Groundwater-dependent ecosystem map exposes global dryland protection needs
Bibliographic record
Abstract
Groundwater’s role in supporting ecosystems worldwide is rarely acknowledged. Groundwater-dependent ecosystems (GDEs), which depend on groundwater for some or all of their water needs, are diverse and include desert springs, mountain meadows and streams, coastal wetlands and forests. However, the location of these ecosystems worldwide has been largely unknown, hindering our ability to track impacts, establish protective policies, and implement conservation projects.Here, leveraging Earth Observation datasets, random forest modelling, and multiple national and state-level GDE mapping initiatives, we map GDEs across global drylands at high resolution (1 arc-second, roughly 30 m pixels). We find GDEs present on over 8.3 million km2 -- more than one-third of areas analyzed, including important biodiversity hotspots. GDEs are found to be more extensive and contiguous in pastoral landscapes with lower rates of groundwater depletion, suggesting that many GDEs are likely to have already been lost due to land and water use practices. Over half of GDEs exist within regions showing declining trends in regional groundwater storage, and only one-fifth of GDEs exist on protected lands or in jurisdictions with sustainable groundwater management policies, invoking a call to action to protect these vital ecosystems.Cultural and socio-economic linkages with GDEs further underpin these protection needs. The Greater Sahel serves as a case study of these factors, where GDEs play an essential role in supporting biodiversity and rural livelihoods, and which we use as a basis to discuss other means for GDE protection in politically unstable regions. Our GDE map provides critical information for prioritizing and developing policies and protection mechanisms across various local, regional or international scales to safeguard these important ecosystems and the societies dependent on them. An interactive version of our global GDE and GDE probability maps are available at https://codefornature.projects.earthengine.app/view/global-gde. ReferenceRohde, M.M., Albano, C.M., Huggins, X. et al. Groundwater-dependent ecosystem map exposes global dryland protection needs. Nature 632, 101–107 (2024). https://doi.org/10.1038/s41586-024-07702-8
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".