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300 results for “Urban area”
Long-term monitoring of stormwater runoff and water quality in urbanized watersheds of the greater Phoenix metropolitan area, ongoing since 2008
Urbanization alters dramatically watershed ecosystem processes. Land-use change and anthropogenic activities contribute to increased inputs of nutrients and other materials, while changes to land cover alter hydrology and the corresponding movement of materials. These changes have ramifications for both watershed processes and downstream systems. The impacts of urbanization on aquatic systems are well-studied, and frequently encapsulated in the ‘urban stream syndrome’ (Walsh et al. 2005) that describes, among others, increased nutrient loading and stream flashiness. However, there is some evidence that aridland cities behave differently (Grimm et al. 2004, 2005), and the complex dynamics among catchment characteristics, storm attributes, and runoff in highly urbanized settings of the arid Southwest remains poorly understood. To enhance our understanding of stormwater dynamics and watershed functioning in aridland, urban environments, the Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) program began monitoring stormwater runoff at the outflow of the Indian Bend Wash (IBW) in 2008. The IBW is a tributary to the Salt River in central Arizona, and is a major drainage within the greater Phoenix metropolitan area, encompassing much of the City of Scottsdale. A model of soft engineering, the IBW as it runs through much of the City of Scottsdale is comprised largely of a series of artificial lakes, parks, paths, golf courses, ball fields, and other non-structural elements designed with the dual roles of providing outdoor amenities to the City residents while serving as an effective flood water conveyance feature. A unique biogeochemistry of this novel system is detailed by Roach et al. (2008), and Roach and Grimm (2011). Stormwater sampling is conducted at numerous locations. The longest running sampling location is near the outflow of the IBW ~0.6 km above its confluence with the Salt River. The sampling location coincides with a permanent USGS gauging sta
Stormwater Nitrogen in Arizona (SNAZ): runoff and stormwater-mediated export from urbanized catchments within the greater Phoenix metropolitan area, Arizona, USA (2010-2012)
Urbanization alters dramatically watershed ecosystem processes. Land-use change and anthropogenic activities contribute to increased inputs of nutrients and other materials, while changes to land cover alter hydrology and the corresponding movement of materials. These changes have ramifications for both watershed processes and downstream systems. The impacts of urbanization on aquatic systems are well-studied, and frequently encapsulated in the ‘urban stream syndrome’ (Walsh et al. 2005) that describes, among others, increased nutrient loading and stream flashiness. However, there is some evidence that aridland cities behave differently (Grimm et al. 2004, 2005), and the complex dynamics among catchment characteristics, storm attributes, and runoff in highly urbanized settings of the arid Southwest remains poorly understood. To enhance our understanding of stormwater dynamics and watershed functioning in aridland, urban environments, the Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) program began monitoring stormwater runoff at the outflow of the Indian Bend Wash (IBW) in 2008. The IBW is a tributary to the Salt River in central Arizona, and is a major drainage within the greater Phoenix metropolitan area, encompassing much of the City of Scottsdale. A model of soft engineering, the IBW as it runs through much of the City of Scottsdale is comprised largely of a series of artificial lakes, parks, paths, golf courses, ball fields, and other non-structural elements designed with the dual roles of providing outdoor amenities to the City residents while serving as an effective flood water conveyance feature. A unique biogeochemistry of this novel system is detailed by Roach et al. (2008), and Roach and Grimm (2011). Data and expertise garnered by stormwater monitoring near the outflow of the IBW helped pave the way for a more expansive stormwater research effort facilitated by a leveraged grant from the National Science Foundation (DEB-0918457, NSF Eco
Wildlife in urban neighborhoods of the greater Phoenix, Arizona metropolitan area: patterns that span a social-ecological gradient in 2021
Wildlife communities are structured by numerous ecological filters in cities that influence their populations, and some species even manage to thrive in urban landscapes. CAP researchers were the first to observe “the luxury effect”, the hypothesis that biodiversity is positively related to income of residents. The luxury effect is still being tested worldwide twenty years later and has led to important new research on other socio-demographic factors that shape biodiversity but are vastly understudied, such as race and ethnicity, as well as the interaction of these factors with urban structural inequalities that may be hidden by income. This research aims to unpack the luxury effect by considering other landscape and socio-demographic factors that may influence wildlife communities across neighborhoods of metro Phoenix. Specifically, we are investigating if neighborhood income and ethnicity independently influence mammal occupancy in neighborhoods across the CAP ecosystem. To answer this question, we leveraged a wildlife camera array across CAP within community parks, in which cameras are placed across a gradient of average median household income and percent Latinx of residents. Incorporating socioeconomic data into urban mammal research will allow for the advancement in the understanding of socio-ecological patterns.
Urban Heat and Desert Wildlife: Rodent Body Condition Across a Gradient of Surface Temperatures in the greater Phoenix, Arizona (USA) metropolitan area (2019-2020)
We live-trapped wild rodents from seven field sites spanning three strata of land-surface temperatures in the Phoenix, Arizona (USA) metropolitan area. We captured 116 adult pocket mice (Chaetodipus spp. and Perognathus spp.) and Merriam’s kangaroo rats (Dipodomys merriami) during 2019 and 2020 from mountainous urban parks and open spaces. Animal body condition was quantified as percent body fat (i.e., fat mass divided by body mass). We used a noninvasive quantitative magnetic resonance instrument to measure body condition.
Urban habitat features and patterns of snake removals in the greater Phoenix, Arizona (USA) metropolitan area (March 2021 - March 2022)
In urban and suburban areas, wildlife and people are often in close quarters, leading to human-wildlife interactions (HWI). Understanding how wildlife interact with humans and the built environment is critical as urbanization contributes to habitat change and fragmentation globally. In our study, we partnered with a local business that removes and relocates snakes from homes and businesses in the Phoenix area. The most frequently removed were venomous (family Viperidae, e.g., rattlesnakes) and nonvenomous (family Colubridae, e.g., gophersnakes) snakes. Using these records, we investigated taxa-specific habitat trends at two spatial scales. The neighborhood scale focused on front yard measures of cover and vegetation classes and the landscape scale focused on variables related to vegetation indices and degree of urbanization. Both analyses compared areas where snakes were removed to random locations in the city to represent possible habitat available to snakes. At the neighborhood scale (n=60), we found that removals occurred in yards with abundant cover opportunities. At the landscape scale (n=764), we found species-specific differences with nonvenomous snakes removed from areas of higher urbanization compared to venomous snakes. Understanding these distinct habitat patterns in residential yards can identify areas with potential human-snake conflict.
Urban forest canopy cover, vegetation, and site characteristics, Twin Cities Metro Area, 2022 and 2023.
This data was primarily collected to assess forest quality within the Minneapolis-St. Paul (MSP) Metropolitan Area and to link above-ground and below-ground properties as part of the goals of the MSP-LTER Urban Tree Canopy research group. Here, we sampled vegetation on 48 circular plots with a 12.5 m radius distributed across 18 parks, registering the date of sampling, park and management agency names, the plot number, and geolocation (latitude, longitude, and elevation). The plots were randomly selected based on GEDI (Global Ecosystem Dynamics Investigation instrument) 2021 footprints in the MSP Metropolitan Area along accessible forested areas inside public parks, where the management agency allowed sampling. In each plot, we measured forest structure and diversity metrics, species names and abundance, DBH, height, distance from the plot center, the height where each individual canopy starts, and the relative position, exposure, and density of each canopy. We also measured understory plant structure and diversity in 4 subplots per plot, totaling 192 subplots. In these subplots, we surveyed all individual plants with heights over 20 cm, recording species names and abundance, plant basal diameter, plant height, and the total number of branches. Furthermore, we assessed the canopy openness above each subplot by calculating percent DIFN (diffuse non-interceptance) from fish eye pictures of the canopy at 1.3 meters over the subplot.
Urban Ecological Infrastructure (UEI) in the greater Phoenix, Arizona metropolitan area and surrounding Sonoran desert region (2010-2017)
Urban ecological infrastructure (UEI) encompasses all infrastructure in a city that supports ecological structure and function, and by extension, provides ecosystem services to urban residents and is a broad, all-encompassing concept for "nature in cities". This idea includes commonly recognized forms of infrastructure, such as parks, residential yards, community gardens, lakes and rivers, and street trees. But UEI also includes less recognized forms, such as vacant lots, agricultural fields, canals, and water retention basins. Despite being widely recognized as important to urban landscapes, the wide variety, and various forms of urban ecological infrastructure are rarely documented in a single source. To address this, we consolidated various aquatic, terrestrial, and wetland UEI throughout the Phoenix Metropolitan area so researchers can incorporate this UEI into project designs and models. Since people’s perceptions of UEI differ not only by the three broad classifications but also by the individual characteristics of UEI, each feature is classified not only as aquatic, terrestrial, or wetlands but also given on of fifteen unique classifications. Incorporation of UEI into both planning and research design can promote practices that increase both biodiversity and human well-being while also possibly limiting negative landscape perceptions.
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of s
Share and spatial concentration of social housing in Dutch urban areas
<p>This dataset contains the amount of social housing units of the Netherlands per urban area, as well as the intensity of their spatial autocorrelation and its proportion compared to the total housing stock, for the year 2023. <a href="https://www.cbs.nl/nl-nl/dossier/nederland-regionaal/geografische-data/kaart-van-100-meter-bij-100-meter-met-statistieken">Original data</a> comes from Statistics Netherlands (<em>Centraal Bureau voor de Statistiek</em>) released for 100 m x 100 m grid cells covering a large share of the Dutch territory. Grid cells with missing values were excluded from the analysis. The spatial autocorrelation of social housing was calculated with urban area-level and U-style computations of Global Moran's I based on the share of social housing units compared to the total housing stock of every grid cell. Limits and definition of urban areas are extracted from <a href="https://www.oecd.org/en/data/datasets/oecd-definition-of-cities-and-functional-urban-areas.html">the OECD</a>. Data show considerable variation in the levels of social housing and its spatial concentration among Dutch urban areas.</p>
Short-Term Synchronous and Asynchronous Ambient Noise Tomography in Urban Areas: Application to Karst Investigation
<p>We used DSurfTomo (<a href="https://github.com/HongjianFang/DSurfTomo">HongjianFang/DSurfTomo: Direct inversion of surface dispersion data based on ray tracing (github.com)</a>) for the tomography.</p> <p>ABC2_2023.dat is the travel time of C1 and C2 cross-correlation functions, used in our tomography.</p> <p>ManualDSurfTomoV1.3.pdf is the manual of DSurfTomo, including the data format description for ABC2_2023.dat.</p> <p>yunqiVs3D.txt is the interpolated 3D Vs model, including longitude, latitude, depth (meter), Vs (m/s).</p> <p>Previous version error: I forgot to write the Vs value.</p>
Hyperspectral imagery Research Products - Toulouse urban area 2015 (French ANR HYEP project)
<p>The HYEP project (ANR 14-CE22-0016-01) main goal was to propose a panel of methods and processes designed for hyperspectral imaging, which specificity makes a weighty auxiliary for the monitoring of the elements of the urban area. The main results of the project can be found at</p> <ul> <li><a href="http://doi.org/10.1080/01431161.2017.1410247">G. Roussel, C. Weber, X. Briottet and X. Ceamanos, "Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution", International Journal of Remote Sensing, vol. 39(5), pp. 1593-1614, 2018.</a></li> <li><a href="http://doi.org/10.1109/ECMSM.2017.7945884">F. Z. Benhalouche, M. S. Karoui, Y. Deville, I. Boukerch, A. Ouamri, ``Multi-sharpening hyperspectral remote sensing data by multiplicative joint-criterion linear-quadratic nonnegative matrix factorization'', Proceedings of the 2017 IEEE International Workshop on Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM 2017), May 24-26, 2017, Donostia - San Sebastian</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01903469">Gintautas Mozgeris, Vytaut ̇e Juodkien ̇e, Donatas Jonikaviˇcius, Lina Straigyt ̇e, S ́ebastien Gadal, and Walid Ouerghemmi. Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment. Remote Sensing, 10(10), October 2018.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-02281003">Christiane Weber, Thomas Houet, S ́ebastien Gadal, Rahim Aguejdad, Grzegorz Skupinski, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Cl ́ement Mallet, and Arnaud Le Bris. HYEP HYperspectral imagery for Environmental urban Planning : principaux résultats. In 7ème colloque scientifique du groupe SFPT-GH, Toulouse, France, July 2019. ONERA - SFTP.</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01852844">Christiane Weber, Rahim Aguejdad, Xavier Briottet, Josselin Aval, Sophie Fabre, Jean Demuynck, Emmanuel Zenou, Yannick Deville, Moussa Sofiane Karoui, Fatima Zohra, Sébastien Gadal, Walid Ouerghemmi, Clément Mallet, Arnaud Le Bris, and Nesrine CHEHATA. Hyperspectral Imagery for Environmental Urban Planning. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2018, pages 1628–1631, Valencia, Spain, July 2018a. IEEE.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01854904">Christiane Weber, Rahim Aguejdad, X Briottet, J Avala, S. Fabre, J Demuynck, E Zenou, Y. Deville, M. Karoui, F Z Benhalouche, S Gadal, W Ourghemmi, C. Mallet, A. Le Bris, and N. Chehata. HYPERSPECTRAL IMAGERY FOR ENVIRONMENTAL URBAN PLANNING. In IGARSS 2018, Valencia, Spain, 2018b. </a></li> <li><a href="http://doi.org/10.5194/isprs-archives-XLII-1-W1-167-201">W. Ouerghemmi, A. Le Bris, Nesrine CHEHATA, and Clément Mallet. A two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, volume XLII-1/W1, pages 167–174, Hanover, Germany, May 2017. Copernicus GmbH (Copernicus Publications).</a></li> <li><a href="https://hal.inria.fr/hal-02384455">Christiane Weber, Sébastien GADAL, Xavier Briottet, and Clément Mallet. Apport de l’imagerie hyperspectrale pour la planification urbaine. In Karine Emsellem, Diego Moreno, Christine Voiron-Canicio, and Didier Josselin, editors, SAGEO 2016 - Spatial Analysis and Geomatics, Actes de la conférence SAGEO’2016 - Spatial Analysis and GEOmatics, pages 454–462, Nice, France, December 2016. </a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01359643">Gintautas Mozgeris, S ́ebastien Gadal, Donatas Jonikaviˇcius, Lina Straigyte, Walid Ouerghemmi, and Vytaut ̇e Juodkiene. Hyperspectral and color-infrared imaging from ultra-light aircraft: Potential to recognize tree species in urban environments. In University of California Los Angeles, editor, 8th Workshop in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pages 542–546, Los Angeles, United States, August 2016.</a></li> <li><a href="https://hal.inria.fr/hal-02384458">Alexandre Hervieu, Arnaud Le Bris, and Cl ́ement Mallet. Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas. In ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, volume III-3, pages 457–464, Prague, Czech Republic, July 2016.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01888126">Christiane Weber, Thomas Houet, Sebastien GADAL, Rahim Aguejdad, Grzegorz Skupinski, Aziz Serradj, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Clément Mallet, and Arnaud Le Bris. ANR HYEP ANR 14-CE22-0016-01Hyperspectral imagery for Environmental urban Planning HyepProgramme Mobilité et systèmes urbains 2014. Research report, CNRS UMR TETIS, ESPACE, LETG ; ONERA ; GIPSA-lab ; IRAP ; IGN, October 2018c. </a></li> <li><a href="https://doi.org/10.1080/01431161.2019.1579937">Josselin Aval, Sophie Fabre, Emmanuel Zenou, David Sheeren, Mathieu Fauvel & Xavier Briottet (2019) Object-based fusion for urban tree species classification from hyperspectral, panchromatic and nDSM data, International Journal of Remote Sensing, 40:14, 5339-5365, DOI: 10.1080/01431161.2019.1579937 </a></li> <li><a href="https://doi.org/10.3390/rs11111269">Charlotte Brabant, Emilien Alvarez-Vanhard, Achour Laribi, Gwenaël Morin, Kim Thanh Nguyen et al. Comparison of Hyperspectral Techniques for Urban Tree Diversity Classification Remote Sensing, MDPI, 2019, 11 (11), pp.1269. ⟨10.3390/rs11111269⟩ hal-02191084v1 </a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191363v1">C. Brabant, Emilien Alvarez-Vanhard, Gwenaël Morin, Thanh Ngoc Nguyen, Achour Laribi et al. Evaluation of dimensional reduction methods on urban vegetation classification performance using hyperspectral data IGARSS 2018, Jul 2018, Valencia, Spain halshs-02191363v1</a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191097v1">Charlotte Brabant, Emilien Alvarez-Vanhard, Thomas Houet. Improving the classification of urban tree diversity from Very High Spatial Resolution hyperspectral images: comparison of multiples techniques Joint Urban Remote Sensing Event (JURSE 2019), May 2019, Vannes, France halshs-02191097v1</a></li> </ul> <p>This Dataset contains five research outputs of this project that were produced on the basis of Hyperspectral data obtained during an acquisition campaign led on Toulouse (France) urban area on July 2015 using Hyspex instrument which provides 408 spectral bands spread over 0.4 – 2.5 μ. Flight altitude lead to 2 m spatial resolution images.</p> <ul> <li><strong>Fields_samples.7z: </strong> ESRI Shape Format. Supervised SVN classification results for 600 urban trees according to a 3 level nomenclature: leaf type (5 classes), family (12 & 19 classes) and species (14 & 27 classes). The number of classes differ for the two latter as they depend on the minimum number of individuals considered (4 and 10 individuals per class respectively). Trees positions have been acquired using differential GPS and are given with centimetric to decimetric precision. A randomly selected subset of these trees has been used to train machine SVM and Random Forest classification algorithms. Those algorithms were applied to hyperspectral images using a number of classes for family (12 & 19 classes) and species (14 & 27 classes) levels defined according to the minimum number of individuals considered during training/validation process (4 and 10 individuals per class, respectively). Global classification precision for several training subsets is given by Brabant et al, 2019 (<a href="https://www.mdpi.com/470202">https://www.mdpi.com/470202</a>) in terms of averaged overall accuracy (AOA) and averaged kappa index of agreement (AKIA).</li> <li><strong>HySPex-2m.7z: </strong>full hyperspectral VNIR-SWIR ENVI standard image obtained from the coregistration of both VNIR and SWIR ones through a signal aggregation process that allowed to obtain a synthetic VNIR 1.6 m spatial resolution image, with pixels exactly corresponding to natif SWIR image ones. First, a spatially resampled 1.6 m VNIR image was built, where output pixel values were calculated as the average of the VNIR 0.8 m pixel values that spatially contribute to it. Then, ground control points (GCP) were selected over both images and SWIR one was tied to the VNIR 1.6 m image using a bilinear resampling method using ENVI tool. This lead to a 1.6 m spatial resolution full VNIR-SWIR image.</li> <li><strong>HYPXIM-4m.7z, HYPXIM-8m.7z, Sentinel2-10m.7z</strong>: hyperspectral ENVI standard simulated images. Spatial and spectral configurations generated correspond to ESA SENTINEL-2 instrument that was lunched on 2015, and HYPXIM sensor which was under study at that time. </li> </ul>
Urban land expansion in area with decreased urban sprawl at global, national, and city scales during 2000 to 2020
<p>I used calibrated population density thresholds from the year 2000 and 2020 Worldpop population model to measure area and densities for urban and suburban density classes (≥ 250 humans per km<sup>2</sup>) at global and national scales and both broad multi-city agglomerations and fine city cores.</p>
Dataset for Method for Delivery Planning in Urban Areas with Environmental Aspects
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Michał Lasota, Aleksandra Zabielska, Marianna Jacyna, Piotr Gołębiowski, Renata Żochowska, Mariusz Wasiak. Method for Delivery Planning in Urban Areas with Environmental Aspects. Sustainability 2024, 16(4), 1571. https://doi.org/10.3390/su16041571 - published online: 2024-02-13, which a method of large-criteria decision-making support was developed in the field of urban supply planning, taking into account the minimization of harmful compound emissions.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.xlsx: Contains the input data in the model. The data is presented in three tables.</li> <li>OutputOptimization.xlsx: Contains the output optimization data. The data is prsented in four tables.</li> <li>OutputSummary.xlsx: Contains contains the final results of the aggregated variable.</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</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.
Fig. 3 in Role of urban forests as a source of diversity of carabids (Coleoptera: Carabidae) in urbanised areas
Fig. 3. DCA ordination diagram of ecological groups of Carabidae (Eu – eurytopic species, Fo – forest-related sp., OA – open-area-related sp., Pb – peatbog-related sp., H – hygrophilic sp., Mh – mesohygrophilic sp., M – mesophilic sp., Mxe – mesoxerophilic sp., Xe – xerophilic sp., Ph – phytophages, Hz – hemizoophages, Lz – large zoophages, Mz – medium zoophages, Sz – small zoophages, Au – autumn breeders, Sp – spring breeders, ab – abundance, r – richness, "- trap in the site A, %- trap in the site B, %- trap in the site C).
Fig. 2 in Role of urban forests as a source of diversity of carabids (Coleoptera: Carabidae) in urbanised areas
Fig. 2. RDA ordination diagram of the relationship between species dominance of Carabidae and environmental variables (presence of deciduous and coniferous trees, soil cover, anthropopressure, humidity)
Deciphering anthropogenic and biogenic contributions to selected NMVOC emissions in an urban area
<p>This netcdf data file is related to a study published in ACP by Peron et al., 2024. Selected concentration, fluxes of NMVOC as well as meteorological data are reported for the urban area of Innsbruck, Austria. For a complete site description we also refer to Ward et al., 2022: https://doi.org/10.5194/acp-22-6559-2022 and Karl et al., 2020: https://doi.org/10.1175/BAMS-D-19-0270.1</p>
Evaluation of high-resolution WRF simulation in urban areas - Effect of different physics schemes on simulation performance in the Rhine-Main-Neckar area
<p>This dataset contains data sampled from a WRF sensitivity study saved in NetCDF format. The study was run over 4 months of the year 2020. The folders contain the following data:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Datasets</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>wrf_met_sample_full.nc</td> <td>all</td> <td>WRF meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>wrf_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>wrf_met_sample_full_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_met_sample_full_and_quant_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full_and_quant.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_pblh_sample_full.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain</td> </tr> <tr> <td>wrf_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_met_sample_full.nc</td> <td>all</td> <td>ERA5 meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>era5_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain, resampled onto the measured meteorology and with summary statistics</td> </tr> </tbody> </table> <p>Each of these files contains the samples and statistics as NetCDF Variables. These Variables have multiple dimensions, which describe the individual datapoints. For the WRF samples, these dimensions are:</p> <table> <tbody> <tr> <td><strong>Dimension</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Time</td> <td>time since start of simulation</td> </tr> <tr> <td>station_id</td> <td>the ID of the station where the sample was taken (meteo - length 19, PBLH - length 2)</td> </tr> <tr> <td>pbl</td> <td>Planetary Boundary Layer scheme (Bou-Lac / MYJ / YSU)</td> </tr> <tr> <td>lsm</td> <td>Land Surface Model scheme (N / NMP)</td> </tr> <tr> <td>slm</td> <td>Surface Layer Model scheme (MM5 / MO)</td> </tr> <tr> <td>urb</td> <td>Urban Parametrization scheme (SLUCM / BEP)</td> </tr> </tbody> </table> <p>Not all combinations between different simulation schemes exist, so some values in the NetCDF Variables are NaNs.</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>
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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.