Find research datasets worth reusing

openCC0Oct 2024View details →

Point-count bird censusing: long-term monitoring of bird abundance and diversity along the Salt River in the greater Phoenix metropolitan area, ongoing since 2013

Waterways are often the focus of restoration efforts in urban areas. In arid regions, passive discharge of urban water sources may stimulate the recovery or growth of wetland and riparian features in dewatered or ephemeral aquatic systems. In the greater Phoenix metropolitan area (GPMA), sections of the Salt and Gila Rivers have been the targets of active restoration through seeding, planting, and irrigation. At the same time, revegetation has occurred in some sections of the rivers in response to runoff from urban water sources (e.g., storm drains). This dataset catalogs the results of bird surveys conducted at several locations along the Salt River in and around the GPMA beginning in March 2013. Monitoring locations focus on reaches of the river with different characteristics, including: (1) urbanized with perennial water and actively restored (n=2 reaches), (2) urbanized with perennial water and passively restored (n=2 reaches), (3) urbanized with ephemeral water but not restored (n=2 reaches), and (4) non-urban reference areas with perennial water (n=1 reach). This program expands on bird monitoring that the CAP LTER conducts at other locations in and around the GPMA, and complements herpetological surveys that are performed at these locations along the Salt River where the bird surveys are performed. This is a long-term monitoring effort of the CAP LTER with on-going data collection.

openCC0Jul 2024View details →

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

openCC (other)Mar 2022View details →

Bird Abundances at the Hubbard Brook Experimental Forest (1969-present) and on three replicate plots (1986-2000) in the White Mountain National Forest

Bird abundances have been determined from timed censuses, territory maps and nest locations at the Hubbard Brook Experimental Forest from 1969 to the present. This data set includes counts of the number of adult birds (males and females) per 10 ha at HBEF (1969 - present) and on three additional plots within the White Mountain National Forest (1986 - 2000). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Dec 2021View details →

The effects of changing vegetative composition on the abundance, species diversity and activity of birds at the Jornada Basin LTER site, 1997

This data package contains bird abundance data collected in plots that have had various plant functional groups or species experimentally removed at the Jornada Basin LTER site in southern New Mexico, USA. This data was collected in an effort to distinguish the differential effects of plant community biomass, plant community functional groups, and biodiversity within functional groups on plant community function, including effects on animals. To make these distinctions, treatments were established by the selective removal of plant species or functional groups within experimental plots. There are eight treatments: control (C, no removals); four functional group removal treatments (PG, perennial grass removed; S, shrubs removed; SSh, subshrubs removed; Succ, succulents removed), and three species richness manipulation treatments. Richness manipulations included a simplified treatment (Simp), where only the single most abundant species of each growth form is preserved and all other species in the growth form are removed, a reduced‐Larrea treatment (rL), where the Larrea is assumed to be the dominant and is removed while minority components remain, and a reduced-Prosopsis treatment (rP), where Prosopis rather than Larrea is removed as the shrub dominant. Following treatments, bird abundance and habitat preference data was collected in 1997. This data set consists of plot number, treatment type, and time of bird presence by taxa and by habitat and behavior. This study is complete.

openCC (other)Nov 2023View details →

SBC LTER: Beach: Time series of abundance of birds and stranded kelp on selected beaches, ongoing since 2008

Dataset contains the distribution, abundance and seasonal occurrence of birds, humans, dogs, and of freshly stranded giant kelp (Macrocystis pyrifera) plants and holdfasts present during monthly low-tide (<= 2.5 ft) weekday surveys of standard 1 km long transects on selected sandy beaches of the mainland coast of the Santa Barbara Channel. The abundance of humans, dogs, marine mammals and dead or oiled wildlife is also recorded during each survey. Sampling began in 2008 and is ongoing. See Methods for more information.

opencc-zeroMar 2016View details →

Bird migration case study dataset v1.1

Bird migration case study dataset, updated during the WG3 workshop ‘Visualisations: from show cases to production’ in 2015 by @peterdesmet. Changeset <ul> <li>Add aggregation instructions for bird migration altitude profiles</li> <li>Add instructions to create basemap</li> <li>Add forward trajectory data</li> <li>Change license to CC0</li> <li>Update README & documentation where necessary</li> </ul>

opencc-by-4.0Aug 2024View details →

Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe

The data in this repository were used to conduct the analysis outlined in the following bioRxiv preprint: <ul> <li>Sarah Hayes, Joe Hilton, Joaquin Mould-Quevedo, Christl Donnelly, Matthew Baylis, Liam Brierley (2025) "Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe" bioRxiv doi:10.1101/2024.07.17.603912</li> </ul> The codes used for the analyses are available at https://github.com/sarahhayes/avian_flu_sdm/  The following lookup table can be used to cross-reference between the variable descriptions in Tables 1 and 2 of the preprint and the files in this repository:   <table> <tbody> <tr> <td> <h3> Variable description </h3> </td> <td> <h3> Filename </h3> </td> </tr> <tr> <td> Minimum elevation (metres above sea level)  </td> <td> elevation_min_10kres.tif </td> </tr> <tr> <td> Maximum elevation (metres above sea level)  </td> <td> elevation_max_10kres.tif </td> </tr> <tr> <td> Difference between minimum and maximum elevation   </td> <td> elevation_diff_10kres.tif </td> </tr> <tr> <td> Modal elevation (metres above sea level)  </td> <td> elevation_mode_10kres.tif </td> </tr> <tr> <td> Normalised Difference Vegetation Index (NDVI)  </td> <td> ndvi_*_quart_2022_eco_rasts.tif </td> </tr> <tr> <td> Land cover </td> <td> landcover_output_full_2022_10kres.tif </td> </tr> <tr> <td> Distance to coast </td> <td> dist_to_coast_10kres.csv </td> </tr> <tr> <td> Distance to inland water </td> <td> dist_to_water_output_10kres.csv </td> </tr> <tr> <td> Relative humidity </td> <td> mean_relative_humidity_q*_10kres_eco_quarts.tif </td> </tr> <tr> <td>Seasonal weighted mean of the month-wise difference in the minimum temperature and maximum temperature (degrees Celsius)  </td> <td> mean_diff_*_quart_eco_rasts.tif  </td> </tr> <tr> <td>Seasonal weighted mean of monthly mean temperatures (degrees Celsius) (Mean monthly temperature for each month calculated using: Mean temperature = Minimum temperature + diurnal range/2)</td> <td> mean_mean_*_quart_eco_rasts.tif  </td> </tr> <tr> <td>Seasonal temperature variation (degrees Celsius) (Difference between the maximum and minimum of mean monthly temperature values across months majority-represented within the season)</td> <td> variation_in_quarterly_mean_temp_q*_eco_rasts.tif </td> </tr> <tr> <td> Precipitation  </td> <td> mean_prec_*_quart_eco_rasts.tif  </td> </tr> <tr> <td>Seasonal mean of daily zero-degree isotherm (metres above sea level) </td> <td> isotherm_mean_q*_eco_rasts.tif </td> </tr> <tr> <td>Number of days the zerodegree isotherm was below 1 metre at midday at Coordinated Universal Time (UTC)  </td> <td> isotherm_midday_days_below1_q*_eco_quarts.tif </td> </tr> <tr> <td> Chicken density </td> <td> chicken_density_2010_10kres.tif </td> </tr> <tr> <td> Duck density </td> <td> duck_density_2010_10kres.tif </td> </tr> <tr> <td> Anatinae (dabbling ducks) </td> <td> anatinae_rast_eco_bds.tif </td> </tr> <tr> <td> Anserinae (swans and geese) </td> <td> anserinae_rast_eco_bds.tif </td> </tr> <tr> <td> Ardeidae (herons)  </td> <td> ardeidae_rast_eco_bds.tif </td> </tr> <tr> <td> Arenaria/Calidris (turnstones and sandpipers) </td> <td> arenaria_calidris_rast_eco_bds.tif </td> </tr> <tr> <td> Aythyini (diving ducks)</td> <td> aythyini_rast_eco_bds.tif</td> </tr> <tr> <td> Laridae (gulls) </td> <td> laridae_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding within 2m of water surface </td> <td> around_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding >2m below water surface </td> <td> below_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet plants </td> <td> plant_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet scavenging </td> <td> scav_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet endothermic vertebrates </td> <td> vend_rast_eco_bds.tif </td> </tr> <tr> <td> Congregative </td> <td> cong_rast_eco_bds.tif </td> </tr> <tr> <td> Migratory </td> <td> migr_rast_eco_bds.tif </td> </tr> <tr> <td> Below threshold phylogenetic distance to known host species  </td> <td> host_dist_rast_eco_bds.tif </td> </tr> <tr> <td> Species richness </td> <td>  species_richness_rast_eco_bds.tif </td> </tr> </tbody> </table>

opencc-by-4.0Oct 2023View details →

Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.

The data files for figures in Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:Figure 1The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:<ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables.  </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables.  </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul>Figure 2<ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul>Figure 3<ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul>Figure 4The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.Figure 5The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.Figure 6The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.  Figure 7The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.Figure 8The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.Figure S1 and Figure S3The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.Figure S2<ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul>Figure S4<ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents.  </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents.  </li></ul>Figure S5There are no data associated with this figure. This figure was made using Microsoft Powerpoint.Figure S6The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.

opencc-by-4.0Dec 2021View details →

Dataset for 'Measuring and explaining disagreement about bird taxonomy'

Dataset used for a research project that measures, classifies and explains disagreement about bird taxonomy. This is version 3, which has new data on research effort for each of the birdlife concepts, and no longer contains data about ecological and geographical predictors. This version of the data is used in the submission to EJT in november 2023.

opencc-by-4.0Dec 2020View details →

European_bird_dispersal: v1.0.3-Edispersal

Standardised empirical dispersal kernels emphasise the pervasiveness of long-distance dispersal in European birds  ABSTRACT: <ol> <li>Dispersal is a key life-history trait for most species and is essential to ensure connectivity and gene flow between populations and facilitate population viability in variable environments. Despite the increasing importance of range shifts due to global change, dispersal has proved difficult to quantify, limiting empirical understanding of this phenotypic trait and wider synthesis. </li> <li>Here we introduce a statistical framework to estimate standardised dispersal kernels from biased data. Based on this, we compare empirical dispersal kernels for European breeding birds considering age (average dispersal; natal, before first breeding; and breeding dispersal, between subsequent breeding attempts) and sex (females and males) and test whether different dispersal properties are phylogenetically conserved. </li> <li>We standardised and analysed data from an extensive volunteer-based bird ring-recoveries database in Europe (EURING) by accounting for biases related to different censoring thresholds in reporting between countries and to migratory movements. Then, we fitted four widely used probability density functions in a Bayesian framework to compare and provide the best statistical descriptions of the different age and sex-specific dispersal kernels for each bird species. </li> <li>The dispersal movements of the 234 European bird species analysed were statistically best explained by heavy-tailed kernels, meaning that while most individuals disperse over short distances, long-distance dispersal is a prevalent phenomenon in almost all bird species. The phylogenetic signal in both median and long dispersal distances estimated from the best-fitted kernel was low (Pagel’s λ < 0.25), while it reached high values (Pagel’s λ >0.7) when comparing dispersal distance estimates for fat-tailed dispersal kernels. As expected in birds, natal dispersal was on average 5 km greater than breeding dispersal, but sex-biased dispersal was not detected.</li> <li>Our robust analytical framework allows sound use of widely available mark-recapture data in standardised dispersal estimates. We found strong evidence that long-distance dispersal is common among European breeding bird species and across life stages. The dispersal estimates offer a first guide to selecting appropriate dispersal kernels in range expansion studies and provide new avenues to improve our understanding of the mechanisms and rules underlying dispersal events. </li> </ol> Content <ul> <li>The workflow for estimating dispersal kernels from ring-recovery data for all of Europe</li> <li>The code to develop the dispersal kernels with ring-recovery data. </li> <li>Dispersal distances for European birds: <a href="/api/files/0afdcc68-9a21-4e61-b8ff-9eb74f3c0d70/Table_S14_ species_dispersal_distances_v1_0_2.csv?versionId=1e3c628b-88c0-4b20-aad7-a5bf06e21bec">Table_S14_ species_dispersal_distances_v1_0_2.csv</a></li> <li>Dispersal kernel parameters for European birds: <a href="/api/files/0afdcc68-9a21-4e61-b8ff-9eb74f3c0d70/Table_S13_species_dispersal_parameters_v1_0_2.csv?versionId=195bd982-9d5e-47a1-b7d8-cf5a101f3603">Table_S13_species_dispersal_parameters_v1_0_2.csv</a></li> </ul>

opencc-by-4.0Apr 2024View details →

Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population

<h1>Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population</h1> <h1> </h1> These files contain data on bacteria present in the guts of wild great tit (Parus major)  obtained from faecal samples and sequenced using Illumina MiSeq. These data resulted from an experiment which provided supplementary mealworms at the nest during the breeding season at number of woodland sites in Cork, Ireland. Approximately half of these nests were given mealworms covered in a freeze dried bacterial powder containing the bacteria Lactobacillus kimchicus, which had been isolated from great tit faeces from the previous season. This treatment aimed to disrupt the gut microbiota of the treatment birds in order to provide evidence for the gut microbiotas role in birds health and fitness. Included here are the 3 elements necessary to create a 'phyloseq object' containing the sample metadata, ASV (Amplicon Sequence Variant) count table and a taxonomy table. The metadata file includes the alpha diversity scores for each individual. The data include all negative control samples taken during sample collection and library preparation, which were removed before the main analyses. All analyses, except for the beta-diversity analyses, were conducted in R. All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697. <h2> </h2> <h2>## Description of the data and file structure </h2> Taxonomy, ASV and metadata files required to create a phyloseq object in R. metadata.csv file contains data on individual birds (i.e. individual samples). The metadata includes descriptions of the bird itself and it's environment, namely: <ul> <li>Rownames: unique sample ID for each sample, corresponds with asvTable.csv. </li> <li>Nest: unique identifier for the nest box associated with the bird being sampled. </li> <li>Sample.ID: unique identifier for the faecal sample or control sample.</li> <li>Bird.ID: Identity of the bird the sample came from, note some individuals sampled twice so some bird.ID's may reoccur in metadata with different Sample.ID.</li> <li>Date: Date the sample was taken dd/mm/yyyy.</li> <li>Day: Date the sample was taken, in days since 1st March.</li> <li>Ring.Mark: British Trust for Ornithology (BTO) metal ring ID where applicable. Birds only ringed at D15 so some young birds do not have IDRings.</li> <li>Site: ID of woodland site  that bird was sampled at.</li> <li>Chick.LetterID: ID letter differentiates between different birds from the same nest. Either 'A'-'F' for nestlings, 'Fe' for females or 'M' for males.</li> <li>Age.code: BTO age code.</li> <li>Age.category: Age category that bird is in. D8 = 8 days post hatching, D15 = 15 days post hatching, adult = 1+ years post hatching.</li> <li>Sex: Bird's sex, only determined for adult birds. Fe = Female, M = Male.</li> <li>Wing_mm: Wing length in mm.</li> <li>Tarsus_mm: minimum tarsus length of bird in mm.</li> <li>Weight_g: bird's weight in grams.</li> <li>Faecal.Sample: bird's age at sampling.</li> <li>newRing: whether bird was fitted with a new BTO ring. Only relevant to adults.</li> <li>Treatment: the experimental treatment group that the bird was in. Either 'Treatment' when nest given L. kimchicus treated mealworms or 'Control' when nest given plain mealworms.</li> <li>Notes: field notes.</li> <li>Main.sample: indicates whether this sample was the main sample to be used for analysis, an alternative sample taken as a backup.</li> <li>Plate: the ID of the PCR plate which the sample was amplified on.</li> <li>Azenta_noPeriod: sample ID given to sequencing facility without special characters. Corresponds to fastq files and ASV table counts.</li> <li>Qubit_prePool: samples qubit score before pooling.</li> <li>Date_extracted: date the sample was extracted on dd/mm/yyyy.</li> <li>SampleType: whehther the sample was a 'main' sample intended for downstream analysis, a 'control' sample for detecting contamination during library preparation, a 'duplicate' for detecting PCR issues, a 'label_error' where sample was suspected of being mislabelled at some point, a 'repeat' sample intended to detect errors or issues, a 'contam' sample which was suspected of being contaminated,  a 'common' sample used across different PCR plates to detect issues. Extraction_notes: notes regarding the DNA extraction of the sample.  </li> <li>LibPrep_notes: notes regarding the library preparation of the sample.</li> <li>Ring.Mark.lab: the ring or sample ID written on the sample tube, recorded to help detect mislabelling.</li> <li>Post_lab_notes: notes regarding issues found post sequencing.</li> <li>NumberOfReads: number of sequence reads associated with the sample.   </li> <li>DistanceToEdge: distance between nest and woodland edge in metres.   </li> <li>BroodSize.D8: number of nestlings in the nest at day-8 post hatching.   </li> <li>BroodSize.D15: number of nestlings in the nest at day-15 post hatching.</li> <li>firstEggLayDate: Date the first egg in the clutch was laid, in days since 1st March.</li> <li>lastEggLayDate: Date the last egg in the clutch was laid, in days since 1st March.</li> <li>Observed: number of unique ASV's (or taxa) detected in the sample.</li> <li>Chao1: Chao1 diversity of the sample.</li> <li>Shannon: Shannon diversity of the sample.</li> </ul> The file 'taxonomy.csv' contains the taxonomic breakdown of each bacterial Amplicon Sequence Variant (ASV) found in the dataset from Phylum to Species. Obtained by using the Naive Bayes Classifier against the Silva (v138) taxonomic database. The file 'asvTable.csv' contains counts of each amplicon sequence variant's occurrence for each individual sample. Samples are rows and taxa are columns.   <h2>Sharing/Access information </h2> All R code is available on GitHub (https://github.com/shan-e-s\).  Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.

opencc-by-4.0Jul 2022View details →

Supplemented material to "Mycobacteriosis in various pet and wild birds from Germany: pathological findings, coinfections, and characterisation of causative Mycobacteria."

This is the supplemented material to the publication "Mycobacteriosis in Various Pet and Wild Birds from Germany: Pathological Findings, Coinfections, and Characterization of Causative Mycobacteria". The causative agents and confounding factors of mycobacteriosis in a set of pet (n=45) and some wild birds (n=5) from Germany were examined in this study. Not only Mycobacterium genavense (Mg), but also M. avium subsp. avium (Maa) and M. avium subsp. hominissuis (Mah), contributed to mycobacteriosis in these birds. The isolates were characterized by a combination of different typing methods. The genetic diversity of isolates belonging to Mg, Maa and Mah differed. Various coinfections by viruses, endoparasites, fungi and other bacterial species did not affect the manifestation of mycobacteriosis. Cross pathological fidings were more often seen in mycobacteriosis caused by Ma compared to Mg suggesting a different pathogenicity of the two species. New genotypes of Mah were identified in these birds that is important for epidemiological studies and for understanding the zoonotic role of this pathogen, as the subsp. hominissuis represents an increasing public health concern. The study provides some evidence of correlation between individual Maa genotypes and virulence which will have to be confirmed by broader studies.

opencc-by-4.0Jul 2024View details →

Data from Davison et al. (2024) Changes in Danish bird communities over four decades of climate and land-use change

Environmental and biodiversity data associated with the article: Davison, C. W., Rahbek, C., & Morueta-Holme, N. (2024) Changes in Danish bird communities over four decades of climate and land-use change. Oikos. https://doi.org/10.1111/oik.10697 Data on local bird species richness, functional diversity, temporal and spatial turnover (beta diversity), abundance, and biomass at volunteer led survey routes across Denmark. Matched habitat data (from volunteers) and historical climate data (E-OBS). Bird observations are a subset of the Common Bird Monitoring programme (DOF – BirdLife Denmark) that include routes surveyed in the summer season, spanning ≥10 years, and with full GPS coordinates. This excel document contains all of the derived (and anomysied) data used in the final analyses and includes metadata describing the variables. Climate and trait data were obtained from open-access databases (see references). Metadata is included in the excel file. <ul> <li>Danish Common Bird Monitoring programme – Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. Dansk Ornitologisk Forening.</li> <li>E-OBS European gridded climate data – Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., & New, M. (2008). A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. Journal of Geophysical Research Atmospheres, 113(20). https://doi.org/10.1029/2008JD010201</li> <li>AVONET bird traits data – Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581–597. https://doi.org/10.1111/ele.13898</li> </ul>

opencc-zeroApr 2023View details →

Functional traits database for North American birds

Estimation of functional diversity in biological communities requires extensive and complete data on numerous functional traits of species or even individuals. When estimating functional diversity at large scales, this fact possesses an issue that may be hard to overcome: for many species, there might not be sufficient data on their functional traits. In such cases, even if there is missing information on functional trait value for one species in a community, this makes the trait impossible to use for the estimation of the functional diversity of a community. On the other hand, there are available datasets on the functional traits of all extant species within certain lineages across the world, but such datasets are often limited to very few functional traits, missing some dimensions of species' ecological niches. In this dataset, I compiled the available data from various sources that describe 23 functional traits of 703 bird species that occur in Canada, the United States, and Mexico. These functional traits include the following: diet type, diurnal and nocturnal feeding, diet items, feeding methods, feeding substrate, nest type, nest substrates, breeding system, chick development at hatching, nest aggregation, clutch size, first breeding age, number of clutches a year, breeding success, adult annual survival, mean biomass, maximum lifespan, hand-wing index, kleptoparasitism, nest parasitism, and the extent of dependency on other species for building a nest.

opencc-by-4.0Aug 2022View details →

Arctic specimens in the NHMO DNA bank Bird collection 2022

All Arctic specimens in the NHMO DNA bank Bird collection as of August 2022. See Johannessen et al. 2023 "Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)" for further details.

opencc-by-4.0Aug 2022View details →

Arctic specimens in the NHMO Bird collection 2022

All Arctic specimens in the NHMO Bird collection as of August 2022. See Johannessen et al. 2023 "Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)" for further details.

openCC (other)Jun 2023View details →

Frugivoria: A trait database for birds and mammals exhibiting frugivory across contiguous Neotropical moist forests

Biodiversity in many areas is rapidly shifting and declining as a consequence of global change. As such, there is an urgent need for new tools and strategies to help identify, monitor, and conserve biodiversity hotspots. One way to identify these areas is by quantifying functional diversity, which measures the unique roles of species within a community and is valuable for conservation because of its relationship with ecosystem functioning. Unfortunately, the trait information required to evaluate functional diversity is often lacking and is difficult to harmonize across disparate data sources. Biodiversity hotspots are particularly lacking in this information. To address this knowledge gap, we compiled Frugivoria, a trait database containing dietary, life-history, morphological, and geographic traits, for mammals and birds exhibiting frugivory, which are important for seed dispersal, an essential ecosystem service. Accompanying Frugivoria is an open workflow that harmonizes trait and taxonomic data from disparate sources and enables users to analyze traits in space. This version of Frugivoria contains mammal and bird species found in contiguous moist montane forests and adjacent moist lowland forests of Central and South America– the latter specifically focusing on the Andean states. In total, Frugivoria includes 45,216 unique trait values, including new values and harmonized values from existing databases. Frugivoria adds 23,707 new trait values (8,709 for mammals and 14,999 for birds) for a total of 1,733 bird and mammal species. These traits include diet breadth, habitat breadth, habitat specialization, body size, sexual dimorphism, and range-based geographic traits including range size, average annual mean temperature and precipitation, and metrics of human impact calculated over the range. Frugivoria fills gaps in trait categories from other databases such as diet category, home range size, generation time, and longevity, and extends certain traits, once only a

openCC (other)Dec 2023View details →

Bird Communities in Fragmented, Non-Native Pine Plantations in the Oak Openings Region of Northwest Ohio

Comprehensive surveys, while preferred, are not always feasible due to time, logistical, and funding constraints. However, limited surveys of focal taxa, such as birds, coupled with vegetation surveys, can provide critical information to guide land management. In the 1930s non-native conifers were planted in the Oak Openings Region of northwestern Ohio, a biodiversity hotspot. The stands are declining, and management is needed, but restoration to native habitat is time consuming and expensive. Our research utilized an avian perspective of ecological function of introduced pine plantations versus native remnants to guide management. We surveyed bird activity May through July 2020 with point-counts in nine sites (1.3-2.3 ha) with three each of white pine, red pine, and oak forest sites. At each site, we estimated bird richness, abundance, and diversity, as well as structural characteristics (e.g., canopy cover), composition (e.g., vegetation types), and landscape context (e.g., landcover). Superficially, the pine sites appear to be beneficial as pine habitat for breeding birds, with high Simpson’s indices (up to 0.89) and high species richness compared to oak sites. However, our results reveal that the pines are not truly functioning as pine habitat for birds based on the limited occurrence of pine specialist species, proportion of generalists to pine specialists, and landscape context. Simple measures of diversity with no consideration as to species identity and without the environmental context fail to provide reliable measures of ecological value. Instead, we recommend selective sampling and consideration of landscape context, vegetation structure, and species classification to guide management.

openCC0Jul 2021View details →

CBP01 Variable distance line-transect sampling of bird population numbers in different habitats on Konza Prairie (Reformatted to the ecocomDP Design Pattern)

This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-knz/26/11. The abstract below was extracted from the Level 0 data package and is included for context: Records of bird species based on line transect sampling, giving perpendicular distance of sighting from the transect line on 16 separate transects. Bird surveys were conducted 2-4 times per year in January, April, June, and October for a 29-year period from 1981 to 2009. Transects were designed to determine bird communities and population numbers associated with tallgrass prairie habitats with different experimental treatments (fire frequency, grazed by bison vs. ungrazed), riparian habitats on forest edge, and gallery forests dominated by oak woodland.