Fabien Laroche
email: fabien.laroche@irstea.fr

Philippe Jarne

Thomas Perrot

Francois Massol

The evolution of the competition-dispersal trade-off affects α and β-diversity in a heterogeneous metacommunity

Keywords: Neutral theory, trade-off, metacommunity, species diversity, evolutionary branching, null models, coalescence, T-statistics

Difference in dispersal ability is a key driver of species coexistence in metacommunities.

However, the available frameworks for interpreting species diversity patterns in natura often overlook trade-offs and evolutionary constraints associated with dispersal. Here we build a metacommunity model accounting for dispersal evolution and a competition-dispersal tradeoff. Depending on the distribution of carrying capacities among communities, species dispersal values are distributed either around a single strategy (ESS; evolutionarily stable strategy), or around distinct strategies (EB; evolutionary branching). We show that limited dispersal generates spatial aggregation of dispersal traits in ESS and EB scenarios, and that the competition-dispersal trade-off strengthens the pattern in EB scenario. Importantly, individuals in larger (resp. smaller) communities tend to harbour lower (resp. higher) dispersal, especially under the EB scenario. We explore how dispersal evolution affects species diversity patterns by comparing those from our model to the predictions of a neutral metacommunity model. The most marked difference is detected under EB, with distinctive values of both alpha-and beta-diversity (e.g., the dissimilarity in species composition between small and large communities was significantly larger than neutral predictions). We conclude that, from an empirical perspective, jointly assessing community carrying capacity with species dispersal strategies should improve our understanding of diversity patterns in metacommunities.

MAIN TEXT

INTRODUCTION

Ecological differentiation is considered as a driver of stable coexistence among competing species since the origin of community ecology [START_REF] Lotka | Elements of physical biology[END_REF][START_REF] Macarthur | The limiting similarity, convergence, and divergence of coexisting species[END_REF][START_REF] Chesson | Mechanisms of Maintenance of Species Diversity[END_REF]. In this context, resource partitioning plays an important role, but numerous theoretical [START_REF] Hastings | Disturbance, coexistence, history, and competition for space[END_REF][START_REF] Tilman | Competition and biodiversity in spatially structured habitats[END_REF][START_REF] Calcagno | Coexistence in a metacommunity: the competition-colonization trade-off is not dead[END_REF] and experimental [START_REF] Cadotte | On Testing the Competition-Colonization Trade-Off in a Multispecies Assemblage[END_REF][START_REF] Livingston | Competition-colonization dynamics in experimental bacterial metacommunities[END_REF] studies emphasized that trade-offs between local competitive ability and dispersal of species in patchy environments (so-called "competition-colonization" or "competition-dispersal" trade-offs) also favour coexistence. Both empirical studies and field surveys suggest that these trade-offs are a distinctive characteristic of communities from many natural systems [START_REF] Jakobsson | A comparative study of seed number, seed size, seedling size and recruitment in grassland plants[END_REF][START_REF] Nadell | A fitness trade-off between local competition and dispersal in Vibrio cholerae biofilms[END_REF], and are sometimes associated with wide variation in dispersal among species belonging to the same community (e.g., [START_REF] Muller-Landau | Interspecific variation in primary seed dispersal in a tropical forest[END_REF] for the tree community of Barro Colorado Island). However, whether (and how) they affect patterns of species diversity in natural communities remains debated [START_REF] Yu | An empirical model of species coexistence in a spatially structured environment[END_REF][START_REF] Lei | Spatial dynamics of two competing specialist parasitoids in a host metapopulation[END_REF]. One reason is that studies trying to explain such patterns using empirical data on dispersal variation and potential trade-offs with competitiveness remain scarce and focused on simple communities including a few species at best [START_REF] Yu | An empirical model of species coexistence in a spatially structured environment[END_REF][START_REF] Lei | Spatial dynamics of two competing specialist parasitoids in a host metapopulation[END_REF]. Gathering such data is indeed a highly demanding endeavour in complex natural communities.

A less demanding, though more indirect, approach to the role of trade-offs on species diversity patterns is the "pattern-to-process" approach in which these patterns are used to infer the underlying processes, and therefore to distinguish among coexistence theories. However, the theory of species coexistence based on the competition-dispersal trade-off has mostly been developed in terms of "patch dynamics" [START_REF] Tilman | Competition and biodiversity in spatially structured habitats[END_REF][START_REF] Levins | Regional Coexistence of Species and Competition between Rare Species[END_REF][START_REF] Leibold | The metacommunity concept: a framework for multi-scale community ecology[END_REF], considering monospecific communities and focusing on species diversity at the "metacommunity" scale only. Such assumptions are far from realistic for most natural systems and prevent studying diversity patterns within sites.

The metacommunity framework provides more general models, giving more or less weight to basic processes such as dispersal, demographic stochasticity, or competition (see [START_REF] Leibold | The metacommunity concept: a framework for multi-scale community ecology[END_REF] for a review). In particular, neutral models [START_REF] Caswell | Community structure: a neutral model analysis[END_REF][START_REF] Bell | Neutral macroecology[END_REF][START_REF] Hubbell | The unified neutral theory of biodiversity and biogeography[END_REF] constitute an interesting entry into metacommunity dynamics: they assume ecological equivalence of species [START_REF] Calcagno | Coexistence in a metacommunity: the competition-colonization trade-off is not dead[END_REF] and attribute a major role to the balance between demographic stochasticity and dispersal. Importantly, these models yield accurate quantitative descriptions of community structure [START_REF] Volkov | Patterns of relative species abundance in rainforests and coral reefs[END_REF]. An outstanding example is the fit of a neutral model to the rank-abundance diagram of tropical trees in Barro Colorado Island [START_REF] Hubbell | Neutral theory and the evolution of ecological equivalence[END_REF]. We suggest here that neutral models could be used for building predictions about the effects of interspecific competition-dispersal trade-offs on diversity patterns, providing a more sophisticated basis than patch-dynamics models for pattern-toprocess approaches.

We only know of two studies that assessed the impact of interspecific heterogeneity of dispersal on species diversity patterns by comparing these patterns to neutral predictions. Liu et al. [START_REF] Liu | Asymmetry in species regional dispersal ability and the neutral theory[END_REF] introduced several dispersal levels in an otherwise neutral model. They showed that the rank-abundance diagram of a community could not be used to detect heterogeneous dispersal because this pattern is already very well predicted by a purely neutral model. Janzen et al. [START_REF] Janzen | A sampling formula for ecological communities with multiple dispersal syndromes[END_REF] showed that using seed-dispersal traits when analysing rank-abundance patterns of tree species in Barro Colorado Island could lead to reject a purely neutral model in favour of an alternative hypothesis involving interspecific dispersal. Both [START_REF] Liu | Asymmetry in species regional dispersal ability and the neutral theory[END_REF] and [START_REF] Janzen | A sampling formula for ecological communities with multiple dispersal syndromes[END_REF] considered the effect of interspecific heterogeneity of dispersal on community structure through variation in propagule pressure: species with higher dispersal exert a stronger propagule pressure and are therefore better represented locally. However, in such models, dispersal does not trade off with local competitive ability (assumed to be identical among species): species with higher dispersal always thrive in all communities. The conditions for regional coexistence are unfortunately not considered in these studies which focus on species diversity within communities. Introducing a metacommunity structure in which species can coexist within local communities and a competition-dispersal trade-off in models such as those of [START_REF] Liu | Asymmetry in species regional dispersal ability and the neutral theory[END_REF][START_REF] Janzen | A sampling formula for ecological communities with multiple dispersal syndromes[END_REF] should allow to study conditions for the regional coexistence of dispersal strategies as a result of a trade-off with local competition. This is what is done in our study.

Dispersal has a genetic basis in many species [START_REF] Haag | A candidate locus for variation in dispersal rate in a butterfly metapopulation[END_REF][START_REF] Saastamoinen | Heritability of dispersal rate and other life history traits in the Glanville fritillary butterfly[END_REF][START_REF] Charmantier | Heritability of short-scale natal dispersal in a large-scale foraging bird, the wandering albatross[END_REF] and can evolve quickly under selection [START_REF] Cheptou | Rapid evolution of seed dispersal in an urban environment in the weed Crepis sancta[END_REF][START_REF] Fronhofer | Eco-evolutionary feedbacks during experimental range expansions[END_REF], suggesting that interspecific variation along a competition-dispersal tradeoff should not only be ecologically stable, but also evolutionarily stable, at the metacommunity scale. The evolution of dispersal has been considered in several theoretical studies [START_REF] Ronce | How does it feel to be like a rolling stone? Ten questions about dispersal evolution[END_REF][START_REF] Duputié | An empiricist's guide to theoretical predictions on the evolution of dispersal[END_REF], though mostly in single-species metapopulations, especially to explore whether dispersal is stabilised by selection around an evolutionarily stable strategy (ESS), or whether it can diversify toward several strategies (evolutionary branching; EB). We are particularly interested in the latter scenario in which the coexistence among strategies verifies both ecological and evolutionary stability. Several processes can generate EB on dispersal (e.g. spatial and temporal fluctuations of environmental conditions [START_REF] Mathias | Divergent evolution of dispersal in a heterogeneous landscape[END_REF][START_REF] Massol | Evolution of dispersal in spatially and temporally variable environments: The importance of life cycles[END_REF][START_REF] Johst | Evolution of complex dynamics in spatially structured populations[END_REF]). In particular, Massol et al. [START_REF] Massol | Asymmetric patch size distribution leads to disruptive selection on dispersal[END_REF] showed that variation in the strength of kin selection due to variation in carrying capacity among populations is sufficient to generate disruptive selection on dispersal and EB, without calling for fluctuations. Their model includes both an explicit metapopulation structure and a competition-dispersal trade-off. We upscaled it to metacommunity level which allowed us to explore whether dispersal polymorphism has an effect on diversity within communities and dissimilarity among communities compared to a neutral model without dispersal polymorphism. This extends the current framework for interpreting species distribution to the competition-dispersal coexistence theory.

METHODS

Model with dispersal evolution (model M 1 )

Model M 1 simultaneously describes the evolutionary dynamics of dispersal and the temporal change in species diversity in a metacommunity. Individuals share several attributes: a species label, a dispersal parameter ݀, the cost of dispersal ܿ, the probability (ߤ) and magnitude (ߪ) of mutations on dispersal, and the speciation probability ߥ. These attributes are real numbers between 0 and 1, except ߪ which lies between 0 and 0.1. Individuals belong to communities which are described by their carrying capacity ܭ (i.e. the number of individuals they can harbour). The metacommunity is made of N communities which can have different carrying capacities.

Mortality occurs continuously, and dead individuals are immediately replaced through a lottery process. Each death-replacement cycle starts with a "death" event: a randomly chosen individual is removed from the metacommunity. Each individual constantly produces a very large number of propagules normalised to 1 for the lottery. A fraction 1 -݀ of propagules remains in the community and a proportion ݀ is dispersed in the metacommunity, which induces a trade-off between local competition and dispersal. A proportion ܿ of dispersed propagules are lost (dispersal cost). Dispersed propagules have equal chance of reaching all communities, meaning that an individual exports ݀ሺ1 -ܿሻ ܰ ⁄ propagules to each community. The model is therefore spatially implicit. A dead individual is replaced by either a local or an imported propagule in proportion to their occurrence (lottery). The new individual has the same dispersal probability as its parent (݀) with probability 1 -ߤ. When mutation occurs (probability ߤ), the dispersal probability differs from that of the parent by ±ߪ minሺ10݀, 10ሺ1 -݀ሻ, 1ሻ, ߪ being the maximum amplitude of a mutation. This function was chosen to ensure both (i) a non-biased change in dispersal whatever the value of ݀ and (ii) a mutant dispersal probability within the (0,1) interval (ߪ = 0.01 in our simulations). In addition, with probability ߥ, the new individual belongs to a new species that never occurred before in the metacommunity (i.e. point speciation; see [START_REF] Hubbell | The unified neutral theory of biodiversity and biogeography[END_REF]). Speciation has no effect on dispersal and does not interact with the mutation dynamics. A new cycle then starts. Calling ܬ = ܭܰ ഥ the total number of individuals in the metacommunity, the expected life-time of an individual is ܬ cycles, such that a "generation" can be defined as ܬ cycles.

Following [33, equations 4 and 5], the mean ܭ ഥ , squared coefficient of variation 1 and Appendix 1). The symmetric metacommunity is made of an equal number of small and large communities (ߨ ଵ = 0.5), while the asymmetric one is essentially made of small communities (ߨ ଵ ≈ 0.97). These metacommunities display the same ܭ ഥ and ߛ ଶ values, as well as the same number of communities (N). Keeping ܭ ഥ and ܰ constant yields the same overall number of individuals in the metacommunity, which implies the same rate of occurrence of new species and the same singular value of dispersal ݀ * = min൫1 ൫ܭ ഥ ሺܿ + ߛ ଶ ሻ൯ ⁄ , 1൯ in the two metacommunities (i.e. the value at which branching or evolutionary stability may occur, see [START_REF] Massol | Asymmetric patch size distribution leads to disruptive selection on dispersal[END_REF]). We set the mean carrying capacity of communities to ܭ ഥ = 8 and the number of communities in the metacommunity to ܰ = 1110 (see Appendix 1), so that both metacommunities contained ܬ = 8880 individuals.

ߛ ଶ = ॱሾሺܭ -ܭ ഥ ሻ ଶ ሿ ܭ ഥ ଶ ⁄ and standardized skewness ߛ ଷ = ॱሾሺܭ -ܭ ഥ ሻ ଷ ሿ ቀߛ ଶ ଷ ଶ ൗ ܭ ഥ ଷ ቁ ൗ of
At the beginning of the first cycle, all individuals harboured the same dispersal value set to the singular value ݀ * . Species identities were attributed using the urn sampling scheme developed for the neutral model of species diversity with multiple communities [START_REF] Etienne | A neutral sampling formula for multiple samples and an 'exact' test of neutrality[END_REF]. In both cases, mutation and speciation parameters were set to: ߤ = 0.01, ߪ = 0.01, ߥ = 10 ିସ . These parameters were chosen so as to (i) remain close to the assumption of adaptive dynamics (limiting the rate and size of mutations of dispersal), (ii) ensure evolutionary dynamics that are fast enough to exhibit the outcomes of interest with simulations of tractable length and size, and (iii) keep a separation of timescales between speciation and mutation processes. We explored three values of dispersal cost ( ܿ =0, 0.1 and 0.5). As the results were qualitatively similar, we only reported results for ܿ =0.1 in the main text (full results in Appendix).

For both symmetric and asymmetric metacommunities, 100 independent simulations were run over 20000 generations (ca. 1.77 × 10 ଼ death-replacement cycles), a value large enough to reach a stationary dispersal distribution. Simulations were run using the Java programming language (source code DOI: 10.6084/m9.figshare.3145831).

Neutral model (model M 0 )

Model M 0 is a particular case of model M 1 in which all individuals from the metacommunity display the same dispersal value and dispersal does not evolve (i.e. µ = 0). M 0 is thus a neutral model in which individual dispersal is explicitly described. Expected patterns of species diversity can be derived analytically in this model using a coalescent approach (inspired from [START_REF] Wakeley | The coalescent in an island model of population subdivision with variation among demes[END_REF]; Appendix 4).

Trait and species diversity patterns

We explored the spatial structure of dispersal trait in the final state of simulations produced by model M 1 using ܶ-statistics ( [START_REF] Violle | The return of the variance: intraspecific variability in community ecology[END_REF]), a tool to decompose the metacommunitywide variance of dispersal into the relative contributions of several levels of organization:

individual (I), community (C) and metacommunity (R, for regional). We focused on ܶ ூ/ூோ , the proportion of dispersal variance at metacommunity level explained by variation of individual dispersal within communities. Low ܶ ூ/ூோ means that individuals from a community tend to be similar, which we call "dispersal clumpiness". We tested whether ܶ ூ/ூோ is lower than expected under two null models using a randomization procedure. First, we preserved the species label and dispersal value of individuals, but permuted their community identity among communities (keeping community size constant). Under the second model, we permuted dispersal values among species but preserved the occurrence and relative abundances of species in communities. In this second test, we overlooked intraspecific variation by first attributing to individuals the average metacommunity trait value of their species. Randomization proceeded such that individuals from species A were attributed the trait from species B, etc. The mean observed ܶ ூ/ூோ values in the 100 replicated simulations was compared to the mean of the null distribution, obtained by applying the randomization procedure to each simulation output and re-computing ܶ ூ/ூோ .

When comparing predictions from models M 0 and M 1 in terms of species diversity patterns, we focused on measures of (i) diversity within communities (α-diversity), (ii) differentiation among communities (β-diversity) and (iii) diversity at metacommunity scale (ߛ-diversity). We defined ܦ ఈ ሺܭ ሻ as the probability that two individuals sampled in the same community with carrying capacity ܭ belong to different species. ܦ ఈ ሺܭ ሻ is a standard index of diversity usually called the Simpson index [START_REF] Magurran | Measuring biological diversity[END_REF]. To evaluate the differentiation between community pairs, we used ܦ ఉ ൫ܭ , ܭ ൯ defined as the probability that two individuals drawn from distinct communities with carrying capacities ܭ and ܭ belong to different species. ܦ ఊ , defined as the probability that two individuals randomly sampled in the metacommunity belong to different species, was used at the metacommunity scale.

RESULTS

When allowing dispersal to evolve (model M 1 ), our simulations corroborated the theoretical predictions of adaptive dynamics (Figure 1). In the symmetric metacommunity The proportion of variance of dispersal among individuals within communities was significantly lower than expected under a random permutation of individual position (i.e.

(
ܶ ூ/ூோ was significantly lower than random expectation using the first randomization procedure; Figure 3A), especially in the asymmetric metacommunity. When permuting species traits while preserving their abundance in each community (Figure 3B), ܶ ூ/ூோ under the ESS scenario was not significantly different from the randomized value while a significantly lower value was observed under the EB scenario. We further observed that the average dispersal was significantly larger in communities with small carrying capacity than in those with large carrying capacity in the two metacommunities (Figure 3C), a pattern that we call the "carrying capacity -dispersal relationship" below. Appendix Under the ESS scenario, all species harboured similar dispersal strategies, and the carrying capacity-dispersal relationship was weak (but still significant). We therefore expected species diversity patterns ܦ( ఈ , ܦ ఉ and ܦ ఊ ) to closely match the predictions of our neutral model M 0 . Under the EB scenario, dweller and drifter species emerged, as well as a strong carrying capacity-dispersal relationship. We therefore expected species diversity patterns to strongly deviate from model M 0 predictions. In particular, we expected a higher ܦ ఉ ሺܭ ଵ , ܭ ଶ ሻ than under model M 0 because small communities are occupied by drifter species while large communities are occupied by dweller species. We derived analytical expressions of ܦ ఈ ሺܭ ሻ, ܦ ఉ ൫ܭ , ܭ ൯ and ܦ ఊ in model M 0 (Appendix 4) which were validated using simulations (Figure A4.2). ܦ ఈ ሺܭ ሻ and ܦ ఉ ൫ܭ , ܭ ൯ depend on the dispersal rate (݀ * ), the dispersal cost (ܿ), the speciation rate (ߥ) and the mean, variance and asymmetry of the distribution of the carrying capacities across the metacommunity, but not on the carrying capacities ܭ and ܭ . Higher asymmetry of the distribution always had a negative impact on ܦ ఈ , ܦ ఉ and ܦ ఊ (Figure A4.3). In the examples considered here, in which the coefficient of variation was kept constant but asymmetry varied, the neutral diversity at community ܦ( ఈ ሻ and metacommunity levels ܦ( ఊ ) as well as the dissimilarity among communities ܦ( ఉ ሻ in the symmetric metacommunity were thus predicted to be higher than in the asymmetric metacommunity (Figure 4).

We computed the unbiased ܦ estimates (defined in Appendix 5) of ܦ ఈ ሺܭ ଵ ሻ,

ܦ ఈ ሺܭ ଶ ሻ, ܦ ఉ ሺܭ ଵ , ܭ ଵ ሻ, ܦ ఉ ሺܭ ଵ , ܭ ଶ ሻ, ܦ ఉ ሺܭ ଶ , ܭ ଶ ሻ
and ܦ ఊ from our simulations of model M 1 and compared them to the analytical predictions of M 0 (Figure 4). In the symmetric metacommunity (dispersal ESS), ܦ ఈ ሺܭ ଵ ሻ ≈ ܦ ఈ ሺܭ ଶ ሻ, and ܦ ఉ ሺܭ ଵ , ܭ ଵ ሻ ≈ ܦ ఉ ሺܭ ଵ , ܭ ଶ ሻ ≈ ܦ ఉ ሺܭ ଶ , ܭ ଶ ሻ, in line with neutral predictions. However, all ܦ estimates were lower than the neutral predictions. In the asymmetric metacommunity (EB; Figure 4), the ܦ ఈ ݏ were also similar between small and large communities, in line with the predictions of model ܯ , but this time larger than the neutral expectations. ܦ ఉ ሺܭ ଵ , ܭ ଶ ሻ and ܦ ఉ ሺܭ ଶ , ܭ ଶ ሻ were, as expected, higher than the neutral expectations. These deviations from the neutral predictions of both ܦ ఈ and ܦ ఉ resulted in a larger ܦ ఊ than predicted by the neutral model (Figure 4). Note also that ܦ ఉ ሺܭ ଵ , ܭ ଵ ሻ and ܦ ఈ ሺܭ ଵ ሻ were very similar in the asymmetric metacommunity (Figure 4), indicating that, when sampling two individuals in small communities, the probability of getting two different species did not depend on whether individuals were drawn from the same or from different communities. These results were robust to changing the dispersal cost (Appendix 5, Figures A5.1 and A5.2).

DISCUSSION

Dispersal in metacommunities

We built a metacommunity model including dispersal evolution which reproduces the central result obtained by [START_REF] Massol | Asymmetric patch size distribution leads to disruptive selection on dispersal[END_REF] at the metapopulation scale: increasing asymmetry in the distribution of carrying capacities generates variance in the strength of kin competition among communities, which triggers evolutionary branching and the emergence of distinct dispersal strategies in the metacommunity. We could therefore compare an ESS scenario in which individual dispersal shows little variation around a single value (symmetric metacommunity) and an EB scenario in which dispersal is distributed around two distinct levels (asymmetric metacommunity).

Our analysis of dispersal distribution within and among communities showed that under both scenarios, a negative carrying capacity -dispersal relationship emerges. This pattern stems from the three following effects. First, all communities have similar immigrant http://mc.manuscriptcentral.com/prsb Submitted to Proceedings of the Royal Society B: For Review Only pool compositions because dispersal is not limited by distance in our model. Second, in small communities, the temporal dynamics of average species abundances in these small communities are mostly driven by immigration, and are similar to those of the immigrant pool. Third, the deterministic local competition effect dominates in large communities, which induces a shift of relative abundances toward the dominance of low dispersers and the extinction of high dispersers (i.e. competitive exclusion).

The carrying capacity-dispersal relationship echoes several observations on withinspecies dispersal polymorphism in natural metapopulations. For example, a majority of migrants originates from small ephemeral patches, rather than from large patches, in a rock pool metapopulation of the cladoceran Daphnia magna [START_REF] Altermatt | Populations in small, ephemeral habitat patches may drive dynamics in a Daphnia magna metapopulation[END_REF]. However, a competitiondispersal trade-off may not be the only driver of such patterns in natural metapopulations as variation in both age (when extinctions occur) and connectivity of populations play an important role in the distribution of dispersal phenotypes among populations [START_REF] Hanski | Variation in migration propensity among individuals maintained by landscape structure[END_REF]. Whether the carrying capacity -dispersal relationship occurs at the interspecific level in natural metacommunities is less documented to date. Our work predicts that species occurring preferentially in communities with large (resp. small) carrying capacities should be dwellers (resp. drifters). A comparison of traits assumed to be good proxies of dispersal, such as dispersal attributes of seeds in plant communities (see [START_REF] Lindborg | Evaluating the distribution of plant life-history traits in relation to current and historical landscape configurations[END_REF] for an example in grassland communities) might be a first approach to test this prediction. More refined statistical approaches based on dispersal kernel estimation [START_REF] Muller-Landau | Interspecific variation in primary seed dispersal in a tropical forest[END_REF][START_REF] Clark | Comparative seed shadows of bird-, monkey-, and wind-dispersed trees[END_REF] is a further possibility. A combination of genetic and demographic data collected in several species might also return relevant information on dispersal, as already done in metapopulations of single species (e.g., [START_REF] Lamy | Testing metapopulation dynamics using genetic, demographic and ecological data[END_REF])

A strong carrying capacity-dispersal relationship (like in EB scenario) has potential to generate dispersal clumpiness, as small (resp. large) communities tend to harbour more drifters (resp. dwellers) than expected under random permutation of individual in space.

However, dispersal clumpiness can emerge even in the absence of strong carrying capacitydispersal relationship as exemplified in the ESS scenario. This stems from the fact that when dispersal is limited, individuals which are in the same community tend to be more closely related than individuals randomly sampled at the metacommunity scale. This generates clumpiness of both species [START_REF] Mcgill | Towards a unification of unified theories of biodiversity[END_REF] and values of heritable traits, which in turn yields a lower ܶ ூ/ூோ than expected when permuting individuals across the metacommunity. Interestingly, this should caution against interpreting all significantly low ܶ ூ/ூோ as signals of some kind of "environmental filtering" associated to the considered trait (as suggested by [START_REF] Violle | The return of the variance: intraspecific variability in community ecology[END_REF]) when using a null model randomizing individual position in space. By contrast, our second randomizing procedure (permuting average species trait only) exhibits two properties addressing the abovedescribed limits: it preserves the variation of species abundances among communities and it removes the effects of increased relatedness within populations by considering average species traits. Under the ESS scenario, the variance within communities was not significantly lower than the one expected under the null model, which corroborates the idea that the significantly low ܶ ூ/ூோ observed when randomizing individual positions is mostly due to the effect of limited dispersal and not to dispersal polymorphism (which is limited in this case).

Under the EB scenario, the dispersal strategies were still significantly more similar within communities than predicted by the null model. Permuting average species traits can therefore be used to disentangle the effects of limited dispersal on spatial patterns of dispersal (null model not rejected) from those of dispersal polymorphism (null model rejected), and should be preferred to permuting individual positions whenever limited dispersal is thought to play a role in diversity patterns.

Neutral predictions on species diversity patterns

Our neutral model makes two important predictions about species diversity patterns.

First, α-and β-diversity should not vary (in expectation) among communities with different carrying capacities. In several neutral metacommunity models, Simpson diversity (used here) can vary among patches [START_REF] Economo | Species diversity in neutral metacommunities: a network approach[END_REF][START_REF] Economo | Network isolation and local diversity in neutral metacommunities[END_REF]. However, this variation is mostly explained by differences in connectivity (i.e. the number of immigrants per generation) among patches induced by specific network structures. As we were primarily interested in the relationship between the evolutionary dynamics of dispersal and diversity patterns, we focused on a simple spatial structure in which connectivity is similar between all patches irrespective of their carrying capacity, a situation for which we could derive evolutionary predictions. Our model might be extended to more complex community networks in which the neutral expectations have already been derived and that better describe the spatial structure of some real metacommunities, such as dendritic networks for rivers [START_REF] Muneepeerakul | Neutral metacommunity models predict fish diversity patterns in Mississippi-Missouri basin[END_REF][START_REF] Seymour | Dendritic network structure and dispersal affect temporal dynamics of diversity and species persistence[END_REF]. The limiting step here would be to derive eco-evolutionary predictions about dispersal polymorphism in complex networks with variable connectivity among patches, a point that has little been explored up to now [START_REF] Henriques-Silva | On the evolution of dispersal via heterogeneity in spatial connectivity[END_REF].

Second, our neutral model predicts that species ߛ-diversity should decrease with the asymmetry of the carrying capacity distribution in the metacommunity. This can be intuitively explained by the fact that, in more asymmetric metacommunities, drift is stronger because more individuals belong to small communities. Here, we focused on the prediction about asymmetry as it is the feature of carrying capacity distribution that discriminates dispersal evolution scenarios [START_REF] Massol | Asymmetric patch size distribution leads to disruptive selection on dispersal[END_REF]. Yet, the analytical results derived from our neutral model may allow a more comprehensive exploration of the effects of carrying capacity distribution on species ߛ-diversity (Figure A4.3) and thus contribute to current research in conservation biology, in particular with respect to reserve design [START_REF] Van Teeffelen | Species in a dynamic world: Consequences of habitat network dynamics on conservation planning[END_REF]. However, this is beyond the scope of the present work.

In the two examples of metacommunities considered in our study, we derived neutral predictions about diversity patterns when a priori knowing the relevant parameters (carrying capacities and dispersal cost). However, it may not be straightforward to obtain these parameters in natural systems. One alternative would be to fit the neutral model to observed diversity patterns in order to estimate their most likely values. The sampling formulas that have been derived for classic neutral models considering spatially implicit metacommunities allow computing maximum likelihood estimates [START_REF] Etienne | A neutral sampling formula for multiple samples and an 'exact' test of neutrality[END_REF][START_REF] Etienne | A new sampling formula for neutral biodiversity[END_REF][START_REF] Etienne | A novel genealogical approach to neutral biodiversity theory[END_REF]. Such a formula is not available for our model, but the carrying capacities of communities and the dispersal cost could be estimated by looking for values that yield predictions about expected diversity and dissimilarity that fit the best to the observed diversity and dissimilarity estimates (e.g., using a least-square approach).

Effects of dispersal polymorphism on species diversity

Our neutral predictions (model M 0 ) incorporate the influence of speciation, drift and limited dispersal. Comparing the neutral predictions of species diversity patterns with model M 1 , in which dispersal can evolve, reveals the additional effects (i) of mutational variance around the selective optima in the ESS scenario and (ii) of disruptive selection of dispersal in the EB scenario. To our knowledge, our study is the first to suggest studying the effects of a competition-dispersal trade-off as an evolutionarily driven deviation from the neutral theory of metacommunity assembly.

In the ESS scenario (symmetric metacommunity), both the diversity within communities and the dissimilarity among communities do not depend on carrying capacity, which is consistent with the neutral predictions. This was rather expected as species are distributed around the same dispersal value, with some variation due to mutation, leading to metacommunity dynamics close to neutrality. However, lower diversity and lower dissimilarity than expected under a neutral model suggest that the mutational variance of dispersal generates fitness differences among individuals, which scale up to a slight fitness heterogeneity among species. Slightly maladapted species are more quickly eliminated than expected under the neutral model which yields a lower diversity at the metacommunity scale and consequently decreases both the diversity within communities and the dissimilarity among communities. More generally, our results suggest that when there is a unique selective optimum on a given trait (i.e. stabilizing selection), neutral models which overlook intra-and inter-specific variance in this trait may overestimate species diversity.

Under EB, the following general picture of the metacommunity emerged: (i) each large community harbours a mostly endemic community of species that are good local competitors but nearly never disperse (dwellers); (ii) drifter species freely disperse across the metacommunity, dominating small communities and maintaining themselves at low frequency through mass effect in large communities although they are locally outcompeted by dwellers.

From point (i) derives the higher dissimilarity among large communities than expected under the neutral model. Point (ii) explains why the diversity of small communities equals the dissimilarity among small communities. The mass effect of drifters in large communities explains why diversity/dissimilarity in small communities ܦ( ఈ ሺܭ ଵ ሻ) matches the diversity observed in a single large community ܦ( ఈ ሺܭ ଶ ሻ) although the former includes many more individuals. We also emphasize that, when EB occurs, both α-and β-diversities are higher than predicted by the neutral model. Dispersal polymorphism increases the species coexistence potential at metacommunity scale.

Our work suggests that the study of diversity patterns in metacommunities would greatly benefit from documenting dispersal traits of species, so as to identify dwellers and drifters. This point was previously made by Janzen et al. [START_REF] Janzen | A sampling formula for ecological communities with multiple dispersal syndromes[END_REF] in their study of the Barro Colorado tropical tree community: they concluded from combining local abundances of species with seed dispersal traits that immigration was stronger for biotically dispersed tree species (which would therefore be drifters) than for abiotically dispersed ones (which would therefore be dwellers

Conclusion

To our knowledge, our study proposes the first theoretical model integrating dispersal evolution in a multi-species context and deriving predictions about species diversity patterns.

Interestingly it includes three of the four paradigms of metacommunity assembly identified by Leibold et al. [START_REF] Leibold | The metacommunity concept: a framework for multi-scale community ecology[END_REF]: it simultaneously borrows from (i) the "patch-dynamics" view by considering a competition-dispersal trade-off as a coexistence mechanism, (ii) the "masseffect" view as drifter species can persist in patches due to immigration, although they are less fit locally, and (iii) the neutral view as species diversity in the metacommunity ultimately stems from an equilibrium between speciation and stochastic extinction. Our approach should now be extended to other traits contributing to the local adaptation of species in patches with heterogeneous environmental conditions (see [START_REF] Jabot | A stochastic dispersal-limited trait-based model of community dynamics[END_REF]) but keeping the focus on the evolutionary and ecological significance of the considered polymorphisms, as we did here with dispersal.

This would integrate the fourth metacommunity paradigm, species sorting [START_REF] Leibold | The metacommunity concept: a framework for multi-scale community ecology[END_REF], and might pave the way to an unified framework for pattern-based approaches of metacommunity assembly processes. (dark grey) and 2 (light grey). 95% confidence interval of these average value based on central limit theorem approximation are reported for each bar. Testing whether mean dispersal was higher in small communities yielded that the observed difference is statistically significant in both metacommunitites (test based on the central-limit theorem Normal approximation; p=0.0001 in metacommunity 1, p< 10 -12 in metacommunity 2). with ݀ = ݀ * (computed from the left column of table A4.3) for symmetric (dark grey) and asymmetric (light grey) metacommunities. Simulation parameters are given in Table 1. A point is associated to each grey bar, which represents the average estimate value obtained from 100 independent simulations of model M 1 . A 95% confidence interval of the expected diversity/dissimilarity value under model M 1 is reported around each point.

FIGURE AND TABLE CAPTIONS

  3 provides an analytical argument to explain the emergence of this pattern. The carrying capacity -dispersal relationship was much less pronounced in the symmetric metacommunity (ESS) than in the asymmetric one (EB). All these results are robust to variation in the dispersal cost (Appendix 3, Figures A3.1 and A3.2).

  individuals' density when all species are pooled together (null in the white areas). Time is given in generation numbers. Parameters describing dispersal evolution are ߤ = 0.01 and ߪ = 0.01, and the cost of dispersal is ܿ = 0.1.

Figure 2 :

 2 Figure 2: Monitoring of dispersal in three species from the asymmetric metacommunity

Figure 3 :

 3 Figure 3: Analysis of dispersal pattern in symmetric (1) and asymmetric (2) metacommunities. Panel A: Testing ܶ ூ/ூோ using full permutation of individual positions. Bars

Figure 4 :

 4 Figure 4: Estimates and 95% confidence intervals of ܦ ఈ (panel A), ܦ ఉ (B) and ܦ ఊ (C) in the symmetric and asymmetric metacommunities obtained from model M 1 simulations compared

  to the evolutionary dynamics, in agreement with the EB prediction. The difference between these dispersal values decreased with the dispersal cost ܿ (Appendix 2,

	Figure A2.1) and increased with the square coefficient of the variation in carrying capacity
	(ߛ ଶ ; Appendix 2, Figure A2.2). Following species composition of the asymmetric
	metacommunity in time during and after dispersal branching (Figure 2) further showed that (i)
	species that appeared before branching could persist in both branches long after branching,
	but ultimately went extinct because of speciation and demographic stochasticity; (ii) species
	that appeared after branching stayed in their original branch. No species was present in both

metacommunity 1), variation in dispersal remained concentrated around ݀ * , in agreement with the ESS prediction. In the asymmetric metacommunity (metacommunity 2), two distinct values emerged due evolutionary branches at the end of 95 of our 100 replicates; it is therefore possible to characterize "dweller" and "drifter" species, harbouring low and high dispersal values respectively. The situation is very different under the ESS scenario in which, by definition, all species had similar dispersal values at all time.

  ). Importantly enough, biotically dispersed species are dominant in Barro Colorado island. This point cannot be discussed in Janzen et al. framework, as dispersal does not trade-off with any other features (it is a structural property of their model that drifters dominate in any community). By contrast, our model suggests a biological interpretation of drifters' dominance: the carrying capacity of Barro Colorado island may be too small to let dwellers reach large enough population sizes to durably settle and counterbalance the immigration of drifters. Dwellers should here show the same fitness in all communities, irrespectively of size. Second, large communities simultaneously harbour dwellers and drifters the relative abundances of which fluctuate around stable values in time (results not shown), and this is not due to some local coexistence mechanisms but rather to mass effect from the metacommunity.

Beyond analyzing a single community, testing whether small communities (i) have more similar species composition than they have with large communities and (ii) harbour species with higher dispersal abilities than large communities may reveal that a competitiondispersal trade-off affects species coexistence at the metacommunity scale. In addition, the EB scenario of our model illustrates how considering dispersal may contribute to avoid misinterpreting species diversity patterns. First, dwellers species are abundant in one or a few large communities and absent elsewhere, but this is not due to local environmental filtering or adaptation.

Table 1 :

 1 Parameters of symmetric and asymmetric metacommunities, corresponding to the evolutionary predictions and summary statistics of simulations. The two metacommunities verifies: ܭ ഥ = 8 and ߛ ଶ = 9/16 ≈ 0.56, ܰ = 1110 patches. ߨ ଵ represents the proportion of patches with carrying capacity ܭ ଵ . In both cases, other parameters are set to: ߤ = 0.01, ߪ = 0.01, ߥ = 10 ିସ , ܿ = 0.1. ESS: evolutionary stable strategy; EB: evolutionary branching.

Figure 1: Examples of dispersal distributions through time in model M 1 for symmetric (panel A) and asymmetric metacommunity (panel B) respectively (see

Table 1 for metacommunity features). One simulation run is represented in each panel. The grey shades refer to

Table 1 634

 1

	Page 27 of 31
	Metacommunity		symmetric	asymmetric
	Distribution of carrying capacities	ܭ ଵ	2	7
		ܭ ଶ	14	44
		ߨ ଵ	0.5	36/37
	Prediction on dispersal evolution		ESS	EB
	635

http://mc.manuscriptcentral.com/

prsb Submitted to Proceedings of the Royal Society B: For Review Only

http://mc.manuscriptcentral.com/prsb Submitted to Proceedings of the Royal Society B: For Review Only

ACKNOWLEDGEMENTS

Many thanks are due to P. David, N. Loeuille, S. Maurice, F. Munoz, and R. Vitalis for discussions and to F. Altermatt, E. Klein, S. Pavoine, O. Ronce and C. Vacher for their useful comments on the manuscript. We also thank associate editor O. Gaggiotti and two anonymous referees for significant improvements during the revision process. We thank the CEFE (Center for Evolutionary and Functional Ecology) and particularly M.-C. Quidoz (SIE platform) for access to computational resources.

FUNDING

FL was supported by fellowships from AgroParistech, the Agropolis foundation and the Swedish University of Agricultural Science, and by money from IRSTEA. TP was supported by a fellowship from the Université de Montpellier. This work was supported by money from CNRS to PJ and FM, and from the French ANR (AFFAIRS project in the BIOADAPT program -P.I.: Dr. P. David -grant n° 12-ADAP-005; ARSENIC project -P.I.: Dr. F.

Massol -grant n° 14-CE02-0012).

COMPETING INTEREST STATEMENT

The authors declare no competing interest.

AUTHORS' CONTRIBUTION

FL, PJ & FM built the project. FM did the analysis of dispersal evolution. TP ran the simulations. FL derived the neutral predictions and analysed the simulations. FL, PJ and FM wrote the paper.


Regina Bernhaupt
email: regina.bernhaupt@irit.fr

François Manciet
email: francois.manciet@irit.fr

Michael Pirker
email: michael.pirker@ruwido.com

User Experience as a Parameter to Enhance Automation Acceptance: Lessons from Automating Articulatory Tasks

Keywords: user experience, task migration, automation, remote control, second screen. ACM Classification H.5.2. User Interfaces: Interaction Technologies; K.4.3 Organizational Impacts: Automation

If automation in a system is accepted by users is depending on the perceived usefulness and ease of use of the system. How and if user experience has an effect on acceptance is less explored. Goal of this research was to investigate how user experience is changing when a system is automating articulatory tasks. To investigate this relationship we used a case study from the field of interactive television (TV), automating the task of transferring a movie from the TV to the tablet or providing automatically information on the tablet for the movie running on the TV. The automation was indicated for all the tasks by simply bringing a remote control close to the device. Results show that automation in articulatory tasks has a direct positive influence on the overall user experience (UX) and thus is a candidate to further enhance automation acceptance models.

INTRODUCTION

In the software critical systems area, automation is defined as a device or software that accomplishes partially or fully a task that was previously carried out, partially or fully, by a (or a group of) human operator(s) [START_REF] Parasuraman | Humans and automation: Use, misuse, disuse, abuse[END_REF]. But why do people accept automation? The actual usage of a system depends on the perceived usefulness (U) and the perceived ease of use (E) of a system, influencing the attitude towards using a system (A), and when taking into account the behavioural intention to use a system (B) finally describe the actual system use. Figure 1 shows the (standard) technology acceptance model describing these factors [START_REF] Davis | Perceived usefulness, perceived ease of use, and user acceptance of information technology[END_REF].

Figure 1. Technology Acceptance Model (TAM) [9]

The automation acceptance model (AAM) [START_REF] Ghazizadeh | Extending the Technology Acceptance Model to assess automation[END_REF] extends the technology acceptance model taking into account the effect of time: a feedback loop describes that the actual system use will for example change the users perceived usefulness and ease of use of the system.

What is unclear is what role user experience (UX) plays for the acceptance of automation? To investigate if overall there is an influence of user experience on automation acceptance, we decided to perform a pre-study in form of an experiment that investigates the effect of automating an articulatory task on user experience and usability.

As the design and development of a safety-critical application would be rather complicated, we decided to perform this pre-study in a different domain that is known to be using automation over quite some time. We chose the area of interactive TV as: (*) we have access to interactive TV technologies, (*) user groups in the field of interactive TV consumption are broad and represent the majority of the population and (*) participants can be easily recruited to perform series of studies and experiments. (*) Interactive TV is an area were automation has been already well established and (*) the introduction of novel forms of automated support is easy to do in terms of development.

Our study investigated the ability of an interactive system to automatically perform tasks (spanning two or more devices) once the user indicated it. To indicate automation a simple proximity/touch based interaction between two devices was chosen: users can indicate on which device to continue a task by bringing a remote control close to the device (see Figure 2 -dashed arrows indicate how the remote control is central for the interaction). Once the remote is close to the device, the task of selecting the device and performing the action (e.g. transfer the movie) is taken over by the system and performed for the user automatically. To investigate the effect of automation on user experience the experiment measured usability with the standard usability scale questionnaire (SUS) and users rating of perceived ease of use. User experience was measured with the AttrakDiff questionnaire (www.attrakdiff.de) as well as via users rating on naturalness and by interview questions.

STATE OF THE ART Automation in Entertainment and Interactive TV

In the software critical systems area, automation is defined as a device or software that accomplishes partially or fully a task that was previously carried out, partially or fully, by a (or a group of) human operator(s) [START_REF] Parasuraman | Humans and automation: Use, misuse, disuse, abuse[END_REF]. This notion of "partially" and "fully" implies that the degree of automation varies and automation can be on various levels ranging from higher level tasks to lower level interaction techniques. Parasuraman and Riley [START_REF] Parasuraman | Humans and automation: Use, misuse, disuse, abuse[END_REF] proposed a scale for these levels of automation. These different levels go from a complete autonomous system, acting individually (high automation) to a level where absolutely no assistance is offered to the operator (low automation). Main goal of the scale (see Table 1) is to better describe what automation is and to help system designers when designing autonomous systems.

Introducing automation for safety-critical systems has been investigated in detail. While there are benefits, there are also shortcoming including the introduction of so called automation surprises [START_REF] Bainbridge | Ironies of automation[END_REF][START_REF] Palmer | Oops, it didn't arm". -A Case Study of Two Automation Surprises[END_REF] and badly designed automation leading to catastrophic impacts [START_REF] Norman | The 'problem' with automation: inappropriate feedback and interaction, not 'over-automation[END_REF][START_REF] Parasuraman | Humans and automation: Use, misuse, disuse, abuse[END_REF]. Automation is the result of the migration of a task, which was initially carried out by a human, to a system. As a consequence, it is impacting the work of the human operator as it changes the nature of this work. Amongst the known consequences of badly automated system, it has been demonstrated that it induces more cognitive loads to the human operator (the monitoring activity of the system) [START_REF] Sarter | How in the world did we ever get into that mode? Mode error and awareness in supervisory control[END_REF], it is associated to a degradation of skills and is sometimes not well accepted and perceived by the operators [START_REF] Parasuraman | Humans and automation: Use, misuse, disuse, abuse[END_REF]. These three main problems are well known in the area of software critical systems, but seem to be of lesser importance in other areas applying automation. While piloting an aircraft or monitoring a nuclear power plant are activities that might not be compared to entertainment activities such as watching TV, it is striking that automation in these areas is welcomed by users and seems to fail less often.

High

10. The computer decides everything, acts autonomously, ignoring the human Low 9. informs the human only if it, the computer, decides to. Considering the rich ecosystem of the living room, where several devices are available, it is interesting to see how entertainment oriented activities benefit from automation. Antila et al [START_REF] Antila | RoutineMaker: towards end-user automation of daily routines using smartphones[END_REF] have proposed a smart phone application that is able to detect patterns in tasks achieved regularly by users, and enables the user to have these tasks performed automatically. In their study results showed that users were enthusiastic with such an application (showing a positive user experience), and that acceptance of automation was immediate. Difficulties encountered were the perceived complexity of how to create automation (usability), especially the need to describe tasks in a more general way (e.g. "change the profile to silent when entering certain location between certain time"). Similar work has been proposed by Maués et al [START_REF] De | Keep doing what i just did: automating smartphones by demonstration[END_REF]. They have realised a smart phone tool able to detect patterns in users' smart phone activities and to automate some of these activities. In these two cases, the results are identical, users are interested in achieving tasks automatically, given the tool is usable and does not require skills or prior knowledge on programming.

Automation and automated tasks are extremely common in the TV and iTV area. From the early days lots of activities, especially changing the channel, have been automated. With the number of channel and offers growing, various other tasks have become fully automated. Some examples:

(*) the task of finding channels on the different frequencies and storing them (and giving them channel names) is done today automatically once the user selects channel search [START_REF] Tseng | TRIBA: a cable television retrieval & awareness system[END_REF]. Today even the logo detection of channel and their classification is automated [START_REF] Özay | Automatic TV logo detection and classification in broadcast videos[END_REF] (*) recordings of a show or future episodes of a series are performed automatically and even programs that the user might want to watch are stored automatically on the hard disc without notifying the user.

The integration of iTV systems into home automation today enables users to have not only automated heating system control but also to have shutters closed and lights dimmed automatically once the favorite TV show starts [START_REF] Tseng | TRIBA: a cable television retrieval & awareness system[END_REF].

When watching TV the majority of users is not aware of the automation applied to standard tasks. New developments like the need to perform tasks on several devices or to use multi-modal interaction techniques with the remote control are raising awareness now that automation is an essential part in this landscape.

Usability, User Experience and Acceptance

Usability is defined as being the "effectiveness, efficiency and satisfaction with which a specified set of users can achieve a specified set of tasks in a particular environment" (ISO 9241-210). Usability can be measured with a broad range of methods, most typically in experiments or studies where a user is performing a set of tasks. Usability is measured with the number of successful performed tasks, number of errors, ability of the user to learn the system, or the user self-estimation on how satisfactory the system is to perform the given tasks [START_REF] Nielsen | Measuring usability: preference vs. performance[END_REF].

User Experience (UX) is a concept that is fundamentally different from usability. The ISO 9241-210 standard defines UX as "A person's perceptions and responses that result from the use and/or anticipated use of a product, system or service." What is important for the concept of user experience is that it is described as dynamic, time dependant [START_REF] Karapanos | Measuring the dynamics of remembered experience over time[END_REF], and beyond the instrumental [START_REF] Hassenzahl | User experience-a research agenda[END_REF]. Its overall goal is to understand the role of affect as an antecedent, a consequence and a mediator of technology. Its concept focuses on positive emotions and emotional outcomes such as joy, fun and pride [START_REF] Hassenzahl | User experience-a research agenda[END_REF]. Generally, UX is described as focusing on the interaction between a person and a product, and is likely to change over time, depending on the context of use [START_REF] Hassenzahl | User experience-a research agenda[END_REF].

If and how usability and user experience are related is currently unclear. A limited usability must not necessarily lead to a negative UX, while a system that is highly usable must not provide a good user experience.

As user experience in temporarily bound [START_REF] Margeritta Von Wilamowitz-Moellendorff | Dynamics of user experience: How the perceived quality of mobile phones changes over time. In User Experience-Towards a unified view[END_REF], user experience has different aspects: the experience before using the system, during the usage of the system and after the system was used, as well as overall judgement of the user experience. Figure 3 describes that before using a system the user has an anticipated user experience where she imagines the experience when using the system. During the system usage there is the momentary user experience that is established by having the user really use the system and after using the system the episodic user experience is based on the user reflecting on the experience. Over time the cumulative user experience is established as the user will reflect on multiple periods of use [START_REF] Allaboutux | www.allaboutUX[END_REF].

Figure 3. Redrawing of the time spans of user experience [1].

For the area of interactive TV, [START_REF] Pirker | Investigating usability and user experience as possible entry barriers for touch interaction in the living room[END_REF] showed that high usability does not lead to a positive user experience and that a system with low usability can have nevertheless a highly positive user experience. If this relationship is generalizable for the interactive TV domain and beyond is still to be shown. While defining UX is a complicated process, it has been shown that an overall positive UX can be the key to the longer term acceptance and usage of a system [START_REF] Margeritta Von Wilamowitz-Moellendorff | Dynamics of user experience: How the perceived quality of mobile phones changes over time. In User Experience-Towards a unified view[END_REF].

Thus UX is an important dimension to take into account when focusing on automation in interactive systems.

Acceptance of technology is investigated using a variety of models, with the Technology Acceptance Model (TAM) [START_REF] Davis | Perceived usefulness, perceived ease of use, and user acceptance of information technology[END_REF] being one of the most popular ones. The TAM predicts that users' perceived usefulness and ease of use are the main determinants of their attitudes toward a technology, which in turn can predict their intentions to use and accept the system. According to this model, factors affecting automation are task compatibility, UX, and mandatory usage. [START_REF] Karapanos | User experience over time: an initial framework[END_REF] explains the relationship between usability, UX and acceptance: "The impact of novelty in users' experience displayed a sharp decrease after the first week of use. Contrary to common belief that iPhone's success is largely due to its aesthetics and novel interaction style, these aspects were found to play a minimal role in providing positive pro-longed experiences. Next, we found a shift in users' concerns over time from ease-of-use to usefulness. While ease-of-use was the primary predictor of goodness during early orientation experiences, usefulness became an even more dominant predictor during the incorporation phase."

A variation of the TAM is the Automation Acceptance Model of [START_REF] Ghazizadeh | Extending the Technology Acceptance Model to assess automation[END_REF]. Figure 3 describes the extension of the TAM to take into account compatibility between (automated) system and task to be performed and trust. It also takes into account the effect of continued usage of a system. Once users have actually used a system this will affect their perception of the compatibility of the (automated) system with their tasks, their trust as well as the usefulness and perceived ease of use.

Interaction Techniques supporting Cross-Device Tasks

There is a broad range of prototypical implementations of interaction techniques that support users when performing tasks involving several devices. These interaction techniques include several levels of automation and automatically support performing tasks while interacting with the system. Unfortunately the majority of this body of literature does not describe in detail when and how the automation is performed.

Gesture Interaction

Gestures interaction appears as a promising interaction technique to enable interaction between second screens and the big screen. Nonetheless it is demonstrated that when designing such interactions, what is important is to combine mobile devices and large displays in a seamless manner. When this combination is not done adequately, it is highly probable that users prefer standard user interfaces for control. [START_REF] Lorenz | The ambient media player: a media application remotely operated by the use of mobile devices and gestures[END_REF] and [START_REF] Fleury | Evaluating user preferences for video transfer methods from a mobile device to a TV screen[END_REF] have compared mid-air gestures and classical user interfaces with directions arrow, and they have found than user are less efficient using gestures interactions.

Touch Gestures and Pointing

Touch gestures on mobile phones and tablets, especially flick gestures, have become a common standard today [START_REF] Rukzio | An experimental comparison of physical mobile interaction techniques: touching, pointing and scanning[END_REF]. When using touch to enable users to change devices or to transfer content from one device to another, the user can simply use the flick gesture to automatically move content to a connected device [START_REF] Fleury | Evaluating user preferences for video transfer methods from a mobile device to a TV screen[END_REF][START_REF] Rukzio | An experimental comparison of physical mobile interaction techniques: touching, pointing and scanning[END_REF]. For standard TV environment including different device brands there is a broad range of technological difficulties reported including limitations related for example to RFID technologies [START_REF] Rukzio | An experimental comparison of physical mobile interaction techniques: touching, pointing and scanning[END_REF].

Body Interaction

Based on user's movements, body interaction allows the tracking of each body part. [START_REF] Centieiro | Enhancing remote live sports experiences through an eyes-free interaction[END_REF] and [START_REF] Leroy | Second screen interaction: an approach to infer tv watcher's interest using 3d head pose estimation[END_REF] have shown that the tracking of head gestures can help to dynamically enhance the TV watching experience by providing different information depending on where the user looks at. Nonetheless, these interactions are technically challenging.

Contact interaction

First introduced by Rekimoto et al. [START_REF] Rekimoto | Pick-and-drop: a direct manipulation technique for multiple computer environments[END_REF], contact based interaction provides an efficient way to connect devices that are physically close. Originally designed to be used with an object to carry the information (a pen in [START_REF] Rekimoto | Pick-and-drop: a direct manipulation technique for multiple computer environments[END_REF]), this interaction technique has evolved, replacing the physical support object with a mobile device. Contact interaction has been reused in many fields and for several purposes: to share pictures between people or to enable cross-application interaction [START_REF] Selmer Nielsen | JuxtaPinch: exploring multi-device interaction in collocated photo sharing[END_REF]. When using contact interaction, [START_REF] Rukzio | An experimental comparison of physical mobile interaction techniques: touching, pointing and scanning[END_REF] has shown that people prefer to touch things that are close to avoid any physical efforts. This interaction technique is judged the easiest, because it has a low cognitive load and limits the number of possible interactions. When a longer distance between the devices is introduced (e.g. touch the TV screen [START_REF] Fleury | Evaluating user preferences for video transfer methods from a mobile device to a TV screen[END_REF]), this interaction is less preferred than interactions with a remote control.

Available End-Consumer Solutions

Industries tend to use private protocols to connect devices of the same brand (Amazon Fire TV, Apple AirPlay, or Google Chromecast). Here the connection between the devices is done through another device plugged onto the TV system. The devices used could be a dongle or a box. With these mechanisms, the flow of content between devices is unilateral. The TV system is used to stream content (videos, photos, music) that are sent from the mobile device. The connectivity is handled on the side of the second screen. The physical context is here important as devices have to be connected to the same network to be able to share information. As soon as a device leaves the network, the connectivity is lost. For the following study no industrial solution is currently available that can be applied directly.

Remote Controls

The majority of people is associating watching TV with the use of a remote control. While the limitations of standard remote controls have been reported in detail [START_REF] Bernhaupt | A set of recommendations for the control of IPTV-systems via smart phones based on the understanding of users practices and needs[END_REF], the majority of TV-related devices is still controlled with a remote control. Remote controls have evolved slower than the associated functionalities on the interactive TV environment, with the majority still following standard button layouts with infrared as transmission technology [START_REF] Bernhaupt | A set of recommendations for the control of IPTV-systems via smart phones based on the understanding of users practices and needs[END_REF].

Recent enhancement for remote control included considerations to use 2D gesture [START_REF] Neil | Touchpad-based remote control devices[END_REF], mid-air gesture [START_REF] Gurrin | Browsing large personal multimedia archives in a lean-back environment[END_REF], free-hand gesture via input on a hand that is filmed by camera [START_REF] Lee | Actual remote control: a universal remote control using hand motions on a virtual menu[END_REF], natural language and speech commands [START_REF] Aseel | Speech enhanced remote control for media terminal[END_REF] or particular types of feedback [START_REF] Lee | Actual remote control: a universal remote control using hand motions on a virtual menu[END_REF].

Concepts to enhance the control of a TV included a multitude of apps available for mobile phones and tablets [START_REF] Hao Lü | Gesturemote: interacting with remote displays through touch gestures[END_REF], as well as the dematerialization of the remote control to be replaced with new physical devices [START_REF] Baudel | Charade: remote control of objects using free-hand gestures[END_REF]. Contrary to scientific efforts of enhancing the control in the living room a current European household has on average 2.1 actively used remote controls [START_REF] Bernhaupt | A set of recommendations for the control of IPTV-systems via smart phones based on the understanding of users practices and needs[END_REF].

RESEARCH GOAL

Automation acceptance models describe that users accept automation if the system supports well the tasks they want to perform, if the system is perceived as useful and easy to use. The (actual) usage of the system can influence the attitude toward using the system and the behavioral intention to use. What seems clear is that acceptance is influenced by what users anticipate and is changing once users have been using the system and especially after using the system (and reflecting on it). This is very similar to the overall user experience and it seems reasonable to investigate the relationship between acceptance and user experience. Goal of this study is to shed light on the relationship between automation acceptance and user experience. In a first step we investigated the effect of automation on user experience (lower part) and the relationship of perceived usefulness and perceived ease of use. The next steps will be the investigation of the other related factors and how these factors change over time. The relationship between the introduction of automation

EXPERIMENT

Goal of the experiment was to investigate the overall relationship of user experience and usability when introducing automation, to verify if such a relation is existing and is useful to explore in terms of influence for overall acceptance.

Research Goal & Hypothesis

The main hypothesis was: If the system provides support for user-induced automatic task migration (to different devices) the user experience is rated higher and usability (in terms of efficiency, effectiveness and satisfaction) is higher for device-cross-over tasks.

We call a system automated when the user does not have to support the tasks of connection between two devices. Related to standard definitions of automation [START_REF] Selmer Nielsen | JuxtaPinch: exploring multi-device interaction in collocated photo sharing[END_REF] this type of automation is induced by the user (where to perform the task next) and is then conducted completely automatically without any further user involvement. Once the content is transferred the user can continue its interaction but typically cannot interfere during the transfer.

Task migration is related to the distribution of content through devices and the distribution of input/output into a system. We talk about task migration when the device used to complete and finish a task is different from the device used to start the task. User-induced task migration will happen when for example the user's mental resources required for a task differ on the different devices used.

Reading a text (more than 5 lines) on TV is harder than on a tablet. Entering text with a remote control is requiring more cognitive effort and is typically longer in terms of interaction than on a tablet.

PROTOTYPE Design Considerations

When starting to design possible concepts to support automation with or without an associated interaction technique the following considerations and constraints have been taken into account:

(1) The concept has to be simplistic enough to be integrated in a prototype without bringing too many changes in terms of usability and user interaction concepts to a traditional interactive TV system;

(2) The interaction technique chosen should be usable, it should feel natural and easy to learn and remember;

(3) The automation and its interaction technique should be applicable to interactive TV systems currently available in the market.

Selection of Interaction Technique

Based on the scientific state of the art and industrial systems we rated the proposed types of interaction on the design considerations (simplistic, natural, easy and applicable). Table 2 summarizes the advantages and disadvantages of the different proposed types of interaction.

Based on this classification we decided to use a contact or close proximity type of interaction.

While there is a strong believe in the TV and entertainment research community that remote controls will disappear in the near future, the remote control (and a TV screen) is today the only device that is available in all households regardless of technical infrastructure and the only device that is most probably accessible for all household members. We thus decide to choose the remote control as central means for indicating automation due to its broad availability and familiarity for a broad range of users. We decided to combine a remote control with the most advantageous interaction, namely the contact interaction. This type of interaction limits possible usability problems and allows to investigate the automation aspect.

Modality

EXPERIMENT ON USER-INDUCED AUTOMATION Prototype

To perform the experiment we developed a prototype based on a standard user interface for an Internet Protocol TV system (IPTV) that was already proven to be highly usable [START_REF] De | Keep doing what i just did: automating smartphones by demonstration[END_REF]. The IPTV System allows users to access live TV and provides typical IPTV services such as video on demand, libraries, program guide (see Figure 5), web applications, or administration settings. It was used with a standard remote control and is designed to be operable with only using six navigation buttons.

The system was available in two versions: with and without automation. There were two tasks enhanced with automation:

(1) When the user is watching a program on the TV, and if she wants to have information about this movie on her tablet, or to watch it on the tablet, the system will automatically provide the links to relevant information source (e.g. the Wikipedia web page). The support is called when the user touches the tablet with the remote control. A menu pops up on the tablet's screen offering the user the choice to select what she wants, within three possibilities (reviews, plot, and movie transfer or trailer when the movie was not available). If the system is not automated, the user will not be supported during these tasks. This means that she will have to do the research manually (open a web browser, enter her query and find the appropriate link) and to collect the links alone, without assistance from the system. (2) If a user is watching a program she can automatically transfer the movie to the tablet, simply pointing with the remote control on the tablet. In the not automated case, the user has to find the right video in the folders of the tablet's system / or to plug a cable on tablet to transfer the video / or to find the site where the channel is broadcasted.

Additionally, a tutorial explaining the interaction mechanism was created and added into the main menu of the IPTV system, to better support users in their discovery of the contact interaction.

Experimental Design

The experiment had as independent variable the task support (Condition Automation: automation vs no automation). We additionally varied the type of remote control (using two remote controls) for other research purposes that are not reported in this study. Participants were assigned to the conditions by chance and conducted the tasks once with and once without automation. All conditions and task orders were counter-balanced.

Tasks included: Task 0: Trial task to discover the system (change channels and access video on demand section) Task 1: Get more information (number of episodes) about a series in the program guide; Task 2: Get extra information about a movie (number of Oscar / ratings); Task 3: Store information (ratings) about movies before buying them to compare them; Task 4: Continue to watch a movie on the second screen. Dependent variables were usability and user experience. For usability, the metrics task completion time, successful and unsuccessful completion of tasks, rating of the task (on a scale from 1 to 5, 1 being very easy and 5 very difficult), level of acceptance of automation as well as users feeling of naturalness of the interaction technique used (again with the automated system) were measured. Usability was additionally measured applying the SUS [START_REF] Brooke | SUS-A quick and dirty usability scale[END_REF] questionnaire.

For user experience the AttrakDiff [START_REF] Hassenzahl | The interplay of beauty, goodness, and usability in interactive products[END_REF] questionnaire was used.

Participants

Thirty-two students in computer science from the blind University took part in the study. Twenty-four were male and 8 were female. The age of participants ranged from eighteen to twenty-five, with an average of 21.7 (SD=1.65).

Procedure

The experiment took place at IRT in an office of the university. The room was arranged with two sofas, one table and the desk were the TV screen was placed, with the audio system. The TV screen used was in fact a 21.5" computer screen, full HD. The second screen used was a tablet Google Nexus 7 with android 4.4. Users were taped during the session. The study was structured into four parts.

During the first one, we asked users questions about their media consumption habits, as well as their knowledge about interactive TV systems and second screen applications related to watching TV. The second part was dedicated to the use of the first system. The experimenter gave the participant basic information about how the system works.

When the system A (with automation) was used, the experimenter told the user that this system has a special interaction technique that will help her to achieve tasks, without telling her what this interaction technique was. Each participant conducted four experimental tasks with the first system. For each task, a short introduction into the scenario was given, followed by an explicitly formulated task assignment. Hints were provided after a predefined time period. Additionally, each task had a time limit. If a participant needed more time the task was stopped, counted as not solved and the correct way to solve the task was explained to the user. After performing the four tasks, and answering questions about each tasks (difficulty, comfort, was the interaction technique appreciated, automation acceptance), we asked user to fill out the AttrakDiff and the SUS questionnaires.

The third part was the same as the second one, except that the system were changed. Tasks included some selections of different movies in the video on demand or electronic program guide on the TV and the research of information about the movie on the tablet, or the transfer of the movie to the tablet.

The final part was to interview the participant and debrief. We asked participants a set of interview questions about the system they preferred and why they preferred the system and what their feelings about the interaction technique and the automated support provided by the system were.

RESULTS

Usability was investigated using the SUS questionnaire. A closer inspection of the SUS scores revealed that the type of the system did have an observable influence on the SUS score (System A -automated: mean = 83.2, SD = 13.0; System B -not automated: mean = 68.2, SD = 15.5). Thus, the type of system (with or without automation) showed statistically different results (Mann Whitney U, Z = -3.981, p > .000).

Table 3 shows the results for each task for the experiment. It presents how many of the participants succeeded (S), succeeded with help (H) or failed (F) for a specific task, the average time for each task in seconds (standard deviation in brackets), as well as the mean rating for the perceived difficulty (R) on a scale from 1 (very easy) to 5 (very difficult), followed by the standard deviation (SD) for this rating. The hints given were related six times (of the total of 45 hints given) to the contact interaction. Three times because people did not understand the provided tutorial (which was written in English); they did not know how to translate the word "remote control" into French. Additionally, during task 4A, three users did not see the tutorial in the main menu of the TV system. In task 2A, all the hints given were related to where the information was located on the Wikipedia page.

Task

A paired samples t-test revealed significant differences for task times for the different task sets (automated tasks vs non-automation). Tasks 1, 2 and 3 were significantly different in task time (p > 0.000) for automated vs nonautomated task times and task ratings for tasks 1 and 3 were significantly different (p > 0.000), rating automated tasks as easier.

User Experience was investigated using the AttrakDiff questionnaire. As can be seen in Figure 3, System A (automated) scored a lot better regarding the user experience compared to System B. This is also supported by statistical analysis (Mann-Whitney: Pragmatic Quality: Z = -2.21, p=0.027; Hedonic Quality: Z = -5.48, p=0.000, Attractiveness: Z = -4.27, p = 0.000). The final interview revealed that almost all the users (31/32) preferred to use the automated system as interaction and automation, because of its simplicity, its rapidity and its novelty. All of them have appreciated to be supported by the system during the tasks.

Usability, UX and Acceptance

To investigate the interrelations between the system, the user experience metrics (pragmatic quality, hedonic quality and its sub-dimensions identification and stimulation, and attractiveness), as well as the usability metric (SUS), correlation coefficients (Spearman's roh) were computed among the indicator scales.

The results of the correlation analysis show that the correlations of system with hedonic quality (HQ -including sub-dimensions identification HQ-I and stimulation HQ-S), attractiveness (ATT), as well as the SUS usability metric was significant at a 0.01 level, while for pragmatic quality (PQ) it was still significant at a 0.05 level (System and PQ: r(62) = -.279, p = .026; system and HQ: r(62) = -.462, p < .000; system and HQ-S: r(62) = -.738, p < .000; system and HQ: r(62) = -.690, p < .000; system and ATT: r(62) = -.538, p < .000; system and SUS: r(62) = -.502, p < .000).

Additionally, to investigate the interrelations between the different UX indicators, correlation analysis was computed among the UX metrics and the usability metric. The results of the correlation analyses show that all correlations between pragmatic quality, hedonic quality, attractiveness, and the SUS usability metric were highly significant (p < 0.01) and all were greater or equal to 0.554 (PQ and ATT: r(62) = .684, p < .000; PQ and HQ: r(62) = .554, p < .000; PQ and SUS: r(62) = .719, p < .000; AT and HQ: r(62) = .817, p < .000; ATT and SUS: r(62) = .750, p < .000; HQ and SUS: r(62) = .608, p < .000).

These results suggest that in this study, the user experience factors were highly interrelated, which goes in line with the opinion of most UX researchers in the HCI community that the elements of user experience are heavily interrelated and influencing each other mutually; it also replicates previous findings [START_REF] Pirker | Investigating usability and user experience as possible entry barriers for touch interaction in the living room[END_REF], [START_REF] Bernhaupt | Evaluating User Experience for Interactive Television: Towards the Development of a Domain-Specific User Experience Questionnaire[END_REF].

As we additionally found correlations of the UX and usability metrics with the degree of automation of the system, this suggests -in combination with the results from the final interview -that automation can positively influence users' perceptions and experiences with an IPTV system. As user experience is changing over time and usability is becoming more important for long term usage, the findings can be interpreted in two ways: (1) for the introduction of a new system the novelty is important to enhance user experience. (2) for longer term usage it seems possible to influence the user experience continuously, so positive user experience leads to a higher acceptance of the system, the incorporation into users' daily lives, and finally an emotional attachment to the product [START_REF] Karapanos | User experience over time: an initial framework[END_REF].

DISCUSSION

Our results validated the hypothesis, that if the system provides support for user-induced automatic task migration (to different devices) the user experience is rated higher and usability (in terms of efficiency, effectiveness and satisfaction) is higher for device-cross-over tasks.

This finding needs to be investigated in more detail. Here we have just scratched the surface of what automation (automatically performing articulatory tasks) and task migration (changing devices for media consumption) can support within such an application domain.

Lessons learned For Automation

When designing for automation in the area of interactive TV it is essential to note that usability might be the most important system property. If an entertainment system like a TV cannot be used by a broad audience (typically TV is watched in almost all age groups) it will simply fail in terms of acceptance. This might be an explanation why automation has been accepted by users from the beginning. Today automation is used to support users during higher level tasks: tasks are performed automatically when users are navigating, choosing or selecting content, when manipulating content e.g. recording but also forwarding/scanning in a video. At lower levels, especially when taking into account new forms of remote controls that support multi-modal interaction techniques automation is used to allow the user to perform transfer, copy or selection tasks automatically.

What the experiment shows is that combining automation with a novel and usable form of interaction can enhance the user experience, especially the perceived hedonic quality of the system. Positive user experience is perceived as a central factor to support acceptance of a technology [START_REF] Davis | Perceived usefulness, perceived ease of use, and user acceptance of information technology[END_REF].

Results of our study have only investigated the small area of perceived usefulness and perceived ease of use and the influence of user experience: showing that a high user experience leads to a higher perceived usefulness and perceived ease of use at the momentary user experience.

Application for Safety-Critical Systems Design

The application from results and findings from one domain to another has to be done very carefully. As for the concept of usability where it has been shown in detail that usability is heavily influenced by the application domain (Twintide Project, http://www.twintide.org/.), recent findings suggest that also user experience as a concept [START_REF] Lai | Towards a UX manifesto[END_REF] and especially dimensions like aesthetics, emotion or social connectedness are different for varying application domains. On the contrary user experience has been shown to be closely related to humans and how they overall perceive interactive systems. It is thus likely that the findings of the case study in the entertainment area indicate a generally applicable outcome, that the automation of articulatory tasks can overall enhance user experience. Positive user experiences have been shown to increase acceptance of technologies [START_REF] Hassenzahl | The interplay of beauty, goodness, and usability in interactive products[END_REF]. Especially for the field of safety-critical systems were acceptance of automated systems is still critically discussed, this might be an interesting point to investigate: using automation to enhance the user experience of the system and on the long(er) term increasing technology acceptance of the automated system.

CONCLUSION AND NEXT STEPS

What could be shown in this experiment is that automation is enhancing user experience in the area of entertainment contrary to literature from other fields like safety-critical systems and automation [START_REF] Parasuraman | Humans and automation: Use, misuse, disuse, abuse[END_REF]. It shows that user experience can be a candidate to further enhance automation acceptance models, as user experience as well as usability is affected by the introduction of automation. The contextual and timely bound nature of UX makes it a good candidate to explain possible feedback-loops for automation acceptance models. Next steps will be the investigation of all the factors of the automation acceptance model as well as user experience in a set of experiments investigating the long-term usage of an automated system.

Figure 2 .

 2 Figure 2. The design landscape for TV and video consumption including connected devices like TV sets, tablets, smart phone and disconnected devices.

Figure 4 .

 4 Figure 4. Automation Acceptance Model (AAM) from [12]. In grey the original TAM model, dashed lines show the feedback cycle proposed by the authors.

Figure 4 :

 4 Figure 4: Extended TAM [10] taking into account feedback based on anticipated (*), momentary (**) and episodic user experience (***). In grey: assumed influence of UX on all stages of the TAM model that have to be investigate in future work. In yellow focus of the first experimentation.

Figure 5 .

 5 Figure 5. Standard IPTV user interface requiring only six buttons for accessing the three-level-menu structure.

Figure 3 .

 3 Figure 3. In white, system with automation score, in black system without automation score.

Table 1 . Levels of automation of decision and action selection [30]

 1

8. informs the human only if asked or, 7. executes automatically, then necessarily informs the human, and 6. allows the human a restricted time to veto before automatic execution, or 5. executes the suggestion if the human approves, or 4. suggests one alternative 3. narrows the selection down to a few, or 2. the computer offers a complete set of decision/action alternatives, or 1. the computer offers no assistance: human must take all decisions and actions

Table 2 . Comparison of Interaction Techniques and additional modalities for remote controls: (-) indicates reported disadvantages; (+) reported advantages.

 2

	Simplistic Natural	Easy	Applicable

Table 3 -Tasks results: Tasks labelled A indicate automated system; B system without automation.

 3

	S	H	F	Time (sec)	R	SD
	T1A 24	4	4	95 (38.3)	1.7	0.82
	T1B 20	7	5	142 (64.8)	2.7	1.35
	T2A 15	10	7	171 (54.8)	2.4	1.10
	T2B 25	6	1	115 (54.8)	2.1	1.07
	T3A 28	4	0	142 (51.0)	1.5	0.67
	T3B 22	9	1	198 (73.7)	2.3	0.99
	T4A 23	3	6	74 (34.4)	1.7	1.09
	T4B 29	2	1	70 (32.1)	1.7	0.90

ACKNOWLEDGMENTS

This project was funded by ruwido Austria. We thank all participants of the experiment and the ICS team at IRIT.