Potential Distribution Modeling and Conservation Gap Identification for Rare and Endangered Plant Species: A Case Study of 10 Species in Hubei Province
Bibliographic record
Abstract
ABSTRACT With the continuous increase in the demand for land and natural resources, driven by population growth and economic expansion, the conservation of rare and endangered species faces mounting pressure. Exploring how to achieve the target of conserving 30% of the global terrestrial area proposed by the Kunming–Montreal Global Biodiversity Framework is of great significance for biodiversity and species protection. This study employed a combined SDMs–InVEST modeling approach to predict the current and future potential distributions, habitat quality, and carbon storage of 10 protected plant species in Hubei Province. Using the high‐potential distribution areas of these 10 species as conservation targets, combined with regions of high habitat quality and carbon storage, Marxan was applied to identify conservation gaps for these 10 protected plant species in Hubei Province. Results indicate that the province‐wide mean Habitat Quality Index (HQI) is projected to increase gradually from 0.355 to 0.366, while spatial heterogeneity of HQI will become more pronounced—western mountainous areas show marked HQI improvements, whereas HQI around central–eastern urban agglomerations declines significantly. Total ecosystem carbon storage in Hubei is projected to rise from 2.11 × 10 9 t to 2.13 × 10 9 t. On the basis of 423 occurrence records spanning 10 species (10 genera, 9 families), ensemble SDMs found climate to be the primary determinant of potential distributions; however, the future influence of anthropogenic disturbance and effects of habitat patches (EHPs) is projected to increase, leading to a 2.6% contraction in the total area of core potential distribution zones. These findings provide spatially explicit scientific guidance for optimizing regional protected‐area networks and for aligning biodiversity conservation with carbon management objectives under China's dual‐carbon strategy. Furthermore, multi‐period systematic conservation planning revealed significant protection gaps in interprovincial mountainous regions, forming four key aggregation zones: the Jinqian River source region (Shiyan–Shaanxi), the Tongbai Mountain belt (Suizhou–Henan), the Mufu Mountain region (Xianning), and the Wuling Mountain corridor (Enshi–Chongqing). These regions represent future conservation priorities. Our findings indicate that priority should be given to establishing new nature reserves selected from the 219 conservation gap planning units identified in this study, in order to strengthen the regional conservation‐planning system for rare and endangered plants in Hubei Province and to provide scientific and theoretical support for achieving the targets of the Kunming–Montreal Global Biodiversity Framework.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".