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803 results for “population (urban)”
LAGOS-US HUMAN v2: Data module of human population(1990-2020), urbanization classification, and lake access in the conterminous U.S.
The LAGOS-US HUMAN v1 data package is an extension module of the LAGOS-US research platform that includes data characterizing human population (population count, race, ethnicity, socioeconomic information), urbanization, and lake access of 479,950 lakes larger than or equal to 1 ha in the conterminous U.S. (48 states plus the District of Columbia). This data module contains four data tables linked through the unique lake identifier for the LAGOS-US research platform, lagoslakeid. Human population characteristics (race, ethnicity, and socioeconomic factors) were derived from U.S. census data for 1990, 2000, 2010, and 2020. Lakes were classified as urban or not using two different classifications: one based on the ‘Developed’ land category in the National Land Cover Dataset; and another based on the 2020 Census Urban Areas category. Metrics for lake access were developed from national datasets on public boat launches, transportation, and public lands. LAGOS-US HUMAN v1 provides a link between lake data and human contexts, facilitating interdisciplinary research in limnology, urban ecology, environmental justice, and conservation. To facilitate such studies, users are encouraged to use the other three core data modules of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds); GEO (geospatial ecological context at multiple spatial and temporal scales); and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.
Rural population count at 1 km for 2000-2020 based on WorldPop and GHS-SMOD urbanization level
<p>Rural population count at 1 km grid in EPSG:4326 for 2000-2020 (annual). This is only an estimate of the rural population. This probably misses many rural areas, especially in the tropics. The maps were derived using two data sources:</p> <ol> <li><a href="https://hub.worldpop.org/geodata/listing?id=64">WorldPop population counts at 1 km</a>;</li> <li><a href="https://human-settlement.emergency.copernicus.eu/download.php?ds=smod">GHS-SMOD urbanization levels at 1 km</a>;</li> </ol> <p>Rural population is estimated using the following translation rules for GHS-SMOD (note: these are arbitrary rules based on the GHS-SMOD documentation):</p> <ul> <li>Class 30: “Urban Centre grid cell” = 0% rural</li> <li>Class 23: “Dense Urban Cluster grid cell” = 0.5% rural</li> <li>Class 22: “Semi-dense Urban Cluster grid cell” = 2% rural</li> <li>Class 21: “Suburban or per-urban grid cell” = 15% rural</li> <li>Class 13: “Rural cluster grid cell” = 95% rural</li> <li>Class 12: “Low Density Rural grid cell” = 100% rural</li> <li>Class 11: “Very low density rural grid cell” = 100% rural</li> </ul> <p>The nighttime images are based on: <a href="https://doi.org/10.5281/zenodo.7750174">https://doi.org/10.5281/zenodo.7750174</a></p> <ul> <li>Schiavina, Marcello; Melchiorri, Michele; Pesaresi, Martino (2023): GHS-SMOD R2023A - GHS settlement layers,<br>application of the Degree of Urbanisation methodology (stage I) to GHS-POP R2023A and GHS-BUILT-S R2023A,<br>multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) [Dataset] doi:<br>10.2905/A0DF7A6F-49DE-46EA-9BDE-563437A6E2BA PID: <a href="http://data.europa.eu/89h/a0df7a6f-49de-46ea-%209bde-563437a6e2ba">http://data.europa.eu/89h/a0df7a6f-49de-46ea-</a><br><a href="http://data.europa.eu/89h/a0df7a6f-49de-46ea-%209bde-563437a6e2ba">9bde-563437a6e2ba</a></li> </ul>
Data and code from: "Building multidimensional tolerance landscapes to predict the population dynamics of bacteria exposed to antibiotics in urban sewers"
<p>City sewers harbor diverse bacterial communities exposed to various antibiotic residues resulting from human consumption and excretion. Although these residues typically occur at sub-inhibitory concentrations, they can still impact the growth rate and yield of susceptible wastewater bacteria. Many bacteria exhibit antibiotic tolerance through transient phenotypic changes. Antibiotic residues, combined with complex environmental factors like temperature and salinity, especially in coastal cities, contribute to non-additive interactions that modulate antibiotic tolerance and affect population dynamics.</p> <p>To better understand these interactions, we developed continuous multivariate tolerance landscapes for three bacterial species: <strong><em><span>Escherichia coli</span></em></strong>, the emerging pathogen <strong><em><span>Streptococcus suis</span></em></strong>, and the sewer-inhabiting <strong><em><span>Arcobacter cryaerophilus</span></em></strong>. We modeled their intrinsic growth rates and carrying capacities across complex environments, incorporating temperature, salinity, and concentrations of two antibiotics (ciprofloxacin and azithromycin).<span> Using</span> these multivariate tolerance curves, we predicted microbial population dynamics in two sewers of Barcelona, highlighting the importance of environmental complexity in shaping microbial responses to antibiotic stressors.</p> <p> </p> <p><strong>Usage</strong></p> <p>Users can perform the analysis by running the R script (TC3D.R) after the installation of all</p> <p>package mentioned in the preamble,<span> </span></p> <p>This folder contains:</p> <p>- 3 datasets with OD measures for the 3 species:</p> <p><span> </span>* data_acrya.xlsx</p> <p><span> </span>* data_ecoli.xlsx</p> <p><span> </span>* data_ssuis.xlsx</p> <p>- 1 excel files with metadata (plate, well, species, environmental conditions)</p> <p><span> </span>* map_plate_all.xlsx</p> <p>- 4 datasets giving time series of the flow and several measures including <span> </span>conductivity and <span> </span>temperaturefor 2 sewers of Barcelona obtained from sample cabines <span> </span>set during the implementation of SCOREWATER (ID:820751)</p> <p><span> </span>* carmel_flow.csv</p> <p><span> </span>* carmel_quality.csv</p> <p><span> </span>* poblenou_flow.csv</p> <p><span> </span>* poblenou_quality.csv</p> <p>- 1 C++ script compiled and run with the R TMB package:</p> <p><span> </span>* fit_growth_r_K_SS_treatment.cpp : computes the negative loglikelihood for r and K, and state DOs, given the observed DO, for the populations under one same environmental treatment (salinity * temperature * antibiotic), and computes the density-dependence parameter alpha from r and K using the Delta Method.</p> <p><br><br></p>
A survey of scorpion (Scorpiones) populations along an urbanization gradient in the greater Phoenix metropolitan area, Arizona, USA (summer 2019)
The goal of this research project was to evaluate how scorpion populations responded to the gradient of urbanization. We conducted 50 night-time walking transects across the gradient of urbanization from downtown Phoenix to nearby wildland areas during Summer 2019. We commonly documented three scorpion species. Data present whether a species was detected at a site during this time period.
Figure 3 in Population structure of a native and an alien species of snail in an urban area of the Atlantic Rainforest
Figure 3. Detectability probability (A), abundance (B) and recruitment (C) estimated for Achatina fulica during the study. The error bars show 90% confidence intervals.
Genomic footprints of (pre) colonialism: Population declines in urban and forest túngara frogs coincident with historical human activity
<p>Urbanisation is rapidly altering ecosystems, leading to profound biodiversity loss. To mitigate these effects, we need a better understanding of how urbanisation impacts dispersal and reproduction. Two contrasting population demographic models have been proposed that predict that urbanisation either promotes (facilitation model) or constrains (fragmentation model) gene flow and genetic diversity. Which of these models prevails likely depends on the strength of selection on specific phenotypic traits that influence dispersal, survival, or reproduction. Here, we a priori examined the genomic impact of urbanisation on the Neotropical túngara frog (<em>Engystomops pustulosu</em>s), a species known to adapt its reproductive traits to urban selective pressures. Using whole-genome resequencing for multiple urban and forest populations we examined genomic diversity, population connectivity and demographic history. Contrary to both the fragmentation and facilitation models, urban populations did not exhibit substantial changes in genomic diversity or differentiation compared to forest populations, and genomic variation was best explained by geographic distance rather than environmental factors. Adopting an a posteriori approach, we additionally found both urban and forest populations to have undergone population declines. The timing of these declines appears to coincide with extensive human activity around the Panama Canal during the last few centuries rather than recent urbanisation. Our study highlights the long-lasting legacy of past anthropogenic disturbances in the genome and the importance of considering the historical context in urban evolution studies as anthropogenic effects may be extensive and impact non-urban areas on both recent and older timescales. </p>
Rapid evolutionary divergence of a songbird population following recent colonization of an urban area
<p>Colonization of a novel environment by a small group of individuals can lead to rapid evolutionary change, yet evidence of the relative contributions of neutral and selective factors in promoting divergence during the early stages of colonization remain scarce. Here, we use genome-wide SNP data to test the role of neutral and selective forces in driving the divergence of a unique urban population of the Oregon junco (<em>Junco hyemalis oreganus</em>), which became established on the campus of the University of California at San Diego (UCSD) in the early 1980s. Previous studies based on microsatellite loci documented significant genetic differentiation of the urban population as well as divergence in sexual signaling and life-history traits relative to nearby montane populations. However, the geographic origin of the colonization and the factors involved in the onset of the differentiation process remained uncertain. Our genome-wide SNP dataset confirmed the marked genetic differentiation of the UCSD population, and phylogenomic analysis identified the coastal subspecies <em>pinosus</em> from central California as its sister group instead of the neighboring mountain population. Demographic inference based on site frequency spectra recovered a time of separation from <em>pinosus</em> as recent as 20 to 32 generations, and a strong bottleneck at the time of colonization, suggesting a relevant role of founder effects and drift in the genetic differentiation of the UCSD population. However, we also found significant associations between environmental parameters characterizing the urban habitat of UCSD and genome-wide variants linked to functional genes. Some of the identified gene functions, like heavy metal detoxification and high-pitched hearing, have been reported as potentially adaptive in birds inhabiting urban environments. These results suggest that the interplay between founder events and directional selection may result in rapid shifts in both neutral and adaptive loci across the genome, and reveal the UCSD population of juncos as an ongoing case of divergence following the colonization of an anthropic environment.</p>
Potential local adaptation in populations of invasive reed canary grass (Phalaris arundinacea) across an urbanization gradient
<p>Urban stressors represent strong selective gradients that can elicit evolutionary change, especially in non-native species that may harbor substantial within-population variability. To test whether urban stressors drive phenotypic differentiation and influence local adaptation, we compared stress responses of populations of a ubiquitous invader, reed canary grass (Phalaris arundinacea). Specifically, we quantified responses to salt, copper, and zinc additions by reed canary grass collected from four populations spanning an urbanization gradient (natural, rural, moderate urban and intense urban). We measured ten phenotypic traits and trait plasticities, because reed canary grass is known to be highly plastic and because plasticity may enhance invasion success. We tested the following hypotheses: 1) source populations vary systematically in their stress response, with the intense urban population least sensitive and the natural population most sensitive, and 2) plastic responses are adaptive under stressful conditions. We found clear trait variation among populations, with the greatest divergence in traits and trait plasticities between the natural and intense urban populations. The intense urban population showed stress tolerator characteristics for resource acquisition traits including leaf dry matter content and specific root length. Trait plasticity varied among populations for over half the traits measured, highlighting that plasticity differences were as common as trait differences. Plasticity in root mass ratio and specific root length were adaptive in some contexts, suggesting that natural selection by anthropogenic stressors may have contributed to root trait differences. Reed canary grass populations in highly urbanized wetlands may therefore be evolving enhanced tolerance to urban stressors, suggesting a mechanism by which invasive species may proliferate across urban wetland systems generally.</p>
A gridded dataset on population densities, real estate prices, transport and land use inside 192 worldwide urban areas
<p>This dataset provides, on a systematic basis, gridded population densities, rents, real estate prices, and transport times (both in<br> public transport and private car) in 192 cities across the world.</p>
IuliaMargineanGitHub/Projecting-Heat-Stress-Vulnerable-Populations-at-Intra-Urban-Scales: Projecting heat stress vulnerable populations at intra-urban scales
<p>This repository provides the data and scripts necessary for full reproduction of results, as well as example datasets that were used for data generation and analysis in the manuscript Marginean et al., "High-resolution Modelling and Projecting Local Dynamics of Differential Vulnerability to Urban Heat Stress".</p> <p>The folders in this repository contain the following:</p> <ol> <li>The input datasets: <ul> <li>shares of educational attainment in 2012 and 2020, by sex, age group and census tract</li> <li>internal migration by sex, age group and census tracts</li> <li>decadal mortality and fertility, by sex, age group, scenario, and census tract</li> </ul> </li> <li>Projected decadal data (2012 to 2050) by sex, age group, scenario and census tract</li> <li>Scripts for generating high resolution projections for three Shared Socioeconomic Pathways (SSPs): one script for SSP 2 projections and one scripts for SSP 1 and SSP 3 projections</li> <li>Example input data to reconstruct the projections in SSP 1, 2 and 3 for females ages 25 to 64</li> </ol>
Figure 1 in Perceptions of the Andean condor in the urban population of Ecuador
Figure 1. Study site, highlights the sierra region of Ecuador and the Antisana National Park (Antisana N. P.).
Figura 2 in Perceptions of the Andean condor in the urban population of Ecuador
Figura 2. Modelo logístico que explica la probabilidad de identificación del cóndor andino por parte de la población urbana del Ecuador. P = probabilidad; línea gris = mujeres; línea negra = hombres.
Figure 6 in Far from urban areas: plastic uptake in fish populations of subtropical headwater streams
Figure 6. Median plastic particle distribution in fishes per sample site (whiskers = min - max values, dots = outliers, stars = extreme outliers, horizontal line = median, box = 50% tile).
Figure 3 in Far from urban areas: plastic uptake in fish populations of subtropical headwater streams
Figure 3. Plastic particles abundances in benthic and water column feeders (whiskers = min - max values, dots outliers, stars extreme outliers, horizontal line median, box 50% tile).
Figure 1. Study area. A. South America and Brazil. B in Far from urban areas: plastic uptake in fish populations of subtropical headwater streams
Figure 1. Study area. A. South America and Brazil. B. Brazil and the state of Rio Grande do Sul. C. Rio Grande do Sul and the Sinos River Basin. D. The numbers from 1 to 7 in the white dots show the sampling sites in the upper section of the Sinos River basin. The colour gradient represents the terrain elevation (light green elevations of 30m altitude and dark brown elevations of 980m). The red polygons are the urban areas.
Figure 5 in Far from urban areas: plastic uptake in fish populations of subtropical headwater streams
Figure 5. Total abundances of food items per category in comparison with ingested plastic particles abundances. (Pla=Pastics, Dip=Diptera, Hem=Hemiptera, Tri=Tricoptera, Lep=Lepidoptera, Eph=Ephemeroptera, Ple=Plecoptera, Col=Coleoptera, Gas=Gastropoda, Odo=Odonata, Veg= Plant).
Figure 2. The adult female with a in Different shades of snake: Peculiar coloration in an urban population of the Grass Snake, Natrix natrix (Linnaeus, 1758
Figure 2. The adult female with a peculiar blue coloration. From the top to bottom: blue coloration present at the margin of the last row of dorsal scales and ventral scales. The white underside of the throat is clearly visible; Dorsal view of the individual; The ventral color switches from white to black towards the tail and the blue color intensifies.
Figure 1. A in Different shades of snake: Peculiar coloration in an urban population of the Grass Snake, Natrix natrix (Linnaeus, 1758
Figure 1. A melanistic individual (top) and an individual from the subspecies N. n. persa (middle). A common occurring individual with black spots behind the head (bottom) captured at the locality.
Fig. 1 in Borrelia miyamotoi infection in Apodemus spp. mice populating an urban habitat (Warsaw, Poland)
Fig. 1. Scheme of the study area (city of Warsaw, Poland) showing the arrangement of mice-trapping locations. Black points – locations where B. miyamotoi infected mice were present; White points – locations where none of the captured mice were B. miyamotoi infected; N1–N3 – locations within northern suburbs; C1–C5 - locations within city centre; S1–S2 – locations within southern suburbs; numbers in boxes – B. miyamotoi prevalence in mice inhabiting respective areas.
Figure 1 in Ecological impact and population status of non-native bees in a Brazilian urban environment
Figure 1 Bipartite network and non-native bees sampled in Curitiba. a) Bipartite network, non-native plant and bees colored, b) Anthidium manicatum, female; c) Distributional range of A. manicatum (SpeciesLink); d) Melipona scutellaris worker on Calliandra brevipes; e) Distributional range of M. scutellaris (SpeciesLink), natural records in green.
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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)
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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.