Safeguarding eucalypt diversity through conservation‐focused tree planting
Bibliographic record
Abstract
Abstract The UN decade on ecosystem restoration calls for the preservation of global biodiversity. Safeguarding biological diversity is crucial for the well‐being of people and the persistence of nature. Tree planting offers a nature‐based solution to carbon abatement with significant potential to benefit biodiversity, but how well different aspects of biodiversity are captured in plantings is not well known. We assemble a database of 218 species and >80,000 observations from environmental tree plantings in Australia, and investigate how well they capture eucalypt diversity, including possible climate change effects. We compare this to hypothetical planting scenarios that maximize biodiversity benefits for species of conservation interest. Eucalypt species that are rare, endemic, evolutionarily distinct or listed in the Environment Protection and Biodiversity Act (EPBC) are prioritized. We find that environmental tree plantings are dominated by very common species likely to benefit from climate change, with less than 1% of rare or evolutionarily distinct species represented. All conservation scenarios outperform business‐as‐usual. The scenario prioritizing endemic species while considering climate change more than doubles the average extent of occurrence for species of conservation interest. It allocates four times more planting area to species of conservation interest compared to a business‐as‐usual approach and decreases weighted endemism (a favourable outcome here) by 38% on average, demonstrating the positive impact of conservation‐focused tree planting on biodiversity. Synthesis and applications. Everywhere, governments are making commitments to halt biodiversity loss. Conservation‐focused tree planting can advance our goal of safeguarding biodiversity while still contributing to carbon capture. We show that eucalypt species of conservation interest can benefit substantially from tree planting where the primary goal is biodiversity conservation, compared to the current approach to planting, which we have found largely benefits commonly occuring species. We measure potential distribution gains for eucalypt species that are rare, endemic, evolutionarily distinct and at risk of extinction (listed), and identify the most important eucalypt species to plant across Australia, taking the effects of climate change into account. Our framework for selecting species to plant based on a range of conservation goals can serve as a guide for future environmental tree planting initiatives.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".