Carlos Lara

Keywords:

Heterogeneous SoC

A system-on-a-chip (SoC) is a device which integrates multiple processing elements in the same die. An heterogeneous SoC may feature different processors, hardware accelerators, and even an FPGA.

Intrinsic channels Intrinsic channels

The shared resources of the SoC may be leveraged to covertly transfer information.

Attack goals

• Bypass isolation policies • Change the nature of the channel 1 Case studies

  have niche applications but are difficult to mitigate • Their transmission rate is low, but the volume of information required is not high • We need to rethink the design of SoC platforms to introduce more effective isolation strategies Carlos LARA Thanks !

Carlos LARA

https://shop.trenz-electronic.de/Download/?path=Trenz_Electronic/Development_Boards/TE0802/REV02/Documents Carlos LARA

4 https://shop.trenz-electronic.de/Download/?path=Trenz_Electronic/Development_Boards/TE0802/REV02/Documents Carlos LARA

Acknowledgments

This work has been supported by the French government in the framework of the France 2030 initiative under project ARSENE (ANR-22-PECY-0004).


I Tanya Handa

Rien Aerts

Frank Berendse

Matty P Berg

Andreas Bruder

Olaf Butenschoen

Eric Chauvet

Mark O Gessner

Je ´re ´my Jabiol

Marika Makkonen

Brendan G Mckie

Bjo ¨rn Malmqvist{

Edwin T H M Peeters

Stefan Scheu

Bernhard Schmid

Jasper Van Ruijven

Veronique C A Vos

Stephan Ha ¨ttenschwiler

Mellander

Consequences of biodiversity loss for litter decomposition across biomes

Data Table 1 | Characteristics of aquatic and terrestrial ecosystems at five widely dispersed locations * Means were calculated based on 10-year records between 1998 and 2008 from the closest possible meteorological station. { Soluble reactive phosphorus < ortho-phosphate.

The decomposition of dead organic matter is a major determinant of carbon and nutrient cycling in ecosystems, and of carbon fluxes between the biosphere and the atmosphere [START_REF] Wardle | Communities and Ecosystems: Linking the Aboveground and Belowground Components[END_REF][START_REF] Bardgett | The Biology of Soil: A Community and Ecoystem Approach[END_REF][START_REF] Parton | Global-scale similarities in nitrogen release patterns during longterm decomposition[END_REF] . Decomposition is driven by a vast diversity of organisms that are structured in complex food webs [START_REF] Bardgett | The Biology of Soil: A Community and Ecoystem Approach[END_REF][START_REF] Gessner | Diversity meets decomposition[END_REF] . Identifying the mechanisms underlying the effects of biodiversity on decomposition is critical [START_REF] Gessner | Diversity meets decomposition[END_REF][START_REF] Ha ¨ttenschwiler | Biodiversity and litter decomposition in terrestrial ecosystems[END_REF][START_REF] Cardinale | The functional role of producer diversity in ecosystems[END_REF] given the rapid loss of species worldwide and the effects of this loss on human well-being [START_REF] May | Why should we be concerned about loss of biodiversity[END_REF][START_REF] Naeem | The functions of biological diversity in an age of extinction[END_REF][START_REF] Cardinale | Biodiversity loss and its impact on humanity[END_REF] . Yet despite comprehensive syntheses of studies on how biodiversity affects litter decomposition [START_REF] Gessner | Diversity meets decomposition[END_REF][START_REF] Ha ¨ttenschwiler | Biodiversity and litter decomposition in terrestrial ecosystems[END_REF][START_REF] Cardinale | The functional role of producer diversity in ecosystems[END_REF][START_REF] Hooper | A global synthesis reveals biodiversity loss as a major driver of ecosystem change[END_REF] , key questions remain, including when, where and how biodiversity has a role and whether general patterns and mechanisms occur across ecosystems and different functional types of organism [START_REF] Gessner | Diversity meets decomposition[END_REF][START_REF] Cardinale | Biodiversity loss and its impact on humanity[END_REF][START_REF] Hooper | A global synthesis reveals biodiversity loss as a major driver of ecosystem change[END_REF][START_REF] Balvanera | Quantifying the evidence for biodiversity effects on ecosystem functioning and services[END_REF][START_REF] Loreau | Linking biodiversity and ecosystems: towards a unifying ecological theory[END_REF] . Here, in field experiments across five terrestrial and aquatic locations, ranging from the subarctic to the tropics, we show that reducing the functional diversity of decomposer organisms and plant litter types slowed the cycling of litter carbon and nitrogen. Moreover, we found evidence of nitrogen transfer from the litter of nitrogen-fixing plants to that of rapidly decomposing plants, but not between other plant functional types, highlighting that specific interactions in litter mixtures control carbon and nitrogen cycling during decomposition. The emergence of this general mechanism and the coherence of patterns across contrasting terrestrial and aquatic ecosystems suggest that biodiversity loss has consistent consequences for litter decomposition and the cycling of major elements on broad spatial scales.

Biological diversity that directly influences litter decomposition exists at multiple trophic levels [START_REF] Gessner | Diversity meets decomposition[END_REF] . This diversity includes plants that produce litter mixtures of varying quality, microbial decomposers and invertebrate consumers of varying body size, the last two of which selectively use the heterogeneous resources provided by litter mixtures [START_REF] Gessner | Diversity meets decomposition[END_REF][START_REF] Reiss | Emerging horizons in biodiversity research and ecosystem functioning[END_REF] . General principles of the effects of biodiversity on litter decomposition have proved elusive: both pioneering work [START_REF] Wardle | Biodiversity and plant litter: experimental evidence which does not support the view that enhanced species richness improves ecosystem function[END_REF] and recent syntheses have highlighted contrasting effects of litter species richness on decomposition [START_REF] Gessner | Diversity meets decomposition[END_REF][START_REF] Ha ¨ttenschwiler | Biodiversity and litter decomposition in terrestrial ecosystems[END_REF][START_REF] Cardinale | The functional role of producer diversity in ecosystems[END_REF][START_REF] Gartner | Decomposition dynamics in mixed-species leaf litter[END_REF][START_REF] Lecerf | Incubation time, functional litter diversity, and habitat characteristics predict litter-mixing effects on decomposition[END_REF] . In part, this variation appears to be due to site-specific conditions, including contrasts between aquatic and terrestrial ecosystems, as well as between geographic settings. Further differences may arise from variation in experimental protocols, the plant species studied and the types of decomposers included in a given experiment. Such methodological discrepancies have complicated syntheses across studies, hindering the emergence of common patterns and mechanisms.

Here we report the results of the first concerted experiments studying the effects of biodiversity on decomposition by manipulating diversity across trophic levels and distinct biomes in both forest floor and stream habitats (Extended Data Table 1). We proposed that the functional diversity of decomposers (variation in body size) and of leaf litter (variation in litter quality) promotes C and N cycling across contrasting locations (subarctic to tropical) and ecosystem types (terrestrial versus aquatic). Body size encapsulates numerous species traits that are relevant to ecosystem functioning, and extinction scenarios project that the larger species will be preferentially lost from biological communities [START_REF] Duffy | Biodiversity loss, trophic skew and ecosystem functioning[END_REF][START_REF] Woodward | Body size in ecological networks[END_REF] . Similarly, plant functional types reflect differences in leaf quality traits that determine litter decomposition independently of geographical location [START_REF] Cornwell | Plant species traits are the predominant control on litter decomposition rates within biomes worldwide[END_REF] . Plant functional types are defined here in terms of plant C allocation strategies (deciduous versus evergreen), N acquisition strategies (N-fixing versus non-N-fixing) and litter recalcitrance (rapidly decomposing versus slowly decomposing) (Extended Data Table 2).

Mixing leaf litter from various plant functional types together resulted in accelerated C and N dynamics, as indicated by the overall net positive effects on C and N loss (that is, increased C and N loss with increasing functional diversity) (Fig. 1, C loss; Extended Data Fig. 1, N loss; P , 0.05, C and N loss). However, C loss from litter mixtures was only 2.9 6 0.8 mg g 21 (mean 6 s.e.m.) of initial litter dry mass greater than the expected loss based on data from single litter functional types, indicating only a modest increase in C cycling as a result of litter mixing. Although also statistically significant (P , 0.01), the difference in the loss of N across all litter mixtures was very small (0.1 6 0.2 mg N g 21 of initial litter dry mass; mean 6 s.e.m.; Extended Data Fig. 1). The net litter diversity effect on C loss was stronger in terrestrial than in aquatic ecosystems (P , 0.001, Fig. 1 and Extended Data Table 3), supporting theoretical predictions [START_REF] Gessner | Diversity meets decomposition[END_REF] but contrasting the results of a meta-analysis in which diversity effects on decomposition were significant only for streams [START_REF] Cardinale | The functional role of producer diversity in ecosystems[END_REF] . Sorting the litter mixtures into species at the end of the experiments enabled us to explore potential reasons for this discrepancy, by partitioning the net diversity effects into complementarity effects (that is, the effects resulting from synergistic or antagonistic interactions) and selection effects (that is, the effects arising when the presence of a particular functional type with high (or low) process rates dominates a mixture) [START_REF] Loreau | Partitioning selection and complementarity in biodiversity experiments[END_REF] . The observed net diversity effects were clearly driven by complementarity effects that were stronger than selection effects (Fig. 1). Overall, the complementarity effect was a similar strength to the net effect for C loss (3.4 6 0.9 mg C g 21 ), and even stronger than the net effect for N loss (1.0 6 0.2 mg N g 21 ). By contrast, the mean selection effects were not significant. The characteristics of the forest floor habitat that may favour complementarity effects include strong fluctuations in temperature and humidity and a homogenous litter cover [START_REF] Gessner | Diversity meets decomposition[END_REF] . Conversely, the observed negative complementarity effects in subarctic and tropical streams could reflect a low density and low taxon richness of litter consumers (Extended Data Table 4) and thus limited potential for complementary resource use [START_REF] Frainer | When does diversity matter? Species functional diversity and ecosystem functioning across habitats and seasons in a field experiment[END_REF] .

Our experiments also show that completeness of the decomposer community, which is rarely considered in large-scale studies, is important for C and N dynamics during litter decomposition (Fig. 2, Table 1 and Extended Data Table 5). The presence of medium-sized invertebrates (#1 mm in diameter) in the decomposer community increased the average C and N loss across all sites by 2.1 6 0.8% and 2.0 6 1.0%, respectively. The complete decomposer community (which included organisms up to 5 mm in diameter) increased the average C loss across all sites by 10.6 6 1.0% and the average N loss across all sites by 11.1 6 1.2% (Fig. 2). This effect was consistently positive across all but the Mediterranean terrestrial site. Thus, the presence of large fauna clearly has a major impact on decomposition (Table 1), as reported previously [START_REF] Woodward | Continental-scale effects of nutrient pollution on stream ecosystem functioning[END_REF][START_REF] Wall | Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent[END_REF][START_REF] Garcı ´a-Palacios | Climate and litter quality differently modulate the effects of soil fauna on litter decomposition across biomes[END_REF] ; however, in line with previous studies, the importance of large fauna varies among locations in aquatic [START_REF] Woodward | Continental-scale effects of nutrient pollution on stream ecosystem functioning[END_REF] and terrestrial ecosystems [START_REF] Wall | Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent[END_REF][START_REF] Garcı ´a-Palacios | Climate and litter quality differently modulate the effects of soil fauna on litter decomposition across biomes[END_REF] . In our study, the strong effects of the complete decomposer community at the temperate and tropical locations correspond to high relative abundances of millipedes and termites at the terrestrial temperate and tropical sites, respectively (Extended Data Table 6). Similarly, the large effect of the complete decomposer community at the temperate aquatic site corresponds to the high abundance of a particularly efficient amphipod detritivore (Extended Data Table 4). Our data clearly indicate that the largebodied organisms are the most critical for decomposition. These animals also tend to face the greatest extinction risk [START_REF] Duffy | Biodiversity loss, trophic skew and ecosystem functioning[END_REF] .

Litter mixing and completeness of the decomposer community interacted with each other to affect C and N loss, although this interaction explains less of the variance than the main effects (Table 1). C loss and, even more so, N loss increased in the presence of particular plant functional types and with increasing completeness of the decomposer community (Table 1 and Extended Data Table 7). Although the type of decomposer community did not significantly change the net effect of diversity on C loss (P 5 0.67) or N loss (P 5 0.30) (Extended Data Table 3), it emerged as a significant factor in the selection effect for both C loss (P , 0.05) and N loss (P , 0.05). Additionally, the interaction between the rapidly decomposing litter type and the decomposer community was significant in explaining the selection effect and the overall net diversity effect on C loss (P , 0.05) and N loss (P , 0.05), suggesting that large decomposers are particularly important drivers of C and N loss from litter mixtures that contain rapidly decomposing litter. The food preference behaviour of decomposers could be important in accounting for this result, as has previously been implied for terrestrial [START_REF] Vos | Macrodetritivore identity drives leaf litter diversity effects[END_REF] and aquatic [START_REF] Swan | Preferential feeding by an aquatic detritivore mediates non-additive decomposition of speciose leaf litter[END_REF] ecosystems.

A key result of our large-scale study is that the effects of litter diversity on C and N dynamics can be largely explained by the presence of particular functional plant types in litter mixtures, supporting the idea that the range and relative abundance of plant traits in ecosystems underlie the effects of species richness on ecosystem processes [START_REF] Garnier | Plant functional markers capture ecosystem properties during secondary succession[END_REF][START_REF] Cadotte | Beyond species: functional diversity and the maintenance of ecological processes and services[END_REF] . The effects of the presence of litter from particular plant functional types, or the interactions among these, were consistent across locations at both terrestrial and aquatic sites, together accounting for about 10% of the total variance as shown in the full analysis of variance model (Table 1, P , 0.05; mixtures on C loss. The net diversity effect is the deviation from the expected mean based on C loss measured from litter consisting of single species. The blue and brown circles show the mean effects (6s.e.m.) on C loss from litter mixtures in forest streams and on forest floors, respectively, in subarctic (SUB), boreal (BOR), temperate (TEM), Mediterranean (MED) and tropical (TRO) locations. Each circle to the right of the dashed lines shows the mean effect per ecosystem type (that is, aquatic versus terrestrial), as calculated across the three types of decomposer community (n 5 165 litter mixtures per location and ecosystem type; see Extended Data Table 3 for statistical analyses). The circles to the left of the dashed lines show the overall mean across all locations.

Decomposer community effect (%) Extended Data Table 7). Beyond the presence or absence of particular plant functional types, we found no significant effect of the richness of plant functional types in the litter on C loss (P 5 0.93), although a positive effect was observed on N loss (P , 0.001) (Table 1). The effect on N loss was strongest when the most complete decomposer communities had access to the litter (litter richness 3 decomposer community interaction; P , 0.05). Our results indicate that partitioning the diversity effects into the separate contributions of the presence or absence of particular plant functional types in litter and their interactions can help move interpretations of biodiversity-ecosystem functioning experiments beyond the current dichotomy between broad generalizations and claims of idiosyncratic compositional effects [START_REF] Ha ¨ttenschwiler | Biodiversity and litter decomposition in terrestrial ecosystems[END_REF][START_REF] Wardle | Biodiversity and plant litter: experimental evidence which does not support the view that enhanced species richness improves ecosystem function[END_REF][START_REF] Gartner | Decomposition dynamics in mixed-species leaf litter[END_REF] . An intriguing finding in this context is that the strongest positive interaction emerged between two particular litter functional types: N-fixing plants and rapidly decomposing deciduous plants (Extended Data Table 7). When these types were present together in litter mixtures, the average C loss was 13.5% greater than the average C loss of all litter combinations, and the N loss was 32.5% greater. This general pattern holds across terrestrial and aquatic ecosystems from the subarctic to the tropics. Moreover, relative to the total amount of N in the litter initially, less N remained in the litter of N-fixing plants when rapidly decomposing litter was present than when it decomposed alone (Fig. 3 and Extended Data Table 8). The rapidly decomposing litter, in turn, contained more N when litter from N-fixing plants was present than when it decomposed alone (Fig. 3 and Extended Data Table 8). On average across all of the sites, the litter of N-fixing plants lost 20.6% of its initial N when it decomposed alone but 25.0% when it decomposed in the presence of litter from rapidly decomposing plants. By contrast, the litter of rapidly decomposing plants lost 18.1% of its N when it decomposed alone but 13.4% when litter from N-fixing plants was present. This striking pattern across locations and ecosystems suggests, for the first time from field data, that N can be transferred between litter types. A plausible mechanism for this effect is that fungal decomposers tap the nutrient reservoir of the N-fixing plant litter, boosting C use and fungal growth in the N-deficient litter, which provide high-quality C [START_REF] Schimel | Nitrogen transfer between decomposing leaves of different N status[END_REF] (see section Extended discussion on litter N transfer in Methods). The average net differences in N fluxes between single-species litter and litter mixtures of these two plant functional types account for approximately 0.25 g N per square metre of ground area, representing up to one-tenth of the total annual N input from leaf litter fall. Thus, although the biodiversity effects that we report here, in line with recent syntheses [START_REF] Cardinale | Biodiversity loss and its impact on humanity[END_REF][START_REF] Hooper | A global synthesis reveals biodiversity loss as a major driver of ecosystem change[END_REF][START_REF] Balvanera | Quantifying the evidence for biodiversity effects on ecosystem functioning and services[END_REF] , are smaller than those noted for other ecosystem processes such as plant biomass production, these changes in N fluxes can have important consequences for the ecosystem. Even slight differences in the N dynamics in litter mixtures compared with the respective single-species litter can substantially change the N supply to primary producers and other organisms over large spatial and temporal scales [START_REF] Finzi | Non-additive effects of litter mixtures on net N mineralization in a southern New England forest[END_REF] .

The implications of our results are that changes in C and N cycling in response to biodiversity loss are largely predictable across vastly different latitudes in both terrestrial and aquatic ecosystems, by taking into account relatively simple plant traits and the structural characteristics of decomposer communities. To provide robust projections of how ecosystems respond to a loss of biodiversity, it is essential to identify the mechanisms that result from specific interactions between the components of biodiversity as we describe here. With the consistent patterns and mechanisms of biodiversity effects that we have shown, such projections now seem to be within reach. 8 for statistical analyses) for litter decomposing in terrestrial (brown) and aquatic (blue) ecosystems at five locations. The relative net difference was calculated as

[(N i,m 2 N f,m )/N i,m ] - [(N i,a -N f,a )/N f,a ],
where N i,m and N i,a are the initial (i), and N f,m and N f,a are the final (f), amounts of N in a particular litter type in a mixture (m) or alone (a). * Plant species (trees or shrubs) were selected to represent the same four functional types at each location (N-fixing, evergreen, rapidly decomposing deciduous and slowly decomposing deciduous). Linear functional type richness was fitted before litter functional type compositions. { An alternative model omitting richness and testing in detail the litter functional type compositions in a full factorial analysis with contrasts for functional type presence/absence and interactions is presented in Extended Data Table 7. That model highlights the importance of the interaction between the litter of the N-fixing functional type and the rapidly decomposing functional type, hinting at a N-transfer mechanism.

METHODS SUMMARY

The field experiments followed an identical protocol at ten sites, encompassing both aquatic (forest stream) and terrestrial (forest floor) ecosystems at five locations across a latitudinal gradient spanning from the subarctic to the tropics, with intermediate locations in boreal, temperate and Mediterranean climates (Extended Data Table 1).

Leaf litter from native tree or shrub species representing four common functional types (evergreen, deciduous with slowly decomposing litter, deciduous with rapidly decomposing litter, and N-fixing) that naturally occur across all locations (18 species in total; Extended Data Table 2) was exposed to decomposers in a total of 2,250 experimental microcosms set up in the field with all possible location-specific single-species and multi-species combinations. We used a randomized block design with five blocks per site. Each block contained 1 replicate of 15 combinations of litter types (that is, all possible combinations of 4 litter species) 3 3 microcosm mesh sizes (totalling 45 microcosms per block). The three mesh sizes used to construct the field microcosms allowed us to establish three increasingly complete decomposer communities (small, medium-sized and complete) in the microcosms. Small-sized decomposer communities included microorganisms and fauna that passed through 50-mm and 250-mm mesh screens (DIATEX) in terrestrial and aquatic ecosystems, respectively. The medium-sized decomposer communities contained all organisms (including invertebrates) that passed through 1-mm mesh screens, whereas the complete decomposer communities included all organisms that passed through 5-mm mesh screens.

Litter mass loss was allowed to proceed to the same defined decomposition stage (40-50% of the mass of the least recalcitrant litter type remaining at each location; Extended Data Table 9) to ensure the comparisons of C and N loss, as well as the effects of diversity, at a similar decomposition stage among sites, using analysis of variance models (see Methods section).

Online Content Any additional Methods, Extended Data display items and Source Data are available in the online version of the paper; references unique to these sections appear only in the online paper.

METHODS

Experimental design. Our field experiments followed an identical protocol at a total of ten sites, representing either an aquatic ecosystem (forest stream) or a terrestrial ecosystem (forest floor). Five locations were selected across a broad latitudinal gradient spanning from the subarctic to the tropics, with intermediate locations in boreal, temperate and Mediterranean climates (Extended Data Table 1). Across all five locations, and in both the stream and forest ecosystems, the experiments consisted of a randomized block design in which the leaf litter from 4 common native plants (corresponding to the functional types shown in Extended Data Table 2) and 11 mixtures of these litter types (corresponding to all possible litter combinations within a location) were enclosed in nylon mesh screens (DIATEX) and placed in the field in five blocks (n 5 5 locations 3 2 ecosystem types 3 15 litter combinations 3 3 mesh sizes 3 5 blocks 5 2,250 microcosms). The four functional plant types represent distinct plant C allocation strategies (deciduous versus evergreen), N acquisition strategies (N fixer versus not a N fixer) and litter recalcitrance of deciduous non-N-fixers (rapidly decomposing versus slowly decomposing).

The three mesh sizes used to construct the microcosms enabled us to distinguish three increasingly complete decomposer communities (small, medium-sized and complete) that established themselves on the decomposing litter. Small decomposers included microorganisms and small-sized fauna that passed through 50-mm and 250-mm mesh screens in terrestrial and aquatic systems, respectively. The mediumsized decomposer communities contained all organisms (including invertebrates) that passed through 1-mm mesh screens, whereas the complete decomposer communities included all decomposers that passed through 5-mm mesh screens. Litter mass loss was allowed to proceed to the same defined decomposition stage (40-50% of the litter mass of the least recalcitrant litter type remaining at each site; Extended Data Table 9) to ensure meaningful comparisons of C and N loss among all sites. At all ten sites, extra microcosms containing the fastest decomposing litter type served as benchmark indicators of decomposition rates. Site characterization. The five stream locations were characterized in terms of their geomorphological, physical and chemical features (Extended Data Table 1). Water samples were collected for chemical analyses at the time of establishing the experiments. Samples for inorganic N and phosphorus determination were filtered over 0.45-mm pore-size cellulose acetate membrane filters and transported to the laboratory in a cooler at about 5 uC, where they were frozen for later analysis at Eawag, Switzerland.

At the five forest sites (Extended Data Table 1), the leaf area index was measured at breast height on a uniformly cloud-covered day when the forest canopy was fully developed, using an LAI-2000 Plant Canopy Analyzer (LI-COR) for the subarctic location, an LAI-2200 Plant Canopy Analyzer (LI-COR) for the Mediterranean and tropical locations, and a SunScan Canopy Analysis System (Delta T Devices) for the temperate locations. Data for the boreal location were provided by K. Bishop & P.-E. Mellander. To characterize the soil at each of the terrestrial sites, three samples from each experimental block were taken with a soil corer (5 cm diameter, 10 cm height), pooled, stored in plastic bags at 4 uC, then sent cooled to the University of Go ¨ttingen (Germany). Sieved soil samples (,2 mm sieve pore size) were analysed for pH (2 g soil in 20 ml 0.01 M CaCl 2 ) and C and N concentration (using an NA 1500 Carlo Erba elemental analyser). The microbial biomass of the soil was estimated using the substrate-induced respiration (SIR) method. The microbial respiratory response was measured in an electrolytic O 2 -microcompensation apparatus at 22 uC. These measurements were made hourly for 24 h. The microbial biomass was measured after the addition of glucose (8 mg C g 21 dry soil) as a substrate to saturate the catabolic activity of the microorganisms. The maximum initial respiratory response (MIRR; ml O 2 g 21 dry mass h 21 ) was calculated as the mean of the lowest three readings within the first 10 h, and the microbial biomass was calculated as C mic 5 38 3 MIRR (in mg C mic g 21 soil dry mass).

Data loggers (SL52T, Signatrol) were installed in some microcosms at all ten sites, to record the temperature every 2 h. These temperature measurements were taken in the same litter treatment for all three mesh sizes in three of the five experimental blocks. Leaf litter collection. A total of 20 litter types were collected at the 5 locations of our coordinated experiment. This litter corresponded to the same four functional types per location that were introduced above: N-fixing plants, rapidly decomposing deciduous plants, slowly decomposing deciduous plants and evergreen plants (Extended Data Table 2). Litter from these four functional types varies with respect to several quality traits [START_REF] Makkonen | Highly consistent effects of plant litter identity and functional traits on decomposition across a latitudinal gradient[END_REF] (Extended Data Table 2). The selected species were common native trees or, in two cases, native woody shrubs (Vaccinium vitis-idaea and Rhododendron tomentosum) occurring at each location. The litter was collected during location-specific leaf senescence either by hand (V. vitis-idaea and R. tomentosum) or by using litter traps. An exception was litter from the temperate evergreen species Ilex aquifolium, which was obtained by cutting branches in the field and simulating senescence in the laboratory for three weeks. Leaves with signs of herbivory or disease were discarded. The litter from multiple individual trees or shrubs of each species was pooled and dried at 40 uC. Leaf litter field incubations. Stream experiments were conducted by exposing 5 g litter batches in tetrahedral mesh microcosms (17 cm 3 25 cm) made of one of three mesh sizes (250 mm, 1 mm or 5 mm) to provide access to decomposer communities differing in body size. The microcosms were randomly attached (about 40 cm distance between microcosms) to five 20-m metal chains, each in a separate riffle (short, relatively shallow sections of streams with non-stagnating water) 20 m or farther apart from each other (experimental blocks). The chains were fixed in the stream with reinforcing bars in fairly homogeneous sand-gravel stream sections, where leaves accumulated naturally. All microcosms were submerged at depths sufficient to ensure that they were not exposed to air when water levels dropped. Care was taken to expose the litter to constant flow conditions, avoiding deep depositional areas (that is, pools and backwaters) with slow or no flow and rocky riffles with broken flow.

Terrestrial experiments on the forest floor were conducted by incubating 8 g location-specific litter (4 g only in the subarctic because of limited litter availability for some species) in field microcosms made of polyethylene cylinders (15 cm diameter, 10 cm height) covered with 50-mm mesh at the top and bottom to allow the passage of water but to prevent the entry of natural litter fall from above and the loss of small litter particles from the bottom. Two windows (5 cm 3 18 cm) were cut into the cylinders and covered with 50-mm, 1-mm or 5-mm mesh to provide access to decomposer communities differing in body size. The windows were cut close to the bottom of the cylinders to ensure that decomposers had access to a continuous layer of litter outside and inside the microcosms. An additional 1.5-cm height plastic ring of the same diameter as the cylindrical microcosms was attached at the bottom of the microcosm, making it possible to push the microcosms gently into the top soil (to a depth of 1.5 cm). This ring held the terrestrial microcosms properly in place while the bottom mesh was in intimate contact with the soil surface. In cases in which pushing the microcosms into the soil was difficult (for example, in the tropical forest with its dense superficial tree roots), the 1.5-cm rings were fitted with a separate plastic or metal ring before placing the microcosms. The microcosms were separated from each other by at least 50 cm. They were randomly distributed within blocks that were established at least 20 m apart from each other. Sample harvest and processing. We removed the decomposing litter of all species from the field when 40-50% of the initial litter mass of the fastest decomposing species was remaining. As a consequence, the duration of litter decomposition varied among locations and ecosystem types (Extended Data Table 9). This procedure ensured that similar decomposition stages were sampled at all sites, facilitating meaningful comparisons of decomposition rates and litter diversity effects. All litter samples were separated into the constituent species immediately after litter retrieval. The litter recovered from the streams was gently washed to remove any adhering material and invertebrates. The litter from the terrestrial sites was cleaned by gently brushing off any dirt without using water, to prevent the leaching of nutrients. The litter samples were then dried at 65 uC for 48 h. A correction factor was used to convert the initial litter mass (weighed after drying at 40 uC) to the final dry mass, based on ten randomly selected samples per litter type that were successively dried and weighed in the laboratory first at 40 uC and then at 65 uC. Litter C and N loss. The initial C and N concentrations of each of the 20 individual litter types were determined from 5 random samples. The final C and N concentrations after retrieval of the litter from the field were also measured for each individually sorted litter type from each microcosm. This process resulted in a total of 5,400 samples for which to calculate the percentage C and N loss for each litter type under the various conditions. Following the determination of litter dry mass, all initial and final samples were ground with a ball mill (Retsch PM 400) to a fine homogeneous powder. Subsamples of 3 mg were analysed for C and N concentrations using a CHN elemental analyser (Flash EA 1112 Series, Thermo Finnigan). C and N loss (%) from the litter during field exposure was calculated as 100 3

[(M i 3 CN i ) 2 (M f 3 CN f )] / (M i 3 CN i )
, where M i and M f are the initial and final litter dry mass, respectively, and CN i and CN f are the initial and final C or N concentration (% of litter dry mass). Using C loss (%) rather than total litter mass loss allowed us to correct for any possible inorganic contamination of the litter retrieved from the field. Analyses of diversity effects and statistical models. The net diversity effects, comprising complementarity and selection effects, on both C and N loss were calculated in species mixtures [START_REF] Loreau | Partitioning selection and complementarity in biodiversity experiments[END_REF] . The net diversity effect was calculated as the sum of the complementarity and selection effects and contrasts the actual C and N loss observed for mixtures of plant functional types with that expected based on the C and N loss measured in single-species treatments. The net diversity effect represents the sum of synergistic or antagonistic interactions (that is, complementarity effects) and those due to the presence of a dominant species (that is, selection effects). Data were square-root transformed (keeping the original negative and positive signs for the transformed values) to meet the assumptions for the analysis of variance of net diversity, complementarity and selection effects (see details below).

Analysis of variance models based on sequential sums of squares (type I) were used to assess the effects of diversity (the richness of plant litter functional types or the presence or absence of a given functional type and its interaction with other functional types), the completeness of the decomposer community (small, medium and large (complete)), the location across the latitudinal gradient and the ecosystem type (terrestrial versus aquatic) on percentage C and N loss. To ensure meaningful comparisons across the locations, several standardization methods were tested to remove any variation associated with the differences in incubation length. These methods included standardizing relative to the following: 1) a standard litter type from a non-native plant, Ailanthus altissima, that decomposed at all locations during the experiments; 2) the overall mean C or N loss per mesh size across locations; and 3) the mean C or N loss per mesh size of the rapidly decomposing functional type across locations. Because the results were consistent irrespective of standardization, the final model is presented using the non-standardized data.

The model terms were fitted to account for the dependency between the richness of plant litter functional types and the functional type composition (the presence or absence of a given functional type and interactions between functional types). First, functional type composition was partitioned into a contrast for richness and residual functional type composition (Table 1 and Extended Data Table 5). Second, as shown in Extended Data Table 7, we omitted the richness term and instead resolved the functional type composition into a full factorial analysis with contrasts for functional type presence or absence and interactions. In this model, the decomposer community was fitted as a log-linear contrast (small to large mesh size was expressed as the logarithm of the mesh size of the microcosms, which produced a linear relationship of the three mesh sizes). We also removed all of the other nonsignificant interaction terms in multiple successive model-fitting steps. These two alternative analyses reflect different partitionings of the functional type composition term into contrasts; they allowed us to compare the explanatory power of the richness contrast with the presence/absence contrast. A perfect linear richness effect would be found if all presence/absence contrasts had equal coefficients and did not interact. In this case, the mean squares or the richness effect with only one degree of freedom would be much larger than that of the combined mean squares of the presence/absence main effects of the four litter types with four degrees of freedom. In both models, the terms 'location' and 'ecosystem type' were tested at the block level. All other terms were tested against the residuals.

A similar analysis of variance approach was used to test independently for the effects of these same factors on complementarity and selection effects, as well as on net diversity effects (Extended Data Table 3). In a separate analysis of variance (Extended Data Table 8), we also tested whether the net loss of the total amount of N relative to the initial amount of N differed when litter of particular plant functional types (for example, rapidly decomposing litter and litter of N-fixing plants) decomposed together as opposed to decomposing separately, which we interpreted as an indication of N transfer between litter species. The location and ecosystem type were also included in this analysis. All statistical analyses were performed with R software version 2.8.0.

Extended discussion on litter N transfer. Although our data suggest that N was transferred from the litter of N-fixing plants to rapidly decomposing litter, alternative mechanisms cannot be entirely ruled out. In particular, N incorporated into decomposing litter can originate not only from another co-occurring litter type but also from the N pool in the soil or stream water or from microbial N fixation [START_REF] Vitousek | Heterotrophic nitrogen fixation in decomposing litter: patterns and regulation[END_REF] . However, N transfer from such alternative N sources does not readily explain the concomitant reciprocal changes that we observed between the litter of N-fixing plants and rapidly decomposing litter. Moreover, the idea that N transfer occurred between the two litter types is further supported by a positive net diversity effect on C loss that we observed only when these two particular litter functional types were both present (Extended Data Table 3). Additional support for our interpretation comes from [START_REF] Gartner | Decomposition dynamics in mixed-species leaf litter[END_REF] N tracer studies in microcosms with tropical [START_REF] Cadotte | Beyond species: functional diversity and the maintenance of ecological processes and services[END_REF] and temperate [START_REF] Lummer | Connecting litter quality, microbial community and nitrogen transfer mechanisms in decomposing litter mixtures[END_REF] forest litter, which are proof of principle that active biological transfer of N through microorganisms, particularly saprotrophic fungi, can occur. Our large-scale field experiment suggests that this phenomenon might be widespread across terrestrial and aquatic ecosystems and across a wide variety of forest types and climatic conditions.

It had been proposed that N transfer is driven by a gradient in N concentration between litter types [START_REF] Gessner | Diversity meets decomposition[END_REF][START_REF] Ha ¨ttenschwiler | Biodiversity and litter decomposition in terrestrial ecosystems[END_REF] , the rationale being that the element that limits the decomposition rate is N. However, the scenario now unfolding from our experiment (Fig. 3) and the recent isotope tracer studies under laboratory conditions [START_REF] Cadotte | Beyond species: functional diversity and the maintenance of ecological processes and services[END_REF][START_REF] Lummer | Connecting litter quality, microbial community and nitrogen transfer mechanisms in decomposing litter mixtures[END_REF] is that N transfer is stoichiometrically controlled. The crucial determinant that defines the gradient along which N will be transferred in litter mixtures seems to be the demand for N relative to the availability of C (and possibly that of other elements critical for decomposer growth), rather than differences in the N concentration. A litter with high C quality favours rapid microbial growth, which in turn entails a high demand for N (and other nutrients), resulting in N acquisition from neighbouring nutrient pools. In extreme cases, the N source litter may even have a lower N concentration than the N sink litter [START_REF] Lummer | Connecting litter quality, microbial community and nitrogen transfer mechanisms in decomposing litter mixtures[END_REF] , provided that the C quality of both litter types is sufficiently different. In accordance with this mechanism, the decomposition of recalcitrant litter types in our study (slowly decomposing and evergreen plant functional types) was not accelerated by the presence of litter from N-fixing plant species (Extended Data Table 7), although those recalcitrant litter types had similarly low or lower initial N concentrations than the rapidly decomposing litter species, which consistently benefited from the presence of N-fixing plant litter.

Extended Data Figure 1 | Net diversity, complementarity and selection effects of plant litter mixtures on N loss. The net diversity effect is the deviation from the expected mean based on N loss measured from litter consisting of single species. The blue and brown circles show the mean effects (6s.e.m.) on N loss from litter mixtures in forest streams and on forest floors, respectively, in subarctic (SUB), boreal (BOR), temperate (TEM), Mediterranean (MED) and tropical (TRO) locations. Each circle to the right of the dashed lines shows the mean effect per ecosystem type (that is, aquatic versus terrestrial), as calculated across the three types of decomposer communities (n 5 165 litter mixtures per location and ecosystem type; see Extended Data Table 3 for statistical analyses). The circles to the left of the dashed lines show the overall mean across all locations (n 5 825 litter mixtures per ecosystem type).

Figure 1 |

 1 Figure 1 | Net diversity, complementarity and selection effects of plant litter

2 SUBFigure 3 |

 23 Figure 3 | Relative change in the total amount of litter N. The relative net difference between two-species mixtures (containing litter from the N-fixing and the rapidly decomposing plants) and monocultures of N-fixing plant litter (left) or rapidly decomposing plant litter (right) is shown (mean 6 s.e.m., n 5 15; see Extended Data Table8for statistical analyses) for litter decomposing in terrestrial (brown) and aquatic (blue) ecosystems at five locations. The relative net difference was calculated as[(N i,m 2 N f,m )/N i,m ] -[(N i,a -N f,a )/N f,a ],where N i,m and N i,a are the initial (i), and N f,m and N f,a are the final (f), amounts of N in a particular litter type in a mixture (m) or alone (a).

Received 28

 28 November 2013; accepted 11 March 2014.

Table 1 |

 1 Variance in C and N loss associated with diversity and sitesThe relative contributions of variance in C and N loss associated with diversity and sites (expressed in percentage sums of squares (% SS)) in a large-scale leaf litter decomposition experiment. The main factors are italicized. ***, P , 0.001, analysis of variance based on sequential sums of squares (see Methods). See Extended Data Table5for details. DF, degrees of freedom; FT, functional type.

	Factor

Acknowledgements We thank A. Lecerf and P. Garcı ´a-Palacios for comments on the manuscript. We are grateful to numerous technicians in Montpellier (France) for building field microcosms, in Du ¨bendorf (Switzerland) for water chemical analyses, and in Du ¨bendorf and Go ¨ttingen (Germany) for grinding litter samples. We also thank M. Schindler for assistance, B. Buatois, R. Leclerc, P. Schevin and L. Sonie ´for analyses performed at the Plate-Forme d'Analyses Chimiques en Ecologie, LabEx CeMEB (France), G. Larocque for help with R code and our many colleagues at the field sites and research institutes for their support in various ways. This study is part of the BioCycle research project funded by the European Science Foundation (ESF) as part of its EUROCORES programme EuroDIVERSITY. BioCycle has been endorsed by DIVERSITAS as contributing towards their scientific research priorities in biodiversity science.

Author Contributions All authors contributed to the experimental design, data acquisition and revision of the final manuscript. Statistical analyses were performed by I.T.H., B.S., J.V.R. and B.G.M., and the manuscript was written by I.T.H., S.H. and M.O.G.

Extended Data Table 2 | Plant functional types, species identity and litter quality traits Leaf litter was sampled from location-specific native tree species corresponding to four functional types (top) varying in quality traits associated with decomposition (bottom). * All data are shown as percentage dry mass (mean 6 s.e.m., n 5 5). Methods are described in detail in ref. 31.

Extended Data Table 3 | Results of analyses of variance testing for the net diversity effect (NE), complementarity effect (CE) and selection effect (SE) on C loss (top) and N loss (bottom) from decomposing leaf litter* * Interaction terms omitted from the final model are not significant for any of the three response variables. { Location, ecosystem type and their interaction were tested against the block rather than against the residual.

Author Information Reprints and permissions information is available at www. nature.com/reprints. The authors declare no competing financial interests. Readers are welcome to comment on the online version of the paper. Correspondence and requests for materials should be addressed to S.H. (stephan.hattenschwiler@cefe.cnrs.fr).

Extended Data Table 4 | Characteristics of stream macroinvertebrate communities at the five tested locations* * The mean density and total taxon richness of detritivores, their main invertebrate predators, and the mean proportion of Plecoptera, Trichoptera and Gammarus as a percentage of total detritivore abundance (mean 6 s.d.) are shown. All samples were collected using a 500-mm mesh net at the same time of year as the main experiment (although in different years in some cases). Specific sampling protocols differed between locations, with the density standardized to the number of individuals per metre squared. For the subarctic site, six replicate kick samples were taken, each from an area of 1 m 3 0.35 m for 1 min, during September 2006. Identification was mostly to the species level [START_REF] Lepori | Deterministic control on community assembly peaks at intermediate levels of disturbance[END_REF] . For the boreal site, four replicate Surber samples per year were taken for three years, during October 2010-2012, with a quadrat size of 0.25 m 3 0.5 m. Identification was mostly to the species level (B.G.M. and P.-O. Hoffsten, unpublished observations). For the temperate site, five replicate sweep net samples were taken, each from an area of 0.3 m 3 5 m, in October 1992. Identification was mostly to the species level [START_REF] Nijboer | Macrofaunagemeeenschappen in de Periode 1970-1995[END_REF] . For the Mediterranean site, five replicate Surber samples were taken, with a quadrat size of 0. { Detritivore community composition data do not sum to 100% at all locations, owing to the presence of other dipteran (Tipulidae), lepidopteran (Pyralidae) and crustacean (Asellidae) shredders at the temperate site, and tipulid and pyralid shredders at the tropical site. { The caddisfly Micrasema (Brachycentridae) was common at the Mediterranean site but was small and was not regarded as a shredder.

Extended Data Table 5 | Full model output of the relative contributions of variance associated with diversity and sites to explain C and N loss Variance associated with diversity and sites is expressed in percentage sums of squares (% SS); levels of significance are *P , 0.05, **P , 0.01, ***P , 0.001. { Plant species were selected to represent the same four functional types (FTs) at each location (N-fixing, evergreen, rapidly decomposing deciduous trees or shrubs, or slowly decomposing deciduous trees or shrubs). Litter FT richness (linear contrast) and litter diversity (factorial contrast) were fitted before litter FT compositions. { An alternative model omitting richness and testing in detail the litter FT compositions in a full factorial analysis with contrasts for FT presence/absence and interactions is presented in Extended Data Table 7. That model highlights the importance of the interaction between the litter of the N-fixing FT and the rapidly decomposing FT, hinting at a N-transfer mechanism.

Extended Data Table 6 | Characteristics of soil fauna communities at the five tested locations* * The mean density, the total taxon richness and the proportion of dominant taxa as a percentage of total community abundance are shown (mean 6 s.d.). Communities are divided into mesofauna and macrofauna, reflecting an increase in body size that relates to mesh size differences in the field microcosms. { Taxon richness is based on the number of observed families. { Dominant taxa data are based on a lower taxonomic resolution than taxon richness, mainly order or class level. The community composition data do not always sum to 100% at all locations, owing to the presence of other taxa. * The decomposer community was fitted as a log-linear contrast and not a factorial contrast (a factorial contrast is shown in Table 1). { The location and ecosystem type and their interaction were tested against the block rather than against the residual.

Extended Data Table 8 | Analysis of variance testing for the proportional change in total litter N content

The test compares specific two-species combinations, including the particular functional types of N-fixing and rapidly decomposing plants, to their respective single-species treatment across location and ecosystem type.

* A significant difference in the mixture 3 functional type interaction is taken as an indication of N transfer between litter species. { Location and ecosystem type and their interaction were also included as factors in this analysis and were tested against the block rather than the residuals.


Gaspard Rihm

Bruno Fady
email: bruno.fady@inrae.fr

Caractériser l'enveloppe climatique future des jardins communs forestiers : une approche essentielle pour mieux raisonner l'adaptation des espèces

Keywords: niche climatique, test de provenances, adaptation génétique, plasticité phénotypique climate niche, provenance test, genetic adaptation, phenotypic plasticity OUTILS ET MÉTHODES

Messages clés :

• Les jardins communs sont des outils pour tester l'adaptation des essences au climat futur.

• Les conditions climatiques actuelles et futures dans ces jardins ont été évaluées.

• La zone méditerranéenne préfigure les conditions du futur dans une grande partie de la France.

Caractériser l'enveloppe climatique future des jardins communs forestiers : une approche essentielle pour mieux raisonner l'adaptation des espèces L'adaptation au changement climatique est un enjeu de gestion majeur pour les forêts françaises. Pour décrire et comprendre la variabilité génétique et la plasticité phénotypique des caractères d'intérêt pour l'adaptation, les jardins communs (dont les tests de provenances) sont un outil de choix. Nous décrivons dans ce travail un des réseaux forestiers français majeur de tests en jardins communs (GEN4X), son amplitude écologique et, via une approche de modélisation de niche, sa sensibilité à un climat qui va en se réchauffant et s'asséchant au cours du siècle. Une attention particulière a été portée aux dispositifs situés en région méditerranéenne.

Characterising the future climate envelope of common forest gardens: a key approach to better rationalise species adaptation

Adaptation to climate change is a major management stake for French forests. Common gardens (including provenance tests) are choice tools to describe and understand the genetic variability and phenotypic plasticity of traits of interest for adaptation. The present work describes i) one of the major forest networks dedicated to common garden tests in France (GEN4X), its ecological range, and ii) through a niche modelling approach, its sensitivity to climate, which is going to grow warmer and drier in the course of the century. Special attention was paid to the devices located in the Mediterranean region.

L es forêts sont un des habitats les plus diversifiés et les plus riches en espèces au monde [START_REF] Myers | Biodiversity hotspots for conservation priorities[END_REF]. Elles fournissent de multiples services écosystémiques tels que la régulation du climat et du cycle de l'eau, le contrôle de l'érosion et la production de biomasse [START_REF] Thompson | Forest biodiversity and the delivery of ecosystem goods and services: Translating science into policy[END_REF]. Ces services sont et devraient être largement et négativement impactés par le changement climatique global [START_REF] Thom | Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests[END_REF]. L'augmentation des températures à la surface du globe devrait significativement affecter la productivité et la pérennité des peuplements forestiers via l'augmentation de la fréquence et de l'intensité des feux de forêts, des pullulations d'insectes et de pathogènes et des sécheresses, notamment [START_REF] Choat | Global convergence in the vulnerability of forests to drought[END_REF][START_REF] Seidl | Forest disturbances under climate change[END_REF][START_REF] Gallagher | Safety margins and adaptive capacity of vegetation to climate change[END_REF][START_REF] Coop | Wildfire-Driven Forest Conversion in Western North American Landscapes[END_REF][START_REF] Forzieri | Emergent vulnerability to climate-driven disturbances in European forests[END_REF].

Ces conséquences indirectes du changement climatique global affecteront la survie des arbres qui ne manquent cependant pas d'atouts pour réagir, notamment si les changements ne dépassent pas ceux sur lesquels les Nations unies se sont entendus dans le cadre des accords de Paris sur le climat en 2015 (https://unfccc.int/fr/processus-et-reunions/l-accord-de-paris/l-accord-de-paris). Ces atouts sont en particulier leur capacité à modifier leur physiologie lorsque l'environnement change (plasticité phénotypique) et leur capacité à produire une descendance abondante et souvent fréquente dont les carac-tères phénotypiques pourront évoluer vers un nouvel optimum dans de nouvelles conditions environnementales sous l'effet de la sélection naturelle (adaptation génétique). Plasticité phénotypique et adaptation génétique procurent un potentiel d'adaptation non négligeable aux arbres forestiers face à des changements rapides [START_REF] Aitken | Adaptation, migration or extirpation: climate change outcomes for tree populations[END_REF][START_REF] Alberto | Potential for evolutionary responses to climate change -evidence from tree populations[END_REF].

Un bon outil expérimental pour étudier la réponse à la sélection de populations différentes est le test en « jardins communs ». Cet outil consiste à installer dans un environnement commun des individus issus d'origines génétiques diverses (par exemple des origines géographiques différentes, les provenances, ou des familles différentes issues d'un même peuplement forestier), collectivement appelées « génotype ». Du fait de cet environnement commun, qui peut être décrit plus ou moins finement par la constitution de blocs, les différences phénotypiques mesurées entre génotypes différents dans un jardin commun pourront être attribuées à de la variabilité génétique (figure 1, ci-dessous). Les différences observées chez un même génotype planté dans des jardins communs différents relèveront de la plasticité phénotypique, avec adaptation locale si les génotypes locaux ont systématiquement une meilleure performance dans leur environnement d'origine que les autres génotypes [START_REF] Kawecki | Conceptual issues in local adaptation[END_REF][START_REF] Lefèvre | Les processus biologiques de réponse des arbres et forêts au changement climatique : adaptation et plasticité phénotypique[END_REF][START_REF] De Villemereuil | Méthodes pour l'étude de l'adaptation locale et application au contexte de l'adaptation aux conditions d'altitude chez la plante alpine Arabis alpina[END_REF]. On trouve les premières traces des jardins communs dès le XVIII e siècle. Le botaniste et inspecteur-général de la Marine française, Duhamel du Monceau, imagine alors les premiers tests de provenances de Pinus sylvestris, dans l'optique d'augmenter les stocks de bois pour la construction de navires [START_REF] Langlet | Two hundred years genecology[END_REF]. C'est dans les années 1820 que les tests de provenances sont réellement étudiés grâce à Vilmorin, qui reprend les questionnements de Duhamel du Monceau, mais cette fois-ci publie les résultats en 1862. S'ensuit un intérêt croissant pour les tests de provenances, bien représenté dans l'Union internationale des Instituts de Recherches forestières (International Union of Forest Research Organizations, IUFRO) dès sa création en 1892 [START_REF] Matyas | Climatic adaptation of trees: rediscovering provenance tests[END_REF]. Parmi les objectifs premiers de l'IUFRO, on retrouve l'initiation et la coordination de tests de provenances internationaux. Ce sont les débuts de ce que l'on appelle la « génécologie », qui donnera ensuite lieu à de plus en plus d'études sur les liens entre provenances, lignées évolutives, climats, sols, phénotypes, dans une optique d'amélioration et d'augmentation de la ressource forestière disponible pour la plantation, mais aussi de connaissance fondamentale, dont l'adaptation sous l'effet de la sélection naturelle.

Ainsi, les jardins communs sont des dispositifs anciennement utilisés, mais qui restent, aujourd'hui encore, à l'heure de la génomique, et malgré les coûts financiers certains qu'ils engendrent, des outils de choix indispensables pour caractériser la diversité génétique et la plasticité phénotypique de caractères d'importance pour l'adaptation locale et globale [START_REF] Lefèvre | Les processus biologiques de réponse des arbres et forêts au changement climatique : adaptation et plasticité phénotypique[END_REF][START_REF] Martínez-Sancho | Post-glacial re-colonization and natural selection have shaped growth responses of silver fir across Europe[END_REF].

En France, INRAE gère un réseau très conséquent de jardins communs forestiers, appelé GEN4X, acronyme de « réseau de génétique forestière pour la recherche et l'expérimentation » (https://www6.inrae.fr/in-sylvafrance/Services/In-Situ/Reseau-GEN4X). Le réseau comprend plus de 2 000 ha de tests pour un total d'environ du Groupe d'experts intergouvernemental sur l'évolution du climat (GIEC) : RCP2.6 et RCP8.5 [START_REF] Moss | Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies[END_REF].

Pour évaluer les enjeux futurs en termes de recherche et avec une vision spatialisée, nous avons considéré que l'ensemble des jardins communs du réseau GEN4X occupait une niche climatique, à l'image de ce qui se fait pour les espèces [START_REF] Thuiller | Patterns and uncertainties of species' range shifts under climate change[END_REF] Statistics (TSS) comme métrique d'évaluation de la pertinence du modèle choisi [START_REF] Allouche | Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)[END_REF]

RÉSULTATS description taxonomique et écologique des jardins communs GeN4X

La base de données du réseau GEN4X référence actuellement 1 208 dispositifs en jardin commun répartis sur l'ensemble du territoire français pour lesquels des coordon-

Présence des espèces méditerranéennes dans le réseau des jardins communs GeN4X

Les espèces d'arbres forestiers méditerranéens (au sens de Médail et al., 2019 pour les espèces européennes et dont la majorité de l'aire de répartition est en climat méditerranéen pour les autres) sont présentes dans 15 % des jardins communs GEN4X. Elles sont absentes du Nord-Est et leur présence est essentiellement limitée à la région bioclimatique méditerranéenne (figure 4, ci-dessus).

Représentativité climatique actuelle et future des jardins communs GeN4X

Que ce soit en termes de variables climatiques moyennes, de saisonnalité ou d'extrêmes, le réseau couvre actuellement très bien la variabilité climatique française métropolitaine, à l'exception des régions montagneuses à climat froid (figure 5

Modélisation de la « niche » climatique actuelle et future des jardins communs GeN4X

Parmi les 19 variables climatiques fournies par ClimateEU, 6 variables non-colinéaires ont été retenues, qui expliquent la majeure partie de la variabilité climatique des jardins communs du réseau GEN4X suite à une analyse en composantes principales : température moyenne annuelle, moyenne mensuelle de l'amplitude des températures diurnes, température maximale du mois le plus chaud, précipitation moyenne annuelle, saisonnalité des précipitations et précipitations cumulées du trimestre le plus chaud.

Le modèle qui permet de décrire le mieux le jeu de données des dispositifs GEN4X en utilisant ces variables climatiques de température et de pluviométrie les plus pertinentes, est la méthode d'apprentissage automatique « Random Forest » (score TSS = 0,630). Ce score étant néanmoins relativement faible (l'adéquation parfaite entre données et modèle étant obtenue pour une valeur TSS = 1), nous avons construit un modèle d'ensemble, une fonctionnalité de biomod2 permettant de maximiser les performances des algorithmes individuels testés [START_REF] Hao | A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD[END_REF]. Utilisant la méthode des « weighted mean of probabilities », le score TSS du modèle d'ensemble basé sur les cinq algorithmes testés est ainsi passé à 0,728,

Figure 2 lFigure 3 l

 23 Figure 2 l Répartition taxonomique et dates de plantation de chacun des jardins communs du réseau GEN4X

Figure 4 lFigure 5 l

 45 Figure 4 l Carte de localisation des dispositifs en jardin commun du réseau Gen4X en France contenant des espèces d'arbres forestiers méditerranéens

Figure 6 l

 6 Figure 6 l Changement d'occupation spatiale de la niche climatique modélisée des jardins communs GEN4X à l'horizon 2050-2080 sous deux scénarios climatiques : RCP2.6 (ligne du haut) et RCP8.5 (ligne du bas) et deux jeux de données : France entière (panneaux de gauche) et GRECO Méditerranée (panneaux de droite) En gris clair les zones d'absence, en rouge les zones où la niche climatique n'existe plus, en vert clair les zones inchangées et en vert foncé les zones où la niche climatique est nouvelle par rapport à l'époque actuelle (1990-2020).

  Photo 1 l Un jardin commun d'intérêt pour l'identification de génotypes pour la diversification de la forêt française dans le contexte du changement climatique : le test de provenances / descendances de sapin de Céphalonie (Abies cephalonica Loud.) en forêt domaniale de St Lambert, Vaucluse, récemment homologué « verger à graines » Photo : © INRAE

Example de jardin commun en blocs complet à composition aléatoire Chaque génotype est présent dans chaque bloc et est répété 54 fois dans le dispositif 10 provenances 3 individus par provenance bloc 18 blocs zone impossible à planter : pierrier zone impossible à planter : pierrier

	Figure 1 l Représentation schématique d'un jardin commun (en blocs complet à composition aléatoire)
	Chaque génotype est présent dans chaque bloc et est répété 54 fois dans le dispositif (10 génotypes, 18 blocs, 3 individus par géno-
	type et par bloc). Une partie de la zone a été simulée comme non plantable. Le dispositif est entouré de plants « de bordure », isolant
	le dispositif du milieu environnant et supprimant les effets de bordure pour les plants expérimentaux. Chaque individu est identifiable
	par sa position géographique (chiffres en bleu). La position de deux génotypes (bleu et vert) est donnée à titre d'exemple.
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	o	x	x	x	x	x	x	x	x	x	x						x	x	x	x	x	x	x	x	x	x	o
	o	x	x	x	x	x	x	x	x	x	x						x	x	x	x	x	x	x	x	x	x	o
	o	x	x	x	x	x	x	x	x	x	x						x	x	x	x	x	x	x	x	x	x	o
	o	x	x	x	x	x	x	x	x	x	x						x	x	x	x	x	x	x	x	x	x	o
	o	x	x	x	x	x	x	x	x	x	x						x	x	x	x	x	x	x	x	x	x	o Génotype A
	o	x	x	x	x	x	x	x	x	x	x						x	x	x	x	x	x	x	x	x	x	o Génotype B
	o	x	x	x	x	x	x	x	x	x	x						x	x	x	x	x	x	x	x	x	x	o x = plant expérimental
	o	x	x	x	x	x	x	x	x	x	x						x	x	x	x	x	x	x	x	x	x	o o = plant de bordure
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	9 o	x	x	x	x	x	x	x	x	x	x						x	x	x	x	x	x	x	x	x	x	o
	8 o	x	x	x	x	x	x	x	x	x	x						x	x	x	x	x	x	x	x	x	x	o
	7 o	x	x	x	x	x	x	x	x	x	x						x	x	x	x	x	x	x	x	x	x	o
	6 o	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	o
	5 o	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	o
	4 o	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	o
	3 o	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	o
	2 o	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	o
	1 o	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	o
	o	o	o	o	o	o	o	o	o	o	o	o	o	o	o	o	o	o	o	o	o	o	o	o	o	o	o
		1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25

  , p. 501). Par contre, et quel que soit le scénario de changement climatique envisagé, les climats actuels des jardins communs seront en grande majorité absents des climats moyens, de saisonnalité et extrêmes de la seconde moitié du XXI

e siècle. Seuls les climats actuels des jardins communs méditerranéens échantillonnent encore des climats existant dans le futur, sauf pour ce qui est des valeurs extrêmes sous scénario RCP8.5 (figure

5

, p. 501).

  Le jeu de données sur lequel nous avons travaillé ne contient pas tous les jardins communs génétiques présents en France métropolitaine. De nombreux tests INRAE contenant du Pin maritime, du Douglas ou du Peuplier ne sont pas encore archivés dans des bases de données en accès libre. De même, les tests mis en place par le Centre national de la propriété forestière (CNPF) ou l'Office national des forêts (ONF) ne figurent pas dans cette base de données. Néanmoins, le jeu de données sur lequel nous avons travaillé est très conséquent, taxonomiquement et spatialement. Nous pensons que nos conclusions sont robustes même s'il y a bien un enjeu à répertorier in extenso et valoriser l'information concernant tous les jardins communs français, par exemple via l'infrastructure nationale de recherche IN-Sylva France.

  Quelques régions demeurent stables, voire gagnent en compatibilité climatique au cours de la seconde moitié du XXI e siècle (Bretagne, Normandie, régions froides des montagnes). Le nombre de jardins communs y est actuellement plutôt faible voire inexistant. Y développer de nouveaux jardins communs avec une large palette de génotypes issus des taxons actuellement présents dans l'ensemble du réseau nous semble une piste à suivre en matière de recherche et développement.La région méditerranéenne française mérite une mention particulière. Sous le scénario RCP8.5, elle se rajoute aux régions dont l'enveloppe climatique des jardins communs actuels disparaît (figure 6, p. 502, zones rouges). Elle contient des ressources génétiques (essentiellement testées localement) d'intérêt pour les régions fortement impactées par le changement climatique (photo 1, p. 503). À ce double titre, ses jardins communs méritent d'être suivis comme des vigies des effets des changements climatiques. La région méditerranéenne paraît aussi adaptée à l'installation de deux types de jardins communs :-à court terme avec des espèces forestières majeures de l'économie forestière française, permettant de tester les limites adaptatives de leurs divers génotypes et origines géographiques à des stades précoces (niche de germination et niche de recrutement) ;-à long terme en introduisant des essences rustiques résistantes à la sécheresse, en comparaison avec les génotypes locaux. On peut penser à des autochtones comme le Pin d'Alep (récemment intégré à la norme NF B52 001 des bois de structure) et le Chêne vert qui couvrent de vastes espaces en Méditerranée et en France, au discret Érable de Montpellier, ou aux exotiques comme le Pin brutia, le Thuya de Berbérie, les sapins méditerranéens ou les chênes méditerranéens dont l'acclimatation en France n'est testée que dans quelques régions climatiques. Il est urgent d'établir une liste de ces espèces, plutôt méconnues, de les conserver et de les tester, dans un contexte dominé par l'incertitude sur les extrêmes climatiques, mais dont la tendance est bien aux climats plus chauds et secs en Europe occidentale.

Rev. For. Fr., 74, 4-2023 © AgroParisTech, 2023

Remerciements

Les données des jardins communs ont été vérifiées par leurs référents régionaux INRAE respectifs que nous remercions : Denis Vauthier (Unité expérimentale Entomologie et Forêts Méditerranéenne, Avignon), Frédéric Bernier (Unité expérimentale Forêt Pierroton, Bordeaux) et Dominique Veisse (Unité expérimentale GBFOR, Orléans).

Les données climatiques actuelles et modélisées pour le futur ont été transmises par Maurizio Marchi (Institute of Biosciences and BioResources, Florence, Italie). Elles sont issues de l'outil de désagrégation spatiale des données climatiques qu'il a développé : https://www. ibbr.cnr.it/climate-dt/.

Nous remercions aussi Marta Benito Garzón (UMR Biogeco, INRAE Bordeaux) et Wilfried Thuiller (CNRS, Laboratoire d'écologie alpine, Grenoble) pour leurs conseils d'utilisation du modèle de niche utilisé et des analyses en découlant.


Claudiu Constantin Manole

C Pîrvu

F Maury

I Demetrescu

NOVEL APPROACH TO SURFACE PLASMON RESONANCE: A THIRD DIMENSION IN DATA INTERPRETATION THROUGH SURFACE ROUGHNESS CHANGES

Keywords: Surface Plasmon Resonance, Atomic Force Microscopy, Roughness, Potentiostatic

In a Surface Plasmon Resonance (SPR) experiment two key parameters are classically recorded: the time and the angle of SPR reflectivity. This paper brings into focus a third key parameter: SPR reflectivity. The SPR reflectivity is proved to be related to surface roughness changes.

Practical investigations on i) gold anodizing and ii) polypyrrole film growth in presence of oxalic acid is detailed under potentiostatic conditions. These experimental results reveal the potential of using the SPR technique to investigate real-time changes both on the gold surface, but also in the gold film itself. This extends the versatility of the technique in particular as sensitive in-situ diagnostic tool.

INTRODUCTION

In 1968, A. Otto reported the excitation of the surface plasma waves by total reflection between a dielectric and noble metal [1]. Then E. Kretschmann published a subsequent report for an optical system in which the surface plasma was excited inside the metal and treated its implication on the optical constants [2]. Shortly after, this phenomena named Surface Plasmon Resonance (SPR) was used in technological applications as a versatile technique for gas sensors [3], in the study of optical properties of metals [4], or as a microscopy technique [5]. Recently, the SPR technique was integrated into a powerful biosensing system [START_REF] Homola | Surface Plasmon Resonance Based Sensors[END_REF][START_REF] Abbas | [END_REF][8].

The optical SPR device is composed of a prism, a thin gold layer (around 50 nm thick) and a sensing layer forming an interface between the gold film and the surrounding medium.

Each component has its own refractive index values, noted in Fig. 1a with n p for the prism, n m for gold (continuous metal thin film) and n s for the sensing layer. Different approximations were used to study this phenomenon, with increased accuracy [9,10]. However using the Fresnel equations [START_REF] Sernelius | Surface Modes in Physics[END_REF], we can look at an approach which provides a relationship to the classical Lambert-Beer law while also highlighting practical aspects related to the surface features.

The SPR technique was also coupled with an electrochemical system to simultaneously monitor the electrochemical reactions and the SPR behavior. A previous study described some aspects related to the synergetic use of these techniques [START_REF] Pirvu | [END_REF]. However, in the present paper we followed an original approach which consists in using the electrochemical system only to produce the chemical reactions, while the SPR was used as an in-situ diagnostic tool to investigate them. In order to comply with the theoretical approach, a potential was applied potentiostatically so that its value can be considered as a constant. Practical SPR implication on in-situ polypyrrole doping is detailed. The changes at the gold interface (i.e. polymerization) and in the gold layer itself (anodizing) is investigated by considering the reflectivity angle change, as described in the fundamental part (usual approach) but also the reflectivity intensity (an original view describing a third parameter), as highlighted in the next section.

FUNDAMENTAL PRINCIPLES

When an electromagnetic wave of light is passing from one medium to another, it changes its direction as shown schematically in Fig. 1a, according to the Snell law:

m m p p n n T T sin sin (1)
where ș p is the angle of the incident electromagnetic wave and ș m is the emergent one. When the ș m angle reaches the value 90°, the light wave is totally reflected. Under the total reflection conditions, the value of ș p is also called the critical angle, and it can be determined by inserting ș m = 90° in Eq. (1).

The SPR apparatus uses a p-polarized incident light. The total reflected amplitude of this incident light at the prism-metal interface is given by:

m p p m m p p m pm n n n n r T T T T cos cos cos cos (2)
where

p m p m n n T T 2 2 2 sin 1 cos (3)
Under these total internal reflection conditions, an evanescent wave is formed at the interface of the medium with a magnitude of the wave vector k ev [13]. This parallel evanescent wave is amplified in the presence of the gold film. This generates collective oscillations of the plasmons (free electrons in the metal network) at the metal-sensing layer interface. The wave vector of these surface plasmons can be defined as [13]:

2 2 2 2 2 s m s m SPR n n n n k O S (4)
Under these conditions, internal reflection will occur inside the metal layer. The amplitude of this reflection is described by:

s m m s s m m s ms n n n n r T T T T cos cos cos cos (5)
where

p s p s n n T T 2 2 2 sin 1 cos (6)
The refractive index is a complex quantity that can be written as:

) 1 ( ~j j j j j i n i n n N .
, where j is the p, m and s components [START_REF] Abbas | [END_REF] An overview on the total reflection intensity (R) of the incident monochromatic light for the whole SPR device is given by [START_REF] Sernelius | Surface Modes in Physics[END_REF]:

2 2 2 2 2 2 2 2 sin 4 exp 1 sin 4 exp 1 • © § 1 • © § p p m ms pm p p m ms pm h i r r h i r r r R T N N O S T N N O S (8)
The exponential term in Eq. 8 describes the intensity decrease in the reflected monochromatic light. The parameter ț m is associated to the "internal" intensity loss in the metal, while ț p represents the intensity that is lost into the prism. The thickness of the metal-sensing layers (h) has also an important role in the reflection decrease, as evidenced by the Eq. 8 since it is involved in the exponential term.

One of the conditions required for the SPR technique is that k ev = k SPR . Considering this, the SPR resonance angle (ș SPR ) can be calculated as:

1 • © § 2 2 2 2 2 sin s p m p p SPR n n n n T T (9)
In the SPR technique we are usually monitoring the changes of the reflectivity intensity and the reflectivity angle as a function of time (in the range of a few seconds). The first one is described by the real part of the refractive index (Eq. 8), while the second one by the imaginary part (the exponents in Eq 8). Punctually, with the SPR apparatus we have a series of constant parameters. For example, by taking n m = constant, Ȝ = constant (with C as integration constant)

and n p = x, we can define the refractive index rate of change as:

³ ³ ³ 1 • © § 1 • © § f i SPR SPR s f i s s f i s SPR d dn n x C dn n x C R T T T 2 2 2 2 2 cos 2 sin ) ( (10)
Since ț p and ț m are intrinsic properties of the prism and of the metal respectively, therefore they are constant, and with in addition Ȝ = constant one can define the reflectance intensity rate of change as:

SPR f i SPR f i SPR I d I h d h Z C I R ³ ³ ) ( (11)
where Z(h) represents the height profile function, while dh represents infinitesimal changes of thickness at the interface. Therefore, the gold film thickness is an important parameter in achieving optimum SPR reflectivity [2]. The thickness profile is directly related to the surface profile, which in turn reflects the surface roughness. In a basic overview, the changes in surface roughness perturb the SPR wave by local changes of the surface normal, and implicitly of the incidence angle from the metal to the sensing layer as indicated in Fig. 1b. This defines practical implications for another SPR key parameter: the reflectivity intensity. Its relationship with surface roughness will be further detailed with the experimental results of this work.

EXPERIMENTAL DETAILS

The An Atomic Force Microscope (AFM) from APE Research, Italy, was used to obtain the microscopy details on the surface features. The AFM recording had been achieved with a contact mode cantilever of silicon by applying a 5 nN loading force to scan with a rate of 5 µm/sec the 5x5 µm 2 area and using an acquisition delay of 0.01 ms. The modular program Gwyddion 2.36 was used for data processing.

RESULTS AND DISCUSSION

The insight into the third key parameter which provides an expansion in SPR data interpretation will be focused on: (i) the implications of Au anodizing in SPR data; (ii) how anodizing changes the roughness of the Au thin film and (iii) what are the practical aspects when sensing the reactions at the interface during real time monitoring of electrochemical polymerization.

Gold Anodizing by Oxalic Acid

Chemical reactions can occur at the metal interface with the surrounding medium or the sensing layer, without any changes of the gold surface. An example for two aqueous solutions (ultrapure water and Na 2 SO 4 electrolyte) is shown in Fig. 2a, from 0 V up to the potentiodynamic range where water electrolysis occurs (1 V). These changes are associated to the refractive index of aqueous solutions which induces a shift of the SPR angle of a few millidegrees, but no significant change in the reflectivity intensity [START_REF] Pirvu | [END_REF].

However, an anodizing process can influence changes in the gold layer, and implicitly in its surface roughness. Such a change in roughness can influence the reflectivity intensity according to Eq. ( 11) and as shown schematically in Fig 1b . In Fig. 2b such a change in the reflectivity intensity is experimentally observed for an oxalic acid electrolyte at 0.8 V by comparing the curves after 1 s and 95 s. As reported by K. Nishio and H. Masuda [14], in anodizing conditions such as the ones dealt in this paper, the reflectivity increases considerably (roughly four times in our case).

There are two electrochemical processes that can be electrochemically detected at the Au surface, namely the i) oxidation of the adsorbed species and ii) the Au oxidation. The oxidation of the adsorbed OH to O onto the Au surface had been reported up to 0.9 V vs Ag/AgCl [15].

Subsequently, the oxidation of gold to its trivalent state (Au 2 O 3 ) should occur at around 1.2 V vs Ag/AgCl [16]. The electrochemical mechanism of Au anodizing in oxalic acid at this low potential is a particular case open for clarification. However, after the anodizing process the formation of nanoporous gold was reported [17]. An AFM study was thus made in order to elucidate the changes that occur in this particular case at the Au surface after the anodizing process.

Surface Roughening

As observed in Fig. 3a, the bare gold surface closely resembles the ones as-deposited by sputtering [18,19]. After the potentiostatic treatment, a clear surface roughening is observed in Fig. 3b, with the appearance of heightened surface features. In the selected linear profile the surface height varies with around 4 nm (Fig. 4a) for the bare gold, while the roughened one has a selected surface feature of the height of an order of magnitude higher, up to 70 nm (Fig. 4b).

Since these local readings do not highlight the overall feature characteristic of the complete area of interest, a general parameter is required for characterization. A standardized statistic parameter which describes the surface features is the Average Surface Roughness (or R a ) [START_REF]Surface Texture: Surface Roughness, Waviness and Lay; ASME B[END_REF]:

h d h Z N R N a ³ 0 1 ( 12
)
where N is the number of points obtained during the evaluation. The close resemblance of Eq. 12 and Eq. 11 emphasizes the close relationship between the surface roughness and the recorded reflectivity. For the bare Au the calculated roughness is 2 nm, while for the anodized gold the surface roughness increased almost 4 fold, to 7.6 nm. The optimum control of the Au surface roughness is reported as an useful tool not only to prevent the unwanted effect of radiation dampening for SPR [START_REF] Velinov | [END_REF], but also to obtain the specific SPR-related Enhancement for Raman Spectroscopy, also called SERS [15,22].

The AFM results coupled with the Reflectivity changes show a practical relationship between the surface roughness and the SPR response. The anodizing implication on the change in reflectivity adds a new dimension to the SPR data interpretation that is to our knowledge unprecedented.

In previous studies we had used the SPR phenomenon as Au nanoparticles marker in a polymeric film as in the paper [23]. Since the oxalic acid behaves also as a marker by decreasing the SPR reflectance, we can monitor its behavior in competing chemical reactions. For this, we can consider as a practical example the in-situ polymerization of pyrrole (Py).

Practical Approach: in situ Pyrrole Polymerization

Polypyrrole (PPy) polymerizes electrochemically by oxidation at +0.8 V vs Ag/AgCl.

During the PPy growth, charge-compensating anions are accommodated. The anion accommodation in the PPy film (also called doping) can be achieved according to the reaction [24]:

> @ o ne n A PPy nA PPy n oxidation
where A -is the anion that compensates the positive charges generated during oxidation.

The anion involved in the PPy doping can be the oxalate anion (C 2 O 4

2-

). However, the large polystyrene sulfonate ([C 8 H 7 SO 3 -] n ; namely PSS -) anion can also be accommodated. Consequently, we have set the conditions for a doping competition between the small oxalate anion and the large PSS -one, by mixing both species in the same aqueous solution.

The same average number of doping anions was reported for both the small (ClO 4 -) and the large (PSS -) anion [25]. Consequently, the density (mass/volume) of the overall film was reported to be 19% higher for the large PSS molecular weight than for the small anion [25]. In Fig. 5a the in-situ SPR angle response shows that the increasing concentration of the large PSS - anion in the solution leads to an increase in angle. The angle increase implies an increase in weight [START_REF] Homola | Surface Plasmon Resonance Based Sensors[END_REF] due to changes in refractive index (see Eq. 9). This confirms that more mass is deposited with PSS -anion than with oxalate anion. Because of the limiting amount of anions which can be accommodated into the film, a saturation trend at the increased NaPSS concentrations can also be observed (for 100 and 200 µM).

About the ion diffusion in the PPy film, the relative mobility of the ions was reported to be related to their size [26], i.e. the oxalate anion has a high mobility into the PPy film, while the large PSS -anion has considerable low mobility. In addition, denser films lead to lower ion diffusion. Thus, in the PSS -denser film, the oxalate diffusion decreases due to low mobility and increased density. Considering the overall PSS -and oxalate anions competition, the oxalate should dope to a higher degree the PPy film.

The SPR reflectivity in Fig. 5b indicates that, although the PSS -anion prevented the oxalate doping, the oxalate-favoring decrease in reflectance is still observable. Furthermore, the reflectivity decreases notably in the presence of PSS -(for instance from 14% to 11% for 50 µM NaPSS -). Between 20 and 80 s of polymerization, PSS -has the most significant contribution to the doping, with respect to the oxalate (Fig. 5b). In the region until 80 s there are complex processes that occur simultaneously with different rates. Such processes are, for example, pyrrole (Py) adsorption, doping anions adsorption, formation of PPy nucleation sites, continuous polymerization on the already formed sites and film doping [27]. Also, Pt dissolution in highly acidic media has been previously reported [28,29]. However the low concentration of oxalic acid, the Pt complexing properties of oxalate [30] and the increased electron density at Pt interface (due to electrode polarization) indicate a potential manner in which the risk of film contamination with Pt is minimized.

After 80 s a relatively stable response is observed for all of the polymerization conditions (Fig. 5b).

Next to the classical increase in weight investigated by the SPR angle dependency, we obtained a complementary insight into the in-situ ion diffusion of oxalic acid during PPy polymerization. This is achieved by monitoring the intensity of reflectivity as a third key SPR parameter.

CONCLUSIONS

Beside the already consecrated SPR parameters (e.g. time and SPR angle) we brought into focus a third key parameter: the SPR reflectance intensity. Considering this parameter, we acquired practically two times more analytical results using the same SPR measurements. The reflectivity intensity change was directly related to the changes in surface roughness of the thin Au film.

The highly sensitive SPR was not only used in a classical manner to monitor weight changes at the interface, but also to monitor in-situ changes in roughness. In this manner, any changes (either electrochemically or of any other nature) which occur in the Au film itself can be investigated by SPR. This allowed investigating the in-situ competitive behavior during PPy polymerization between oxalate and PSS -. Consequently, the dependence of ion diffusion on the film density had been confirmed to occur even during the PPy growth.

These results expand the sensing range of applicability not only at the gold surface, but also to the changes in the gold film itself. b)

Captions for Figures

  experiments were made with an Autolab ESPRIT SPR device. The electrochemical cell is composed of the SPR cuvette with three electrodes: Au working electrode, Pt counter electrode and Ag/AgCl reference electrode. The connection of the electrodes was made to an Autolab PGSTAT 320N potentiostat-galvanostat. Three types of electrolytes were used: i) ultralow ionic solution of deionized water (Millipore 18.2 Mȍ cm), ii) Na 2 SO 4 50 mM and iii) oxalic acid (H 2 C 2 O 4 ) 50 mM. For polymerization, polypyrrole PPy 200 mM was added in the oxalic acid electrolyte, and subsequently polystyrene sulfonate (NaPSS), Mw = 70,000, as competing dopant at varying concentrations (50, 100 and 200 µM).

Fig. 1 .

 1 Fig. 1. Schematic view of the SPR device in the three layer configuration, for a) planar surface

Fig. 2 .

 2 Fig. 2. SPR reflectivity vs SPR angle for ultrapure water and Na 2 SO 4 50 mM at 0 and +1 V (vs.

Fig. 3 .

 3 Fig. 3. AFM 3D profile for a) smooth and b) electrochemically roughened Au thin film. The vertical line (1) corresponds to the linescan giving the 2D profile represented in Fig. 4.

Fig. 4 .Fig. 5 .

 45 Fig. 4. AFM 2D profile for a) smooth and b) electrochemically roughened Au thin film

Fig. 1 Fig. 2 .Fig. 3 .Fig. 4 .Fig. 5 .

 12345 Fig. 1.

Acknowledgement: The work has been funded by the Sectoral Operational Programme Human Resources Development 2007-2013 of the Ministry of European Funds through the Financial Agreement POSDRU/159/1.5/S/134398.