Taxonomic bias in biodiversity data and societal preferences. — TEI-tagged view

Julien TroudetpersName, Philippe GrandcolaspersName, Amandine BlinpersName, Régine Vignes-LebbepersName, Frédéric LegendrepersName (2017date). “Taxonomic bias in biodiversity data and societal preferences..” Scientific Reportstitle·j.

Object of study — taxonomic biasterm·dom in GBIFterm·mth biodiversity occurrenceterm·key data across 24 classes

Research question — how societal preferencesterm·dom and taxonomic research relate to biodiversity dataterm·key gathering

Contribution — societal preferencesterm·dom, not research activityterm·dom, strongly correlate with taxonomic biasterm·dom in biodiversity dataterm·key

Full text — tagged

The reference list is not part of the text we hold for this paper. Our copy was extracted from the publisher's page and the bibliography did not survive that extraction, so the article below ends before its references. Nothing has been removed by us.

Taxonomic biasterm·dom in biodiversity dataterm·key and societal preferencesterm·dom.

Abstract Studying and protecting each and every living species on Earth is a major challenge of the 21 st century. Yet, most species remain unknown or unstudied, while others attract most of the public, scientific and government attention. Although known to be detrimental, this taxonomic biasterm·dom continues to be pervasive in the scientific literature, but is still poorly studied and understood. Here, we used 626 million occurrencesterm·key from the Global Biodiversity Information Facilityterm·mth (GBIFterm·mth), the biggest biodiversity dataterm·key portal, to characterize the taxonomic biasterm·dom in biodiversity dataterm·key. We also investigated how societal preferencesterm·dom and taxonomic research relate to biodiversity dataterm·key gathering. For each species belonging to 24 taxonomic classes, we used the number of publications from Web of Science and the number of web pages from Bing searches to approximate research activityterm·dom and societal preferencesterm·dom. Our results show that societal preferencesterm·dom, rather than research activityterm·dom, strongly correlate with taxonomic biasterm·dom, which lead us to assert that scientists should advertise less charismatic speciesterm·key and develop societal initiatives (e.g. citizen scienceterm·key) that specifically target neglected organisms. Ensuring that biodiversity is representatively sampled while this is still possible is an urgent prerequisite for achieving efficient conservation plans and a global understanding of our surrounding environment.

Introduction Since the first Convention on Biological Diversity in 1992, biodiversity and the consequences of its destruction have become a central concern for biologists 1 – 3 . From scientists to the lay public or policy makers and practitioners, the need to study and protect biodiversity is growing, and scientists have shown that it must be tackled at the gene, species and ecosystem level 4 . Within a context of global change and accelerated biodiversity loss, this necessity has become a major concern and challenge for the 21 st century 5 , 6 . However, discussions on biodiversity often only focus on a small subset of species, while most of the eukaryotic biodiversity remains unknown or ignored 7 , 8 .

Taxonomic biasterm·dom, also referred to as taxonomic chauvinismterm·dom 9 , is pervasive in biodiversity research. This bias stems from disparities in our knowledge of different organisms, and in the extent to which they are the focus of scientific research, across a wide range of biological disciplines. Some organisms – mostly plants and vertebrates – are over-represented in various scientific fields 7 , 9 , 10 , are more likely to raise funds 11 , or are considered ecologically more important than others 12 . It has been shown, however, that focusing on a few, often charismatic, species, prevents reaching global conclusions and developing efficient conservation plans 7 , 13 , 14 . Rare, small or uncharismatic creatures do play pivotal functions in ecosystems 15 , 16 . In addition, biomimicry, i.e. the application of the properties of living organisms to technology, and bioprospecting activities, i.e. the search for new natural products in wild species, cannot be performed efficiently when less than 1% of known species have been carefully studied 17 . Thus, given its scientific and societal significance, describing taxonomic biasterm·dom in the study of biodiversity and understanding its underlying causes are undeniable priorities.

Taxonomic biasterm·dom in science has long been recognized 10 , 18 , 19 but its origin is less clear. Obviously, some organisms are more difficult to study than others because they live in remote habitats, are local endemics or are microscopic and difficult to identify 20 . But these intrinsic features alone cannot fully explain the pervasive taxonomic biasterm·dom observed in science. Two hypotheses on the role of two extrinsic factors can then be put forward: the ‘taxonomic research’ hypothesis and the ‘societal preferencesterm·dom’ hypothesis. The ‘societal preferencesterm·dom’ hypothesis suggests that societal intereststerm·dom influence and bias the choice of study organismsterm·dom 21 , 22 . The ‘taxonomic research’ hypothesis implies that scientific reasons and limitations will lead and orientate biodiversity dataterm·key gathering 20 . Because of the intricate interactions between scientists, citizens and funding agencies, and their mixed influence 23 , the underlying mechanisms are unclear. Nevertheless, these hypotheses deserve to be explored and confronted at a global taxonomic scale. Moreover, the recent development of citizen scienceterm·key 24 may have increased the impact of societal preferencesterm·dom. Here, to investigate the relative impact of ‘societal preferencesterm·dom’ and ‘taxonomic research’ on biodiversity dataterm·key, we used the number of webpagesterm·mth from Bing searches and the number of publications retrieved from Web of Science, as proxies, respectively (see Methods).

The study of biodiversity is a daunting task – ca. 10 million species are estimated to inhabit the planet – that requires deploying a considerable workforce to gather and analyse biodiversity dataterm·key 25 . Fortunately, for ethical and scientific reasons 26 – 28 , data sharing practices and tools like the Global Biodiversity Information Facilityterm·mth (GBIFterm·mth) were developed, facilitating access to species occurrenceterm·key records. The GBIFterm·mth portal was chosen because it hosts the biggest open access primary biodiversity database and, even though the big data paradigm does not mean that big datasets are devoid of flaws, they offer a significant potential for new and broad insights 29 . Moreover, although open access primary biodiversity dataterm·key are heterogeneous, resulting from the good will of contributors and not from a well-planned sampling protocol 30 , they reflect our knowledge and practices in the study of biodiversity. Thus, they can be used to investigate taxonomic biasterm·dom on a large geographical and taxonomic scale.

Here, we aim to quantify taxonomic biasterm·dom in biodiversity dataterm·key using 626 million of GBIF-mediated occurrencesterm·key covering 24 classes of organisms. After careful data validation procedures, we characterized biodiversity gaps, a necessary first step before trying to bridge these gaps 31 . We did not assess the validity of GBIFterm·mth mediated data, which is an issue that has already been raised and discussed repeatedly 32 – 36 . Instead, we quantified taxonomic biasterm·dom and imprecision (i.e. when an occurrenceterm·key has been identified not at the species level but only at a higher taxonomic level) and related them to information provided in the occurrenceterm·key records information (data origin, record date and locality). We tested the relative impact of societal preferencesterm·dom and taxonomic research on taxonomic biasterm·dom, using public interestterm·dom (i.e. the number of webpagesterm·mth) and taxonomic research quantity (i.e. the number of publications) as explaining variables, respectively. Opposing these hypotheses enabled us to suggest future directions for developing strategies for representative sampling of biodiversity while this is still possible.

Results Global taxonomic coverage and taxonomic precisionterm·key 24 classes of organisms recorded in the GBIFterm·mth database had more than 1 million occurrencesterm·key, with widely variable numbers of occurrenceterm·key recordings (Table 1 ). More than half of the records were bird (Avesterm·key) occurrencesterm·key (345 million occurrencesterm·key; 53% of the GBIFterm·mth mediated data), even though birds represent only 1% of the total number of species catalogued in GBIFterm·mth. Avesterm·key was also the class with the highest median number of occurrencesterm·key per species (med /sp = 371). By contrast, and despite being three times more speciose, Arachnida had only 2.17 million occurrencesterm·key and one of the lowest median numbers of occurrencesterm·key per species (med /sp = 3). The lowest values of the median number of occurrencesterm·key per species (i.e. below 7) were found for several classes of Arthropods (Insectaterm·key, Maxillopoda, Arachnida, Malacostraca), some fungi (Agaricomycetes) and diatoms (Bacillariophyceae). Magnoliopsida and Insectaterm·key, two highly speciose classes, were the ones with the highest number of species recorded. Only six of the 24 classes had a median number of occurrencesterm·key per species higher than 20. Table 1 Biodiversity occurrenceterm·key data statistics for 24 taxonomic classes. nb occ (millions) p >1 (thousands) med /sp (mad) Taxonomic precisionterm·key Avesterm·key 345.11 12.82 371 (541) 0.99 Magnoliopsida 118.21 261.01 19 (25) 0.92 Insectaterm·key 46.78 352.78 3 (3) 0.77 Liliopsida 36.75 68.99 15 (19) 0.95 Actinopterygii 14.18 30.73 27 (37) 0.92 Mammalia 10.78 11.53 15 (21) 0.88 Bryopsida 6.06 18.85 7 (9) 0.95 Gastropoda 5.85 46.99 7 (9) 0.69 Reptilia 4.98 11.30 24 (34) 0.88 Lecanoromycetes 4.97 17.79 8 (10) 0.93 Polypodiopsida 4.91 12.65 23 (31) 0.95 Amphibia 3.94 5.89 54 (76) 0.91 Agaricomycetes 3.80 23.53 4 (4) 0.93 Malacostraca 2.73 30.16 6 (7) 0.73 Globothalamea 2.68 4.07 10 (13) 0.74 Arachnida 2.17 38.11 3 (3) 0.77 Bivalvia 2.02 14.02 9 (12) 0.70 Bacillariophyceae 1.96 11.19 2 (1) 0.70 Maxillopoda 1.87 9.98 4 (4) 0.58 Pinopsida 1.57 0.91 110 (160) 0.95 Jungermanniopsida 1.41 6.93 7 (9) 0.91 Polychaeta 1.29 8.77 6 (7) 0.73 Florideophyceae 1.07 5.78 17 (24) 0.88 Anthozoa 1.03 8.64 7 (9) 0.59 Total for 24 classes 626.13 1013.39 7 (9) 0.94 Total in the GBIFterm·mth 649.79 1200.38 6 (7) 0.93 The number of occurrencesterm·key (nb occ ) was obtained before the use of any filter. The number of species (p >1 ) corresponds to the number of unique scientific names having at least one occurrenceterm·key. In bold are the eight classes selected to study the taxonomic biasterm·dom at the ordinal level. med /sp is the median number of occurrencesterm·key per species and mad is the associated median deviation. Taxonomic precisionterm·key is the proportion of taxa identified at least at the species level.

With regard to taxonomic precisionterm·key, 94% of GBIFterm·mth occurrencesterm·key were identified (at least) at the species level (88% not counting Avesterm·key). The lowest levels of taxonomic precisionterm·key were found in Maxillopoda and Anthozoa (58% and 59% of occurrencesterm·key, respectively), whereas the highest levels were found in the different classes of Plantae (91 to 95% of occurrencesterm·key in Magnoliopsida, Liliopsida and Pinopsida), Fungi (93% in Agaricomycetes and Lecanoromycetes) and Avesterm·key (99%).

Taxonomic biasterm·dom Of the 2.2 million of species referenced in the GBIFterm·mth taxonomic backbone, 1.2 million species can be found in the GBIFterm·mth published datasets and 1.01 million belong to the 24 classes selected here. The number of recorded species per class was not proportional to their known species richness, highlighting a strong taxonomic biasterm·dom. Avesterm·key and Insectaterm·key were, by far, the most over- and under-represented classes, respectively. Mammalia, Liliopsida, Actinopterygii, Amphibia and Magnoliopsida were also over-represented, whereas Arachnida, Gastropoda, Agaricomycetes, Malacostraca and Bacillariophyceae were under-represented (Fig. 1 and Supplementary Fig. 1 ). This taxonomic biasterm·dom was already apparent more than 50 years ago, meaning that classes that were over- or under-represented in the 1950′s are still over- or under-represented today (Fig. 2 ). Nonetheless, we found an increase in taxonomic biasterm·dom over time, mostly due to the faster accumulation of data for birds compared to other classes (Fig. 3 top ; 283 million bird occurrencesterm·key recorded between 2000 and 2016). Recently, data has accumulated faster than ever before for most classes (Fig. 3 top, middle and Supplementary Fig. 2 ) however, for Amphibia, Reptilia and Florideophyceae, the number of occurrencesterm·key recorded per year has stagnated or even declined over the past 40 years (Fig. 3 bottom ). Figure 1 Taxonomic biasterm·dom in biodiversity occurrenceterm·key data. The vertical line at x = 0 depicts the ‘ideal’ number of occurrencesterm·key per class, where each class is sampled proportionally to its number of known species. Green and red bars show the classes that are over- and under-represented in the GBIFterm·mth mediated database compared to this ‘ideal’ sampling, respectively. Insects lack >200 millions occurrencesterm·key and birds have an excess of >200 millions occurrencesterm·key compared to an unbiased taxonomic sampling. Because birds and insects are greatly over- and under-represented, respectively, an inverse hyperbolic sine transformation was used for the x-axis. Figure 2 Evolution over time of the taxonomic biasterm·dom for each class. The larger the circle, the higher the deviation from I, the ‘ideal’ number of occurrencesterm·key per class if no taxonomic biasterm·dom is observed. Red dots indicate negative deviations (i.e. shortfall in occurrencesterm·key = under-represented classes); green dots indicate positive deviations (i.e. excess of occurrencesterm·key = over-represented classes). Figure 3 Biodiversity occurrencesterm·key recorded in GBIFterm·mth between 1900 and 2006. For each curve, the number of occurrencesterm·key was plotted yearly. Top : black = all 24 classes considered together, yellow = Avesterm·key; Middle : yellow = Magnoliopsida, blue = Insectaterm·key, green = Liliopsida; Bottom : green = Actinopterygii, yellow = Mammalia, light blue = Reptilia, dark blue = Amphibia, orange = Florideophyceae, purple = Globothalamea.

Twenty out of 24 classes had more than 50% of their described species referenced at least once in GBIFterm·mth, and, for 14 of these classes, these statistics rose to 70% or more. By contrast, only 35% of Insectaterm·key and 36% of Arachnida species were referenced at least once in GBIFterm·mth (Fig. 4 top ). Furthermore, species were more or less intensely recorded in GBIFterm·mth: 21% had only one occurrenceterm·key (i.e. 212,911 species), 44% had between 2 and 19 occurrencesterm·key (i.e. 446,643 species), and 35% had 20 or more occurrencesterm·key (i.e. 353,843 species). This density of recordings per species was unevenly distributed between classes (Fig. 4 top ). Only three classes (Avesterm·key, Amphibia and Actinopterygii) had more than half of their species with at least 20 occurrencesterm·key, and only Avesterm·key had more than half of its species “decently” sampled (i.e. with 20 spatially distinct occurrencesterm·key). This contrasted strikingly with the Arthropod classes, where, at best, 9% of species were “decently” sampled, even though Malacostraca had 68% of its species recorded in the GBIFterm·mth. Figure 4 Taxonomic heterogeneity in sampling, occurrenceterm·key data origin and quality for 24 taxonomic classes. Top : Proportion of species per class recorded in GBIFterm·mth with at least one occurrenceterm·key (light green: p >1 ), with more than 20 occurrencesterm·key (green: p >20 ), and with more than 20 spatially distinct occurrencesterm·key (i.e. “decently” sampled – dark green: p >20d ). For all classes, except Avesterm·key, less than 1/3 of all species are “decently” sampled. Classes are ranked according to their proportion of “decently” sampled species. Middle : Occurrenceterm·key origin ( basisOfRecord ) for each class. Some classes like Amphibia have a high proportion of occurrencesterm·key based on specimens (blue: living or preserved specimen, material samples or fossils), whereas others like Avesterm·key have a majority of occurrencesterm·key based on observation (orange: machine or human observation, literature). Grey bars show occurrencesterm·key where the record basis is unknown. Classes are ranked according to their proportion of specimen-based occurrencesterm·key. Bottom : Data incompleteness. Proportion of occurrencesterm·key with spatial (purple) or temporal (yellow) inaccuracies for each class. Spatial inaccuracy corresponds to an occurrenceterm·key lacking coordinates or tagged has having geospatial issues by GBIFterm·mth. Temporal inaccuracy corresponds to a sampling event with no specified month or year. Classes are ranked according to their proportion of occurrencesterm·key with spatial issues.

This taxonomic biasterm·dom recurs at a lower taxonomic scales. We selected eight classes and showed that, for all of them, some orders were better represented in the GBIF-mediated database than others (Table 2 and Supplementary Table S1 ). For instance, the median number of occurrencesterm·key varied largely within each class, some orders having medians that were more than 50 times higher than those of other orders of the same class (e.g. m Phaethontiformes = 5504 vs m Sphenisciformes = 2; m Chiroptera = 107 vs m Cetacea = 2). The smallest difference in medians was found within poorly represented classes, in which all orders have medians less than 20. Taxonomic precisionterm·key was also estimated and found to be highly heterogeneous between orders of the same class. The largest differences were observed within Insectaterm·key. More than 90% of occurrencesterm·key were identified at the species level for four orders (Siphonaptera, Odonata, Orthoptera and Psocodea), whereas taxonomic precisionterm·key ranged from 35 to 0.5% for Grylloblattodea, Mantophasmatodea and Strepsiptera. Taxonomic precisionterm·key within Mammalia was also very heterogeneous ranging from 22% (Perissodactyla) to 99% (Monotrema and Notoryctemorphia). Conversely, taxonomic precisionterm·key was less variable between orders of Lecanoromycetes (over 89% taxonomic precisionterm·key for all orders), Magnolopsida (82% and above) and Avesterm·key (77% and above). Table 2 Biodiversity occurrenceterm·key data statistics for the orders (maximum 10) with the most occurrencesterm·key within eight selected classes. Order nb occ (millions) p > 1 (thousands) med/sp (mad) Taxonomic precisionterm·key Order nb occ (millions) p > 1 (thousands) med/sp (mad) Taxonomic precisionterm·key Agaricomycetes Agaricales 1.80 14.14 4 (4) 0.93 Lecanoromycetes Lecanorales 2.86 9.24 8 (10) 0.93 Russulales 0.53 1.96 7 (9) 0.94 Teloschistales 0.75 2.38 11 (15) 0.94 Polyporales 0.53 2.72 4 (4) 0.92 Peltigerales 0.58 1.45 15 (21) 0.92 Hymenochaetales 0.27 0.74 7 (9) 0.91 Pertusariales 0.26 1.10 7 (9) 0.91 Boletales 0.25 1.39 4 (4) 0.90 Ostropales 0.20 2.75 5 (6) 0.90 Cantharellales 0.10 0.59 4 (4) 0.98 Umbilicariales 0.09 0.14 21 (30) 0.98 Thelephorales 0.08 0.41 5 (6) 0.97 Baeomycetales 0.08 0.16 15 (21) 0.97 Auriculariales 0.05 0.26 4 (4) 0.98 Candelariales 0.07 0.08 19 (27) 0.98 Gomphales 0.04 0.30 7 (9) 0.92 Acarosporales 0.05 0.35 6 (7) 0.92 Corticiales 0.02 0.25 4 (4) 0.95 Agyriales 0.01 0.06 12 (16) 0.95 Amphibia Anura 2.85 5.03 54 (76) 0.82 Magnoliopsida Asterales 17.02 33.12 16 (21) 0.82 Caudata 1.06 0.60 172 (246) 0.93 Lamiales 13.03 28.01 17 (22) 0.93 Gymnophiona 0.02 0.16 14 (19) 0.89 Fabales 11.27 24.13 25 (34) 0.89 Caryophyllales 10.14 15.14 17 (22) 0.96 Avesterm·key Passeriformes 185.57 7.34 368 (525) 0.95 Rosales 9.45 13.76 12 (16) 0.95 Charadriiformes 37.63 0.48 2538 (3760) 0.94 Malpighiales 7.06 19.68 22 (30) 0.94 Anseriformes 34.12 0.21 5135 (7609) 0.93 Gentianales 5.06 21.70 17 (22) 0.93 Accipitriformes 14.83 0.34 579 (855) 0.95 Ericales 4.74 14.07 20 (27) 0.95 Piciformes 13.81 0.48 467.5 (650) 0.93 Myrtales 4.63 14.71 26 (34) 0.93 Columbiformes 11.41 0.38 261 (366) 0.93 Apiales 4.55 5.93 20 (27) 0.93 Pelecaniformes 11.01 0.16 1517 (2248) 0.90 Mammalia Rodentia 3.62 3.59 25 (36) 0.90 Gruiformes 4.98 0.28 148.5 (219) 0.94 Chiroptera 2.23 1.31 107 (154) 0.94 Suliformes 4.57 0.09 777 (1150) 0.93 Carnivora 1.62 0.89 10 (13) 0.93 Apodiformes 4.17 0.53 565 (802) 0.97 Diprotodontia 0.63 0.22 48.5 (70) 0.97 Insectaterm·key Lepidoptera 17.41 64.11 3 (3) 0.76 Artiodactyla 0.63 0.98 8 (10) 0.76 Coleoptera 9.77 96.27 3 (3) 0.93 Soricomorpha 0.48 0.67 16 (22) 0.93 Hymenoptera 8.23 58.02 3 (3) 0.88 Cetacea 0.40 0.54 2 (1) 0.88 Diptera 4.70 63.99 2 (1) 0.90 Lagomorpha 0.28 0.19 20.5 (29) 0.90 Hemiptera 1.97 33.81 2 (1) 0.22 Perissodactyla 0.20 0.52 9 (12) 0.22 Trichoptera 1.27 6.69 3 (3) 0.78 Primates 0.12 0.80 12 (16) 0.78 Odonata 1.20 3.48 11 (15) 0.90 Reptilia Squamata 4.49 9.16 37 (52) 0.90 Orthoptera 0.96 9.57 3 (3) 0.78 Testudines 0.37 0.63 16 (22) 0.78 Ephemeroptera 0.40 1.27 4 (4) 0.61 Crocodylia 0.05 0.16 3 (3) 0.61 Plecoptera 0.23 1.92 4 (4) 0.88 Rhynchocephalia 0.00 0.02 2 (1) 0.88 Statistics and abbreviations as in Table 1 .

Explanatory variables In GBIFterm·mth, recorded occurrencesterm·key can refer to a collected specimen (or object) or an observation. The proportion of specimen- vs observation-based occurrencesterm·key differed greatly between classes (Fig. 4 middle ). Some classes had 90% or more of their occurrencesterm·key based on observation (e.g. Globothalamea, Avesterm·key), whereas others had between 70 and 80% of occurrencesterm·key based on specimens (e.g. Amphibia, Gastropoda, Reptilia and Bivalvia). Between these extremes, the relative proportion of specimen- vs observation-based occurrencesterm·key in the 24 classes formed a continuum, with a few classes having an almost equivalent number of occurrencesterm·key of both origins (e.g. Insectaterm·key). Three of the four groups of Tetrapods (Amphibia, Reptilia and Mammalia) had occurrencesterm·key based mainly on specimens, whereas birds had the highest proportion of observation-based occurrencesterm·key (94%).

Although temporal and geographical information can also be added to a record, these fields are informed with more or less precision. The proportion of spatial and temporal inaccuracies (p -time and p -space ) differed greatly between classes (Fig. 4 bottom ). Only 4% of Avesterm·key occurrencesterm·key had temporal and/or spatial inaccuracies, whereas 48% and 55% of Gastropoda occurrencesterm·key had spatial and temporal inaccuracies, respectively. Along with Gastropoda, the classes with the highest inaccuracy rates were Amphibia, Bivalvia and Reptilia, and these four classes were the ones with the highest proportion of specimen-based occurrencesterm·key.

All Multiple Correspondence Analyses (MCA) showed that occurrencesterm·key recorded before 1975 were grouped with specimen-based occurrencesterm·key and with occurrencesterm·key with spatial issues (Fig. 5 ). Conversely, more recent occurrencesterm·key were grouped with complete and observation-based occurrencesterm·key. Most of the classes, and in particular Amphibia, Reptilia and Florideophyceae, were in the upper right section of the graph (old, incomplete specimen-based occurrencesterm·key), whereas Avesterm·key was in the lower left section, characterized by recent and complete observations. Figure 5 Relation between age, origin and quality of the occurrenceterm·key data for 24 taxonomic classes. Graph showing the first two axes of a Multiple Correspondence Analysis (MCA) performed on 5 million random occurrencesterm·key. Labels in black represent the categories considered for all occurrencesterm·key. Classes’ names (in green) are placed at the average position of the class occurrencesterm·key. Occurrenceterm·key age contains eight time intervals and an Unknown Year category; data origin contains three categories: Specimen for specimen-based occurrencesterm·key, Observation for observation-based occurrencesterm·key, and Unknown for unknown origins; data quality contains four categories: Temporal issue for the lack of year or month, Spatial issues for the lack of coordinates, Both issues and No issue .

Public interestterm·dom (inferred from the number of web pages referenced by a search engine) and taxonomic research effort (inferred from the number of publications in Web of Science) were assessed and used in Generalized Linear Modelsterm·mth (GLMterm·mth). The number of web pages (with the keyword “species” added to the species’ scientific name) ranged from 0 to 1.8 million with a median number of 1,480 pages for the 24,000 best-represented species (1,000 species for each class) and 22 pages for the randomly chosen species. The number of publications, tallied for 453 orders, ranged from 0 (for eight orders) to 72 426 for Coleoptera, with a median number of 229 publications.

For most classes, GLMsterm·mth suggested a positive and significant correlation between public interestterm·dom and the number of occurrencesterm·key in GBIFterm·mth (Table 3 ). A few negative correlations were found but were never significant. The quantity of research was not significantly correlated with the number of occurrencesterm·key for most classes, and, when the correlation was significant, it was either positive (e.g. Mammalia) or negative (e.g. Agaricomycetes). A significant correlation between public interestterm·dom and research quantity was found in 10 out of 47 cases. Table 3 GLMterm·mth results assessing the link between research quantity, public interestterm·dom and their combined interaction on the amount of biodiversity dataterm·key per class. Class Selected species Nb species Public interestterm·dom influence pval Research influence pval Interaction influence pval Actinopterygii Best 930 (+) 0.000* (+) 0.780 (-) 0.023* Random 883 (+) 0.000* (+) 0.004* (-) 0.014* Agaricomycetes Best 951 (+) 0.000* (-) 0.002* (+) 0.055 Random 738 (+) 0.000* (-) 0.032* (+) 0.659 Amphibia Best 916 (+) 0.573 (-) 0.000* (+) 0.058 Random 875 (+) 0.076 (+) 0.024* (-) 0.714 Anthozoa Best 910 (+) 0.304 (-) 0.273 (+) 0.000* Random 744 (+) 0.002* (-) 0.101 (+) 0.198 Arachnida Best 930 (-) 0.376 (-) 0.021* (+) 0.000* Random 799 (+) 0.029* (-) 0.624 (-) 0.632 Avesterm·key Best 930 (-) 0.376 (-) 0.021* (+) 0.000* Random 850 (+) 0.000* (+) 0.182 (+) 0.277 Bacillariophyceae Best 885 (+) 0.000* (-) 0.616 (+) 0.174 Random 780 (+) 0.000* (-) 0.011* (-) 0.230 Bivalvia Best 928 (+) 0.000* (-) 0.082 (+) 0.160 Random 755 (+) 0.000* (+) 0.313 (-) 0.087 Bryopsida Best 905 (+) 0.000* (+) 0.000* (-) 0.079 Random 846 (+) 0.000* (+) 0.366 (-) 0.672 Florideophyceae Best 904 (+) 0.000* (-) 0.070 (+) 0.000* Random 818 (+) 0.000* (+) 0.002* (+) 0.665 Gastropoda Best 718 (+) 0.683 (+) 0.183 (+) 0.045* Random 521 (+) 0.033* (+) 0.110 (-) 0.738 Globothalamea Best 886 (+) 0.005* (+) 0.000* (-) 0.599 Random 793 (+) 0.015* (-) 0.310 (+) 0.106 Insectaterm·key Best 967 (+) 0.000* (-) 0.246 (-) 0.216 Random 769 (+) 0.013* (+) 0.369 (-) 0.601 Jungermanniopsida Best 905 (+) 0.000* (+) 0.405 (+) 0.013* Random 850 (+) 0.001* (+) 0.999 (+) 0.558 Lecanoromycetes Best 961 (+) 0.000* (-) 0.667 (-) 0.851 Random 804 (+) 0.000* (-) 0.584 (+) 0.560 Liliopsida Best 931 (+) 0.000* (+) 0.060 (-) 0.168 Random 856 (+) 0.000* (+) 0.000* (+) 0.615 Magnoliopsida Best 959 (+) 0.000* (+) 0.003* (-) 0.205 Random 768 (+) 0.001* (-) 0.170 (+) 0.863 Malacostraca Best 906 (+) 0.000* (-) 0.002* (-) 0.001* Random 757 (+) 0.156 (-) 0.392 (+) 0.154 Mammalia Best 913 (+) 0.000* (+) 0.024* (-) 0.000* Random 800 (+) 0.000* (+) 0.049* (-) 0.100 Maxillopoda Best 889 (+) 0.000* (+) 0.017* (-) 0.540 Random 835 (+) 0.012* (-) 0.898 (+) 0.510 Pinopsida 796 (+) 0.000* NA NA Polychaeta Best 790 (+) 0.000* (-) 0.053 (+) 0.389 Random 712 (+) 0.010* (-) 0.212 (+) 0.519 Polypodiopsida Best 938 (+) 0.000* (-) 0.174 (+) 0.335 Random 785 (+) 0.000* (+) 0.048* (-) 0.473 Reptilia Best 940 (+) 0.180 (+) 0.627 (+) 0.190 Random 794 (+) 0.040* (+) 0.104 (+) 0.448 A positive correlation between public interestterm·dom and the number of occurrencesterm·key was found in most classes. Values followed by * have a significant p-value at a 5% threshold. (+) indicates a positive influence while (-) indicates a negative influence of the variable on the number of occurrencesterm·key. Nb species = number of species used in the GLMterm·mth after removing outliers; pval = p-values; NA = not available (because no order information and therefore no research quantity was available for Pinopsida).

Discussion Taxonomic biasterm·dom, i.e. the fact that some taxa are more investigated than others, is a well-known problem for the study of biodiversity. How can we infer general principles and put in place effective strategies for biodiversity conservation when some taxa are over-studied while others are ignored? Although known for a long time, taxonomic biasterm·dom is currently receiving an increasing attention. However most studies on taxonomic biasterm·dom have been restricted to a few taxa or areas 9 , 19 , 30 , 37 – 39 . By analysing data from the biggest biodiversity dataterm·key repository available, we emphasize here the prevalence of taxonomic biasterm·dom in biodiversity dataterm·key.

Unsurprisingly, and as previously reported regarding GBIFterm·mth mediated data 33 , we show that birds are over-represented in biodiversity dataterm·key. Some studies highlighted the over-representation of birds in diverse disciplines ranging from behavioural ecology to evolution and conservation 9 , 40 . The ever-growing number of observations that bird enthusiasts report undoubtedly amplify bias. Other vertebrate classes (Actinopterygii and Mammalia, and to a lesser extent Reptilia and Amphibia) are relatively well represented in the GBIF-mediated database, as are most Plantae classes, especially Liliopsida and Magnoliopsida. On the other hand, Arthropods (Insectaterm·key, Arachnida, Malacostraca and Maxillopoda) and Mollusca (Gastropoda and Bivalvia) are under-represented, with insects being particularly mis-represented. Birds and insects are obvious outliers but, beyond these two classes, the taxonomic biasterm·dom in biodiversity dataterm·key remains blatant.

Taxonomic biasterm·dom is even more apparent when considering “decently” sampled species, namely species sampled in at least 20 different points on the globe. For any study requiring a number of different sampling points, like those relying on niche modelling, the field of investigation is restricted to vertebrates and plants on land and Actinopterygii in aquatic habitats. Invertebrates and fungi, on the other hand, have to be virtually ignored because of insufficient data at the scale of the planet. Given that these neglected organisms have a high diversity and play crucial roles in diverse ecosystems 2 , 3 , 15 , this situation will inevitably result in an unbalanced fundamental knowledge of biodiversity, risky guesses and uninformed conservation decisions 7 , 14 , 19 , 41 , 42 . A similar taxonomic biasterm·dom, with equivalent outcomes, is found between orders within each class.

More disturbingly, we show that the taxonomic biasterm·dom in biodiversity dataterm·key, although known for a few decades 18 , has remained broadly the same since the 1950’s. The evolution of taxonomic biasterm·dom over time has rarely been investigated, and never at a large taxonomic scale. Bonnet et al . 9 , focusing on vertebrates, showed there had been no changes in taxonomic chauvinismterm·dom in ecology and behavioural research. Similarly, Stahlschmidt 21 reported a static taxonomic biasterm·dom from 2001 to 2010 in parental care research. He noted, however, that the absolute number of publications on parental care in birds increased significantly over this period. Along the same lines, Di Marco et al . 8 emphasized that, in conservation science, some historically under-studied taxa were receiving more attention today, but underlined that a taxonomic biasterm·dom toward taxa that are threatened or less rich in biodiversity still exists. Our results confirm this status quo situation at a larger taxonomic scale: most classes that were under- or over-represented in the GBIFterm·mth mediated database in 1950 are still under- or over-represented today. Even though most classes are better recorded today than before, the gap between birds and the rest of biodiversity (i.e. ∼99% of known biodiversity) increases with time because bird occurrencesterm·key accumulate much faster than other class occurrencesterm·key. Thus, while most of biodiversity remains to be described 25 , the same taxa are preferentially studied and recorded over and over again.

The large taxonomic scale approach we used here comes with a few limitations. First, it must be emphasized that big datasets, like all sampling, are biased so that conclusions must be drawn accordingly 29 . Second, this large-scale approach implies that each species is equivalent and directly comparable, which is obviously arguable. Third, it neglects scale effects: species richness in insects is so large that whatever the means used, this class is always at risk of being understudied. Still, this approach enabled us to highlight the pervasiveness of taxonomic biasterm·dom and bring new insights into the nature of this bias.

The underlying causes of taxonomic biasterm·dom must be identified if one wants to reverse it. We suggest here that societal preferencesterm·dom, and not taxonomic research, orientate which biodiversity dataterm·key are gathered. The most popular species on the web are also the species with the most records in GBIFterm·mth. Moreover, the best-supported model, where the interaction between taxonomic research effort and the number of web pages was taken into account, indicated a significant effect of public interestterm·dom on biodiversity dataterm·key gathering. The role played by the general public in the study and conservation of biodiversity has already been established: positive links exist between public opinion, scientific productions and conservation policies, however the directionality of these interactions remains unclear 23 , 43 . Our analyses confirm these interactions but do not allow us to clarify the causality issue. Although inevitable biases occur when using internet searches, such as the inability to distinguish scientific web pages from other web pages, particularly at such a broad taxonomic scale, “ many (30–80%) web pages containing the scientific names of species have little or nothing to do with scientific research “ 22 indicating that our results are presumably related to societal preferencesterm·dom. Surveys to determine public preferences could help counteract this issue but should be carried out at large taxonomic scales.

Studying invasive alien species, Wilson et al . 22 concluded that “ the choice of research subject in biology reflects the interests of society ”. Because of public interestterm·dom, and not specifically for their scientific interest, studies of ‘public-aware’ taxa are more likely to be funded and receive more funding 11 , 23 , 44 . Our results provide further evidence of this trend, highlighting the active role of the general public in biodiversity dataterm·key collecting, given that, for instance, the biggest dataset was provided by eBird (211 million occurrencesterm·key), a collective enterprise devoted to birds and partly relying on citizen scienceterm·key 45 . For multiple reasons (e.g. the difficulty of obtaining permits, more and more endangered species, citizen scienceterm·key programmes, population decline, etc.), less specimen-based occurrencesterm·key are now reported. Amphibia, Gastropoda and Reptilia, the three classes with the highest proportion of specimen-based occurrencesterm·key, are also the classes with a decreasing or stabilizing trend in data accumulation. We thus anticipate an increasing bias between taxa mostly known from observation-based occurrencesterm·key and taxa mostly known from specimen-based occurrencesterm·key. In addition, a lot of records are old and incomplete, and could soon, or already, be obsolete 46 , which risks reinforcing the taxonomic biasterm·dom against classes with relatively few recent occurrencesterm·key.

The good news is that the observed taxonomic biasterm·dom can be corrected. Shine & Bonnet 47 showed how snakes, which were under-represented in ecology among terrestrial vertebrates until 1990, have grown in popularity in this scientific field, illustrating that acting on taxonomic biasterm·dom is possible. Similarly, for most classes, occurrencesterm·key accumulate at a much faster rate now that 50 or 30 years ago, which is an encouraging trend. Obviously, this trend can also result from changes in data-sharing practices, and not simply from overall data collection. Still, as we are accumulating more and more biodiversity dataterm·key, the question of how to efficiently sample the whole of biodiversity remains open. The biodiversity knowledge chain is complex and its links influence one another. Scientists play a key role in this chain. However, our results show that they alone cannot ensure that biodiversity is sampled adequately and that societal preferencesterm·dom are too important to be ignored. Scientists must reach out to the lay audience 22 , 23 , 48 and advertise under-represented organisms to the general public. For instance, the crucial role of protists in ecosystem functioning probably seems too obscure to generate any interest from the general public 49 . New practices or methods, from citizen scienceterm·key to metagenomics, should also help increase public awareness and would have even more impact if programmes were developed jointly between science and society 20 , 50 . The expected gain would be colossal and would achieve more than a well-balanced sampling of biodiversity: new vocations in science, more efficient citizen sciencesterm·key programmes, influence on funding and political decisions, etc.

Citizen scienceterm·key and data gathering by non- professionals might be decisive in the near future. The contribution of citizen scienceterm·key to the most over-represented class of GBIF-mediated data, birds, dates back more than a hundred years 51 . Different fields of research from molecular engineering 52 to quantum science 53 and neurosciences 54 have greatly benefited from the involvement of non-professionals, and it has been shown that a well-made citizen scienceterm·key programme can could produce in two years the same amount of data that scientists can produce in a decade 55 . Yet, the use of citizen scienceterm·key for studying taxa that are not as charismatic as birds or mammals is still in its infancy 55 , 56 . Efforts must be made to develop such initiatives, probably by relying on new technologies such as smartphones and dedicated applications 55 , 57 . Citizen scienceterm·key cannot, and must not, replace standard scientific practices 58 ; they are complementary approaches with different strengths and limitations. However, citizen scienceterm·key could substantially contribute to our knowledge of biodiversity, especially if adapted programmes devoted to neglected taxa are highlighted 24 .

Considering the whole of biodiversity, and not only charismatic organisms, is a prerequisite for the development of efficient conservation plans, of prolific bioprospecting activities, and for enhancing our understanding of biodiversity on a global scale 8 , 17 , 59 . Many international projects have been developed since the Convention on Biological Diversity, illustrating an increased awareness of the astonishing diversity of functions and services that biodiversity supports 2 , 3 . Nevertheless, while biodiversity declines at an unprecedented rate 60 , taxonomic biasterm·dom is still a burden on biodiversity studies. It is urgent that we get rid of this burden and that we start embracing the whole of biodiversity.

Methods Dataset We downloaded all available occurrenceterm·key records from the GBIFterm·mth data portal in June 2016 ( http://doi.org/10.15468/dl.hqesx6 ). 649 million occurrencesterm·key were saved as a Darwin Core archive. Occurrencesterm·key from this archive were extracted and imported into a SQL database, where data were indexed to reduce the computation time of subsequent queries. We focused on 24 taxonomic classes out of the 297 referenced in GBIFterm·mth, excluding classes with less than 1 million occurrencesterm·key (9.4 million occurrencesterm·key from 19,000 species, had no class affiliation). We ended up with 626 million occurrencesterm·key (NB occ ) and 1.01 million species, representing more than 96% of the total number of occurrencesterm·key and 84% of the total number of species in GBIFterm·mth. All statistics were computed from this dataset.

Taxonomic errors: imprecision and bias For each class, we quantified the level of taxonomic precisionterm·key as the proportion of occurrencesterm·key with information at the species level or lower. We assessed taxonomic biasterm·dom by computing and comparing the following statistics for each class: the total number of occurrencesterm·key (nb occ ), the median number of occurrencesterm·key per species (med /sp ) and the median absolute deviation, the proportion of species with at least one occurrenceterm·key (p >1 = n >1 /N), and the proportion of species with at least 20 occurrencesterm·key (p >20 = n >20 /N), where n >i is the number of species with at least i occurrencesterm·key and N is the number of known species for a given class. N was obtained using the GBIFterm·mth taxonomic backbone (accessible at: http://doi.org/10.15468/39omei ), by counting the number of distinct species with either the ‘accepted’ or ‘doubtful’ taxonomic status. This method excluded synonyms. Furthermore, we computed p >20d , the proportion of species with at least 20 spatially distinct occurrencesterm·key. Two occurrencesterm·key were considered spatially distinct when, using a global grid of 10*10 km cells based on the pseudocylindrical equal-area map projection Eckert IV, they fell in two different cells. We chose a threshold of 20 spatially distinct occurrencesterm·key because it is a common threshold in niche modelling analyses 61 . Occurrencesterm·key without spatial coordinates were excluded when computing the number of spatially distinct occurrencesterm·key. We calculated how each class deviates from an ‘ideal’ sampling I, where each class is sampled proportionally to its number of known species (N). I = NB occ *(N/N tot ) where N tot is the total number of known species. To investigate the evolution of taxonomic biasterm·dom over time, we excluded i) occurrencesterm·key without a collection year and ii) occurrencesterm·key recorded during the last 10 years because of the lag between recording and integration in the GBIFterm·mth database (S. Gaiji, pers. comm.). The ‘ideal’ sampling I was calculated every ten years between 1956–2006 and deviations from these ‘ideal’ samplings were plotted for each class.

Statistics were computed at the ordinal level for Agaricomycetes, Amphibia, Avesterm·key, Insectaterm·key, Lecanoromycetes, Magnoliopsida, Mammalia and Reptilia using the same methods. These classes were chosen due to their relatively high number of occurrencesterm·key and/or species, and because of the diversity of patterns they exhibited in our preliminary results. We also tried to cover a large taxonomic range (Tetrapods, Arthropoda, Plantae, Fungi) to include as much biodiversity as possible.

Explanatory variables computed from the GBIFterm·mth dataset Data origin . In GBIFterm·mth, the origin of an occurrenceterm·key can be specified using a controlled vocabulary in the ‘basisOfRecord’ field. We delimited three categories, depending on whether recorded occurrencesterm·key refer to a specimen (or object), an observation, or was of unknown origin. The “specimen” category (O spec ) contained: fossil specimens, living specimens, material samples and preserved specimens. The “observation” category (O obs ) consisted of: human observations, machine observations, unclassified observation and literature. The third category corresponded to the “unknown” option (O unk ).

Date and Locality precision (Data completeness) For each class, the proportion of temporal (p -time ) and spatial inaccuracies (p -space ) was computed as follows: p -time = O -time /nb occ and p -space = O -space /nb occ , where O -time is the number of occurrencesterm·key lacking information regarding either the month, year or both, and O -space is the number of occurrencesterm·key missing coordinates or flagged as having geospatial issues in GBIFterm·mth.

External explanatory variables Taxonomic research and societal preferencesterm·dom Taxonomic research was quantified through the number of publications. We searched the Web of Science portal (apps.webofknowledge.com) with the following query for each order: “taxonomy” AND (“[order name]” OR “[family names]”), over the 1900–2016 period. The number of systematists, who are the producers of primary biodiversity dataterm·key, would have been a better indicator but this could not be obtained due to the current architecture of Web of Science. We therefore used the publication metrics for taxonomic research from Web of Science as done previously 37 .

Public interestterm·dom for a given species was estimated through the number of web page results, a proxy that has been proven to be reliable 22 . These numbers were obtained from Bing searches using the exact Latin name (e.g. “ Corvus corax ”) or a combination of the Latin name and the keyword “species” (e.g. “ Corvus corax ” + species). Bing and Google searches yielded similar results for the 4,000 species tested with both search engines (Supplementary Fig. 3 ), but only Bing allowed us to carry out a high number of searches. For each class, these searches were performed on the 1,000 species with the most occurrencesterm·key (except for Pinopsida, which only had 902 species recorded in the GBIFterm·mth) and then on a further 1,000 randomly chosen species. Each search was run twice to check for consistency.

Statistical analyses We favored medians (m) over means because of their robustness to outliers. For the same reason, we used the median absolute deviation (mad), which represents the median of the absolute deviation from the median, as a measure of statistical dispersion. In all analyses needing spatial or temporal information, O -space and O -time occurrencesterm·key were ignored, respectively.

The relationship between data origin, completeness and year of record was investigated using multiple correspondence analyses (MCA). Analyses were done on three samples of five million random occurrencesterm·key from our dataset. The variables were: class (24 categories), year of the record (categories: ‘<1900’, ‘1900–1949’, ‘1950–1974’, ‘1975–1999’, ‘2000–2004’, ‘2005–2009’, ‘2010–2014’, ‘> = 2015’), data origin (categories: specimen, observation, unknown), data completeness (categories: no problem, missing temporal information, missing spatial information, missing both). Because results can be hard to interpret when categories with very few observations are used 59 , each analysis was performed a second time ventilating the categories represented in less than 0.5% of the dataset.

To explore the relative impact of public interestterm·dom and taxonomic research quantity on taxonomic biasterm·dom, we used generalized linear modelsterm·mth 62 , 63 (GLMterm·mth). For each of the 24 classes, we looked at the effect of these two variables and their interaction on the number of occurrencesterm·key per species in GBIFterm·mth. We used an identical model for all classes, which was fitted using a negative binomial distribution to take into account overdispersion. Half of the GLMsterm·mth were computed using the 1,000 best-represented species in GBIFterm·mth (Best), while the other half used 1,000 random species referenced in GBIFterm·mth (Random). Only one GLMterm·mth was computed for Pinopsida because they had less than 1,000 species. Initial models were strongly influenced by extreme values and had poor resolution. Therefore we excluded outliers, which were identified when the number of occurrencesterm·key or web search results was >Q 3 + 4 * IQR, where Q 3 is the third quartile value and IQR is the interquartile range. For each GLMterm·mth, we checked the validity of the model by plotting the values of residuals against predicted values to test the homogeneity of residuals.

We performed all analyses using the R statistical software version 3.3.2 ( https://www.R-project.org ) with associated packages: FactoMineR 64 , ggplot2 65 , gridExtra 66 , MASS 67 , plyr 68 and scales 69 .

Electronic supplementary material Supplementary information