BUSCADOR RECOLECTA

[Methods] Experiment locations and climate Trans-Mediterranean translocation of Posidonia oceanica fragments took place between Catalunya (Spain), Mallorca (Spain) and Cyprus in July 2018 and were monitored until July 2019 (Fig. 1). Sea surface temperature data for each transplant 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 the transplant sites in Catalunya, Mallorca and Cyprus and recorded hourly temperatures throughout the duration of the experiment (one year). In order to obtain an extended time series of temperature at transplant sites, 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. Local climate data was also compared to the global thermal distribution of P. oceanica to assess how representative experimental sites were of the thermal distribution of the species (Supplementary materials). Collectively, seawater temperatures from the three locations span the 16th - 99th percentile of temperatures observed across the global thermal distribution of P. oceanica. As such Catalunya, Mallorca and Cyprus are herein considered to represent the cool-edge, centre and warm-edge of P. oceanica distribution, respectively. Transplantation took place toward warmer climates and procedural controls were conducted within each source location, resulting in six source-to-recipient combinations (i.e. treatments, Fig. 1). Initial collection of P. oceanica, handling and transplantation was carried out simultaneously by coordinated teams in July 2018 (Table S1). Each recipient location was subsequently resampled four times over the course of the experiment, in August/September 2018 (T1), October 2018 (T2), April 2019 (T3) and May/June 2019 (T4, Table S1). Between 60-100 fragments were collected for each treatment. A fragment was defined as a section of P. oceanica containing one apical shoot connected with approximately five vertical shoots by approximately 10-15 cm of rhizome with intact roots. Collection occurred at two sites within each location, separated by approximately 1 km. Within sites, collections were conducted between 4 – 5 m depth and were spaced across the meadow to minimise the dominance of a single clone and damage to the meadow. Upon collection, fragments were transported for up to one hour back to the nearest laboratory in shaded seawater. Handling methods In the laboratory, fragments were placed into holding tanks with aerated seawater, at ambient temperature and a 14:10 light-dark cycle. All shoots were clipped to 25 cm length (from meristem to the tip of the longest leaves), to standardise initial conditions and reduce biomass for transportation. For transport by plane or ferry between locations, fragments were packed in layers within cool-boxes. Each layer was separated by frozen cool-packs wrapped in wet tea towels (rinsed in sea water). All fragments spent 12 hrs inside a cool-box irrespective of their recipient destination, including procedural controls (i.e. cool-cool, centre-centre and warm-warm) to simulate the transit times of the plants travelling furthest from their source location (Fig. 1a). On arrival at the destination, fragments were placed in holding tanks with aerated seawater at ambient temperature as described above in their recipient location for 48 hrs, prior to field transplantation. Measurement methods One day prior to transplantation, fragments were tagged with a unique number and attached to U-shaped peg with cable-ties. Morphological traits for each fragment were measured and included: 1) length of the longest apical leaf, width and number of leaves 2) total number of bite marks on leaves of three vertical shoots per fragment, 3) number of vertical shoots, 4) leaf count of three vertical shoots per fragment and 5) overall horizontal rhizome length. A subset (n=10) of fragments per treatment were marked prior transplantation to measure shoot growth. To do this, all shoots within a single fragment were pierced using a hypodermic needle. Two holes were pierced side-by-side at the base of the leaf/top of the meristem. Transplant methods All transplant sites were located in 4 – 5 m depth in area of open dead-matte, surrounded by P. oceanica meadow. In Mallorca and Cyprus, fragments were distributed between two sites, separated by approximately 1 km. In Catalunya, a lack of suitable dead matte habitat, meant that all fragments were placed in one site. Fragments were planted along parallel transects at 50 cm intervals and with a 50 cm gap between parallel transects (Fig. S1). Different treatments were mixed and deployed haphazardly along each transect. Resampling methods and herbivory On day 10 of the experiment, a severe herbivory event was recorded at both warm-edge translocation sites. Scaled photos of all fragments were taken at this time to record the effects of herbivory on transplants. At the end of each main sampling period (T0 – T1, T1-T2 and T3 – T4), all pierced fragments were collected and taken back to the laboratory to measure shoot growth. At T1, T2 and T3, additional sets of fragments (n = 10 per treatment) were marked using the piercing method to record growth in the subsequent time period. In addition, at T1 and T3, n = 20 shoots within the natural meadow at each site were marked to compare growth rates between the native meadow and transplants. Underwater shoot counts and a scaled photo was taken to record fragment survivorship, shoot mortality, bite marks, and shoot length among all remaining fragments within each site and sampling time. In the laboratory, morphological measurements (described above) were repeated on the collected fragments and growth of transplant and natural meadow shoots was measured. Growth (shoot elongation, cm d-1) of the marked shoots was obtained by measuring the length from the base of meristem to marked holes of each leaf (new growth) of the shoot and dividing the leaf elongation per shoot by the marking period (in days). For each shoot, total leaf length (cm shoot-1) and the number of new leaves was also recorded. The rate of new leaf production (new leaves shoot-1 d-1) was estimated dividing the number of new leaves produced per shoot and the marking period. New growth was dried at 60 ºC for 48 hrs to determine carbon and nitrogen content of the leaves, and carbon to nitrogen (C:N) ratios. Carbon and nitrogen concentrations in the new growth leaf tissue was measured at the beginning of the experiment and each subsequent time point for each treatment. Nutrient analyses were conducted at Unidade de Técnicas Instrumentais de Análise (University of Coruña, Spain) with an elemental analyser FlashEA112 (ThermoFinnigan). Underwater photos of shoots were analysed using ImageJ software (https://imagej.net). Maximum leaf length on each shoot in warm-edge transplant sites (cool-warm, centre-warm and warm-warm) were recorded for the initial (day 10) herbivore impact, T1, T2 and T3 time-points and related to transplant nutrient concentrations. Herbivore impact was estimated as the proportional change in length of the longest leaf relative to initial length at T0. Thermal stress Long term maximum temperatures were recorded as the average of annual maximum daily temperatures in each transplant site, averaged between years from 1981-2019. Maximum thermal anomalies were calculated as the difference between daily temperatures in a recipient site over the course of the experiment and the long-term maximum temperature in the source site for each corresponding population. ‘Heat stress’ and ‘recovery’ growth periods of the experiment were defined as T0 -T2 (July-October) and T2-T4 (November-June), respectively, corresponding to periods of positive and negative maximum thermal anomalies. Thermal anomalies experienced by the different transplant treatments were plotted using the ‘geom_flame’, function in the ‘HeatwavesR’ package (Schlegel & Smit 2018) of R (version 3.6.1, 2019) ., 1. The prevalence of local adaptation and phenotypic plasticity among populations is critical to accurately predicting when and where climate change impacts will occur. Currently, comparisons of thermal performance between populations are untested for most marine species or overlooked by models predicting the thermal sensitivity of species to extirpation. 2. Here we compared the ecological response and recovery of seagrass populations (Posidonia oceanica) to thermal stress throughout a year-long translocation experiment across a 2800 km gradient in ocean climate. Transplants in central and warm-edge locations experienced temperatures >29 ºC, representing thermal anomalies >5ºC above long-term maxima for cool-edge populations, 1.5ºC for central and <1ºC for warm-edge populations. 3. Cool, central and warm-edge populations differed in thermal performance when grown under common conditions, but patterns contrasted with expectations based on thermal geography. Cool-edge populations did not differ from warm-edge populations under common conditions and performed significantly better than central populations in growth and survival. 4. Our findings reveal that thermal performance does not necessarily reflect the thermal geography of a species. We demonstrate that warm-edge populations can be less sensitive to thermal stress than cooler, central populations suggesting that Mediterranean seagrasses have greater resilience to warming than current paradigms suggest., Australian Research Council, Award: DE200100900. Horizon 2020 Framework Programme, Award: 659246. Fundación BBVA., Peer reviewed

6 pages. -- Supplementary Figure 1. Current mean maximum summer temperature (average 𝑇!"# """""" for the period 1980-2005) across potential seagrass distribution. -- Supplementary Figure 2. Difference between current mean maximum summer temperature ( 𝑇!"# """""" ) and the Tlimit as a function of latitude. Negative and positive latitude values for southern and northern hemispheres, respectively. -- Supplementary Figure 3. Uncertainty associated to the time (in years) for mean maximum summer temperature to reach seagrass upper thermal limit (Tlim) at the warming rates projected under the RCP8.5 scenario around potential seagrass sites. -- Supplementary Figure 4. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP8.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas. -- Supplementary Figure 5. The time (in years) to reach Tlimit at the warming rates predicted under the RCP4.5 scenario around potential seagrass sites. -- Supplementary Figure 6. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP4.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas., Seagrasses have experienced major losses globally mostly attributed to human impacts. Recently they are also associated with marine heat waves. The paucity of information on seagrass mortality thermal thresholds prevents the assessment of the risk of seagrass loss under marine heat waves. We conducted a synthesis of reported empirically- or experimentally-determined seagrass upper thermal limits (Tlimit) and tested the hypothesis that they increase with increasing local annual temperature. We found that Tlimit increases 0.42± 0.07°C per°C increase in in situ annual temperature (R2 = 0.52). By combining modelled seagrass Tlimit across global coastal areas with current and projected thermal regimes derived from an ocean reanalysis and global climate models (GCMs), we assessed the proximity of extant seagrass meadows to their Tlimit and the time required for Tlimit to be met under high (RCP8.5) and moderate (RCP4.5) emission scenarios of greenhouse gases. Seagrass meadows worldwide showed a modal difference of 5°C between present Tmax and seagrass Tlimit. This difference was lower than 3°C at the southern Red Sea, the Arabian Gulf, the Gulf of Mexico, revealing these are the areas most in risk of warming-derived seagrass die-off, and up to 24°C at high latitude regions. Seagrasses could meet their Tlimit regularly in summer within 50-60 years or 100 years under, respectively, RCP8.5 or RCP4.5 scenarios for the areas most at risk, to more than 200 years for the Arctic under both scenarios. This study shows that implementation of the goals under the Paris Agreement would safeguard much of global seagrass from heat-derived mass mortality and identifies regions where actions to remove local anthropogenic stresses would be particularly relevant to meet the Target 10 of the Aichi Targets of the Convention of the Biological Diversity., Peer reviewed

The dataset contains data on sediment C02 efflux rates, carbon emissions during the experiment (gm-2), % Organic Carbon, Organic Matter content of the Posidonia oceanica seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions. Sediments used in the experiment were extracted in October 2017 from the P. Oceanica meadow of Pollença in Mallorca Island at six-meter depth Figure (1). Sediments were sampled in October 2017 using sediment cores (9 cm ID and 30cm long) and directly transported to the laboratory. Only the top 10 cm of the sediment cores were used since this fraction is the most susceptible to erosion. Living seagrass tissues (roots, rhizomes, and leaves) were removed and sediment was mixed and homogenized. 40ml of sediments were poured into glass containers of 750ml with 500ml of seawater. Finally, each recipient contained a sediment layer of approximately 1.1cm in each container. Containers were placed at five different temperature baths (26,27.5, 29, 30.5, 32 ºC) simulating summer temperatures in the bay (Garcias-Bonet et al., 2019) at different agitation regimes (agitation/repose) to simulate exposed and sheltered conditions.10 containers were sampled right after the experiment started to provide initial sediment conditions. Five containers per temperature and agitation treatment were removed 7, 21, 43, 67, and 98 days from the experiment start, to analyse sediment organic matter and CaCO3 content. CO2 incubations were run 5, 14, 56, and 91 days from the experiment start. Sampling times were distributed considering that organic matter remineralisation was likely to follow an exponential trend, including a rapid phase of loss of the more labile material followed by a slower loss of more recalcitrant substrates (Arndt et al., 2013). The experiment was run in the dark to avoid photosynthesis in an isothermal chamber at 21ºC., Organic carbon analysis: In each sampling time, organic matter content in sediments (OM %DW) was estimated as the percentage weight loss of dry sediment sample after combustion at 550ºC for 4 hours. Organic carbon (Corg) was calculated from OM content using the relation described in (Mazarrasa et al., 2017b) y = 0.29x – 0.64; (R2=0.98, p< 0.0001, n=60) OM and POC stocks along the experiment (mg OM ml-1 and mg POC ml-1) were estimated by multiplying the OM and POC (%DW) by the sediment dry weight (mg) remaining in each experimental unit and standardized to the initial volume of sediment (40 ml) introduced in every glass container. Inorganic carbon was estimated as the percentage weight loss of already combusted sediment (550ºC) after combustion at 1000ºC., Sediment CO2 production: Container headspace CO2 gas concentration was measured during 20 minutes continuum incubations (4 replicates) in each temperature and agitation treatment in all sampling times. CO2 air concentration measures were carried out using an Infra Red Gas Analyser EGM4 from PPSystems. Concentration of dissolved CO2 in seawater (in μmol CO2 L−1) was calculated from the concentration of CO2 (in ppm) measured in headspace air samples after equilibration as described in (Garcias-Bonet and Duarte, 2017; Wilson et al., 2012). Briefly, we calculate the dissolved CO2 remaining in seawater after equilibration with the air phase ([CO2]SW−eq) by, [CO2]SW−eq = 10−6 β [C CO2]Air P where β is the Bunsen solubility coefficient of CO2, calculated according to Wiesenburg and Guinasso (1979), as a function of seawater temperature and salinity; [CO2]Air is the CO2 concentration measured in containers headspace air (in ppm) and P is the atmospheric pressure (in atm) of dry air that was corrected by the effect of multiple sampling applying Boyle’s Law. Then, the initial CO2 concentration in seawater before the equilibrium ([CO2]SW−before eq) was calculated (in ml CO2 /ml H2O) by [CO2]SW−before eq = ([CH4]SW−eq VSw + 10−6 ([CO2]Air −[CO2]Air background) VAir)/VSW Where VSw is the volume of seawater in the core or in the seawater closed circuit, [CO2]Air background is the atmospheric CO2 background level and VAir is the volume of the headspace or the closed air circuit. Finally, the initial CO2 concentration was transformed to µmol CH4 L−1 by applying the ideal gas law. CO2 efflux values were calculated from CO2 variation per time unit. Then, we converted the rates to aerial (taking in account container surface) base, and thickness (in μmol m-2 s-1)., The dataset provides data on sediment C02 efflux rates (μmol CO2 m-2 s-1), carbon emissions during the experiment (gm-2), % Organic Carbon, Organic Matter content (g m-2) of the Posidonia oceanica seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions., CO2 efflux.xlsx, dataset_units.xlsx, Sediment_Organic_Carbon.xlsx, Peer reviewed

5 pages. -- Table S1. Summary table with description of sites and fish species studied at four locations across the Mediterranean Sea. -- Fig. S1 Trend in minimum (1st percentile of daily temperatures) and maximum (99th percentile of daily temperatures) sea surface temperatures from 1981 to 2019. -- Fig. S2 Ivlev’s electivity index for herbivorous fishes across the Mediterranean Sea. Ivlev’s index standardizes food consumed by food availability within the habitat and scales from -1 (extreme selection against food source) to 1 (extreme selection for food source) (Jones & Norman 1986), Peer reviewed

Growth rates of Cystoseira compressa from Crete have been removed from the dataset and analysis due to poor condition of the plants., 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., [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., [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., [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)., [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., [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)., [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., [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., 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, Trans_Mediterranean_metabolic_rates_upload; Trans_Mediterranean_seagrass_growth_rates_upload; Trans_Mediterranean_seaweed_growth_rates_upload; TransMediterranean_seaweed_growth_rates_upload, Peer reviewed

[Geographic location of data collection] Mediterranean basin, seagrass meadows of Posidonia oceanica and Cymodocea nodosa in coastal regions, max. depth 17m., [File List] datacompilation_med_seagrass_metabolic_rates_hendriks.csv, readme.txt., [Relationship between files, if important] readme provides background information for csv datafile., [Additional related data collected that was not included in the current data package] dissolved nutrients for author data (available upon request)., [Description of methods used for collection/generation of data] Data on metabolic rates was extracted from the literature, through a literature search (March 2020) on SCOPUS and the Web of Science using the keywords “Posidonia”, OR “Cymodocea”, OR “Seagrass”, AND “Productivity”, OR “Metabolism” and manually screened for data on metabolism in the Mediterranean basin. This database was extended with unpublished data from the authors and data from dedicated sampling campaigns in 2016 in Mallorca (Western Mediterranean) and 2017 in the Eastern basin (Crete and Cyprus). We compiled data from multiparametric sensors, and data using the benthic chambers methodology with a temporal cover from 1982 to 2019., [Methods for processing the data] For benthic chambers, reported metabolic rates were extracted from the literature. For measurements with multiparametric sensors we used time series of dissolved oxygen (DO, in mg/L), salinity and temperature (C) measured in P. oceanica and/or C. nodosa meadows. With the time series of dissolved oxygen (DO), temperature (°C) and salinity we calculated the metabolic rates of the seagrass habitats using a modification of the model of Coloso et al., (2008) implemented in MATLAB (version 7.5. the Mathworks Inc.) explained in detail in Vaquer-Sunyer et al., (2012). Wind speed was estimated at each station for the same interval as oxygen measurements to predict k660 (air-sea gas transfer velocity for oxygen at 20º C and salinity 35) based on Kihm et al., (2010) and Cole et al., (1998). Schmidt number equations for seawater according to Wanninkhof (1992) were used for the k calculation from k660. As the cubic model equals the model proposed by Wanninkhof et al., (1999) for short-term winds this parameterization by Kihm et al., (2010) is used. Meteorological data (windspeed) for the deployment period was obtained from the Agencia Estatal de Meteorología (AEMET) for the stations in Mallorca, from the Cyprus Department of Meteorology for Cyprus sampling sites and from the Hellenic National Meteorological Service for the locations in Crete.--, [Standards and calibration information] Sensors were calibrated before each deployment; oxygen sensors (Hach LDOTM) were calibrated using the water saturated air method calibration. For validation of salinity, specific conductance calibrations were performed with 50.000uS/cm buffers. For depth measurements, pressure readings were corrected for specific conductance., [Environmental/experimental conditions] Coastal seagrass meadows with max. 17m depth., [Describe any quality-assurance procedures performed on the data] Negative respiration rates (oxygen production) at night for sensor deployments, were discarded as this was interpreted as an indication for the influence of lateral advection and passing of different water masses. Therefore, we trimmed the dataset to contain only measurements where this influence was not detected. Respiration rates were notated as oxygen consumption (positive values, literature reports differ in notation)., [People involved with sample collection, processing, analysis and/or submission, please specify using CREDIT roles https://casrai.org/credit/: Conceptual idea IEH and NM. Data collection in the field MW, SF, RVS, IEH, NM. Literature compilation IEH and AEM. Data curation AEM and IEH., [Data-specific information] 1. Number of variables: 21. 2. Number of cases/rows: 151. 3. Variable List: Reference, Journal, Methodology, Year, Month, Season, Site, Region, Latitude, Longitude, Species, Temperature_C, Salinity, Depth, NCP, NCP_SD, CR, CR_SD, GPP, GPP_SD, Wind_m_s. 4. Missing data codes: Empty cell. 5. Specialized formats or other abbreviations used: C (degree Celcius), SD (Standard Deviation), m_s (Meter per second). Depth in meter. Latitude and Longitude in Decimal Degrees (DD)., The data is a compilation of information on metabolic rates of Mediterranean seagrasses obtained by two different methodologies (benthic incubations and multiparametric sensors) from published literature and data from the authors., The Spanish Ministry of Economy and Competitiveness (Project MEDSHIFT, CGL2015-71809-P). Project RTI2018-095441-B-C21 (SUMAECO) from the Spanish Ministry of Science, Universities and Innovation. SF was supported by a “Margalida Comas” postdoctoral scholarship, funded by the Balearic Islands Government. Also funding was received from “projectes de recerca La Caixa en àrees estratègiques” (2018) through a grant to IEH at the University of the Balearic islands., datacompilation_med_seagrass_metabolic_rates_hendriks.csv, readme.txt, Peer reviewed

[Description of methods used for collection/generation of data] Collection of data from extraction of articles retrieved from the literature (Web of Knowledge and SCOPUS, accessed July 2015 and updated May 2021). Papers reporting estimates of the effect of coastal plants on current and wave attenuation in vegetated coastal habitats identified using search terms: “Seagrass*” [All Fields] OR “Mangrove*” [All Fields] OR “Salt marsh*” [All Fields] OR “Macrophyte*” [All Fields] AND “engine*” [All Fields] OR “wave attenuation” [All Fields] OR “flow modification” [All Fields]. The in total 963 papers retrieved were analyzed for quantitative estimates, supplemented with papers and documents containing data meeting the requirements of the analyses contained within the references of the papers retrieved, resulting in a data set containing a total of 1372 estimates derived from 95 individual articles with a temporal cover from 1982 to 2020., [Methods for processing the data] Results from field and laboratory studies were used, but not numerical models. When information was given for multiple observations with different vegetation parameters and/or hydrodynamic parameters, we included several data points per study, but only included 1 measurement (max. distance) when the same structural parameters had repeated measurements for different distances within the vegetation. Where authors reported values for current reduction these were used directly, always making sure a non-vegetated (bare) reference value was used to calculate reduction in the vegetation. When data was (re)calculated from separate reported values the formulas used for current reduction, dU, were calculated as: dU/U0 = (U0-Uv)/U0 With U as the current speed over a reference unvegetated region U0 and through a vegetated region Uv in m s-1 respectively. Where the information was provided in the selected studies, we calculated the wave energy reduction, dE, defined as (Knutson et al. 1982): dE/E0 = ((E0-Ev))/E0 Where E is the energy without vegetation (E0) and within the vegetation (Ev) respectively. The wave height reduction per meter r (Mazda et al 1997) was calculated as: r = dH/(H0x) = ((H0-Hv))/(H0x) Where x is the length of the vegetation field. When multiple measurements were done with the same vegetation settings (i.e. density, water height) at different distances into the vegetation, we took the maximum distance evaluated. The effect of vegetation on current and wave attenuation was represented by the decay coefficients, KiH, (Kobayashi et al., 1993) and KiU (m-1), representing the relative decrease in significant wave height (KiH), and current velocity (KiU) with distance into the vegetated fringe (x, bed length) calculated as, kiH=1/x ln(1-dH/H0 )=1/x ln(Kt ) and kiU=1/x ln(1-dU/U0 ) Where Kt is the wave transmission coefficient. We used the same literature sources that were used for the data were collection, to compile relevant vegetation structural parameters, specifically, shoot or stem density and emergence ratio (defined as hveg/h). For stiffness we used Young’s bending modulus (E, in N mm-2), when this parameter was not available from the same source, we completed the data with species specific values from literature (e.g. Zhu et al. 2020 for salt marshes, de los Santos et al. 2016; La Nafie et al. 2012; Soissons et al. 2017 for seagrasses and van Hespen et al. 2021 for mangroves). When no value was known, the value for the family was used or an average for the group (i.e., saltmarsh, seagrass, etc.) obtained from the compiled values., [Relationship between files] Readme provides background information for xlsx datafile., [People involved with sample collection, processing, analysis and/or submission] https://casrai.org/credit . Idea and concept C.M.D and I.J.L, design and discussion of content during workshops I.E.H., N.M., B.v.W., T.J.B., I.J.L, C.M.D. Database compilation I.E.H, M.M., A.G.M and N.M. Analysis of data I.E.H.. All authors contributed to the writing and editing of the manuscript., Funding for this data collection supplied by the MedShift project, CGL2015-71809-P (MINECO/FEDER) and baseline funding from King Abdullah University of Science and Technology to C.M.D. I.E.H. was supported by grant RYC-2014-15147, co-funded by the Conselleria d'Innovació, Recerca i Turisme of the Balearic Government (Pla de ciència, tecnologia, innovació i emprenedoria 2013-2017) and the Spanish Ministry of Economy, Industry and Competitiveness., Data_coastal_vegetation_adaptation.xlsx, readme.txt, Peer reviewed

The dataset compiles seagrass upper thermal limits (Tlimit) for survival, growth or biomass loss published in the literature and obtained by conducting a search on Web of Knowledge using the keywords combinations seagrass AND (temperature OR warming) and seagrass AND ("thermal limit" OR "thermal threshold" OR "critical temperature" OR "thermal niche”). The reference lists of the papers obtained with these searches were screened for additional relevant data. The dataset only includes data of seagrass populations growing submersed within their native geographical range. Tlimit are derived from empirical observations of seagrass die-off events attributed to heat waves, in combination with other simultaneous stressors (hypersalinity, Carlson et al 2018; low light availability, Moore and Jarvis 2008, Moore et al., 2014), or mesocosm experiments. Seagrasses in mesocosm experiments were exposed to at least 2 temperature treatments above average in situ summer temperature that extended the experimental thermal range beyond the Tlimit. Seagrasses were exposed to experimental temperatures for 6 to 120 days depending on the study. The Tlimit was defined as: a) the upper temperature at which shoot survival, shoot growth or biomass above optimal temperature started to decline in experimental studies; or b) the seawater temperature during the heat wave that triggered die-off events. For each study, the compiled dataset includes the species name, location and coordinates of the population studied, the Tlimit, the approach (i.e. experimental or empirical), the year the study was conducted and the data source. For experimental studies, the dataset also includes the temperature treatments seagrasses were exposed to. For each population studied, we obtained mean annual seawater temperature values for the 5 years before the thermal tolerance experiment or observation was conducted from the ORAS4 ocean reanalysis (Balmaseda, Mogensen, Weaver, 2013), which provides monthly 3D temperature global fields from 1958 to present with a spatial resolution of 1 degree in the horizontal and ~10 m in the vertical. Those temperatures aim at representing the regional characteristics, rather than the local features which cannot be captured by the coarse spatial resolution, [Relationship between files] The file "variables_Marbà_et_al_ 2022.xlsx" defines the variables used in the dataset. The full references of the sources of data compiled in the dataset are provided in the file "References_Dataset_Marba_et_al_2022.docs"., [Environmental/experimental conditions] The dataset includes target experimental temperatures and average annual seawater temperature natural populations were exposed to, calculated for the 5 years before conducting the experiment or the occurrence of seagrass mass-mortality event., Dataset of seagrass upper thermal limits for survival, growth or biomass loss derived from empirical observations of seagrass die-off events attributed to heat waves or mesocosm experiments., This work was funded by the Spanish Ministry of Economy, Industry and Competivness with the projects MedShift (CGL2015-71809-P), SumaEco (RTI2018-095441-B-C21) and Clifish (CTM2015-66400-C3-2-R), the European Union’s Horizon 2020 SOCLIMPACT project (grant agreement No 776661) and the King Abdullah University of Science and Technology (KAUST subaward number 3834). S.B. was supported by a Juan de la Cierva Formación contract funded by the Spanish Ministry of Economy, Industry and Competitiveness., File List: - variables_Marbà_et_al_ 2022.xlsx - dataset_Marbà_et_al_2022_(seagrass thermal limits).xlsx - References_Dataset_Marba_et_al_2022.docs, Peer reviewed