Michael Kücken
email: michael@gsystech.de

Baruch Rinkevich

Lee Shaish

Andreas Deutsch

Nutritional resources as positional information for morphogenesis in the Stony Coral

Keywords:

We are interested in deciphering the mechanisms for morphogenesis in the Red Sea scleractinian coral Stylophora pistillata with the help of mathematical models. Previous mathematical models for coral morphogenesis assume that skeletal growth is proportional to the amount of locally available energetic resources like diffusible nutrients and photosynthetic products. We introduce a new model which includes factors like dissolved nutrients and photosynthates, but these resources do not serve as building blocks for growth but rather provide some kind of positional information for coral morphogen-

Introduction

Skeletal growth in scleractinian corals occurs by the deposition of calcified matrix which is excreted by the polyp calicoblastic tissue. This process relies on complex interactions between the environment and intrinsic genetic control [1,2,3,4]. Two major environmental factors that affect coral growth and morphology are light, that is used as an energy source for the zooxanthellae [5,6,7,8], and water currents [9]. A strong genetic influence is reflected in the bewildering range of shapes that colonies of different coral species can attain. Even within a species genetic factors have a strong influence on morphology [10]. However, very little is known about the genetic control of coral architectures, pattern formation and on the variety of shapes each coral species may present [4,10,11].

In complex biological phenomena such as morphogenesis very often fundamental principles are not very well understood. In this situation mathematical modeling is increasingly considered to be a very useful tool [12]. Through mathematical modeling we can test hypotheses, connect seemingly unrelated observations and establish some order in the confusing plethora of biological observations.

Modeling coral growth has so far focused on the role of environmental factors such as light and water currents. The particular morphology of the coral was proposed to provide an ecological advantage such as better access to light or dissolved nutrients [13,14]. In [15] coral tissue is considered analogous to porous media and growth is proposed to be the result of an interaction between water flow and chemical processes.

By far the most elaborate models appeared in a series of papers by Kaandorp, Merks and coworkers [16,17,18,19,20]. These studies analyzed the influence of hydrodynamic flow on coral morphology. The coral is modeled as a three-dimensional surface and growth takes place perpendicular to this surface according to a so-called growth function. In the above models the amount of skeletal growth is directly dependent on the concentration of lo-cally available nutrients that are transported to the coral by the water flow. Locations of the coral exposed to the water flow will thus exhibit increased growth in contrast to areas that have less access to the water flow. Variations of the model consider diffusion of nutrients inside the coral and the effect of light.

These models succeed in obtaining branching coral architectures that resemble some corals actually found in nature but cannot -as we will argue below -sufficiently describe morphogenesis in the scleractinian coral Stylophora pistillata. This species forms calcified skeletons characterized by well-defined branches that arise through splitting of existing branches and sprouting. In the course of colony astogeny complex, yet not haphazard, branch patterns arise. Note, that morphogenesis by branch bifurcations and sprouting is a common mechanism in morphogenesis -examples are the formation of the tracheal tree in the lung and kidney formation [21,22].

Stylophora pistillata colonies can be generated either from a small fragment (nubbin) down to the size of a single polyp or from a planula larva that attaches to the ground. At first the tissue around the attached fragment or the primary polyp spreads laterally and new polyps appear as nodes in a seemingly hexagonal lattice. In the case of planula larva settlement, at some point the coral tissue excretes calcified matrix and a branched skeleton is produced. In both scenarios new branches then arise by two mechanisms. First, an existing branch may split into two branches. Second, a new branch may form lateral to an already existing branch -a process that we call sprouting. A quantitative characterization of these first stages of astogeny was given in [10]. We use this data for fine-tuning our model.

Undamaged Stylophora colonies in the wild that are free to grow into all directions usually exhibit a round, almost hemispherical shape. Also, it is observed in young colonies that originate from a single branch that the overall shape is first very elongated and almost planar [10,23,24] (see Figure 7 a) and b)) and only later approaches the spherical shape seen in larger colonies. Another important feature of Stylophora is the fact that the branches fill out the colony space quite evenly. Generally, branches avoid each other and gaps in the colony are filled in by new branches [25].

There is a large degree of stochasticity in colony growth. Colonies of the same genotype grown under the same conditions exhibit considerable variation as to the exact location of new side branches and splitting events. The morphometric parameters that were extracted from colonies by Shaish et. al. [10] have some fairly high standard deviations as well. Nevertheless, it was shown that differences between genotypes were still larger than stochastic variation within a genotype.

If we observe young colonies that originate from a single separated branch as has been done by Shaish et. al. [10] we notice that significant skeletal growth takes place only at the branch tip and a few locations along the branch giving rise to lateral branches. However, most places of the coral surface exhibit no or very little skeletal growth even though all parts of the branch have equal access to nutrients in the water. This behavior is in contrast to Kaandorp-Merks' model that relates local growth to the amount of locally available resources and predicts initial skeletal growth of the branch on the whole surface in this situation. However, this is not the case in these young colonies and therefore Kaandorp-Merks' model is not suitable for describing morphogenesis in Stylophora pistillata.

In the following we will call the small circular regions that form a significant amount of skeletal matrix growth zones. By definition they are located at a branch tip -but not every branch tip is necessarily a growth zone (if the branch ceased growing). Due to their calcifying activity growth zones likely have a different genetic expression profile than noncalcifying areas. Survival of the growth zone will lead to a continued growth of the branch, a new generation of a growth zone will lead to a lateral branch, a splitting of a growth zone will lead to a branch bifurcation and the extinction of a growth zone will lead to a stop in branch growth.

The lack of significant calcification outside the growth zones indicates that the decision for calcification is not only dependent on the local access to nutrients in the water. Rather, some sort of intracolonial signaling is likely involved. This could for instance be mediated by a substance which is excreted by the growth zone tissue, diffuses to the neighboring tissue and suppresses calcification there. It could also be due to a depletion of resources needed for calcification in the non-calcifying tissue that are transported to the growth zone. The situation we encounter here has parallels to well-known organizing regions such as the Hydra head. Most significantly, formation of a new Hydra head can only take place some distance away from an existing head [26]. Organizing regions have been well described by simple mathematical models that assume a slowly diffusing activator and a faster diffusing inhibitor [26,[START_REF] Meinhardt | The Algorithmic Beauty of Sea Shells[END_REF][START_REF] Meinhardt | Models of Biological Pattern Formation[END_REF]. In this context the conditions for the emergence and splitting of organizer regions are well-understood. A similar mechanism could well explain the permanence of growth zones and the repression of growth in the neighboring tissue. This is not to say that energy resources like nutrients or sunlight are not important for colony growth -quite the opposite, as their involvement is considered axiomatic in the field. However, they likely play a different role in colony astogeny than the one suggested by Kaandorp and Merks. We propose that they influence colony shape not directly as building material for the skeleton but by providing cues for the growth direction of branches and by controlling branch bifurcations and the generation of new branches. In this sense the spatial distribution of energy resources provides positional information to the tissues of the developing colony. The situation is somewhat different to the classical notion of positional information in morphogenesis, which is usually connected to more or less elusive morphogens [START_REF] Wolpert | [END_REF].

In our model we assume that energy resources influence morphogenesis by controlling the survival, disappearance and splitting of growth zones. Generally, high concentrations of resources will lead to the survival, new generation and splitting of growth zones whereas low concentrations will lead to an extinction of a growth zone. An example where nutritional resources indeed have an impact on the emergence of organizing regions is Hydra magnipapillata where overfeeding leads to multiple head formation along the body axis [30]. We assume that energy resources reach the coral from the water medium and are created by photosynthesis. Photosynthates are modeled to be transported inside the coral, which is strongly supported by experiments [31,32,33] Due to the lack of specific information we do not outline a speculative scenario on low-level genetic control but rather include genetic factors in our model parameters. The model considers the coral at the branch level meaning that we do not consider single polyps and the details of branch formation and splittings but rather attempt to understand how colony architecture emerges.

The model results were compared with data on morphogenesis in Stylophora pistillata that were collected by Shaish et. al. [10]. In this work young colonies of several different genotypes of Stylophora pistillata were observed for one year. We used their genotypes A, B and C as these were shown to have significantly different growth parameters and are representative for the other genotypes considered in [10].

Model

The starting point of our modeling is the realization that significant skeletal growth in Stylophora takes place almost exclusively at the tips of the branches. This observation is supported by the fact that branches do not much thicken towards their base. The branch tips are not only interesting as the main sites for calcification but also for some physiological peculiarities. In most Stylophora corals the branch tip is less pigmented which indicates that fewer zooxanthellae are present at the tips. A study of the tips of Acropora revealed that the tips are not only low in zooxanthellae numbers, but also low in photosynthetic products and interestingly very high in ATP [34]. The authors of this study concluded that photosynthetic products are transported from the tissue of the branches toward the tips where they are rapidly converted into ATP. This energy can then aid the calcification process. In Stylophora this kind of study has not been performed but results in [31,32] suggest a similar phenomenon. The observation emphasizes that tissue in different parts of the colony has different functions: the branches produce photosynthetic products that are used up at the branch tip and aid the growth process there. This difference in tasks is likely manifested by a different gene expression profile in the tips. Unlike in plants there is neither apical polarity nor apical dominance in branch growth, a conclusion supported by the fact that isolated branches develop new branches at either end of the branch [24].

Because colony growth takes place predominantly at the branch tips we can represent the coral shape by the midlines of the coral branches. Thus, the coral colony can be viewed as a tree of curves that are growing at the free ends. The curves themselves are modeled as a collection of line segments, all with the same length Δx, that are attached to each other (see Figure 1 a) b)). The endpoints of the line segments we call nodes and denote them by r i . This approach reduces the three-dimensional structure of the coral to a collection of one-dimensional objects on which mathematical algorithms perform much faster.

In this representation morphogenesis of the coral is described by the following processes:

• growth of a branch tip Figure 1: a) The three-dimensional coral skeleton is represented by its midline curve. This curve is itself represented as a collection of line segments, each of length Δx. Growing line segments are represented in grey. b) Quantity N is taken up at every node R, its amount decreases by φ(d) at a point Q with distance d to R. c) We assume that P is produced in the branch, diffuses and is taken up completely at the branch tip. d) The local concentration of N and P provides information about the location within the coral colony. N is large at the periphery and small in the center of the coral. P is large in the center and at long branches but small in areas of many growing branch tips.

•
factors that guide these processes. For our mathematical model we define two quantities N and P that we interpret as concentrations of nutrients in the water medium and photosynthetic products in the coral branches, respectively. These quantities are used for defining growth direction as well as splitting and sprouting events.

The quantity N represents the concentration of dissolved nutrients in the water current, similar as in Kaandorp and Merks' models [18,19,20]. The work of these authors has shown that under broad assumptions the distribution of nutrients in the water medium around the coral is not uniform. Assuming that nutrients reach the coral by advection and diffusion and are taken up by the coral tissue, zones with depleted nutrients arise. Generally, N will be large in the coral periphery and small in the center of the coral (see Figure 1 d) and Figure 2 a)). Therefore the magnitude of N provides information to the coral tissue about its relative location in the coral colony.

The movement of nutrients in the sea water is very complicated because advection, diffusion, time-dependent currents, tidal flows and turbulence all play a role. We do not explicitly take these effects into account and define N in a very simple way. We assume that far away from the coral a maximal nutrient concentration N max is achieved. The coral tissue decreases this maximal nutrient concentration by taking up nutrients itself. We remind that the coral is given by the collection of nodes { r i } (the endpoints of the line segments), which we use to find N at an arbitrary point r. Then we assume that every node point r i decreases the amount of nutrients present at r by φ(| r i -r|) where φ is a decreasing function (see Figure 1 b)). Thus, we have

N( r) = N max -i φ(| r i -r|) N max . (1)
In our simulations we used

φ(r) = 1 r α : r ≥ R (α+2)R-(α+1)r R α+1 : r < R
with α = 1.7 and R is of the order of magnitude of the branch thickness d. Function φ(r) is constructed such that it is continuously differentiable and that it does not diverge for r = 0. Our choice for N( r) is motivated by a Green's function approach to the solution of the diffusion equation. Indeed, for α = 1.0 we have the exact solution of Poisson's equation. Choosing a larger α (as we did) highlights short-distance influences and decreases longdistance influences. Equation (1) does not yet consider the sea floor (which we assume to be planar) on which the coral is growing. We can incorporate the sea floor by extending the sum in (1) to all points r * i that are obtained as reflection of r i at the sea floor.

According to equation (1) N( r) can be negative, which is clearly nonbiological. This can be avoided by making φ dependent on N but this greatly complicates the calculation of N. In our model we set N to zero if the sum exceeds N max . This choice ensures a value of N between 0 and 1. As an alternative for ensuring nonnegative N we could have solved the diffusion equation with sinks that are dependent on the local nutrient concentration, but that would have been computationally significantly more expensive.

The quantity P represents the local availability of photosynthates that are produced in the branches of the coral and are transported through the coral. P is defined on the nodes { r i } only. We assume that P is produced evenly by the coral with rate λ, is completely taken up at the tips (i.e. we have P = 0 at the tips) and is transported by diffusion (see Figure 1 c)). Therefore, P will be large in the center of the coral and at long branches with no side branches. It will be small in areas with many growing branch tips (see Figure 1 d) and Figure 2 b)).

Additionally, we assume that there is a maximal concentration of photosynthates P max . Due to scaling arguments we can set P max = 1. Thus, values for P are in the interval from 0 to 1. On a particular branch, P is given by the steady-state diffusion equation

P xx + λ = 0 ,
where P = 0 at branch tips, P x = 0 at the base of the coral tree and P is conserved at branch nodes. The equation is solved using a straight-forward iterative procedure. If after an iteration step P exceeds P max it is set to P max .

Let us now continue with the rule for growth at the branch tip. We assume the same growth speed v for all growing tips. Unfortunately, there are no long-term measurements of the growth speed of individual branches although experiments for settling this question would be easy to perform. Assuming a constant growth rate is still well-supported in face of the spherical growth of colonies. Also we observe after a splitting event that the two new branches have about the same length.

Note that it frequently happens in corals that branch tips stop growing. This is often the case for interior branches, and may be due to the lack of access to dissolved nutrients from the flow and lack of light. Apparently, the disposition to growth is switched off completely in these cases. This behavior was partly incorporated in the model. Growth of a branch was stalled if it was to collide with another branch or with the surface on which the coral grows. For the small colonies that were mostly considered the effect was not important.

For the growth direction of a branch we use the gradient of N (direction of largest increase in N) measured at the tip of the branch. This way growth of the branches is generally directed away from the colony center as it is actually observed in virtually all colonies. Furthermore, this rule reproduces the fact that branches avoid and grow away from each other. The rule is supported by observations in [25]. It was shown that branches whose tips are placed close to each other grow away from each other or stop growing at all. This property of the coral branches to grow away from each other is certainly one of the main reasons why the coral fills out space evenly with its branches. We do not include phototropism in our model because it does not seem to play a primary role for determining the growth direction in the young colonies that we observed [24]. Sprouting branches are important to fill in space for colonies that originate from a single branch such as in the experiments of [10,23,24]. In many young colonies that originate from a separated branch the number of sproutings significantly exceeds the number of branch splittings. We assume that sprouting takes place at a certain location in a given iteration step with a probability S(N, P ) which increases as resources N and P increase. For simplicity we assume that S(N, P ) = βNP . This choice for S(N, P ) implies that sproutings only occur in areas where neither N nor P are too small. It ensures that interior branches fill out the space between existing branches and that new branches do not form in areas that are already nutrient-depleted. Other, slightly different choices for S(N, P ) give similar results. Note that our rule is not deterministic but stochastic, which takes into account the large variability that is observed during the first stages of colony formation.

The direction of a new sprout is not directly given by the gradient of N. Instead, points in different directions are sampled and the direction in which the largest N was found is used as the direction of the branch. This direction is in some cases very different from ∇N.

Branch splitting is the common mechanism for the generation of new branches in larger colonies but also occurs frequently in the young colonies investigated in [10]. Observations suggest that the two new branches split with an angle of about 35 o although variations do occur in real specimens. We assume that the two new arising branches are located in a single plane with the branch from which they originate and that the new branches are symmetric around the axis of the old branch. This assumption is well-supported by observations. The last rotational degree of freedom is determined by the position of the new branches that maximizes the sum of N on the tip of the two branches. Splittings are modeled to occur randomly with probability of T (N, P ) = γN + δP x . Here P x is the derivative of P along the branch and proportional to the flux of P into the tip. Note that this implies that splitting occurs if N is large (meaning that the branch is well-exposed to the fluid flow) or P x is large (meaning that the branch is very long already with no side branches).

Of course, other forms for the probabilities S(N, P ) and T (N, P ) could be used. Our choices are simple, do not contain many parameters and give good results.

We continue with a summary of our main model assumptions:

• the coral grows exclusively at the tips with a fixed growth rate

• the coral consumes dissolved nutrients N from the water along the branches

• photosynthetic products are produced along the branches, diffuse and are taken up completely at the branch tips

• branches grow into the direction of the steepest decrease of N into the water medium

• side branches sprout randomly with a probability proportional to the product of N and P

• branch tips split randomly with a probability proportional to the sum of N and P

In our simulations we start with a single branch growing upward with length H init . This is in analogy to the experiments reported in [10]. After the initialization a number of iteration steps is performed. At each step the following calculations take place:

• increase the segment of the growing tips by a fixed amount For the small colonies that we consider the algorithm is very fast, but it greatly slows down for larger colonies. After a simulation run the morphometric quantities of coral growth in Table 1 were extracted. We strove for consistency in the computation of these quantities with [10]. For each genotype 30 simulation runs were performed and averages and standard deviations of the morphometric quantities were computed and compared to the corresponding values found experimentally [10].

The visualization tool TRIVIZ written by H. Gerdes was used for graphic output. This tool is a C++ interface to the more general OpenGL and allows an efficient visualization of three-dimensional structures.

Results

Three sets of parameters were fitted by hand corresponding to three genotypes (genotype A, B and C in the experiments of [10]). For all simulations we used Δx = 0.125 cm, α = 1.7 and λ = 22.0, N max = 12.9 and a splitting angle of 36 o . 100 simulation steps correspond to one year. The remaining parameters for each genotype are given in Table 2. The initial colony height H init was chosen as the average initial height in the experiments for the respective genotype.

Snapshots of a typical simulation run for genotype C are presented in Figure 6. The same simulation, but seen from above and for more iterations, is shown in Figure 7 c)-f). As in experiments we observe a planar shape of the coral and later a transition to a more rounded, spherical shape. A large colony corresponding to three years development is seen in Figure 5. This colony exhibits a spherical shape and qualitatively resembles corals in the field.

Comparisons between simulations and observed colonies for genotypes A, B and C are shown in Figure 4. Clearly, a resemblance between experiments and simulations is visible. Exact correspondence cannot be expected because of the large component of stochasticity in colony development.

In Table 3 morphometric parameters of experiments and simulations are compared. The same data are presented visually in Figure 3. In spite of the sometimes large error bars each genotype has a distinct profile of morphometric parameters that has been reproduced by the simulations.

In our simulation runs we observed that height, the number of branches, ecological volume and length could be very well reproduced. The simulation data are almost always in the confidence interval of the experimental data.

a) f) b) c) d) e)
The average branch length is consistently too large in the simulations which may be due to slightly different definitions of branch length. Furthermore, the largest order of branches Ω in the simulations is usually too large and N2, the number of branches of order 2, is too small. Again, a different technique in determining order is likely to be the culprit for this difference. Note that not only the averages correspond well to each other but also the standard deviations of some parameters. In both experiments and simulations for the same settings quite different colonies were obtained which supports our view that there is a large stochastic component in the beginning of astogeny.

Along the environmental factors there is a large genetic influence on morphogenesis. We observed that genotype A has a much smaller growth rate than the other two genotypes and that genotype B has far fewer branches than genotypes A and C. Genotype A also has fewer branches than genotype C, which is due to the slow growth because the splitting and sprouting probabilities are actually the same in A and C. This means that splitting and sprouting probabilities are not dependent on the growth rate. Small colonies can already exhibit an intricate and complex branched structure (like genotype A) and, vice versa, relatively large colonies can still have relatively few branches (like genotype B).

There is not only quantitative agreement between simulations and experiments but also qualitative agreement in features that are more difficult to quantify. The visual appearance of the simulated structures resembles real corals. The branches in the simulations seek out to fill the space around the coral. Furthermore, the beginning of astogeny is characterized by a planar colony shape, exactly as it is actually observed (see for example Figure 4a) and b)).

Discussion

Our model not only qualitatively reproduces the early stages of coral development but also compares very well quantitatively to the experimental data. This is the more remarkable as measuring the morphometric parameters occurs by very different tools in experiments and simulations. Due to the one-dimensional nature of the model, calculations can be executed very efficiently and many runs can be performed easily and fast.

We postulate the following hypotheses regarding coral morphogenesis in Stylophora:

• dissolved nutrients and the distribution of photosynthates provides positional information for coral morphogenesis

• coral growth takes place at the branch tips only

• growth at the branch tips is uniform

• sprouting and splitting are stochastic events that are regulated by the presence of dissolved nutrients and photosynthates Crucial for the model is the realization that significant skeletal growth only takes place in one zone -at the branch tip. Sprouting can be understood as the formation of a new growth zone and splittings can be seen as a large growth zone that is dividing into two growth zones. If a growth zone has been established we propose that the growth rate is constant for all growing tips. If the tip should encounter an area of few resources growth is halted completely as it is in fact frequently observed in inner branches of the colony with poor access to the flow around the coral. In our model we have not yet considered this feature as it is not relevant for young colonies.

The idea of the growth zone can be used to understand growth in different coral species. In a branching coral like Stylophora pistillata the growth zone has the shape of a disk. How this shape is established is not part of our model but could be addressed by a model that represents the coral as a surface in three dimensions -possibly by a reaction-diffusion mechanism that is regulated by the availability of resources. Note that reaction-diffusion patterns can also be line patterns which might underlie the linear growth zones of some corals such as Turbinaria and some Montipora species.

There are similarities in our approach to the model by Kaandorp and Merks by emphasizing the role of energetic resources such as solved nutrients and photosynthates. However, energetic resources play a different role in our model compared to Kaandorp-Merks' model. In their model skeletal growth was proportional to the amount of locally available nutrients. In our model nutrients control the emergence and splitting of growth zones. Skeletal growth does not take place outside of growth zones even though nutrient concentration might be high. In Kaandorp-Merks' model one observes a continuous range of growth speeds along the coral. In our model there is either growth at a constant speed or no growth at all.

The model also has similarity to a classic model by Meinhardt for the development of branched structures [35]in tissues. One version of the model consists of an activator A that leads to differentiation in the tissue. This activator is paired to a classic inhibitor I that prevents the occurrence of a new activator center in the near vicinity and a diffusible substance S that is guiding the developing branch away from already differentiated tissue. Linear structures with branch bifurcations and lateral branches arise in the course of a simulation. Although the formulation of Meinhardt's model is different and it is applied to differentiation, not growth, its logic is very similar to ours. The activator A and the differentiation process correspond to the branch tip that is growing at a constant speed. The substance S corresponds to our N in that it is guiding the branch -and the inhibitor I corresponds to both N and P in that it is controlling the formation of side branches. There is no explicit inhibitor in our model but inhibition occurs by depletion of the resources N and P .

Our model does not downplay the importance of genetic factors, which are contained implicitly. For instance, the maintenance of a relatively small, circular growth zone at the branch tip requires a genetic basis. In our model we do not address how this growth zone is established but we point out that circular dot patterns are ubiquitous in nature and can be the result of many different mechanisms. Furthermore, the way positional information is used for determining growth direction, splitting and sprouting probabilities necessarily requires a genetic basis. We do not outline the details of this process, which would be pure speculation only with the lack of knowledge on genetics in Stylophora pistillata.

It is important to note that the introduced quantities N and P can be interpreted differently. Instead of N we could have introduced the quantity

I( r) = φ(| r i -r|) ,
as a representation of an inhibitor of growth that is excreted by the coral branches. The mathematical structure of such a kind of model is very similar to our model and it is in fact very difficult to propose an experiment that discriminates between the two scenarios. Similarly, P could also be interpreted in a different way. Instead of a quantity taken up at the tips and promoting sprouting and splitting we could define a substance that is excreted at the tip and represses sprouting and splitting along the branch. Again, it is impossible with current knowledge to decide what mechanism is correct. Therefore we do not rule out in our model the possibility that the coral regulates its own growth by true morphogens that are acting through the branches and the water medium. On the other hand there is strong evidence that dissolved nutrients and photosynthetic products are not distributed evenly so that they can serve as positional information, and there is no necessity for morphogens that do the same job.

Finally, it appears that some of the presented ideas can be used to find more general growth rules for a wider class of coral species. Especially, the idea that growth takes place in certain growth zones only may also apply to other coral species.

  splitting of a branch tip into two tips• sprouting of branches on existing branchesAs we have outlined in the introduction we propose that the distribution of locally available resources (dissolved nutrients in the water and distribution of photosynthetic products) provides positional information to genetic

Figure 2 :

 2 Figure 2: Concentrations of a) N b) P andc) sprouting probability in a colony of genotype C after 100 simulation steps. Red/yellow corresponds to large values and blue/green to small values. N is small in the center of the coral and large at the branch tips. P is large in the center and small in the periphery. The largest probability for a sprout is found at the left lower branch and in the middle of the main branch.

Figure 3 :

 3 Figure 3: Comparison of experiments and simulations. The data are scaled to the average of the experimental data for the three genotypes.

Figure 4 :Figure 5 :Figure 6 :Figure 7 :

 4567 Figure 4: Comparison of experiments and simulations for genotypes A a), d) B b), e) and C d), f)

Table 1 :

 1 • if the segment is longer than Δx start a new line segment but give a chance for splitting and calculate the new growth direction using ∇N List of morphometric parameters that were used in the quantitative comparison of experiments and simulations. Refer to details of how the parameters were measured to[10].

	Parameter Explanation
	H	colony height
	nB	number of branches
	N2	number of branches of order 2
	N3	number of branches of order 3
	Le	colony length
		largest lateral extension
	Ev	ecological volume
		πHr 2 where r = widthlength/4
	SI	percentage of branches from splitting
	Bal	average branch length
	Ω	largest order of a branch
	parameter	genotype A genotype B genotype C
	v (mm/step) 0.0875	0.144	0.144
	H init (mm)	28.6	28.0	32.0
	β	0.01	0.004	0.01
	γ	0.02	0.00625	0.02
	δ	0.02	0.00875	0.02

• calculate P and N on the branches • give a chance for sprouting at every node point

• calculate P again if sprouting took place

Table 2 :

 2 Choice of model parameters for genotype A, B and C.

Table 3 :

 3 Quantitative comparison of simulations and experiments for genotypes A, B and C.

Acknowledgements

This research was funded by the European project 012547 CORALZOO. We thank Heiko Gerdes (Dresden) for the visualization of the model results. We thank the editor for useful remarks that greatly improved the paper.

[1] J. P. Gattuso, M. Frankignoulle, I. Bourge, S. Romaine, R. W. Buddemeier, Global Planet Change 18 (1998) 37-46.


Minal Patel
email: minal.patel@ucl.ac.uk

Hayley Mulhall
email: h.mulhall@surrey.ac.uk

Khalid Al-Quatani

Mark Lewis
email: mark.lewis@beds.ac.uk

Dr Ivan Wall
email: i.wall@ucl.ac.uk

Muscle-derived precursor cells isolated on the basis of differential adhesion properties respond differently to capillary flow

Keywords: differential adhesion, muscle-derived precursor cells, osteogenic differentiation, shear stress, stem cells Mathematical annotation

Capillary shear stress can improve osteogenic differentiation in muscle-derived precursor cells (MDPCs). This has implications for large-scale bioprocessing of cell therapies where capillary transfer is needed. The recovery, viability and osteogenic differentiation potential of two subsets of MDPCs, early-adherent pre-plate 1 (PP1) and late-adherent PP3 populations,.have been examined: PP1 MDPCs produced a greater degree of osteogenic differentiation than PP3 MDPCs, quantified by Alizarin Red S staining intensity (p<0.05). For both cell populations, capillary flow induced significant increases in Alizarin Red S staining (p<0.05). However, PP1 cells were more susceptible to capillary flow-induced damage than PP3 cells and this was dependent on duration of exposure. Overall, results indicate that different cell subsets, even from within a single tissue, can respond variably to capillary shear stress, necessitating its precise monitoring and control.

Introduction

Capillary wall shear stress, such as that encountered during bioprocessing of regenerative cell populations, can contribute to osteogenic differentiation in muscle-derived precursor cells [START_REF] Mulhall | Effect of capillary shear stress on recovery and osteogenic differentiation of muscle-derived precursor cell populations[END_REF]). If osteoid cell material is not the desired differentiation product of the process, then shear stresses encountered in the bioprocessing environment during capillary transfer of cell suspensions need to be carefully regulated and their effects defined, to ensure purity of the cell product is not compromised.

It is widely recognized that bone marrow-derived stromal cells and adipose-derived precursors undergo enhanced osteogenic differentiation in response to laminar flow and for adhered cells, 0.23-1.2 N m -2 of laminar flow-induced shear can promote this [START_REF] Grellier | Responsiveness of human bone marrow stromal cells to shear stress[END_REF][START_REF] Knippenberg | Adipose tissuederived mesenchymal stem cells acquire bone cell-like responsiveness to fluid shear stress on osteogenic stimulation[END_REF][START_REF] Kreke | Effect of intermittent shear stress on mechanotransductive signaling and osteoblastic differentiation of bone marrow stromal cells[END_REF]). The particular method by which laminar flow is applied can also influence osteogenic responses in cells. In this paper, we have adopted a different approach, instead applying capillary shear forces representative of events such as trypsinization and suspension of these adherent cells that is necessary for the 'whole bioprocess' of automated cell expansion and differentiation [START_REF] Zoro | Regenerative medicine bioprocessing: concentration and behavior of adherent cell suspensions and pastes[END_REF].

Cell populations isolated from adult human tissues are extremely heterogeneous, due to factors such as stochastic variation in biological age of individual cells, differential responses to environmental stimuli etc.

Isolating cells on the basis of differential adhesion properties can reduce this heterogeneity, as progenitor cells in culture will adhere rapidly when plated due to intrinsic elevated integrin activation, whereas mature cell subsets will require longer periods of time to adhere [START_REF] Davies | A Multipotent Neural Crest Derived Progenitor Cell Population is Resident within the Oral Mucosa Lamina Propria[END_REF][START_REF] Jones | Separation of human epidermal stem cells from transit amplifying cells on the basis of differences in integrin function and expression[END_REF][START_REF] Dowthwaite | The surface of articular cartilage contains a progenitor cell population[END_REF]. In muscle, it has been reported that early adherent cells have progenitor properties whereas late adherent cells have a more mature myogenic phenotype and slow growth properties [START_REF] Lee | Clonal isolation of muscle-derived cells capable of enhancing muscle regeneration and bone healing[END_REF][START_REF] Sinanan | Muscling in on stem cells[END_REF].

In this paper, we exposed both early-adherent preplate (PP) -1 and late-adherent (PP3) subsets of MDPCs to laminar flow through a narrow-gauge capillary to determine whether distinct profiles in immediate recovery, viability in culture and osteogenic differentiation potential were evident in the two populations.

Materials and methods

Muscle-derived stem cells

MDPCs were obtained from masseter muscle of p5 male Royal College of Surgeons rats following enzymic digestion of tissue, as previously described [START_REF] Rando | Primary mouse myoblast purification, characterization, and transplantation for cellmediated gene therapy[END_REF]. The cell suspension was serially plated into three different plates, pre-plate 1, 2 and 3 (PP1, PP2, and PP3 respectively), based on their adhesion properties following a previously described method [START_REF] Lee | Clonal isolation of muscle-derived cells capable of enhancing muscle regeneration and bone healing[END_REF]. After 1 h of initial plating, non-adherent cells in suspension were removed and transferred to a new flask for a period of 48 h. Fresh GM was added to the 1 st set of adherent cells (PP1). The non-adherent cells in suspension from the 2 nd flask (PP2) (after the 48 h) were transferred to a new flask for about 5-7 days (PP3). Cells were cultured in growth medium (DMEM + 10% v/v foetal calf serum + 1% antibiotic/antimycotic solution; all from Invitrogen) and cultured at 37 C and 5% CO 2.

Cells were used for experiments within the first 2 passages.

Induction of capillary shear stress

Cells (3x10 5 cells/ml) were loaded into a 20 ml syringe barrel connected to an opposing 20 ml syringe by a small diameter capillary of 0.318 mm (24 gauge) x 10 mm, in 316 stainless steel (Cooper's Needleworks, Birmingham, UK). A Harvard PHD 2000 infuse/withdraw apparatus (Harvard Apparatus, Edenbridge, UK) was used to drive cells through the capillary from one barrel to the other either 10, 20 or 30 times. Cells were passed through the capillary at a constant, controlled rate of 50 ml/min. Cells not exposed to capillary shear were used as a control. The capillary entrance and exit zones were tapered to avoid high extensional stress on entry. We assumed the predominant force experienced by cells within the system to be capillary wall shear stress ( ) and calculated the magnitude of force using the equation derived by [START_REF] Zoro | Regenerative medicine bioprocessing: concentration and behavior of adherent cell suspensions and pastes[END_REF]:

32Q / d 3
where Q = fluid flow rate; d = capillary diameter; = suspension viscosity.

Suspension viscosity of the dilute suspension was assumed to be similar to that of water at 25 C (8.9 x 10 -4

Pa.s) and the flow rate of 50 ml/min equated to a capillary wall shear stress of 235 Pa.

Cell recovery

Cell recovery was measured immediately following exposure to shear using the trypan blue exclusion method by mixing cells in suspension with trypan blue dye at a ratio of 1:1. Cell counts were preformed using a haemocytometer and non-viable cells were determined on the basis of uptake of dye.

Cell viability

Cell viability after 48 h was determined using MTT dye reduction assay to determine relative amount of viable cells after capillary shearing. Cells that were either capillary-sheared or non-sheared were seeded into 96 well plates at 5x10 3 cells/well (in 100 l) and after 48 h, 25 l MTT (5 mg/ml) was added to each well. After 4 hr incubation at 37 C, medium was removed and 100 l extraction buffer (20% v/v SDS, 0.5 M dimethyl formamide) was added to each well and cells incubated overnight at 37 C. Cell viability was determined by reading absorbance at 570 nm using a Dynex spectrophotometer.

Osteogenic induction of MDPCs after capillary shear

Capillary-sheared MDPCs were seeded into 24 well plates at 10 5 cells/well. After 24 h, medium was replaced with osteogenic medium consisting of standard culture medium supplemented with 100 nM dexamethasone, 50 μM ascorbate 2-phosphate and 10 mM β-glycerophosphate. Medium was changed every 3 days.

Myogenic culture conditions

Capillary-sheared MDPCs were seeded into 24 well plates at 10 5 cells/well. After 24 h, medium was replaced with myogenic medium consisting of DMEM supplemented with 2% (v/v) FBS and 10 ng IGF-1/ml (Sigma-Aldrich). Medium was changed every 3 days.

Alizarin Red S quantitation of early mineralization

Alizarin Red S was used to detect calcified matrix (an early indicator of mineralized matrix deposition). After 2 weeks of culture in either osteogenic or myogenic medium, MDPCs were fixed with 70% (v/v) ethanol for 1 h at 4 °C, then stained with 2% (w/v) Alizarin Red S (pH 4.3) in deionised water for 10 min at room temperature.

Photomicrographs were taken on a Nikon Eclipse TE2000-U microscope (Nikon, Kingston-on-Thames, UK).

For quantitation, cetylpyridinium chloride (10% w/v in 10 mM sodium phosphate, pH 7.0) was used to elute the stain. 100 µl aliquots were then transferred to a 96 well plate and absorbance was measured at 570 nm.

Statistical analyses

All cell culture experiments were carried out in triplicate and analysed using one-way ANOVA followed by Bonferroni correction for multiple testing. Statistical significance was assumed if p<0.05.

Results and Discussion

Cells were passed through the capillary at 50 ml min -1 , and the first response we assessed was cell viability immediately following capillary flow of duration 10 capillary passes (duration of 12 s per ml of cell suspension), 20 capillary passes (24 s ml -1 ) or 30 capillary passes (36 s ml -1 ). For both PP1 and PP3 MDPCs, there was a small but steady decline in viability compared with cells not exposed to shear stress (Figure 1). For PP1 cells, decline in cell viability was statistically significant at 30 capillary passes (p<0.05 vs 0 passes). We also compared cell viability in PP1 and PP3 MDPCs 48 h after exposure to capillary shear as we previously found that late-adherent cells recovered from the capillary passage regime by 48 h and were similar to MDPCs that had not been passed through the capillary at this time [START_REF] Mulhall | Effect of capillary shear stress on recovery and osteogenic differentiation of muscle-derived precursor cell populations[END_REF].

PP1 MDPCs showed a trend of increasing viability up to 20 capillary passes but a substantial drop in viability was noted at 30 passes (p<0.05 versus 20 capillary passes; Figure 2). However, the late-adherent PP3 MDPCs all had a similar viability to the non-capillary exposed MDPCs. This data indicates that the capillary flow method we used causes differential effects on cell viability that are consistent between the two MDPC populations over time. In addition, the number of passes and hence duration of exposure to capillary flowinduced shear stress appears to be important, with presumable stimulation of cell number up to 20 capillary passes for PP1 cells; but a significant drop in viability at 30 passes. [START_REF] Born | Estimation of disruption of animal cells by laminar shear stress[END_REF] reported that capillary wall shear stress of over 100 N m -1 was required to cause irreversible damage to suspensions of TB/C3 murine hybridomas as a benchmark for the magnitude of forces that mammalian cells in suspension can withstand during bioprocessing. There are some substantial differences in the protocols employed that include duration of capillary flow and phenotypic characteristics of individual cell populations. To elaborate on the latter, the robustness of the individual cell type assessed will vary considerably between different tissues and, as we report here, even within a tissue as the PP3 (late-adherent) cells were less affected by capillary shear stress than PP1 cells in terms of immediate cell viability and later cell recovery/survival.

Cells in different tissues of the body are responsive to physical stimuli which in vivo result from local cell-cell and cell-matrix interactions, along with exposure to forces, such as compressive stress (cartilage and bone), tensile strain (tendons and muscles) and shear stress (blood and endothelia). In vitro, application of forces that mimic the in vivo environment can promote cell responses and numerous studies have demonstrated that enhanced osteogenic induction of bone marrow-derived stromal cells can be achieved using laminar flow in both monolayer [START_REF] Grellier | Responsiveness of human bone marrow stromal cells to shear stress[END_REF][START_REF] Kreke | Effect of intermittent shear stress on mechanotransductive signaling and osteoblastic differentiation of bone marrow stromal cells[END_REF] and in 3D bioreactors [START_REF] Bancroft | Fluid flow increases mineralized matrix deposition in 3D perfusion culture of marrow stromal osteoblasts in a dose-dependent manner[END_REF][START_REF] Gomes | Effect of flow perfusion on the osteogenic differentiation of bone marrow stromal cells cultured on starch-based three-dimensional scaffolds[END_REF][START_REF] Stiehler | Effect of dynamic 3-D culture on proliferation, distribution, and osteogenic differentiation of human mesenchymal stem cells[END_REF]. We have previously demonstrated that passing late-adherent MDPCs through a narrow-gauge capillary can enhance osteogenic differentiation even though the cells are of a muscle origin [START_REF] Mulhall | Effect of capillary shear stress on recovery and osteogenic differentiation of muscle-derived precursor cell populations[END_REF]). This has obvious implications for bioprocessing of cell material for therapeutic purposes as it indicates that forces that might be encountered during large-scale, automated expansion of progenitor cell populations might promote differentiation events that could be undesirable (particularly if osteogenic cells are not the desired target cells of the process).

In this study we compared the osteogenic differentiation potential of early-adherent PP1 and late-adherent PP3

MDPCs. Cells were exposed to capillary flow, plated and then cultured in either osteogenic or myogenic medium for two weeks. PP1 MDPCs cultured in osteogenic medium produced >3 fold increase in Alizarin Red S staining which indicates early events in mineralization of extracellular matrix that typifies osteogenic differentiation, compared with cells cultured in standard culture medium (Figure 3A and Table 1). PP3 MDPCs cultured in osteogenic medium only underwent a >2-fold increase compared with normal growth medium (Figure 3B), reflecting their greater commitment to the myogenic lineage and reduced capacity to undergo osteogenic differentiation. Capillary wall shear stress produced an additive effect to the osteogenic medium by inducing a small but significant increase in osteogenic induction after 30 capillary shear passes compared with unsheared cells (p<0.01; Figure 3A). This also occurred in the instance of PP3 MDPCs (p<0.01; Figure 3B).

Even when cultured in myogenic medium, the intrinsically more myogenic PP3 MDPCs exposed to capillary flow exhibited small but significant increases in Alizarin Red S staining (Figure 3D), indicating that the kind of forces experienced in the bioprocessing environment might induce a degree of heterotopic differentiation that will potentially compromise the therapeutic cell population. Generally though, capillary-sheared PP3 MDPCs cultured in myogenic medium produced only 0.4-fold Alizarin Red staining compared with cells grown in standard growth medium, whereas the intrinsically more multipotent PP1 produced elevated levels of 0.7-fold staining in myogenic medium compared to standard growth medium (Figure 3C-D). Exposing PP1 cells cultured in myogenic medium to capillary wall shear stress did not induce significant changes in Alizarin Red S staining (p>0.1; Figure 3C) whereas PP3 cells produced small but significant increases in staining after 20 and 30 capillary passes (p<0.05; Figure 3D).

This study differs from previous studies of shear stress and osteogenic differentiation as we characterized the effects of capillary flow, one aspect of the whole bioprocess that cells are exposed to during preparation for therapy, rather than the effect of laminar flow on cells that are already adhered to a substrate. We considered the effect of capillary flow in terms of wall shear stress, which based on previous studies, is assumed to be the highest magnitude force in the system [START_REF] Zoro | Regenerative medicine bioprocessing: concentration and behavior of adherent cell suspensions and pastes[END_REF]. Furthermore, we assessed the differential effects on different subsets of cells that were separated on the basis of differential adherence. Consequently we have demonstrated that a capillary wall shear stress of 235 N m -2 can be applied subsets of MDPCs and that these cells have differential capacities to withstand the effects of capillary shear stress. The capacity for MDPCs (derived from skeletal muscle) to withstand shear stress is somewhat greater than that of aortic smooth muscle cells tested in previous studies [START_REF] Zoro | The impact of process stress on suspended anchorage-dependent mammalian cells as an indicator of likely challenges for regenerative medicines[END_REF]. For large-scale manufacturing of regenerative cell therapies, something that will become an integral part of the bioprocessing industry over the next few decades, it will be important to understand how bioprocessing forces can influence behaviour of the therapeutic cells being produced.

Figure 1 :

 1 Figure 1: Recovery of MDPCs immediately following exposure to capillary wall shear stress. Early-adherent PP1 cells underwent a gradual decline in recovery with increasing duration of capillary shear. The same conditions did not affect recovery of late-adherent cells. N = number of viable cells after treatment; N 0 = number of viable cells in unsheared cell samples; * p<0.05 versus 0 passes.

Figure 2 :

 2 Figure2: Viability of MDPCs was assessed in adherent populations of cells 48h after exposure to capillary shear stress using the MTT assay. Early-adherent PP1 underwent a gradual increase in cell number compared to nontreated cells after up to 20 capillary passes, but then a significant reduction was observed after 30 capillary passes. For PP3 cells, no significant variation in cell viability was evident. N = number of viable cells after treatment; N 0 = number of viable cells in unsheared cell samples; * p<0.05 versus 20 passes.

Figure 3 :

 3 Figure 3: Osteogenic capacity of MDPCs after 2 weeks in culture, measured as a function of Alizarin Red S staining intensity. PP1 cells produced >3-fold increase in Alizarin Red S stain compared to cells grown in normal medium (A). PP3 cultured in osteogenic medium underwent less osteogenic induction (producing >2fold increase in staining) compared to normal growth medium control (B). Both PP1 (C) and PP3 (D) MDPCs cultured in myogenic medium underwent reduced Alizarin Red S staining compared even to normal medium controls (0.7-fold and 0.4-fold respectively). For A, B and D, exposure of MDPCs to capillary shear stress promoted additive osteogenic effects. * p<0.05, ** p<0.01, both versus 0 passes.

  Table1I: Fold-change in Alizarin Red S staining intensity versus non-capillary-sheared cells cultured in normal

	growth medium
	Capillary	Osteogenic medium	Myogenic medium
	passes	PP1	PP3	PP1	PP3
	0	3.29	2.1	0.71	0.42
	10	3.23	2.31	0.71	0.37
	20	3.48	2.36	0.76	0.46
	30	3.72	2.40	0.72	0.47


Christiane Goodfellow
email: christiane.goodfellow@wiwi.uni-muenster.de

Martin T Bohl

D82 -Asymmetric and Private Information < D8 -Information and Uncertainty < D -Microeconomics, G14 -Information and Market Efficiency|Event Studies < G1 -General Financial Markets

Keywords: Informed Trading, After-hours Trading, Exchange JEL Classification: D82, G14, G20 Informed Trading, After-hours Trading, Exchange Regulation JEL Classification: D82, G14, G20 Informed Trading, After-hours Trading, Exchange Regulation

This paper contributes to the debate about the relative qualities of floor and electronic trading systems by analysing the effects of bringing forward the Xetra closing time from 8.00pm to 5.30pm in November 2003, while the Frankfurt floor remains open until 8.00pm. This natural experiment provides a 'cleaner' institutional setting than in Venkataraman (2001), as it enables us to investigate trading quality on both platforms for the same stocks in the same country before and after the event. The empirical evidence suggests that primarily institutional investors trading in large stocks transfer to the floor when Xetra closes earlier. It appears that investors remain with Xetra for informed trading though.

Boerse announced this change in opening hours in a press release on 3 September 2003. This release claims that market participants prefer uniform trading hours across Europe as stock trading becomes increasingly international (Deutsche Boerse, 2003).

We analyse the effects of this event on the trading qualities on both platforms, thereby overcoming the limitations of [START_REF] Venkataraman | Automated versus floor trading: an analysis of execution costs on the Paris and New York Exchanges[END_REF] as pointed out by [START_REF] Madhavan | Discussion of Venkataraman[END_REF]. [START_REF] Venkataraman | Automated versus floor trading: an analysis of execution costs on the Paris and New York Exchanges[END_REF] analyses the trade execution costs for the non-anonymous New York Stock Exchange (NYSE) compared with the anonymous Paris Bourse for large and liquid stocks. He finds trading costs in Paris to be higher than at the NYSE by 0.14 percent of the trading volume and concludes that human intermediation may increase liquidity on a stock exchange. In a critical discussion, [START_REF] Madhavan | Discussion of Venkataraman[END_REF] demonstrates several substantial difficulties with this analysis: [START_REF] Venkataraman | Automated versus floor trading: an analysis of execution costs on the Paris and New York Exchanges[END_REF] examines two stock exchanges in different countries with different regulatory settings and different sets of stocks that are matched using various algorithms. Moreover, two auction markets are investigated which makes the interpretation of the spreads ambiguous. As we compare the same stocks traded simultaneously on both platforms in continuous trading at the same stock exchange, we are able to report more reliable results than [START_REF] Venkataraman | Automated versus floor trading: an analysis of execution costs on the Paris and New York Exchanges[END_REF].

Moreover, our direct comparison of the electronic trading system with the floor provides further evidence regarding the [START_REF] Seppi | Equilibrium block trading and asymmetric information[END_REF] hypothesis that broker-facilitated upstairs markets are preferred to electronic downstairs markets by those traders who can credibly convey that their trades are uninformed. [START_REF] Bessembinder | Does an electronic stock exchange need an upstairs market?[END_REF] find empirical evidence supporting [START_REF] Seppi | Equilibrium block trading and asymmetric information[END_REF] and report total execution costs, and their adverse selection component, to be lower in the upstairs market than downstairs. They argue that the presence of brokers drives down transaction costs in part because these brokers have information about unexpressed liquidity [START_REF] Grossman | The informational role of upstairs and downstairs markets[END_REF], and in part because the information asymmetry is lower in a non-anonymous market. [START_REF] Bessembinder | Does an electronic stock exchange need an upstairs market?[END_REF] conclude that an electronic exchange needs an upstairs market.

By presenting empirical evidence on the comparison of two trading systems in a clean institutional setting, we also contribute to the more general debate on whether anonymous electronic trading systems provide more or less favourable conditions for investors than the non-anonymous floor. While a few studies find evidence for the electronic system offering lower execution costs than the floor (Pirrong, 1996, Domowitz and[START_REF] Domowitz | Automation, trading costs, and the structure of the securities trading industry[END_REF], the majority of authors argue that higher information asymmetries in computerised trading lead to higher transaction costs ( [START_REF] Theissen | Floor versus screen trading: evidence from the German stock market[END_REF], Barclay et al., 2003[START_REF] Jain | Informed trading in parallel auction and dealer markets: The case of the London Stock Exchange[END_REF]. In fact, [START_REF] De | Price effects of trading and components of the bid-ask spread on the Paris Bourse[END_REF] find substantial permanent price effects of trading on the anonymous Paris Bourse, while the price impact of off-exchange transactions appears non-existent. These findings imply that the anonymous market tends to attract informed trading.

Previous studies either compare floor and electronic trading platforms at the same time [START_REF] Venkataraman | Automated versus floor trading: an analysis of execution costs on the Paris and New York Exchanges[END_REF][START_REF] Grammig | Knowing me, knowing you: trader anonymity and informed trading in parallel markets[END_REF][START_REF] Theissen | Floor versus screen trading: evidence from the German stock market[END_REF], or they investigate how the market properties change over time when an electronic system is introduced instead of the existing trading floor [START_REF] Gilbert | How is futures trading affected by the move to a computerized trading system? Lessons from the LIFFE FTSE 100 contract[END_REF]. In our study, this is reversed in that part of the trading time in the electronic system is abolished. This reversal is intriguing in itself as one might expect the traditional floor trading to be more prone to cuts in opening hours than highly automated electronic trading platforms. Moreover, this setting provides a natural experiment to study potential transfers of investors from Xetra to the floor as it enables us to examine continuous trading of the same stocks in two trading mechanisms at the same stock exchange.

Generally, the higher the information asymmetry is, the less liquid and less deep a market.

While traders on the floor have better knowledge about the order flow and the identity of their trading counterparties, those who trade on a computerised system have easier access to fundamental information and prices on other markets [START_REF] Pirrong | Market liquidity and depth on computerized and open outcry trading systems: a comparison of DTB and LIFFE Bund contracts[END_REF]. Pirrong compares theoretically and empirically the liquidity of German Bund Futures contracts traded simultaneously on the open-outcry platform LIFFE and electronically on Deutsche Terminboerse DTB. He finds that the computerised system offers lower spreads and is more liquid and deeper than the floor system. [START_REF] Domowitz | Automation, trading costs, and the structure of the securities trading industry[END_REF] examine the total execution costs on various U.S. trading platforms and hypothesise that electronic trading systems compete for traders with floor-based ones. The degree of automation and the resulting lower running costs of a computerised platform should lead to lower overall execution costs in an electronic system than the floor can offer. In fact, they find that electronic markets provide more favourable conditions to investors than the floor for trading in over-the-counter stocks. As for trading listed issues, commissions for traditional brokers are so high that they over-compensate the lower implicit execution costs on the floor.

In contrast to these studies, there is evidence for the floor offering lower execution costs and hence more favourable conditions for investors than electronic trading systems. [START_REF] Theissen | Floor versus screen trading: evidence from the German stock market[END_REF] compares the transaction costs on the Frankfurt floor with those in the electronic system (then IBIS, now Xetra) and finds the electronic system to offer lower bid-ask spreads for actively traded stocks. The floor, however, offers more favourable conditions for less frequently traded stocks, with the market share of less liquid stocks being lower in the electronic system than on the floor.

Theissen attributes the higher execution costs on the electronic platform to the adverse selection Several related studies investigate the influence of market makers on information asymmetry. [START_REF] Jain | Informed trading in parallel auction and dealer markets: The case of the London Stock Exchange[END_REF] examine the price impact for stocks traded parallel in the electronic system SETS and on the dealer market at the London Stock Exchange. They find empirical evidence for the computerised trading system to attract relatively more informed trades than the non-anonymous dealer market. Similarly, Barclay et al. (2003) conclude for Nasdaq stocks that informed traders prefer electronic systems over market makers. [START_REF] Benveniste | What's special about the specialist?[END_REF] construct a theoretical model for the spread whose primary implication is that information asymmetries are lower in a non-anonymous market. However, [START_REF] Lehmann | Trading and liquidity on the Tokyo Stock Exchange: a bird's eye view[END_REF] examine empirically the Tokyo Stock Exchange's success in providing liquidity without a specialist on the floor and the electronic system. They conclude that the Tokyo Stock Exchange is a well-functioning, liquid market despite the lack of market makers. [START_REF] Ding | An analysis of transactions data for the Stock Exchange of Singapore: patterns, absolute price change, trade size and number of transactions[END_REF] investigate how the Stock Exchange of Singaporewhich includes a screen-based dealer system that has no specialist -performs relative to the NYSE and Nasdaq. They find, to a large extent, similarities between the Singapore Stock Exchange and the NYSE and Nasdaq.

Bringing forward the closing time of Xetra is not an endogenous variable (Deutsche Boerse, 2003) and therefore provides an ideal institutional setting to study the extent to which investors transfer from Xetra to the floor, and to ascertain the type of investors that accept trading on a non-anonymous system when the anonymous one closes earlier. 1 This should yield insights into the perceptions of traders regarding the relative market qualities of both trading platforms. As investors choose the platform through which to route their orders, the 'selection bias' is an integral part of this study and does not need to be corrected for or explicitly modelled empirically. Since information asymmetry impacts directly on market quality, we examine whether it is primarily uninformed investors that accept the floor as a substitute of the electronic system. [START_REF] Theissen | Floor versus screen trading: evidence from the German stock market[END_REF] finds differences in relative execution costs between large and small stocks. This is consistent with [START_REF] Dennis | Who's informed? An analysis of stock ownership and informed trading[END_REF], who suggest that institutional investors are generally better informed than individual ones, and [START_REF] Falkenstein | Preferences for stock characteristics as revealed by mutual fund portfolio holdings[END_REF], who concludes that mutual funds prefer large stocks.

We therefore conduct our analysis separately for large and for small stocks, which might give further indications regarding the type of investors that transfer to the floor. Finally, we draw conclusions regarding the design of a trading platform.

We expect trading volume to increase sharply on the floor in November 2003 after 5.30pm, driven by uninformed trades which can thereby be settled at a time when Xetra is closed. As informed traders prefer the anonymity of the electronic system (e.g. Barclay et al., 2003[START_REF] De | Price effects of trading and components of the bid-ask spread on the Paris Bourse[END_REF], this might lead, under otherwise equal conditions, to a higher fraction of informed traders in Xetra, thereby increasing the adverse selection costs and hence making the computerised platform less favourable for investors. This might induce further transfers to the floor. Alternatively, it is possible that the fraction of informed traders increases on the floor if the transferring traders are a representative subset of all traders in Xetra. If primarily institutional (individual) investors move to the floor, we expect especially trading volume for large (small) stocks to increase there in November 2003. If mostly uninformed traders transfer to the floor, we conclude that a trading platform should be non-anonymous in order to avert informed trading. This latter finding might shed light on the justifiability of abolishing floor trading systems from the perspective of market microstructure.

In order to empirically analyse the relative market quality on both trading platforms over time, section 2 introduces the dataset and the methodology before section 3 describes the empirical results. Section 4 summarises our findings and concludes. This portfolio captures roughly 90 percent of the equities market in Xetra (Deutsche Boerse, 2004).

Data and Methodology

The transactions data include timestamp, price, and number of shares. They were obtained from the University of Karlsruhe database and the quotes data from Deutsche Boerse AG.

The 136 stocks were selected based on the instrument groups set up by Deutsche Boerse which contain predominantly German companies' shares. The DAX index comprises 30 blue-chip stocks, the MDAX contains 50 stocks whose size ranks immediately below that of the DAX ones, the TecDAX covers 30 medium-sized stocks in the technology sector, and the SDAX consists of 50 small-cap stocks. Out of these four indices, we choose those shares that were traded on both platforms throughout the six months under investigation.

Quotes are only available for Xetra as this platform has one orderbook from which we received the data. On the floor, by contrast, several market makers operate who are not obliged to publish their orderbooks. For this institutional reason, it is impossible to obtain a comprehensive set of quotes for the floor. Quotes data for Xetra comprise bid and ask prices with timestamps and the respective number of shares. The relative spreads, which are reported multiplied with 100, are calculated as the difference between ask and bid prices divided by the spread midpoint.

We conduct common plausibility checks on the data (Madhavan et al., 1997, Chung and[START_REF] Chung | Order handling rules, tick size, and the intraday pattern of bid-ask spreads for Nasdaq stocks[END_REF] and eliminate implausible observations with negative spreads or with negative or zero trading volume, as well as overnight returns. There were no entries with changes in absolute spreads, spread midpoints or returns larger than 50 percent. We only use data from continuous trading, i.e.

we exclude auctions and observations outside the trading times. This results in a dataset containing 1,106,147 transactions for the floor, 10,376,549 transactions for Xetra, and 28,165,410 quotes.

Transactions that market makers settle in their own accounts should also be eliminated since they do not pay spreads to themselves. However, we cannot identify such transactions in the dataset, and their impact on overall trading volume should be negligible for large stocks in which institutional investors trade actively. Since our results are driven by large stocks, we do not pursue this further.

Methodology

In order to analyse the anonymous and the non-anonymous trading platforms over time, we apply descriptive methods from the market microstructure literature, and we decompose the bid-ask spreads following [START_REF] George | Estimation of the bid-ask spread and its components: a new approach[END_REF]. The methodology of [START_REF] Venkataraman | Automated versus floor trading: an analysis of execution costs on the Paris and New York Exchanges[END_REF] cannot be applied here because quotes data are not available for both markets that we are comparing. For the descriptive analysis, we choose the turnover as a proxy for trading volume, transaction size as an indicator for the presence of institutional investors, and return volatility (standard deviation as in Madhavan et al., 1997 or Ding and[START_REF] Ding | An analysis of transactions data for the Stock Exchange of Singapore: patterns, absolute price change, trade size and number of transactions[END_REF] and the relative bid-ask spread [START_REF] Glosten | Bid, ask and transaction prices in a specialist market with heterogeneously informed traders[END_REF] to reflect information asymmetry. The trading time is split into 15-minute intervals, and within each interval the average is calculated per share on each trading day following [START_REF] Abhyankar | Bid-ask spreads, trading volume and volatility: intra-day evidence from the London Stock Exchange[END_REF]. Rather than aggregating the shares into a portfolio, this method preserves the characteristics of each stock in each interval.

Turnover is calculated as the cumulative product of price and number of shares, transaction size gives the number of shares per trade, and return volatility is computed cumulatively across each interval with the return being (p t -p t-1 )/p t-1 . 2 The volatility and the spreads are reported in percent in Table 1. We employ t-tests to determine whether the means in the indicators have changed significantly over time. Statistically, this is identical to estimating dummy regressions and t-testing the estimated coefficients.

2 For the descriptive analysis, we follow [START_REF] Roll | A simple implicit measure of the effective bid-ask spread in an efficient market[END_REF] and do not take the logarithm of the prices. As there are no quotes data available for the floor, we estimate the spread following [START_REF] Roll | A simple implicit measure of the effective bid-ask spread in an efficient market[END_REF] for the descriptive analysis of the total spread.3 Alternatively, transaction costs could be approximated as in [START_REF] Lesmond | A new estimate of transaction costs[END_REF]. Their results are strongly correlated with those of [START_REF] Roll | A simple implicit measure of the effective bid-ask spread in an efficient market[END_REF] so that the choice of model will have very little impact on the interpretation of the estimates in our setting. The basis of the spread estimation in [START_REF] Roll | A simple implicit measure of the effective bid-ask spread in an efficient market[END_REF] is the autocovariance γ T of the returns:

Spread T = (-4)γ T , ( 1
)
where T is the last period for which a return observation is available. Spread T can be interpreted as the average relative spread since the returns that feed into the estimation are relative. In case of γ T > 0 it is not defined. [START_REF] George | Estimation of the bid-ask spread and its components: a new approach[END_REF] propose two methods for decomposing the spread into its order processing and adverse selection cost components. Under the first approach, the realised return of an individual stock R i,t = ln(p i,t /p i,t-1 ) over each 15-minute interval needs to be purged of its time-varying expected return component that is approximated by the return of an equal-weighted size-based portfolio R p to which stock i belongs. We use the four portfolios DAX, MDAX, TecDAX, and SDAX described in section 2.1. The regression

R i,t = α 0 + α 1 R p,t + u i,t (2)
is used to extract the expected return of stock i. u i,t are the residuals, whose autocovariance across 2-hour intervals form the basis of the synthetic spread Spread calc,i,t which is otherwise calculated as in equation ( 1). In order to decompose the observed spread into its order processing and adverse selection cost components, we estimate the regression

Spread calc,i,t = β 0 + β 1 Spread obs,i,t + i,t (3)
where Spread obs,i,t is the average observed spread across each 2-hour interval. β 1 measures the order processing costs and (1 -β 1 ) are the adverse selection costs. While the constant has to be included in the model specification [START_REF] George | Estimation of the bid-ask spread and its components: a new approach[END_REF], it is not needed to determine the estimated components of the spread. When β1 < 0, the adverse selection costs are set to 1. As there are no observed spread data Spread obs,i,t available for the floor, we can only decompose the spreads from Xetra.

The starting point of the second method proposed by [START_REF] George | Estimation of the bid-ask spread and its components: a new approach[END_REF] is the difference If the aim of this paper was to directly compare spreads between the two trading platforms, we would need to report the [START_REF] Roll | A simple implicit measure of the effective bid-ask spread in an efficient market[END_REF] measure for Xetra as well. However, we observe spreads before and after bringing forward the Xetra closing time for each trading system separately, so that it is not meaningful to analyse an inferior proxy spread measure (i.e. [START_REF] Roll | A simple implicit measure of the effective bid-ask spread in an efficient market[END_REF] for Xetra when observed spreads are available. Likewise, the asymmetric information spread model proposed by [START_REF] Glosten | Estimating the components of the bid/ask spread[END_REF] or the models introduced by De Jong et al. (1996) could be estimated for both the floor and Xetra. For Xetra, however, we have observed spread data available, so that interpreting or decomposing any spread estimate would be unnecessarily unreliable. Moreover, in order to follow [START_REF] Glosten | Estimating the components of the bid/ask spread[END_REF] or [START_REF] De | Price effects of trading and components of the bid-ask spread on the Paris Bourse[END_REF], the trade direction would need to be inferred from previous price changes. A number of studies show that these estimates are inaccurate (e.g. [START_REF] Aitken | The accuracy of the tick test: evidence from the Australian Stock Exchange[END_REF]Frino, 1996, Ellis et al., 2000), and Odders-White (2000) reports spread components to be biased as a result.

Empirical Results

We expected a sharp increase in trading volume on the floor in November 2003 after 5.30pm. In fact, turnover almost doubles there in the evening. As can be seen from Panel A in Table 1, it increases slightly in the daytime but grows from approximately 15,400 Euros in the evening in October to 29,700 Euros at the same time in November. This increase coincides with bringing forward the closing time in Xetra on 3 November 2003, which we view as empirical evidence for investors transferring to the floor rather than remaining with the electronic trading platform and trading in the daytime instead.

Insert while daytime turnover in Xetra for small stocks increases. This is an indication that primarily institutional investors transfer to the floor in the evening, while individual investors trading in small stocks remain with the electronic trading platform and adjust their trading time (see [START_REF] Falkenstein | Preferences for stock characteristics as revealed by mutual fund portfolio holdings[END_REF] for the relationship between the size of stocks and investor type). Since Xetra has a larger market share in large stocks than in small stocks [START_REF] Theissen | Floor versus screen trading: evidence from the German stock market[END_REF], it is not surprising that large stocks are especially affected by the earlier closing time of Xetra.

In Xetra, the trading volume increases steadily from August 2003 to November 2003, drops in December 2003 and grows strongly in January 2004. This time series behaviour, along with the fact that only a small fraction of pre-event evening trading volume in Xetra is visible on the floor post-event, suggests that other factors play a part in Xetra in addition to bringing forward its closing time.

Since transactions in Xetra tend to be larger than those settled on the floor, an increase in transaction size on the floor could be interpreted as further evidence for investors transferring to the floor when Xetra is closed. As Panel B of Table 1 shows, the average number of shares traded per transaction on the floor increases significantly from about 394 to 535 in November 2003 after 5.30pm. On average, transactions in the evening are then larger than during the daytime. Thus, the substantial increase in trading volume on the floor is driven by the growth in transaction size, and is interpreted as further evidence for the increased trading volume originating from Xetra.

Similarly to turnover, transactions in Xetra grow steadily from August 2003 to October 2003, drop in size in November 2003 and resume growing thereafter.

The transaction size on the floor increases substantially for large stocks in November 2003 after 5.30pm, while the transaction size in Xetra grows slowly across the six months under investigation.

We conclude that investors trading in large stocks transfer to the floor when Xetra closes earlier.

As a larger fraction of market participants in Xetra are informed traders than on the floor (Barclay et al., 2003), and since those traders that appear to have transferred tend to be well informed, we expect this movement of investors to increase information asymmetry on the floor in November 2003. We investigate this by analysing return volatility and the bid-ask spread.

However, the volatility decreased on both trading systems, with the floor showing a significant reduction in the daytime and in the evening (Panel C, Table 1). Following Barclay and Hendershott (2003), we interpret this as a reduction in asymmetric information on the floor. It therefore appears that the transfer of investors from Xetra to the floor does not result in higher information asymmetries on the floor. This can be explained in two ways: Either those investors that are prepared to transfer to the floor are not informed, or they are informed but the trades they choose to settle on the floor are not information-based. Either way, this transfer should increase information asymmetry in Xetra under otherwise equal conditions. Since we are investigating a dynamic market across six months, holding everything else equal is too restrictive an assumption. Therefore, we also observe an overall reduction in volatility in Xetra which could be attributed to a growth in liquidity shown in Panels A and D of Table 1.

The bid-ask spread is another measure of information asymmetry as it contains a component that compensates the market maker for losses incurred when trading against informed investors.

The overall spread decreases in both trading systems, with the reduction being particularly sharp on the floor in the evening in November 2003 (Panel D, Table 1). For actively traded stocks, the decrease in the spread is so large that the usual pattern of lower spreads in the daytime than in the evening reverses. Thus, for large stocks, the daytime spread is larger than that in the evening from November 2003 onwards. The reduction in spreads in Xetra could be driven by the increase in liquidity or other factors beyond the transfer of trades to the floor.

Overall, return volatility is higher on the floor than in Xetra, implying a larger degree of information asymmetry on the floor compared with the electronic system. At the same time, the total spreads on the floor are lower relative to Xetra. Since the spreads are also a proxy for asymmetric information, these empirical results are, at first sight, surprising. However, since the non-anonymous floor is likely to have a larger share of uninformed trades than Xetra (Barclay et al., 2003), stock prices on the floor might be noisier than in Xetra. As the primary goal of this paper is the change in either trading platform over time, rather than a comparison of the two trading systems, we do not investigate this in greater depth.

As explained in section 2.2, the adverse selection component of the spread can be analysed for Xetra only. Table 2 shows the mean adverse selection costs for the first version of the spread decomposition according to [START_REF] George | Estimation of the bid-ask spread and its components: a new approach[END_REF]. For DAX and MDAX shares, the adverse selection costs before November 2003 are smaller in the evening than in the daytime. Thus, the trades that are affected by bringing forward the closing time of Xetra are informed to a smaller extent than the trades in the daytime. We therefore expect transfers inside Xetra from the evening to the daytime to lower adverse selection costs under otherwise equal conditions. Indeed, we observe minor, though statistically significant reductions in adverse selection costs for large and mediumsize stocks, with the changes for small stocks being insignificant. This is plausible since small shares are not actively traded in Xetra in the evening, which implies that they are not much affected by shortening the Xetra trading time. While the changes are very small, their statistical significance gives an indication of the direction in which adverse selection costs have changed over time.

In light of the results of the descriptive analysis, one might expect an increase in adverse selection costs in November 2003, since primarily informed investors appear to remain with Xetra, while uninformed ones prefer the floor. However, liquidity in Xetra increased over time, which reduces spreads and adverse selection costs. While the adverse selection costs for the TecDAX stocks decrease over the six months under investigation, there is no significant effect at the time when Xetra begins to close earlier.

The level of the adverse selection costs across all four portfolios is suprisingly high. George et al.

( 1991) report that only about 10 percent of the spread compensate the market maker for adverse selection costs, and their results change substantially when varying interval lengths. However, our results are robust against the choice of interval length and the decomposition approach as we also followed [START_REF] Roll | A simple implicit measure of the effective bid-ask spread in an efficient market[END_REF] and the second method presented by [START_REF] George | Estimation of the bid-ask spread and its components: a new approach[END_REF]. This discrepancy between our results and those of [START_REF] George | Estimation of the bid-ask spread and its components: a new approach[END_REF] could be attributed to technical innovations in the past 20 years lowering order processing costs, thereby increasing the fraction of the spread that is driven by information asymmetries.

Insert Table 2 about here

It should be noted that the German stock market is fragmented from 9.00am to 5.30pm into floor and Xetra trading platforms. After 5.30pm, only the floor exists, which enables market makers to observe the entire market more easily than in the daytime. They can therefore learn faster, so that information is incorporated more promptly into quotes. This will also make floor trading in the evening more attractive than before November 2003 and might thus account for some of the observed transfers.

For turnover, transaction size, return volatility and spread, large stocks drive the empirical results. There are at least two possible explanations for this finding. First, Xetra has a much larger market share in large stocks than in smaller ones. It is therefore plausible that primarily large stocks are affected by bringing forward the Xetra closing time, which is what we see reflected in the data. Second, consistent with this interpretation is [START_REF] Falkenstein | Preferences for stock characteristics as revealed by mutual fund portfolio holdings[END_REF] result that institutional investors prefer large stocks over small ones. Therefore it is likely that primarily institutional investors transferred to the floor in November 2003, but continued to settle informed trades on the anonymous electronic platform. Consistently with [START_REF] De | Price effects of trading and components of the bid-ask spread on the Paris Bourse[END_REF], it can be concluded that non-anonymous trading platforms offer favourable conditions to uninformed traders, while the anonymity of a computerised platform is preferred for settling informed trades.

As a robustness check, we conducted our analysis of turnover, transaction size, volatility, spread and the spread decomposition for each of the six months from August 2003 to January 2004 separately for either trading platform. The results are not reported in detail but are available from the authors on request. The prominent changes in all these time series occurred in November 2003, which we regard as support for our attributing those changes to the earlier closing time in Xetra. The aim of this paper is to contribute to the debate about the relative qualities of floor and electronic trading systems. This issue is investigated in a natural experiment on the German stock market: The trading hours on the anonymous electronic trading system Xetra are reduced, while the non-anonymous floor remains open from 9.00am to 8.00pm. Previous studies either compare both trading platforms operating parallel, or they examine how the market properties change over time when a computerised system is introduced to replace the trading floor. Our setting enables us to observe potential transfers of investors from Xetra to the floor, and it overcomes the limitations of [START_REF] Venkataraman | Automated versus floor trading: an analysis of execution costs on the Paris and New York Exchanges[END_REF] as pointed out by [START_REF] Madhavan | Discussion of Venkataraman[END_REF].

We find empirical evidence for primarily investors trading in liquid shares transferring to the floor after 5.30pm when Xetra closes early. The transactions they settle on the floor are, on average, not informed. Our insights are consistent with Barclay et al. (2003), [START_REF] Jain | Informed trading in parallel auction and dealer markets: The case of the London Stock Exchange[END_REF][START_REF] Benveniste | What's special about the specialist?[END_REF][START_REF] De | Price effects of trading and components of the bid-ask spread on the Paris Bourse[END_REF][START_REF] Venkataraman | Automated versus floor trading: an analysis of execution costs on the Paris and New York Exchanges[END_REF] who find that informed traders prefer the anonymity of the electronic trading system. Uninformed investors should choose nonanonymous trading systems, and they should settle their transactions before 5.30pm. The lack of fragmentation in the German stock market after 5.30pm from November 2003 onwards further contributes to lower transaction costs as market makers learn faster when the entire market can be observed more easily.

Likewise, if information-based trading is to be reduced, the trading platform should not be anonymous. This implies the recommendation to stock exchanges to refrain from closing floor trading even if highly automated electronic systems have lower running costs than the floor. However, there are exchanges that have completely abolished floor trading, e.g. Italy. Like [START_REF] Bessembinder | Does an electronic stock exchange need an upstairs market?[END_REF], we argue that an electronic exchange benefits from a broker-facilitated upstairs market, especially if the floor has already been closed.

There are various alleys for methodological extensions of this paper. Firstly, a model as in [START_REF] Easley | Liquidity, information, and infrequently traded stocks[END_REF] could be estimated. However, this approach is based on the assumption that information events occur only outside the trading hours, and the market maker learns the information throughout the trading day. As a result, splitting the trading time into 9.00am to 5.30pm and 5.31pm to 8.00pm is not meaningful. In light of our empirical results, which underscore the sharp differences on the floor between daytime and evening, we would not be able to interpret the probabilities of informed trading with respect to the research questions in this paper.

Secondly, the price impact of a trade could be estimated as in [START_REF] Hasbrouck | Measuring the information content of stock trades[END_REF]. The core of this method is to model the interactions between trades and quotes. As quotes data are not available for the floor, this can only be estimated for Xetra. Like with the spread decomposition performed above, this means that results cannot be compared between the floor and the electronic trading platforms. Thirdly, the effective spread could be used for the descriptive analysis and for the spread decomposition separately to the quoted spreads. This spread measure allows for trade executions within the quoted spread [START_REF] Venkataraman | Automated versus floor trading: an analysis of execution costs on the Paris and New York Exchanges[END_REF]. Once again, this would only be possible for Xetra. Finally, depth is another indicator of market quality and could also be examined in the context of this paper. However, [START_REF] Lee | Spreads, depths, and the impact of earnings information: an intraday analysis[END_REF] find that spreads are negatively correlated with depth so that qualitative results regarding the latter can be inferred from our analysis of spreads.

More generally, further empirical research should take into account the explicit trading costs as well. Fees and commissions that investors face for transactions they settle on the floor might outweigh the relative benefits of the non-anonymous trading platform [START_REF] Domowitz | Automation, trading costs, and the structure of the securities trading industry[END_REF].

Since institutional investors tend to have their own dealers on the floor, they might be able to save on those fees. This could be another reason why we observe primarily institutional investors transferring to the floor.

Acknowledgements

  2003, the closing time of the German electronic trading platform Xetra was brought forward from 8.00pm to 5.30pm, while the Frankfurt floor remained open until 8.00pm. Deutsche

2. 1

 1 Data Our dataset captures three months prior to, and after, the bringing forward of the Xetra closing time on 3 November 2003 from 8.00pm to 5.30pm. This results in 66 trading days from August 2003 to October 2003 and 60 trading days from November 2003 to January 2004. The data comprise transactions and quotes data for 136 shares traded contemporaneously on both Xetra and the floor.

  R i and the logarithm of the bid price return to compute a synthetic spread Spread calc .[START_REF] Roll | A simple implicit measure of the effective bid-ask spread in an efficient market[END_REF] spread measure as given in equation (1) can only be used as Spread calc if the expected return of a security is constant over time, and if the adverse selection costs in the market equal zero.[START_REF] George | Estimation of the bid-ask spread and its components: a new approach[END_REF] test their models with daily and weekly data of stocks traded on the NYSE and on Nasdaq from 1983 to 1987. Their major result is that adverse selection costs are smaller than previously reported in the literature and account for 8 to 13 percent of the observed spread.[START_REF] George | Estimation of the bid-ask spread and its components: a new approach[END_REF] conclude that order processing costs are the predominant component of the quoted spread.

  Venkataraman,

	2001,
	F o r
	P
	e
	e r
	R
	e
	v i e
	w

Table 1 :

 1 Previous versions of this paper were presented at the INFER Workshop on Bounded Rationality in Economics and Finance, at the EMG Conference on Microstructure of Capital Markets, at the 7th Doctoral Meeting in International Trade and Finance, and at the research seminar at Westfaelische Wilhelms-University. The authors would like to thank an anonymous referee, Mark P. Taylor, Descriptive Statistics, Continued

	Carsten Burhop, Thomas Goodfellow, Robert Hudson, Wing Lon Ng, Maureen O'Hara, Christian
	Salm, David Sondermann, Mark Trede, Simon Turvey, Kumar Venkataraman, and Thomas Wu for
	their helpful comments and suggestions on earlier drafts. All remaining errors are ours. Martin T.
	Bohl is indebted to the Alexander von Humboldt Foundation for financial support.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

18 Page 21 of 21 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	F o r P e e r R e v i e w DAX MDAX TecDAX SDAX H 0 : equal means H 0 : equal means H 0 : equal means H 0 : equal means mean t-test P -value mean t-test P -value mean t-test P -value mean t-test P -value Aug03-Oct03 daytime 0.9945 -2.7407 0.0062 0.9935 -3.6298 Nov03-Jan04 daytime 0.9942 0.9924 0.9826 Oct03 Nov03 Aug03-Oct03 evening 0.9924 0.9932 0.9871 daytime 0.9924 0.9009 daytime 0.9948 -10.8685 <0.0001 0.9949 -187.8081 <0.0001 0.9821 0.3150 0.9864 0.0003 0.9864 -4.1547 <0.0001 0.9949 0.4796 0.6315 0.9953 0.9948 0.7528 0.9943 -0.2416 0.8091 0.9940

Table 2 :

 2 Relative Adverse Selection Costs[START_REF] George | Estimation of the bid-ask spread and its components: a new approach[END_REF] for equal-weighted size-based portfolios in Xetra

19 Page 22 of 21 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

While liquidity-motivated trades are likely to have a strong time preference and will thus readily transfer to the floor when Xetra closes earlier, we hypothesise that informed trades will remain with the anonymous Xetra and be settled in the daytime from November

onwards.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

[START_REF] Kara | A model and empirical test of the strong form efficiency of US capital markets: more evidence of insider trading profitability[END_REF] also follow the[START_REF] Roll | A simple implicit measure of the effective bid-ask spread in an efficient market[END_REF] approach to estimate the transaction costs of insider trades and argue that this is a valid method in a market with information asymmetries.