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40 results for “ecosystem condition”
Data from: A systematic approach to evaluate the influence of environmental conditions on eDNA detection success in aquatic ecosystems
The use of environmental DNA (eDNA) to determine the presence and distribution of aquatic organisms has become an important tool to monitor and investigate freshwater communities. The successful application of this method in the field, however, is dependent on the effectiveness of positive DNA verification, which is influenced by site-specific environmental parameters. Factors affecting lower eDNA concentrations in aquatic ecosystems include flow conditions, and the presence of substances that possess DNA-binding properties or inhibitory effects. In this study we investigated the influence of different environmental parameters on the detection success of eDNA using the invasive goby Neogobius melanostomus. In a standardized laboratory setup, different conditions of flow, sediment-properties, and fish density were compared, as well as different potential natural inhibitors such as algae, humic substances, and suspended sediment particles. The presence of sediment was mainly responsible for lower eDNA detection in the water samples, regardless of flow-through or standing water conditions and a delayed release of eDNA was detected in the presence of sediment. Humic substances had the highest inhibitory effect on eDNA detection followed by algae and siliceous sediment particles. The results of our study highlight that a successful application of eDNA methods in field surveys strongly depends on site-specific conditions, such as water flow conditions, sediment composition, and suspended particles. All these factors should be carefully considered when sampling, analyzing, and interpreting eDNA detection results.
Supplementary material 7 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 7
Supplementary material 4 from: Nedkov S, Zhiyanski M, Dimitrov S, Borisova B, Popov A, Ihtimanski I, Yaneva R, Nikolov P, Bratanova-Doncheva S (2017) Mapping and assessment of urban ecosystem condition and services using integrated index of spatial structure. One Ecosystem 2: e14499. https://doi.org/10.3897/oneeco.2.e14499
Contains descriptions of urban ecosystem subtypes and their relation to EUNIS habitat classess
Supplementary material 3 from: Nedkov S, Zhiyanski M, Dimitrov S, Borisova B, Popov A, Ihtimanski I, Yaneva R, Nikolov P, Bratanova-Doncheva S (2017) Mapping and assessment of urban ecosystem condition and services using integrated index of spatial structure. One Ecosystem 2: e14499. https://doi.org/10.3897/oneeco.2.e14499
Map of urban ecosystem condition representing an example of map sheets that cover the whole country
Supplementary material 2 from: Kokkoris I, Dimopoulos P, Xystrakis F, Tsiripidis I (2018) National scale ecosystem condition assessment with emphasis on forest types in Greece. One Ecosystem 3: e25434. https://doi.org/10.3897/oneeco.3.e25434
Diversity Shannon/index
Data and Code Supporting Hagy et al. Quantifying coastal ecosystem condition and a trophic state index with a Bayesian analytical framework
<p>The code includes several R markdown files and an associated file containing R functions. The main body of code <em>coastal_TSI_ts.Rmd</em> loads the function file located in the functions folder. Raw data is included as .csv files in the raw directory and various data sets created as intermediate products are in the data folder. Variable definitions are included in a data dictionary.</p> <p>The code has been used to generate the analysis reported in</p> <p>Hagy, JD, B. Kreakie, M. Pelletier, F. Nojavan, J. Kiddon, and A. Oczkowski. Quantifying coastal ecosystem condition and a trophic state index with a Bayesian analytical framework</p> <p>This file contains a zip of a github repository, https://github.com/USEPA/-cTSI</p>
Supplementary material 8 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 8
Supplementary material 2 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 2
Supplementary material 9 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 9.
Supplementary material 5 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 5
Supplementary material 6 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 6
Supplementary material 3 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 3
Supplementary material 1 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 1.
Supplementary material 4 from: Tanács E, Bede-Fazekas Á, Csecserits A, Kisné Fodor L, Pásztor L, Somodi I, Standovár T, Zlinszky A, Zsembery Z, Vári Á (2022) Assessing ecosystem condition at the national level in Hungary - indicators, approaches, challenges. One Ecosystem 7: e81543. https://doi.org/10.3897/oneeco.7.e81543
Appendix 4
Data from: Trophic and non-trophic interactions influence the mechanisms underlying biodiversity–ecosystem functioning relationships under different abiotic conditions
Open the record for dataset details and reuse information.
Data from: A systematic approach to evaluate the influence of environmental conditions on eDNA detection success in aquatic ecosystems
Open the record for dataset details and reuse information.
Water use partitioning of native and non-native tree species in riparian ecosystems under contrasting climatic conditions
<p>We aimed at evaluating water-source partitioning between native and non-native tree species coexisting in central Spain floodplains; determining the dependency on drought stress of such water-sources-use; and assessing if the reliance on deeper water sources relates with physiological and growth performance. We assessed water-uptake depth, leaf functional traits related to physiological performance and growth of native (<em>Populus alba</em>) and non-native trees <em>(Ailanthus altissima, Robinia pseudoacacia</em>) coexisting in riparian forests under different drought conditions (drier, intermediate and wetter). We analyzed δ<sup>2</sup>H and δ<sup>18</sup>O isotopes in xylem water and in soil water from top, mid and deep soil depths and determined the contribution of each water source to plant xylem water. Leaf traits related with resource use and secondary growth were assessed for each species.</p>
H2O-F - A PROSPECTIVE, MULTI-COUNTRY, MULTI-CENTRE, MULTI-CONDITION FEASIBILITY STUDY EVALUATING THE ESTABLISHMENT OF THE NATIONAL HEALTH OUTCOMES OBSERVATORY (H2O) ECOSYSTEM
ClinicalTrials.gov study NCT06833892. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Grassland ecosystem responses to elevated CO2 are contingent on nitrogen conditions
GEO Series GSE67531. uncultured bacterium; Bacteria. 48 samples. Type: Other.
Seascape context matters more than habitat condition for fish assemblages in coastal ecosystems
<p>Data collect for the a project between University of the Sunshine Coast and Healthy Land and Water for manuscript <em>Seascape context matters more than habitat condition for fish assemblages in coastal ecosystems</em></p>
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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.