The AVONET database contains comprehensive functional trait data for all birds, including six ecological variables, eleven continuous morphological traits, and information on range size and location. Raw morphological measurements are available from 90020 individuals of 11009 extant bird species sampled from 181 countries. These data are also summarised as species averages in three taxonomic formats, allowing integration with a global phylogeny, geographical range maps, IUCN Red List data, and the eBird citizen science database. The full AVONET dataset including raw morphological measurements as well as species averages for each taxonomy is provided in 'AVONET Supplementary dataset 1' Data on duplicate measurements for a subset of individuals are provided in 'Supplementary dataset 2'Data and Code to reproduce the analyses and figures presented in Tobias et al 2022 (Ecology Letters doi: https://doi.org/10.111/ele.13898) is included in the 'ELEData' and 'ELECode' zip files.

This dataset combines modified data from the OBBA (2001-2005) (Bird Studies Canada, 2008), the Avonet dataset (Tobias et al., 2022), and the Ontario Land Cover Compilation v2 (Ontario Ministry of Natural Resources and Forestry, 2014), specifically covering the Canadian portion of the Great Lakes Basin Watershed. It includes data on 211 species from the OBBA, though all location-identifying information has been excluded. The full dataset can be requested via the Nature Counts Portal (https://naturecounts.ca/nc/default/explore.jsp#download). Species trait data originate from the Avonet dataset, with detailed trait descriptions available from the original Avonet dataset.

Land cover classifications were simplified into two categories: "Natural" and "Human Modified." Although an "Other" category was initially present, it was removed prior to model development. The data was resampled into various pixel sizes, ranging from the original 15m resolution to 150m, 300m, 500m, and 1000m.

The dataset is divided into a 70/30 train-test split and was used in building random forest models. For further details, please refer to the original dataset sources.

References

Bird Studies Canada, Environment Canada’s Canadian Wildlife Service, Ontario Nature, 553 Ontario Field Ornithologists and Ontario Ministry of Natural Resources. (2008). Ontario 554 Breeding Bird Atlas Database. https://naturecounts.ca/nc/default/explore.jsp#download Ontario Ministry of Natural Resources and Forestry. (2014). Ontario Land Cover Compilation 705 Data Specifications Version 2.0. https://ws.gisetl.lrc.gov.on.ca/fmedatadownload/Packages/OntarioLandCoverComp-v2.zip Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Sayol, F., Neate‐Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño‐Centellas, F. A., Claramunt, S., Darski, B., Freeman, B. G., Bregman, T. P., … Coulson, T. (2022). AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581–597. https://doi.org/10.1111/ele.13898

Data and code to reproduce "The influence of geographic ranges, climatic niches, and temperature fluctuations on population variability"Please, cite the appropriate data sources if using any of the data below.The analyses_tencatenPRSB.r file has the code needed to reproduce the analyses and figures present in the manuscript. The functions_tencatenPRSB.r file has the functions needed to run the code in the analyses_tencatenPRSB.r file.Resident_ranges.zip has the birdLife expert geographic range shape files for the species considered in our study. This file needs to be extracted in order to be able to import geographic ranges.BBS_Phylo.nex has the phylogeny obtained from Jetz et al. (2012) used in our study.BBS_Traits.csv has the body size and dispersal traits from the AVONET database (Tobias et al. 2022) for the species used in our study.ChelseaCV.tif and ChelseaequalCV.tif are the rasters with temperature variability that were estimated from the CHELSA database (Karger et al. 2017) for conformal and Behrmann's equal area projections respectively.Bio_PCA.tif and Bio_PCA_equal.tif are the rasters with the two first axes of the PCA of the 19 bioclimatic variables available in CHELSA (Karger et al. 2017) for conformal and Behrmann's equal area projections respectively.aaw1313_data_s1.xlsx table with data used to obtain information on which species sampled by BBS that are resident. This table is available in the supporting information of Rosenberg et al. (2019)Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K., & Mooers, A. O. (2012). The global diversity of birds in space and time. Nature, 491(7424), 444-448.Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., ... & Kessler, M. (2017). Climatologies at high resolution for the earth’s land surface areas. Scientific data, 4(1), 1-20.Rosenberg, K. V., Dokter, A. M., Blancher, P. J., Sauer, J. R., Smith, A. C., Smith, P. A., ... & Marra, P. P. (2019). Decline of the North American avifauna. Science, 366(6461), 120-124.Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J., Yang, J., Sayol, F., ... & Schleuning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581-597.

Aim: Mountain ecosystems are hotspots of biodiversity due to their high variation in climate and habitats. Yet, above average rates of climate change and enhanced forest disturbance regimes alter local climatic conditions and vegetation structure, which should impact biodiversity. Here, we investigated the impact of vegetation and climate as well as their interactions on bird communities to improve our ability to predict climate-change effects on bird communities. Location: European Alps, Germany Methods: We studied patterns and drivers of bird communities at 213 plots along gradients in vegetation density and elevation using autonomous sound recorders. Bird species were identified from soundscapes by Convolutional Neural Networks (BirdNET) and taxonomists. Results: Bird diversity and community metrics were moderately to strongly correlated for data based on either identification by BirdNET or taxonomists, and ecological findings were overall similar for both datasets. Vegetation density 1-2 m and >2 m above ground strongly affected bird diversity and community composition and mediated effects of elevation. Community composition changed with elevation more strongly in habitats with low than high vegetation density >2 m. Species numbers decreased with elevation in habitats with low vegetation density 1-2 m and >2 m above ground, but increased in habitats with high vegetation density. Overall, functional and phylogenetic diversity increased with elevation indicating lower habitat filtering, but patterns were also mediated by vegetation density. Main conclusions: Our results indicate that bird communities in the German Alps are determined by strong interactive effects of elevation and vegetation, underlining the importance to consider variation in vegetation in studies of biodiversity patterns along elevation gradients and under climate change. Combining remote sensing data and biodiversity monitoring based on autonomous sampling and AI-based species identification opens new avenues for bird monitoring and research in remote areas. Methods Bird sampling We used bioacoustic audio recorders (BAR, Frontier Labs, Salisbury, Australia; standard settings) to capture soundscapes in 2021. Recorders had to be moved between plots and could not be installed permanently due to the limited availability of recorders. On each plot, recording took place on four to five days distributed evenly between late winter (mid March) and late summer (mid August) in the submontane, montane, and subalpine zone. In the subalpine and alpine zone, only three to four recordings were conducted between late April and mid August due to snow cover restricting access in spring. Recording was limited to days with no or negligible rain and low wind speed. Recorders were placed at approximately 1.8 m height close to the plot centre, either attached to a tree or wooden pole. Recorders were programmed to record for two minutes every twelve minutes from two hours before to four hours after sunrise and from three hours before sunset to three hours after sunset. Bird identification For species identification by taxonomists, we selected the first 2 min of every hour of the morning recording, that is, 12 min per plot and sampling day. However, since owls typically sing early in the season (Südbeck et al., 2005), we omitted the recording from 2 h before sunrise from the second sampling on and only used the subsequent five recordings, that is, 10 min per plot and sampling day. Taxonomists (R.M., Lu.G., and others (see acknowledgments)) identified vocalizing species and documented each species as presence/ and absence for each recording. For further analyses, we excluded all species which are not breeding bird species of 225 terrestrial habitats of the region to avoid spurious results due to species not associated with the environmental conditions of our plots. For species identification with BirdNET (version 2.4), a Convolutional Neural Network (Kahl et al., 2021), we used all recordings, that is, 60 min around sunrise and sunset. All species that are not breeding bird species of terrestrial habitats of the region were then excluded. We validated 7399 classifications across 89 (out of 98 species identified by CNN) in order to identify species specific confidence thresholds that maximize the separation between correct and incorrect identifications. R.M. reviewed 5527 3-s segments and categorized the BirdNET classifications either as true or false positive. We further used annotations of our recordings done by Lu.G. at the Bird Sounds Global platform (https://bsg.laji.fi/) of the LIFEPLAN research programme (https://www.helsinki.fi/en/projects/lifeplan). The annotations were provided with a timestamp which allowed us to match and categorize 1874 additional classifications. For all species with more than 30 true positive classifications, we fitted Conditional Inference Trees to identify species-specific confidence thresholds. For species with 5 to 30 true positive classifications, we visually inspected the distribution of true and false positives along the confidence axis. If the distribution of true and false positives showed a discriminable pattern, we assigned them to one of three threshold classes (0.3, 0.5 and 0.8). If true and false positives were similarly distributed along the confidence axis or if less than 5 true positive classifications were available, we used the highest threshold class (0.8). Trait data and phylogeny We downloaded the bird megatree by Jin and Qian (2023) based on Jetz et al. (2012), which was pruned to the species identified by one of the two methods applied in our study. Moreover, we compiled information on 11 morphometric traits, two habitat-related traits, migratory behaviour and trophic level from the AVONET database (Tobias et al., 2022). Morphometric traits were corrected for their relationship with body size by taking residuals from linear models with respective traits as response (log-scale) and body mass (log-scale) as predictor (Hagge et al., 2021). Based on correlations among morphometric traits we selected the continuous traits body mass, hand wing index, beak length, beak width, tail length and tarsus length for further analyses. In addition, analyses included preferred habitat (ordinal: 1 = dense, 2 = semi-open, 3 = open), migratory behaviour (ordinal: 1 = sedentary, 2 = partially migratory, 3 = migratory) and trophic 260 level (categorical: herbivore, carnivore, and omnivore). The trait trophic level was converted into two binary traits herbivore (0/1) and carnivore (0/1), whereas omnivores were binned 1 in both binary traits. Environmental data We measured the coordinates and elevation of each plot centre using a Trimble r12i GNSS receiver. To characterize vegetation at each plot, we used a high-resolution LiDAR dataset acquired in September 2021 during leaf-on conditions using a helicopter-mounted Riegel VQ-780i sensor with average point density of ~50 points m2 (Mandl et al., 2023). Vegetation parameters were calculated within a 25 m radius around the plot. Vegetation density >2 m above ground and 1-2 m above ground were calculated as the proportion of returns within these height layers, and the variation in vegetation height was characterized as the standard deviation of LiDAR returns. In addition, we used data on herb layer cover (<1 m above ground) and shrub layer cover (1-5 m above ground) from ground-based vegetation surveys conducted on one 4 m x 4 m vegetation survey area per plot (Braziunas et al., 2024). We tested for collinearities between vegetation characteristics calculating pairwise Pearson'´s correlation coefficients and by conducting a principal component analysis. Based on these results (Figure S3), we selected LiDAR-based vegetation density >2 m above ground and vegetation density 1-2 m above ground as predictors for bird analyses since they represent different vegetation layers, were not correlated strongly and reflected a larger area around the plot centre than the parameters derived from the vegetation surveys.

This dataset and R code support a study about the effects of landscape structure on the functional diversity of bird assemblages in the Upper Magdalena River Valley, Colombia. The research explores how habitat loss and fragmentation impact bird communities by analyzing functional traits across different land-use gradients. Bird abundance data were collected through mist nets, point counts and eBird records, while functional traits were obtained from the AVONET database. The dataset includes morphological and ecological trait data, landscape metrics and functional diversity indices. The provided R scripts enable users to conduct statistical analyses, including functional diversity calculations, generalized linear models (GLMs), and multivariate analyses (PCA, db-RDA, RLQ and fourth-corner analysis).

Socially monogamous birds may break up their partnership by a so-called ’divorce’ behaviour. Divorce rate immensely varies across avian taxa that have a predominantly monogamous social mating system. Although a range of factors associated with divorce have been tested, broad-scale drivers of divorce rate remain contentious. Moreover, the impact of sexual roles in divorce still needs further investigation due to the conflicting interest of males and females. Here we applied phylogenetic comparative methods to analyse one of the largest datasets ever compiled that included divorce rates from published studies of 186 avian species from 25 orders and 61 families. We tested correlations between divorce rate and a group of factors: ‘promiscuity’ of both sexes (propensity of polygamy), migration distance, and adult mortality. Our results showed that only male promiscuity, but not female promiscuity, had a positive relationship with divorce rate. Furthermore, migration distance was positively correlated with divorce rate, while adult mortality rate showed no direct relationship with divorce rate. These findings indicated that divorce might not be a simple adaptive (by sexual selection) or non-adaptive strategy (by accidental loss of a partner), but could be a mixed response to sexual conflict and stress from the ambient environment. Methods We used data from Kenny et al. (2017), Liker et al. (2014), Botero et al. (2012), Handbook of the Birds of the World (https://birdsoftheworld.org) and other published literature (cited in the table). For migration distance, we used data from Delhey et al. 2021. Adult mortality rate was extracted from the AVONET database. Our final dataset contains 232 avian species from 25 orders and 61 families, and the number of species with the full dataset is 186.

Botero, C.A., Dustin, R. , & Rubenstein. (2012). Fluctuating environments, sexual selection and the evolution of flexible mate choice in birds. PLoS ONE, 7(2), e32311. 10.1371/journal.pone.0032311

Delhey, K. , Dale, J. , Valcu, M. , & Kempenaers, B. . (2021). Migratory birds are lighter coloured. Current Biology, 31(23), R1511-R1512. 10.1016/j.cub.2021.10.048

Kenny, K. , Birkhead, T. R. , & Green, J. P. (2017). Allopreening in birds is associated with parental cooperation over offspring care and stable pair bonds across years. Behavioral Ecology, 28(4), 1142-1148. 10.1093/beheco/arx078

Liker, A. , Freckleton, R. P. , & Székely, T. (2014). Divorce and infidelity are associated with skewed adult sex ratios in birds. Current Biology, 24, 880–884. 10.1016/j.cub.2014.02.059 Tobias, J.A., Sheard, C., Pigot, A.L., Devenish, A.J.M., Yang, J., Sayol, F., et al. (2022) AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25, 581– 597. 10.1111/ele.13898

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.

• 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.
• 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
• 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

This repository contains the data and code needed to reproduce the analyses in:Ten Caten, C., Dallas, T., Latitudinal specificity of plant-avian frugivore interactions.Please, If using any of the datasets below, cite the source that compiled the data (full citations are provided when applicable)net.long.txt file contains the interaction data compiled by Fricke, Evan C., and Jens-Christian Svenning. "Accelerating homogenization of the global plant–frugivore meta-network." Nature 585.7823 (2020): 74-78.Species_phylogeny_Diff.txt contains that harmonized names of some bird species output.nex contains the frugivore phylogenies obtained from Jetz, Walter, et al. "The global diversity of birds in space and time." Nature 491.7424 (2012): 444-448.AVONET_Raw_Data.csv has the bird functional data compiled by Tobias, Joseph A., et al. "AVONET: morphological, ecological and geographical data for all birds." Ecology Letters 25.3 (2022): 581-597.28554.txt, 27783.txt, and 27782.txt are files that have functional data obtained from the TRY database. Kattge, Jens, et al. "TRY plant trait database–enhanced coverage and open access." Global change biology 26.1 (2020): 119-188.Functions_JAE.R has all the functions used in the Analyses_JAE.R file. Functions_JAE.R should be compiled before running the code in Analyses_JAE.RAnalyses_JAE.R has all the code necessary to reproduce the analyses presented in the manuscript.

Resources for: Spatio-temporal integrated Bayesian species distribution models reveal lack of broad relationships between traits and range shifts ## Overview This repository is supporting information for the manuscript: Wiethase, J. H., Mostert, P.S., Cooney, C., O'Hara, R.B. & Beale, C. M. Spatio-temporal integrated Bayesian species distribution models reveal lack of broad relationships between traits and range shifts. The scripts cover the entire workflow from data preparation to model building, sensitivity analysis, result extraction, regression analysis, and visualization. Also included are the required model data, model output as a .csv file, output figures, and supporting source files containing functions. The species observation data were derived from the Tanzania Bird Atlas and eBird. ## Contents - scripts (folder R) 1. 1_Make_SDM_general_data.R * Prepares general data for the INLA Species Distribution Models (SDMs). Original species observation data will have to be obtained from eBird (https://science.ebird.org/en/use-ebird-data/download-ebird-data-products) or the Tanzania Bird Atlas (http://www.tanzaniabirds.net). 2. 2_make_species_SDM_data.R * Creates species-specific SDM data. 3. 3_make_model_files.R * Creates model files for the INLA models. 4. 4_Run_prior_mesh_sensitivity_analysis.R * Conducts sensitivity analysis related to priors and mesh choice in the INLA models. 5. 5_check_effort_effect.R * Analyses the effect of sampling effort on INLA model results. 6. 6_Make_final_SDMs.R * Creates the final INLA SDMs, based on the sensitivity analysis. 7. 7_Get_INLA_results.R * Extracts results from the final INLA models. 8. 8_Make_regression_data.R * Prepares data for regression analysis of range shifts ~ traits. 9. 9_Run_trait_analysis.R * Runs the regression analysis of range shifts ~ traits. 10. 10_Make_manuscript_figures.R * Creates figures and tables for the manuscript. ## Contents - scripts (folder source) 1. Various * Collection of custom R functions used in different stages of the analysis. ## Contents - data (folder model_data) 1. trait_model_data.csv * The data set used for the for regression analysis of range shifts ~ traits. Each row consists of transition metrics as well as traits for a given species. Trait variables correspond broadly to metadata tab in Avonet2_eBird.xlsx. Additional trait variables are sensitivity traits as percentage of variation explained by environmental covariates alone (with name imp), as well as traits related to habitat breadth, as range of values where the probability of presence is above 0.5 in the effect plots (with name breadth). temp_imp, temp_breadth: Hottest temperature, rain_imp, rain_breadth: annual rainfall, dry_imp, dry_breadth: longest dry spell, BG_imp, BG_breadth: bare ground cover, HFP_imp, HFP_breadth: human footprint. Relevant range shifts used for the analysis are the log-proportional change in range size between the two time periods in the study (log_prop_change), relative expansion on the log scale (log_relative_expansion) and relative contraction on the log scale (log_relative_contraction). 2. regression_data.RData * The full data used for the for regression analysis of range shifts ~ traits. Contains the linear combinations used for regression models with either habitat breadth (all_lc_breadth) or sensitivity traits (all_lc_sens). Object all_species is the full data containing species transition scores and traits, model_data is filtered to those species with no missing value in column avg.r. 3. plumage_lightness.csv * The specific dorsal reflectance trait values for each species (column avg.r), based on reflectance across the whole uv-vis region (300-700 nm). 4. Avonet2_eBird.xlsx * The full Avonet traits database. See tab Metadata for details on the trait variables used in the analysis. 5. model_files_E...RData * Combined files used for the INLA models at different mesh resolutions, resolution specified by the number following E in the name string, in degrees. Contains objects for creating the SDM predictions (predcoords, projgrid, spatial_points), the triangulated mesh (mesh), the covariates sampled at integration points during the data integration step (NearestPredCovars), and the duplicated version of teh sampled covariates corresponding to the two time periods in the study (covs_duplicated). 6. TZ_INLA_model_data.RData * General combined files used for the INLA models, not specific to different mesh resolutions. Contains the linear combinations and sequences used to draw the effect plots (all_lc and all.seq), the processed bird observation data sets corresponding to Tanzania Bird Atlas (altas_filtered) and eBird (ebird_filtered) ...

Environmental and biodiversity data associated with the article: Davison et al. (2023) Vegetation structure from LiDAR explains the local richness of birds across Denmark, Journal of Animal Ecology.

Bird richness and abundance at points across Denmark, with matched land cover and LiDAR structural data. Bird observations are a subset of the Common Bird Monitoring programme (DOF – Birdlife Denmark) and pooled from summer counts of 2014, 15, and 16. Bird functional group assignments and environmental data are from open access data sets (see below).

    Data source
    Reference


    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.


    EcoDes-DK15 LiDAR data set of Denmark
    Assmann, J. J., Moeslund, J. E., Treier, U. A., & Normand, S. (2022). EcoDes-DK15: high-resolution ecological descriptors of vegetation and terrain derived from Denmark’s national airborne laser scanning data set. Earth System Science Data, 14(2), 823–844. https://doi.org/10.5194/essd-14-823-2022


    Pan-European land cover map of the year 2015 
    Pflugmacher, D., Rabe, A., Peters, M., & Hostert, P. (2019). Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote Sensing of Environment, 221, 583–595. https://doi.org/10.1016/j.rse.2018.12.001


    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


    Birds of the Palearctic - original source of trait data 
    Cramp, S. (2006). The birds of the western Palearctic interactive. Oxford University Press and BirdGuides.


    Life-history characteristics of European birds - trait database
    Storchová, L., & Hořák, D. (2018). Life-history characteristics of European birds. Global Ecology and Biogeography, 27(4), 400–406. https://doi.org/10.1111/geb.12709

The dataset file (CSV) includes all the variables used in the study by Santangeli et al. titled What drives our aesthetic attraction to birds published in The dataset file (CSV) includes all the variables used in the study by Santangeli et al. titled What drives our aesthetic attraction to birds? published in 2023 in NPJ Biodiversity (DOI : 10.1038/s44185-023-00026-2).The dataset (file named: "monsterALL2_2_2023.csv") is structured so as to have one or two rows per species, depending if the species is sex dichromatic or not. This structure is dictated and adjusted by the availablility of the species by sex aesthetic attractiveness score.Sex monochromatic species have only one attractiveness score, and appear as only one row in the database. Sex dichromatic species may have up to two available attractiveness scores, one for eaither sex (male or female), and therefore may have up to two rows in the database, if attractiveness for both sexes is available.In terms of the variables, the dataset includes taxonomy related variables, such as the species latin name, family and order level information, as well as sex, with three classes: male, female or average (for the monochromatic species).Attractiveness is given with two variables, including the average attractiveness score by species and sex, and the standard deviation associated to that score as a measure of uncertainty.These are followed by a selection of AVONET (https://figshare.com/articles/dataset/AVONET_morphological_ecological_and_geographical_data_for_all_birds_Tobias_et_al_2021_Ecology_Letters_/16586228/1) bird traits, such as morphometric measurements (e.g. beak and tarsus length), as well as data on the habitat, migration ecology and trophic level of each species.Then the color related variables are presented, including the plain colors (such as blue and green) separately for their dark and light version (e.g. blueD and blueL, respectively), as well as other variables representing color diversity (n.loci), color elaboration (elaboration), sex dichromatism score. Other variables following are the length of the crest (crest), IUCN red list category and population trend, as well as biogeographical variables related to the species range (e.g. range size). The last column (BirdTREE) represents the bird species taxonomy matching the BirdTREE taxonomy (available at: https://birdtree.org/).The script file (Santangeli et al Attractivness_final_script) includes all the documented steps to reproduce the analyses, which also require custom made functions available in the file "Functions".

We assessed the importance of deciduous and evergreen habitats for forest birds (including several endemic and threatened species) within South Andaman Island, India. To this end, we compared the composition and diversity (taxonomic, functional and phylogenetic) of forest birds across the two habitat types, and evaluted species-specific responses to habitat variables within the island.

The dataset collected and R codes used in this study are published here. Bird species traits were sourced from Tobias et al. (2022) and Wilman et al. (2014). Phylogenetic trees were sourced from birdtree.org (Jetz et al. 2012).

Taxonomic Coverage: Birds, 54 species

Geographic Coverage: South Andaman Island, Andaman and Nicobar Islands, India

Temporal Coverage: March-May 2022 and December 2022-April 2023

Brief summary of field methods:
We conducted line-transect surveys for forest birds across evergreen and deciduous forests in South Andaman Island, India.
Twenty-seven transects were sampled either in the morning (between 0515 – 0930 hrs) or in the afternoon (between 1515 – 1730 hrs) from March-May 2022 and December 2022-April 2023. For every bird detected, we noted species identity, group size (if visible), time of detection, and whether the bird(s) was seen or heard. Detections of flying birds and nocturnal species (owls and nightjars) were excluded.
For each transect, we measured -
a. tree density and basal area (using the point-centered quarter method, or PCQ);
b. canopy cover, proportion of deciduous trees, and presence of cane, bamboo runners and clumps of standing bamboo (averaged across PCQ points);
c. number of large trees and cut logs (counted within a 20m-wide belt along each transect); and
d. distance from nearest settlement/village (using Google Earth Pro).

The dataset published here contains eight data files and two R code files, along with a ReadMe.txt file that explains each of these files.

Funding:

Science and Engineering Research Board (Govt. of India) - SRG/2021/001523

The Rufford Foundation

The Rauf Ali Fellowship

Arvind Datar

Rohini Nilekani Philanthropies

References:

Jetz, W., G. H. Thomas, J. B. Joy, K. Hartmann, and A. O. Mooers. 2012. The global diversity of birds in space and time. Nature 491:444–448.
Tobias, J. A., C. Sheard, A. L. Pigot, A. J. M. Devenish, J. Yang, et al. 2022. AVONET: morphological, ecological and geographical data for all birds. Ecology Letters 25:581–597.
Wilman, H., J. Belmaker, J. Simpson, C. de la Rosa, M. M. Rivadeneira, and W. Jetz. 2014. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology 95:2027–2027.

This dataset includes the raw data for the analysis of breeding bird community assembly in the Hengduan Mountains, as well as the analysis and visualization code. Specifically, the raw data comprises a occurrence matrix along elevational gradients and trait data of breeding birds in the Hengduan Mountains. We got occurrence matrix by establishing sampling sites every 100 m along elevational gradients. For trait data, it was from the dataset on the life-history and ecological traits of Chinese birds (http://doi.org/10.24899/do.202109003(opens in new window), AVONET dataset (https://figshare.com/s/b990722d72a26b5bfead(opens in new window), and IUCN Red List Categories and Criteria (https://portals.iucn.org/library/node/10315/(opens in new window). The analysis include two methods: a framework to quantitatively infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis (iCAMP) and the dispersal–niche continuum index (DNCI).The analysis and visualization are both conducted in R software.

Animal communication plays a crucial role in biology, yet the wide variability in vocalizations is not fully understood. Previous studies in birds have been limited in taxonomic and analytical breadth. Here we analyse an extensive dataset of >140,000 recordings of vocalisations from 10,124 bird species, representing nearly every avian order and family, under a structural causal model framework, to explore the influence of eco-evolutionary traits on acoustic frequency characteristics. We find that body mass, beak size, habitat associations, and geography influence acoustic frequency characteristics, with varying degrees of interaction with song acquisition type. We find no evidence for the influence of vegetation density, sexual dimorphism, range size and competition on our measures of acoustic frequency characteristics. Our results, built on decades of researchers’ empirical observations collected across the globe, provide a new breadth of evidence about how eco-evolutionary processes shape bird communication. Methods Acoustic signal characteristics of bird sounds We accessed the recordings of bird vocalisations (both songs and calls) of all the species from the online repository xeno-canto. First, we downloaded the meta-data of all the bird recordings available on xeno-canto in December 2019, using the R package warbleR. We then cleaned the meta-data file by removing unidentified species, such as ‘Mystery mystery’ and non-avian sounds. For species that have a distinct song and call, we limited our analysis to songs only, based on the existing distinctions within Xeno-canto. For species without a clear distinction between songs and calls, such as raptors, we included all vocalizations in our analysis. For each species we then hierarchically downloaded the best available recording (refer Fig. S9 in manuscript), using the R package warbleR. The quality of the recordings in xeno-canto is ranked from A to E, with A representing the highest and E the lowest quality, with a few recordings having “no score”. We selected the highest available rank, without requiring a minimum number of recordings for each species. For example, if Copsychus malabaricus (White-rumped Shama) had recordings of quality A, B and C, we selected only and all those ranked as A. If another species, Copsychus saularis (Oriental Magpie-robin) had no recording of quality A, but only of quality from B to E, we chose B recordings. We then analysed individual recordings for each species to detect and measure acoustic frequency values (refer Fig. S2 in manuscript). For each recording, we first automatically detected all acoustic signals therein, each between a duration of 1 – 5 sec - acoustic note, (refer Fig. S2b in manuscript). We used 0 and 22 kHz as our lower and upper limits of a frequency bandpass filter, to capture the entire range of the acoustic frequencies, with a Hanning window of default length of 512. We did not use any proportional reduction of amplitude envelopes through thinning. We only used converted Waveform Audio File Format (.wav) recordings that had a sampling rate of 44.2 kHz, after decompressing them from mp3 format. While this has been shown not to significantly bias any of the acoustic or similarity measurements, it has been shown to affect the precision of acoustic parameters, such as peak frequency. However, the negative effect of compression is assumed to be less problematic when comparing acoustic frequency values across species, as the differences between species are usually stronger, than within species. For each acoustic note from a recording, we calculated the Signal-to-Noise Ratio (SNR) to discard low-quality (e.g., high background noise) selections, by following the recommendation from Araya-Salas et. al 2019, because background noise has been shown to bias most energy distribution-related parameters such as spectral entropy and affect the precision of most acoustic parameters. Here we used the default value of not using any lower and upper limits of a frequency bandpass filter, or a window length, as they were previously used to automatically detect the acoustic note in the previous step. We used a very small margin value of 0.04, adjacent to the start and end points of selection over which to measure noise. For each recording, we then selected the top 100 non-overlapping acoustic notes, i.e., those with the highest SNR, to measure acoustic characteristics of each species (refer Fig. S1c-d in manuscript ): (i) minimum frequency (ƒmin), (ii) maximum frequency (ƒmax), (iii) dominant frequency (ƒdom), (iv) standard deviation of ƒmin (σfmin), (v) standard deviation of ƒmax (σfmax), and (vi) interquartile range of acoustic notes (Δƒ). We define ƒmin as the start frequency of the amplitude peak containing the highest amplitude, ƒmax as the end frequency of the amplitude peak containing the highest amplitude; ƒdom as the average dominant frequency measured across the spectrogram of the acoustic note; and Δƒ as the frequency range between the first quartile (frequency at 25% energy of the spectrum) and the third quartile (frequency at 75% energy of the spectrum). ƒmin, ƒmax, and ƒdom, forms the fundamental frequency measures, σfmin and σfmax characterize the variability found within minimum and maximum fundamental frequencies respectively, and Δƒ characterizes the frequency bandwidth. We also modelled Δƒ, measured as the difference between ƒmin and ƒmax, which we elaborate in Supplementary Information in the manuscript. Assuming the recordist manipulated their equipment and recording strategy such that the acoustic signal of the target species would be the clearest and loudest in the recording, we repeatedly estimated each of these six response variables under a series of thresholds - amplitude of every 5 dB between 5 and 50 dB (refer to Fig. S1e in manuscript). Median values of the estimates of the six response variables, from each of the threshold percentages (refer Fig. S1e, in manuscript) were used to estimate acoustic characteristics for each species, across all recordings (refer Fig. S1f, in manuscript). We used the R package warbleR to calculate the acoustic characteristics. We specifically chose these measures of bird vocalisation because they have all been previously linked to the evolutionary forces of physiology, inter-specific competition, distribution, and environmental variables (Fig. 1 & Table S1 in manuscript ). Additionally, they were also the easiest acoustic measures that could be consistently extracted from thousands of recordings of our focus bird species. Zoogeographic distribution of species We divided the zoogeographic distribution of our study species into 12 major zoogeographic realms (refer Fig S9 in manuscript). In addition to the 11 zoogeographic realms identified by Holt et al. 2013, namely, Afrotropical, Australian, Madagascan, Nearctic, Neotropical, Oceanian, Oriental, Palearctic, Panamanian, Saharo-Arabian, and Sino-Japanese, Oceanian, we also included species from Antarctica and surrounding islands under a new realm, South Polar, based on our data from xeno-canto. We listed a species under multiple zoogeographic realms if its distribution spanned multiple realms, either due to its natural or introduced distribution. We calculated the frequency values separately for species that occurred in more than one zoogeographic realm. Species traits We collated information on the morphology, behaviour, ecology, and geographical data of birds from AVONET and BirdBase. We used log(e) transformed body mass (g) as a measure of body mass, results are presented as back-transformed values in the main text. We calculated beak size (log(e) transformed) by multiplying beak length, width, and depth (mm3). As a measure of vegetation density, we used two categorical variables: (i) habitat density and (ii) primary habitat, both from AVONET. We estimated overall species richness as a proxy for competition for acoustic frequency space, by calculating the mean number of terrestrial vertebrates: amphibians and birds using global richness data from BiodiversityMapping.org. We extracted these values as means within a species’ breeding range, which we calculated as a circular area, equivalent to the species range size, centred around its centroid latitude and longitude. To account for plasticity in song acquisition, we divided the bird species into two major groups, (i) song learners – species that learn songs from adult tutors to use in mate choice and territorial displays, i.e., hummingbirds, parrots, and oscine passerines and (ii) innate songsters – species that do not learn their vocalisation. i.e., suboscine passerines and all remaining birds. Even though there are exceptions (e.g., a few species of Ant wrens and Wood creepers), in general, song learners tend to have more complex vocalisation. Additionally, song learners, show a slower rate of evolution and diversification due to their significantly greater variability in acoustic traits (by 54 – 79%) compared to innate songsters.

While environmental variability is theorized to impact the life history characteristics of organisms, these hypotheses have not been thoroughly tested with empirical data. To fill this gap, we synthesized a global data set of environmental variability metrics and life history characteristics across the ranges of 7,477 non-migratory, non-marine avian species. These data are derived from the ERA5 climate reanalysis, AVONET, BirdTree, and BirdLife databases as well as previously published research. By extracting environmental variability values across individual species' ranges, this data set allows users to evaluate avian species' pace of life in response to environmental change.

Glucose is a central metabolic compound used as a source of energy across all animal taxa. There is high interspecific variation in glucose concentration between taxa, the origin and the consequence of which remain largely unknown. Nutrition may affect glucose concentrations because carbohydrate content of different food sources may determine the importance of metabolic pathways in the organism. Birds sustain high glucose concentrations that may entail the risks of oxidative damage. We collected glucose concentration and life history data from 202 bird species from 171 scientific publications; classified them into seven trophic guilds and analysed the data with a phylogenetically controlled model. We show that glucose concentration is negatively associated with body weight and is significantly associated with trophic guilds with a moderate phylogenetic signal. After controlling for allometry, glucose concentrations were highest in carnivorous birds, which rely on high rates of gluconeog..., , , # Trophic guilds differ in blood glucose concentrations: A phylogenetic comparative analysis in birds

Description

This study is a broad-scale comparative analysis of the association between avian glucose concentration and nutritional types (trophic guilds) and how this variation is related to the longevity of bird species. To explore this relationship, we applied phylogenetically controlled PGLS and MCMCglmm models while controlling for the allometric relationship between body weight and blood glucose concentration. We collected glucose concentration data from 202 bird species from scientific publications, and we also collected body weight data of these species. We classified the bird species into seven different trophic guilds (with equivocal cases having an alternative classification). We also used an independent trophic classification (AVONET trophic niches, see below). The dataset also consists of longevity data of 159 bird species with references.

Authors:

Endre Z. S...

Trait-based prediction of extinction across terrestrial data See the full OA article here: In this study we analysed the generality of trait-based prediction of extinction risk across terrestrial (including freshwater) vertebrates, invertebrates, and plants at a global scale. We compiled data for 10 potential biological predictors and the extinction risk of species (IUCN 2021-3).

Predictors: Body size, offspring size, fecundity, generation length, diet breadth, trophic level, dispersal ability, microhabitat, habitat breadth, and altitudinal range. Taxonomical groups: - Mammals, birds, reptiles, amphibians, freshwater fixes; - Dragonflies, butterflies, grasshoppers, spiders, snails; - Bryophytes, ferns, gymnosperms, monocots, legumes. Files - 20220730 main.R: R script to run the analyses - 20220730_Dataset: .xslx file containing all compiled traits for each group. Check the Legend tab for more information. - my_mammals.csv: validation dataset for mammals (most of the data retrieved from the COMBINE dataset - my_birds.csv: validation dataset for birds, most of it retrieved from AVONET. Running the analyses 1. Make sure all the downloaded files are unzipped in the same working directory. 2. Open main.R and source it (make sure main.R is in your R working directory) 3. Some of the supplementary analyses may take long to run (not more than 1 hour) 4. The script retrives Fig 1, Fig S1, S2, and S3, as well as several exploratory tables (those are not exported)

For more details check the methodology section of the paper.