Youssef El Jabbar
email: ysf.eljabbar@gmail.com

Hind Lakhlifi

Rachida El Ouatib

Lahcen Er-Rakho

Sophie Guillemet-Fritsch

Bernard Durand

Preparation and characterisation of green nano-sized ceramic pigments with the spinel structure AB 2 O 4 (A = Co, Ni and B = Cr, Al)

Keywords: Conceptualization, Writing-Original Draft, Data curation Inorganic pigment, Substitution, Shades, Morphology

Preparation and characterisation of green nano-sized ceramic pigments with the spinel structure AB 2 O 4 (A = Co, Ni and B = Cr

.Introduction

The visual appearance of an object can play an important role in the industrial and artistic sectors. Colour and shine have become criteria for appreciating many products and evaluating the purchase decision. In the fields of surface engineering and ceramic industries, the use of inorganic pigments improves certain physical properties such as hardness, impermeability and corrosion resistance. Pigments are thus judged for both their tinting strength and for their heat stability and light from the external environment, as well as attacks from chemical agents in additives such as the mixtures, like binders and varnishes, used during coating operations [START_REF] Soong | Assessing the automotive paint evidence in Singapore via population and discrimination studies[END_REF][START_REF] Rossi | Study of the Durability and Aesthetical Properties of Powder Coatings Admixed with Pearlescent Pigments[END_REF][START_REF] Maile | Effect pigments-past, present and future[END_REF][START_REF] Hamzah | CoAl 2 O 4 /TiO 2 nano composite as an anti-corrosion pigment[END_REF][START_REF] Burgos-Fernández | Texture Evaluation of Automotive Coatings by Means of a Gonio-Hyperspectral Imaging System Based on Light-Emitting Diodes[END_REF][START_REF] Wang | Improvement of Corrosion Resistance and Solid Content of Zinc Phosphate Pigmented Alkyd Coating by Methacrylated Cardanol[END_REF].

The granulometry of the pigments is also of particular interest. In fact, powders with fine particle sizes and high specific surfaces offer high tinting strength [START_REF] Wang | High-performance spherical urchin-like CoAl 2 O 4 pigments prepared via microemulsion-hydrothermalprecipitation method[END_REF][START_REF] Hao | Preparation of Ultrafine Pigment Dispersion and Investigation of its Adsorption Performance on Cationized Flax Substrate[END_REF]. In general, inorganic pigments are prepared by traditional methods based on mixtures of oxides, hydroxides or carbonates. These methods have the advantage of being inexpensive and simple to implement. However, they require high temperatures and long heat treatments to obtain the homogenous phase; in addition, the materials produced by this process exhibit heterogeneous particle size distribution and low specific surface area [START_REF] Chavarriaga | Synthesis by solution combustion of inorganic blue pigments MFe 2 (P2O7) 2 : Influence of the cation M (Zn 2+ , Co 2+ , Cu 2+ , and Mg 2+ ) on their optical properties[END_REF][START_REF] Zou | Influence of crystallite size on color properties and NIR reflectance of TiO 2 @NiTiO 3 inorganic pigments[END_REF][START_REF] Natarajan | Photoluminescence investigations of Zn2SiO4 co-doped with Eu 3+ and Tb 3+ ions[END_REF][START_REF] Zhang | Synthesis and characterization of (Ni, Sb)-co-doped rutile ceramic pigment via mechanical activation-assisted solid-state reaction[END_REF][START_REF] Dohnalová | Preparation and selected properties of pigments on base of Ln-doped CaSnO 3[END_REF]. Recently, other synthetic routes using a wet process based on soft chemistry have been developed to avoid these disadvantages. Indeed, several works based on sol-gel synthesis, coprecipitation methods, hydrothermal techniques and others make it possible to produce fine powders with high chemical and morphological homogeneities [START_REF] Surya | One-pot Cajanus cajan (L.) Millsp. leaf extract-mediated preparation of MgFe 2 O 4 nanoparticles: Optical, structural, morphological and particle size analyses[END_REF][START_REF] Zhang | Synthesis and characterization of ZnFe 2 O 4 nanoparticles on infrared radiation by xerogel with sol-gel method[END_REF][START_REF] Rosowska | The effect of iron content on properties of ZnO nanoparticles prepared by microwave hydrothermal method[END_REF][START_REF] Kothandan | Structural, morphological and optical properties of Ba 1-x Cu x TiO 3 (X = 0.2, 0.4, 0.6, 0.8) nanoparticles synthesizedby hydrothermal method[END_REF].

For industrial needs, ongoing research work in this area has focused on the development of inorganic materials that meet demand criteria. Among the most preferred materials to be used as pigment are the oxides of spinel structure with the general formula AB 2 O 4 due to their many advantageous properties, including high chemical and thermal stability, and their interesting optical properties. In these oxides, (A) represents the divalent metal ions, while (B) J o u r n a l P r e -p r o o f represents trivalent metal ions. The addition of various chromophores (mainly transition elements) provides a wide variety of colours [START_REF] Tang | Effects of colouration mechanism and stability of CoAl 2 O 4 ceramic pigments sintered on substrates[END_REF][START_REF] Tan | Research on the lowtemperature synthesis of cobalt aluminum spinel type blue pigments[END_REF][START_REF] Zhou | Synthesis and characterization of Co 1-x Ca x Al 2 O 4 composite blue nano-pigments by the polyacrylamide gel method[END_REF].

The present paper focuses on green pigments-based on spinel oxides. Various shades of green pigment are used in ceramic manufacturing. Uvarovite (Ca 3 Cr 2 (SiO 4 ) 3 ) is a green pigment obtained from potassium dichromate (K 2 Cr 2 O 7 ), calcium carbonate (CaCO 3 ) and quartz (SiO 2 ) [START_REF] Verger | Synthesis, properties and uses of chromium-based pigments from the Manufacture de Sèvres[END_REF]. Eskolaite (Cr 2 O 3 ) is the most heavily used oxide in ceramic manufacturing and doping with other elements such as ZnO and MgAl 2 O 4 and the silicates MgAl 2 Si 3 O 12 can lead to various shades of green pigments [START_REF] Verger | Reactivity of chromium-based pigments in a porcelain glaze[END_REF]. Other types of inorganic green pigments have been studied based on phosphates such as xenophyllite (Na 4 Fe 7 (PO 4 ) 6 ) and vivianite (Fe 3 (PO 4 ) 2 ) [START_REF] Onoda | Synthesis of novel green phosphate pigments in imitation of natural ores[END_REF]. In this work we report on the synthesis of CoCr 2 O 4 ultrafine powders doped with nickel and aluminium. The effect of the metal substitution on the structural, optical and morphological properties is discussed.

Experimental procedure

Results and discussion

Thermal analysis

Thermogravimetry-differential thermal analysis (TG-DTA) was performed to monitor the thermal decomposition process under an air atmosphere of the precursors obtained after pyrolysis (300 ° C/12h) up to 1000 ° C with a temperature rise rate of 10°C/min and sample sizes between 15-25 mg in open Pt crucible were used. Fig. 2 represents the TGA and DTA curves of the precursor produced from the initial synthesis based on the preparation of the cobalt chromite (CoCr 2 O 4 ). The TG-DTA curves for the substituted compositions show similar thermal behaviours. The TGA thermogram shows a total weight loss of around 16%. This is due to the elimination of water adsorbed on the surface of the powder and the removal of organic residues. After 900°C, no discernible mass loss was observed on the TGA curves. DTA curves showed slight endothermic inflections at around 925 °C, which can be attributed to the reduction of Co 3+ to Co 2 + [START_REF] Jabbar | Structure, microstructure, optical and magnetic properties of cobalt aluminate nanopowders obtained by sol-gel process[END_REF].

J o u r n a l P r e -p r o o f

X-ray diffraction

Based on the TG-DTA results, the precursors obtained by pyrolysis were calcined in a muffle furnace under air atmosphere and the heat treatment temperature was adjusted to 900 ° C for 5 hours. The produced powders were analysed by X-ray diffraction. The XRD patterns of the obtained samples demonstrated a single phase of spinel (see, Fig. 3). The characteristic peaks of sample (a) are indexed to the crystallographic planes of CoCr 2 O 4 (JCPDS files no. 080-1668). Samples (b) and (c) have a similar system of lines with a slight shift. This confirms that the Ni and Al cations have been successfully inserted into the spinel oxides without forming any other phase. Rietveld refinement of the XRD patterns was performed using FullProf suite software in order to obtain more structural information. The refined parameters include the scale factor, sample displacement, background, peak shape, lattice constants, atomic positions for the oxygen atoms and the isotropic thermal parameters for each atom.

The quality of the refined structural models was checked using the reliability index parameters. The refinements were carried out in the Fd-3m space group. The refined parameters obtained and the graphical illustration (experimental, calculated and difference signals) for the substituted sample Ni 0.5 Co 0.5 CrAlO 4 are depicted in Fig. 4. The refinement of cations occupancy at 8a sites and 16d sites confirms the direct spinel-type structure of the sample with negligible inversion.

Optical properties

The shades of the colour green of the synthesised powders CoCr 2 O 4 , CoCrAlO 4 and Ni 0.5 Co 0.5 CrAlO 4 are shown in the images presented in Fig. 5. The diffuse reflectance spectra obtained by UV-Visible analysis show the presence of broad bands located in the visible region (530-670 nm) (Fig. 5).These absorptions can be attributed to the allowed transitions 4 A 2 (F) → 4 T 1 (P) and 3 T 1 (F) → 3 T 1 (P), respectively, of the Co 2+ and Ni 2+ ions in the J o u r n a l P r e -p r o o f tetrahedral sites and to the transitions 4 A 2g → 2 T 2g , 4 A 2g → 2 T 1g and 4 A 2g → 2 E g of chromium in the octahedral site [START_REF] Prim | Pink ceramic pigments based on chromium doped M(Al 2-x Cr x )O 4 , M=Mg, Zn, normal spinel[END_REF][START_REF] Torkian | Simple and Efficient Rout for Synthesis of Spinel Nanopigments[END_REF][START_REF] Ianoş | Characterization of Mg 1-x Ni x Al 2 O 4 solid solutions prepared by combustion synthesis[END_REF][START_REF] Hedayati | Synthesis and characterization of Co 1-x ZnxCr 2-y Al y O 4 as a near-infrared reflective color tunable nano-pigment[END_REF]. The colorimetric coordinates (L *, a * and b *) obtained by colorimetric analysis make it possible to quantify the colour of the pigments (see Table 1).

For the composition doped with 50% of Al (sample 2), it was found that component (-a*), which is responsible for the green colour, was reduced by almost half and component (-b*), which is responsible for the blue colour, was slightly decreased. The ratio between these components (a*/b*), which indicates the saturation of the green hue, was reduced from 1.21 to 0.71. The aditional substitution by 50% of Ni allows this ratio to 1.41. This is due to the decrease in the -b * component, a characteristic of the blue coloration, which depends on the cobalt concentration.

Morphological properties

The morphology of the synthesised green powders was examined using SEM analysis. As shown in Fig. 6, the micrograph of the CoCr 2 O 4 powder (Fig. 6 (a)) shows grains of different shapes and an agglomerated state. The grain size was estimated to be 75-200 nm. The CoCrAlO 4 powder (Fig. 6 (b)) is formed of grains with a quasi-spherical shape more or less agglomerated with sizes estimated to range between 35 nm and 100 nm. The image of Ni 0.5 Co 0.5 CrAlO 4 (Fig. 6 (c)) shows a fine powder with zones of agglomeration and consisting of quasi-spherical grains whose sizes were estimated to range between 50 nm and 75 nm.

Measurement of the specific surface area by the BET method shows that the substitution of 50% of Al in the CoCr 2 O 4 spinel makes it possible to increase the surface area from 6 m 2 /g to 12.4 m 2 /g. The second substitution by 50% of Ni resulted in a surface area of about 18 m 2 /g. The grain sizes determined from the values of the specific surface areas were estimated by considering the hypothesis that the grains are monodispersed and spherical [START_REF] Akbari | Particle size characterization of nanoparticles-a practicalapproach[END_REF]. The obtained values were in the same order of size as those observed in the SEM micrographs.

J o u r n a l P r e -p r o o f

Ceramic colouring efficiency tests

Ceramic colouring efficiency tests were used to evaluate the efficiency of the green colour coverage of the synthesised pigments on the ceramic substrates. The Ni 0.5 Co 0.5 CrAlO 4 nanoparticles were mixed in different amounts with transparent glaze powders, the chemical composition of which is given in Table 2. The mixtures were homogenised and deposited on to the ceramic substrates and then fired at 920 ° C for 30 minutes with a heating rate of 10 °C/min. Fig. 7 shows the images of the coloured surfaces of the substrates obtained after firing. It was observed that mixing the glaze with a small amount of the nanoparticle pigment results in green colour coverage. The L*a*b* colorimetric parameters measured on the coloured surface are given in Table 3. The hue variation between the two samples can be calculated by using CIEΔE2000 color-difference formula (ΔE 00 ) [START_REF] Wee | Use of a porcelain color discrimination test to evaluate color difference formulas[END_REF] and by the following [START_REF] Hu | Synthesis and chromatic properties of V-doped and V/Y-codoped ZrO 2 yellow pigments[END_REF]. We noted that a quantity of 4% of the pigment mixed with the glaze is sufficient for obtaining a contrast of less than 2, which can be difficult to distinguish with the human eye.

equation ΔE*= [(ΔL*) 2 +(Δa*) 2 +(Δb*) 2 ] 1/2

Conclusion

Green pigments based on the spinel structure were successfully synthesised using the polymeric route. The substitution of 50% of Al and Ni in the spinel CoCr 2 O 4 leads to improving the morphology of the powders with different shades of the colour green. The obtained nanoparticles consist of grains of almost spherical shapes. The specific surface area increases after the substitution from 7 m 2 /g to 18 m 2 /g. Ceramic colouring efficiency tests show that an amount of less than 4% wt of the prepared pigment was sufficient for good coverage of the green colour.  Ceramic colouring efficiency tests show that an amount of less than 4% wt of the prepared pigment was sufficient for good coverage of the green colour.

J o u r n a l P r e -p r o o f

J o u r n a l P r e -p r o o f

Declaration of interests

 The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

J o u r n a l P r e -p r o o f

  of 1: 2, then poured into a solution of polyacrylic acid (10% by weight) as a chelating and gelling agent. The pH of the mixture was adjusted to 7 by using ammonium hydroxide solution under constant stirring. The initial synthesis was based on the preparation of CoCr 2 O 4 spinel oxide, after which the synthesis was based on a substitution of 50% of the tetrahedral and octahedral sites by nickel and aluminium, respectively. The experimental protocol is summarized in Fig. 1. Thermal analysis was carried out by thermogravimetric/differential thermal analyzer (APPARTUS SHIMADZU). XRD patterns J o u r n a l P r e -p r o o f were obtained at room temperature using a Bruker D8 employing CuKα radiation as the X-ray source. The surface morphology of the samples was examined by JEOL JSM 6700F scanning electron microscopy (SEM) using a 30 kV voltage. Diffuse reflectance was measured with a Perkin Elmer Lambda 35 UV-Vis spectrophotometer. The specific surface area of the powder was measured using Brunauer-Emmet-Teller (BET) method by Micrometrics Flowsorb II 2300 instrument. The colorimetric measurements were performed in the CIE L*a*b* (L*a*b* colorimetric system of the Comission Internationale de l'Eclairage) using a CHROMA METER CR-400/410 colorimeter (KONICA MINOLTA).

Fig. 1 .Fig. 2 .

 12 Fig. 1. Flowchart for the preparation of Co 1-x Ni x Cr 2-y Al y O 4 (0≤x≤1, 0≤y≤2) powdered pigments.

Fig. 5 .Fig. 6 .Fig. 7 .

 567 Fig. 5. Shades of the green colour and diffuse reflectance spectra of (a): CoCr 2 O 4 , (b): CoCrAlO 4 and (c): Ni 0.5 Co 0.5 CrAlO 4 powders. J o u r n a l P r e -p r o o f



  r n a l P r e -p r o o f Highlights: Ultrafine powders were prepared by the polymeric route.  CoCr 2 O 4 spinel and the spinels substituted by nickel and aluminium cations are in the crystalline phase at 900°C. The effect of the metal substitution on the structural, optical and morphological properties was discussed. The substitution of 50% of Al and Ni in the spinel CoCr 2 O 4 leads to improving the morphology of the powders with different shades of the colour green.

cobalt nitrate Co(NO 3 ) 2 •6H 2 O (99.0%, Sigma Aldrich), aluminium nitrate Al(NO 3 ) 3 •9H 2 O (99.0%, Sigma Aldrich) and chromium nitrate Cr(NO 3 ) 3 •9H 2 O (99.0%, Sigma Aldrich). The reagents were dissolved in distilled water with a molar ratio of

	All the chemicals used to begin this work were of pure grade: nickel nitrate Ni(NO 3 ) 2 •6H 2 O
	(99.0%, Sigma Aldrich), the cations metal A 2+ /B 3+

Table 1

 1 Colorimetric coordinates of CoCr 2 O 4 , CoCrAlO 4 and Ni 0.5 Co 0.5 CrAlO 4 powders.

Table 2

 2 Chemical composition of the transparent glaze.

	Oxide	K 2 O Al 2 O 3	SiO 2 CaO MgO Fe 2 O 3 Na 2 O
	Content/% 5.77 17.08	62.68 3.90 0.32 0.29	9.93

Table 3

 3 Colorimetric coordinates of the coloured surface of ceramic substrates measured after firing.

	L*a*b* of Ni 0.5 Co 0.5 AlCrO 4 pigment	L* 41.64 -10.26 -7.32 a* b*	ΔE* ΔE 00
		2% 45.64	-7.87	-5.25 5.01 4.45
	wt% of pigment	3% 43.87	-8.35	-5.75 3.33 2.80
	added in the glazes	4% 42.35	-8.95	-6.11 1.92 1.49
		5% 40.12	-9.42	-6.88 1.79 1.55


Thierry De Meeûs
email: thierry.demeeus@ird.fr

Sophie Ravel

Philippe Solano

Jérémy Bouyer

Negative density dependent dispersal in tsetse flies: a risk for control campaigns?

Keywords: Glossina, Sleeping sickness, Nagana, Control campaigns, Effective population size, Migration rate

Tsetse flies are vectors of parasites that cause diseases responsible for significant economic losses and health issues in sub-Saharan Africa, including sleeping sickness in humans and nagana in domestic animals. Efficient vector control campaigns require good knowledge of the demographic parameters of the targeted populations. In the last decade, population genetics emerged as a convenient way to measure population densities and dispersal in tsetse flies. Here by revealing a strong negative density-dependent dispersal in two dimensions, we suggest that control campaigns might unleash dispersal from untreated areas. If confirmed by direct measurement of dispersal before and after control campaigns, area wide and/or sequential treatments of neighboring sites will be necessary to prevent this issue.

2

Tsetse fly control and population genetics tools

Flies of the genus Glossina are the vectors of trypanosomes, parasites responsible for diseases that lead to a significant disease burden and economic losses in sub-Saharan Africa. In humans, tsetse flies transmit trypanosomiasis, also known as sleeping sickness" which is usually mortal if the patient is not treated [START_REF] Jamonneau | Untreated human infections by Trypanosoma brucei gambiense are not 100% Fatal[END_REF]. In animals, particularly cattle, it causes nagana, a disease known to cause considerable economic losses in sub-Saharan Africa [START_REF] Van Den Bossche | A changing environment and the epidemiology of tsetse-transmitted livestock trypanosomiasis[END_REF][START_REF] Solano | How can tsetse population genetics contribute to African trypanosomiasis control?[END_REF]. Vector control has become a recognized key component in the management of these plagues [START_REF] Solano | How can tsetse population genetics contribute to African trypanosomiasis control?[END_REF][START_REF] Bouyer | Mapping landscape friction to locate isolated tsetse populations candidate for elimination[END_REF][START_REF] Diall | Developing a progressive control pathway for African animal trypanosomosis[END_REF]. Nevertheless, to optimize control campaigns, good knowledge of the biology of the target populations is needed, in particular their density and dispersal ability [START_REF] Vreysen | Tsetse flies: Their biology and control using area-wide integrated pest management approaches[END_REF]. Lack of such knowledge has indeed been implicated in the failure of some elimination campaigns due to the raid reinvasion of treated populations by local flies or by flies from neighboring sites [START_REF] Adam | Genetic comparison of Glossina tachinoides populations in three river basins of the upper west region of Ghana and implications for tsetse control[END_REF]. Population genetics (see Glossary) offers useful tools [START_REF] De Meeûs | Population genetics and molecular epidemiology or how to "débusquer la bête[END_REF] in particular for tsetse flies [START_REF] Solano | How can tsetse population genetics contribute to African trypanosomiasis control?[END_REF].

Considerable work has been undertaken in recent years to study the population biology of different species of tsetse flies in a range of different countries with varying success in terms of population density and dispersal estimates (e.g. see [START_REF] Bouyer | Mapping landscape friction to locate isolated tsetse populations candidate for elimination[END_REF][START_REF] Adam | Genetic comparison of Glossina tachinoides populations in three river basins of the upper west region of Ghana and implications for tsetse control[END_REF][START_REF] Kone | Contrasting population structures of two vectors of African trypanosomoses in Burkina Faso: consequences for control[END_REF][START_REF] Manangwa | Detecting Wahlund effects together with amplification problems: cryptic species, null alleles and short allele dominance in Glossina pallidipes populations from Tanzania[END_REF][START_REF] Mélachio | Population genetics of Glossina palpalis palpalis from central African sleeping sickness foci[END_REF][START_REF] Opiro | Genetic diversity and population structure of the tsetse fly Glossina fuscipes fuscipes (Diptera: Glossinidae) in Northern Uganda: Implications for vector control[END_REF]).

The best way to accurately estimate dispersal of a given population is isolation by distance. Isolation by distance is a common feature of population structure in which the genetic relatedness (or genetic distance) between individuals or subpopulations (see Box 1) is a decreasing function of the geographic distance separating them (the shorter the distance between them, the more similar they are). When such genetic distances are known, an isolation by distance model can be built and its significance tested (Box 2). If it is significant and if effective population density De can be estimated, then dispersal δ can be extracted from the model (Box 2).

In the last decade, several data sets have provided the opportunity for such inferences for different tsetse fly species in different countries in Africa: Glossina palpalis gambiensis along the northern reaches [START_REF] Bouyer | Population sizes and dispersal pattern of tsetse flies: rolling on the river?[END_REF] and the southern reaches of the Mouhoun River [START_REF] Bouyer | Population structure of Glossina palpalis gambiensis (Diptera: Glossinidae) between river basins in Burkina-Faso: consequences for area-wide integrated pest management[END_REF] in Burkina-Faso (in one dimension=1D, see Box 2); G. palpalis palpalis in Cameroon (two dimensions=2D, see Box 2) [START_REF] Mélachio | Population genetics of Glossina palpalis palpalis from central African sleeping sickness foci[END_REF][START_REF] Mélachio | Effect of sampling methods, effective population size and migration rate estimation in Glossina palpalis palpalis from Cameroon[END_REF]; G. palpalis gambiensis and G. tachinoides in Southern Burkina Faso (2D) across river basins [START_REF] Kone | Contrasting population structures of two vectors of African trypanosomoses in Burkina Faso: consequences for control[END_REF]; G. tachinoides in Ghana (2D) [START_REF] Adam | Genetic comparison of Glossina tachinoides populations in three river basins of the upper west region of Ghana and implications for tsetse control[END_REF]; G. fuscipes fuscipes from Uganda [START_REF] Opiro | Genetic diversity and population structure of the tsetse fly Glossina fuscipes fuscipes (Diptera: Glossinidae) in Northern Uganda: Implications for vector control[END_REF][START_REF] Hyseni | The population structure of Glossina fuscipes fuscipes in the Lake Victoria basin in Uganda: implications for vector control[END_REF], in Tanzania and Kenya [START_REF] Manangwa | Genetic diversity of Glossina fuscipes fuscipes along the shores of Lake Victoria in Tanzania and Kenya: implications for management[END_REF] (2D); G. pallidipes from Kenya Nguruman escarpment and Ruma [START_REF] Okeyo | Temporal genetic differentiation in Glossina pallidipes tsetse fly populations in Kenya[END_REF] and from the Serengeti Park Reserve in Tanzania (2D) [START_REF] Manangwa | Detecting Wahlund effects together with amplification problems: cryptic species, null alleles and short allele dominance in Glossina pallidipes populations from Tanzania[END_REF]. This offered the opportunity to check if any relationship existed between these different inferences of population density and dispersal. The objective of the present paper is to discuss the evidence that tsetse dispersal may be density dependent.

Regression between dispersal and effective population density

The detailed protocol for using or reanalyzing available data and computing the necessary parameters are given in the supplementary file S1. Positive density dependence dispersal occurs when dispersal increases with density, as expected if individuals leave to escape overcrowding. Negative density dependent dispersal happens when crowded populations can no longer accept any immigrants. To investigate evidence for density-dependent dispersal in tsetse, we computed effective population densities as the ratio of effective population size (Ne) to the average surface area occupied by a population (Box 2). For the sake of homogeneity across studies, Ne was estimated using linkage disequilibrium methods [START_REF] Bartley | Use of linkage disequilibrium data to estimate effective size of hatchery and natural fish populations[END_REF][START_REF] Do | NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data[END_REF][START_REF] Peel | Accounting for missing data in the estimation of contemporary genetic effective population size (Ne)[END_REF][START_REF] Waples | Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution[END_REF] and the surface area occupied by a subpopulation corresponds to the surface of subsamples when available or the disc defined by the minimum distance between the closest subsamples taken as the diameter of a population (Box 2). Like for dispersal, we extracted slope estimates and 95% bootstrap confidence intervals over loci (95%CI) (when available) to compute dispersal (δ) as explained in Box 2. Because the relationship is exponential, we log transformed both geographic and genetic distances to perform the regression. We kept only data with 2D population structure regressions [START_REF] Adam | Genetic comparison of Glossina tachinoides populations in three river basins of the upper west region of Ghana and implications for tsetse control[END_REF][START_REF] Kone | Contrasting population structures of two vectors of African trypanosomoses in Burkina Faso: consequences for control[END_REF][START_REF] Manangwa | Detecting Wahlund effects together with amplification problems: cryptic species, null alleles and short allele dominance in Glossina pallidipes populations from Tanzania[END_REF][START_REF] Mélachio | Population genetics of Glossina palpalis palpalis from central African sleeping sickness foci[END_REF]. For some studies [START_REF] Opiro | Genetic diversity and population structure of the tsetse fly Glossina fuscipes fuscipes (Diptera: Glossinidae) in Northern Uganda: Implications for vector control[END_REF][START_REF] Mélachio | Effect of sampling methods, effective population size and migration rate estimation in Glossina palpalis palpalis from Cameroon[END_REF][START_REF] Hyseni | The population structure of Glossina fuscipes fuscipes in the Lake Victoria basin in Uganda: implications for vector control[END_REF][START_REF] Manangwa | Genetic diversity of Glossina fuscipes fuscipes along the shores of Lake Victoria in Tanzania and Kenya: implications for management[END_REF][START_REF] Okeyo | Temporal genetic differentiation in Glossina pallidipes tsetse fly populations in Kenya[END_REF], we needed to re-compute several statistics, as explained in detail in supplementary file S1. Only two studies where 1D dispersal inferences were available along the Mouhoun River in Burkina Faso [START_REF] Bouyer | Population sizes and dispersal pattern of tsetse flies: rolling on the river?[END_REF][START_REF] Bouyer | Population structure of Glossina palpalis gambiensis (Diptera: Glossinidae) between river basins in Burkina-Faso: consequences for area-wide integrated pest management[END_REF] were not included. According to Watts et al. [START_REF] Watts | Compatible genetic and ecological estimates of dispersal rates in insect (Coenagrion mercuriale: Odonata: Zygoptera) populations: analysis of 'neighbourhood size' using a more precise estimator[END_REF], high mutation rates have a stronger effect on the accuracy of the method in linear habitats than in two-dimensional habitats and this may affect analyses of riparian systems. The fact that the metric in 1D is in m and in 2D in m² also makes comparisons difficult. Additionally, we wanted to check that the relationship was not due to a systematic bias in De and δ estimates by using independent estimates of De and census densities (see below). Ne can strongly depend on the reproductive system, on fluctuations in population sizes and/or generation overlap.

This last point should be minimized if samples taken at intervals of not more than two months are used, as we recommend (Supplementary File S1). We also expect that Ne is strongly correlated with the census size of the population, otherwise all population genetics studies of tsetse flies would need to be called into question. Finally, a census of flies captured during the studies, when available, was also analysed and, likewise, was seen to be correlated with the real census of the corresponding tsetse populations [START_REF] Barclay | Probability models to facilitate a declaration of pest-free status, with special reference to tsetse (Diptera: Glossinidae)[END_REF].

The results of the regressions are presented in Figure 1, in 1a with effective population densities (De), and in 1b with census population densities (Dc). An exponential, tight and negative relationship can be seen between population density and dispersal. The regression explains most of the variance (R²≈0.85 for De; R²≈0.86 for Dc). Spearman's rank correlation testing with Rcmdr package [START_REF] Fox | The R commander: a basic statistics graphical user interface to R[END_REF][START_REF] Fox | Extending the R commander by "plug in" packages[END_REF] for R [START_REF]R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing[END_REF] gave significant p-values (0.0056 and 0.0167 for De and Dc respectively).

Possible causes for negative density dependent dispersal

Negative density dependence appears to be much less frequent than positive density dependence (the intuitively expected sign of correlation), in particular in insects [START_REF] Baines | The interactive effects of competition and predation risk on dispersal in an insect[END_REF][START_REF] Ronnås | Limited emigration from an outbreak of a forest pest insect[END_REF]. "The occurrence of negative density dependent dispersal is in agreement with the ''social fence'' and related hypotheses which have been proposed in particular for small mammal populations, where movements are increasingly inhibited by aggression at high densities" [START_REF] Matthysen | Density-dependent dispersal in birds and mammals[END_REF]. Negative density dependence can result from Allee effects or because other factors, such as predation, interact with density to negatively influence dispersal [START_REF] Baines | The interactive effects of competition and predation risk on dispersal in an insect[END_REF].

In the case of insects, if there is a trade-off between wing development and reproductive capacity, dispersal may also be promoted by harsh local conditions, which can be correlated with low levels of population density, especially when dispersal is costly [START_REF] Kamioka | Evolution of density-dependent wing polymorphism in insects[END_REF].

There are nevertheless several reported cases of negative density dependent dispersal. Experimental studies on insects have shown that juvenile hormone titer, which is influenced by the diet at earlier stages, can influence wing size and hence dispersal capacities [START_REF] Kamioka | Evolution of density-dependent wing polymorphism in insects[END_REF]. Negative density dependence has also been reported in the northern pine processionary moth Thaumetopoea pinivora [START_REF] Ronnås | Limited emigration from an outbreak of a forest pest insect[END_REF].

Dispersal is strongly density dependent in tsetse flies

Negative density dependent dispersal in tsetse populations has been known for some time (Box 3) but its intensity has not been measured to date. The exponential negative density dependence of dispersal observed in the present study raises several questions. The phenomenon implies that competition for space is very harsh and probably occurs mainly during feeding on the host (Box 3). When densities are high, all the sites are crowded and both hosts and local tsetse are accustomed to each other (Box 3). Naïve immigrants from remote sites are characterized by low feeding success, thus increasing their mortality. When population densities are low, densities may vary in both space and time, which renders immigration much easier and allows long-range immigration. In an empty spot with no other tsetse competitors and naïve hosts, even exhausted immigrants from remote sites can safely settle. This pattern could also reflect favorable host densities.

When densities of favorable hosts are low, tsetse flies need to keep moving to find a suitable blood meal.

Since control campaigns aim at considerably reducing tsetse densities, they may unleash dispersal and relatively rapid reinvasion of treated zones from neighboring or even remote sites, as may have occurred in Ghana [START_REF] Adam | Genetic comparison of Glossina tachinoides populations in three river basins of the upper west region of Ghana and implications for tsetse control[END_REF] and certainly occurred in Ivory Coast [START_REF] Rogers | A review of density-dependent processes in tsetse populations[END_REF]. Reinvasion of flies in areas depleted as a result of vector control has been well documented for quite some time and emphasizes the role played by fly movement in the development of control strategies [START_REF] Gouteux | A density-dependent model with reinvasion far estimating tsetse fly populations (Diptera : Glossinidae) through trapping[END_REF]. Some evidence suggests strong densitydependence associated with this phenomenon: the bigger the decrease in the population, the higher the potential for reinvasion in a continuous tsetse belt [START_REF] Gouteux | A density-dependent model with reinvasion far estimating tsetse fly populations (Diptera : Glossinidae) through trapping[END_REF]. These authors also report that Hargrove's models use density-dependent mortality, but not dispersal [START_REF] Hargrove | Tsetse eradication: sufficiency, necessity and desirability[END_REF]. This limitation has already been reported and actually concerns all existing models [START_REF] Peck | Mathematical modeling, spatial complexity, and critical decisions in tsetse control[END_REF].

Density-dependent dispersal was only recently incorporated in a tsetse population dynamics model, based on a sigmoidal density-dependent dispersal rate adapted for individuals competing for access to resources [START_REF] Cecilia | Environmental heterogeneity drives tsetse fly population dynamics[END_REF]. Such immigrations are dangerous for two reasons: i) because they can severely jeopardize the sustained success of control campaigns; ii) these new immigrants can bring with them pathogenic agents or more virulent strains that were not present before the campaign and were unable to invade the zone because of competition for space; and iii) these immigrants may replace the local population and adapt to local pathogens differently and exhibit new vectorial capacities compared to the former population. What is more, negative density-dependent dispersal may partially explain some observed failures. As underlined by Rogers and Randolph [START_REF] Rogers | A review of density-dependent processes in tsetse populations[END_REF]: "Control programmes must recognize that the efficacy of population suppression may well be reduced at the low levels of density that need to be maintained in order to reduce the rates of disease transmission, because at these low levels, tsetse may show entirely unexpected demographic vigor, due to the absence of normal density dependent constraints". If suitable host density is the only force driving the observed pattern, then control campaigns would not be expected to have any impact on dispersal.

Concluding Remarks

More data on isolation by distance slopes and effective population density estimates are needed to confirm the exact relationship found so far. One-dimensional isolation (along river courses) is particularly rare up to now. Most species we studied belong to the palpalis subgenus and one species (G. pallidipes) to the morsitans subgenus. The trends highlighted here probably apply to all tsetse flies but more studies are needed to generalize the trends we found to the whole genus. Studies should also focus on areas where tsetse control has been implemented. Considering that our results strongly support negative density-dependent dispersal, it will be crucial to evaluate the strength of the phenomenon using direct methods like mark-release-recapture in the field before and after control, for instance, combined with population genetics analyses. Alternatively, the prediction that neighboring flies will recolonize the treated area after the campaign ends can easily be assessed with sampling and genotyping before and after a control campaign in the treated area and its surroundings. If confirmed, and if one wants to avoid reinvasions and their associated issues (see Outstanding Questions), it will be even more important to find a way to isolate zones after treatment [START_REF] Bouyer | Mapping landscape friction to locate isolated tsetse populations candidate for elimination[END_REF] and to sequentially treat neighboring areas harboring tsetse flies. Finally, several published results could not be included in the present review because of data unavailability. Data availability is now mandatory in several journals but we think this should be generalized.

Glossary

Allee effect: A phenomenon where the individual fitness (survival and/or reproduction) is positively correlated with population size or density.

Alleles:

The different states of a locus or a gene (e.g. the three alleles of the ABO system for blood groups).

Bootstrap: A randomization procedure where an item series (e.g. loci) are resampled with replacement (the same item can be resampled several times) until the number of items present in the raw data is reached. At each resampling, a statistic is measured (e.g. genetic distance, see Box 1). The procedure is repeated a great number of times (e.g. 5000), which produces a distribution of possible values for the statistic. Excluding the 2.5% smallest and the 2.5% biggest values of the bootstrap distribution provides the 95% bootstrap confidence interval of the statistic.

Correlation coefficient:

A measure of the covariation between two variables.

Effective population density:

The ratio of effective population size to the surface area occupied by a given population.

Effective population size:

Quantifies the rate at which a population loses its genetic diversity. Indeed, the reciprocal of the effective size (1/Ne) gives the long-term probability that two randomly sampled genes in the population are replicates (or descend) from a single gene in the parental generation. It can be roughly defined as the number of adults in a population that will leave a genetic signature to the next generation. It is generally smaller than the census size Nc, except when coalescence is delayed due to a particular system of mating (negative assortative mating) or in very small dioecious populations. In any case, Ne and Nc must be strongly positively correlated in most, if not all, situations.

Immigration rate:

The proportion of individuals in a subpopulation that come from other subpopulations of the total population.

Inbreeding: A concept describing how alleles, individuals or subpopulations can be related. Formally, it corresponds to the probability to randomly draw two identical alleles that are identical by descent, i.e. that come from a common ancestor.

Linkage disequilibrium: A measure of the statistical correlation between alleles at two or more loci. If in equilibrium, then the occurrence of alleles at two loci is simply equal to the product of corresponding allele frequencies in the population for the two loci.

The main forces influencing linkage disequilibrium are reproductive systems, selection and genetic drift. It can be used to measure effective population size (small populations generate and maintain higher linkage disequilibria than bigger ones).

Locus: A specific segment of the genome, not necessarily a coding sequence (gene).

Population genetics:

The study of the distribution of genetic variation in space and time and its evolution with random genetic drift, selection, mutation, migration etc.

Regression: A mathematical model explaining the relationship between a response variable and one (or several) explanatory variable(s).

Riparian: A riparian system describes the interface between land and a river or stream.

Slope:

The parameter of a regression that describes how many units ordinates go up or down for each unit increase in the abscissa.

Box 1: Measuring genetic distances

Several genetic distances exist that are used in isolation by distance procedures (see [START_REF] Séré | Comparison of different genetic distances to test isolation by distance between populations[END_REF] for a more complete overview). The most popular, Wright's FST [START_REF] Wright | The interpretation of population structure by F-statistics with special regard to system of mating[END_REF] is not a real genetic distance but a measure of the effect of subdivision on inbreeding. The parametric definition of this parameter is (e.g. [START_REF] De Meeûs | Population genetics and molecular epidemiology or how to "débusquer la bête[END_REF]):

= - 1 -
where QS is the probability of identity between two alleles from two individuals of the same subpopulation and QT is the probability of identity between two alleles from two subpopulations of the total population. Its value varies between 0 (no subdivision) and 1 (all subsamples fixed for one or the other allele present, i.e. absolute subdivision). It is mainly influenced by subpopulation sizes and immigration rate (or dispersal). This parameter is estimated with Weir and Cockerham's unbiased estimator θ [START_REF] Weir | Estimating F-statistics for the analysis of population structure[END_REF]. For isolation by distance situations, it has been shown that the use of FST_R=θ/(1-θ) is more useful because it is linearly related to geographic distances without losing its relation to other demographic parameters [START_REF] Rousset | Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance[END_REF]. An equivalent measure between individuals ar, and it's unbiased estimator â, was also designed by Rousset [START_REF] Rousset | Genetic differentiation between individuals[END_REF] as:

= - 1 -
where Qw is the probability of identity of two genes within an individual and Qr is the probability of identity of genes at (geographical) distance r. Another statistic ê can be used in case of very important neighborhood (see [START_REF] Watts | Compatible genetic and ecological estimates of dispersal rates in insect (Coenagrion mercuriale: Odonata: Zygoptera) populations: analysis of 'neighbourhood size' using a more precise estimator[END_REF] for more details).

In other instances it may be more appropriate to use another genetic distance, e.g. Cavalli-Sforza and Edward's chord distance [START_REF] Cavalli-Sforza | Phylogenetic analysis: model and estimation procedures[END_REF]. DCSE is more appropriate for tree topology design [START_REF] Takezaki | Genetic distances and reconstruction of phylogenetic trees from microsatellite DNA[END_REF] and more powerful in some cases of isolation by distance testing [START_REF] Séré | Comparison of different genetic distances to test isolation by distance between populations[END_REF]

: = 2 2
where r is the number of loci, j the locus name (from 1 to r), i the allele name (from 1 to mj), mj the number of alleles at locus j, xij and yij are the frequencies of allele i at locus j for subpopulations x and y, respectively.

Box 3: Tsetse fly atypical reproduction and density dependence

Female tsetse flies do not lay eggs but larviposit a single mature larva (3rd instar, L3) in humid soil one at a time. The larva develops feeding from the uterine glands of the mother (adenotrophic viviparity). After larviposition, the larva quickly burrows into the soil surface for pupation. It was shown in G. morsitans that a larviposition pheromone is deposited to attract other females to the same site, leading to a strong aggregation of pupae [START_REF] Who | Control and surveillance of human African trypanosomiasis: report of a WHO expert committee, World Health Organization & WHO Expert Committee on the Control and Surveillance of Human African Trypanosomiasis, World Health Organization[END_REF]. The adult emerges 20-80 days later. Thus, in nature, each female produces no more than 3-5 offspring during its total life. Lifespan is around 3 months for females, 2 months for males. As a result, the intrinsic rate of tsetse population growth is theoretically low. Both females and males feed on vertebrate blood and are therefore both vectors.

Learning capacities of tsetse flies may increase their hunting efficiency with age and encourage those returning to their first host [START_REF] Bouyer | Learning influences host choice in tsetse[END_REF].

Evidence for negative density dependence in tsetse flies has been reported in several studies. Interactions between flies and the irritation of the host animal are responsible for a decreased proportion of fed G. morsitans morsitans as the numbers of tsetse flies arriving increase, and frequently disturbed spots might encourage tsetse flies to leave without feeding, simultaneously increasing dispersal and associated mortality and decreasing local density [START_REF] Rogers | A review of density-dependent processes in tsetse populations[END_REF][START_REF] Vale | Direct observations on the responses of tsetse flies (Diptera, Glossinidae) to hosts[END_REF]. Host irritation and tsetse learning are important parameters driving the survival and dispersal of tsetse flies.

In dense tsetse populations, the populations are self-sustained and are considerably reduced after insecticidal spray, but are recolonized from neighboring sites, which takes several months to complete [START_REF] Randolph | Local variation in the population dynamics of Glossina palpalis palpalis (Robineau-Desvoidy) (Diptera: Glossinidae). II. The effect of insecticidal spray programmes[END_REF]. Alternatively, low-density sites are naturally sustained by immigrants from neighboring sites and insecticidal treatments do not have as much impact on the total population [START_REF] Rogers | A review of density-dependent processes in tsetse populations[END_REF][START_REF] Randolph | Local variation in the population dynamics of Glossina palpalis palpalis (Robineau-Desvoidy) (Diptera: Glossinidae). II. The effect of insecticidal spray programmes[END_REF][START_REF] Rogers | Local variation in the population dynamics of Glossina palpalis palpalis (Robineau-Desvoidy) (Diptera: Glossinidae). I. Natural population regulation[END_REF]. The same symbols indicate the same species. Scales were log-transformed for both axes.

Neighborhood:

  The number of individuals connected through migration in an isolation by distance framework. Parametric test: Statistical tests using population parameters (average and variance). If the constraints to apply such tests (e.g. normality of data, homogeneity of variances) are not met, non-parametric tests must be applied instead (e.g. rank tests or Mantel tests).

Figure 1 :

 1 Figure 1: Strong negative density-dependent dispersal in tsetse fly populations. The

Acknowledgements

We thank Winnie Okeyo, Norah Saarman, Oliver Manangwa, Trésor Tito Tanekou Melachio, Adalgisa Caccone and Chaz Hyseni for providing assistance with their data sets, which meant we could compute parameters that were missing in their original papers and considerably enriched our perception of density dependence in tsetse flies in the present review. We also thank Dr Raphaël Leblois for his advice concerning theoretical issues in isolation by distance.

Box 2: Measuring and testing isolation by distance and parameter inferences

Isolation by distance is measured through a regression of geographic distances (explanatory variable) DGeo between individuals or subsamples and corresponding genetic distances (response variable) DGenet. Inferences follow particular models of regression, depending on whether the population structure occurs in one dimension (1D) or in two dimensions (2D) [START_REF] Watts | Compatible genetic and ecological estimates of dispersal rates in insect (Coenagrion mercuriale: Odonata: Zygoptera) populations: analysis of 'neighbourhood size' using a more precise estimator[END_REF][START_REF] Rousset | Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance[END_REF][START_REF] Rousset | Genetic differentiation between individuals[END_REF]. 1D structures correspond to shores, ecotones or river courses, as it is the case for Glossina palpalis gambiensis along forest galleries in savannas. 2D structures are more common, or at least more often reported. Three dimension models (dense forests or water columns for aquatic organisms) remain poorly The average surface (S) occupied by a subpopulation can be computed as the surface area occupied by the different traps used in a given survey site. If only one trap is available per site, the distance between the two closest sites (Dmin) can be taken as the raw proxy of the distance between the centers of two neighboring subpopulations and hence as their diameter: S=π×(Dmin/2)². If the average effective population size Ne is computed with appropriate algorithms ( e.g. see [START_REF] Do | NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data[END_REF][START_REF] Balloux | Heterozygote excess in small populations and the heterozygoteexcess effective population size[END_REF][START_REF] Vitalis | ESTIM 1.0: a computer program to infer population parameters from one-and two-locus gene identity probabilities[END_REF][START_REF] Wang | Estimating effective population size and migration rates from genetic samples over space and time[END_REF]), then De=Ne/S, and a rough proxy of parent-offspring average distance (dispersal) can be computed as [START_REF] Séré | Comparison of different genetic distances to test isolation by distance between populations[END_REF]:

In 2D, immigrants from neighboring subpopulations at each generation can be estimated as Nem=1/2πb [START_REF] Rousset | Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance[END_REF].

19

For isolation by distance between individuals, â should be used instead of ê when Nb<10000 in 1D or when Nb<50 in 2D [START_REF] Watts | Compatible genetic and ecological estimates of dispersal rates in insect (Coenagrion mercuriale: Odonata: Zygoptera) populations: analysis of 'neighbourhood size' using a more precise estimator[END_REF].

Significance testing cannot be undertaken with a parametric test since distance measures are autocorrelated (paired comparisons). The significance of the slope can be tested by a bootstrap over loci based 95% confidence interval (95%CI). If 0 is not included in 95%CI, then the slope is significantly above 0. Otherwise, a correlation coefficient (e.g. Pearson) is computed between the two distance matrices and cells of one of those permuted a great number of times (Mantel test [START_REF] Mantel | The detection of disease clustering and a generalized regression approach[END_REF]). The p-value of the test is the proportion of time the randomized correlation was as big as or bigger than the observed one. If the 95%CI is not above 0, the Mantel test may be more powerful if the genetic distance used is DCSE, at least for highly variable markers like microsatellite loci [START_REF] Séré | Comparison of different genetic distances to test isolation by distance between populations[END_REF].