Biodiversity in India: Present Threats, Conservation Policies and the Role of Indian Legislation
Bibliographic record
Abstract
India is one of the 17 megadiverse countries, hosting approximately 7–8% of all recorded species on just 2.4% of the world’s land area. With four global biodiversity hotspots (Himalaya, Indo-Burma, Western Ghats–Sri Lanka and Sundaland), the country supports over 55,481 plant taxa and 105,244 animal species as of 2024 (Botanical Survey of India, 2025; Zoological Survey of India, 2025). Despite robust legal and policy frameworks, biodiversity faces severe threats from habitat fragmentation, deforestation (18,200 ha of primary humid forest lost in 2024), invasive alien species, pollution, overexploitation and climate change. This manuscript synthesizes the latest data from the Botanical Survey of India (BSI), Zoological Survey of India (ZSI), Global Forest Watch, India State of Forest Report 2023 and IUCN Red List, while critically examining India’s biodiversity-related legislation: the Wildlife (Protection) Act 1972 (as amended 2022), Biological Diversity Act 2002 (Rules 2004 & Amendment 2023), Forest (Conservation) Act 1980 (amended 2023), Environment (Protection) Act 1986, Scheduled Tribes and Other Traditional Forest Dwellers (Recognition of Forest Rights) Act 2006 and recent regulations under the Jan Vishwas Act 2023 and Biological Diversity (Access and Benefit Sharing) Regulations 2025 (Government of India). The analysis reveals implementation gaps, judicial interventions and emerging opportunities under the Kunming-Montreal Global Biodiversity Framework. Strengthening enforcement, community rights, landscape-level planning and climate-resilient corridors is essential for achieving national targets of protecting 30% of land and water by 2030.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".