Uncovering biodiversity priorities for appalachian streams through taxonomic and functional diversity mapping
Bibliographic record
Abstract
• Most biodiverse streams occur in degraded lowland areas outside protection. • INLA-SPDE approach successfully predicts functional diversity distributions. • Current protected areas miss 95% of taxonomic diversity hotspots in region. • Elevation and precipitation drive taxonomic and functional diversity patterns. With freshwater diversity facing unprecedented global decline driven by human activities, the Kunming-Montreal Global Biodiversity Framework’s 30x30 initiative aims to protect 30 % of land and water by 2030. However, effective conservation requires understanding the spatial distribution of biodiversity and ensuring protected areas align with biodiversity hotspots. We developed spatial models of taxonomic and functional diversity of stream fishes across West Virginia using Integrated Nested Laplace Approximation with Stochastic Partial Differential Equations. We analyzed 505 sampling sites to map species richness, functional richness, and functional divergence, and assessed their alignment with protected areas. Elevation emerged as a consistent driver across all diversity metrics, with higher diversity in lowland streams. Precipitation positively influenced functional divergence, while species richness was surprisingly higher in areas with greater land degradation, likely reflecting historical human settlement in biodiversity-rich areas. Current protected areas of West Virginia (∼17.12 % of total area) predominantly encompass regions of low taxonomic and functional diversity, with less than 5 % of taxonomic diversity hotspots and less than 10 % of functional diversity hotspots under protection. The mismatch between diversity patterns and protection status creates significant conservation challenges, as many biodiverse streams now flow through urbanized or industrialized areas. Our findings highlight the need for conservation strategies that target not only traditional wild areas but also urban and degraded waterways supporting diverse fish assemblages. By utilizing routine agency surveys and publicly available databases, our approach offers a replicable framework that can inform conservation planning and management across different regions, supporting global goals for freshwater ecosystem protection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".