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Micro- and nanoscale patterned monolayers of plasmonic nanoparticles were fabricated by combining concepts from colloidal chemistry, self-assembly, and subtractive soft lithography. Leveraging chemical interactions between the capping ligands of pre-synthesized gold colloids and a polydimethylsiloxane stamp, we demonstrated patterning gold nanoparticles over centimeter-scale areas with a variety of micro- and nanoscale geometries, including islands, lines, and chiral structures (e.g., square spirals). By successfully achieving nanoscale manipulation over a wide range of substrates and patterns, we establish a powerful and straightforward strategy, nanoparticle chemical lift-off lithography (NP-CLL), for the economical and scalable fabrication of functional plasmonic materials with colloidal nanoparticles as building blocks, offering a transformative solution for designing next-generation plasmonic technologies.

[Experimental design: thermal performance experiments] All experiments were run in climate-controlled incubation facilities of the Institut Mediterrani d’Estudis Avançats (Mallorca, Spain). Following 48 hrs under ambient (collection site) conditions, samples were transferred to individual experimental aquaria, which consisted of a double layered transparent plastic bag filled with 2 L of filtered seawater (60 μm) (following Savva et al. 2018). 16 experimental bags were suspended within 80L temperature-controlled baths. In total, ten baths were used, one for each experimental temperature treatment. Bath temperatures were initially set to the acclimatization temperature (i.e. in situ temperatures) and were subsequently increased or decreased by 1 °C every 24 hours until the desired experimental temperature was achieved. Experimental temperatures were: 15, 18, 21, 24, 26, 28, 30, 32, 34 and 36°C (Table S2). For each species, four replicate aquarium bags were used for each temperature treatment with three individually marked seagrass shoots or three algal fragments placed into each bag. For P. oceanica, each marked plant was a single shoot including leaves, vertical rhizome and roots. For C. nodosa, each marked individual consisted of a 10 cm fragment of horizontal rhizome containing three vertical shoots. Individually marked seaweeds contained the holdfast, and 4-5 fronds of P. pavonica (0.98 ± 0.06 g FW; mean ± SE) or a standardised 5-8 cm fragment with meristematic tip for C. compressa (3.67 ± 0.1 g FW; mean ± SE). Experimental plants were cleaned of conspicuous epiphytes. Once the targeted temperatures were reached in all of the baths, experiments ran for 14 days for the algal species and 21 days for seagrasses to allow for measurable growth in all species at the end of the experiment. Experiments were conducted inside a temperature-controlled chamber at constant humidity and air temperature (15 °C). Bags were arranged in a 4x4 grid within each bath, enabling four species/population treatments to be run simultaneously. Bags were mixed within each bath so that one replicate bag was in each row and column of the grid, to minimise any potential within bath effects of bag position. Replicate bags were suspended with their surface kept open to allow gas exchange and were illuminated with a 14h light:10h dark photoperiod through fluorescent aquarium growth lamps. The water within the bags were mixed with aquaria pumps. The light intensity within each bag was measured via a photometric bulb sensor (LI-COR) and ranged between 180-258 μmol m-2 s-1. Light intensity was constant between experiments and did not significantly differ between experimental treatments (p > 0.05). The temperature in the baths was controlled and recorded with an IKS-AQUASTAR system, which was connected to heaters and thermometers. The seawater within the bags was renewed every 72 hrs and salinity was monitored daily with an YSI multi-parameter meter. Distilled water was added when necessary to ensure salinity levels remained within the range of 36-39 PSU, typical of the study region. Carbon and Nitrogen concentrations in the leaf tissue were measured at the end of the experiment for triplicates of the 24ºC treatment for each species and location (Fig. S2) at Unidade de Técnicas Instrumentais de Análise (University of Coruña, Spain) with an elemental analyser FlashEA112 (ThermoFinnigan). [Species description and distribution] The species used in this study are all common species throughout the Mediterranean Sea, although differ in their biological traits, evolutionary histories and thermo-geographic affinities (Fig. S1). P. oceanica is endemic to the Mediterranean Sea with the all other Posidonia species found in temperate Australia (Aires et al. 2011). The distribution of P. oceanica is restricted to the Mediterranean, spanning from Gibraltar in the west to Cyprus in the east and north into the Aegean and Adriatic seas (Telesca et al. 2015) (Fig. S1A). C. nodosa distribution extends across the Mediterranean Sea and eastern Atlantic Ocean, where it is found from south west Portugal, down the African coast to Mauritania and west to Macaronesia (Alberto et al. 2008) (Fig. S1B). Congeneric species of C. nodosa are found in tropical waters of the Red Sea and Indo-Pacific, suggesting origins in the region at least prior to the closure of the Suez Isthmus, approximately 10Mya. Like C. nodosa, Cystoseira compressa has a distribution that extends across the Mediterranean and into the eastern Atlantic, where it is found west to Macaronesia and south to northwest Africa (Fig. S1C). The genus Cystoseira has recently been reclassified to include just four species with all congeneric Cystoseira spp. having warm-temperate distributions from the Mediterranean to the eastern Atlantic (Orellana et al. 2019). The distribution of Padina pavonica is conservatively considered to resemble C. nodosa and C. compressa, spanning throughout the Mediterranean and into the eastern Atlantic. We considered the poleward distribution limit of P. pavonica to be the British Isles 50ºN (Herbert et al. 2016). P. pavonica was previously thought to have a global distribution, but molecular analysis of the genus has found no evidence to support this (Silberfeld et al. 2013). Instead it has been suggested that P. pavonica was potentially misclassified outside of the Mediterranean, due to morphological similarity with congeneric species (Silberfeld et al. 2013). Padina is a monophyletic genus with a worldwide distribution from tropical to cold temperate waters (Silberfeld et al. 2013). Most species have a regional distribution, with few confirmed examples of species spanning beyond a single marine realm (sensu Spalding et al. 2007). [Metabolic rates] Net production (NP), gross primary production (GPP) and respiration (R) were measured for all species from the four sites for five different experimental temperatures containing the in-situ temperature during sampling up to a 6ºC warming (see SM Table S3 for details). Individuals of the different species were moved to methacrylate cylinders containing seawater treated with UV radiation to remove bacteria and phytoplankton, in incubation tanks at the 5 selected temperatures. Cylinders were closed using gas-tight lids that prevent gas exchange with the atmosphere, containing an optical dissolved oxygen sensor (ODOS® IKS), with a measuring range from 0-200 % saturation and accuracy at 25ºC of 1% saturation, and magnetic stirrers inserted to ensure mixing along the height of the core. Triplicates were measured for each species and location, along with controls consisting in cylinders filled with the UV-treated seawater, in order to account for any residual production or respiration derived from microorganisms (changes in oxygen in controls was subtracted from treatments). Oxygen was measured continuously and recorded every 15 minutes for 24 hours. Changes in the dissolved oxygen (DO) were assumed to result from the biological metabolic processes and represent NP. During the night, changes in DO are assumed to be driven by R, as in the absence of light, no photosynthetic production can occur. R was calculated from the rate of change in oxygen at night, from half an hour after lights went off to half an hour before light went on (NP in darkness equalled R). NP was calculated from the rate of change in DO, at 15 min intervals, accumulated over each 24 h period. Assuming that daytime R equals that during the night, GPP was estimated as the sum of NP and R. To derive daily metabolic rates, we accumulated individual estimates of GPP, NP, and R resolved at 15 min intervals over each 24 h period during experiments and reported them in mmol O2 m−3 day−1. A detailed description of calculation of metabolic rates can be found at Vaquer-Sunyer et al. (Vaquer-Sunyer et al. 2015). [Thermal distribution and thermal safety margins] We estimated the realised thermal distribution for the four experimental species by downloading occurrence records from the Global Biodiversity Information Facility (GBIF.org (11/03/2020) GBIF Occurrence Download). Occurrence records from GBIF were screened for outliers and distributions were verified from the primary literature (Alberto et al. 2008, Draisma et al. 2010, Ni-Ni-Win et al. 2010, Silberfeld et al. 2013, Telesca et al. 2015, Orellana et al. 2019) and Enrique Ballesteros (pers. comms) (Fig. S1). Mean, 1st and 99th percentiles of daily SST’s were downloaded for each occurrence site for the period between 1981-2019 using the SST products described above (Table S4). Thermal range position of species at each experimental site were standardised by their global distribution using a Range Index (RI; Sagarin & Gaines 2002). Median SST at the experimental collection sites were standardized relative to the thermal range observed across a species realized distribution, using the equation: RI = 2(SM- DM)/DB where SM = the median temperature at the experimental collection site, Dm = the thermal midpoint of the species global thermal distribution and DB = range of median temperatures (ºC) that a species experiences across its distribution. The RI scales from -1 to 1, whereby ‘-1’ represents the cool, leading edge of a species distribution, ‘0’ represents the thermal midpoint of a species distribution and ‘1’ represents the warm, trailing edge of a species distribution (Sagarin & Gaines 2002). Thermal safety margins for each population were calculated as the difference between empirically derived upper thermal limits for each population and the maximum long term habitat temperatures recorded at collection sites. Each population’s thermal safety margin was plotted against its range position to examine patterns in thermal sensitivity across a species distribution. [Growth measurements and statistical analyses] Net growth rate of seagrass shoots was measured using leaf piercing-technique (Short & Duarte 2001). At the beginning of the experiment seagrass shoots were pierced just below the ligule with a syringe needle and shoot growth rate was estimated as the elongation of leaf tissue in between the ligule and the mark position of all leaves in a shoot at the end of the experiment, divided by the experimental duration. Net growth rate of macroalgae individuals was measured as the difference in wet weight at the end of the experiment from the beginning of the experiment divided by the duration of the experiment. Moisture on macroalgae specimens was carefully removed before weighing them. Patterns of growth in response to temperature were examined for each experimental population using a gaussian function: g = ke[-0.5(TMA-μ)2/σ2], where k = amplitude, μ = mean and σ = standard deviation of the curve. Best fit values for each parameter were determined using a non-linear least squares regression using the ‘nlstools’ package (Baty et al. 2015) in R (Team 2020). 95% CI for each of the parameters were calculated using non-parametric bootstrapping of the mean centred residuals. The relationship between growth metrics and the best-fit model was determined by comparing the sum of squared deviations (SS) of the observed data from the model, to the SS of 104 randomly resampled datasets. Growth metrics were considered to display a significant relationship to the best-fit model if the observed SS was smaller than the 5th percentile of randomised SS. Upper thermal limits were defined as the optimal temperature + 2 standard deviations (95th percentile of curve) or where net growth = 0. Samples that had lost all pigment or structural integrity by the end of the experiment were considered dead and any positive growth was treated as zero. Comparative patterns in thermal performance between populations have fundamental implications for a species thermal sensitivity to warming and extreme events. Despite this, within-species variation in thermal performance is seldom measured. Here we compare thermal performance between-species variation within communities, for two species of seagrass (Posidonia oceanica and Cymodocea nodosa) and two species of seaweed (Padina pavonica and Cystoseira compressa). Experimental populations from four locations spanning approximately 75% of each species global distribution and a 6ºC gradient in summer temperatures were exposed to 10 temperature treatments (15ºC to 36ºC), reflecting median, maximum and future temperatures. Experimental thermal performance displayed the greatest variability between species, with optimal temperatures differing by over 10ºC within the same location. Within-species differences in thermal performance were also important for P. oceanica which displayed large thermal safety margins within cool and warm-edge populations and small safety margins within central populations. Our findings suggest patterns of thermal performance in Mediterranean seagrasses and seaweeds retain deep ‘pre-Mediterranean’ evolutionary legacies, suggesting marked differences in sensitivity to warming within and between benthic marine communities. [Sample collection] Sample collections were conducted at two sites, separated by approximately 1 km, within each location. Collections were conducted at the same depth (1-3 m) at each location and were spaced across the reef or meadow to try and minimise relatedness between shoots or fragments. Upon collection, fragments were placed into a mesh bag and transported back to holding tanks in cool, damp, dark conditions (following Bennett et al. 2021). Fragments were kept in aerated holding tanks in the collection sites at ambient seawater temperature and maintained under a 14:10 light-dark cycle until transport back to Mallorca, where experiments were performed. Prior to transport, P. oceanica shoots were clipped to 25 cm length (from meristem to tip), to standardise initial conditions and remove old tissue for transport. For transport back to Mallorca, fragments were packed in layers within cool-boxes. Cool-packs were wrapped in damp tea towels (rinsed in seawater) and placed between layers of samples. Samples from Catalonia, Crete and Cyprus experienced approximately 12hrs of transit time. On arrival at the destination, samples were returned to holding tanks with aerated seawater and a 14:10 light-dark cycle. [Sea temperature measurements and reconstruction] Sea surface temperature data for each collection site were based on daily SST maps with a spatial resolution of 1/4°, obtained from the National Center for Environmental Information (NCEI, https://www.ncdc.noaa.gov/oisst (Reynolds et al. 2007). These maps have been generated through the optimal interpolation of Advanced Very High Resolution Radiometer (AVHRR) data for the period 1981-2019. Underwater temperature loggers (ONSET Hobo pro v2 Data logger) were deployed at each site and recorded hourly temperatures throughout one year. In order to obtain an extended time series of temperature at each collection site, a calibration procedure was performed comparing logger data with sea surface temperature from the nearest point on SST maps. In particular, SST data were linearly fitted to logger data for the common period. Then, the calibration coefficients were applied to the whole SST time series to obtain corrected-SST data and reconstruct daily habitat temperatures from 1981-2019. [Field collections] Thermal tolerance experiments were conducted on two seagrass species (P. oceanica and Cymodocea nodosa) and two brown seaweed species (Cystoseira compressa and P. pavonica) from four locations spanning 8 degrees in latitude and 30 degrees in longitude across the Mediterranean (Fig. 1, Table S1). These four species were chosen as they are dominant foundation species and cosmopolitan across the Mediterranean Sea. Thermal performance experiments from Catalonia and Mallorca were conducted simultaneously in June 2016 for seaweeds (P. pavonica and C. compressa) and in August 2016 for seagrasses (P. oceanica and C. nodosa). Experiments for all four species were conducted in July 2017 for Crete and in September 2017 for Cyprus. Horizon 2020 Framework Programme, Award: 659246; Juan de la Cierva Formacion, Award: FJCI-2016-30728; Spanish Ministry of Economy, Industry and Competitiveness, Award: MedShift, CGL2015-71809-P; Spanish Ministry of Science, Innovation and Universities, Award: SUMAECO, RTI2018-095441-B-C21

The aim of this study was to quantify direct and indirect relationships between soil microbial community properties (potential basal respiration, microbial biomass) and abiotic factors (soil, climate) in three major land-cover types. Location: Europe Time period: 2018 Major taxa studied: Microbial community (fungi and bacteria) We collected 881 soil samples from across Europe in the framework of the Land Use/Land Cover Area Frame Survey (LUCAS). We measured potential soil basal respiration at 20ºC and microbial biomass (substrate-induced respiration) using an O2-microcompensation apparatus. Climate and soil data were obtained from previous LUCAS surveys and online databases. Structural equation modeling (SEM) was used to quantify relationships between variables, and equations extracted from SEMs were used to create predictive maps. Fatty acid methyl esters were measured in a subset of samples to distinguish fungal from bacterial biomass. Soil microbial properties in croplands were more heavily affected by climate variables than those in forests. Potential soil basal respiration and microbial biomass were correlated in forests but decoupled in grasslands and croplands, where microbial biomass depended on soil carbon. Forests had a higher ratio of fungi to bacteria than grasslands or croplands. Soil microbial communities in grasslands and croplands are likely carbon-limited in comparison with those in forests, and forests have a higher dominance of fungi indicating differences in microbial community composition. Notably, the often already-degraded soils of croplands could be more vulnerable to climate change than more natural soils. The provided maps show potentially vulnerable areas that should be explicitly accounted for in coming management plans to protect soil carbon and slow the increasing vulnerability of European soils to climate change. [Methods] Soil samples were collected during the 2018 LUCAS soil sampling campaign. Soil chemical and physical properties were measured at the Joint Research Centre in Ispra, Italy (Orgiazzi et al., 2018). Soil microbial respiration and biomass, as well as water content and water holding capacity, were measured in the Eisenhauer lab of the German Centre for Integrative Biodiversity Research. Fungi/Bacteria was measured by fatty acid analysis by Felipe Bastida at CEBAS CSIC. Climate and geographical data were harvested from various databases, which are listed in Appendix 1 (data sources) of the associated paper. For more details on the soil sampling and physical and chemical properties, see: Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A., & Fernández-Ugalde, O. (2018). LUCAS Soil, the largest expandable soil dataset for Europe: a review. European Journal of Soil Science, 69(1), 140-153. https://doi.org/10.1111/ejss.12499 For more details on the measurements of soil microbial respiration and biomass, fatty acids, and water holding capacity, see the supplementary methods of the associated paper (Appendix 2). [Usage Notes] Fatty acid analysis was performed for a subset of 267 samples. Water holding capacity and associated measurements of basal respiration was analyzed in a subset of 100 samples. The samples that were not in these subsets have NA values for the columns associated with these measurements. In order to protect the precise locations of the LUCAS sampling sites, latitude and longitude values could not be given. The approximate location of each sampling site is instead described by the NUTS3 region. If you wish to replicate the structural equation modeling described in the paper, for which latitude is required, please get in touch. A description of each column is available in the associated metadata file. Deutsche Forschungsgemeinschaft, Award: FZT 118-202548816. European Research Council, Award: 694368. European Commission. Directorate-General for the Environment. Direction Générale Opérationnelle Agriculture, Ressources Naturelles et Environnement du Service Public de Wallonie. Eurostat. Peer reviewed

Peer reviewed

Abstract. Dynamic global vegetation models are used to predict the response of vegetation to climate change. They are essential for planning ecosystem management, understanding carbon cycle–climate feedbacks, and evaluating the potential impacts of climate change on global ecosystems. JULES (the Joint UK Land Environment Simulator) represents terrestrial processes in the UK Hadley Centre family of models and in the first generation UK Earth System Model. Previously, JULES represented five plant functional types (PFTs): broadleaf trees, needle-leaf trees, C3 and C4 grasses, and shrubs. This study addresses three developments in JULES. First, trees and shrubs were split into deciduous and evergreen PFTs to better represent the range of leaf life spans and metabolic capacities that exists in nature. Second, we distinguished between temperate and tropical broadleaf evergreen trees. These first two changes result in a new set of nine PFTs: tropical and temperate broadleaf evergreen trees, broadleaf deciduous trees, needle-leaf evergreen and deciduous trees, C3 and C4 grasses, and evergreen and deciduous shrubs. Third, using data from the TRY database, we updated the relationship between leaf nitrogen and the maximum rate of carboxylation of Rubisco (Vcmax), and updated the leaf turnover and growth rates to include a trade-off between leaf life span and leaf mass per unit area.Overall, the simulation of gross and net primary productivity (GPP and NPP, respectively) is improved with the nine PFTs when compared to FLUXNET sites, a global GPP data set based on FLUXNET, and MODIS NPP. Compared to the standard five PFTs, the new nine PFTs simulate a higher GPP and NPP, with the exception of C3 grasses in cold environments and C4 grasses that were previously over-productive. On a biome scale, GPP is improved for all eight biomes evaluated and NPP is improved for most biomes – the exceptions being the tropical forests, savannahs, and extratropical mixed forests where simulated NPP is too high. With the new PFTs, the global present-day GPP and NPP are 128 and 62 Pg C year−1, respectively. We conclude that the inclusion of trait-based data and the evergreen/deciduous distinction has substantially improved productivity fluxes in JULES, in particular the representation of GPP. These developments increase the realism of JULES, enabling higher confidence in simulations of vegetation dynamics and carbon storage.

ABSTRACTUnicellular eukaryotic predators have a crucial role in the functioning of the ocean ecosystem by recycling nutrients and energy that are channeled to upper trophic levels. Traditionally, these evolutionary-diverse organisms have been combined into a single functional group (Heterotrophic flagellates), overlooking their organismal differences. Here we investigate four evolutionary related species belonging to one cosmopolitan family of uncultured marine picoeukaryotic predators: MAST-4 (species A, B, C, and E). Co-occurrence and distribution analyses in the global surface ocean indicated contrasting patterns in MAST-4A & C, suggesting adaptation to different temperatures. We then investigated whether these spatial distribution patterns were mirrored by MAST-4 genomic content using Single-Cell Genomics. Analyses of 69 single-cells recovered 66-83% of the MAST-4A/B/C/E genomes, which displayed substantial inter-species divergence. MAST-4 genomes were similar in terms of broad gene functional categories, but they differed in enzymes of ecological relevance, such as glycoside hydrolases (GHs), which are part of the food degradation machinery in MAST-4. Interestingly, MAST-4 species featuring a similar GH composition co-excluded each other (A & C) in the surface global ocean, while species with a different set of GHs appeared to be able to co-exist (species B & C) suggesting further niche diversification associated to prey digestion. We propose that differential niche adaptation to temperature and prey type has promoted adaptive evolutionary diversification in MAST-4. Altogether, we show that minute ocean predators from the same family may have different biogeography and genomic content, which need to be accounted to better comprehend marine food webs.