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802 results for “urban data”
Data from: Urbanization and primary productivity mediate the predator-prey relationship between deer and coyotes
<p>Predator-prey interactions are important to regulating populations and structuring communities but are affected by many dynamic, complex factors, across larges-scales, making them difficult to study. Integrated population models (IPMs) offer a potential solution to understanding predator-prey relationships by providing a framework for leveraging many different datasets and testing hypotheses about interactive factors. Here, we evaluate the coyote-deer (<em>Canis latrans</em> – <em>Odocoileus virginianus</em>) predator-prey relationship across the state of North Carolina (NC). Because both species have similar habitat requirements and may respond to human disturbance, we considered net primary productivity (NPP) and urbanization as key mediating factors. We estimated deer survival and fecundity by integrating camera trap, harvest, biological and hunter observation datasets into a two-stage, two-sex Lefkovich population projection matrix. We allowed survival and fecundity to vary as functions of urbanization, NPP and coyote density and projected abundance forward to test eight hypothetical scenarios. We estimated initial average deer and coyote densities to be 11.83 (95% CI: 5.64, 20.80) and 0.46 (95% CI: 0.02, 1.45) individuals/km<sup>2</sup>, respectively. We found a negative relationship between current levels of coyote density and deer fecundity in most areas which became more negative under hypothetical conditions of lower NPP or higher urbanization, leading to lower projected deer abundances. These results suggest that coyotes could have stronger effects on deer populations in NC if their densities rise, but primarily in less productive and/or more suburban habitats. Our case study provides an example of how IPMs can be used to better understand the complex relationships between predator and prey under changing environmental conditions.</p>
Data for 'Global Assessment of Interannual Hazard Variability in Coastal Urban Areas and Ecosystems'
<p>This dataset supports Odériz et al. (2024). 'Global Assessment of Interannual Hazard Variability in Coastal Urban Areas and Ecosystems'</p>
Outdoor NB-IoT and 5G coverage and channel information data in urban environments
<p>This dataset includes data for NB-IoT and 5G networks as collected in two cities: Oslo, Norway (NB-IoT only) and Rome, Italy (both NB-IoT and 5G).</p> <p>Data were collected using the Rohde & Schwarz TSMA6 mobile network scanner. 7 measurement campaigns are provided for Oslo, and 6 for Rome. Additional data collected in Rome are provided in the following large-scale dataset, focusing on the two major mobile network operators: <a href="https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements">https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements</a> </p> <p>The dataset includes a metadata file providing the following information for each campaign: </p> <ul> <li>date of collection;</li> <li>start time and end time of collection;</li> <li>length;</li> <li>type (walking/driving).</li> </ul> <p>Two additional metadata files are provided: two .kml files, one for each city, allowing the import of coordinates of data points organized by campaign in a GIS engine, such as Google Earth, for interactive visualization.</p> <p>The dataset contains the following data for NB-IoT:</p> <ul> <li>Raw data for each campaign, stored in two .csv files. For a generic campaign <X>, the files are: <ul> <li>NB-IoT_coverage_C<X>.csv including a geo-tagged data entry in each row. Each entry provides information on a Narrowband Physical Cell Identifier (NPCI), with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator, Country Code, eNodeB-ID) and RF signal (RSSI, SINR, RSRP and RSRQ values);</li> <li> NB-IoT_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a NPCI, with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator ID, Country Code, eNodeB-ID) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file for each city: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <NPCI, Operator ID, eNodeB-ID> unique combination detected at the coordinates of the data point.</li> <li>Estimated positions of eNodeBs, stored in a csv file for each city;</li> <li>A matlab script and a function to extract and generate processed data from the raw data for each city.</li> </ul> <p>The dataset contains the following data for 5G:</p> <ul> <li>Raw data for each campaign, stored in two .xslx files. For a generic campaign <X>, the files are: <ul> <li>5G_coverage_C<X>.xslx including a geo-tagged data entry in each row. Each entry provides information on a Physical Cell Identifier (PCI), with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator, Country Code) and RF data (SSB-RSSI, SSS-SINR, SSS-RSRP and SSS-RSRQ values, and similar information for the PBCH signal);</li> <li> 5G_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a PCI, with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator ID, Country Code) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <PCI, Beamforming Index, Operator ID> unique combination detected at the coordinates of the data point.</li> <li>A matlab script and a supporting function to extract and generate processed data from the raw data.</li> </ul> <p>In addition, in the case of the Rome data additional matlab workspaces are provided, containing interpolated data in the feature dimensions according to two different approaches:</p> <ul> <li>A campaign-by-campaign linear interpolation (both NB-IoT and 5G);</li> <li>A bidimensional interpolation on all campaigns combined (NB-IoT only).</li> </ul> <p>A function to interpolate missing data in the original data according to the first approach is also provided for each technology. The interpolation rationale and procedure for the first approach is detailed in:</p> <p>L. De Nardis, G. Caso, Ö. Alay, U. Ali, M. Neri, A. Brunstrom and M.-G. Di Benedetto, "Positioning by Multicell Fingerprinting in Urban NB-IoT networks," Sensors, Volume 23, Issue 9, Article ID 4266, April 2023. <span>DOI: </span><a href="https://doi.org/10.3390/s23094266" target="_blank" rel="noopener"><span>10.3390/s23094266</span></a>.</p> <p>The second interpolation approach is instead introduced and described in:</p> <p>L. De Nardis, M. Savelli, G. Caso, F. Ferretti, L. Tonelli, N. Bouzar, A. Brunstrom, O. Alay, M. Neri, F. Elbahhar and M.-G. Di Benedetto, " Range-free Positioning in NB-IoT Networks by Machine Learning: beyond WkNN", under major revision in IEEE Journal of Indoor and Seamless Positioning and Navigation.</p> <p>Positioning using the 5G data was furthermore in investigated in: </p> <p>K. Kousias, M. Rajiullah, G. Caso, U. Ali, Ö. Alay, A. Brunstrom, L. De Nardis, M. Neri, and M.-G. Di Benedetto, "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," <span>IEEE Communications Magazine, Volume 62, Issue 5, pp</span><span>. 44-49, May</span><span> 202</span><span>4</span><span>. DOI: </span><a href="https://doi.org/10.1109/MCOM.011.2200707" target="_blank" rel="noopener"><span>10.1109/MCOM.011.2200707</span></a><span>.</span></p> <p><span>G. Caso, M. Rajiullah, K. Kousias, U. Ali, N. Bouzar, L. De Nardis, A. Brunstrom, Ö. Alay, M. Neri and M.-G. Di Benedetto,"The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution", IEEE Open Journal of the Communications Society, Volume 5, pp. 7380 - 7399, 2024. DOI: <a href="https://doi.org/10.1109/OJCOMS.2024.3499370" target="_blank" rel="noopener"><span>10.1109/OJCOMS.2024.3499370</span></a>.</span></p> <p>Please refer to the above publications when using and citing the dataset. </p>
Data for: Serotonin transporter (SERT) polymorphisms, personality and problem-solving in urban great tits
<p class="CxSpFirst"><span><span><span><span><span><span><span><span><span><span><span>Understanding underlying genetic variation can elucidate how diversity in behavioral phenotypes evolves and is maintained. Genes in the serotonergic signaling pathway, including the serotonin transporter gene (<i>SERT)</i>, are candidates for affecting animal personality, cognition and fitness. In a model species, the great tit (<i>Parus major</i>), we reevaluated previous findings suggesting relationships between <i>SERT</i> polymorphisms, neophobia, exploratory behavior and fitness parameters, and performed a first test of the relationship between single nucleotide polymorphisms (SNPs) in SERT and problem-solving in birds. We found some evidence for associations between <i>SERT </i>SNPs and neophobia, exploratory behavior and laying date. Furthermore, several SNPs were associated with behavioral patterns and success rates during obstacle removal problem-solving tests performed at nest boxes. In females, minor allele homozygotes (AA) for nonsynonymous SNP226 in exon 1 made fewer incorrect attempts and were more likely to problem-solve. In both sexes, there was some evidence that minor allele homozygotes (CC) for SNP84 in exon 9 were more likely to problem-solve. Only one SNP-behavior relationship was statistically significant after correcting for multiple comparisons, but several were associated with substantial effect sizes. Our study provides a foundation for future research on the genetic basis of behavioral and cognitive variation in wild animal populations. </span></span></span></span></span></span></span></span></span></span></span></p>
Data archive for paper "Machine Learning Emulation of Urban Land Surface Processes"
<p>This archive contains models, data* (Overview), as well as the Singularity image to optionally rerun experiments described in "<a href="https://doi.org/10.1029/2021MS002744">Machine Learning Emulation of Urban Land Surface Processes</a>".</p> <p><strong>Prerequisites</strong></p> <ul> <li>Linux or macOS with Bash shell.</li> <li><a href="https://sylabs.io/">Singularity</a> (tested with version 3.6.3-1.el8)</li> </ul> <p>Please note that all steps require <a href="https://sylabs.io/">Singularity</a> to be installed on your system. If you are looking for information on how to install or use Singularity, please refer to the <a href="https://sylabs.io/docs">Singularity documentation</a>.</p> <p><strong>Overview</strong></p> <p>A general overview of the repository structure is given below. Due to licensing restrictions analysis and forcing data (*) cannot be included and need to be requested separately (see Initialization). Data derivatives (**) from either analysis or forcing, as well as intermediary data (***), are not included as they can be generated by rerunning experiments (see Usage).</p> <pre><code>. ├── data │ ├── analysis* │ ├── forcing* │ ├── teb │ ├── utils │ ├── wps │ └── wrf ├── hpc ├── models │ ├── teb │ ├── unn │ ├── wps │ └── wrf-unn ├── notebooks ├── outputs │ ├── analysis** │ ├── benchmark*** │ ├── forcing** │ ├── kerastuner*** │ ├── notebooks │ ├── tabular │ ├── teb** │ ├── unn** │ ├── wps*** │ └── wrf ├── paper │ └── figures ├── singularity └── tools </code></pre> <p><strong>Initialization</strong></p> <p>Forcing and analysis data need to be requested separately. The following directories should map to their respective data archives:</p> <ul> <li><code>./data/analysis</code> -> <a href="http://doi.org/10.5281/zenodo.4678387">Grimmond et al. (2013)</a></li> <li><code>./data/forcing</code> -> <a href="http://doi.org/10.5281/zenodo.4679279">Grimmond et al. (2021)</a></li> </ul> <p><strong>Usage</strong></p> <p>To rerun all experiments and reproduce results, run <code>tools/run_all.sh</code> from your command prompt. After completion, all results are saved in the <code>outputs</code> directory. Note that WRF simulations require high CPU time and may take hours or days to complete.</p> <p>Alternatively, if <a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System (PBS)</a> is available on your system, the following helpers may be used instead:</p> <pre><code>qsub hpc/submit_init.pbs qsub hpc/submit_tuner.pbs qsub hpc/submit_unn.pbs qsub hpc/submit_find_median_unn.pbs qsub hpc/submit_wrf.pbs qsub hpc/submit_postprocess.pbs qsub hpc/submit_benchmark.pbs </code></pre> <p>Note that you may need to modify PBS helper scripts to suit your specific environment.</p> <p><strong>Development notes</strong></p> <p>See DEVELOP.md.</p> <p><strong>License</strong></p> <p>The source code developed for this work is licensed under MIT (<code>LICENSE_CODE.txt</code>). For licensing information of third-party software see licenses under the <code>models</code> directory. Data files in this archive, including the initial and boundary condition data from the European Centre for Medium-Range Weather Forecasts (<code>data/wps/ungrib</code>), are licensed under CC BY-NC 4.0 (<code>LICENSE_DATA.txt</code>).</p>
Data from: Turnover in floral composition explains species diversity and temporal stability in the nectar supply of urban residential gardens
<p>Residential gardens are a valuable habitat for insect pollinators worldwide, but differences in individual gardening practices substantially affect their floral composition. It is important to understand how the floral resource supply of gardens varies in both space and time so we can develop evidence-based management recommendations to support pollinator conservation in towns and cities.</p> <p>We surveyed 59 residential gardens in the city of Bristol, UK, at monthly intervals from March to October. For each of 472 garden surveys, we combined floral abundances with nectar sugar data to quantify the nectar production of each garden, investigating the magnitude, temporal stability, and diversity and composition of garden nectar supplies.</p> <p>We found that individual gardens differ markedly in the quantity of nectar sugar they supply (from 2 g to 1662 g), and nectar production is higher in more affluent neighbourhoods, but not in larger gardens. Nectar supply peaks in July (mid-summer), when more plant taxa are in flower, but temporal patterns vary among individual gardens. At larger spatial scales, temporal variability averages out through the portfolio effect, meaning insect pollinators foraging across many gardens in urban landscapes have access to a relatively stable and continuous supply of nectar through the year.</p> <p>Turnover in species composition among gardens leads to an extremely high overall plant richness, with 636 taxa recorded flowering. The nectar supply is dominated by non-natives, which provide 91% of all nectar sugar, while shrubs are the main plant life form contributing to nectar production (58%). Two thirds of nectar sugar is only available to relatively specialised pollinators, leaving just one third that is accessible to all.</p> <p><i>Synthesis and applications</i>. By measuring nectar supply in residential gardens, our study demonstrates that pollinator-friendly management, affecting garden quality, is more important than the size of a garden, giving every gardener an opportunity to contribute to pollinator conservation in urban areas. For gardeners interested in increasing the value of their land to foraging pollinators we recommend planting nectar-rich shrubs with complementary flowering periods and prioritising flowers with an open structure in late summer and autumn.</p>
TXRF and TXRF-XANES data for characterization of unique aerosol pollution episodes in urban areas
<p>The dataset contains the raw data for elemental composition and copper/bromine speciation determined in size-fractionated aerosol particles sampled by a May-type cascade impactor. </p> <p>These data are related to the journal article <strong>Characterization of unique aerosol pollution episodes in urban areas using TXRF and TXRF-XANES </strong>by Otto Czömpöly, Endre Börcsök, Veronika Groma, Simone Pollastri and Janos Osan, published in Atmospheric Pollution Research Volume 12, Issue 11, November 2021, 101214. <a href="https://doi.org/10.1016/j.apr.2021.101214">https://doi.org/10.1016/j.apr.2021.101214</a></p> <p>The elemental composition data were produced by total-reflection X-ray Spectrometry (TXRF) at Centre for Energy Research, Budapest, Hungary. </p> <p>The file "TXRF.zip" contains raw spectra as "*.spe", fitting results as "*.asr", calibration file "aer_mo.cal" and calculated elemental masses along the 20-mm stripe samples as "*.apr", all as text files in AXIL/QXAS format.</p> <p>The copper and bromine speciation data were produced by X-ray absorption near-edge structure (XANES) recorded in the TXRF detection mode at the XRF beamline of Elettra Sincrotrone Trieste, Italy.</p> <p>The file "XANES.zip" contains average XANES spectra of several energy scans for each sample as "*.xmu" and linear combination fitting results as "*.lcf", all as text files in Athena/Ifeffit format.</p> <p>The files are grouped in folders related to aerosol particles collected during the five pollution episodes (A-E) and near pollution sources as presented in the publication. The digit following the sample number denotes the impactor stage number (3-9), numbered from large (4.5-8.9 um) to small (70-180 nm) particle fractions. </p>
Data from: Predicting habitat suitability and connectivity for management and conservation of urban wildlife: A real-time web application for grassland water voles
<ol> <li>Natural habitats in urban areas provide benefits for both humans and biodiversity. However, to achieve biodiversity gains we require new techniques to determine habitat suitability and ecological connectivity that will inform urban planning and development.</li> <li>Using an example of an urban population of water voles (<i>Arvicola amphibius</i>) we developed a habitat suitability model and a resistance-surface-based model of landscape connectivity to identify potential connectivity between areas of suitable habitat. We then updated the environmental variables according to new urban development plans and used our models to generate spatially explicit predictions of both habitat suitability and connectivity.</li> <li>To make models accessible to urban and conservation planners we developed an interactive mapping tool that provided users with a graphical user interface (GUI) to inform conservation planning for this species.</li> <li>The model found that habitat suitability for water voles was related to distance from key environmental variables, such as built-up areas and urban green spaces, while the connectivity model identified important corridors connecting areas of potential distribution for this species.</li> <li>Future development plans altered the potential spatial distribution of the water vole population, reducing the extent of suitable habitat in some core areas. The interactive mapping tool made available suitable habitat and connectivity maps for conservation managers to assess new planning applications and for the development of a conservation action plan for water voles.</li> <li>Synthesis and applications: We believe this approach provides a framework for future development of nature conservation tools that can be used by planners to inform ecological decision making, increase biodiversity and reduce human-wildlife conflict in urban environments.</li> </ol>
Survey Data Results for Project Strategies for Mitigating Congestion in Small Urban and Rural Areas
<p>File contains the Survey Data Results for Project Strategies for Mitigating Congestion in Small Urban and Rural Areas.</p> <p>Row 1 is the question number.</p> <p>Row 2 is the question.</p> <p>Remaining Rows are individual survey responses.</p>
Data from: Landscape context and substrate characteristics shape fungal communities of dead spruce in urban and semi-natural forests
<p>Data from:</p> <p>Korhonen, A., Miettinen, O., Kotze, D.J., & Hamberg, L. (2022). Landscape context and substrate characteristics shape fungal communities of dead spruce in urban and semi-natural forests. Environmental Microbiology. DOI: 10.1111/1462-2920.15903</p> <p>This dataset contains occurrence data of 475 fungal ITS2 sequence clusters (OTUs) across 360 sequenced samples originating from dead wood of Norway spruce (<em>Picea abies</em>). Wood samples were collected from 90 downed spruce trunks, at four points from each trunk. Trunks were at intermediate stages of decay and located in spruce-dominated stands in 24 urban forests (66 trunks) and in 8 rural semi-natural forests (24 trunks) in southern Finland. The data consists of an OTU table (sequence read counts in each sample) and sample metadata (locality information, trunk characteristics and environmental variables).</p>
A framework to identify priority wetland habitats and movement corridors for urban amphibian conservation - Raw Data
<p>Call of the Wetland Program observation data reported by citizen scientists at wetlands in the City of Calgary during three amphibian seasons from 2017 to 2019. Wetlands were surveyed up to 11 times per season and participants reported observations to a smartphone application – where they documented species, and type of observation (eggs, adult, juvenile, tadpole or call). Participants also recorded when they participated in a survey and there were no observations. Observations associated without a site ID represent opportunistic observations at non-survey wetlands. This data was used to validate habitat suitability models derived from the occupancy modeling results. <br> <br> Survey wetland location shapefile (centroid point of wetland) also included. </p>
Data set for "Local and landscape correlates of coccinellid species richness, abundance, and assemblage change along a rural–urban gradient in Quintana Roo, Mexico."
<p>Data set for original article "Local and landscape correlates of coccinellid species richness, abundance, and assemblage change along a rural–urban gradient in Quintana Roo, Mexico"<strong> </strong>accepted at Biotropica.</p> <p>We studied changes in coccinellid assemblage composition, diversity, species richness and abundance in domestic gardens along a rural to urban gradient in southeastern Mexico, and identified local and lanscape variables that determine their species richness and abundance. </p>
Sample Input Data and Supporting Files for the SELECT Model of Urbanization
<p>Sample Input Data and Supporting Files for the SELECT Model of Urbanization</p> <p>Code available at: https://github.com/IMMM-SFA/select</p>
Data supporting: Invader at the edge - genomic origins and physiological differences of round gobies across a steep urban salinity gradient
<p>Species invasions are a global problem of increasing concern, especially in highly connected aquatic environments. Despite this, salinity conditions can pose physiological barriers to their spread and understanding them is important for management. In Scandinavia's largest cargo port, the invasive round goby (Neogobius melanostomus), is established across a steep salinity gradient. We used 12 937 SNPs to identify the genetic origin and diversity of three sites along the salinity gradient and round goby from <span>western,</span> <span>central and </span>northern Baltic Sea, as well as north European rivers. Fish from two sites<span> from the extreme ends of the gradient</span> were also acclimated to freshwater and seawater, and tested for respiratory and osmoregulatory physiology. Fish from the high salinity environment in the outer port showed higher genetic diversity, and closer relatedness to the other regions, compared to fish from lower salinity upstream the river. Fish from the high salinity site also had higher maximum metabolic rate, fewer blood cells and lower blood Ca2+. Despite these genotypic and phenotypic differences, salinity acclimation affected fish from both sites in the same way: seawater increased the blood osmolality and Na+ levels, and freshwater increased the levels of the stress hormone cortisol. Our results show genotypic and phenotypic differences over short spatial scales across this steep salinity gradient. These patterns of the physiologically robust round goby are likely driven by multiple introductions into the high salinity site, and a process of sorting, likely based on behaviour or selection, along the gradient. Since this euryhaline fish risks spreading from this area, seascape genomics and phenotypic characterisation can inform management strategies even within an area as small as a coastal harbour inlet.</p>
Data from the paper "Learning to clusterize urban areas: two competitive approaches and an empirical validation"
<p>Data for urban clustering used in the paper "Learning to clusterize urban areas: two competitive approaches and an empirical validation". We release two datasets for urban clustering based on data acquired in Santiago de Chile. The first dataset is computed at the level of urban blocks. The second dataset is computed at the level of individuals using a uniform sample of Santiago inhabitants. Both datasets comprises features based on social characteristics (e.g., SES), land use, and aesthetic visual perception of the city. The features of each data unit (blocks or individuals) are provided using row packing (each row is a data unit) in CSV files. We release PCA (Principal Components Analysis) features for both datasets.</p>
Replication data for: Occupations and their impact on the spreading of COVID-19 in urban communities
<p>This dataset contains real-world COVID-19 human-to-human transmission network data. The data mimics how COVID-19 infections may have spread from one individual to another in Bucharest (Romania) during August 1<sup>st</sup> and October 31<sup>st</sup>, 2020. The information refers to COVID-19 patients (referees) and their contacts (referrals), i.e., the people they interacted with before being tested COVID-19 positive. The dataset is structured as an edge-list file (referee - referral ties). For each referee (referral), we provide the following attributes: sex (male/female), age, sector (public/private), a job in the medical sector (yes/no), ISCO-08 one-digit code, ISCO-08 two-digit code, ISCO-08 three-digit code, employability (active/non-active), age class (minor, adult, pensioner), confirmation month (when a patient was tested positive for COVID-19 infection), confirmation day (when a patient was tested positive for COVID-19 infection). The data were analyzed using relational hyperevent modeling (<a href="https://github.com/juergenlerner/eventnet">https://github.com/juergenlerner/eventnet</a>). </p> <p>This dataset allows replication of the analysis reported in the manuscript entitled: Occupations and their impact on the spreading of COVID-19 in urban communities (Hâncean M-G, Lerner J, Perc M, Oană I, Bunaciu D-A, Stoica AA & Ghiță M-C). </p>
Data from: Wild bee abundance declines with urban warming, regardless of floral density
<p>As cities expand, conservation of beneficial insects is essential to maintaining robust urban ecosystem services such as pollination. Urban warming alters insect physiology, fitness, and abundance, but the effect of urban warming on pollinator communities has not been investigated. We sampled bees at 18 sites encompassing an urban warming mosaic within Raleigh, NC, USA. We quantified habitat variables at all sites by measuring air temperature, percent impervious surface (on local and landscape scales), floral density, and floral diversity. We tested the hypothesis that urban bee community structure depends on temperature. We also conducted model selection to determine whether temperature was among the most important predictors of urban bee community structure. Finally, we asked whether bee responses to temperature or impervious surface depended on bee functional traits. Bee abundance declined by about 41% per °C urban warming, and temperature was among the best predictors of bee abundance and community composition. Local impervious surface and floral density were also important predictors of bee abundance, although only large bees appeared to benefit from high floral density. Bee species richness increased with floral density regardless of bee size, and bee responses to urban habitat variables were independent of other life-history traits. Although we document benefits of high floral density, simply adding flowers to otherwise hot, impervious sites is unlikely to restore the entire urban pollinator community since floral resources benefit large bees more than small bees.</p>
Data and code for "A decade of monitoring micropollutants in urban wet-weather flows: what did we learn?"
<p><strong>Data and code for publication</strong></p> <p><em>Lena Mutzner, Viviane Furrer, Hélène Castebrunet, Ulrich Dittmer, Stephan Fuchs, Wolfgang Gernjak, Marie-Christine Gromaire, Andreas Matzinger, Peter Steen Mikkelsen, William R. Selbig, Luca Vezzaro,<br> A decade of monitoring micropollutants in urban wet-weather flows: what did we learn?<br> Water Research, 2022.<br> https://doi.org/10.1016/j.watres.2022.118968</em></p> <p><strong>Data</strong></p> <p>Micropollutants concentrations in µg/l (incl. heavy metals) for 77 wet-weather discharge sites (36 combined sewer overflows, 41 stormwater outlets). The provided data set is a collection of raw data sets based on publications referenced below. The data description is provided in the folder Data\AA_DataDescription.txt</p> <p><strong>Abstract</strong></p> <p>Urban wet-weather discharges from combined sewer overflows (CSO) and stormwater outlets (SWO) are a potential pathway for micropollutants (trace contaminants) to surface waters, posing a threat to the environment and possible water reuse applications. Despite large efforts to monitor micropollutants in the last decade, the gained information is still limited and scattered. In a metastudy we performed a data-driven analysis of measurements collected at 77 sites (683 events, 297 detected micropollutants) over the last decade to investigate which micropollutants are most relevant in terms of 1) occurrence and 2) potential risk for the aquatic environment, 3) estimate the minimum number of data to be collected in monitoring studies to reliably obtain concentration estimates, and 4) provide recommendations for future monitoring campaigns. We highlight micropollutants to be prioritized due to their high occurrence and critical concentration levels compared to environmental quality standards. These top-listed micropollutants include contaminants from all chemical classes (pesticides, heavy metals, polycyclic aromatic hydrocarbons, personal care products, pharmaceuticals, and industrial and household chemicals). Analysis of over 30,000 event mean concentrations shows a large fraction of measurements (> 50%) were below the limit of quantification, stressing the need for reliable, standard monitoring procedures. High variability was observed among events and sites, with differences between micropollutant classes. The number of events required for a reliable estimate of site mean concentrations (error bandwidth of 1 around the “true value) depends on the individual micropollutant. The median minimum number of events is 7 for CSO (2 to 31, 80%-interquantile) and 6 for SWO (1 to 25 events, 80%-interquantile). Our analysis indicates the minimum number of sites needed to assess global pollution levels and our data collection and analysis can be used to estimate the required number of sites for an urban catchment. Our data-driven analysis demonstrates how future wet-weather monitoring programs will be more effective if the consequences of high variability inherent in urban wet-weather discharges are considered.</p>
Data from: Does urbanization ameliorate the effect of endoparasite infection in kangaroo rats?
<p>Urban development can fragment and degrade remnant habitat. Such habitat alterations can have profound impacts on wildlife, including effects on population density, parasite infection status, parasite prevalence, and body condition. We investigated the influence of urbanization on populations of Merriam's kangaroo rat (<i>Dipodomys merriami</i>) and their parasites. We predicted that urban development would lead to reduced abundance, increased parasite prevalence in urban populations, increased probability of parasite infection for individual animals, and decreased body condition of kangaroo rats in urban versus wildland areas. We live trapped kangaroo rats at 5 urban and 5 wildland sites in and around Las Cruces, NM, USA from 2013-2015, collected fecal samples from 209 kangaroo rats, and detected endoparasites using fecal flotation and molecular barcoding. Seven parasite species were detected, although only two, parasitic worms <i>Mastophorus dipodomis</i> and <i>Pterygodermatites dipodomis</i>, occurred frequently enough to allow for statistical analysis. We found no effects of urbanization on population density or probability of parasite infection. However, wildland animals infected with <i>P. dipodomis</i> had lower body condition scores than infected animals in urban areas or uninfected animals in either habitat. Our results suggest that urban environments may buffer Merriam's kangaroo rats from the detrimental impacts to body condition that <i>P. dipodomis</i> infections can cause. </p>
Temporal data from camera trap captures of raccoons (Procyon lotor) and coyote (Canis latrans) across urban-rural gradient Michigan 2015-2020
<p>Temporal data and trap success for raccoons (<em>Procyon lotor</em>) and coyotes (<em>Canis latrans</em>) across an urban-rural gradient in Michigan, from 2015 to 2020. These data are associated with the article "Temporal refuges of a subordinate carnivore vary across rural-urban gradient" in the journal Ecology and Evolution. </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
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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
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