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Data includes the measured environmental concentrations (MEC) of the summer copper (Cu) concentration in the German Bight from 1986 to 2021 (MUDAB database, https://geoportal.bafg.de/MUDABAnwendung/), including sampling points coordinates, year of sampling and Cu concentration. Additionally the Hazard quotient (HQ) is provided by dividing the MEC with the predicted no effect concentration (PNEC), defined as EC10 estimates from Crassostrea gigas embryos exposed for 48 h at 18°C and LC10 estimates from C. gigas larvae exposed for 24 h at 24°C, divided by an assessment factor (AF) of 5.
The data represent species counts (cells L-1) of the three AZA-producing dinoflagellate species Azadinium spinosum, Az. poporum and Amphidoma languida (all members of the taxonomic family Amphidomataceae) of water samples taken during in total six different field expeditions on several research vessels (RV Heincke, RV Uthörn, RV Polarstern) and on in total five stationary sampling stations (Scapa Flow/Scotland, Cuxhaven/Germany, Helgoland/Germany, Wilhelmshaven/Germany, Sylt/Germany) between 2015 and 2019. The water samples have been taken using Niskin bottles (on research vessels attached to a CTD). After DNA extraction, the species cell numbers have been calculated by quantitative PCR (qPCR) analysis using respective standard curves. These samples gained from different geographical areas in the eastern North Atlantic have been analyzed as part of the RIPAZA Project (funded by the German BMBF; in cooperation with the Third Institute of Oceanography, Xiamen/China) and the results are presented and discussed in the doctoral thesis of Stephan Wietkamp (Suppl.Tab.S6, Suppl.Tab.S7). Aim of the project and especially of this data set was to provide first reference data on the biogeography (geographical distribution and seasonality) of toxigenic Amphidomataceae in the eastern North Atlantic.
Leaf damage data on European beech leaves from saplings and mature trees from Lower Saxony, Germany. Three forest stand types were studied (European beech/Douglas fir mixture, European beech monoculture, and European beech/Norway spruce mixture) at six different sites, three in southern (site 1-3) and three (4-6) in northern Lower Saxony, Germany. At each plot 20 leaves from 20 saplings and 80 leaves from 5 mature trees were sampled in August 2019. After sampling the dorsal side of leaves was scanned and the amount of damage was estimated with an image processing software called ImageJ. Damage is distinguished into total, pathogen and herbivory damage which is further distinguished into chewing, sucking, mining, skeletonizing and gall damage.
The data represent species counts (cells L-1) of the three AZA-producing dinoflagellate species Azadinium spinosum, Az. poporum and Amphidoma languida (all members of the taxonomic family Amphidomataceae) of water samples taken during in total six different field expeditions on several research vessels (RV Heincke, RV Uthörn, RV Polarstern) and on in total five stationary sampling stations (Scapa Flow/Scotland, Cuxhaven/Germany, Helgoland/Germany, Wilhelmshaven/Germany, Sylt/Germany) between 2015 and 2019. The water samples have been taken using Niskin bottles (on research vessels attached to a CTD). After DNA extraction, the species cell numbers have been calculated by quantitative PCR (qPCR) analysis using respective standard curves. These samples gained from different geographical areas in the eastern North Atlantic have been analyzed as part of the RIPAZA Project (funded by the German BMBF; in cooperation with the Third Institute of Oceanography, Xiamen/China) and the results are presented and discussed in the doctoral thesis of Stephan Wietkamp (Suppl.Tab.S6, Suppl.Tab.S7). Aim of the project and especially of this data set was to provide first reference data on the biogeography (geographical distribution and seasonality) of toxigenic Amphidomataceae in the eastern North Atlantic.
Partly taken from the materials and methods of https://doi.org/10.1016/j.baae.2022.12.003: To compare the activity densities of ground-dwelling predators between treatments with and without RAPs, spiders were sampled using pitfall traps, which were set up after each round of aphid counting (one per plot, twice per year; Brown & Matthews, 2016). The traps (with a volume of 400 ml and a width of 90 mm) were filled with a mixture of water and ethylene glycol (1:1; 120 ml) and dug at ground level into the middle of each plot. The traps were covered with a plastic roof and a metal grid (15 × 15 mm grid size) to avoid overflowing during rain and accidental rodent catches (Császár et al., 2018). The traps were activated for 7 days. Subsequently, all arthropods were transferred into 70% ethanol. Spiders were identified to species according to Nentwig et al. (2019). Spider hunting strategy (active hunter or web-builder) was used as the feeding trait according to Cardoso et al. (2011).
Taken from the methods of https://doi.org/10.1016/j.agee.2020.107237: The effect of rare arable plants on soil nutrient concentration was measured by taking soil samples in the 1st and 2nd study year (March 2018 and August 2019). One soil sample per plot was taken to a 20 cm depth and analyzed by the AGROLAB Group (Landshut, Germany) for soil organic matter [%] and nitrogen concentration [%] (DIN EN 15936; 2012 and DIN EN 16168; 2012-11).
Partly taken from the materials and methods of https://doi.org/10.1016/j.baae.2022.12.003: To compare the activity densities of ground-dwelling predators between treatments with and without RAPs, carabids were sampled using pitfall traps, which were set up after each round of aphid counting (one per plot, twice per year; Brown & Matthews, 2016). The traps (with a volume of 400 ml and a width of 90 mm) were filled with a mixture of water and ethylene glycol (1:1; 120 ml) and dug at ground level into the middle of each plot. The traps were covered with a plastic roof and a metal grid (15 × 15 mm grid size) to avoid overflowing during rain and accidental rodent catches (Császár et al., 2018). The traps were activated for 7 days. Subsequently, all arthropods were transferred into 70% ethanol. Carabids were identified to species according to Hůrka (1996). Carabid feeding behavior was classified according to Homburg et al. (2014). To simplify the dataset, carabid feeding behavior was classified as predominantly granivorous (species mainly feed on seeds and fruits) or as carnivorous/omnivorous, because carnivorous and omnivorous species are potentially feeding on aphids and other non-plant material.
Taken from the methods of https://doi.org/10.1016/j.agee.2020.107237: Vegetation surveys were performed once in July for both study years. Plant species were classified and each species' percent cover for both the arable plant community and the crop were visually estimated per plot. Species were divided into rare arable plants, other spontaneously occurring arable plants, the sown crop species, and volunteer crops that re-emerged after cultivation in previous years. To measure the productivity of our plots, both the crop biomass and arable plant biomass (rare arable plants + volunteer crops + spontaneous arable plants) were collected in July and August in both study years. For the arable plant biomass, three 0.5 m × 0.5 m sampling quadrats were randomly placed in the plot, harvested, and dried at 65 °C for 48 h. Crop biomass was measured after cutting, drying, and weighing three randomly selected crop rows per plot. To minimize edge effects, the outmost crop rows were excluded from the sampling. Arable plants and crop biomasses were projected as g m ⁻².
Taken from materials and methods of https://doi.org/10.1016/j.baae.2022.12.003: Aphids were counted on 50 randomly selected shoots in two crop rows (100 shoots in total) per plot and sampling round. To reduce edge effects, rows with less than 20 cm to the edge were excluded. Counting took place twice a year, that is, once during crop flowering (BBCH 61; beginning of aphid population growth) and once during crop milk ripening stage (BBCH 75).
The considered data set contains measurements of 4 automatic weather stations from 1997 until 2018 installed by Helmholtz Centre for Environmental Research - UFZ GmbH. Here, the main station corresponded to typical sensor technology and equipment of the German Meteorological Service [Deutscher Wetterdienst, DWD]. These data were quality-checked and processed as daily values. As far as possible, erroneous values were replaced by means of other stations on site. Very short failures could be supplemented by interpolation or averaging. Hence, values of measured variable can originate from different stations. However, only one value for each measured variable is provided for every day within the table. A unique label for individual weather stations is used to identify the exact origin of data.
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