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Research data keyboard_double_arrow_right Dataset 2020Publisher:PANGAEA Funded by:AKA | Topoclimate, land surface..., EC | PETA-CARBAKA| Topoclimate, land surface conditions and atmospheric feedbacks ,EC| PETA-CARBAuthors:Karjalainen, Olli;
Karjalainen, Olli
Karjalainen, Olli in OpenAIRELuoto, Miska;
Luoto, Miska
Luoto, Miska in OpenAIREAalto, Juha;
Aalto, Juha
Aalto, Juha in OpenAIREEtzelmüller, Bernd;
+4 AuthorsEtzelmüller, Bernd
Etzelmüller, Bernd in OpenAIREKarjalainen, Olli;
Karjalainen, Olli
Karjalainen, Olli in OpenAIRELuoto, Miska;
Luoto, Miska
Luoto, Miska in OpenAIREAalto, Juha;
Aalto, Juha
Aalto, Juha in OpenAIREEtzelmüller, Bernd;
Etzelmüller, Bernd
Etzelmüller, Bernd in OpenAIREGrosse, Guido;
Grosse, Guido
Grosse, Guido in OpenAIREJones, Benjamin M;
Jones, Benjamin M
Jones, Benjamin M in OpenAIRELilleøren, Karianne Staalesen;
Lilleøren, Karianne Staalesen
Lilleøren, Karianne Staalesen in OpenAIREHjort, Jan;
Hjort, Jan
Hjort, Jan in OpenAIREThis dataset contains spatial predictions of the potential environmental spaces for pingos, ice-wedge polygons and rock glaciers across the Northern Hemisphere permafrost areas. The potential environmental spaces, i.e. conditions where climate, topography and soil properties are suitable for landform presence, were predicted with statistical ensemble modelling employing geospatial data on environmental conditions at 30 arc-second resolution (~1 km). In addition to the baseline period (1950-2000), the predictions are provided for 2041-2060 and 2061-2080 using climate-forcing scenarios (Representative Concentration Pathways 4.5 and 8.5). The resulting dataset consists of five spatial predictions for each landform in GeoTIFF format.The data provide new information on 1) the fine-scale spatial distribution of permafrost landforms in the Northern Hemisphere, 2) the potential future alterations in the environmental suitability for permafrost landforms due to climate change, and 3) the circumpolar distribution of various ground ice types, and can 4) facilitate efforts to inventory permafrost landforms in incompletely mapped areas.
PANGAEA - Data Publi... arrow_drop_down PANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2020License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1594/pangaea.922771&type=result"></script>'); --> </script>
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more_vert PANGAEA - Data Publi... arrow_drop_down PANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2020License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1594/pangaea.922771&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:PANGAEA Authors:Schild, Laura;
Schild, Laura
Schild, Laura in OpenAIREKruse, Stefan;
Kruse, Stefan
Kruse, Stefan in OpenAIREHeim, Birgit;
Heim, Birgit
Heim, Birgit in OpenAIREStieg, Amelie;
+7 AuthorsStieg, Amelie
Stieg, Amelie in OpenAIRESchild, Laura;
Schild, Laura
Schild, Laura in OpenAIREKruse, Stefan;
Kruse, Stefan
Kruse, Stefan in OpenAIREHeim, Birgit;
Heim, Birgit
Heim, Birgit in OpenAIREStieg, Amelie;
Stieg, Amelie
Stieg, Amelie in OpenAIREvon Hippel, Barbara;
Gloy, Josias; Smirnikov, Viktor; Töpfer, Nils;von Hippel, Barbara
von Hippel, Barbara in OpenAIRETroeva, Elena I;
Troeva, Elena I
Troeva, Elena I in OpenAIREPestryakova, Luidmila A;
Pestryakova, Luidmila A
Pestryakova, Luidmila A in OpenAIREHerzschuh, Ulrike;
Herzschuh, Ulrike
Herzschuh, Ulrike in OpenAIREVegetation 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.
PANGAEA - Data Publi... arrow_drop_down PANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2023License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert PANGAEA - Data Publi... arrow_drop_down PANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2023License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1594/pangaea.955784&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Publisher:PANGAEA Authors:Wolfe, Kennedy;
Nguyen, Hong D; Davey, Madeline;Wolfe, Kennedy
Wolfe, Kennedy in OpenAIREByrne, Maria;
Byrne, Maria
Byrne, Maria in OpenAIRECoastal and intertidal habitats are at the forefront of anthropogenic influence and environmental change. The species occupying these habitats are adapted to a world of extremes, which may render them robust to the changing climate or more vulnerable if they are at their physiological limits. We characterized the diurnal, seasonal and interannual patterns of flux in biogeochemistry across an intertidal gradient on a temperate sandstone platform in eastern Australia over 6 years (2009–2015) and present a synthesis of our current understanding of this habitat in context with global change. We used rock pools as natural mesocosms to determine biogeochemistry dynamics and patterns of eco‐stress experienced by resident biota. In situ measurements and discrete water samples were collected night and day during neap low tide events to capture diurnal biogeochemistry cycles. Calculation of pHT using total alkalinity (TA) and dissolved inorganic carbon (DIC) revealed that the mid‐intertidal habitat exhibited the greatest flux over the years (pHT 7.52–8.87), and over a single tidal cycle (1.11 pHT units), while the low‐intertidal (pHT 7.82–8.30) and subtidal (pHT 7.87–8.30) were less variable. Temperature flux was also greatest in the mid‐intertidal (8.0–34.5°C) and over a single tidal event (14°C range), as typical of temperate rocky shores. Mean TA and DIC increased at night and decreased during the day, with the most extreme conditions measured in the mid‐intertidal owing to prolonged emersion periods. Temporal sampling revealed that net ecosystem calcification and production were highest during the day and lowest at night, particularly in the mid‐intertidal. Characterization of biogeochemical fluctuations in a world of extremes demonstrates the variable conditions that intertidal biota routinely experience and highlight potential microhabitat‐specific vulnerabilities and climate change refugia. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2020) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2020-11-30.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1594/pangaea.925299&type=result"></script>'); --> </script>
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more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1594/pangaea.925299&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2018Publisher:PANGAEA Authors: Ferger, Stefan;Schleuning, Matthias;
Schleuning, Matthias
Schleuning, Matthias in OpenAIREBöhning-Gaese, Katrin;
Böhning-Gaese, Katrin
Böhning-Gaese, Katrin in OpenAIREFresh biomass of invertebrates in sweep net samples of all 60 plots plus 4 sun-coffee-plantations. We used 100 sweeps with a 30-cm diameter sweep net along two parallel, permanently marked 50-m transects on the plots to sample invertebrate biomass per plot in the warm dry season and cold dry season. All invertebrates ≥ 1 mm were lumped to one sample per plot and season, which was immediately weighed to obtain fresh biomass. Fresh biomass of invertebrates in sweep net samples of all 60 plots plus 4 sun-coffee-plantations.Biomass of invertebrates = sweep net sample of 2x50 sweeps along the transects
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1594/pangaea.895655&type=result"></script>'); --> </script>
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more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1594/pangaea.895655&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Embargo end date: 13 Jul 2020Publisher:Dryad Funded by:SNSF | Host-parasite interaction..., FCT | SFRH/BPD/91527/2012SNSF| Host-parasite interactions on the move - mechanisms and cascading consequences of malaria infections in migratory birds ,FCT| SFRH/BPD/91527/2012Authors:Briedis, Martins;
Bauer, Silke; Adamík, Peter; Alves, José; +10 AuthorsBriedis, Martins
Briedis, Martins in OpenAIREBriedis, Martins;
Bauer, Silke; Adamík, Peter; Alves, José; Costa, Joana; Emmenegger, Tamara; Gustafsson, Lars; Koleček, Jaroslav; Krist, Miloš; Liechti, Felix; Lisovski, Simeon; Meier, Christoph; Procházka, Petr; Hahn, Steffen;Briedis, Martins
Briedis, Martins in OpenAIREAim: Animal migration strategies balance trade-offs between mortality and reproduction in seasonal environments. Knowledge of broad-scale biogeographical patterns of animal migration is important for understanding ecological drivers of migratory behaviours. Here we present a flyway-scale assessment of the spatial structure and seasonal dynamics of the Afro-Palearctic bird migration system and explore how phenology of the environment guides long-distance migration. Location: Europe and Africa. Time period: 2009–2017. Major taxa studied: Birds. Methods: We compiled an individual-based dataset comprising 23 passerine and near-passerine species of 55 European breeding populations where a total of 564 individuals were tracked migrating between Europe and sub-Saharan Africa. In addition, we used remote sensed observations on primary productivity (NDVI) to estimate the timing of vegetation green-up in spring and senescence in autumn across Europe. First, we described how individual breeding and non-breeding sites and the migratory flyways link geographically. Second, we examined how migration timing along the two major Afro-Palearctic flyways is tuned with vegetation phenology en route and at the breeding sites. Results: While we found the longitudes of individual breeding and non-breeding sites to be strongly positively related, the latitudes of breeding and non-breeding sites were negatively related. In autumn, timing of migration was similar along the Western and the Eastern flyways and happened ahead of the autumnal senescence of vegetation. In spring, migration timing was approximately two weeks later along the Eastern flyway than on the Western flyway which coincided with the later spring green-up in Eastern Europe. Main Conclusions: Migration of the Afro-Palearctic landbirds follows a longitudinally parallel leap-frog migration pattern where migrants track vegetation green-up in spring and depart before vegetation senescence in autumn. However, the ongoing global change have the potential to disrupt this spatiotemporal synchronization between migration timing and spring green-up with variable effects on different migrant populations.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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visibility 3visibility views 3 download downloads 1 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2020Publisher:PANGAEA Authors:Shevtsova, Iuliia;
Shevtsova, Iuliia
Shevtsova, Iuliia in OpenAIREKruse, Stefan;
Kruse, Stefan
Kruse, Stefan in OpenAIREHerzschuh, Ulrike;
Herzschuh, Ulrike
Herzschuh, Ulrike in OpenAIREBrieger, Frederic;
+4 AuthorsBrieger, Frederic
Brieger, Frederic in OpenAIREShevtsova, Iuliia;
Shevtsova, Iuliia
Shevtsova, Iuliia in OpenAIREKruse, Stefan;
Kruse, Stefan
Kruse, Stefan in OpenAIREHerzschuh, Ulrike;
Herzschuh, Ulrike
Herzschuh, Ulrike in OpenAIREBrieger, Frederic;
Brieger, Frederic
Brieger, Frederic in OpenAIRESchulte, Luise;
Schulte, Luise
Schulte, Luise in OpenAIREStuenzi, Simone Maria;
Stuenzi, Simone Maria
Stuenzi, Simone Maria in OpenAIREPestryakova, Ludmila A;
Zakharov, Evgenii S;Pestryakova, Ludmila A
Pestryakova, Ludmila A in OpenAIREField investigations were performed in four areas: a treeless mountainous tundra (16-KP-04; Lake Rauchuagytgyn area), tundra-taiga transition zone (16-KP-01, Lake Ilirney area) and a northern taiga (18-BIL-01, 18-BIL-02). In total, 39 sites were investigated. The sites were placed to cover different vegetation communities that characterise central Chukotka.Fifteen-meter radius sample plots (sites) were demarcated in the most homogeneous locations. Heterogeneity was accommodated by roughly assorting vegetation into two to three vegetation types per sampling plot. Within each area of roughly estimated vegetation types we selected one 0.5 x 0.5 m subplot for representative ground-layer above-ground biomass (ABG) harvesting (major taxa and other). For moss and lichen AGB harvesting inside 0.5 x 0.5 m subplots representative 0.1 x 0.1 m subplots were chosen. All ground-layer vegetation AGB assessments were calculated for the fifteen-meter radius plot in g m^2 for each sample plot. Tree (Larix cajanderi) AGB was assessed using partial harvesting of three representative individual trees per sample plot, specifically developed for the study area allometric equations and measurements of all trees' heights on the fifteen-meter radius plot. AGB of tall shrubs (Alnus fruticosa, Pinus pumila and Salix spp. (non-creeping)) was assessed from harvested subsamples and projective cover on the fifteen-meter radius sample plot.All harvested AGB samples were weighed fresh in the field. In general, AGB samples with a weight of more than 15 g were subsampled. All samples were oven dried (60 °C, 24 h for ground-layer and moss and lichen samples, 48 h for shrub and tree branch samples, up to one week for tree stem discs) and weighed again.All data was collected by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia.
PANGAEA - Data Publi... arrow_drop_down PANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2020License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
more_vert PANGAEA - Data Publi... arrow_drop_down PANGAEA - Data Publisher for Earth and Environmental ScienceDataset . 2020License: CC BYData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1594/pangaea.923719&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 10 Mar 2022 SpainPublisher:Dryad Funded by:EC | DPaTh-To-AdaptEC| DPaTh-To-AdaptAuthors:Bennett, Scott;
Bennett, Scott
Bennett, Scott in OpenAIREMarba, Nuria;
Vaquer-Sunyer, Raquel;Marba, Nuria
Marba, Nuria in OpenAIREJordá, Gabriel;
+2 AuthorsJordá, Gabriel
Jordá, Gabriel in OpenAIREBennett, Scott;
Bennett, Scott
Bennett, Scott in OpenAIREMarba, Nuria;
Vaquer-Sunyer, Raquel;Marba, Nuria
Marba, Nuria in OpenAIREJordá, Gabriel;
Forteza, Marina; Roca, Guillem;Jordá, Gabriel
Jordá, Gabriel in OpenAIREhandle: 10261/311232
[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
Recolector de Cienci... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTADataset . 2022 . Peer-reviewedData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
visibility 21visibility views 21 download downloads 19 Powered bymore_vert Recolector de Cienci... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTADataset . 2022 . Peer-reviewedData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:World Data Center for Climate (WDCC) at DKRZ Authors:Dix, Martin;
Bi, Daohua;Dix, Martin
Dix, Martin in OpenAIREDobrohotoff, Peter;
Fiedler, Russell; +30 AuthorsDobrohotoff, Peter
Dobrohotoff, Peter in OpenAIREDix, Martin;
Bi, Daohua;Dix, Martin
Dix, Martin in OpenAIREDobrohotoff, Peter;
Fiedler, Russell;Dobrohotoff, Peter
Dobrohotoff, Peter in OpenAIREHarman, Ian;
Harman, Ian
Harman, Ian in OpenAIRELaw, Rachel;
Law, Rachel
Law, Rachel in OpenAIREMackallah, Chloe;
Mackallah, Chloe
Mackallah, Chloe in OpenAIREMarsland, Simon;
O'Farrell, Siobhan; Rashid, Harun;Marsland, Simon
Marsland, Simon in OpenAIRESrbinovsky, Jhan;
Srbinovsky, Jhan
Srbinovsky, Jhan in OpenAIRESullivan, Arnold;
Sullivan, Arnold
Sullivan, Arnold in OpenAIRETrenham, Claire;
Trenham, Claire
Trenham, Claire in OpenAIREVohralik, Peter;
Vohralik, Peter
Vohralik, Peter in OpenAIREWatterson, Ian;
Williams, Gareth;Watterson, Ian
Watterson, Ian in OpenAIREWoodhouse, Matthew;
Woodhouse, Matthew
Woodhouse, Matthew in OpenAIREBodman, Roger;
Bodman, Roger
Bodman, Roger in OpenAIREDias, Fabio Boeira;
Dias, Fabio Boeira
Dias, Fabio Boeira in OpenAIREDomingues, Catia M.;
Hannah, Nicholas;Domingues, Catia M.
Domingues, Catia M. in OpenAIREHeerdegen, Aidan;
Heerdegen, Aidan
Heerdegen, Aidan in OpenAIRESavita, Abhishek;
Savita, Abhishek
Savita, Abhishek in OpenAIREWales, Scott;
Allen, Chris; Druken, Kelsey;Wales, Scott
Wales, Scott in OpenAIREEvans, Ben;
Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon;Evans, Ben
Evans, Ben in OpenAIRESnow, Kate;
Snow, Kate
Snow, Kate in OpenAIREWard, Marshall;
Yang, Rui;Ward, Marshall
Ward, Marshall in OpenAIREProject: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CSIRO-ARCCSS.ACCESS-CM2.ssp245' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Australian Community Climate and Earth System Simulator Climate Model Version 2 climate model, released in 2019, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: CABLE2.5, ocean: ACCESS-OM2 (GFDL-MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE5.1.2 (same grid as ocean). The model was run by the CSIRO (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia), ARCCSS (Australian Research Council Centre of Excellence for Climate System Science). Mailing address: CSIRO, c/o Simon J. Marsland, 107-121 Station Street, Aspendale, Victoria 3195, Australia (CSIRO-ARCCSS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2018Publisher:Zenodo Funded by:EC | AQUACROSS, EC | MARSEC| AQUACROSS ,EC| MARSAuthors: Pletterbauer, Florian; Graf, Wolfram;Dataset on dispersal of alien species in relation to the historic development of hydropower generation and Navigation along the River Danube.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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visibility 31visibility views 31 download downloads 6 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 SpainPublisher:Elsevier BV Funded by:EC | TROPOS, EC | H2OCEAN, EC | MARIBE +1 projectsEC| TROPOS ,EC| H2OCEAN ,EC| MARIBE ,EC| MERMAIDAuthors:Carlos V.C. Weiss;
Carlos V.C. Weiss
Carlos V.C. Weiss in OpenAIREJarbas Bonetti;
Jarbas Bonetti
Jarbas Bonetti in OpenAIREMarinez E.G. Scherer;
Marinez E.G. Scherer
Marinez E.G. Scherer in OpenAIREBárbara Ondiviela;
+2 AuthorsBárbara Ondiviela
Bárbara Ondiviela in OpenAIRECarlos V.C. Weiss;
Carlos V.C. Weiss
Carlos V.C. Weiss in OpenAIREJarbas Bonetti;
Jarbas Bonetti
Jarbas Bonetti in OpenAIREMarinez E.G. Scherer;
Marinez E.G. Scherer
Marinez E.G. Scherer in OpenAIREBárbara Ondiviela;
Bárbara Ondiviela
Bárbara Ondiviela in OpenAIRERaúl Guanche;
José A. Juanes;Raúl Guanche
Raúl Guanche in OpenAIREThe marine environment has been in the spotlight of economic development due to the growing demand for areas to promote activities associated with the concept of Blue Economy. This is the case of the renewable energy and aquaculture sectors, whose expansion towards offshore is determined by the increase global demand for energy and food, and by exceeding of the carrying capacity of coastal and terrestrial systems. In this context, the multi-use strategy can be an alternative to minimize conflicts between activities and impacts on the surrounding social-ecological environment. This contribution presents a preliminary approach to identify opportunities for individual exploitation and the possibilities of multi-use between wind energy, wave energy and aquaculture in Brazil?s Exclusive Economic Zone. Technical, operational, and biological aspects were evaluated, through a Suitability Index validated in previous works, to identify zones with favorable conditions for energy exploitation and farming of six fish species. Additionally, overlaps between conservation areas and multi-use zones were considered to analyze possible spatial conflicts. Zones with multi-use possibilities with different combinations between these sectors were identified: i) wave energy and aquaculture presented the largest areas for multi-use, distributed in the south, southeast and northeast; ii) possibility of combining wind energy and aquaculture was identified in the northeast; and iii) multi-use possibilities in the south for marine energies. Zones with multi-use possibilities were identified in protection and conservation areas, such as the combination of wave exploitation and Greater Amberjack farming, with 63% overlap. Therefore, this case study is a guide for future local studies in the marine region of Brazil, mainly in the selection of sites for analysis. The present contribution represents a starting point for the discussion about multi-use in the country C.V.C. Weiss is grateful to the Brazilian National Council for Scientific and Technological Development (CNPq) for the PDJ (Pós-doutorado Junior) fellowship granted (151228/2020–5), and the financial support from the Universidad de Cantabria (UC) through the Agusto González de Linares and Margarita Salas Grants (POS-UC-2019-06 and RMS-04, respectively). J. Bonetti is a Research Fellow of CNPq (Grant 306633/2019–1). Raúl Guanche acknowledges the Grant RYC-2017-23260 funded by MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future”. This work is framed in the project “ACUFLOT”, supported by the Biodiversity Foundation of the Ecological Transition and Demographic Challenge Ministry of Spain and the IHCantabria - Instituto de Hidráulica Ambiental de la Universidad de Cantabria
Ocean & Coastal Mana... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAArticle . 2023License: CC BYData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
visibility 11visibility views 11 download downloads 6 Powered bymore_vert Ocean & Coastal Mana... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAArticle . 2023License: CC BYData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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