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358 results for “fine-scale”
Long-term demographic dataset for Cladonia perforata, including fine-scale cover, occupancy, and subpopulation area data, 2011-2024
This dataset includes all data pertaining to a long-term demographic study of Cladonia perforata (perforate reindeer lichen), a federally endangered lichen endemic to Florida, including fine-scale cover, occupancy, and population area data, conducted by the Archbold Biological Station Plant Ecology Program. This includes 13 years of data (2011-2024) from nine subpopulation (including seven at Archbold Biological Station, and two at the Lake Wales Ridge Wildlife and Environmental Area, Royce Unit), all located in rosemary scrub habitat within the Lake Wales Ridge metapopulation. This study sought to characterize the fire ecology and long-term population trends for the species, and thus also includes data on prescribed burn severity and time since fire. Data were collected using a stratified random plot design, with occupancy plots (presence/absence within 1.5 meter radius) throughout the subpopulation and a subset of these designated as cover plots only, with this cover data collected as point intercept hits within a 48x48cm area. Cover data also includes microhabitat data – canopy cover in densiometer reading and dominant ground cover. Cover and occupancy data were taken every 3 years for each subpopulation (subpopulations were on different yearly schedules). Subpopulation area was mapped using a submeter GPS unit every 6 years. Subpopulations were resampled for all metrics as soon as possible following a fire, and the sampling schedule was then reset.
Fine-scale meteorological observations from walking traverses in two Phoenix Area Social Survey (PASS) 2017 neighborhoods (2019)
This dataset includes human-biometeorological observations from 2.5 km walking traverses with a mobile weather station. The traverses occurred in two 2017 Phoenix Area Social Survey neighborhoods (U18: South Phoenix/Salt River (Audubon) and W15: Camelback Mountain) on one day in June and October, at 12pm and 4pm on each day. Specifically, air temperature, humidity, wind speed, and radiant energy (infrared and solar radiation) in 3-dimensions were measured at 2-second intervals. Additionally, mean radiant temperature was calculated from the radiation measurements. The meteorological observations are spatially referenced with latitude and longitude coordinates. The paths through the neighborhoods were chosen to maximize proximity to PASS 2017 participants’ homes.
Fine-scale structure of the 2016-2017 Central Italy Seismic Sequence from data recorded at the Italian National Network
<p><strong>Data Set </strong></p> <p>Catalog of 33,983 earthquakes located during the 2016-2017 Central Italy seismic sequence. The velocity model used is the 1D gradient P- and S-wave velocity models (after Carannante et al., 2013). We used the highest quality P- and S-wave arrival times manually picked by analysts of the National Institute of Geophysics and Volcanology (INGV) seismic monitoring room, having an uncertainty lower than 0.6 s. </p> <p>Events were located by means of a 2-step procedure: the INGV routine absolute locations computation for all events with ML ≥ 1.5 that occurred in the study area between August 2016 and January 2018, using the method described in Chiaraluce et al. (2017); the determination of relative locations by applying the HypoDD code (Waldhauser, 2001) to the catalog picks and phase delay times measured from waveform cross correlation.</p> <p>The time domain cross-correlation method (Schaff et al., 2004; Schaff and Waldhauser, 2005) was applied to seismograms of all pairs of events separated by 3 km or less and recorded at common stations. Seismograms were filtered in the 1-15 Hz frequency range using a 4 pole, zero phase band‐pass Butterworth filter. The correlations measurements were performed on 0.7 s long window for P-waves and 1 s windows for S-waves. Only measurements with correlation coefficients greater than 0.7 were kept, resulting in a total of ~4.4 million P and ~1.1 million S wave delay times. </p> <p>We sub-divided the entire dataset in 18 rectangular boxes, containing a maximum of 6000 earthquakes, orthogonal to and centered on the mean strike of the seismic sequence. The overlap between neighboring boxes is 50% with respect to the NW-SE extension. HypoDD is run separately on each box. Resulting relative locations from all boxes were combined into a single catalog, computing the weighted mean of double hypocenters in the overlapping regions (Waldhauser and Schaff, 2008).</p> <p>The final double-difference catalog includes 33,982 events occurring between 24<sup>th</sup> of August 2016 and 18<sup>th</sup> of January 2018.</p> <p>The catalog is in csv format, semicolon separator, ordered by origin time and the header content is the following:</p> <ul> <li>Id-ingv: ingv eventid, useful to link to the QuakeML phase file through the INGV fdsnws/event webservice (<a href="https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2Fwebservices.ingv.it%2Fswagger-ui%2Fdist%2F%3Furl%3Dhttps%3A%2F%2Fingv.github.io%2Fopenapi%2Ffdsnws%2Fevent%2F0.0.1%2Fevent.yaml">http://webservices.ingv.it/swagger-ui/dist/?url=https://ingv.github.io/openapi/fdsnws/event/0.0.1/event.yaml</a>) and to the reported magnitude;</li> <li>Latitude(°) expressed in decimal degrees;</li> <li>Longitude(°) expressed in decimal degrees;</li> <li>Depth(km) hypocentral depth expressed in kilometers;</li> <li>Year of origin time in the format yyyy;</li> <li>Month of origin time in the format mm;</li> <li>Day of origin time in the format dd; </li> <li>Hour of origin time in the format hh;</li> <li>Minute of origin time in the format min;</li> <li>Second of origin time in the format ??.?????? s;</li> <li>Magnitude: the value available at the phases downloading time (see Id-ingv fdsnws/event)</li> </ul> <p> </p> <p> </p> <p> </p> <p><br> </p>
One-hectare fine-scale dataset of a fynbos plant community in the Cape Floristic Region
<p>Cape fynbos, which forms part of the Cape Floristic Region (CFR) of South Africa, a global biodiversity hotspot, is renowned for its high levels of plant species endemism and diversity. This extraordinary ecosystem, characterised by nutrient-poor soils and fire-adapted vegetation, is a treasure trove of endemic flora. However, this fragile system faces increasing threats from habitat loss, climate change, and invasive species. Pristine fynbos, naturally high in plant diversity and which forms a large part of the CFR, presents an ideal opportunity to gather fine-scale data on community assembly patterns. Most fynbos vegetation surveys use a plot size of about 100 m2, with no spatial structures within plots to demarcate individual subplots. Here, a groundbreaking dataset is presented that fully covers 1-hectare of pristine fynbos, systematically gridded into 50 × 50 subplots, each measuring 2 × 2 m, arranged evenly within a square-shaped survey site. Each plot was assigned a unique Y–X coordinate combination. For each plot, all plant species present were recorded, along with their total percentage covers and maximum height values. Total percentage covers were also recorded for bare soil, rock, and termite mounds. This dataset provides a valuable contribution to the field of fynbos ecology, as well as plant community ecology in general, and establishes a benchmark for future one-hectare surveys of similar fynbos vegetation types, delineating the fine-scale composition and structure of fynbos in the CFR. The dataset will be useful for a wide audience, including community and spatial ecologists, plant and environmental scientists, and biodiversity informaticians and statistical ecologists, offering ideal data for testing new metrics of diversity and compositional turnover. Data in Brief, Volume 59, April 2025, 111334: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2025.111334" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.dib.2025.111334</span></span></a></p>
Dataset generated to evaluate in situ sampling strategies to reconstruct fine-scale ocean currents in the context of SWOT satellite mission (H2020 EuroSea project)
<p><strong>Dataset generated in Subtask 2.3.1 of the H2020 EuroSea project.</strong></p> <ul> <li> <p><em>H2020 EuroSea project:</em><br> The H2020 EuroSea project aims at improving and integrating the European Ocean Observing and Forecasting System (see official website: <a href="https://eurosea.eu/">https://eurosea.eu/</a>). It has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626).</p> </li> <li> <p><em>Task 2.3:</em><br> Task 2.3 has the objective to improve the design of multi-platform experiments aimed to validate the Surface Water and Ocean Topography (SWOT) satellite observations with the goal to optimize the utility of these observing platforms. Observing System Simulation Experiments (OSSEs) have been conducted to evaluate different configurations of the in situ observing system, including rosette and underway CTD, gliders, conventional satellite nadir altimetry and velocities from drifters. High-resolution models have been used to simulate the observations and to represent the “ocean truth”. Several methods of reconstruction have been tested: spatio-temporal optimal interpolation, machine-learning techniques, model data assimilation and the MIOST tool. The planned OSSEs are detailed in this public report <a href="https://doi.org/10.3289/eurosea_d2.1">Barceló-Llull et al. (2020)</a> and the complete analysis is available here <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>. Contributors to Task 2.3 are CSIC (Spain), CLS (France), SOCIB (Spain), IMT-Atlantique (France) and Ocean-Next (France).</p> </li> <li> <p><em>Subtask 2.3.1:</em><br> Subtask 2.3.1 aims to evaluate different in situ sampling strategies to reconstruct fine-scale ocean currents (~20 km) in the context of SWOT. An advanced version of the classic optimal interpolation used in field experiments, which considers the spatial and temporal variability of the observations, has been applied to reconstruct different configurations with the objective to evaluate the best sampling strategy to validate SWOT.</p> </li> <li> <p><em>Where?</em><br> The analysis focuses on two regions of interest: (i) the western Mediterranean Sea and (ii) the Subpolar North West Atlantic. In the western Mediterranean Sea, the target area is located within a swath of SWOT, while in the North West Atlantic the region of study includes a crossover of SWOT during the fast-sampling phase.</p> </li> </ul> <p><strong>Report with the full analysis</strong></p> <p>The complete analysis can be found in this report: <a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al. (2022)</a>.</p> <p><strong>Codes for the analysis</strong></p> <p>The codes generated to develop Subtask 2.3.1 can be found on GitHub: <a href="https://github.com/bbarcelollull/EuroSea_subTask_2.3.1">https://github.com/bbarcelollull/EuroSea_subTask_2.3.1</a></p> <p><strong>The dataset</strong></p> <p>The dataset includes:</p> <p>1) Model outputs used to simulate the observations in different configurations in both regions of study. The folder "2D_model_outputs" contains 2D data used to simulate SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42). The folder "3D_model_outputs" contains 3D model outputs used to simulate observations of temperature and salinity. Note that eNATL60 outputs have been interpolated onto a new regular grid. </p> <p>2) Simulated configurations (or sampling strategies) in each region (PKL file format).</p> <p>3) Observations simulated in each configuration in both regions of study. The observations simulated are temperature and salinity. ADCP horizontal velocities are also simulated, however for eNATL60 they will be corrected in the future to account for the rotated original axes. File format: region_configuration_period_model.nc. The folder "SSH" includes the simulated SSH observations for the analysis of the temporal correlation scale (<a href="https://doi.org/10.3289/eurosea_d2.3">Barceló-Llull et al., 2022</a>, p. 28-42).</p> <p>4) Reconstructed fields with the spatio-temporal optimal interpolation. File format: region_configuration_period_model_stOI_Lx_Lt_cd_YYYYMMDDhhmm_var.nc (stOI = spatio-temporal optimal interpolation, Lx = spatial correlation scale, Lt = temporal correlation scale, cd = map on the central date of the sampling, YYYYMMDDhhmm = date and time of the map, var = variable interpolated (temperature and salinity) or the derived variables (dynamic height, geostrophic velocities and the Rossby number)).</p> <p>5) Compared fields (ocean truth from model outputs vs. reconstructed fields) for each region and model (PKL file format).</p> <p> </p>
Fine-scale population spatialization data of China in 2018 based on real location-based big data
<p><strong>This data contains a geospatial population raster layer in GeoTIFF format with 1*1 km resolution for 31 provincial regions (2851 counties) of China in 2018 (pop2018.tif). It also provides the Tencent positioning data in 2018 (TN_hSum2018.tif), the table of statistical population of 2851 counties (statistical_population_2018_china_county.xls) and its vector map (statisitcal_pop.shp) and codes (code.docx).</strong></p>
Fine-scale anthropogenic nutrient input data for 8 watersheds along the Saint Lawrence River (1981, 2021)
<p>We quantified Net Anthropogenic Nitrogen and Phosphorus Inputs (NANI-NAPI) at two scales (the finest one, the municipality, and a coarser one, the county) for all municipalities and counties of 8 watersheds in Québec, Canada, for 1981 and 2021.</p> <p>The datasets here report 1) watershed NANI and NAPI values accounted from both scales for 1981 and 2021, 2) municipality-scale NANI values for each municipality in the 8 watersheds, and 3) certain components of the municipality-scale NANI for all municipalities in the Yamaska watershed.</p>
Fine-scale changes in speed and altitude suggest protean movements in homing pigeon flights
<p>The power curve provides a basis for predicting adjustments that animals make in flight speed, for example in relation to wind, distance, habitat foraging quality and objective. However, relatively few studies have examined how animals respond to the landscape below them, which could affect speed and power allocation through modifications in climb rate and perceived predation risk. We equipped homing pigeons (<i>Columba livia</i>) with high-frequency loggers to examine how flight speed, and hence effort, varies in relation to topography and land cover. Pigeons showed mixed evidence for an energy-saving strategy, as they minimized climb rates by starting their ascent ahead of hills, but selected rapid speeds in their ascents. Birds did not modify their speed substantially in relation to land cover, but used higher speeds during descending flight, highlighting the importance of considering the rate of change in altitude before estimating power use from speed. Finally, we document an unexpected variability in speed and altitude over fine scales; a source of substantial energetic inefficiency. We suggest this may be a form of protean behaviour adopted to reduce predation risk when flocking is not an option, and that such a strategy could be widespread.</p>
Beneath the Antarctic sea-ice: Fine-scale analysis of Weddell seal (Leptonychotes weddellii) behaviour and predator-prey interactions, using micro-sonar data in Terre Adélie
<p>In this study, we tried to assess:<br>i) whether and how female Weddell seals feed (frequency, depth, duration) during lactation,<br>ii) what is their utilization of a limited foraging area (benthic or pelagic dives) as they are spatially constrained by the presence of their pup, and<br>iii) how can we characterize their foraging dives and the approach/catching phases using new tools providing a more detailed description of their behaviour.<br>Sonar tags were deployed on three breeding female Weddell seals in Terre Adélie (East Antarctica) in November 2019, to study animals' movements and dives at high resolution (3D acceleration, magnetometry, time and depth and GPS location), as well as information on prey and predator-prey interactions using acoustic data.</p>
Fine-scale map reveals highly variable recombination rates associated with genomic features in the Eurasian blackcap
<p>This project estimates historical recombination rates of the Eurasian blackcap (<em>Sylvia atricapilla</em>) and the garden warbler (<em>Sylvia borin)</em> using a linkage disequilibrium-based approach. The population-scaled recombination rates were inferred using 'pyrho' considering the population's demographic information and mutation rates. The mutation rate was taken from a closely related species (flycatcher, <em>Ficedula albicollis</em>).</p> <p>Here we report the recombination rate per site per generation (r, output of pyrho) for all the chromosomes along the genome for both species. </p> <p>Blackcap dataset file: WholeGenome_chromosomes_blackap_n38_Pen20_W50.gz</p> <p>Garden warbler dataset file: WholeGenome_chromosomes_SylBor_n10_Pen20W50.gz</p> <p> </p> <p>Additionally, we annotated the blackcap genome to evaluate the association between recombination rates and specific annotation features (e.g., genes, transcription start sites, promoters, 5'UTR, and 3'UTR regions). The annotation of the genome was based on RNA-seq and Iso-Seq data. It can be found with the name bSylAtr1.1.gff.</p>
Assessing size at sexual maturity and fine-scale population structure in a direct developing whelk (Buccinum undatum) in Southern Newfoundland, Canada
<p>R script file used to filter genotype data, estimate L50, and analyze patterns of population structure of <em>Buccinum undatum </em>in Southern Newfoundland, Canada. Also included are the following files required to run the script:</p> <p>populationsNWA.snps.vcf - Northwest Atlantic group output at the conclusion of the Stacks de novo pipeline<br>pop_map_NWA.txt - Population map for the Northwest Atlantic group<br>genlightNWAFullFilt.rds - Filtered genotype data for the Northwest Atlantic group<br>populations3Ps.snps.vcf - 3Ps group output at the conclusion of the Stacks de novo pipeline <br>pop_map_3Ps.txt - Population map for the 3Ps group<br>genlight3PsFullFilt.rds - Filtered genotype data for the 3Ps group<br>maturity_data.csv - Data set containing, shell length, sex, and maturity status for samples.<br>sample_site_coordinates_3Ps.csv - Data set containing coordinates of 3Ps sample sites</p> <p> </p>
Environment and density-dependency explain the fine-scale distribution of tree recruits before and after thinning
<p>Dataset used in the article "Environment and density-dependency explain the fine-scale distribution of tree recruits before and after thinning" in PeerJ</p>
Broad-scale patterns of geographic avoidance between species emerge in the absence of fine-scale mechanisms of coexistence
<p>Aim: The need to forecast range shifts under future climate change has motivated an increasing interest in better understanding the role of biotic interactions in driving diversity patterns. The contribution of biotic interactions to shaping broad-scale species distributions is however, still debated, partly due to the difficulty of detecting their effects. We aim to test whether spatial exclusion between potentially competing species can be detected at the species range scale, and whether this pattern relates to fine-scale mechanisms of coexistence.</p> <p>Location: Western Palearctic</p> <p>Time period: Anthropocene</p> <p>Taxa: bats (Chiroptera)</p> <p>Methods: We develop and evaluate a measure of geographic avoidance that uses outputs of species distribution models to quantify geographic exclusion patterns expected if interspecific competition affects broad-scale distributions. We apply the measure to 10 Palearctic bat species belonging to four morphologically similar cryptic groups in which competition is likely to occur. We compare outputs to null models based on pairs of virtual species and to expectations based on ecological similarity and fine-scale coexistence mechanisms. We project changes in range suitability under climate change taking into account effects of geographic avoidance.</p> <p>Results: Values of geographic avoidance were above null expectations for two cryptic species pairs, suggesting that interspecific competition could have contributed to shaping their broad-scale distributions. These two pairs showed highest levels of ecological similarity and no trophic or habitat partitioning. Considering the role of competition modified predictions of future range suitability.</p> <p>Conclusions: Our results support the role of interspecific competition in limiting the geographic ranges of morphologically similar species in the absence of fine-scale mechanisms of coexistence. This study highlights the importance of incorporating biotic interactions into predictive models of range shifts under climate change, and the need for further integration of community ecology with species distribution models to understand the role of competition in ecology and biogeography.</p>
Environmental DNA reveals fine-scale habitat associations for sedentary and resident marine species across a coastal mosaic of soft and hard-bottom habitats
<p>Accurate knowledge on spatiotemporal distributions of marine species and their association with surrounding habitats is crucial to inform adaptive management actions responding to coastal degradation across the globe. Here, we investigate the potential use of environmental DNA (eDNA) to detect species-habitat associations in a patchy coastal area of the Baltic Sea. We directly compare species-specific qPCR analysis of eDNA with baited remote underwater video systems (BRUVS), two non-invasive methods widely used to monitor marine habitats. Four focal species (cod Gadus morhua, flounder Platichthys flesus, plaice Pleuronectes platessa and goldsinny wrasse Ctenolabrus rupestris) were selected based on contrasting habitat associations (reef- vs. sand-associated species), as well as differential levels of mobility and residency, to investigate whether these factors affected the detection of species-habitat associations from eDNA. To this end, a species-specific qPCR assay for goldsinny wrasse is developed and made available herein. In addition, potential correlations between eDNA signals and abundance counts (MaxN) from videos were assessed. Results from Bayesian multi-level models revealed strong evidence for a sand association for sedentary flounder (98% posterior probability) and a reef association for highly resident wrasse (99% posterior probability) using eDNA, in agreement with BRUVS. However, contrary to BRUVS, eDNA sampling did not detect habitat associations for cod or plaice. We found a positive correlation between eDNA detection and MaxN for wrasse (posterior probability 95%), but not for the remaining species and explanatory power of all relationships was generally limited. Our results indicate that eDNA sampling can detect species-habitat associations on a fine spatial scale, yet this ability likely depends on the mobility and residency of the target organism, with associations for sedentary or resident species most likely to be detected. Combined sampling with conventional non-invasive methods is advised to improve detection of habitat associations for mobile and transient species, or for species with low eDNA concentrations. </p>
Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"
<h1><strong>Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"</strong></h1> <p>This repository contains pre-processed data sets and code scripts to reproduce the data processing and analyses that are presented in the corresponding manuscript. Some minor pre-processing was completed before presenting this -- namely, the land cover raster was clipped to the study area of Knox County, Tennessee, USA, prior to placing in the repository to reduce the file size. </p> <h2><strong>How to use this repository to reproduce results </strong></h2> <p>This repository is designed to support the reproduction of analyses in the associated manuscript. The entire project can be downloaded and stored anywhere on your computer, as long as the file structure is not altered. The project contains folders with all data sets and code scripts necessary for analysis.</p> <p><strong>What you will need: </strong><br> - Installed R and RStudio for purely spatial cluster and global model analyses<br> - Basic understanding of how to open R and run code </p> <p><strong> You do NOT need:</strong><br> - To download or install R packages on your own; that is taken care of within this environment<br> - To write any code <br> - To set up any working directories in R </p> <h3><strong>Important: Using `renv`</strong></h3> <p>Short Version: When you open the R project, run `renv::restore()` and follow the prompts to install the necessary R packages. </p> <p>The R package `renv` was used to create a <strong>project library</strong>, which contains all R packages that are used by the project. The packages in the project library are <strong>the versions used during the original analysis</strong>. This means that if any packages are updated by developers in ways that would change the results of the analysis, this project can still produce the original results because of `renv`. When you open this project for the first time, `renv` will automatically download and install itself and ask you to run `renv::restore()`. <strong>You should run `renv::restore()` to automatically download and install all of the packages within this reproducible environment</strong>. </p> <h2><strong>## Basic step-by-step guide:</strong></h2> <p>- 1. Download the entire repository by clicking "Code -> Download ZIP" on GitHub or by downloading the ZIP file in Zenodo<br>- 2. Extract the ZIP file anywhere on your computer (do not change the structure of the files once extracted)<br>- 3. In RStudio, click *File -> Open Project* and browse to the location where you extracted the repository; in the repository file, open the knoxaedeslandcover R Project file <br>- 4. Open any of the R scripts in the `analysis/` folder<br>- 5. Run the code `renv::restore()` in the script or in the console and follow the prompt to install the packages <br> - Now you can run the R Scripts; start from the top with loading the packages and data, then work your way down line-by-line</p> <h3><strong># `analysis/` Folder</strong></h3> <p>The `analysis/` folder contains scripts for processing data and conducting analyses. Each file is an R script that should be opened in R studio. The first shows how to process and aggregate the various raw data files; if you are only interested in reproducing analyses from the manuscript, you can skip to the second file and work from there. </p> <p><strong><em>## Files within the `analysis/` folder</em></strong></p> <p>The files are numbered in the order that they were run for the original analysis. In this case, none of the analyses are dependent on the others, so they can technically be used in any order. The numbers associated with each file describe the order that the analyses would normally be run. </p> <p> - `(1)dataprep.R` contains the code for cleaning and combining the land cover, climate, and mosquito data -- this includes calculating the land cover percentages at different scales and calculating weekly and timelagged climate values<br> - `(2)summary_analysis.R` contains code for reproducing summary data and creating graphs from the manuscript<br> - `(3)variable_selection.R` contains code for asssessing collinearity and fitting models to identify the best fitting variables for each speceis<br> - `(4)finalmodels.R` contains code for fitting the final models using the selected variables for each species </p> <h3><strong># `data/` Folder</strong></h3> <p>This folder contains several datasets, including one that compiles them all for analyses (`knox_joined`). The raw data are included to show how the data was processed and aggregated, but the individual raw data files are not needed for analyses. See `data dictionary.txt` for a description of all attributes contained within each file. </p> <p><strong><em>## Files within the `data/` folder</em></strong></p> <p> - `knox22_joined.RDS` contains a cleaned and joined version of land cover, climate, and mosquito data in R Data Serialization format, which maintains predefined factor and numeric designations for columns. <br> - `knox22_joined.csv` contains a cleaned and joined version of land cover, climate, and mosquito data in CSV format -- identical to 'knox22_joined.RDS'<br> - `sites22.csv` contains the names, site codes, and coordinates of the study sites<br> - `aedes22_clean.csv` contains the raw mosquito collection data for the study without any climate or land cover information <br> - `NLCD_2019_landcover_clippedtoKnox.tif` contains the NLCD land cover data, already clipped to Knox County, TN, USA<br> - `knox22_temperature.csv` contains raw daily temperatures for the city of Knoxville in 2022<br> - `knox22_rainfall.csv` contains raw daily precipitation for the city of Knoxville watersheds in 2022<br> - `rainfall_stations.csv` contains the descriptions, approximate street addresses, and geographic coordinates for rainfall monitoring sites <br> - `data dictionary.txt` file that defines column names and other data attributes for every dataset </p> <h3><strong># `renv/` Folder</strong></h3> <p>The `renv/` folder contains bits and pieces needed for the `renv` package. Nothing should be altered in this folder. </p> <p> </p> <h2><strong>References for source data </strong></h2> <p> - Some of the data in this repository were originally obtained from open access sources. </p> <p> - Land cover data was obtained from the National Land Cover Database (NLCD) 2019 data product, specifically the "NLCD 2019 Land Cover (CONUS)" product. The original, unclipped raster can be freely downloaded here: https://www.mrlc.gov/data/nlcd-2019-land-cover-conus</p> <p> - Temperature data was downloaded from the United States National Oceanic and Atmospheric Administration (NOAA) weather station for Knoxville, Tennessee. The source data can be downloaded from this site: https://www.weather.gov/mrx/tysclimate</p> <p> - Rainfall data was obtained from the City of Knoxville rainfall data website, located here: https://www.knoxvilletn.gov/government/city_departments_offices/engineering/stormwater_engineering_division/rainfall_data</p> <p> - All mosquito collection data was collected directly by the manuscript authors</p>
Figure 2 in Fine-scale abundance variation in New Zealand migratory and non-migratory Galaxias fish species
Figure 2. – Boxplot showing the abundance of migratory (galbre; Galaxias brevipinnis) and non-migratory (galpau; G. paucispondylus and galvul; G. vulgaris) species at sites upstream of lakes (n = 18) and sites without lakes (n = 8). Boxplots show medians (horizontal line), 25th and 75th percentiles (upper and lower box limits), maximum and minimum values (bars), and mean (red dots). Outliers are presented by black circles.
Figure 1 in Fine-scale abundance variation in New Zealand migratory and non-migratory Galaxias fish species
Figure 1. – Map of survey sites, including sites upstream of Lakes Ohau, Pukaki and Tekapo (white boxes), and non-lake stream sites (grey shaded boxes). The number of fish caught at the different sites is shown within the boxes, with Galaxias brevipinnis at the top and non-migratory Galaxias spp. at the bottom of the boxes.
Figure 5 in Fine-scale abundance variation in New Zealand migratory and non-migratory Galaxias fish species
Figure 5. – Principal component analysis (PCA) biplot of the microhabitat environmental factors. Each dot repre- sents one sampling point. The symbols indicate sites upstream of lakes (circles) and sites without lakes (triangles), with the 95% confidence ellipses enclosing sample units from each group. Ellipses that do not overlap represent groups that differ significantly. Dim1, the first PCA axis; Dim2, the second PCA axis.
Figure 4 in Fine-scale abundance variation in New Zealand migratory and non-migratory Galaxias fish species
Figure 4. – Representation of size-class structure of Galaxias brevipinnis in lake tributary sites with different distance categories from their recruitment sources (lakes). Sample sizes (n) and distances (in km) are shown inside the panels.
Figure 3 in Fine-scale abundance variation in New Zealand migratory and non-migratory Galaxias fish species
Figure 3. – The relationship between square-root transformed galbre Galaxias brevipinnis and non-migratory species (galpau; G. paucispondylus) abundance with distance from the lakes.
ScienceDex guides
Understand access before you commit
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.