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Urban Riparian Wetland Water Quality Dataset_Stormwater Capture in Beaver-mediated Wetlands along Walnut Creek, Raleigh, North Carolina, USA
<p><span>This is the initial release of a </span><strong><span>water quality</span></strong><span> dataset pertaining to the <strong>riparian floodplain wetlands</strong> alongside Walnut Creek in Raleigh, North Carolina USA. Walnut Creek is the main drainage channel in an <strong>urbanized watershed</strong> (HUC-12: 030202011101) in central North Carolina. There are several riparian floodplain wetlands along the creek which are largely supplied by <strong>urban stormwater</strong> runoff including directed <strong>storm sewer flows</strong> and regular <strong>overbank flooding</strong> events. In many of these wetlands local water retention and residence time in the surface ponds is mediated by the damming activity of <strong>North American beavers (</strong><em><strong>Castor canadensis</strong></em><strong>)</strong>. This dataset contains data specific to the water quality values of <strong>Walnut Creek</strong>, its tributary <strong>Little Rock Creek</strong>, and the surface ponds and groundwater at the <strong>Walnut Creek Wetland Park</strong> which is actively influenced by resident beavers. The period of this dataset is from <strong>January </strong></span><strong><span>5</span><span>, 2023 through </span></strong><strong><span>October 28</span><span>, 2023</span></strong><span>. </span></p> <p><span>This dataset includes a variety of common <strong>water quality parameters</strong> measured in situ by use of a <strong>YSI Pro water quality meter</strong>, as well as <strong>dissolved nutrient values</strong> determined by <strong>laboratory analysis</strong> of collected water samples.<span> </span>YSI data was collected on a <strong>weekly</strong> basis and water samples were collected for laboratory analysis on a <strong>monthly</strong> basis. Additional measurements and collection took place during <strong>six large rainfall events</strong> to allow comparison between baseflow and stormflow conditions across the site.<span> </span>This dataset aims to provide a comprehensive look at the water quality of Walnut Creek in comparison with the surface ponds and groundwater in the Walnut Creek Wetland Park, which are all ultimately sourced from <strong>urban stormwater runoff</strong>. </span></p> <p><span>This water quality dataset is intended to accompany the <u>separate</u> <strong>hydrology dataset</strong> published on Zenodo at URL: <a href="https://doi.org/10.5281/zenodo.10709630">https://doi.org/10.5281/zenodo.10709630</a>. Together, these datasets are meant to support an improved understanding of the water availability and water quality found in connection with beaver-mediated stormwater capture in an urbanized watershed in the North Carolina Piedmont.</span></p> <p><span> </span><span>This dataset resulted from research supported with a Graduate Student Research Grant awarded by the <strong>North Carolina Water Resources Research Institute (WRRI)</strong>, under Project Number 23-10-W: "Stormwater Diversion, Storage, and Treatment by Beaver-enhanced Floodplain Wetlands in Piedmont Urban Watersheds". </span></p> <p><span>This material is based upon work supported by the <strong>National Science Foundation (NSF)</strong> Graduate Research Fellowship Program (GRFP) under Grant No. (DGE 2137100). Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.</span></p> <p><span>Special thanks to <strong>Raleigh Parks</strong> and <strong>Walnut Creek Wetland Park</strong> for making this work possible.</span></p> <p><span>Laboratory analysis support for evaluation of dissolved nutrients (nitrate+nitrite, TKN, total phosphorus, and total organic carbon) was provided by the <strong>NC State Environmental and Agricultural Testing Services (EATS)</strong> laboratory, Department of Crop and Soil Sciences.</span></p> <p><span> </span><span>Additional laboratory analysis support for evaluation of dissolved nutrients (TKN and total phosphorus) was provided by the <strong>NC State Environmental Analysis Laboratory (EAL)</strong>, Department of Biological and Agricultural Engineering (BAE).</span></p> <p><span> </span><span>Usage of and technical support for the YSI Pro water quality meter used in this study was made possible by the <strong>Osburn Lab</strong>, Department of Marine, Earth and Atmospheric Sciences (MEAS), NC State University.</span></p>
Figure 10 in Ecological Features And Anthropogenic Transformation Of Wetlands As Part Of Urban Floras Of Ukraine
Figure 10. Distribution of plant communities by soil aeration and nitrogen content in soil (legend explanation is given in figure 9).
Figure 9 in Ecological Features And Anthropogenic Transformation Of Wetlands As Part Of Urban Floras Of Ukraine
Figure 9. Distribution of plant communities by variability of damping and total salt regime, where 1: Typhetum angustifoliae Pignatti 1953; 2: Typhetum latifoliae Nowiñski 1930; 3: Phragmitetum australis Savič 1926; 4: Sparganietum erecti Roll 1938; 5: CariciRumicion hydrolapathi Passarge 1964; 6: Senecionion fluviatilis Tx. ex Moor 1958; 7: ChelidonioAcerion negundi L. Ishbirdina et A. Ishbirdin 1991.
Figure 6 in Ecological Features And Anthropogenic Transformation Of Wetlands As Part Of Urban Floras Of Ukraine
Figure 6. Proportion of number of native and adventive plant species in wetland flora in studied territories.
Figure 7 in Ecological Features And Anthropogenic Transformation Of Wetlands As Part Of Urban Floras Of Ukraine
Figure 7. Proportion of number of wetlands plant species by the degree of urbanization in studied territories
Figure 2 in Ecological Features And Anthropogenic Transformation Of Wetlands As Part Of Urban Floras Of Ukraine
Figure 2. Proportion of number of plant species within families of wetland flora in studied territories.
Figure 3 in Ecological Features And Anthropogenic Transformation Of Wetlands As Part Of Urban Floras Of Ukraine
Figure 3. Proportion of number of plant species within soil water regime ecogroups in studied territories.
Figure 4 in Ecological Features And Anthropogenic Transformation Of Wetlands As Part Of Urban Floras Of Ukraine
Figure 4. Proportion of number of plant species within total salt regime ecogroups in studied territories.
Figure 1 in Ecological Features And Anthropogenic Transformation Of Wetlands As Part Of Urban Floras Of Ukraine
Figure 1. The location of the studied cities in Ukraine. (The map from Nations online: https://www. nationsonline.org/oneworld/map/ukrainepoliticalmap.htm).
Hull Springs (Baliles Center) Wetland Data from 2021-09-18 to 2021-10-28
<p>General Metadata for Hull Springs Restored Wetland Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment and sensor can be found as text files with the file format of:</p> <pre><code>HS_wetland_DO_YYYY-MM-DD_metadata.txt HS_wetland_Depth_YYYY-MM-DD_metadata.txt HS_wetland_CT_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab <a href="https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts">https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts</a>.</p> <p>File Created</p> <ul> <li>2021-06-16 by KF</li> </ul> <p>File Modified</p> <ul> <li>2021-07-22 by KF - added general metadata for the pressure transducer and the CT sensor.</li> <li>2021-11-10 by KF - updated to include the depth calculations from the water level logger.</li> </ul> <p>Description</p> <p>These data are from the sampling station in the restored wetland at the Baliles Center for Environmetal Education at Hull Springs. The sensors are in the NE corner of the shallow pond portion of the restored wetland (38.119289, -76.667252).</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Water Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Water Temperature (dC) and Water Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Water Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger * Air Temperature (dC) and Barometric Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger mounted in the air next to the wetland.</code></pre> <p>The sensors are sampled every 15 minutes</p> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * observation.DO, .CT, .press, or .BP - the incremental number of each observation from the DO, conductivity, water pressure, or barometric pressure sensor. * timestamp.DO, .CT, .press, or .BP - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) from the DO, conductivity, water pressure, or barometric pressure sensor. * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp.DO, .CT, .press, or .BP - the temperature (dC) from the DO, conductivity, water pressure, or barometric pressure sensor. * Pressure.press or .BP - the pressure recorded by the pressure transducer (kPa) on the water pressure or barometric pressure sensor. * Z - the depth of the water (cm). * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) * press.g.cm2 - the pressure from the water pressure sensor (g/cm^2) * BP.g.cm2 - the barometric pressure from the barometric pressure sensor (g/cm^2)</code></pre>
Hull Springs (Baliles Center) Wetland Data from 2021-07-16 to 2021-08-14
<p>General Metadata for Hull Springs Restored Wetland Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment and sensor can be found as text files with the file format of:</p> <pre><code>HS_wetland_DO_YYYY-MM-DD_metadata.txt HS_wetland_Depth_YYYY-MM-DD_metadata.txt HS_wetland_CT_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab <a href="https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts">https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts</a>.</p> <p>File Created</p> <ul> <li>2021-06-16 by KF</li> </ul> <p>File Modified</p> <ul> <li>2021-07-22 by KF - added general metadata for the pressure transducer and the CT sensor.</li> <li>2021-11-10 by KF - updated to include the depth calculations from the water level logger.</li> </ul> <p>Description</p> <p>These data are from the sampling station in the restored wetland at the Baliles Center for Environmetal Education at Hull Springs. The sensors are in the NE corner of the shallow pond portion of the restored wetland (38.119289, -76.667252).</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Water Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Water Temperature (dC) and Water Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Water Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger * Air Temperature (dC) and Barometric Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger mounted in the air next to the wetland.</code></pre> <p>The sensors are sampled every 15 minutes</p> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * observation.DO, .CT, .press, or .BP - the incremental number of each observation from the DO, conductivity, water pressure, or barometric pressure sensor. * timestamp.DO, .CT, .press, or .BP - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) from the DO, conductivity, water pressure, or barometric pressure sensor. * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp.DO, .CT, .press, or .BP - the temperature (dC) from the DO, conductivity, water pressure, or barometric pressure sensor. * Pressure.press or .BP - the pressure recorded by the pressure transducer (kPa) on the water pressure or barometric pressure sensor. * Z - the depth of the water (cm). * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) * press.g.cm2 - the pressure from the water pressure sensor (g/cm^2) * BP.g.cm2 - the barometric pressure from the barometric pressure sensor (g/cm^2)</code></pre>
Hull Springs (Baliles Center) Wetland Data from 2021-08-14 to 2021-09-18
<p>General Metadata for Hull Springs Restored Wetland Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment and sensor can be found as text files with the file format of:</p> <pre><code>HS_wetland_DO_YYYY-MM-DD_metadata.txt HS_wetland_Depth_YYYY-MM-DD_metadata.txt HS_wetland_CT_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab <a href="https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts">https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts</a>.</p> <p>File Created</p> <ul> <li>2021-06-16 by KF</li> </ul> <p>File Modified</p> <ul> <li>2021-07-22 by KF - added general metadata for the pressure transducer and the CT sensor.</li> <li>2021-11-10 by KF - updated to include the depth calculations from the water level logger.</li> </ul> <p>Description</p> <p>These data are from the sampling station in the restored wetland at the Baliles Center for Environmetal Education at Hull Springs. The sensors are in the NE corner of the shallow pond portion of the restored wetland (38.119289, -76.667252).</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Water Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Water Temperature (dC) and Water Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Water Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger * Air Temperature (dC) and Barometric Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger mounted in the air next to the wetland.</code></pre> <p>The sensors are sampled every 15 minutes</p> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * observation.DO, .CT, .press, or .BP - the incremental number of each observation from the DO, conductivity, water pressure, or barometric pressure sensor. * timestamp.DO, .CT, .press, or .BP - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) from the DO, conductivity, water pressure, or barometric pressure sensor. * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp.DO, .CT, .press, or .BP - the temperature (dC) from the DO, conductivity, water pressure, or barometric pressure sensor. * Pressure.press or .BP - the pressure recorded by the pressure transducer (kPa) on the water pressure or barometric pressure sensor. * Z - the depth of the water (cm). * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) * press.g.cm2 - the pressure from the water pressure sensor (g/cm^2) * BP.g.cm2 - the barometric pressure from the barometric pressure sensor (g/cm^2)</code></pre>
Fig. 3 in Impact Of Coastal Wetland Restoration Strategies In The Chongming Dongtan Wetlands, China: Waterbird Community Composition As An Indicator
Fig. 3. Densities of Charadriidae (a), Anatidae (b), Ardeidae (c), and Laridae (d) among autumn, winter and spring in four sites. Error bars represent ±1 SE.
Fig. 3 in Waterbird Distribution Patterns And Environmentally Impacted Factors In Reclaimed Coastal Wetlands Of The Eastern End Of Nanhui County, Shanghai, China
Fig. 3. Non-metricmulti-dimensionalscaling(NMDS) ordinationplotsshowingwaterbird communitystructurefromsixstudysites.
The site of an experimental oil spill located at a freshwater wetland along the St. Lawrence River re-visited after 21 years
<p>In 1999 a wetland close to Ste. Croix de Lotibiniere (Quebec, Eastern Canada) and located along the St. Lawrence River was the site of a simulated oil spill. Test plots were set up, contaminated with crude oil and subsequently used to test natural attenuation, nutrient amendment or vegetation cropping as remediation treatments. In 2020, this study revisited the former test plots to investigate any lingering effects of the original Ste. Croix study. Test plot sediments and control sediments featured detectable quantities (75 - 165 g/kg) of mid-chain n-alkanes (C10-C36), but no other kinds of hydrocarbons. Differences in hydrocarbon, total organic carbon, nitrogen and phosphorous content were not significantly different between test plot sediments and control sediments. A microbial analysis did not detect significant differences in microbial load, microbial diversity or microbial community composition between test plot sediments and control sediments. Key genes for the aerobic and anaerobic degradation of n-alkanes as well as for the aerobic degradation of polycyclic aromatic hydrocarbons were detected in all sediment samples. Abundances of these genes did not differ significantly between formerly oil-contaminated sediments and control sediments. This study shows that after 21 years, previously oil-contaminated sediments of the Ste. Croix wetland can be considered completely remediated irrespective of the performed remediation treatment.</p> <p>In this file archive are included, metadata, metagenome co-assembly, functional and taxonomic annotations, contigs and genes abundance matrices and MAGs files.</p> <p>Raw Illumina sequence data was deposited in the NCBI SRA portal under accession PRJNA818909.</p>
Data from: Recovering wetland biogeomorphic feedbacks to restore the world's biotic carbon hotspots
<p>These datafiles are part of a study that reviews how feedbacks between geomorphology and landscape-building vegetation underlie carbon storage and sequestration, and how feedback disruption can switch wetlands from carbon sinks into sources carbon stocks. Included ecosystems: ocean, forests, peatlands, mangrove forests, salt marshes, and seagrass meadows. Data were collected from other studies.</p> <p>Additional information regarding the methods, references or results of these datasets can be found in: Ralph J.M. Temmink, Leon P.M. Lamers, Christine Angelini, Tjeerd J. Bouma, Christian Fritz, Johan van de Koppel, Robin Lexmond, Max Rietkerk, Brian R. Silliman, Hans Joosten, Tjisse van der Heide. 2022. Recovering wetland biogeomorphic feedbacks to restore the world’s biotic carbon hotspots. <em>Science</em>.</p> <p> </p>
Dynamics of global wetlands by TOPMODEL
<p>Dynamics of global wetlands are closely linked to biodiversity conservation, hydrology, and greenhouse gas emissions. However, long-term time series of global wetland products are still lacking. Using a TOPMODEL-based diagnostic model, we produced an ensemble of 28 gridded maps of monthly global/regional wetland extent products at 0.25° × 0.25° spatial resolution based on four observation-based wetland products and seven reanalysis soil moisture datasets for the period 1980–2020. The parameters of the model are calibrated on grid-scale against four observation-based wetland products. Overall, our products can capture the spatial distributions, seasonal cycles, and interannual variabilities of observed wetland extent well, and also show a good agreement with satellite-based terrestrial water storage estimates. The resulting mean annual maximum global wetland area fluctuates within a range of 3.3–5.6 Mkm<sup>2</sup>. The long temporal coverage beyond the era of satellite datasets, the global coverage and the opportunity to provide real-time update from ongoing SM data make these products helpful for various applications such as analyzing the wetland-related methane emission.</p>
Fig. 4 in The Amount And Distribution Of The Red Data Book Bird Wetland Species In The Azov-Black Sea Region Of Ukraine According To The Results Of August Counts 2004-2015
Fig. 4. Distribution of wetlands number depending on number of species in them (axis X — number of species, axis Y — number of wetlands).
Fig. 5 in The Amount And Distribution Of The Red Data Book Bird Wetland Species In The Azov-Black Sea Region Of Ukraine According To The Results Of August Counts 2004-2015
Fig. 5. Distribution of wetlands depending on species number (axis X) and average amount of birds in them (axis Y).
Experimental disturbance treatments of wetland vegetation
<p><em>Study sites</em></p> <p>Our study sites were located in Kastbjerg Ådal (river valley) in Eastern Jutland, Denmark. It is within the Natura 2000 and habitat area no. 223 appointed because of the wide stretch of fens and mires among other qualities. The water course is in good ecological status according to the Water Framework Directive. Nitrogen deposition in this area is low to moderate, 12.5-14.5 kgN/ha/yr (Ellermann et al. 2021). Meadows and fens dominate the study area, known for ‘the longest stretch of rich fen’ in Denmark. Large parts of the river valley are heavily degraded by drainage, fertilization and scrub encroachment, but there have also been recent efforts to restore the watercourse and the valuable rich fens in the valley. Most fens and wet meadows have been abandoned and are now increasingly dominated by tall grasses, tall forbs and willow scrub, but summer grazing occurs in some areas and efforts are made to ensure grazing in the most valuable fens. The drier meadows are typically mown by heavy machinery. The sites were selected to represent gradients in soil moisture from moist to wet and gradients in nutrient status or productivity from poor to rich and included rich fens with characteristic species, fens dominated by <em>Juncus subnodulosus</em> and by <em>Equisetum fluviatile</em>, drained fens encroached by <em>Phragmites australis</em> and natural meadows with characteristic species and encroached by <em>Epilobium hirsutum</em> and meadows characterized by clovers and cultural grasses.</p> <p>The nine sites were of 10 m<sup>2</sup>, each with ten 1 m<sup>2</sup> plots. The 10 plots within each site had treatments assigned randomly. Despite the location in the same river valley, the sites were considered independent because of their different management history and starting conditions and a typical inter-site distance of c. 225 meters. The experiment was established in June 2017 and treatments were repeated monthly during summer and bimonthly during winter, depending on treatment. Responses were recorded in July 2019.</p> <p> </p> <p><em>Experimental set-up and treatments</em></p> <p>Each of the 9 sites were divided into ten 1 m × 1 m plots each with a 0.5 m × 0.5 m inner square and a surrounding plot buffer zone with a control and the following treatments: burning, mowing, trampling, intensive summer grazing (SI), intensive summer grazing with trampling (SIT), extensive summer grazing (SE), extensive summer grazing with trampling (SET, year-round grazing (YR), and year-round grazing with trampling (YRT). Treatments were allocated randomly to each plot with the restriction that the control plot was always in one corner. The experiment was multifactorial with respect to grazing and trampling, whereas burning and mowing were stand-alone treatments. Initial biomass in each plot was estimated at the beginning of the experiment in June 2017 as follows: all standing biomass and litter was removed from the plots by manual cutting at the soil surface and following the micro-topography. Bryophytes were harvested by hand plucking. Biomass, litter and bryophytes from the plot buffer zone were cut separately from the inner square. To estimate the species abundances, a representative sample of the inner square was sorted into litter and live biomass (including bryophytes) by species as sorting the complete biomass was not feasible. All species, litter and biomass from the buffer zone were dried at 55° C and weighed. Using the relative abundance of species in the representative sample and with respect to the weight of the total biomass in the inner square, we estimated the abundance of the species in the inner square.</p> <p>Burning was simulated in March 2018 and 2019. We used wooden boards to shield and adjacent areas were watered before burning the focal plot with a gas weed burner. We burned on a calm day following a dry period with frost to ensure minimum risk of igniting underlying peat and fire spreading over ground, but ensuring that the standing biomass and litter would be dry enough to ignite. This is not a simulation of a naturally occurring wildfire, but corresponds to the conditions that managers would prefer for prescribed conservation burning at larger scales. We simulated mowing as a biomass removal in June 2018. Biomass was removed uniformly across the whole plot in a height of c. 5 cm depending on microtopography. This corresponds to conservation mowing in management but without the added disturbance and pressure from machines. Trampling disturbance was applied using short stilts that could be attached to the field biologist’s boot. The surface of the stilt was 49 cm<sup>2</sup> which corresponds to a pressure of 1.3-1.5 kg/cm<sup>2</sup> with the added weight of the field biologist. This again corresponds to the pressure of a hoof of cattle weighing c. 300-400 kg. Trampling was applied by stepping into the field randomly 60 times once every month from May to September and was the same treatment in combination with intensive, extensive and year round grazing. Grazing was simulated by cutting the above-ground biomass using a 1 m<sup>2</sup> frame divided into a 10 cm coordinate system using the letters A-J on the x-axis and the numbers 1-10 on the y-axis. We cut tufts of biomass within the coordinate system using a list of random combinations of letters and numbers. This system enables “ungrazed” individuals to flower and set seeds. Based on our experience with grazing as an agri-environmental management practice in Denmark, we defined intensive summer grazing as taking place between May and September with the goal of removing all standing biomass by September. Extensive summer grazing also takes place May-September, but we carried this out at half the intensity as intensive summer grazing. Year-round grazing obviously takes place during the whole year (here administered May-September and November, January and March) with the goal of removing all standing biomass by the end of winter (March) before the beginning of a new growing season. We used the initial standing biomass (June 2017) as a measurement of plot productivity and estimated the amount of biomass to be removed during “grazing” as c. 20 % of the initial productivity each month May-September in intensive plots and with all standing biomass “grazed” in September. For extensive plots, we estimated removed biomass as c. 10 % of the initial productivity each month May-September leaving some standing biomass in September. Year-round grazing biomass removal was estimated as c. 10 % of yearly productivity removed every month May-September and November and 20 % removed in January and March resulting in no standing biomass at the end of the winter. As expected plot productivity changed as a result of the treatments, the amount of biomass removed had to be adjusted throughout the experiment. In practice, we aimed for removing twice the amount of biomass in intensive plots relative to extensive plots within the same site and always ensuring that no standing biomass was left in intensive plots in September, c. 50 % of the standing biomass was left in extensive plots in September and no standing biomass was left in year-round grazing plots in March (see actual removed biomass by treatment in Appendix A). All treatments were applied to the whole plot (1 m × 1 m), while the biomass response was only measured in the inner square (0.5 m × 0.5 m), leaving a buffer zone between plots with different treatments.</p> <p> </p> <p><em>Response variables</em></p> <p>A full plot (1 m × 1 m) species list was recorded in the field at the end of the experiment. From this total plot richness, vascular plant plot richness, bryophyte plot richness and number of indicator species per plot were calculated. Indicator species of conservation status are species considered moderately to very sensitive towards habitat degradation as defined by Fredshavn et al. (2010, see Appendix C). Indicator species are often adapted to relatively infertile habitats revealed by low Ellenberg N values and high Grime’s S values reflecting tolerance to nutrient shortage.</p> <p>Mean plot Grime’s C and S values (Grime et al. 1989) were calculated based on vascular plant species lists. We converted Grime’s life strategies to numerical values based on Ejrnæs and Bruun (2000).</p> <p>We performed a Nonmetric Multi-dimensional Scaling analysis (NMDS) on the presence-absence of vascular plant and bryophyte species at the end of the experiment using the function metaMDS in R-package ‘vegan’ (Oksanen et al. 2017) in R version 4.0.3 (R Core Team 2017), using Sørensen dissimilarity and a four-dimensional solution (k =4). The plot coordinates at the three first NMDS axes were extracted (NMS4 was discarded as noise) and these, along with the four richness variables as well as Grime’s C and S values, were used as response variables in Linear Mixed Models (LME) as described in ‘Statistical analyses’.</p> <p>Supplementary to regression models of single response variables we carried out a quadratic discriminant analysis (QDA) as described in ‘Statistical analyses’ using the change in six indicators during the course of the experiment. The difference between plot species richness at the beginning and end of the experiment was calculated based on the species lists from sorted initial biomass and end biomass (0.5 m × 0.5 m). Start-end differences were also calculated separately for vascular plant species richness, bryophyte species richness, richness of indicator species, the ratio between biomass of forbs and graminoids (grasses, sedges and rushes) and Grime’s C and S mean site values.</p> <p> </p> <p><em>Explanatory and co-variables</em></p> <p>Leaf nitrogen, carbon and phosphorous were determined from plot level sampling of leaf plates of grasses, i.e., the most abundant species group across sites. Fresh leaf plates were collected at the beginning and end of the project and then dried, ground and analyzed in the lab. Soil moisture (% volumetric water content) was measured as the mean of four measurements per plot at the beginning and end of the project using a FieldScout TDR 300 Soil Moisture Meter.</p> <p>The total number of species found in each site was used as a co-variable in species richness models reflecting the local species pool.</p> <p> </p> <p><em>Data processing</em></p> <p>All species names were checked for synonyms using the national database arter.dk.</p> <p> </p> <p>References:</p> <p> </p> <p>Ejrnæs, R. and H. H. Bruun (2000). "Gradient analysis of dry grassland vegetation in Denmark." Journal of Vegetation Science <strong>11</strong>(4): 573-584.</p> <p>Ellermann, T., R. Bossi, J. Nygaard, J. H. Christensen, P. Løfstrøm, C. Monies, C. Geels, I. E. Nielsen and M. B. Poulsen (2021). Atmosfærisk deposition 2019. NOVANA. Aarhus, Aarhus Universitet, DCE - Nationalt Center for Miljø og Energi.</p> <p>Fredshavn, J., R. Ejrnæs and B. Nygaard (2010). "Teknisk anvisning for kortlægning af terrestriske naturtyper. TA-N3, Version 1.04. Fagdatacenter for Biodiversitet og Terrestriske Naturdata, Danmarks Miljøundersøgelser. 18 s. ."</p> <p>Grime, J. P., J. G. Hodgson and R. Hunt (1989). Comparative plant ecology: a functional approach to common British species. London, Unwin Hyman.</p> <p>Oksanen, J., F. G. Blanchet, R. Kindt, P. Legendre, R. B. O'Hara, G. L. Simpson, P. Solymos, M. H. H. Stevens and H. Wagner (2017). "Package 'vegan': Community Ecology Package. Version 2.4-3. <a href="http://cran.r-project.org/web/packages/vegan/vegan.pdf">http://cran.r-project.org/web/packages/vegan/vegan.pdf</a>."</p> <p> </p> <p>R Core Team (2017). R: A language and environment for statistical computing. Vienna, Austria, R Foundation for Statistical Computing.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.