Douglas Martins Araujo

Gérard Meunier

Olivier Chadebec

Jean Louis Coulomb

Loic Rondot

3-D Hybrid FEM-BEM Using Whitney Facet Elements and Independent Loops

Keywords: Boundary element method (BEM), facet finite elements, hybrid FEM-BEM, magnetostatics

This paper presents a hybrid formulation using Whitney facet elements. This formulation is efficient for the modeling of unbounded 3-D magnetostatic problems. Boundary element method is used to model the unbounded regions and finite element method to model the ferromagnetic ones. Using equivalent circuit representation, the problem is changed, the degrees of freedom becoming magnetic flux in loops instead of faces. As a result, the dimension of the system to be solved is decreased.

To study the distribution of magnetic quantities (as magnetic field h and induction b) in a given device and neglecting temporal variations, Maxwell's equations for a magnetostatic problem are considered curl h = j [START_REF] Carpentier | Modeling of magneto-mechanical coupling using magnetic volume integral and mechanical finite-element methods[END_REF] div b = 0.

(

) 2
Magnets may optionally be introduced, leading to the following constitutive Law: b = μ(hh c ) where μ = μ(h) [START_REF] Vishnevsky | Modeling magnetization using Whitney facet elements[END_REF] where h c is the magnet's coercive field. The total magnetic field is split into two parts, as follows:

h = h 0 -grad ϕ ( 4
)
where ϕ is the reduced magnetic scalar potential, h 0 is the source field created by the nonmeshed coil, and h red = -grad ϕ is the reduced magnetic field.

Because vb = -h c + h, we have

vb = -h c + h 0 -grad ϕ ( 5
)
where v is the magnetic reluctivity.

III. FEM USING WHITNEY FACET ELEMENTS The total induction b could be interpolated using facet shape functions, as follows [START_REF] Dular | Mixed finite elements associated with a collection of tetrahedra, hexahedra and prisms[END_REF]:

b(x) = N f j I. INTRODUCTION
N UMERICAL tools based on finite element met- hod (FEM) provide to the user an effective representation of the device. Compared with analytical methods, the geometrical and physical properties are both accurately and quickly described. Furthermore, FEM can solve a wide range of problems. FEM-based models need a full domain representation including magnetic nonlinear regions and the surrounding region. As a result, the number of degrees of freedom (DoFs) can dramatically increase.

To tackle this problem, integral formulations have been considered [START_REF] Carpentier | Modeling of magneto-mechanical coupling using magnetic volume integral and mechanical finite-element methods[END_REF]. Integral volume methods can be used to represent nonlinear regions and consider implicitly the surrounding linear region. A drawback of these methods is the building of a fully populated matrix. Thus, the assembly time can be prohibitive.

The boundary element method (BEM) is used to model linear unbounded regions. In this method, the DoFs are located on the domains interface (bounded and unbounded).

The use of Whitney facet elements for approximation of induction in FEM-BEM magnetostatic formulations has already been proposed in [START_REF] Bandelier | A mixed B-oriented FEM for magnetostatics in unbounded domains[END_REF]. The originality of our approach is to impose the solenoidality of b through independent loops techniques associated with an equivalent circuit associated with the dual mesh [START_REF] Vishnevsky | Modeling magnetization using Whitney facet elements[END_REF]. This new formulation has been validated thanks to the modeling of a U-shaped contactor and obtained result compared with pure FEM.

II. MAGNETOSTATIC PROBLEM

We suppose a magnetic domain m , containing eventually magnets and surrounded by an unbounded domain 0 with permeability μ 0 . The surrounded region contains a nonmeshed coil in which flows an electric current density j. Fig. 1 shows the problem. w j (x) j [START_REF] Boeykens | Efficient calculation of coupling matrices for a decoupled FE/BIE formulation[END_REF] where w j is the j facet shape function, j is the magnetic flux through the facet j, and N f is the number of internal and external faces generated from the discretization. Let us notice the main property of this kind of element family: the normal component of w j is conserved through each facet, ensuring the conservation of normal induction. Equation ( 5) is projected on the domain m using facet shape functions to get a Galerkin equation

m w i •vbd - m w i • h red d = m w i •(-h c + h 0 )d . (7)
With ( 6) and introducing the reduced scalar magnetic potential, we get

N f j m w i • vw j j d - m w i • grad ϕ m d = m w i • (-h c + h 0 )d . ( 8
)
By integrating by parts, the second integral of ( 8) is

m w i •grad ϕ m d = m ϕ m w i •nd - m ϕ m divw i d . ( 9
)
Looking at the right side of ( 9), both terms could be interpreted as averaged reduced potentials. Because w i • n = 1/S i , S i being the surface of element i , we can conclude that the first integral represents the averaged potential on this boundary face

m ϕ m w i • nd = φ m . ( 10
)
Similarly, the second integral represents the averaged potential on the volume of elements. Considering an element e and one of its facets j , then divw j = ±1/V e , where V e is the volume of element e, we get

m ϕ m divw j d = φm . ( 11
)
Let us notice that the sign of the preceding term depends on the orientation of facet j . Hence, (8) can be rewritten, as follows:

[R] - φ m φm = U ( 12
)
where φm is the variation of averaged potentials between adjacent volumes elements, φ m is the variation of averaged potentials of a volume element and a face on boundary if the face is located on m , is the magnetic flux through the facets and surface, and [R] is the stiffness matrix, sparse which is similar to a matrix obtained during a classical FEM assembly, such as

R i j = m w i • vw j d . ( 13
)
U is the source term from coils and magnets, such as

U i = m w i • (-h c + h 0 )d . ( 14
)
System (12) can be considered as a matrix representation of an equivalent electric circuit. The centroids of elements of the primal mesh are now the nodes of the dual mesh [START_REF] Bossavit | Mixed-hybrid methods in magnetostatics: Complementarity in one stroke[END_REF]. Similarly, the facets of elements of the primal mesh are now the branches of the dual mesh.

Each DoF carries two different quantities. The first one is the magnetic flux of branches and it represents the analogy of electric current for magnetic problems. This flux is the same that crosses the faces and was presented in the previous equations.

This electric circuit has to be completed. To impose div b = 0, a circuit solver will be used (Section V). Fig. 2 shows an example of dual mesh used to build an equivalent circuit from the primal mesh and the different potential averages involved.

The external part of the circuit has to be considered. BEM involves two quantities: the magnetic potential and the magnetic flux on boundary. This will allow us to develop a perfect coupling between the method presented above and the BEM and complete the electric circuit.

IV. BOUNDARY ELEMENT METHOD

The main idea is to solve Laplace equation transforming the volume integral equation over all domain 0 , into a surface integral on the boundary m . This transformation can be made thanks to Green's identity [START_REF] Boeykens | Efficient calculation of coupling matrices for a decoupled FE/BIE formulation[END_REF].

Considering the third Green's identity applied in the air region to the reduced magnetic potential ϕ, we get

c(x 0 )ϕ(x 0 ) = m ∇ n G(x 0 , x)ϕ -G(x 0 , x) 1 μ 0 b-h 0 • n d (15)
where ϕ(x 0 ) is the reduced magnetic scalar potential at x 0 point, G(x 0 , x) is the 3-D Green's function (1/r ), r is the norm of a distance vector linking x 0 and a point x of the boundary, and c(x 0 ) is the solid angle subtended by the boundary m at x 0 . Let us consider a uniform distribution for the potential and its normal derivative on the border (zero-order interpolation function for both equivalent to degenerated facet shape functions on m ) [START_REF] Dular | Mixed finite elements associated with a collection of tetrahedra, hexahedra and prisms[END_REF]

ϕ(x) = N j w j (x)ϕ j , (x) = N j w j (x) j S j ( 16
)
where j = b nj S j and N is the number of facets of the boundary. By applying Galerkin projection to (15) and choosing the same weighting functions and interpolation functions used to describe reduced magnetic potential and normal magnetic induction distributions (16), we get

[H]ϕ ext + [T] = [T 1 ]h 0n . ( 17
)
[T], [T 1 ], and [H] matrices are associated to the following expressions:

T i j = mi 1 μ 0 S j m j G(x i , x j )d d , T 1i j = S j T i j H i j = mi c ii - m j ∇ n G(x i , x j )d d (18)
where c ii is similar to 2π, integration point being located on the regular m surface. There are many ways to perform integration of (18) [START_REF] Aimi | Hypersingular kernel integration in 3D Galerkin boundary element method[END_REF]. The simple layer potential integral is computed thanks to well-known analytical expression. Integral associated with double layer is expressed thanks to a Gaussian quadrature rule. Let us notice that [H] matrix is an invertible one.

V. COUPLING BOTH METHODS

As Fig. 3 shows, the circuit built using the dual mesh will be completed by adding the contribution of the external reduced potential averaged in the air φ ext . BEM will be used to evaluate this potential.

Because the potential is imposed ϕ(∞) = 0, we get

φ ext = ϕ -ϕ(∞). ( 19
)
To ensure the solenoidality of b, a variational formulation can be used [START_REF] Bandelier | A mixed B-oriented FEM for magnetostatics in unbounded domains[END_REF]. In our approach, a circuit solver resolution based on the determination of independent loops [START_REF] Nguyen | An independent loops search algorithm for solving inductive PEEC large problems[END_REF] is used. The general principle consists in finding small topological loops, by minimizing the number of branches, allowing a very quick research of independent loops.

The boundary condition can be easily considered in the formulation, by simply suppressing the associated DoFs in the equivalent circuit located on m . There are no nodes of the dual mesh at the magnetic boundary and the last branch connects the centroid of a volume element and the infinite node

φ = φ m + φ ext . ( 20
)
The last term of (20) is deduced from (17), as follows: Fundamental circuit equations are expressed in terms of external and internal average potentials

φ ext = [H] -1 [T 1 ]h 0n -[H] -1 [T] . (21)
[M] φ φm = 0 (22)
where [M] is the branch-loop matrix. By correlating (12), (21), and (22), we get

[M] R+ H -1 T 0 0 0 [M] t M = [M] U+ H -1 T 1 h 0n 0 ( 23
)
where M is the loop's magnetic flux. By solving the linear system, we obtain the currents flowing on each branch by applying the following equation:

= [M] t M .

VI. RESULTS

Fig. 4 shows a magnetic circuit with a coil. The distribution of magnetic induction was studied and it is shown in Fig. 5.

The magnetic force versus variation of the air gap is considered and compared with an FEM reference (Fig. 6) computed with a very dense mesh. Meshes with different number of DoFs are considered: FEM reference, FEM dense, FEM-BEM, FEM-BEM medium, and FEM-BEM coarse (Table I).

Once the problem has been solved, the numerical evaluation of magnetic force is achieved thanks to sources method [START_REF] Bobbio | Equivalent sources methods for the numerical evaluation of magnetic force with extension to nonlinear materials[END_REF]. Fig. 7 shows the relative error of each solution, FEM with a very dense mesh being used as reference. The new B-oriented H(div)-conforming formulation provides increased accuracy on the normal component of the magnetic induction. Therefore, it is well adapted to magnetic forces evaluations in which good accuracy is obtained using only a very few DoFs. This is the main advantage of our approach compared with the classical FEM-BEM formulation [START_REF] Meunier | Hybrid finite element boundary element solutions for three dimensional scalar potential problems[END_REF].

This comparison was used to validate our new approach. The convergence analysis shows that for the real case FEM-BEM is outperforming FEM in terms of DoF for the magnetic force evaluation. Further discussions concerning computation time, when accelerating techniques are applied, have been extensively made in [START_REF] Sabariego | Fast multipole acceleration of the hybrid finite-element/boundaryelement analysis of 3-D eddy-current problems[END_REF].

VII. CONCLUSION

In this paper, we have presented an original hybrid FEM-BEM formulation using facet elements. The formulation has a small relative error in comparison with a 3-D reference FEM with a high mesh density. The convergence of the results is quickly reached and with very coarse meshes, we get acceptable results. This new formulation seems very attractive because it enables an easy treatment of the unbounded region without meshing the air. Coupling of FEM and BEM by using Whitney facet elements which degenerate to zero-order shape function on the boundary is natural and does not necessitate interpolation process. Moreover, multiple connected regions are naturally treated.

The obtained matrix is composed of a fully dense matrix from BEM and sparse matrix from FEM. Using equivalent circuit representation, the problem is changed, the DoF becoming magnetic flux in loops instead of faces. As a result, the dimension of the system to be solved is decreased.

As we have no element in the unbounded region, the total number of DoF decreases drastically. If the number of elements on the boundary increases, the time spent on the matrix integration and solving becomes prohibitive. However, to solve problems involving a high number of DoF, numerical techniques can be used to reduce time and memory storage consumption. Using, for example, fast multipole acceleration technique, we observe a great reduction in computation time [START_REF] Sabariego | Fast multipole acceleration of the hybrid finite-element/boundaryelement analysis of 3-D eddy-current problems[END_REF].

Fig. 1 .

 1 Fig. 1. Classical representation of magnetostatic problem: different domains, materials, and magnetic sources.

Fig. 2 .

 2 Fig. 2. Primal and dual meshes. Black points: centroid of elements. Dotted lines: branches of the dual mesh. Both of the averaged potential configurations are shown (internal and border branches).

Fig. 3 .

 3 Fig. 3. Primal and dual meshes. Black points: centroid of elements and the imposed potential at the infinite. Internal and external contributions are represented.

Fig. 4 .Fig. 5 .

 45 Fig. 4. Magnetic circuit with a linear permeability and a nonmeshed coil.

Fig. 6 .

 6 Fig.6. Comparison between FEM and the formulation presented for the computation of magnetic force.

TABLE I DEGREES

 I OF FREEDOM Fig.7. Relative error for magnetic force. FEM with very dense mesh is the reference.


Emmanuel Giguet

Gaël Lejeune

Jean-Baptiste Tanguy

Daniel@FinTOC '2 Shared Task: Title Detection and Structure Extraction

We present our contributions for the two tracks of the 2020 FinTOC Shared Tasks: Table of Content (ToC) extraction in English documents and French documents. We describe separately our work on Title Detection and ToC Extraction. For ToC Extraction, we propose an approach that combines information from multiple sources: the table of contents, the wording of the document, and lexical domain knowledge. For the title detection part, we compare surface features to character-based features on various training configurations. We show that title detection results are very sensitive to the kind of training dataset used.

Introduction

This paper describes our participation to the Financial Document Structure Extraction shared tasks [START_REF] Bentabet | FinTOC-2020 Shared Task:"Financial Document Structure Extraction[END_REF]. We submitted results for the Title Detection and the ToC Structure Extraction. Structure Extraction is an important issue for Natural Language Processing and Document Analysis. Rich logical structures can be exploited for document classification and clustering [START_REF] Doucet | Unsupervised classification of text-centric xml document collections[END_REF][START_REF] Ait Elhadj | Using structural similarity for clustering XML documents[END_REF]. In the Document Analysis field, ToC generation aims to retrieve or extract a ToC from documents where the logical structure is not explicitly marked, or difficult to detect. ToC generation makes it easier to access information, in particular in the domain of Digital Humanities where documents can be long and structured in parts, chapters, appendices. Title Detection plays an important role for extracting the structure by helping to get candidates to populate the ToC. The position of sentences with respect to titles is used to improve the results in some NLP tasks: text classification [START_REF] Lejeune | Added-value of automatic multilingual text analysis for epidemic surveillance[END_REF], Terminology Acquisition [START_REF] Daille | Ambiguity Diagnosis for Terms in Digital Humanities[END_REF] or Keyphrase Extraction [START_REF] Florescu | PositionRank: An unsupervised approach to keyphrase extraction from scholarly documents[END_REF]. The paper is organized as follows. Section 2 gives a quick background for both subtasks. Section 3 describes our contribution to the ToC Extraction task and Section 4 our contribution to the Title Detection task. We give some words of conclusion in Section 5.

Background

Usually, the logical structure of natural language data is not explicitly encoded within a document or within an information stream: it is the case for native PDF documents like financial prospectuses. The organization of the information has to be inferred from the layout and the style of text blocks. Positional and contrastive features allows the recovery of the underlying structure. Recovering the global structure of a document is probably one of the most important process that has to be achieved towards relevant information extraction. In this regard, document structure analysis certainly precedes sentence analysis. By the way, neither one of them can be seen as preprocessing stage. Both are fully part of a natural language processing system. We may consider that these processes relate to skimming and scanning reading techniques. While skimming allows a reader to get a first glance of a document, scanning is the process of searching for a specific piece of information in a document. Different part of the document may be spotted by the reader and seeked for specific information using a zoom-in/zoom-out strategy [START_REF] Jeyafreeda | Modeldriven Web Page Segmentation for Non Visual Access[END_REF]. Concerning global structure, important information is found in the titles and subtitles, making the detection of titles important for improving web indexation [START_REF] Changuel | A general learning method for automatic title extraction from html[END_REF] or downstream NLP tasks [START_REF] Huttunen | Relevance prediction in information extraction using discourse and lexical features[END_REF][START_REF] Daille | Ambiguity Diagnosis for Terms in Digital Humanities[END_REF][START_REF] Tkaczyk | Who did what?: Identifying author contributions in biomedical publications using naïve bayes[END_REF]. We can see two main strategies for ToC extraction: detecting the ToC pages and relying on the book content. The ICDAR Book Structure Extraction competitions results [START_REF] Doucet | Overview of the ICDAR 2013 Competition on Book Structure Extraction[END_REF] showed that hybrid systems are promising which is consistent with more recent results from [START_REF] Nguyen | Enhancing table of contents extraction by system aggregation[END_REF] who combined different systems to get better results.

3 Contribution to the ToC Extraction Shared Task

From Table of Content Extraction to Document Structure Extraction

In previous INEX Book Structure Extraction Competitions, we used to consider the whole wording of the document (Giguet and Lucas, 2010a;Giguet and Lucas, 2010b;[START_REF] Giguet | Resurgence for the book structure extraction competition[END_REF]. This is a minority approach, it is more common to rely on the recognition and the parsing of the ToC since most books contain one and since ToCs are usually quite easy to locate. Taking into account the whole wording of the document presents several advantages. First, it allows to consistently handle documents with and without ToC. Second, it permits to extract titles that are not included in the ToC, such as lower-level titles or preliminary titles. Third, it avoids having to process erroneous ToCs. Indeed, the ToC of a document may not be accurately synchronized if the authors forgot to update it. It may also contain entries that are not titles, for instance a paragraph incorrectly labelled as a title, or wrong page numbers. This cases often occur when documents are published without the supervision of an editorial board.

In the first edition of FinTOC [START_REF] Giguet | Daniel@fintoc-2019 shared task: Toc extraction and title detection[END_REF] our strategy mainly relied on the detection of the Table of Contents combined to a simple fallback strategy when no ToC is found. We locate ToC pages to extract their content, and we submit the result as the document structure. Our expectations was to have a good precision and a low recall due to missing or incomplete ToCs. ToCs belong to the category of index lists. Whether they are table of contents, list of figures, or list of tables, index lists together form a network of links starting from the periphery of the document and pointing to the inner content. These links facilitate direct access to information and enables alternative reading strategies. They also provide an "at-a-glance" snapshot of the complexity of the book structure. While Document Structure Extraction do not consist in ToC extraction, it would be unfortunate to get rid of the information contained in the ToCs. Therefore, ToC recognition and parsing is fully integrated to our extraction method. And linking ToC entries to headings of the main text stream is the first step of our integration process.

Title Extraction from the Whole Content

As documents do not all contain ToCs, alternative ways have to be found to capture the hierarchy of headings. The main stream of content is the most natural source of information. However, it needs to be accurately and reliably detected. The task is not straightforward since the content is fragmented into pieces of texts. In order to retrieve the main text stream from the document content, page layouts have to be inferred in order to exclude the headers and the footers which break the linearity of the main text stream. Floating objects such as figures, tables, graphics and framed texts have to be excluded as well.

As the shared task relates to financial document analysis, we solely focus on table detection and removal: they are the most frequent floating objects. One positive side effect: table removal reduces the search space and prevents considering table content when searching title candidates, thereby reducing the number of false positives. The table detection module parses the PDF vectorial shapes that are extracted by the pdf2xml command [START_REF] Déjean | pdf2xml open source software[END_REF]. Text background and framed content are first inferred. The algorithm then builds table grids from adjacent framed content interpreted as possible table cells.

Once the main text stream is extracted, titles are located with the help of two complementary strategies: the Numbered List Detection strategy and the Salient Text Detection strategy.

The Numbered List Detection strategy detects coherent series of numbered lines that may correspond to numbered titles. The strategy takes into account various features of text lines: the numbering style type (i.e., decimal, lower-latin, upper-latin, lower-roman, upper-roman), the numbering pattern (e.g., prefixes such as Chapter, Section, hierarchical numbering system such as A.2, 3.1.b). The Text Saliency Detection strategy is a contrastive approach to title detection. Titles are salient objects that stands out from the surrounding background. The background corresponds to text blocks (i.e., paragraphs, list items) that share common stylistic properties (i.e., font properties, line spaces, background, alignment).

Taking advantages of Prospectus Document Model and Specific Document Models

The structure of financial prospectuses is driven by strong expectations from potential buyers and authorities. Thus, a model tends to emerge among the producers of financial information: across organizations, prospectuses tend to share common features in terms of macro structure. Certain sections and subsections are expected and are present with an expected naming. Moreover, prospectuses issued by an organization tend to share the same structure over products and over years. Moreover they also often share exact or similar document or page layout models. It is interesting to take benefit of these features, whether they are related to the genre or related to a specific organization. In this regard, titles from the training set are stored as lexical entries and reused as an external knowledge source for title detection.

Results

Table 1 exhibits the results our system as obtained on the train dataset. Compared to our first contribution [START_REF] Giguet | Daniel@fintoc-2019 shared task: Toc extraction and title detection[END_REF], our system outperforms the previous version which mainly relies on the ToC detection and extraction. The current version still demonstrates good precision and considerably improves recall. The choice to favour precision is much more sensitive with the Xerox metrics than with the Inex08 metrics. Our results on the test set are given in Table 2. One can see that, contrary to other systems, it performed better on the English data than on the French data. This in accordance with the results obtained on the train set.

4 Contribution to the Title Detection Shared Task

Datasets

The training and testing sets of the shared task are composed of segments labelled Title or Not Title. We also used the Fintoc-2019 (https://wp.lancs.ac.uk/cfie/shared-task/) corpus for improving English title detection and the DEFT-2011 (https://deft.limsi.fr/2011/) for French. In order to observe the impact of adding training data, we kept the split in training and testing sets. Both the FinTOC-2019 and the DEFT-2011 corpora are made up of segments labelled as Title or Not Title. A segment in the FinTOC-2019 corpus (as in the Fintoc-2020 corpus) refers to a physical component that is, in practice, a line. In the DEFT-2011 corpus, a segment refers to a logical component : a Title, a section, a subsection, etc. We must be clear that DEFT-2011's documents are scientific papers. We experiment several training sets combinations in order to assess the impact of the language and the text genre. To test the models, we selected randomly 20% of each dataset to use it as development data (see details in Appendices).

Methods

We propose baselines mainly relying on surface features and a character n-gram approach. For both methods we use a Random Forest classifier with 50 estimators since it outperformed other classifiers.

Baselines n-gram method It consists in vectorizing the segments by counting the frequency of character ngrams. We explore different values for n min and n max minimum and maximum size of the n-grams. From the results obtained in both languages with the baselines (Table 3 for English and Table 4 for French ) we can observe that Baseline B6 gives the best results in all cases. This result is in accordance with the observations we made in previous edition [START_REF] Giguet | Daniel@fintoc-2019 shared task: Toc extraction and title detection[END_REF]. Regarding the training datasets, we can observe that the Fintoc-2020 train set gives the best results. We observe the same pattern for the character n-gram method, the F score does not achieve 80% without this data. We also observed that using bilingual datasets does not improve results (see appendixes).

Results

Conclusion

In this article we proposed approaches for two shared tasks of FinTOC 2020. Regarding the ToC Extraction Shared Task, we propose a hybrid approach. It consists in combining the output of multiple modules dedicated or related to title detection. Title candidates are extracted from the table of contents thanks to a ToC Detection and Extraction module. Candidates are also extracted from the main text stream with the help of two complementary modules: a Numbered List detection module and a Text Saliency Detection module. In order to enrich the approach, titles from the training set are used to detect domain-specific titles. The title candidates are merged to generate a complete Table of Contents. For the Title Detection task, we proposed to use two types of features: surface features and character n-grams). We showed that stylometric features (frequency of punctuation, numbers and capitalized letters) combined with visual characteristics (bold, italic. . . ) achieve better results than the character n-gram approaches.

Table 4 :

 4 Baseline results on the F-2020-fr-dev dataset, learned from: (i) D-2011-TRAIN, (ii) D-2011-TRAIN+TEST, (iii) D-2011-TRAIN+TEST + F-2020-fr-TRAIN and (iv) F-2020-fr-TRAIN

Table 1 :

 1 Title candi-Results obtained on the train dataset

		Xerox measures		Inex08 measures	Error count
	P	R	F1	Title P	R	F1	Title Level Pb Title Pb Level Err
	French 89.6 53.6 64.4 62.0 40.7 25.7 30.5 45.4 9.3	1765	3061	461
	English 89.8 63.9 70.3 68.8 50.0 35.8 39.7 54.5 29.9	2713	4256	974
		Team	Inex F1			Team	Inex F1
		DNLP	0.37				DNLP	0.34
		taxy.io	0.32				Daniel 2 0.28
		Baseline 0.32				taxy.io	0.24
		Daniel 2 0.22				Amex 1 0.23
							Baseline 0.18
	(a) Results for the test dataset (French)			(b) Results for the test dataset (English)

Table 2 :

 2 Official results (Inex F1) for the ToC extraction task dates are searched among the salient remaining text lines. A text line is salient if its stylistic properties generates enough contrasts with the surrounding text background. Salient texts sharing identical stylistic properties are clustered in order to build sets of titles acting at the same level of the hierarchy.

Appendices


Andrea Scaccabarozzi

Andrea Cattoni

Gilles Patriarche

Laurent Travers

Stéphane Collin

Jean-Christophe Harmand

Frank Glas

Fabrice Oehler

Stable and high yield growth of GaP and GaAs nanowires arrays using In as a catalyst †

We report the first investigation of indium (In) as the vapor-liquid-solid catalyst of GaP and GaAs nanowires by molecular beam epitaxy. A strong asymmetry in the Ga distribution between the liquid and solid phases allows one to obtain pure GaP and In 0.2 Ga 0.8 As nanowires while the liquid catalyst remains nearly pure In. This uncommon In catalyst presents several advantages. First, the nanowire morphology can be tuned by changing the In flux alone, independently of the Ga and group V fluxes. Second, the nanowire crystal structure always remains cubic during steady state growth and catalyst crystallization, despite the low contact angle of the liquid droplet measured after growth (95 • ). Third, the vertical yield of In-catalyzed GaP and (InGa)As nanowires arrays on patterned silicon substrates increases dramatically. Combining straight sidewalls, controllable morphologies and a high vertical yield, In-catalysts provide an alternative to the standard Au or Ga alloys for the bottom-up growth of large scale homogeneous arrays of (InGa)As or GaP nanowires.

(InGa)As NWs 27,28 , for which no liquid droplet is present dur-47 ing growth 29 . As for GaP, the growth of [Au]-GaP 11 and 48 [Ga]-GaP 15,30 NWs is reported but the case of [In]-GaP NWs has 49 J o u r n a l N a me , [ y e a r ] , [ v o l . ] , 1-8 | 1 never been investigated. 50 2 Solid-liquid distribution 51 The chemical compositions of the liquid (catalyst) and solid (NW) 52 in the VLS growth of III-V NWs are often very different. The group 53 V is an obvious example: it constitutes 50% of the solid phase 54 but at most a few percent of the liquid 19,31-33 . Group III alloys 55 can also present a severe asymmetry between the solid and liq-56 uid phases. For example, the strong distribution asymmetry of 57 (Al,Ga) was used by Priante et al. to obtain sharp (AlGa)As het-58 erostructures 21 . 59 The detailed modeling of the VLS growth of NWs from liquid al-60 loy particles has recently been performed using nucleation theory 61 (NT) 25,32,34 . Alternatively, Standard equilibrium thermodynam-62 ics (SET) is a simpler method 30 . In Fig. 1, we compare these two 63 methods by plotting the pseudo-binary distribution coefficient of 64 a solid In 1-x Ga x P NW in contact with an [InGaP] liquid catalyst

Introduction

Regular arrays of III-V semiconducting nanowires (NWs) are of great interest for the future generation of opto-electronic devices [1][2][3] In the bottom-up approach, the fabrication always includes the use of patterned substrates, fabricated by lithography, to obtain regular arrays of holes in a dielectric mask [4][5][6] or to localize gold particles 3,7,8 . The type of catalyst, or the absence thereof, is a critical choice which impacts all subsequent steps. For GaAs and GaP NWs fabricated by molecular beam epitaxy (MBE), growth usually proceeds via the Vapor-Liquid-Solid (VLS) mechanism 9 . Regarding regular Ga(AsP) NW arrays, only two catalysts are reported to provide high yield, stable growth and smooth morphologies: gold (Au-catalyzed) 10,11 or gallium (selfcatalyzed) [12][13][14][15] .

The self-catalyzed variant 16 provides an elegant route to complex multi-shell structures in a single growth run 14 by consuming the Ga VLS catalyst in situ 17,18 . However this can lead to crystal phase heterostructures due to the changing contact angle of the Ga catalyst as its volume shrinks 19 . Moreover, as both Au-and Ga-catalysts contain significant amounts of Ga, there is a limit to the sharpness of axial group III heterostructures due to a resera Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, 91120, Palaiseau, France. E-mail: fabrice.oehler@c2n.upsaclay.fr b Institut Photovoltaïque d'Ile-de-France, 91120, Palaiseau, France. † Electronic Supplementary Information (ESI) available: Equilibrium group V concentration in the droplet for [In]-(InGa)As and [In]-(InGa)P NWs; Thermodynamic reference data; Crystal structure of [Ga]-GaP NWs which catalyst has been crystallized using P. See DOI: 00.0000/00000000. voir effect 20,21 . There is thus an interest to identify new catalysts To compare quantitatively the NW morphologies, we use the demonstrates that the improved vertical yield is not due to the increased volume of the initial droplet 13 , but relates to the presence of In in the liquid catalyst.

The fraction of vertical NWs is known to depend on the droplet contact angle 42,43 , hole diameter and mask thickness 44 , as well as droplet size 12,13 . In our case, the ratio of hole diameter over mask thickness is about 3.5, which is slightly below the reported optimum range 44 . This may explain the relatively poor vertical yield in our [Ga]-GaAs NW arrays (χ = 0.5). As seen previously, the transition from Ga-to In-catalyst is associated with a strong reduction of the contact angle (from β Ga = 130 • to β In = 95 • , measured after growth). An optimum value of 90 • is reported in the literature for Ga 42,43 . This could explain the vertical yield improvement with In-catalyst observed both in our GaP and GaAs NW arrays.

On the other hand, the vertical yield enhancement from GaAs to GaP NW arrays remains unexplained, as both start from the same droplets arrays and have the same contact angle.

Conclusion

We have proposed and demonstrated the VLS growth of GaAs and GaP NWs catalyzed by In droplets, exploiting the strong asymmetry in the distribution of Ga between the liquid and solid phases.

Experimental

All samples were grown on 2" p-type (111) Si wafers with 0.01-0.02 Ω.cm resistivity, covered with 25±2 nm of SiO x using plasma-enhanced chemical vapor deposition. The thickness of the oxide layer was measured for every sample by ellipsometry and reflectometry. On each sample, a hexagonal array of disks with 20 nm nominal diameter and 300 nm pitch was written by electron-beam lithography (EBL, Raith EBPG 5000+) in a polymethylmethacrylate (PMMA) resist layer and developed with a solution of methyl-isobutyl-ketone and isopropyl-alcohol. The EBL pattern was transferred into the oxide layer by reactive ion etching, creating a pattern of holes. Organic residues on the samples were cleaned with solvents and an oxygen plasma. A final wet etch step in diluted hydrofluoric acid (HF) was performed immediately before loading the samples in the MBE system to obtain a fully deoxidized Si(111) surface inside the holes. After this, the thickness of the silicon oxide mask is reduced to 14 nm. Before loading into the growth chamber, the samples were outgassed at about 600 • C, then further outgassed in situ at 700 • C. Scanning electron microscopy (SEM) inspection prior to the growth but after the outgassing steps reveals an average hole diameter of 50 nm.

The growth was performed in a Riber 32 solid-source MBE reactor. Ga-catalyzed NWs were grown at a substrate temperature

Notes and references

22 forFig. 1

 221 Fig. 1 Distribution coefficient of [InGaP] (black) and [InGaAs] (blue) liquids respectively in contact with pseudo-binary In 1-x Ga x P and In 1-x Ga x As solids at three different temperatures. Nucleation theory (NT) is compared with standard equilibrium (SET) and experimental data (Exp) at 520 • C from this work.

  108

Figures 2 .

 2 Figures 2.a and 2.b present overviews of [In]-(InGa)P and

Figures 2 . 8 J

 28 Figures 2.c and 2.d present chemical maps and High Angle An-119

Fig. 2

 2 Fig. 2 (a-b) SEM overviews of the [In]-(InGa)P and [In]-(InGa)As NW arrays grown using a Ga:In flux ratio of 1:1. (c-d) STEM-HAADF images and EDS maps of the above NWs dispersed on TEM grids. (e) Gallium composition profiles along the growth axis extracted from the EDS maps. Dotted lines are guides to the eye. Absolute errors are ± 0.01 in the Ga composition and a few nm in length, respectively.

157 4

 4 Aspect ratio and morphology 158 Although there is no significant change of the solid composition 159 when supplying In in our experiments, we observe an obvious 160 evolution of NW morphology. [In]-GaP NWs (Fig. 3.b-c) are wider 161 and shorter than [Ga]-GaP NWs (Fig. 3.a).

Fig. 3

 3 Fig. 3 SEM bird eye view of the NW array (300 nm pitch) after 20 min growth. (a) [Ga]-GaP NWs. (b) [In]-GaP NWs grown using 1:1 (Ga:In) flux ratio. (c) [In]-GaP NWs, 1:4 flux ratio.

Table 1 Fig. 4

 14 Fig. 4 (a) Bright field TEM image of the catalyst of a [Ga]-GaP NW showing the contact angle β Ga , measured after growth. (b) HR-STEM bright field image showing the crystal structure of the same [Ga]-GaP NW. (c-d) Similar images for the contact angle β In and crystal structure of a [In]-GaP NW grown using 1:4 flux ratio. Cubic phase segments (ZB, yellow color), alternate with twinned cubic segments (ZB twin, purple).

Figure 5 Fig. 5 243 Figure 5

 552435 Figure 5 presents an [In]-GaP NW, which catalyst has been

274 7

 7 Vertical yield improvement 275 Besides the overall stabilization of the ZB phase, the addition of 276 In also induces a substantial increase of the vertical yield of NWs 277 grown on patterned SiO 2 /Si(111) substrates. Figure 6 presents 278 representative SEM overviews of the NW arrays after growth. No 279 empty holes are found, which indicates that the Si(111) surface 280 is exposed fully in each hole. However, some wires grow in titled 281 directions (i.e. not vertical) or as a irregular crystals extending 282 over the mask. The average fraction of vertical NWs, i.e. the ver-283 tical yield (χ), is summarized in Table 2. We find that [Ga]-GaAs 284 NWs have the lowest yield, which substantially improves as In is 285 provided ([In]-(InGa)As). Arrays of [Ga]-GaP NWs start with a 286 higher vertical yield but also improve slightly when In is supplied 287 ([In]-GaP).

Fig. 6 Table 2

 62 Fig. 6 SEM overviews of NWs arrays on patterned Si(111). (a,b) [Ga]-GaAs and [Ga]-GaP NWs. (c,d) [In]-(InGa)As and [In]-GaP, grown using 1:1 ratio of Ga:In fluxes.

Figure 7

 7 Figure 7 presents the SEM images after the droplet deposition step and after 150 s of subsequent NW growth. A single droplet is present in each hole, for all samples and group III fluxes. The droplets grown using the largest group III fluxes (2:0 Fig. 7.b and 1:1 Fig. 7.c) are bigger than those from the (1:0) experiment (Fig. 7.a). After 150 s of growth, the vertical yield difference is already visible. Fig 7.c confirms the yield improvement in the presence of In, as previously observed during the longer growth experiments (Fig. 6). Since the (2:0) flux with large pure Ga droplets also results in a poor vertical yield (Fig. 7.b), this

PureFig. 7

 7 Fig. 7 SEM observations of Ga-and In-catalyzed GaAs NW arrays grown using different Ga:In flux ratio, after the droplet deposition (left) and 150 s of subsequent growth (right). (a) Reference Ga-catalyzed sample, ratio 1:0. (b) Ga-catalyzed sample grown using twice the Ga flux, ratio 2:0. (c) Sample growth using simultaneous In and Ga fluxes, ratio 1:1. Scalebar is 200 nm for all images.

  of 600 • C and In-catalyzed NWs at 520 • C. The Ga flux (from a standard effusion cell) corresponds to a growth rate of 1.5 Å.s -1 for planar (001) GaAs, as determined using Reflection High Energy Electron Diffraction (RHEED). The In flux (standard effusion cell) was calibrated from (001) InAs RHEED measurements. The As 4 flux (Riber VAC500, cracker temperature 600 • C) is set to 1.4 As/Ga effective atomic flux ratio from (001) GaAs RHEED. The P 2 flux (Riber KPC, cracker temperature 900 • C) is fixed to a 1.2 P:Ga effective atomic flux using growth rate measurements on a (001) GaP surface. Prior to the exposure to group V, Ga or InGa droplets are deposited for 2 min, which completely fills each hole of our silica mask. The subsequent NW growth duration is 20 min. The morphological characterizations were performed by SEM (FEI Magellan 400L), while structural analyses were performed by TEM and STEM/HAADF (FEI Titan Themis operated at 200 kV). The NW chemical composition was measured by EDS (Brucker super X) during the STEM observations.

Table 1

 1

reports aver-162 age measurements over 10 wires for each sample.

1-8 J o u r n a l N a me , [ y e a r ] , [ v o l . ] ,

J o u r n a l N a me , [ y e a r ] , [ v o l . ] ,

Conflicts of interest


Yan Lee

Juan Manuel Peralta-Sánchez

Manuel Martínez-Bueno

Anders Pape Møller

Miguel Rabelo-Ruiz

Carmen Zamora-Muñoz

Juan José Soler
email: jsoler@eeza.csic.es

Manuel Martínez- Bueno

The gut microbiota of brood parasite and host nestlings reared within the same environment: Disentangling genetic and environmental effects

Keywords: Brood parasitism, Diet, Gut microbiota, Genetic factors, Great spotted cuckoo, Magpie

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The study of microbiotas associated with different environments, including animal hosts, has become one of the most fruitful areas of biological research during the last decade [START_REF] Thompson | A communal catalogue reveals Earth's multiscale microbial diversity[END_REF][START_REF] Parfrey | Introduction: The host-associated microbiome: Pattern, process and function[END_REF][START_REF] Moran | Evolutionary and ecological consequences of gut microbial communities[END_REF]. Reasons include that microorganisms should have profound impact on animal life and evolution, particularly those in close contact with animals, as is the case of gut microbiota [START_REF] Mcfall-Ngai | Animals in a bacterial world, a new imperative for the life sciences[END_REF][START_REF] Colston | Microbiome evolution along divergent branches of the vertebrate tree of life: what is known and unknown[END_REF]. Gut microbiota shares intricate relationships with their hosts and are essential for host health and survival [START_REF] O'hara | The gut flora as a forgotten organ[END_REF][START_REF] Browne | Transmission of the gut microbiota: spreading of health[END_REF]. For instance, it plays a key role in the development of a healthy immune system [START_REF] Mao | Innate and adaptive lymphocytes sequentially shape the gut microbiota and lipid metabolism[END_REF][START_REF] Belkaid | Role of the microbiota in immunity and inflammation[END_REF], brain physiology and behavior [START_REF] Sherwin | Microbiota and the social brain[END_REF], host protection from pathogenic infections [START_REF] Buffie | Microbiota-mediated colonization resistance against intestinal pathogens[END_REF], nutrient absorption [START_REF] Clemente | The impact of the gut microbiota on human health: an integrative view[END_REF] and provisioning of hosts with essential or otherwise inaccessible nutrients from food [START_REF] Rosenbaum | The gut microbiota in human energy homeostasis and obesity[END_REF]. Despite the importance of gut microbiotas for animal life, we are still far from a general understanding of the processes involved in shaping their composition and evolution [START_REF] Foster | The evolution of the host microbiome as an ecosystem on a leash[END_REF].

Controlled experimental work under lab conditions with animal models, as well as human studies, clearly detected strong environmental components determining gut microbiota with little evidence of genetic effects [START_REF] Knowles | Species identity dominates over environment in shaping the microbiota of small mammals[END_REF][START_REF] Henderson | Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range[END_REF]. Diet is, perhaps, the main environmental factor since experimental modifications of diets resulted in drastic changes in gut microbiota [START_REF] Sonnenburg | Diet-induced extinctions in the gut microbiota compound over generations[END_REF][START_REF] Rachel | Diet Dominates Host Genotype in Shaping the Murine Gut Microbiota[END_REF][START_REF] David | Diet rapidly and reproducibly alters the human gut microbiome[END_REF]. Social and physical environments are also important as both experimentally cohoused mice [START_REF] Seedorf | Bacteria from Diverse Habitats Colonize and Compete in the Mouse Gut[END_REF][START_REF] Hildebrand | Inflammation-associated enterotypes, host genotype, cage and interindividual effects drive gut microbiota variation in common laboratory mice[END_REF] and cohabiting unrelated humans [START_REF] Schloss | The dynamics of a family's gut microbiota reveal variations on a theme[END_REF][START_REF] Song | Cohabiting family members share microbiota with one another and with their dogs[END_REF] showed convergent gut microbiota composition. Studies of wild animals have also reported important variation in gut microbiotas in relation to, for instance, seasonal and habitat differences [START_REF] Maurice | Marked seasonal variation in the wild mouse gut microbiota[END_REF][START_REF] Ren | Seasonal, spatial, and maternal effects on gut microbiome in wild red squirrels[END_REF]. Effects of genetic factors determining gut microbiota at the intraspecific level are considered rather weak [START_REF] Rachel | Diet Dominates Host Genotype in Shaping the Murine Gut Microbiota[END_REF][START_REF] Wang | Of genes and microbes: solving the intricacies in host genomes[END_REF][START_REF] Rothschild | Environment dominates over host genetics in shaping human gut microbiota[END_REF]. Conversely, most evidence of genetic factors influencing gut microbiota comes from comparative interspecific studies showing consistent species-specific differences [START_REF] Knowles | Species identity dominates over environment in shaping the microbiota of small mammals[END_REF][START_REF] Amato | Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes[END_REF][START_REF] Nishida | Rates of gut microbiome divergence in mammals[END_REF] or even phylogenetic covariation between animals and their gut microbial communities [START_REF] Brooks | Phylosymbiosis: Relationships and functional effects of microbial communities across host evolutionary history[END_REF][START_REF] Ochman | Evolutionary Relationships of Wild Hominids Recapitulated by Gut Microbial Communities[END_REF][START_REF] Kartzinel | Covariation of diet and gut microbiome in African megafauna[END_REF]. Different species, however, also differ in diet, feeding behavior and environmental characteristics of inhabited areas, and, therefore, most of the phylogenetic patterns may also be driven by environmental factors that vary interspecifically, making environmental factors co-vary with phylogenetic differences [START_REF] Kartzinel | Covariation of diet and gut microbiome in African megafauna[END_REF][START_REF] Muegge | Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans[END_REF]. This makes it difficult to disentangle the effects of environmental and genetic (i.e. phylogenetic) factors on gut microbiota.

To cope with this problem, Knowles et al. [START_REF] Knowles | Species identity dominates over environment in shaping the microbiota of small mammals[END_REF] explored the effect of habitat (i.e., location) and common evolutionary history (i.e., species identity) in species of three widespread mammal genera, some of them living in sympatry with each other. This approach allowed them to statistically separate the effects of habitat and species, concluding that the latter was dominant over the former factor [START_REF] Knowles | Species identity dominates over environment in shaping the microbiota of small mammals[END_REF]. Interestingly, interspecific differences in gut microbiota positively covaried with dietary differences, and, thus, the detected genetic component may also reflect the species-specific dietary niche of hosts. However, gut morphology and physiological characteristics of hosts are usually closely related to dietary niche [START_REF] Muegge | Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans[END_REF][START_REF] Delsuc | Convergence of gut microbiomes in myrmecophagous mammals[END_REF][START_REF] Ley | Evolution of mammals and their gut microbes[END_REF]. Consequently, effects of gut microbiota that are a priori attributable to dietary niche might be over-interpreted, and should be controlled not only for host phylogeny but also for gut morphology [START_REF] Amato | Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes[END_REF]. Thus, the genetic effect of gut microbiota of animals due to anatomical or physiological characteristics should be determined after controlling for the effect of diet, sociality, and physical environment. Avian brood parasitism model systems bring us a golden opportunity to dissect this relationship [START_REF] Ruiz-Rodríguez | Gut microbiota of great spotted cuckoo nestlings is a mixture of those of their foster magpie siblings and of cuckoo adults[END_REF].

Brood parasitism is a form of reproductive strategy by which parasites lay their eggs in the nests of other species so that when their eggs hatch in the host's nest, the host adults subsequently rear their offspring [START_REF] Davies | Cuckoo adaptations: trickery and tuning[END_REF]. Brood parasites and their hosts are usually phylogenetically distant species and, during growth, nestlings of both species receive a similar diet in identical social and physical environments [START_REF] Payne | The cuckoos[END_REF]. In addition, brood parasite and host adults may differ in their dietary niche and consequently in gut morphology and physiology. In this case, interspecific differences in gut microbiota of cuckoo and host nestlings would be hardly explained by environmental factors [START_REF] Ruiz-Rodríguez | Gut microbiota of great spotted cuckoo nestlings is a mixture of those of their foster magpie siblings and of cuckoo adults[END_REF]. The brood parasite -host system formed by the great spotted cuckoo (Clamator glandarius) (hereafter cuckoo) and their magpie (Pica pica) host fits all these characteristics. They are phylogenetically distant species [START_REF] Prum | A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing[END_REF], brood parasite and host nestlings may develop together within the same nest and are fed with a similar diet by adult magpies [START_REF] Soler | Preferential allocation of food by magpie Pica pica to great spotted cuckoo Clamator glandarius chicks[END_REF][START_REF] Soler | Coevolutionary interactions in a hostparasite system[END_REF]. Moreover, while adult magpies have an omnivorous diet [START_REF] Birkhead | The Magpies. The ecology and behaviour of black-billed and yellowbilled magpies[END_REF], adult great spotted cuckoos mainly feed on hairy caterpillars [START_REF] Payne | The cuckoos[END_REF], and, consequently, morphology of their guts differs even at the fledgling stage [START_REF] Ruiz-Rodríguez | Differences in intestinal microbiota between avian brood parasites and their hosts[END_REF]. We already know that the gut microbiota of cuckoo nestlings differ from that of host nestlings [START_REF] Ruiz-Rodríguez | Gut microbiota of great spotted cuckoo nestlings is a mixture of those of their foster magpie siblings and of cuckoo adults[END_REF][START_REF] Ruiz-Rodríguez | Differences in intestinal microbiota between avian brood parasites and their hosts[END_REF] and is intermediate between those of cuckoo adults and magpie nestlings [START_REF] Ruiz-Rodríguez | Gut microbiota of great spotted cuckoo nestlings is a mixture of those of their foster magpie siblings and of cuckoo adults[END_REF]. However, the cuckoo and magpie nestlings used for comparison in these previous studies did not share their nests with nestlings of the other species. Thus, it is possible that differences in diet and/or in transmitted microbiota from adult magpies to nestlings were partially responsible for the detected interspecific differences, and, as a result, the genetic component of gut microbiota may have been over-interpreted.

To demonstrate further differences in the genetic component of the gut microbiota of cuckoo and magpie nestlings, we here experimentally moved cuckoo eggs among magpie nests so that the cuckoo eggs hatched a few days later than magpie eggs in these nests. This procedure dramatically increased the likelihood of nestlings of both species surviving until the fledging stage while sharing identical social and physical environments [START_REF] Soler | Coevolutionary interactions in a hostparasite system[END_REF]. Moreover, we collected bacterial samples from the cloaca and from the nestlings' beak (esophagus), which included those of saliva and food received from magpie adults. These samples allowed us to explore for the first time the possibility that interspecific differences detected in these esophageal microbiotas explained previously detected differences in cloacal microbial communities.

Within this framework, a genetic component determining interspecific differences in the gut microbiota will be identified if esophageal microbiotas of cuckoo and host siblings do not vary, but clear differences in cloacal microbiota are detected. This prediction assumes that cuckoos and magpies that share the same nests are fed a similar diet that bestows similar food associated microbiotas. Here, we tested these predictions and assumptions of genetic component explaining interspecific differences in gut microbiotas of magpies and great spotted cuckoos. Bacterial groups responsible for interspecific differences in gut microbiotas of magpies and great spotted cuckoo have been described elsewhere [START_REF] Ruiz-Rodríguez | Gut microbiota of great spotted cuckoo nestlings is a mixture of those of their foster magpie siblings and of cuckoo adults[END_REF], and, thus, here we concentrate on exploring the associations between esophageal and intestinal bacterial communities, as well as the association between microbiotas and diet.

Materials and Methods

Study area and species

The study was performed in the Hoya de Guadix (37º18' N, 3º11' W), southern Spain; a high-altitude plateau where magpie nests are frequently parasitized by great spotted cuckoos [START_REF] Soler | Brood parasitism correlates with the strength of spatial autocorrelation of life history and defensive traits in Magpies[END_REF]. The vegetation is sparse with oak (Quercus ilex), almond (Prunus dulcis) and pine (Pinus halepensis and Pinus pinaster) trees where a relatively high density of magpie nests is present.

Magpies lay one egg per day until they complete a typical clutch size of six or more eggs. Normally, magpies start to incubate when the 4 th egg is laid, and their eggs hatch after 21 days of incubation. The great spotted cuckoo is a non-evictor cuckoo, and it can share a nest with foster siblings.

Nonetheless, the incubation period of cuckoo eggs is 5-6 days shorter than that of magpie eggs [START_REF] Soler | Coevolutionary interactions in a hostparasite system[END_REF]. Thus, in the case that brood parasitism occurs before magpie incubation starts, cuckoo eggs hatch some days before those of magpies, which confers a great advantage to cuckoo nestlings when competing for food with foster siblings during the nestling period [START_REF] Soler | Preferential allocation of food by magpie Pica pica to great spotted cuckoo Clamator glandarius chicks[END_REF][START_REF] Moreno-Rueda | Rules of food allocation between nestlings of the black-billed magpie Pica pica, a species showing brood reduction[END_REF].

In most cases, magpie nestlings are outcompeted by cuckoo nestlings unless brood parasitism occurs after magpies finish laying their eggs [START_REF] Soler | Duration of sympatry and coevolution between the great spotted cuckoo (Clamator glandarius) and its primary host, the magpie (Pica pica)[END_REF]. The nestling periods also differ according to species, with cuckoos spending 16-18 days and magpies 21-23 days in the nest [START_REF] Soler | Growth and development of great spotted cuckoos and their magpie host[END_REF].

Field work

At the beginning of April 2018, we searched intensively on a weekly basis for new magpie nests in the study area, which allowed us to detect new magpie nests during the egg laying stage. In the case of brood parasitism, surveillance of magpie nests allowed us to envisage hatching time of magpie and cuckoo eggs. To maximize the number of nests where great spotted cuckoo and magpie nestlings developed together, we translocated cuckoo and magpie eggs among nests to adjust expected hatching day of magpie chicks to be two days in advance of cuckoo eggs (Fig. 1). We did so in eight magpie nests from which both great spotted cuckoos and magpies successfully fledged.

When magpie and cuckoo nestlings in experimental nests were about 12 and 10 days old respectively, we collected esophageal samples, i.e. the food and the saliva they received from adult parents, by means of neck-collar trials [START_REF] Martín-Gálvez | Benefits associated with escalated begging behaviour of black-billed magpie nestlings overcompensate the associated energetic costs[END_REF] (Fig. 1). Briefly, we used plastic zip ties of three millimeters width and of different color for nestling recognition. These neck-collars were carefully placed around nestlings' necks in such a way that it allowed normal respiration, but ingestion of food provided by adult magpies was prevented.

Once neck-collars were adjusted, we cut the remaining length of zip ties and returned the chicks to the nest. After approximately two hours, we returned to the experimental nest, collected food samples from nestlings' esophagus, and carefully cut the zip ties with scissors. Since adult magpies transported food to the nests in their gular cavity and regurgitated food to begging nestlings, nestlings received not only food, but also saliva from their parents. Therefore, the liquid part of the collected samples consisted of a mixture of adult magpie and nestling saliva. Samples were immediately transferred into 500 µL of lysis buffer (50 mM Tris-HCl, 0,5 % SDS, 2 mM EDTA, 100 mM NaCl) and kept at 4ºC in a portable fridge until arrival at the laboratory a few hours later, where the samples were frozen at -20ºC for further analysis.

Collar trials were only performed in experimental nests (number of nests = 8, number magpie nestlings sampled = 17, number of cuckoo nestling sampled = 8).

Cloacal samples were collected from magpie and cuckoo nestlings close to the time of abandoning their nests (15-16 and 18-19 days old cuckoos and magpies, respectively). Only the two heavier host nestlings and all cuckoo nestlings found in magpie nests were sampled. These samples were collected by injecting and repipetting 500 µL of sterile phosphate buffer (0.1 M Na2HPO4 and 0.1 M NaH2PO4, pH 7.4) in nestlings' cloaca using sterile tips and an automatic micropipette. Collected samples were stored in 500 µL of lysis buffer (50 mM Tris-HCl, 0,5 % SDS, 2 mM EDTA, 100 mM NaCl), and kept at 4ºC in a portable fridge until the portable fridge was transported back to the laboratory a few hours later. Samples were then stored at -20 ºC until further analysis.

Sample preparation

For food item identifications, samples were laid out on a petri dish where food items were separated from each other. Since we were interested in characterizing bacterial communities of food, to avoid contamination, manipulation of prey items was conducted under aseptic condition, with previously sterilized tweezers while wearing gloves previously washed with ethanol. Once food items were separated from each other, we took photos that were later used for identification. Whenever possible, invertebrate animal food was classified to the family level via visual inspection (ESM-Figure 1). Detected vegetable food items were in all cases green barley grain.

Identification of some small pieces of food items was not possible and they were labelled as 'unidentified'.

Esophageal samples contained solid tissues in suspension with buffer lysis and saliva from adult and nestling birds. The liquid content (hereafter saliva) were separated from the solid one and kept in different 2 mL microfuge tubes. Collar samples containing solid tissues (hereafter, esophageal samples) and cloaca samples were first mechanically homogenized using zirconia ceramic beads.

Briefly, 2 mL microcentrifuge tube filled with zirconia (100 mg) ceramic beads of approximately 2 mm depth were added with 800 µL of cloacal samples; or with 100 mg of previously mixed esophageal samples adding solution 2 (Xtrem biotech®) to make up 800 µL. The tubes were then vortexed for three minutes for mechanical digestion of solid matters. Tubes containing saliva sample was centrifuged in 18,000 rpm for two minutes and the precipitate was suspended in 700 µL of lysis solution. Afterward, the tubes containing the three types of samples were added with 10 µL of proteinase K (10 mg/mL) and incubated in thermal blocks in two rounds of incubation (55°C for 15 min, and 90°C for eight min). The tubes were then cooled at room temperature before being centrifuged at 14,000 rpm for five minutes. 700 µL of supernatants were transferred to new 1.5 mL microcentrifuge tubes and 250 µL of solution 3 (Xtrem biotech®) was added into each tube followed by vortex for 30 seconds. The tubes were then incubated in ice for 10 minutes and centrifuged in 14,000 rpm for five minutes. 700 µL of supernatant from each tube was then transferred to new microcentrifuge tubes. 650 µL of binding buffer (20 mM HCl-Tris pH 8, 4 M Guanidinium isothiocinate, 1 mM DTT) and 50 µL of silica beads (Sigma S-5631) dissolved in 6 M Guanidinium Chloride [START_REF] Hoss | DNA extraction from Pleistocene bones by a silica-based purification method[END_REF] was added to each tube followed by agitation by inversion. The tubes were then centrifuged in 10,000 rpm for one minute. The supernatant was discarded by vacuum suction and the precipitate was then resuspended in 500 µL of binding buffer for two minutes. The tubes were centrifuged again in 10,000 rpm for one minute and the supernatant was discarded via vacuum suction. 1000 µL of wash buffer (20 mM Tris-HCl pH 7.4, 1 mM EDTA, 50 mM NaCl, 50 % ethanol) was added to suspend the precipitate for two minutes and the tubes were then centrifuged in 10,000 rpm for one minute. The supernatant was discarded, and the pellet was dried in vacuum using a drying machine. For DNA elution, 200 µL of MilliQ water were added and the tubes were incubated in 55 °C for five minutes. Finally, the tubes were centrifuged in 18,000 rpm for 2 minutes. This step was repeated once to increase the purity of DNA from the tube. The supernatant was collected for further analysis.

DNA amplification for high throughput sequencing

We amplified the region spanning from V6 to V8 of the bacterial gene 16S RNA following procedure described in Ruiz-Rodríguez et al. [START_REF] Ruiz-Rodríguez | Gut microbiota of great spotted cuckoo nestlings is a mixture of those of their foster magpie siblings and of cuckoo adults[END_REF]. First PCR was carried out using two sets of primers, B969F_bar1 and BA1406R and B969F_bar2 and BA1406R. PCR was performed in 25 µL reactions containing 2.5 µL of 3 mM of each primer, 2.5 µL of PCR water, 12.5 µL of iProof HF Master Mix (BioRad®) and 5 µL of diluted DNA 1:10. PCR conditions included 98 °C for one minute, followed by 25 cycles of 98 °C for 10 seconds, 52 °C for 20 seconds and 72 °C for 15 seconds, and a final elongation step at 72 °C for 5 minutes. PCR products were purified using HigherPurity TM DNA Purification SPRI Magnetic Beads (Canvax®) according to user manual and eluted in 40 µL of distilled water. Successful amplification was checked by electrophoresis in 1 % agarose.

Second PCR was applied to add the 'barcodes' (Nextera XT barcodes) designed to tag individual samples. These barcodes contain sequences that overlap with the sequences of the primers used in the first PCR [START_REF] Hamady | Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex[END_REF]. PCR was performed in 25 µL reactions containing 3.3 µL of 3 mM of each primer, 0.9 µL of PCR water, 12.5 µL of iProof HF Master Mix (BioRad®) and 5 µL of purified PCR product from first PCR. This PCR was conducted by 98°C for one minute, followed by 8 cycles of 98°C for 10 seconds, 55°C for 20 seconds and 72°C for 15 seconds, and a final elongation step at 72°C for five minutes. Purification steps from the first PCR were repeated and eluted in 40 µL of distilled water and electrophoresis in 1 % agarose were performed to determine successful amplification.

Concentration of DNA of PCR product was measured using Qubit® 3.0 Fluorometer. The sample with the lowest concentration of DNA was chosen as the baseline concentration and the concentration of each sample was adjusted to be equimolar to this baseline concentration before all the samples were pooled. High throughput sequencing was performed in a single cartridge in Illumina MiSeq platform (López Neyra Institute, Granada, Spain).

Sequence analyses

Sequence data were analyzed with QIIME 2 v2018.8 (Quantitative Insights Into Microbial Ecology) [START_REF] Caporaso | QIIME allows analysis of highthroughput community sequencing data[END_REF][START_REF] Bolyen | Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2[END_REF]. Primer trimming and sequence quality filtering were performed using default parameters. Paired sequences were joined following default parameters in QIIME2. We then used Deblur, a sub-Operational Taxonomic Unit (sOTU) to further remove sequencing errors and to create the sub-OTU table [START_REF] Amir | Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns[END_REF] with a fragment length set at 380 bp.

Afterwards, sequences were aligned and followed by building a de novo phylogenetic tree following Janssen et al. [START_REF] Janssen | Phylogenetic Placement of Exact Amplicon Sequences Improves Associations with Clinical Information[END_REF]. Taxonomy assignment was performed based on Greengenes 13_08 with a similarity of 99% [START_REF] Desantis | Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB[END_REF]. Lastly, non-bacterial, chloroplastidial and mitochondrial sequences were removed from the sub-OTU table as well as non-taxonomically assigned sequences at the phylum level.

Sequences from next-generation sequencing techniques may include reagent contaminants due to DNA extraction, amplification, and library preparation kits. This may influence characterization of bacterial communities, mainly of those with very small microbial biomass [START_REF] Salter | Reagent and laboratory contamination can critically impact sequence-based microbiome analyses[END_REF][START_REF] De Goffau | Recognizing the reagent microbiome[END_REF].

However, we analyzed cloacal and esophageal samples, which contained relatively high bacterial biomass. More importantly, we were not interested on characterizing microbiotas, but exploring expected differences between microbiotas that had been characterized with identical protocols and, thus, included identical reagent contaminants if any. Thus, reagent contaminants would negligibly influence our results.

Statistical analyses and sample sizes

We examined the effect of genetic and non-genetic components in the determination of gut microbiota by exploring the influence of species identity and gut site in alpha and beta microbial diversity [START_REF] Whittaker | Evolution and measurement of species diversity[END_REF]. Alpha diversity is the diversity within a sample, while beta diversity measures the between samples differences. Alpha diversity of gut microbiotas was explored by means of Shannon's diversity index, which considers the number of detected OTUs and abundance of each of them [START_REF] Shannon | A mathematical theory of communication[END_REF]. Interspecific differences were explored in General Linear Mixed Models (GLMM) by full crossing the two independent variables, i.e. species identity (magpie and cuckoo) and gut region (saliva, food and cloaca) as the independent factors, and nest identity as random factors. The model also included interactions between fixed and random factors to account for the repeated measurement nature (i.e. within nest comparisons) of our data sets. Residuals were visually inspected for normality and homoscedasticity, justifying the use of parametric tests. Alpha diversity statistical tests were performed in Statistica 10.0. Beta diversity matrices were calculated using weighted and unweighted UniFrac distance [START_REF] Lozupone | UniFrac: a new phylogenetic method for comparing microbial communities[END_REF][START_REF] Lozupone | Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities[END_REF] based on a rarefied OTU table at 4000 sequence per sample. Principal Coordinate Analysis (PCoA) plot was generated using these UniFrac distance matrices. While weighted Unifrac weighs sequence according to the number of sequence found per OTU in a given sample, unweighted UniFrac simply counts the absence or presence of an OTUs in a given sample. In other words, for any given sample, weighted UniFrac gives more importance to the most abundant bacteria while unweighted UniFrac gives more importance to bacteria found in minority.

Both weighted and unweighted UniFrac distance matrices were then used in non-parametric multivariate statistical test, PERMANOVA, implemented in PRIMER-7 (Anderson, 2001) with 9,999 permutations. Briefly, the models testing for interspecific and gut location differences included these two factors as fixed effects, and nest identity nested within the interaction of fixed factors as random effects. To explore interspecific differences of microbiota associated to the cloaca, saliva and food of magpies and great spotted cuckoos, we ran three separate models that included species and nest identity as the fixed and the random factors, respectively. Similarly, to explore within species differences in microbiota due to gut locations, we ran two sets of models, one for great spotted cuckoos and another for magpies that included gut location and nest identity as fixed and random factors, respectively. Post-hoc comparisons between pairs of gut locations were explored in separate models with two-level fixed factor (i.e. pairwise). By including nest identities as random factor, all these PERMANOVAs assumed within-nests comparisons and, thus, only considered nests with the two species and the three gut locations, which drastically reduces degrees of freedom. However, because the effects of fixed factors rarely depended on nest identity (see Results), to increase statistical power, we also ran models that did not include the random factor and showed results in Electronic Supplementary Material (ESM, Table 2).

The associations between diet and microbiotas were explored by means of Mantel tests in the R environment (Version 3.6.1.). Matrices of dissimilarities of diet were calculated based on Bray Curtis dissimilarity index. Matrices of dissimilarities of species identity were built with 0's (when the pairwise of individual was of the same species) and 1's (when the pairwise of individual was of different species). As matrices of similarities of bacterial communities, we used both weighted and unweighted UniFrac distance matrices. Afterward, by using the R package 'ecodist' version 1.2.3 [START_REF] Goslee | The ecodist package for dissimilarity-based analysis of ecological data[END_REF], we performed autocorrelation analyses (Mantel tests with 9,999 permutations), with matrices of bacterial communities at different locations as dependent variables, and matrices of species identity and diet as explanatory variables. We estimated the strength of the correlation (Beta values), and whether these values differed significantly from zero (P values).

Unfortunately, nestlings were not individually marked at the time of sampling their diet and, thus, we were not able to match diet with cloaca samples at the level of the individual. To solve this problem, we randomly matched cloaca and diet samples within the same nests and species. We did so four times and, because results did not vary qualitatively, we used average estimates and associated p-values.

We sampled microbiota of 33 magpie and 22 great spotted cuckoo nestlings in 18 magpie nests. In ten of these magpie nests, great spotted and magpie nestlings grew together, in six only magpies, and in two nests only cuckoos developed. As a rule, only two magpies and all cuckoo nestlings were sampled per nest, the cloaca was always sampled, but saliva and food were only sampled in 8 nests containing cuckoo and magpie nestlings.

Bacterial DNA amplification and sequencing failed for some samples and, thus, samples sizes shown in the ESM (Table 1) differed among nests, species, and sample location.

Results

Gut microbiota of great spotted cuckoo nestlings were not significantly more diverse than those of the magpie host nest-mates, independently of gut location (GLMM, F = 0.18, d.f. = 1, 11.8, P = 0.68). Moreover, independently of species (GLMM, interaction between species and gut location, F = 0.03, d.f. = 2, 10.6, P = 0.97), cloacal microbiota tends to be more diverse than the microbiota of saliva or food (GLMM, F = 3.28, d.f. = 2, 10.6, P = 0.064, Fig. 2).

In beta diversity analyses, we found that microbiota of nestlings reared in magpie nests depended on species identity (magpie vs. great spotted cuckoo) and gut location (cloaca, saliva and food) (Species x Gut location comparisons in Table 1, P < 0.001, and ESM-Table 2). The effect of species identity was, therefore, due to the microbiota detected when comparing cloacal samples, since microbiota of esophageal samples (saliva and food) did not differ interspecifically (see species comparisons in Table 1).

Accordingly, principal coordinate analyses on both unweighted and weighted UniFrac distances showed clear clustering of cloacal samples (P < 0.012), but those of saliva (P > 0.5) or food samples (P > 0.5) of both species were distributed randomly between species (Table 1, ESM-Table 2, Fig. 3).Intraspecific differences due to gut microbiota locations reached statistical significance in both great spotted cuckoo ( P < 0.003) and magpie (only for weighted UniFrac distances, P = 0.004) nestlings (Table 1, ESM-Table 2, Fig. 4). These differences were mainly due to those between cloacal microbiota and microbiota of esophageal (both saliva and food) samples (Table 1, ESM-Table 2), which were more apparent in cuckoo than in magpie samples (Fig. 4).

Finally, diet collected from great spotted cuckoos and their magpie foster siblings explained differences in gut microbiota at different locations after controlling for the effect of species identity (Table 2). The strength of the positive associations, however, varied depending on the location and the diversity index used to estimate distance matrices of microbiota. In terms of unweighted UniFrac differences, diet of great spotted cuckoo and magpie nestlings explained microbiota composition of esophageal samples (Saliva: P = 0.049; Food: P = 0.001), but not of cloaca samples (P = 0.32).

Discussion

Although esophageal microbiota of magpies and great spotted cuckoos raised within the same environment (nest) did not vary significantly, the microbiota of cloacal samples showed clear interspecific differences.

Analyses were performed by comparing microbiotas of great spotted cuckoos with those of their magpie foster siblings, with which they shared environmental conditions and parental care from the same adults. Thus, interspecific differences can only be interpreted as being the result of genetic factors determining gut microbiota. Moreover, diet of great spotted cuckoo and magpie nestlings explained microbiota composition of esophageal samples, but not of cloaca samples, pointing out the well-known role of diet determining the gut microbiota. Below, we discuss the importance of these results, possible caveats of the statistical inferences, and the advantages of using parasitism as a model system to disentangle genetic and environmental factors determining gut microbiota, and to explore mechanisms underlining interspecific differences as well as functional consequences.

Given the importance of gut microbiota for animal life, including brain physiology and behavior of hosts [START_REF] Sherwin | Microbiota and the social brain[END_REF], determining the importance of environmental and genetic factors explaining the gut microbiota of animals is a major topic not only in microbiology, but also in ecology and evolution [START_REF] Moran | Evolutionary and ecological consequences of gut microbial communities[END_REF]. Most support for a genetic component of gut microbiota in animals came from comparative studies where species-specific differences and phylogenetic relatedness influences were considered as evidence of genetic factors determining gastro-intestinal microbiota [START_REF] Brooks | Phylosymbiosis: Relationships and functional effects of microbial communities across host evolutionary history[END_REF][START_REF] Kartzinel | Covariation of diet and gut microbiome in African megafauna[END_REF][START_REF] Ley | Evolution of mammals and their gut microbes[END_REF]. However, different species also varied in environmental conditions that they experience and their lifestyles, including those related to habitat characteristics, foraging behavior and diet; all of which being well-known environmental factors determining gut microbiota of animals.

To overcome such problems when exploring genetic influences, several recently published papers carried out comparative analyses while controlling for interspecific differences in diet composition [START_REF] Amato | Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes[END_REF][START_REF] Kartzinel | Covariation of diet and gut microbiome in African megafauna[END_REF], habitat exploitation [START_REF] Knowles | Species identity dominates over environment in shaping the microbiota of small mammals[END_REF][START_REF] Ruiz-Rodríguez | Gut microbiota of great spotted cuckoo nestlings is a mixture of those of their foster magpie siblings and of cuckoo adults[END_REF], and/or host physiology [START_REF] Kartzinel | Covariation of diet and gut microbiome in African megafauna[END_REF]. Briefly, Amato et al. [START_REF] Amato | Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes[END_REF] compared the gut microbiota of 18 species of wild non-human primates that represented a range of gut morphological specialization. They concluded that the influence of host phylogeny was much stronger than that of host dietary niche, mainly because of the importance of host physiology as a determinant of gut microbiota. The rationale of the study is that the represented species do not share identical environment and, thus, authors statistically controlled for host geographic locations and actual dietary intake. In contrast, Knowles et al. [START_REF] Knowles | Species identity dominates over environment in shaping the microbiota of small mammals[END_REF] explored the microbiota and diet of sympatric small mammal species across multiple habitats, which allowed for statistically disentangling environmental (habitat and diet) and genetic (species identity) components of gut microbiota. They also found that diet shapes microbiota but due to species-specific diet, they concluded in favor of a large influence of genetic factors explaining gut microbiota of small mammals. However, another study of artiodactyls that also explored the gut microbiota of pairs of sympatric species across the Americas failed to detect phylogenetic effects explaining gut microbiota, although each geographic area displayed a unique gut microbiota composition [START_REF] Moeller | Dispersal limitation promotes the diversification of the mammalian gut microbiota[END_REF]. Finally, Ruiz-Rodríguez et al. [START_REF] Ruiz-Rodríguez | Gut microbiota of great spotted cuckoo nestlings is a mixture of those of their foster magpie siblings and of cuckoo adults[END_REF] took advantage of the brood parasitism model-system and detected that gut microbiota of magpie nestlings differed from that of great spotted cuckoo nestlings although both species were reared in different nests of the same species. Here we went one step further and experimentally forced cuckoos and magpies to develop within the same nests, so that they share the same parents under identical environmental conditions. Our results confirm interspecific differences in cloacal microbiota, therefore, suggesting that previous findings were not due to magpies and great spotted cuckoos developing in different magpie nests.

Several non-exclusive possibilities might explain interspecific differences after controlling for environmental conditions. The first one is related to the existence of processes of host-symbiont co-diversification due to vertical inheritance of microbiota; a hypothesis predicting that gut microbiota diversification should increase with phylogenetic distances for which some studies found support [START_REF] Knowles | Species identity dominates over environment in shaping the microbiota of small mammals[END_REF][START_REF] Moeller | Cospeciation of gut microbiota with hominids[END_REF][START_REF] Groussin | Unraveling the processes shaping mammalian gut microbiomes over evolutionary time[END_REF]. However, vertical transmission of gut microbiota cannot explain the detected interspecific differences between magpies and cuckoos because brood parasitic nestlings do not have contact with cuckoo adults until nest independence [START_REF] Soler | Innate versus learned recognition of conspecifics in great spotted cuckoos Clamator glandarius[END_REF].

Another possibility is that closely related species, or individuals of the same species, are more likely to share genetic or behavioral mechanisms that allow horizontal acquisition of similar bacteria from the environment [START_REF] Colston | Microbiome evolution along divergent branches of the vertebrate tree of life: what is known and unknown[END_REF].

This mechanism includes dietary preferences, which cannot explain the detected interspecific differences in cloacal microbiotas of cuckoos and magpies because of our results and previous research [START_REF] Soler | Preferential allocation of food by magpie Pica pica to great spotted cuckoo Clamator glandarius chicks[END_REF] showed that magpies and great spotted cuckoos received a similar diet from magpie adults. Moreover, the microbiota of food collected from esophageal samples did not differ between species and, thus, external environmental conditions related to diet cannot explain interspecific differences in the intestinal microbiota of these two species. Interestingly, and in agreement with this inference, among-individual differences in diet were positively related to differences in esophageal microbiota, but among individual differences in diet did not associate with differences in intestinal microbiotas of these species. These results, on the one hand, point to the importance of diet determining esophageal microbiotas, and, on the other, suggest that similar diets might result in significantly different intestinal microbiota when comparing different species. Therefore, these results strongly suggest a genetic component of intestinal microbiota that is independent of external environmental factors including diet.

The interspecific differences should therefore be explained by anatomical or physiological characteristics that influence gut microbiota, and that differ between magpies and great spotted cuckoos. Innate and adaptive immune system, gut morphology and mucus characteristics can differentially select components of the intestinal microbiota [START_REF] Amato | Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes[END_REF][START_REF] Brooks | Phylosymbiosis: Relationships and functional effects of microbial communities across host evolutionary history[END_REF][START_REF] Donaldson | Gut microbiota utilize immunoglobulin A for mucosal colonization[END_REF][START_REF] Thaiss | The microbiome and innate immunity[END_REF][START_REF] Sicard | Interactions of intestinal bacteria with components of the intestinal mucus[END_REF]. We know that great spotted cuckoos and magpies differ in their immune system [START_REF] Soler | Interactions between a brood parasite and its host in relation to parasitism and immune defence[END_REF]; that cellular immunity of magpies and body condition of great spotted cuckoos predicted cloacal microbiota [START_REF] Ruiz-Rodríguez | Bacterial diversity at the cloaca relates to an immune response in magpie Pica pica and to body condition of great spotted cuckoo Clamator glandarius nestlings[END_REF]; and that, different from magpies, the intestine morphology of cuckoos is shorter and contains a relative large caecum [START_REF] Ruiz-Rodríguez | Differences in intestinal microbiota between avian brood parasites and their hosts[END_REF]. These interspecific morphological and physiological differences are therefore likely explanations for the detected interspecific differences in the intestinal microbiota that deserve further investigation.

Future studies could, for instance, explore the effect of the immune system by experimentally enhancing cellular immune response [START_REF] Soler | Trade-off between immunocompetence and growth in magpies: an experimental study[END_REF], and exploring differential interspecific effects in intestinal microbiotas of magpies and of great spotted cuckoos developing under identical environmental conditions.

The study system would also allow exploring the consequences of having different intestinal microbiota but feeding similar diets in terms of, for instance, nutrient absorption capacity. Intestinal digestibility of great spotted cuckoo nestlings is less efficient than that of magpie host nestlings [START_REF] Soler | Intestinal digestibility of great spotted cuckoo nestlings is less efficient than that of magpie host nestlings[END_REF], so this might be related to the detected differences in their intestinal microbiota.

The association between intestinal microbiotas and nutrients in the feces of cuckoo and host nestlings growing in the same nest would allow detecting bacteria responsible for the interspecific differences in nutrient absorption.

Shotgun metagenomics and multi-omic methodologies applied to the cuckoo-host system will allow us to understand the causes and consequences of the detected interspecific differences, and, thus, exploring genetic mechanisms behind the genetic effect of microbiota while controlling for environmental conditions.

To summarize, our results strongly suggest a genetic component determining the intestinal microbiota of the study species while controlling for the effect of several environmental components including both abiotic and biotic conditions related to nest of rearing as parental identity, behavior and diet. The fact that interspecific differences in esophageal microbiota were not strong strengthens the possibility that interspecific differences in gut morphology and physiology are responsible for the observed differences in gut microbiota. Similar to laboratory experiments, the reproductive strategy of brood parasitism allows researchers to locate genetically unrelated individuals under identical environmental conditions and diet, where exploration of the influence of the genetic component on gut microbiota under natural conditions is made possible. This approach escapes from the undesired effects of captivity in gut microbiota [START_REF] Amato | Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes[END_REF][START_REF] Clayton | Captivity humanizes the primate microbiome[END_REF][START_REF] Kohl | Captivity results in disparate loss of gut microbial diversity in closely related hosts[END_REF], and, thus, brood parasitism should be considered a model system for exploring genetic and environmental factors affecting gut microbiota of animals as well as mechanisms and related consequences. microbiotas of brood parasitic and host nestlings. The research group benefits from facilities, including an apartment, provided by the city hall of Guadix where a small lab to quickly process the samples was installed.

Financial support was provided by the Spanish Ministerio de Ciencia, Innovación y Universidades and European (FEDER) funds (CGL2017-83103-P). All procedures were conducted under licence from the Environmental Department of the Regional Government of Andalucía, Spain (reference SGYB/FOA/AFR). All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. We also show raw data collected from great spotted cuckoo and magpie nestlings.

Figure 1 :

 1 Figure 1: Time schedule scheme and fieldwork protocol followed to synchronizing great spotted cuckoo and magpie eggs hatching within the same magpie nest. Incubation period of great spotted cuckoo eggs is on average four days shorter than that of magpie eggs. Magpie females lay one egg per day and start to incubate with the fourth egg. Thus, cuckoo eggs detected in magpie nests before incubation started were moved (black arrows connected two nests) to magpie nests where the expected hatching date of host eggs will occur in advance of or the same day as the experimental cuckoo egg. Date of collecting esophageal (saliva and food) and cloacal samples are shown in red and purple colors, respectively.

Figure 2 :

 2 Figure 2: Shannon index values of alpha diversity of microbiota of cloaca, food and saliva samples of great spotted cuckoo and magpie nestlings. Values are weighted means ± Standard Errors (Boxes), 95% CI (Whiskers).

Figure 3 :

 3 Figure 3: Principal coordinate analyses on both unweighted and weighted UniFrac distances of microbiotas of great spotted cuckoo (Cuckoos) and Magpie nestlings from cloaca and esophageal (saliva and food) samples. We show percentage of variance explained by the two first axes.

Figure 4 :

 4 Figure 4: Principal coordinate analyses on both unweighted and weighted UniFrac distances of microbiotas of great spotted cuckoo (Cuckoos) and magpie nestlings from cloaca and esophageal (saliva and food) samples. We show percentage of variance explained by the two first axes.

Figure 1 :

 1 Figure 1: Shannon index values of alfa-diversity of microbiota of cloaca, prey and saliva samples of great spotted cuckoos and magpie nestlings. Values are weighted means ± Standard Errors.

Fig 2 :

 2 Fig 2: Principal coordinate analyses on both unweighted and weighted UniFrac distances of microbiotas of great spotted cuckoo (Cuckoos) and Magpie nestlings from cloaca and esophageal (saliva and prey) samples. We show percentage of variance explained by the two first axes as well as the coordinates of each sample on them.

Figure 3 :

 3 Figure 3: Principal coordinate analyses on both unweighted and weighted UniFrac distances of microbiotas of great spotted cuckoo (Cuckoos) and Magpie nestlings from cloaca and esophageal (saliva and prey) samples. We show percentage of variance explained by the two first axes as well as the coordinates of each sample on them.

Table 1 :

 1 Number of magpie and great spotted cuckoo nestlings with cloaca, 824 saliva and prey sampled in different nests. Total collected samples (Tot S) 825 from cloaca, saliva and prey of both species are also shown. Nests with

Table 2 :

 2 Results from PERMANOVAs testing for the effects of species identity on the microbiota of cloaca and esophageal (saliva and prey) samples of great spotted cuckoos and magpies that shared identical environmental conditions (i.e. nest identity) during development.

			Unweighted UniFrac	Weighted UniFrac
			Pseudo-			Pseudo-
			F	df	P	F	df	P
	Species x gut location comparisons
	Species	Fixed	1.66	1, 50 0.0076	3.49	1, 50 0.0045
	Gut loc	Fixed	2.36	2, 50 0.0001	7.79	2, 50 0.0001
	Sp x G	Fixed	1.45	2, 50 0.0074	2.31	2, 50 0.0098
	Nest (Sp*G)	Rnd	1.17 50, 43 0.0001	1.21	50, 43 0.0521
	Species comparisons
	Cloacal samples
	Species	Fixed	2.85	1, 8 0.0109	4.96	1, 8 0.0111
	Nests	Rnd	1.12	17, 8 0.0826	1.3	17, 8 0.1242
	Nest*Species Rnd	1.17	8, 19 0.1052	1.88	8, 19 0.0093
	Esophageal samples
	Saliva
	Species	Fixed	0.95	1, 6 0.5315	0.56	1, 6 0.6126
	Nests	Rnd	1.12	7, 6 0.1669	0.51	7, 6 0.9837
	Nest*Species Rnd	0.83	6, 13 0.9308	0.87	6, 13 0.6346
	Prey
	Species	Fixed	0.88	1, 5 0.5857	0.21	1, 5 0.9397
	Nests	Rnd	1.61	7, 5 0.0009	1.56	7, 5 0.0617
	Nest*Species Rnd	1.01	5, 11 0.4836	0.97	5, 11 0.4859
	Gut location comparisons
	Great spotted cuckoos
	locations	Fixed	2.03	2, 11 0.0026	3.84	2, 11 0.0012
	Nests	Rnd	1.46 11, 11 0.0157	1.25	11, 11 0.2788
	Nest*loc	Rnd	1.12	11, 7 0.2847	0.84	11, 7 0.7161
	Loc (Cl -Sa) Fixed	1.97	1, 11 0.0102	4.90	1, 11 0.0037
	Nests	Rnd	1.30	11, 6 0.0132	1.42	11, 6 0.0899
	Nest*loc	Rnd	1.21	5, 6 0.0772	1.10	5, 6 0.3688

Acknowledgments:

We thank Manuel Martin-Vivaldi Martínez and Jarkko Rutila for discussion on the interest of exploring genetic and environmental factors of gut

samples from great spotted cuckoos and magpies are in bold font.

827

PERHAPS MOVE TO APPENDIX

Magpies

Great spotted cuckoos TOT S Nests ID Cloaca Saliva Prey Tot S Cloaca Saliva Prey Tot S G34