Code and data for: Traits, threats, and popularity explain extinction risk of bird
Bibliographic record
Abstract
README for Supplementary Materials<b>Title of Paper:</b> Traits, threats, and popularity explain extinction risk of birds globally<br><b>Authors:</b> Janaina Serrano, Lars Iversen, Laura Pollock<br>DescriptionThis README file provides an overview of the dataset, columns, and code files included in the supplementary materials for the paper titled <i>Traits, threats, and popularity explain extinction risk of birds globally</i>. The dataset contains information on bird species, their traits, threats they face, habitat characteristics, and popularity metrics. The associated code files are provided to facilitate reproducibility and further exploration.Files in Supplementary Materials1. <b>Dataset File</b><b>Filename:</b> dat<b>Format:</b> CSVDataset DescriptionColumns in the Dataset<b>Column Name</b><b>Description</b><b>species</b>Scientific name of the bird species.<b>threatened</b>Binary indicator of whether the species is classified as threatened (1/0).<b>Habitat</b>Primary habitat type for the species.<b>Order1</b>Taxonomic order to which the species belongs.<b>total_threats</b>Total number of threats affecting the species.<b>Migration</b>Migration status of the species (e.g., migratory or non-migratory).<b>HabitatBreadth</b>Breadth of habitat types the species can occupy (numerical value).<b>Range.Size</b>Geographical range size of the species (in square kilometers).<b>Mass</b>Body mass of the species (in grams).<b>gbif_mean</b>Mean number of GBIF records indicating species' data availability.<b>mean</b>Mean number of Google search hits for the species (popularity metric).<b>pollution</b>Impact of pollution threats on the species.<b>logging</b>Impact of logging threats on the species.<b>invasive</b>Impact of invasive species threats on the species.<b>agriculture</b>Impact of agricultural activities on the species.<b>climate_change</b>Impact of climate change threats on the species.<b>hunting</b>Impact of hunting threats on the species.<b>Mass.log</b>Log-transformed body mass.<b>Range.Size.log</b>Log-transformed range size.<b>gbif_mean.log</b>Log-transformed GBIF mean value.<b>HabitatBreadth.log</b>Log-transformed habitat breadth.<b>google.log</b>Log-transformed Google search popularity.<b>Migration1</b>Categorical indicator of migration type.<b>Mass.log.cs</b>Centered and scaled log-transformed body mass.<b>Range.Size.log.cs</b>Centered and scaled log-transformed range size.<b>gbif_mean.log.cs</b>Centered and scaled log-transformed GBIF mean value.<b>HabitatBreadth.log.cs</b>Centered and scaled log-transformed habitat breadth.<b>google.log.cs</b>Centered and scaled log-transformed Google popularity.<br>2. <b>Code Files</b>Descriptions of the provided code files:<b>Filename</b><b>Description</b><b>dataprep_birdtraits</b>Script for preparing and cleaning the bird trait dataset. Includes processes like data wrangling, standardization of column names, log-transformations of variables, and preparation of final input data for analysis.<b>model_figures_script</b>Script to generate the main figures from the paper. This includes plotting extinction risk models, trait relationships, and visualizations of the species' threats and popularity metrics.<b>model_evaluation_bivmap</b>Script for model evaluation and bivariate mapping. Evaluates the predictive performance of models and creates spatial maps to visualize overlaps modeled extinction risk and observed conservation status of birds.<b>gtrends</b>Script for collecting and processing Google Trends data related to species' popularity. Retrieves search hit data, processes it for analysis, and calculates summary metrics like mean hits and log-transformed values.<b>GBIF_download</b> Script for collecting and processing GBIF number of observations for birds globally from 2004-2021. Retrieves GBIF data, processes it for analysis, and calculates average species observations per year.Data Usage and CitationThe dataset is provided as supplementary material for the paper and can be used for academic purposes. If you use this dataset, please cite the paper as follows:<i>Serrano J., Iversen L., Pollock L. (2025). Traits, threats, and popularity explain extinction risk of birds globally.</i>Contact InformationFor questions about the dataset or paper, please contact:<b>Janaina Serrano</b>: janaina.serrano@mail.mcgill.ca<b>Lars Iversen</b>: lars.iversen@mcgill.ca<b>Laura Pollock</b>: laura.pollock@mcgill.ca<br>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".