources contributing to the GBIF-Index over time (February 2012) 



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(red: Plantae, green: Animalia) 

 

 

 

Figure 24. Grid occupancy (1/2 degree grid) of the GBIF-Index over time (February 2012) 

 

 



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Figure 25. Grid occupancy (1/10 degree grid) of the GBIF-Index by Kingdom (February 2012) 



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Figure 26. Grid occupancy (1/10 degree grid) of the GBIF-Index by Classes (February 2012) 



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Figure 27. Distribution of families for FFU-ENM – Kingdom Animalia (February 2012) 

 



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Figure 28. Distribution of families for FFU-ENM – Kingdom Plantae (February 2012) 

 



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Figure 29. Selection of fit-for-use records in the GBIF Index for ecological niche modelling (ENM).