Predicting habitat suitability of Dalbergia latifolia Roxb. (Indian rosewood) using MaxEnt: implications for conservation and sustainable forest management
Bibliographic record
Abstract
BACKGROUND: Dalbergia latifolia Roxb. (Indian rosewood) is a leguminous tropical hardwood of high ecological and economic value, native to India’s tropical and subtropical forests. Its richly coloured, durable heartwood and acoustic qualities make it a prized timber in domestic and international markets. Ecologically, the species contributes to forest health by stabilising soils, fixing atmospheric nitrogen, and supporting associated biodiversity. However, populations have sharply declined due to illegal logging, unsustainable harvesting, and habitat loss. It is currently classified as Vulnerable on the IUCN Red List (2020, Criteria A1cd), Near Threatened in India (IUCN, 2018), and has been listed in CITES Appendix II since 2016. India remains the largest global supplier, with Karnataka contributing over 50% of national output. Yet, less than 1% of its standing stock is under managed cultivation due to slow growth, long rotations, and restrictive harvest policies. While initiatives such as CAMPA and the National Agroforestry Policy have encouraged enrichment planting, efforts remain fragmented and lack spatially explicit data for prioritisation. RESULTS: Using Maximum Entropy (MaxEnt) modelling with high-resolution climate data, we generated the first range-wide habitat suitability maps for D. latifolia in India. The model showed high predictive accuracy (AUC = 0.912), identifying tropical dry and moist deciduous zones as primary habitats. High-suitability areas were concentrated in the southern Western Ghats, especially Karnataka, Kerala, and Tamil Nadu, with additional patches in Maharashtra and Madhya Pradesh. Among 19 bioclimatic variables, annual mean temperature, temperature seasonality, and precipitation of the driest quarter were most influential. Accuracy improved through spatial filtering and validation of occurrence records. Despite broad climatically suitable areas, only 17.2% overlapped with existing Protected Areas, revealing major conservation gaps. CONCLUSIONS: This study provides the first high-resolution, range-wide habitat suitability assessment for Dalbergia latifolia in India. The findings offer a scientific basis for conservation prioritisation, in-situ restoration, ex-situ conservation, and ecologically informed plantation design. The results are directly relevant to national initiatives such as CAMPA and the CAMPA-funded AICRP-28 on D. latifolia, where spatially explicit data can guide resource allocation, site prioritisation, and restoration planning. Beyond rosewood, the MaxEnt framework demonstrated here can be applied to other threatened or commercially important tropical tree species. Future modelling that integrates land-use change and high-resolution climate projections will further strengthen adaptive management and ensure the long-term conservation of climate-sensitive species under changing environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".