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This data set describes the population dynamics of Wilson's Storm Petrels (Oceanites oceanicus) at King George Island (Isla 25 de Mayo, Antarctica) over a forty year period (1978 – 2020). It includes all available data on Wilson's Storm Petrels from two colonies: around the Argentinian Base Carlini (62°14′S, 58°40′W; CA, formerly called Base Jubany) and the Henryk Arctowski Polish Antarctic Station (62°09′S, 58°27′W; HA). Data on population productivity (number of nests, eggs, chicks and fledglings) was collected by regular visits to the colonies and searching for nest burrows, or monitoring of the egg or chick if found. Data on adult abundance and estimated age categories (i.e., presence of foot spots; Quillfeldt et al. (2000, doi:10.1007/s003000000167) were collected at CA by using the same size mistnet every study year in the same location within the breeding colony. Chicks were measured regularly (varying intervals depending on the study) at both CA and HA. Chick tarsus was measured using callipers (vernier or digital depending on the study year) to the nearest 0.1 mm, chick wing length was measured using wing rulers to the nearest 1 mm, and chick body mass was measured using mechanical or digital scales depending on the study year to the nearest 0.1 g. Chick growth rates were calculated based on the linear growth period following Ausems et al. (2020, doi:10.1016/j.scitotenv.2020.138768). Chick food loads (g) were recorded at CA and determined based on changes in chick body mass on consecutive days (Gladbach et al. (2009, doi:10.1007/s00300-009-0628-z); Kuepper et al. (2018, doi:10.1016/j.cbpa.2018.06.018). This study was further supported by the Erasmus+ programm and thee German Academic Exchange Service (DAAD)

The CARBO-ACID research cruise (EUROFLEETS+ SEA02_10) was carried out on the RV Ramón Margalef between August 2nd and August 11st, with departing from Vigo – Spain and ending in Lisbon – Portugal. The main objective of this cruise was to collect data and samples to study the potential effects of ocean acidification on carbonate marine organisms (coccolithophores, pteropods, planktonic and benthic foraminifera, and corals) along the Iberian margin. With this objective, oceanographic data and water samples, plankton, cold-water corals and sediment samples were collected during an upwelling season, along two transects coinciding with the two persistent upwelling filaments off the Iberia Margin: the Cape Finisterra and the Cape Roca. In this dataset is guiven all the acquired data recollected onboad. During the CARBO-ACID cruise we did a total of 7 stations, 4 stations along the Cape Finisterra transect (from W to E: CA3, CA2, CA7, CA8) and 3 stations at the Cape Roca (from W to E: CA6, CA5, CA4) transect (Fig). At each station we usually started with a multibeam survey, a CTD and Rosette cast. These initial operations allowed to identify the different water masses present in this area, characterize their physical properties and to recover seawater samples at specific depth levels. The seawater samples were onboard subsampled, preserved in cold conditions or with chemicals and/ or filtered for several further analysis in the shore-based laboratories: DNA, chlorophyll, fitoplankton, coccolithophores, pH, alkalinity, stable isotopic composition, trace elements concentration and Suspend Particulate Matter. Subsequently to these operations, at each station, two vertical tows with a plankton multinet (with 5 nets) were done on the top 700 m of the water column to sample the planktonic communities of the different water depths. After this, sediment samples were recovered with a box-corer to study the past oceanographic conditions, between the pre-industrial Era and the Present, with multi-proxies used in paleoceanography and sedimentology. A total of 10 box-cores were recollected and each of them was onboard sub-sampled for eDNA, enzymes and benthic foraminifera. Fifteen shipek grab samples were recollected at the Fontanelas seamount (Estremadura Spur), station CA6, to characterize the sedimentary cover and to evaluate the presence of deep cold-water corals. Preliminary results show that the stations CA7, CA8 and CA4, located close to the coast, as expected, are the most influenced by the coastal upwelling, exhibiting colder surface water, higher values of fluorescence, and more zooplankton content reflecting higher phyto-zooplankton concentrations, as typical of the upwelling waters. At station CA4 temperature was higher and fluorescence showed lower values, indicative of less phytoplankton, and interpreted as indicating a different upwelling source water from that upwelled further north. Based on the CTD data, the Cape Roca transect is more influenced by the subtropical East North Atlantic Central Water (ENACWst), while the Cape Finisterra transect is more under the influence of the subpolar branch (ENACWsp). Seafloor sediment samples showed significant differences between the stations. Along the northern transect (Cape Finisterra) the seafloor sediments show an increase in grain size from the offshore to the coast. The offshore stations CA3 and CA2 revealed finer grained sediments, CA8 were composed of coarser sand and the station CA7, the shallowest station 77 m, presented the sediment composed mainly of shell fragments and coarse grain sand. Along the southern transect (Cape Roca), the offshore station CA6 (Fontanelas seamount) has coarser sandy sediments with rock clasts and cold-water coral fragments, and the stations CA5 and CA4 with fine sand to muddy sediments. The detailed CA6 bathymetry allowed to verify the existence of small plateaus on the slope of the Fontanelas seamount, where the fossil cold-water corals fragments were found, suggesting that this area is a very interesting system deserving further study with a ROV, and to characterize the corals fields and verify if there are live corals. These recollected data and samples will allow not only to reconstruct the pH variability under different environmental conditions, but also to estimate the biogeochemical changes along the coastal ocean waters as the anthropogenic influence increases. These results will contribute to better understand and model the effects on the biota under the future expected oceans pH changes.

The main objective of this Sternfahrt-8, from 10th to 16th September 2021, was to assess the temporal variance of oceanographic real time data in the Elbe influence area of the German Bight (North Sea). Therefore, the participating Ships should repeat the same tracks for four days (see map). One ship (RV Uthörn) covered the western part between Cuxhaven and Heligoland, the second ship (RV Littorina) went to the northern part between Heligoland and Büsum and the third vessel (RV Ludwig Prandtl) should have covered the middle part of the study area, but due to vandalism damage it could not participate on the cruise. During the whole cruise chemical and physical data were recorded continuously along the tracks. Additionally, discrete water samples were taken on six stations along the way for further analysis in the laboratory. The latter data is not included in the present dataset, and can be accessed via https://doi.pangaea.de/10.1594/PANGAEA.963455. For more information about the MOSES campaign and the "Sternfahrten" cruises see article cited in references.

Vegetation surveys were carried out in four different study areas in the Sakha Republic, Russia: in the mountainous region of the Verkhoyansk Range within the Oymyakonsky and Tomponsky District (Event EN21-201 - EN21-219), and in three lowland regions of Central Yakutia within the Churapchinsky, Tattinsky and the Megino-Kangalassky District (Event EN21220 - EN21264). The study area is located within the boreal forest biome that is underlain by permafrost soils. The aim was to record the projective ground vegetation in different boreal forest types studied during the RU-Land_2021_Yakutia summer field campaign in August and September 2021.Ground vegetation was surveyed for different vegetation types within a circular forest plot of 15m radius. Depending on the heterogeneity of the forest plot, multiple vegetation types (VA, VB, or VC) were chosen for the survey. The assignment of a vegetation type is always unique to a site. Their cover on the circular forest plot was recorded in percent.In total, 84 vegetation types at 58 forest plots were assessed. All data were collected by scientists form the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) Germany, the University of Potsdam Germany, and the North-Easter Federal University of Yakutsk (NEFU) Russia.

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases.  Please see the README document ("README.md") and the accompanying published article: Braun, C. D., M. C. Arostegui, N. Farchadi, M. Alexander, P. Afonso, A. Allyn, S. J. Bograd, S. Brodie, D. P. Crear, E. F. Culhane, T. H. Curtis, E. L. Hazen, A. Kerney, N. Lezama-Ochoa, K. E. Mills, D. Pugh, N. Queiroz, J. D. Scott, G. B. Skomal, D. W. Sims, S. R. Thorrold, H. Welch, R. Young-Morse, R. Lewison. In press. Building use-inspired species distribution models: using multiple data types to examine and improve model performance. Ecological Applications. Accepted. DOI: < article DOI will be added when it is assigned >

This data was collected as a part of a mesocosm study to investigate the ecosystem impacts of ocean alkalinity enhancement, within the EU H2020 OceanNETs project. Nine mesocosms were deployed in Taliarte Harbour (Gran Canaria, Spain) and were regularly sampled using integrated water samplers between 10th September-25th October 2021. A gradient design was used in this experiment with a total of nine different alkalinity concentrations. Seawater alkalinity ranged between ambient (0 µeq kg-1 added alkalinity, OAE0) and 2400 µeq kg-1 additional alkalinity (OAE2400). The alkalinity levels increased in equal intervals of 300 µeq kg-1 across nine mesocosms (OAE0, OAE300, OAE600, OAE900, OAE1200, OAE1500, OAE1800, OAE2100, OAE2400). This data set contains metazoan zooplankton biomass (µgC per L) from these nine mesocosms. Biomass was calculated based on zooplankton abundances transformed using carbon mass conversion factors. Metazoan zooplankton were sampled with apstein net (ø17cm, mesh size 55µm, 64.06285L) hauls taken every two days (except for days 5 and 9). Zooplankton were size fractioned and assessed in the correspondent size class (small: 55-200µm; medium: 200-500µm; large: 500µm-3mm). Within each size class, all organisms were counted and identified to the lowest possible taxonomic level, and developmental stages were differentiated where possible. Zooplankton abundances (individuals per L) converted to carbon biomass (µgC per L) using biomass conversion factors. Conversion factors are obtained from different sources (Sanchez et al. (in prep)). Briefly: i) metazoan zooplankton functional groups were sampled and measured for carbon biomass using an elemental analyser at specific points throughout the experiment, ii) individual zooplankton were photographed, measured, and their biovolumes and carbon masses derived using standard conversions cited in the literature, iii) zooplankton conversion factors from KOSMOS Gran Canaria 2019 (https://doi.pangaea.de/10.1594/PANGAEA.971765). The experiment, which lasted 33 days, was divided into four response phases (see Sánchez et al. (in prep)): i) pretreatment (days 1 to 4, treatment was implemented on day 4), ii) immediate (days 5-10), iii) shorter term (days 11-22), iv) longer term (days 23 to 33). This data set is associated to the submission by Paul et al. (in review) (https://doi.pangaea.de/10.1594/PANGAEA.966941), so we refer to this data set for basic parameters like water temperature, salinity, pH and carbonate chemistry, to avoid repetition.

# Can niche plasticity mediate species persistence under ocean acidification? [https://doi.org/10.5061/dryad.x0k6djhtq](https://doi.org/10.5061/dryad.x0k6djhtq) This dataset originates from a study investigating the impact of ocean acidification on a temperate rocky reef fish assemblage using natural CO2 vents as analogues. The dataset covers various niche dimensions, including trophic, habitat, and behavioural niches. The study focused on how fish niches are modified in response to ocean acidification, assessing changes in breadth, shift, and overlap with other species between the acidified site and the control site. ## Description of the data and file structure #### Raw\_single\_niche\_data The “*Raw_single_niche_data*” dataset consists of seven spreadsheets, each sharing two essential columns: 'group' and 'community'. These columns are crucial for subsequent analysis using the SIBER framework. **group** = species * Common = common triplefin, *Forsterygion lapillum* * Yaldwyn = Yaldwyn’s triplefin, *Notoclinops yaldwyni* * Blue_eyed = blue-eyed triplefin, *Notoclinops segmentatus* * Blenny = crested blenny, *Parablennius laticlavius* **community** = treatment * C = control * V = CO2 vents **Description of the seven spreadsheets:** 1. **Isotopes -** the dataset includes ratios of 13C/12C and 15N/14N expressed in the conventional δ notation as parts per thousand deviation from international standards. Stable isotopes were derived from a total of 251 fishes collected across three years of sampling. iso1= δ13C iso2= δ15N 2. **Stomach volumetric** - The dataset includes estimated volumetric measures of stomach contents, where the volume contribution of each prey category relative to the total stomach content (100%) was visually estimated. Data were collected between 2018 and 2019. The stomach content was analysed with this method for common triplefin, Yaldwyn's triplefin, blue eyed triplefin and crested blenny. There are 19 prey categories. 3. **Stomach count** - All prey items were counted in 10 prey categories: copepods, ostracods, polychaetes, amphipods, gastropods, bivalves, tanaids, mites, isopods , and others. Digested items that were not identifiable were excluded from the analysis. The stomach content was analysed with this method for common triplefin, Yaldwyn's triplefin and blue eyed triplefin. 4. **Stomach biomass -** The dataset includes calculated biomass derived from the mass of prey subsamples within each category, multiplied by their count. 5. **Habitat** - The microhabitat occupied and habitat orientation (horizontal, angled and vertical) was recorded using free roaming visual surveys on SCUBA (February 2018). *Microhabitat types:* t. = turf algae <10 cm in height ca. = erect calcareous algae cca. = crustose coralline algae b. = bare rocky substratum sp. = encrusting fleshy green algae cobble. = cobbles (~0.5–2 cm in diameter) *Type of surface orientation:* hor = horizontal angle = angled vert = vertical 6. **Behaviour** - Behavioural variables quantified from underwater footage and expressed as rates per minute. The behaviours are: swimming, jumping, feeding, attacking and fleeing from an attack. 7. **Aquarium**: Data from an aquarium experiment involving *Forsterygion lapillum and Notoclinops yaldwyni*, showing the proportion of time spent in available habitat types to assess habitat preference in controlled conditions. Time in each habitat type and spent in activity was derived from video recordings of 10 minutes and expressed as a proportion of total observation time. Common = common triplefin, *Forsterygion lapillum* Yaldwyn = Yaldwyn’s triplefin, *Notoclinops yaldwyni* Common.c = common triplefin in presence of Yaldwyn’s triplefin Yaldwyn.c = Yaldwyn’s triplefin in presence of common triplefin turf.horizontal = time spent on horizontal turf substratum bare.horizontal = time spent on horizontal bare substratum turf.vertical = time spent on vertical turf substratum bottom = time spent on the bottom of the tank swimming = time spent swimming aquarium.wall = time spent on the walls of the tank switches = numbers of changes between habitats #### Unified\_overlap\_dataset The *“Unified_overlap_dataset”* consists of ten spreadsheets, each sharing “id”, “year”, “location” and “species “column (with few exceptions detailed). These first columns need to be factors for analysis using the Unified overlap framework. We used the R scripts provided in the original study ([Geange et al, 2011](https://doi.org/10.1111/j.2041-210X.2010.00070.x)), as detailed in the manuscript. Data for control and vents are in separate data sheets, with C = control and V = vent. **Id**: sample number **Year:** year the data were collected **Location:** North (n) or South (s), site location **Species**: fish species * Common = common triplefin, *Forsterygion lapillum* * Yaldwyn = Yaldwyn’s triplefin, *Notoclinops yaldwyni* * Blue_eyed = blue-eyed triplefin, *Notoclinops segmentatus* * Blenny = crested blenny, *Parablennius laticlavius* We used the same data as per previous section. **Isotopes C and Isotopes V:** * iso1= δ13C * iso2= δ15N **Diet V and Diet C:** For **stomach content**: we used only volumetric stomach content data as inclusive of all species of interest. It is not raw data, but we used the reduced dimension obtained from nonmetric multidimensional scaling (nMDS), thus the 2 columns resulting from this analysis are vol1 and vol2. Raw data are in the datasheet **Stomach volumetric** in the “*Raw_single_niche_data*” dataset. **Habitat association C and Habitat association V** / **Habitat - C and Habitat - V** For **Habitat association**, the columns are id, species, habitat and position. The habitat association for each species is categorical based on habitat occupied and position (e.g., turf - vertical). Information for Crested blenny were extracted from the behavioural video recordings (with each video being a replicate). The dataset is then linked to **Habitat cover** in both control (C) and vent (V) sites to determine the choice of the habitat based on habitat availability. Therefore, the habitat cover only presents the percentage cover of each habitat type at control and vent. *Habitat:* turf = turf algae <10 cm in height ca = erect calcareous algae cca = crustose coralline algae barren = bare rocky substratum sp = encrusting fleshy green algae cobble = cobbles (~0.5–2 cm in diameter) sand = sand *Position:* hor = horizontal angle = angled vert = vertical **Behaviour C and Behaviour V**: Behavioural variables quantified from underwater footage and expressed as rates per minute. The behaviours are: swimming, jumping, feeding, attacking and fleeing from an attack. Reference: Geange, S. W., Pledger, S., Burns, K. C., & Shima, J. S. (2011). A unified analysis of niche overlap incorporating data of different types. *Methods in Ecology and Evolution*, 2(2), 175-184. [https://doi.org/10.1111/j.2041-210X.2010.00070.x](https://doi.org/10.1111/j.2041-210X.2010.00070.x) We used a small hand net and a mixture of ethanol and clove oil to collect the four species of interest (Forsterygion lapillum, Notoclinops yaldwyni, Notoclinops segmentatus and Parablennius laticlavius) at both control and vent sites over four years. For stable isotope analysis, white muscle tissue was extracted from each fish and oven-dried at 60 °C. The dried tissue was subsequently ground using a ball mill. Powdered muscle tissue from each fish was individually weighed into tin capsules and analysed for stable δ 15N and δ13C isotopes. Samples were combusted in an elemental analyser (EuroVector, EuroEA) coupled to a mass spectrometer (Nu Instruments Horizon) at the University of Adelaide. We then analysed the isotopic niche in SIBER. For stomach content analysis the entire gut was extracted from each fish. Using a stereomicroscope, for count and biomass, all prey items in the stomach were counted first. For each prey category, well-preserved individuals were photographed and their mass was calculated based on length and width. The average mass per individual for each category was then multiplied by the count to determine total prey biomass. For the volumetric method, the volume contribution of each prey category relative to the total stomach content was visually estimated (algae were accounted for). Digested items that were not identifiable were excluded from the analysis. Each stomach content dataset was reduced to two dimensions with non-metric multidimensional scaling (nMDS) to be then analysed in SIBER. To assess habitat choice, visual surveys were conducted on SCUBA, to record the microhabitat type and orientation occupied by Forsterygion lapillum, Notoclinops yaldwyni and Notoclinops segmentatus. The resulting dataset comprised a total of 17 distinct combinations of habitat types and surface orientations. The dataset was simplified to two dimensions using correspondence analysis (CA) for subsequent SIBER analysis. Fish behaviour was assessed using GoPro cameras both in situ and during controlled aquarium experiments. In the field, recordings lasted 30 minutes across 4 days, with analysis conducted using VLC. Initial acclimation and periodic intervals (10 minutes every 5 minutes) were excluded from analysis. In controlled aquarium settings, individuals of Forsterygion lapillum and Notoclinops yaldwyni were observed both in isolation and paired. Their habitat preference, surface orientation, and activity levels were recorded for 10 minutes to assess behaviour independent of external influences. Both datasets were dimensionally reduced for analysis in SIBER: non-metric multidimensional scaling (nMDS) was applied to the in situ behavioral data, while principal component analysis (PCA) was used for the aquarium experiments. Unified analysis of niche overlap We quantified the local realised niche space for each fish species at control and vent along the four niche classes, adapting the data as follows: isotopes (continuous data): raw data. stomach content (continuous data): reduced dimension from the volumetric measure of the previous step. habitat association (elective score): habitat and orientation preference linked to Manly’s Alpha association matrix. behaviour (continuous data): raw data. Global change stressors can modify ecological niches of species, and hence alter ecological interactions within communities and food webs. Yet, some species might take advantage of a fast-changing environment, and allow species with high niche plasticity to thrive under climate change. We used natural CO2 vents to test the effects of ocean acidification on niche modifications of a temperate rocky reef fish assemblage. We quantified three ecological niche traits (overlap, shift, and breadth) across three key niche dimensions (trophic, habitat, and behavioural). Only one species increased its niche width along multiple niche dimensions (trophic and behavioural), shifted its niche in the remaining (habitat), and was the only species to experience a highly increased density (i.e. doubling) at vents. The other three species that showed slightly increased or declining densities at vents only displayed a niche width increase in one (habitat niche) out of seven niche metrics considered. This niche modification was likely in response to habitat simplification (transition to a system dominated by turf algae) under ocean acidification. We further show that at the vents, the less abundant fishes have a negligible competitive impact on the most abundant and common species. Hence, this species appears to expand its niche space overlapping with other species, consequently leading to lower abundances of the latter under elevated CO2. We conclude that niche plasticity across multiple dimensions could be a potential adaptation in fishes to benefit from a changing environment in a high-CO2 world. 

[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

Associated data and R code for the paper Epstein & Roberts 2022 - Identifying priority areas to manage mobile bottom fishing on seabed carbon in the UK. This repository contains the primary output data from a desk-based investigation of seabed sediment organic carbon (OC) and mobile demeresal fishing in the UKEEZ. Best available published datasets were combined to produce unified maps of predicted seabed OC stocks, mean annual mobile bottom fishing disturbance, mean value of fish landed by mobile bottom fishing, and mean annual cummulative disturbance to seabed carbon from mobile bottom fishing. This data was combined with modeling of estimated fishing displacement to idenitfy priority areas for managmement and/or future research. For further methodological information please refer to the full paper published at PLOS Climate.