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opencc-by-4.0Nov 2024View details →

Data for "Combining 13C, 15N, and 2H tracer to measure feeding and metabolic activity in marine, shallow-water sponges – A pilot study"

This dataset includes raw data used in the paper "Combining 13C, 15N, and 2H tracer to measure feeding and metabolic activity in marine, shallow-water sponges – A pilot study" (JEMBE).

opencc-by-4.0Aug 2024View details →

Occurrence Record Dataset from "Depth Matters for Marine Biodiversity"

This is the final occurrence record dataset produced for the manuscript "Depth Matters for Marine Biodiversity". Detailed methods for the creation of the dataset, below, have been excerpted from Appendix I: Extended Methods. Detailed citations for the occurrence datasets from which these data were derived can also be foud in Appedix I of the manuscript. We first assembled a list of all recognized species of fishes from the orders Scombiformes (Betancur-R et al., 2017), Gadiformes, and Beloniformes by accessing FishBase (Boettiger et al., 2012; Froese & Pauly, 2017) and the Ocean Biodiversity Information System (OBIS; OBIS, 2022; Provoost & Bosch, 2019) through queries in R (R Core Team, 2021). Species were considered Atlantic if their FishBase distribution or occurrence records on OBIS included any area within the Atlantic or Mediterranean major fishing regions as defined by the Food and Agriculture Organization of the United Nations (FAO Regions 21, 27, 31, 34, 37, 41, 47, and 48; FAO, 2020). The database query script can be found on the project code repository (<a href="https://github.com/hannahlowens/3DFishRichness/blob/main/1_OccurrenceSearch.R">https://github.com/hannahlowens/3DFishRichness/blob/main/1_OccurrenceSearch.R</a>). We then curated the list of names to resolve discrepancies in taxonomy and known distributions through comparison with the Eschmeyer Catalog of Fishes (Eschmeyer & Fricke, 2015) , accessed in September of 2020, as our ultimate taxonomic authority. The resulting list of species was then mapped onto the Global Biodiversity Information Facility’s backbone taxonomy (Chamberlain et al., 2021; GBIF.org, 2020a) to ensure taxonomic concurrence across databases (Supplementary Table 1). The final taxonomic list was used to download occurrence records from OBIS (OBIS, 2022) and GBIF (GBIF.org, 2020b) in R through robis (Provoost & Bosch, 2019) and occCite (Owens et al., 2021).  For each species, duplicate points were removed from two- and three-dimensional species occurrence datasets separately, and inaccurate depth records were removed from 3D datasets (all records with and without depth information were retained for the 2D dataset). Depth records were based on the “depth” field in both the GBIF and OBIS datasets, which define the field as “depth below the surface in meters”. We chose this value over incorporating information from “minimumDepthInMeters” and “maximumDepthInMeters” because more records contained information from the “depth” field than either of the two other fields (although when these fields were both supplied, “depth” appears to have been often, but not always, derived by calculated the mean between minimum and maximum depth). We also initially included the “depthAccuracy” field from both datasets but did not ultimately use this field as it was not complete enough to be useful. Instead, we determined depth inaccuracy based on extreme statistical outliers (values greater than 2 or less than -2 when occurrence depths were centered and scaled), depths that exceeded bathymetry at occurrence coordinates, and occurrence depths far outside known depth ranges obtained from FishBase, Eschmeyer’s Catalog of Fishes, and/or congeneric depth ranges in the dataset. Once the resulting data were mapped and curated to remove records with putatively spurious coordinates, under-sampled regions and species were augmented with data from publicly available digital museum collection databases not served through OBIS or GBIF, as well as a literature search. Finally, for datasets with more than 20 points remaining after data curation, occurrence data were downsampled to the resolution of the environmental data; that is, to 1 point per 1 degree grid cell in the 2D dataset, and to one point per depth slice per 1 degree grid cell in the 3D dataset.   References: Betancur-R, R., Wiley, E. O., Arratia, G., Acero, A., Bailly, N., Miya, M., Lecointre, G., & Ortí, G. (2017). Phylogenetic classification of bony fishes. BMC Evolutionary Biology, 17(1), 162. <a href="https://doi.org/10.1186/s12862-017-0958-3">https://doi.org/10.1186/s12862-017-0958-3</a> Boettiger, C., Lang, D. T., & Wainwright, P. C. (2012). rfishbase: exploring, manipulating and visualizing FishBase data from R. Journal of Fish Biology, 81(6), 2030–2039. <a href="https://doi.org/10.1111/j.1095-8649.2012.03464.x">https://doi.org/10.1111/j.1095-8649.2012.03464.x</a> Chamberlain, S., Barve, V., McGlinn, D., Oldoni, D., Desmet, P., Geffert, L., & Ram, K. (2021). rgbif: Interface to the Global Biodiversity Information Facility API. <a href="https://CRAN.R-project.org/package=rgbif">https://CRAN.R-project.org/package=rgbif</a> Eschmeyer, & Fricke, W. N. &. (2015). Taxonomic checklist of fish species listed in the CITES Appendices and EC Regulation 338/97 (Elasmobranchii, Actinopteri, Coelacanthi, and Dipneusti, except the genus Hippocampus). Catalog of Fishes, Electronic Version. Accessed September, 2020. <a href="https://www.calacademy.org/scientists/projects/eschmeyers-catalog-of-fishes">https://www.calacademy.org/scientists/projects/eschmeyers-catalog-of-fishes</a> FAO. (2020). FAO Major Fishing Areas. United Nations Fisheries and Aquaculture Division. <a href="https://www.fao.org/fishery/en/collection/area">https://www.fao.org/fishery/en/collection/area</a> Froese, R., & Pauly, D. (2017). FishBase. Accessed September, 2022. www.fishbase.org GBIF.org. (2020a). GBIF Backbone Taxonomy. Accessed September, 2020. GBIF.org GBIF.org. (2020b). GBIF Occurrence Download. Accessed November, 2020. <a href="https://doi.org/10.15468">https://doi.org/10.15468</a> OBIS. (2020). Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. Accessed November, 2020. www.obis.org Owens, H. L., Merow, C., Maitner, B. S., Kass, J. M., Barve, V., & Guralnick, R. P. (2021). occCite: Tools for querying and managing large biodiversity occurrence datasets. Ecography, 44(8), 1228–1235. <a href="https://doi.org/10.1111/ecog.05618">https://doi.org/10.1111/ecog.05618</a> Provoost, P., & Bosch, S. (2019). robis: R Client to access data from the OBIS API. <a href="https://cran.r-project.org/package=robis">https://cran.r-project.org/package=robis</a> R Core Team. (2021). R: A Language and Environment for Statistical Computing. <a href="https://www.R-project.org/">https://www.R-project.org/</a>

opencc-by-4.0Jul 2024View details →

Europe-wide public preferences for plankton-based ecosystem services and marine biodiversity from a series of Deliberative Monetary Valuation workshops

The data were collected as part of the Horizon Europe project BIOcean5D – Marine Biodiversity Assessment and Prediction Across Spatial, Temporal and Human Scales. The aim of the study was to elicit public preferences for marine biodiversity and ecosystem services, with a particular focus on plankton. This dataset comprises responses from a series of Deliberative Monetary Valuation workshops held across Europe, along with the corresponding supplementary material. The responses encompass a range of data, including socio-demographic information, personal characteristics, beliefs, prior knowledge, preferences derived from a Discrete Choice Experiment, and the associated motivations of each respondent. In total, 15 workshops were conducted between October 2023 and February 2024 in the following locations: Poland (Poznan and Sopot), Italy (Padova and Chioggia), the Basque Country (Vitoria-Gasteiz and Bilbao), Germany (Bremerhaven and Hannover), and France (Rennes and Brest).

STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: RLG Dataset Coniston A

This dataset contains the files corresponding to which results have been included in the journal paper. The full descirption of conducted trials and data structure is mentioned in the attached pdf document.STREAM trials were conducted by the University of Birmingham (UoB) and the University of St. Andrews from 29/08/2022 - 02/09/2022 at Coniston Lake in the UK. The primary aim was to gather propagation data across lakes and measure the returns from the lake surface. The data will be used to develop algorithms to extract the information needed for pilotage.The experiments were performed with radars operating in the 79, 150, and 300 GHz bands to investigate the Doppler and imaging capabilities of these radars.This report describes the measurement scenarios and data structure of INRAS Radarlog (76 GHz – 81 GHz) used for data collection campaign.Contact: a.a.a.pirkani@bham.ac.uk or m.s.gashinova@bham.ac.uk

opencc-zeroNov 2023View details →

dryad40/100

Ecological forecasts for marine resource management during climate extremes

Forecasting weather has become commonplace, but as society faces novel and uncertain environmental conditions there is a critical need to forecast ecology. Forewarning of ecosystem conditions during climate extremes can support proactive decision-making, yet applications of ecological forecasts are still limited. We showcase the capacity for existing marine management tools to transition to a forecasting configuration and provide skilful ecological forecasts up to 12 months in advance. The management tools use ocean temperature anomalies to help mitigate whale entanglements and sea turtle bycatch, and we show that forecasts can forewarn of human-wildlife interactions caused by unprecedented climate extremes. We further show that regionally downscaled forecasts are not a necessity for ecological forecasting and can be less skilful than global forecasts if they have fewer ensemble members. Our results highlight capacity for ecological forecasts to be explored for regions without the infrastructure or capacity to regionally downscale, ultimately helping to improve marine resource management and climate adaptation globally.

Fig. 8 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)

Fig. 8. Plot of the non-metric multidimensional scaling (nMDS) based on the by Bray–Curtis similarity index for logarithmic values of meiobenthos taxa density in the recognized habitats of the Snake Island MPA (Black Sea).

Fig. 7 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)

Fig. 7. Cluster analysis dendrogram based on meiobenthos density on the different habitats in MPA of the Snake Island (Black Sea).

Fig. 4 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)

Fig. 4. The average density (N, means ± SE ind.·m–2) and biomass (B, means ± SE mg·m–2) of the total meiobenthos with contribution permanent and temporary taxa in the different habitats of the Snake Island MPA (Black Sea).

Fig. 3 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)

Fig. 3. Meiobenthic community structure of different substrate types in the MB143 habitat of the Snake Island MPA (Black Sea).

Fig. 2 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)

Fig. 2. The average density (N, means ± SE ind.·m–2) and biomass (B, means ± SE mg·m–2) of the total meiobenthos of different substrate types in the MB143 habitat of the Snake Island MPA (Black Sea).

Fig. 1 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)

Fig. 1. The map-scheme of the study area near the Snake Island (north-western Ukrainian shelf of the Black Sea).

Fig. 6 in Meiofaunal Biodiversity In A Marine Protected Area: A Case Study In The Rocky And Sedimentary Shores Of The Snake Island (North-Western Black Sea)

Fig. 6. The contribution (%) of each meiobenthic taxon to the average density and biomass in the different habitats of the Snake Island MPA (Black Sea).

Figure 1 in Amphi-Atlantic distribution of the Mancocumatinae (Cumacea: Bodotriidae), with description of a new genus dwelling in marine lava caves of Tenerife (Canary Islands)

Figure 1. Speleocuma guanche gen. et sp. nov. A, Ovigerous female, whole animal in lateral view. B, Adult male, whole animal in lateral view.

Figure 4 in Amphi-Atlantic distribution of the Mancocumatinae (Cumacea: Bodotriidae), with description of a new genus dwelling in marine lava caves of Tenerife (Canary Islands)

Figure 4. Speleocuma guanche gen. et sp. nov., ovigerous female. A, Right mandible. B, Pars incisa of the left mandible showing the lacinia mobilis. C, Maxillule. D, Maxilla. E, Maxilliped 1.

Figure 3 in Amphi-Atlantic distribution of the Mancocumatinae (Cumacea: Bodotriidae), with description of a new genus dwelling in marine lava caves of Tenerife (Canary Islands)

Figure 3. Speleocuma guanche gen. et sp. nov., ovigerous female A, Antenna 1. B, Maxilliped 2. C, Maxilliped 3. D, Pereopod 2. E, Pereopod 3. F, Pereopod 4. G, Last abdominal somite and uropod.

Figure 2 in Amphi-Atlantic distribution of the Mancocumatinae (Cumacea: Bodotriidae), with description of a new genus dwelling in marine lava caves of Tenerife (Canary Islands)

Figure 2. Speleocuma guanche gen. et sp. nov., preadult female, SEM micrographs. A, Anterior half of the carapace. B, Microstructure of the carapace showing the denticulate scales.

Figure 5 in Amphi-Atlantic distribution of the Mancocumatinae (Cumacea: Bodotriidae), with description of a new genus dwelling in marine lava caves of Tenerife (Canary Islands)

Figure 5. Speleocuma guanche gen. et sp. nov., adult male. A, Antennae 1 and 2. B, Mandible. C, Maxilula. D, Maxila. E, Maxilliped 2. F, Maxilliped 3. G, Pereopod 2. H, Pereopod 3. I, Pereopod 4. J, Pleopod. K, Uropod.

opencc-by-4.0Apr 2023View details →

Figs 1, 2. Auxis spp. from Syrian marine waters. 1, A in First record of frigate tuna Auxis thazard (Osteichthyes: Scombriformes: Scombridae) in the Syrian marine waters, the Eastern Mediterranean

Figs 1, 2. Auxis spp. from Syrian marine waters. 1, A. thazard (Lacepède, 1800); 2, A. rochei (Risso, 1810). A, corselet; B, vertical line indicating where the tip of the pectoral fin reaches; C, dark wavy lines in the dorsal scaleless area.

opencc-by-4.0Oct 2023View details →

A dataset of seabird collision and displacement vulnerability factors relatively to marine wind farms in Portugal

The implementation of marine wind farms has grown considerably along northern European's northern Atlantic coasts (e.g. Baltic and North Sea) and a boom in these infrastructures is expected to take place along Europe's entire Atlantic and Mediterranean coasts. Accordingly, the Portuguese government has recently proposed priority sites for the construction of wind farms along the mainland coast. We used sensitivity mapping (Garthe & Hüppop, 2004) to assess which areas along the Portuguese coast are most sensitive for seabirds and to what extent the proposed sites for wind farm construction overlap with these areas.This dataset contains the base data to estimate a seabird Species Sensitivity Index (SSI) (following Bradbury et al., 2014, Certain et al., 2015), including scores for 11 species-specific ecological and behavioural factors related with seabird species' (i) vulnerability to collision with wind farms (4 factors), (ii) vulnerability to displacement due to disturbance by wind farms and associated maintenance (3 factors), and (iii) conservation status (4 factors). We reviewed the literature to mine and compile data on these factors for 34 seabird species that regularly occur along the Portuguese mainland coast. We updated factor scores, particularly for those factors that have been studied in greater detail in recent years using tracking technologies (Clairbaux & Jessopp, 2021). However, in many cases empirical data were unavailable and we used the scores presented in previous sensitivity mapping studies (Garthe & Hüppop, 2004; Bradbury et al., 2014; Certain et al., 2015; Wade et al., 2016; Serratosa & Allinson, 2022).

SeaPaCS graphic elaboration of the Protocol for marine micro-plastic collection and monitoring in citizen science and for building a L.A.D.I. trawling tool

This  is a graphic elaboration  (in Italian) of the protocol "SeaPaCS deliverable - protocol for plastic monitoring in citizen science" in English and Italian is a deliverable of the SeaPaCS project (Participatory Citizen Science Against Marine Pollution), funded by IMPETUS (project ID 101058677). The protocol  and the visual elaboration has been freely adapted from "LADI and the Trawl" by Coco Coyle with Melissa Novaceski, Emily Wells and Max Liboiron, as published by the Civic Laboratory for Environmental Action Research, August 2016.  The graphic elaboration (as the protocol) in both languages, consists of three parts: 1) how to build a DIY low cost manta trawl device (LADI - Low-Tech Aquatic Detection Debris Instrument) to monitor plastic pollution,  adjusted to materials availability and costs in Italy; 2) how to monitor (the sampling itself and towing procedure); and 3) how to categorize plastic debris back on land.