Keywords: Primary biodiversity data, specimen, observation, database, ancillary data, biodiversity occurrences, big data

Primary biodiversity data represent the fundamental elements of any study in systematics and evolution. They are, however, no longer gathered as they used to be and the mass-production of observation-based occurrences is overthrowing the collection of specimen-based occurrences.

Although this change in practice is a major upheaval with significant consequences in the study of biodiversity, it remains understudied and has not attracted yet the attention it deserves. Analyzing 536 million occurrences from the Global Biodiversity Information Facility (GBIF) mediated data, we show that this spectacular change affects the 24 eukaryote taxonomic classes we targeted: from 1970 to 2016 the proportion of occurrences marked as traceable to tangible material (i.e. specimen-based occurrences) fell from 68 to 18 %; moreover, most of those specimen based-occurrences cannot be readily traced back to a specimen because the necessary information is missing. Ethical, practical or legal reasons responsible for this shift are known, and this situation appears unlikely to be reversed.

Still, we urge scholars to acknowledge this dramatic change, embrace it and actively deal with it. Specifically, we emphasize why specimen-based occurrences must be gathered, as a warrant to allow both repeating evolutionary studies and conducting rich and diverse investigations. When impossible to secure, voucher specimens must be replaced with observation-based occurrences combined with ancillary data (e.g. pictures, recordings, samples, DNA sequences). Ancillary data are instrumental for the usefulness of biodiversity occurrences and we show that, despite improving technologies to collate them, they remain rarely shared. The consequences of such a change are not yet clear but we advocate collecting material evidence or ancillary data to ensure that primary biodiversity data collected lately do not partly become obsolete when doubtful.

observations, biodiversity data accumulate faster than ever [START_REF] Bisby | The Quiet Revolution: Biodiversity Informatics and the Internet[END_REF][START_REF] Kitchin | Big Data, new epistemologies and paradigm shifts[END_REF]), but the link to specimens in natural history collections is being lost. We argue here that the change in biodiversity data gathering [from specimen-based (SB) to observation-based (OB) occurrences] has strong consequences in systematics and evolutionary biology. This change must then be analysed, acknowledged, and its effects integrated in our practices; the sooner, the better.

Biodiversity occurrences are not equivalent to one another and, according to their nature (SB or OB, old or recent, with ancillary data or not, etc.), they offer more or less research opportunities (Fig. 1). Generally, a biodiversity occurrence contains a taxonomic identification, a localisation and a date [START_REF] Ariño | Approaches to estimating the universe of natural history collections data[END_REF]. These three pieces of information can be provided for SB or OB occurrences, and, in both cases, can be accurate or not, and more or less precise. Accuracy and precision mostly depend on the collector's skills and equipment, but they are also related to the nature of the primary biodiversity occurrence. In addition, a biodiversity occurrence, be it SB or OB, can be complemented with ancillary data such as pictures or samples, increasing the information content of biodiversity occurrences and their usefulness [START_REF] Gaiji | Content assessment of the primary biodiversity data published through GBIF network: Status, challenges and potentials[END_REF][START_REF] Garrouste | The "wild shot": photography for more biology in natural history collections, not for replacing vouchers[END_REF]Fig. 1). Most ancillary data, however, cannot be gathered a posteriori of an OB occurrence, whereas it can be for a SB occurrence.

Thus, the way primary biodiversity data are collected impacts their provided information content for current and future investigations. This change in practice (proportionally much more OB than SB occurrences) is a major upheaval with spectacular consequences for systematics and evolutionary studies. Since the very beginning of systematics, specimens have been collected and used to inventory the diversity of life and later to decipher the relationships within the tree of life (Giribet 2015). Natural history collections (NHC), which now support biodiversity, morphology or molecular databases, have been put together and used for species identification and description, comparative anatomy, and phylogenetic studies, to name a few practices embodying their usefulness [START_REF] Garner | Are natural history collections coming to an end as time-series? Front[END_REF][START_REF] Kemp | The endangered dead[END_REF][START_REF] Buerki | Collections-based research in the genomic era[END_REF]Fig. 1). Databases containing mainly unvouchered observations would not be as profitable as data repositories composed of specimens but they have positive sides in return (e.g. the pace at which biodiversity occurrences are shared; datasets with higher statistical value, etc.) and can be complemented with diverse media. It is pivotal to accommodate to this shift now to make the best of it. As often, good legacy of previous practices and fruitful innovations must be retained and developed, while bad legacy must be put aside [START_REF] Godfray | Challenges for taxonomy[END_REF].

We argue that specimens belong to the good legacy and are too important to be put aside.

Even though specimens, like digital data, are not everlasting, their existence offers a high guarantee for repeatability in the study of biodiversity [START_REF] Huber | The importance of voucher specimens, with practical guidelines for preserving specimens of the major invertebrate phyla for identification[END_REF][START_REF] Schilthuizen | Specimens as primary data: museums and 'open science[END_REF][START_REF] Turney | Non-repeatable science: assessing the frequency of voucher specimen deposition reveals that most arthropod research cannot be verified[END_REF][START_REF] Ceríaco | Photography-Based Taxonomy Is Inadequate, Unnecessary, and Potentially Harmful for Biological Sciences[END_REF][START_REF] Grandcolas | Loosing the connection between the observation and the specimen: a byproduct of the digital era or a trend inherited from general biology?[END_REF][START_REF] Gutiérrez | Specimen collection crucial to taxonomy[END_REF] and the possibility to apply technical advances on them. The recent revolutions in systematics, i.e. the use of DNA and much recently the advent of next generation sequencing (NGS), illustrate this point because they rely on specimens or samples [START_REF] Pellens | Biodiversity conservation and phylogenetic systematics: preserving our evolutionary heritage in an extinction crisis[END_REF][START_REF] Gutiérrez | Specimen collection crucial to taxonomy[END_REF]. Even better, these technical advances are qualified as revolutionary because specimens are available to use them on, enabling us to engage in new research agenda (e.g. [START_REF] Anmarkrud | Complete mitochondrial genomes of eleven extinct or possibly extinct bird species[END_REF]. Similarly, in the era of phylogenomics, several authors have recently underlined the necessary revival of morphological studies in systematics, which, again, rely on specimens (e.g. [START_REF] Jenner | Accepting Partnership by Submission? Morphological Phylogenetics in a Molecular Millennium[END_REF][START_REF] Wiens | The Role of Morphological Data in Phylogeny Reconstruction[END_REF][START_REF] Smith | Morphology's Role in Phylogeny Reconstruction: Perspectives from Paleontology[END_REF][START_REF] Yassin | Phylogenetic classification of the Drosophilidae Rondani (Diptera): the role of morphology in the postgenomic era[END_REF][START_REF] Pyron | Post-molecular systematics and the future of phylogenetics[END_REF][START_REF] Wanninger | Morphology is dead -long live morphology! Integrating MorphoEvoDevo into molecular EvoDevo and phylogenomics[END_REF][START_REF] Wipfler | A review of methods for analysing insect structures -the role of morphology in the age of phylogenomics[END_REF].

Beyond specimens, good practices about items providing additional information content (e.g. samples or pictures) should be advocated to assist the change in biodiversity data gathering (e.g.

Garrouste 2017

). Every item of data associated with an occurrence (be either an unvouchered observation or a specimen) is an additional evidence to fight against one or several of the seven currently identified biodiversity shortfalls [START_REF] Hortal | Seven shortfalls that beset large-scale knowledge of biodiversity[END_REF]. The Linnean shortfall, the gap between the described species and the actual number of species, undoubtedly requires specimen collection [START_REF] Ceríaco | Photography-Based Taxonomy Is Inadequate, Unnecessary, and Potentially Harmful for Biological Sciences[END_REF][START_REF] Dubois | The need for reference specimens in zoological taxonomy and nomenclature[END_REF][START_REF] Pine | What Is an 'Extant' Type Specimen? Problems Arising from Naming Mammalian Species-Group Taxa without Preserved Types[END_REF]; see [START_REF] Pape | Taxonomy: Species can be named from photos[END_REF] for an opposite opinion). But other shortfalls could be filled, in certain cases, as efficiently with samples or pictures than with specimens. A picture or a DNA sample of a well-known species would efficiently contribute to reduce the Prestonian shortfall, i.e. the lack of knowledge about the abundance of species and their population dynamics in space and time [START_REF] Cardoso | The seven impediments in invertebrate conservation and how to overcome them[END_REF]. Indeed, when doubtful, a species attribution can be checked consulting the picture or sequencing DNA, so that observational occurrences with ancillary data constitute appropriate datasets for evolutionary studies.

When a paradigm shift is on the way, measures are required to guide this shift and ensure its maximal usefulness now and in the future. Here, we test whether a shift in the study of biodiversity (i.e. primary data are not SB anymore but mainly OB) is on the rise and whether it is restricted to some organisms. We also investigate when it started, whether it comes with more ancillary and precise data, and how it might affect the fields of systematics and evolution. Analysing 536 million occurrences from the GBIF (Global Biodiversity Information Facility) in 24 taxonomic classes, we show empirically that this shift is widely shared across eukaryotes. From then on, because current decisions will shape the future and to ensure maximal benefits of biodiversity occurrences in systematics and biodiversity research in general, we provide guidelines for primary biodiversity data gathering and sharing, guidelines easily met from individual research to broad citizen science programs.

MATERIALS AND METHODS

Data set

We downloaded all the data available from the GBIF portal in June 2016 (http://doi.org/10.15468/dl.hqesx6). These 649 million occurrences were saved as a Darwin Core archive (www.tdwg.org). Occurrences from this archive were extracted and imported into a SQL database, where they were indexed to reduce computation time of later queries. We focused on 24 taxonomic classes out of the 297 referenced in the GBIF, excluding the classes with less than 1 million occurrences (9.4 million occurrences, distributed into 19 thousands species, had no class affiliation).

This filtering reduced the dataset to 626 million of occurrences (NBocc) and 1.01 million species, representing more than 96 % of the total number of occurrences and 84 % of the total number of species in the GBIF. Finally, because we computed statistics over time, data without a year of collection were excluded. We ended up with 536 million occurrences, which is the dataset used to compute all statistics. A lag exists between an occurrence event recording and its integration in the GBIF database (S. Gaiji comm. pers.) and it might be related to the type of occurrences (i.e. specimenor observation-based). Consequently, even though we show results until 2016, we avoid interpreting the last ten years results in the plots to limit the risk of hazardous conclusions.

Data Quantity

To calculate data quantity in the GBIF mediated data, the number of occurrences collected per year was counted. Then, a data accumulation curve was computed.

Data Origin

In the GBIF, the origin of an occurrence can be specified using a controlled vocabulary in the 'basisOfRecord' field. As in [START_REF] Troudet | Taxonomic bias in biodiversity data and societal preferences[END_REF], we distinguished "specimen-based occurrences" linked to tangible material from "observation-based occurrences" (or disconnected observations). The category "specimen" regrouped fossil specimen, living specimen, material sample, and preserved specimen. The category "observation" regrouped human observation, machine observation, observation, and literature. Literature occurrences could have been placed in the specimen category, a solution we have discarded because the link to specimen is not straightforward. This choice does not affect the conclusions drawn here because only 497,231 occurrences (i.e. <0.1 %) have a literature origin. A third category, corresponding to the option "unknown", was also kept.

Supporting Files

Supporting files (or links leading to such files) can be associated to an occurrence in the GBIF.

They contribute to improve the traceability between a taxon name and a given occurrence. Two kinds of supporting files are mainly used: DNA sequences and multimedia files. For each of those supporting data, we computed 1) the quantity of both DNA sequences and multimedia files per year, and 2) the yearly numbers of DNA sequences and multimedia files divided by the yearly number of occurrences. This last number approximates (because a same occurrence can have several supporting files) the proportion of occurrence with supporting files.

To further understand the structure of the GBIF mediated data we also classified occurrences with supporting files according to their origin (i.e. 'basisOfRecord'). Thus, we distinguished the number of specimen-based occurrences with multimedia supporting files from the observation-based and unknown occurrences with multimedia supporting files.

Development of Data Completeness

Primary biodiversity data are all the more useful as they are associated to a lot of information.

The DarwinCore format currently in use in the GBIF [START_REF] Wieczorek | Darwin Core: An Evolving Community-Developed Biodiversity Data Standard[END_REF] provides 234 columns to record information as diverse as the ethology of a living specimen or the geological strata of a fossil specimen. A complete occurrence would never require these 234 columns to be filled, because there are always inapplicable columns for a given occurrence. Nevertheless, the development of data completeness over time can be estimated from the evolution of the proportion of columns containing information. We thus averaged the proportion of non-null (non-empty) columns per occurrence per year.

Development of Taxonomic and Spatial Precision

In general, a primary biodiversity occurrence is associated to a scientific name, which can be more or less precise depending on the skills of the identifier but also on the state and availability of taxonomic knowledge. We estimated taxonomic precision (in number and proportion per year) differentiating occurrences identified at least at the species level from supra-specific occurrences. The proportion of occurrences identified at the species or infraspecific level was used to estimate the taxonomic precision of the GBIF mediated occurrences. As for the development of spatial imprecision, it was calculated as the number and proportion, per year, of occurrences lacking coordinates or flagged in the GBIF as data with coordinate issues.

RESULTS AND DISCUSSION

A Shift in the Recording of Primary Biodiversity Data

In the current context of biodiversity crisis, numerous pleas have incited the scientific community to collect as much biodiversity data as possible, out of the fear it might disappear before we even knew of its existence (May 2004;[START_REF] Butchart | Global Biodiversity: Indicators of Recent Declines[END_REF]). These calls have been heard and, indisputably, biodiversity data accumulate faster than ever (Fig. 2 and Supporting Information), a trend most classes of organisms exhibit even though for a few of them the trend is not so strong [START_REF] Troudet | Taxonomic bias in biodiversity data and societal preferences[END_REF]. The >57 million occurrences submitted to the GBIF in 2014, more than five times the amount of data submitted ten years earlier (i.e. 11 million occurrences in 2004), embody this report (Supporting Information). With this spectacular acceleration, the amount of data available to scientists is so huge that the study of biodiversity has entered into the "Big Data" era [START_REF] Hampton | Big data and the future of ecology[END_REF][START_REF] Joppa | Filling in biodiversity threats gaps[END_REF][START_REF] Kelling | Data-Intensive Science: A New Paradigm for Biodiversity Studies[END_REF]). Arguably, this trend reflects the advent of new scientific communities taking or renewing their interest in biodiversity. It may also suggest an increasing appeal of the public for biodiversity. In both cases, this situation offers new opportunities that will enrich our understanding of biodiversity and generate a higher awareness of conservation issues or of biodiversity shortfalls [START_REF] Hortal | Seven shortfalls that beset large-scale knowledge of biodiversity[END_REF]. Other benefits followed such as an increased power in statistical analyses because of larger datasets or the possibility to tackle issues at large taxonomical, temporal or spatial scales [START_REF] Rosenheim | Ecoinformatics (Big Data) for Agricultural Entomology: Pitfalls, Progress, and Promise[END_REF]. However, the large volume of data is also a curation challenge that must be handled to avoid passing on a dubious source of knowledge to future generations because of a fall in data quality [START_REF] Howe | Big data: The future of biocuration[END_REF]), a criticism regularly brought up for GBIF mediated data (e.g. [START_REF] Yesson | How Global Is the Global Biodiversity Information Facility?[END_REF]). This acceleration is triggered, at least partly, by a change in the way biodiversity data are recorded. The origin of biodiversity data has shifted from a majority of specimen-based (SB) to a majority of observation-based (OB) occurrences. This shift has been previously suspected [START_REF] Gaiji | Content assessment of the primary biodiversity data published through GBIF network: Status, challenges and potentials[END_REF]) and we show here that, from 1970 to 2016, the proportion of occurrences traceable to tangible material (i.e. specimens) fell from 68 % to 18 %. This decrease is not due to recent digitization initiatives because we used the collection/observation date, and not the digitization date, to compute these statistics. This result applies to the 24 classes studied, except for a few eccentric cases such as Globothalamea and Polychaeta (Figs. 2 and3). Likely, these exceptions relate to specific practices for observing, collecting or curating these organisms, or to their low volume of primary biodiversity data, which might cast doubt on their atypical trends. Besides, this shift might be slightly inflated because it presumably requires less time to integrate OB than SB occurrences in the GBIF. Still, ignoring the last ten years to limit this potential bias (shaded area in Fig. 2), this shift remains striking. It started, for most of the organisms, in the second half of the 20 th century and kept intensifying ever since. On the opposite, the number of SB occurrences has stagnated, or increased marginally at best, in the past 40 years. More worrying, most of SB occurrences cannot be readily traced back to a specimen: Only 238 000 occurrences have a filled "materialsampleid" column, representing only 0.28 % of the 84 million SB occurrences. This number illustrates that the way SB occurrences are recorded in biodiversity databases must be improved. Even though a specimen exists somewhere, it cannot be located without a potentially complex investigation procedure. This situation hampers the verification process, a founding step in scientific practice [START_REF] Turney | Non-repeatable science: assessing the frequency of voucher specimen deposition reveals that most arthropod research cannot be verified[END_REF]. Although scientists can be delighted with the pace at which biodiversity data accumulate, they cannot be satisfied with a biodiversity research relying mainly on unverifiable or hardly verifiable occurrences. Divergent causes, not necessarily exclusive, may explain this practice shift. In a context of massive biodiversity loss, a sense of urgency fueled the pleas for accelerated data collection [START_REF] Hampton | Big data and the future of ecology[END_REF]) and encouraged the accumulation of unvouchered observations, less destructive and easier to produce, share and store than specimen-based occurrences. Ethical considerations and conservation issues that hinder specimen collections have commonly been put forward (e.g. [START_REF] Minteer | Avoiding (Re)extinction[END_REF], although they are debatable in some situations [START_REF] Dubois | Does Nomenclatural Availability of Nomina of New Species or Subspecies Require the Deposition of Vouchers in Collections?[END_REF][START_REF] Dubois | Endangered species and endangered knowledge[END_REF][START_REF] Dubois | The need for reference specimens in zoological taxonomy and nomenclature[END_REF][START_REF] Löbl | Assessing biodiversity: a pain in the neck[END_REF]. The adoption of the Nagoya protocol by many countries and the development of mobile applications have undoubtedly contributed to this shift as well. Concurrently, [START_REF] Grandcolas | Loosing the connection between the observation and the specimen: a byproduct of the digital era or a trend inherited from general biology?[END_REF] suggested that this shift started when biodiversity sciences merged with general biology, more interested in discovering general patterns and laws than in documenting diversity that is supposedly already known enough. Others underlined the lack of human and economic resources to ensure both the gathering of specimens and the curation of natural history collections [START_REF] Kemp | The endangered dead[END_REF]. These reasons could have favoured a decrease in specimen collection. On the other hand, the number of observationbased occurrences has dramatically increased with, for instance, the rise of citizen science that enable to rapidly produce a vast amount of observational data [START_REF] Dickinson | The current state of citizen science as a tool for ecological research and public engagement[END_REF]) and that will certainly become more attracting and rewarding for the public in the future (e.g. [START_REF] Silvertown | Crowdsourcing the identification of organisms: A case-study of iSpot[END_REF]. Given the multiple origins of this trend, it seems unlikely to be reversed in the near future and must be organised and guided to ensure maximal benefits for the study of biodiversity.

Primary Biodiversity Data for systematics and evolutionary studies in the 21st Century: Are

We There Yet?

The importance of collecting specimens in taxonomy, evolution and ecology cannot be overemphasized [START_REF] Huber | The importance of voucher specimens, with practical guidelines for preserving specimens of the major invertebrate phyla for identification[END_REF][START_REF] Schilthuizen | Specimens as primary data: museums and 'open science[END_REF] and two main points, previously discussed in the literature, must be reiterated. First, specimens are needed for species description and for the study of biodiversity in general [START_REF] Krell | Specimen collection: Plan for the future[END_REF]Rocha et al. 2014;[START_REF] Ceríaco | Photography-Based Taxonomy Is Inadequate, Unnecessary, and Potentially Harmful for Biological Sciences[END_REF][START_REF] Dubois | The need for reference specimens in zoological taxonomy and nomenclature[END_REF][START_REF] Gutiérrez | Specimen collection crucial to taxonomy[END_REF]Pine andGutiérrez 2018 contra Minteer et al. 2014;[START_REF] Marshall | New species without dead bodies: a case for photo-based descriptions, illustrated by a striking new species of Marleyimyia Hesse (Diptera, Bombyliidae) from South Africa[END_REF][START_REF] Pape | Taxonomy: Species can be named from photos[END_REF]. A crucial argument is the utility of specimens for checking species identification. The spectacular growth in biodiversity occurrences is a fantastic opportunity for researchers if, and only if, occurrence quality can be somehow evaluated. [START_REF] Goodwin | Widespread mistaken identity in tropical plant collections[END_REF] assessed that up to half of tropical plant identifications in museum collections were false. Correcting identification errors can be done after examining specimens, but is impossible for unvouchered observations. If Goodwin et al.'s estimation is correct and generalizable to most primary data, the need for some specimens, or at least ancillary data to observation occurrences, is critical. Encouragingly, [START_REF] Kosmala | Assessing Data Quality in Citizen Science[END_REF] showed that high quality data were obtained in diverse citizen science programs, suggesting that biodiversity data gathered for well-known taxa [START_REF] Troudet | Taxonomic bias in biodiversity data and societal preferences[END_REF] or geographic areas [START_REF] Meyer | Multidimensional biases, gaps and uncertainties in global plant occurrence information[END_REF]) might contain fewer errors than in the study of [START_REF] Goodwin | Widespread mistaken identity in tropical plant collections[END_REF].

Second, the revived focus on morphology advocated lately in systematics requires specimens [START_REF] Jenner | Accepting Partnership by Submission? Morphological Phylogenetics in a Molecular Millennium[END_REF][START_REF] Wiens | The Role of Morphological Data in Phylogeny Reconstruction[END_REF][START_REF] Smith | Morphology's Role in Phylogeny Reconstruction: Perspectives from Paleontology[END_REF][START_REF] Yassin | Phylogenetic classification of the Drosophilidae Rondani (Diptera): the role of morphology in the postgenomic era[END_REF][START_REF] Pyron | Post-molecular systematics and the future of phylogenetics[END_REF][START_REF] Wanninger | Morphology is dead -long live morphology! Integrating MorphoEvoDevo into molecular EvoDevo and phylogenomics[END_REF][START_REF] Wipfler | A review of methods for analysing insect structures -the role of morphology in the age of phylogenomics[END_REF]. Authors recommending this revival underlined that comparative morphology not only brings phylogenetic characters but also allows including fossil taxa in phylogenetic analyses (e.g. Pyron 2011; [START_REF] Wood | Treating Fossils as Terminal Taxa in Divergence Time Estimation Reveals Ancient Vicariance Patterns in the Palpimanoid Spiders[END_REF], enabling us to better estimate the structure and branch length of the reconstructed trees [START_REF] Wiens | Combining Phylogenomics and Fossils in Higher-Level Squamate Reptile Phylogeny: Molecular Data Change the Placement of Fossil Taxa[END_REF][START_REF] Pyron | Post-molecular systematics and the future of phylogenetics[END_REF]. Given that phylogenetic thinking has become of paramount importance in biology, improvements in phylogenetic estimation offer large potentialities in comparative analyses and evolutionary studies, and in the study of biodiversity in general [START_REF] Losos | Evolutionary Biology for the 21st Century[END_REF][START_REF] Buerki | Incorporating evolutionary history into conservation planning in biodiversity hotspots[END_REF].

To happen, this encouraging prospect must be supported with the adequate facilities and workforce to host, curate, describe and identify these specimens. Worryingly, many institutions devoted to these tasks face budget cuts [START_REF] Kemp | The endangered dead[END_REF]. Museums and curators can neither handle the large amount of specimens collected over the world nor ensure the best preservation conditions of these specimens and their identification. It is a critical topic to urgently consider together with this paradigm shift [START_REF] Kemp | The endangered dead[END_REF][START_REF] Schilthuizen | Specimens as primary data: museums and 'open science[END_REF], and unvouchered observations complemented with ancillary data can contribute to limit this issue.

A specimen is not always necessary for a primary biodiversity data to be useful. Instead of specimens, and in complement to unvouchered observations, digital data or molecular data can be collated. New technologies offer a wide range of tools and methods to collect concrete specimen evidence in nature, and it is now relatively easy and affordable to obtain DNA sequences, images and sound recordings. Then, using molecular and digital data should now be a common practice in the study of biodiversity, as the exponential growth of molecular data and phylogenies, and the development of morphological databases and ontogenies would suggest [START_REF] Lathe | Genomic data resources: challenges and promises[END_REF][START_REF] Parr | Evolutionary informatics: unifying knowledge about the diversity of life[END_REF][START_REF] Deans | Time to change how we describe biodiversity[END_REF][START_REF] Deans | Finding Our Way through Phenotypes[END_REF]. We show here that digital and DNA data are increasingly used (63,271 and 878,308 ancillary data were collected in 1950 and 2010, respectively) but these data remain patently underemployed (Fig. 4). Only 2.5 % of all the GBIF-mediated occurrences for the 24 focal classes were linked to digital data and 1.5 % to DNA sequences. Worse, proportionally, they become more and more negligible, regarding the large quantity of observations without supporting data. This situation might be improving lately, but the post-2008 tendency observed demands to be confirmed in future years (Fig. 4), which would be happening only if scholars take up the pedagogic and practical challenge of highlighting the importance of ancillary data for biodiversity occurrences. Moreover, and quite inconsistently, digital and DNA data were less used for OB than for SB occurrences (Fig. 5).

They would yet be more useful for OB biodiversity data given that they would constitute the only way to independently check or update observation occurrences, whereas one can refer to specimens, as long as those are kept and the traceability chain is not broken, for SB occurrences (Page 2015 ;[START_REF] Nualart | Assessing the Relevance of Herbarium Collections as Tools for Conservation Biology[END_REF]. The high proportion of sequences associated to primary biodiversity data of unknown origin could suggest that when a sample is performed, occurrences are often classified in the catch-all class 'unknown origin'.

In addition to ancillary data, the usefulness of primary biodiversity occurrences can be maximized through a higher level of precision and completeness in recordings. We expect biodiversity data occurrences to be more precise and complete now than before because tools that are more efficient have been developed. Whatever the nature of the occurrence, spatial coordinates for instance can be easily provided with a high precision level given the democratization of GPS. Data completeness should also improve because of the growing awareness that a global and comprehensive picture of biodiversity is needed. Our results showed that, in proportion, data precision does improve but that data completeness stagnates (Fig. 6 and Supporting Information). The proportion of data with geospatial issues in the GBIF (i.e. data with low spatial precision) decreased from 50.2 % in 1900 to 0.6 % in 2014 in spite of a larger number of occurrences with spatial imprecision -this number being quite stable over the past 30 years (Fig. 6A). Over the same period, records identified at the species level augmented from 89.6% to 99.4%, with once again an increase of supra-species records (Fig. 6B).

While species identification and spatial precision improves, so does niche modelling results for instance, which promises significant advances in biogeography (e.g. [START_REF] Meseguer | Integrating Fossils, Phylogenies, and Niche Models into Biogeography to Reveal Ancient Evolutionary History: The Case of Hypericum (Hypericaceae)[END_REF][START_REF] Töpel | SpeciesGeoCoder: Fast Categorization of Species Occurrences for Analyses of Biodiversity, Biogeography, Ecology, and Evolution[END_REF]. In this regard, important gains for systematics and evolutionary studies can be anticipated from the increasing level of precision in primary biodiversity data.

Given the progresses of technology and the proportion of people owning smartphones with photo and GPS capabilities, targeting a higher level of completeness in biodiversity data is legitimate but the reasons and the necessity of this objective must be well-advertised, a task that falls to scholars.

They have the power to modulate the current trend, demanding a minimal amount of ancillary data when designing their personal or collaborative research projects, including citizen science programs.

Taking pictures or samples, not necessarily systematically but more often than now, should be part of the scientific protocol. This will not replace the wealth that specimens in natural history collection offer [START_REF] Funk | Systematic Data in Biodiversity Studies: Use It or Lose It[END_REF][START_REF] Buerki | Collections-based research in the genomic era[END_REF] but would limit the risk that entire datasets become useless when data inaccuracy is suspected. In a recent study, [START_REF] Silvertown | Crowdsourcing the identification of organisms: A case-study of iSpot[END_REF] show, for instance, that accurate species identification was achieved using pictures of organisms found in the UK. Whatever its nature and quantity of ancillary data, primary biodiversity data must be made available, and this evolution would require the adequate infrastructures to support the massive amount of data one can foresee. Several data storage and compression options are currently investigated (e.g. [START_REF] Marx | Biology: The big challenges of big data[END_REF]Numanagic et al. 2016), which suggests it will not be an insurmountable hurdle. The costs that should be deployed are substantial but are worth it for evolutionary biologists and for the society.

Besides, these efforts would result in large image and DNA databases, whose usefulness, accuracy and automatic search efficiency would augment together with their supply, as a virtuous circle.

The fear of biodiversity disappearance has triggered a vague of biodiversity data accumulation.

We are in the middle of a paradigm change where most biodiversity data are not anymore gathered like it used to be. This paradigm change has been undergone without any supervision. Even though some aspects of these changes are highly beneficial, others are suboptimal and must not be ignored.

We must act now to allow a better monitoring of the biodiversity research agenda and to continue shaping how biodiversity data should be gathered, diversifying the objects of collection (e.g. specimens, samples, DNA, images, etc. - [START_REF] Knapp | The changing role of collections and field research[END_REF]. We argue that ancillary data (samples, DNA, pictures) must be collected more methodically than today [START_REF] Joppa | Filling in biodiversity threats gaps[END_REF], to avoid disillusionment complemented with ancillary data such as multimedia files or DNA sequences. For observations, these additional data must be acquired when the observation is performed; it cannot be performed later. On the opposite, for specimens -as long as they are well-curated, which is unfortunately not always possible -ancillary data can be gathered later (this feature is symbolized through the continuous background and the arrows). b) Three hypothetical case studies -Because data can be acquired later, a specimen occurrence offers a wide range of studies and analyses. Conversely, for observation occurrences, the spectrum of analyses depends on the existence or not of ancillary data: an unvouchered observation will not allow as many studies as an observation combined with a DNA sample (the interdiction signs cover studies that cannot be achieved). Pictograms for specimen, observation, DNA and photos were designed by FreepiK from Flaticon. tagged as having geospatial issues in the GBIF (plain line). Yet, the proportion of those occurrences is decreasing (dashed line). The plot b) shows the number of occurrences identified at least at the species level or at a higher taxonomic rank. The number of occurrences identified at a higher taxonomic rank is increasing with time. Yet, the proportion of occurrences identified at least at the species level is increasing.

Figure captions

Figure 1 :

 1 Figure 1: Illustrations of observation-based and specimen-based primary biodiversity

Figure 2 :

 2 Figure 2: Number of primary biodiversity occurrences per year and origin from 1900 to today.

Figure 3 :

 3 Figure 3: Proportion of occurrences per year of collection and origin for a particular class. For

Figure 4 :

 4 Figure 4: The increase of ancillary data to biodiversity occurrences does not keep pace with

Figure 5 :

 5 Figure 5: Occurrences with ancillary data are mainly specimen occurrences. Occurrences with

Figure 6 :

 6 Figure 6: a) Spatial and b) taxonomic precision in the GBIF mediated data improve over time in

ACKNOWLEDGMENTS

This study was developed as part of a Ph.D. project and was funded as a grant by the Ministère de la Recherche to JT. We would like to thank Mark Judson for his comments on an early draft of this manuscript, as well as Marianne Elias and Colin Fontaine for commenting on the latest version. We thank Anne-Sophie Archambeau, Samy Gaiji, Marie-Elise Lecoq, Sophie Pamerlon, Roseli Pellens, Tim Robertson, Dmitri Schigel, Jérôme Sueur and Wilfried Thuillier for fruitful discussions. We thank Alexandre Antonelli and three anonymous reviewers for their constructive comments that contributed to clarify and improve this manuscript.

) )


Jonas Gouraud

Arnaud Delorme

Bruno Berberian

Running head: INFLUENCE OF AUTOMATION OVER MIND WANDERING 1 Influence of Automation over Mind Wandering Frequency in Sustained Attention

Keywords: mind wandering, automation, vigilance, oculometry, complacency, decoupling

Recent evidences showing that mind wandering might fill the time saved by automation are particularly worrying when taking into account the negative effect of mind wandering on short-term performance. 17 participants performed an obstacle avoidance task under manual and automated conditions in 2 sessions lasting 45 minutes each. We recorded attentional probes, oculometry and answers to the Task Load Index after each session.

Subjects perceived the manual condition as more demanding than the automated one. We highlighted a significant influence of automation on the mind wandering frequency after some time. Multiple phenomena may play a role, such as complacency and decoupling from the task at hand. Pupil diameter decreased during mind wandering versus focus periods, with a stable amplitude. Mind wandering knowledge could be used in a near future to characterize and quantify an operator's state of mind regarding automation related problems.

Introduction

In order to continuously improve system safety, the critical systems industry makes extensive use of automation (Baxter, Rooksby, Wang, & Khajeh-Hosseini, 2012; [START_REF] Parasuraman | Human-computer monitoring[END_REF]. In cockpits [START_REF] Wise | Managing automation in the cockpit[END_REF], in cars [START_REF] Naujoks | Secondary task engagement and vehicle automation -Comparing the effects of different automation levels in an on-road experiment[END_REF], and in power plant consoles (Cummings, Sasangohar, Thornburg, Xing, & D'Agostino, 2010), automation has been introduced to increase performance and respond to new safety requirements. Unfortunately, while implementing higher levels of automation indeed improves the efficiency and capacity of a system, it also creates new challenges for human operators. Particularly, the externally imposed task to maintain sustained attention focused for long periods of time in low probability environments causes progressive vigilance decrement -or invigilance increment [START_REF] Hancock | In search of vigilance: The problem of iatrogenically created psychological phenomena[END_REF]) -preventing efficient automation supervising [START_REF] Amalberti | Automation in aviation: A human factors perspective[END_REF]. As targets are hidden -naturally, voluntarily or because of poor display design -, the task to detect and react to these targets is often stressful and increasingly difficult [START_REF] Mackworth | The breakdown of vigilance during prolonged visual search[END_REF]. These problems result in out-of-the-loop (OOTL) performance problem, referring to a performance decrease whenever attempts are made to regain manual control after a critical system failure.

Such problems have been studied in laboratories [START_REF] Endsley | The out-of-the-loop performance problem and level of control in automation[END_REF][START_REF] Sarter | Automation surprises[END_REF], but are also regularly reported in operational conditions.

Mosier and collaborators (1994) examined NASA's Aviation Safety Reporting System (ASRS) database and reported that 77% of the incidents involved an over-reliance on automation leading to a probable vigilance failure. Similarly, [START_REF] Gerbert | The causes of causes: determinants and background variables of human factor incidents and accidents[END_REF] studied German aviators' anonymous responses to questionnaires about automation-related incidents and pointed out failures of vigilance as the largest contributor to human error. Several studies showed that efficient sustained attention for hours cannot be achieved [START_REF] Cabon | Human vigilance in railway and long-haul flight operation[END_REF][START_REF] Mackworth | The breakdown of vigilance during prolonged visual search[END_REF][START_REF] Methot | Effects of signal probability on individual differences in vigilance[END_REF]. Such a context may favor the occurrence of mind wandering (MW) episodes. MW is a family of experiences relating to the mind's tendency to engage in thoughts unrelated to the here and now [START_REF] Smallwood | The restless mind[END_REF]. It is an ubiquitous phenomenon that can be intentional or spontaneous (Golchert et Given that MW diverts an operator's attention from his primary task, it could play an important role in vigilance failures observed in highly reliable automated environments. [START_REF] Casner | Vigilance impossible: Diligence, distraction, and daydreaming all lead to failures in a practical monitoring task[END_REF] studied the impact of automation on MW in an aeronautical context. Their results on 16-minute sessions did not show a significant correlation between automation and the frequency of MW reports. However, the propensity to mind wander appeared to increase when everything seemed under control.

Supervising ultra-reliable systems could encourage operators to decrease cognitive resources allocated to the monitoring task. In that context, time saved by automation, which should normally be used for other productive tasks and for monitoring, could instead be filled by task-unrelated thoughts. Operators in such a state would not be prepared to regain manual control over the system in response to rare critical events. The protocol was conducted in accordance with the Declaration of Helsinki. Task Environment. We used the LIPS (Laboratoire d'Interactions Pilote-Système, or Pilot-System Interactions Laboratory) environment developed at the ONERA organization to program our experiment (see Figure 1). An unmanned air vehicle (UAV) depicted as a plane seen from above stayed at the center of a 2D radar 22-inch screen and moved following waypoints arranged in a semi-straight line with clusters of obstacles along the way (every 45s on average). Each cluster contained between 1 to 5 obstacles, including one on the trajectory. The participants were instructed to control the movements of the UAV to avoid obstacles. The LIPS environment includes a physics engine to reproduce convincing Rafale aircraft motion behavior. The LIPS was displayed on a screen within the SIMPIT environment shown in Figure 1. minutes, the probe appeared on a secondary 10-inch screen next to the main screen. For technical reasons, the obstacle avoidance task was not paused when the probe was displayed. Participants were asked to fill it as soon as it appeared, and any successful or failed trial during this interval would not be taken into account. Participants were informed that the probe was not part of the evaluation to lower the impact of instructions over their natural propensity to mind wander. Participants were required to answer the following questions (originally in French, see Figure 2): "When this probe appeared, where was your attention directed?" Answers could be "On the task" (e.g., thinking about the next obstacle, the decision to make, the incoming waypoint), "Something related to the task" (e.g., thinking about performance, interface items, last trial), "Something unrelated to the task" (e.g., thinking about a memory, their last meal, or a body sensation, hereafter defined as MW) or "External distraction" (e.g., conversation, noise). The preceding examples were given to participants to illustrate each category. We were primarily interested in reports of being "On the task" and MW reports. Reports of thoughts "Related to the task" were integrated to avoid participants to report MW when thinking about their performance [START_REF] Head | The troubling science of neurophenomenology[END_REF]. Noises were integrated to avoid participants to report MW if they were focused on any external signal. was your attention directed?" Answers could be "On the task", "Something related to the task", "Something unrelated to the task" or "External distraction" Conditions. Two conditions were proposed. The first one was the "manual" condition and required participants to manually avoid obstacles. The system detected obstacles on the trajectory 13s before impact. Then, an orange circle appeared around the UAV and the participant could initiate an avoidance maneuver. Participants were able to choose the way in which they wished to avoid the obstacle by clicking on "Evitement Gauche" (left maneuver) or "Evitement Droite" (right maneuver). Once they clicked, the simulator turned the trajectory of the UAV on the chosen side, following a predefined angle. Each obstacle had a safe circle similar to that of the UAV (see Figure 1). A collision warning -i.e., an orange circle around both the UAV and the obstacle with a message "Collision" -was displayed if the UAV safe circle penetrated inside the obstacle safe circle. A trial with a collision warning triggered was marked as failed. To resume the initial trajectory, they had to click on the "Retour trajectoire" (return to original trajectory) button. If no action was taken within 16 seconds after the first change in trajectory, the aircraft automatically resumed the trajectory and the trial was marked as failed.

The second condition was the "automated" condition. Participants were required to monitor the system avoiding obstacles. They had to click on an "Acquittement" [START_REF] Hart | Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research[END_REF]. This questionnaire includes questions pertaining to mental load, time pressure, physical strain, effort, frustration, and perceived performance. Participants were asked to answer each question using a horizontal line, ranging from 0 to 20. Although a TLX questionnaire completed at each block would allow precise workload monitoring, we believe that MW would have been artificially lower due to the disruption. Therefore, the TLX was only filled at the end of each session. Oculometry. Oculometric data was recorded using the hardware SmartEye Pro 3.0 and the software SmartEye 6.2.4. The system included 2 infrared illuminators and 3 cameras (120Hz) placed above the screen to avoid any direct contact with the participant (see Figure 1). Gaze calibration was performed using a 4-point grid.

Performance. We recorded button clicks throughout both conditions. Each button click was saved along with its timestamp within a CSV text format by the LIPS "Something related to the task" were classified as "Focus" to avoid any influence of poor performance on subsequent attentional reports [START_REF] Head | The troubling science of neurophenomenology[END_REF]. Extracts before "Something unrelated to the task" were classified as "MW". Extracts before "External distraction" were discarded as noise. We performed filtering on pupillometry using the R package reshape [START_REF] Wickham | Reshaping data with the reshape package[END_REF], psych [START_REF] Revelle | psych: Procedures for Personality and Psychological Research (Version 1.7.5)[END_REF] when they were required to do so. Reaction time were computed by comparing participants button click time delay in the manual condition to the moment the system detected an obstacle, and in the automated condition to the time at which they acknowledged each automation decision.

Results

Mind Wandering Frequency

We split the 45-minute sessions into 4 blocks lasting 10-minutes and containing 5 reports each. MW propensity was calculated as a percentage of all reports in the block (see Figure 3). Participants reported MW episodes for almost half of the probes (M = 49%, SD = 30%). This rate is consistent with previous studies on the subject [START_REF] Kam | Slow fluctuations in attentional control of sensory cortex[END_REF][START_REF] Smallwood | The restless mind[END_REF], 2015). Each participant reported on average 4% "external distraction" thoughts in each session (SD = 5). Such a low rate justified discarding "external distraction" reports as noise without thwarting subsequent analysis.

We used the ezANOVA function [START_REF] Lawrence | ez: Easy Analysis and Visualization of Factorial Experiments (Version 4.4-0[END_REF] to perform a two-way repeated measure ANOVA. We entered time (block) and level of automation (condition) as independent variables. We used the MW frequency as a dependent variable. Mauchly's test indicated that the assumption of sphericity had been verified for the main effect of block, W = 0.64, p = .251, and block × condition, W = 0.90, p = .906. There were significant main effects of time over the MW frequency, F(3, 48) = 8.88, p < .001, as well as of the level of automation over the MW frequency, F(1, 16) = 12.67, p = .003.

There was also a significant interaction effect between the time and the level of automation, F(3, 48) = 5.22, p = .003. Without specific a priori predictions on the evolution of MW frequency through time, we conducted Tukey's post-hoc tests on the model including Block variable for each condition separately. We used the glht [START_REF] Hothorn | multComp: Simultaneous Inference in General Parametric Models (Version 1.4-8[END_REF] and mes (AC Del Re, 2014) functions. For the manual condition, all differences were non-significant (p > .366). For the automated condition, the third and fourth blocks had significantly higher MW frequency compared to the first block, p = .001, d = 0.54 and p = .003, d = 0.32, respectively. Similarly, the blocks 3 and 4 had significantly higher MW frequency compared to the block 2, p = .007, d = 0.12 and p = .016, d = 0.12, respectively. analyzed using the 10 seconds preceding each probe. We used the lmer function (Bates, Maechler, Bolker, & Walker, 2015, p. 4) to perform a linear mixed-effect analysis despite missing values -"external distraction" reports and bad quality extracts excluded. As random effects, we had intercepts for subjects. Visual inspection of residual plots did not reveal any obvious deviations from normality or homoscedasticity. P-values were obtained by likelihood ratio tests using ANOVA on the full model against the models with no fixed effect, with only block, with block and condition, and with block and condition and their interaction. The results are shown in Table 1. On average, participants showed a significantly smaller pupil during MW episodes (see Figure 4). There was no main effect of MW on other markers. Influence of time and automation on oculometric differences. We looked for any influence of time or automation over the pupillometric differences previously observed between MW and focus periods. We used the lmer function to perform the linear mixed-effect analysis, as in the previous paragraph. As fixed effects, we entered time (block), level of automation (condition) and their interaction. As random effects, we had intercepts for subjects, as well as by-subject random slopes for the effect of block and condition. Visual inspection of residual plots did not reveal any obvious deviations from normality and homoscedasticity. Results are gathered in Table 2. Pupillometric difference remained stable through time and condition.

Discussion

We studied the impact of automated compared to manual environments on MW and its behavioral and oculometric markers. The automated condition revealed significantly lower TLX scores compared to the manual condition, showing a protocol in line with usual goals regarding automation introduction [START_REF] Wiener | Cockpit automation[END_REF]. Performance remained very high throughout both conditions whereas MW increased in the automated condition, ruling out the possibility that our attentional reports might be significantly influenced by poor performance [START_REF] Head | The troubling science of neurophenomenology[END_REF]. This demonstrates that our results were as close as possible to ecological settings. Building on this, three main results have been shown: (1) MW increases after some time has elapsed in an automated environment, (2) there is a difference in pupil diameter between MW and focus episodes but not for other oculometric markers and (3) pupillometric difference between attentional states remains stable through time and condition. We discuss these results

below.

The first result is the significant increase of the MW frequency under the automated condition between blocks two and three. No significant time-related evolution of MW was observed under the manual condition. Since both conditions lasted the same . Nevertheless, the level of automation alone cannot explain the observed data. Even though MW frequency highlighted significant differences between conditions, the trend did not evolve linearly with time-on-task and showed no difference between conditions for the first two blocks. Moreover, this evolution happened despite TLX scores remaining low for both conditions, which rules out the possibility that MW may be explained by workload evolution. Together, these findings argue for an effect linked to time spent supervising automation. There are two explanations, which may be complementary, for this interaction between time and level of automation over MW frequency. First, complacency might be generated by the high reliability of the system and lower monitoring performance.

Complacency is an issue of monitoring automation generated by an uncritical reliance on , 1993). In our experiment, participants encountered no error during the first three blocks. Given that the system never did any miss or error, participants may have thought that it would remain perfectly reliable. In this context, their perception of the required workload might evolve: since the automated system does not seem to require their attention to function properly, participants would redirect their cognitive resources towards more personal matters and mind-wander more. The higher perceived workload under the manual condition supports our analysis. Moreover, this could explain why participants, novice in supervising the system, exhibited an increase of MW frequency only after some time, while pilots in [START_REF] Casner | Vigilance impossible: Diligence, distraction, and daydreaming all lead to failures in a practical monitoring task[END_REF], who were pilots with thousands of hours dealing with autopilot, mind wandered immediately without temporal evolution. These evidences suggest a mediating influence of system familiarity in MW Operators save cognitive resources allocated to the low-event automated task in order to perform better on another task -MW -, which is considered more interesting or useful, independently of experiment instructions. The second possible explanation is a decoupling of operators' attention from the task at hand. When dealing with automation, operators give up their direct control over the system for a monitoring role in the supervisory control loop [START_REF] Moray | Monitoring behavior and supervisory control[END_REF][START_REF] Sheridan | Telerobotics, automation, and human supervisory control[END_REF]. They may experience a loss of agency -i.e., the ability to feel in control [START_REF] Wegner | The Illusion of Conscious Will[END_REF]. Multiple studies pointed to a limit to the automation level beyond which users felt less in control (Berberian, Sarrazin, Le Blaye, & Haggard, 2012; Coyle, Moore, Kristensson, Fletcher, & Blackwell, 2012), leading to a form of disengagement from the task at hand [START_REF] Haggard | Sense of agency in the human brain[END_REF]. Interestingly, Szalma (2014) described a similar disengagement when applying the Self-Determination Theory [START_REF] Ryan | Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being[END_REF] to human-system interactions. The inability of a system to support autonomous behavior may lower motivation and create an externalization of task goals -i.e. a process by which operators rejects the value of a goal. In our experiment, since participants do not validate but rather only acknowledge the system's actions, they could firstly experience a loss of agency, their motivation would decrease, leading to a faint sense of responsibility. This process chain could lead participants to reallocate cognitive resources from the task to MW, unconsciously trying to optimize time and mental resources from their perspective.

Further studies are needed to distinguish the respective impacts of agency drop and complacency on MW emergence.

Our second result concerns oculometric measures. We highlighted a lower pupil diameter during MW, as did several studies on MW (Faber, Bixler, & D'Mello, 2017; workload is not to be overlooked. The use of electroencephalograms would allow continuous measurement to precisely assess its impact over MW frequency. However, such a protocol requires the influence of perceived workload and MW over neural measures to be discriminated. Eventually, the expected outcome is to design automated systems able to adapt themselves to operators' MW episodes. We hope that such a system may enhance safety in critical automated environments.

Figure 1 .

 1 Figure 1. Screenshot of the LIPS interface and the environment. One of the screen is used for the task and the other one for questionnaire probes. For the task, the plane in the center is static and the surround (yellow and red numbered symbols) are moving. During, left and right avoidance maneuvers, again the plane is static and the background is rotated.

Figure 2 .

 2 Figure 2. Screenshot of the French MW probes. The question is "When this probe appeared, where

(

  acknowledgement) button to acknowledge automated avoidance decisions as soon as they saw it -twice per trial, once to acknowledge avoidance of the object and once to acknowledge the return to normal trajectory after avoiding the object. A feedback message was displayed to the participants. The acknowledgement ensured that participants would have the same motor input under both the manual and the automated conditions. If participants detected an automation error, i.e. choosing the wrong side for avoidance trajectory, they were instructed to click on the button "Changement d'altitude" (change altitude) so that the UAV would perform an emergency descent. A feedback message was displayed in that case as well. The altitude change ensured that participants were facing a supervision task.Procedure. Participants were explicitly instructed that detection accuracy was more important than speed in button clicks. Each participant performed the two conditions on two separate days in a counterbalanced way. Each day started with an explanation of the task, followed by a 10-minute training period and a 45-minutes session under the proper condition. Each session contained 60 clusters of obstacles. Each cluster was considered a trial. They were separated by 45 seconds on average. 20 probes were answered under each condition. The distribution of probes was not correlated with events on the obstacle avoidance task in order to avoid performance to influence MW reports (Head & Helton, 2016). The automated condition included 8 conflicts with a probe within the 10-seconds interval following the conflict. Participants encountered one system error (where they had to click on the "Changement d'altitude" button) during training, and another during the automated condition at the end of the third block. Under the manual condition, participants encountered at the end of the third block a conflict impossible to avoid. Both the automation error and this conflict were not followed by an attentional probe for at least 10 seconds after. Data recording MW Probes. Comma Separated Value (CSV) text files were used to store all answers. The exact appearance time was saved along with each answer, in order to synchronize probes data with the pupillometric signal. Post-task questionnaire. We used a validated French version of the NASA Task Load Index (TLX) questionnaire to evaluate the required amount of cognitive resourcesequated as workload -along several dimensions (Cegarra & Morgado, 2009; Hart &

  environment.Data Analysis MW probes. Participants' clicks and probe answers were saved in CSV text format. We used R-Studio and R 3.4.1 (R Core Team, 2016; RStudio Team, 2015) to analyze the data. Oculometry. The 10 seconds preceding each probe were extracted from oculometric data. This period length is in line with the literature investigating MW and oculometric markers (Bixler & D'Mello, 2014, 2015; Franklin, Broadway, Mrazek, Smallwood, & Schooler, 2013; He et al., 2011). Extracts before "On the task" and

Figure 3 :

 3 Figure 3: MW frequency evolution according to the condition. Error bars show the 95% confidence intervals based on bootstrap

Figure 4 :

 4 Figure 4: Normalized pupil diameter. Evolution during the 30-second interval preceding probes display -the grey part of the signal is used for computation

  amount of time, had similar number of actions and pursued the same goal -avoid incoming obstacles -, time-related phenomena (drowsiness, habituation, tiredness) cannot explain entirely the fact that MW increased only under the automated condition. The absence of MW increase in the manual condition is interesting considering the wellestablished vigilance decrement observed in sustained attention (Cabon et al., 1993; Davies & Parasuraman, 1982; Jeroski, Miller, Langhals, & Tripp, 2014; Mackworth, 1948). It may point to a fundamental difference between MW and vigilance decrement when considering the influence of automation. However mediating factors have still to be investigated, such as anxiety and motivation, which have demonstrated essential link with both phenomena separately (Killingsworth & Gilbert, 2010; Szalma et al., 2004; Szalma & Matthews, 2015)

  the system[START_REF] Parasuraman | Performance Consequences of Automation-Induced "Complacency[END_REF]. Complacency has been linked to higher reaction time (Bahner, Hüper, & Manzey, 2008; Manzey, Bahner, & Hüper, 2006), loss of situation awareness (Endsley & Kiris, 1995) and failures of detection (Parasuraman et al.

  frequency temporal evolution. This position would introduce a third possibility within the overload/underload theory debate (Pattyn et al., 2008; Warm, Parasuraman, & Matthews, 2008). Although the task complexity does not change, the operator's perception could evolve based on their trust in the system and their feelings toward the overall situation. As pointed by Seli and colleagues (Seli, Carriere, & Smilek, 2015; Seli et al., 2016), there is strong evidence that people can exert some control over their MW. This follows Casner and Schooler's (2015) results, who demonstrated that cognitive resources freed by automation in peaceful situations are not allocated to task planning, but rather to MW. Our analysis is in line with studies that observed MW increase in a low probability signal environment (Berthié et al., 2015; Casner & Schooler, 2015; Galera et al., 2012), with the time elapsed performing the task (McVay & Kane, 2009; Smallwood, Baracaia, Lowe, & Obonsawin, 2003; Smallwood, Riby, Heim, & Davies, 2006) and the view of complacency as a multiple-task strategy (Bahner et al., 2008; Moray & Inagaki, 2000).

  decoupling. Indeed, the study Cheyne and colleagues (2009) falls under the concerns expressed by[START_REF] Head | The troubling science of neurophenomenology[END_REF], see next paragraph). Further neural studies are necessary to answer this question.One could argue about the absence of analysis of performances, in order to clarify the relation between MW and stimuli processing. However, our study aimed to explore a different question: the impact of automation on MW occurrence. Addressing both questions would have required modifications to our protocol -add more conflicts, increase the duration, synchronize probes with conflicts -, with the possibility to introduce biases and produce an environment far from an ecological one. In such condition, OOTL phenomenon occurrence would be difficult to induce. Nevertheless, the question of how MW influences performance remains to be answered. The extended literature on the subject (Esterman, Noonan, Rosenberg, & DeGutis, 2013; Smallwood et al., 2003; Smilek, Carriere, & Cheyne, 2010a; Thomson, Seli, Besner, & Smilek, 2014) was recently criticized by Head and Helton (2016). They put forward the possibility that poor performance observed before MW may influence subsequent attentional reports, and not the other way around. The result of their reading study did not show any significant link between MW and awareness of stimuli. Certainly, studies using high rates of discrete events without high cognition -like the Sustained Attention to Response Task (SART) -may be particularly biased by this logical flaw, as performance monitoring and self-corrections are easy to perform. On the other hand, continuous metrics -as in tracking tasks (Kam et al., 2012; Yanko & Spalek, 2014)cannot be similarly biased, as poor performance evaluation is harder and would lead to a direct correction visible in the signal. Similarly, studies measuring stimuli awareness or recognition -like reading -may avoid this flaw, as performance is evaluated either at the end of the session (Franklin et al., 2013), or not at all (Uzzaman & Joordens, 2011). Be that as it may, further research is needed to identify parameters mediating the perceptual decoupling induced by MW. In the near future, the massive use of automation in everyday systems will reinforce the OOTL phenomenon. Our results show that automation increases MW frequency after some time. The MW literature in ecological tasks already highlighted how the phenomenon increases the risks in critical environments. Such results stress the necessity to study in more detail the relation between MW and the OOTL performance problem. Possible improvements include the study of reliability and complacency by manipulating the number of conflicts and automation errors. Another possibility is to highlight the impact of the operator's engagement in the task. Finally, perceived

Figure 1 .

 1 Figure 1. Screenshot of the LIPS interface and the environment. One of the screen is used for the task and the other one for questionnaire probes. For the task, the plane in the center is static and the surround (yellow and red numbered symbols) are moving. During, left and right avoidance maneuvers, again the plane is static and the background is rotated.

Figure 2 Figure 2 .

 22 Figure 2

Figure 3 :

 3 Figure 3: MW frequency evolution according to the condition. Error bars show the 95% confidence intervals based on bootstrap

Figure 4 :

 4 Figure 4: Normalized pupil diameter. Evolution during the 30-second interval preceding probes displaythe grey part of the signal is used for computation

  , ggplot2[START_REF] Wickham | ggplot2: Elegant Graphics for Data Analysis[END_REF], p. 2) and robfilter(Fried,

	Schettlinger, & Borowski, 2014). Filtering was done in two passes, following a method
	already used in the literature (Grandchamp, Braboszcz, & Delorme, 2014). Firstly, we
	filtered the signal. Pupil diameter had to be between 1 and 10 mm (due to the physical
	limits of pupil diameter, see (Lemercier, 2014), had to be less than 80% different from
	the preceding value (due to pupil dynamic limits) and had to be of a quality (computed by

the SmartEye software) over 0.01. Extracts were discarded if their resulting pupil diameter series consisted of less than 70% compliant values. The proportion of extracts excluded due to low quality (9.6%) is in line with that excluded in other investigations

[START_REF] Smallwood | Pupillometric Evidence for the Decoupling of Attention from Perceptual Input during Offline Thought[END_REF]

. Resulting extracts were completed using basic linear interpolation. A second filtering pass was applied with a median filter (moving window of 50 frames). Finally, the data of each participant were normalized by subtracting the mean and dividing by the root mean square of all good-enough quality extracts for this participant.

Fixations, saccades and blinks were computed by the SmartEye Pro software.

Blinks were computed using sliding windows of 700ms. Saccades were defined in SmartEye Pro parameters as gaze velocity over 35 deg/s. Saccades were limited to 200ms. Fixations were frames where the gaze velocity remained below 15 deg/s. Performance. Performance was assessed by determining if participants clicked

Table 1 : Comparison of oculometric measures during MW and focus episodes

 1

	Parameter	MW values	Focus values	Mental State model
		M	SD	M	SD	χ²(1)	p-value
	Pupil size (mm)	4.90	0.97	5.05	0.97	7259	<.001
	Saccade frequency (sacc/s)	3.92	2.36	3.89	2.39	0.07	.795
	Mean fixation duration (s)	2.87*10 -1	6.65*10 -1	3.22*10 -1 6.52*10 -1	0.08	.774
	Blink frequency (blink/s)	6.90*10 -2	1.10*10 -1	5.43*10 -2 9.81*10 -2	2.09	.148

Table 2 : Influence of time and level of automation over the difference Parameter Time model Time + Condition model Time*Condition model

 2

		χ²(3)	p-value	χ²(1)	p-value	χ²(3)	p-value
	Pupil size	1.83	.609	0.40	.528	0.30	.959

  [START_REF] Grandchamp | Oculometric variations during mind wandering[END_REF][START_REF] Mittner | When the Brain Takes a Break: A Model-Based Analysis of Mind Wandering[END_REF]. Our probes included action required for 8 out of 20 probes in each condition, ruling out the possibility that performance may have Our result could point to important task mediators of MW influence over oculometric markers, such as event rate or cognitive demands. Finally, the last result is the stability of pupillometric markers with respect to automation and time. Cheyne and colleagues (2009) recently proposed the integration of intensity of environment decoupling as a characteristic of MW episodes. They used a Sustained Attention to Response Task (SART, a form of GO/NOGO task; see (Robertson, Manly, Andrade, Baddeley, & Yiend, 1997) to match errors and reaction time evolution with each level of their model. If this model were true, there is little doubt that physiological markers would show some sensibility to intensity of MW. However, no influence over oculometric markers was observed. Several explanations can be proposed.

	significantly influenced subsequent attentional reports (Head & Helton, 2016). Moreover,
	literature on vigilance already linked a lower pupil baseline to periods of lower sensibility
	to external stimuli (K. McIntire, P. McIntire, Mckinley, & Goodyear, 2014; Nishiyama,
	Tanida, Kusumi, & Hirata, 2007). Taken together, these results are in line with the view
	of MW as a phenomenon inducing a decoupling from the environment. However, other
	research linked large pupils with slow and inaccurate responses (Gilzenrat, Nieuwenhuis,
	Jepma, & Cohen, 2010; Smallwood et al., 2011), or more directly to MW during a word-
	by-word reading task (Franklin et al., 2013). A recent study by Konishi et al. (2017) was
	aimed at explaining these results to all appearances contradictory. During 0-back and 1-
	back tasks, they observed a smaller pupil preceding MW reports. They also linked a
	higher pupil baseline and slower or inaccurate responses, highlighting a different state of
	under-processing of external stimuli and ruling out a potential increase in pupil diameter
	during MW episodes. These results corroborate our study and stress the need to
	investigate these attentional states. Contrary to pupillometry, other oculometric measures
	did not exhibit significant sensitivity to MW. However, our experiment is the first to our
	knowledge to investigate MW influence over blink, saccade frequency and fixation
	duration in operational settings. Indeed, previous research used most exclusively reading
	tasks (Bixler & D'Mello, 2014, 2015; Reichle, Reineberg, & Schooler, 2010; D. Smilek
	et al., 2010; Uzzaman & Joordens, 2011), with the notable exception of meditation
	(Grandchamp et al., 2014).

First, our protocol, which differ from previous protocols, may not be able to uncover such a tendency. Second, intensity may not regulate MW impact over pupillometry. Third, there may not be any intensity in MW episodes, each inducing the same environment

Table 1

 1

Table 1 : Comparison of oculometric measures during MW and focus episodes

 1

	Parameter	MW values	Focus values	Mental State model
		M	SD	M	SD	χ²(1)	p-value
	Pupil size (mm)	4.90	0.97	5.05	0.97	7259	<.001
	Saccade frequency (sacc/s)	3.92	2.36	3.89	2.39	0.07	.795
	Mean fixation duration (s)	2.87*10 -1	6.65*10 -1	3.22*10 -1 6.52*10 -1	0.08	.774
	Blink frequency (blink/s)	6.90*10 -2	1.10*10 -1	5.43*10 -2 9.81*10 -2	2.09	.148

Acknowledgements

We thank the Direction Générale de l'Armement (DGA) for their financial support to the first author. This work was supported by the Agence Nationale de la Recherche (grant ANR-15-CE26-0010-01).