Supplementary Code and Data for Wong Hearing et al: "Humans could become the greatest driver of biosphere net gain in Earth history, but are currently the second fastest driver of biosphere loss"

Biosphere disruptors Supplementary code and data for Wong Hearing _et al._: "Humans could become the greatest driver of biosphere net gain in Earth history, but we are currently the second fastest driver of biosphere loss". The preprint is available on bioRXiv: https://doi.org/10.64898/2026.04.10.715592. This manuscript has been submitted for peer-review at Proceedings of the Royal Society B: Biological Sciences and is subject to change following review. This is an archived version of the live repositories which can be found on GitHub and will continue to be updated. - disruptors: https://github.com/twwh01/disruptors- planetary boundaries: https://github.com/twwh01/planetary_boundaries_plots Repository contents The repository is split into two self-contained subdirectories, each with its own README, data, and outputs. This is because the subdirectories are archives of the live repositories referenced above. The two subdirectories are independent of each other. disruptors/ holds the `R` analysis code that produces figures 2, 3, S1, and S2. pbs/ holds the `Python` code that produces the two panels of figure 1. disruptors/ Supplementary data and `R` code for analysing the impact of humans on the biosphere in the context of biosphere disruptions in deep time. See [disruptors/README.md](disruptors/README.md) for the required packages and how to install them, including details of the versions used in this analysis. disruptors/dependencies.R: checks for the required `R` packages and installs any that are missing (only needs to be run once per machine). disruptors/scripts/rspb_disruptors_figure_2.R: produces figure 2, a schematic, hypothetical figure of the potential range of biosphere disruptor impacts over time; self-contained with the schematic data being defined within the script. disruptors/scripts/rspb_disruptors_figure_3-S1-S2.R: produces figure 3 and supplementary figures S1 and S2, the main results of this study, from the supplementary data tables; reads sheets `table_s3_events` and `table_s5_biosphere_changes` of the supplementary data workbook. disruptors/data/: the supplementary data workbook (described in detail below). disruptors/plots/: the output figures. `fig_1_pbs_2025_ptme.png`: figure 1, the two planetary boundary panels produced by [pbs/](pbs/) composited side by side with a shared legend (this was produced by putting the two separate plots next to each other in Microsoft Powerpoint). `fig_2_schematic.png`: figure 2, from `rspb_disruptors_figure_2.R`. `fig_3_rates_of_change_ordered.png`: figure 3, from `rspb_disruptors_figure_3-S1-S2.R`. `fig_S1_event_timescales.png`: figure S1, from `rspb_disruptors_figure_3-S1-S2.R`. `fig_S2_deep_time_habitability.png`: figure S2, from `rspb_disruptors_figure_3-S1-S2.R`. `disruptors.Rproj`: RStudio project file. Opening it sets the working directory correctly for the relative paths (`data/`, `plots/`) used by the scripts. pbs/ `Python` code for plotting Earth's planetary boundaries as a polar diagram for different intervals in time. See [pbs/README.md](pbs/README.md) for the required packages and how to create the conda environment. - [pbs/planetary_boundary_classes.py](pbs/planetary_boundary_classes.py): defines the three classes used by the plotting scripts, and the plotting routine itself. `ControlVariable` holds the baseline, boundary, upper limit, and current value of a single control variable and normalises them onto a common scale (0 = baseline, 1 = the planetary boundary, 2 = the upper limit of the zone of increasing risk, above 2 = high risk zone); `PlanetaryBoundary` groups control variables into one boundary; `PlanetarySystem` groups the boundaries and draws the final polar plot.- [pbs/pbs_2025.py](pbs/pbs_2025.py): plots the 2025 planetary boundaries from the `2025` sheet of the data workbook.- [pbs/pbs_PTME.py](pbs/pbs_PTME.py): plots the biosphere integrity boundary for the Permo-Triassic Mass Extinction from the `PTME` sheet.- [pbs/environment.yml](pbs/environment.yml): defines the `pbs-env` conda environment with all required packages.- [pbs/data/](pbs/data/): the planetary boundaries data workbook (described in detail below).- [pbs/plots/](pbs/plots/): the output figures, `pbs_2025` and `pbs_PTME`, each written as both `.png` (600 dpi) and `.pdf`. These are the two panels of figure 1. Data Both data files are Excel workbooks. Neither is modified by the code — the scripts only read them. disruptors/data/disruptors_supplementary_data_tables_s1-s5.xlsx The supplementary data tables for the manuscript. Tables S1 and S2 are the descriptive, human-readable compilations; tables S3–S5 are the machine-readable tables used in the analyses. Sheet: Contents `README`: describes each of the other sheets. `references`: the 115 literature sources cited across the tables, as `short_ref` (the key used in `table_s5_biosphere_changes`) and `full_ref`. `table_s1_transient`: Table S1; examples of transient disruptors in Earth's deep past associated with substantial habitat loss, with age, duration, proximate cause, background climate state, temperature change, ocean deoxygenation and acidification, carbon isotope shift, and biosphere impact. `table_s2_persistent`: Table S2; examples of persistent disruptors in Earth's deep past associated with substantial biosphere change, plus humans as a near-past to contemporary and possible future persistent disruptor. `table_s3_events`: Table S3; reference table of numerical age, duration, and background climate state for every disruption episode used in the analyses. `table_s4_summaries`: Table S4; summary statistics (mean, median, standard deviation, maximum, minimum, count) of the transient and persistent disruptor durations in Table S3. `table_s5_biosphere_changes`: Table S5; quantitative changes in biosphere metrics for each episode. 106 records. Columns of `table_s3_events`: - `interval_name`: full name of the disruption episode. `interval_abbreviation`: short label used in the figures. `big5` | `yes`: if the episode is one of the "big five" mass extinctions; blank otherwise. `big3_hatfield` | `yes`: for the three most severe extinctions following Hatfield et al. (2025). `type` | disruptor category: `transient`, `persistent`, `humans so far`, `business as usual`, or `sustainable stewardship`; the last three are the human scenarios. `background_climate`: following categories of Judd et al. (2024). `age_estimate_ma`: estimated age of episode in millions of years ago (Ma); negative values are future scenarios (for example −0.0011 for the year 3100). `event_duration_min_yr`, `event_duration_max_yr`: shortest and longest estimated duration of the episode, in years. `event_duration_est_yr`: central estimate of the duration of maximum impact of each episode, in years. `event_duration_notes`: notes and sources for the duration estimates. Columns of `table_s5_biosphere_changes` (the script reads columns 1–13): `interval_name`, `interval_abbreviation`, `type`, `age_estimate_ma`: as in `table_s3_events`; these are keys joining the two sheets. `reference`: literature source for the value as a `short_ref` key into the `references` sheet. `change_type`: the biosphere metric used; either `species` or `genus` (richness), `biomass`, or `productivity` (NPP). `change_group`: the taxonomic or functional group the value applies to (for example `global`, `global marine`, `terrestrial vertebrates`, `corals`, `terrestrial primary productivity`). `change_metric`: the unit of the biosphere change value; currently `percent` throughout but other values can be accepted. `change_value`: the magnitude and sign of the biosphere change (positive for gain and negative for loss); values range from −95 (PTME) to +188000 per cent. `event_duration_est_yr`: central duration estimate for the episode (in years); keyed in from `table_s3_events` sheet. `change_per_msy_mgy`: biosphere change expressed per million species-years or million genus-years. `percentage_change_rate_Myr`: percentage change per million years; the rate plotted in figure 3. `notes`: free-text notes on the record. Three further columns to the right of the data (`change_group`, `change_type`, `change_type_definition`) are a legend listing permitted vocabulary for those fields; they are not read by the script. pbs/data/planetary_boundaries_data.xlsx The control variable values used to draw the planetary boundary diagrams. The workbook comprises three sheets (`2023`, `2025`, and `PTME`) with identical structure of one row per control variable (13 rows). Sheet `2025` holds values from the 2025 Planetary Health Check and is read by `pbs_2025.py`. Sheet `PTME` holds the values reconstructed for the Permo-Triassic Mass Extinction and is read by `pbs_PTME.py`; currently only the two biosphere integrity variables are quantified and the other boundaries are left blank; these blank values plot as grey "not quantified" segments. Sheet `2023` holds the equivalent values from the 2023 report and is retained for reference; no script in this repository uses it (see active GitHub repo for the script that does). Columns of each sheet in `planetary_boundaries_data.xlsx`: `Earth_system_component`: the planetary boundary the control variable belongs to (for example `Climate change`, `Biosphere integrity`). `control_variable`: control variable name; plotting scripts select rows by matching on this field. `description`: description of what the control variable measures. `units`: units of control variable values (for example `ppm CO2`, `Wm-2`, `E/MSY`, `% HANPP`, `DU`). `baseline`: the baseline pre-industrial or reference value for the control variable; plotted at radius 0. `planetary_boundary`: the boundary value; the upper edge of the safe operating space; plotted at radius 1. `upper_limit`: the upper edge of the zone of increasing risk; plotted at radius 2. `current_value`: the present-day (or, for geological intervals, reconstructed) value; sets the length of the plotted bar; blank where the boundary has not been quantified. `notes`: source and caveats for the values. The four value columns (`baseline`, `planetary_boundary`, `upper_limit`, and `current_value`) are ordered rather than absolute because direction of the boundary variable matters. For some variables, lower values are safer (for example, carbon dioxide), but for other variables higher values are safer (for example, ozone). `ControlVariable.norm()` detects which case applies from the relative order of the baseline and boundary and normalises accordingly.