Conservation strategies for mammals in data-deficient regions: Analysing research trends and identifying conservation priority areas
Bibliographic record
Abstract
Biodiversity conservation is crucial for sustaining ecosystem integrity, especially in species-rich, endemic regions like North-East India (NEI), part of the Indo-Burma biodiversity hotspot. NEI hosts 269 mammal species across 136 genera, 38 families, and 11 orders, with 48 threatened and 13 endemic species, highlighting its global conservation significance. However, habitat loss, deforestation, and fragmentation due to anthropogenic pressures necessitate urgent, informed conservation strategies. This study analyses research trends on NEI’s mammalian fauna and identifies conservation priority areas. A bibliometric analysis of 350 publications (1920–2023) shows significant research growth, with biodiversity and taxonomy (n = 215), ecology and behavior (n = 75), and conservation biology (n = 62) as dominant themes. Despite their ecological importance, knowledge gaps persist, particularly for small-bodied mammals like bats and rodents. Carnivores (n = 85), primates (n = 57), and Cetartiodactyla (n = 47) dominate research, revealing biases. We developed a Regional Conservation Priority Index (RCPI) integrating IUCN threat status, regional endemism, global range restriction, and NEI range restriction. Using Area of Habitat (AOH) data, we mapped species richness and RCPI-weighted richness for large-bodied (>10 kg) and small-bodied (≤10 kg) mammals. Morphological Spatial Pattern Analysis (MSPA) identified core habitat zones, and a Conservation Score Map (CSM) integrated functional richness with conservation urgency. Priority areas include Arunachal Pradesh, northern West Bengal, Assam, and Meghalaya, aligning with global biodiversity targets like the Kunming–Montreal Framework’s 30 × 30 and SDG 15. This data-driven framework offers a replicable approach for conservation prioritization in data-deficient regions, supporting evidence-based policymaking and adaptive management to protect NEI’s mammalian fauna.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".