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This file contains carbon and mineral data of organic matter fractions obtained from two permafrost drill cores L14-02 (73.33616° N; 141.32776° E) and L14-05 (73.34994° N; 141.24156° E) from Bol’shoy Lyakhovsky Island in NE Siberia in 2014. The datasets contain mass fractions of different size and density fractions, OC concentrations, OC/N ratios, data on organic matter composition based on 13C-NMR, radiocarbon (14C) data, as well as data on iron (Fe) mineral phases and CO2 production rates of mineral-associated organic matter. Further, carbon and organic biomarker data (n-alkanes) of the bulk sediment are included. The data were created to study mass partitioning of Pleistocene permafrost OC among different organic matter fractions to assess the bioavailability and stability of the organic matter. Please refer to the publication listed below for more information. {"references": ["Jannik Martens, Carsten W. Mueller, Prachi Joshi, Christoph Rosinger, Markus Maisch, Andreas Kappler, Michael Bonkowski, Georg Schwamborn, Lutz Schirrmeister, Janet Rethemeyer. Stabilization of mineral-associated organic carbon in Pleistocene permafrost. Nature Communications."]}

Aggregated moss layer projective vegetation cover is given in percent for each taxon for 57 sites. The cover of different vegetation types at the sites is given in percent as well.The vegetation surveys were carried out in four different study areas in the Sakha Republic, Russia: in the mountainous region of the Verkhoyansk Range within the Oymyakonsky and Tomponsky District (Event EN21-201 - EN21-219), and in three lowland regions of Central Yakutia within the Churapchinsky, Tattinsky and the Megino-Kangalassky District (Event EN21220 - EN21264). The study area is located within the boreal forest biome that is underlain by permafrost soils. The aim was to record the projective ground vegetation in different boreal forest types studied during the RU-Land_2021_Yakutia summer field campaign in August and September 2021.The ground vegetation projective cover in percent was assessed within a circular forest plot of 15m radius. Depending on the heterogeneity of the forest plot, multiple vegetation types (VA, VB, or VC) were surveyed separately. The assignment of a vegetation type is always unique to a site. Up to four quadrats of 2x2 m were surveyed per vegetation type and projective cover in percent recorded separately for herbaceous and moss layers. All vegetation smaller than 40 cm was recorded. Additionally, ground vegetation projective cover was surveyed in 4 rings of 50 cm width around the center of the circular forest plot. Photos of quadrats were taken at the time of survey.Average ground vegetation cover per plot was calculated by using an average weighted by vegetation types for each site. The ring survey data was not included in the plot average.In total, 491 quadrats at 57 forest plots were investigated. 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.

Replication data for Figures 1 through 8. The data is stored in MATLAB’s proprietary format (*.mat), but it can be imported using open-source software like Python. Attached is MATLAB live script to import the data. This live script reproduces draft versions of Figures 1 through 8 from the paper Atmosphere circulation patterns synchronize pan-Arctic glacier melt and permafrost thaw by Sasgen et al. (2024). The primary goal of this script is to facilitate the further use of the data. By producing the figures, we aim to clarify the units and demonstrate how the data are stored in the .mat file. For additional information, please contact Ingo Sasgen at ingo.sasgen@awi.de .

This repository contains the data associated with the paper Tedersoo et al. (2022) Global patterns in endemicity and vulnerability of soil fungi // Global Change Biology. DOI:10.1111/gcb.16398 Fungi are highly diverse organisms and provide a wealth of ecosystem functions. However, distribution patterns and conservation needs of fungi have been very little explored compared to charismatic animals and plants. Here we assess endemicity patterns, global change vulnerability and conservation priority areas for functional groups of soil fungi based on six global surveys using a high-resolution, long-read metabarcoding approach. Endemicity of all fungi and most functional groups peaks in tropical habitats, including Amazonia, Yucatan, West-Central Africa, Sri Lanka and New Caledonia, with a negligible island effect compared with plants and animals. We also found that fungi are vulnerable mostly to drought, heat and land cover change, particularly in dry tropical regions with high human population density. Fungal conservation areas of highest priority include herbaceous wetlands, tropical forests and woodlands. We suggest that there should be more attention focused on the conservation of fungi, especially tropical root symbiotic arbuscular mycorrhizal and ectomycorrhizal fungi, unicellular early-diverging groups and macrofungi in general. Given the low overlap between endemicity of fungi and macroorganisms, but high matching in conservation needs, detailed analyses on distribution and conservation requirements are warranted for other microorganisms and soil organisms in general. This repository contains the following data associated with the publication: Supplementary tables S1 - S6 (`Tables_S1-S6.xlsx`): - Table S1. Definition of ecoregions and assignment of samples to ecoregions - Table S2. GSMc dataset used for endemicity analyses - Table S3. Dataset used for modeling endemicity values - Table S4. Dataset used for calculating and mapping vulnerability scores - Table S5. Dataset used for calculating and mapping conservation value - Table S6. Additional funding sources by authors OTU distribution by samples and ecoregions (`Data_taxon_assignment_to ecoregions.xlsx`) Gridded maps: Conservation priorities for all fungi and fungal groups - ConservationPriority_AllFungi.tif - ConservationPriority_AM.tif - ConservationPriority_EcM.tif - ConservationPriority_Moulds.tif - ConservationPriority_NonEcMAgaricomycetes.tif - ConservationPriority_OHPs.tif - ConservationPriority_Pathogens.tif - ConservationPriority_Unicellular.tif - ConservationPriority_Yeasts.tif The average vulnerability of all fungi and fungal groups and the model uncertainty estimates - AverageVulnerability_AllFungi.tif - AverageVulnerability_AM.tif - AverageVulnerability_EcM.tif - AverageVulnerability_Moulds.tif - AverageVulnerability_NonEcMAgaricomycetes.tif - AverageVulnerability_OHPs.tif - AverageVulnerability_Pathogens.tif - AverageVulnerabilityUncertainty_AllFungi.tif - AverageVulnerabilityUncertainty_AM.tif - AverageVulnerabilityUncertainty_EcM.tif - AverageVulnerabilityUncertainty_Moulds.tif - AverageVulnerabilityUncertainty_NonEcMAgaricomycetes.tif - AverageVulnerabilityUncertainty_OHPs.tif - AverageVulnerabilityUncertainty_Pathogens.tif - AverageVulnerabilityUncertainty_Unicellular.tif - AverageVulnerabilityUncertainty_Yeasts.tif - AverageVulnerability_Unicellular.tif - AverageVulnerability_Yeasts.tif The relative importance of predicted vulnerability of all fungi - RelativeImportanceOfVulnerability_AllFungi.tif Vulnerability to drought, heat, and land cover change for all fungi - Vulnerability_AllFungi_Heat-Drought-LandCoverChange.tif - VulnerabilityUncertainty_AllFungi_Heat-Drought-LandCoverChange.tif Human footprint index based on the Land-Use Harmonisation (LUH2; Hurtt et al., 2020, doi:10.5194/gmd-13-5425-2020) - `LandCoverChange_1960-2015.tif` MD5 checksums for all files (`MD5.md5`) Fungal groups: - AM, arbuscular mycorrhizal fungi (including all Glomeromycota but excluding all Endogonomycetes) - EcM, ectomycorrhizal fungi (excluding dubious lineages) - NonEcMAgaricomycetes, non-EcM Agaricomycetes (mostly saprotrophic fungi with usually macroscopic fruiting bodies) - Moulds (including Mortierellales, Mucorales, Umbelopsidales and Aspergillaceae and Trichocomaceae of Eurotiales and Trichoderma of Hypocreales) - Putative pathogens (including plant, animal and fungal pathogens as primary or secondary lifestyles) - OHPs, opportunistic human parasites (excluding Mortierellales) - Yeasts (excluding dimorphic yeasts) - Unicellular, other unicellular (non-yeast) fungi (including chytrids, aphids, rozellids and other early-diverging fungal lineages) Detailed processing steps can be found here: https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability This repository contains the data associated with the paper Tedersoo et al. (2022) Global patterns in endemicity and vulnerability of soil fungi // Global Change Biology. DOI:10.1111/gcb.16398 Fungi are highly diverse organisms and provide a wealth of ecosystem functions. However, distribution patterns and conservation needs of fungi have been very little explored compared to charismatic animals and plants. Here we assess endemicity patterns, global change vulnerability and conservation priority areas for functional groups of soil fungi based on six global surveys using a high-resolution, long-read metabarcoding approach. Endemicity of all fungi and most functional groups peaks in tropical habitats, including Amazonia, Yucatan, West-Central Africa, Sri Lanka and New Caledonia, with a negligible island effect compared with plants and animals. We also found that fungi are vulnerable mostly to drought, heat and land cover change, particularly in dry tropical regions with high human population density. Fungal conservation areas of highest priority include herbaceous wetlands, tropical forests and woodlands. We suggest that there should be more attention focused on the conservation of fungi, especially tropical root symbiotic arbuscular mycorrhizal and ectomycorrhizal fungi, unicellular early-diverging groups and macrofungi in general. Given the low overlap between endemicity of fungi and macroorganisms, but high matching in conservation needs, detailed analyses on distribution and conservation requirements are warranted for other microorganisms and soil organisms in general.

Climate change is impacting virtually all marine life. Adaptation strategies will require a robust understanding of the risk to species and ecosystems and how those propagate to human societies. We develop a unified and spatially explicit index to comprehensively evaluate the climate risks to marine life. Under high emissions (SSP5-8.5), almost 90% of ~25,000 species are at high or critical risk, with species at risk across 85% of their native distributions. One-tenth of the ocean contains ecosystems where the aggregated climate risk, endemism, and extinction threat of their constituent species are high. Climate change poses the greatest risk for exploited species in low-income countries with high dependence on fisheries. Mitigating emissions (SSP1-2.6) reduces the risk for virtually all species (98.2%), enhances ecosystem stability, and disproportionally benefits food-insecure populations in low-income countries. Our climate risk assessment can help prioritize vulnerable species and ecosystems for climate-adapted marine conservation and fisheries management efforts.

This data set describes the population dynamics of Wilson's Storm Petrels (Oceanites oceanicus) at King George Island (Islas 25 de Mayo, Antarctica) over a forty year period (1978 - 2020). It includes all available data on Wilson's Storm Petrels from two colonies: around the Argentinian Base Carlini (62°14′S, 58°40′W; CA, formerly called Base Jubany) and the Henryk Arctowski Polish Antarctic Station (62°09′S, 58°27′W; HA). Data on adult abundance and estimated age categories (i.e., presence of foot spots; Quillfeldt et al. (2000, doi:10.1007/s003000000167) were collected at CA by using the same size mistnet every study year in the same location within the breeding colony. Adults were ringed with a metal leg ring, and their foot webs were checked for foot spots. This study was further supported by the Erasmus+ programm and thee German Academic Exchange Service (DAAD)

Phytoplankton community structure and their physiological response in the vicinity of the Antarctic Polar Front (APF; 44°S to 53°S, centred at 10°E) were investigated as part of the ANT-XXVIII/3 Eddy-Pump cruise conducted in austral summer 2012. Our results show that under iron-limited View the MathML source(0.6mgm−3) can be observed at stations with deep mixed layer View the MathML source(>60m) across the APF. In contrast, light was excessive at stations with shallower mixed layer and phytoplankton were producing higher amounts of photoprotective pigments, diadinoxanthin (DD) and diatoxanthin (DT), at the expense of TChl-a, resulting in higher ratios of (DD+DT)/TChl-a. North of the APF, significantly lower silicic acid (Si(OH)4) concentrations View the MathML source(5mmolm−3) region south of the APF, on the contrary, was dominated by microphytoplankton (diatoms and dinoflagellates) with lower ratios of (DD+DT)/TChl-a, despite having been exposed to higher levels of irradiance. The significant correlation between nanophytoplankton and (DD+DT)/TChl-a indicates that differences in taxon-specific response to light are also influencing TChl-a concentration in the APF during summer. Our results reveal that provided mixing is deep and Si(OH)4 is replete, TChl-a concentrations higher than View the MathML source0.6mgm−3 are achievable in the iron-limited APF waters during summer.

This paper introduces a semiqualitative approach to analyse the joint dynamics of fleets and stocks in a multispecies, multifleet fishery. We ask whether changes in fleets affect resource dynamics and whether trends in resource influence fleet dynamics more than external drivers do. External drivers include vessel buyback, fuel price, and fish prices, as well as environmental fluctuations. Resource status is measured by abundance and length metrics; fleet capacity is measured by total horse power, and economic metrics such as profitability and earnings are examined as well. A maximum likelihood approach is used to identify the combined metric trends with the largest support in the data. The approach is applied to the French Bay of Biscay fisheries in 2000–2007. Combined-metric time trends suggest that decreases in fleet capacity did not result in decreasing fishing impacts; trends in stocks and fish prices were not the major drivers of changes in fleets either. Rather, the vessel buyback program might have been the main factor determining fleet dynamics over that period.