Neige Calonne
email: neige.calonne@gmail.com

Maurine Montagnat

Margret Matzl

Martin Schneebeli

The layered evolution of fabric and microstructure of snow at Point Barnola, Central East Antarctica

Keywords: snow microstructure, c-axis orientation, temperature gradient metamorphism, Antarctica

Snow fabric, defined as the distribution of the c-axis orientations of the ice crystals in snow, is poorly known. So far, only one study exits that measured snow fabric based on a statistically representative technique. This recent study has revealed the impact of temperature gradient metamorphism on the evolution of fabric in natural snow, based on cold laboratory experiments. On polar ice sheets, snow properties are currently investigated regarding their strong variability in time and space, notably because of their potential influence on firn processes and consequently on ice core analysis. Here, we present measurements of fabric and microstructure of snow from Point Barnola, East Antarctica (close to Dome C). We analyzed a snow profile from 0 to 3 m depth, where temperature gradients occur. The main contributions of the paper are (1) a detailed characterization of snow in the upper meters of the ice sheet, especially by providing data on snow fabric, and (2) the study of a fundamental snow process, never observed up to now in a natural snowpack, namely the role of temperature gradient metamorphism on the evolution of the snow fabric. Snow samples were scanned by micro-tomography to measure continuous profiles of microstructural properties (density, specific surface area and pore thickness). Fabric analysis was performed using an automatic ice texture analyzer on 77 representative thin sections cut out from the samples. Different types of snow fabric could be identified and persist at depth. Snow fabric is significantly correlated with snow microstructure, pointing to the simultaneous influence of temperature gradient metamorphism on both properties. We propose a mechanism based on preferential grain growth to explain the fabric evolution under temperature gradients.

Our work opens the question of how such a layered profile of fabric and microstructure evolves at depth and further influences the physical and mechanical properties of snow and firn. More generally, it opens the way to further studies on the influence of the snow fabric in snow processes related to anisotropic properties of ice such as grain growth, mechanical response, electromagnetic behavior.

Introduction

The ice crystal has various properties that depend on the crystallographic orientation. Snowflakes are the result of an enhanced growth rate along either the basal plane (plate crystal) or prismatic plane (column crystal), depending on the temperature conditions during growth [START_REF] Furukawa | Handbook of Crystal Growth: Fundamentals[END_REF]. Non-basal deformation of ice crystals requires a stress at least 60 times larger than that for a basal slip at the same strain rate [START_REF] Duval | Rate-controlling processes in the creep of polycrystalline ice[END_REF], showing an extreme mechanical anisotropy. Anisotropy also exists in the dielectric [START_REF] Matsuoka | Precise measurement of dielectric anisotropy in ice ih at 39 ghz[END_REF] and light scattering [START_REF] Takano | Solar radiative transfer in cirrus clouds. part ii: Theory and computation of multiple scattering in an anisotropic medium[END_REF] properties of ice. Snow on the ground is constituted of sintered single ice crystals that have each a distinct crystallographic orientation. We define snow fabric as the distribution of the c-axis orientations of ice crystals in snow, the c-axis orientation being the axis perpendicular to the basal plane. The question of how the distribution of crystal orientations in snow affects its physical and mechanical properties has never been truly addressed due to the lack of data on snow fabric itself.

de [START_REF] De Quervain | The institute for snow and avalanche research at weissfluhjoch/davos: the first five years (1943 to 1948)[END_REF] analyzed a few alpine snow samples that tend to show a preferential vertical orientation of the c-axes in depth hoar, but data were too few to draw any definitive conclusions. From Antarctic snow, [START_REF] Stephenson | Preliminary results of the glaciological work on the trans-antarctic expedition, 1955-58[END_REF], [START_REF] Stephenson | Some considerations of snow metamorphism in the antarctic ice sheet in the light of ice crystal studies[END_REF], and [START_REF] Stephenson | Some aspects of shallow snow metamorphism at Southice, Antarctica[END_REF] indicate that fabric may be affected by metamorphism, but again the interpretation is limited due to the low number of samples. Up to now, the only significant work on snow fabric is by [START_REF] Riche | Evolution of crystal orientation in snow during temperature gradient metamorphism[END_REF]. Based on laboratory experiments, they showed that the snow fabric evolves during temperature gradient metamorphism.

Temperature gradient metamorphism has been widely investigated in terms of microstructure evolution (e.g. [START_REF] Pfeffer | Temperature gradient and initial snow density as controlling factors in the formation and structure of hard depth hoar[END_REF]Calonne et al., 2014a) and physical mechanisms involved (e.g. [START_REF] Kaempfer | Phase-field modeling of dry snow metamorphism[END_REF]Calonne et al., 2014b[START_REF] Calonne | Macroscopic modeling of heat and water vapor transfer with phase change in dry snow based on an upscaling method: Influence of air convection[END_REF]. Temperature gradients in snow lead to re-crystallization of the ice structure [START_REF] Pinzer | Vapor flux and recrystallization during dry snow metamorphism under a steady temperature gradient as observed by time-lapse micro-tomography[END_REF] by sublimation of ice and deposition of water vapor coupled with heat and mass transport. The morphology evolves typically from new snow or rounded grains to faceted crystals and depth hoar.

More attention has been recently given to snow properties on polar ice sheets and how they might be transformed during burial and influence firn properties. Understanding firn processes is crucial to correctly interpret the Antarctic and Greenlandic ice cores in view of Earth's climate history [START_REF] Alley | The two-mile time machine: ice cores, abrupt climate change, and our future[END_REF]. Recent studies showed that the layering of snow properties such as density [START_REF] Mitchell | Observing and modeling the influence of layering on bubble trapping in polar firn[END_REF] or microstructural properties [START_REF] Gregory | Impact of physical properties and accumulation rate on pore close-off in layered firn[END_REF] influences firn densification and the close-off depth. Other properties such as snow impurity content [START_REF] Hörhold | On the impact of impurities on the densification of polar firn[END_REF][START_REF] Freitag | Impurity-controlled densification: a new model for stratified polar firn[END_REF] or snow fabric [START_REF] Montagnat | Measurements and numerical simulation of fabric evolution along the Talos Dome ice core, Antarctica[END_REF][START_REF] Montagnat | Fabric along the neem ice core, greenland, and its comparison with GRIP and NGRIP ice cores[END_REF] have been suggested, but not deeply investigated up to now, partly because of the lack of data on surface snow properties. This paper presents measurements of snow fabric and microstructure of a natural snowpack from Point Barnola, East Antarctica. We analyzed a snow profile from 0 to 3 m depth where temperature gradients occur. The vertically continuous measurement of the snow microstructure using micro-computed tomography (micro-CT) revealed complex layering, sometimes only a few millimeter thick. Within layers, we cut out horizontal thin sections and measured the fabric with an Automatic Ice Texture Analyzer. Different types of snow fabric could be identified and persist at depth. In the discussion part, we first present the possible depositional and post-depositional processes that might explain the observed snow stratigraphy. Secondly, we highlight the simultaneous impact of temperature gradient metamorphism on the fabric and microstructure of snow, and suggest a possible mechanism based on preferential grain growth to explain the fabric evolution.

Finally, we discuss the potential influence of snow fabric on snow mechanical behavior and on the evolution of the snow and firn layering at depth. [START_REF] Stenni | Three-year monitoring of stable isotopes of precipitation at concordia station, east antarctica[END_REF] and the snow accumulation is about 25 kg m -2 yr -1 [START_REF] Frezzotti | Spatial and temporal variability of snow accumulation in east antarctica from traverse data[END_REF][START_REF] Urbini | Historical behaviour of dome c and talos dome (east antarctica) as investigated by snow accumulation and ice velocity measurements[END_REF].

Methods

Snow samples

Three snow blocks, each 1 m heigh, 0.5 m thick and 0.5 m wide, were sampled from the surface to 3 m depth and transported to Davos in insulated boxes at -20 o C. At 5 meters apart from this 3 m deep snow pit, a 1.2 m deep snow pit was also dug and snow samples were directly impregnated with diethyl phthalate (see subsection 2.3) to prevent any microstructural changes of the snow until analysis. In addition, a block of snow of 1 meter height was collected at a mega-dune site (78 o 18.7984'S, 110 o 34.322'E) located 430 km South West from Point Barnola. Steps to measure the c-axis orientation of ice crystals from a thin section. a) thin section of 100 µm thick glued on a glass plate seen by the naked eye and before dissolving the diethyl phthalate with tetralin; the ice crystals are transparent while the diethyl phthalate is opaque. b) the same section seen across polarized light after dissolving the diethyl phthalate; individual ice single crystals can already be identified by the nuance of colors. c) c-axes orientation map of the section obtained after processing; each color corresponds to a particular c-axis orientation given by the color loop; the pure black background correspond to the initial air pores in snow.

Computed tomography and microstructural parameters

The snow microstructure was studied based on 3-D images obtained by micro Computed Tomography (micro-CT). From each of the three blocks, cylindrical snow samples of 60 mm height and 36 mm diameter were cut out using a scroll saw in adjacent rows in the top 45 cm. Samples of 60 mm height and 50 mm diameter were cut out below 45 cm such as successive samples overlap vertically by about 30 mm. All samples were scanned with a micro-CT 80 (ScancoMedical) located in a cold-room at -15 o C. The gray-scale 3-D images were reconstructed by a cone-beam algorithm, filtered by a Gaussian filter to remove the image noise, and binarised by visually determining the optimum threshold. Images have a nominal resolution of 18 µm for the samples collected above 0.45 m depth, where snow shows finer ice structures, and of 25 µm for the ones collected below. A total of 43 volumes showing the snow microstructure continuously from 0 to 2.9 m depth was obtained.

Snow density ρ (kg m -3 ), specific surface area SSA (m 2 kg -1 ), and mean pore thickness Th p (mm) were evaluated from the 3-D images using the Image Processing Language (ScancoMedical) [START_REF] Thomsen | Stereological measures of trabecular bone structure: comparison of 3d micro computed tomography with 2d histological sections in human proximal tibial bone biopsies[END_REF]. The mean pore thickness was determined by filling maximal spheres into the structure using the distance transform algorithm [START_REF] Hildebrand | Direct three-dimensional morphometric analysis of human cancellous bone: microstructural data from spine, femur, iliac crest, and calcaneus[END_REF]. All the parameters were computed over sub-volumes of 1.8 mm height, 10.8 mm thickness and 10.8 mm width for the samples collected above 0.45 m depth, and of 3 mm height, 15 mm thickness and 15 mm width for the samples below 0.45 depth. We ensured that the dimension of the sub-volumes corresponded to the dimension of the representative elementary volume for the considered properties or were larger.

Thin sectioning and fabric analysis

Immediately after tomography each cylindrical sample was transferred in a plastic vial and impregnated with diethyl phthalate (CAS No. 84-66-2) in a cold room at -4 o C [START_REF] Heggli | Snow replica method for three-dimensional X-ray microtomographic imaging[END_REF]. This operation allows to obtain a sample that can not evolve anymore and that is solid for the thin sectioning required to extract the c-axis orientations.

The thin sections were cut out at -15 o C from the impregnated snow samples using a band saw and then microtomed with a Leica Polycut. All thin sections were cut parallel to the snow surface (perpendicular to gravity) and had a size of about 2×3 cm 2 and a final thickness of 100-150 µm. In total, we cut out 77 thin sections from the snow samples between 0 and 3 m depth. Before analysis, tetralin (CAS No. 119-64-2), a hydrocarbon in liquid state above -35 o C, was used to liquefy the birefringent crystallized diethyl phthalate contained in the thin section. This procedure does not modify relative grain positions, as verified by [START_REF] Riche | Evolution of crystal orientation in snow during temperature gradient metamorphism[END_REF]. A picture of a typical thin section is shown in Figure 1a.

The fabric of each thin sections was determined using the G50 fabric analyzer (Russel-Head Instrument), an automated polarizing optical microscope [START_REF] Peternell | A technique for recording polycrystalline structure and orientation during in situ deformation cycles of rock analogues using an automated fabric analyser[END_REF]. The analyzer provides the c-axis orientation c k of every pixel k in the field of view of the microscope. In our case, a pixel has a size of 10×10 µm 2 and the entire area of about 2×3 cm 2 of the thin section was mapped. Each c-axis orientation c k is defined in a spherical coordinate system by an inclination θ k (angle in the vertical plane between 0-90 o ) and an azimuth ϕ k (angle in the horizontal plane between 0-360 o ) given in a local reference frame R, with the third axis perpendicular to the thin section. The expression of c k in this reference frame is

c k = (cos ϕ k sin θ k , sin ϕ k sin θ k , cos θ k ) (1)
Orientation measurements are provided together with a quality factor ranging from 0 to 100 for each pixel.

Only pixels with a quality factor higher than 70 were considered here [START_REF] Peternell | A technique for recording polycrystalline structure and orientation during in situ deformation cycles of rock analogues using an automated fabric analyser[END_REF]. For each thin section, we ensured that at least 100 snow crystals were analyzed to obtain a representative sample. An example of a c-axis orientation map is shown in Figure 1c.

To characterize the c-axis orientation distribution obtained over a thin section, we used the second order orientation tensor a (2) defined as [START_REF] Durand | Ice microstructure and fabric: an up-to-date approach for measuring textures[END_REF])

a (2) = (1/N p ) Np k=1 c k ⊗ c k (2)
where N p is the total number of pixels over which c k has been calculated, c k is given by Eq.( 1), and ⊗ is the tensor product. By construction, a (2) is symmetric and a symmetry reference frame R sym exists

in which a (2) is diagonal. Let a (2) 1 , a (2)
2 , and a

(2) 3

denote the three corresponding eigenvalues that satisfy

1 > a (2) 1 > a (2) 2 > a (2)
3 > 0 and a

(2)

1 + a (2)
2 + a

(2) 3 = 1. These eigenvalues correspond to the length of the axes of the ellipsoid that fits best the distribution of the c-axis orientations. Three different types of fabric can be defined:

• Isotropic fabric: the c-axes are oriented randomly in all directions, a

(2) 2 a

(2) 3

1/3.

• Girdle fabric: the c-axes are oriented randomly within a preferential plane, a

(2) 1 a

(2) 2

> 1/3. By looking at the Schmidt plots of the c-axis distribution obtained for snow in this study, we can already precise that the preferential plane for snow is the horizontal one.

• Cluster fabric: the c-axes are oriented toward one direction, a

(2) 2 a

(2)

3 < 1/3. Measurements show that this direction is the vertical one for snow.

We define the girdle index n G = ln(a

(2)
2 /a

(2)

3 ) (the higher, the more girdle-type is the fabric) and the

cluster index n C = ln(a (2)
1 /a

(2)

2 ) (the higher, the more cluster-type is the fabric). The type of the fabric is given by the shape index n shape = ln(a

(2) 1 /a (2)
2 )/ ln(a

(2) 2 /a (2)
3 ), which is lower than 1 for a girdle-type fabric and larger than 1 for a cluster-type fabric, and the higher the index, the more pronounced the fabric shape.

The strength of the fabric is defined by the strength index n strength = ln(a

(2) 1 /a (2)
3 ), which is 0 for a perfect isotropic fabric and increases the more the fabric moves away from the isotropic-type, independently of the direction (toward the girdle-or the cluster-type). Illustrations of the above indexes can be found in Fisher

Results

Microstructure and fabric of the snow profile

An overall picture of the microstructure along the 3 meter snow profile is given in Figure 2. 3-D images of the entire snow profile are in the supplementary material. Looking at the density and SSA evolution, the snowpack can be divided in two main parts. Above 0.45 meter, the snow properties develop gradually with depth, the density decreases from 460 to 350 kg m -1 , while the SSA increases from 9.5 to 13.8 m 2 kg -1 . The snow consists only of small rounded grains, as illustrated in Fig. 2d). Below 0.45 m depth, the snowpack is strongly stratified and the properties change rapidly at transitions, sometimes within a 1 cm thickness. In this part of the snowpack, the density fluctuates with depth between around 200 and 450 kg m -3 . No significant linear correlation between density and depth is found (r = -0.22, p-value = 0.066).

The SSA decreases with depth from around 10 to 5 m 2 kg -1 , showing a significant anti-correlation with depth (r = -0.78, p-value < 0.0001). Within this main trend, small but abrupt variations of the SSA can be observed. The snowpack consists mainly of large faceted grains with some depth hoar-like structures, as shown in Fig. 2e), with thin layers of denser rounded grains in between. Figure 3 presents an example of such a stratification for a snow sample taken at 2.8 m depth. For this sample, the snow density evolves from around 300 kg m -1 in the large faceted grains layer at the top and bottom, up to 420 kg m -1 within the 1 cm layer of rounded smaller grains in the middle. The SSA increases from around 5.5 m 2 kg -1 to 7.4 m 2 kg -1 , while the mean pore thickness decreases from around 0.80 mm to 0.35 mm. Different types of snow fabric are observed, as shown by the variation of the eigenvalues of a (2) along the 2a). Type and strength of the snow fabric are better revealed in Figure 4 by plotting the cluster index against the girdle index. The 1:1 line represents the threshold between girdle and cluster fabric. The fabric type range between isotropic, weak cluster, and weak girdle. In average, the girdle-type prevails slightly, with an average shape index of 0.89. No significant correlations were found between the fabric indexes and depth (Tab. 1), showing that the fabric evolves independently of depth, in opposition with the common observations in firn and ice cores (e.g. [START_REF] Montagnat | Measurements and numerical simulation of fabric evolution along the Talos Dome ice core, Antarctica[END_REF][START_REF] Montagnat | Fabric along the neem ice core, greenland, and its comparison with GRIP and NGRIP ice cores[END_REF]. This relationship is further investigated in Figure 4 and Table 1. Looking at the link with density first, we observe that denser snow shows a more isotropic fabric, while less dense snow tends to a slightly more anisotropic fabric, mainly toward the girdle-type fabric (Fig. 4). Density and strength index are indeed significantly anti-correlated: when density decreases, fabric become less isotropic (Tab. 1). The correlation of the strength index was also evaluated against SSA and mean air pore thickness, and is significant (Tab. 1).

Relationship between fabric and microstructure

Snow samples with high SSA and small pores show preferentially an isotropic fabric, while snow samples with low SSA and large pores show a more pronounced fabric, either a weak girdle-or cluster-type fabric. This relationship is also indicated by the correlation between the cluster and girdle index and the microstructural properties. Note that the correlation of the girdle index with density and SSA is at the limit of the significance threshold of < 0.05.

The fabric described above from the main profile at Point Barnola are compared in Figure 4 to 13 snow samples from the mega-dune site (triangle symbols) and 5 snow samples from Point Barnola that were directly impregnated in the field (square symbols). A comparable evolution of the fabric with density is found.

Discussion

Understanding the snow stratigraphy

Point Barnola is a low snow accumulation site, close to Dome C (Concordia Station) where the average annual accumulation rate is about 25 kg m -2 yr -1 . However, snow accumulation in this area is highly variable in space [START_REF] Frezzotti | Spatial and temporal variability of snow accumulation in east antarctica from traverse data[END_REF][START_REF] Urbini | Historical behaviour of dome c and talos dome (east antarctica) as investigated by snow accumulation and ice velocity measurements[END_REF], notably due to wind effects such as drift [START_REF] Libois | Modeling the impact of snow drift on the decameter-scale variability of snow properties on the antarctic plateau[END_REF] and wind direction during snowfall [START_REF] Urbini | Historical behaviour of dome c and talos dome (east antarctica) as investigated by snow accumulation and ice velocity measurements[END_REF]. The local accumulation and ablation can be several times higher than the average annual accumulation [START_REF] Frezzotti | Spatial and temporal variability of snow accumulation in east antarctica from traverse data[END_REF], making hard to retrieve the snowpack history. By neglecting a potential accumulation hiatus, we can estimate that around 40 years of snow is preserved in the 2.8 meter long snow profile, based on the above accumulation rate and an average snow density of 350 kg m -3 .

We suggest that the first rather homogeneous 45 cm of the snowpack is the remaining part of a single sastrugi that has been eroded and shows now a flat snow surface as observed during sampling. Such wind packed structures are built in winter and their height often exceeds the average annual accumulation [START_REF] Gow | On the accumulation and seasonal stratification of snow at the south pole[END_REF]. The decay process of sastrugi is not well established. A potential mechanism is the formation of horizontal temperature gradients due to the low sun angle and subsequent softening of snow by water vapor transport [START_REF] Orheim | Surface snow metamorphosis on the antarctic plateau[END_REF], which can then be more easily eroded by wind. This first 45 cm dense layer illustrates the strong spatial redistribution of snow at the surface of the ice sheet, which leads here to a snow accumulation about 6 times higher than the annual accumulation (45 cm versus 7 cm). Within this layer, the SSA increases and the density decreases gradually with depth, in opposition with common observations. We believe that this layer is not representative of the "steady state" snowpack and its characteristics are strongly related to surface processes. Understanding such processes is beyond the scope of this paper and would require a dedicated study.

Below 45 cm, the snowpack is finely stratified and shows layers as thin as 1 cm thick. The snow consists of thin layers of small rounded, slightly faceted, grains located in between layers of large faceted to depth hoar grains. The latter snow type corresponds to some degree to the "hard depth hoar" as described by [START_REF] Pfeffer | Temperature gradient and initial snow density as controlling factors in the formation and structure of hard depth hoar[END_REF]. Significant changes in density, SSA, and mean pore thickness were observed between these two types of layers. Similar features in the snow stratigraphy and in the density and SSA evolution have been reported at Dome C (e.g. [START_REF] France | Snow optical properties at dome c (concordia), antarctica; implications for snow emissions and snow chemistry of reactive nitrogen[END_REF][START_REF] Gallet | Vertical profile of the specific surface area and density of the snow at dome c and on a transect to dumont d'urville, antarctica -albedo calculations and comparison to remote sensing products[END_REF]. It is likely that the dense small grain layers originate from wind packed layers at the surface and might correspond to buried eroded sastrugi, as described above. The large faceted grain layers might originally be formed in an area in between sastrugi and be constituted by snow eroded from the sastrugi. Again, understanding such processes would require a dedicated study. The stratigraphy is however not only the result of surface processes but also of in-depth evolution processes. While compaction by overburden pressure seems to be quasi inactive, as the increase in density with depth is not significant (Section 3.1 and Fig. 2b), temperature gradient metamorphism plays an important role here.

Temperature gradient metamorphism affects significantly the first meters of snow and is crucial to un- [START_REF] Brucker | Modeling time series of microwave brightness temperature at Dome C, Antarctica, using vertically resolved snow temperature and microstructure measurements[END_REF]. Between these two depths, a macroscopic temperature gradient of 10 K m -1 is thus present and changes its direction from upward to downward seasonally. Locally, changes in snow density between layers induce changes in thermal conductivity and consequently modifications of the temperature gradient at the layer scale (e.g. [START_REF] Calonne | Numerical and experimental investigations of the effective thermal conductivity of snow[END_REF]. At Point Barnola, the thermal conductivity of the faceted large grain layers is estimated at 0.18 W m -1 K -1 , while the one of the dense small grain layers is estimated at 0.41 W m -1 K -1 based on [START_REF] Calonne | Numerical and experimental investigations of the effective thermal conductivity of snow[END_REF]. The temperature gradient and thus the re-crystallization rate of the ice structure is thus significantly higher in the large faceted grain layers than in the dense small grains layers. We pointed out here a mechanism that enhances the initial differences between layers and reinforces the layering. Calonne et al.

(2014a) described in detail snow metamorphism during a constant temperature gradient of 43 K m -1 . The main features consist in the coarsening of grains and pores, no significant changes in density, faceting and striation of the grains (faceted crystals and depth hoar), together with the formation of vertically elongated snow structures. Despite the differences in temperature conditions, similar characteristic are shown in the large faceted grain layers at Point Barnola (Fig. 2e) and 3). The structural anisotropy index (vertical over horizontal), estimated for 31 representative samples based on the chord length distribution as described in [START_REF] Krol | Relating optical and microwave grain metrics of snow: The relevance of grain shape[END_REF], ranges from 1.04 to 1.21, with a mean value of 1.12 that indicates an overall slight preferential vertical direction of ice structures in snow. Note that no significant differences in structural anisotropy have been observed between the large faceted grain and the dense small grain layers.

Influence of temperature gradient metamorphism on snow fabric

The only available study of snow fabric evolution during temperature gradient is the recent cold laboratory work of [START_REF] Riche | Evolution of crystal orientation in snow during temperature gradient metamorphism[END_REF]. The initial snow used for their experiments was fresh natural alpine snow with partly decomposed particles and showed a cluster-type fabric with c-axes preferentially oriented toward the vertical. After 3 months of evolution under a temperature gradient of 50 K m -1 at an absolute mean temperature of -20 o C, the snow became depth hoar and showed a weak girdle-type fabric with most of the c-axes oriented in the horizontal plane. The girdle and cluster index evolved from 0.09 and 0.79 at the beginning of their experiment, to 0.32 and 0.27 at the end, respectively, as shown and compared to our data in Fig. 4. The shape index reflects an evolution from a well-expressed cluster-type to a weak girdle-type fabric, with values evolving from 8.9 to 0.84. By performing the experiment with an initial snow 30% denser [START_REF] Furukawa | Handbook of Crystal Growth: Fundamentals[END_REF]. The larger the brown arrow, the higher the growth rate on the considered ice surface.

than the first one, no significant changes in the snow fabric were observed by [START_REF] Riche | Evolution of crystal orientation in snow during temperature gradient metamorphism[END_REF]. During the metamorphism of 3 months duration, the snow grains underwent at least 11 complete re-crystallization cycles, i.e. the ice grains have been completely re-built at least 11 times, based on a turnover rate estimation [START_REF] Pinzer | Vapor flux and recrystallization during dry snow metamorphism under a steady temperature gradient as observed by time-lapse micro-tomography[END_REF].

Our results support the fact that both microstructure and fabric evolve during temperature gradient metamorphism, as shown by the significant correlation between both properties. Dense small grain layers, subjected to a smaller temperature gradient and less affected by re-crystallization, show an isotropic fabric, while large faceted grain layers, subjected to a higher temperature gradient and more affected by re-crystallization, show a more anisotropic fabric, mainly of girdle-type. We made the same observation for a snow block sampled at the mega-dune site, suggesting that the influence of metamorphism is widespread on the Central East Antarctica plateau. We also observed this relationship for both techniques of transportation used, snow impregnated directly in the field and snow blocks not impregnated maintained at equi-temperature conditions at -20 o C, excluding major changes during transport or preparation.

We interpret the evolution of snow fabric as a consequence of the favored growth of some ice grains at the expense of the other grains during temperature gradient metamorphism, following [START_REF] Adams | Ice crystals grown from vapor onto an orientated substrate: application to snow depth-hoar development and gas inclusions in lake ice[END_REF] and [START_REF] Riche | Evolution of crystal orientation in snow during temperature gradient metamorphism[END_REF]. This preferential growth is driven by the combination of (1) the anisotropic growth properties of ice depending on the absolute temperature [START_REF] Furukawa | Handbook of Crystal Growth: Fundamentals[END_REF], which results in differences of growth and decay rate between the basal and prismatic faces of the ice crystals at a given temperature, and

(2) the orientation of the ice crystals with respect to the temperature gradient direction [START_REF] Adams | Ice crystals grown from vapor onto an orientated substrate: application to snow depth-hoar development and gas inclusions in lake ice[END_REF], given that sublimation and condensation are mostly occurring on ice surfaces oriented perpendicularly to the temperature gradient direction, i.e. perpendicularly to the vapor flux. Combining these two processes, we can define an optimum configuration for grain growth, when the grain configuration is such as the faces showing an enhanced growth rate are located perpendicularly to the temperature gradient, and, by opposition, less optimum growth configurations. Grains in optimum configuration will develop at the expense of the less optimum grains, and it will consequently impact the distribution of the crystal orientations in snow. This is supported by [START_REF] Riche | Evolution of crystal orientation in snow during temperature gradient metamorphism[END_REF] who showed that the number of crystals is decreasing during temperature gradient metamorphism, a process that seems to further enhance the survival of the faster growing crystals.

Figure 5 helps understanding the suggested process for the fabric evolution. Hexagonal ice crystals are displayed to easily identify the crystalline orientations, but could be replaced by any single crystal observed in snow. We describe here the example presented in Figure 5a of a vertical temperature gradient at an average temperature of -20 o C. It is known that the growth rate at a temperature of -20 o C is enhanced on the prismatic faces compared to the basal faces of an ice crystal (e.g. [START_REF] Furukawa | Handbook of Crystal Growth: Fundamentals[END_REF]. Combining the two processes explained above, for the example at -20 o C, the optimum growth configuration for an ice grain is reached when its prismatic faces are oriented perpendicular to the temperature gradient direction, i.e. when its c-axis is oriented horizontally. The preferential growth of grains in the optimum configuration leads thus in this case to a snow fabric evolution toward a girdle-type.

The growth rate of ice crystals is enhanced on the prismatic faces between 0 and -4 o C, basal faces between -4 o C and -10 o C, prismatic faces between -10 o C and -22 o C, and basal faces below -22 o C (e.g. [START_REF] Furukawa | Handbook of Crystal Growth: Fundamentals[END_REF].

Hence, we expect that when re-crystallization occurs in a snow layer at temperatures between 0 and -4 o C, and between -10 o C and -22 o C, a girdle-type fabric will develop as illustrated in Figure 5a, while between -4 o C and -10 o C, and below -22 o C, a cluster-type fabric will be created, as illustrated in Figure 5b. The alternation in preferential grain growth direction suggested here is supported by the result of Riche et al.

(2013) who observed at an average temperature of -20 o C the development of a weak girdle-type fabric (Fig. 4). At Point Barnola, metamorphism occurs at an average temperature of about -45 o C, where a cluster-type fabric is supposed to develop following our reasoning. However, we measured snow fabrics that are in average slightly represented by a weak girdle-type fabric, although weak cluster-type fabrics are also observed (Fig. 4). The week anisotropy signal in the fabric at Point Barnola limits further interpretation. We also point out that the relationship between temperature, water vapor saturation, and growth rate has been shown for temperatures down to -40 o C but not below [START_REF] Libbrecht | Growth rates of the principal facet of ice between -10 • C and -40 •[END_REF], as far as we know. Our fabric observations might thus reflect growth rate dependencies that are not described in current studies. Such considerations highlight the need of cold-laboratory experiments, under well controlled conditions, to better understand the preferential growth process in snow and the fabric evolution under temperature gradients.

4.3. On the potential influence of snow fabric at depth in polar ice sheets

Understanding firn densification is crucial to interpret the Antarctic and Greenland ice cores in view of Earth's climate history (e.g. [START_REF] Alley | The two-mile time machine: ice cores, abrupt climate change, and our future[END_REF]. Most of the firn densification models simulate only mean density evolution with depth, ignoring firn stratification and so variations in firn physical properties. However, recent works emphasize the influence of the layered snow properties, such as density [START_REF] Hörhold | The densification of layered polar firn[END_REF][START_REF] Mitchell | Observing and modeling the influence of layering on bubble trapping in polar firn[END_REF], microstructure [START_REF] Gregory | Impact of physical properties and accumulation rate on pore close-off in layered firn[END_REF][START_REF] Fujita | Densification of layered firn in the ice sheet at dome fuji, antarctica[END_REF], impurity content [START_REF] Hörhold | On the impact of impurities on the densification of polar firn[END_REF][START_REF] Freitag | Impurity-controlled densification: a new model for stratified polar firn[END_REF][START_REF] Fujita | Densification of layered firn in the ice sheet at dome fuji, antarctica[END_REF], or snow fabric [START_REF] Montagnat | Measurements and numerical simulation of fabric evolution along the Talos Dome ice core, Antarctica[END_REF][START_REF] Montagnat | Fabric along the neem ice core, greenland, and its comparison with GRIP and NGRIP ice cores[END_REF] on firn behavior and close-off depth. Concerning the latter property, close to perfect topographical domes, fabrics along firn and ice cores evolve generally with depth toward very well expressed cluster-type (e.g. [START_REF] Alley | Fabrics in polar ice sheets: Development and[END_REF], by dislocation creep deformation and dynamic recrystallization [START_REF] De La Chapelle | Dynamic recrystallization and texture development in ice as revealed by the study of deep ice cores in antarctica and greenland[END_REF] due to the overburden pressure reached at these depths. Nevertheless, [START_REF] Montagnat | Fabric along the neem ice core, greenland, and its comparison with GRIP and NGRIP ice cores[END_REF] observed slightly clustered fabrics in the NEEM firn core (Greenland) that could not be explained by the low level of strain reached at these depths, based on modeling estimations [START_REF] Montagnat | Measurements and numerical simulation of fabric evolution along the Talos Dome ice core, Antarctica[END_REF]. This observation would be in favor of some fabric evolution in snow or firn, not entirely controlled by creep deformation.

Snow is a porous material and its mechanical response is known to depend strongly on its density and microstructure. Although most of the mechanical simulations and physical models of compaction of low density snow are based on the hypothesis of a strong impact of grain re-arrangement by grain boundary sliding (e.g. [START_REF] Arnaud | Modeling of the densification of polar firn characterization of the snow-firn transition[END_REF][START_REF] Johnson | Identifying microstructural deformation mechanisms in snow using discrete-element modeling[END_REF], recent studies suggest that intragranular viscoplastic deformation could also play an important role [START_REF] Rolland Du Roscoat | Analysis of snow microstructure by means of x-ray diffraction contrast tomography[END_REF][START_REF] Theile | Simulating creep of snow based on microstructure and the anisotropic deformation of ice[END_REF]. Furthermore, these studies suggest that the crystal orientation, owing to the strong viscoplastic anisotropy of ice, would come into play in the mechanical response of snow close to the surface [START_REF] Theile | Simulating creep of snow based on microstructure and the anisotropic deformation of ice[END_REF].

Such recent results combined with our measurements of fabric in natural snowpacks suggest that the snow fabric, whose layering follows the microstructure layering, might have to be considered to better understand snow and firn properties deeper at depth. Fabric evolution could be viewed as an additional parameter to better constrain the physical models of snow and firn layering evolution. Our study should be followed by high resolution measurements of the fabric along cores covering the transition between snow and firn, to investigate the fabric evolution under an increasing pressure.

Conclusion

This study presents for the first time a joint analysis of the preferred crystallographic orientation (fabric) and microstructure of Antarctic snow. Different types of fabric are revealed in the upper 3 m of the snowpack in clear correlation with the layering of the microstructure. Randomly orientated c-axes were observed in the denser layers of rounded grains, while a slight preferential orientation in the horizontal plane (girdle-type fabric) was mainly observed in the large faceted grain layers.

We highlighted that the variations measured in snow fabric and microstructure are not a function of depth, as observed along firn and ice cores, but are the result of temperature gradient metamorphism.

We presented a preferential crystal growth mechanism to explain the fabric evolution under temperature gradients.

Our work showed that not only changes in microstructure but also changes in fabric characterize the natural snowpacks. This leads to the question of how such layered properties are modified during burial and how they might interact with firn densification down to the close-off depth. More generally, our work opens the way to further studies on the influence of the fabric in snow processes related to anisotropic ice properties such as mechanical response, optic or dielectric macroscopic behavior.

Figure 1 :

 1 Figure 1:Steps to measure the c-axis orientation of ice crystals from a thin section. a) thin section of 100 µm thick glued on a glass plate seen by the naked eye and before dissolving the diethyl phthalate with tetralin; the ice crystals are transparent while the diethyl phthalate is opaque. b) the same section seen across polarized light after dissolving the diethyl phthalate; individual ice single crystals can already be identified by the nuance of colors. c) c-axes orientation map of the section obtained after processing; each color corresponds to a particular c-axis orientation given by the color loop; the pure black background correspond to the initial air pores in snow.

  et al. (1987) section 3.4.

Figure 2 :

 2 Figure 2: Evolution of the snow properties with depth for the snow profile at Point Barnola: a) eigenvalues of a (2) (black cross: a (2) 3 , blue triangle a (2) 2 , red circle: a (2) 1 , the dotted line indicates the 1/3 value), b) density, and c) specific surface area SSA. Vertical slices from the 3D images show the microstructure of d) rounded grains and e) large faceted grains.

Figure 3 :

 3 Figure 3: Snow microstructure and fabric for a sample collected between 2.82 and 2.86 m depth at Point Barnola. A vertical slice of the 3-D image is shown together with the evolution of the microstructural parameters. c-axis orientation maps from the top and middle part of the sample and fabric indexes are given.

Figure 3

 3 Figure3suggests a link between fabric and microstructure. The large faceted crystals at the top of the snow sample show a girdle-type fabric, with a girdle index of 0.50 and a strength index of 0.65. In contrast, the rounded grains of the middle layer have a strength index of 0.09 that characterizes an isotropic fabric.

Figure 4 :

 4 Figure4: Type and strength of the snow fabric for the Point Barnola samples (77 data, circles), the Point Barnola samples impregnated in the field (5 data, square), and the mega dune site samples (13 data, triangle). Density is given according to the color bar. Note that very well-expressed fabrics have values of ln(a 1 /a 2 ) and ln(a 2 /a 3 ) typically around 6[START_REF] Fisher | Statistical analysis of spherical data[END_REF] Black star symbols correspond to the fabric measured by[START_REF] Riche | Evolution of crystal orientation in snow during temperature gradient metamorphism[END_REF] during a temperature gradient metamorphism at the beginning (cluster-type fabric) and end (weak girdle-type fabric) of the experiment using fresh initial snow (Group 1).

  derstand the stratification. It is especially important at low accumulation rate sites where snow is subjected to temperature gradients during a long period of time, here up to about 40 years. At Concordia Station, the snow temperature at 0.1 m depth varies between around -25 o C in summer and -70 o C in winter, while at 5 m depth, temperatures are always around -55 o C (

Figure 5 :

 5 Figure 5: Schematic of the growth of a single ice crystal under a vertical temperature gradient at a mean temperature of a) -20 o C and b) -8 o C. The crystal faces that have an enhanced growth rate are shown in orange, based on the Nakaya diagram (e.g.[START_REF] Furukawa | Handbook of Crystal Growth: Fundamentals[END_REF]. The larger the brown arrow, the higher the growth rate on the considered ice surface.

  Snow samples were collected in Central East Antarctica at Point Barnola (75 o 42'S, 123 o 15'E, 3236 masl), located 66 km south from Concordia Station (Dome C) during the EXPLORE expedition in 2011-2012. At Concordia Station, which is the closest site to Point Barnola where climate conditions are recorded, the daily mean air temperature varies from about -80 o C in winter to -25 o C in summer

Table 1 :

 1 Pearson correlation coefficients rp and p-values between fabric indexes and microstructural properties from the snow samples collected between 0.45 and 2.9 m depth (permanent snowpack) at Point Barnola. Asterisks indicate a significant correlation (p-values < 0.05).

		Correlation coefficient; p-value
		Girdle index	Cluster index	Strength index
	depth	0.10; 0.397	0.17; 0.149	0.18; 0.13490
	density	-0.22; 0.064	-0.45; <0.001*	-0.43; <0.001*
	SSA	-0.20; 0.091	-0.41; <0.001*	-0.39; <0.001*
	pore thickness	0.26; 0.025*	0.48; <0.001*	0.48; <0.001*
	snow profile in Figure

Acknowledgment

We greatly thank an anonymous reviewer for his/her interesting comments and constructive review that contribute to improve the manuscript. M. Schneebeli thanks Michel Fily, Jerome Chappellaz and the EXPLORE team for the excellent support. N. Calonne was supported by grant 152845 from the Swiss National Science Foundation, M. Schneebeli by the French Agence Nationale de la Recherche (ANR) project DOME A (ANR-07-BLAN-0125) and by the French polar research institute IPEV (EXPLORE project 1052). M. Montagnat is supported the French CNRS (Centre National de la Recherche Scientifique), institutes INSIS (Institut des Sciences de l'Ingenierie et des Systemes) and INSU (Institut National des Sciences de l'Univers). We acknowledge the European Science Foundation (ESF) for travel grant supports in the frame of the RNP Micro-DICE.

Authors contributions

M. Schneebeli collected the snow samples and managed the project. M. Montagnat and N. Calonne performed the fabric measurements and M. Matzl performed the micro-CT measurements. N. Calonne analyzed the data and prepared the manuscript with input from M. Montagnat and M. Schneebeli.


César Ducruet

Liehui Wang
email: lhwang@re.ecnu.edu.cn

Keywords: China, connectivity, maritime network, port system, vessel movements

China's global shipping connectivity had been somewhat overlooked as the bulk of related studies predominantly focused on the throughput volume of its own port cities. This article tackles such lacunae by providing a relational perspective based on the extraction of vessel movement archives from the Lloyd's List corpus. Two complementary analyses are proposed: long-term dynamics with all ships included (1890-2008) and medium-term dynamics focusing on container flows . Each analysis examines China's maritime connectivity in various ways and ondifferent spatial scales, from the global to the local, in terms of concentration, vulnerability, and expansion. The main results underline the influence of technological, economic, and political factors on the changing distribution of connectivity internally and externally. In particular, China has managed to reduce its dependence upon external transit hubs, to increase the internal connectivity of its own port system, and to strengthen its dominance towards an increasing number of foreign nodes and trade partners through the maritime network.

Introduction

The global shipping network had become an increasingly popular research topic in the last decade or so (Wang et al., 2016;[START_REF] Ducruet | Multilayer dynamics of complex spatial networks: the case of global maritime flows (1977-2008)[END_REF]. Related works are mainly motivated with the analysis of the network's topological structure and spatial distribution at different levels, from ports to countries and world areas. In the empirical literature however, it remains unclear whether China and its port cities have actually become integrated into this global shipping network, alongside decades of socio-economic and trade development.

Although many aspects of China's port and maritime activities had been documented extensively from various perspectives from the distant past up to nowadays [START_REF] Ng | China's seaport development during the early Open Door Policy period[END_REF], the majority of such studies still remain focused on containerization dynamics taking place in the recent period [START_REF] Rimmer | China's container-related dynamics, 1990-2005[END_REF]. Another limitation of this multifaceted research field is the dominant focus on specific ports or regions, mainly due to the enormous size of the country. Pioneer works at local level include more qualitative analyses of the vital role of Hong Kong in South China's port modernization and connectivity [START_REF] Wang | A container load center with a developing hinterland: a case study of Hong Kong[END_REF][START_REF] Song | Regional container port competition and co-operation: the case of Hong Kong and South China[END_REF]; these were later followed by more quantitative approaches to port competition and throughput concentration within main port regions such as the Yangtze port system [START_REF] Veenstra | The development of the Yangtze River container port system[END_REF]Wang and Ducruet, 2012;[START_REF] Cao | The evolution course and mechanism of the port system along the Yangtze River[END_REF][START_REF] Wu | Spatial-temporal evolution of main cargo structure in the port system of Yangtze River Delta[END_REF][START_REF] Yang | Analysis of the development potential of bulk shipping network on the Yangtze River[END_REF], the Pearl River Delta port system (Wang et al., 2012;[START_REF] Liu | Development of a container port system in Pearl River Delta: path to multi-gateway ports[END_REF][START_REF] Cheng | Evolution and dynamic mechanism of container port system in the Pearl River Delta[END_REF], and the Bohai Rim or Yellow Sea port system (An and [START_REF] Han | The problem on the construction of container load center in north China[END_REF][START_REF] Han | Research on the construction and optimization of container transportation network in northeast China[END_REF][START_REF] Feng | Peripheral challenge by small and medium sized ports (SMPs) in multi-port gateway regions: the case study of northeast of China[END_REF], to name but a few.

Yet, some studies have provided an analysis of the whole Chinese port system in terms of port throughput dynamics including all commodities and on the very long-term [START_REF] Wang | Regional resilience and spatial cycles: Long-term evolution of the Chinese port system (221BC-2010AD)[END_REF], restricted to container throughput in the more recent period such as 1998-2010 [START_REF] Pan | New tendency of Chinese container port system: 1998-2010[END_REF], or focusing on other aspects such as port integration mechanisms and clusters (Wang et al., 2015;[START_REF] Wang | The evolution and competition situation of container port cluster system in China[END_REF]. Some studies have even expanded the analysis to the entire East Asian port system (Notteboom, 2006a). A global perspective on the diffusion of containerization across ports of the world classified most (mainland) Chinese ports in the "fourth wave" entitled "global standard" from the mid-1990s [START_REF] Guerrero | The waves of containerization: shifts in global maritime transportation[END_REF], defined by the standardization of containerization across the world, the emergence of postpanamax ships, increased regionalization among East Asian countries, and the entry of major Chinese ports in the global shipping network. In such a category, Chinese ports are grouped with mainly transshipment hubs (e.g. Salalah, Gioia Tauro, Colon, Freeport, Tanjung Pelepas) as well as gateway ports in emerging countries (e.g. Vietnam, Mexico, India, Brazil). A handful of other Chinese ports belonged to the "fifth wave" labelled "peak growth" by the authors, such as Yingkou and Taicang, which benefited from congestion in large gateways, alongside Tangier Med, Caucedo, and Prince Rupert. It is important to mention pioneering works about the maritime connectivity of the Chinese port system [START_REF] Wang | Organization networks of Chinese marine container transportation[END_REF][START_REF] Xu | Empirical analysis of the ship-transport network of China[END_REF] using graph theory and complex networks as analytical tools.

Yet, very little attention has been paid to the external relations, or shipping connectivity, of Chinese port cities with the outside world, just like studies of maritime forelands and overseas linkages / connectivity in general in port geography for instance [START_REF] Ng | Port geography at the crossroads with human geography: between flows and spaces[END_REF]. Port throughput volumes, and other important aspects of ports such as management, planning, and policies, are good proxies of China's evolving trade relations externally, with a gradual adaptation to global standards in terms of technology and governance. However, they cannot provide a precise understanding of the evolution of shipping flow patterns; namely how Chinese port cities have integrated the global maritime network through successive phases during its contemporary history. Such an approach is particularly relevant nowadays, given the recent emphasis put by the Chinese government on its "One Belt, One Road" vision and policy, and the gradual reintegration of Hong Kong, a major container hub, into China's territory and economy, since its partial retrocession in 1997. This article takes its inspiration from a few existing contributions on the matter, with the ambition to propel them further. A first group of studies takes China as the core subject of investigation, questioning its maritime connectivity in various ways. For instance, Comtois (1999) directly addressed China's port system integration into global container shipping.

Although his work provided many clues about interrelated factors such as port privatization, port expansion, the modification of transactional networks and the emergence of a new business environment in China, it did not formally analyze the physical integration through shipping flows. Some years later, [START_REF] Comtois | Géopolitique et transports : nouvelles perspectives stratégiques dans le détroit de Taiwan[END_REF] as well as [START_REF] Rimmer | China's extra-and intra-Asian liner shipping connections, 1990-2000[END_REF] investigated more directly the changing patterns of shipping lines (e.g., Evergreen, COSCO) that included Chinese ports in their scheduled services, showing important changes in the design of those services at intra-and extra-Asian levels. Looking back in time, Wang et al. (2015) analyzed the spatial distribution of British and Japanese maritime networks in China and Asia in the 1920s, revealing interesting functional and regional differentiations at different scales between the two networks. More focused on specific mainland Chinese ports, [START_REF] Wang | The geographical connectedness of Chinese seaports with foreland markets: A new trend?[END_REF] found that existing studies tended too often to overlook non-hub Chinese seaports, despite their potential role to rival established hub ports, and shed new light on how China is divided by globalization through trade relations with different regions around the world. Yet, Wang and Hong (2016) adopted a complex network perspective to investigate the changing pattern of cross-strait liner shipping networks between mainland China and Taiwan.

A second group of studies offered a broader vision of China's maritime connectivity, by shifting from a China-centered view to a more regional (transnational) or even global view. The Chinese geographer Chengjin [START_REF] Wang | Spatial organization networks of world marine container transportation[END_REF] can be considered as a pioneer by putting Chinese ports in a global perspective, revealing their paramount dependence upon adjacent (foreign) transshipment hubs. Later on, [START_REF] Ducruet | Centrality and vulnerability in liner shipping networks: Revisiting the Northeast Asian port hierarchy[END_REF] noticed that although Northeast Asian ports could not compete with major Chinese ports (e.g., Shanghai, Shenzhen, Qingdao, Tianjin) in terms of throughput volume and growth between 1996-2006, Busan and Hong Kong maintained their centrality and dominance in the regional liner shipping network. Such a divergence between throughput volume and network centrality was also found at global level, due to the persistent domination of Singapore and the aforementioned hubs of the Asian and even global systems [START_REF] Ducruet | The worldwide maritime network of container shipping: Spatial structure and regional dynamics[END_REF]. Alongside other possible reasons such as the fiscal system or other geopolitical considerations, the principal explanation was the fact that the main function of (mainland) Chinese ports was to serve domestic demand, yet without any ambition to supersede existing hubs for international transshipment. Barrier effects in the global maritime network linking cities of the world were underlined by Ducruet et al. (2015a) in the case of China. For instance, in 1951, Shanghai stood out for its large demographic size but with limited centrality in the maritime network, being reduced to a minor and peripheral hub in a subnetwork comprising only four other Chinese port cities, mainly due to political circumstances at the time. In the subsequent years under study , its maritime centrality still remained limited, but Shanghai became better integrated within a larger subcomponent of the global network polarized by Tokyo. Other global-level studies such as those by [START_REF] Tran | Empirical analysis of the container liner shipping network on the East-West corridor (1995-2011)[END_REF], [START_REF] Li | Centrality in global shipping network basing on worldwide shipping areas[END_REF], and [START_REF] Xu | Evolution of regional inequality in the global shipping network[END_REF] remained too aggregated, at the level of large world regions (e.g. East Asia), to account for a precise view of China's position in global maritime flows.

The ambition of this paper is thus to provide a more dynamic view on the evolution of China's external connectivity through maritime transport networks, as existing studies remain highly static. In addition, it is our goal to extend the focus to other sectors than sole container shipping.

Based on largely untapped data on global vessel movements obtained from Lloyd's List, a world leader in maritime insurance and shipping intelligence, we propose for the first time to analyze the changing connectivity of Chinese (port) cities in global maritime flows for two different periods. A long-term perspective including all vessel circulation connecting Chinese ports mainly serves to verify the impact of political and technological change on shipping connectivity. The more recent period on container shipping flows documents from the Open Door Policy launched in 1978 up to the present how containerization, from its initial adoption at a few nodes, has diffused nation-wide over time as well as externally.

Our central research question can thus be formulated as follows: how have Chinese cities become integrated in the global maritime network, and how has this integration fostered or reduced excessive traffic concentration internally and dependence or vulnerability externally?

Answers to such questions are proposed based on relatively classic methods originating from graph theory and spatial statistics. The connectivity of Chinese ports in this paper is mainly measured by degree centrality (i.e., the number of links to other ports, at node, sub-region, and national level), and also by looking at domestic inter-port linkages. Another useful measure is hub dependence, explored by [START_REF] Ducruet | Hub dependence in constrained economies: The case of North Korea[END_REF] in the case of North Korean ports, to evaluate the vulnerability level of ports by their dependence upon external nodes. Such measures are compared with the more common ones of traffic volumes but also traffic concentration. The global port-to-port adjacency matrix also serves to measure the changing geographic distribution of China's maritime forelands by world region, based on both continents and political blocks. The remainder of this paper is as follows: in the second section, we present the results obtained from the usage of maritime traffic statistics covering the 1890-2008 period in order to describe important connectivity changes that occurred across different political regimes and technological innovations. The third section, focusing on container flows since the Open Door Policy, will apply similar methods up to the year 2016, insisting more on traffic inequality issues. The last section will discuss and conclude with the lessons learned from this study for further academic research and policy recommendations in the fields of ports, maritime transport, but also networks and urban development in general.

Data and methodology

In this paper, we compare the evolution of China's shipping connectivity over two different time scales, namely 1868-2008 and 1978-2016. These two periods complement each other by the fact that the first comprises all types of ships, and the second focuses on containerships only. Long-term and medium-term analyses are useful to grasp essential features, such as China's resilience or adaptation to changing trade patterns, (geo)political circumstances, and technological standards. The analysis of China's connectivity since the 19 th century is based on two complementary sources. One first database was assembled by [START_REF] Wang | Regional resilience and spatial cycles: Long-term evolution of the Chinese port system (221BC-2010AD)[END_REF] and updated by the authors of this paper to provide a general background on China's evolving port and maritime activity over the longest possible period (1868-2014), based on customs data and ministry data. The rest of the analysis is based on the Lloyd's List corpus, which was extracted on the basis of one publication of the Shipping Index every five years or so between 1890 and 2008, providing a snapshot of global maritime flows based on the latest movement of each recorded vessel at the time of the selected publication, around the period of April-May for the sake of comparability (see Ducruet et al., 2015b for a full description of this corpus). The Lloyd's database is rather unique in that it provides the possibility to focus on the external linkages of port cities, complementing the analysis of tonnage by a relational perspective, albeit over a shorter period (1890-2008).

There are two major differences with the former approach when it comes to analyze container flows linking China and the rest of the world. They have in common to focus on vessel movements, from the same source Lloyd's List, but the study of containerization leaves behind other ship types, and of course, concentrates on a shorter time period . Firstly, the 1978-2016 database comprises full months of daily vessel movements, making it possible to track complete maritime routes instead of just one-stop calls between previous, current, and next port. This potentially has enormous implications on the way China's shipping connectivity can be understood, especially from a geographic perspective. For the sake of comparability, the month of June was retained each year, with the exception of 1981, 1986, 1991, 2001, 2007, and 2008 for which it is replaced by the month of March due to data availability issues. Around 30 days of continuous circulation thus compose the yearly snapshot of connectivity. Secondly, vessel capacity, measured in twenty-foot equivalent units (TEUs) is taken into account, compared with the sole count of vessel calls in the previous section, which inevitable excluded the size effect and was another cause for the over-or underestimation of certain linkages between China and the rest of the world.

The proposed analyses consist in applying standard graph-theoretical and statistical measures to the aforementioned matrices of inter-port vessel flows, such as degree centrality (i.e. number of adjacently connected port nodes) and concentration measures (i.e. Gini coefficient, Herfindhal index). It is complemented by a look at the average kilometric distance of China's overseas shipping linkages, the distribution of vessel traffic by main cargo types, the global and regional distribution of China's overseas traffic, and the share of China at external nodes and regions. Finally, we investigate the changing distribution of inter-port linkages within Northeast Asia, based on the single linkage analysis method, to reveal China's preferential attachments with its immediate neighbors, but also internally, based on domestic or coastal shipping linkages.

China's long-term maritime connectivity (1868-2008)

As seen in Figure 1, the evolution of tonnage is a good starting point to document the changing A closer look at Figure 1 reveals interesting regional dynamics taking place within the country. While all of the three main port ranges have followed the national trend, and the central region (or Yangtze River Valley) always ranked first among them, one can observe an inversion between North China and South China that persisted from the early 1900s until nowadays. Even during the major disruption of the late 1940s and early 1950s, North China went through a lesser decline than the two other regions. One reason is geopolitical, as North China, and especially Manchuria, rapidly became the focus of important foreign interests and interventions (e.g., Japan, Russia, Germany), resulting in fostered port development such as in Tianjin [START_REF] Todd | Political and technical factors impinging on port operations: The case of Manchuria[END_REF]. The other factor is both cultural and political, as South China had been traditionally more liberal and trade-oriented for centuries, notably since the Treaty of Nanking (1842) through which the British opened a number of treaty ports in South China (e.g. Shanghai, Guangzhou, Xiamen, Ningbo, and Fuzhou). However, South China became somewhat peripheral under the new regime installed in 1949 and based in Beijing, which largely neglected (or even disregarded) foreign trade in the subsequent and so-called period of "closure". In addition, as port activity is measured in tons, this unit gives more advantage to North China even after the opening of 1978, as North China handles mainly raw materials such as coal and iron ore, while South China, though gradually becoming the world's factory, handled lighter goods produced by manufacturing industries.

Lloyd's database is rather unique in that it provides the possibility to focus on the external linkages of port cities, complementing the analysis of tonnage by a relational perspective, albeit over a shorter period . The evolution of the number of shipping linkages between China and the rest of the world, as defined by the number of foreign ports connected (Figure 2), closely matches the previous figure based on total tonnage, despite a marked decline in the recent period. Chinese port cities have deployed their external connectivity substantially during specific periods, such as the Russo-Japanese war (1905) The changing political regime caused a drastic reorientation of internal economic priorities as well as external trade relationships, resulting, as seen also in Figure 2, in a dispersion of external traffic (i.e. decreasing Gini coefficient) but at the same time, it led to a concentration at a handful of largest external nodes (i.e. increasing Herfindhal index). This corresponds exactly to the impacts observed in the study of North Korea's external shipping linkages when it lost its main trade partners during the collapse of the USSR [START_REF] Ducruet | Political and economic factors in the evolution of North Korea's maritime connections[END_REF], thereby providing grounds for more generic approaches to the impact of major shocks and disruptions on the evolution of network topology.

Another crucial dimension of connectivity is its geographic distribution (Figure 3), given that major spatial shifts may occur as already observed in the Yellow Sea in recent years, in the context of rapid economic growth and increasing trade relationships between China and South Korea [START_REF] Lee | Trade reorientation and its effects on regional port systems: The Korea-China link along the Yellow Sea Rim[END_REF]. Two dimensions are proposed, each of them grouping foreign ports and their host country into world regions (left) and political groups (right). World regions mainly overlap in the sharing of continents, with Asia split between East and West; the first being a North-South area from Indonesia to Far-East Russia, and the second an East-West area from Myanmar to Syria. Political regions are defined by the dominant ideology of the country's constitution, with reference to the Cold War era when the world was divided amongst three main groups. Although such a typology does not apply to the 120 years under consideration, it does provide a benchmark to situate shipping connectivity according to other factors than sole physical geography.

[Insert Figure 3 about here] Results can be explained by changes in both trade relationships and shipping network design.

Before the 1960s, China's shipping connectivity was dominated by its linkages with the Western world (Europe and America), which evolved from 88.1% in 1890 to 80.2% in 1935, before going down to 61.0% in 1940, i.e. in the early phase of the Second World War. The rest of the period is marked by the continued decline of such a relationship, with the exceptional importance of North America (mainly the United States) in 1946 (62.6%, against 1.7% for Europe), when vessels sailed all over the world to support recovery from war losses and strengthen geopolitical control; the link with North America fell to zero percent in 1951. In parallel, East Asia increasingly occupied the lion's share of China's shipping connectivity, up to 55.8% in 2008. While such recent figures seem to contradict trade figures which, on the contrary, show a tremendous growth of China-Europe and China-US flows from the 1980s onwards, this fading connectivity with the Western world stems from the fact that shipping networks (especially container shipping) have been reorganized in the form of scheduled, pendulum services calling at a limited number of large gateways and transit hubs, thereby artificially causing spatial shrinkage as the data is based on the previous or next port of call.

This system has gradually replaced the port-to-port configuration, less centralized and more long-distance, based on origin-destination shipments. This explains why North America had regained substantial importance after China's resuming of its trade relations with the West, being more directly accessible across the Pacific, compared with the China-Europe route, organized along successive intermediary stops. The temporary increase of Africa's share in the 1960s-1970s is directly attributable to China's foreign policy promoted by Zhou Enlai through the signature of the first official bilateral trade agreements with Algeria, Egypt, Guinea, Somalia, Morocco and Sudan, mainly to encourage decolonization and the fight against capitalist super-powers.

When it comes to political regions, Figure 3 confirms the aforementioned trends, with a shrinking dominance of capitalist partners during the period (from 89.5% in 1890 to 52.5% in 2008), a rise of socialist partners in the years following political change, the highest share being 30.2% in 1951, but gradually declining since then. As for Europe in general, connectivity with the USSR and the socialist block via the China-Europe route (Black Sea, Baltic Sea) became underestimated given the reorganization of shipping networks. Non-aligned countries often located in Asia, for instance, kept a substantial share up to the present day, with 36.5% in 2008.

Thus, shipping connectivity well overlapped political and trade relationships until their disentanglement provoked by technological progress in shipping and port operations. Such a consideration motivates us to zoom in on the more recent period in the next section, to look at how China's connectivity had evolved through the particular vector of container shipping. In particular, and based on another database for the 1977-2008 period covering all ship types, it has been possible to compare the share of container traffic with that of other types in China's total traffic (Figure 4). It must be noted, however, that although the first container line started operating in 1973, dedicated container handling facilities were developed in Tianjin port only in 1980. While all traffic categories increased over time, container traffic both in absolute and relative values increased from the lowest to the highest score relatively rapidly. It superseded general cargo and liquid bulks in the mid-1990s, as well as solid bulks in the early 2000s, although nowadays China still handles enormous amounts of raw materials, such as coal, (Wand and Ducruet, 2014). By comparison, general cargo has substantially stagnated, partly because such merchandise was increasingly containerized. This drastic transition in China's maritime traffic structure motivates a further look in the next section at container shipping dynamics, especially in terms of overseas connectivity.

[Insert Figure 4 about here]

China's container shipping connectivity (1978-2016)

There are two major differences between the previous and current sections in terms of connectivity measurement based on vessel movements, in addition to the type of fleets (all vessels vs. containerships) and the time period (1890-2008 vs. 1978-2016). Firstly, the 1978-2016 database comprises full months of daily vessel movements, making it possible to track complete maritime routes instead of just one-stop calls between previous, current, and next port.

This potentially has enormous implications on the way China's shipping connectivity can be understood, especially from a geographic perspective. For the sake of comparability, the month of June was retained each year, with the exception of 1981, 1986, 1991, 2001, 2007, and 2008 for which it is replaced by the month of March due to data availability issues. Around 30 days of continuous circulation thus compose the yearly snapshot of connectivity. Secondly, vessel capacity measured in twenty-foot equivalent units (TEUs) is taken into account, compared with the sole count of vessel calls in the previous section, which inevitably excluded the size effect and was another cause for the over-or under-estimation of certain linkages between China and the rest of the world.

A first analysis considers simple indicators such as total overseas traffic, degree centrality (number of external linkages), number of domestic nodes being connected with the outside, and the level of traffic concentration within the country (Figure 5). In accordance with the previous Figure 4, we observe a rapid growth in the number of connected nodes (internally and externally), albeit following different trends, namely more regular for domestic nodes, and exponential for foreign ports. This means that the development and spatial diffusion of containerization had been rather gradual internally, but slow and then very fast externally. This means too that China had to catch up with containerization until it became able to deploy its connectivity with foreign partners. As in many port systems worldwide (Notteboom, 2006b), the spread of containerization reinforced traffic inequalities within China, as seen with the constant increase of the Gini coefficient, from about 0.5 in the early 1980s to about 0.8 in recent years. The gradual decline of the Herfindahl index (HHI) suggests that the Chinese port system had become more polycentric, whereby larger ports keep concentrating traffic but with a parallel growth of small and medium-sized ports.

[Insert Figure 5 about here] When it comes to the distribution China's external linkages (Figure 6), there was a rapid concentration of traffic at foreign ports until the mid-1990s, and a slight increase of this concentration since then, up to the present day. It means that China's external connectivity has evolved from dispersion to concentration, favoring fewer but larger hubs. At the same time, the share of the largest linkage (cf. hub dependence) and of the top linkages (Herfindahl index or HHI) have both decreased over time, after exceptional values in the late 1970s and early 1980s due to the rarity of connections. This suggests that although China concentrated its external traffic at a few large hubs, recalling the process of vulnerability [START_REF] Ducruet | Hub dependence in constrained economies: The case of North Korea[END_REF], it managed to diversify its connectivity by a parallel spread of its linkages to other ports as well in addition to the largest ones. Most interesting is the gradual expansion of China's geographic radius in terms of the average kilometric distance (orthodromic) of its container shipping linkages with foreign ports, despite certain fluctuations, and fall-off in recent years. This overall trend goes along with the decline of the share of its surrounding region (Northeast Asia) in total foreign traffic, although it still remains nowadays around 60%. The share of the neighboring region of Southeast Asia, an important crossroad connecting Europe and Asia, has noticeably increased from zero percent in the early 1980s to more than 30% in latest years. But such an expansion and growing integration in the global shipping network does not contradict the fact that intranational (or domestic, coastal) container shipping had decreased. On the contrary, the share of coastal shipping had grown at a modest but regular pace until the early 2000s, faster afterwards, reaching no less than above 50% of China's total container traffic in 2016. The same methodology could be applied to specific Chinese ports and regions, as a possible pathway for further research.

[Insert Figure 6 about here]

We have chosen to move forward to the observation of the geographic distribution of these external linkages, at the level of Northeast Asia's main partners and by world region (Figure 7). While China's first container lines connected Japan in the early period of containerization, the latter gradually lost its prominence over time, from 100% in 1978 to less than 20% in 2016.

In relative terms, China's traffic with its adjacent neighbors gradually shifted to South Korea as well as Taiwan, especially from the mid-1990s. This evolution is partly explained by the growing centralization of Northeast Asia's liner shipping networks upon transshipment hubs such as Busan in Korea and Kaohsiung in Taiwan [START_REF] Ducruet | Centrality and vulnerability in liner shipping networks: Revisiting the Northeast Asian port hierarchy[END_REF], as well as increasing trade between China and South Korea [START_REF] Lee | Trade reorientation and its effects on regional port systems: The Korea-China link along the Yellow Sea Rim[END_REF]. By comparison, Hong Kong kept its role as a major external node, although one can observe a gradual decline of its share since its partial retrocession in 1997, which directly provoked a shift in liner shipping network configurations, with the first direct call of Evergreen, a major shipping line, to a Chinese mainland port, Yantian, in 1998 [START_REF] Wang | A container load center with a developing hinterland: a case study of Hong Kong[END_REF]. Hong Kong's share within Northeast Asia thus declined from around 40% to less than 30% since then, with a transfer of cargo handling operations to adjacent ports such as Shenzhen.

[Insert Figure 7 about here] A rapid zoom in on the share of the two major hubs of Busan and Hong Kong in China's total and regional container traffic is worthy of investigation to complement such a broad view on the external vulnerability (or hub dependence) of Chinese ports. While Hong Kong's share in China's total foreign traffic gradually declined from 54.6% in 1978 to 7.7% in 2016, especially after British retrocession (their share was still around 20% in 1997); the same share had declined everywhere but remains higher for South China in 2016 (11.3%), compared with Central/Yangtze (6.3%) and North China (3.7%), for obvious reasons such as spatial proximity but also close business ties. In the case of Busan, a similar process is observable, but with a growing share in China's total external traffic up to 2005 (11.1%), and a gradual decline since then, down to 6.6% in 2016. North China, which is closer to Busan, remained more dependent than other Chinese port regions, culminating at 16.9% in 2005 (compared with 12.2% for Central/Yangtze and 8.2% for South China), and remaining higher than average in 2016 with a score of 8.1% for North China, 7.9% for Central/Yangtze, and 4.3% for South China. China thus remains internally differentiated in terms of vulnerability upon external transshipment hubs, but managed to overcome such a situation by deploying its connectivity outside the regions and increasingly bypass these hubs with direct calls from major shipping lines.

The geographic spread of China's shipping connectivity by world region had been rather chaotic until the early 1990s, and has stabilized since then with specialization centered on North America (cf. transpacific trade), West Asia, as an intermediary zone between Europe and East Asia (cf. Middle East and Indian Ocean), and Europe itself. Other continents occupy a relatively minor share, although the recent growth of Africa, albeit still moderate, confirms growing Chinese investments in this continent, recently seen as a major factor in the shift of Africa's connectivity towards Asia in general [START_REF] Chaponnière | Le basculement de l'Afrique vers l'Asie. Enjeux pour les ports africains[END_REF].

The other side of the coin is also worthy of interest, namely to what extent foreign ports and regions are dependent on China. Starting with Figure 8, it was possible to calculate the share in the total number of ports and container traffic in the world related with China. After reaching a peak value in 2007 (38.5%), the share of ports connected directly or indirectly with China gradually fell to 28.1% in 2016. Still, it means that nearly one-third of the world's container ports are involved in routes and services including Chinese ports, which is rather impressive, compared with 1.6% in 1978. In terms of world container traffic, China's share rose to 21.5% in 2016, its highest value, meaning that no less than one-fifth of global container shipping is related to China, which moreover currently hosts most of the top 20 container ports in the world.

Looking at the figures when split by world region, we note that China's container traffic share had become substantial in the middle of the period (early 2000s), when it joined the World Trade Organization (WTO) in December 2001. Still, these numbers should be analyzed with caution, as China's share was calculated in the total container traffic of each world region, including intra-regional traffic as well, thus causing a bias. Perhaps, further analyses should consider recalculating this share only based on extra-regional flows. This might explain why the most China-dependent regions are East Asia and West Asia, due to their geographic proximity, and why this share had even decreased in very recent years in all other regions, perhaps also due to lower economic growth for China itself, recently reported as a general economic slowdown.

[Insert Figure 8 about here] Yet, and it is the goal of our last analysis, China's traffic share has risen impressively at the level of individual foreign ports (Figure 9). The latter nodes were differentiated according to the period from which they started to be connected with Chinese ports, keeping only the main nodes and the three first "generations". Interestingly, whereas China had become less and less dependent upon Hong Kong for external traffic, Hong Kong is increasingly dependent on China, accounting for up to 32.8% of its total traffic in the last period. It is followed by a number of major Japanese ports, but also Port Klang and Penang in Malaysia, albeit at lower levels.

Among the second generation ports, (i.e., connected from the period 1982-1986), Busan stands out as the most China-dependent port, reaching 32.2% of its total traffic in the last period. This is again an interesting inversion as its share in China's external traffic had constantly decreased.

It is followed by a number of South Asian ports (Manila, Keelung, Bangkok), as well as US West Coast ports and also Antwerp in Europe, albeit at a much lower level for the latter (0.1% in the last period). Among the third-generation ports, some nearby nodes have the highest share of China-related traffic (Incheon, Sakai, Vostochnyy), followed by a number of ports that are more and more distant geographically speaking, reaching as far as North Europe, the Middle East, and Australia. All these evolutions have been extremely rapid, although certain losses are felt at a number of Japanese ports (Moji, Shimizu) but also at Brisbane and Seattle.

[Insert Figure 9 about here] The last part of this analysis is the mapping of the major shipping linkages of mainland Chinese ports at two complementary levels: domestic and international. For the sake of clarity, we have restricted foreign linkages to Northeast Asia, bearing in mind that only very few of those were actually reaching outside the region (for a similar methodology applied to the case of North Africa, see [START_REF] Mohamed-Chérif | Regional integration and maritime connectivity across the Maghreb seaport system[END_REF]. Such an approach allows us to test the coevolution of domestic and international linkages with reference to concepts of hub dependence, port system integration, and network vulnerability. The main results (Figure 10) demonstrate that although China as a whole had, as showed in previous sections, deployed its global shipping connectivity in ways previously unthinkable, and reduced its dependence upon neighboring foreign transit hubs, it still relies up to nowadays, on a limited number of external nodes to access the global maritime network of container shipping. Another recurrent finding is that mainland China has always been divided between North and South in terms of the geographic coverage of its external hubs. During the early periods, North China was more polarized by the main Japanese ports (i.e. Kobe, Yokohama, Kitakyushu), and by the main South Korean ports in the late periods (i.e. Busan and Incheon). In constant evidence is the polarization of South China by Hong Kong, notwithstanding certain exceptions at different periods, and noticeable fluctuations of this influence. Up to the mid-1990s, Hong Kong was gradually expanding its dominance from South to North, but since then, it is rivalled mainly by Busan, which even supersedes Hong Kong in the period 2002-2006, a period of strong port growth in South Korea following its global strategy of becoming Northeast Asia's logistics hub.

In the last period, it is the first time that Shanghai shifts from Hong Kong's influence to the influence of Busan. From then onwards, North and South China remained rather separate in terms of foreign linkages, Nantong, in the middle of the country, being the only Chinese port predominantly connected with Japan in recent times.

Conclusion

The analysis of China's maritime connectivity from the local to the global and over the longterm is a new contribution to both transport studies and Chinese studies in academic literature.

China deserves such an ambitious approach, given the economic importance it has gained over the years, and its growing role in global shipping. Yet, such an exercise greatly depended on huge efforts to collect and extract massive statistical data covering not only China's port activity itself but also its relationships with the rest of the world through its port cities. The only possible source being the Lloyd's List, this article carefully selected the most relevant time series to highlight key features that characterized China's contemporary connectivity in the worldwide maritime network. While this approach is only at its eve and further research is needed, additional evidence could be provided, such as important geographic and functional shifts occurring at different time

  scale and geographic distribution of port activities in mainland China in the last 150 years or so. Different phases can be differentiated in terms of total tonnage: stability and moderate growth until the late 1880s, namely following and during the Treaty Ports era mainly based on foreign (European) intervention; accelerated growth during the era of leased territories or concessions to Japan, Russia, Germany, United Kingdom, and France until the late 1920s; lack of statistical data on the 1930s and 1940s; major traffic disruption and decline during the political transition towards the empowerment of Mao Zedong and the People's Republic of China in the late 1940s and early 1950s followed by the Korean War (1950-1953) and trade embargoes; recovery in the early Cold War period of the 1960s based on new trade circuits with preferential partners such as the USSR and the socialist block; instability in the late 1950s and 1960s with traffic growth during the Great Leap Forward (1958-1960); the revival of certain trades with Japan and Western Europe; fluctuations and decline due to priority given to agriculture, railways, and heavy industries (on the development of the Chinese railway network between 1906 and 2000, see Wang et al., 2009) before and during the Cultural Revolution; traffic growth during the economic revitalization and modernization under Zhou Enlai and the Gang of Four (1970-1976); uninterrupted and fast traffic growth following China's Open Door Policy launched by Deng Xiaoping, and the only slight decline being felt in 2009 due to the global financial crisis, which affected not only China but also the rest of the world. [Insert Figure 1 about here]

  taking place in the Yellow Sea and Manchuria, economic prosperity during the Nanjing decade led by Chiang Kai-Shek until Japan's invasion ofManchuria (1937), and after the Open DoorPolicy (1978), notwithstanding a noticeable decline in the years 2000 and 2008 for the following reasons, which are also derived from Figure2.After suddenly reaching its lowest value in 1951, the Gini coefficient, a measurement of inequality applied on the distribution of vessel calls between China and foreign ports, increased tremendously in subsequent years, reaching nearly 0.75 in 2008. Such a trend suggests that although China had witnessed skyrocketing increases in traffic volumes in the latter period, its external linkages had become increasingly concentrated at fewer main external nodes. As a consequence, the number of external nodes decreased latterly. This trend is also confirmed by the growing Herfindhal index, which summarizes the share of the largest connected nodes in China's total foreign traffic, also reaching its peak value in 2008. One selected example is Hong Kong, whose share in China's total foreign traffic increased from its lowest value in 1965 (7%) to about 23% in 2008. Although Hong Kong's role as China's gateway had been crucial following the Open Door Policy but tended to stagnate or even decline in recent years due to the shift and growth of port operations at mainland Chinese ports[START_REF] Wang | From a hub port city to a global supply chain management center: A case study of Hong Kong[END_REF], it had remained a major connector (or bridge) between China and the rest of the world, at least until mainland Chinese ports became relatively autonomous for handling large containerships without needing transit trade through the Hong Kong hub. In parallel, the share of domestic linkages in total vessel traffic increased very fast in recent years, from an average of about 45% until 1985 to about 73% in 2008, thereby translating, mainly, Shanghai's strengthening role as a domestic transshipment hub between North and South China, and for the whole Yangtze River Valley(Wang and Ducruet, 2012).[Insert Figure2about here] Such topological and spatial considerations show to what extent China's shipping connectivity translates various events, shocks, and transitions of a different nature. The political transition of the late 1940s clearly had the most intense influence on such connectivity, in accordance with the model of[START_REF] Ducruet | Hub dependence in constrained economies: The case of North Korea[END_REF] on the level of hub dependence in constrained economies.

  Internally, there have been drastic changes in the way China had become connected through coastal shipping. Up the early 2000s, North and South China remain barely connected (based on dominant, largest flow linkages), as each region is more connected internally, or through the intermediate central or Yangtze region such as through Shanghai or Ningbo main ports. Each region, in parallel to this evolution, is characterized by the emergence of local or regional hubs polarizing their surrounding neighbors, such as Tianjin (China's first container port) in the first two periods, Shanghai in the 1980s, Qingdao and Xiamen in the period 1997-2001, and from the period 2002-2006 one can observe a more polycentric system emerging, well connected through these hubs. The late periods witness the emergence of Shenzhen as a very strong hub, with even more dominant linkages than Shanghai, although it is spatially restricted to South China itself. [Insert Figure 10 about here]

  scales and spatial scopes, internally and externally. Massive shocks and disruptions during the contemporary era have been absorbed and overcome differently across the country, and such a geographic differentiation still persists even though port and shipping technologies have greatly changed and improved. While the influence of physical geography such as distance, resulting in transport costs, certainly plays a role in the emergence of certain patterns of flows, other factors are also at stake, such as the cultural and economic variety of China, as reflected in the recurrent differentiation between North and South. The extent to which China's reliance on external, foreign transit hubs to connect global maritime networks is a structural vulnerability or a conjectural phenomenon that may change in the near or distant future remains to be verified.Many port cities of the world are becoming increasingly dependent upon Chinese trade or transit, but at the same time, China has not yet become the dominant hub of Asia, despite its spectacular traffic growth. Backed by more qualitative evidence, further research should investigate in greater detail the factors at stake behind such logic, which certainly goes beyond pure transport economics, and may lead us to different research arenas such as political geography, political economy, and geopolitics. Another research pathway is to update the current vessel movement database including all ship types to better document the recent role played by raw materials, not only containers, on China's increasing insertion in the current and future world system. Finally, it would be extremely necessary to integrate shipping data with socio-economic and trade data into multivariate analyses to better understand their mutual influences overtime.

Figure 1 .

 1 Figure 1. Throughput evolution of mainland Chinese ports, 1868-2014Source: updated from[START_REF] Wang | Regional resilience and spatial cycles: Long-term evolution of the Chinese port system (221BC-2010AD)[END_REF]

Figure 2 .

 2 Figure 2. Maritime connectivity of mainland Chinese ports, 1890-2008 Source: own elaboration based on Lloyd's List data and Wessa software

Figure 3 .

 3 Figure 3. China's maritime foreland distribution by world region, 1890-2008 Source: own elaboration based on Lloyd's List data

Figure 4 .

 4 Figure 4. Traffic distribution by main vessel types at Chinese ports, 1977-2008 Source: own elaboration based on Lloyd's List data

Source: own elaboration based on Lloyd's List data 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 2 1 2 2 3 2 4 3 3 2 2 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 4 4 5 4 5 7 7 10 12 14 13 15 17 19 20 22 31 34 37 37 36 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 3 4 5 6 6 6 7 6 8 7 8 7 8 7 7 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 1 1 1 2 4 5 6 4 5 7 9

6 7 8 7 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 4 4 4 5 7 7 5 6 10 8 8 8 7 4

Africa

East Asia

Europe

Latin America North America

Oceania

West Asia 1 9 7 8 1 9 7 9 1 9 8 0 1 9 8 1 1 9 8 2 1 9 8 3 1 9 8 4 1 9 8 5 1 9 8 6 1 9 8 7 1 9 8 8 1 9 8 9 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6