DOI: 10.5061/dryad.83bk3jb08
title: Data from: Bird species’ tolerance to human pressures and associations with population change

description: Aim: Some species thrive in human-dominated environments, while others are
 highly sensitive to all human pressures. However, standardised estimates
 of species’ tolerances to human pressures are lacking at large spatial
 extents and taxonomic breadth. Here, we quantify the world's bird
 species’ tolerances to human pressures. The associated precision values
 can be applied to scientific research and conservation. Location: Global.
 Time period: 2013–2021. Major taxa studied: 6090 bird species. Methods: We
 used binary observation data from eBird and modeled species’ occurrences
 as a function of the Human Footprint Index. Using these models, we
 predicted how likely each species was to occur under different levels of
 human pressures. Then, we calculated each species’ Human Tolerance Index
 (HTI) as the level of the Human Footprint Index where predicted occurrence
 probability was reduced to 50% of the maximum species’ occurrence
 probability. We used resampling to obtain estimates of uncertainty on the
 Human Tolerance Indices. We also compared tolerances across species with
 increasing, stable, and decreasing population trends. Results: We found
 that 22% of the bird species tolerated the most modified human-dominated
 environments, whereas 0.001% of species only occurred in the intact
 environments. We also found that HTI varied according to species’
 population trend category, whereby species with decreasing population
 trends had a lower tolerance than species with increasing or stable
 population trends. Main conclusions: The estimated HTI indicates the
 potential of species to exist in a landscape of intensifying human
 pressures. It can identify species unable to tolerate these environments
 and inform subsequent conservation efforts. We found evidence that
 species’ sensitivity to human-dominated environments may be driving birds’
 use of space. Bird species’ tolerances are also linked to their population
 trends, making the tolerances a relevant addition to conservation
 planning.
description: We used binary observation data from eBird and model species’
 occurrences as a function of the Human Footprint Index. Using these
 models, we predicted how likely each species was to occur under different
 levels of human pressures. Then, we calculated species’ Human Tolerance
 Index (HTI) as the level of the Human Footprint Index where predicted
 occurrence probability was reduced to 50% of the maximum species’
 occurrence probability for 6094 bird species. We used resampling to obtain
 estimates of uncertainty on the Human Tolerance Indices. 
description: # Bird species’ tolerance to human pressures and associations with
 population change Codes for filtering and analysing eBird data to
 calculate bird species' tolerances to human pressures and the
 calculated tolerances as dataset README file created by Emma-Liina
 Marjakangas on December 8th 2023 \--- We used binary observation data from
 eBird and model species’ occurrences as a function of the Human Footprint
 Index to quantify the world's bird species’ tolerances to human
 pressures. We included also the associated precision; values which can be
 applied to scientific research and conservation. \## Description of the
 data and file structure Data for analysis steps are freely available from
 eBird (see below). Dataset containing all calculated bird tolerances to
 human pressures are included in the excel-file
 "bird_tolerances". The first sheet "Tolerances"
 includes the tolerance values, while the second sheet "Metadata"
 includes the explanation for each column name and ranges of the presented
 values. File: "bird_species_tolerances_dataset_240228.xlsx" \##
 Sharing/Access information The data that support the analyses of this
 study are all freely available for research purposes, and can either be
 directly downloaded from the respective websites or are available on
 demand. Specifically, bird species checklists are available at
 [http://ebird.org](http://ebird.org). Bird species’ distribution maps
 (BirdLife 2022) are available at
 [http://datazone.birdlife.org](http://datazone.birdlife.org). Bird
 species’ IUCN Red List statuses and population trend categories (IUCN Red
 List of Threatened Species, downloaded 24.04.2023) are available at
 [http://iucnredlist.org](http://iucnredlist.org). This analysis package
 does not include input data files as they are available freely available
 via the original platforms. \## Code/Software All analyses were conducted
 in R software (version 4.0). The commented codes for the analysis steps
 are included in the data package. Each code file contains code for the
 analysis of one continent (Asia) as an example to reduce repetition. The
 code files are run sequentally from step1 to step5 as indicated in the
 code file names. Before the steps outlined below, eBird sampling event and
 species observation data need to be downloaded from eBird website. It is
 recommended to keep all necessary files in one directory folder or use a
 full path to the underlying datafiles needed for each step. The folder
 paths of the The general workflow of the code files is: 1. Filter eBird
 sampling location data to even out the spatial and temporal distribution
 of sampling event data 2. Obtain Human Footprint Index data for eBird
 sampling locations 3. Filter eBird species observation data to exclude
 poor-quality data 4. Quantify tolerances for each species within a
 continent 1. Resample the data 50 times to obtain uncertainty estimates 2.
 Fit Generalized Additive Models using eBird and Human Footprint Index data
 (core model structure occurrence ~ Human Footprint Index) 3. Predict bird
 occurrences across the entire Human Footprint Index continuum to obtain a
 response curve 4. Calculate three tolerance measures (Peak, Conservative
 and Maximum HTI) from the response curve 5. Summarize distributions of HTI
 values within continents 6. Summarize distributions of HTI values across
 continents and link them to species’ population trend and threat level
 information Files: step1_compile_ebird_data.R: Filter eBird sampling event
 and species observation data within continents step2_resolve_taxonomies.R:
 Create a taxonomy crosswalk between different bird taxonomies used in
 different datasets step3_quantify_tolerances.R: Loop through all species
 within a continent to fit occurrence~human pressure models, predict
 response curves and calculate tolerance values with uncertainty estimates
 step4_summarize_tolerances_within_continent.R: Summarize tolerances of
 species within continents step5_summarize_tolerances_across_continents.R:
 Summarize tolerances across continents and analyze links to population
 trends and threat statuses