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    Seasonal variation in daily activity patterns of snow leopards and their prey

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    Obscured fishing activity

    Welch and colleagues analysed 3.7 billion AIS messages recorded between 2017 and 2019 in the global Fishing Watch AIS dataset, identifying more than 55,000 suspected intentional disabling events in waters more than 50 nautical miles from shore, amounting to 6% ( >4.9 million hours) of obscured vessel activity. Hotspots of disabling activity were located near several regions of IUU concern and transshipment hotspots, including in the exclusive economic zones of Argentina and West African nations and in the Northwest Pacific. Using individual boosted regression tree models for the four dominant gear types (squid jiggers, trawlers, tuna purse seines and drifting longlines) and a full model that included all suspected disabling events (that is, the four gear types listed above and additional gears such as gillnet and troll), Welch and colleagues found that loitering by transshipment vessels (a proxy for potential transshipment events) was the most important driver in the full model and squid jigger model and more than half of the disabling events by squid jiggers were close enough to undertake transshipment to refrigerated cargo vessels. More

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    Biodiversity–stability relationships strengthen over time in a long-term grassland experiment

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    Marine phytoplankton community data and corresponding environmental properties from eastern Norway, 1896–2020

    Sampling strategies and dataThe inner Oslofjorden phytoplankton dataset is a compilation of data mostly assembled from the monitoring program, financed since 1978 by a cooperation between the municipalities around the fjord, united in the counsel for technical water and sewage cooperation called “Fagrådet for Vann- og avløpsteknisk samarbeid i Indre Oslofjord”. The monitoring program started in 1973 and is ongoing. The program has sampled environmental parameters and chlorophyll since 1973, but for the first 25 years, phytoplankton data is only reported for the years 1973, 1974, 1988/9, 1990, 1994 and 1995. Since 1998, yearly sampling has been conducted, and from 2006 to 2019, the sampling frequency was approximately monthly. In addition, we have compiled research and monitoring data from researchers at the University of Oslo from 1896 and 1916, 1933–34 and 1962–1965.The records from 1896 and 1897 were collected using zoo-plankton net13. The phytoplankton collection in 1916–1917 used buckets or Nansen flasks for sampling. From 1933 to 1984, phytoplankton samples were collected using Nansen bottles and then from 1985–2020 with Niskin bottles from research vessels. The exception is the period from 2006 to 2018 when samples were also collected with FerryBox- equipped ships of opportunity14 with refrigerated autosamplers (Table 2).Since the 1990s, quantitative phytoplankton samples have mostly been preserved in Lugol’s solution, except for spring and autumn samples in the period 1990–2000 that were preserved in formalin. The records from 1896, 1897 and 1916 were preserved in ethanol, and between 1933 and 1990, samples were preserved in formalin. Sampling strategies and methods are listed in Table 2.The records from 1896 and 1897 were quantified by weight, and taxon abundance is categorised as “rare” (r), “rather common” (+), “common” (c) and “very common” (cc)13. In 1916 and 1917, Grans filtration method15 was used, and the number was given in cell counts per litre. From 1916 to 1993, the data is reported only as phytoplankton abundance (N, number of cells per litre). For most years after 1994, the dataset includes both abundance and biomass (μg C per litre), except for 2003, 2004, 2017 and 2018. Phytoplankton was identified and quantified using the sedimentation method of Utermöhl (1958)16. Biovolume for each species is calculated according to HELCOM 200617 and converted to biomass (μg C) following Menden-Deuer & Lessards (2000)18.Data inventoryThe inner Oslofjorden Phytoplankton dataset was compiled in 2020, comprising quantitative phytoplankton cell counts from inner Oslofjorden since 1896. Previously, parts of the data have been available as handwritten or printed tables in reports and published sources19,20,21 (Fig. 2). All sources are digitally available from the University of Oslo Library, the website for “Fagrådet” (http://www.indre-oslofjord.no/) or the NIVA online report database (https://www.niva.no/rapporter). Data from 1994 and onwards have been accessed digitally from the NIVA’s databases. They are also available from client reports from the monitoring project for inner Oslofjorden from the online sites listed above.The first known, published investigation of hydrography and plankton in the upper water column of the inner Oslofjorden was by Hjort & Gran (1900)13. Samples were collected during a hydrographical and biological investigation covering both the Skagerrak and Oslofjorden. There is only one sampling event from Steilene (Dk 1), but some phytoplankton data were obtained at Drøbak, just south of the shallow sill separating the inner and outer Oslofjorden, from winter 1896 to autumn 1897. Twenty years later, Gran and Gaarder (1927)22 conducted a study that included culture experiments at Drøbak field station (at the border between the inner and outer Oslofjorden) in March – April 1916 and August – September 1917. A higher frequency investigation was carried out from June 1933 to May 1934, covering 12 stations in inner and outer Oslofjorden where phytoplankton was analysed by microscopic examination23. The extensive program (the Oslofjord Project) conducted from 1962–1964 covered many parameters, and we have extracted the data for phytoplankton. From 1973 and onward, the research vessel-based monitoring program was financed by the municipalities around the fjord, and since 2006 NIVA has supplemented the monitoring program using FerryBox ships of opportunity. Samples from 4 m depth were collected using a refrigerated autosampler system (Teledyne ISCO) connected to a FerryBox system on M/S Color Festival and M/S Color Fantasy through cooperation between NIVA and Color Line A/S. Since 2018, the FerryBox has been part of the Norwegian Ships of Opportunity Program research infrastructure funded by the Research Council of Norway.The indicated depth of 3.5–4 m is an estimated average, as the actual sampling depth depends on shipload and sea conditions.Several other research projects have sampled from inner Oslofjorden between 1886 and 2000 with different aims. Data from relevant projects reporting on the whole phytoplankton community have also been included in this database.Data compilationThe data already digitalised were compiled from MS Excel files, and other data were manually entered into the standard format in MS Excel files. All collected data were then integrated into one MS Excel database, and this file was used for upload into GBIF. Data can be downloaded from GBIF in different formats and be linked together by the measurementsorfacts table.Quality control and standardisationAfter compilation, the data were checked for errors that could occur during manual digitalisation or just the compilation process. Duplicates and zero values were removed (Fig. 2). The major quantitative unit is phytoplankton abundance in cells per litre. Due to varying scopes of sampling and the development of gear and instruments, the number of species identified may vary between projects. Some of the earliest records were registered as “present”, indicating the amount in comments.Metadata, such as geographical reference, depth and methodology accessed from papers and reports, were accessible from the data source. When data was accessed from the NIVA internal databases, the metadata information was provided by the database owners/researchers.TaxonomyThe taxonomy of microalgae is in constant revision as new knowledge and techniques for identification are developing. Several historical species names recorded in this database are synonyms of accepted names in 2021. We have used the original names in our database and matched them to accepted names and Aphia ID using the taxon match tool available in the open-access reference system; World Register of Marine Species (Worms)24. The taxon match was conducted in March 2021.The nomenclature in Worms is quality assured by a wide range of taxonomic specialists. The Aphia ID is a unique and stable identifier for each available name in the database24. We also cross-checked the last updated nomenclature in Algaebase25 (March 2022) to assign species to a valid taxon name. When Algaebase and Worms were not in accordance, Algaebase taxonomy was usually chosen except in the case of Class Bacillariophyceae.Before matching the species list, the original species names were cleaned from spelling mistakes or just spelling mismatches like spaces, commas, etc. The original name is, however, left in one column in the database. For registrations where a species identification is uncertain, e.g. Alexandrium cf. tamarense, we used only Alexandrium. For registrations where the full name is uncertain, e.g. cf. Alexandrium tamarense, we used the name and Aphia ID for higher taxa, in this case, order. For others, e.g. “pennate diatoms” or “centric diatoms“, we used the name and Aphia ID for class. When names for, e.g. order and class were not recognised automatically by the matching tool in World Register of Marine Species (WoRMS), these were matched manually. Only very few records, mostly “cysts” and “unidentified monads”, could not be matched neither automatically nor manually but were assigned to general “protists” with affiliated ID. More

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    Dynamics of rumen microbiome in sika deer (Cervus nippon yakushimae) from unique subtropical ecosystem in Yakushima Island, Japan

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