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Youth Baseline & Endline
<p>These datasets are part of EC Horizon 2020 project REFUGE-ED Effective practices in education, mental health and psychosocial support for the integration of refugee children (101004717).</p> <p>It corresponds to the results obtained from the questionnaires applied to migrant and refugee children and young people.</p>
A SSP1-Low emission land use scenario based on LCM2019 for Scotland - baseline 2019 and scenario 2050 (nov22)
<p>This set of datasets contains a land use change scenario (2050) for Scotland within the scope of a SSP1 - Low emissions scenario (Shared Socio-Economic Pathways). For achieving a low-emission scenario, simulated land use change targeted woodland expansion (including silvo-arable and silvo-pastoral) and decreased grazing intensity, both land use changes also aimed at benefitting four aspects of ecosystem services: carbon storage through tree planting, emission reduction through deintensification, biodiversity enhancement through tree planting, and pollination to support food production.</p> <p>The baseline dataset is based on the Land Cover Map 2019 (Morton et al, 2020) aggregated at 100m resolution. Grazing intensity was added to it by using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). From the baseline dataset, the land use scenario map was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). The attached land use scenario map for 2050 is not an optimised result, but it is only one possibility that meets all the constraints stipulated for the scenario.</p> <p><strong>For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a></strong></p> <p>This analysis was conducted as part of the Land use Transformations (<a href="https://landusetransformations.hutton.ac.uk/">https://landusetransformations.hutton.ac.uk/</a>) project (JHI-C3-1) in the Scottish Government funded Strategic Research Programme 2022-27.</p> <p> </p> <p><strong>-------------------------</strong></p> <p><strong>Datasets accessible here : <a href="https://openscience.hutton.ac.uk/dataset/low-emission-land-use-scenarios">SSP1-Low Emission Land Use Scenarios - Dataset - Natural Asset Register Data Portal (hutton.ac.uk)</a></strong></p> <p><strong>License</strong>: CC BY-NC 4.0 namely “Creative Commons Attribution-NonCommercial 4.0 International“ <a name="_Hlk161153952"></a>(https://creativecommons.org/licenses/by-nc/4.0/)</p> <p><strong>Copyright to display of the datasets</strong>: <br>“Contains Data owned by UK Centre for Ecology & Hydrology © Database Right/Copyright UKCEH. Based on Data from LPIS and JAC (Scottish Government, 2019).”</p> <p><strong>2 Main files :</strong></p> <ul> <li><strong>SSP1LEonLCM19_2019.tif </strong>: original land uses (2019) on which the scenario is based on. This land use map, of a resolution of 100m, is based on the Land Cover Map 2019 (Morton et al, 2020), estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021).<br><br><strong>Contributions </strong>to the baseline dataset (SSP1LEonLCM19_2019.tif) : <ul> <li>100% of 100m cells: Land Cover Map 2019 (Morton et al, 2020)</li> <li>66.84% of 100m cells: the LCM 2019 was subdivided by grazing intensity using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This impacts the grasslands, heathers, bogs and arable classes.</li> <li>Estimated overall contributions: 90% UKCEH, 10% JHI</li> </ul> </li> </ul> <ul> <li><strong>SSP1LEonLCM19_2050.tif</strong> : land use scenario (2050), which is within the scope of a SSP1 - Low emissions scenario (Shared Scocio-Economic Pathways). The scenario was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a><br><br><strong>Contributions</strong> to the scenario dataset (SSP1LEonLCM19_2050.tif) : <ul> <li>cf. contribution to the baseline (above)</li> <li>14% of 100m cells: modelled land use change</li> <li>Estimated overall contributions: 85% UKCEH, 15% JHI</li> </ul> </li> </ul> <p> </p> <p><strong>Main references:</strong></p> <p>Morton, R. D., Marston, C. G., O’Neil, A. W., & Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, GB) [Data set]. NERC Environmental Information Data Centre. <a href="https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC">https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC</a></p> <p>Wardell-Johnson, D. (2022) Stocking rates derived from IACS 2019 version 4. <br>Based on data from Land Parcel Information System (2019) courtesy of Rural Payments and Inspections Division, Scottish Government.<br>Based on data from the June Agricultural Census (2019) courtesy of Rural and Environment Science and Analytical Services, Agricultural Statistics team, Scottish Government.</p> <p>Chapman, P. (2007) Conservation Grazing of Semi-natural Habitats. Technical note TN586. SAC tn586-conservation.pdf (sruc.ac.uk)</p> <p>FAS (2021) Practical Guide: Managing Peatlands and Upland Habitats. <a href="https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/">https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/ </a>(author: Paul Chapman)</p> <p>Castellazzi, M.S.; Gimona, A. (2021) SLM-OptionsTool, a land use change tool for Ecosystem Services (arcgis toolbox and user manual included, part of RESAS Deliverable-O1.4.2ciiD27).</p> <p>Castellazzi, M.S., Matthews, J., Angevin, F., Sausse, C., Wood, G.A., Burgess, P.J., Brown I., Conrad, K.F., Perry J.N. (2010). Simulation scenarios of spatio-temporal arrangement of crops at the landscape scale . Environmental Modelling and Software 25, 1881-1889. <a href="https://doi.org/10.1016/j.envsoft.2010.04.006">https://doi.org/10.1016/j.envsoft.2010.04.006</a> </p> <p><a href="https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts">https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts</a></p>
Lower baseline immunity in invasive Egyptian Goose compared to sympatric native waterfowls
<p>Successful invasive species out-compete native species for vital resources. To increase their spreading success, invasive species need to trade off nutritional and metabolic resources allocated to reproduction and range expansion with other costly body functions. One proposed mechanism for the reallocation of resources is a trade-off with the immune function. According to the evolution of the increased competitive ability hypothesis, a reduced investment in immunity would be favoured by parasite loss in colonised habitats. It is also suggested that invaders would reallocate resources among different immune effectors depending on the new pathogens they encounter in the new habitat. Reallocation of resources may also involve the regulation of oxidative status given its fundamental link with the immune system. Relying on a panel of blood-based markers of immune function and oxidative status quantified in an invasive species (Egyptian Goose) and two native competing species (Mallard and Mute Swan) in Germany, we tested the hypothesis that the invasive species would have a lower investment in immune function compared to the native species. We predicted lower levels of baseline immune markers associated with systemic inflammatory response, and higher levels of certain low-cost immunological effectors (e.g. humoral effectors) in the invasive species compared to the two native species. If Egyptian Geese reduced their investment in immune function, we would also expect that geese generated less oxidative damage and had a lower expression of antioxidant defences than native species. We found lower levels of several immune markers associated either with inflammatory response or humoral innate and adaptive immunity in the invasive species compared to the two native species. The bacteria-killing ability of plasma was the only immune marker that was upregulated in Egyptian Geese. Markers of oxidative status were higher in Mallards compared to the other species. The results of our study point to an overall reduced investment in immune function in the invasive species as a possible energy-saving immunological strategy due to the loss of parasites in the newly colonised habitats, as observed in a previous study. Thus, a lower investment in immune function may benefit other energy-demanding activities, such as reproduction, dispersal, and territoriality.</p>
Meteorological Data from Chios: June 2024 Baseline Measurements for the MUSICA Project
<h2><strong>June 2024 – Chios (Chiostown)</strong></h2> <h3>Introduction</h3> <p>The present meteorological data is collected from the weather station in Chiostown, located in Chios, and is published on the Zenodo platform for open access. The station is positioned at an elevation of 32 meters, and the data includes measurements of temperature, rainfall, wind speed, and wind direction, covering the period from June 1st to June 30th, 2024. These measurements are conducted as part of the <strong>MUSICA</strong> project, which aims to monitor climate changes in the Chiostown area and the broader region of Chios. The data for June 2024 captures the onset of summer, with rising temperatures and the absence of rainfall, reflecting typical seasonal patterns for the region.</p> <p>The files include:</p> <ul> <li><strong>Date and time of recording</strong>: For accurate time tracking of the data.</li> <li><strong>Temperature</strong>: Daily average, maximum, and minimum temperatures in degrees Celsius (°C).</li> <li><strong>Rainfall</strong>: Daily rainfall in millimeters (mm).</li> <li><strong>Wind speed</strong>: Average and maximum daily wind speed in kilometers per hour (km/h).</li> <li><strong>Wind direction</strong>: The prevailing wind direction of the day.</li> </ul> <h3>Data Highlights for June 2024</h3> <ul> <li><strong>Highest temperature</strong>: 36.7°C, recorded on June 13th, 2024, at 16:50.</li> <li><strong>Lowest temperature</strong>: 16.6°C, recorded on June 1st, 2024, at 04:00.</li> <li><strong>Total rainfall</strong>: No rainfall was recorded throughout June 2024.</li> <li><strong>Highest wind speed</strong>: 53.1 km/h, recorded on June 21st, 2024, at 16:20.</li> </ul> <h3>Data Usage</h3> <p>The data is free to use. Users are welcome to download, analyze, and utilize the data for personal, educational, or research purposes, as well as for developing applications and tools that contribute to understanding and addressing weather and climate phenomena.</p>
Meteorological Data from Kardamyla, Chios: July 2024 Baseline Measurements for the MUSICA Project
<p>The analysis of climatological data is crucial for understanding weather patterns in a region. July 2024 was characterized by intense summer conditions in Kardamyla, featuring hot days, minimal rainfall, and steady winds. The following data, collected throughout the month, provides valuable insights into the observed weather phenomena.</p> <h4><strong>Temperature:</strong></h4> <ul> <li>Average temperature: <strong>28.7°C</strong>.</li> <li>Highest temperature: <strong>37.8°C</strong> on July 18.</li> <li>Lowest temperature: <strong>18.9°C</strong> on July 2.</li> <li>Days with temperatures ≥ 32°C: <strong>26 days</strong>.</li> </ul> <h4><strong>Rainfall:</strong></h4> <ul> <li>Total rainfall: <strong>4.8 mm</strong>, all recorded on July 4.</li> <li>Days with rainfall > 0.2 mm: <strong>1 day</strong>.</li> </ul> <h4><strong>Wind:</strong></h4> <ul> <li>Average wind speed: <strong>6.0 km/h</strong>.</li> <li>Maximum wind speed: <strong>48.3 km/h</strong> on July 3.</li> <li>Dominant wind direction: <strong>North (N)</strong>.</li> </ul> <h4><strong>Conclusion:</strong></h4> <p>July was marked by hot temperatures with notable peaks, limited rainfall, and steady winds primarily from the north. The region experienced intense thermal conditions, especially after mid-July, reinforcing the image of a dry and hot summer.</p>
Meteorological Data from Chios: July 2024 Baseline Measurements for the MUSICA Project
<p>July 2024 presented intense summer conditions in Chios Town, with persistent high temperatures, no rainfall, and steady winds. The data collected throughout the month showcases the extreme summer weather experienced in the region.</p> <h4><strong>Temperature:</strong></h4> <ul> <li>Average temperature: <strong>30.4°C</strong>.</li> <li>Highest temperature: <strong>39.0°C</strong> on July 18.</li> <li>Lowest temperature: <strong>22.1°C</strong> on July 3 and 4.</li> <li>Days with temperatures ≥ 32°C: <strong>28 days</strong>.</li> </ul> <h4><strong>Rainfall:</strong></h4> <ul> <li>Total rainfall: <strong>0.0 mm</strong>.</li> <li>Days with rainfall: <strong>None</strong>.</li> </ul> <h4><strong>Wind:</strong></h4> <ul> <li>Average wind speed: <strong>10.9 km/h</strong>.</li> <li>Maximum wind speed: <strong>54.7 km/h</strong> on July 6, 9, and 15.</li> <li>Dominant wind direction: <strong>North (N)</strong>.</li> </ul> <h4><strong>Conclusion:</strong></h4> <p>July was characterized by extremely high temperatures, no precipitation, and persistent winds from the north. This weather profile reinforces the intensity of summer in the region, highlighting the need for heat and wind monitoring during such periods.</p>
Meteorological Data from Kardamyla, Chios: November 2024 Baseline Measurements for the MUSICA Project
<p>November 2024 exhibited a clear shift to winter conditions in Kardamyla. With lower temperatures, increased rainfall, and occasionally strong winds, the month marked a transition to cooler weather. The following analysis provides detailed insights into the climatic trends observed during November.</p> <h4><strong>Temperature:</strong></h4> <ul> <li>Average temperature: <strong>14.4°C</strong>.</li> <li>Highest temperature: <strong>23.1°C</strong> on November 21.</li> <li>Lowest temperature: <strong>4.7°C</strong> on November 25 and 27.</li> <li>Days with temperatures ≥ 32°C: <strong>0 days</strong>.</li> </ul> <h4><strong>Rainfall:</strong></h4> <ul> <li>Total rainfall: <strong>51.2 mm</strong>.</li> <li>Maximum daily rainfall: <strong>17.2 mm on November 16</strong>.</li> <li>Days with rainfall > 0.2 mm: <strong>10 days</strong>.</li> </ul> <h4><strong>Wind:</strong></h4> <ul> <li>Average wind speed: <strong>8.6 km/h</strong>.</li> <li>Maximum wind speed: <strong>70.8 km/h</strong> on November 21.</li> <li>Dominant wind direction: <strong>North (N)</strong>.</li> </ul> <h4><strong>Conclusion:</strong></h4> <p>November brought significant changes, with cooler temperatures and consistent rainfall, signaling the onset of winter. Strong winds from the north occasionally dominated, reflecting the more dynamic weather patterns typical of the season.</p>
Meteorological Data from Kardamyla, Chios: September 2024 Baseline Measurements for the MUSICA Project
<p>September 2024 marked the transition from the intense heat of summer to milder conditions. Significant rainfall was recorded, including one day of heavy precipitation, while temperatures remained relatively moderate. The data provides insights into the climatic behavior of the region during this month.</p> <h4><strong>Temperature:</strong></h4> <ul> <li>Average temperature: <strong>23.9°C</strong>.</li> <li>Highest temperature: <strong>32.8°C</strong> on September 5.</li> <li>Lowest temperature: <strong>14.1°C</strong> on September 25 and 26.</li> <li>Days with temperatures ≥ 32°C: <strong>3 days</strong>.</li> </ul> <h4><strong>Rainfall:</strong></h4> <ul> <li>Total rainfall: <strong>62.8 mm</strong>.</li> <li>Days with rainfall > 0.2 mm: <strong>3 days</strong>.</li> <li>Day with maximum rainfall: <strong>62.2 mm on September 11</strong>.</li> </ul> <h4><strong>Wind:</strong></h4> <ul> <li>Average wind speed: <strong>6.5 km/h</strong>.</li> <li>Maximum wind speed: <strong>53.1 km/h</strong> on September 10 and 13.</li> <li>Dominant wind direction: <strong>Northwest (NNW)</strong>.</li> </ul> <h4><strong>Conclusion:</strong></h4> <p>September exhibited milder temperatures compared to the preceding months, with significant rainfall on September 11 distinguishing it from the dry summer. Winds remained steady, predominantly from the northwest, emphasizing the seasonal shift.</p>
Meteorological Data from Kardamyla, Chios: October 2024 Baseline Measurements for the MUSICA Project
<p>October 2024 reflected the gradual shift to autumn conditions in Kardamyla. With cooler temperatures, no rainfall, and steady winds predominantly from the north, the month was characterized by stable weather patterns. Below is an analysis of the observed data for October, shedding light on the climatic trends of the region.</p> <h4><strong>Temperature:</strong></h4> <ul> <li>Average temperature: <strong>19.9°C</strong>.</li> <li>Highest temperature: <strong>30.1°C</strong> on October 6.</li> <li>Lowest temperature: <strong>9.2°C</strong> on October 27 and 28.</li> <li>Days with temperatures ≥ 32°C: <strong>0 days</strong>.</li> </ul> <h4><strong>Rainfall:</strong></h4> <ul> <li>Total rainfall: <strong>0.0 mm</strong>.</li> <li>Days with rainfall: <strong>None</strong>.</li> </ul> <h4><strong>Wind:</strong></h4> <ul> <li>Average wind speed: <strong>8.6 km/h</strong>.</li> <li>Maximum wind speed: <strong>53.1 km/h</strong> on October 17.</li> <li>Dominant wind direction: <strong>North (N)</strong>.</li> </ul> <h4><strong>Conclusion:</strong></h4> <p>October marked the end of the dry season, with mild to cool temperatures and no rainfall. The north winds remained dominant, often reaching significant speeds, reflecting the stable yet shifting weather patterns typical for autumn in Kardamyla.</p>
Meteorological Data from Kardamyla, Chios: August 2024 Baseline Measurements for the MUSICA Project
<p>August 2024 continued the trend of high temperatures but with slightly milder intensity compared to July. The absence of rainfall, consistent winds, and ongoing drought painted a typical summer landscape for Kardamyla. The following data reflects the weather conditions observed during the month.</p> <h4><strong>Temperature:</strong></h4> <ul> <li>Average temperature: <strong>27.6°C</strong>.</li> <li>Highest temperature: <strong>37.1°C</strong> on August 15.</li> <li>Lowest temperature: <strong>18.4°C</strong> on August 2.</li> <li>Days with temperatures ≥ 32°C: <strong>18 days</strong>.</li> </ul> <h4><strong>Rainfall:</strong></h4> <ul> <li>Total rainfall: <strong>0.0 mm</strong>.</li> <li>Days with rainfall: <strong>None</strong>.</li> </ul> <h4><strong>Wind:</strong></h4> <ul> <li>Average wind speed: <strong>5.6 km/h</strong>.</li> <li>Maximum wind speed: <strong>43.5 km/h</strong> (observed twice, on August 10 and 24).</li> <li>Dominant wind direction: <strong>North (N)</strong>.</li> </ul> <h4><strong>Conclusion:</strong></h4> <p>August maintained typical summer conditions with hot days, clear skies, and mostly northern winds contributing to a sense of coolness. The absence of rainfall highlights the need to monitor drought conditions that may impact the region.</p>
Classification results extracted by the baseline RFSS+SI+GLCM and U-Net models.
<p>Selected indicative cases demonstrate (A) S2_12-12-20_16PCC_6, (B) S2_22-12-20_18QYF_0, (C) S2_27-1-19_16QED_14 and (D) S2_14-9-18_16PCC_13 patches on test set.</p>
A Brand-New and High-Quality Baseline Climatology Surface for China (ChinaClim_baseline)
<p>Incorporation satellite-driven data (TRMM3B43 and MODIS LST) and more 2000 weather stations in optimal TPS interpolation, ChinaClim_baseline is a brand-new and high-quality baseline climatology surface for China at spatial resolution of 1km. The data includes average monthly precipitation and temperatures (average, maximum and minimum temperature) over the most recent three full decades (1981-2010). The scale factor of precipitation and temperatures are 0.01 and 0.1, respectively.</p>
Prediction of the broadcasting model and various baselines
<p>The ground truth values, and the predictions from the broadcasting model and the baselines (SC and CMAQ) in the submission <em>Development of an LSTM-Broadcasting deep-learning framework for regional air pollution forecast improvement</em>. The namelists of the WRF and CMAQ models are also included.</p>
DIHnamic proyect RCT Baseline questionnaire
<p>The DIHnamic Project has carried out under the framework of a European initiative that aims to provide innovation support to European agencies as key intermediary agents with small and medium-sized enterprises (SMEs). Innovation in the said agencies is becoming increasingly relevant, with the arrival of new business models and emerging technologies, having to adapt and innovate the way in which support to SMEs is delivered.</p> <p>In addition, this programme has aimed to analyse with an evidence-based approach, using methodologies that provide a rigorous impact measurement, such as RCTs.</p> <p>The impact of this project has been analysed through a Randomised Control Trial, which is a requisite for the evaluation of all projects funded under this call. </p> <p>The baseline information started to be collected since the publication of the results of the call for proposals, where each company was assigned to one of the two services. Filling in the baseline questionnaire was a requisite to accept the support. </p> <p>The baseline and follow-up questionnaires were completed through an online platform set up for this purpose. A protocol for its correct application was also provided in which it was specified that, regardless who completed the online questionnaire, it should have been validated and consolidated by the company's management team, as a key agent in decision-making on digitalisation, and a common agent for all companies.</p>
Where have all the lions gone? Establishing realistic baselines to assess decline and recovery of African lions
<p><strong>Aim</strong>: Predict empirically the current and recent-historical (c. 1970) landscape connectivity and population size of the African lion as a baseline against which to assess conservation of the species.</p> <p><strong>Location</strong>: Continental Africa.</p> <p><strong>Methods</strong>: We compiled historical records of lion distribution to generate a recent-historical range for the species. Historical population size was predicted using a generalised additive model. Resistant kernel and factorial least-cost path analyses were used to predict recent-historical landscape connectivity and compare this with contemporary connectivity at continental, regional and country scales.</p> <p><strong>Results</strong>: We estimate a baseline population of ~92,054 (83,017 – 101,094 95% CI) lions in c1970, suggesting Africa's lion population has declined by ~75%, over the last five decades. Although greatly reduced from historical extents (c. 1500 AD), recent-historical lion habitat was substantially connected. However, in comparison, contemporary population connectivity has declined dramatically, with many populations now isolated, as well as large declines within remaining population core areas. This decline was most marked in the West and Central region, with a 90% decline in connected habitat, and in the <span>Congo Basin </span>where connected habitat is reduced to 17% of its c. 1970 level. The Eastern and Southern regions have experienced lower, though significant, declines in connected habitat (44% and 55% respectively). Contemporary populations are connected by three non-core habitat linkages and 15 potential corridors (spanning unconnected habitat) that may allow dispersal and gene flow. Declining connectivity mirrors recent studies showing loss of genetic diversity and increasing genetic isolation of lion populations.</p> <p><strong>Main conclusions</strong>: We provide an empirically derived baseline for African lion population size, habitat extent and connectivity in c. 1970 and at present against which to evaluate contemporary conservation of the species, avoiding a shifting baseline syndrome where conservation success/failure is measured only against recent population size or range. We recommend priorities for conservation of existing habitat to avoid further fragmentation.</p>
Multivariate curve resolution -alternating least squares augmented with partial least squares baseline correction applied to mid-IR laser spectra resolves protein denaturation by reducing rotational ambiguity
<p>This record contains mid-IR spectra of a titration of beta-lactoglobulin with two detergents. Spectra are stored in the form of Matlab .mat files. More details on the data and data processing can be found in the associated publication.</p><ul><li>F_smooth: Surfactant calibration spectra</li><li>initial_estimate_4c: Intitial estimate of spectra used for MCR-ALS</li><li>protein_spectra_new: Protein spectra obtained by subtracting the background surfactant titration spectra (surftitrationnewwvcorr) from the protein</li><li>titration spectra (prtntitrationnewwvcorr)</li><li>tit_concat_new_mcr: Input for standalone MCR-ALS</li><li>true_conc_sds_nis_pr_cal: Concentrations of SDS and C12E8 as well as protein used in training data for PLS</li></ul>
Data from: Challenging trophic position assessments in complex ecosystems: calculation method, choice of baseline, trophic enrichment factors, season and feeding guild do matter. A case study from Marquesas Islands coral reefs
<p>Assessments of ecosystem functioning are a fundamental ecological challenge and an essential foundation for ecosystem-based management. An understanding of species trophic positions (TP) is essential to characterize food web architecture. However, despite the intuitive nature of the concept, empirically estimating TP is a challenging task due to the complexity of trophic interaction networks. Various alternative methods are proposed to assess TPs, including different approaches to account for the different sources of organic matter at the base of the food web (the "baseline"). However, it is often not clear which methodological approach and which baseline choices are the most reliable. Using an ecosystem-wide assessment of a tropical reef (Marquesas Islands, French Polynesia, with available data for 70 coral reef invertebrate and fish species), we tested whether different commonly used TP estimation methods yield similar results and, if not, whether it is possible to identify the most reliable method. We found significant differences in TP estimates of up to 1.7 TP for the same species, depending on the method and the baseline used. When using bulk stable isotope data, the choice of the baseline significantly impacted TP values. Indeed, while δ<sup>15</sup>N values of macroalgae led to consistent TP estimates, those using phytoplankton generated unrealistically low TP estimates. The use of a conventional enrichment factor (i.e. 3.4 ‰) or a "variable" enrichment factor (i.e. according to feeding guilds) also produced clear discrepancies between TP estimates. Regarding the use of different calculation methods, TPs obtained with δ<sup>15</sup>N values of source amino acids (compound specifics isotope analysis) were close to those assessed with macroalgae, but evidenced the opposite seasonal pattern, with significantly lower TPs in winter than in summer for the majority of assessed species, with particularly pronounced differences for lower TP species. We use the observed differences to discuss possible drivers of the diverging TP estimates and the potential ecological implications.</p>
Data for: Bivalve δ15N isoscapes provide a baseline for urban nitrogen footprint at the edge of a World Heritage coral reef
<p>This dataframe presents the d<sup>15</sup>N signature of 348 long-lived benthic bivalves from 12 species. Individuals were trapped at 27 sites in 2012 around the Peninsula of Nouméa, New Caledonia. For each bivalve specimen, muscle tissues were dissected and stored frozen. Muscles were freeze-dried, ground into powder, and weighed in tin cups for isotopic analysis (about 1mg in 4 × 6 mm tin cups). Muscle samples were analyzed using a Thermo Delta Advantage mass spectrometer in continuous flow mode connected to a Costech Elemental Analyzer via a ConFlo IV at Union College (Schenectady, NY, USA). Ammonium sulfate [IAEA-N-2], caffeine [IAEA-600], and an in-house acetanilide were used as standards; measurements of δ<sup>15</sup>N are reported to atmospheric nitrogen. The uncertainty for δ<sup>15</sup>N measurements was ±0.15‰ based on repeated analysis of an in-house acetanilide standard.</p> <p>The data base is made of 348 raws corresponding to specimens, and 5 columns presenting an ID, the collection site, the position within the laggon , the species and the measured D15N value. </p>
Fig. 5 in Herpetofauna diversity in Zamrud National Park, Indonesia: baseline checklist for a Sumatra peat swamp forest ecosystem
Fig. 5. Principal Component Analysis (PCA) of the distribution of herpetofauna species.
Fig. 2 in Herpetofauna diversity in Zamrud National Park, Indonesia: baseline checklist for a Sumatra peat swamp forest ecosystem
Fig. 2. Locations of the survey sites in Zamrud National Park.
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.