Find research datasets worth reusing

opencc-by-4.0Jul 2021View details →

Dataset: Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window

Uploaded igor pxp files are the data to generate all figures of the results from our publication in Atmospheric Chemistry and Physics with the name of "Time dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling PMF window" by Chen et al. (2021). This study deployed a novel and advanced source apportionment technique on a dataset measured in Magadino. Rolling PMF allows retrieving more realistic, time-dependent and detailed information of the organic aerosol sources. This work highlights the strength of the rolling PMF mechanism by comparing it with the results derived from conventional seasonal PMF. Overall, this comprehensive interpretation of chemical speciation monitor (ACSM) data could be a role model for similar analyses.

opencc-by-4.0May 2023View details →

Dataset: Country-wide mapping of harvest areas and post-harvest forest recovery using Landsat time series data in Japan

Description: This version of the repository holds an updated dataset of the annual forest disturbance agents/stable land cover maps across Japan using Landsat time series data. This version covers the period for 1985-2023 (1985-2019 in the original dataset). Harvest, Conversion, Thinning, and Other disturbances are mapped as different disturbance agents. The maps cover the entire country of Japan, excluding several isolated islands. The maps are available in compressed GeoTIFF format in Albers Equal Area Conic projection. The maps can be browsed in <a href="https://dulvrq3317.users.earthengine.app/view/japan-harvest-year-1985-2023">Google Earth Engine Apps</a>. Citation: Shimizu, K. and Saito, H. (2021) Country-wide mapping of harvest areas and post-harvest forest recovery using Landsat time series data in Japan. International Journal of Applied Earth Observation and Geoinformation 104: 102555. <a href="https://doi.org/10.1016/j.jag.2021.102555">https://doi.org/10.1016/j.jag.2021.102555</a> Period: 1985 to 2023 annually Spatial resolution: 30m Projection: Albers Equal Area Conic Projection (Two standard parallels: 33N and 44N, Central Meridian: 135E)

opencc-by-4.0Dec 2022View details →

Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches

Raw experimental data associated with the manuscript "Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches", by Sosa-Carrillo and colleagues, bioRxiv, 10.1101/2022.11.02.514931. The code to analyze this raw data and generate the figures for the manuscript is available on GitLab at https://gitlab.inria.fr/InBio/Public/yeastcybersecretion.

opencc-by-4.0Jan 2023View details →

IRIS preprocessed data used in paper "Multi variables time series information bottleneck"

Prprocessed data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code This dataset is created from a public available dataset of observations performed by IRIS, a NASA small explorer mission developed and operated by LMSAL with mission operations executed at NASA Ames Research Center and major contributions to downlink communications funded by ESA and the Norwegian Space Centre. Multiple Time Series of IRIS level 2 data are available <a href="https://iris.lmsal.com/search/">here</a> The selected data was labeled using these definitions: QS: Quiet Sun AR: Active Regions of the Sun FL: Flare A time series is labeled QS when every single time step refer to a quiet sun activity. When a given time series is partially composed of flaring events, the global time series is labeled as FL. The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True Loaded data is then a python dict described bellow. Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=240 representing a range between 2793.8401Å and 2806.02Å). Each sample is given a 'position' which is a list of length 4: position[1] is a string that gives the name of the event position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data Data file info : Type: .npz Size: 11.89GB *** Key: 'data_TR_QS' ndarray data of length 2467 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TR_AR' ndarray data of length 1042 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TR_FL' ndarray data of length 1055 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_VAL_QS' ndarray data of length 325 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_VAL_AR' ndarray data of length 1042 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_VAL_FL' ndarray data of length 714 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TE_QS' ndarray data of length 1428 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TE_AR' ndarray data of length 792 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TE_FL' ndarray data of length 356 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_TR' ndarray data of length 4564 containing np.ndarray of shapes ['various', 240]   *** Key: 'data_VAL' ndarray data of length 2081 containing np.ndarray of shapes ['various', 240] *** Key: 'data_TE' ndarray data of length 2576 containing np.ndarray of shapes ['various', 240]   *** Key: 'position_TR_QS' ndarray data of length 2467 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TR_AR' ndarray data of length 1042 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TR_FL' ndarray data of length 1055 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_VAL_QS' ndarray data of length 325 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_VAL_AR' ndarray data of length 1042 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_VAL_FL' ndarray data of length 714 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TE_QS' ndarray data of length 1428 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TE_AR' ndarray data of length 792 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TE_FL' ndarray data of length 356 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TR' ndarray data of length 4564 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_VAL' ndarray data of length 2081 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}   *** Key: 'position_TE' ndarray data of length 2576 containing ndarray data of length 4 containing mix of types {'ndarray', 'int', 'str'}

opencc-by-4.0Feb 2023View details →

Dataset for "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices"

Dataset for the paper "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices" The dataset contains localization measurements acquired with UWB devices. We compare the proposed localization method, called FlexTDOA, with a classic TDOA implementation, and with TWR-based localization. For more information about the localization methods, please refer to the paper. The dataset contains the measurements necessary to generate all the plots in the paper. For code examples on how to read and plot the data, please check out the associated Github repository: https://github.com/lauraflu/flextdoa If you find the dataset useful, please consider citing our work: <blockquote> Pătru, G. C., Flueratoru, L., Vasilescu, I., Niculescu, D., & Rosner, D. (2023). FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices. IEEE Access. </blockquote>

opencc-by-4.0May 2023View details →

CAELUS: Classification of sky conditions from 1-min time series of global solar irradiance using variability indices and dynamic thresholds

CAELUS, a novel classification algorithm that relies on various thresholds to separate all possible sky conditions into six classes, is presented in Ruiz-Arias and Gueymard (2023, doi: <a href="https://doi.org/10.1016/j.solener.2023.111895">10.1016/j.solener.2023.111895</a>). This dataset was used to develop, validate and benchmark CAELUS. It is made up by 1-min quality-assured observations of global horizontal irradiance (GHI) and diffuse horizontal irradiance at 54 stations of the Baseline Surface Radiation Network (BSRN) archive, which is publicly available (see download instructions in https://bsrn.awi.de/data). The dataset includes 5 years of data per station, except in two of them (Petrolina, Brazil, and Solar Village, Saudi Arabia), combined with other variables that are required to run CAELUS, namely: solar zenith angle (sza), extraterrestrial horizontal solar irradiance (eth), clear-sky GHI (ghics) and GHI in a clean and dry atmosphere (ghicda). In addition, the dataset also provides the sky classification obtained with CAELUS. Further details about CAELUS and the dataset compilation is available in Ruiz-Arias and Gueymard (2023, doi: <a href="https://doi.org/10.1016/j.solener.2023.111895">10.1016/j.solener.2023.111895</a>). A Python implementation of CAELUS is available in https://github.com/jararias/caelus.

opencc-by-4.0Jun 2023View details →

Datasets used in the study "Trends in medication use after the onset of the COVID-19 pandemic in the Republic of Ireland: an interrupted time series study"

This record contains datasets analysed as part of the study "Trends in medication use after the onset of the COVID-19 pandemic in the Republic of Ireland: an interrupted time series study". Two datasets were used, one relating to therapeutic subgroups defined by ATC codes (atc_wide_freq_avg.csv) and one relating to individual medications (drugs_wide_freq_avg.csv). Datasets were collated by combining monthly data reported by HSE Primary Care Reimbursement Services in Ireland relating to dispensing on the General Medical Services scheme at https://www.sspcrs.ie/portal/annual-reporting/ Code used to collate datasets and for data management is included in Stata format (compile_data_export_for_analysis_final.do). The study protocol is available at https://doi.org/10.17605/OSF.IO/B4RTM

opencc-by-4.0May 2023View details →

High resolution and high cadence time series of land surface categories, land use land cover, and land use land cover changes

A prototype of monthly, 10 m resolution land surface categories, land use land cover (LULC) cover, and LULC change maps derived from Sentinel-2 data over three areas within Belgium, Portugal, and Sicily for the period 2018-2020. The LULC and LULC change maps were independently validated by IIASA. All products were generated within the framework of the RapidAI4EO project, funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356. The data description can be found below. The validation report of the LULC and LULC change maps can be found in validation_LULC.pdf and validation_change.pdf, respectively, and the validation dataset can be found in Lesiv et al. (2023). Data description Increasing the cadence of the land cover updates from the typical (multi-)annual to monthly cadence poses several challenges. First, several land cover types are difficult to discriminate without any knowledge of temporal dynamics. For instance, croplands are characterized by a dynamic of vegetation growth and a harvest period (i.e. cycles of bare soil, sparsely vegetated and vegetated periods). This contrasts with grasslands that often lack the harvest period resulting in a bare soil cover. Without this temporal information, it is difficult to distinguish a vegetated cropland field from grassland. Second, phenological changes may introduce a large intra-class variability and thus also confusion between classes. For example, the shedding of leaves during autumn or wilting of herbaceous vegetation in dry summer periods introduces spectral variability within land cover classes. To overcome these challenges, we developed a workflow with two main phases. The first phase aims to map land surface categories (LSC) at a monthly resolution. The next phase uses the resulting monthly LSC probability time series to classify land cover. Land surface category (LSC) These LSC represent basic, observable bio-geophysical properties (categories) of the Earth surface that can be predicted directly from individual monthly composites. LSC classes contain a set of vegetated and non-vegetated surface categories. Discrete LSC classification legend: <table> <tbody> <tr> <td> Map code </td> <td> Land cover class </td> </tr> <tr> <td> 11 </td> <td> Tree (leaf-on) </td> </tr> <tr> <td> 12 </td> <td> Shrubland (leaf-on) </td> </tr> <tr> <td> 13 </td> <td> Grassland </td> </tr> <tr> <td> 14 </td> <td> Woody vegetation (leaf-off) </td> </tr> <tr> <td> 15 </td> <td> Wilted herbaceous vegetation </td> </tr> <tr> <td> 21 </td> <td> Bare/sparse vegetation </td> </tr> <tr> <td> 22 </td> <td> Water </td> </tr> <tr> <td> 24 </td> <td> Built-up </td> </tr> </tbody> </table> In order to predict the LSC, we trained a CatBoost model (Dorogush et al., 2018) using a DEM, spectral bands and vegetation indices, country, the timing (month) of the spectral data, and the pseudo-probability of a U-Net model trained to segment built-up surfaces as input. Labels were derived by post-processing the land cover labels of the ESA WorldCover product (Zanaga et al., 2021). Please note that the collection of these labels was suboptimal, likely having an impact on the LULC and change maps generated in the prototype. Land use land cover After predicting LSC over the three AOI’s, we trained a CatBoost model using the LSC probabilities over a window of one year, country, and the timing (month) as independent variable. The use of LSC probabilities over multiple months allows to incorporate information about dynamics, which is necessary to discriminate some classes (e.g. cropland and grassland or cropland and bare). Similar to the LSC labels, the LULC labels were derived from the ESA WorldCover product v100 (year 2020), resulting in a similar legend system. The use of a moving window approach to predict LULC allows to (i) incorporate temporal information that is necessary to discriminate land cover classes and (ii) is expected to lead to more consistent land cover maps. It however has the disadvantage that (i) no land cover predictions are available at the beginning and the end of the time series and (ii) the timing of the predicted land cover change is not always accurate. To resolve these issues, we applied a post-processing step that compares and integrates the LULC predictions and cleaned LSC predictions. Discrete LC classification legend: <table> <tbody> <tr> <td> Map code </td> <td> Land cover class </td> </tr> <tr> <td> 10 </td> <td> Tree cover </td> </tr> <tr> <td> 20 </td> <td> Shrubland </td> </tr> <tr> <td> 30 </td> <td> Grassland </td> </tr> <tr> <td> 40 </td> <td> Cropland </td> </tr> <tr> <td> 50 </td> <td> Built-up </td> </tr> <tr> <td> 60 </td> <td> Bare/sparse vegetation </td> </tr> <tr> <td> 80 </td> <td> Permanent water bodies </td> </tr> <tr> <td> 90 </td> <td> Herbaceous wetland </td> </tr> </tbody> </table> Land use land cover change Monthly change maps were finally derived from the land cover maps. The pixel values within the change maps represent the percentage of pixels that changed with respect to the previous month over an area of 90x90m. The maps contain values between 0-100, with larger values assigned to larger change patches. A value of 100 indicates that all pixels within an area of 90x90m around the pixel were flagged as change. Files The zip files contain the following data: <ul> <li>lsc.zip: land surface category maps over the three AOI’s</li> <li>lc.zip: LULC maps over the three AOI’s</li> <li>change.zip: change maps over the three AOI’s</li> </ul> These maps are generated for each month over the period 2018-2020 for each of the tiles (see tiles.gpkg for an overview of all tiles). The files names use the following naming convention: “tile-year-month.tif”. References Myroslava Lesiv, Halyna Bun, & Martina Duerauer. (2023). Validation data set on land cover changes for RapidAI4EO project [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7825963                                                                   Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363. Zanaga, D., et al.., 2021. ESA WorldCover 10 m 2020 v100. <a href="https://doi.org/10.5281/zenodo.5571936 ">https://doi.org/10.5281/zenodo.5571936 </a>

openOpenDec 2008View details →

Pond Area Estimates: Nine Study Regions in Alaska for 3 time periods (1950s, 1978-1982, 1999-2001) using remotely sensed images

The data are ArcGIS shapefiles by USGS quadrangle within 9 study regions: Arctic Coastal Plain, Stevens Village area, Yukon Flats, Minto Flats, Denali Flats, Talkeetna, Innoko Flats, Tetlin Flats, and Copper River Basin. Each shapefile polygon represents the shoreline of a pond as visually interpreted from each georectified remotely sensed image. All images were rectified based on at least 25 control points from 1:63 360 USGS digital raster graphics topographic maps using a second-order polynomial with a RMS error of less than one satellite image pixel (30 meters). All closed-basin ponds greater than 0.2 hectares were visually delineated and manually traced as polygons using ArcGIS. Each pond polygon has an ID and Hectares field representing the pond ID and area in hectares for the time period of the remotely sensed image.

openCC (other)Jan 2020View details →

PIE LTER plant biomass associated with marsh sites used in space for time sea level rise study, Rowley, MA.

This dataset contains the mass of dried grasses collected from clip plots in the PIE LTER Space for Time Sea Level Rise study. The space for time study uses an intensive and comprehensive approach to compare low elevation, Spartina alterniflora marsh areas to higher elevation Spartina patens marsh areas. Grasses are sampled in plots from 4 transects per site with five plots per site, arranged from the tidal creek's edge to no more than 150 meters back from the creek. Other related data files include: HTL-RO-ST-MAR-Sites, HTL-RO-ST-MAR-Birds, HTL-RO-ST-MAR-Quads, HTL-RO-ST-MAR-Sediments, HTL-RO-ST-MAR-Bites, HTL-RO-ST-MAR-Sticky, HTL-RO-ST-MAR-Decomp, HTL-RO-ST-MAR-Traps, HTL-RO-ST-MAR-Deep_pitfalls

openCC (other)Jan 2022View details →

PIE LTER herbivory measurement associated with marsh sites used in space for time sea level rise study, Rowley, MA.

This dataset contains aggregated observations of predation and herbivory on tethered bait in each quadrat of sites around the Rowley River and the south side of Sawyer Island at the Plum Island LTER. Measurements consist of the consumption status of tethered squid or kelp pieces as a measure of energy transfer between trophic levels. Pieces were left in the field for five days and observers recorded the status of bait over time as either entirely missing, partially consumed, having scrape marks, or fully intact. These measurements can be used to calculate consumption rates (i.e. energy transfer) over time. Notes include fields discussing any additional observations - e.g., if a stick was found missing.

openCC (other)Dec 2021View details →

PIE LTER fish and crab trap data associated with marsh sites used in space for time sea level rise study, Rowley, MA.

This dataset consists of assessments of crab and fish abundances using traps placed in creeks adjacent to marsh community survey transects at sites around the Rowley River and the south side of Sawyer Island at the Plum Island LTER. At each site, four sets of crab and fish traps were deployed for one week. Traps were sampled daily and all individuals were identified and then returned to the creek away from the area where traps were placed. See HTL-RO-ST-MAR-Sites for site description.

openCC (other)Dec 2021View details →

PIE LTER quadrat percent cover associated with marsh sites used in space for time sea level rise study, Rowley, MA.

To assess community structure, first each 1 square meter is first examined by moving grass around to scan the substrate for mussels, crab burrows, amphipod burrows, and Littorina littorea snails. Researchers then estimates the percent live cover of a variety of plant species, checking against a standard list of species. Species not on the list are also recorded, and, if unknown, for identification in the lab after sampling. Each species has its cover recorded individually, which could lead to greater than 100% cover. Percent cover of bare space, detritus, and wrack are also recorded. Last, Melampus bidentata snails are recorded in 10 cm x 10 cm at each corner of the quadrat.

opencc-by-4.0Mar 2020View details →

Woody Cover Mapping in the Kruger National Park using Sentinel-1 time series and LiDAR data

This data repository presents a workflow to derive woody cover information for the Kruger National Park, South Africa, from freely available Sentinel-1 C-Band time series and LiDAR data (modified from Smit et al. 2016) using machine learning (MLR and Ranger in R). The methodology is described in following publication: Urban, M., K. Heckel, C. Berger, P. Schratz, I.P.J. Smit, T. Strydom, J. Baade & C. Schmullius (2020): Woody Cover Mapping in the Savanna Ecosystem of the Kruger National Park Using Sentinel-1 C-Band Time Series Data. Koedoe. In order to derive woody cover percentage information, download all files into one folder and run the R-Files consecutively from 01_ to 04_. Follow the instruction within each of the R-Files, which are written as comments in the programming code. The data repository consist of the following files: R-Files: 1. Script 1: 01_MLR_tune_spatial_final 2. Script 2: 02_MLR_cross_validation_spatial_final 3. Script 3: 03_MLR_RANGER_train_final 4. Script 4: 04_MLR_prediction_woody_cover_final   Training dataset - ENVI FILE (layerstack of Sentinel-1 VH and VV backscatter between 2016 and 2017 and the woody cover reference derived from the LiDAR data) : 1. S1_A_VH_VV_16_17_lidar   Data for prediction - ENVI FILES (3 example regions in the Kruger National Park): 1. S1_A_VH_VV_16_17_subset_example_Letaba_Rest_Camp 2. S1_A_VH_VV_16_17_subset_example_Lower_Sabie 3. S1_A_VH_VV_16_17_subset_example_Pafuri   Final woody cover maps of the Kruger National Park: 1. xx_woody_cover_map_final.rar (contains final maps in 10m, 30m, 50m and 100m spatial resolution as .tif and a QGIS project)   References: Smit, I.P.J., Asner, G.P., Govender, N., Vaughn, N.R. & Wilgen, B.W. van, 2016, ‘An examination of the potential efficacy of high-intensity fires for reversing woody encroachment in savannas’, Journal of Applied Ecology, 53(5), 1623–1633.

opencc-by-4.0Dec 2020View details →

Semantic Segmentation of Time Series Imagery Using Deep Convolutional Neural Networks: A Case Study of Sandbars in Grand Canyon

This dataset contains imagery used to train and test Deep Convolutional Neural Networks for the purpose of binary semantic segmentation of a time series of oblique imagery capturing sandbar monitoring sites in The Grand Canyon. In addition the scripts needed for removing image distortion, registering, rectifying, and labeling imagery is present.

opencc-by-4.0Dec 2020View details →

Time Series used in the Forecasting Benchmark

This data set contains the time series used in Libra (GitHub: <a href="https://github.com/DescartesResearch/ForecastBenchmark">https://github.com/DescartesResearch/ForecastBenchmark</a> ; CodeOcean: <a href="https://doi.org/10.24433/CO.3240518.v1">https://doi.org/10.24433/CO.3240518.v1</a>). Libra is a forecasting benchmark that automatically evaluates and ranks forecasting methods based on their performance in a diverse set of evaluation scenarios. The benchmark comprises four different use cases, each covering 100 heterogeneous time series taken from different domains.

opencc-by-4.0Oct 2019View details →

Using Blender EEVEE for the Generation of Real Time Background Plate in Green Screen Movie Shots

This talk will introduce you to the use of blender EEVEE for green screen shots for a short movie. Green (and blue) screen shots are notoriously difficult to get the lighting condition right, since the image from the camera is dominated by the bright green background. It is very helpful on the set to see in real time the final composition of the scene with the proper background to make adjustments of the camera and the lighting position and the lighting intensity and color. For the shots a large volume mocap solution (Optitrack) was used to track the movie camera (Arri Alexa) and the transformation data was sent to Blender to animate the virtual camera. The previously laser scanned background was rendered in real time in EEVEE. To combine the camera image and the rendered image a dedicated live-keying hardware was used. The described set-up was used in connection with the research project Virtually Real – Aesthetics and Perception of Virtual Spaces in Film by the Zurich University of the Arts and the University of Bern, funded by the Swiss National Science Foundation.

dryad40/100

Data for: Land use change and coastal water darkening drive synchronous dynamics in phytoplankton and fish phenology on centennial time scales

At high latitudes, the suitable window for timing reproductive events is particularly narrow, promoting tight synchrony between trophic levels. Climate change may disrupt this synchrony due to diverging responses to temperature between e.g. the early life stages of higher trophic levels and their food resources. Evidence for this is equivocal, and the role of compensatory mechanisms are poorly understood. Here, we show how a combination of ocean warming and coastal water darkening drive long-term changes in phytoplankton spring bloom timing in Lofoten Norway, and how spawning time of Northeast Arctic cod responds in synchrony. Spring bloom timing was derived from hydrographical observations dating back to 1936, while cod spawning time was estimated from weekly fisheries catch and roe landing data since 1877. Our results suggest that land use change causing coastal water darkening has gradually delayed the spring bloom up to 1990 after which ocean warming has caused it to advance. The cod appear to track phytoplankton dynamics by timing gonadal development and spawning to maximize overlap between offspring hatch date and predicted resource availability. This finding emphasises the importance of land-ocean coupling for coastal ecosystem functioning, and the potential for fish to adapt through phenotypic plasticity.

opencc-zeroJan 2024View details →

dryad40/100

Testing alternative hypotheses for the decline of cichlid fish in Lake Victoria using fish fossils time series from sediment cores

Lake Victoria is well known for its high diversity of endemic fish species that provide livelihoods for millions of people. The lake garnered widespread attention during the twentieth century as major environmental and ecological changes modified the fish community with the extinction of ~40% of endemic cichlid species by the 1980s. Suggested causal factors include anthropogenic eutrophication, fishing, and introduced non-native species but their relative importance remains unresolved because monitoring data started in the 1970s when changes were already underway. Here, for the first time, we reconstruct two time series, covering the last ~200 years, of fish assemblage using fish teeth preserved in lake sediments. Two sediment cores Lake Victoria (Mwanza Gulf), were subsampled continuously at intra-decadal resolution, and teeth were identified to major taxa: Cyprinoidea, Haplochromini, Mochokidae, and Oreochromini. None of the fossils could be confidently assigned to non-native Nile Perch. Our data show significant decreases in haplochromine and oreochromine cichlid fish abundances began long before Nile Perch's arrival, while cyprinoids have generally been increasing. Our study is the first to reconstruct a time series of fish assemblage in Lake Victoria extending deeper back in time than the past 50 years, helping shed light on processes underlying Lake Victoria's biodiversity loss.

opencc-zeroMar 2024View details →