Hans Joachim Poethke
email: poethke@biozentrum.uni-wuerzburg.de

Andreas Gros
email: andreas@necsi.edu

Thomas Hovestadt
email: hovestadt@biozentrum.uni-wuerzburg.de

The ability of individuals to assess population density influences the evolution of emigration propensity and dispersal distance

Keywords: density dependent, kin-competition, distance-dependent dispersal cost, metapopulation, individual-based model

We analyze the simultaneous evolution of emigration and settlement decisions for actively dispersing species differing in their ability to assess population density. Using an individualbased model we simulate dispersal as a multi-step (patch to patch) movement in a world consisting of habitat patches surrounded by a hostile matrix. Each such step is associated with the same mortality risk. Our simulations show that individuals following an informed strategy, where emigration (and settlement) probability depends on local population density, evolve a lower (natal) emigration propensity but disperse over significantly larger distances -i.e. postpone settlement longer -than individuals performing density-independent emigration. This holds especially when variation in environmental conditions is spatially correlated. Both effects can be traced to the informed individuals' ability to better exploit existing heterogeneity in reproductive chances. Yet, already moderate distance-dependent dispersal costs prevent the evolution of multi-step (long-distance) dispersal, irrespective of the dispersal strategy.

Introduction

The evolution of dispersal is driven by the balance between dispersal related cost, e.g. the mortality-risk during transitions or the energy and time spent on dispersal [START_REF] Rankin | The cost of migration in insects[END_REF]Burchsted 1992, Zera and[START_REF] Zera | Physiology and ecology of dispersal polymorphism in insects[END_REF], and numerous potential benefits. These benefits include avoidance of inter-or intraspecific competition for resources (Lambin et al. 2001, Poethke and[START_REF] Poethke | Evolution of density-and patch-size-dependent dispersal rates[END_REF], minimization of kin-competition (e.g. [START_REF] Hamilton | Dispersal in stable habitats[END_REF][START_REF] Comins | Evolutionarily stable strategies for localized dispersal in 2 dimensions[END_REF][START_REF] Frank | Dispersal Polymorphisms in Subdivided Populations[END_REF][START_REF] Comins | The dynamics of predation and competition in patchy environments[END_REF][START_REF] Rousset | Evolution of the distribution of dispersal distance under distance-dependent cost of dispersal[END_REF][START_REF] Kisdi | Conditional dispersal under kin competition: extension of the Hamilton-May model to brood size-dependent dispersal[END_REF][START_REF] Poethke | The relative contribution of individual and kin selection to the evolution of density-dependent dispersal rates[END_REF]) avoidance of inbreeding depression [START_REF] Bengtsson | Avoiding inbreeding -at what cost?[END_REF][START_REF] Motro | Avoiding inbreeding and sibling competition -the evolution of sexual dimorphism for dispersal[END_REF][START_REF] Perrin | Inbreeding, kinship, and the evolution of natal dispersal[END_REF] or coping with the temporal variability of resource availability [START_REF] Levin | Dispersal Strategies in patchy environments[END_REF], Travis and Dytham 1999[START_REF] Gandon | Multiple causes of the evolution of dispersal[END_REF]. The possibility to colonize new habitats, another benefit of dispersal, is critical for the persistence of a species [START_REF] Alsos | Frequent long-distance plant colonization in the changing Arctic[END_REF].

So far theoretical studies on dispersal of actively moving organisms have mainly focused on the emigration of individuals, while the dispersal process itself and the question how far to disperse has been mostly ignored. Instead, the dispersal distance has been tackled with rather arbitrary assumptions like nearest neighbour (e.g. Travis et al. 1999[START_REF] Gros | Evolution of local adaptations in dispersal strategies[END_REF] or global (e.g. [START_REF] Poethke | Evolution of density-and patch-size-dependent dispersal rates[END_REF] dispersal. The growing awareness of colonization events through long-distance dispersal in plants [START_REF] Nichols | The genetic consequences of long-distance dispersal during colonization[END_REF][START_REF] Nathan | Mechanisms of long-distance dispersal of seeds by wind[END_REF][START_REF] Bohrer | Effects of long-distance dispersal for metapopulation survival and genetic structure at ecological time and spatial scales[END_REF][START_REF] Nathan | Long-distance dispersal of plants[END_REF][START_REF] Alsos | Frequent long-distance plant colonization in the changing Arctic[END_REF]) has inspired the introduction and investigation of "fat-tailed" dispersal kernels [START_REF] Higgins | Predicting plant migration rates in a changing world: The role of long-distance dispersal[END_REF][START_REF] Bullock | Long distance seed dispersal by wind: measuring and modelling the tail of the curve[END_REF][START_REF] Hovestadt | Evolution of reduced dispersal mortality and 'fat-tailed' dispersal kernels in autocorrelated landscapes[END_REF][START_REF] Austerlitz | Using genetic markers to estimate the pollen dispersal curve[END_REF][START_REF] Katul | Mechanistic analytical models for long-distance seed dispersal by wind[END_REF] in theoretical studies on seed dispersal. However, dispersal kernels describe the probability distribution of dispersal distances away from a source. The use of dispersal kernels has thus mostly been confined to studies of seed or pollen dispersal, or that of other organisms with passive dispersal [START_REF] Ezoe | Optimal dispersal range and seed size in a stable environment[END_REF][START_REF] Gros | Evolution of local adaptations in dispersal strategies[END_REF]) in continuous landscapes.

Although kernels have occasionally been estimated for actively moving animals [START_REF] Baguette | Long distance dispersal and landscape occupancy in a metapopulation of the cranberry fritillary butterfly[END_REF][START_REF] Gilbert | Long-distance dispersal and human population density allow the prediction of invasive patterns in the horse chestnut leafminer Cameraria ohridella[END_REF][START_REF] Chapman | Modelling population redistribution in a leaf beetle: an evaluation of alternative dispersal functions[END_REF]) and certain kernels are implicitly assumed in metapopulation models [START_REF] Hanski | A practical model of metapopulation dynamics[END_REF], they appear inappropriate to capture the behaviour of actively moving individuals. Such animals presumably do not leave habitat patches accidentally [START_REF] Van Dyck | Dispersal behaviour in fragmented landscapes: Routine or special movements?[END_REF], but because of a decision to do so. Moreover, they are believed to more or less continuously monitor the status of their surroundings during movement [START_REF] Nathan | A movement ecology paradigm for unifying organismal movement research[END_REF]. It is likely that they can distinguish between suitable and unsuitable habitat and can assess the expected benefit of further movement [START_REF] Baker | Incremental costs and benefits shape natal dispersal: Theory and example with Hemilepistus reaumuri[END_REF]. Habitat selection of actively moving animals has been object to several theoretical and empirical studies (for a review see [START_REF] Morris | Toward an ecological synthesis: a case for habitat selection[END_REF]. Numerous examples show that animals react not only to habitat quality but also on local population density [START_REF] Morris | Active density-dependent habitat selection in a controlled population of small mammals[END_REF]. For this reason it is rational to consider dispersal of actively moving organisms as a three phase process with different factors influencing decisions concerning i) emigration, ii) movement and iii) settlement [START_REF] Clobert | Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations[END_REF].

When suitable habitat is patchily distributed and patches of habitat are embedded into an environment unsuitable for reproduction, animals should continue their movement until they find a suitable patch [START_REF] Baker | Incremental costs and benefits shape natal dispersal: Theory and example with Hemilepistus reaumuri[END_REF]Rao 2004, Stamps et al. 2005). In this case the realized dispersal distance of individuals will crucially depend on the mean distance between patches of suitable habitat (e.g. [START_REF] Hein | Patch density, movement pattern, and realised dispersal distances in a patch-matrix landscape -a simulation study[END_REF]). Yet, once dispersing individuals have discovered a patch of suitable habitat, they may either settle or decide to continue their search for a new breeding site. In this case, the final dispersal distance of actively moving animals will strongly depend on their perseverance i.e. the probability to continue dispersal after having reached a patch of suitable habitat. It is conceivable that an individual able to estimate the quality of its natal site is also able to assess the quality of a site it has arrived in, and may consequently continue to disperse if the quality of that site is perceived as too low.

We thus propose that dispersal distances should strongly depend on the capability of individuals to use information on habitat quality in their dispersal decision whereas uninformed dispersers should typically not continue dispersal once they have reached a new potential breeding habitat. In the following we expand a previously published simulation model [START_REF] Poethke | Evolution of density-and patch-size-dependent dispersal rates[END_REF] to test this hypothesis. We compare the evolution of dispersal distances for different degrees of spatial correlation in environmental conditions.

Local population density is a critical determinant of intraspecific competition and dispersal is strongly motivated by the search for less populated habitats [START_REF] Ruxton | Fitness-dependent dispersal in metapopulations and its consequences for persistence and synchrony[END_REF], Travis et al. 1999[START_REF] Poethke | Evolution of density-and patch-size-dependent dispersal rates[END_REF]. To account for organisms differing in their capacity to assess local population density, we thus implement informed (in this case density dependent) as well as uninformed (density independent) dispersal into the model and compare the effect of either strategy on the evolution of dispersal.

More specifically, we test a number of hypotheses. (1) The number of successive dispersal steps decreases with increasing dispersal costs (cf. [START_REF] Murrell | The evolution of dispersal distance in spatially-structured populations[END_REF]. (2) The number of dispersal steps increases with increasing spatial correlation in environmental conditions. With increasing spatial correlation, animals have to disperse further to escape poor conditions (cf. [START_REF] Frank | Spatial aspects of metapopulation survival -from model results to rules of thumb for landscape management[END_REF][START_REF] Murrell | The evolution of dispersal distance in spatially-structured populations[END_REF][START_REF] Kallimanis | Patchy disturbance favours longer dispersal distance[END_REF]). (3) Most importantly, we test the hypothesis that short distance (that from the natal site to the first suitable site encountered) dispersal prevails for uninformed dispersers, while dispersal events that cover more than one step will only be found -in significant proportion -for the case of informed (density dependent) dispersal.

The model

We use a modified version of the individual-based metapopulation model of [START_REF] Poethke | Evolution of density-and patch-size-dependent dispersal rates[END_REF]. The model simulates the population dynamics, inter-patch dispersal, and the evolution of dispersal propensity in a meta-population of a diploid, sexually reproducing species with discrete generations and density-dependent local population growth. The metapopulation consists of habitat patches arranged in a square lattice of 24x24=576 cells. To avoid edge effects the metapopulation is closed into a torus (edges are wrapped). Each patch (i) supports a local population with carrying capacity K = 40; total carrying capacity of the system is thus ca. 23.000 individuals. Simulation experiments are initialised by placing K individuals in each of the 576 patches.

Within each patch populations grow according to a logistic growth model suggested by [START_REF] Hassell | Patterns of dynamical behavior in singlespecies populations[END_REF]. Similar to [START_REF] Murrell | The evolution of dispersal distance in spatially-structured populations[END_REF] and [START_REF] Poethke | Evolution of density-and patch-size-dependent dispersal rates[END_REF], a female gives birth to 2 x O offspring where O is Poisson distributed with mean O mean (t, i). To account for environmental variability, O mean (t, i) (specific for patch i at timestep t) is drawn from a log-normal distribution with mean O = 2 and a standard deviation of V = 1.

Offspring are randomly allocated to either sex. Offspring survival during maturation is dependent on the total number of offspring hatched in a patch (N i ). Thus, offspring mature with a density dependent survival probability s where

> @ i i i N K K s 1 O .
In all of our simulations, individuals are characterized by four alleles (n 1 , n 2 , f 1 , f 2 ) at two diploid loci (n, f). The first of these loci (n) determines the individual's propensity for natal dispersal (P n ), the second (f) determines its perseverance, that is the probability (P f ) of continuing to disperse once it has successfully reached a suitable habitat patch. These alleles take continuous values and are initialised with uniformly distributed random numbers within the interval [0.3, 0.5]. Preliminary simulations showed that similar dispersal traits evolved independently of the starting values taken for initialising alleles (see appendix figure A2).

Dispersal "decisions" can be either density-dependent (DD) or density-independent (DI). We performed simulation experiments for three different scenarios differing in the assumptions concerning the animals' capacity to perceive local population density and to adjust their dispersal behaviour accordingly. In the first scenario (scenario DI n /DI f ) natal dispersal propensity as well as perseverance, i.e. dispersal propensity for all further dispersal moves, is density-independent. In the second scenario (DD n /DI f ) we assume that individuals can asses the population density in their natal patch, but do not have ability to assess the density of any patch they reach thereafter. Thus, natal dispersal propensity is density-dependent (DD n ) but perseverance is density-independent (DI f ). In the third scenario (DD n /DD f ) all dispersal steps are density-dependent.

In the case of density independent dispersal, dispersal probability (the phenotype) is calculated according to the mean value of both alleles at the respective loci. This holds for density independent natal dispersal probability (P DI,n = (n 1 +n 2 )/2) as well as for the probability to continue dispersal (P DI,f = (f 1 +f 2 )/2) in the two scenarios with density independent perseverance (DI n ,DI f and DD n ,DI f ).

In the case of density dependent natal dispersal, individual alleles do not determine the dispersal probability but a threshold density C n = (n 1 +n 2 )/2 for dispersal. The actual dispersal probability P DD,n is then calculated according to the model derived by [START_REF] Poethke | Evolution of density-and patch-size-dependent dispersal rates[END_REF] as

°°® t n i i n n i n DD C C if C C C C if P 1 0 , (1a)
Accordingly, the probability to continue dispersal in the case of density dependent perseverance (DD n ,DD f ) is calculated as

°°® t f i i f f i f DD C C if C C C C if P 1 0 , ( 1b
)
with

C f = (f 1 +f 2 )/2
For a full discussion concerning the adequacy of this model structure for density dependent dispersal see [START_REF] Poethke | Evolution of density-and patch-size-dependent dispersal rates[END_REF] and [START_REF] Hovestadt | Information processing in models for density-dependent emigration: A comparison[END_REF]. It should be noted that Ruxton and Rohani (1998) already utilized a model very similar to our DD n /DD f model version to address questions concerning the stabilizing effects of dispersal on population dynamics.

The three scenarios may correspond to three types of dispersers with different sensory capacities. The first scenario (DI n /DI f ) could apply to animals such as certain butterflies that leave habitats independent of population density (e.g. [START_REF] Boughton | The dispersal system of a butterfly: A test of source-sink theory suggests the intermediate-scale hypothesis[END_REF]. The second scenario (DD n /DI f ) may apply to animals that acquire important information about population density during their larval stage but disperse regardless of density as adults, such as e.g., migratory locusts [START_REF] Fuchs | Neural correlates to flight-related densitydependent phase characteristics in locusts[END_REF], or some crickets [START_REF] Fowler | Dispersal of early instars of the mole cricket, Scapteriscus tenuis, as a function of density, food, and pathogens (Orthoptera, Gryllotalpidae)[END_REF]). In the third setting (DD n /DD f ) we assume animals that have the ability to assess local population density at any time. This most likely applies to long-lived animals like mammals, birds, and a few insect species, e.g. some corixid species investigated by [START_REF] Pajunen | Habitat selection in rock pool corixids: the effect of local density on dispersal[END_REF].

For natal dispersal an individual randomly chooses one of eight neighbouring patches as destination and then assesses successive patches along this trajectory thus keeping its direction in each following step (see Figure 1). Dispersing individuals either reach the next habitat patch or die during the transition with probability μ. Exploratory simulation runs showed that individuals very rarely continued their dispersal for more than 10 consecutive steps (see also results section), and we therefore limited the maximum number of movement steps to 15. This had a negligible effect on simulation results but significantly reduced computation time.

We modelled spatial correlation in habitat quality by aggregating patches into clusters and assigning an identical value of O mean (t, i) to all k patches within such a cluster. To account for different spatial dimensions of correlation we vary the side-length of clusters from l=1

(cluster-size k = 1; no spatial correlation) to l=4 (cluster-size k = 16; see fig 1). Reproductive conditions (O mean (t, i)) for each cluster were drawn anew every generation (t). The spatial configuration of clusters does not change over time, so that each patch always belongs to the same cluster.

When inheriting its parent's genes, mutations occur with a small probability (m= 0.01) per allele. If mutations occur, the affected alleles are altered by adding a random value drawn from a uniform distribution within the interval [-0.1, 0.1]. Preliminary simulation experiments showed that an evolutionary equilibrium is usually reached after approximately 3000 generations (see appendix figure A2), yet each simulation run included 10000 generations. All results were recorded in the last generation. We varied stepwise dispersal mortality μ as follows: μ = {0.025, 0.05, 0.10, and 0.20}.

Results

In all scenarios we find that uninformed dispersers (DI n ) evolve higher probabilities of natal dispersal than informed dispersers (DD n ; fig. 2a), whilst consecutive dispersal steps occur with higher probability for informed dispersers (DD f ) than for uninformed dispersers (DI f ; fig. 2b). Not surprisingly, natal dispersal probability (fig. 2a) as well as the mean number of additional steps moved (fig. 2b) decreases rapidly with increasing dispersal mortality (P). In general, individuals are thus inclined to settle in the first patch they arrive in as soon as dispersal mortality rises beyond even moderate levels (P ≥ 0.1). The effect of dispersal mortality on natal dispersal is more pronounced for the case of density independent natal dispersal than for informed dispersal (fig. 2a).

Evolution of elevated natal dispersal probabilities for uninformed (DI n , density independent) compared to informed natal dispersal (DD n ) has already been described by [START_REF] Enfjall | The evolution of dispersal -the importance of information about population density and habitat characteristics[END_REF] and [START_REF] Hovestadt | Information processing in models for density-dependent emigration: A comparison[END_REF]: density dependent dispersal more efficiently exploits inter-patch differences in population density, but also more efficiently homogenizes population density across patches. To demonstrate this figure 3 compares the demographic benefits, that is, the mean difference in population size between the source and destination patch for the first five dispersal steps of emigrants. Evidently, the very first dispersal step is the most profitable one, i.e. is associated with the largest decline in pre-(natal patch) compared to post-dispersal (target patch) density. This holds for density dependent as well as for density independent dispersal. For example, with low dispersal cost (P = 0.025) and an accordingly high emigration probability, uninformed natal dispersers (DI n /DI f ) arrive after their first dispersal step in a patch with on average 10.8 fewer competitors than in their natal patch (full dots, fig. 3a). If these individuals continue to disperse their second step will bring them to a patch with on average only 1.1 fewer individuals then their first target. With higher dispersal cost (P=0.200) emigration probabilities become generally lower but, correspondingly, the demographic benefits of natal dispersal increases (fig. 3b). Thus, with higher dispersal mortality (P=0.200) uninformed natal dispersers (DI n /DI f ) arrive after their first dispersal step in a patch with on average 13.7 fewer individuals than in their natal patch (full dots, fig. 3b).

Informed dispersal allows a more efficient exploitation of inter-patch differences in population density. Even for the case of low dispersal cost (and relatively high emigration probability), density dependent natal dispersers (DD n /DI f and DD n /DD f ) arrive in patches with on average 22.8 fewer individuals than in their natal patch (grey dots and open dots in fig. 3a).

For the case of high dispersal cost (P = 0.200) the mean reduction in local population size achieved increases to 38.6 individuals (grey dots and open dots in fig. 3b).

The difference in the potential demographic benefit gained by dispersal between informed and uninformed strategies is not restricted to the first dispersal step: it is even more pronounced for the steps following natal dispersal. While the mean demographic benefit of a second dispersal step (i.e. the mean difference in population density between the target of their first (natal) dispersal step and the target of their second step) for completely uninformed dispersers (DI n /DI f ) is only 1.1 individuals (largely independent of dispersal cost, see filled circles in fig.

3a and fig. 3b), completely informed dispersers (DD n /DD f ) will realize a mean demographic benefit of 5.2 individuals in the case of low dispersal cost (P = 0.025; open circles in fig. 3a) and of 12.1 individuals for high dispersal cost (P = 0.200; open circles in fig. 3b) with a second dispersal step. Even those individuals that show informed dispersal in their first step but density independent dispersal in all other steps (DD n /DI f , grey circles) do profit slightly more from steps following natal dispersal than completely uninformed dispersers.

Natal dispersal equalizes population density to a considerable degree, thus reducing the potential benefits of further steps, while the costs for any such step remain the same.

Consequently, even in scenarios with a rather benign cost of dispersal (e.g. Spatial correlation has only a negligible effect on the probability of natal dispersal (fig. 4a) but the number of additional dispersal steps taken is strongly influenced by the spatial correlation in environmental conditions (fig. 4b); here we only show results for scenario DI n /DI f but results for the two other strategies are analogous (see appendix). At low dispersal mortality (P=0.025) the mean number of dispersal steps increases from 1.08 to 1.58 as cluster dimension increases from k=1 to k=16. Nonetheless, only 34% of all emigrants will continue dispersal after the first dispersal step (see black dots in fig. A1). However, at a dispersal mortality of P=0.1, the mean number of dispersal steps increases only from 1.02 to 1.05.

Discussion

The results of our evolutionary simulation experiments show that organisms with active dispersal should typically settle into the first patch of suitable habitat they encounter, i.e.

disperse over short distances. Even for rather low levels of dispersal mortality individuals hardly ever continue dispersal once they have reached a suitable patch. This supports the findings by [START_REF] Barton | The evolution of an 'intelligent' dispersal strategy: biased, correlated random walks in patchy landscapes[END_REF], who modelled the (evolution of) movement rules of organisms during transition. They found that organisms should target their movement to the first (nearest) patch when patches become rare and movement in the matrix thus becomes more costly. Selection for settlement in the first patch encountered particularly evolves for uninformed dispersers and for spatially uncorrelated environmental conditions. Yet even in spatially correlated environments density independent dispersers mostly settle in the neighbouring patch as soon as step-wise dispersal mortality becomes moderate (e.g. P ≥ 0.1).

Thus, most of these dispersers did not even leave the range of environmental correlation in the scenarios with extended spatial clusters.

Under density dependent natal dispersal, the general tendency of individuals to leave the natal patch decreases. However, at the same time, the average dispersal distance (number of consecutive dispersal steps) is increased. Thus, the largest mean number of dispersal steps recorded emerges for completely informed dispersal (DD n /DD f ) in spatially correlated landscapes. However, even in these scenarios, individuals typically move less than two steps as soon as stepwise mortality risk becomes moderately large (P ≥ 0.1).

As the costs for any dispersal step remain the same in our simulations, this decline in dispersal probability must be attributed to a declining benefit of additional dispersal steps as the number of steps increases. This declining benefit has two main causes: (1) Kin competition, a major driver of dispersal [START_REF] Hamilton | Dispersal in stable habitats[END_REF][START_REF] Rousset | Evolution of the distribution of dispersal distance under distance-dependent cost of dispersal[END_REF][START_REF] Poethke | The relative contribution of individual and kin selection to the evolution of density-dependent dispersal rates[END_REF], is already largely reduced with the first dispersal step, because siblings are spread over eight neighbouring patches. Note that individual based simulation models of the evolutionary process account for kin competition by default [START_REF] Poethke | The relative contribution of individual and kin selection to the evolution of density-dependent dispersal rates[END_REF]). ( 2) The chance to reach a habitat with fewer competitors decreases with every dispersal step simply because any round of dispersal homogenizes population density ever further. The fact that spatial correlation has little effect on natal emigration probabilities (see fig. 4a) despite the fact that demographic benefits decline, supports the notion that this step is to a large degree driven by avoidance of kin-competition. Only selection on taking further steps seems to be sensitive to demographic benefits. The ability to control these latter benefits -moving from high-to low-density populations -is responsible for the significant differences between informed and uninformed dispersal strategies.

The evolution of elevated dispersal probabilities for uninformed (DI n , density independent) compared to informed natal dispersal (DD n ) can be traced to the fact that density dependent dispersal more efficiently exploits inter-patch differences in population density, but also more efficiently homogenizes population density across patches [START_REF] Enfjall | The evolution of dispersal -the importance of information about population density and habitat characteristics[END_REF]Leimar 2009, Hovestadt et al. 2010). This is the flip side of the larger demographic benefit achieved by dispersing individuals following a density-dependent emigration strategy (fig. 3). Clearly, homogenizing occurs most rapidly when the overall number of emigrants is large, i.e. when dispersal mortality is low.

Only the DD strategy is adjustable in a way that allows 'compensating' increased dispersal costs by also increasing the benefits as individuals emigrate only from especially densely populated populations and thus collect especially large demographic fitness benefits given they survive dispersal [START_REF] Poethke | The relative contribution of individual and kin selection to the evolution of density-dependent dispersal rates[END_REF]). This explains the fact that under densitydependent emigration natal dispersal is rarer, but consecutive dispersal steps occur with higher probability.

Even when individuals disperse with a fixed probability (DI n ), more natal emigrants come, for simple statistical reasons, from high density patches. Apart from the effect of kin-competition [START_REF] Hamilton | Dispersal in stable habitats[END_REF]May 1977, Poethke et al. 2007), it is this simple truism that makes even density independent natal dispersal a beneficial strategy in landscapes that exhibit spatiotemporal variability in population density. For dispersal steps following natal dispersal this argument does not hold however. Only immigrants, i.e. individuals that previously dispersed already, are allowed to decide whether they want to continue dispersal or not, but a correlation between population density and the number of immigrants no longer exists.

On the other hand, with completely density dependent dispersal (DD n /DD f ) immigrants can 'recognize' that they have arrived in a high-density patch and decide to move on. The probability that this is indeed the case is supported by another mechanism already described by [START_REF] Hovestadt | The control of emigration and its consequences for the survival of populations[END_REF]: under density dependent dispersal the distribution of the number of emigrants becomes much more leptokurtic with occasional 'mass emigration' from patches that have particularly good years. Such events also imply that large numbers of individuals immigrate into the neighbouring patches. Consequently, dispersal itself creates a spatial correlation in population density and makes it more profitable to continue dispersal over several steps to leave the zone of correlation in population density. It is indeed this later effect that explains why, rather surprisingly, the mixed strategy (DD n /DI f ) also evolves higher probabilities for consecutive dispersal steps. Due to the aforementioned effect, the recognition of high density in the natal patch allows already predicting that, after the first 'round' of dispersal, population density in neighbouring patches will be large too, even if individuals do not have the ability to perceive the actual density there.

In our model we assume that individuals either have information on local population density or not and that information acquisition is not costly. In real systems information acquisition may come with a cost, e.g. as investment into sensual capabilities or into time needed for information acquisition. Thus animals often use environmental cues (e.g. Stamps andKrishnan 2005, Ichiki et al 2011) or information derived from the presence of heterosopecifics (e.g. Monikkonen et al. 1999) to assess local habitat quality. Such indirect information will necessarily be less precise. Thus, situations of intermediate information status will exist, too.

It requires further investigation to understand how information precision and investment into its acquisition would affect the evolution of conditional dispersal strategies.

Despite the enormous interest of theoretical ecologists in the evolution of dispersal and its ecological consequences, the great majority of studies on dispersal evolution have so far focused on the evolution of dispersal propensity (e.g. [START_REF] Hamilton | Dispersal in stable habitats[END_REF][START_REF] Janosi | On the evolution of density dependent dispersal in a spatially structured population model[END_REF], Travis and Dytham 1999, Travis et al. 1999[START_REF] Poethke | Evolution of density-and patch-size-dependent dispersal rates[END_REF][START_REF] Parvinen | Evolution of dispersal in metapopulations with local density dependence and demographic stochasticity[END_REF], while a smaller number of papers investigate the evolution of dispersal distance (e.g. [START_REF] Ezoe | Optimal dispersal range and seed size in a stable environment[END_REF][START_REF] Savill | Spatially induced speciation prevents extinction: the evolution of dispersal distance in oscillatory predator-prey models[END_REF], 1999[START_REF] Hovestadt | Evolution of reduced dispersal mortality and 'fat-tailed' dispersal kernels in autocorrelated landscapes[END_REF][START_REF] Murrell | The evolution of dispersal distance in spatially-structured populations[END_REF][START_REF] Rousset | Evolution of the distribution of dispersal distance under distance-dependent cost of dispersal[END_REF], Higgins et al. 312 2003[START_REF] Muller-Landau | Theoretical perspectives on evolution of long-distance dispersal and the example of specialized pests[END_REF][START_REF] Starrfelt | Parent-offspring conflict and the evolution of dispersal distance[END_REF][START_REF] Travis | Towards a mechanistic understanding of dispersal evolution in plants: conservation implications[END_REF].

Usually the evolution of dispersal distances and the prediction of evolutionarily stable dispersal kernels are analysed under the assumption of 'passive' and uncontrollable movement as it occurs in seed dispersal, while investigating dispersal propensity has been a focus of animal ecologists. At least in part this dichotomy is explained by the different mechanisms that govern dispersal in plants and animals. In active as well as in passive dispersers, dispersal incurs different types of cost. First, species that belong to either dispersal group must invest in equipment that allows dispersal. This may be the flight apparatus of wind dispersed seeds [START_REF] Tackenberg | Assessment of wind dispersal potential in plant species[END_REF], the elaiosoma of ant dispersed seeds [START_REF] Pfeiffer | Myrmecochorous plants use chemical mimicry to cheat seed-dispersing ants[END_REF], the wings and flight muscles of Orthoptera (e.g. [START_REF] Zera | The physiology of life history trade-offs in animals[END_REF], or the silk thread of spiders (e.g. [START_REF] Bonte | Environmental and genetic background of tiptoe-initiating behaviour in the dwarfspider Erigone atra[END_REF]. Such investment costs are paid once and are more or less independent of the distance an individual will ultimately travel. However there are further costs that are related to dispersal distance. With passive dispersal (especially in plants) the number of propagules produced is typically large and dispersal is under maternal control. From the perspective of a tree, it is the overall distribution of offspring that counts and it may pay in evolutionary terms to allocate at least some offspring to risky long-distance dispersal. This promotes the evolution of "fat tailed" dispersal kernels [START_REF] Hovestadt | Evolution of reduced dispersal mortality and 'fat-tailed' dispersal kernels in autocorrelated landscapes[END_REF]; see also [START_REF] Roy | Butterfly numbers and weather: predicting historical trends in abundance and the future effects of climate change[END_REF][START_REF] Rousset | Evolution of the distribution of dispersal distance under distance-dependent cost of dispersal[END_REF][START_REF] Starrfelt | Parent-offspring conflict and the evolution of dispersal distance[END_REF].

However, as explained in the introduction these approaches appear ill suited for the case of actively moving organisms where the dispersal distances observed should be traced to the interaction between landscape attributes and the rules governing the movement and especially the settlement of organisms. Actively moving animals may continuously monitor their surroundings (Getz and[START_REF] Nathan | A movement ecology paradigm for unifying organismal movement research[END_REF]) and decide whether they continue to move or stay once they have found a patch of habitat. At this moment the dispersing individual may decide to either settle or move on. According to our results the tendency to move on will clearly depend on the risk associated with searching for other suitable habitats (cost of dispersal). In addition we would predict that species with well developed sensory (and cognitive) abilities that are readily able to assess population density or habitat quality are more likely to move on than species that do not have such abilities. On the other hand, individuals that need much time (in relation to life-expectancy) or energy to collect such information would typically settle in the first habitat they find.

Dispersal distance is an emergent property depending on behavioural rules on the one hand and landscape attributes on the other (cf. Baguett and Van Dyck 2007). As 'smart' animals are unlikely to settle just somewhere in the 'matrix' the distribution of dispersal distances will strongly be defined by the typical distances between patches of suitable habitat. We believe that our, certainly simplifying approach, is a useful step to better understand the evolution of dispersal distances in actively moving organisms as an emergent property defined by landscape attributes on the one hand and settlement rules on the other.

In summary we predict that with uninformed dispersal multiple dispersal steps should evolve only if step-wise dispersal costs are rather low. Multiple dispersal steps are most likely to evolve in species able to assess the quality of at least their natal habitat and in landscapes that show strong spatial correlation of environmental conditions. However, as it presumably requires time to collect information about habitat quality we expect that secondary dispersal will most likely occur among long-lived species with considerable sensory and cognitive abilities like mammals or birds.

  P=0.1), only 2-5% of the emigrants take more than one dispersal step. With spatially correlated environmental fluctuations (figs. 3c and 3d) however, the demographic benefit for natal dispersal decreases while the benefit for all further dispersal steps increases. An increase in the demographic benefits gained by moving additional steps particularly emerges for individuals following uninformed secondary dispersal (for DI n /DI f compare filled dots in figs. 3a and b with figs 3c and d; for DD n /DI f compare grey dots in figs. 3a and b with figs. 3c and d) and it is largest for individuals that show density dependent natal dispersal but density independent dispersal in all other dispersal steps (DD n /DI f ).

Figure A2 Exemplary

 A2 Figure A2

Figure Captions Figure 1

 1 Figure Captions

Figure 2

 2 Figure2

Figure 3

 3 Figure 3

Figure 4

 4 Figure 4

Acknowledgements

This work was supported by funding of the German Science Foundation to HJP and TH (DFG PO 244/3-1) and by the DLR (project TenLamas, EU FP6 BiodivERsA Eranet) to TH. We thank Joseph Chipperfield and two anonymous reviewers for their helpful comments on this manuscript.

Figure A1

Probability to perform at least n additional dispersal steps following initial natal dispersal, with n plotted on x-axis, as a function of dispersal cost (P; rows) and the mode of dispersal


Thomas Paviot
email: thomas.paviot@supmeca.fr

Pascal Morenton
email: pascal.morenton@ecp.fr

Vincent Cheutet
email: vincent.cheutet@supmeca.fr

Samir Lamouri
email: samir.lamouri@supmeca.fr

MultiCAD/MultiPDM integration framework

Keywords: CAD/PDM integration, Product Structure, EBOM, SOA, UML

whether they are published or not. The documents may come

Introduction

In order to cope with the necessity of reducing design time and time-to-market, large industrial companies (especially automotive or aeronautical companies) have become extended enterprises in which subcontractors become co-partners of the product development. The use of a coherent information system ensures project/product data coherency [START_REF] Cui | Research on Integration Technology for Product Lifecycle Management System[END_REF]. Each operator of the project accesses a single repository thus ensuring an effective collaborative work. In this paper, we focus on the mechanical Computer Aided Design (CAD) tools and Product Data Management systems (PDM).

For major projects, the chain of subcontracting may include several levels. SME involved in these projects, acting as subcontractors at level 3 or 4, are located at the crossroads of several extended enterprises (several of these major groups can be their customers). Designers have to use many CAD systems and connect the PDM systems of their customers, as well as their own PDM. Commercial CAD/PDM integrations are often expensive, and require significant training before they can be used. SME do not have the technical skills and financial resources to deploy these solutions quickly and efficiently. The CAD/PDM integration is then a major issue for these companies, since the ability to integrate the extended enterprises' information technology system tends to become an order qualifier. On one hand, the SME were identified as having the following needs:

• a low-cost CAD/PDM integration, easy to use, deploy and maintain,

• an installation that would not cause any important modification of the current system, both on server and client side, • an extensible integration, i.e. easily allow the interoperability with another CAD or PDM system. On the other hand, the following specific needs were expressed by designers:

• export the product structure to the PDM in order to manage an EBOM and CAD documents dependencies, • allow an asynchronous collaborative design on a part of the mock up,

• enable data exchange with customers or suppliers, • enable a project review based on 3D visualization. Two issues appear as crucial for SME: reduction of design time and risks of mistakes in the product structure management thus enabling them to compete for a call to tender.

When working with CAD and PDM systems without their native integration (whether it is too costly or the editors did not develop it), designers must reintegrate the data manually in the PDM thus causing a waste of time and a big risk of error. Data exchange using a standard data format (like the Standard for the Exchange of Product model data (STEP) for example) is also possible, but the incomplete implementation of STEP by CAD editors limits the scope of features needed by users (these points are developed in chapter 3).

The main issues thus identified are: Is there a way to overcome limitations in the implementation of STEP standard to allow an efficient collaborative design? What is the process of conversion of a CAD product structure to an Engineering Bill Of Materials (EBOM)? After a brief description of the state-of-the-art in CAD/PDM integration (chapter 2), we propose an approach based on a technique that obtains the information from the product structure before the STEP processor. We then develop a graph-based model of dependencies in the Digital Mock-Up (DMU) description, and conversion rules to export the product structure to EBOM (cf. chapter 3). Chapter 4 is relevant to the implementation architecture and the results obtained.

CAD / PDM integration

In order to integrate a CAD system with a PDM system, a data translator has to be developed. According to Fowler [START_REF] Fowler | STEP for Data Management, Exchange and Sharing, Technology Appraisals[END_REF], there are four different solutions for the data exchange problem: the manual reintegration of data, the direct translation, the neutral format translation and the product database sharing. The last two solutions are reasonable, flexible and adaptable. Direct translation is the most accurate solution, but the number of translators increases exponentially with the number of systems involved. Guyot et al. [START_REF] Guyot | Product data and digital mock-up exchange based on PLM[END_REF] calculate that for n systems, the number of translators is n²-n for the direct translation, and only 2n with a neutral format. If we consider using a commercial native CAD/PDM integration, which is an example of direct translation, the total cost of ownership (TCO) is then O(n²-n). The exponential growth of TCO is not compatible with SME resources.

A priori the solution of a neutral format translation appears to be adapted [START_REF] Fenves | Product Information Exchange : Practices and Standards[END_REF]. STEP file format is an internationalized standard [START_REF] Pratt | ISO 10303: the STEP standard for product data exchange, and its capabilities[END_REF] that offers various means of storing, exchanging and archiving the product data in a long-term approach. Oh et al. [START_REF] Oh | Mapping product structures between CAD and PDM systems using UML[END_REF] develop a CAD/PDM integration based upon EXPRESS mapping language and an UML mapping diagram. Methodology of Zhang et al. [START_REF] Zhang | An Internet based STEP data exchange framework for virtual enterprises[END_REF] maps IGES, STEP AP203 and STEP AP209 standard. However, although the STEP Schema has been specified for exchanging data usually stored by PDM systems [START_REF] Machner | Mapping the user requirements from the VDAORG with the STEP PDM Schema[END_REF], the use of STEP file format to extract the DMU information leads to two problems: CAD editors partially implement the ISO standard [START_REF] Oh | Mapping product structures between CAD and PDM systems using UML[END_REF] and STEP processor acts like a filter on native data that is problematic in the case of an homogeneous CAD/PDM integration, i.e. both designers working with the same CAD tool (cf. Chapter 3).

Proposed approach

The article is the key-item of the enterprise since it is used by every department: design, maintenance, manufacture, distribution, inventory management and Material Requirement Planning (MRP). Each of them view the BOM 'as maintained', 'as built', 'as designed' etc. Parts are necessary to manage the engineering changes requests as well as the product configuration [START_REF] Estublier | Reconciling Software Configuration Management and Product Data Management[END_REF]. In order to allow the connection to the Information Technology (IT) system of the extended enterprise, we consider that it is important that our CAD/DPM integration generates an EBOM. We then have two different structures describing the product: a CAD document structure, which defines the links between CAD files, and an EBOM that describes the structural links between PDM parts. This chapter presents the methodology used to convert a CAD product structure and all the dependencies to this double structure in the PDM system.

Dependencies in a DMU

CAD editors now offer the same kind of features. By studying the different CAD software that we are used to working with, we noticed that a DMU is completely defined by:

• a set of CAD files that describe the geometry of the system. Each software uses a proprietary file format to store geometric data, but the information is structured by two kinds of files: parts and assemblies (CATPart and CATProduct for CATIA V5 TM , SLDPRT and SLDASM for SolidWorks TM , IPT and IAM for Inventor TM , PRT and ASM for Pro/E), • a set of files that define the different configurations of a part. These are usually text CSV formatted files that can come from a spreadsheet application, • a set of components linked with parent/child relations. Components can be instances of CAD files. These dependencies are presented on fig. 1 These data must be stored into the PDM to ensure their coherence during the design process.

Graph model of dependencies

In this subchapter, we define a graph-model of these dependencies that leads to the data model to be implemented.

F is the set of all the necessary and sufficient files needed to completely describe the geometry of the digital mock-up. Each of these files describes a part of the system.

I is the set of components that appear in the design tree view that is generally displayed on the left part of the CAD software window. These components can be, or not, an instance of an element of F . C is the set of files that define different configurations for a part or an assembly.

We identified four different types of links between these elements:

• structural links ( L S )

Properties of this model

Each file in F can be instantiated more than once. The current trend to standardize products leads to a reduction in the elements of F to describe the geometry of a product.

We can then write the following cardinality property:

) ( ) ( ) ( 0 I card F card C card ≤ ≤ ≤ (

Product structure to export to PDM

Our integration must take into account the EBOM of the product. This approach is necessary to connect the Material Requirement Planning (MRP) system, or to enable the product configuration management.

"As Design" EBOM graph model

Basically, we can define an EBOM as a graph ) , ( L P E , P being parts and L structural links (cf. fig. 3). E is a directed, weighted 1-graph. Weight i w of edge i L is given by the number of elements in its parent. The EBOM can be exploded into n 1-level EBOM in order to simplify the treatment.

Product Structure to EBOM conversion rule

We then have to determine which CAD components have to be converted to PDM parts and the conversion rules that need implementing. The conversion rules must solve these two issues:

• the rule for converting CAD instances into parts,

• the rule of constructing EBOM links from the product structure, i.e. define a morphism from ) , ( L S I PS to ) , ( L P E .

Converting CAD components to PDM parts

The first approach we investigated is the one that is usually implemented in the CAD softwares: each new instance of the ( L P E . A special case concerns the instances referring to a part with no mass: in an early design stage, each part may be modelized by a few geometric characteristics (point, line, plane etc.) on which future mass will stand. Another usual design methodology uses master sketches that drive the geometry of the DMU, since they allow a robust and flexible design. These master sketches must not be converted to an EBOM part: they have to be considered as tools in the DMU building process. We decide to put an "MS" tag (for Master Sketch) in the name of the instance, to specify to the translator that it should be skipped. These considerations lead to the following single rule: all instances that are not "MS" tagged are converted into parts.

A E D C B 2 1 1 1 2 1 D F C C E 4 A E D C B E C F 2 1 1 2 4 1 1 E
We must also take into account another aspect: when the designer uses a standard component or a purchased component (cf. fig. 4), he uses the 3D model of the component obtained from the supplier database (cf. fig. 4a). This model is generally available in the STEP format, and once it is instantiated in the CAD session, many components are created (cf. fig. 4b). In this case, it is not necessary to create the PDM parts corresponding to each item, but only the one corresponding to the root node of the structural tree (cf. fig. 4c). This is achieved by adding a "PURCHASED" tag to the instance name.

Implementation

This chapter explains the implementation of the previous algorithm: subchapter 4.1 focuses on the data model whereas 4.2 presents the functional architecture of the implementation.

Data model

Eynard et al. [START_REF] Eynard | PDM system implementation based on UML[END_REF] define a complete UML class diagram of a product structure. However two problems subside: on one hand, the dependencies between the CAD files are not described; on the other hand this model is strongly linked to the PDM system they study and is not easily portable to another system. We propose the below model (fig. 5) that uses elementary classes available on any PDM system (Part, CAD Document, Generic Document) and for which implementation does not require any customization on the PDM server side. The double Part and CAD document structures appear on this diagram.

The "Structure links" connect the "Part Master" class with the "Part" class that can be iterated during the design process. Each part is described by 3 "Generic Documents" that embed the 3D Preview, exchange files and configuration table files. Each "Part" is described by 0 or 1 "CAD Document" which refers to a native CAD file.

Implementation architecture

The implementation architecture relies on two technologies (cf. fig. 6):

• Component Oriented Model (COM): this Microsoft TM Windows TM specific technology is designed to allow the automation of manual processes. The data model of each CAD software is specific, but we implemented a COM client that relies on a set of generic basic classes/methods that are available in each software. A simple XML mapping file is used to instantiate our generic client object. COM client parses product structure, export files as STEP or 3DXML. • Web Services (Service Oriented Architecture): a service is an implementation independent interface with an explicit definition; a service is loosely coupled, location-transparent and called by interoperable communication protocol; a service encapsulates re-usable business functions.

During the last years, PDM editors have made many efforts to provide standardized SOA [START_REF] Lee | An implementation methodology of SOA based PLM system[END_REF] that make their system interoperable. Simple Object Application Protocol (SOAP) [START_REF]Simple Object Access Protocol[END_REF] requests deal with part/documents/links creation on the PDM. For the CAD softwares that do not provide any COM server, or that run on other operating systems, we developed a simple STEP parser that can extract the product structure information [START_REF] Oh | Mapping product structures between CAD and PDM systems using UML[END_REF].

3D Visualization

To achieve the 3D visualization of our integration, a number of file formats are available that can satisfy the needs of all users by allowing 3D data, and sometimes other data such as 2D drawings and documents, to be viewed from a fairly simple and free of charge viewing tool. These include formats such as UGS'JT, Dassault Systèmes'3DXML, 3D Industry Forum's (3DIF) U3D, Tech Soft's HOOPS Stream, and Web 3D Consortium's VRML and X3D.

3D visualization files are created from the CAD session with the appropriate COM method, exported to the PDM and converted into 3D preview "Generic Document" (cf. fig. 5). These documents can be accessed by any authorized user even if the CAD software is not installed on his computer. It's especially useful for PDM systems accessible from a lightweight client: a collaborative project review can be done from almost any computer, as long as the appropriate viewer is installed and configured.

Results

We developed a demonstrator (released under the terms of a free software license) that implements previous work: CAD Shuttle [13]. We identified four elementary processes that we implemented to achieve a simple CAD centric collaborative design:

• export of the CAD data to the PDM,

• download of the CAD data from the PDM,

• request a lockup of a part (checkout) and begin a new design process,

• upload modifications to the PDM (check-in) and allow other designers to access new part iteration. We exported the product structure of the example of fig. 1 from Autodesk Inventor TM to PTC Windchill TM PDM. We also investigated further with bigger and more complex DMUs (cf Table 1

Conclusion

In this paper, we presented a methodology to convert a CAD product structure to an EBOM. We defined an UML data model that was implemented to provide an easy to use/deploy/maintain and low-cost bidirectional CAD/PDM bridge. Basic features of collaborative design work are fully functional but we still encounter problems to properly handle events sent by the CAD or the PDM when a modification occurs on both CAD or PDM sides. Further short term work will focus on issues of change management in product structure and synchronization of data. In the long term, we plan to develop a methodology to convert an Engineering Bill Of Material into a Manufacturing Bill Of Material (MBOM), so that our demonstrator can connect to an Enteprise Ressource Planing (ERP) system [START_REF] Cimdata | PLM and ERP Integration: Business Efficiency and Value[END_REF] and cover a larger scope of extended enterprise needs.

Figure 1

 1 Figure 1 Dependencies for a simple DMU (screenshots from Autodesk Inventor TM )

Figure 2

 2 Figure 2 Graph model for the instances/files dependencies Let D be the set including all DMU data ( I F C D ∪ ∪ = ) and L the family of dependencies between these data. The graph ) , ( L D DMU contains the required information that has to be stored in the PDM database. We define the subgraph ) , ( L S I PS as the "product structure".

  of the necessary dependencies to keep the CAD data coherent, once the current work of the designer is saved to his local hard disk, DMU is a connected and directed graph. An element of I cannot handle a reference to himself so if , Morenton P., Cheutet V. and Lamouri S.

Figure 3

 3 Figure 3 EBOM graph model

Figure 4 I

 4 Figure 4 Product structure conversion for standard components

Figure 5

 5 Figure 5 UML data model of the translator

Figure 6

 6 Figure 6 Implementation architecture based on COM and SOAP technologies

Table 1

 1 ). Results of CAD export to a distant on-demand PLM platform CAD Shuttle has been successfully tested with 2 other CAD software and 1 PDM system, using the same and simple graphical user interface.

	DMU name	Number of I Number of parts DMU Size	Total export time
	Grip	10	6	3.5Mb	5mn
	Aircraft	100	90	200Mb	45mn


Johanne Seguin

Céline Nicolazzi

Nathalie Mignet

Daniel Scherman

Guy G Chabot

Vascular density and endothelial cell expression of integrin alpha v beta 3 and E-selectin in

Keywords: Animals, Cell Adhesion Molecules, metabolism, Cell Line, Tumor, E-Selectin, metabolism, Endothelial Cells, metabolism, Female, Integrin alphaVbeta3, metabolism, Mice, Neoplasms, Experimental, blood supply, metabolism, Neovascularization, Pathologic, metabolism mouse tumors, vascularization, integrin alpha v beta 3, E-selectin, melanoma, colon, mammary, pancreas, sarcoma, lung

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Angiogenesis, which is required for the growth of solid tumours and the metastatic dissemination of tumour cells, is probably the most important parameter for the control of cancer progression 1 . Angiogenesis is the formation of new capillaries from pre-existing [ ] microvessels and consists in a multi-step process, in which endothelial cells degrade their extracellular matrix, migrate into the perivascular spaces, proliferate and align to form cell-cell contacts to construct patent blood vessels (reviewed in 2 ). This process is [ ] mediated by endothelial cell adhesion molecules (CAMs) represented by four principal classes: the selectins, which are transmembrane proteins subdivided in three groups E, L and P, expressed by endothelial cells, leucocytes and platelets, respectively; the integrins, which are transmembrane glycoproteins composed of two subunits and ; the membrane proteins immunoglobulin superfamily; and, the α β cadherins, mediators of cell-cell interactions 3 . These CAMs also play an important role in the process of metastasis development and [ ] migration 4, 5 . Compared to normal tissues, the expression pattern of the different CAMs vary widely between tumour types 6 .

[ ] [ ]
Therefore, the knowledge of specific CAM expression in tumours is of importance because it can allow a better understanding of the biology of tumour metastasis and also contribute to better predict the tumour evolution in vivo.

Among the CAMs, two proteins appear more particularly involved in the angiogenesis process, i.e., the integrin alpha v beta 3 ( v 3)

α β
and the E-selectin Varner, 1996 10294/id; [START_REF] Kraling | E-selectin is present in proliferating endothelial cells in human hemangiomas[END_REF]

[ ]

Although the importance of angiogenesis has become central to a better understanding of the cancer process, as well as to the search for more specific treatments, it is surprising that most preclinical murine models are poorly characterized with regard to their vascular density and their expression of the principal CAMs. This information could be helpful in order to select the best tumour models for angiogenic studies and to follow experimental therapeutic approaches.

The aim of the present study was to evaluate the vascular density of 15 frequently employed murine solid tumours using PECAM-1 (a constitutively expressed CAM; CD31), and to characterize the expression pattern of the integrin v 3 and E-selectin using immunological α β methods. Our results show that each tumour type possesses a unique expression pattern of CAMs that could influence the tumour biology and response to therapy.

Materials and methods

Mouse tumours

Mouse tumours were obtained from the Aventis Oncology Department (now Sanofi, Vitry sur Seine, France) and were maintained in the mouse strain of origin. Briefly, tumour fragments (30 60 mm ) were implanted subcutaneously using a 12 gauge trocar into the flanks isopentane immersed in liquid nitrogen and stored at 70 C until cutting on microtome and prepared for immunohistological analysis.

-°Primary

antibodies used for immunostaining

Monoclonal rat antibodies anti-PECAM-1 (CD31, clone: MEC13.3), anti-E-selectin (CD62E, clone: 10E9.6) and hamster antibody anti-integrin v 3 (CD61, clone: 2C9.G2, directed against sub-unit ) were obtained from BDBiosciences (Le Pont de Claix, France).

α β β 3
These antibodies were shown to cross-react with the corresponding mouse antigens. The working dilutions for immunostaining were 50-fold for anti-PECAM-1 (15.6 g/mL) and 20-fold for anti-E-selectin (62.5 g/mL) and anti-integrin v 3 (250 g/mL). Omission of the μ μ α β μ primary antibody was used for negative controls.

Immunohistological analysis

Ten-micron frozen tissue sections were placed on Superfrost Plus slides. Immunostaining of the CAMs was performed using a three-step procedure as previously described 8 . Endogenous peroxidase was inactivated with 0.3 H O in phosphate-buffered saline

[ ] % 2 2
(PBS) for 30 min. Non-specific binding sites were blocked with 10 serum of the species used for the secondary antibody in PBS for 30 % min. Slides were then incubated for 1 h (CD31, anti-PECAM-1) or 2 h (CD62E, anti-E-selectin and CD61, anti-integrin v 3) at 37 C in a α β °humidified chamber with primary antibody. After 3 rinses of 5 min each, slides were incubated for 30 min with the corresponding biotinylated-secondary antibody (dilution 1/400 for the goat anti-rat antibody (0.5 mg/mL) and 1/200 (125 g/mL) for the mouse μ anti-hamster antibody). After 3 rinses, slides were re-incubated with the streptavidin-conjugated peroxidase (Sigma-Aldrich, St. Quentin Fallavier, France) according to the manufacturer s instructions (dilution 1/400). The DAB (3,3 -diamino-benzidine, Sigma-Aldrich)

' ′
substrate was then added for 5 to 7 min until a brown precipitate was visible. Tissues were counterstained with Gill s hematoxylin ' (Sigma-Aldrich) and treated with 30 mmol/L NH OH to generate a blue nuclear stain. Slides were dehydrated in graded ethanol solutions 4 and xylene and mounted with Eukitt (Sigma-Aldrich).

®

Immunostaining semi-quantitative analysis

The density of CAMs was expressed as a percent range of total cells, as follows: -, no staining detected; <1 , few positive cells; 1 5

% -%
, some positive cells in all tumour sections; 5 15 , positive cells observed in all fields; >15 , high density of positive cells in all fields.

-% %

The immunostaining was assessed by two independent investigators in a blinded manner.

Results

Choice of tumour types

In this study, we investigated 15 solid murine tumours to evaluate their vascular density and to assess their expression of important molecular markers involved in angiogenesis. The tumours were chosen among the most frequently used mouse tumour models, and also to be representative of the most frequent human tumour types. The murine tumours examined are listed in Table 1 and included 1 melanoma, 3 colon carcinomas, 7 mammary adenocarcinomas, 2 pancreatic tumours, 1 osteosarcoma and 1 lung carcinoma. Along with the tumour code names are presented the mouse strain used for propagation and the tumour main characteristics including doubling time, histological type and metastasis potential (invasiveness).

Tumour vascular density

Medium sized tumours of approximately 200 mm were harvested and analyzed for the endothelial expression of PECAM-1 (CD31), presented a poor vascular density (<5 ), whereas colon C26 (5 15 ), and pancreatic adenocarcinoma PO2 (>15 ) were well and highly % -% % vascularized, respectively. Table 2 presents the comparison of the vascular density for the 15 tumours investigated. It is noteworthy that only the B16 melanoma was poorly vascularized, whereas all the colon and the mammary tumours were well vascularized (5 15 ). The -% pancreatic adenocarcinomas (PO2, PO3), sarcoma (GOS) and the lung carcinoma (LLC) were the most vascularized tumours in our study (>15 ).

%

Tumour vascular distribution

The vascular distribution pattern of each tumour type is presented in Table 2. Most tumours showed a homogeneous and regular distribution of blood vessels (colon, pancreas, sarcoma and lung). However, two tumour types presented a different profile: the B16 melanoma presented small spots with numerous vessels; and, all the mammary tumours showed a heterogeneous distribution of blood vessels.

Expression of integrin v 3 in tumours α β

The percent expression of positive vessels for integrin v 3 is presented in Table 2. Seven tumours expressed the v 3 integrin,

α β α β
although at various levels. The B16 melanoma, one colon tumour (C26), 3 mammary tumours (MA17/Adr, MA25, MA44), one pancreatic tumour (PO2) and the Lewis lung carcinoma expressed this integrin. However the v 3 integrin was expressed at higher levels in the

α β
Lewis lung carcinoma and the PO2 pancreatic adenocarcinoma. Representative photographs depicting a low expression (<5 ) of v 3 in

% α β
B16 melanoma, MA17/Adr, MA25 and MA44 mammary adenocarcinoma are shown in Fig. 2, whereas high expression (5 15 ) is shown

-%
for the PO2 pancreatic adenocarcinoma and LLC Lewis lung carcinoma.

The distribution pattern of this integrin was however different for the expressing tumours. The MA17/Adr, MA25 and MA44 mammary adenocarcinomas expressed the v 3 integrin on some vessels distributed all over the tumour section, whereas the C26 colon α β carcinoma and B16 melanoma showed v 3 restricted to some areas localized at the tumour periphery. The high expression of this integrin

α β
in the LLC lung carcinoma and the PO2 pancreatic adenocarcinoma was however distributed differently: in the LLC, v 3 was

α β
homogeneously distributed, whereas in PO2, the expressing cells formed clusters.

Expression of E-selectin in tumours

The E-selectin expression in the various tumours is presented in Table 2. E-selectin was detected in only four tumours (B16, C26, C51, PO3). Representative photographs are presented in Fig. 3, where MA17/Adr and PO2 did not show detectable levels of E-selectin. In B16 melanoma, only few positive cells showed E-selectin expression (Fig. 3), similarly to the C51 colon adenocarcinoma and the PO3 pancreatic adenocarcinoma which also showed some isolated vessels expressing E-selectin. The C26 colon carcinoma expressed E-selectin to a high degree (5 15 ), with numerous vessels expressing this selectin, although with a heterogeneous distribution, as depicted in Fig. 3.

-%

Co-expression of integrin v 3 and E-selectin in tumours α β

To study the localization of v 3 and E-selectin in the two tumours that co-expressed these CAMs, i.e., B16 and C26, we used α β adjacent histological sections presented in Fig. 4. The co-expression of v 3 and E-selectin was not homogeneously distributed and was α β only observed at the tumour periphery for B16 and C26 tumours. In addition, we noted that the detection of PECAM-1 expression exhibited a denser vascularization in these areas.

Discussion

The aim of this study was to better characterize the vascular density and the CAMs expression in murine solid tumours frequently employed in preclinical research. These data could facilitate the choice of the best tumour models for testing therapeutic approaches aiming at blocking angiogenesis or destroying existing tumour vascularization.

Concerning the vascular density, assessed with the staining of a CAM constitutively expressed by endothelial cells (PECAM-1, CD31), we found significant differences between the various tumours examined. Indeed, the least vascularized tumour was the B16 melanoma, whereas the colon and mammary tumours were all well vascularized. Also of note, the pancreatic tumours, the sarcoma (GOS) and the lung (LLC) tumours were found to be the most vascularized tumours in our series. There was apparently no correlation between the vascular density and the metastatic potential, because some poorly vascularized tumours are known to be highly metastatic (e.g., B16;

compare Tables 1 and2), whereas other tumours were highly vascularized and are moderately metastatic (e.g., GOS). Although these results may appear to contradict observations showing that high vascularization often correlates with metastatic potential within the same tumour type (reviewed in 30 ), it should be noted that in our study, different tumours from various tissue origins were compared, and not

[ ]
at various growth stages for the same tumour type. Also, the results showing poor vascularization for the B16 melanoma could support the hypothesis that a tumour hypoxic environment could favour metastasis as previously reported 31 .

[ ]

The vascular distribution pattern in tumours was also of interest, because it revealed a well-organized vascularization in all subcutaneously transplanted tumour models. Most tumour types presented a regular and homogeneous distribution of blood vessels (colon, pancreas, sarcoma and lung). However, in the B16 melanoma and the mammary tumours, the blood vessel distribution was found heterogeneous. For the mammary tumours, it was noteworthy that tumours of a same organ origin exhibited a similar vascularization pattern, even if they were transplanted in different mouse strains.

In humans, high tumour vessel density has been shown to be associated with metastasis, tumour progression and decreased survival time in several tumour types, including breast, lung, melanoma, colon, cervix, prostate and bladder 32, 33 . Although increased vascularity [ ] could be anticipated to favor oxygenation and drug delivery to tumours, it is possible that the more vascularized tumours metastasize to distant sites and therefore become more difficult to treat because of their dissemination throughout the organism 33 .

[ ]

In this study, we have also assessed the expression of two endothelial CAMs involved in angiogenesis in frequently employed mouse tumour models. These CAMs, including the integrin v 3 and the E-selectin, are particularly important molecules because they are

α β
involved in the process of angiogenesis which is required for tumour growth, cell migration and metastasis Weis, 2011 10351/id .

{ }

Integrin v 3 is not constitutively expressed by normal endothelial cells and is therefore considered diagnostic of proliferative α β endothelial cells in neovascularized areas 9 . In the present study, the v 3 integrin was expressed in 7/15 of the tumours examined (B16,

[ ] α β
C26, MA17/Adr, MA25, MA44, PO2, LLC). It is noteworthy that there was no correlation between the tumour type and the expression of this integrin because only 1/3 of colon tumours and 3/7 of mammary tumours expressed this integrin. For the distribution pattern of this integrin in expressing tumours, it was interesting to observe that mammary and lung tumours have a homogenous distribution, whereas C26 and B16 melanoma presented a peripheral distribution. Similar results obtained by magnetic resonance imaging have recently been published for the B16 melanoma where it was also observed that v 3 is peripherally distributed 34 . Similarly to mouse tumours, the

α β [ ]
integrin v 3 has also been found expressed in the endothelium of several human tumours including melanoma, breast, colon, prostate, α β cervix, brain, and pancreas, colon, and lung 11, 19,

[ ] E-selectin was expressed in only 4 mouse tumours, including B16 melanoma, C26 and C51 colon tumours and pancreatic PO3 tumours. In the C26 colon tumour and B16 melanoma, E-selectin expression was restricted to areas localized at the tumour periphery, which suggests that these areas correspond to tumour neovascularization zones which also expressed the v 3 integrin. This localized

α β
expression of E-selectin at tumour periphery has already been observed in previous studies 37, [START_REF] Fox | The increased expression of adhesion molecules ICAM-3, E-and P-selectins on breast endothelium[END_REF] . Like integrin v 3 E-selectin is not

[ ] α β
expressed by normal endothelial cells but was found on microvessels that showed proliferating endothelial cells 8 . With regard to the [ ] E-selectin clinical applications, antagonists have been developed to target cellular interactions with this CAM including antibodies, ligand inhibitors and metabolic carbohydrate mimetics 39 . E-selectin has also recently been used as a target for drug delivery 40 .

[ ] [ ]

We noticed that only half of the tumours expressed integrin v 3 and a quarter expressed the E-selectin. Previous studies have also

α β
reported heterogeneity in CAMs expression by tumour endothelium, i.e., some tumours are deficient while others highly express CAMs 6

[ ]
. The deficient CAMs expression could be due to the fact that CAMs are also involved in the inflammatory responses and recruitment processes. A reduced endothelial CAM expression in tumour microvessels could facilitate tumour progression in avoiding the patrolling by circulating lymphocytes 6 . In addition, the heterogeneity of CAMs expression has been shown to depend on tumour type, implantation [ ] mode, growth site and host strain 41, 42 . In the present study, subcutaneously implanted tumours have been used and these models may

[ ]
not be the exact reflection of tumours, for which more complex interactions between the different CAMs appear to regulate tumour in situ angiogenesis 43 .

[ ] As previously mentioned, tumour viability, growth and metastasis depend on tumour angiogenesis. Integrin v 3 and E-selectin α β mediate the processes of microvessel neoformation, and detection of the expression of both CAMs allows to determine whether angiogenesis occurs in a tumour. Indeed, several studies have reported the use of specific angiogenesis specific markers as targeting ligands for systemic drug or gene delivery to cancer 44 46 or to other vascular diseases 47 .

[ -] [ ]

The expression of these CAMs in tumours appears to be shared by murine and human tumours as well. For example, v 3 has indeed With regard to the E-selectin, antagonists have been developed to target cellular interactions with this CAM including antibodies, ligand inhibitors and metabolic carbohydrate mimetics 39 . E-selectin has also recently been used as a target for drug delivery 40 .

[ ] [ ]

In addition to therapeutic applications, the identification of these CAMs in tumours has also permitted the use of this knowledge for molecular imaging. The integrin v 3 has been targeted for imaging purposes with near-infrared fluorescent dye-RGD peptide conjugates, α β their multivalent analogs, and nanoparticle conjugates 50, 52 . E-selectin has also been used as a target for molecular imaging 40 .

[ ] [ ]

In conclusion, the assessment of the vascular density and the expression of the important integrin v 3 and E-selectin in a series of α β widely used murine solid tumour models has allowed the identification of several tumours expressing these CAMs. We have also identified two tumours expressing both v 3 and E-selectin (B16 and C26). These data may prove useful for the choice of appropriate α β tumour models for the study of the biology of tumour angiogenesis, the evaluation of antiangiogenic therapies and the validation of tumour imaging modalities targeting these CAMs.

Figure 1 Figure 2 Figure 4

 124 Figure 1 Vascular density in representative mouse tumours B16 melanoma, poorly vascularized (<5 ); C26 colon carcinoma, well vascularized (5 15 ); PO2 pancreatic adenocarcinoma, highly % -% vascularized (>15 ). Mouse tumour slices of 10 m were prepared and stained using PECAM-1 (CD31) antibody as described in Materials % μ and Methods. Original magnification 100, bar 20 m.× μ

  9166/id . The integrin v 3 is not (or weakly) constitutively expressed in normal

	{	}	α β
	vascular endothelial cells, whereas it is selectively expressed on proliferating vascular endothelial cells 9 and on vascular cells in tumours [ ]
	10 12 . Several studies have indeed reported the requirement of integrin v 3 for angiogenesis Brooks, 1994 10300/id;Eliceiri, 1999 [ -] α β {
	10301/id;Weis, 2011 10351/id . Similarly, E-selectin is also detected at very low levels in normal adult blood vessels, but is highly }
	expressed in newly formed tumour capillaries 15 and in activated endothelium 4, 8 . In addition, several investigators have shown that [ ] [ ]

E-selectin is involved in angiogenesis 8, 16, 17 . By their angiogenic and adhesion properties, both CAMs are involved in tumour growth

[ ]

and metastasis and can therefore influence cancer prognosis and/or response to antiangiogenic therapy 4, 5, 18, 19 .

transplantable ductal adenocarcinomas of the pancreas in C57BL/6 mice . Cancer Res . 44 : 717 -726 28 . Glasgow LA , Crane JL Jr , Kern ER , Youngner JS . 1978 ; Antitumor activity of interferon against murine osteogenic sarcoma in vitro and in vivo . Cancer Treat Rep .

Acknowledgements:

We are grateful to the Oncology Department of Aventis Pharma, S.A. (now Sanofi, S.A.), for providing the murine tumour samples. This work was supported in part by Gencell S.A., the Centre National de la Recherche Scientifique (CNRS), the Institut National de la Sant et de é la Recherche M dicale (INSERM) and the Ecole Nationale Sup rieure de Chimie Paris (ENSCP). We also thank the Institut National du é é

Cancer for grant support to GGC (INCa, Boulogne Billancourt, France).

>15%

Homogeneous vessels distribution, some small spots with numerous vessels 5 15

Homogeneous vessels distribution --Lung

LLC

>15%

Homogeneous vessels distribution 5 15

-% a Three independent samples were evaluated for each tumour type by 2 independent investigators, giving similar results. Tumour volumes were approximately 200 mm .