Fabio Benedetti

François Guilhaumon

Fanny Adloff

Sakina-Dorothée Ayata

Investigating uncertainties in zooplankton composition shifts under climate change scenarios in the Mediterranean Sea

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potential diversity losses or gains, and can therefore help in determining priority areas for biodiversity management [START_REF] Bellard | Impacts of climate change on the future of biodiversity[END_REF]). The niche modelling procedure is sprinkled with decisional steps, from the initial conditions to the projections. Each step requires a decision among alternative options, and each option has a consequence on the final inference, thus generating variability (or uncertainty) around the mean projection [START_REF] Araújo | Ensemble forecasting of species distributions[END_REF][START_REF] Beaumont | Why is the choice of future climate scenarios for species distribution modelling important?[END_REF]. Uncertainty maps are often not provided with ENMs predictions [START_REF] Rocchini | Accounting for uncertainty when mapping species distributions: the need for maps of ignorance[END_REF]. Studies quantifying and mapping uncertainties associated with niche models predictions are scarce and are generally based on terrestrial taxa such as New World birds [START_REF] Diniz-Filho | Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change[END_REF], freshwater fishes in France [START_REF] Buisson | Uncertainty in ensemble forecasting of species distribution[END_REF], African vertebrates [START_REF] Garcia | Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates[END_REF], and European trees [START_REF] Goberville | Uncertainties in the projection of species distributions related to general circulation models[END_REF]. While these studies have identified the choice of the statistical technique to be the main driver of uncertainty, there is still no consensus on the best model. Ensemble forecasting has therefore been suggested as a solution to handle the variability of predictions based on different methods [START_REF] Araújo | Ensemble forecasting of species distributions[END_REF]. But distinct modelling strategies often rely on different theoretical assumptions and data properties (e.g. presence-only versus presence-absence data, or distancebased versus regression-based methods), and therefore may not approximate the same facet of a species niche [START_REF] Soberón | Niches and distributional areas: concepts, methods, and assumptions[END_REF][START_REF] Sillero | What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods[END_REF]. Consequently, more research is needed to summarise the relative importance of the different choices made within the niche modelling framework (i.e. aims of the study, species' ecological properties, ENM type and complexity, data characteristics etc.). Oceans play a key role in regulating Earth's climate [START_REF] Sabine | The oceanic sink for anthropogenic CO2[END_REF]) and provide energetic and food resources for people throughout the world. Since climate change is threatening marine biodiversity and the associated ecosystem services (Hoegh-Guldberg and Bruno 2010; [START_REF] Gattuso | Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios[END_REF], it is crucial to better understand how species may respond to environmental fluctuations and how the later may impact ecosystem functioning. Additionally, marine taxa live in habitats with different characteristics compared to their terrestrial peers: there are fewer barriers to dispersal (Steele 1991), and temperature gradients are declined over much larger spatial scales. Moreover, marine taxa present original life cycles and ecological characteristics (ontogenetic shifts, spatial aggregation, mobility), as well as peculiar data properties (and biases) which may pose new challenges for niche modelling [START_REF] Robinson | Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities[END_REF]. Occurrence-based niche modelling has been relatively ignored in marine ecology [START_REF] Robinson | Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities[END_REF]. Rather, marine ecologists have focused on the available abundance records to model the population dynamics of high trophic level taxa and their impact on food supplies [START_REF] Cheung | Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change[END_REF], or to relate their distribution shifts to climate fluctuations [START_REF] Perry | Climate change and distribution shifts in marine fishes[END_REF][START_REF] Pinsky | Marine taxa track local climate velocities[END_REF]. Amid all oceanic basins, the Mediterranean Sea occupies a particular position: this semi-enclosed basin is a hotspot for biodiversity [START_REF] Myers | Biodiversity hotspots for conservation priorities[END_REF][START_REF] Bianchi | Marine Biodiversity of the Mediterranean Sea: Situation, Problems and Prospects for Future Research[END_REF] that undergoes intense human pressures [START_REF] Mermex | Marine ecosystems' responses to climatic and anthropogenic forcings in the Mediterranean[END_REF][START_REF] Coll | The Mediterranean Sea under siege: spatial overlap between marine biodiversity, cumulative threats and marine reserves[END_REF] and strong rates of warming due to anthropogenic climate change [START_REF] Giorgi | Climate change hot-spots[END_REF]. Ongoing and future warming of the basin will likely trigger biodiversity losses across different benthic communities [START_REF] Danovaro | Biodiversity response to climate change in a warm deep sea[END_REF][START_REF] Garrabou | Mass mortality in Northwestern Mediterranean rocky benthic communities: effects of the 2003 heat wave[END_REF][START_REF] Coma | Global warming-enhanced stratification and mass mortality events in the Mediterranean[END_REF]. Pelagic communities are also likely to experience alterations in species composition due to the northwards migrations of species tracking their optimal thermal niche [START_REF] Sabatès | Sea warming and fish distribution: the case of the small pelagic fish, Sardinella aurita, in the western Mediterranean[END_REF]. Northwards migration would promote the replacement of cold-water species by warm-water competitors [START_REF] Rais Lasram | The Mediterranean Sea as a "cul de sac" for endemic fishes facing climate change[END_REF][START_REF] Albouy | Combining projected changes in species richness and composition reveals climate change impacts on coastal Mediterranean fish assemblages[END_REF], thus leading to alterations in the food-web structure and modifications of the functional and phylogenetic components of biodiversity [START_REF] Albouy | From projected species distribution to food-web structure under climate change[END_REF][START_REF] Albouy | Projected impacts of climate warming on the functional and phylogenetic components of coastal Mediterranean fish biodiversity[END_REF]. However, previous projections only concerned coastal fish assemblages. Potential community shifts in the lower trophic levels (i.e. the plankton) are still lacking despite their major ecological importance. Mediterranean fishes primarily feed on mesozooplankton [START_REF] Stergiou | Feeding habits and trophic levels of Mediterranean fish[END_REF][START_REF] Costalago | Seasonal comparison of the diets of juvenile European anchovy Engraulis encrasicolus and sardine Sardina pilchardus in the Gulf of Lions[END_REF], a group that is dominated (in terms of abundance and diversity) by copepods (Siokou-Frangou et al. 2010;Mazzocchi et al. 2014). Copepods represent a key group in pelagic trophic webs and are major contributors to the marine carbon cycle [START_REF] Mauchline | The biology of calanoid copepods[END_REF][START_REF] Beaugrand | Climate, plankton and cod[END_REF]. Climate variability has been shown to modify copepod biogeography and phenology [START_REF] Beaugrand | Diversity of calanoid copepods in the North Atlantic and adjacent seas: species associations and biogeography[END_REF][START_REF] Mackas | Changing zooplankton seasonality in a changing ocean: Comparing time series of zooplankton phenology[END_REF], therefore altering upper trophic levels through bottom-up processes [START_REF] Beaugrand | Climate, plankton and cod[END_REF]. Copepods have been identified as "beacons of climate change" [START_REF] Richardson | In hot water: zooplankton and climate change[END_REF]) meaning they are an ideal group for monitoring changes in environmental conditions, and the ensuing ecosystem modifications. For these reasons, ENMs are appropriate and necessary tools to estimate climate change impacts on copepod assemblages [START_REF] Sunday | Thermal tolerance and the global redistribution of animals[END_REF]. Plankton belong to the taxa that are the least studied through occurrence-based niche modelling [START_REF] Robinson | Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities[END_REF]). To our knowledge, only four studies have used ENMs to model species niches for oceanic phytoplankton [START_REF] Irwin | Phytoplankton niches estimated from field data[END_REF][START_REF] Pinkernell | Potential effects of climate change on the distribution range of the main silicate sinker of the Southern Ocean[END_REF][START_REF] Brun | Ecological niches of open ocean phytoplankton taxa[END_REF][START_REF] Barton | Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities[END_REF]. Zooplankton has been subjected to more niche-based studies, yet nearly all ENMs were developed for the North Atlantic and they implied a limited set of model algorithms and species [START_REF] Reygondeau | Future climate-driven shifts in distribution of Calanus finmarchicus[END_REF][START_REF] Beaugrand | Applying the concept of the ecological niche and a macroecological approach to understand how climate influences zooplankton: advantages, assumptions, limitations and requirements[END_REF][START_REF] Chust | Are Calanus spp. shifting poleward in the North Atlantic? A habitat modelling approach[END_REF][START_REF] Villarino | Modelling the future biogeography of North Atlantic zooplankton communities in response to climate change[END_REF][START_REF] Brun | The predictive skill of species distribution models for plankton in a changing climate[END_REF]. These studies have focused on predicting shifts in habitat suitability for a few species and on inferring changes in local biodiversity (α diversity). To account for changes in species assemblages composition, one can use indices of ß diversity (representing the variation of species assemblages composition; [START_REF] Anderson | Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist[END_REF], and combine them with α diversity estimates to identify the patterns driving dissimilarity between present and future species assemblages [START_REF] Dobrovolski | Climatic history and dispersal ability explain the relative importance of turnover and nestedness components of beta diversity[END_REF]. The aims of our study are to: (i) estimate potential shifts in zooplankton surface assemblages composition in the Mediterranean Sea under several climate change scenarios; and (ii) quantify the relative importance of uncertainty sources. Shifts in assemblages composition are derived from a set of ten ENMs, covering the majority of the commonly-used algorithms, and six different configurations of a regional circulation model. These configurations allow investigating the effects of greenhouse gas (GHG) emission scenarios and boundary forcings (BF), the consequences of the latter having never been explored before though it has been shown to be at least of the same order of magnitude as the one related to the GHG emission scenario [START_REF] Adloff | Mediterranean Sea response to climate change in an ensemble of twenty first century scenarios[END_REF]. In addition, five different levels in species prevalences are tested to investigate this potential source of substantial uncertainty (see below).

Material and Methods

Species data

To select the copepod species to be modelled, we filtered those with more than 50 occurrences in the Mediterranean Sea, based on a regional dataset (Supplementary material Appendix 1, Table A1) and, since these are not endemic, we selected only those represented globally in the Ocean Biogeographic Information System (OBIS; http://www.iobis.org), which lead to a total of 106 copepod species. Models were therefore calibrated using global data to avoid truncated distributions (Thuiller et al. 2004). The species list represents nearly 20% of the total number of copepod species reported in the Mediterranean Sea (n = 560, Razouls et al. 2005Razouls et al. -2016)). Most of the missing species being rare, our species list does represent the most commonly sampled species in the basin (Siokou-Frangou et al. 2010, Mazzocchi et al. 2014). Observations were aggregated within the 0.25°x0.25° grid cells of the World Ocean Atlas 2013 (WOA13; Levitus et al. 2013). Ultimately, only the presences recorded across a defined 30-year baseline period were kept. See Supplementary material Appendix 2, Table A2, for the final list of copepod species names and their corresponding numbers of global and regional occurrences after re-sampling on WOA13's grid. Present and future climatic data Sea surface temperature (SST) and sea surface salinity (SSS) were used as environmental predictors. These variables are commonly used when modelling copepod distributions because copepods are: poikilothermic, passively dispersed, and not exploited by human activities [START_REF] Richardson | In hot water: zooplankton and climate change[END_REF][START_REF] Reygondeau | Future climate-driven shifts in distribution of Calanus finmarchicus[END_REF][START_REF] Chust | Are Calanus spp. shifting poleward in the North Atlantic? A habitat modelling approach[END_REF]. Since the use of 30-year climatologies is often advocated for calibrating ENMs when predicting species distributions under climate change scenarios [START_REF] Roubicek | Does the choice of climate baseline matter in ecological niche modelling?[END_REF][START_REF] Harris | Climate projections for ecologists[END_REF], 30-year climatologies for the baseline period were constructed as follows. First, four global seasonal (spring/summer/fall/winter) SST and SSS in situ climatologies were retrieved from the WOA13 (Locarnini et al. 2013;[START_REF] Zweng | Mediterranean Sea response to climate change in an ensemble of[END_REF]; available at: https://www.nodc.noaa.gov/cgibin/OC5/woa13/woa13.pl) at a 1/4° resolution, for each of the three decades constituting the chosen baseline periods: 1965-1974, 1975-1984, 1985-1994. For each variable, global decadal climatologies were computed by averaging the four initial seasonal climatologies. In addition, the standard deviation of SST was computed to obtain decadal climatologies of the seasonal variation of SST (σSST) for each of the three periods. The newly-defined decadal climatologies were used to compute the final estimates of average SST, average SSS, and average σSST for the 1965-1994 period, which were used to calibrate the ENMs. Future predictions of SST, SSS and σSST over the Mediterranean Sea were obtained from the regional ocean general circulation model NEMOMED8 [START_REF] Beuvier | Modeling the Mediterranean Sea interannual variability during 1961-2000: focus on the Eastern Mediterranean transient[END_REF] under multiple forcing configurations [START_REF] Adloff | Mediterranean Sea response to climate change in an ensemble of twenty first century scenarios[END_REF]. Its horizontal resolution is 1/8° (~9 to 12 km grid cells depending on latitude) and it has 43 vertical levels. NEMOMED8 has been previously used to project fish distributions under climate change scenarios [START_REF] Rais Lasram | The Mediterranean Sea as a "cul de sac" for endemic fishes facing climate change[END_REF][START_REF] Albouy | Combining projected changes in species richness and composition reveals climate change impacts on coastal Mediterranean fish assemblages[END_REF][START_REF] Hattab | Towards a better understanding of potential impacts of climate change on marine species distribution: a multiscale modelling approach[END_REF]). This ocean model presents three main sources of boundary forcing: the Atlantic hydrography, the river runoff and the atmospheric surface fluxes. To assess projection uncertainty related to the choice of the GHG emission scenario and to the choice of the different boundary forcings, we used the 6-member ensemble scenario simulations of [START_REF] Adloff | Mediterranean Sea response to climate change in an ensemble of twenty first century scenarios[END_REF]. In their numerical experiments, the origin of the model boundary forcings (surface flux, river runoff, and Atlantic hydrography) was alternatively changed, and three different scenarios of GHG emission were considered. The GHG emission scenarios used in their study are based on the Special Report on Emission Scenarios (SRES) of the Intergovernmental Panel for Climate Change (IPCC 2007). The authors' annotations for the model runs were kept (A2, A2-F, A2-RF, A2-ARF, A1B-ARF, B1-ARF) with A2, A1B, and B1 indicating the GHG emission scenario, F the updated surface fluxes conditions, R the updated river runoff conditions, and A the updated Atlantic hydrography conditions. The sensitivity to each of the ocean model boundary forcings can be assessed through the "one to one" comparison among the simulations A2, A2-F, A2-RF, and A2-ARF. The comparison between A2-ARF, A1B-ARF, and B1-ARF allows to assess the uncertainty related to the choice of the GHG emission scenario. The latest IPCC report provides more recent GHG concentration scenarios (Representative Concentration Pathways RCPs; IPCC 2013), but to date there is no regionalized climate change model for the Mediterranean Sea under RCP scenarios. Monthly outputs of the ocean model were used to calculate seasonal and decadal climatologies of mean SST, mean SSS, and mean σSST following the WOA13 mode (Levitus et al. 2013) for two future 30-year periods: 2020-2049 and 2069-2098. Similarly, monthly outputs for the baseline period were used to construct additional climatologies of mean SST, SSS and σSST. These were used to compute the modelled climatological anomalies for the three environmental predictors. The modelled climatological anomalies were added to the baseline in situ climatologies to obtain future fields of SST, SSS, and σSST that are corrected for the bias between NEMOMED8 outputs and the observations. Therefore, the ENMs that were calibrated on observational climatologies were not directly projected on modelled data [START_REF] Hattab | Towards a better understanding of potential impacts of climate change on marine species distribution: a multiscale modelling approach[END_REF]. The final climatologies were then used to project the habitat suitabilities of the 106 species in 2020-2049 and 2069-2098 using the ENMs (Fig. 1). See Supplementary material Appendix 3, Table A3, for a comparison of NEMOMED8 outputs against WOA13 observations, and a summary of the climatic anomalies used for predicting species habitat suitabilities in 2020-2049 and in 2069-2098. Environmental niche modelling and uncertainty due to species prevalences To investigate the uncertainty due to the choice of the ENM, we used ten algorithms that cover the complexity range of the commonly-used statistical methods [START_REF] Merow | What do we gain from simplicity versus complexity in species distribution models?[END_REF]): three regression-based models: Generalized Linear Model (GLM), Generalized Additive Model (GAM), Multivariate Adaptive Regression Splines (MARS); one classification-based model: Flexible Discriminant Analysis (FDA); two tree-based models: Random Forest (RF), Classification Tree Analysis (CTA); and three machine learning models: Boosted Regression Trees (BRT), Artificial Neural Networks (ANN), and Maximum entropy (MAXENT; [START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF]). These nine models require either presence/absence (P/A) data or presence-background (P/B) data (for MAXENT, [START_REF] Yackulic | Presence-only modelling using MAXENT: when can we trust the inferences?[END_REF][START_REF] Guillera-Arroita | Maxent is not a presence-absence method: a comment on Thibaud et al[END_REF]). An additional presence-only (P/O) ENM was also used: Surface Range Envelope (SRE, equivalent to the Bioclim model; [START_REF] Busby | BIOCLIM-a bioclimate analysis and prediction system[END_REF]. For the nine algorithms requiring P/A (or P/B) data, pseudo-absences (psA) were randomly generated after defining both environmental and spatial weighting [START_REF] Engler | An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data[END_REF][START_REF] Hengl | Spatial prediction of species' distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging[END_REF], to place them in regions of lowest environmental suitability and far from the known presences [START_REF] Hattab | Towards a better understanding of potential impacts of climate change on marine species distribution: a multiscale modelling approach[END_REF]. The method employed to generate pseudo-absences strongly impacts ENMs outputs, and should be chosen in light of the species' ecological characteristics [START_REF] Chefaoui | Assessing the effects of pseudo-absences on predictive distribution model performance[END_REF]VanDerWal et al. 2009;[START_REF] Barbet-Massin | Selecting pseudo-absences for species distribution models: how, where and how many?[END_REF]. Zooplankton is composed of ectotherms whose population dynamics are tightly coupled to climate [START_REF] Hays | Climate change and marine plankton[END_REF][START_REF] Richardson | In hot water: zooplankton and climate change[END_REF][START_REF] Beaugrand | Applying the concept of the ecological niche and a macroecological approach to understand how climate influences zooplankton: advantages, assumptions, limitations and requirements[END_REF], and whose individuals are passively dispersed over very large spatial scales in relatively short time periods [START_REF] Jönsson | The timescales of global-surface ocean connectivity[END_REF]. Building on the results of [START_REF] Chefaoui | Assessing the effects of pseudo-absences on predictive distribution model performance[END_REF], we chose to employ the following method to randomly draw psA.

Firstly, the reverse environmental weighting was based on a P/O ecological niche factor analysis (ENFA; [START_REF] Hirzel | Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data?[END_REF]. This multivariate ordination technique allocates a degree of similarity (ranging between 0 and 100) to each cell (at the global scale for ENM calibration) by comparing the species environmental envelope to the environmental conditions (using the three selected predictors). It therefore provides an habitat suitability index (HSI) that was used together with the distance to presences to produce the following probability distribution τ [START_REF] Hengl | Spatial prediction of species' distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging[END_REF]:

𝛕 𝑥 = [ 𝑑 𝑥 + (100 -𝐻𝑆𝐼 𝑥 ) 2 ]
2 with dx being the distance to presences normalized by the maximum distance, and pseudo-absences are increasingly drawn at the edge of low HSI values because of the squared term. τ was used as a probability density function to randomly simulate pseudo-absences in unsuitable habitats and further away from known presences. We argue that this method allows to draw the psA in the environment that is theoretically reachable for the studied species (as recommended by [START_REF] Barve | The crucial role of the accessible area in ecological niche modeling and species distribution modeling[END_REF] because at decadal time scales, planktonic communities are well-connected anywhere in the ocean, as recently demonstrated by [START_REF] Jönsson | The timescales of global-surface ocean connectivity[END_REF]. The chosen number of psA also impacts ENMs projections [START_REF] Barbet-Massin | Selecting pseudo-absences for species distribution models: how, where and how many?[END_REF]Meynard et al. 2013). Considering that the regional occurrence data do not allow to approximate each species' prevalences in the Mediterranean Sea (because of the low surface coverage of scientific cruises), we chose to draw a varying number of psA for each species. This allowed to investigate the relative amount of uncertainty related to species prevalence. Different levels of species prevalences were considered by increasing the ratio of drawn pseudo-absences over the number of presences (which was kept constant at the number of observations): 1 (npsA = nP), 0.67 (npsA = 1.5*n), 0.5 (npsA = 2*nPres), 0.1 (npsA = 10*nP), and 0.02 (npsA = 50*nP). These 5 prevalence levels were finally used in the variance analysis (see below) as an additional uncertainty factor, together with ENM choice, SRES, BF and the ensuing interaction terms. To account for the stochasticity in the psA generation process, 10 different psA realisations were carried out for each species (so each species presents 50 P-psA datasets). For every species and every ENMs algorithm, the 50 P/psA datasets were split into a calibration set (80%) and a testing set (20%). Models were evaluated according to the True Skill Statistic (TSS) criterion [START_REF] Allouche | Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)[END_REF]) with a threefold cross-validation. See Supplementary material Appendix 4 and 5, Fig. A4-5, for the species and the ENMs evaluation scores. Mapping future shifts in species assemblages P/A distribution maps were generated from the habitat suitabilities over the Mediterranean Sea for each species (Fig. 1), and for each combination of ENM (n=10), cross-validation runs (n=3), prevalence level (n=5), pseudo-absence realisation (n=10) and hydrodynamical model's boundary forcings (n=6). This was done for the two future time periods. Species assemblages (i.e. the sum of species modelled as present or absent in each cell grid) were built by stacking all the species P/A maps (according to the identity of the above-mentioned parameters). Species assemblages for the baseline period were simulated in the same manner, but for a single set of environmental conditions (WOA13 baseline climatologies).

By comparing the present to the future assemblages, indices of community shifts related to α and ß diversity were computed within each grid cell: difference in species richness (∆SR), and Jaccard's dissimilarity index (ßjac). For each period, the sum of the species modelled as present was used to estimate species richness. ∆SR was computed as the difference between future species richness and the baseline species richness, and was used to assess whether climate changes would promote or weaken copepod α diversity. Pairwise Jaccard's dissimilarity index (ranging between 0 and 1) is given by: ß 𝑗𝑎𝑐 = 𝑏 + 𝑐 𝑎 + 𝑏 + 𝑐 where a is the number of species present at both time periods, b is the number of species present in the baseline period only, and c is the number of species present in the future time period only. It was used to assess the temporal changes in species assemblages composition. In addition, by applying the framework of [START_REF] Baselga | betapart: an R package for the study of beta diversity[END_REF], ßjac values were decomposed into its two additive components: nestedness (ßjne) and turnover (ßjtu). The latter expresses species replacement without the influence of ∆SR between time steps as follows:

ß 𝑗𝑡𝑢 = 2min (𝑏, 𝑐) 𝑎 + 2min (𝑏, 𝑐)
The difference between ßjac and ßjtu expresses the nestedness component ßjne that accounts for the amount of dissimilarity that is due to differences in richness (ßjne = ßjac -ßjtu). It is formulated as follows:

ß 𝑗𝑛𝑒 = max(𝑏, 𝑐) -min (𝑏, 𝑐) 𝑎 + 𝑏 + 𝑐 × 𝑎 𝑎 + 2min (𝑏, 𝑐) Moreover, the ratio between ßjne and ßjtu (ßratio = ßjne/ ßjac) was computed and related to ∆SR in order to understand which component has the highest contribution to future changes in the species assemblages [START_REF] Dobrovolski | Climatic history and dispersal ability explain the relative importance of turnover and nestedness components of beta diversity[END_REF][START_REF] Albouy | Combining projected changes in species richness and composition reveals climate change impacts on coastal Mediterranean fish assemblages[END_REF]. For instance, a ßratio value greater than 0.5 indicates that the observed dissimilarity is driven by nestedness, which can occur under both increases or decreases in richness. Alternatively, a value lower than 0.5 indicates the shift is driven by species replacement. When ∆SR is positive and turnover drives the dissimilarity in the assemblage, it means that climate change promotes diversity by creating favourable conditions for species that were not present previously. When ∆SR is positive and nestedness drives the dissimilarity, climate change promotes richness while not changing the initial assemblage composition. Processing novel climate conditions To identify where niches are projected into novel combinations of environmental predictors [START_REF] Zurell | Predicting to new environments: tools for visualizing model behaviour and impacts on mapped distributions[END_REF][START_REF] Mesgaran | Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models[END_REF], cells where ENMs extrapolation occurs were determined according to the species Multivariate Environmental Similarity Surface (MESS; [START_REF] Elith | The art of modelling range-shifting species[END_REF]. It enables to evaluate how dissimilar the environment used for projecting the ENMs is from the species native range (i.e. reference envelope used for ENMs calibration). The MESS maps present both positive and negative values, the later indicating the cells where novel climate conditions occur. Since the maps depend on the calibration dataset, MESS values were computed for each species and for every combination of prevalence level and future circulation model forcing conditions (i.e. SRES and BF, thus 30 maps per species). The psA realisations or the ENMs' cross-evaluation runs do not significantly affect the MESS, so they were not taken into account when identifying novel climate conditions. Within each cell, the species presenting negative MESS values were discarded from the assemblage's species list, and changes in α and ß diversity were computed without them. The ratio of species being discarded was computed and mapped for each prevalence and NEMOMED8 configuration (data not shown) in order to assess where non analog climates have the strongest impact. In the cases where novel conditions do not allow to predict changes in copepod diversity (because all species had to be discarded from the assemblages), the corresponding cells were left blank in the consensus projections and were ignored in the subsequent variance analysis (see below). All analyses were also carried out while ignoring MESS outputs, but as it did not alter the main results, only those obtained when accounting for non analog climates are presented. Density distributions in future Mediterranean environmental ranges were visually compared to the current ones to identify the combination of predictors that may lead to the appearance of novel climate conditions.

Consensus projections and partitioning sources of uncertainties

To identify the dominant patterns in assemblages shifts, consensus maps were drawn for each of the calculated indices by averaging their values within each cell and across every model run. The associated standard deviation was used to assess variability between runs as well as its spatial distribution over the basin. Three-way ANOVA was used to assess the contributions of the different uncertainty sources to the overall variability in ∆SR [START_REF] Diniz-Filho | Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change[END_REF][START_REF] Garcia | Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates[END_REF]): ENMs, emission scenario (SRES), circulation model boundary forcing (BF), species prevalence, and the associated interaction terms. BF and SRES were treated separately in the variance analyses because not all BF have been coupled with every SRES. The relative amount of uncertainty attributable to the sources was estimated as the proportion of sum of squares with respect to the total sum [START_REF] Diniz-Filho | Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change[END_REF][START_REF] Garcia | Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates[END_REF]). The three-way ANOVA was performed in a linear mixed-effect model framework that allowed to account for the variation in effect size produced when iterating 3 crossvalidation runs within 10 psA realisations. To further examine how projections differ under combinations of ENMs and ocean model configurations, values of ∆SR were averaged to obtain consensus projections for each combination of BF/SRES and ENMs (e.g. SRE-A2, MAXENT-B1ARF, GLM-A2F etc.). Similarity between these projections were assessed by analysing their loadings on the first principal component of an unscaled Principal Component Analysis (PCA; Legendre and Legendre 2012). All statistical analyses were conducted under the R environment (R Core Team 2014) using the biomod2 package [START_REF] Thuiller | biomod2: Ensemble platform for species distribution modeling[END_REF] for ENMs and the betapart package [START_REF] Baselga | betapart: an R package for the study of beta diversity[END_REF] for biodiversity indices' estimates.

Results

Consensus patterns of α and ß diversity show that species nestedness with decreases in richness is the main pattern driving the dissimilarity between present and future copepod assemblages (∆SR < 0 and ßratio < 0.5; Fig. 2). Indeed, 91.63% and 95.85% of the cells exhibit a decline in species richness by 2020-2049 and 2069-2098 respectively. Mean ∆SR is -2.13 for the mid-century and -5.13 for the end-ofcentury period (mean SR for the baseline period being 75.83). The projected patterns are spatially structured. The largest decreases in richness are observed in the eastern Mediterranean (Fig. 2c-d). The few cells that show positive ∆SR (8.37% by 2020∆SR (8.37% by -2049;;4.15% by 2069-2098) are located in the northwestern area (Gulf of Lions, Ligurian Sea), the Alboran Sea, and the northernmost parts of the Adriatic and the Aegean Seas (Fig. 2c-d), which are characterised by milder temperature and lower salinity waters, both now and in the future. Gains and losses in richness are mainly associated with nestedness. Most of Mediterranean cells show mean ßratio values higher than 0.5 for 2020-2049 (total mean ßratio = 0.63). By 2069-2098, 98,31% of the cells exhibit a higher contribution of nestedness than turnover in assemblages' dissimilarity (total mean ßratio = 0.77). Gains in species richness associated with turnover only appear in the Alboran and Marmara Seas by 2069-2098 (Fig. 2d). As shown on Fig. 2c,d, non analog conditions occur in the central Aegean Sea and the easternmost part of the Levantine basin by 2069-2098, due to non analog SSS values (future SST and σSST were always within the range of the calibration data). The entire Levantine basin is actually affected by novel salinity conditions, as nearly 50% of the species had to be discarded from the assemblages, depending on the considered model forcings and prevalences (data not shown). Standard deviations of ∆SR were computed within each cell to assess its variability across runs (Fig. 3). The amplitude of the predicted losses in species richness scales with its variability (R 2 = 0.54, P-value < 2.2x10 -16 ), which is also true for the cells that exhibit positive ∆SR value (R 2 = 0.59, P-value < 2.2x10 -16 ). The strongest differences in richness and assemblage dissimilarity rates occur in the eastern part of the Mediterranean basin. Uncertainties across model runs are therefore larger for these regions. Three-way ANOVA based on linear mixed-effects models helped disentangling the relative contribution of different sources of uncertainty. For both future time periods, the choice of the ENMs explains most of the variability in projections of ∆SR (Fig. 4a,b). The same result was obtained for the dissimilarity indices, and are therefore not presented. On average, the relative contribution of ENMs to the total sum of squares increases from 72.68% in 2020-2049 to 74.14% in 2069-2098, when accounting for the choice of BF. When accounting for SRES rather than BF, ENMs' average contribution decreases from 82.25% in 2020-2049 to 68.66% in 2069-2098. The second most important contributing factors are the interaction terms, indicating divergent ENMs responses according to different BF and SRES configurations (23.16% in 2020-2049and 17.96% in 2069-2098 when accounting for BF;14.44% in 202014.44% in -204914.44% in and 19.81% in 206914.44% in -2098 with SRES) with SRES). The choice of the BF, of the SRES, or of the prevalence level, always show mean relative contributions lower than 6%. Predicted shifts in species assemblages are mainly driven by the ENMs and their interactions with either the BF or the SRES. Therefore similarities across the ENMs' average ∆SR projections were examined through a PCA. The first two Principal Components (PC1 and PC2) explain 87.73% of total variance. However the SRE predictions' relative contribution to PC1 and PC2 reaches 48.85% (Fig. 5a). Since all PCs are orthogonal to one another and the P/A ENMs are contributing to nonsignificant components, SRE projections should not be averaged together with the other methods in an ensemble forecasting framework. When averaging SRE projections, higher consensus values of negative ∆SR and nestedness are obtained (Fig. 5b). Mean ∆SR in the Mediterranean Sea for 2069-2098 decreases to -11.21 while the mean ßratio increases up to 0.95. The proportion of cells exhibiting strictly positive ∆SR slightly increases from 4.15% to 7.85%. A second PCA was performed with the P/psA models' projections only. Again, the loading coordinates are used to explore their degree of similarity in their ∆SR forecasts (Fig. 6). Examining the maps of mean ∆SR for each P/psA ENM revealed that the loadings along PC1 (64.00% of total variance) are to be interpreted as an increasing gradient in the predicted species loss. All nine p/psA ENMs present the same spatial pattern in species richness increases, while the range of their predicted decreases vary. ENMs with higher loadings on PC1 are those that predict greater declines in richness (Fig. 6). MARS projections are the most pessimistic regarding ∆SR values, with all its configurations having higher loadings than the other ENMs. The more pessimistic models include MARS, CTA and ANN (in order of pessimism). The least pessimistic forecasts are produced by the remaining models: MAXENT, RF, FDA, GAM, GBM and GLM. The ENMs predicting the most negative values of ∆SR are the ones with the most variability in loadings along PC1 (MARS followed by CTA and ANN). A significant correlation was found between the ENMs average loadings and the range of loadings (the distance between the least conservative configuration (A2ARF) and the most conservative one (B1ARF) (R 2 = 0.82, P-value < 0.001). This indicates the most pessimistic ENMs are also the most sensitive to the choice of the BF and SRES.

Discussion

In the present study, we explored the uncertainties in future composition changes in Mediterranean copepod assemblages. Different combinations of species prevalences, ENMs, circulation model boundary forcings, and emission scenarios were used to forecast shifts in species assemblages for the 21 st century. Our results have implications for studies aiming to forecast changes in habitat suitability for planktonic species with ENMs, from both technical and theoretical perspectives. Main sources of uncertainties Our results are in agreement with previous studies [START_REF] Diniz-Filho | Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change[END_REF][START_REF] Buisson | Uncertainty in ensemble forecasting of species distribution[END_REF][START_REF] Garcia | Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates[END_REF]) that documented the variability in forecasts related to the differences between ENMs outputs (Fig. 4). The main divergence occurs between the SRE model, the sole P/O ENM considered here, and the other ENMs that were all based on P/psA (or P/B for MAXENT) data (Fig. 5). Dissimilarity between present and future assemblages was much greater when predicted by the SRE than with the other ENMs, but nestedness in species losses remained the dominant pattern driving the dissimilarity. Previous studies comparing P/O to P/A (or P/psA and P/B) methodologies consistently found SRE models to underestimate species ranges, which translated into more pessimistic forecasts of changes in richness [START_REF] Pearson | Model-based uncertainty in species range prediction[END_REF][START_REF] Hijmans | The ability of climate envelope models to predict the effect of climate change on species distributions[END_REF]. The SRE uses percentile distribution to draw a rectangular "box" (a range envelope) around the presence data in environmental space [START_REF] Busby | BIOCLIM-a bioclimate analysis and prediction system[END_REF]. SRE projections depend on the overlap between the defined envelope and the future conditions, whereas P/A models (e.g. GLM-derived response curves) allow to recognise favourable areas even beyond the range of the observed presences. The large discrepancies between the SRE and the P/psA models are also due to the methodology chosen to generate psA. Environmental and spatial weightings were applied such that psA were assigned in unsuitable areas as far as possible from known presences [START_REF] Hengl | Spatial prediction of species' distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging[END_REF]. Therefore the environmental range captured by the SRE is much narrower than the one captured by the other models. The similarity between correlative ENMs along the first component of a PCA can be related to the similarity between the models' algorithms (Fig. 6). Regression-based methods forecasts are quite similar to one another (GLM, GAM, FDA). The same is evidenced for complex classification-trees (GBM, RF). GBM predictions are similar to regression models because the algorithm used here is equivalent to boosted regression trees [START_REF] Ridgeway | The state of boosting[END_REF][START_REF] Friedman | Greedy function approximation: a gradient boosting machine[END_REF]. MAXENT projections are similar to both GBM's and regression-based models', as its core algorithm contains a machine-learning piece [START_REF] Elith | A statistical explanation of MaxEnt for ecologists[END_REF], and it may be close to GLMs depending on the tuning of its parameters [START_REF] Guillera-Arroita | Maxent is not a presence-absence method: a comment on Thibaud et al[END_REF][START_REF] Halvorsen | Opportunities for improved distribution modelling practice via a strict maximum likelihood interpretation of MaxEnt[END_REF]. MARS-based projections forecasted the greatest rates of species loss among P/psA models. It may seem surprising that MARS projections were not closer to regressionbased models since they rely on a non-parametric regression procedure that is often seen as an extension of GLMs and GAMs [START_REF] Friedman | Multivariate Adaptive Regression Splines[END_REF][START_REF] Franklin | Mapping species distributions: spatial inference and prediction[END_REF]. The greater species losses predicted by MARS models could be due to the first-order interactions that were enabled between the predictive variables (they were disabled for the other models). The interaction terms between ENMs and BF, and/or the choice of the SRES, were identified as the second uncertainty-generating factor (Fig. 4). The BF can have an important but very local impact, meaning it is restricted to the few cells located near the forcing fluxes (e.g. the Alboran Sea for the Atlantic hydrography, and the Northern Aegean sea for the river runoff, since the Black Sea is treated as a river in this circulation model). The sensitivity to the choice of the SRES slightly increases between the two future periods (Fig. 4b) consistently with the response of the physical variables in climate scenarios. Due to the long lifetime of anthropogenic CO2 in the atmosphere, the magnitude of oceanic response is smaller in the first half of the 21 st century. Our results have important implications for interpreting previous studies that have predicted shifts in habitat suitability for fishes over the Mediterranean continental shelf [START_REF] Rais Lasram | The Mediterranean Sea as a "cul de sac" for endemic fishes facing climate change[END_REF][START_REF] Albouy | Combining projected changes in species richness and composition reveals climate change impacts on coastal Mediterranean fish assemblages[END_REF]. Their results were all based on species habitat suitabilities that were estimated through a weighted average consensus across seven ENMs, including the SRE. Combining the SRE in ensemble predictions will lead to less conservative changes (i.e. higher predicted rates of species loss) than excluding it based on the evaluation criterion score (which is the most common criterion for weight attribution). From the present results, we argue that P/O and P/psA (or P/B) models outputs should be compared (e.g. with a PCA) before being mixed together as they rely on different data and assumptions, and are likely to model different components of the species niche [START_REF] Brotons | Presence-absence versus presence-only modelling methods for predicting bird habitat suitability[END_REF][START_REF] Sillero | What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods[END_REF]. Indeed, the PCA identified these two types of methods as two different "visions" of the species niches, both leading to two different scenarios of biodiversity change. Guidelines for modelling zooplankton with ENMs Modelling habitat suitabilities through P/O or P/A methods holds different implications depending on the ecological properties of the species [START_REF] Hernandez | The effect of sample size and species characteristics on performance of different species distribution modeling methods[END_REF][START_REF] Tsoar | A comparative evaluation of presence-only methods for modelling species distribution[END_REF]Jimenez-Valverde et al. 2008). One group of methods might be better suited than another according to the taxa of interest. [START_REF] Brotons | Presence-absence versus presence-only modelling methods for predicting bird habitat suitability[END_REF] suggested that P/O models may be more accurate for species that are far from equilibrium with their environment. For several reasons, we argue that correlative models, based on environmentally-weighted psA, are appropriate for modelling zooplanktonic taxa. First, they are short-lived ectotherms whose physiology and population dynamics are tightly coupled with climate variability [START_REF] Hays | Climate change and marine plankton[END_REF][START_REF] Richardson | In hot water: zooplankton and climate change[END_REF]). [START_REF] Sunday | Global analysis of thermal tolerance and latitude in ectotherms[END_REF]2012) showed that the spatial ranges of marine ectotherms closely match their thermal tolerance limits. Most of the zooplankton are not commercially exploited, so in absence of direct human harvesting they are likely to be near equilibrium with the environment, and their geographical distribution is a good indicator of their abiotic preferences.

Copepods exhibit very broad latitudinal ranges (Razouls et al. 2005(Razouls et al. -2016) ) which result from both wide environmental preferences and huge dispersal potential due to turbulent oceanic circulation [START_REF] Jönsson | The timescales of global-surface ocean connectivity[END_REF]. However, the potentially worldwide distributions of these organisms [START_REF] Finlay | Global dispersal of free-living microbial eukaryote species[END_REF]Cermeño and Falkowski 2009;[START_REF] De Vargas | Eukaryotic plankton diversity in the sunlit ocean[END_REF], combined with spatially (and temporally) biased data sets, limit the ENMs' capacity to link species occurrences to environmental predictors properly. In consideration of the datasets attributes (large spatial autocorrelation due to sampling biases coarse resolution etc.), future niche modelling studies should not focus on using complex ENM algorithms. Since they are likely to fit spurious relationships, or natural stochasticity (visible through noisy response curves), and thus to be less transferable in time and space (Jimenez-Valverde et al. 2008;Heinanen et al. 2012;[START_REF] Merow | What do we gain from simplicity versus complexity in species distribution models?[END_REF]. We advocate that P/psA methods applied to zooplankton should be coupled with environmentally-weighted simulations of pseudo-absences because (i) absence data are impossible to ascertain in the plankton realm, and (ii) marine ectotherms are at equilibrium with their environments. Multiple methods of model evaluation and comparison should be considered [START_REF] Brun | The predictive skill of species distribution models for plankton in a changing climate[END_REF], such as niche transferability tests in space and time (niche hindcasting), or comparison with mechanistic models outputs and/or response curves from laboratory experiments. Habitat suitability estimates along environmental gradients will be of great use for marine ecologists as they can easily be coupled with functional traits data (Benedetti et al. 2016) to better explore trait biogeography, and their link with ecosystem functioning [START_REF] Albouy | Projected impacts of climate warming on the functional and phylogenetic components of coastal Mediterranean fish biodiversity[END_REF]. Future shifts in zooplankton surface assemblages Previous studies modelling climate change impacts on zooplankton have generally focused on changes of habitat suitability or species richness [START_REF] Reygondeau | Future climate-driven shifts in distribution of Calanus finmarchicus[END_REF][START_REF] Beaugrand | Future vulnerability of marine biodiversity compared with contemporary and past changes[END_REF]. Here, we extended the use of planktonic niche models for measuring ß diversity. We predicted that climate change might lead to a loss of copepod diversity throughout most of the surface of the Mediterranean Sea (although some northern regions exhibit increases in species richness), with nestedness as the main pattern driving the dissimilarity between present and future assemblages (Fig. 2). Our results imply that future copepod assemblages in most of the surface Mediterranean Sea will be composed of less species, all remnants being present in the initial assemblages. Areas of potential future increases in copepod diversity are restricted to the coldest regions: the Gulf of Lions, the Alboran Sea and the northern Adriatic and Aegean Seas. Again, our predictions imply that climate change might make these areas suitable for new species, without removing the ones present in the initial assemblages. This pattern may be explained by the northward shifts of temperature and salinity sensitive species towards the Gulf of Lions, and the northern Adriatic and Aegean Seas. These results are in agreement with the rate of climate change estimated by [START_REF] Burrows | Geographical limits to species-range shifts are suggested by climate velocity[END_REF], who tracked SST isotherms modelled by a global ocean model that was forced by the RCP 8.5 emission scenario. Nestedness was also the dominant pattern in forecasted changes in Mediterranean coastal fish assemblages [START_REF] Albouy | Combining projected changes in species richness and composition reveals climate change impacts on coastal Mediterranean fish assemblages[END_REF]. However, the rates of changes in species richness we found for copepods are arguably much lower. In addition, [START_REF] Albouy | Combining projected changes in species richness and composition reveals climate change impacts on coastal Mediterranean fish assemblages[END_REF] predicted higher proportions of cells displaying increases in species richness, and in more diverse locations throughout the basin. They predicted increases in fish diversity in the central Adriatic, the central and northern Aegean, and the coastal Levantine. Meanwhile, we predict higher diversity losses in these regions, that will experience the strongest rates of warming and saltening [START_REF] Adloff | Mediterranean Sea response to climate change in an ensemble of twenty first century scenarios[END_REF]. The fact that planktonic copepods present broader environmental niches, compared to coastal fishes, may explain this discrepancy. The considered fishes include endemic species with much narrower thermal amplitudes [START_REF] Rais Lasram | The Mediterranean Sea as a "cul de sac" for endemic fishes facing climate change[END_REF]) than planktonic species characterized by global scale distributions. This is in line with the results from Mediterranean coastal time series that demonstrated the strong resilience of copepod communities to highly variable conditions, over pluridecadal scales (Siokou-Frangou et al. 2010;[START_REF] Mazzocchi | Zooplankton associations in a Mediterranean long-term time-series[END_REF]. The comparison between our results and previous studies conducted on other components of Mediterranean marine ecosystems implies that different components of the pelagic food web may not respond to climate changes in unique ways. Consequently, predicting climate-induced shifts in ecosystems requires to account for multiple trophic levels. Limitations A notable limitation of our study is that we were unable to test the relative contribution of the choice of the regional circulation model because of data availability. Previous studies have shown this factor to be the second-most important in explaining variability across predictions, persistently ranking in front of interaction terms [START_REF] Diniz-Filho | Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change[END_REF][START_REF] Buisson | Uncertainty in ensemble forecasting of species distribution[END_REF][START_REF] Garcia | Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates[END_REF]. Consequently, it is reasonable to believe that it could represent a second-order uncertainty factor in our case as well. But it is not likely to overstep ENMs as the major source of uncertainty [START_REF] Garcia | Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates[END_REF]. It is noteworthy that different regional ocean circulation models generally agree on the future impacts of climate change on the overall Mediterranean circulation and physical conditions [START_REF] Dubois | Future projections of the surface heat and water budgets of the Mediterranean Sea in an ensemble of coupled atmosphere-ocean regional climate models[END_REF][START_REF] Gualdi | The CIRCE simulations: regional climate change projections with realistic representation of the Mediterranean Sea[END_REF]). So we are confident our consensus patterns of shifts might not drastically change when switching to another regional circulation model. Additionally, zooplankton is known to perform diel vertical migrations that can span several hundreds of meters depending on the species [START_REF] Roe | Observations on the diurnal vertical migrations of an oceanic animal community[END_REF][START_REF] Ohman | The demographic benefits of diel vertical migration by zooplankton[END_REF]). So it is crucial to note that our habitat suitability predictions are only valid for the surface waters of the Mediterranean Sea. Changes in the species surface habitat suitability could lead to horizontal spatial range shifts as the species track optimal growth conditions [START_REF] Sunday | Thermal tolerance and the global redistribution of animals[END_REF][START_REF] Poloczanska | Global imprint of climate change on marine life[END_REF], but it could also trigger a deepening of their distribution [START_REF] Dulvy | Climate change and deepening of the North Sea fish assemblage: a biotic indicator of warming seas[END_REF]. The exact depth of the species occurrences is difficult to establish for each observation which limits the development of three-dimensional niche models [START_REF] Bentlage | Plumbing the depths: extending ecological niche modelling and species distribution modelling in three dimensions[END_REF]. Still, we point out that the majority of the occurrence data used here comes from surface layers (0-200m depth), and that only a few of the studied copepod species do perform large vertical migrations in the Mediterranean basin [START_REF] Di | Vertical zonation patterns for Mediterranean copepods from the surface to 3000 m at a fixed station in the Tyrrhenian Sea[END_REF]Benedetti et al. 2016).

Supplementary material (Appendix EXXXXX at <www.oikosoffice.lu.se/appendix>). Appendix 1-2.

Figure captions

Fig. 1) Schematic summary of the analytical framework of the study. For each 106 species, environmental weighting and spatial buffering are used to generate 10 P/psA datasets and each is used to calibrate 10 different ENMs. The niche models are used to project species habitat suitabilities in present and future (2020-2049; 2069-2098) time periods at the regional scale. Different configurations (SRES and BF) of the regional circulation model are used to investigate their relative contribution to uncertainties. Habitat suitability maps are transformed into binary (P/A) maps (maximizing TSS threshold criterion) which are used to generate the species assemblages. Differences in species richness (∆SR) and composition (ßdiv indices) are computed by comparing the assemblages of 2020-2049 and 2068-2098 to the 1965-1994 assemblages. Fig. 2) Changes in species richness and composition between the baseline period (1965-1994) and 2020-2049 (a,c) and 2069-2098 (b,d) for the copepod assemblages of the surface Mediterranean Sea. Changes in species composition are quantified using Jaccard's dissimilarity index ßjac (a,b) and the ßratio index (c,d). Numbers in (d) indicate the main Mediterranean sub-basins: 1) Alboran Sea 2) Algerian and Tunisian waters 3) Gulf of Lions 4) Ligurian Sea 5) Tyrrhenian Sea 6) Sicilian Strait 7) Tunisian and Libyan shelf waters 8) Ionian Sea 9) Adriatic Sea 10) Levantine Sea 11) Aegean Sea. Fig. 3) Linear relationships between the average value of predicted ∆SR and the associated standard deviation, for the Mediterranean cells exhibiting both losses and increases in richness. Mean ∆SR values come from all ENMs predictions for the 2069-2098 period. The two linear model statistics are given in the figure. Fig. 4) Proportion of the total sum of square attributed to the following source of uncertainties: (a) ENM, GHG emission scenario (SRES), species prevalence, and associated interaction terms, and (b) ENM, boundary forcings, species prevalence and associated interaction terms, for the two future time periods (2020-2049; 2069-2098). and the future (2069-2098) time periods, based on SRE models only, quantified using the Jaccard's dissimilarity index ßjac. Fig. 6) ENMs loadings (excluding SRE) on the first Principal Component (72.36% of explained variance) of an un-scaled PCA based on their mean projections of ∆SR for the 2069-2098 period. Colors and shapes describe the forcing configuration of the circulation model (BF and SRES). PC1 represents a gradient in predicted species losses (negative ∆SR), ENMs with higher loadings are the ones predicting greater rates of species loss. Within-ENM spreading of the loadings along PC1 indicates the method sensitivity to the choices of the SRES/BF (i.e. the strength of interactions). Supplementary material Appendix Supplementary Table A1 -References used to build the copepod species list, with the associated number of occurrences, the covered subregion of the Mediterranean Sea and the time period encompassing the occurrences. Supplementary Table A2 -Copepod species list and corresponding numbers of global, and regional, presence cells (after re-sampling on WOA13's grid, 0.25x0.25° resolution). Supplementary Table A3 -Summary of the regional ocean circulation model (NEMOMED8) outputs in terms of SST, σSST and SSS for each of its configurations (i.e. combination of SRES and BF). Differences against WOA13 climatologies and modelled anomalies are also given. Supplementary Figure A4 -Species average TSS scores (plus the associated confidence intervals). ENMs were cross-validated by dividing each P/psA dataset into a training set (80% of the data) and a testing set (remaining 20%). Supplementary Figure A5 -ENMs average TSS (plus the associated confidence intervals). ENMs were cross-validated by dividing each P/psA dataset into a training set (80% of the data) and a testing set (remaining 20%). Supplementary material Appendix, Tables captions Table A1) References used to build the regional occurrence database and the copepod species names list. The associated number of occurrences, the covered subregion of the Mediterranean Sea, and the time period covering the occurrences are given. Table A2) Copepod species list and corresponding numbers of global, and regional, presence cells (0.25x0.25° resolution). The species names list was obtained as follows: several regional (i.e. Mediterranean) datasets were merged (see Suppl. Table S1) to build a regional occurrence database which recorded 361 different species; then, only the species with at least 50 different records (meaning at least 50 different observation points in time and space) were kept in order to get rid of species rarely occurring in the basin of interest (n=193); finally, in order to avoid truncated response curves (Thuiller et al. 2004) as all species are not proven endemic to the Mediterranean Sea (Razouls et al. 2005(Razouls et al. -2016)), the species name list was further restricted to species presenting several occurrences in other oceanic basins on the OBIS online database (assessed on the 11 December 2014). It consists of the species that are the most commonly sampled in the surface (0-300m depth) of the Mediterranean Sea (Siokou-Frangou et al. 2010;Mazzocchi et al. 2014;Benedetti et al. 2016). The occurrences of the final 106 chosen species were re-sampled on the WOA13 grid cell (0.25x0.25° resolution; Levitus et al. 2013). The final number of global, and regional, grid cells with presences are given here. In the regional database, species can present several occurrences belonging to a single 0.25x0.25° cell (because of records that are spatially close), so that their final number of regional presence cells is less than 50.

Table A3) Summary of the regional ocean circulation model (NEMOMED8) outputs in terms of SST, σSST and SSS for each of its configurations (i.e. combination of SRES and BF). Differences against WOA13 climatologies and modelled anomalies are also given. For the baseline period , average values of SST, σSST and SSS come from WOA13 climatologies and the two historical configurations of NEMOMED8 (HIF and HIS-F, see [START_REF] Adloff | Mediterranean Sea response to climate change in an ensemble of twenty first century scenarios[END_REF] for full details). Mean differences between NEMOMED8 and WOA13 outputs are shown. For the two future time periods (2020-2049; 2069-2098), mean values are computed on the final climatologies (i.e. the ones used to project the ENMs) that result from the addition of NEMOMED8 anomalies (as shown in the table) on WOA13 climatologies. Said anomalies were computed by subtracting historical climatologies (based on HIS runs for the A2 configuration, and HIS-F for all the others, [START_REF] Adloff | Mediterranean Sea response to climate change in an ensemble of twenty first century scenarios[END_REF] to the climatological future runs. Figure A4) Frequency distribution of the species' average TSS scores, per bins of 0.01 TSS values. The scores were computed with the maximum-threshold method. ENMs were cross-validated by dividing each P/psA dataset into a training set (80% of the data) and a testing set (remaining 20%). Figure A5) ENMs' average TSS scores (plus associated standard deviations), computed with the maximum-threshold method. ENMs were cross-validated by dividing each P/psA dataset into a training set (80% of the data) and a testing set (remaining 20%). References used in the Supplementary materials' captions:


Mieczysław A Kłopotek

What is the value of information -search engine's point of view

Keywords:

Within the domain of Information Retrieval, and in particular in the area of Web Search Engines, it has become obvious long time ago that there is a deep discrepancy between how the information is understood within computer science and by the man-in-the-street. We want to make an overview of ways how the apparent gap can be closed using tools that are technologically available nowadays.

The key to a success probably lies in approximating (by means of artificial intelligence) the way people judge the value of information.

Introduction

Information Science, especially Information Retrieval, and in particular Search Engines needs to measure information value for at least a couple of reasons:

document ranking in search engines -for typical queries, consisting of a few words, hundreds or even thousands matching documents are found, so to satisfy the user, one may use additional criteria for ranking, like the value of contained information filtering information according to one's interests (such as fresh information, information without advertisement, formulated in a simple language, etc.), -classification of information, -selection of a representative of a group of documents, -selection of information to store in a database search engine -necessary because of the enormity of the Internet as compared to the capacity of even the largest search engine databases From user's point of view, the value of information should be measured at least along the following dimensions:

quality -agreement with the state of the real world (positive for true, negative for false information) -utility -usefulness for the purposes of the user (positive if useful, negative, when harmful) -actuality -about the current state of the world or from the past -intelligibility -whether or not the receiver can capture the content of the message accessibility -whether or not the message can reach the user for whatever reasons, (e.g. whether or not it has been sent)

Now the big question is if we are capable to capture the value of information along these dimensions. The answer is: at least partially yes. The subsequent sections will be devoted to show how with current technology these measurements may be performed.

Befolre proceeding let us however point at difficulties when matching these user expectations. Measuring information has a long tradition in Computer Science. As early as in 1948 Shannon [START_REF] Shannon | A mathematical theory of communication[END_REF] laid foundations for development of information theory quantifying the information as the entropy of the source of signal which defined the minimal bandwidth through which a given information may pass. Shannon's entropy measure was also for years considered as an upper limit for compression that only the best compression algorithms could approach. In the recent time however a new perspective is opening. In the domain of lossless compression a new branch of visually lossless compression emerged [START_REF] Li | Block-based segmentation and adaptive coding for visually lossless compression of scanned documents[END_REF], bringing dozens of times higher compression rate than predicted by Shannon's information theory. With the advent of HTML and the separation of text and images also degrees of information compression far beyond original Shannon's concepts were achieved. Recently, the concept of excess entropy [START_REF] Dębowski | Excess entropy in natural language: Present state and perspectives[END_REF] has been coined to point at the fact that going beyond the pure statistical evaluation of the source of information (sender) by taking into account the structure (syntax), semantics, pragmatics and apobetics 1 we can achieve higher information levels than at the statistical level.

These sample developments point at two important points missed by the original proposal of Shannon: the significance of information receiver (like in the visually lossless compression) and the structure of information (like in HTML example). Hence the definition of information itself has been broadened beyond Shannon's concept and is understood as the content of a message sent from an information source (sender) to an information sink (receiver) increasing the state of knowledge of the latter. This imples that any information stems from an intelligent sender. And this is exactly the reason why the information value can be measured along the aforementioned dimensions in spote iof the fact that our computer technology is far from a through understanding of the real world -to measure information stemming from an intelligent sender we can apply artificial intelligence tools and methods as we will subsequently see.

1

The pragmatic level of information means the activity of the receiver intended/achieved by the sender via the message. The issue of apobetics has been probably first raised by Gitt [START_REF] Gitt | In the Beginning was Information (Am Anfang war die Information)[END_REF]. and it means the goal of the sender pursued when sending the message.

As mentioned, the quality of the information is the degree of compliance with the state of the real world, which a Web page describes (can be positive when the line and negative if it is inconsistent).

Can we really say how the information is consistent with the state of the real world? Not at this stage of development, but we can now at least point to situations where non-compliance is suspected :

other users were not interested in reading it creator is not understanding the topic creator does not care about the form of the page, -author prepared a propagandist text the text is in fact an advertisement Whether or not the other users were interested in reading it, we can tell by measuring the time spent on the site and extent of returning to this page.

To detect, whether the developer worked carelessly, without much attention to content, we can measure linguistic correctness (care, the spelling, the analysis of syntax and elements of semantic analysis), as well as calculating the so-called badrank [START_REF] Kolda | Generalized badrank with graduated trust[END_REF] to see of the author cares about the relevance of links. It may be useful to distinguish the spelling mistakes of the so-called "Typos" (e.g., by measuring the so-called edit distance properly loaded on the keyboard). Spelling errors may indicate a low level of education, and thus knowledge, and "typos"little attention to detail paid by the creator (perhaps not read their texts). The measure should take into account not only the absolute number of errors, but the length of the text.

Web statistics can allow to distinguish among these pages with probably the highest value in terms of care and representativeness. Word documents can be assessed using methods based on tfidf (term frequency, inverse document frequency). Term frequency is the number of occurrences of words in the document, while the inverse document frequency is the inverse of the number of documents in which the term occurs (or respective logs of them). The value of the text would represent the sum of the individual words tfidf in document. Instead tfidf also the variance in the number of instances of documents and other measures of diversity of the words between documents can be used.

The above-mentioned concept of PageRank can be used to assess the value of web pages in various ways. The quality assessment is of course the basic PageRank itself. But also its variant called badrank. Badrank [START_REF] Kolda | Generalized badrank with graduated trust[END_REF] is a measure of "spamming" by the creator as part of the so-called link farm. It is assumed that the WWWW pages pointing at the spamming page are also involved in the spamming action. Therefore, the mechanism is constructed similar to PageRank [START_REF] Page | The pagerank citation ranking: Bringing order to the web[END_REF] except that we reverse the directions of the arrows in the Web graph. The so-called personalized random walk is assumed that is the random walker makes his out-of-boring jump to the group U of web pages which are manually qualified as spamming pages. The probability of hitting on the page is a measure of the spam. Beside spam farms [START_REF] Wu | Identifying link farm spam pages[END_REF] there are also many other techniques of spamming and corresponding methods of spam detection, based on reverting the spamming technologies. Let us mention some, described e.g. in [START_REF] Ntoulas | Detecting spam web pages through content analysis[END_REF] inserting multiple keywords to the content -antispamming by a comparison of histogram of occurrence of words in queries and other categories of words (e.g. participation in the common words [stop-words]) in general and the distribution of popular etc. words in the document inserting multiple keywords to the title -antispamming as above inserting words that are keywords pasted together (e.g. freedownload) -antispamming by observation over the average length of words inserting a large part of the text as anchor text -antispamming by proper statistics a side effect of effort put into spamming is usually neglecting the Web page appearance -antispamming by checking for a small number of HTML tags compared to the length of text duplicating content -antispamming by checking for high compressibility gzip -more than four times the typical compression rate spam generated by a random mechanism -antispamming by computing the conditional independence of the occurrence of words in n-grams on a page

Besides spam there are also other types of harmful content on the Web, like pornography [START_REF] Wen Bing Horng | Classification of age groups based on facial features[END_REF] (which is relatively easy to recognize), illegal trade (fake brand products), criminal, violent racist and other harassment pages, as well as advertisement pages for harmful products aiming at distorting the people and the society. In this case one relies also on a set of predefined vocabulary, focused crawling, group of editors, creating directories of harmful and / or safe pages, with support of methods of event extraction and analysis. One can assess goodness of other pages by personalized PageRank in normal Web graph and badness by personalized PageRank in the reverse graph, and then compare goodness and badness of each page.

Next one can look into such as inserted by the creator of the links are actually in line with the theme of the document. You can highlight the following techniques:

clustering (cluster analysis), links within the cluster is considered to be topical -Links from / to spam -to be non-topical matching topic subject links to the document -similarity of the text near the anchor to the vector of 20 words with the highest tfidf in the target document [5] -as previously, but with separate treatment of links within the Web domain and going outside, classifying ranks relative to these groups (e.g., above the median) -topical triangles: if A is related to B, A points B, and C points to A and to B, then the links from C to A and B may be considered topical. -link between the pages A and B is topical if both pages are often found high ranking in many queries -links of type hubs / Authorities in HITS and SALSA technology comparative analysis of the structure of the web pages according to the recommendations of various guidelines of web page construction (e.g. elimination of navigational links by identifying their permanent place)

In most of techniques for detecting spam or other harmful content some numerical values are computed. These values can be the ratios of frequency of occurrences as compared with the "gold standard" or results of some statistical tests of significance aiming to reject the hypothesis that the website does not represent the spam.

Beside looking for signs of page misconduct, also efforts were made to define positive tests of page quality. So in [START_REF] Fogg | How do users evaluate the credibility of web sites?: a study with over 2,500 participants[END_REF] it is proposed to consider a page a good one if there are no errors of any kind, if the creator of the web page can be clearly identified (with contact details), verifying that the site belongs to a serious organization having experts in the field presented by the Web page, and the inclusion of references (links) to relevant pages on the topic. Here, of course, it is necessary to use a shallow analysis of natural language, identifying named entities and the use of appropriate semantic resources (lists of individuals or organizations or types of organizations that are trustworthy). Also one needs methods for appropriate classification of the content of the page [START_REF] Ciesielski | Wikipedia-based document categorization[END_REF] to match it against the list of experts. [START_REF] Dondio | Computational trust in web content quality: A comparative evalutation on the wikipedia project[END_REF] proposes a number of methods for assessing the quality of Web pages edited by communities, in which the method of time series analysis of changes and of the list of readers / writers is exploited. Unfortunately, not all suggestions of [START_REF] Dondio | Computational trust in web content quality: A comparative evalutation on the wikipedia project[END_REF] are plausible, just to mention the requirement "Neutral point of view". Though today technology is close to properly assessing the attitude of the author (via called a sensual analysis) and "neutrality" can in principle be measured automatically, this measure has nothing to do with the quality measurement as our primary goal is to ensure that the contents are true (reflect actual state of the world) and hence it cannot be not neutral with respect to the truth.

Measuring utility

Utility is the degree to how much information brings the viewer to learn about the world to the extent necessary for the running existence / immediate action (can be positive if the beneficial and negative when it is harmful).

The difference to the quality lies in the fact that we now care for receiver's needs. So high quality information is not enough, it may prove useless for the current goals, therefore reading it may be waste of time and hence harmful.

Hence, the role of search engines is to provide the users with what they currently need. Thus, the utility can be seen in the context of the user's query.

Utility may be again judged by viewing reaction of other receivers. For example, if page advertised product and the customer bought it, it means that the information provided was useful.

Previously there were efforts to profile the user claiming that the content returned by the search engine should best fit user's profile. Nowadays such an approach is deemed to be a failure because we cannot predict from the past user's information need in the future (the user can look now for something else).

Therefore a different pathway is steered at. Rather then trying to find user's profile, a search engine reply with diverse topics is aimed at. One presents best fitting the query but differing from the preceding ones on the list, not only by content but also by topic (in fact similarity to a query is weighted against the difference to the predecessors). So the value of a page is not only judged by its similarity to the query but also its new content compared to what is provided by better pages.

In general, measuring utility requires climbing up to at least semantic level of the message. Our group is engaged in developing a semantic search engine covering the whole Polish Internet. To ensure appropriate quality of responses, we seek a well-founded method of matching user's query to the document contents. Let us briefly explain the notion of semantic search engine and its impact on ranking method requirements.

As already stressed, semantics of information expresses the meaning of this information. In linguistics research on the semantics tries among others to relate symbols (like words, phrases, characters) to (real) beings which they mean (so-called denotations) therefore related areas like morphological and syntactic analysis is engaged. Understanding of semantics may prove useful in comprehending pragmatics (the expected acting of the recipient upon obtaining the information) and apobetics (the goal of the sender when sending the information).

Identification of the meaning of an information has been subject of intense research. So-called "semantic search" is deemed to be a method of improvement of search engine response by means of understanding of user intent as well as of the search terms in the context of the document space. If we take into account advances in natural language research, we easily guess that there is virtually no chance to realize the goal of semantic search, formulated in this way, in near future. Computers have no chance to understand semantics of textual messages as they have no "experience" with the reality surrounding us humans. Access to semantics of real world appears to be a remote goal.

Therefore in our research project NEKST we reformulated in a significant way the task of semantic analysis of Internet documents by understanding the task in an operational way. Instead of trying to pretend that the machine understands the meaning of the text, we use the fact that both the information sender and the recipient are human beings. Hence not the search engine but the man has to understand the text, and the search engine only supports him in this understanding. This support has the form of so-called semantic transformations on the text which on the one hand enrich the text with new features extending search characteristics and on the other hand may move the text to other space than the document space that is into the space of objects the documents are about.

So the semantic transformation means such a transformation of the document and/or query content that allows for traditional document search via a semantically related query [START_REF] Ciesielski | Semantic information within the BEATCA framework[END_REF][START_REF] Mieczysław | Towards the notion of typical documents in large collections of documents[END_REF].

Within the system NEKST the following types of semantic transformations have been implemented:

user suggestions [START_REF] Sydow | Introducing diversity to log-based query suggestions to deal with underspecified user queries[END_REF], -substitution with synonyms, hypernyms, hyponyms and other related concepts, -concept disambiguation [START_REF] Ciesielski | Wikipedia-based document categorization[END_REF], -document categorization [START_REF] Ciesielski | Wikipedia-based document categorization[END_REF], -personalized PageRank [START_REF] Mieczysław | A calculus for personalized PageRank[END_REF], -cluster analysis and assignment of cluster keywords to documents [START_REF] Chojnacki | Grupowanie stron w polskim internecie[END_REF], -explicit separation of document cluster and document search, -extraction of named entities and relations between them [START_REF] Wróblewska | Dependency-based extraction of entityrelationship triples from polish open-domain texts[END_REF], -diversification of responses to queries, -dynamic summarizing [START_REF] Kłopotek | Conceptual Maps of Document Collections in Internet and Intranet. Coping with the Technological Challenge[END_REF], and identification and classification of harmful contents.

Of course English language there exists a significant body of research on the above-mentioned topic, but with our system we demonstrated that the concepts are also implementable for non-English and in particular for highly inflectional languages with flexible grammar like Polish.

If you take the semantic transformation view then it is obvious that you need all the traditional mechanisms of a search engine also under semantic search, including the ranking mechanisms.

Note that traditional PageRank itself is a career of a (limited) amount of semantic information. One usually assumes that a link is added to a page with some semantic relation to the pointed page in mind. But various variants of PageRank are considered in conjunction with semantic search because the links are not perfect semantic relation indicators. So one variant, implemented also by us, is the TrustRank [START_REF] Gyöngyi | Combating web spam with trustrank[END_REF] where you may give more weight to one outgoing link and less to another (e.g. based on textual similarity between the pointing and the pointed page). Still another approach is to create a kind of "topic sensitive" PageRank [START_REF] Taher | Topic-sensitive PageRank: A context-sensitive ranking algorithm for web search[END_REF]. You may split the collection into a set of rough categories and for each of these categories one can compute a separate PageRank. If one recognizes that a query belongs to a particular topic, the personalized PageRank may be used. It may, however, happen that one query is related to two or more categories. An efficient way of computing personalized PageRank for a mixture of categories is proposed in [START_REF] Mieczysław | A calculus for personalized PageRank[END_REF].

Actuality Measurement

As mentioned the actuality tells us whether the Web page contains up-to-date information or was accurate in the past only.

Though we cannot confront the web content with the state of the real world, we can nonetheless check whether or not the creator of the Web page cares for its content.

Actuality can be measured by tracking the activity of web page authors. One can use the measures of deadrank or refresh rate, etc.

In the old days, to verify the actuality of a web page, one could query the date of the last update, but today many servers update artificially the creation date or the pages are dynamically generated. However, local servers are not able to overcome the dynamic nature of the Internet. Due to the frequent deletion of web pages, one can discover not maintained pages by looking at their dead links (the author did not notice disappearance of pages).

This fact gave rise to the DeadRank. Deadrank is based on the PageRank on the reverse Web graph, though it is slightly different from badrank. One thinks about the DeadRank as the probability of visiting the page by a random walker starting his journey on non-existent web pages and jumping back to them upon being bored. Not maintained pages will have high Deadrank. Of course, the problem here is to obtain information about the pages that do not exist. Usually, we can learn about the non-existence from a suitable server error, but the server can also cheat, redirecting to another page, or creating one on the fly. One can compensate for this effect by investigating what answer is given by the server if you specify an address that you are sure does not exist. Comparison of responses will give us an indication of whether the page exists or it is an artifact of the server.

Refresh rates of Web pages are also subject of manipulation by servers nowadays, but there were technologies developed for search engines guiding the visit frequency of spiders depending on some measures of refresh rate. The article [START_REF] Dai | Capturing page freshness for web search sigir[END_REF] proposed the so-called temporal freshness, consisting of a mixture of (1) content freshness (PF), expressing updates of the page as such and (2) in-link freshness (INF), reflecting updating of the links pointing to the page. At the time i, these quantities are:

P F t i (p) = j<i e (j-1) * a * ∆t k w k * C(j, k, p) InF t i (p) = j<i e (j-1) * a * ∆t l w l * C(j, l, p)
where w k and w l are weights of activities of type k on the Web page and activities of type l on in-links of the Web page resp. C(j, k, p) and C(j, k, p) tell if the respective activity appeared or not on the web page at time point j. Based on these data the quantity TFC (temporal correlation freshness) is defined, which is the ratio of a temporal correlation between the measurements of PF and INF. Intuition for this measurement is as follows: the site is "fresh" if its changes are seen by the links. These measurements assume that we have a historical copies of pages available (for example, InternetArchive).

The concept of intelligibility specifies whether or not the information can be decoded by the receiver / mirrors what the sender wanted to send.

Intelligibility can be evaluated, e.g. by examining whether you can build profile of customers visiting the given page from the click stream and if the prevailed clicking behavior on the page corresponds to the obtained profile.

Other methods rely on an assessment of "the literary" quality. One examines the length of the words used on the page in terms of the number of letters and syllables, sentence length counted in words, syllables and/or letters. One also examines the variation of the words (the ratio of the number of words used to words in general, the cumulative distribution function of the words used etc.). Out of these statistics some complexity measures are derived.

One of the best known methods in this area is called Flesch Reading Ease index F Ri = 206.876 -1.015ASL -84.6ASW

where ASL is the average number of syllables per word, and ASW -the average number of words in a sentence2 . More sophisticated methods can utilize the available semantic resources, including the classification of the conceptual difficulties of individual words. Usually lists of difficult words are difficult to obtain, but lists of easy words are easier to get.

Although measures related directly or indirectly to the length of the sentences are fairly good indicator of clarity, in case of hypertext documents on the internet we have to handle the problems of punctuation -it is neglected and thus the easiness may be underestimated.

But it turns out that a significant part of the easy pages point generally to the easy (easy to understand) ones, while the difficult pages point to difficult ones. Therefore, for example, one can use so-called TrustRank method [START_REF] Gyöngyi | Combating web spam with trustrank[END_REF] (an analogue of PageRank in social networks) to assess the page easiness based on prior assessment by humans of a set of seed pages.

Measuring availability

Availability means whether or not the information was sent by the sender.

The availability of information on the Internet can be tested by determining whether the page is a search result for search for meaningful question asked. But for the modern Internet technology the detection of availability can be much more sophisticated. First, we can ask what is the probability that a random wanderer will go to the respective information (website / web page). This concept is close to the so-called PageRank. Second, we consider the wanderer applying the backspace key, which leads to a measure of RBS. Thirdly, the wanderer can look for certain keywords, characterizing the respective information then we will have to deal with so-called Query-Dependent PageRank. Finally, instead of asking about the probability we can think about the ranking for the mentioned measurement in general or for a given query, consisting of one, two or three words (the minimum distance from the top ranking after these words / phrases).

We also have to take into account the technical possibilities of the presentation and the ability of human perception. From a technical point of view, it is essential to tell the format of information (standard HTML, PDF, or one of over 300 different formats of text documents, which are used to store information) and match it against the palette of formats supported by user's web browser. The issue is further extended by the problems of active sites, using JavaScript, Basic or applets and information presented in the form of files. Here we can calculate the probability of format compatibility between the source and the mouth of information.

The so-called Web Accessibility Guidelines3 , developed by W3C, indicate further need for considering the latest standards of presentation (e.g. CSS in conjunction with HTML for ornamental purposes), the usage of explanatory text, the usage of non-textual elements (graphics, sound, Flash), as well as alternatives to or features of HTML, which may be not supported by one's browser (frames, iframe, embed, etc.). Based on the use / non-utilization of such features, software has been developed such as Valet4 , which counts the number of violations of these formalist readings in the form of a linear model that presents a measure of the availability of information (see [START_REF] Montero Simarro | Quality models for automated evaluation of web sites usability and accessibility[END_REF]).

Moreover, nowadays there is a tendency to restrict access to the Internet. Under the new legislative proposals such as the U.S. availability of information on the Internet regulation5 is the future depend on the wealth of internet users. ISPs can freely censor information available. Therefore, the measurement will be available not just answer "yes / no", but the function of the funds available to the surfer, as well as the selection of service providers by issuing and seeking the document.

Concluding Remarks

Over six decades of rapid development of computer science have passed since the seminal paper of Shannon, who dared to ask how much information we can pass through an information channel. By asking this, he implied that information is an objective reality that needs to be quantified somehow. It has to be regretted that over the years one has forgotten that information is not a product of computer science and cannot be arbitrarily defined. With the advent of Web technologies it became again an acute problem get an in-depth understanding of nature and properties of information (as an immaterial dimension of the real world) and how they should be measured in order to provide search engine users with most valuable output. To have a valuable output we must establish the value of each piece of information. This is somehow contrary to the current philosophical trends that hamper the actual reflection on the nature of information.

We have tried to demonstrate in this presentation why it is necessary to go beyond Shannon model, why it is important to go beyond statistical level to appropriately capture the value of the information. We have pointed to the five dimensions of information evaluation: quality, utility, actuality, intelligibility and availability. Last not least we have demonstrated that with current tools or ones available in near future it is possible to measure the information value according to these dimensions. Surely, we do not have a proper understanding of all aspects of information and the technical realization of some concepts is far from satisfactory. But we can nonetheless hope that in near future the search engines will be able to tell us far more about the web pages we are looking for just accelerating our search for helpful information. The search engine designers have to keep in mind that the people do not live to search but rather they search to live.

See http://www.editcentral.com/gwt1/EditCentral.html for alternative measures like Automated readability index, Flesch-Kincaid grade level, Coleman-Liau index, Gunning fog, SMOG index.

http://www.w3.org/WAI/WCAG20/quickref/

http://valet.webthing.com/access/url.html

http://www.fcc.gov/openinternet