Caroline Brunel
email: caroline.brunel@ird.fr

Robin Pouteau

Wayne Dawson

Michael Pester

Kelly S Ramirez

Mark Van Kleunen

Towards Unraveling Macroecological Patterns in Rhizosphere Microbiomes

come   L'archive ouverte pluridisciplinaire

It has long been hypothesized that the macroecological patterns (i.e., ecological patterns across large spatial scales) of the rhizosphere microbiome will relate to the macroecological patterns of plants [START_REF] Berg | Plant microbial diversity is suggested as the key to future biocontrol and health trends[END_REF]. However, few studies have found clear relationships between plant and rhizosphere diversity [START_REF] Leff | Predicting the structure of soil communities from plant community taxonomy, phylogeny, and traits[END_REF][START_REF] Ramirez | Range-expansion effects on the belowground plant microbiome[END_REF]. This is likely because there is a disconnect between the plant and the microbial scales (Box 1). For other (non-rhizosphere) microbiomes, large-scale sampling campaigns are leading the way by generating standardized raw data and metadata (e.g., the Earth Microbiome Project [START_REF] Gilbert | The Earth Microbiome project: successes and aspirations[END_REF] or the Human Microbiome Project [START_REF] Lloyd-Price | Strains, functions and dynamics in the expanded Human Microbiome Project[END_REF]). For macro-organisms, macroecological patterns are frequently identified by collating existing and newly generated data from individual studies [e.g., based on the Global Biodiversity Information Facility (GBIF) i ; Map of Life]. However, to our knowledge, the same has not been done for rhizosphere

Highlights

The processes shaping rhizosphere microbial communities are currently unclear because of both a lack of knowledge about biogeographical patterns and the disconnection between plant and microbial scales.

Sequence databases have now collected a sufficient amount of data covering a range of biomes and plant taxa to allow synthesis across studies. Recently, new bioinformatic methods have been developed that allow us to overcome former spatial and taxonomic limitations.

Understanding the processes that shape rhizosphere microbial communities will provide important insights into plant ecology and evolutionary biology, and can enable us to manage microbial and plant ecosystem services. microbiomes. Synthesizing rhizosphere data represents a fundamental way to connect belowground diversity with aboveground diversity. Yet, to successfully synthesize rhizosphere microbiome data and better predict macroecological patterns in the rhizosphere microbiome, the following challenges must be addressed: (i) obtain sufficient geographical and plant-host taxonomic coverage; and (ii) quantify between-study heterogeneity in sequence data. In this review, we highlight how new bioinformatic tools coupled with meta-analyses offer an opportunity to better understand the rules of rhizosphere microbiome assembly.

Rhizosphere Microbiomes Research Remains Incomplete

To reveal what currently restricts our understanding of rhizosphere microbiome assembly and identify steps to move the field forward, we summarize here the most dominant research themes on rhizosphere microbiomes in 2018. A literature search using the topic queries 'rhizosphere microbiome' and 'rhizosphere microbial communities' in Web of Science revealed a total 662 articles published in 2018, of which 382 were suitable for analysis (see the supplemental information online). From these 382 studies, we extracted the main topics, and information on their geographical, temporal, and taxonomic scales, the studied microbial taxa, the origins of the samples, and the sequencing analytic approaches used (Figure 1). Overall, scientists considering rhizosphere microbiomes were predominantly interested in the following four topics: (i) the rhizosphere environment (exudates, autofeedback effects of microbial communities, or exploratory Box 1. The Macroecology of Rhizosphere Microbiomes At larger spatial scales, rhizosphere microbiome assembly results partly from the metabolic synchronization of microbial substrate-utilization traits and plant-root exudation [START_REF] Beattie | Metabolic coupling on roots[END_REF]. From this postulate, macroecological patterns in rhizosphere microbiomes are expected to result from the biogeography of both free-living soil microbes (determined by geography, dispersal, environment, and biotic interactions [START_REF] Prosser | The role of ecological theory in microbial ecology[END_REF]) and the host plants (e.g., species, developmental stage, and symbiosis establishment [START_REF] Uroz | Plant symbionts are engineers of the plant-associated microbiome[END_REF], and functional traits). Currently, a large body of evidence challenges the 'everything is everywhere, but, the environment selects' hypothesis [START_REF] Baas-Becking | Geobiologie; of Inleiding tot de Milieukunde, WP Van Stockum & Zoon NV 86[END_REF], claiming that microorganisms are not cosmopolitan [86], and implying that, in addition to the environmental drivers, dispersal limitation also controls the assembly of root-associated microorganisms [START_REF] Peay | Spore dispersal of basidiomycete fungi at the landscape scale is driven by stochastic and deterministic processes and generates variability in plant-fungal interactions[END_REF]. Therefore, quantifying the dispersal ability of microbes and vectors of dispersal is the first step in understanding the macroecology of rhizosphere microbiomes.

However, from the perspective of a microbe, a whole plant may already be approaching a macroecological spatial scale. Consider Figure I. Assembly of a plant community at a location might be determined by dispersal ability of species in the regional pool (light blue); abiotic (orange), and biotic filters (green) should then act in concert to shape plant and rhizosphere microbial communities [START_REF] Cadotte | Should environmental filtering be abandoned?[END_REF]. However, biotic filters can operate at the spatial scale of the plant or root scale to shape plant and microbial communities, respectively. While filters shaping plant communities at larger spatial scales influence the pool of species from which microbial communities are constructed, abiotic and biotic filters constrain the species pool further at the rootsystem scale (Figure I, root system box). If we zoom in to the scale of individual roots, we would expect rhizosphere microbial community composition to be shaped by root characteristics such as root thickness, root hairs, root-associated fungi, priority effects resulting from other microbes, and exudate compounds (Figure I, 'Root Characteristics'; in green).

To fully understand patterns of microbiome diversity, composition and functions, we need to measure and study rhizosphere microbiomes at the scale of individual roots and within root systems. For example, we do not know how much variation there is in microbial communities and their functioning among different roots within a single root system. We need to learn more about how this variation relates to the age of roots, root depth, and neighboring plants (Figure I). We do not know how much variation in rhizosphere microbiomes there is at this scale between different plants, different plant populations, and different regions. Moreover, we also do not fully understand how or why rhizosphere microbiomes vary in composition temporally, and what the effects of this variation might be for plant-soil interactions and their consequences over time. Finally, we lack information on whether microbial diversity in the rhizosphere is structured any differently from bulk soils at different spatial and temporal scales. While evidence runs counter to the 'everything is everywhere, and the environment selects' idea, we may not be testing this idea at scales most relevant to microbes. For example, the 'regional' species pool from which rhizosphere communities are assembled might largely comprise all the microbes occurring in a single root system and surrounding bulk soil, and in the root systems of neighboring plants. Associations between plant and soil microbial communities might also be organized at the scale of 'root communities', determined by the identity and distribution of the roots of species in a volume of soil [START_REF] Leff | Predicting the structure of soil communities from plant community taxonomy, phylogeny, and traits[END_REF]. We argue that, to fully understand the macroecology of rhizosphere microbiomes, we need to refocus our lens of enquiry to consider the factors governing microbiome assembly at finer spatial scales and how they relate to the larger scale patterns of diversity and composition more typical of macroecological studies. studies); (ii) the relation to the host plant; (iii) external biotic factors (i.e., other than the holobiont, e.g., pathogens, microbiomes of other plants, bulk-soil microbiome or mesofauna); and (iv) the effects of abiotic factors (anthropogenic or geoclimatic parameters).

Community assembly is assumed to work as a hierarchy of nested constraints (i.e., dispersal constraints and abiotic together with biotic filters) of potentially varying strengths acting at different spatial scales [START_REF] Scherrer | Disentangling the processes driving plant assemblages in mountain grasslands across spatial scales and environmental gradients[END_REF][START_REF] Trøjelsgaard | Ecological networks in motion: micro-and macroscopic variability across scales[END_REF]. Despite covering a variety of environmental filters, our synthesis found that current research exhibits a pronounced specialization and compartmentalization of studied topics, ~43% of the studies focused on only one of the four topics (i.e., the rhizosphere environment, the relation to the host plant, external biotic factors or abiotic factors; Figure 1). Furthermore, there was a limitation in 'scales' studied. Approximately 78% of the reviewed literature focused on a single plant species, 69% had sampled only at a single time point, and 60% did not consider spatial scales (Figure 1). The detection of low-abundance taxa is extremely limited when focusing on very small scales of study [START_REF] Ramirez | Detecting macroecological patterns in bacterial communities across independent studies of global soils[END_REF], which represents a major issue because these taxa have a leading role in microbial community structure and functions [START_REF] Ramirez | Detecting macroecological patterns in bacterial communities across independent studies of global soils[END_REF][START_REF] Jousset | Where less may be more: how the rare biosphere pulls ecosystems strings[END_REF][START_REF] Wei | Rare taxa of alkaline phosphomonoesteraseharboring microorganisms mediate soil phosphorus mineralization[END_REF][START_REF] Cerro-Galvez | Microbial responses to anthropogenic dissolved organic carbon in the Arctic and Antarctic coastal seawaters[END_REF] and in the way that microbial communities interact with plants [START_REF] Dawson | A small number of low-abundance bacteria dominate plant species-specific responses during rhizosphere colonization[END_REF]. At the same time, few studies examined the assembly of rhizosphere microbiomes at the plant scale. For example, ~16% of the studies (i.e., 61 out of 382) described variation across plant traits, developmental stages, or generations (Figure 1). Approximately 23% (i.e., 87 out of 382) focused on the effect of plant identity

Glossary

Amplicon sequence variant (ASV): a higher-resolution analog of the traditional OTU. ASVs are inferred regardless of other sequences or reference databases by a process that sufficiently controls errors so that sequences can be resolved exactly, down to the level of singlenucleotide differences. Consequently, ASVs combine advantages of simple merging between independently processed data sets and accurate measurement of diversity with applicability to communities lacking deep coverage in reference databases [START_REF] Callahan | Exact sequence variants should replace operational taxonomic units in marker-gene data analysis[END_REF].

Holobiont: an integrated unit including a host and its associated microorganisms, which must be considered as a whole to understand evolutionary and ecological processes [START_REF] Moran | The hologenome concept: helpful or hollow?[END_REF][START_REF] Bordenstein | Host biology in light of the microbiome: ten principles of holobionts and hologenomes[END_REF].

Low-abundance microbial taxa: unlike the few microbial species that are very abundant and account for most of the biomass and carbon cycling, most microbial species have an individual relative abundance of b0.1% and are defined as the 'rare biosphere' [START_REF] Sogin | Microbial diversity in the deep sea and the underexplored 'rare biosphere[END_REF]. Their contribution to community dynamics is still unclear. Owing to their low relative abundance, traditional molecular techniques [clone libraries and fingerprinting methods, such as denaturing gradient gel electrophoresis (DGGE), and terminal restriction fragment length polymorphism (T-RFLP)] could not retrieve them [START_REF] Pedrós-Alió | The rare bacterial biosphere[END_REF][START_REF] Shade | Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity[END_REF].

Macroecology: the study of relationships between organisms and their environment at large spatial scales to characterize and explain patterns of abundance, distribution, and diversity. Microbiome: the set of microorganisms present at a given site, with consideration often given to their genomes and the host or surrounding habitat [START_REF] Marchesi | The vocabulary of microbiome research: a proposal[END_REF].

Operational taxonomic unit (OTU): sequence clusters that constitute the standard unit of marker-gene analysis.

There are two main methods of defining molecular OTUs. Closed-reference methods involve searching for a sequence in a reference database that is sufficiently similar to reads and subsequent recruitment of reads into a corresponding OTU. De novo methods involve grouping of reads grouped into OTUs as a function of their pairwise sequence similarities [START_REF] Callahan | Exact sequence variants should replace operational taxonomic units in marker-gene data analysis[END_REF].

Orthologs: genes found in different species that evolved from a common ancestral gene, and usually have retained the same function. Ortholog identification is a critical process for reliable prediction of gene function in metagenomes.

Trends Trends in in Plant Plant

Rhizophere: this term, coined by Lorenz Hiltner in 1904 [START_REF] Hartmann | Lorenz Hiltner, a pioneer in rhizosphere microbial ecology and soil bacteriology research[END_REF], refers to the narrow zone of soil around plant roots, which is under the chemical, physical, and biological influence of root exudates.

The size of the rhizosphere (affected by soil type, plant species, and phenological growth stage) ranges from b1 mm to several centimeters around the root system [START_REF] Sharifi | Chatting with a tiny belowground member of the holobiome: communication between plants and growth-promoting rhizobacteria[END_REF]. The rhizosphere microbiome differs from but interacts with the endosphere microbiome (i.e., microorganisms living inside the roots), the rhizoplane microbiome (i.e., the biofilm surrounding the roots), and the bulk soil microbiome (i.e., microorganisms that do not directly interact with the roots, but may indirectly benefit from metabolites derived from the rhizosphere soil microbiota).

(i.e., species, varieties, or genotypes), and only 4% (16 out of 382) included the plant community scale (i.e., considered the influence of surrounding plants; Figure 1). Collectively, this suggests that, for a better understanding of the rhizosphere microbiome, research could benefit from more studies that consider a geographical gradient or temporal approach. Specifically, experimental designs and analytic tools could be deployed to resolve variation not only between plant individuals or composite samples, as is commonly done, but also within root systems, (Figure 1).

In addition to scale, microbial taxonomic and functional diversity must be more fully assessed.

Metabarcoding has proven to be a valuable tool and has been widely used for characterizing the microbial diversity in different environments [START_REF] Quince | Shotgun metagenomics, from sampling to analysis[END_REF]. Specifically, we found that sequencing approaches were used in ~73% of the cases, and amplicon sequencing of taxonomic or functional marker genes was by far the most popular approach (used in ~66% of the cases; Figure 1). Furthermore, experiments were designed to improve knowledge of gene and metabolite expression profiles involved in plant-microbe interactions by using pairwise interactions under controlled conditions: most studies used semiartificial experimental set-ups: ~61% of the rhizosphere microbiome studies were conducted under controlled conditions, whereas 39% were described as being conducted in situ (Figure 1). While these experiments are valuable, they are admittedly difficult to scale up to natural ecosystems [START_REF] Sergaki | Challenges and approaches in microbiome research: from fundamental to applied[END_REF]. Furthermore, research on rhizosphere microbiomes was often limited to a phylum-level perspective and bacteria had received special attention (~88% of the studies analyzed bacterial communities), while complex interactions among fungi, bacteria, archaea and the plant host remained poorly studied (Figure 1). The heterogeneity in sampling methods (Figure 1) also highlights the vague definition of 'rhizosphere' that can complicate comparisons across studies. Additionally, a standardized protocol for rhizosphere soil collection could be developed for each life form (e.g., tree, shrub, or herbaceous) to reduce disparity of results. Such standardization steps are especially important if different processes dominate at different nested scales within rhizosphere microbiomes. This overview underlines the need for a more holistic approach to studying rhizosphere microbiomes considering large spatial, temporal, and taxonomic dimensions.

Can We Transcend Taxonomic and Geographic Limitations?

When a large-scale sampling campaign is not feasible, the compilation of existing data with sufficient geographical and plant-host taxonomic coverage is a viable alternative. Data sharing among scientists has been encouraged by public data repositories as well as the demands from journals for making sequence data publicly available as a condition for publication. For example, the International Nucleotide Sequence Database Collaboration (which includes the DNA Data Bank of Japan, the European Nucleotide Archive, and the National Center for Biotechnology Information; hereafter NCBI) solicits the banking of complete sequence data and metadata [START_REF] Blaxter | Reminder to deposit DNA sequences[END_REF][START_REF] Salzberg | Databases: reminder to deposit DNA sequences[END_REF]. These databases now provide access to thousands of sequence data sets from rhizosphere samples. The metadata associated with the rhizosphere metagenomes (in NCBI, referring mainly, but not only, to amplicon-gene sequencing) are essential for finding, retrieving, and reusing data stored in online repositories. The metadata include environmental data (e.g., details about the host plant or the geographical location of sampling) and methodological data (e.g., the applied protocol). Collectively, these data provide great opportunities to address questions of community assembly and co-existence rules of rhizosphere microbiomes, and to connect belowground diversity to aboveground diversity. To get an impression of the amount and quality of data available, we used the NCBI BioSample database [START_REF] Barrett | BioProject and BioSample databases at NCBI: facilitating capture and organization of metadata[END_REF], which stores all sequence metadata submitted to NCBI. We obtained information on the publication date, plant host, and geographical coordinates of the collection locations of the root and rhizosphere microbiomes [i.e., deriving from the taxonomic id 'txid939928' entitled 'rhizosphere metagenome' (Figure 2) 38 146 metadata records in August 2019]. NCBI BioSample promotes the use of the MIMS and MIMARKS host-or plant-associated packages to implement standardization and description of sequence-based data and to provide some control over metadata submission [START_REF] Yilmaz | Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications[END_REF]. Nevertheless, we found that rhizosphere sequence metadata were not always registered in the corresponding specialized packages, because ~72% were recorded in nondedicated packages, which do not provide a relevant description of the samples. Most submitters used the generic package, which has no specific requirements.

Thousands of rhizosphere microbiome studies are now available spanning a range of terrestrial ecosystems (Figure 2). We found a clear exponential increase in the number of 'rhizosphere metagenome'-associated samples published each year, and data came from all continents, which provides opportunities for global analyses (Figure 2). Moreover, according to the metadata, these samples were associated with 264 different host-plant species belonging to 134 families (Figure 2). The majority of these plant species (~72%) have economic uses, with plants used for environmental (mainly ornamental) purposes (~14%), as genetic resources (~13%), as human food (~10%) and medicines (~9%) being well represented (Figure 2). Taken together, these numbers suggest that the recent exponential accumulation of sequence data and their associated metadata provide useful information for meta-analyses. However, out of the 38 146 rhizosphere samples recorded, ~46% were not geographically localized (and some were incorrectly localized, e.g., mixed up latitude and longitude or used indeterminate coordinate systems) and 79% were not associated with a standardized plant species name (i.e., according to the Plant List ii ). Thus, there is still much scope for improvements in data reporting.

The metadata describing sampling location, associated plants, and other important details, such as nucleotide extraction and sequencing protocols, were also often not included with the published supplemental data sets. This problem is also common to other fields of research. A recent metaanalysis of 11.4 million metadata of samples used in biomedical experiments revealed multiple anomalies and highlighted the need for a more robust approach to reporting metadata [START_REF] Gonçalves | The variable quality of metadata about biological samples used in biomedical experiments[END_REF]. Inclusion of metadata is crucial for finding suitable data sets for meta-analyses, will help to ensure interoperability of the metadata, and ultimately will allow us to begin to understand how the host plant (the environment of the rhizosphere) shapes macroecological patterns in rhizosphere microbiomes. Thus, it is vital that public repositories implement stricter control of submitted data for standardization, and that authors add sufficient details about sample origin and nucleotide sequences, and carefully use formal language when submitting metadata. Transcending geographical and taxonomic boundaries already appears feasible by using shared data, but increased awareness of the importance of metadata would multiply the strength of this approach.

Improvements and Limitations of Sequence-Based Data

Describing microbial functions is required to understand what connects a microbe to its habitat, particularly in the rhizosphere, where plant exudates and microbial substrate preferences constitute an important driver of microbial community assembly [START_REF] Zhalnina | Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly[END_REF]. Furthermore, linking information from plant and microbial data sources can help to answer macroecological questions (Box 2).

Box 2. Plant Reference Databases

For plant ecology, the collation of local data sets from independent studies into global databases has revealed global patterns and provided insights into the underlying ecological processes [START_REF] Franklin | Big data for forecasting the impacts of global change on plant communities[END_REF]. Plants have a critical role in biogeochemical cycling [START_REF] Wieder | Global soil carbon projections are improved by modelling microbial processes[END_REF], the supply of food and goods [START_REF] Mercado-Blanco | Belowground microbiota and the health of tree crops[END_REF], and ecosystem resilience [START_REF] Scheffer | Catastrophic shifts in ecosystems[END_REF]. Similarly, our understanding of rhizosphere microbiomes and their contribution to ecosystem services [START_REF] Saccá | Ecosystem services provided by soil microorganisms[END_REF] would be greatly improved from coordinated efforts that are global in scope. It is known that the rhizosphere microbiome has a fundamental role in numerous processes related to plant and ecosystem functioning. For example, rhizosphere processes are quantitatively important drivers of soil organic matter decomposition and nutrient release at the ecosystem scale, with consequences for global C cycling and vegetation feedbacks to global change [START_REF] Kivlin | Fungal symbionts alter plant responses to global change[END_REF][START_REF] Finzi | Rhizosphere processes are quantitatively important components of terrestrial carbon and nutrient cycles[END_REF][START_REF] Dijkstra | Rhizosphere priming: a nutrient perspective[END_REF][START_REF] Philippot | Biochemical cycling in the rhizosphere having an impact on global change[END_REF].

We have largely described rhizosphere data from the perspective of the microbe, yet, to date, there are no global efforts to link rhizosphere data to global plant or vegetation data. If we consider plants and their associated microbiomes as holobionts, we need to combine data on plant ecology and physiology with rhizosphere microbiome data to understand rhizosphere microbiome composition and function. This would be valuable for studies involving a range of plant taxa, but such studies are still scarce. We need to link plant databases to rhizosphere microbiome studies, allowing us to relate plant identity to microbial community composition to address specific macroecological questions (see Outstanding Questions). Databases describing plant species functional traits [START_REF] Kattge | TRY plant trait databaseenhanced coverage and open access[END_REF], phylogeny [START_REF] Durka | Daphne: a dated phylogeny of a large European flora for phylogenetically informed ecological analyses[END_REF], and native and exotic ranges [100] are available. Data processing has been facilitated by the development of bioinformatic tools allowing, for example, plant-name assignment [101], collection of plant occurrences through the GBIF database, and placement of species in phylogenetic trees [102]. Such tools and databases have increased our understanding of many macroecological patterns and ecosystemlevel processes involving plants. The amount of available data describing plant root trait is increasing [103], and plant data sets could be used for studying plant-microbe interactions. Recently, forest-plot data and known associations of specific tree species with ectomycorrhizal and arbuscular mycorrhizal fungi were used to make the first global maps of the distribution of these mycorrhizal groups [START_REF] Steidinger | Climatic controls of decomposition drive the global biogeography of forest-tree symbioses[END_REF]. The patterns revealed demonstrate the critical role of root symbionts in shaping the distribution of tree species. Thus, we call for deeper levels of collaboration between plant and microbial ecologists, so that mutually informative linkages between plant and microbial data and macroecological studies can be forged. To begin with, microbiome-focused studies should better record the taxonomy and locations of the host plant(s) and describe plant traits while providing details on the sequencing methods. Gathering all these data in a dedicated database, or developing existing database packages for associated metadata will be crucial for improving knowledge on the macroecology of rhizosphere microbiomes. At the same time, it will also promote scientists' awareness of the importance of data sharing. Ideally, in the future, plant ecologists may be able to extract rhizosphere microbiome DNA from study species and locations for sequencing as a standard practice, as sequencing costs continue to fall.

There are currently three main methods that use microbial sequence information: taxonomic classification with rRNA-gene and internal transcribed spacer (ITS) sequences, microbial metaomics, and complete genome sequencing. No one method is without bias, yet all provide critical information for understanding the ecology of rhizosphere microbiomes.

As for all microbiome sequence data sets, taxonomic classification of rhizosphere microbiomes relies on the assignment of sequences to taxonomic and functional units via referencesequence databases (recently presented and compared in [START_REF] Balvočiūtė | NCBI and OTThow do these taxonomies compare?[END_REF]). The amount and reliability of data in these databases is improving due to contributions from researchers worldwide and the colossal efforts of global initiatives such as the Earth Microbiome Project iii and to the ongoing efforts of database managers to carefully curate and align high quality sequences. Thus, these reference databases cover more and more microbial taxa and offer great opportunities for global scale analyses on, for example, microbial diversity. Nevertheless, despite the improved methods and data availability, microbial diversity may be underestimated when conducting 16S-and 18S-rRNA gene or ITS analyses, if the assignment relies on incomplete reference gene databases [START_REF] Choi | Strategies to improve reference databases for soil microbiomes[END_REF].

In 2011, ~100 000 fungal species were described, but the extent of global fungal diversity was estimated at 800 000-5 100 000 species [START_REF] Blackwell | The fungi: 1, 2, 3 … 5.1 million species?[END_REF]. Thus, only 2-13% of all fungal taxa have been described so far. In 2016, Locey and Lennon [START_REF] Locey | Scaling laws predict global microbial diversity[END_REF] estimated that the Earth may host 10 12 microbial taxa (encompassing Bacteria, Archaea, and microscopic fungi). By contrast, Overmann and colleagues determined that only ~14 000 species of Bacteria and Archaea, probably representing 0.001-0.1% of the estimated global species number, have so far been cultivated and formally described [START_REF] Overmann | Relevance of phenotypic information for the taxonomy of not-yet-cultured microorganisms[END_REF]. The entirety of the Earth Microbiome Project has cataloged fewer than 10 7 taxa, of which only one third have been detected at least twice. The latter could indicate that many microbial taxa are indeed low-abundance taxa [START_REF] Gilbert | The Earth Microbiome project: successes and aspirations[END_REF]. Efforts to understand this so-called rare biosphere are now ongoing [START_REF] Lynch | Ecology and exploration of the rare biosphere[END_REF].

Coverage of the bacterial and archaeal 16S-rRNA-gene sequence space will continue to improve as metagenome-assembled genomes reveal previously unknown genes that had not been detected by universal primers [START_REF] Yarza | Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences[END_REF]. This trend will also generally hold true for the 18S-rRNA-gene of eukaryotic microorganisms. For now, taxonomic delineation of microorganisms remains difficult because minor differences in 16S-or 18S-rRNA gene sequences may not necessarily translate into different traits of microorganisms. Furthermore, observed differences in taxonomic marker-gene sequences might also originate from sequencing errors. New denoising algorithms that correct sequencing errors and determine real biological sequences (i.e., exact amplicon sequence variants, ASVs) have recently been developed, but the few hundred bases generated by Illumina sequencing often provide insufficient taxonomic resolution. For a reliable taxonomic assignment across all taxonomic levels, 1300 base pairs are currently recommended [START_REF] Yarza | Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences[END_REF]. With emerging single-molecule sequencing technologies (e.g., Oxford Nanopores and PacBio), taxonomic assignments will improve in the near future. However, even if most of the microbial taxa have been discovered by now based on their 16S-rRNA gene [START_REF] Yarza | Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences[END_REF], the fact that most of them are not cultured [START_REF] Konstantinidis | Uncultivated microbes in need of their own taxonomy[END_REF] means that we still have limited information about their physiology and ecology [START_REF] Overmann | Relevance of phenotypic information for the taxonomy of not-yet-cultured microorganisms[END_REF][START_REF] Nesme | Back to the future of soil metagenomics[END_REF].

Beyond taxonomic classifications, the use of functional groups of microorganisms should be prioritized to help improve the field of microbial meta-omics [START_REF] Fierer | Embracing the unknown: disentangling the complexities of the soil microbiome[END_REF]. However, this requires consistent ways of sorting phylotypes into ecologically meaningful categories. For example, a recently published global atlas of the dominant bacteria found in soil sorted them into ecological groups, albeit at a coarse resolution [START_REF] Delgado-Baquerizo | A global atlas of the dominant bacteria found in soil[END_REF]. Moreover, databases that classify microbial taxa according to their lifestyles or guilds are growing. For example, the FunGuild database [START_REF] Nguyen | FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild[END_REF] classifies fungal taxa as having a symbiotroph, pathotroph, or saprotroph lifestyle, while the FunGene database provides a repository for the most common functional marker genes used in microbial ecology [START_REF] Fish | FunGene: the functional gene pipeline and repository[END_REF]. Additionally, the nifH (encoding the dinitrogenase reductase subunit of nitrogenase) database facilitates analyses of the evolution and ecology of nitrogen-fixing organisms [START_REF] Gaby | A comprehensive aligned nifH gene database: a multipurpose tool for studies of nitrogenfixing bacteria[END_REF], and the PhytoPath database records data on plant pathogen species [START_REF] Pedro | PhytoPath: an integrative resource for plant pathogen genomics[END_REF]. This offers opportunities to assess macroecological questions on functional diversity of rhizosphere microbiomes.

At the same time, sequencing of complete microbial genomes is being successfully and more regularly used to identify the metabolic potential of microbes through gene identification [START_REF] Sasse | Feed your friends: do plant exudates shape the root microbiome?[END_REF]. This provides an alternative way to reveal functional groups of microbes, and can help to reduce the overall complexity of rhizobiomes, because orthologous genes can be assigned and functions of uncultivated microbes might be inferred. Some initiatives already give interesting perspectives in this regard. For example, OrthoMCL [START_REF] Li | OrthoMCL: identification of ortholog groups for eukaryotic genomes[END_REF], KEGG [START_REF] Kanehisa | KEGG: new perspectives on genomes, pathways, diseases and drugs[END_REF], and related packages [START_REF] Aßhauer | Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data[END_REF][START_REF] Huang | Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources[END_REF] allow ortholog prediction among multiple species and raise the possibility of assigning a microbial lifestyle through a genome signature [START_REF] Dutta | Microbial lifestyle and genome signatures[END_REF][START_REF] Hacquard | Survival trade-offs in plant roots during colonization by closely related beneficial and pathogenic fungi[END_REF]. Besides being limited by incomplete genomes and/or the quality of gene annotation, the predictions can be biased because the presence of genes in a metagenome does not necessarily mean that they are in fact expressed.

To reduce such biases, assigning functions should ideally be complemented by following in situ expression and/or translation of assigned genes using metatranscriptomic [START_REF] Hausmann | Long-term transcriptional activity at zero growth of a cosmopolitan rare biosphere member[END_REF] or metaproteomic [START_REF] Zecchin | Rice paddy Nitrospirae carry and express genes related to sulfate respiration: proposal of the new genus 'Candidatus sulfobium[END_REF] data, and the predicted function of a gene should be tested, such as with enzymatic assays. Despite the current limitations, new approaches related to deeper taxonomic assignment and functional description of microorganisms are promising and open new avenues for a better understanding of the macroecological patterns in rhizosphere microbiomes.

Methods to Synthesize across Studies

Heterogeneity of data describing microbial communities due to differences in methodology represents a significant obstacle for pooling data across studies. Before the recent advances in molecular biology, meta-analytical efforts focused on measuring the diversity or abundance of major microbial taxa derived from a variety of analytical methods. For example, Hendershot et al. [START_REF] Hendershot | Consistently inconsistent drivers of microbial diversity and abundance at macroecological scales[END_REF] performed a global meta-analysis on belowground diversity (including taxonomic and phylogenetic indices) and abundance (including microbial biomass, phospholipid-derived fatty acids abundance, and fungal colonization rate) of 325 soil communities across 20 studies along temperature and soil-pH gradients. They addressed inconsistencies among studies by using meta-regression to explore relationships between the microbiome and abiotic factors both within and among studies, but they did not find any such relationships. Possibly, no patterns were detectable due to a mismatch between the scale of community sampling and the scale at which the data on the environmental variables are available.

Due to the emergence and rapid growth of high-throughput sequencing studies, it has now become possible to describe the taxonomic (approximated by operational taxonomic units, OTUs) and functional group composition of microbial communities with near-complete coverage. Therefore, the merging of numerous meta-barcoding studies now represents a good opportunity to detect macroecological patterns and infer their drivers. However, comparing microbiome-sequence data from independent studies remains challenging due to the lack of standard protocols. Potential biases include the variety of methods used to collect samples, to extract DNA, the use of different primers, PCR bias, the different sequencing platforms and sequencing depths, the clustering methods, and the taxonomic classification pipelines [START_REF] Walters | Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys[END_REF][START_REF] Klindworth | Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies[END_REF][START_REF] Tremblay | Primer and platform effects on 16S rRNA tag sequencing[END_REF].

Each step of the workflow, from sampling to data set standardization, can produce variations that might blur biological patterns.

Trends in Plant Science

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Despite these challenges, it is possible to compare microbiomes across multiple data sets, if one uses a reference population and standardized bioinformatic protocols. In a seminal study, Lozupone et al. [START_REF] Lozupone | Meta-analyses of studies of the human microbiota[END_REF] successfully compared human microbiomes using raw data derived from 12 independent data sets. The authors used sequences from studies that targeted different nonoverlapping regions of the 16S-rRNA gene, and then related them to near full-length sequences from a reference database. This protocol allowed for comparison of sequences generated from different regions of the 16S-rRNA gene, but was in the meantime limited by the partial detection of taxa associated with primer affinity [START_REF] Tremblay | Primer and platform effects on 16S rRNA tag sequencing[END_REF]. They showed that variation in the composition of microbiomes from different studies was consistently larger than the variation caused by differences in methods among the studies, and they demonstrated that cross-study comparisons of human microbiomes are possible, at least when the variable of interest has a large effect size. Subsequent studies have then applied comparable methodologies with the analysis of mammal gut microbiomes [START_REF] Holman | Meta-analysis to define a core microbiota in the swine gut[END_REF][START_REF] Duvallet | Meta-analysis of gut microbiome studies identifies disease-specific and shared responses[END_REF], and initiated the establishment of international research consortia to collect data and set up databases [START_REF] Wang | Meta-analysis of human genomemicrobiome association studies: the MiBioGen consortium initiative[END_REF]. Such studies and initiatives are still missing for rhizosphere microbiomes.

Recently, Ramirez et al. [START_REF] Ramirez | Detecting macroecological patterns in bacterial communities across independent studies of global soils[END_REF] combined data from multiple independent studies of soils from around the globe, which they then analyzed with a machine-learning technique to assess macroecological patterns in soil bacterial communities. They used the metadata of the studies to identify technical biases and estimated with in silico analyses the biases of primer pairs used to produce the data. Then, they disentangled technical and environmental effects from taxonomic effects using random-forest techniques. This way, they were able to jointly analyze microbial community data from 30 individual studies comprising 1998 soil microbiome samples from 21 countries. They found that a subset of taxa with low relative abundances that occurred in most soil samples were more important for structuring soil communities compared with abundant taxa. These lowabundance taxa were also better predictors of community structure compared with environmental factors, which were often confounded across studies. Similar patterns on a smaller scale had previously been observed but required more controlled experimental conditions [START_REF] Dawson | A small number of low-abundance bacteria dominate plant species-specific responses during rhizosphere colonization[END_REF].

Recently, bioinformaticians have developed ASV-based methods that provide the finest-scale taxonomic partitioning of microbial phylotypes possible and allow simpler merging of sequence data across studies [START_REF] Callahan | DADA2: high-resolution sample inference from Illumina amplicon data[END_REF][START_REF] Amir | Deblur rapidly resolves single-nucleotide community sequence patterns[END_REF][START_REF] Edgar | Updating the 97% identity threshold for 16S ribosomal RNA OTUs[END_REF][START_REF] Callahan | Exact sequence variants should replace operational taxonomic units in marker-gene data analysis[END_REF]. Generated phylotypes can be rapidly and efficiently compared across independently processed datasets, as long as the underlying sequence data has been derived from the same genetic locus (amplified with the same primers). The first meta-analysis using this methodology was recently published by Rocca et al., combining 606 microbiomes that had been exposed to various environmental conditions sampled within different habitats [START_REF] Rocca | The Microbiome Stress Project: toward a global meta-analysis of environmental stressors and their effects on microbial communities[END_REF]. The authors selected nine independent studies on 16S-rRNA-gene amplicon sequencing with publicly available raw 16S-rRNA-gene sequencing data from the Illumina MiSeq V4 hypervariable region (reprocessed into ASVs). Their results contrast with the earlier mentioned results of Hendershot [START_REF] Hendershot | Consistently inconsistent drivers of microbial diversity and abundance at macroecological scales[END_REF], which could not identify global patterns related to temperature using other analytic methods. Thus, the study by Rocca et al. [START_REF] Rocca | The Microbiome Stress Project: toward a global meta-analysis of environmental stressors and their effects on microbial communities[END_REF] proved the efficiency of combining sequence data using ASVs and generated a more detailed understanding of how specific environmental parameters influence microbiome responses. Applied to the rhizosphere, the use of ASVs coupled with meta-analyses across large-scale studies would allow us to infer assembly mechanisms of plant-associated microbial communities.

Concluding Remarks

Recent advances in molecular biology are helping to unravel macroecological patterns in rhizosphere microbiomes. However, current work remains highly specialized and lacks the coverage of large temporal, geographical, and taxonomic scales. Deciphering the rules of rhizosphere Outstanding Questions

Deciphering the macroecology of rhizosphere microbiomes could help us to answer the following fundamental and applied questions:

What are the evolutionary and environmental drivers of rhizosphere microbial community composition?

How do different functional groups of microorganisms differ in their biogeographical distributions?

What are the mechanisms within and across plant hosts that mediate microbe-microbe and host-microbe interactions?

Which are the major microbial taxa in control of plant performance (e.g., pathogens, plant disease suppressors, or phytohormone producers)?

What is the role of rhizosphere microbiomes in large-scale patterns of plant invasion, and how can the rhizosphere microbiome be effectively included in risk assessments of invasive non-native species?

How will rhizosphere microbiomes in different parts of the world respond to climate change (e.g., CO 2 increase and warming), and what will be the consequences for individual plant species, plant communities, and biogeochemical processes (e.g., greenhouse gas emissions or modification of carbon and nitrogen cycles)? community assembly requires not only scaling up to detect macroecological patterns in the traditional sense, but also scaling down to understand spatial structure of rhizosphere microbiomes within root systems. In this review, we considered that the emerging new bioinformatic tools, specifically the use of exact sequence variants, coupled with existing and newly generated data, could be used to fill the knowledge gaps. This information will enable researchers to better explain and predict community and ecosystem responses to environmental change (see Outstanding Questions). To pursue this path, we encourage microbial and plant ecologists to collaborate in: (i) building links between databases; (ii) designing sampling campaigns guided by approaches used in macroecology and community ecology; and (iii) designing a standard protocol for recording of microbiome metadata and sampling of rhizosphere soil and DNA. These efforts will go a long way to unifying microbial ecology with macroecology and to advance our understanding of rhizosphere microbiomes.

Trends

  Figure I. Hypothetical Filters Affecting Assembly of Plant Communities and Rhizosphere Microbiomes in Plant Root Systems. Abiotic filters shaping communities include temperature, precipitation (climate), soil structure, temperature, moisture, and pH (soil characteristics). Biotic filters shaping plant communities include plant-plant interactions (competition [-] and facilitation [+]), and plant-non-plant interactions (herbivory, pollination, and fungal pathogens/mutualists). Biotic filters shaping rhizosphere microbial communities include vegetation cover and root characteristics of focal and neighboring plants (root thickness, root hairs, root-associated fungi, and exudate compounds).

Figure 1 .

 1 Figure 1. How Did Scientists Consider the Rhizosphere Microbiomes in 2018? Overview of the 382 articles, published in 2018, that responded to a 'rhizosphere microbiome' and 'rhizosphere microbial communities' topic query in Web of Science (query done in March 2020, see details in the supplemental information online). (A) Venn diagram representing the topics and subtopics raised in each article. The number of studies considering each of them are indicated in the boxes with matching colors (each publication can account for several topics and subtopics). (B) Venn diagram representing the repartitioning of studied microbial taxa. (C) Temporal scales ('None' stands for a single time point). (D) Geographical scales ('None' refers to experimental conditions ). (E) Numbers of studied plant species. (F) Rhizosphere soil-sampling method. (G) Sequencing approaches used to characterize rhizosphere communities ('None' refers to methods that do not rely on sequencing, e.g., the description of catabolic profiles or phospholipid-derived fatty acids measurements). (H) Sample origins.

Figure 2 .

 2 Figure 2. The Rhizosphere Microbiome Collection. Out of the 38 146 rhizosphere microbiomes collected worldwide (data stored in the NCBI Biosample repository with the taxonomy ID: 'txid939928' recorded before August 2019), only metadata of 10 670 (~28%) were recorded in the dedicated MIMS/MIMARKS plant-associated and host-associated packages, 20 460 (~54%) were geolocalized, 7845 (~20%) were assigned to host plant species (264 different plant species), and only 4465 (~12%) fulfilled all three criteria. (A) Temporal trend of data publication (N = 38 146) in the generic and nonspecialized packages (in gray), in specialized package (i.e., packages allowing a relevant description of associated metadata, in blue) and in specialized packages with information about the host species and the localization (in red). (B) Plant economic use (according to the GRIN database [81]) of the 264 different plant species. (C) World map showing collection locations of the 20 460 geolocalized rhizosphere microbiome samples. (D) Phylogenic representation of all vascular plant families (including angiosperms, gymnosperms, and ferns) and associated rhizosphere microbiomes (N = 7845, all family names are given in Figure S1 in the supplemental information online). In (C) and (D), different colors represent the sample species richness, point size represents the number of samples, and gray points stand for data without information about the host plant.

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Acknowledgments

This review was initiated by C.B. and greatly benefited from the work of all co-authors. C.B. acknowledges financial support from the research project of Taizhou University No. 2018YQ001 (China). W.D. acknowledges funding from the Royal Society Research Grant No. RG160281 (United Kingdom).

Resources

Supplemental information

Supplemental information associated with this article can be found online at https://doi.org/10.1016/j.tplants.2020.04.015.


Romain Jattiot
email: jattiot@uni-bremen.de

Benjamin Latutrie
email: benjamin.latutrie@gmail.com

André Nel
email: anel@mnhn.fr

The first damselfly (Odonata, Lestidae) from the upper Eocene of Monteils (Gard, France)

Keywords: Odonatoptera, late Eocene, Monteils Formation, paleobiogeography, wing venation

The discovery of the first damselfly Lestes regina from Monteils (Gard, France) supports the identity of late Eocene age of this outcrop with the historical outcrop of Célas, type locality for the type series of this species. Lestes regina is also documented from the late Eocene Isle of Wight basin, confirming the presence of significant contacts between this southern area and the anglo-Parisian lacustrine basin at that time. Nearly all the Eocene and Oligocene fossil Lestes from Western Europe have a particular character, viz. the presence of a supplementary row of cells between the veins MP and CuA. This character is much less frequent in extant Lestes and is still unknown among Neogene representatives of the genus.

Introduction

Even though the Lestoidea are recorded from the Lower Cretaceous with Cretalestes martinae [START_REF] Jarzembowski | The Mesozoic non-calopterygoid Zygoptera: description of new genera and species from the Lower Cretaceous of England and Brazil and their phylogenetic significance (Odonata, Zygoptera, Coenagrionoidea, Hemiphleboidea, Lestoidea)[END_REF], the oldest accurate fossil record of the extant family Lestidae comes from the late Eocene, as the Paleocene Lestes zalesskyi Piton, 1940 is based on a poor and lost fossil of Zygoptera of uncertain position (Piton, 1940;[START_REF] Nel | Les Lestoidea (Odonata, Zygoptera) fossiles: un inventaire critique[END_REF]. The oldest known Lestidae have been found in the lacustrine outcrops of the Isles of Wight (UK) and Célas (France). The discovery of new Eocene fossils of this family can be of great interest. Here, we describe the first fossil Odonata found in the upper Eocene outcrop of Monteils (Gard, France). This outcrop is very rich in fossil plants, fishes and insects, although rather few of these have been described [START_REF] Laurent | Flore des calcaires de Célas[END_REF][START_REF] Laurent | Etude phytologique sur le bassin de Célas[END_REF][START_REF] Feist-Castel | Sur les charophytes fossiles du bassin tertiaire d'Alès (Gard)[END_REF][START_REF] Nel | Une nèpe fossile de l'Eocène supérieur du bassin d'Alès (Hemiptera, Heteroptera, Nepidae)[END_REF][START_REF] Séméria | Paleochrysopa monteilsensis gen. et sp. nov., a new fossil of Chrysopidae from the Upper Eocene formation of Monteils (France with a review of the known Chrysopid fossils (Insecta, Neuroptera)[END_REF][START_REF] Rückert-Ülkümen | Hemitrichas vardinis (Sauvage, 1883) (Pisces, Teleostei) aus dem Alt-Tertiär (Obereozän) von Gard, Südfrankreich[END_REF][START_REF] Nel | New genus of leaf-mimicking katydids (Orthoptera: Tettigoniidae) from the Late Eocene -Early Oligocene of France and England[END_REF][START_REF] Legalov | New and little known weevils (Coleoptera: Curculionoidea) from the Paleogene and Neogene[END_REF][START_REF] Perrard | A new paper wasp from Late Eocene of France (Hymenoptera: Vespidae: Polistinae)[END_REF][START_REF] Cai | A new genus and species of Steninae from the late Eocene of France (Coleoptera, Staphylinidae)[END_REF][START_REF] Skartveit | Revision of fossil Bibionidae (Insecta: Diptera) from French Oligocene deposits[END_REF].

Material and method

The studied specimen was collected from the yellowish late Eocene (Priabonian) shale of Alès-Monteils in Gard, southern France (44°05′9″N, 04°11′39″E, after the BRGM geological maps 1/50 000, numbers 912 -Alès, XXVIII -40 and 938 -Anduze, XXVIII-41). It was examined using a Leica M300 binocular and photographed using a Nikon D5100 camera equipped with a Nikon AF 60 mm Micro lense.

We follow the wing venation nomenclature of [START_REF] Riek | A new interpretation of dragonfly wing venation based upon Early Carboniferous fossils from Argentina (Insecta: Odonatoidea) and basic characters states in pterygote wings[END_REF], modified by [START_REF] Nel | Les 'Anisozygoptera' fossiles. Phylogénie et classification (Odonata)[END_REF], [START_REF] Bechly | Morphologische Untersuchungen am Flügelgeäder der rezenten Libellen und deren Stammgruppenvertreter (Insecta; Pterygota; Odonata), unter besonderer Berücksichtigung der Phylogenetischen Systematik und des Grundplanes der Odonata[END_REF][START_REF] Bechly | Phylogenetic systematics of Odonata. -homepage on Internet site[END_REF] and [START_REF] Jacquelin | New insights on basivenal sclerites using 3D tools and homology of wing veins in Odonatoptera (Insecta)[END_REF].

Abbreviations. Ax, primary antenodal crossvein; arc, arculus; dc, discoidal cell; sn, subnodus; C, Costa; Pt, pterostigma; RA, Radius anterior; RP, Radius posterior; IR, Interradius; MA, Media anterior; MP, Media posterior; CuA, Cubitus anterior. Add: N, nodus, which has indicated in the Fig. 2.

Systematic paleontology

Order Odonata [START_REF] Fabricius | Entomologia systematica emendata et aucta, secundum classes, ordines, genera, species, adjectis synonymis, locis, observationibus, descriptionibus[END_REF]Family Lestidae Calvert, 1901 Genus Lestes [START_REF] Leach | Entomology[END_REF] Lestes regina Théobald, 1937

(Figs 1-2)
Material. Specimen UBGD 32320 (imprint, a nearly complete damselfly with all legs and wings preserved, sex unknown), stored at the Université de Bourgogne, Géologie, Dijon, France; counterimprint in Coll. Benjamin Latutrie.

Description. Body black, wings hyaline with pterostigma dark brown. Head deformed as in all fossil Lestidae, with mouthparts anteriorly projected, 2.6 mm long, and 3.5 mm wide. Thorax 6.0 mm long, and 3.4 mm wide. Fore-and hindwing nearly identical, in particular discoidal cells of same shapes and widths; fore wing 19.9 mm long, 4.5 mm wide, ratio L/W 4.4, width at nodus level 3.2 mm; pterostigma 2.3 mm long, and 0.5 mm wide; distance from base to arculus 3.7 mm, from arculus to nodus 3.7 mm, from nodus to pterostigma 9.8 mm, and from subnodus to base of RP2 2.8 mm; oblique vein 'O' two cells distal of base of RP2; a supplementary row of 5-6 cells between MP and CuA. Abdomen 27.9 mm long, 1.3 mm wide; cerci present but poorly preserved.

Discussion

The wing venation of this fossil is characteristic of the Lestidae and more precisely of the genus Lestes: long pterostigmata; bases of RP3/4 and IR2 midway between arculus and nodus; an oblique crossvein 'O'; distal discoidal vein MAb very oblique, with distal angle of discoidal cell very acute; MA strongly zigzagged; area between IR2 and RP3/4 distally strongly widened with three rows of cells between these two veins; fore-and hindwing discoidal cells of the same width and shape. One species of this genus was already described from the uppermost Eocene of Célas (in a lost outcrop at the old trench at the entrance to an abandoned railway tunnel), an outcrop situated at 4 km to and contemporaneous with Monteils [START_REF] Cavelier | La limite "Eocène -Oligocène" en Europe occidentale[END_REF], viz. Lestes regina [START_REF] Théobald | Les insectes fossiles des terrains oligocènes de France[END_REF]. The holotype of this species is probably lost and Nel & Paicheler (1994: 10-11, fig. 5) designated a lectotype and redescribed it. Mentioned below are the very few differences between L. regina and the herein described specimen.

The fore wing of the specimen from Monteils is 19.9 mm long, 4.5 mm wide, ratio L/W 4.4, width at nodus level 3.2 mm; while in the holotype of Lestes regina, the fore wing is 21.5 mm long, 4.6 mm wide with a ratio L/W 4.7 [START_REF] Théobald | Les insectes fossiles des terrains oligocènes de France[END_REF]. In the lectotype, the fore wing is 22.0 mm long, 4.5 mm wide with a ratio L/W 4.8, width at nodus level 3.0 mm [START_REF] Nel | Les Lestoidea (Odonata, Zygoptera) fossiles: un inventaire critique[END_REF]. The body dimensions are less reliable because of possible deformations due to taphonomic processes. Nevertheless, the abdomen is shorter than in the holotype of L. regina (27.9 mm long vs. 33.0 mm), and broader (1.3 mm vs. 0.75 mm). An additional difference could be the presence of three cells between base of RP2 and oblique vein 'O' in the lectotype of L. regina, whereas there are only two in the specimen from Monteils. In our opinion, the aforementioned differences are not sufficient to clearly separate this latter from Lestes regina. This discovery confirms that the two outcrops of Célas and Monteils are contemporaneous or nearly so, despite the different sediment aspect between them (darker limestone at Célas, compared to the white to grey blue limestones at Monteils).

Conclusion

The occurrence of Lestes regina in the three localities of Célas, Monteils and Isle of Wight suggests that there were significant contacts between the region of Alès and the northern Anglo-Parisian lacustrine basin. [START_REF] Feist-Castel | Sur les charophytes fossiles du bassin tertiaire d'Alès (Gard)[END_REF] came to the same conclusion based on charophytes. The presence of the same genus of Orthoptera Tettigoniidae at Célas and at Isle of Wight further strengthens this hypothesis [START_REF] Nel | New genus of leaf-mimicking katydids (Orthoptera: Tettigoniidae) from the Late Eocene -Early Oligocene of France and England[END_REF].

The presence of a supplementary row of cells between MP and CuA is encountered in several Eocene-Oligocene Lestes spp. from Europe, viz. Lestes regina, Lestes aff. regina (late Eocene, Isles of Wight, UK, [START_REF] Nel | Dragonflies and damselflies (Insecta: Odonata) from the Late Eocene of the Isle of Wight[END_REF], L. ceresti [START_REF] Nel | Description d'une nouvelle espèce fossile de Lestes leach, 1815, du Stampien de Céreste (Alpes-de-Haute-Provence) (Odon. Lestidae)[END_REF], Lestes brisaci Nel et al., 1997 (both from the late Oligocene, France), L. statzi Schmidt, 1958 (late Oligocene, Germany), L. foersteri Hess, 1895 (early Oligocene, France), and L. aquisextanus [START_REF] Nel | Sur la présence d'un Lestes Leach (1815) fossile de la lignée de Lestes regina Théobald dans les calcaires stampiens d'Aix-en-Provence. (Odon., Lestidae)[END_REF] (latest Oligocene, France) [START_REF] Hess | Beitrag zur Kenntnis der tertiären Agrioniden: eine neue Lestes-Art aus dem plattigen Steinmergel von Brunstatt bei Mülhausen i. E. Wissenschaftliche Beilage zum Programm der Oberrealschule, Gewerbeschule für das Schuljahr 1894-95[END_REF][START_REF] Schmidt | Bemerkungen über Lestiden. 3. Über Oligolestes grandis Statz, 1935 und eine neue Lestes-Art aus dem mittel-Oligozän von Rott im Siebengebirge[END_REF][START_REF] Nel | Sur la présence d'un Lestes Leach (1815) fossile de la lignée de Lestes regina Théobald dans les calcaires stampiens d'Aix-en-Provence. (Odon., Lestidae)[END_REF][START_REF] Nel | Description d'une nouvelle espèce fossile de Lestes leach, 1815, du Stampien de Céreste (Alpes-de-Haute-Provence) (Odon. Lestidae)[END_REF][START_REF] Nel | New Tertiary fossil Odonata from France. (Sieblosiidae, Lestidae, Coenagrioniidae, Megapodagrionidae, Libellulidae)[END_REF]. The two lestid species of uncertain generic affinities Lestes vicina [START_REF] Hagen | Zwei Libellen aus der Braunkohle von Sieblos[END_REF] (early Oligocene, Germany) and Lestes plicata Piton & Théobald, 1939 (early Oligocene, France) also have this supplementary row of cells [START_REF] Hagen | Zwei Libellen aus der Braunkohle von Sieblos[END_REF][START_REF] Piton | Poissons, crustacés et insectes fossiles de l'Oligocène du Puydu-Mur, Auvergne[END_REF][START_REF] Nel | Les Lestoidea (Odonata, Zygoptera) fossiles: un inventaire critique[END_REF]. Conversely, the Miocene European species of Lestes do not exhibit this particular character. Compte-Sart (2014) indicated that this supplementary row of cells between MP and CuA can be found also in some specimens but not in others, of extant Lestes species (L. dryas), and therefore considered it of minor importance. Yet, it would be somewhat significant to find it in all specimens of L. ceresti and L. regina. Naturally, these fossils are still too few (ca. 10 specimens of L. ceresti) to represent a valid statistical basis. More specimens are necessary to draw any conclusion on the frequency and stability of this character among the Eocene-Oligocene species of Lestes. Nevertheless, it seems that this character was more frequent among the Paleogene Lestes than in the Neogene ones.

FIGURE 1 .

 1 FIGURE 1. Lestes regina Théobald, 1937, UBGD 32320, Monteils. General habitus. Scale bar = 10 mm.

FIGURE 2 .

 2 FIGURE 2. Lestes regina Théobald, 1937, UBGD 32320, Monteils. (A) Fore wing; (B) Hindwing, arrow supplementary row of cells between MP and CuA. Scale bar = 10 mm.

Acknowledgements

We thank a lot two anonymous referees for their helpful remarks on the first version of the paper.

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Johan Linderholm
email: johan.linderholm@umu.se

| Paul Geladi

Nathalie Gorretta

| Ryad Bendoula

Alexia Gobrecht

Near infrared and hyperspectral studies of archaeological stratigraphy and statistical considerations

Keywords: archaeological stratigraphy, chemometrics, hyperspectral Imaging, near infrared spectroscopy, soil formation

The paper proposes a methodology based on near-infrared (NIR) spectrometry for studying stratigraphy and depth profiles in archaeological excavations. The NIR spectra can be used to describe and complement the wet chemical analysis. Soil samples were collected from a 0.8 m deep stratigraphy of a Neolithic site that were analyzed by three different NIR instrumentations. Phosphate-and magnetic susceptibility and inductively-coupled plasma mass spectrometry measurements were also conducted as reference analysis.

Principal component analysis on the data from three different NIR instrumentations gave useful score plots that allowed grouping of the samples. The results from the lab spectrometer were most useful, although the hyperspectral NIR camera was the fastest method to obtain spectra of many samples from one image. The paper shows how the NIR spectral data can be used for multivariate analysis to get meaningful conclusions on archaeological soils and sediments, especially in terms of understanding site development/phases and soil formation.

| INTRODUCTION

In archaeology, stratigraphy and soil depth profiles are important because they can reveal human settlement activities as a function of time, soil formation, processes of sedimentation and deposition.

Important indicators of human settlement and human activities are redistribution and reworking of soils and sediments [START_REF] Goldberg | Practical and theoretical Geoarchaeology[END_REF]. Also, accumulation of soil-phosphate (emanating from general waste, dung, and bones) [START_REF] Holliday | Methods of soil P analysis in archaeology[END_REF] and of carbon compounds (accumulated organic matter and from the use of fire) into the soil system are general features. Soils and sediments from archaeological sites often differ significantly in this respect from the general background as they become recipients of waste and general amelioration, resulting in an excess of 10-100 times of phosphate content and other nutrients compared with the background. The organic matter that accumulates oxidizes and decomposes over time but is incorporated in the humic matter and retains some of its chemical properties or "signatures". An improved understanding of this process and the nature of these soils will improve the understanding of the cultural processes forming them but that requires some form of analysis results as a function of depth.

Traditional wet chemical measurements of the above-mentioned indicators are comparably slow and usually expensive, so faster techniques are sometimes needed to replace or supplement them and especially techniques that provide robust data. The wet chemical analysis gives direct (quantitative) chemical information but with limited depth/time resolution. This is where near-infrared (NIR) spectroscopy can be helpful.

In soil-sedimentary contexts, NIR applications have been tested to analyze soil characteristics [START_REF] Udelhoven | Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: A feasibility study[END_REF][START_REF] Viscarra Rossel | Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties[END_REF]. In examples of studies in archaeology, NIR spectroscopy has been used to further enhance remote sensing sensibility [START_REF] Grøn | Interpretation of archaeological small-scale features in spectral images[END_REF]. Here it was shown that hyperspectral image analysis could be used for identifying archaeological features. [START_REF] Matney | In situ shallow subsurface reflectance spectroscopy of archaeological soils and features: A case-study of two Native American settlement sites in Kansas[END_REF] presented an archaeological prospection study where it was shown that in situ shallow subsurface features, such as hearths and storage pits could be spectrally identified. [START_REF] Araújo | Determining soil properties in Amazonian Dark Earths by reflectance spectroscopy[END_REF] used NIR spectroscopy to study Amazonian dark Earths to predict soil properties, such as soil organic carbon etc, using Partial Least Squares regression (PLS) modeling. The outcome of this study was that most parameters except soil P could be successfully predicted.

Bone materials in archaeological contexts have been studied by several authors with different approaches. In [START_REF] Thomas | Near infrared analysis of fossil bone from the Western Cape of South Africa[END_REF] and [START_REF] Linderholm | Identification of fragmented bones and their state of preservation using near infrared hyperspectral image analysis[END_REF] Near Infrared spectroscopic measurements have two advantages: they are faster than collecting samples in containers and analyzing them in the lab and, therefore, they can provide a higher resolution in time (more depths) and they are nondestructive. Furthermore, spectra can be collected in field/in situ. A field instrument probe can just be pointed towards a spot in the excavation. Besides NIR spectroscopy in the field, in-field hyperspectral imaging would also be a possibility, allowing a direct spatialization of the spectral information. We were unable to carry out imaging in field, but include a comparison based on laboratory imaging. Another important issue is that in an ideal situation, the scarce sample based wet chemical and abundant in situ spectroscopic information can be related by a regression model so that the wet chemical parameters can be predicted also with higher spatial and temporal resolution.

| MATERIALS AND METHODS

| The bastuloken site

The soil sequence that has been analyzed derives from the Neolithic site Bastuloken, Raä 158, Ramsele parish, Ångermanland Sweden (N 63°40.3622′/E 16°24.1107′) (Figure 2). The site is located on a sandy glacifluvium with dominating vegetation of pine (Pinus sylvestris), assorted lichens (Cladonia), blueberry and lingonberry (Vaccinium). Acid Podzols is the general soil type with well-developed eluviation-and illuviation horizons and subsequent translocation of F I G U R E 1 A schematic and actual view over a profile, samples (B1-B16) and measurements used in this paper. Not all wet chemical measurements could be made for all the sampled depths. Spectroscopic measurements were easy and cheap to make in triplicate using three different NIR instrumentations. NIR: near infrared [Color figure can be viewed at wileyonlinelibrary.com] elements such as Fe-Al through the soil profile [START_REF] Melkerud | Chemical, mineralogical and morphological characterization of three podzols developed on glacial deposits in Northern Europe[END_REF].

At this site, four oval semisubterranean structures (embankments) have been localized and three of them in close connection to the lake Bastuloken. The site has been excavated on three occasions and several phases were observed in the sedimentation of the walls [START_REF] Engelmark | Rapport över arkeologisk förundersökning av Raä 183, Ramsele sn, Ångermanland[END_REF][START_REF] Larsson | Människan och älgen vid Bastuloken: En delundersökt boplatsvall från neolitikum i Västernorrland[END_REF][START_REF] Larsson | Climate Change, Moose and Humans in Northern Sweden 4000 cal. yr BP[END_REF]. The embankments consisted of large amounts of unburned elk bones, fire-cracked stones, reaching a height of 0.8-1 m above the initial soil surface [START_REF] Storå | Osteological analysis of unburnt and bunt skeleton findings from the Stone Age site Bastuloken, Raä 183, Ramsele parish, County of Västernorrland[END_REF]. Artefacts were predominantly scrapers, slate points and general lithic debris [START_REF] Engelmark | Rapport över arkeologisk förundersökning av Raä 183, Ramsele sn, Ångermanland[END_REF][START_REF] Larsson | Climate Change, Moose and Humans in Northern Sweden 4000 cal. yr BP[END_REF], Linderholm et al., 2013). It is quite clear that massive hunting activities were going on at the site but the scale of bone amounts deposited, point toward activities dealing with more than just food production and consumption. Large scale elk skin processing is one suggested activity connected to these constructions.

Relating depth with time 14 C dating is useful and these data are available [START_REF] Larsson | Människan och älgen vid Bastuloken: En delundersökt boplatsvall från neolitikum i Västernorrland[END_REF] and [START_REF] Larsson | Climate Change, Moose and Humans in Northern Sweden 4000 cal. yr BP[END_REF]. The 14 C analysis from these studies showed initial dates to the late Neolithic (approximately 2500 BC) and encompasses 600-year span from base to top of the profile sequences.

| Sampling during excavation

The 2005 excavation was conducted by opening a square pit of one by one meter and excavating in 10 cm levels (Figure 3a) [START_REF] Engelmark | Rapport över arkeologisk förundersökning av Raä 183, Ramsele sn, Ångermanland[END_REF]. During the course of excavation every ten cm layer was sieved to 2 mm and bulk samples were collected for the analysis of macrofossils and bone while fire-cracked stones were recorded at different excavated levels of depth (DN1-DN8) (Table 1). Once the full profile was exposed, 16 depth profile samples were collected (B1-B16), every five cm from 0-80 cm depth (Figure 3b). They were dried and packaged for analysis. Before any analysis, samples were passed through a 2 mm sieve and homogenized using mechanized mortar ().

| Wet chemical and other analysis

An overview of all analyses carried out for the 16 depths is presented in Table 2. All 16 samples were analyzed using loss on ignition (LOI) at 550°C, 2% citrate soluble phosphate (Cit-P) and magnetic susceptibility (MS) [START_REF] Linderholm | The Soil as a Source Material in Archaeology: Theoretical Considerations and Pragmatic Applications[END_REF]. inductively-coupled plasma mass spectrometry (ICP-MS) measurement was only conducted on half of the samples because of time and cost considerations.

The analytical methods used to analyze the collected samples are described in some previous studies [START_REF] Arrhenius | Fosfathalten i skånska jordar[END_REF][START_REF] Grabowski | Functional interpretation of Iron Age longhouses at Gedved Vest, East Jutland, Denmark: Multiproxy analysis of house functionality as a way of evaluating carbonised botanical assemblages[END_REF][START_REF] Linderholm | The Soil as a Source Material in Archaeology: Theoretical Considerations and Pragmatic Applications[END_REF].

In this study, the focus is on the analysis of 2% citric acid soluble, mainly inorganic phosphate (inorganic-PO4, Soil P). Human-driven accumulation and circulation of phosphate in settlement and other contexts are well known in archaeology. Loss on Ignition was performed on samples dried to constant weight (105°C) and subsequent combusting of sediment material in crucibles at 550°C for 4 hr [START_REF] Carter | Soil sampling and methods of analysis[END_REF]. Usually, the LOI is assumed to correspond to the crude organic mass of the soil, but will also include partly combusted materials something that may be most significant in soil and sediments affected by human activities. MS of the soil samples was analyzed with a Bartington system (MS2 with MS2B dual sensor [START_REF] Dearing | Environmental magnetic susceptibility: Using the Bartington system, Bartington Instruments[END_REF]). MS is useful to characterize soils and sediments and especially in relation to processes of heating. All MS data reported here are low-frequency measurements (χlf 10 -8 m 3 kg -1 mass-specific susceptibility) on 10 -g soil samples. A subset from 16 samples originals was selected for further analysis of ICP-MS (table 2). For the ICP-MS analysis, samples were dissolved using a mix of nitric and perchloric acids (HNO 3 +HClO 4 ), in an Open beaker system. The instrumentation used was a ICP/MS-DRC-Elan 6100, Perkin-Elmer, Norwalk, Connecticut [START_REF] Linderholm | The Soil as a Source Material in Archaeology: Theoretical Considerations and Pragmatic Applications[END_REF]). Here we report data from sulfur (S), phosphorous (P) and calcium (Ca) as these elements partly represent bone matter in the analyzed context. 3.

F I G U R E 3 (a)
All materials analyzed by NIR were also subjected to wet chemical analysis (see Section 2.3).

| JDSU MicroNIR field spectrometer measurement

The JDSU MicroNIR 1700 is a portable instrument to be connected to a tablet (Windows 7 based software) or laptop computer for field work.

The probe is 42 mm (diam) by 42 mm (length) in size (see also [START_REF] Linderholm | Field-based near infrared spectroscopy for analysis of Scandinavian Stone Age rock paintings[END_REF]. It is based on an InGaAs detector and two built in quartz halogen lamps and it gives absorbances for 125 wavelengths (908-1,676 nm). This is approximately a wavelength every 6 nm.

The obtained data set was thus 16 × 125. Calibration was done by using dark current for 0% and reflections from a Spectralon (Labsphere, Inc., North Sutton, NH) standard for 100%. The spectra in reflectance were transformed into absorbance. Three measurements per sample were collected and subsequently averaged. The JDSU is the only portable instrument used for the data in this paper and it is therefore important

because it has a high potential for field use. Data on the MicroNIR can also be found in the references [START_REF] Zumba | Near infrared and hyperspectral studies of archaeological stratigraphy and statistical considerations[END_REF][START_REF] Wang | Detection of adulteration in milk powder with starch near infrared[END_REF].

| ASD LabSpec 4 measurement

In the experimental setup for the ASD LabSpec (Analytical Spectral Devices Inc., Boulder, CO), a halogen light source (150 W) was coupled with a 940 µm core diameter optical fiber and collimated by an aspheric lens. The incident beam was a 2 cm diameter circular spot. The reflected light was collected by an optical fiber bundle (44 fibers with a 200 µm core diameter each). This fiber bundle was connected to a LabSpec 4 Bench spectroradiometer, featuring a detection range of 350-2500 nm, whose spectral sampling (resp. spectral resolution) was 1.4 nm (resp.

3 nm) in the visible and near infrared range and 1.1 nm (resp. 10 nm) in the short-wave infrared range. A constant angle of 30°was maintained between the excitation and collection arms. This angle was chosen to optimize the intensity of the reflected beam and to avoid specular reflection. For each sample, dark current was recorded from all measured spectra and subtracted. A Spectralon (Labsphere, Inc.) white reference was used as a reference to standardize spectra from nonuniformities of all components of the instrumentation. Three measurements per sample were collected and subsequently averaged to give a 16 × 2151 data matrix of absorbance values [START_REF] Araújo | Determining soil properties in Amazonian Dark Earths by reflectance spectroscopy[END_REF][START_REF] Cambou | Prediction of soil organic carbon stock using visible and near infrared reflectance spectroscopy (VNIRS) in the field[END_REF].

| Hyspex SWIR measurements

The NEO Hyspex (Norsk Elektro Optikk, Lørenskog, Norway), a pushbroom hyperspectral camera was set up on a translation stage operated by a stepper motor and a computer supported by NEO software (Norsk Elektro Optikk). An illumination unit including 150 W tungsten halogen source mounted at a 45°angle was used to illuminate the sample. The hyperspectral camera covers the spectral range from 1,000-2,500 nm in 256 bands (spectral resolution of 6 nm) with 320 pixels over the cross-track field of view (FOV). The camera was equipped with a 30 cm lens that produced a nominal pixel size of approximately 0.287 mm across and 0.427 mm along the FOV, respectively [START_REF] Mishra | Application of independent components analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration[END_REF].

Settings of acquisition parameters were performed by means of the software provided by NEO. Raw images were first corrected in radiance using sensor characteristics (e.g., spectral sensitivity) provided by the manufacturer. As acquired radiance images depended on lighting condition, a reflectance correction was performed by using a 99%

reflectance Spectralon (Labsphere, Inc.) surface placed within each scene. The average reflectance spectrum from the total surface of each sample was extracted. Finally, absorbance images (-log 10 ) were computed for use in further analysis.

The powdered samples were transferred from plastic bags into Al cups of 4 cm diameter for imaging. The images were cleaned of background and other irregularities and used both as images and as an average spectrum. The average spectra formed a matrix of size 16 × 256.

The Hyspex images were made into a mosaic containing all samples B1-B16 in depth order. The images contained shadows from the illumination and also reflections from the Al cups. Fortunately, a Abbreviations: ASD: analytical spectral devices; NIR: near infrared.

spot of about 1 cm 2 in the middle of each image was correctly illuminated. After some background and shading removal, an errorfree square cm was extracted.

| Comparison of NIR spectra from the different instruments

This study focuses on the NIR wavelength range for sake of comparison and only the InGaAs detector for the ASD instrument was used for the comparison, above 1,000 nm.

Figure 4 shows an example of three different averaged spectra on the 16 samples, acquired with the three instruments. In spite of the differences in spectral range, some similar traits can be studied if a multivariate analysis is used. The visible peaks at 1,400 and 1,950 nm are related to OH bonds (water in different forms).

| Multivariate data analysis

The data produced by the NIR spectrometer and imaging equipment consisted of many variables and required multivariate data analysis.

Before all analysis, all wavelengths below 1,000 nm were removed because these only represent the visible light reflection (color) of the samples.

Principal component analysis (PCA) was done to get an overview of large multivariate data sets with possibly correlated variables. A few principal components were extracted to represent the spectral information contained in the data [START_REF] Wold | Principal component analysis[END_REF]. The Evince software was used for making and testing PCA models (Prediktera 2.7.4).

PCA was used on average spectra from the NIR probes, but also on the hyperspectral images from the NEO images. Especially in imaging, a correction for scattering and offset of each pixel spectrum is often useful.

Standard normal variate correction (SNV) [START_REF] Barnes | Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra[END_REF]) means that for each spectrum the mean is subtracted and the spectrum is divided by its standard deviation. This is an accepted method for removing specular reflection and penetration depth differences (topography) between the samples. For the hyperspectral images, SNV correction and columnwise mean-centering were used.

| RESULTS

| Profiles from wet chemistry

To begin with, we present the vertical profile of Cit-P, LOI, and the MS (Figure 5). Furthermore, the data subset from the ICP-MS study for S, P, and Ca is also presented in Figure 5. These data display similar trends as the above mentioned, although the ICP-MS analyzed P concentrations are significantly higher than that of the Cit-P. Also, it should be noted the extreme levels of Ca (and P) in sample B6 (at percentage levels). These classical human indicator elements show that human activity has varied considerably over time.

| Multivariate analysis of NIR spectra

The PCA was performed on the ASD and MicroNIR spectra. Components 1 and 2 are shown for the ASD data (Figure 6). In Figure 6, the first component (t1) separates the samples in two clusters (positive-negative).

On the negative side, there is also a separation in two clusters in the t2-component (positive-negative). Similar PCA clustering was also obtained with the Hyspex (not shown here). For the MicroNIR, separation of objects was found only for Component 1. The second and higher components were too noisy to show anything useful.

In Figure 6, two objects (B10-B11) do not belong to the neighboring group. This is purely based on visual observation in the

| Hyperspectral image analysis of the Hyspex images

The Hyspex imaging device is able to produce hundreds of thousands of pixels per image, where each pixel is a spectrum. This provides a different way of analyzing the data using imaging techniques. Two Both scores clearly show a progression in color when moving from sample level 1-16. Figure 9 shows clearly that there is a potential for also using imaging in field situations. Another advantage of imaging that is easily be seen in Figure 9 is that also inhomogeneity inside the samples can be observed and studied. Still, calculating average spectra for each image gives almost the same information as in Figure 6.

| DISCUSSION

The real world situation of archaeological observations is different from that of process industry or food quality assurance in that all samples are unique and final and that repeats are not possible to acquire. We only have data from the endpoint state of all previous events as in this case to understand the process forming the archaeological record. The conditions are not fully controllable or repeatable, which is a very different situation to most experimental situations. Still, the results in this study show that it is possible to use a data analysis approach similar to the process industrial one to get new information from archaeological contexts.

It was shown the NIR spectral information combined with multivariate analysis could be used to find soil horizon traits. The NIR measurements on the soils samples show that classes similar to those obtained by wet chemistry can be obtained that clearly shows the potential of using NIR spectroscopy in archaeological contexts.

The three instruments showed approximately the same potential for interpretation but the one with the largest wavelength range/ spectral resolution was slightly better. The NIR-imaging data can be used for making average spectra, but it could also give images where homogeneity within a sample can be studied.

Field sampling and subsequent analysis of collected materials is still the most common way of analyzing materials from excavations.

However, the spectroscopic instruments can also be taken out in the field for in situ measurements. Hyperspectral imaging for field use is still in its early stages and, usually, laboratory measurements are necessary to ensure spectral reproducibility. Imaging techniques open a way for future studies where intact profiles are documented with spectroscopic and spatial information and less physical sampling is needed.

Images have the advantage of allowing two modes of measurement. It is possible to integrate an area in the image to obtain an integrated spectrum that is comparable to that of the spectrometers.

It is also possible to study the images for spatial information on particle size and distribution. For some types of materials, this image mode is necessary. If NIR measurement and Imaging can be developed for use in the field then potentially less soil sampling is needed. The better suggestion is to use field imaging where hundreds of thousands of spectra over a large area can be collected and analyzed by hyperspectral image analysis as Figure 9 clearly shows.

This would also add to the general documentation of excavations in general, combining the strength of images and chemical information.

NIR analysis provides information that enables interpretations in terms of soil formation and cultural impact intensity. Improved understanding of this process and the nature of these soils will improve the understanding of the cultural processes forming them.

This is where NIR spectroscopy is helpful. The paper describes a test of this idea on one stratigraphy but the results can be generalized to a more complete analysis.

  the use of NIR hyperspectral imaging was used to analyze bone taphonomy in different contexts. Furthermore, Cascant et al. (2017) provides a NIR study on how to predict alkaline earth elements in bone remains using PLS predictive modeling. Examples of using NIR-Imaging techniques in field was done at the fortified city of Carcassonne where walls and mortars were analyzed in situ (Allios et al., 2016). In archaeology, sedimentation, stratigraphic development, and depth are related to time: the deeper the sample is taken the older it is. Gradients in depth of chemical concentrations give an indication of human activities over time. We aim to show that also gradients in NIR spectra have this functional relationship. For the analyses presented in this paper, soil material was collected during an archaeological excavation in 2005, from a trench covering a full depth profile (from top to subsoil). The samples were analyzed by different wet chemical and physical methods in the lab and the same samples were again analyzed in 2016 when also NIR spectroscopy/imaging was conducted using three different NIR instruments. Figure 1 explains how the different samples were presented to the analysis methods. The schematic trench profile is presented in this figure. More details are given in the methods and materials section. The spectroscopic data has the potential of giving better spatial and time resolution, however, this requires finding a relation between wet chemistry and spectral response. The main topic of the paper is then to present a number of ways of making the spectroscopic data make sense, either on its own or in combination with the wet chemical data obtained. NIR radiation can penetrate to a depth of a few mm into a powdered solid material. Because of a combination of refraction inside particles and reflection on surfaces (combined into transflection), some of the radiation comes out of the material again and can be measured by a sensor or collected with fiber optics. In this process also absorption occurs which gives the collected spectrum a chemical fingerprint of the material. Near Infrared spectroscopy has been used for classifying and quantifying solid materials, including soils. In this study, two different acquisition techniques have been used namely probe and imaging.

F

  I G U R E 2 Overview of Sweden and the site Bastuloken (raä 183, Ramsele parish, Ångermanland, Northern Sweden) [Color figure can be viewed at wileyonlinelibrary.com]

  Photo of one of the semisubterranean structures at Bastuloken, with the lake in the background. Inner lowest part indicated by a dotted line and the three persons are standing on the top of one embankment. A square marks the location of the excavated trench. (b) Photo of analyzed profile sequence at Bastuloken with sampled part indicated by a rectangle and sample positions marked. Note parts of the excavated fire-cracked stones in the upper right corner [Color figure can be viewed at wileyonlinelibrary.com]

2. 4 |

 4 NIR Instrumentation and spectral acquisition In this study, three different NIR instrumentations were used. The first two techniques use either a fiberoptic probe for simultaneous illumination and collection of transflected radiation or a probe with separated illumination and collection devices in the probe head. As an imaging option a more recent technique was used, where a whole scene is illuminated and the transfected radiation is collected pixelwise in a line scanning camera, giving a complete image. The three NIR instrumentations used were: (a) JDSU MicroNIR, (b) Analytical Spectral Devices (ASD) LabSpec 4, and (c) Hyspex line scanning camera. Technical details for the NIR instruments are presented in Table

  First, the sequence shows a large variation in Cit-P F I G U R E 4 Average (Vis and) NIR spectra for three instruments MicroNIR, ASD-LabSpec 4, Hyspex (sample in aluminum cup). Spectral data in below 1,000 nm was excluded from all models. ASD: analytical spectral devices; NIR: near infrared [Color figure can be viewed at wileyonlinelibrary.com] F I G U R E 5 A stratigraphic compilation of samples and analytical results; loss on ignition, citric soluble phosphate and magnetic susceptibility, and ICP-MS wet chemistry data, (sulfur (S), phosphorous (P) and calcium (Ca)). ICP-MS: inductively-coupled plasma mass spectrometry [Color figure can be viewed at wileyonlinelibrary.com] concentrations and also what appears to be two main and strong accumulation events, in this respect. This is corroborated by the MS readings that demonstrate a similar pattern. The LOI data are quite high compared to the surrounding forest podzol soils and the LOI readings show a corresponding two-peak pattern in the stratigraphic sequence.

  figure. The clusters defined here have too few members for allowing a deeper statistical clustering analysis. However, samples closely related in depth do form clusters which make sense stratigraphically. It is possible to see a grouping in depth and this grouping is indicated by using different colors and symbols. Class I and II (B1 and B14-16) relates to podzol soil formation as the buried (Eb-horizon) and the upper "contemporary" (E-horizon) eluviation horizon show the same characteristics. Buried B-horizon samples retain similar traits as the later formed ones. Also, the sedimentary process, where subsoil (C-horizon) material is redeposited on the top part of the profile probably also carry traits from this soil horizon. It becomes quite clear that the NIR information together with the PCA gives a good platform for identifying podzolized paleosols. Samples B7-13 together with sample B5 form a dense group covering the mid part of the sequence. The samples B10 and B11 seem to be outliers and they both show comparably low readings in phosphate and MS. Samples B2-4 and B6 show a less dense grouping. One may assume that the non-background samples show human impact but in a different form for Class I and for Classes III and IV. This shows how PCA can be used for finding outliers (if any exist) and for classification of samples.

Figure 8

 8 Figure 8 combines the archaeological observations of bone material and fire-cracked stone retrieved during the cause of excavation. Also, the classes (I-IV are shown) from Figure 6 are shown in corresponding colors. There is not a perfect correspondence between NIR classes and archaeological and geological observations. However, there is an apparent sampling issue that affects this outcome. The bone/ stone observations represent a volume of 100 dm 3 that was retrieved during the cause of excavation. The subsamples used for NIR and other analysis are represented by a volume of 0,125 dm 3 and the fraction <1,25 mm.This means that the correlation between the different sample groups is not necessarily absolute. Also, the smaller samples were collected along a horizontal line in the corner of the excavated square and in that sense do not represent the full volume of the excavated volume, however, the vertical resolution is higher.Where low amounts of bone and fire-cracked stone are recorded, this corresponds well to the soil formation processes observed in the field and also found in the NIR classification. The NIR data provides more possibilities on how to interpret the profile sequence.Another striking observation is related to bone corrosion and erosion is evident in the upper part of the sequence, leaving high amounts of phosphate and low amounts of retrieved bones. Also, the retrieved bones from the excavation showed a reversed corrosion pattern as the oldest (deepest deposited) were the best preserved[START_REF] Linderholm | Identification of fragmented bones and their state of preservation using near infrared hyperspectral image analysis[END_REF].

F

  I G U R E 8 The archaeological profile interpretation and amounts of unburnt bone and fire-cracked stone in each 10 cm documentation level of one square meter. Classes added from NIR-PCA in Figure 6 (PC 1 and 2) [Color figure can be viewed at wileyonlinelibrary.com] F I G U R E 9 Hyspex hyperspectral results for the cleaned image. Shown are PCA t1 (left) and t2 (right) for the 16 samples (B1 top-B16 bottom) in sample cups. The variation in color represents score value. Classes from figure 8 are added for comparison [Color figure can be viewed at wileyonlinelibrary.com] PC-components were considered meaningful to show (score t1 71,3%, score t2 7,8%) as mosaic images and in Figure 9, these 2 components are shown as depth profiles. The color of the samples indicates the score value. There is a clear distinction between the spots that conforms very well to classes found in the score plot of the ASD scores in Figure 6.

ACKNOWLEDGMENTS

Marcus & Amalia Wallenberg Foundation (MAW 2012.0136) and The Kempe Foundation (SMK-1749) for financial support. The authors would also like to thank the three anonymous reviewers for constructive comments.

Funding information

Marcus & Amalia Wallenberg Foundation, Grant/Award Number: MAW 2012.0136; The Kempe Foundation, Grant/Award Number: SMK-1749 Scientific editing by Sarah Sherwood