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178,491 Research products

  • European Marine Science
  • 2019-2023
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  • European Marine Science

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  • Authors: Cyr, Frédéric; Coyne, Jonathan;

    Mooring data collected on the Newfoundland and Labrador (NL) shelf in support of the Overturning in the Subpolar North Atlantic Program (OSNAP). The Canadian mooring 1 along Seal Island section (OSNAP mooring CSI1) is equipped with Conductivity-Temperature-Depth (CTD), Acoustic Doppler Current Profiler (ADCP) and other temperature-only sensors. It has been deployed since July 2020 at geographical location 53.4490 degrees north and 55.2995 degrees west. Please refer to the OSNAP data management plan for more details regarding the OSNAP data policy and management plan. Before the use and publication of any OSNAP data, users are strongly encouraged to read the full OSNAP data policy. We draw your attention to the following excerpts from that policy: Any person making use of OSNAP observational data and/or numerical results must communicate with the responsible investigators at the start of the analysis and anticipate that the data collectors will be co-authors of published results. In cases where investigators choose not to be co-authors on publications that rely on their data, the parties responsible for collecting the data and the sponsoring funding agencies should be acknowledged, including reference to any relevant publications by the originating authors describing the data sets and a reference to the data set itself using its DOI. OSNAP data are intended for scholarly use by the academic and scientific community, with the express understanding that any such use will properly acknowledge the originating investigator. HOW TO ACKNOWLEDGE DATA FROM THE OSNAP PROJECT: OSNAP data were collected and made freely available by the OSNAP (Overturning in the Subpolar North Atlantic Program) project and all the national programs that contribute to it (www.o-snap.org).

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  • Authors: Cyr, Frédéric; Coyne, Jonathan;

    Mooring data collected on the Newfoundland and Labrador (NL) shelf in support of the Overturning in the Subpolar North Atlantic Program (OSNAP). The Canadian mooring number 0 (OSNAP mooring C0) is equipped with Conductivity-Temperature-Depth (CTD), Acoustic Doppler Current Profiler (ADCP) and other temperature-only sensors. It has been deployed since July 2020 at geographical location 52.6228 degrees north and 52.2163 degrees west. Please refer to the OSNAP data management plan for more details regarding the OSNAP data policy and management plan. Before the use and publication of any OSNAP data, users are strongly encouraged to read the full OSNAP data policy. We draw your attention to the following excerpts from that policy: Any person making use of OSNAP observational data and/or numerical results must communicate with the responsible investigators at the start of the analysis and anticipate that the data collectors will be co-authors of published results. In cases where investigators choose not to be co-authors on publications that rely on their data, the parties responsible for collecting the data and the sponsoring funding agencies should be acknowledged, including reference to any relevant publications by the originating authors describing the data sets and a reference to the data set itself using its DOI. OSNAP data are intended for scholarly use by the academic and scientific community, with the express understanding that any such use will properly acknowledge the originating investigator. HOW TO ACKNOWLEDGE DATA FROM THE OSNAP PROJECT: OSNAP data were collected and made freely available by the OSNAP (Overturning in the Subpolar North Atlantic Program) project and all the national programs that contribute to it (www.o-snap.org).

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Liu, Yijing; Wang, Peiyan; Elberling, Bo; Westergaard-Nielsen, Andreas;

    To quantify the seasonal transition dates, we used NDVI derived from Sentinel-2 MultiSpectral Instrument (Level-1C) images during 2016–2020 based on Google Earth Engine (https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2). We performed an atmospheric correction (Yin et al., 2019) on the images before calculating NDVI. The months from May to October were set as the study period each year. The quality control process includes 3 steps: (i) the cloud was masked according to the QA60 band; (ii) images were removed if the number of pixels with NDVI values outside the range of -1–1 exceeds 30% of the total pixels while extracting the median value of each date; (iii) NDVI outliers resulting from cloud mask errors (Coluzzi et al., 2018) and sporadic snow were deleted pixel by pixel. NDVI outliers mentioned here appear as a sudden drop to almost zero in the growing season and do not form a sequence in this study (Komisarenko et al., 2022). To identify outliers, we iterated through every two consecutive NDVI values in the time series and calculated the difference between the second and first values for each pixel every year. We defined anomalous NDVI differences as points outside of the percentiles threshold [10 90], and if the NDVI difference is positive, then the first NDVI value used to calculate the difference will be the outlier, otherwise, the second one will be the outlier. Finally, 215 images were used to reflect seasonal transition dates in all 5 study periods of 2016–2020 after the quality control. Each image was resampled with 32 m spatial resolution to match the resolution of the ArcticDEM data and SnowModel outputs. To detect seasonal transition dates, we used a double sigmoid model to fit the NDVI changes on time series, and points where the curvature changes most rapidly on the fitted curve, appear at the beginning, middle, and end of each season (Klosterman et al., 2014). The applicability of this phenology method in the Arctic has been demonstrated (Ma et al., 2022; Westergaard-Nielsen et al., 2013; Westergaard-Nielsen et al., 2017). We focused on 3 seasonal transition dates, i.e., SOS, NDVImax day, and EOF. The NDVI values for some pixels are still below zero in spring and summer due to topographical shadow. We, therefore, set a quality control rule before calculating seasonal transition dates for each pixel, i.e., if the number of days with positive NDVI values from June to September is less than 60% of the total number of observed days, the pixel will not be considered for subsequent calculations. As verification of fitted dates, the seasonal transition dates in dry heaths and corresponding time-lapse photos acquired from the snow fence area are shown in Fig. 2. Snow cover extent is greatly reduced and vegetation is exposed with lower NDVI values on the SOS. All visible vegetation is green on the NDVImax day. On EOF, snow cover distributes partly, and NDVI decreases to a value close to zero. # Data from: Drivers of contemporary and future changes in Arctic seasonal transition dates for a tundra site in coastal Greenland The dataset includes all original images used in this study to extract seasonal transition dates and corresponding results. ## Description of the data and file structure Datasets included: (1) The spatial distribution of NDVI values for this study region (168 rows and 166 columns). Each file is named in the form of '' year-month-day''. For example, a file named "2016-05-02'' represents the data for 2nd, May of 2016. The normal NDVI values in each file range from -1 to 1, and NaN represents no valid value. The folder named 'unique_date_NDVI' refers to the spatial distribution of NDVI for all available dates, directly acquired from satellite images. The folder named 'unique_date_NDVI_rm_outlier' refers to the spatial distribution of NDVI after quality correction for each date using the described method. (2) The extracted phenology indicators for each pixel in this study region. Five tables named 'Phe_pixel_XXXX.xlsx' include the extracted seasonal transition dates during 2016–2020, pixel by pixel. There are 9 columns in each table, they are row number and column number (used to describe the specific location of pixel), year, start of spring, middle of spring, end of spring, start of fall, middle of fall, and end of fall. ## Sharing/Access information All functions regarding the extraction of seasonal transition dates can be found here: * All parameters and associated functions regarding the SnowModel can be found here: * All original meteorological data in this study is from: * Climate change has had a significant impact on the seasonal transition dates of Arctic tundra ecosystems, causing diverse variations between distinct land surface classes. However, the combined effect of multiple controls as well as their individual effects on these dates remains unclear at various scales and across diverse land surface classes. Here we quantified spatiotemporal variations of three seasonal transition dates (start of spring, maximum Normalized Difference Vegetation Index (NDVImax) day, end of fall) for five dominant land surface classes in the ice-free Greenland and analyzed their drivers for current and future climate scenarios, respectively.

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    Dataset . 2023
    License: CC 0
    Data sources: Datacite; ZENODO
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      Dataset . 2023
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    Authors: Long, Chunyan; Yang, Fan; Zhang, Qian; Cheng, Xiaoli;

    # Different dynamics and controls of enzyme activities of leaf and root litter during decomposition These data include a data sheet with field collected data compiled across two years. Each litter types has a unique label including leaf and root litter. All traits (i.e., β‐1,4‐glucosidase (BG, EC 3.2.1.21), the sum of the leucine aminopeptidase (LAP, EC 3.4.11), N‐acetyl‐β‐glucosaminidase (NAG, EC 3.2.1.14), acid phosphatase (AP, EC 3.1.3.2)) were determined in the laboratory by standard methods. To analyze the enzymatic C:N:P stoichiometry, the vector length (i.e., quantifying the relative C versus nutrient acquisition) was calculated as the square root of the squared sum of the values of x and y [length = Sqrt (x2 + y2)], and the vector angle (i.e., quantifying the relative P versus N acquisition) was calculated as the arctangent of the point (x, y) [angle (degrees) = degrees (Atan2(x, y))], where x represents the relative proportion of C- to C+P-acquiring enzyme activities and y represents the relative proportion of C- to C+N-acquiring enzyme activities. The columns in the dataset refer to: Types = Leaf or root; Litter mass loss = The initial litter weight minus the litter weight of sampling time divided by the initial litter weight; BG = β‐1,4‐glucosidase (BG, EC 3.2.1.21); NAP = The sum of the leucine aminopeptidase (LAP, EC 3.4.11), N‐acetyl‐β‐glucosaminidase (NAG, EC 3.2.1.14); AP = Acid phosphatase (AP, EC 3.1.3.2); Vector_length = The square root of the squared sum of the values of x and y, where x represents the relative proportion of C- to P-acquiring enzyme activities and y represents the relative proportion of C- to N-acquiring enzyme activities (Moorhead et al., 2016); Vector_angle = The arctangent of the point (x, y) [angle (degrees) = degrees (Atan2(x, y))], where x represents the relative proportion of C- to P-acquiring enzyme activities and y represents the relative proportion of C- to N-acquiring enzyme activities (Moorhead et al., 2016); Litter C loss = The initial litter C minus the litter C of sampling time divided by the initial litter C; Litter lignin loss = The initial litter lignin minus the litter lignin of sampling time divided by the initial litter lignin; Litter cellulose loss = The initial litter cellulose minus the litter cellulose of sampling time divided by the initial litter cellulose; Litter hemicellulose loss = The initial litter hemicellulose minus the litter hemicellulose of sampling time divided by the initial litter hemicellulose; Litter N loss = The initial litter N minus the litter N of sampling time divided by the initial litter N; Litter P loss = The initial litter P minus the litter P of sampling time divided by the initial litter P; Soil temperature = ℃; soil moisture = Volumetric water content of soil; Litter C concentration = %; Litter lignin concentration = %; Litter cellulose concentration = %; Litter hemicellulose concentration = %; Litter N concentration = %; Litter P concentration = mg kg-1; Liiter C:N = unitless; Liiter N:P = unitless; Liiter lignin:N = unitless; Bacterial biomass = μg g-1; Fungi biomass = μg g-1; Total PLFAs biomass = μg g-1. We selected three sites with different land use types (i.e., woodland, shrubland, and cropland). At each study site, three independent 10 m × 10 m plots were selected. Each plot was located 100 m apart from one another. We conducted a 784-day in situ litter decomposition experiment, where litter from each species and site was incubated at their own site (e.g., crop litter was placed back into the cropland). In each woodland and shrubland, freshly fallen senesced leaves were collected from litter traps (1 m × 1 m). Roots (diameter ≤2 mm) were excavated using a root auger at a depth of 20 cm under each tree in the two plantations. For cropland, the leaves and roots (diameter ≤2 mm) of maize were collected after harvesting. Living roots were selected as the experimental materials based on color, luster, and elasticity. The sampled roots were placed in an incubator and transported to the laboratory. Adherent soil particles and extraneous organic material were gently removed from the root samples. Thereafter, all collected leaf and root litter samples were oven-dried (55 °C) in the laboratory. Subsequently, the leaf and root litters were sterilized at 121 °C for 20 min to ensure that the most likely import of the microbial community was through the soil rather than litter. Finally, all collected leaf and root litter samples were oven-dried (55 °C) in the laboratory for further experiments and analysis. On October, 2016, a total of 5.0 g of oven-dried leaves and roots were placed in nylon bags (15 cm × 20 cm, with a 1-mm mesh size). A total of 450 litterbags prepared for this experiment (3 plots × 3 species × 2 litter types ×5 decomposition times × 5 replicates). After removing the floor litter (if any), leaf litterbags were fixed to the mineral soil surface. The root litterbags were placed in the soil at a depth of 10 cm at an angle of approximately 30° to the vertical. An additional set of samples from each litter was prepared for initial morphological traits and chemical analyses. After 77, 168, 265, 419, and 784 days of decomposition, we measured soil temperature and moisture in each plot with a portable instrument (SIN-TH8, SinoMeasure, China). At each decomposition time, five leaf and root litterbags were retrieved separately from each plot to ensure sufficient sample testing for all variables. Litterbags were transported to the laboratory where the exterior of the bags was brushed free of adhering soil. Two subsamples of litter were immediately frozen and stored at -20 °C until the analysis of enzyme activity and phospholipid fatty acid (PLFA); the remaining litters were carefully cleaned of mineral soil particles, living soil animals, and debris adhering to the litter materials, and then dried (55 °C for 48 h) in an oven before weighing. See Table S1 for a detailed description of replication statement. Litter enzyme dynamics are strongly shaped by litter, soil, and microbial attributes during decomposition, however, enzyme dynamics of leaf and root litter remains unresolved due to contrasting differences in rates and controls on leaf and root litter decomposition. Herein, we conducted a 784-day field experiment to evaluate the relative importance of litter, alkaline soil, and microbial attributes to enzyme activities and their C:N:P stoichiometry of leaf and root litter during decomposition under subtropical land use change of China. We found that only the C- and N-acquiring enzyme activities of shrub leaves were greater than those of wood and crop, and there was no significant difference in P-acquiring enzyme activity among the three species of leaves. Both the C- and P-acquiring enzyme activities of crop roots were significantly lower than those of afforested lands (i.e., woodland and shrubland). The N-acquiring activities of wood roots were significantly lower than those of shrub and crop. At the temporal dynamics, the C-, N-, and P-acquiring enzyme activities of the leaves decreased with mass loss, which was affected by the shift in litter nutrients (e.g., N and P) and soil moisture during decomposition. In contrast, the three enzyme activities of roots increased with mass loss, largely due to the increase in microbial biomass of bacteria regulated by litter stoichiometry. The enzymatic C:nutrient (N and P) ratios declined with mass loss, but the enzymatic P:N ratios remained relatively constant with mass loss during the leaf litter decomposition. Whereas, both of the enzymatic C:nutrient ratios and enzymatic P:N ratios decreased with mass during the root litter decomposition. Our results showed that the enzymatic C:N:P stoichiometry of decaying leaves and roots was predominantly predicted by microbial biomass and bacterial biomass, respectively. Overall, we outlined the pattern of contrasting contributions of litter, soil, and microbial attributes to enzyme dynamics during decomposition, which provided a framework for better understanding litter C, N, and P dynamics in relation to microbial resource allocation strategy during decomposition.

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    DRYAD; ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: Datacite; ZENODO
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      DRYAD; ZENODO
      Dataset . 2023
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    Authors: AKPINAR, AYHAN;

    Due to space limitations of the journal, detailed versions of the tables can be accessed from this link. In addition, the references of the articles in the tables are available here.

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    Dataset . 2023
    License: CC BY
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    Dataset . 2023
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      Dataset . 2023
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      Dataset . 2023
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    Authors: Mihretu, Getahun Yismaw;

    This Dataset was collected in an experiment conducted in the field in west Dembia district under irrigation from January to April 2021 to investigate the effect of mulches on growth and fruit yield of watermelon varieties. Then all phenological, growth, yield and yield component parameters were collected and subjected to analysis of variance and the analysis was carried out using the SAS version 9.4 software computer program's General Linear Model (GLM) procedure. The economic feasibility analysis was also performed by following CIMMYT procedure and correlation analysis was performed using the Pearson correlation procedure found in SAS.

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    Mendeley Data
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    Mendeley Data
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      Mendeley Data
      Dataset . 2023
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    Authors: Wasilewski, Adam;

    The dataset includes 1. online store customer behavior data (clickstream) from 1.04.-30.11.2023, used to cluster customers and evaluate the effectiveness of implemented modifications (catalog: learning-dataset) 2. clustering results to verify the effectiveness of implemented changes (catalog: clustering) 3. detailed data for calculation of macro-conversion indicators (catalog: macro-conversion-indicators) 3. detailed data for calculation of micro-conversion indicators (catalog: micro-conversion-indicators)

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    Authors: José dos Reis Filho, Ivan;

    This dataset encompasses a comprehensive collection of soybean market news articles, meticulously curated and labeled for relevance. Collected from the prominent Brazilian website "noticiasagricola.com.br" from January 2015 to June 2023, the dataset features a diverse range of content, including international, national, and regional perspectives. Features: Data Attributes: Date, headline, content, label, and embeddings from three pre-trained BERT models (Paraphrase Multilingual, Distilbert Multilingual, and BERTimbau). Labeling: News articles are labeled as either relevant or irrelevant, providing a binary classification for ease of analysis. Coverage: The dataset spans various aspects of the soybean market, offering insights into climate conditions, research findings, consulting information, technological advancements, diseases and pests, and logistics. Language: Portuguese Potential Applications: Natural Language Processing (NLP) tasks; Machine Learning Task(ML); Multimodal Predictions. Benefits: Diverse sources (544 international, national, and regional providers); Enriched with embeddings for advanced NLP applications Covers a wide range of soybean market aspects Usage: Researchers and practitioners in the fields of agriculture, economics, and data science can leverage this dataset for in-depth analyses, model development, and trend exploration within the soybean market.

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    Authors: Hajek, Olivia; Sturchio, Matthew; Knapp, Alan;

    # Data from: A test of the seasonal availability of water hypothesis in a C3/C4 mixed grassland Abstract: To better understand how shifts in the seasonal availability of water can affect ecosystem function in a northern mixed grass prairie in southeastern Wyoming, we reduced early season rainfall (April June 2021) using rainout shelters and added the amount of excluded precipitation during the latter half of the growing season (July-September), effectively shifting spring rainfall to summer rainfall. As expected, this shift in precipitation seasonality influenced patterns of soil water availability, leading to increased soil respiration in the summer months and sustained canopy greenness throughout the growing season. Despite these responses, there were no significant differences in C3 aboveground net primary production (ANPP) between the seasonally shifted treatment (SEAS) and the plots that received ambient (AMB) precipitation. This was likely due to the high levels of spring soil moisture present before rainout shelters were deployed that sustained C3 grass growth. However, in plots with high C4 grass cover, C4 ANPP increased significantly in response to increased summer rainfall. Overall, we provide the first experimental evidence that shifts in the seasonality of precipitation, with no change in temperature, will differentially impact C3 vs. C4 species, altering the dynamics of carbon cycling and canopy albedo in this extensive semi-arid grassland. Contact: Olivia Hajek () Data collection location: USDA-ARS High Plains Research Station (41.20, -104.88) Data collection time period: 2021-04-10 2021-09-23 File information: 10 files are included in this folder. 1. README. Contains detailed information regarding files 2-9. File format: .pdf 2. ANPP. Aboveground net primary production of the experimental plots collected at the beginning of September. File format: .csv Variables: Plot: Plot number of sample (Plots 1-20) Trt: Treatment (AMB = Ambient, SEAS = Seasonally shifted treatment) Cover_C4: Categorization of percent C4 grass cover for the plot (high corresponds to > 25%; low corresponds to < 5%) Rep: Experimental replication of sample (0.1 m2, 2 reps/plot) C3: Mass of C3 perennial grasses in sample, in grams Understanding how cool-season C3 and warm-season C4 grasses will respond to climate change is critical for predicting future grassland functioning. With warming, C4 grasses are expected to increase relative to C3 grasses. But, alterations in the seasonal availability of water may also influence C3/C4 dynamics because of their distinct seasons of growth. C4: Mass of C4 perennial grasses in sample, in grams Forb: Mass of forbs in sample, in grams Woody: Mass of woody species in sample, in grams Annual: Mass of C3 annual grasses in sample, in grams Cactus: Percent cover of cactus in the sample Total_biomass: Total mass of entire sample, in grams ANPP_total: Aboveground net primary production in g m-2 (total_biomass converted to an area) 3. Canopy greenness. Plot greenness from repeat digital photography, as calculated with the green chromatic coordinate (GCC), capturing the growing season phenology dynamics (2021-04-09 2021-09-23). File format: .csv Variables: Date: Date of measurement (dd-mm-yy) Plot: Plot number of sample (Plots 1-20) Trt: Treatment (AMB = Ambient, SEAS = Seasonally shifted treatment) Greenness: Average green chromatic coordinate of the pixels for each plot image 4. Soil moisture. Soil moisture measured integrated across the upper 20 cm from 2021- 04-10 2021-09-23. File format: .csv Variables: Date: Date of measurement (dd-mm-yy) Plot: Plot number of sample (Plots 1-20) Trt: Treatment (AMB = Ambient, SEAS = Seasonally shifted treatment) Season: Corresponds to the first or second half of the growing season (Pre = roofs in place; post = after roof removal) sm_20: soil moisture measured as the % volumetric water content integrated over the upper 20 cm of soil 5. Soil respiration. Soil CO2 efflux of the plot in mol CO2 m-2 s-1 from 2021-05-05 2021-09- 23 File format: .csv Variables: Date: Date of measurement (dd-mm-yy) Plot: Plot number of sample (Plots 1-20) Trt: Treatment (AMB = Ambient, SEAS = Seasonally shifted treatment) Season: Corresponds to the first or second half of the growing season (Pre = roofs in place; post = after roof removal) Efflux: CO2 efflux (soil respiration) of the plot in mol CO2 m-2 s-1. 6. Soil temperature. Soil temperature to a depth of 10cm of the plot in degrees Celsius from 2021-05-07 2021-09-23 File format: .csv Variables: Date: Date of measurement (dd-mm-yy) Plot: Plot number of sample (Plots 1-20) Trt: Treatment (AMB = Ambient, SEAS = Seasonally shifted treatment) Season: Corresponds to the first or second half of the growing season (Pre = roofs in place; post = after roof removal) Soil_temp: Measurement of soil temperature to a depth of 10 cm for each plot, in degrees Celsius 7. Water potential. Water potential in MPa measured throughout the growing season for a dominant C3 grass (Pascopryum smithii; 2021-05-27 2021-09-23) and the dominant C4 grass (Bouteloua gracilis; 2021-06-15 2021-09-23). File format: .csv Variables: Date: Date of measurement (dd-mm-yy) Plot: Plot number of sample (Plots 1-20) Trt: Treatment (AMB = Ambient, SEAS = Seasonally shifted treatment) Season: Corresponds to the first or second half of the growing season (Pre = roofs in place; post = after roof removal) Species: 4-letter code for each plant species (PASM = Pascopryum smithii; BOGR = Bouteloua gracilis) Functional_Group: Functional group that the plant belongs to (C3 = C3 perennial grass; C4 = C4 perennial grass) WP: Measured water potential in MPa 8. Species Composition. Visual estimate of the plot cover by species for all experimental plots, assessed in mid-August File format: .csv Variables: Species: Name of plant species present in the plot Functional_group: Functional group that the plant belongs to p_1 p_20: Correspond to plot where measurement was taken (ie. p_1 refers to plot 1) and the value represents the % cover of each species 9. Light transmission. Measurement of the ratio of light transmitted through the rainout shelters observed on three different sunny days. File format: .csv Variables: Date: Date of measurement (dd-mm-yy) Plot: Plot number of sample (Plots 1-20) Trt: Treatment (SEAS = Seasonally shifted treatment) Outside: PAR, radiation in the 400- to 700-nm waveband, representing the portion of the spectrum that plants use for photosynthesis, observed outside of the shelter Underneath: PAR, radiation in the 400- to 700-nm waveband, representing the portion of the spectrum that plants use for photosynthesis, observed underneath the shelter t: ratio of below-canopy PAR measurements to the most recent above-canopy PAR measurement, calculated automatically by the LP-80. 10. Photosynthesis. Photosythesis measurements taken with a LI-6400 (LiCor., Inc, Lincoln NE, USA) in June prior to roof removal and in July post roof removal for a dominant C3 grass and the dominant C4 grass. File format: .csv Variables: HHMMSS: Realtime clock FTime: Time since logging started Photo: Photosynthetic rate (mol CO2 m-2 s-1) Cond: Stomatal conductance mol m-2 s-1 Ci: intercellular CO2 concentration, mol CO2 mol air-1 Trmmol: Transpiration in mmol m-2s-1 VpdL: Vapor pressure deficit based on leaf temp in kPa Area: In-chamber leaf area cm-2 StmRat: Stomatal ratio estimate BLCond: Total boundary layer conductance for the leaf in mol m-2 s-1 Tair: Chamber air temperature in degrees Celsius Tleaf: Leaf thermocouple in degress Celsius Tblk: IRGA Block Temp C CO2R: Reference cell CO2 concentration in mol mol-1 CO2S: Sample cell CO2 concentration in mol mol-1 H2OR: Reference cell H2O concentration in mmol mol-1 H2OS: Sample cell H2O concentration in mmol mol-1 RH_R: Reference relative humidity % RH_S: Sample relative humidity % Flow: Flow rate into chamber in mol mol-1 PARi: In-chamber PAR mol m-2 s-1 PARo: External PAR mol m-2 s-1 Press: Atmospheric pressure in kPa CsMch: Sample CO2 offset mol mol-1 HsMch: Sample H2O offset mmol mol-1 StableF: Stability status as a decimal value Status: Primary numerical status information on the state of the system KEEP: Y means included in analyses; N is excluded Date: Date of measurement (mm/dd/yy) Plot: Plot number of sample (Plots 1-20) Trt: Treatment (AMB = Ambient, SEAS = Seasonally shifted treatment) C: value 3 correspond to C3 (Pascopryum smithii) and 4 corresponds C4 (Bouteloua gracilis) plant species Experimental Design Before the 2021 growing season, we established twenty 1 m2 plots (n=10 per treatment). Plots were separated by at least 3 m, and aluminum flashing was installed (10 cm belowground and 5 cm aboveground) 20 cm outside of the plot perimeter to reduce surface and shallow soil water movement into and out of each plot. Rainout shelter roofs (2.44 m 3.05 m made of clear corrugated polycarbonate, Suntuf, Palram Americas) that were larger than the 1 m2 plots were then placed over ten of the plots. Roofs were initially installed 80 cm above the ground at a slight angle to allow water to drain away from the plot; later in the season, the shelters were raised to 100 cm. Although previous work has demonstrated that these shelters have minimal influence on the microclimate (Loik et al. 2019; Post and Knapp 2020; Hoover et al. 2022), we monitored soil temperatures at 10 cm weekly and evaluated light transmission under the roofs using a 1-m linear quantum light sensor (Decagon AccuPAR, model LP-80). We altered the seasonal dynamics of soil water availability (seasonally shifted treatment, SEAS) by using the clear roofs to exclude all precipitation from April 10 June 30 (some minor blow-in of precipitation during storms was inevitable). We removed the shelters July 1 and added the equivalent amount of water excluded in the spring to these plots in addition to the ambient precipitation received. The additional precipitation was applied manually throughout July September replicating the distribution of rainfall events from the spring (i.e., additions were similar in event frequency and magnitude, Table S1). This ensured that total growing season precipitation was similar for the SEAS and the ambient (AMB) treatment, which received natural precipitation for the entire growing season. Precipitation was recorded at a nearby NOAA weather station (Cheyenne Weather Forecast Office, (41.1516, -104.80622)). Treatments (n=10) were randomly assigned, and because there was some minor topographical variation in the landscape (10 plots were slightly uphill from the others), we assigned treatments within two blocks to control for any effects topography. Block effects were non-significant in our analyses, but after plants became active, we noted that the cover of C3 and C4 functional groups varied widely among plots. Thus, we estimated total plant cover by species in each plot to account for this variation in our analyses. Measured Responses We measured soil moisture (volumetric water content, VWC) weekly in all plots throughout the experiment (April 10 September 23) with a 20 cm handheld soil moisture time-domain reflectometry probe (Campbell-Scientific Hydrosense II). This instrument integrates soil moisture in the top 20 cm of soil where most of the root biomass in this grassland is located (Sun et al. 1997; Carillo et al. 2014). To assess treatment effects on plant water status, we estimated mid-day (12:00 - 14:00hr MST) leaf water potential with a Scholander pressure chamber (PMS instruments) for a dominant C3 grasses, Pascopyrum smithii, and the dominant C4 grass, Bouteloua gracilis. Fully expanded, mature canopy leaves (1-2 leaves per plot, n=6 per treatment) were collected each week. Because the C3 and C4 grasses become active at different times of the growing season, we measured P. smithii water status from May 27 Sept. 16 and B. gracilis from June 15 - Sept. 16. To evaluate how differences in the seasonal availability of water influenced C3 and C4 dynamics and ecosystem function, we measured canopy greenness and soil CO2 efflux weekly, photosynthetic rates for the primary C3 and C4 species in June and July, and ANPP at the end of the growing season (September). Canopy greenness, measured to assess canopy-scale phenological responses and to serve as a proxy for potential ecosystem carbon uptake, was estimated with repeat digital photography (following the methods of Post and Knapp 2020, Hoover et al. 2022). Briefly, an iPhone camera was positioned directly above a marked 50 cm x 50 cm frame in a corner of each plot, and each photograph was then cropped to contain only the interior area of the frame. These cropped photos were processed using the R package EBImage (Pau et al. 2010) to calculate the average green chromatic coordinate (GCC) index (Filippa et al. 2016). The GCC index accounts for variation in pixel brightness (Filippa et al. 2016), thus avoiding background light levels and potential infrastructure impacts. Soil respiration was measured weekly (May 5 Sept. 23) to quantify how the treatments influenced this important carbon flux (Hashimoto et al. 2015). Permanent PVC collars (10 cm in diameter, n=6 per treatment) were installed in locations between grasses at the end of April (2.4 cm belowground and 2 cm aboveground), and all vegetation within the collars was removed (clipped at the base). Before each measurement, any new vegetation growth was also gently removed. Soil respiration was measured using a 6400-09 soil flux chamber attached to an LI-6400XT (LiCor., Inc, Lincoln NE, USA). Measurements were taken mid-day (between 8:30hr 12:30hr MST) at ambient CO2 concentration, humidity, and temperature. Leaf gas-exchange was measured (June 23-24) prior to roofs coming off and after the roofs were removed (July 24-25). On each date, a portable photosynthesis system (LI-6400, LiCor., Inc, Lincoln NE, USA) was used to measure the CO2 uptake (net photosynthesis, or A) on 12 fully expanded mature upper canopy leaves for both C3 (P. smithii) and C4 (B. gracilis) individuals in each treatment. The LI-6400 was fitted with a 32 cm cuvette head and a red-blue LED light source. For all measurements, flow rate was held constant at 600 mol s-1. The temperature exchanger was set to an average midday summer temperature of 30 C. Leaf temperature (Tleaf) was measured with a thermocouple and averaged 31 1.7 C (standard deviation) across all measurement dates. Relative humidity conditions in the chamber were controlled near ambient levels but did vary slightly depending upon water vapor fluxes from the leaf. Photosynthetic photon flux density in the chamber was set at 1800 mol m-2 s-1, approximating full-sun conditions to measure light-saturated net photosynthesis (Asat) and stomatal conductance to water vapor (gs, Fig. S1) at a chamber reference [CO2] of 420 mol mol-1. All measurements occurred between 10:00hr and 15:00hr MST Finally, ANPP was estimated near the end of the growing season (Sept. 1-2) in all plots as plants began to senesce. For each plot, all aboveground vegetation within two 0.1 m2 subplots was harvested to ground height, sorted by functional group (C3 grass, C4 grass, forb, woody, or annual grass), and then dried at 60 C for 48 hours before being weighed to the nearest 0.01g. Previous years growth was easily distinguished from current year growth and was not included. Experimental Design Before the 2021 growing season, we established twenty 1 m2 plots (n=10 per treatment). Plots were separated by at least 3 m, and aluminum flashing was installed (10 cm belowground and 5 cm aboveground) 20 cm outside of the plot perimeter to reduce surface and shallow soil water movement into and out of each plot. Rainout shelter roofs (2.44 m × 3.05 m made of clear corrugated polycarbonate, Suntuf, Palram Americas) that were larger than the 1 m2 plots were then placed over ten of the plots. Roofs were initially installed 80 cm above the ground at a slight angle to allow water to drain away from the plot; later in the season, the shelters were raised to 100 cm. Although previous work has demonstrated that these shelters have minimal influence on the microclimate (Loik et al. 2019; Post and Knapp 2020; Hoover et al. 2022), we monitored soil temperatures at 10 cm weekly and evaluated light transmission under the roofs using a 1-m linear quantum light sensor (Decagon AccuPAR, model LP-80). We altered the seasonal dynamics of soil water availability (seasonally shifted treatment, SEAS) by using the clear roofs to exclude all precipitation from April 10 – June 30 (some minor blow-in of precipitation during storms was inevitable). We removed the shelters July 1 and added the equivalent amount of water excluded in the spring to these plots in addition to the ambient precipitation received. The additional precipitation was applied manually throughout July – September replicating the distribution of rainfall events from the spring (i.e., additions were similar in event frequency and magnitude, Table S1). This ensured that total growing season precipitation was similar for the SEAS and the ambient (AMB) treatment, which received natural precipitation for the entire growing season. Precipitation was recorded at a nearby NOAA weather station (Cheyenne Weather Forecast Office, (41.1516, -104.80622)). Treatments (n=10) were randomly assigned, and because there was some minor topographical variation in the landscape (10 plots were slightly uphill from the others), we assigned treatments within two blocks to control for any effects topography. Block effects were non-significant in our analyses, but after plants became active, we noted that the cover of C3 and C4 functional groups varied widely among plots. Thus, we estimated total plant cover by species in each plot to account for this variation in our analyses. Measured Responses We measured soil moisture (volumetric water content, VWC) weekly in all plots throughout the experiment (April 10 – September 23) with a 20 cm handheld soil moisture time-domain reflectometry probe (Campbell-Scientific Hydrosense II). This instrument integrates soil moisture in the top 20 cm of soil where most of the root biomass in this grassland is located (Sun et al. 1997; Carillo et al. 2014). To assess treatment effects on plant water status, we estimated mid-day (12:00 - 14:00hr MST) leaf water potential with a Scholander pressure chamber (PMS instruments) for a dominant C3 grasses, Pascopyrum smithii, and the dominant C4 grass, Bouteloua gracilis. Fully expanded, mature canopy leaves (1-2 leaves per plot, n=6 per treatment) were collected each week. Because the C3 and C4 grasses become active at different times of the growing season, we measured P. smithii water status from May 27 – Sept. 16 and B. gracilis from June 15 - Sept. 16. To evaluate how differences in the seasonal availability of water influenced C3 and C4 dynamics and ecosystem function, we measured canopy greenness and soil CO2 efflux weekly, photosynthetic rates for the primary C3 and C4 species in June and July, and ANPP at the end of the growing season (September). Canopy greenness, measured to assess canopy-scale phenological responses and to serve as a proxy for potential ecosystem carbon uptake, was estimated with repeat digital photography (following the methods of Post and Knapp 2020, Hoover et al. 2022). Briefly, an iPhone camera was positioned directly above a marked 50 cm x 50 cm frame in a corner of each plot, and each photograph was then cropped to contain only the interior area of the frame. These cropped photos were processed using the R package EBImage (Pau et al. 2010) to calculate the average green chromatic coordinate (GCC) index (Filippa et al. 2016). The GCC index accounts for variation in pixel brightness (Filippa et al. 2016), thus avoiding background light levels and potential infrastructure impacts. Soil respiration was measured weekly (May 5 – Sept. 23) to quantify how the treatments influenced this important carbon flux (Hashimoto et al. 2015). Permanent PVC collars (10 cm in diameter, n=6 per treatment) were installed in locations between grasses at the end of April (2.4 cm belowground and 2 cm aboveground), and all vegetation within the collars was removed (clipped at the base). Before each measurement, any new vegetation growth was also gently removed. Soil respiration was measured using a 6400-09 soil flux chamber attached to an LI-6400XT (LiCor., Inc, Lincoln NE, USA). Measurements were taken mid-day (between 8:30hr – 12:30hr MST) at ambient CO2 concentration, humidity, and temperature. Leaf gas-exchange was measured (June 23-24) prior to roofs coming off and after the roofs were removed (July 24-25). On each date, a portable photosynthesis system (LI-6400, LiCor., Inc, Lincoln NE, USA) was used to measure the CO2 uptake (net photosynthesis, or A) on 12 fully expanded mature upper canopy leaves for both C3 (P. smithii) and C4 (B. gracilis) individuals in each treatment. The LI-6400 was fitted with a 3×2 cm cuvette head and a red-blue LED light source. For all measurements, flow rate was held constant at 600 μmol s-1. The temperature exchanger was set to an average midday summer temperature of 30 °C. Leaf temperature (Tleaf) was measured with a thermocouple and averaged 31 ± 1.7 °C (standard deviation) across all measurement dates. Relative humidity conditions in the chamber were controlled near ambient levels but did vary slightly depending upon water vapor fluxes from the leaf. Photosynthetic photon flux density in the chamber was set at 1800 μmol m-2 s-1, approximating full-sun conditions to measure light-saturated net photosynthesis (Asat) and stomatal conductance to water vapor (gs, Fig. S1) at a chamber reference [CO2] of 420 μmol mol-1. All measurements occurred between 10:00hr and 15:00hr MST Finally, ANPP was estimated near the end of the growing season (Sept. 1-2) in all plots as plants began to senesce. For each plot, all aboveground vegetation within two 0.1 m2 subplots was harvested to ground height, sorted by functional group (C3 grass, C4 grass, forb, woody, or annual grass), and then dried at 60 °C for 48 hours before being weighed to the nearest 0.01g. Previous year’s growth was easily distinguished from current year growth and was not included. Understanding how cool-season C3 and warm-season C4 grasses will respond to climate change is critical for predicting future grassland functioning. With warming, C4 grasses are expected to increase relative to C3 grasses. But, alterations in the seasonal availability of water may also influence C3/C4 dynamics because of their distinct seasons of growth. To better understand how shifts in the seasonal availability of water can affect ecosystem function in a northern mixed grass prairie in southeastern Wyoming, we reduced early season rainfall (April – June 2021) using rainout shelters and added the amount of excluded precipitation during the latter half of the growing season (July-September), effectively shifting spring rainfall to summer rainfall. As expected, this shift in precipitation seasonality influenced patterns of soil water availability, leading to increased soil respiration in the summer months and sustained canopy greenness throughout the growing season. Despite these responses, there were no significant differences in C3 aboveground net primary production (ANPP) between the seasonally shifted treatment (SEAS) and the plots that received ambient (AMB) precipitation. This was likely due to the high levels of spring soil moisture present before rainout shelters were deployed that sustained C3 grass growth. However, in plots with high C4 grass cover, C4 ANPP increased significantly in response to increased summer rainfall. Overall, we provide the first experimental evidence that shifts in the seasonality of precipitation, with no change in temperature, will differentially impact C3 vs. C4 species, altering the dynamics of carbon cycling and canopy albedo in this extensive semi-arid grassland.

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    Authors: Zhu, Er-Lin;

    Raw data for all figures in the manuscript "Zirconium isotope tracing the magmatic-hydrothermal transition"

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  • Authors: Cyr, Frédéric; Coyne, Jonathan;

    Mooring data collected on the Newfoundland and Labrador (NL) shelf in support of the Overturning in the Subpolar North Atlantic Program (OSNAP). The Canadian mooring 1 along Seal Island section (OSNAP mooring CSI1) is equipped with Conductivity-Temperature-Depth (CTD), Acoustic Doppler Current Profiler (ADCP) and other temperature-only sensors. It has been deployed since July 2020 at geographical location 53.4490 degrees north and 55.2995 degrees west. Please refer to the OSNAP data management plan for more details regarding the OSNAP data policy and management plan. Before the use and publication of any OSNAP data, users are strongly encouraged to read the full OSNAP data policy. We draw your attention to the following excerpts from that policy: Any person making use of OSNAP observational data and/or numerical results must communicate with the responsible investigators at the start of the analysis and anticipate that the data collectors will be co-authors of published results. In cases where investigators choose not to be co-authors on publications that rely on their data, the parties responsible for collecting the data and the sponsoring funding agencies should be acknowledged, including reference to any relevant publications by the originating authors describing the data sets and a reference to the data set itself using its DOI. OSNAP data are intended for scholarly use by the academic and scientific community, with the express understanding that any such use will properly acknowledge the originating investigator. HOW TO ACKNOWLEDGE DATA FROM THE OSNAP PROJECT: OSNAP data were collected and made freely available by the OSNAP (Overturning in the Subpolar North Atlantic Program) project and all the national programs that contribute to it (www.o-snap.org).

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  • Authors: Cyr, Frédéric; Coyne, Jonathan;

    Mooring data collected on the Newfoundland and Labrador (NL) shelf in support of the Overturning in the Subpolar North Atlantic Program (OSNAP). The Canadian mooring number 0 (OSNAP mooring C0) is equipped with Conductivity-Temperature-Depth (CTD), Acoustic Doppler Current Profiler (ADCP) and other temperature-only sensors. It has been deployed since July 2020 at geographical location 52.6228 degrees north and 52.2163 degrees west. Please refer to the OSNAP data management plan for more details regarding the OSNAP data policy and management plan. Before the use and publication of any OSNAP data, users are strongly encouraged to read the full OSNAP data policy. We draw your attention to the following excerpts from that policy: Any person making use of OSNAP observational data and/or numerical results must communicate with the responsible investigators at the start of the analysis and anticipate that the data collectors will be co-authors of published results. In cases where investigators choose not to be co-authors on publications that rely on their data, the parties responsible for collecting the data and the sponsoring funding agencies should be acknowledged, including reference to any relevant publications by the originating authors describing the data sets and a reference to the data set itself using its DOI. OSNAP data are intended for scholarly use by the academic and scientific community, with the express understanding that any such use will properly acknowledge the originating investigator. HOW TO ACKNOWLEDGE DATA FROM THE OSNAP PROJECT: OSNAP data were collected and made freely available by the OSNAP (Overturning in the Subpolar North Atlantic Program) project and all the national programs that contribute to it (www.o-snap.org).

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    Authors: Liu, Yijing; Wang, Peiyan; Elberling, Bo; Westergaard-Nielsen, Andreas;

    To quantify the seasonal transition dates, we used NDVI derived from Sentinel-2 MultiSpectral Instrument (Level-1C) images during 2016–2020 based on Google Earth Engine (https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2). We performed an atmospheric correction (Yin et al., 2019) on the images before calculating NDVI. The months from May to October were set as the study period each year. The quality control process includes 3 steps: (i) the cloud was masked according to the QA60 band; (ii) images were removed if the number of pixels with NDVI values outside the range of -1–1 exceeds 30% of the total pixels while extracting the median value of each date; (iii) NDVI outliers resulting from cloud mask errors (Coluzzi et al., 2018) and sporadic snow were deleted pixel by pixel. NDVI outliers mentioned here appear as a sudden drop to almost zero in the growing season and do not form a sequence in this study (Komisarenko et al., 2022). To identify outliers, we iterated through every two consecutive NDVI values in the time series and calculated the difference between the second and first values for each pixel every year. We defined anomalous NDVI differences as points outside of the percentiles threshold [10 90], and if the NDVI difference is positive, then the first NDVI value used to calculate the difference will be the outlier, otherwise, the second one will be the outlier. Finally, 215 images were used to reflect seasonal transition dates in all 5 study periods of 2016–2020 after the quality control. Each image was resampled with 32 m spatial resolution to match the resolution of the ArcticDEM data and SnowModel outputs. To detect seasonal transition dates, we used a double sigmoid model to fit the NDVI changes on time series, and points where the curvature changes most rapidly on the fitted curve, appear at the beginning, middle, and end of each season (Klosterman et al., 2014). The applicability of this phenology method in the Arctic has been demonstrated (Ma et al., 2022; Westergaard-Nielsen et al., 2013; Westergaard-Nielsen et al., 2017). We focused on 3 seasonal transition dates, i.e., SOS, NDVImax day, and EOF. The NDVI values for some pixels are still below zero in spring and summer due to topographical shadow. We, therefore, set a quality control rule before calculating seasonal transition dates for each pixel, i.e., if the number of days with positive NDVI values from June to September is less than 60% of the total number of observed days, the pixel will not be considered for subsequent calculations. As verification of fitted dates, the seasonal transition dates in dry heaths and corresponding time-lapse photos acquired from the snow fence area are shown in Fig. 2. Snow cover extent is greatly reduced and vegetation is exposed with lower NDVI values on the SOS. All visible vegetation is green on the NDVImax day. On EOF, snow cover distributes partly, and NDVI decreases to a value close to zero. # Data from: Drivers of contemporary and future changes in Arctic seasonal transition dates for a tundra site in coastal Greenland The dataset includes all original images used in this study to extract seasonal transition dates and corresponding results. ## Description of the data and file structure Datasets included: (1) The spatial distribution of NDVI values for this study region (168 rows and 166 columns). Each file is named in the form of '' year-month-day''. For example, a file named "2016-05-02'' represents the data for 2nd, May of 2016. The normal NDVI values in each file range from -1 to 1, and NaN represents no valid value. The folder named 'unique_date_NDVI' refers to the spatial distribution of NDVI for all available dates, directly acquired from satellite images. The folder named 'unique_date_NDVI_rm_outlier' refers to the spatial distribution of NDVI after quality correction for each date using the described method. (2) The extracted phenology indicators for each pixel in this study region. Five tables named 'Phe_pixel_XXXX.xlsx' include the extracted seasonal transition dates during 2016–2020, pixel by pixel. There are 9 columns in each table, they are row number and column number (used to describe the specific location of pixel), year, start of spring, middle of spring, end of spring, start of fall, middle of fall, and end of fall. ## Sharing/Access information All functions regarding the extraction of seasonal transition dates can be found here: * All parameters and associated functions regarding the SnowModel can be found here: * All original meteorological data in this study is from: * Climate change has had a significant impact on the seasonal transition dates of Arctic tundra ecosystems, causing diverse variations between distinct land surface classes. However, the combined effect of multiple controls as well as their individual effects on these dates remains unclear at various scales and across diverse land surface classes. Here we quantified spatiotemporal variations of three seasonal transition dates (start of spring, maximum Normalized Difference Vegetation Index (NDVImax) day, end of fall) for five dominant land surface classes in the ice-free Greenland and analyzed their drivers for current and future climate scenarios, respectively.

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    Authors: Long, Chunyan; Yang, Fan; Zhang, Qian; Cheng, Xiaoli;

    # Different dynamics and controls of enzyme activities of leaf and root litter during decomposition These data include a data sheet with field collected data compiled across two years. Each litter types has a unique label including leaf and root litter. All traits (i.e., β‐1,4‐glucosidase (BG, EC 3.2.1.21), the sum of the leucine aminopeptidase (LAP, EC 3.4.11), N‐acetyl‐β‐glucosaminidase (NAG, EC 3.2.1.14), acid phosphatase (AP, EC 3.1.3.2)) were determined in the laboratory by standard methods. To analyze the enzymatic C:N:P stoichiometry, the vector length (i.e., quantifying the relative C versus nutrient acquisition) was calculated as the square root of the squared sum of the values of x and y [length = Sqrt (x2 + y2)], and the vector angle (i.e., quantifying the relative P versus N acquisition) was calculated as the arctangent of the point (x, y) [angle (degrees) = degrees (Atan2(x, y))], where x represents the relative proportion of C- to C+P-acquiring enzyme activities and y represents the relative proportion of C- to C+N-acquiring enzyme activities. The columns in the dataset refer to: Types = Leaf or root; Litter mass loss = The initial litter weight minus the litter weight of sampling time divided by the initial litter weight; BG = β‐1,4‐glucosidase (BG, EC 3.2.1.21); NAP = The sum of the leucine aminopeptidase (LAP, EC 3.4.11), N‐acetyl‐β‐glucosaminidase (NAG, EC 3.2.1.14); AP = Acid phosphatase (AP, EC 3.1.3.2); Vector_length = The square root of the squared sum of the values of x and y, where x represents the relative proportion of C- to P-acquiring enzyme activities and y represents the relative proportion of C- to N-acquiring enzyme activities (Moorhead et al., 2016); Vector_angle = The arctangent of the point (x, y) [angle (degrees) = degrees (Atan2(x, y))], where x represents the relative proportion of C- to P-acquiring enzyme activities and y represents the relative proportion of C- to N-acquiring enzyme activities (Moorhead et al., 2016); Litter C loss = The initial litter C minus the litter C of sampling time divided by the initial litter C; Litter lignin loss = The initial litter lignin minus the litter lignin of sampling time divided by the initial litter lignin; Litter cellulose loss = The initial litter cellulose minus the litter cellulose of sampling time divided by the initial litter cellulose; Litter hemicellulose loss = The initial litter hemicellulose minus the litter hemicellulose of sampling time divided by the initial litter hemicellulose; Litter N loss = The initial litter N minus the litter N of sampling time divided by the initial litter N; Litter P loss = The initial litter P minus the litter P of sampling time divided by the initial litter P; Soil temperature = ℃; soil moisture = Volumetric water content of soil; Litter C concentration = %; Litter lignin concentration = %; Litter cellulose concentration = %; Litter hemicellulose concentration = %; Litter N concentration = %; Litter P concentration = mg kg-1; Liiter C:N = unitless; Liiter N:P = unitless; Liiter lignin:N = unitless; Bacterial biomass = μg g-1; Fungi biomass = μg g-1; Total PLFAs biomass = μg g-1. We selected three sites with different land use types (i.e., woodland, shrubland, and cropland). At each study site, three independent 10 m × 10 m plots were selected. Each plot was located 100 m apart from one another. We conducted a 784-day in situ litter decomposition experiment, where litter from each species and site was incubated at their own site (e.g., crop litter was placed back into the cropland). In each woodland and shrubland, freshly fallen senesced leaves were collected from litter traps (1 m × 1 m). Roots (diameter ≤2 mm) were excavated using a root auger at a depth of 20 cm under each tree in the two plantations. For cropland, the leaves and roots (diameter ≤2 mm) of maize were collected after harvesting. Living roots were selected as the experimental materials based on color, luster, and elasticity. The sampled roots were placed in an incubator and transported to the laboratory. Adherent soil particles and extraneous organic material were gently removed from the root samples. Thereafter, all collected leaf and root litter samples were oven-dried (55 °C) in the laboratory. Subsequently, the leaf and root litters were sterilized at 121 °C for 20 min to ensure that the most likely import of the microbial community was through the soil rather than litter. Finally, all collected leaf and root litter samples were oven-dried (55 °C) in the laboratory for further experiments and analysis. On October, 2016, a total of 5.0 g of oven-dried leaves and roots were placed in nylon bags (15 cm × 20 cm, with a 1-mm mesh size). A total of 450 litterbags prepared for this experiment (3 plots × 3 species × 2 litter types ×5 decomposition times × 5 replicates). After removing the floor litter (if any), leaf litterbags were fixed to the mineral soil surface. The root litterbags were placed in the soil at a depth of 10 cm at an angle of approximately 30° to the vertical. An additional set of samples from each litter was prepared for initial morphological traits and chemical analyses. After 77, 168, 265, 419, and 784 days of decomposition, we measured soil temperature and moisture in each plot with a portable instrument (SIN-TH8, SinoMeasure, China). At each decomposition time, five leaf and root litterbags were retrieved separately from each plot to ensure sufficient sample testing for all variables. Litterbags were transported to the laboratory where the exterior of the bags was brushed free of adhering soil. Two subsamples of litter were immediately frozen and stored at -20 °C until the analysis of enzyme activity and phospholipid fatty acid (PLFA); the remaining litters were carefully cleaned of mineral soil particles, living soil animals, and debris adhering to the litter materials, and then dried (55 °C for 48 h) in an oven before weighing. See Table S1 for a detailed description of replication statement. Litter enzyme dynamics are strongly shaped by litter, soil, and microbial attributes during decomposition, however, enzyme dynamics of leaf and root litter remains unresolved due to contrasting differences in rates and controls on leaf and root litter decomposition. Herein, we conducted a 784-day field experiment to evaluate the relative importance of litter, alkaline soil, and microbial attributes to enzyme activities and their C:N:P stoichiometry of leaf and root litter during decomposition under subtropical land use change of China. We found that only the C- and N-acquiring enzyme activities of shrub leaves were greater than those of wood and crop, and there was no significant difference in P-acquiring enzyme activity among the three species of leaves. Both the C- and P-acquiring enzyme activities of crop roots were significantly lower than those of afforested lands (i.e., woodland and shrubland). The N-acquiring activities of wood roots were significantly lower than those of shrub and crop. At the temporal dynamics, the C-, N-, and P-acquiring enzyme activities of the leaves decreased with mass loss, which was affected by the shift in litter nutrients (e.g., N and P) and soil moisture during decomposition. In contrast, the three enzyme activities of roots increased with mass loss, largely due to the increase in microbial biomass of bacteria regulated by litter stoichiometry. The enzymatic C:nutrient (N and P) ratios declined with mass loss, but the enzymatic P:N ratios remained relatively constant with mass loss during the leaf litter decomposition. Whereas, both of the enzymatic C:nutrient ratios and enzymatic P:N ratios decreased with mass during the root litter decomposition. Our results showed that the enzymatic C:N:P stoichiometry of decaying leaves and roots was predominantly predicted by microbial biomass and bacterial biomass, respectively. Overall, we outlined the pattern of contrasting contributions of litter, soil, and microbial attributes to enzyme dynamics during decomposition, which provided a framework for better understanding litter C, N, and P dynamics in relation to microbial resource allocation strategy during decomposition.

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