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62 results for “Anthropogenic impacts”
FIGURE 3 in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 3 Schematic drawing of the ear of Gadus morhua (anterior is to the left): (a) top view of the body showing the location of the ears in the cranial cavity as well as the proximity of the rostral end of the swim bladder to the ear; (b) lateral and (c) top view of the same ear. Each ear is set at an angle relative to the midline of the fish., The otolith organs,, the semicircular canals (enlarged areas are the ampullae regions that contain the sensory cells);, the dense calcarious otolith lying in close proximity to the sensory epithelium (). Also see Figure 4. Fig. © 2018 Anthony D. Hawkins, all rights reserved
FIGURE 5 in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 5 The sensory epithelia of the end organs of the inner ear have numerous mechanoreceptive sensory hair cells. The apical ends of these cells, directed into the lumen of the epithelia, have ciliary bundles (inserts in the figure) consisting of a single kinocilium (longest of the cilia) and graded stereocilia. Bending of the ciliary bundle during sound stimulation results in neurotransmitter release to stimulate the 8th cranial nerve. The sensory cells on the otolith maculae are organized into orientation groups, with all of the cells in each group having their kinocilia in the same general direction. In this typical saccular epithelium (anterior to the left, dorsal to the top), the cilia on the rostral end are oriented rostrally or caudally, while the cells on the caudal end are oriented dorsally and ventrally., The approximate dividing lines between orientation groups)
FIGURE 2 in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 2 Masking in the Gadus morhua and Salmo salar by ambient noise. The thresholds were determined using a pure tone signal at a frequency of 160 Hz. The ambient noise (natural sea noise, augmented by white noise from a loudspeaker) is expressed as the spectrum level at that same frequency (dB re 1 μPa/Hz). Closed symbols, thresholds to natural levels of ambient noise; open symbols, thresholds to anthropogenic noise. n.b., The thresholds in S. salar were only influenced by high noise levels, above the natural ambient levels of noise (data from Hawkins, 1993). Fig. © 2018 Anthony D. Hawkins, all rights reserved
FIGURE 4 A in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 4 A frontal view of the head of Gadus morhua showing a section of the saccule (). The saccular chamber is filled with perilymph and contains the otolith (), which lies close to the sensory hair cells of the epithelium (macula). The hair cells are innervated by the eighth cranial nerve. Fig. © 2018 Anthony D. Hawkins, all rights reserved
FIGURE 1 in An overview of fish bioacoustics and the impacts of anthropogenic sounds on fishes
FIGURE 1 Fish hearing sensitivity (thresholds) obtained under open sea, free-field, conditions in response to pure tone stimuli at different frequencies. The lower the thresholds (y-axis), the more sensitive the fish is to a sound. Thus, Clupea harengus has best hearing of all of these species over a wider range of frequencies. Note that the thresholds in Gadus morhua and C. harengus obtained under quiet conditions may be below natural ambient noise levels, especially at their most sensitive frequencies. In the presence of higher levels of noise, the thresholds would be raised, a phenomenon referred to as masking. Gadus morhua and C. harengus are sensitive to both sound pressure and particle motion, whereas Limanda limanda and Salmo salar are only sensitive to particle motion. The reference level for the particle velocity is based on the level that exists in a free sound field for the given sound pressure level. n.b., For the particle velocity levels in this figure to match the sound pressure levels in a free sound field it is necessary to calculate an appropriate particle velocity reference level. If the standard reference levels are used, then the curves will not match one another and so they are not included here to keep the figure relatively simple. Fig. © 2018 Anthony D. Hawkins, all rights reserved
Data from: A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography
<p>1. The management of sustainable harvest of animal populations is of great ecological and conservation importance. Development of formal quantitative tools to estimate and mitigate the impacts of harvest on animal populations has positively impacted conservation efforts.</p> <p>2. The vast majority of existing harvest models, however, do not simultaneously estimate ecological and harvest impacts on demographic parameters and population trends. Given that the impacts of ecological drivers are often equal to or greater than the effects of harvest, and can covary with harvest, this disconnect has the potential to lead to flawed inference.</p> <p>3. In this study, we used Bayesian hierarchical models and a 43-year capture-mark-recovery dataset from 404,241 female mallards (Anas platyrhynchos) released in the North American midcontinent to estimate mallard demographic parameters. Further, we model the dynamics of waterfowl hunters and habitat, and the direct and indirect effects of anthropogenic and ecological processes on mallard demographic parameters.</p> <p>4. We demonstrate that density-dependence, habitat conditions, and harvest can simultaneously impact demographic parameters of female mallards, and discuss implications for existing and future harvest management models.</p> <p>5. Our results demonstrate the importance of controlling for multicollinearity among demographic drivers in harvest management models, and provide evidence for multiple mechanisms that lead to partial compensation of mallard harvest. We provide a novel model structure to assess these relationships that may allow for improved inference and prediction in future iterations of harvest management models across taxa.</p>
Figure 5 in Study of ecosystem of the Sukhum Bay with emphasis anthropogenic impact, Abkhazian Black Sea coast
Figure 5. Numerical density of ichthyoplankton in vertical catches, ind./m2 (a) and horizontal catches, ind./100 m3 (b):: 1 – goatfish, 2 – horse mackerel, 3 – annular sea bream, 4 –anchovy, 5 – corb
Figure 4 in Study of ecosystem of the Sukhum Bay with emphasis anthropogenic impact, Abkhazian Black Sea coast
Figure 4. Dynamics of holoplankton (а): numerical abundance (N, 103∙ ind./m3), biomass (B, mg/m3) and meroplankton (b): numerical abundance (N, 103∙ ind./m3), biomass (B, mg/m3).
Figure 2 in Study of ecosystem of the Sukhum Bay with emphasis anthropogenic impact, Abkhazian Black Sea coast
Figure 2. Dynamics of phytoplankton (а): numerical abundance (N, 106∙ cell/m3), biomass (B, mg/m3), temperature of seawater (°C) and planktonic ciliates (b): numerical abundance (N, 106∙ ind./m3), biomass (B, mg/m3)
Research data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems"
<p><strong>Research Data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em></strong></p> <p>Dear reader,</p> <p>reasearch data are provided for the research article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em>. The authors hope that the research data allows for a better understanding of the modeling workflow. Questions regarding the modeling approach etc. can be directed to the authors, see contact details below.</p> <p>The research data covers the following files:</p> <ul> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for the n=566 model variants. Subfolders for each model variant and corresponding files are stored in the subfolder 'model_variants'. An overview regarding the set-up of the model variants is presented in the .xlsx spreadsheet 'model_variants_overview.xlsx' in the folder 'model_variants'.</li> <li>Base data files, used as input files to iMOD-WQ (Verkaik et al., 2021), stored in the subfolder 'imod_input'. However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consider the corresponding meta-data files and/or get in touch with one of the authors for further information.</li> <li>Post-processed model output data, which was further used for model evaluation, stored in the subfolder 'model_output'.</li> <li>Figure files and the corresponding .py scripts, stored in the subfolder 'figures'.</li> </ul> <p>Meta-data files are provided with data files in the different subfolders for clarification.</p> <p>iMOD-WQ (Verkaik et al., 2021) input and .run-files were executed on the University Oldenburg High-Performance Cluster 'Rosa', funded by DFG through its Major Research Instrumentation Program, INST 184/225-1 FUGG, and the Ministry of Science and Culture (MWK) of the Lower Saxony State.</p> <p>Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/</p> <p>The DFG is thanked for SALTSA project funding (DFG project number MA 3274/9-1) within the Priority Programme ‘Regional Sea Level Change and Society (SeaLevel)’. Research related to this article further benefited from funding of the projects WAKOS (BMBF; support code 01LR2003E) and the DFG research unit FOR 5094: The dynamic deep subsurface of high-energy beaches (DynaDeep).</p> <p>Literature:</p> <p>Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092.</p> <p>Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/</p> <p>Seibert, S. L., Greskowiak, J., Oude Essink, G. H. P., & Massmann, G. (2024). Understanding climate change and anthropogenic impacts on the salinization of low‐lying coastal groundwater systems. Earth's Future, 12, e2024EF004737. https://doi.org/10.1029/2024EF004737<br><br><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Gualbert H.P. Oude Essink (Gualbert.OudeEssink@deltares.nl) or Gudrun Massmann (gudrun.massmann@uol.de)</p>
Рис. 2. Схема станΔартных промеров раковины Δвустворчатых моΛΛюсков по А. А. Зютину: L — ΔΛина раковины; H — тоΛщина раковины; D — ширина / выпукΛость Fig. 2. Scheme of bivalve mollusk shell standard measurements: L — shell length; H — shell thickness; D — width / convexity (according to A. A. Zyutin) in Morphometric characteristics of Black Sea mussels Mytilus galloprovincialis Lam. as biomarkers of the anthropogenic impact on the Black Sea coastal biocenoses in tourist destinations
Рис. 2. Схема станΔартных промеров раковины Δвустворчатых моΛΛюсков по А. А. Зютину: L — ΔΛина раковины; H — тоΛщина раковины; D — ширина / выпукΛость Fig. 2. Scheme of bivalve mollusk shell standard measurements: L — shell length; H — shell thickness; D — width / convexity (according to A. A. Zyutin)
Рис. 1. Схема распоΛожения станций отбора проб (сервис ЯнΔекс.Карты) Fig. 1. Location of the sampling stations (source: Yandex.Maps) in Morphometric characteristics of Black Sea mussels Mytilus galloprovincialis Lam. as biomarkers of the anthropogenic impact on the Black Sea coastal biocenoses in tourist destinations
Рис. 1. Схема распоΛожения станций отбора проб (сервис ЯнΔекс.Карты) Fig. 1. Location of the sampling stations (source: Yandex.Maps)
Рис. 3. Ливневый сток, прохоΔящий через территорию муниципаΛьного пΛяжа «Маяк» (фото авторов) Fig. 3. Stormwater runoff passing through the territory of "Mayak" municipal beach (photo by the authors) in Morphometric characteristics of Black Sea mussels Mytilus galloprovincialis Lam. as biomarkers of the anthropogenic impact on the Black Sea coastal biocenoses in tourist destinations
Рис. 3. Ливневый сток, прохоΔящий через территорию муниципаΛьного пΛяжа «Маяк» (фото авторов) Fig. 3. Stormwater runoff passing through the territory of "Mayak" municipal beach (photo by the authors)
Figure 2 in Impacts of anthropogenic activities and habitat degradation on breeding waterbirds
Figure 2. Distribution and observation frequency of urbanization, industrialization, pollution, overgrazing, disturbance, and illegal reed cutting and burning threats in 2002.
Data from: A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography
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The impact of anthropogenic disturbance on mycorrhizal fungi and their associations with rodents: insights from a temperate forest in Mexico
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Data from: Past volcanic activity predisposes an endemic threatened seabird to negative anthropogenic impacts
<p>Humans are regularly cited as the main driver of current biodiversity extinction, but the impact of historic volcanic activity is often overlooked. Pre-human evidence of wildlife abundance and diversity are essential for disentangling anthropogenic impacts from natural events. Réunion Island, with its intense and well-documented volcanic activity, endemic biodiversity, long history of isolation and recent human colonization, provides an opportunity to disentangle these processes. We track past demographic changes of a critically endangered seabird, the Mascarene petrel <em>Pseudobulweria aterrima</em>, using genome-wide SNPs. Coalescent modeling suggested that a large ancestral population underwent a substantial population decline in two distinct phases, ca. 125,000 and 37,000 years ago, coinciding with periods of major eruptions of Piton des Neiges. Subsequently, the ancestral population was fragmented into the two known colonies, ca. 1,500 years ago, following eruptions of Piton de la Fournaise. In the last century, both colonies declined significantly due to anthropogenic activities, and although the species was initially considered extinct, it was rediscovered in the 1970s. Our findings suggest that the current conservation status of wildlife on volcanic islands should be firstly assessed as a legacy of historic volcanic activity, and thereafter by the increasing anthropogenic impacts, which may ultimately drive species towards extinction.</p>
Surface Ocean Biogeochemistry Regulates the Impact of Anthropogenic Aerosol Fe Deposition on the Cycling of Iron and Iron Isotopes in the North Pacific
<p>Monthly-average (year 2014) netcdf output files of NEMO/PISCES Fe isotope model experiments on the ORCA2 grid for experiments with updated iron deposition fields; "ptrc" files contain model outputs for heavy and light iron tracers, "diad" files contain diagnostic model outputs for iron uptake (of light and heavy iron), primary production (for diatoms and nanophytoplankton), and iron deposition fluxes (from dust, anthropogenic, and fire sources).</p>
Figure 1 in Study of ecosystem of the Sukhum Bay with emphasis anthropogenic impact, Abkhazian Black Sea coast
Figure 1. The map of sampling survey in the port of Sukhum.
Data from: Environmental DNA metabarcoding revealed the impacts of anthropogenic activities on phytoplankton diversity in Dianchi Lake and its three inflow rivers
<p>Phytoplankton diversity is closely related to environmental variables and has been widely used in ecological health assessment of rivers and lakes. Combining the advantages of DNA-based identification and high-throughput sequencing technology, environmental DNA (eDNA) metabarcoding permits a new measurement for biodiversity monitoring in aquatic ecosystems. However, it had rarely been used to explore the variability and similarity of phytoplankton diversity between a lake and its inflow rivers and the effects of environmental variables on phytoplankton. This study utilized eDNA metabarcoding to investigate the spatial distribution of phytoplankton and the impacts of environmental variables on the phytoplankton diversity in Dianchi Lake (one of the most polluted urban lakes in China) and its main inflow rivers (Panlong River, Baoxiang River and Chai River). The OTU table is the original table about phytoplankton taxa. A total of 243 distinct phytoplankton taxa were detected, covering 9 phyla, 30 classes, 84 orders and 132 families, and the taxonomic richness of rivers was higher than that of Dianchi Lake.</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.