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Time series of data corresponding to Evans et al. (2022) "Natural variability and expansion of the nitrogen deficit within the Eastern Tropical North Pacific Oxygen Deficient Zone", containing secondary quality controlled data of 8 cruises in the ETNP ODZ, seven of which on the 110 W line, as well as supplemental sediment core data and CalCOFI oxygen data for comparison. Intermediate data products generated by the code used for this paper are also included, and the code to generate these intermediate products as well as the final outputs has been uploaded onto a separate Zenodo repository, "ETNP_ODZ_time_series_code" at https://doi.org/10.5281/zenodo.6519316. More detailed information is available in the README, but should you have any questions, please reach out to Allan Devol or Natalya Evans.

Model results and data used to make future projections of the effects of climate change on the physiology of tuna in the global ocean ------------------------------------------------- Description: Coupled Model Intercomparison Project Phase 5 (CMIP5) model results were downloaded from here: https://esgf-node.llnl.gov/search/cmip5/ World Ocean Atlas (WOA) 2009 data were downloaded from here: https://www.nodc.noaa.gov/OC5/WOA09/netcdf_data.html The model results and data should only be used to reproduce the analysis described in this publication: Mislan, K. A. S., C. A. Deutsch, R. W. Brill, J. P. Dunne, and J. L. Sarmiento. (2017) Projections of climate driven changes in tuna vertical habitat based on species-specific differences in blood oxygen affinity. Global Change Biology. The Zenodo archive of the code is here: https://doi.org/10.5281/zenodo.808742 ------------------------------------------------- Instructions: Download the tar.gz file, unzip, and put the folders in the data folder of the CMIP5_p50_tuna code.

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 >

Whether marine fish will grow differently in future high pCO2 environments remains surprisingly uncertain. Long-term and whole-life cycle effects are particularly unknown, because such experiments are logistically challenging, space demanding, exclude long-lived species, and require controlled, restricted feeding regimes—otherwise increased consumption could mask potential growth effects. Here, we report on repeated, long-term, food-controlled experiments to rear large populations (>4,000 individuals total) of the experimental model and ecologically important forage fish Menidia menidia (Atlantic silverside) under contrasting temperature (17°, 24°, and 28°C) and pCO2 conditions (450 vs. 2,200 μatm) from fertilization to a third of this annual species' life span. Quantile analyses of trait distributions showed mostly negative effects of high pCO2 on long-term growth. At 17°C and 28°C, but not at 24°C, high pCO2 fish were significantly shorter [17°C: -5 to -9%; 28°C: -3%] and weighed less [17°C: -6 to -18%; 28°C: -8%] compared to ambient pCO2 fish. Reductions in fish weight were smaller than in length, which is why high pCO2 fish at 17°C consistently exhibited a higher Fulton's k (weight/length ratio). Notably, it took more than 100 days of rearing for statistically significant length differences to emerge between treatment populations, showing that cumulative, long-term CO2 effects could exist elsewhere but are easily missed by short experiments. Long-term rearing had another benefit: it allowed sexing the surviving fish, thereby enabling rare sex-specific analyses of trait distributions under contrasting CO2 environments. We found that female silversides grew faster than males, but there was no interaction between CO2 and sex, indicating that males and females were similarly affected by high pCO2. Because Atlantic silversides are known to exhibit temperature-dependent sex determination, we also analyzed sex ratios, revealing no evidence for CO2-dependent sex determination in this species. 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 2020-12-25.

1. Tidal stream energy converters (turbines) are currently being installed in tidally energetic coastal sites. However, there is currently a high level of uncertainty surrounding the potential environmental impacts on marine mammals. This is a key consenting risk to commercial introduction of tidal energy technology. Concerns derive primarily from the potential for injury to marine mammals through collisions with moving components of turbines. To understand the nature of this risk, information on how animals respond to tidal turbines is urgently required. 2. We measured the behaviour of harbour seals in response to acoustic playbacks of simulated tidal turbine sound within a narrow coastal channel subject to strong, tidally induced currents. This was carried out using data from animal-borne GPS tags and shore-based observations, which were analysed to quantify behavioural responses to the turbine sound. 3. Results showed that the playback state (silent control or turbine signal) was not a significant predictor of the overall number of seals sighted within the channel. 4. However, there was a localised impact of the turbine signal; tagged harbour seals exhibited significant spatial avoidance of the sound which resulted in a reduction in the usage by seals of between 11 and 41% at the playback location. The significant decline in usage extended to 500 m from the playback location at which usage decreased by between 1 and 9% during playback. 5. Synthesis and applications: This study provides important information for policy makers looking to assess the potential impacts of tidal turbines and advise on development of the tidal energy industry. Results showing that seals avoid tidal turbine sound suggest that a proportion of seals encountering tidal turbines will exhibit behavioural responses resulting in avoidance of physical injury; in practice, the empirical changes in usage can be used directly as avoidance rates when using collision risk models to predict the effects of tidal turbines on seals. There is now a clear need to measure how marine mammals behave in response to actual operating tidal turbines in the long term to learn whether marine mammals and tidal turbines can co-exist safely at the scales currently envisaged for the industry. JApEcol_Hastie_etal_observation_data_DryadLand based observer data (.xlsx) used in the analysis of seal responses to tidal turbine sounds. This is effectively counts of seals observed in the water during acoustic playbacks of tidal turbine sound and silent controls. Data were collected by a series of observers located on a clifftop overlooking the study area (Kyle Rhea, Isle of Skye, Scotland) README file is provided as a tab in the file.JApEcol_Hastie_etal_seal_telemetry_data_DryadHarbour seal telemetry data (.xlsx) used in the analysis of changes in usage with distance from the location of playbacks of tidal turbine sound. The data are regularised lat-lon locations from 10 individual harbour seals tagged with GPS telemetry devices. README is provided as a tab in the file.STIMweighted_J11_1hour_withRampSound file (.wav) used during playbacks of simulated tidal turbine sound to harbour seals to investigate avoidance responses. The file has a 10 second ramp at the start and end of the file, and is frequency weighted for use with a J11 underwater speaker.

In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2022) 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 2023-10-17. This dataset includes physiological parameters of three Hawaiian coral species (Porites compressa, Porites lobata, and Montipora capitata) over 22-month mesocosm experiment. The corals were exposed to one of four treatments: control, ocean acidification, ocean warming, or combined future ocean conditions.

Ambient conditions shape microbiome responses to both short- and long-duration environment changes through processes including physiological acclimation, compositional shifts, and evolution. Thus, we predict that microbial communities inhabiting locations with larger diel, episodic, and annual variability in temperature and pH should be less sensitive to shifts in these climate-change factors. To test this hypothesis, we compared responses of surface ocean microbes from more variable (nearshore) and more constant (offshore) sites to short-term factorial warming (+3 °C) and/or acidification (pH -0.3). In all cases, warming alone significantly altered microbial community composition, while acidification had a minor influence. Compared with nearshore microbes, warmed offshore microbiomes exhibited larger changes in community composition, phylotype abundances, respiration rates, and metatranscriptomes, suggesting increased sensitivity of microbes from the less-variable environment. Moreover, while warming increased respiration rates, offshore metatranscriptomes yielded evidence of thermal stress responses in protein synthesis, heat shock proteins, and regulation. Future oceans with warmer waters may enhance overall metabolic and biogeochemical rates, but they will host altered microbial communities, especially in relatively thermally stable regions of the oceans. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2019) 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 2020-10-20.

# Long-term changes in taxonomic and functional composition of European marine fish communities The GitHub linked repository is here: [European_demersal_fish_assemblages (](https://github.com/auroreRECE/European_demersal_fish_assemblages)DOI [10.5281/zenodo.11190119](https://zenodo.org/doi/10.5281/zenodo.11190119)) ## Overview This project is dedicated to studying the influence of environmental conditions and fishing on the functional and taxonomic structure of a demersal fish community in Europe. This GitHub repository provides the code of the Receveur et al. (2024) publication in Ecography. ## Data files description ### df\_MFA.csv This file contains the coordinates resulting from the Multiple Factor Analysis (MFA): * X : the row numbers ; * ID_unique : a unique ID number corresponding to the trawls ; * Dim.1 : the coordinate of each trawl on the first MFA dimension ; * Dim.2 : the coordinate of each trawl on the second MFA dimension ; * Dim.3 : the coordinate of each trawl on the third MFA dimension ; ### df\_PCA.csv This file contains the coordinates * X : the row numbers ; * ID_unique : a unique ID number corresponding to the trawls ; * Dim.1 : the coordinate of each trawl on the first PCA dimension ; * Dim.2 : the coordinate of each trawl on the second PCA dimension ; * Dim.3 : the coordinate of each trawl on the third PCA dimension ; ### df\_env.csv This file contains the following environmental parameters: * X : the row numbers ; * ID_unique : a unique ID number corresponding to the trawls ; * Year : the Year of each trawl ; * Quarter : the Quarter of each trawl ; * Ecoregion : the Ecoregion where each trawl has been done; * Survey : the name of the Survey ; * x_my_spatial_id : the longitude of the ICES rectangle where the trawl has been done ; * y_my_spatial_id : the latitude of the ICES rectangle where the trawl has been done ; * my_spatial_id : an ID for the ICES rectangle where the trawl has been done ; * depth : the bottom depth (meters) ; * depth_span : the bottom depth variability (maximum depth of the ICES cell - minimum depth) (meters) ; * chloro_mea: the mean chlorophyll-a concentration (mg/m³) ; * mlotst_mea : the mean mixed layer depth (meters) ; * oxy_bottom_mea : the mean bottom dissolved oxygen (umol/l) ; * oxy_surf_mea : the mean surface dissolved oxygen (umol/l) ; * temp_bottom_mea : the mean bottom temperature (°C) ; * temp_surf_mea : the mean surface temperature (°C) ; * curr_surf_mea : the mean surface current strength (m/s) ; * curr_bottom_mea : the mean bottom current strength (m/s) ; * sal_surf_mea : the mean surface salinity (PSU) ; * chloro_std : the standard deviation of chlorophyll-a concentration (mg/m³) ; * mlotst_std : the standard deviation of mixed layer depth (meters) ; * oxy_bottom_std : the standard deviation of bottom dissolved oxygen (umol/l) ; * oxy_surf_std : the standard deviation of surface dissolved oxygen (umol/l) ; * temp_bottom_std : the standard deviation of bottom temperature (°C) ; * temp_surf_std : the standard deviation of surface temperature (°C) ; * curr_surf_std : the standard deviation of surface current strength (m/s) ; * curr_bottom_std : the standard deviation of bottom current strength (m/s) ; * sal_surf_std : the standard deviation of surface salinity (PSU). ## Raw Data sources ### Biological data Trawls content is publicly available for the North East Atlantic (DATRAS database). Mediterranean data (MEDITS database) are available upon request to Maritime Affairs and Fisheries (MARE DATACOLLECTIONFRAMEWORK). The project uses the following surveys: | Survey Code | Survey name | Area | Period | References | | :---------- | :----------------------------------------------------- | :------------------------------------- | :-------: | :--------: | | BITS | Baltic International Trawl Survey | Baltic Sea | 1994-2019 | 4 | | BTS | Beam Trawl Survey | Celtic Sea; English Channel; North Sea | 1997-2019 | 7 | | BTS-VIII | Beam Trawl Survey – Bay of Biscay | Bay of Biscay | 2011-2019 | 7 | | DWS | Deepwater Survey | Irish Sea | 2006-2007 | 8 | | DYFS | Inshore Beam Trawl Survey | Southern North Sea | 2002-2019 | 7 | | EVHOE | French Southern Atlantic Bottom trawl Survey | Bay of Biscay and Celtic Sea | 2003-2019 | 1 | | FR-CGFS | French Channel ground Survey | English Channel | 1997-2019 | 2 | | IE-IAMS | Irish Anglerfish and megrim Survey | Scottish rockall and Irish Sea | 2016-2019 | 2 | | IE-IGFS | Irish Groundfish | Ireland Shelf Sea | 2003-2019 | 2 | | MEDITS | International bottom trawl survey in the Mediterranean | Mediterranean Sea | 1994-2018 | 9 | | NIGFS | Northern Ireland Groundfish Survey | Irish Sea | 2009-2019 | 2 | | NS-IBTS | North Sea International Bottom Trawl Survey | North Sea | 1997-2019 | 2 | | PT-IBTS | Portuguese International Bottom Trawl Survey | Portugal Shelf Sea | 2003-2017 | 2 | | ROCKALL | Scottish Rockall Survey (until 2010) | Rockall plateau | 2003-2009 | 2 | | SCOROC | Scottish Rockall Survey (from 2011) | Scottish plateau | 2011-2019 | 2 | | SCOWCGFS | Scottish West Coast Groundfish Survey | Scottish west coast | 2011-2019 | 2 | | SNS | Sole Net Survey | Southern North Sea | 2002-2019 | 7 | | SP-ARSA | Spanish Gulf of Cadiz Bottom Trawl Survey | Spain | 2003-2019 | 6 | | SP-NORTH | Spanish North Bottom Trawl Survey | North of Spain | 2003-2019 | 2 | | SP-PORC | Spanish Porcupine Bottom Trawl Survey | Irish Sea | 2003-2019 | 5 | | SWC-IBTS | Scottish West Coast International Bottom Trawl Survey | Scotland Shelf Sea | 1999-2010 | 2 | ### Trait data The complete traits data table is available upon request. It is a combination of the publicly available PANGAEA database, Fishbase information, and inference based on the FISHLIFE project. ### Environmental variables The data used are all publicly available on the Copernicus website. ### Fishing data The data used are all publicly available on the Global Fishing Watch website. ## Recommended Citation Please use the following citation: Receveur, A., Leprieur F., Ellingsen K., Keith D., Kleisner K., McLean M., Mérigot B., Mills K., Mouillot D., Rufino M., Trindade-Santos I., Van Hoey G., Albouy C., Auber A. Data for “Long-term changes in taxonomic and functional composition of European marine fish communities.” Dryad Digital Repository. (2024). doi.org/10.5061/dryad.x69p8czsj ## Acknowledgments This research is a product of the MAESTRO group funded by the synthesis center CESAB of the French Foundation for Research on Biodiversity (FRB). We thank France Filière Pêche (FFP) who founded the MAESTRO project. We also warmly thank all those who have contributed in any way to the scientific surveys and data collection/provision (European Institutions and scientists implicated in DATRAS-BTS, MEDITS, and DCF). ## References 1. ICES. The EVHOE survey (France). ICES Documents. (1997). Available at: https://archimer.ifremer.fr/doc/00036/14707/12013.pdf 2. ICES. Manual of the IBTS North Eastern Atlantic Surveys. Series of ICES Survey Protocols SISP 15 (2017). doi:10.17895/ices.pub.3519 3. ICES. Manual for the International Bottom Trawl Surveys Revision VIII. Series of ICES Survey Protocols SISP 10 - IBTS IX. (2015). 4. https://ices-library.figshare.com/articles/report/SISP_7_-*Manual_for_the_Baltic_International_Trawl_Surveys_BITS*/19050986 5. https://gis.ices.dk/geonetwork/srv/api/records/ce94a257-c8b3-44f7-9fd0-6bd7449ce073 6. http://ices.dk/sites/pub/CM%20Doccuments/2002/D/D0302A.pdf 7. https://ices-library.figshare.com/articles/report/SISP_14_-*Manual_for_the_Offshore_Beam_Trawl_Surveys_WGBEAM*/19051328 8. https://gis.ices.dk/geonetwork/srv/api/records/936b4fb7-9baa-4dbc-abd0-b1b7bda16406 9. https://archimer.ifremer.fr/doc/00117/22783/20585.pdf Evidence of large-scale biodiversity degradation in marine ecosystems has been reported worldwide, yet most research has focused on few species of interest or on limited spatiotemporal scales. Here we assessed the spatial and temporal changes in the taxonomic and functional composition of fish communities in European seas over the last 25 years (1994-2019). We then explored how these community changes were linked to environmental gradients and fishing pressure. We show that the spatial variation in fish species composition is more than two times higher than the temporal variation, with a marked spatial continuum in taxonomic composition and a more homogenous pattern in functional composition. The regions warming the fastest are experiencing an increasing dominance and total abundance of r-strategy fish species (lower age of maturity). Conversely, regions warming more slowly show an increasing dominance and total abundance of K-strategy species (high trophic level and late reproduction). Among the considered environmental variables, sea surface temperature, surface salinity, and chlorophyll-a most consistently influenced communities’ spatial patterns, while bottom temperature and oxygen had the most consistent influence on temporal patterns. Changes in communities’ functional composition were more closely related to environmental conditions than taxonomic changes. Our study demonstrates the importance of integrating community-level species traits across multi-decadal scales and across a large region to better capture and understand ecosystem-wide responses and provides a different lens on community dynamics that could be used to support sustainable fisheries management.