Pavel Sebek
email: pav.sebek@gmail.com

Thomas Barnouin

Antoine Brin
email: antoine.brin@purpan.fr

Hervé Brustel
email: herve.brustel@purpan.fr

Marc Dufrêne
email: marc.dufrene@spw.wallonie.be

Frederic Gosselin
email: frederic.gosselin@cemagref.fr

Bruno Meriguet
email: bruno.meriguet@insectes.org

Lilian Micas

Thierry Noblecourt
email: thierry.noblecourt@onf.fr

Olivier Rose

Laurent Velle

Christophe Bouget
email: christophe.bouget@cemagref.fr

A test for assessment of saproxylic beetle biodiversity using subsets of "monitoring species"

Keywords: species richness, deadwood, biodiversity surrogate, identification costs, forest insects

In European forests, large scale biodiversity monitoring networks need to be implementednetworks which include components such as taxonomical groups that are at risk and that depend directly on forest stand structure. In this context, monitoring the species-rich group of saproxylic beetles is challenging. In the absence of sufficient resources to comprehensively survey a particular group, surrogates of species richness can be meaningful tools in biodiversity evaluations. In search of restricted subsets of species to use as surrogates of saproxylic beetle richness, we led a case study in Western Europe.

Beetle data were compiled from 67 biodiversity surveys and ecological studies carried out from 1999 to 2010 with standardized trapping methods in France and Belgium. This large-scale dataset contains 642 forest plots, 1521 traps and 856 species. Twenty-two simplified species subsets were identified as potential surrogates, as well as the number of genera, a higher taxonomic level, taking into account, for each surrogate, the effort required for species identification, the practical monitoring experience necessary, the species conservation potential or the frequency of species occurrence. The performance of each surrogate was analyzed based on the following parameters: overall surrogacy (correlation between subset richness and total species richness), surrogacy vs. identification cost balance, surrogacy variation over a wide range of ecological conditions (forest type, altitude, latitude and bio-geographical area) and consistency with spatial scale. Ecological representativeness and ability to monitor rare species were supplementary criteria used to assess surrogate performance.

The subsets consisting of the identifiable (or only easy-to-identify species) could easily be applied in practice and appear to be the best performing subsets, from a global point of view.

The number of genera showed good prediction at the trap level and its surrogacy did not vary across wide environmental gradients. However, the subset of easy-to-identify species and the genus number were highly sensitive to spatial scale, which limits their use in large-scale studies.

The number of rare species or the species richness of single beetle families (even the best singlefamily subset, the Cerambycidae) were very weak surrogates for total species richness.

Conversely, the German list of monitoring species had high surrogacy, low identification costs and was not strongly influenced by the main geographical parameters, even with our French and Belgian data.

In European-wide monitoring networks, such internationally validated subsets could be very useful with regard to the timing and cost-efficiency of field inventories.

INTRODUCTION

Surrogates of species richness as tools in biodiversity evaluations

In response to the global concern about an ongoing biodiversity erosion, biodiversity evaluation has become an essential part of the monitoring process to quantitatively assess publicly funded conservation programmes (selection of conservation areas, management planning), environmental monitoring schemes and biological compensation programmes [START_REF]Ecosystems and Human Wellbeing. A Framework for Assessment[END_REF].

However, biodiversity evaluation is mainly based on observations such as the apparent loss of certain 'charismatic' key species or the apparent degradation of habitats in general, rather than on precisely known total species numbers [START_REF] Bredemeier | Biodiversity assessment and change -the challenge of appropriate methods[END_REF].

Species richness is one of the simplest and perhaps the most frequently used biodiversity indices because it is easy to interpret, 'sexy' enough to influence decision makers and a valuable tool for communication with the public, even though it does have certain drawbacks inherent to univariate indices [START_REF] Gaston | Biodiversity: Ecology of Numbers and Difference[END_REF]. In the absence of sufficient resources to directly carry out a comprehensive survey of a particular group of species, surrogates of species richness are used as 'shortcuts' for faunal biodiversity assessment and function as proxies to biotic distribution [START_REF] Hirst | Surrogate measures for assessing cryptic faunal biodiversity on macro-algal dominated subtidal reefs[END_REF][START_REF] Caro | Conservation by Proxy: Indicator, Umbrella, Keystone, Flagship, and Other Surrogate Species[END_REF].

Biodiversity surrogates may be indirect indicators of an abiotic or structural nature (environmental variables, diversity of habitats, etc.) or direct indicators closely related to the species themselves [START_REF] Rodrigues | Shortcuts for biodiversity conservation planning: The efectiveness of surrogates[END_REF][START_REF] Mandelik | The relative performance of taxonomic vs. environmental indicators for local biodiversity assessment: A comparative study[END_REF]. Examples of direct surrogates of species richness include (i) the numbers of species that are frequently the focus of conservation measures (flagship, focal, indicator, keystone, and umbrella species;[START_REF] Favreau | Recommendations for assessing the effectiveness of surrogate species approaches[END_REF][START_REF] Caro | Conservation by Proxy: Indicator, Umbrella, Keystone, Flagship, and Other Surrogate Species[END_REF], including rare, endemic and red-listed species [START_REF] Butchart | Biodiversity Indicators Based on Trends in Conservation Status: Strengths of the IUCN Red List Index[END_REF]; (ii) species subsets or combinations of these subsets which are suitable for monitoring because they are relatively easy to identify and are known to be ecologically representative (indicator species; [START_REF] Brin | Species variables or environmental variables as indicators of forest biodiversity: a case study using saproxylic beetles in Maritime pine plantations[END_REF], or (iii) higher taxa at a reduced taxonomic resolution (genera, families, etc.), which are obviously less time-consuming to identify than species [START_REF] Báldi | Using higher taxa as surrogates of species richness: a study based on 3700 Coleoptera, Diptera, and Acari species in Central-Hungarian reserves[END_REF][START_REF] Balmford | Using higher-taxon richness as a surrogate for species richness: I. Regional tests[END_REF][START_REF] Sebastiao | Taxon surrogates among Amazonian mammals: can total species richness be predicted by single orders?[END_REF], or (iv) parataxonomic units, which are heuristically used to determine patterns in taxonomically neglected groups [START_REF] Majka | Parataxonomy: a test case using beetles[END_REF]. Many tests relate to complementary cross-taxon surrogates, and actually test whether one subset (the surrogate taxon) is a good surrogate for another taxon (the target taxon) [START_REF] Sauberer | Surrogate taxa for biodiversity in agricultural landscapes of eastern Austria[END_REF].

Attention is mostly focused on identifying a restricted subset of taxa as a surrogate of the total community richness [START_REF] Mcgeoch | The selection, testing and application of terrestrial insects as bioindicators[END_REF]. Sometimes these surrogates work, but often they do not [START_REF] Magierowski | Robustness of surrogates of biodiversity in marine benthic communities[END_REF][START_REF] Halme | Perennial polypores as indicators of annual and red-listed polypores[END_REF][START_REF] Murphy | A Critical Assessment of the Use of Surrogate Species in Conservation Planning in the Sacramento-San Joaquin Delta, California (USA)[END_REF]. The approach is suitable when the richness of only one specified group of organisms is to be studied. In this case, the subsets of species may be predefined or they may be chosen randomly, for instance as a restricted proportion of all the species in the taxon [START_REF] Vellend | Using subsets of species in biodiversity surveys[END_REF].

A need for surrogates of forest saproxylic biodiversity

In European forests, large scale biodiversity assessments still need to be developed and implemented. From the results of a test phase on intensive forest monitoring plots (Forest Biota), [START_REF] Fischer | Background, main results and conclusions form a test phase for biodiversity assessments on intensive forest monitoring plots in Europe[END_REF] recommended (i) to include direct biodiversity components as additional monitoring elements, especially for taxonomical groups that are directly dependent on trees and stand structure [START_REF] Seidling | Deviances from expected Ellenberg indicator values for nitrogen are related to N throughfall deposition in forests[END_REF], and (ii) to use the MCPFE (Ministerial Conference on the Protection of Forests in Europe) criteria and indicators for sustainable forest management as guidelines for the identification of additional monitoring needs.

Furthermore, the scientific community has been exhorting European governments for decades to prioritarily use the saproxylic organisms, i.e. associated to dead or dying trees [START_REF] Bouget | Nomenclature of wood-inhabiting groups in forest entomology: synthesis and semantic adjustments[END_REF], in the evaluation of forest conservation status (R(88)10 and 11 recommendations from the Council of Europe in 1988). Nevertheless, no practical tools are currently available to help managers reach this objective. Indirect indicators such as the amount of dead wood have been suggested to reflect the saproxylic beetle diversity. However, their efficient use first requires a closer look at spatial scale [START_REF] Lassauce | Deadwood as a surrogate for forest biodiversity: Meta-analysis of correlations between deadwood volume and species richness of saproxylic organisms[END_REF][START_REF] Schiegg | Are there saproxylic beetle species characteristic of high dead wood connectivity?[END_REF][START_REF] Vodka | Habitat preferences of oak-feeding xylophagous beetles in a temperate woodland: implications for forest history and management[END_REF]. About a third of the European forest species need deadwood to some extent for their survival. In Europe, forests have been managed for centuries [START_REF] Grove | Saproxylic insect ecology and the sustainable management of forests[END_REF], and saproxylic biodiversity, which is mostly typical of and restricted to forests, is rich yet threatened due to the loss and fragmentation of habitats with sufficient deadwood and veteran trees. In that, the monitoring of such high saproxylic biodiversity is quite challenging.

Foresters and conservationists are paying more attention to beetles than to other important saproxylic taxa such as fungi or Diptera; this is because more taxonomic expertise currently exists in this field and surveying methods have been standardised [START_REF] Majka | Parataxonomy: a test case using beetles[END_REF]. In addition, saproxylic beetles do account for a large portion of saproxylic biodiversity (just behind fungi). They are widespread, numerous, species-rich and easily sampled; they include representatives of many trophic guilds with a wide range of microhabitat preferences; they are known to exhibit greater site specificity than vertebrates, and they often respond to environmental changes more rapidly than vascular plants or vertebrates. They are therefore assumed to provide valuable information on the quality and continuity of woodland habitats [START_REF] Grove | Saproxylic insect ecology and the sustainable management of forests[END_REF][START_REF] Nieto | European Red List of Saproxylic Beetles[END_REF][START_REF] Schiegg | Are there saproxylic beetle species characteristic of high dead wood connectivity?[END_REF]. However, detailed taxonomic surveys are often prohibitively expensive and time-consuming. Consequently, there is a need for quick and easy biodiversity assessment techniques to monitor and map saproxylic beetle biodiversity.

Potential surrogates should be explored [START_REF] Brin | Species variables or environmental variables as indicators of forest biodiversity: a case study using saproxylic beetles in Maritime pine plantations[END_REF] and the cost-effectiveness of surrogates must be evaluated. Cost-effectiveness assessments have already been carried out for deadwoodassociated polypores [START_REF] Halme | Perennial polypores as indicators of annual and red-listed polypores[END_REF] and these assessments may provide a basis to work from.

In search of surrogates of saproxylic beetle richness: our case study in Western Europe

We investigated the surrogate approach for saproxylic species richness, based on a set of beetle data from temperate Western Europe. We identified and explored the performance of different subsets of taxa as well as one higher taxon subset, genus richness, as surrogates of total community richness. The subsets were defined mainly with respect to their potential use in future exercises. We then analyzed the cost of identifying the subset and the subset sensitivity to changes in environment and spatial scale. One single surrogacy result, whether positive or negative, would not be a useful basis for generalization because results are likely to vary with location, sampling method or forest conditions [START_REF] Hess | Effectiveness of biodiversity indicators varies with extent, grain, and region[END_REF]. To sum up, our objectives were:

1.

to determine which simplified species subsets could be used as relevant surrogates of total species richness, 2. to compare subset surrogacy and identification costs,

3.

to check for variations in subset surrogacy over ecological and methodological gradients,

4.

to investigate the sensitivity of subset surrogacy to spatial scale.

MATERIALS AND METHODS

Compiling French and Belgian datasets

For our analysis, we compiled data on beetles from 67 biodiversity surveys and ecological studies [START_REF] Bouget | Sampling saproxylic beetles with window flight traps: methodological insight[END_REF], a widely used method for collecting saproxylic beetles.

The individual datasets varied greatly in their extent (number of plots, number of traps per plot, number of sampling seasons). To reduce the bias due to differing study designs, we used the beetle records obtained from a single trap in a single sampling season as the fundamental unit for our analysis. The trap level records were then integrated into higher hierarchical levels, the plot and then the dataset levels. In addition, we added the region level, to indicate the geographic location of the dataset at regional scale.

We considered four environmental factors to describe trapping conditions: Forest type (three classes: conifer, deciduous, mixed); Altitude (two classes: highland, lowland; the reference altitude was 1,000 m above sea level); Latitude (two classes: north, south); and Biogeographic area (four classes following the ETCBD (2006): alpine, atlantic, continental, mediterranean). We also took into account the use of bait in the trap as one methodological factor (alcohol-baited, unbaited). The compiled dataset derived from the beetle records in the 67 studies (66 in France, one in Belgium) contained a total of 642 forest plots and 1521 traps. The distribution of the traps across the environmental gradients is shown in Table 1. The location of the study plots is depicted in Fig. 1 (for more detailed information about the sets used, see Appendix A).

Data selection and standardisation

The data from the different sets first had to be harmonised: nomenclature standardisation (Bouget et al., 2000), removal of genus sp. The following families, often difficult to identify (Aderidae, Alexiidae, Cantharidae, Carabidae, Clambidae, Corylophidae, Cryptophagidae, Dermestidae, Dryopidae, Elmidae, Eucinetidae, Latridiidae, Melyridae, Mordellidae, Ptiliidae, Scirtidae, Scraptiidae, Scydmaenidae, Sphaeritidae, Staphylinidae and Throscidae), were not identified at the species level in all of the original datasets and were consequently removed from the compiled dataset. In the Curculionidae family, only the subfamily Scolytinae was included in the analyses.

Four families (Ciidae, Leiodidae, Nitidulidae, Cerylonidae) were not studied in a small number (from one to three) of original datasets. As these families are generally well recognized and informative between saproxylic beetles, they were included in the analyses, but were analyzed with a restricted compiled dataset containing only the datasets where they were studied.

Species characterisation

The 67 datasets to be analyzed contained 856 species (for a complete list see Appendix B). We recorded whether or not each selected species is found on the German List of Monitoring Species [START_REF] Schmidl | Ökologische Gilden xylobionter Käfer Deutschlands und ihr Einsatz in der landschaftsökologischen Praxis -ein Bearbeitungsstandard[END_REF], and on the European Red List of Saproxylic Beetles [START_REF] Nieto | European Red List of Saproxylic Beetles[END_REF]. We defined three levels of identifiability (=ID) as levels of difficulty associated to the identification of the species: 1 being the least difficult (easy to identify, e.g. by picture screening), 2 requiring detailed identification keys, and 3 containing species that can be identified by only a few experts (due to an insufficient literature) or requiring the preparation of genitalia.

Species patrimoniality value (=IP) corresponded to the degree of geographic rarity in France according to [START_REF] Bouget | The French information system on saproxylic beetle ecology (FRISBEE): an ecological and taxonomical database to help with the assessment of forest conservation status[END_REF] and had four levels: (i) common and widely distributed species, (ii) not abundant but widely distributed species, or only locally abundant species, (iii) not abundant and only locally distributed species, (iv) very rare species (known in less than 5 localities or in a single 'county' in France).

For each species, we also included its host tree preference, feeding guild, body size class, cavitypreference (Appendix B) in order to check the range of ecological groups included in each subset (Does the subset reflect a wide range of forest components?). Appendix C shows the species' ID and IP classes and their distribution across feeding guilds.

According to Mac Nally and Fleishman ( 2002), widespread species provide little information about variance in species richness, and rare species have highly specific ecological requirements that are not shared with many other species. We used the frequency of occurrence of the species in the compiled dataset to create a list of 'mid frequent' species (=MidFrequent), that occurred in more than 30% and less than 80% of the sets.

In addition, we defined the list of 'fully saproxylic' genera, that contained only saproxylic species and no non-saproxylic species (see Appendix B).

Data subsetting

As potential surrogates for analysis, we defined 22 species subsets by crossing available species information (Tab. 2). We included different subset types. Some of them reflect the effort required for species identification (2, 10) or the monitoring experience necessary (8); others reflected the species' conservation potential, i.e. they include a high proportion of red-listed or rare species (9,12) or the frequency of the species' occurrence (6,11).

As for the other subsets, we isolated the 5 beetle families that occurred most often (Anobiidae, Cerambycidae, Elateridae, Curculionidae Scolytinae, Tenebrionidae). We also created subsets combining several families according to sorting error risk (18)(19)(20)(21)(22). Indeed, [START_REF] Majka | Parataxonomy: a test case using beetles[END_REF] found that some of these families have a low gross sorting error (Cerambycidae, Tenebrionidae, Scarabaeidae) while others have high sorting errors (Anobiidae, Nitidulidae, Curculionidae Scolytinae, Elateridae).

In the numerous subset, the 4 families with a high average number of individuals caught in traps were grouped; all 19 dominant families with a high average number of species caught in traps were clustered in the domsum subset.

Finally, we created the supra-specific surrogate of the total number of fully saproxylic genera recorded in the dataset (surrogate 23) to include a higher taxonomical level. We restricted the surrogate to fully saproxylic genera to prevent false inclusion of non-saproxylic beetles, in case the surrogate was used in practice.

Data analysis of surrogate quality

All analyses were conducted using the R software (version R 2.13, R Development Core Team, 2011).

Overall surrogacy

For each of the 1521 traps, we computed the total number of species found in the trap (total species richness), the number of species belonging to each subset and the number of fully saproxylic genera (subset richness). We computed R 2 between total species richness and subset richness. We used R 2 (Spearman rank correlation coefficient of determination) as a measure of explained variance.

Comparison between surrogacy and costs

We also evaluated costs and benefits for each species subset (excluding the genus numbersgenera). We used the R 2 value as an indication of the benefit. Since there are no simple shortcuts in collecting data in the field, and since most shortcuts occur during the identification phase, we therefore attributed an identification cost value to each surrogate. We assumed that in a typical community survey, the bulk of the species may be identified quickly, while a relatively small number of species that are quite difficult to identify occupy a disproportionate amount of the researcher's time [START_REF] Colwell | Estimating terrestrial biodiversity through extrapolation[END_REF]. We defined 3 different cost indicators. The first value (P sp ) was the proportion of species caught in trap, that belong to the subset, in other words the proportion of species that need to be identified (a measure of lab work intensity), averaged over the 1521 traps. Secondly, we took E inv = [1 -the proportion of species classified as "easy-to-identify" from the total species list in the subset]. The lower the value, the greater the proportion of easy-to-identify species in the list and the better the surrogate. Thirdly, only for the best surrogates compared at the end of the process, P ind was assessed as a complementary measure of lab work intensity. P ind was actually the proportion of individuals caught in trap, whose species belong to the subset, in other words the proportion of individuals that need to be identified. Nonetheless, we must keep in mind that it may be less time-consuming to identify many individuals of an easy-to-identify species than only a few individuals of a difficult species.

Therefore, a good surrogate should be one that gives a high R 2 value with low P sp , P ind and E inv .

Variations in subset surrogacy over ecological and methodological gradients

We tested the effect of environmental and methodological factors on the predictive value of our surrogates. We used a mixed-effect model with a Poisson distribution, where total species richness (TSR) was a dependent variable, subset richness (SSR) was an explanatory variable and the environmental and methodological factors (EMF) were explanatory fixed-effect factor variables (lme4 R-package). The Generalized Linear Mixed-effects model was written as follows (without between-fixed effects interaction): lmer(TSR~offset(log(SSR+1))+EMF+(1|region/dataset/plot).

The hierarchical agglomeration of trap records (plot / dataset / region) represented random effects. Only one effect was tested at a time (using a Bonferroni correction related to the number of tests) in order to use all records available for a given factor, even if some information was missing concerning the other factors.

Effect of spatial scale on predictive quality

We analysed the influence of the scale on the predictive quality of the surrogates, using only original datasets that contained at least 20 plots (12 datasets out of 67). The levels of plot aggregation were from one to 19 plots in each dataset. At each aggregation level reflecting an increasing scale, we randomly sampled the plots (without replacement) to reach the desired number of plots (50 times) and then figured out the R² value of the surrogate. Finally, we computed the mean R 2 value of the 12 datasets for each surrogate and each plot aggregation level.

Furthermore, we used a linear regression to test the significance of a decline in R 2 across an increasing number of plots. Only the surrogates with R 2 higher than 0.85 at the trap level (as well as the easy-to-identify species group) were tested for the decline.

RESULTS

Overall surrogacy

Seven of the 23 potential surrogates had R 2 greater than 0.85 (significant at p<0.001; Tab. 3).

They were: species from dominant families (domsum with R 2 =0.96), mid-identifiable species (excl. species difficult to identify -identifiable R 2 =0.96), fully saproxylic genera (sgenera R 2 =0.95), common species (common R 2 =0.90), German monitoring species (german R 2 =0.89), identifiable German monitoring species (german identifiable R 2 =0.87) and, finally, easy-toidentify, mid-identifiable and mid-frequent species (subset4 R 2 =0.86).

Some of the subsets explained more than 70% of the variation: the combination of the four most abundant families: Anobiidae, Cerambycidae, Elateridae, Curculionidae (numerous R 2 =0.79), 'mid frequent' species (mid-frequent R 2 =0.78), easy-to-identify species (easy-to-identify R 2 =0.76), the combination of four families: Cerambycidae, Curculionidae, Mycetophagidae, Nitidulidae (combin3 R 2 =0.75) and 'mid frequent' identifiable species (identifiable mid-frequent R 2 =0.73). Large species (more than 10mm in length), which are mainly identifiable species (ID1 or ID2), did not explain a significant proportion of the richness variation (R²=0.62).

The surrogates reflecting the conservation status of species performed poorly: European Red-Listed species (iucn R 2 =0.66) and rare species (rare R 2 =0.28). The correlation values of single beetle families were non significant, the highest value being R 2 =0.50 for Cerambycidae. Only certain combinations of families showed sufficient predictive value: combin3 (R²=0.75) and numerous (R²=0.79).

Some of the further analyses were restricted to the 8 best performing surrogates (i.e. R² greater than 0.85, and the easy-to-identify species subset).

Comparison between surrogacy and costs

R 2 prediction values over 0.85 were only achieved in the subsets where the mean proportion of species to be identified (P sp ) was greater than 50%. From the plot comparing R 2 and P sp (Fig. 2a), the best ranking subsets were german identifiable (R 2 =0.87; P sp =54%) and subset4 (R 2 =0.86; P sp =57%). On the other hand, some of the surrogates provided fair prediction with a comparatively low P sp value, particularly easy-to-identify (R 2 =0.76; P sp =29%). Most of the subsets contained a low proportion of easy-to-identify species, from 10 to 40%. From the figure 2b comparing R 2 and E inv (1 -the proportion of easy-to-identify species in the subset), only one species subset with an R 2 value greater than 0.85, subset4, contained a high proportion of easy-toidentify species (R 2 =0.86; E inv =0.21). Understandably, the easy-to-identify subset performed well as its predictive value was relatively high (R 2 =0.76; E inv =0). The two subsets with the highest R 2 , domsum (R 2 =0.96; P sp =87%; E inv =0.78) and identifiable (R 2 =0.96; P sp =80%; E inv =0.70), turned out to be very costly in both P sp and E inv . These subsets contained low proportions of easy-toidentify species and the mean proportion of species to identify in each trap was very high.

Among the best scoring surrogates the easy-to-identify and german identifiable subsets performed well (Tab. 5), since they contained less than half the number of individuals caught in a trap to be identified on average. The other surrogates had higher P ind values. The domsum and identifiable subsets were very costly in P ind (respectively 90% and 81% of the individuals to be identified).

Variations in subset surrogacy over ecological gradients

Regarding the effect of ecological gradients, only 2 of the 8 best-performing surrogates, easy-toidentify and sgenera, were unaffected by ecological factors. The predictive value of the other surrogates was significantly affected by at least one of the factors (p<0.05, Tab. 3). We computed the correlation values for each factor level if the factor was significant (Tab. 4). In two of the surrogates, the R 2 value remained higher than 0.85 even if the factor effect was significant (identifiable -R 2 from 0.90 to 0.97; domsum -R 2 from 0.92 to 0.98). The predictive value of the other four surrogates fell below the level of 0.85 at least for some of the factor levels: (1) subset4

Was subset surrogacy sensitive to trap bait?

The use of bait in traps (bait factor) significantly influenced the predictive power of three quarters of the surrogates (Tab. 3). As for the 8 best performing surrogates, five of them were unaffected by bait (easy-to-identify, german identifiable, german, common and domsum). Three others, subset4, identifiable and sgenera showed lower surrogacy values in baited traps (Tab. 4). Even so, the only predictive value to fall below 0.85 was for baited traps in subset4 (R 2 =0.81).

Effect of spatial scale on surrogacy

Along the plot aggregation gradient, from the plot level to the forest scale, the decline in R 2 was significant (p<0.05) for all eight of the best-performing surrogates (Fig. 3). Except for identifiable species (identifiable), the R 2 in the higher plot aggregation levels fell below the 0.85 value. The R 2 of all the subsets across all aggregation levels is given in Appendix D.

DISCUSSION

The ideal surrogate should reflect variation in species richness well; should be easily applicable in practice; should provide consistent assessments independently of environmental gradients, methodology or spatial scale; and finally, should also sufficiently detect rare species [START_REF] Colwell | Estimating terrestrial biodiversity through extrapolation[END_REF][START_REF] Noss | Indicators for monitoring biodiversity: A hierarchical approach[END_REF]. We failed to find a surrogate that would meet all the above criteria perfectly. However, attention has to be paid to individual meaning of each criterion and to perspectives of each surrogate in practical work. We summarised the global performance of the best surrogates according to 5 global criteria (Tab. 5): ecological representativeness, conservation interest (i.e. the ability to monitor rare species), identification costs, surrogacy potential and variability in surrogacy across environmental gradients. None of our best scoring surrogates had a biased species composition regarding the host tree group, the feeding guild or the proportion of cavity-dwelling species, when compared with the complete list of species.

The minimum level of desired surrogacy may differ according to the monitoring objectives and may be adjusted to a particular use of the results. When the question is to assess if a particular site is species-rich or not in comparison with other sites, a lower level of prediction could be accepted. When the aim of the survey is to rank several sites according to their species richness, a higher R 2 value should be required.

Analysis of the cost-surrogacy balance

The practicality of a surrogate depends on its simplicity of application. The most preferred surrogates are the ones that save the most time during species identification [START_REF] Magierowski | Robustness of surrogates of biodiversity in marine benthic communities[END_REF] and those that can be handled by a wider spectrum of entomologists without the need for experts. In our study, the most useful surrogates required the least identification time, included the greatest proportion of easily identifiable species and had the fewest number of species that are difficult to identify. Surrogates that explain total species richness well with a limited number of species to identify per trap were deemed useful. In our study, the subsets with a high proportion of easy-to-identify species showed a better cost-surrogacy balance. The number of species in the subset itself is also a very important factor; subsets with many different species are more difficult to use as surrogates because they require more entomological expertise.

Identification effort is the key consideration in biodiversity survey planning [START_REF] Vellend | Using subsets of species in biodiversity surveys[END_REF]. From [START_REF] Bouget | Levels of taxonomic resolution in forest entomology studies: needs, contingencies and perspectives[END_REF], costs in working time for data collection, therefore excluding design planning, data analysis and interpretation, consist in 20% field work (sampling) and 80% lab work (sorting and identification). The last step itself may be divided into 20% rough sorting and 60% species identification. To go further, using a surrogate which saves 50% of the identification costs (30% of global data collection costs) actually saves 1.5 technician hour per sample on average (ca 90€ in a French Institute). In our study, the best performing surrogate from a general point of view was the identifiable subset (easy-to-identify and mid identifiable species). It had the highest prediction value, showed no biases in ecological group composition, included rare species and was very stable across the geographical range, the environmental gradients and the spatial scale (its surrogacy potential remained very high from the trap level to the gamma aggregative forest level). However, the subset contained 71% of the total number of species found in the whole dataset. This makes it inherently highly probable to find many of the selected species in a single trap, and it also means that the surveyor has to be able to recognise 615 species. In contrast, the subset easy-to-identify contained only 203 species and showed quite high predictive power at the trap level. Its identification costs were low since it contained only easy-to-identify species and it required the identification of only 34% of the individuals and 29% of the species caught in a trap on average (Fig. 2a). Using this surrogate would approximately divide the identification costs by four, and consequently save 2.25 technician hour per sample on average (ca 130€ in a French Institute). Nonetheless, the easy-to-identify subset behaved poorly at higher spatial scales, where its predictive ability decreased very rapidly (Fig. 3; Appendix D). The global evaluation of the surrogates (Table 5) showed that the easy-to-identify subset, despite its lower predictive value, is the most easily applicable surrogate of all. Less than 40% of the individuals caught need to be identified, and yet this valuable subset is still able to reflect more than 75% of the richness at the trap level on average (but only 50% of the richness at the gamma scale over a forest; Fig. 3). This subset therefore deserves to be tested over wider geographical and ecological gradients. Its species composition is close to the composition of another pragmatic surrogate (subset4). Both have biased composition in favour of larger species (the proportion of small species is lower than in global assemblages). The main drawback of subset4 is its very low surrogacy potential in conifer forests.

The higher-taxon surrogate, sgenera (the number of genera that contained exclusively saproxylic species), also requires less identification time. Only 349 genera need to be identified, as opposed to 856 species. If rare species are not an issue, sgenera is the second highest scoring subset (Tab.

5). This valuable surrogate showed good predictive power at the trap level and was not affected by environmental gradients. The high correlation between genus and species richness has already been reported in the literature [START_REF] Báldi | Using higher taxa as surrogates of species richness: a study based on 3700 Coleoptera, Diptera, and Acari species in Central-Hungarian reserves[END_REF][START_REF] Balmford | Using higher-taxon richness as a surrogate for species richness: I. Regional tests[END_REF][START_REF] Gaston | Biodiversity: Ecology of Numbers and Difference[END_REF][START_REF] Hirst | Surrogate measures for assessing cryptic faunal biodiversity on macro-algal dominated subtidal reefs[END_REF] though the efficiency of the higher taxa, such as families and orders, has been inconsistent [START_REF] Báldi | Using higher taxa as surrogates of species richness: a study based on 3700 Coleoptera, Diptera, and Acari species in Central-Hungarian reserves[END_REF][START_REF] Sebastiao | Taxon surrogates among Amazonian mammals: can total species richness be predicted by single orders?[END_REF]. In our work, to go beyond the genus level, we would have been limited by the small number of exclusively saproxylic families. The scale dependence of higher-taxon surrogacy [START_REF] Gaston | Biodiversity: Ecology of Numbers and Difference[END_REF][START_REF] Vanderklift | Use of assemblages derived from different taxonomic levels to select areas for conserving marine biodiversity[END_REF] was well confirmed in our results (Fig. 3). We therefore confirm that species-based approaches should be recommended in conservation planning and monitoring when only variables at the species level are of interest (ecological requirements, rarity, etc.;[START_REF] Bouget | Levels of taxonomic resolution in forest entomology studies: needs, contingencies and perspectives[END_REF].

Performance of different surrogate types

We showed that surrogates based on individual beetle families were not able to predict total species richness well. Conversely, [START_REF] Oliver | Designing a cost-effective invertebrate survey: a test of methods for rapid assessment of biodiversity[END_REF] measured a significant correlation between total beetle richness and the richness of three individual families. In our data, even the best family surrogate, i.e. the Cerambycidae, performed poorly. Though this family is difficult to sample with window trapping (Brustel, pers. comm.), it has been recommended as an indicator of saproxylic beetle richness [START_REF] Fayt | Contrasting responses of saproxylic insects to focal habitat resources: the example of longhorn beetles and hoverflies in Belgian deciduous forests[END_REF]; its species richness had the highest Pearson correlation coefficient to the total species richness in Japanese forests (r=0.56; [START_REF] Ohsawa | Beetle families as indicators of Coleopteran diversity in forests: a study using Malaise traps in the central mountainous region of Japan[END_REF].

From [START_REF] Majka | Parataxonomy: a test case using beetles[END_REF], this family is less prone to low sorting, lumping and splitting errors. Our best combinations (combin3, numerous) reached mid to high surrogacy values, but remain costly (high total number of species or difficult species to identify; Tab. 5).

Our results suggest that biodiversity surveys should not be restricted to single beetle families but should adopt a more complex approach.

In our data, the number of rare species was a very weak surrogate for total species richness.

Conversely, [START_REF] Müller | Three-dimensional partitioning of diversity informs state-wide strategies for the conservation of saproxylic beetles[END_REF] supported widely applying conservation practices that focus on red-listed species. However, rare species by definition never appear in high numbers;

their prediction capability may therefore inherently be limited. Often, rare species require a special sampling effort or methodology, occur unpredictably and may be under-represented in collected material [START_REF] Martikainen | Sampling the rarest: threatened beetles in boreal forest biodiveristy inventories[END_REF][START_REF] Martikainen | Sampling saproxylic beetles: lessons from a 10-year monitoring study[END_REF][START_REF] Majka | The Eucnemidae (Coleoptera) of the Maritime provinces of Canada: new records, observations on composition and zoogeography, and comments on the rarity of saproxylic beetles[END_REF][START_REF] Engen | Assessment of species diversity from species abundance distributions at different localities[END_REF]. Biodiversity assessment based on rare species only might easily result in incorrect site evaluations.

The 'identifiable' subset of the German monitoring species (german identifiable) initially proposed by [START_REF] Schmidl | Ökologische Gilden xylobionter Käfer Deutschlands und ihr Einsatz in der landschaftsökologischen Praxis -ein Bearbeitungsstandard[END_REF] also showed relevant predictive ability. In a large-scale German study, [START_REF] Müller | Three-dimensional partitioning of diversity informs state-wide strategies for the conservation of saproxylic beetles[END_REF] observed that their subset of 'German monitoring species' mirrored the diversity pattern of the entire community, and could therefore be used in state-wide monitoring studies. In our French and Belgian data, this german identifiable subset had a high surrogacy (R²=0.87), low identification costs (46% of trapped species and 45% of trapped individuals need to be identified; Tab. 5) and was not influenced by the main geographical parameters but was sensitive to forest type (with a lower surrogacy in conifer than in deciduous and mixed forests). In the calibration of European-wide monitoring networks (see Introduction), such internationally validated subsets could be useful tools (at least in temperate forests in western Europe). The definition of a standardised list of identifiable (or easy-toidentify) species based on the whole checklist of European saproxylic beetle species would be a profitable initiative.

Surrogate sensitivity to environmental conditions, methodological factors and spatial scale

We analysed the sensitivity of surrogate options over a wide range of ecological conditions (geographic areas, forest environment, etc.). The sensitivity of biodiversity surrogates to geographical location and habitat type has already been pointed out by [START_REF] Hess | Effectiveness of biodiversity indicators varies with extent, grain, and region[END_REF]. [START_REF] Halme | Perennial polypores as indicators of annual and red-listed polypores[END_REF] has also recently demonstrated that the subset of perennial polypores can be used as a surrogate for overall polypore species richness, but that the predictive power varies in different management and forest types. In our data, the surrogacy values were quite often influenced by forest type (whose effect was significant on 64% of the surrogates), sometimes by the latitudinal position or by the biogeographic area, and less frequently by the altitudinal position. Only half of the best surrogates compared in Table 5 can be called robust across contrasting forest environments. Moreover, an analysis of the magnitude of the environmental effects on surrogacy, beyond their significance, could be carried out. The ecological relevance of these wellperforming subsets was actually improved by the wide range of feeding guilds, which reflect the different ecological requirements of the species they include (see Appendix C).

From a methodological perspective, we demonstrated a significant effect of trap bait on surrogacy for 74% of the subsets; three of the eight best surrogates were shown to be sensitive to bait (Tab. 5). Indeed, some beetle species are known to be more attracted to alcohol-baited than to unbaited traps [START_REF] Bouget | Levels of taxonomic resolution in forest entomology studies: needs, contingencies and perspectives[END_REF]. Differently baited traps consequently result in different species composition. Similarly, [START_REF] Reyers | Assessment techniques for biodiversity surrogates[END_REF] have mentioned that assessment techniques used have a strong influence on the effectiveness of biodiversity surrogates.

Our study indicates that some of the surrogate subsets are quite robust in terms of alpha diversity.

However, almost all subsets behave differently at both local and regional scales. The surrogacy of only one subset (identifiable) was stable with increasing spatial scale. The predictive values of four surrogates (common, sgenera, subset4, easy-to-identify) strongly decreased with increasing spatial scale. In other words, subset surrogacy was reduced from the local (alpha) diversity to the regional (gamma) diversity (the forest scale made up of aggregated plots). The effectiveness of biodiversity indicators is known to be markedly influenced by spatial scale [START_REF] Hess | Effectiveness of biodiversity indicators varies with extent, grain, and region[END_REF], since species richness is scale dependent [START_REF] Rahbek | The role of spatial scale and the perception of large-scale species-richness patterns[END_REF]. Besides, we did not investigate the contrasts between surrogates to predict the species richness of neighbouring traps, i.e. spatiallyassociated sampling units.

Limits and perspectives

Our study dealt with surrogates of species richness (alpha and gamma diversity) and did not concern species composition (beta diversity). Previous studies have demonstrated that partial subsets may correlate well with total species richness, but may be less robust in reflecting species composition [START_REF] Magierowski | Robustness of surrogates of biodiversity in marine benthic communities[END_REF]Johnson, 2006, Vellend et al., 2008). Generally, the smaller the subset, the weaker the information it provides on composition patterns. From [START_REF] Oliver | Designing a cost-effective invertebrate survey: a test of methods for rapid assessment of biodiversity[END_REF], multivariate analysis of the community structure with species subsets was less powerful in discriminating sites than were whole assemblages. The comparison of inter-site assemblage dissimilarities with a global dataset or with simplified subsets should be carried out [START_REF] Su | Beyond species richness: community similarity as a measure of cross-taxon congruence for coarse-filter conservation[END_REF]. Moreover, we could check that the global variations in abundance/richness of ecological groups are well reflected using species subsets. Some of our best performing subsets were robust in describing alpha diversity and also showed a low variability in surrogacy across ecological gradients. These pragmatic surrogacy subsets may be helpful in obtaining a relevant picture of total species richness in biodiversity monitoring schemes. Our results do not provide an alternative to costly, but necessary, studies on specieshabitat relationships. However, they may provide a valuable technique to help to implement effective European-wide monitoring strategies [START_REF] Müller | Three-dimensional partitioning of diversity informs state-wide strategies for the conservation of saproxylic beetles[END_REF]. Designing a broadscale biodiversity monitoring program that will both address objectives and be statistically sound is a significant challenge [START_REF] Beggs | Approaches to broad scale monitoring of biological diversity -a brief review of international experience[END_REF]. Even though the surrogate ability of our most robust subsets remains spatially stable over our large dataset they may vary over longer time periods or over wider ecological gradients. A regular reassessment of the performance of even pragmatic and low-cost surrogates is required throughout any monitoring program. Indices indicate the performance of the surrogate for each of the 5 criteria mentioned above: 2 = good (well-performing surrogate in the criterion), 1 = average, 0 = bad (badly-performing surrogate in the criterion). P ind (= proportion of individuals to be identified in the species subset from all individuals caught in the trap); P sp (= proportion of species to be identified in the subset from all species caught in the trap); E inv = [1 -the proportion of easy-to-identify species from the total species list in the subset], R 2 = correlation value between subset and total species richness, Sum = Cumulative index (the higher the value, the better), * = estimated value for the highertaxon surrogate.

Figure legends

Fig. 1: Distribution of the plots that were part of the study. Dotted line shows the reference border used for segregation between latitudinal levels (north and south), the altitudinal affiliation of each plot is displayed in full black (lowland) or grey-filled points (highland). The separation into biogeographic areas is depicted by polygons. The plots (point numbers) were part of the following datasets: 1 -Assise; 2 - 11,41,; 54 -Troncais (Velle); 55 -Val-de-Senones;56 -Vauhalaise;57 -Canopee;58 -Haute-Savoie;59 -Jujols;60 -Mantet;61 -Mercantour;62 -Natura 2000;63 -Orlu;64 -Tete-Alpes;65 -Auvergne;66 -Lozere;67 -Bléones;68 -Chamatte;69 -Luberon;70 -Tartonne;71 -Caylus;72 -Landes;73 -Cadarache;74 -Maures (ONF); 75 -Maures (Purpan) Fig. 2: Comparison between surrogacy and cost parameters of the surrogates. (a) between R 2 and P sp (= proportion of species to be identified in the subset from all species caught in the trap); (b) between R 2 and E inv = [1 -the proportion of easy-to-identify species from the total species list in the subset]. Note that only subsets with R 2 greater than 0.5 are displayed. The dashed line shows the value of R 2 =0.85. Point numbers correspond to these subsets: 1 -easy mid-frequent; 2 -easyto-identify; 7german identifiable;: Scale effect. Prediction ability of surrogates (mean R 2 value of 12 datasets) across an increasing number of aggregated plots. Only surrogates with R 2 greater than 0.85 at the trap level are displayed. The dashed line shows the value of R 2 =0.85.

  carried out from 1999 to 2010 by different institutes in France (Institute for Engineering in

	Agriculture and Environment [Cemagref], National Forest Office [ONF], University of Toulouse-
	Purpan Engineering School [EIP] and Office for Insects and their Environment [OPIE]) and the
	Center for Research on Nature, Forest and Wood [CRNFB] in Belgium. The analysis was
	restricted to beetle data obtained from transparent cross-vane window flight-interception traps,
	standardised in size

Table legends Table 1 :

 legends1 Trap numbers in categories representing environmental gradients; based on Biogeographic area, Forest type and Altitude.

	Table 2: Surrogate description. IP 1-2 = number of species with patrimoniality value 1 and 2, IP
	3-4 = number of species with patrimoniality value 3 and 4, MidFrequent = number of species
	present on the list of 'mid frequent' species (see section 2.3. of the text), ID-1 = number of easy-
	to-identify species, ID-2 = number of mid-identifiable species, * = number of genera.

Table 3 :

 3 Surrogacy values and sensitivity to environmental and methodological factors of the 42 subsets. Results of the Spearman correlation between number of species in subsets and total species richness (R 2 is displayed; all the correlations were significant at 0.001 level) and the effect of environmental factors on predictions (level of significance for factor effects: ** = p<0.01; *** = p<0.001; dash = not significant).

Table 4 :

 4 Correlation influenced by factors. The numbers in the table give the R 2 values for each level of ecological and methodological factor variable. The values are displayed only where the effect of the factor was significant. Only the surrogates with R 2 greater than 0.85 (as well as the easy-to-identify subset) were included in the table. The values below 0.85 are in bold.

Table 5 :

 5 Global

surrogate evaluation according to the following criteria: (i) ecological representativeness, (ii) conservation interest, (iii) identification costs, (iv) surrogacy potential and (v) variability in surrogacy across environmental gradients The fields are as follows: groups by preferred host tree, feeding guilds (Saproxylo.=saproxylophagous, Sec. xylo.=secondary wood decayer), large species (>10mm in body size), cavity-dwelling species, common species (IP=1 or 2), rare species (IP=3 or 4). Numbers indicate the proportion (%) of the species in the subset. Variability in surrogacy concerns the effects of forest type, geographical range (latitude, biogeographical region, altitude), alcohol bait, increasing spatial scale (plot aggregation up to the whole forest level) on R² values.

Table 2

 2

	Surrogate	name	species no IP 1-2 IP 3-4 MidFrequent ID-1 ID-2	Description
	1	easy mid-frequent	32	31	1	32	32	0	easy-to-identify mid frequent species (ID=1 x MidFrequent)
	2	easy-to-identify	203	151	52	32	203	0	easy-to-identify species (ID=1)
	3								easy-to-identify mid frequent species and mid identifiable
		identifiable mid-							mid frequent species (ID=1*MidFrequent +
		frequent	84	80	4	84	32	52	ID=2*MidFrequent)
	4								easy-to-identify species and mid identifiable mid frequent
		subset4	255	200	55	84	203	52	species (ID=1 + ID=2*MidFrequent)
	5								easy-to-identify species and mid identifiable species (ID=1 +
		identifiable	615	444	171	84	203 412	ID=2)
	6	mid-frequent	102	98	4	102	32	52 mid frequent species (MidFrequent)
	7	german							easy-to-identify and mid identifiable species present on the
		identifiable	394	276	118	65	150 244	German list of monitoring species (Schmidl & Bussler 2004)
	8	german	499	359	140	74	150 244 all species present on German list of monitoring species
	9								species present on the list of European red-listed species
		iucn	173	111	62	27	63	99	(Nieto & Alexander 2010)
	10	larger10	193	147	46	24	99	79 species with body size greater than or equal to 10 mm
	11	common	256	256	0	63	65	118 common species (IP=1)
	12	rare	217	0	217	4	52	119 rare species (IP=3 or IP=4)
	13	Anobiidae	72	46	26	5	0	21 species family Anobiidae
	14	Cerambycidae	149	111	38	16	69	77 species family Cerambycidae
	15	Curculionidae	95	89	6	13	26	44 species in family Curculionidae
	16	Elateridae	49	25	24	8	12	26 species family Elateridae
	17	Tenebrionidae	55	46	9	3	9	35 species family Tenebrionidae
	18								Cerambycidae, Elateridae, Scarabaeidae, Tenebrionidae
									(generally well known saproxylic species with different
		combin1	268	197	71	29	100 143	habitat preferences)
	19								Cerambycidae, Elateridae, Scarabaeidae, Tenebrionidae,
		combin2	355	254	101	37	101 177	Mycetophagidae, Anobiidae (combin1 extended)
	20								Cerambycidae, Curculionidae, Mycetophagidae, Nitidulidae
		combin3	296	243	53	36	97	147	(families performing well in preliminary analyses)
	21								Anobiidae, Cerambycidae, Elateridae, Curculionidae
									(families with a high average number of individuals caught in
		numerous	365	271	94	88	107 168	traps)
	22	domsum	653	482	171	42	149 320 All 19 dominant families
	23	sgenera	349*	-	-	-	-	-	number of fully saproxylic genera

Table 3

 3

	Surrogate	R 2	Ecological effects Forest type Altitude Latitude	Biogeographic area	Methodological effect Bait
	easy mid-frequent	0.59	***	**	***	**	***
	easy-to-identify	0.76	-	-	-	-	-
	identifiable mid-frequent	0.73	***	**	***	-	**
	subset4	0.86	***	-	-	-	**
	identifiable	0.96		-	-	-	**
	mid-frequent	0.78	***	-	***	**	**
	german identifiable	0.87	***	-	-	-	-
	german	0.89	***	-	-	-	-
	iucn	0.66	***	**	-	**	**
	larger10	0.62	-	-	-	-	-
	common	0.90	-	-	***	***	-
	rare	0.28	-	-	-	-	***
	Anobiidae	0.16	-	-	-	**	***
	Cerambycidae	0.50	-	-	-	-	-
	Curculionidae	0.30	***	-	-	-	***
	Elateridae	0.26	**	-	**	-	***
	Tenebrionidae	0.16	-	-	-	-	***
	combin1	0.60		-	**	-	-
	combin2	0.69	**	-	**	-	-
	combin3	0.75	***	-	-	-	**
	numerous	0.79	***	-	-	**	-
	domsum	0.96	-	-	-	***	-
	sgenera	0.95	-	-	-	-	***

Acknowledgements:

We are grateful to Frédéric Archaux and Jean-Yves Barnagaud for helpful discussions and constructive comments on data analysis, and to Victoria Moore for checking and improving the English language. The manuscript was substantially improved by the refereeing of Dr Panu Halme and an anonymous referee.


Imane Horiya

Soufiene Djahel
email: soufiene.djahel@ucd.ie

Yacine Ghamri-Doudane

A Hidden Markov Model based Scheme for Efficient and Fast Dissemination of Safety Messages in VANETs

Keywords: VANETs, Safety Messages, IEEE 802.11P, Hidden Markov Model, Kalman Filter

Nowadays, Vehicle to Vehicle (V2V) communication is attracting an increasing attention from car manufacturers due to its expected impact in improving driving safety and comfort. IEEE 802.11P is the primary channel access scheme used by vehicles; however it does not provide sufficient spectrum to ensure reliable exchange of safety information. To overcome this issue, many efforts have been devoted to enhance the frequency spectrum utilization efficiency. To this end, the Cognitive Radio (CR) principle has been applied to assist the vehicles to gain extra bandwidth through an opportunistic use of the unused spectrums in their surrounding. In this paper, we focus on safety messages for which we propose an original scheme that makes their exchange among the nearby vehicles more reliable with a significant reduce in their dissemination delay. This improvement is due to the use of a Hidden Markov Model that enables the prediction of the available channels for the subsequent time slots, leading to faster channel allocation for the vehicles. The obtained simulation results confirm the efficiency of our scheme.

I. INTRODUCTION

Vehicular Ad Hoc Networks (VANETs) [START_REF] Hossain | Vehicular telematics over heterogeneous wireless networks: A survey[END_REF] are new paradigm of wireless communications that aim to exploit the recent advances in wireless devices technology to enable intelligent inter-vehicle communication. VANETs are distinguished from other wireless networks by their specific characteristics such as; predictable vehicles movement, high speed, powerful processing units, large storage capacities and new applications scenarios. Additionally, VANETs may ensure wide dissemination of data and safety related information due to the large transmission range of vehicles compared to other wireless devices like sensors and handheld equipments. This wide dissemination is also ensured by the specific routing protocols used, such as GPSR [START_REF] Karp | GPSR: Greedy perimeter stateless routing for wireless networks[END_REF], BROADCOMM [START_REF] Durresi | Emergency broadcast protocol for intervehicle communications[END_REF] and GEOCAST routing approach [START_REF] Maihfer | A survey of geocast routing protocols[END_REF].

Compared to Wireless Sensor Networks (WSNs) and Mobile Ad hoc Networks (MANETs), VANETs do not suffer from energetic resources scarcity since the vehicle's battery can provide a long term energy supply. Although, VANETs are unable to ensure connectivity between vehicles in certain circumstances like in rural areas where the network density is low. Moreover, VANETs may not guarantee timely detection of dangerous road conditions due to the high mobility of vehicles.

The main objectives of Inter Vehicles Communication (IVC) are improving drivers' and passenger's safety and comfort (e.g. anticipation of any danger, accident, emergency braking from the ahead vehicles etc), allowing better traffic information, providing driving assistance and Internet connection. Due to the expected results of IVC, this communication technology has recently attracted a lot of attention from the research community aiming at developing more Intelligent Transportation Systems (ITS) based applications that make driving easier and safer. To achieve this goal, we need first to design an adequate medium access protocol that fulfills the requirements of vehicular environment along with the ITS applications demands in terms of bandwidth. To this end, many researchers have focused their efforts on designing such MAC protocol, dubbed IEEE 802.11P which is commonly used for vehicular communication. Moreover, the US Federal Communication Commission has reserved seven 10MHz wide channels in the 5.9GHz band for the Dedicated Short Range Communication (DSRC) to support the proliferation of ITS applications. Six out of these channels are Service Channels (SCH, dedicated to communications and applications) and the middle channel is the Control Channel (CCH, dedicated to safety message broadcast).

It has been demonstrated in recent studies that the channel bandwidth as designed by IEEE 802.11P [START_REF] Jiang | IEEE 802.11p: Towards an international Standard for Wireless Access in Vehicular Environments[END_REF] standard (i.e., 10 Mhz) might be inadequate to support the heavy requirements of VANET's safety applications, especially during rush hours. Therefore, it becomes mandatory to design alternative dynamic frequency allocation schemes to replace the currently used static techniques. Hence, this may ensure a reliable exchange between the increasing numbers of vehicles and increases the achieved data rates. Indeed, the users are more likely to seek for extra bandwidth to know how long congestion lasts or to find entertainment (e.g. Internet access, video streaming, P2P applications etc). To satisfy this need of bandwidth, we present in this paper an original cooperative sensing and spectrum allocation scheme that exploits the strength of cognitive radio technology as well as the hidden Markov model properties to increase the bandwidth share of users and diminish the transmission delay of emergency messages.

The remainder of the paper is organized as follows. Section II gives an overview on IEEE 802.11P. Next, we present the most significant contributions for spectrum frequency allocation in VANETs and highlight their limitations in section III. In section IV, we introduce our scheme. In section V, we present and discuss the obtained simulation results. Finally, we conclude in Section VI.

II. OVERVIEW OF IEEE802.11P

In order to provide an efficient means of communication in VANET and facilitate its integration with other networks, such as WSNs to constitute the so-called Hybrid Sensors and Vehicular Networks (HSVNs) [START_REF] Djahel | A Framework for Efficient Communication in Hybrid Sensor and Vehicular Networks[END_REF], the IEEE 802.11P task group has defined a set of specifications for Wireless Access in Vehicular Environment (WAVE) to fulfill the requirements of such challenging environment. IEEE 802.11P operates in the frequency band of 5.85-5.925 GHZ, within which the DSRC spectrum is divided to seven channels of 10MHZ each. The control channel (CCH) is exclusively reserved for safety related communications like beacons and event-driven messages whereas up to six service channels (SCHs) are used for non safety data exchange. IEEE802.11P uses the same medium access mechanism of IEEE 802.11e, named Enhanced Distributed Channel Access (EDCA) [START_REF]Wireless LAN medium access control (MAC) and physical layer (PHY) specifications: Amendment 8: Medium access control (MAC) quality of services enhancements[END_REF]. In IEEE 802.11P, the channel time is divided into synchronization periods of 100 ms each, consisting of equallength alternating CCH and SCH intervals. Therefore, the vehicles' devices must switch to the frequency of each channel (i.e., the CCH or one of the SCHs) during its specified interval in order to transmit the type of messages authorized during this period. To make this access scheme more accurate, a period equal to 4ms, called Guard Time, is set at the beginning of each interval to account for the radio switching delay and the timing inaccuracies in the devices. Notice that the coordination between channels is achieved through the use of the Coordinated Universal Time (UTC) offered by a global navigation satellite system.

III. RELATED WORK

Before presenting the most recent works dealing with the spectral frequency scarcity in V2V communication, we first give a brief description of cognitive radio technology.

The cognitive radio is a technology that allows a wireless user to interact with its environment using opportunistic management of spectrum resources and according to its needs in terms of Quality of Service (QoS) and security requirements.

To increase the available bandwidth for V2V communication, particularly over the CCH, the authors of [START_REF] Milota | Cognitive radio: Making software radios more personal[END_REF] have exploited the TV spectrum holes to increase the available bandwidth for vehicles communication through a cognitive radio based scheme. The key idea behind that is as follows; a vehicle senses the radio spectrum in its surrounding to detect its occupancy and usage based on an energydetector scheme. Once one or more holes are detected, the detector vehicle selects one of these holes (i.e., the spectrum of the Primary Users (PU)) to communicate over it. Notice that in the rest of the paper we use the term secondary users to refer to the vehicles that might use the PU's spectrum holes.

In [START_REF] Felice | Analyzing the Potential of Cooperative Cognitive Radio Technology on Inter-Vehicle Communication[END_REF] the authors have proposed a spectrum management framework for cognitive VANET, dubbed Cog-V2V. In this framework, the vehicles can exchange sensing information and detect spectrum holes through a cooperative sensing technique as described below. Each vehicle shares the gathered information regarding the spectrum availability, then it aggregates each received data from its neighbors to make a decision about the channel to use for its transmission. A vehicle can know in advance the spectrum availability by receiving data from other vehicles ahead in the path. The main advantage of this cooperative sensing scheme is that it mitigates the risk of individual detection error. However, the decision about the channel to use is taken individually without coordination with the nearby vehicles. As a consequence, several vehicles may choose the same channel at the same time (i.e., different communications in the same channel may occur), which is the main drawback of this scheme.

In contrast to the previous framework, the authors of [START_REF] Fawaz | Improving reliability of safety applications in vehicle ad hoc networks through the implementation of a cognitive network[END_REF] have conceived an architecture that uses the road side infrastructure as a supervisor that manages the PU's channel holes assignment to the secondary users. A road-side infrastructure records the data gathered by vehicles (regarding spectrum holes) when they are in its transmission range. Then, it assigns the available channels, if any, for each passing vehicle to prevent multiple simultaneous accesses to the same channel. The road-side infrastructure computes a metric called contention metric which assesses whether there is a contention in the CCH channel or not. If so, the system will exploit the spectrum holes detected by the CR component of the passing vehicles. The main shortcoming of this architecture is its high dependence on the infrastructure's performance. Moreover, these road-side units are very costly for their deployment and maintenance.

Despite being a promising solution to the lack of bandwidth, cooperative sensing based schemes, as introduced in [START_REF] Felice | Analyzing the Potential of Cooperative Cognitive Radio Technology on Inter-Vehicle Communication[END_REF], and [START_REF] Fawaz | Improving reliability of safety applications in vehicle ad hoc networks through the implementation of a cognitive network[END_REF], suffer from two major concerns, as explained below.

• The sensing accuracy is highly dependent on the density of the vehicles. • The heterogeneity of primary users' signals makes their detection more difficult; indeed the thresholds of detection differ according to the type of signal (21dB for digital TV, 1dB for analog TV and 12dB for wireless microphone). As opposed to cooperative sensing schemes, in the stand alone mode the secondary users themselves perform the whole process of sensing and decision making. This category of schemes alleviates the problem of density since the vehicles are independent from each others. Despite this advantage over cooperative sensing schemes, this solution has several drawbacks. Simultaneous accesses to the same channel from several secondary users may occur, which leads to troubles. Furthermore, the detection ability of each vehicle relies solely on its own equipment, which may create unfairness among the secondary users as different vehicles are dotted with different equipments and technologies.

To address this issue, [START_REF] Wang | A Novel sensing Coordination Framework for CR-VANET[END_REF] has proposed a novel spectrum sensing coordination scheme that aims at taking the best of both sensing approaches (i.e., cooperative and stand alone) to improve the sensing efficiency and accuracy. Its working principle can be summarized as follows; a coordination node is chosen among the vehicles in the network. This node uses an energy based detection technique to speed up the spectrum holes detection. Once the promising channels have been identified by the coordination node, it assigns a part of the spectrum to a secondary user which in its turn performs an additional stand alone sensing to access the available channels. This scheme leaves some freedom for the vehicle to choose which channel to use. It also diminishes the scope of the master/slave relationship of the architecture proposed in [START_REF] Fawaz | Improving reliability of safety applications in vehicle ad hoc networks through the implementation of a cognitive network[END_REF], which was one of its main drawbacks.

An alternative solution to [START_REF] Wang | A Novel sensing Coordination Framework for CR-VANET[END_REF] is proposed in [START_REF] Husheng | Collaborative Spectrum Sensing in Cognitive Radio Vehicular Ad Hoc Networks: Belief Propagation on Highway[END_REF] where cognitive radio is used to increase bandwidth spectrum through a decentralized cooperative sensing scheme. In this scheme, the authors propose to apply a Belief Propagation (BP) algorithm to manage the spectrum holes detection. The key principle of BP algorithm can be summarized as follows; first, each vehicle broadcasts a message to all the secondary users in VANET to inform them about its belief of the presence of a PU. Afterwards, each receiver vehicle combines its local observations with the received belief to generate a new belief. The major drawback of BP based schemes is the slow process of available PU's channels detection, which may affect the efficiency of the opportunistic use of the available spectrum holes.

In the next section, we present an original scheme that uses a hidden Markov model in order to circumvent the shortcoming of the previously discussed schemes and speed up the PU's holes detection and assignment to secondary users, which leads to reliable and fast dissemination of emergency messages.

IV. THE PROPOSED SCHEME

In our scheme, we consider a cognitive vehicular network within which the vehicles are organized in clusters to make communication easier and more efficient. A cluster of vehicles is composed of a cluster-head and cluster members. The cluster-head assigns channels to cluster members upon request. Whenever a vehicle arrives within a cluster, it first checks whether a cluster-head already exists or not. If so, it updates the cluster-head information with the identity of the current cluster-head. Otherwise, it serves as cluster-head as stated in [START_REF] Felice | Analyzing the Potential of Cooperative Cognitive Radio Technology on Inter-Vehicle Communication[END_REF].

For data transmission, two types of channels will be used; the exclusive channel (a dedicated bandwidth in DSRC) which is reserved for safety related messages transmission and the shared channel that can be used by both PUs and the other vehicles. These vehicles exploit the inactive time slots (i.e., spectrum holes) of the PUs to opportunistically transmit their messages. Therefore, this increases the offered bandwidth of the exclusive channel and leads to fast transmission of safety messages.

In our scheme, spectrum sensing is performed locally by each cluster member in each time slot. Since a cluster is spatially restricted to a small area, we will have redundancy in each vehicle's sensing. We take advantage of this property to estimate the state of the shared channels in the current time slot (i.e., idle or occupied). The final decision about the state of a shared channel is taken by the clusterhead based on the received observations from the vehicles belonging to its cluster. Subsequently, the detected holes in the licensed spectrum are assigned to the vehicles by the cluster-head. The main feature of our scheme is the decrease of the dissemination delay of safety messages compared to the schemes discussed in section III. This is achieved by computing the probability of the expected state (during the subsequent time slot) of each shared channel based on its current status. This probability indicates to the cluster-head how many observations should receive before making the final decision regarding a given shared channel state. Therefore, in the worst case, the cluster-head will wait till receiving the observations from all the cluster members and thus ensures a comparable dissemination delay to the previous schemes; otherwise, it ensures a lower delay that varies according to the computed probability. Note that this probability is computed based on a Hidden Markov Model (HMM) framework as described hereafter.

Let us assume that the state of the channels is a hidden random variable of a HMM. The cluster-head vehicle will estimate the state of each shared channel through a probabilistic computation , thanks to the sensing observations provided by all cluster members. Now, we define our HMM as follows:

• X(t): refers to a state variable in time slot t.

• Y (t): denotes a given observation in time slot t.

• X(t) = (x1(t), x2(t), ..

., xc(t))

such that c denotes the number of available shared channels, which varies in time and space. We set the following conventions:

• xi(t) = 0 if the channel i is idle at time slot t. • xi(t) = 1 if the channel i is busy at time slot t. • Y (t) = (y1(t), y2(t), ..

., yc(t))

where yi(t) is the merged observations of all vehicles in the cluster regarding the channel i. Notice that our model is based on two fundamental but still reasonable assumptions which state that each state X(t) depends only on the last state X(t -1) (i.e., which refers to the Markovian assumption) and a given observation Y (t) depends only on the hidden state X(t). Additionally, we assume that our model is a Linear State Space Model.

We denote by Q(t) the state transition matrix at time slot t, which describes the inner dynamic of the system and by G(t) the measurement matrix at time slot t, which describes the relationship between the hidden state and the measurement. The states and observations are linked by the following equations that constitute the corner stone of our model.

• State equation

X(t + 1) = Q(t)X(t) + ε(t) ( 1
)
• Observation equation

Y (1) = G(t)X(t) + ε ′ (t) (2)
Here ε(t) and ε ′ (t) are additive Gaussian Noise which refer to the noise measurement at time slot t. Note that this well known model allows us to estimate the state of the hidden variables X(t) and X(t + 1) knowing Y (t). Since we are utilising the Linear Gaussian State Space Model, we can use the so-called Kalman filter, described in [START_REF] Hamilton | Time Series Analysis[END_REF] and [START_REF] Masreliez | Robust bayesian estimation for the linear model and robustifying the Kalman filter[END_REF], to provide an estimation of the laws of P [X(t)/Y (t)] and P [X(t + 1)/Y (t)].

A. Kalman Filter overview

The objective is to estimate the random variable X k from Y k in an optimal and recursive manner. To this end, we adopt the standard deviation minimum criterion to estimate the conditional distribution of the random vector X k /Y 0:k , such that Y 0:k denotes the k first observations received by the cluster-head vehicle. Since we are in Gaussian context it is obvious that the variable of interest follows a Gaussian distribution. Hence, we need only to compute the mean and covariance matrix of the distribution. The conditional mean and the covariance matrix are calculated recursively, as shown in Equations 3 and 4, respectively.

Xk = E(X k /Y 0:k ) (3) Xk = E[(X k -(X) k )(X k -(X) k ) * /Y 0:k )] (4)
The Kalman Filter achieves this in a two step process, as described below.

• The prediction step: during which the conditional law of X k knowing Y 0:k-1 is computed, thanks to the Equation. 1.

• The update step: during which the recently available observation

Yt is used to correct the prediction. This enables to compute the mean and covariance matrix of the distribution of interest. Once the channel state is estimated by the filter, the cluster-head vehicle will establish a schedule to gather the states of the channels. Note that the exclusive channel is always available, so we set

X ExcluChannel (t) = 1
The cluster-head will also collect the requests of communication sent by each vehicle, specifying the type of messages to be transmitted (i.e., data, beacon or safety message) and the number of messages in each type. These requests are saved in the so-called schedule table as shown in Table II.

When the cluster-head builds the communication table, it allocates channels to each vehicle following a weighted scheduling scheme. In our scheme, we assign an increasing weight to safety messages in order to speed up their transmission. In next subsection, we give an example to illustrate the functioning of our scheme.

B. Example

We consider a cluster of three vehicles where vehicles V2 and V3 have safety and/or data messages to transmit. The cluster-head (i.e., vehicle V1) assigns an increasing weight to safety messages in order to indicate their priority. When new messages arrive the cluster-head assigns to them a weight equals to 2, 3 etc.

The cluster-head performs channels allocation based on the information contained in Tables I and III. It assigns first the exclusive channel to safety messages with low weight. Then, if the exclusive channel is not congested the cluster-head allocates its remaining bandwidth to the messages with high weight. Afterwards, the other messages will be transmitted over the shared channels having a state equals to 1. If safety messages cannot be all transmitted at time slot t, the expected state of the shared channels at time slot t + 1 is then used to pre-allocate them in order to reduce the channel allocation time.

The main advantage of our model consists in predicting the state of the channel during the next time slot through a probabilistic computation. Hence, we can allocate the data not transmitted at time slot t to the expected free channels during the next time slot. This will significantly reduce the waiting time for the vehicles and consequently decrease safety messages dissemination delay. Therefore, the cluster-head can complete the allocation table with the pre-allocation data for the time slot t + 1.

During the observation collection process performed by the clusterhead, a weight is assigned to each measure provided by the cluster member vehicles. This weight is calculated based on the physical distance between the vehicle and the antenna representing the PU's channels. A higher weight is assigned to vehicles closer to the antenna since their observations are more accurate compared to those provided by a farther vehicles. To this end, it is assumed that the cluster-head knows the GPS coordinates of the antenna and those of each vehicle. Therefore, it is able to compute the corresponding weights.

C. Probability calculation

Here, we will explain in details how we calculate the probability of a channel c being available in next time slot based on its status during the ongoing time slot. This probability is calculated as follows.

P (Xc(t) = Xc(t -1)) = 1 Nv Nv ∑ i=1 Yiωi ( 5
)
where Nv is the number of cluster members that have sent their observations to the cluster-head. Notice that the weight ωi is calculated according to the distance between the vehicle and the antenna. It varies from 1 to 2. ∀i ∈ {1, ..., Nv}1 ≤ ωi ≤ 2

According to the probability calculated in Equation 5, we determine a threshold that defines the number of required observations to confirm the idle status of a given channel.

λmin ≤ P ≤ λmax

If the probability P is smaller than the threshold λmin then the cluster-head waits for more observations. The number of the expected observations to wait for is calculated as follows.

N obs = (1 -λmin).Nv [START_REF] Durresi | Emergency broadcast protocol for intervehicle communications[END_REF] On the other hand, if this probability is greater than λmax then the cluster-head builds a table storing channels states for T + 1. This table helps this cluster-head to speed up the free channels assignment during the subsequent time slot as it waits only for N obs regarding each channel before making decision about its status (free or occupied).

Channel assignment to vehicles is performed in two steps as follows. First, the cluster-head vehicle identifies the idle channels following two different mechanisms. Secondly, it establishes an assignment order among the vehicles requesting channel access according to the type and the number of messages to transmit.

The cluster-head considers a channel as free according to one of the two mechanisms described below:

• Mechanism 1: among the N obs received, if the number of observations equal to 1 is larger than those equal to 0, then the channel is free. Otherwise, the channel is considered occupied.

-Channel 1 S2 1 S3 Shar-Channel1 1 S2 0 - Shar-Channel2 0 - 0 Shar-Channel3 0 - 1 D1 Shar-Channel4 1 S3 1 D3 Shar-Channel5 1 S3
1 -

• Mechanism 2: the cluster-head awaits till receiving N obs observations confirming the free status of a given channel. If the first N obs observations received are equal to 1 then the channel is considered idle, otherwise the cluster-head should wait till the number of observations equal to 1 reaches the value N obs .

If the whole set of observations is received and the N obs of observations equal to 1 is not reached yet, the cluster-head applies the mechanism 1 for the whole set of observations.

V. SIMULATION RESULTS

In this section, we present and discuss the obtained simulation results that evaluate the performance of our scheme. We have conducted our simulation using MATLAB in which we have implemented our scheme and run simulation for several scenarios under various conditions, as summarized in Table V. It is worth mentioning that at the beginning of each simulation, several time slots are used as a training sequence to calculate the probability values to be used in the subsequent time slot. Therefore, during these time slots no PU's channel assignment is performed.

To highlight the effectiveness of our scheme compared to the existing works, we have chosen to measure the following metrics.

• The Average Number of Mini-time Slots (ANMS) required for assigning an idle PU's channel to a vehicle. • The Percentage of the Extra Bandwidth (PEB) gained by each vehicle, as a consequence of applying our scheme, to its total acquired bandwidth fair-share in the CCH. Before discussing the results of these metrics let us first analyze the probability values calculated by our scheme in different scenarios. As depicted in Figure 1, the probability of a channel being free in the next time slot based on its current status varies between 0.4 and 0.8. Its lowest value is 0.44 whereas the highest achieved one is equals to 0.79. We observe from these values and their distribution on the different scenarios for each PU's channel that our scheme ensures efficient prediction of channels status. This is due to the fact that each PU's channel is predicted to be free in the next time slot for at least 1 scenario. Notice that no bar is plotted to represent an occupied channel in a given scenario (i.e., its corresponding probability is equal to 0).

One of the most important metrics for evaluating the effectiveness of channel allocation schemes is the time elapsed between the vehicle request and the channel allocation by the cluster-head. The value of this metric is more critical when we deal with safety related messages, especially in VANETs. In the herein conducted simulation, this metric is measured by the means of ANMS value. Figure 2 compares the ANMS values achieved by our scheme to those of the existing schemes (i.e., the schemes discussed in Section III). The plotted curves show that our scheme (the red curve) outperforms the other schemes (the blue curve) in various scenarios. We observe that the gap between the two curves is important particularly in scenarios 3 and 4 where the channel allocation delay in our scheme is less than the half of that achieved in the other schemes. This is due to the mechanism 1 (see section IV-C) used in these two scenarios to detect the idle status of a channel, which allows significant reduction of the ANMS.

Figure 3 shows the percentage of the extra bandwidth gained by each vehicle (7 vehicles in the case of scenario 5 whose the results are plotted in this figure) when it applies our scheme, under various data rates. We denote that the higher the data rate of the PU's channels is, the larger is the bandwidth acquired by the vehicles. This increase of the bandwidth is justified by the rise of the offered bandwidth at each shared channel. Hence, it is clear that each vehicle will get extra bandwidth even if collisions occur. We also observe that the worst gain achieved by each vehicle under a data rate of 2 mbps (i.e., the lowest data rate in our simulation) is around 20 % of its acquired bandwidth fair share in the CCH, so this confirms the effectiveness of our scheme. We have conducted a comprehensive study on the state of the art contributions dealing with bandwidth scarcity issue, caused by the increasing number of ITS multimedia applications, in dense vehicular networks. We have mainly focused on identifying their advantages and limitations. We then proposed a novel scheme to overcome these limitations by applying cognitive radio technology based techniques to increase the available bandwidth and consequently speed up the transmission of emergency messages over VANETs. This scheme uses Kalman Filter to predict the channel state for the subsequent time slot in order to accelerate its allocation to the requesting vehicles, thus the dissemination delay of emergency messages is significantly reduced. For higher accuracy, the observations received from vehicles closer to the transmitting antenna, in which holes are detected, are given higher importance through an adequate weight scheme. Our scheme has been implemented in MATLAB and the obtained results have proven its efficiency.

VII. ACKNOWLEDGEMENT

Figure 1 :

 1 Figure 1: Probabilities of PU's channels being free in the subsequent time slot

Figure 2 :

 2 Figure 2: ANMS values achieved by our scheme vs. the other schemes in the literature

Figure 3 :

 3 Figure 3: Percentage of the extra bandwidth (PEB) gained by the vehicles under different data rates

Table I :

 I Example of a state table in a given time slot

		X(t)	X(t + 1)
	Exclu-Channel	1	1
	Shar-Channel1	1	0
	Shar-Channel2	1	1
	Shar-Channel3	0	0
	.
	.
	.
	Shar-Channel C	0	1

Table II :

 II Example of the schedule table

	Type of messages	Safety (S)	Data (D)
	No. of messages to transmit	2	1

Table III :

 III Example of the communication table

		Vehicle1	Vehicle2	Vehicle3
	Request	S1	D1	S2	D2	S3	D3
	No. of messages	0	1	2	0	3	1
	Weight	-	3	1	-	2	4

Table IV :

 IV Example of the allocation table

	X(t)	Messages	X(t + 1)	Messages
		allocation		pre-allocation
	Exclu

Table V :

 V Simulation scenarios

		Scenario 1	Scenario 2	Scenario 3	Scenario 4	Scenario 5
	No. of vehicles	10	12	17	20	7
	No. of channels	8	6	9	7	5
	No. of mini-time slots	10	12	17	20	7
	per time slot
	Idle channel	Mechanism 2	Mechanism 2	Mechanism 1	Mechanism 1	Mechanism 2
	identification
	Weight		{1, 1.25, 1.5, 1.75. 2}
	Data rate		{2, 5.5, 11, 18} mbps
	No. of simulation			5
	epochs

This work was supported, in part, by Science Foundation Ireland grant 10/CE/I1855 to Lero -the Irish Software Engineering Research Centre (www.lero.ie).