Vinicius De Almeida
email: vinicius.de-almeida@emse.fr

Eric Serris

Gianluca Lavalle

Ana Cameirão

Jean-Michel Herri

Emilie Abadie

Nicolas Lesage

Annie Fidel Dufour

Mechanisms of Hydrate Blockage in Oil-water Dispersions Based on Flow Loop Experiments

Keywords: flow assurance, petroleum flowlines, hydrate formation, mechanisms of plugging, flow loop experiments, oil-water dispersions

Hydrate formation poses a threat to the safety of petroleum transport in flow lines due to the risk of plugging. In this work, conceptual mechanisms of hydrate formation and plugging in oil-water systems are proposed, based on flow loop tests at 30%, 50% and 80% water cut (water fraction). The experiments were conducted using Kerdane oil, water (with 30 g/L of NaCl) and natural gas at 75 bar and 4 °C. The employment of several instruments, such as acoustic emission, permittivity, and flowmeter, to cite a few, allowed identifying the different flow patterns until hydrate blockage and detecting the phase that carries the hydrates. A model based on density measurement is proposed to calculate the fraction of hydrates flowing locally. It was observed that there are local accumulations of most of the hydrates in a small portion of the flowing volume, which, together with deposition, ends up leading to plugging.

Introduction

The operational safety and economical regards involving multiphase flow transport in petroleum flowlines are studied in an engineering analysis process called flow assurance. Flow assurance's most important issues are the appearance and deposition of solids in deepwater long-distance flow lines due to the high pressures and low temperatures [START_REF] Olajire | Flow assurance issues in deep-water gas well testing and mitigation strategies with respect to gas hydrates deposition in flowlines -A review[END_REF]. In case of pipeline blockage, the operation must be interrupted for remediation, risking the occurrence of accidents and financial losses. Among the flow assurance issues, hydrate problems might be the most significant challenge nowadays [START_REF] Cardoso | Design options for avoiding hydrates in deep offshore production[END_REF][START_REF] Jamaluddin | Hydrate plugging problems in undersea natural gas pipelines under shutdown conditions[END_REF][START_REF] Sloan | Clathrate Hydrates of Natural Gases[END_REF].

Gas hydrates are crystalline compounds formed from a network of hydrogen-bonded water molecules entrapping light natural gas molecules [START_REF] Englezos | REVIEWS Clathrate Hydrates[END_REF][START_REF] Khurana | A review of clathrate hydrate nucleation[END_REF]. These crystals are studied in different applications, including, for example, their use as an energy source [START_REF] Shaibu | An assessment of methane gas production from natural gas hydrates: Challenges, technology and market outlook[END_REF]. In the oil and gas industry, hydrates form when the undesired water content in flow lines is in interaction with gas under high pressure and low temperature [START_REF] Sloan | Fundamental principles and applications of natural gas hydrates[END_REF]Sum et al., 2009). Gas hydrates have a dual effect on multiphase flow: (i) they change the characteristics of the slurry flow and the flow pattern [START_REF] Delahaye | Rheological study of CO2 hydrate slurry in a dynamic loop applied to secondary refrigeration[END_REF][START_REF] Ding | Investigation of natural gas hydrate slurry flow properties and flow patterns using a high pressure flow loop[END_REF]; (ii) the particles may agglomerate or form deposits [START_REF] Bassani | Modeling the effects of hydrate wall deposition on slug flow hydrodynamics and heat transfer[END_REF][START_REF] Chen | Insights into methane hydrate formation, agglomeration, and dissociation in water + diesel oil dispersed system[END_REF][START_REF] Stoner | Perspective on the role of particle size measurements in gas hydrate agglomeration predictions[END_REF][START_REF] Straume | Experimental study of the formation and deposition of gas hydrates in non-emulsifying oil and condensate systems[END_REF]Wang et al., 2020) due to the capillary forces [START_REF] Colombel | Discussion of Agglomeration Mechanisms between Hydrate Particles in Water in Oil Emulsions[END_REF], which affect the rheological properties [START_REF] Chen | Insight into the plugging mechanism in water-continuous hydrate slurries[END_REF][START_REF] Ding | Rheology of natural gas hydrate slurry: Effect of hydrate agglomeration and deposition[END_REF][START_REF] Fidel-Dufour | Rheology of methane hydrate slurries during their crystallization in a water in dodecane emulsion under flowing[END_REF][START_REF] Liu | Hydrate slurry flow characteristics influenced by formation, agglomeration and deposition in a fully visual flow loop[END_REF][START_REF] Pham | Relative pressure drop model for hydrate formation and transportability in flowlines in high water cut systems[END_REF][START_REF] Sinquin | Rheological and Flow Properties of Gas Hydrate Suspensions[END_REF][START_REF] Song | Experimental investigation on the microprocess of hydrate particle agglomeration using a high-speed camera[END_REF], possibly leading to the onset of plugging.

The formation of gas hydrates in oil-water systems has been the object of many research. The first studies proposed that hydrates form a shell around water droplets in water-in-oil systems with an inward growth [START_REF] Melchuna | Topological modeling of methane hydrate crystallization from low to high water cut emulsion systems[END_REF][START_REF] Sloan | Natural Gas Hydrates in Flow Assurance[END_REF]Turner, 2005), or a shell around oil droplets in oil-inwater systems that has an outward growth [START_REF] Melchuna | Topological modeling of methane hydrate crystallization from low to high water cut emulsion systems[END_REF][START_REF] Sloan | Natural Gas Hydrates in Flow Assurance[END_REF]. The models assume that hydrate formation occurs at the oil-water interfaces, as the gas is mainly dissolved in the oil phase.

As a result, for the shell approach, hydrates always entrap the dispersed phase. As an alternative to the shell approach, a "sponge approach" has been recently proposed, where hydrates are porous structures that entrap only the water phase [START_REF] Bassani | A multiscale approach for gas hydrates considering structure, agglomeration, and transportability under multiphase flow conditions: I. phenomenological model[END_REF]. While most of the data available in the literature are limited to oil continuous systems, there is an increasing demand for water continuous test cases. Indeed, in order to maintain a petroleum reservoir explorable, the pressure must be kept constant, and this is done by means of continuous water injection.

A unified hydrate formation flow model for different flow characteristics (oil-dominated, gasdominated, gas condensate, and high water cut system) does not exist. Identifying which phase, oil or water, transports hydrates at different conditions could help to better understand hydrate blockage. The literature has not yet reported a methodology that allows this analysis. Although agglomeration and deposition have commonly been considered the dominant mechanisms of plugging, it is possible that changes in the multiphase flow due to hydrate formation may play an important role in hydrate blockage risk. While solid agglomeration and multiphase flow are often studied separately, an analysis combining these two is necessary for addressing this costly flow assurance problem in offshore petroleum exploration.

In this sense, the objective of this work is to present new insights about the two-way coupling between flow regime and hydrate formation. This paper aims to answer the following questions: (i) How do different oil-water systems affect hydrate formation? (ii) How do hydrates act on the flow pattern and on the flow characteristics? (iii) How do hydrate plugs form? To answer these questions, flow loop experiments were conducted to study the different stages of hydrate slurry formation and plugging at low, intermediate, and high water cuts. The facility is equipped with a unique combination of instruments that allows detecting the continuous phase, tracking hydrate particles, and detect which phase carries the hydrates. The impact of hydrate formation on multiphase flow and the main cause of plugging for each scenario are discussed. An extensive analysis is carried out in order to identify the flow regime at each different stage of the tests. A simplified model using the density measured by a flowmeter is proposed to calculate the local accumulations of hydrates in the flow. Finally, based on this model, a methodology is proposed to classify the different tests according to their heterogeneity in hydrate distribution along the pipe.

Experimental methodology

Flow loop description

The Archimede flow loop, illustrated in Figure 1, consists of a pipeline that is approximately 56 meters long, divided into three sections: a vertical downward section, a slightly inclined or horizontal section, and a vertical upward section. The vertical sections have a pipe internal diameter of 15.7 mm, while the slightly inclined or horizontal section has a pipe internal diameter of 10.2 mm. The temperature is controlled by a cooling system composed of two circulating baths (one for the horizontal section and the other for the vertical sections), which send a refrigerated fluid (mixture of water and ethanol) to the heat exchanger pipes. In order to limit heat exchange with the room, the entire flow loop apparatus is jacketed with an insulating material. A Moineau pump, which allows a flow rate of up to 500 L/h, provides the power for the circulation of the liquid in the pipeline.

The flow loop is equipped with several probes, listed in Table 1, that measure temperature, absolute pressure, pressure drops, the gas flow rate injected by an automatic gas compensation system, density, and flow rate (monitored by means of a Coriolis flowmeter). Furthermore, the apparatus is equipped with a high-speed camera (HSC), a Particle Vision Microscope (PVM), a Focused Beam Reflectance Measurement (FBRM), an acoustic emission technology system (AE) and a permittivity probe.

Table 1. Probes installed in the Archimede flow loop, with their notations and features.

Instrument

Notation / Measurements Feature Temperature probes T1-T10 Nine equally distributed sensors along the loop and one additional for room temperature. Measurements in °C every 1.0 s.

Pressure probe pabs

Absolute pressure of the gas in the separator. Measurements in bar every 1.0 s.

Pressure drops probes

∆p1, ∆p2, ∆p4, ∆ph Pressure drops at the downward and upward vertical sections (∆p1 and ∆p2, in bar), gravitation pressure drop on the right vertical pipe of the separator (∆p4, in mbar), and horizontal section (∆ph, in bar).

Measurements every 1.0 s.

Coriolis flowmeter (density and flow rate)

ρ, Q Measures the density (in kg/m 3 ) and the flow rate (in L/h) at the downward section. Measurements every 1.0 s.

Gas injection pc, QG Automatic gas compensation system with an absolute pressure and a gas flow meter probe. Measurements in NL/min every 1.0 s.

Acoustic emission AE

Measures the absolute energy (in aJ) every 0.5 s. using 4 or 6 sensors, as shown in Figure 1, identified as 1A to 4A (in blue) or from 1B to 6B (in red).

Focused beam reflectance measurement FBRM Measures chord counts (per second) in a 1-1000 µm range every 5.0 s.

Particle vision measurement PVM

Microscope coupled with a camera that takes pictures of the flow at a resolution of 1050 x 825 µm every 5.0 s.

Permittivity probe εr

It measures the relative permittivity near the wall every 3 to 6 s.

High-speed camera HSC

Monochromatic camera installed in front of a 52 x 12 mm visualization window. Up to 1GB/s or 211 fps at a maximum resolution of 2560 x 2048 pixels.

Figure 1. Schematic illustration of the Archimède flow loop.

Experimental conditions and protocol

The experiments are conducted with oil, saline water and natural gas. Table 2 specifies the physical properties of the fluids at room pressure and temperature, while Table 3 provides the composition of the natural gas. Table 4 specifies the control parameters according to their variation range. Each experimental condition, defined by a combination of water cut and flow rate, was repeated at least once.

Parameter Values

Flow rate 200 L/h, 400 L/h

Water cut 30%, 50%, 80%

The experimental protocol, illustrated in Figure 2, can be summarized in four main steps:

i. Controlling the flow rate at 200 or 400 L/h.

ii. Cooling down the temperature to a minimum of 4 °C.

iii. Fast pressurizing to 75 bar.

iv. Maintaining the pressure constant by means of the gas compensation system. During the last step (iv), the injected gas flow rate is measured in order to calculate the amount of gas consumed for hydrate formation. Some intermediate steps (flow rate control to 100 L/h, temperature to 8 °C and absolute pressure to 8 bar) are applied in order to reduce the influence of room temperature on the results during emulsification, as shown in Figure 2.

Data synchronization

Due to the many instruments that are installed in the flow loop, where each one of them is placed in a different position, a methodology to synchronize the data was developed. In order to compare the same portion of the flow when it passes in front of two different instruments, the time required for the flow to move from one of the instruments to the other is calculated. This calculation is a function of the volume between the instruments and the flow rate measured by the Coriolis flowmeter. When this time correction is applied, an indication is given on the x-axis of the graphs (see, for instance, Figure 5). The procedure applied for this paper consisted of fixing the flowmeter as a reference and then correcting the time data set of the other instruments in a way to synchronize them with the time data sets obtained with the flowmeter.

Methodology to calculate the hydrate volume

With the aim to determine the hydrate fraction and the water conversion as a function of time, a model based on mass and volume balances is proposed. The model has the following assumptions:

• The mass of gas dissolved in the oil phase is constant after the onset of hydrate formation.

• The fraction of each gas component being consumed for hydrate formation is proportional to its mole fraction in the free gas.

• The hydrate density is constant and equal to 910 g/L, which is approximately the density of methane hydrates [START_REF] Sloan | Clathrate Hydrates of Natural Gases[END_REF].

• The hydration number, that is, the ratio between the number of water molecules and gas molecules consumed for hydrate formation, is constant and equal to 6.0.

The mass balance, considering the mass of free gas (𝑚 𝐺 ), the mass of free water (𝑚 𝑊 ) and the mass of hydrates (𝑚 𝐻 ), is given by the following equation:

𝑚 𝐺 + 𝑚 𝑊 + 𝑚 𝐻 = 𝑚 𝐺 0 + 𝑚 𝑊 0 + 𝑚 𝐺,𝑖𝑛𝑗 ( 1 )
where the superscript 0 corresponds to the instant of onset of hydrate formation. The term 𝑚 𝐺,𝑖𝑛𝑗 is the mass of gas injected by the gas compensation system since the onset of hydrate formation. The volume balance between gas volume (𝑉 𝐺 ), water volume (𝑉 𝑊 ) and hydrate volume (𝑉 𝐻 ) is represented by the following equation:

𝑉 𝐺 + 𝑉 𝑊 + 𝑉 𝐻 = 𝑉 𝐺 0 + 𝑉 𝑊 0 ( 2 )
One should notice that the oil phase and the dissolved gas were not considered in equations ( 1) and

(2). This is because the oil mass is constant over time, and the amount of dissolved gas is assumed to be constant after the onset of hydrate formation. The gas density can be calculated from the pressure, temperature and composition of the gas using the Soave-Redlich-Kwong equation of state, and can be used to relate the mass and volume of the gas:

𝜌 𝐺 (𝑝, 𝑇) = 𝑚 𝐺 𝑉 𝐺 ( 3 )
As previously mentioned, hydrate density is assumed to remain constant at 910 g/L, and it is the ratio between hydrate's mass and volume:

𝜌 𝐻 = 𝑚 𝐻 𝑉 𝐻 ( 4 )
The water density is also constant and given by:

𝜌 𝑊 = 𝑚 𝑊 𝑉 𝑊 ( 5 )
Finally, the hydration number is:

𝑁 ℎ = (𝑚 W 0 -𝑚 𝑊 ) 𝑀 W ⁄ (𝑚 G 0 + 𝑚 𝐺,𝑖𝑛𝑗 -𝑚 𝐺 ) 𝑀 G ⁄ ( 6 )
where 𝑀 𝑊 is the water molar mass and 𝑀 𝐺 is the gas molar mass. Substituting the equations (3)-(5) into equation (1), and then combining them with equations ( 2) and ( 6), we obtain the following system of equations, where 𝑉 𝑤 , 𝑉 𝐺 and 𝑉 𝐻 are the unknowns to be determined:

[ 𝜌 𝑊 𝜌 𝐺 𝜌 𝐻 1 1 1 𝜌 𝑊 𝑀 𝑊 - 𝑁 ℎ 𝜌 𝐺 𝑀 𝐺 0 ] [ 𝑉 𝑊 𝑉 𝐺 𝑉 𝐻 ] = [ 𝑚 𝐺 0 + 𝑚 𝐺,𝑖𝑛𝑗 + 𝑚 𝑊 0 𝑉 𝐺 0 + 𝑉 𝑊 0 𝑚 𝑊 0 𝑀 𝑊 -𝑁 ℎ ( 𝑚 𝐺 0 + 𝑚 𝐺,𝑖𝑛𝑗 𝑀 𝐺 ) ] ( 7 )
Once the volume of each phase is known, it is possible to calculate the global hydrate fraction in the system relatively to the oil and water phases, which are the phases that circulate in the loop with the hydrates. The hydrate fraction is given by the following expression:

𝛼 𝐻 = 𝑉 𝐻 𝑉 𝐻 + 𝑉 𝑊 + 𝑉 𝑂 ( 8 )
where 𝑉 𝑂 is the oil volume, which is constant.

Results and discussion

The results of the carried-out experiments, summarized in Table 5, are discussed in six subsections.

In the first one, it is introduced the methodology for identifying the continuous phase for characterizing the flow pattern before hydrate formation. The subsequent three subsections present the results of hydrate formation for tests at low, intermediate and high water cut. The fifth subsection presents a methodology to quantify the local hydrate fraction and to characterize the heterogeneity of the system. The last subsection presents a summary of the main outcomes. Identifying the continuous phase and whether the dispersed system is oil-in-water or water-in-oil before hydrate formation is important because hydrates preferentially form at oil-water interfaces, and thus their shape, kinetics and dynamics are strongly affected by the two-phase flow pattern. In this work, a combination of the following three measurements is used to identify the dispersed phase before hydrate formation: the dielectric, the local water fraction calculated from the density, and the images from the camera.

The dielectric of saline water with 30 g NaCl/L is 87.9, while the dielectric of the Kerdane oil is 2.7, both at 4 °C, values obtained from tests in the flow loop apparatus. The local or instantaneous water fraction (𝛼 𝑙,𝑊 ) that passes through the flowmeter is calculated from the measured density (𝜌), using the equation:

𝜌 = 𝜌 𝑊 𝛼 𝑙,𝑊 + 𝜌 𝑂 (1 -𝛼 𝑙,𝑊 ) ( 9 )
where 𝜌 𝑊 and 𝜌 𝑂 are the densities of water and oil.

In order to obtain the dielectric as a function of the water fraction, an algorithm is proposed, as illustrated in Figure 3. In every 30-second slot of the whole measurement, the highest, lowest and timeaveraged values of dielectric are calculated, as depicted in Figure 3(a). The same procedure is performed for the calculated water fraction, as represented in Figure 3(b). These values of dielectric and water fraction are synchronized in such a way that, for each interval of 30 seconds, the highest value of dielectric is coupled with the highest value of the water fraction, the same for their respective average and lowest values, as shown in Figure 3(c). This method is applied to the entire dataset before hydrate formation. During the first three minutes after hydrate formation, as the particles were evenly dispersed in the liquid, the horizontal pressure drop increases significantly (blue line in Figure 5(c)). This increase in pressure drop is believed to be due to an increase in apparent viscosity that occurs as more interfaces are created when hydrates form. However, as the flow starts to became more heterogeneous, at 20.5 minutes, the horizontal pressure drop reduced, due to a reduction of the interfacial area between the phases.

Meanwhile, the local accumulation of hydrates in parts of the pipeline started to cause significant restrictions. This can be noted, for instance, from the pressure drop in the right vertical pipe of the separator (∆p4, cyan line in Figure 5(c)), which reduced when hydrates passed through the separator. This indicates that, when hydrates pass therein, they cause a restriction at the bottom of the separator that temporarily interrupts the flow. This causes the suction of liquid from the right vertical pipe of the separator, explaining why the liquid level in that part of the separator reduced. The hydrate plugging was observed after 30 minutes, when the flow rate indicated zero, causing the end of the experiment. It was therefore a sudden event and was caused by a restriction in the bottom pipeline of the separator.

Figure 5. Experiment at 30% water-cut and 200 L/h (#1 in Table 5), showing the time evolution of (a) dielectric and density, (b) dielectric and absolute energy, and (c) pressure drops and flow rate.

The visual observation that peaks of density and absolute energy (shown in Figure 5) are in phase, which indicates that hydrates are carried by the water phase, can be verified statistically by using a timelagged cross-correlation [START_REF] Cheong | Four ways to quantify synchrony between time series data[END_REF]. This gives the time synchrony between two data sets and consists in incrementally shifting one data set over time and repeatedly calculating the correlation to the other data set. If the peak correlation is at the center (no time lag), this indicates the signals are mostly in-phase at that time interval. To obtain the correlation between two signals, the Pearson correlation can be used [START_REF] Schober | Correlation coefficients: Appropriate use and interpretation[END_REF]. This is a correlation coefficient that ranges from -1 to +1, where a coefficient +1 means that all values from one data set increase as the values from the other one increase in a perfectly linear relationship, while a coefficient -1 means that all values from one data set decrease as the values from the other one increase, also in a linear relationship. A coefficient of zero means that there is no relationship between the data sets.

Figure 6 shows a comparison between density and absolute energy (sensor 1A) at the top panel (the last six minutes of the experiment), and the correlation coefficient between these two data sets for a period of three minutes at the bottom panel. The peak for the correlation coefficient occurs near the center, for a time lag of 4 seconds, which is very low compared to the period it takes for the flow to circulate around the loop. This proves that these signals are in phase.

Figure 6. Time-lagged cross-correlation analysis between density and absolute energy (sensor 1A) for a test at 30% water cut (#1 in Table 5).

Hydrate formation could be detected by the images from the PVM. It is shown in Figure 7 three images: the first one was taken before hydrate formation, the second one was obtained immediately after hydrate formation, and the third one at the end of the test, just before hydrate blockage. It is shown in the first image (Figure 7(a)) a droplet with a perfectly spherical shape, most likely a water droplet dispersed in oil. Once hydrates form, as shown in Figure 7(b-c), they have an irregular shape and the appearance of a porous structure rather than solids with well-defined surfaces.

Figure 7. PVM images for the test at 30% water-cut and 200 L/h (#1 in Table 5).

Three additional tests were performed at 30% water cut, as listed in Table 5, with similar results being observed. The induction time, that is, the time required for the onset of hydrate formation, varied between 14.5 and 24.6 minutes. For all the tests, water was the carrier phase for hydrates. Furthermore, it was observed that plugging at 400 L/h occurs faster than at 200 L/h despite less water conversion. This can be explained by considering that at a higher flow rate, the flow is more homogeneous (see, for instance, Figure 4). Therefore, when hydrates form, the appearance of more interfaces between the phases is expected to increase the apparent viscosity and, as a result, the transportability of the system is reduced.

When summarizing the results from the test at 30% water cut, the most important outcomes are:

• The flow is initially water-in-oil before hydrate formation, as explained in section 4.1 by analyzing the permittivity data and images from the camera.

• During the onset of hydrate formation, hydrates are homogeneously distributed during the first few minutes, as can be deduced from the absolute energy in Figure 5 behavior, with the apparent viscosity being higher immediately after hydrate formation than during the stages of agglomeration, growth, and deposition.

• Water droplets rapidly coalesce, forming large pockets of sponge structures (seen in the PVM images, Figure 7) formed by water and hydrates. This was observed by synchronizing and comparing the peaks of dielectric, density and absolute energy in Figure 5(a-b), after 20 minutes.

Images from the camera, shown in the top panel of Figure 8, also captured an intermittence, with the images becoming darker after hydrate formation (central image) and later presenting an intermittence (two images on the right) between parts of the flow whose images are transparent (mostly with oil) and parts that are dark (due to the presence of hydrates, which block the lighting positioned behind the visualization window). The reason why hydrates stay preferably with the water phase can be due to the higher affinity between hydrates and water than between hydrates and oil [START_REF] Bassani | A multiscale approach for gas hydrates considering structure, agglomeration, and transportability under multiphase flow conditions: I. phenomenological model[END_REF].

• A complete separation into two phases, oil and water with hydrates, is observed. The presence of large pockets of this porous structure containing hydrates and water may easily cause plugging, especially during transit towards points of singularity, where deposition or local accumulation of hydrates increases over time.

Based on test results at 30% water cut, a conceptual model for hydrate formation and plugging at low water cut in oil-water dispersed systems is proposed, shown in Figure 8. The model consists of four stages. The first stage is the entrainment of water droplets into the oil phase due to the shear caused by the flow. The second stage is the formation of hydrates on the surfaces of water droplets.

As hydrates form, they eventually break down inside the water droplets. In the third stage, the water droplets with hydrates become porous structures that may easily adhere to the wall, cause some deposition, and they quickly coalesce to form large pockets of water that carry the hydrate particles.

In the final stage, as the pockets of water with hydrates continue to grow, they eventually plug the pipeline. Plugging is a sudden event, and it occurs when porous-structured pockets containing water and hydrates are large enough to block the pipe. Figure 8. Depiction of the conceptual model for hydrate blockage at low water cut.

Hydrate formation at intermediate water cut

Similar to the previous section, Figure 9 shows the time evolution of dielectric and density (top panel), dielectric and absolute energy (second panel), dielectric and chord counts (third panel), and pressure drops and flow rate (bottom panel). Hydrates formed around 7 minutes, when there was a sudden reduction in dielectric and density (Figure 9(a)) along with a net increase in absolute energy (Figure 9(b)). An intermittent behavior was observed after hydrate formation, with some parts of the flow containing more oil (low density and dielectric) than others. For this test, it was also observed that it was the water phase that transported the hydrates, since regions of high density and dielectric (which contain more water) had the highest values of absolute energy. This is clear by noting in Figure 9(b) that the troughs of dielectric and absolute energy were in phase. Hydrate formation was also detected by the FBRM during the last stage of the experiment, after 12 minutes, when the flow is intermittent. It is possible to see in Figure 9(c) that the chord counts increased and showed similar oscillatory behavior to the dielectric, density and absolute energy, which confirms the presence of more particles and droplets in the part of the flow that contains more water.

There was also a temporal oscillation in the horizontal pressure drop, which is periodic and whose amplitude increases with time (Figure 9(d)). The portion of the flow that contains an accumulation of hydrates is expected to have a local apparent viscosity that increases over time and causes the blockage of the pipe. Plugging probably occurred in the downward section, as the pressure drop in this section (∆p1) decreased whenever the hydrates passed through it and reached zero at the end of the experiment. This occurs because an increase in pressure loss due to viscosity or deposition counterbalances the gravitational differential pressure (positive for downward flow).

Figure 9. Experiment at 50% water-cut and 400 L/h (#10 in Table 5), showing (a) dielectric and density, (b) dielectric and absolute energy, (c) dielectric and chord counts, and (d) pressure drops and flow rate.

For the tests at 50% water cut, hydrate particles have a regular shape, and seem to be solid structures, as observed in the PVM images. Figure 10(a) shows a PVM image before hydrate formation with oil droplets of regular spherical shape. Once hydrates form, as shown in Figure 10(b-c), hydrate particles also have a regular spherical shape, indicating that these particles are entrapping oil droplets.

Figure 10. PVM images for a test at 50% water-cut and 400 L/h (#10 in Table 5).

A total of seven tests were conducted at 50% water cut, three of which were conducted at 200 L/h and four of which were conducted at 400 L/h. The main outcomes from these tests are:

• An oil-in-water dispersed system is the flow regime before hydrate formation, as shown in section 4.1.

• The initial oil-in-water dispersion is completely disrupted after hydrate formation. This is because the hydrates, that formed on the surfaces of oil droplets, have more affinity to the water phase.

Therefore, they are detached from the oil and preferentially carried by the water phase. The conclusion that hydrates are carried by the water phase is based on the correspondence between the density, dielectric, absolute energy and chord counts measurements, whose peaks are in phase, as shown in Figure 9(a-c).

• A separation of phases occurs, with oil droplets coalescing and forming large oil pockets, while hydrates flow suspended in the water phase. This heterogeneous behavior causes an increase in pressure loss in the region of the flow that contains more hydrates. This can be observed in Figure 9(d) due to the increase in horizontal pressure drop (blue line) as hydrates pass through the horizontal section.

• Hydrates apparently entrap part of the oil droplets that are dispersed in the water phase, as seen in the PVM images (Figure 10) where there are particles with a regular shape.

• An intermittent flow forms, characterized by two regions: one containing only oil, and another containing porous hydrate structures that entrap more and more the water phase, and hydrates entrapping oil as well, increasing the pressure loss due to viscosity in these regions. The main cause of plugging was due to a blockage of hydrates in specific parts of the loop, particularly in the downward section, as shown in Figure 9(d) where the pressure drop in the downward section is detected, for all the measurements, after 10 minutes, indicating that the flow is heterogeneous and, once again, parts of the flowing volume contain more hydrates than others: the transit of hydrates is therefore intermittent. Figure 12(a) shows that the density oscillates in a way that the troughs are around 950 kg/m 3 , which can be a mixture of all the phases, while the crests are at more than 1000 kg/m 3 , which means that there is mostly water on this part of the flow. Density and dielectric are in phase, as the time lag for a maximal Kendall correlation between the two data sets is 4 seconds. As a reminder, the tests are carried out with saline water, which has a density of more than 1000 kg/m 3 , while hydrates have a density of around 910 kg/m 3 and oil has a density of around 815 kg/m 3 . Therefore, with hydrates, the flow will be heterogeneous in the sense that there is a region that consists of water as a single phase and another region containing hydrates with oil flowing dispersed in the water continuous phase. Indeed, in Figure 12(b) one can notice that absolute energy and dielectric are in exact antiphase, i.e., the higher values of absolute energy (regions containing more hydrates) correspond to the troughs of dielectric (regions containing more oil).

The results shown in Figure 12(b) indicate that most of the hydrates entrap the oil phase, as evidence by the absolute energy being nearly 80 times higher when there is oil than when there is only water as a single phase. Although most of the hydrates flow entrapping oil droplets, it is expected that some hydrates may break apart from the oil droplets and flow suspended in the water continuous phase (de Almeida et al., 2022).

Figure 12. Experiment at 80% water-cut and 200 L/h (#12 in Table 5), showing the time evolution of (a) dielectric and density, (b) dielectric and absolute energy, (c) dielectric and chord counts, and (d)

horizontal pressure drop and flow rate.

Using the method proposed in subsection 4.1 to calculate the time-lagged cross-correlation between density and absolute energy, for this test at 80% water cut, it is shown in Figure 13 that the time lag between the two data sets is about 1.3 minutes, which is approximately half of the period of oscillation of the curves. One can also notice that the lowest correlation occurs near the center, for a time lag of zero.

This proves that the parts of the flow with more hydrates (peaks of absolute energy) are the same parts of the flow with more oil (valleys of density). Figure 13. Time-lagged cross-correlation analysis between density and absolute energy for the test at 80% water cut (#12 in Table 5).

To gain better understanding of the formation of this oscillatory behavior, Figure 14 shows an enlarged portion of Figure 12(b,c) at two different moments: after a few minutes of hydrate formation (left panels), and close to the end of the experiment (right panels). By analyzing the absolute energy along with the chord counts, one notices a different pattern between the two cases. On the left, hydrates are mostly spread throughout the flow, with a front carrying more particles. In contrast, on the right panel, hydrates are mostly collected at the front, with dispersed hydrates that flow with the continuous phase, while part of the flow will contain mainly water without hydrates or dispersed oil droplets. This is likely due to the difference in velocity between suspended particles of different sizes. Smaller particles accumulate over time behind large hydrate particles because they are faster than the larger ones. 5).

Figure 15. Images from the HSC for the experiment at 80% water cut and 200 L/h (#12 in Table 5)

showing that parts of the flow contain hydrates all the time (images on the left), while others have a reduction in the number of particles over time (images on the right).

We conclude the section proposing a conceptual model for hydrate formation and plugging at high water cut in oil-water dispersed systems, based on our experiments at 80% water cut. The model, represented in Figure 16, contains four stages. The first stage is the formation of an oil-in-water dispersion due to the shear caused by the flow. The second step includes the onset of hydrate formation around oil droplets. Hydrates will trap the oil droplets, while part of the hydrates detaches from the oil surfaces and flow suspended in the water continuous phase. In the third stage, due to the difference in velocity between particles of different sizes, the flow gradually becomes heterogeneous. Small particles seem to accumulate more and more behind large particles, since they can flow faster than the large ones. In the fourth stage, as one region of the flow contains an important local hydrates fraction and has a high enough pressure loss due to viscosity, the plug may eventually occur. For this case, hydrate blockage is a gradual event, and it takes longer than for tests at intermediate or low water cut to occur.

In Figure 16 The density measured by the Coriolis is influenced by the densities of the three phases that flow in the pipeline (water, hydrates and oil). This can be mathematically expressed as:

𝜌 =
It should be noted that the volumes of the three phases (𝑉 𝑊 , 𝑉 𝑂 , 𝑉 𝐻 ) are time-dependent and must be calculated in advance using the model proposed in section 3. Finally, the sum of all phases results in the simple relation:

𝛼 𝑙,𝐻 + 𝛼 𝑙,𝑊 + 𝛼 𝑙,𝑂 = 1 ( 13 )
The model represented by equations ( 10)-( 13) can be used to calculate the local fractions that pass through the Coriolis flowmeter. At low water cut, as illustrated in Figure 8 and Figure 11, where hydrates are transported by the water phase, equations ( 10), ( 11) and ( 13) are used. By combining these equations, the following expression can deduced for the local hydrate fraction:

𝛼 𝑙,𝐻 = 𝜌 -𝜌 𝑂 𝜌 𝐻 -𝜌 𝑂 + (𝜌 𝑊 -𝜌 𝑂 ) 𝑉 𝑊 𝑉 𝐻 ( 14 )
At high water cut, as illustrated in Figure 16, where hydrates flow preferably with the oil phase, it is used equations ( 11), ( 12) and ( 13), from which the following expression is deduced:

𝛼 𝑙,𝐻 = 𝜌 -𝜌 𝑊 𝜌 𝐻 -𝜌 𝑊 + (𝜌 𝑂 -𝜌 𝑊 ) 𝑉 𝑂 𝑉 𝐻 ( 15 )
Figure 17 shows the application of equation ( 14) for a test at 30% water cut and 200 L/h (#1 in Table 5). The hydrate fraction and local hydrate fraction are shown in Figure 17(a), while the absolute energy from sensor 1A is shown in Figure 17(b). The peaks observed in the local hydrate fraction are in phase with the peaks observed in the absolute energy, which indicates that the model is accurately capturing the regions containing more hydrates and regions with more liquid. Despite the final global hydrate fraction in the whole system (in orange) being nearly 7%, the local hydrate fraction can vary between 0 and approximately 18%. It is also important to consider that, due to the porosity, water is trapped inside the hydrate particles and, therefore, the volume fractions of the structures containing hydrates and water (see Figure 8) are expected to be much larger than the calculated local hydrate fractions. Figure 17. Hydrate fractions and absolute energy for a test at 30% water-cut and 200 L/h (#1 in Table 5).

Figure 18 shows hydrate fraction, local hydrate fraction and absolute energy for a test at 50% water cut and 400 L/h (#9 in Table 5). The global hydrate fraction in the system just before plugging (orange line, top panel) is approximately 6.6%, but due to the heterogeneous suspension of hydrates and their accumulation in some parts of the liquid, the local hydrate fraction (blue line, top panel) that passes through the Coriolis varies between 0 and 11.3%. Once more, the peaks of the local hydrate fraction are in phase with the peaks of absolute energy (bottom panel).

Figure 18. Local hydrate fraction and absolute energy for a test at 50% water-cut and 400 L/h (#9 in Table 5).

The application of equation ( 15) for a test at 80% water cut and 200 L/h (#12 in Table 5) is shown in Figure 19. The hydrate fraction (orange line, top panel) is approximately 8.5% just before plugging, but due to the heterogeneity of the flow, the local hydrate fraction may be up to 13.3%. One can notice that the peaks observed for local hydrate fraction are in phase with the absolute energy (bottom panel).

The proposed model can be used to predict plugging by monitoring local peaks of the hydrate fraction.

However, it is important to discuss the limitations of the model that led to the development of equations ( 14) and (15). It can only be used when all the hydrates are being transported by one phase only, water or oil. In this sense, further experiments combined with the implementation of other techniques to obtain the local hydrate fraction could validate the proposed model. Despite its limitations, equations ( 14) and ( 15) can be used for a rapid estimation of the local hydrate in the system and help to better understand the degree of heterogeneity of the flow.

Figure 19. Apparent hydrate fraction and absolute energy for a test at 80% water-cut and 200 L/h (#12 in Table 5).

To conclude the analysis, a methodology to classify the tests according to their heterogeneity is proposed here. It consists of calculating the volume fraction of the flow (relative to the global volume of liquid and hydrates inside the flow loop) that contains 50% of all the hydrates (the upper half of data) formed globally in the system. This means that an integration of the local hydrate fraction (equations ( 14) or ( 15)) over time is performed during the last lap around the loop, just before plugging, in a decrescent order from the highest to the lowest hydrate fraction, while keeping track of the volume as a function of the flow rate measured by the Coriolis. For instance, a system that is highly heterogeneous will have 50% of the hydrates accumulated in a very small fraction of the flow.

This method is applied to all the tests listed in Table 5. The results are shown in Figure 20, which shows the time for plugging since the onset of hydrate formation as a function of the volume (in fraction)

occupied by the 50% of the hydrates (the upper half of data) flowing in the pipeline.

The results indicate that tests at high water cut (in red) are less prone to plug and are more homogeneous, with 50% of the hydrates concentrated in a volume fraction between 26% and 34% of the flow volume. As the water cut reduces, the slurry flow regime becomes increasingly heterogeneous and plugs faster than at high water cut. For the tests at intermediate water cut (in blue), hydrates are concentrated in a volume fraction between 17% and 28% of the volume, while for tests at low water cut, they are concentrated in a volume fraction between 10% and 23%.

Figure 20. Time for plugging as a function of the heterogeneity of the system.

Summary of results

As previously discussed, from the literature, the conceptual models of hydrate formation and plugging in oil-water dispersed systems assume that the formation of large particles that cause plugging occurs mainly due to agglomeration. Agglomeration models have been extensively studied. In this work, it was observed, however, that there are other mechanisms that deserve better attention and could be very important for a better understanding of how plugging occurs.

Separation of phases has been observed in the tests, whose main outcomes are summarized in Table 6. The separation of phases changes the flow regime in a way that presents distinct structures along space, comparable to an intermittent flow. Parts of the flowing volume have higher concentrations of hydrates than others. This can be because structures of different sizes or densities will flow at different velocities, which facilitates the formation of the intermittent flow. It was also observed, especially at low and intermediate water cut, that the dispersed phase rapidly coalesces (large water pockets at low water cut, or large oil pockets at intermediate water cut). This mechanism is not well understood and requires additional studies. The analysis proposed in section 4.5 showed that this separation of phases is more significant at low and intermediate water cut, and it can lead to plugging in a few minutes.

Table 6. Summary of conceptual mechanisms for hydrate blockage from low to high water cuts.

Water cut Model

Low A water-in-oil dispersion is the initial flow regime. When hydrates form, they break into water droplets. Water droplets coalesce, forming large pockets of water with suspended hydrates. Deposition occurs, and hydrates become highly concentrated in a portion of the flowing volume, which eventually causes plugging.

Intermediate

An oil-in-water dispersion is the flow regime in the beginning. When hydrates form, they preferably break into the water phase, while oil droplets coalesce.

This leads to deposition and concentration of hydrates in parts of the flow, eventually causing plugging.

High

An oil-in-water dispersion is the initial flow regime. Hydrates initially form around oil droplets. As they continue forming and entrapping the oil droplets, part of them breaks into the water continuous phase. Suspended particles seem to flow faster than hydrates with entrapped oil, resulting in a gradual transition to an intermittent flow.

Conclusions

In this work, flow loop tests were carried out with hydrate formation in oil-water flow at 30%, 50% and 80% water cut. New mechanisms on how the slurry flow after hydrate formation is and how hydrate blockage occurs were proposed. The main conclusion from the experiments is that there is a formation of a heterogeneous flow that results from a separation of phases. The resulting flow patterns are characterized by parts of the flow containing more hydrates than others. Deposition was also an important mechanism that contributed to plugging formation.

At low water cut, hydrate formation completely modifies from the initial water-in-oil dispersion to a flow pattern composed of large water pockets with hydrates that rapidly plug the system. At intermediate water cut, a rapid separation of phases is observed, disrupting the oil-in-water dispersion and forming a flow with a pattern composed of two main structures: one being a large pocket of oil, and another region containing hydrates suspended in the water phase. At high water-cut, hydrates apparently entrap the oil phase, as it was shown that hydrates flow mostly with the oil phase, although part of the hydrates may break loose and become suspended in the water continuous phase.

A simplified model based on density measurement was proposed to estimate the local hydrate fraction and quantify the heterogeneity of the system. It was shown that tests at high water cut are less heterogeneous, which explains why they flow much longer than at intermediate or low water cut. The extension of the tests to other conditions, the improvement and validation of the model could be conducted in a further work for a better understanding of the mechanism of plugging due to the formation of these heterogeneous flows and the quantification of the transportability of the system.

Figure 2 .

 2 Figure 2. Experimental protocol.

Figure 3 .

 3 Figure 3. Representation of the algorithm to obtain the dielectric as a function of the water fraction.

Figure 4 .

 4 Figure 4. Analysis of the flow regime comparing dielectric vs. water cut (left panels) to images obtained from the camera (right panels) as a function of water cut and flow rate.

  (b), which consistently remains at high values for three minutes after the onset. The horizontal pressure drop (Figure5(c)) has its highest values during this early stage of hydrate formation, as there are more interfaces between the phases and shear due to flow if the system is homogeneous.Ding et al., 2019 observed similar

(

  Figure 11. Depiction of the conceptual model for hydrate blockage at intermediate water cut.

Figure 15

 15 Figure15shows some images which visually confirm the intermittent behavior captured by the signals. The images on the left were taken during the passage of large agglomerates (the "front" part of the flow containing hydrates entrapping oil droplets), while images on the right are taken during the passage of parts of the flow containing mostly water as a single phase. The images observed on the right contain less and less hydrates over time, which corresponds to the reduction of absolute energy in that region of the flow (see Figure14(b), between 86.5 and 87.5 minutes).

Figure 14 .

 14 Figure 14. Comparison between dielectric, absolute energy and chord counts in two 3-minutes intervals for the experiment at 80% water cut and 200 L/h (#12 in Table5).

  , a few images from the camera are shown to illustrate how they compare to the different stages. The first image, on the left, shows the flow before hydrate formation. The second image was taken at the early stages of hydrate formation, when the droplets become darker, as hydrates form around them. The two images on the right are from the two different structures during the last stage, when one part of the flow contains hydrates entrapping oil suspended in water and the other part contains mostly water.

Figure 16 .

 16 Figure 16. Depiction of the conceptual model for hydrate blockage at high water cut.

Table 2 .

 2 Fluids used for the experiments and their properties.

	Fluid	Description	Fluid properties
	Salted water	30 g of NaCl per L of deionized water.	Density: 1023.8 g/L Viscosity: 0.0026 Pa.s
	Kerdane oil	Hydrocarbons, C11-C14, n-alkanes, isoalkanes, cyclics, <2% aromatics.	Density: 815 g/L Viscosity: 0.0017 Pa.s

Table 3 .

 3 Composition of the natural gas.

	Gas	%mol
	Methane	91.7
	Ethane	5.9
	Propane	0.6
	Carbon dioxide 0.8
	Nitrogen	0.8
	i-butane	0.1
	n-butane	0.1

Table 4 .

 4 Control parameters for the experiments.

Table 5 .

 5 List of experiments.

	Experiment	Water cut	Flow rate	Water conversion	Hydrate fraction	Time with hydrates until plug
	#1			18.7%	8.0%	13.4 min
		30%	200 L/h
	#2			11.1%	4.1%	6.1 min

  𝜌 𝐻 𝛼 𝑙,𝐻 + 𝜌 𝑊 𝛼 𝑙,𝑊 + 𝜌 𝑂 𝛼 𝑙,𝑂( 10 ) It was explained in previous sections that hydrates are transported preferentially by the water phase at low and intermediate water cut, or preferentially by the oil phase at high water cut. In this sense, it is assumed that the proportion of local fraction of hydrates relative to the carrier phase is uniform in space and equal to the global proportion of those phases in volume. In other words, locally in the flow, the quantity of hydrates relative to the phase that transports the hydrates is everywhere the same.Therefore, if hydrates are transported by the water phase, it is assumed that, at any given instant, the local fraction of hydrates is proportional to the local water fraction via the global hydrate-to-water volume

	fraction:
	𝛼 𝑙,𝐻 𝛼 𝑙,𝑊	=	𝑉 𝐻 𝑉 𝑊	( 11 )
	However, if hydrates are preferentially transported by the oil phase, it is assumed that, at any given
	time, the local hydrate fraction is proportional to the local oil fraction via the global hydrate-to-oil volume
	fraction:
	𝛼 𝑙,𝐻 𝛼 𝑙,𝑂	=	𝑉 𝐻 𝑉 𝑂

ACKNOWLEDGMENTS

The authors would like to acknowledge the financial support from TotalEnergies in the framework of the Archimede 5 project. We would like to thank the technical support provided by the staff of Mines Saint-Etienne. We also thank Mr. P. Glénat, who recently retired from TotalEnergies, for sharing his valuable experience and knowledge, which greatly contributed to the development of this work.

Sum, A.K., Koh, C.A., Sloan, E.D., 2009. Clathrate hydrates: From laboratory science to engineering practice. Ind Eng Chem Res. https://doi.org/10.1021/ie900679m Turner, D.J., 2005. Clathrate Hydrate Formation in Water-in-oil Dispersions. Golden. Wang, Z., Tong, S., Wang, C., Zhang, J., Fu, W., Sun, B., 2020. Hydrate deposition prediction model for deep-water gas wells under shut-in conditions. Fuel 275. https://doi.org/10.1016/j.fuel.2020.117944


Marco Antonio Chiminazzo
email: marco.chiminazzo@unesp.br

Aline Bertolosi Bombo

Tristan Charles-Dominique

Marco Chiminazzo¹

Aline Bertolosi Bombo¹

Tristan Charles-Dominique²

Alessandra Fidelis¹

Bark production of generalist and specialist species across savannas and forests in the Cerrado

Keywords: bark growth rate, bark thickness, bud protection, fire, savanna-forest boundaries, neotropical savannas, persistence niche, resprouting

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

INTRODUCTION

Savannas have a continuous flammable grass layer that supports frequent fires, creating a harsh environment for trees to persist as their aboveground parts are exposed to flames [START_REF] Bond | Fire and plants[END_REF]. In contrast, forests present a continuous tree canopy, which shades out flammable grasses [START_REF] Hoffmann | Ecological thresholds at the savanna-forest boundary: how plant traits, resources and fire govern the distribution of tropical biomes[END_REF][START_REF] Charles-Dominique | Steal the light: shade vs fire adapted vegetation in forest-savanna mosaics[END_REF]. Tropical savannas and forests are distributed in mosaics mostly maintained by fire [START_REF] Staver | The Global Extent and Determinants of Savanna and Forest as Alternative Biome States[END_REF]Pausas, 2015a), which selects species capable of persisting in fire-prone ecosystems [START_REF] Rossatto | Differences in growth patterns between cooccurring forest and savanna trees affect the forest-savanna boundary[END_REF][START_REF] Hoffmann | Ecological thresholds at the savanna-forest boundary: how plant traits, resources and fire govern the distribution of tropical biomes[END_REF]Charles-Dominique et al., 2015a;[START_REF] Lamont | Evolutionary history of fire-stimulated resprouting, flowering, seed release and germination[END_REF]. Bark thickness of woody species is a key trait that enables their survival during fires by protecting the inner tissues of the stem from the flames and the heat [START_REF] Lawes | Costs and benefits of relative bark thickness in relation to fire damage: a savanna/forest contrast[END_REF][START_REF] Rosell | Bark functional ecology: evidence for tradeoffs, functional coordination, and environment producing bark diversity[END_REF]Pausas, 2015b), thus allowing the species to resprout new branches from their aerial buds [START_REF] Bond | Ecology of sprouting in woody plants: the persistence niche[END_REF][START_REF] Clarke | Resprouting as a key functional trait: how buds, protection and resources drive persistence after fire[END_REF].

Bark tissues are responsible for providing mechanical and chemical protection to the plant inner living tissues and to meristematic regions responsible for the plant regeneration [START_REF] Burrows | Epicormic strand structure in Angophora, Eucalyptus and Lophostemon (Myrtaceae) -implications for fire resistance and recovery[END_REF][START_REF] Vanderweide | Fire resistance of tree species explains historical gallery forest community composition[END_REF][START_REF] Hoffmann | Ecological thresholds at the savanna-forest boundary: how plant traits, resources and fire govern the distribution of tropical biomes[END_REF][START_REF] Schafer | Relative Bark Thickness is Correlated with Tree Species Distributions along a Fire Frequency Gradient[END_REF][START_REF] Scalon | Diversity of functional trade-offs enhances survival after fire in Neotropical savanna species[END_REF]. Bark is not only important for insulating vital tissues, but also for storing important resources like nonstructural carbohydrates which are fundamental for resprouting post-fire [START_REF] Clarke | Resprouting as a key functional trait: how buds, protection and resources drive persistence after fire[END_REF][START_REF] Rosell | Inner bark as a crucial tissue for non-structural carbohydrate storage across three tropical woody plant communities[END_REF]. In fire-prone ecosystems, the rate at which bark is produced (bark growth rate hereafter) is crucial for species persistence, as it determines their capacity to accumulate enough bark between fire events [START_REF] Vanderweide | Fire resistance of tree species explains historical gallery forest community composition[END_REF][START_REF] Schafer | Relative Bark Thickness is Correlated with Tree Species Distributions along a Fire Frequency Gradient[END_REF][START_REF] Charles-Dominique | Fire frequency filters species by bark traits in a savanna-forest mosaic[END_REF][START_REF] Wigley | A handbook for the standardised sampling of plant functional traits in disturbance-prone ecosystems, with a focus on open ecosystems[END_REF].

In flammable ecosystems, woody plants are expected either to prioritize a fast vertical growth when fires are less frequent and less intense (Lanky strategy) or to prioritize a fast bark growth rate when fire is more frequent and intense (Corky strategy; Dantas and Pausas, 2013). Neotropical savannas are largely dominated by plants with the Corky strategy, thus accounting for a greater bark investment when compared to Afrotropical savanna woody species (Dantas and Pausas, 2013). However, different growth forms -like trees and shrubscoexist in those areas. This coexistence is puzzling: while trees can grow their canopy above the flame height and escape the firetrap, shrubs complete all their developmental stages within the flame zone due to their lower stature. These differences suggest that low and high statured plants (i.e., shrubs and trees) would require very different rates of bark production with strong implications over their allocation strategies.

In frequently disturbed environments, the fitness of plants can depend much more on surviving in early stages than outcompeting neighbors or reproducing abundantly [START_REF] Bond | Ecology of sprouting in woody plants: the persistence niche[END_REF]; with species often relying in bud protection and bark production to persist in the environment [START_REF] Clarke | Resprouting as a key functional trait: how buds, protection and resources drive persistence after fire[END_REF]Pausas, 2015b;[START_REF] Pausas | Unearthing belowground bud banks in fire-prone ecosystems[END_REF]. Previous investigations in African savannas revealed that bark growth rate and bud protection are not strongly correlated, suggesting that buds are not simply passively buried under bark but instead maintained actively in or out the bark layer over time [START_REF] Charles-Dominique | Fire frequency filters species by bark traits in a savanna-forest mosaic[END_REF]. Further, the two traits could be expressed in combination to promote fire resistance and to enhance resprouting capacity. As these first observations were made in African vegetation that faces less intense fires than the Cerrado (a species-rich, tropical savanna located in Brazil, where fire is one of the major drivers maintaining the mosaics of savannas and forests; Dantas and Pausas, 2013;[START_REF] Charles-Dominique | Fire frequency filters species by bark traits in a savanna-forest mosaic[END_REF], here we investigate whether these two traits (aerial bud protection and bark growth rate) are more frequently combined in neotropical species exposed to more intense grass fires than those from the Afrotropical savannas.

Although woody communities from savannas and forests differ in composition and structure, some species can inhabit both environments (generalists). We distinguish them from specialist species that cannot overcome the environmental barrier between forest and savanna and are restricted to a single habitat type [START_REF] Futuyma | The Evolution of Ecological Specialization[END_REF]. When strong environmental filters such as fire and shade select species and assemble communities, very contrasted strategies are selected and filtered at the both ends of the spectrum [START_REF] Hoffmann | Ecological thresholds at the savanna-forest boundary: how plant traits, resources and fire govern the distribution of tropical biomes[END_REF]Charles-Dominique et al., 2015b;2017). In the presence of such filters, observing generalist species is particularly interesting as their presence in both ecosystems either means that their biology includes a unique combination of traits that allow them to survive in both ecosystems or they adopt a plastic behavior allowing them to overcome the environmental barrier. Do they optimize bark production to have enough to survive fire but not too much with costs compatible with shaded environments (e.g., "jack-of-all-trades-master-of-none"; MacArthur, 1961)? Or can they adjust their bark production according to the habitat they are developing in, thus behaving like specialists of both systems (e.g., "jack-of-all-trades-masterof-some"; [START_REF] Richards | Jack of all trades, master of some? On the role of phenotypic plasticity in plant invasions[END_REF]?

Both bark production and aerial bud protection are important traits for species survival in fire-prone ecosystems, but to which extent they interact influencing communities and species across contrasting habitats and growth forms needs further investigation. In this study we address these points by exploring four different questions: (i) We first investigate whether species are selected by fire frequency according to their bark growth rate in neotropical savanna-forest mosaics; (ii) second, we explore whether generalist species adjust their bark production according to their growing environment; (iii) third, we test whether bark production differ between species with different predominant growth forms (specifically trees and shrubs that have their aerial biomass exposed differently to the flames). (iv) Last, we test whether a greater bark production is linked to a better aerial bud protection.

MATERIALS AND METHODS

Study area

We conducted the study at the Santa Bárbara Ecological Station (SBES,22º48'59'' S,49º14'12'' W,589 m.a.s.l.), a protected area in southeastern Brazil that comprises a wide diversity of co-existing vegetation types. Mean temperature ranges from 18 to 22 ºC and mean annual rainfall from 1,000 to 1,300 [START_REF] Melo | Plano de Manejo -Estação Ecológica de Santa Bárbara[END_REF]. Climate is seasonal, with a wet and hot summer and a dry and mild winter. We selected four different vegetation types with contrasted fire frequencies (fire events calculated over from 1984 to 2018; [START_REF] Conciani | Human-climate interactions shape fire regimes in the Cerrado of São Paulo state, Brazil[END_REF]: i) open savannas, high fire frequency with 5-9 fire events; defined by high grass cover (up to 80%) co-occurring with short tress and scattered shrubs; ii) woody savannas, low fire frequency with 0-2 fire events; characterized by a lower grass cover (up to 50%) and taller trees capable of suppressing the continuous grass layer by shading locally; iii) cerradão, no fire event; forests composed of a mix of generalists (species occurring in both savannas and forests) and dry forest species; and iv) seasonal semi-deciduous forest, no fire event; forests composed of tall tree species of the Atlantic Rainforest (Fig. 1, [START_REF] Coutinho | Fire in the Ecology of the Brazilian Cerrado[END_REF].

Sampling design and study species

We randomly established 10 transects of 10 x 40 m in each vegetation type. We registered every woody individual with a height between 1.5 and 3 m, because within this height different growth forms (trees and shrubs) are exposed to the flames, thus creating a bottleneck that woody species must overcome to persist in the environment. For each sampled species within the transects, we collected three to five branches from three different individuals in every vegetation type they occurred and scored the predominant growth form according to [START_REF] Abreu | The biodiversity cost of carbon sequestration in tropical savanna[END_REF] and Flora do Brasil (2020).We then classified every species as savanna exclusive, forest exclusive, or generalists (species that inhabits at least one savanna and one forest vegetation) according to [START_REF] Abreu | The biodiversity cost of carbon sequestration in tropical savanna[END_REF] and Flora do Brasil (2020). To compare the bark production of generalist species growing into forests and savannas, we analyzed ten different species that were sampled in at least one savanna and one forest in our study area:

Trait description

We scored the level of bud protection using the Index of bud protection (IBP) after performing at least three observations from three different individuals for each species [START_REF] Burrows | Epicormic strand structure in Angophora, Eucalyptus and Lophostemon (Myrtaceae) -implications for fire resistance and recovery[END_REF]Charles-Dominique et al., 2015a). This index informs, from macro-anatomical observations on main longitudinal sections of main branches (morphologically analogous to trunk), about the degree of bud protection, which ranges from 0 (bud fully exposed to fire) to 3 (bud fully covered by bark) (Charles-Dominique et al., 2015a, Fig. 2a). The index of bud protection for the species in the study area was described in [START_REF] Chiminazzo | Your best buds are worth protecting: Variation in bud protection in a fire-prone cerrado system[END_REF]. We assessed the bark growth rate (BGR) in well-developed branches with similar architecture to the main stem (this method of sampling allows a direct comparison of bark production between single and multi-stemmed species, i.e., trees and shrubs) through transversal sections and considered as bark every tissue external to the wood -cambium region [START_REF] Wigley | A handbook for the standardised sampling of plant functional traits in disturbance-prone ecosystems, with a focus on open ecosystems[END_REF]. We then counted the number of visible growth rings and photographed the samples under a binocular stereomicroscope. When growth rings were not visible, we considered the number of stem growth units [START_REF] Barthélémy | Plant Architecture: A Dynamic, Multilevel and Comprehensive Approach to Plant Form, Structure and Ontogeny[END_REF][START_REF] Wigley | A handbook for the standardised sampling of plant functional traits in disturbance-prone ecosystems, with a focus on open ecosystems[END_REF]. Sampled branches had 3.20 ± 0.69 growth units and 7.09 ± 2.25 mm of diameter. Bark thickness was then measured at the base of the stems in three different parts through radial sections by using the ImageJ software [START_REF] Schneider | NIH Image to ImageJ: 25 years of image analysis[END_REF]. We then divided the mean bark thickness by the number of growth rings/units of the branch at the given section to obtain the bark growth rate [START_REF] Charles-Dominique | Fire frequency filters species by bark traits in a savanna-forest mosaic[END_REF][START_REF] Wigley | A handbook for the standardised sampling of plant functional traits in disturbance-prone ecosystems, with a focus on open ecosystems[END_REF]. To avoid confounding the rate of bark production with bark shedding, we made sure that no bark was shed at the location of the recorded anatomical section by assessing the presence of dead epidermis out of the outermost bark layer (Charles-Dominique et al., 2017) and collected branches with at least three growth units (Fig. 2a,b). We tested whether the variation in bark growth rates was explained either by an overall growth of the plant that could be produced in vegetation types with resource limitation (here light) or by a differential allocation to bark that could be related to the fire frequency experienced by species. We did so by comparing the growth rate to the wood growth rate (length of pith plus wood on a transversal section divided by the numbers of growth units), used here as a proxy for the overall growth of the plant. Lastly, to make sure that differences considering the bark growth rate of generalist species were not driven by changes in abundance and composition across vegetation types, we compared the bark production of 10 generalist species sampled in at least one forest and one savanna vegetation.

Statistical analyses

We used generalized linear mixed effect models (GLMM) to analyze how bark growth rate was distributed across vegetation types with different fire frequencies and between species' growth forms (shrubs and trees). We considered sampled individuals as a random effect and the vegetation type and growth form as fixed effects. After testing the significance of fixed effects, we performed pairwise comparisons using post hoc tests of Tuckey type with Bonferroni correction. We used generalized linear models (GLM) to analyze the relationship between bud protection and bark growth rate and between bark growth rate and fire frequency. We then calculated the community-weighted mean values (i.e., the weight of a trait in a given community -CWM) of the bark growth rate in each plot to analyze how the CWM values were distributed along fire frequencies. We then assessed at which threshold woody communities switch from savannas to forests by considering the 1st and the 3rd quartiles of bark growth rate in each vegetation type. GLMs and post-hoc tests as described above were used to test the differences of CWM according to the fire frequency and to compare the variation of wood and bark growth rates across vegetation types and generalist versus specialist species. To test differences in bark production of generalist species sampled in both savannas and forests, we used linear mixed-effects models considering the vegetation type as a fixed factor and the species and their individuals as random effects. To do so, we first obtained each generalist species mean bark production and compared it with the bark production of each individual sampled in savannas and forests. This approach allowed us to obtain the residuals of each individual bark production (translating into bark adjustment across different vegetation types). Lastly, to test differences between shrubs and trees (regardless the vegetation they occur) we performed a Wilcoxon signed-rank test as bark production of both trees and shrubs were not normally distributed (Shapiro-Wilk normality test, P ≤ 0.001). All analyses were performed on the R software (R Core Team, 2022).

RESULTS

Bark growth rate (BGR) differed between woody communities of forests and savannas. Bark growth rate was greater in species occurring in savannas, with c. three-fold faster bark production than forest species (P ≤ 0.001, Fig. 3a, Table 1). Generalist species produced more bark when growing in savanna compared to individuals growing in forest, on average with an additional 0.1 mm of bark per growth unit in savanna compared to forest (P < 0.01, Fig. 3c). No interaction was detected between bark growth rate of different growth forms and among the similar vegetation types (open/woody savanna and cerradão/semideciduous forests). Contrary to our predictions, the bark production of trees and shrubs did not differ. However, within each vegetation type, trees showed a tendency of producing more bark than shrubs (Fig. 3b). Species BGR scores are shown in Supplementary Information (Table S1).

Community-weighted means (CWM) for bark growth rate varied according to fire frequency (Table 1). Savannas with high fire frequency (open savannas, 5-9 fire events) had CWM values of bark growth rate of 0.379 mm/year, while savannas with lower fire frequency (woody savannas, 1-2 fire events) showed a CWM of 0.293 mm/year. Forest systems (no fire) showed a CWM of 0.107 mm/year. A shift from forest to savanna communities was found at the first quartile ranking species' bark production in open savannas and the third quartile in the semi-deciduous forest, indicating that a minimum bark growth rate of c. 0.13 mm/year is required for species to occur in fire-prone systems (Fig. 3), thus differentiating communities of forests and savannas (P ≤ 0.001, Table 1). Above this threshold, most species occurring in open (33 species, 80% of the community) and woody (40 species, 71%) savannas produce at least 0.13 mm of bark. In forests, only 20 (41%) species from the cerradão and 6 (18%) species from the semi-deciduous forest produced more than 0.13 mm of bark. Out of the 26 species sampled in forest that produced bark above this threshold, 30% are forest specialists, 38% are generalists, and 26% are savanna specialists (in forests, these savanna-specialist species were represented by one or two individuals and were sampled in the cerradão). In savannas, out of the 25 sampled species that produced bark below the threshold, 32% are generalists and 59% are savanna specialists (we did not account species without available information about their habitat of preference and one forest specialist species sampled in the open savanna; Fig. 3a). The ratio between wood growth rate and bark growth rate differed greatly across vegetation types (P ≤ 0.001) and between generalists and specialists (P ≤ 0.01; Fig. S1, Table S2), thus indicating that allocation to bark differed. Lastly, woody individuals with higher degrees of bud protection (IBP = 2, 3) produced bark c. 2 times faster than individuals with low or no bud protection (IBP = 0, 1; Table 1, P ≤ 0.001).

DISCUSSION

The results of our study highlight the importance of bark production in selecting species across contrasting environments such as forests and savannas. As expected, we found that bark growth differs across savanna and forest communities, revealing a minimal threshold of bark (0.13 mm/growth unit) that is required to avoid top-kill and that could be facilitating a greater species survival in fire-prone ecosystems. Interestingly, we observed that a greater bark production is combined with a better aerial bud protectiontwo key traits that improve species persistence in fire-prone ecosystems [START_REF] Burrows | Epicormic strand structure in Angophora, Eucalyptus and Lophostemon (Myrtaceae) -implications for fire resistance and recovery[END_REF][START_REF] Vanderweide | Fire resistance of tree species explains historical gallery forest community composition[END_REF][START_REF] Hoffmann | Ecological thresholds at the savanna-forest boundary: how plant traits, resources and fire govern the distribution of tropical biomes[END_REF][START_REF] Schafer | Relative Bark Thickness is Correlated with Tree Species Distributions along a Fire Frequency Gradient[END_REF][START_REF] Charles-Dominique | Fire frequency filters species by bark traits in a savanna-forest mosaic[END_REF][START_REF] Chiminazzo | Your best buds are worth protecting: Variation in bud protection in a fire-prone cerrado system[END_REF]. Contrary to our predictions, we did not find any differences considering bark growth rate between shrubs and trees. This is particularly interesting because even though different growth forms experience fire differently (the aboveground structures of shrubs are entirely within the flames, while tree canopies can escape the flame zone), both trees and shrubs are generally required to produce significant amounts of bark when growing into fire-prone ecosystems. Further, we did not find differences in the bark production of generalist and specialist species. This later result further revealed that generalist species possibly rely on a plastic production of bark according to the environment they grow in. In the following, we expand on the implications these results have for the persistence of species in fire-prone ecosystems.

On the importance of bark production for generalist and specialist species in savannaforest mosaics

Woody species from savannas produce three times more bark than forest woody species, suggesting that a minimum of 0.13 mm of bark production is required to avoid topkill in fire-prone ecosystems. In fire-free forests, most individuals develop less bark than this threshold. Bark is an important fire-related trait, as it creates a physical barrier against the flames, insulating vital inner tissues from the heat [START_REF] Rosell | Bark functional ecology: evidence for tradeoffs, functional coordination, and environment producing bark diversity[END_REF]Pausas, 2015b).

Accordingly, the rate at which bark is produced is also important in flammable ecosystems, since it determines the capacity of woody plants to have their resprouting stems and young saplings protected before experiencing their first fire event [START_REF] Charles-Dominique | Fire frequency filters species by bark traits in a savanna-forest mosaic[END_REF][START_REF] Wigley | A handbook for the standardised sampling of plant functional traits in disturbance-prone ecosystems, with a focus on open ecosystems[END_REF]. Therefore, the existence of a threshold of bark growth rate between forest and savanna further confirms the role of fire in maintaining savannas and forest as mosaics [START_REF] Hoffmann | Ecological thresholds at the savanna-forest boundary: how plant traits, resources and fire govern the distribution of tropical biomes[END_REF]Dantas et al., 2013;Charles-Dominique et al., 2015b) and the importance of bark investment for Cerrado woody species [START_REF] Hoffmann | Ecological thresholds at the savanna-forest boundary: how plant traits, resources and fire govern the distribution of tropical biomes[END_REF]Dantas and Pausas, 2013;[START_REF] Scalon | Diversity of functional trade-offs enhances survival after fire in Neotropical savanna species[END_REF]2021[START_REF] Loram-Lourenço | A Structure Shaped by Fire, but Also Water: Ecological Consequences of the Variability in Bark Properties Across 31 Species From the Brazilian Cerrado[END_REF]. We further observed that allocation to bark varied across vegetation types, as the bark growth rates were not following the same variation as wood growth rates (similar variation of rates would indicate that the overall growth of the plant varies according to the growing environment, not its allocation to bark).

When fire is suppressed from savannas, generalist species are usually those contributing the most to the density of woody communities [START_REF] Flake | Savannas are not old fields: Functional trajectories of forest expansion in a fire-suppressed Brazilian savanna are driven by habitat generalists[END_REF]. These species can produce bark above the threshold at an equal rate as many savanna-specialist species and could develop in savannas where fire is present. Interestingly, generalist species sampled in forests where fire is absent have bark production that is equivalent to forest specialists. This capacity of generalist species to adjust their bark production according to their environment defines them as "jack-of-all-trades-mater-of-some", a strategy that characterizes the most ecologically successful species, such as invasive ones (see [START_REF] Richards | Jack of all trades, master of some? On the role of phenotypic plasticity in plant invasions[END_REF]. Further studies should focus on the physiological mechanisms explaining this ability to adjust bark production and the associated benefits in terms of cost reduction and increased survival as generalists could have a profound effect on forest-savanna mosaics by allowing transitions between them and induce successional patterns that are not simply driven by increasing shade. Importantly, further investigation could help understand how this bark adjustment is performed by the plant and how it relates to the overall allocation strategies.

Different growth forms, similar bark production? Bark growth rate across trees and shrubs

The bark production of trees and shrubs did not differ in our study area. This result is particularly interesting because we expected the opposite: fire severity is greater at up to 50 cm above the soil surface and reduced below and above this area and drop quickly above the flame height [START_REF] Clarke | Resprouting as a key functional trait: how buds, protection and resources drive persistence after fire[END_REF][START_REF] Graves | Outer bark thickness decreases more with height on stems of fire-resistant than fire-sensitive Floridian oaks (Quercus spp.; Fagaceae)[END_REF][START_REF] Rodrigues | Fire frequency affects fire behavior in open savannas of the Cerrado[END_REF], species with different growth forms face fire differently by being within or outside the flame zone [START_REF] Chiminazzo | Your best buds are worth protecting: Variation in bud protection in a fire-prone cerrado system[END_REF] and should thus display different rates of bark production. Bark production for example varies within the same individual, with bark investment being greater near to the soil when compared to the canopy [START_REF] Graves | Outer bark thickness decreases more with height on stems of fire-resistant than fire-sensitive Floridian oaks (Quercus spp.; Fagaceae)[END_REF][START_REF] Scalon | Diversity of functional trade-offs enhances survival after fire in Neotropical savanna species[END_REF][START_REF] Lawes | Bark functional ecology and its influence on the distribution of Australian half-butt eucalypts[END_REF]. Therefore, the lack of differences in bark production of trees and shrubs may be an indication that both growth forms must invest in producing bark when growing in fireprone ecosystems, thus questioning how much of bark production is traded for a short stature (e.g., Dantas et al., 2013;[START_REF] Scalon | Diversity of functional trade-offs enhances survival after fire in Neotropical savanna species[END_REF], at least in areas with frequent fires.

Out of the few species inhabiting savannas and producing bark below the threshold, most of them are shrubs (66%) and savanna specialists (54%), thus suggesting the presence of alternative strategies for shrub persistence in savannas. Strategies other than investing in bark production aboveground, like trichomes covering buds [START_REF] Chiminazzo | Your best buds are worth protecting: Variation in bud protection in a fire-prone cerrado system[END_REF] and resprouting from belowground bud-bearing organs were reported for most of these species [START_REF] Pilon | The diversity of post-fire regeneration strategies in the cerrado ground layer[END_REF][START_REF] Zupo | Post-fire regeneration strategies in a frequently burned Cerrado community[END_REF][START_REF] Chiminazzo | Your best buds are worth protecting: Variation in bud protection in a fire-prone cerrado system[END_REF]. Therefore, although bark production is strongly linked to savanna communities, shrub species can trade this strategy by allocating most of their biomass belowgroundalso allowing them to persist in the post-fire environment (Klimešová andKlimeš, 2007, Pausas et al., 2018;[START_REF] Ott | The ecology and significance of below-ground bud banks in plants[END_REF][START_REF] Pilon | The diversity of post-fire regeneration strategies in the cerrado ground layer[END_REF][START_REF] Zupo | Post-fire regeneration strategies in a frequently burned Cerrado community[END_REF][START_REF] Chiminazzo | Your best buds are worth protecting: Variation in bud protection in a fire-prone cerrado system[END_REF]. Hence, bark production alone cannot fully explain the assembly of communities with different growth forms in fire-prone ecosystems.

A greater bark production accounts for a better aerial bud protection

Our results indicate that bark growth rate was strongly related to bud protection.

Previous examples on Afrotropical savannas showed that bud protection was only weakly correlated to bark production [START_REF] Charles-Dominique | Fire frequency filters species by bark traits in a savanna-forest mosaic[END_REF]. A possible reason for these divergences may be differences in fire frequencies and intensities, since the Cerrado is exposed to more intense fires than the Afrotopical savannas (Dantas and Pausas, 2013). However, differences in fire intensities/frequencies may not fully explain the bark production-bud protection relationship: conifers exposed to surface fires (e.g., in the Andes and the western North America) can produce thick bark capable of surviving fires (Pausas, 2015b), but this group of plants can present empty axils and meristems embedded near the bark surface translating into a lower bud protection [START_REF] Burrows | Axillary Meristems and the Development of Epicormic Buds in Wollemi Pine (Wollemia nobilis)[END_REF]. Conversely, eucalypts are remarkable for expressing the highest bud protection (embedded within the wood) while having very low bark thickness [START_REF] Burrows | Epicormic strand structure in Angophora, Eucalyptus and Lophostemon (Myrtaceae) -implications for fire resistance and recovery[END_REF]. The relationship between bark production and aerial bud protection should therefore be investigated more widely across different systems and species clades to better understand the ecological meanings of either investing in bud protection, or in greater bark growth rate, or in both.

Conclusions

Bark growth rate is an important trait for sorting woody plant species in savanna and forest mosaics in the Cerrado. In flammable ecosystems, a high bark growth rate is combined with very high aerial bud protection, evidencing that fire is a particularly strong environmental filter selecting species in the Cerrado. The differences in bark production of generalist species question further how plants could adjust their resource allocation to bark depending on the presence of fire or light availability. More studies on the intraspecific variability of fire adaptations are urgently required to understand better how the generalists can bridge two worlds (fire versus shade dominated) and affect the distribution and dynamics of forestsavanna mosaics. Boxplots represent the bark growth rate in a) each vegetation according to the whole community and b) to the species' growth form. Species within the lower dark-gray rectangle produce bark lower than the threshold, while species above the dashed line produce bark higher than the threshold. Variation in bark production of generalist species sampled in both forests and savannas is shown in c. The dashed lines represent the mean bark growth of generalist species in forests (teal) and savannas (orange).

Tables

Table 1 Bark growth rate (BGR, mm/growth units -1 , mean±SD) across species and communities from different vegetation types (open and woody savannas or cerradão and semi-deciduous forests), exposed to different amounts of fire events and with different degrees of bud protection. The index of bud protection ranges from 0 to 3, where 0 corresponds to an unprotected bud and 3 is a bud fully covered by bark. Letters indicate significant differences (P ≤ 0.05) in the pairwise post-hoc test between vegetation types, according to bud protection level and fire frequency. CWM = community-weighted means for BGR.

Vegetation type

Species BGR open savanna 0.37 ± 0.

  Copaifera langsdorffii Desf. (Fabaceae), Erythroxylum pelleterianum A.St.-Hil. (Erythroxylaceae), Luehea grandiflora Mart. & Zucc. (Malvaceae), Moquiniastrum polymorphum (Less.) G. Sancho (Asteraceae), Myrcia multiflora (Lam.) DC. (Myrtaceae), Pera glabrata (Schott) Baill. (Peraceae), Tabernaemontana hystrix Steud. (Apocynaceae), Terminalia glabrescens Mart. (Combretaceae), Trichilia pallida Sw. (Meliaceae) and Vochysia tucanorum Mart. (Vochysiaceae).

Figure 2 .

 2 Figure 2. Bud and bark traits evaluated in savanna and forest species across a fire frequency

Figure 3 .

 3 Figure 3. Dispersion of 123 Cerrado species according to their area of occurrence and their

ACKNOWLEDGMENTS

The authors are thankful to Dhemerson Conciani, Gabriela Damasceno, Giovana Chiari, Mariana Dairel, Vagner Zanzarini, Renon Andrade and the staff of Santa Bárbara Ecological Station for their help during the field work. The authors also thank Dr. Davi Rossatto and Dr.

Marcelo Simon for suggestions on an early version of this paper. We also thank the three anonymous reviewers and the editors who helped improve this manuscript. 2021/09269-9), ABB from FAPESP (2017/02934-1) and AF received grant from CNPq (312689/2021). This study was authorized by the COTEC N. 57/2019 D144/2018 PH.

FUNDING

This study was supported by FAPESP (2015/06743-0) and CNPq (406505/2018-7). MAC received financial support from CNPq (134582/2018-7) and FAPESP (2018/21300-6,