Skip to main content
Environment· 3 figures

Salamander Bsal Watershed Surveillance

Optimize a 25-watershed national surveillance network covering 115 of 184 native salamander species and derive a field-ready 1,650-swab sampling protocol.

What this research found

Where should a national early-detection network for the salamander-killing chytrid fungus Batrachochytrium salamandrivorans (Bsal) sample first? USGS range maps for 184 native salamander species were crossed against a national pathogen swab survey to identify which species and watersheds have never been sampled, and a set-cover optimisation then selected 25 subwatersheds covering 115 of the 184 species and 91.0% of a range-rarity priority weight. A binomial detection model fixed field effort at 66 swabs per site, or 1,650 nationally, which was written up as a field-ready protocol.

  • Existing surveillance — 11,170 swabs over 772 field visits in 458 subwatersheds — leaves most salamander range untouched. Of the 184 native species, 45 have never been swabbed anywhere in their range and 139 only partially; none is fully sampled, and the median species has 98.9% of its watersheds unsampled.
  • Twenty-five subwatersheds suffice to reach 115 of the 184 species and 91.0% of the total priority weight. A mixed-integer solver certified an optimum of 7.0551 out of 7.749 achievable, beating a greedy solution by 0.02%.
  • Priority is dominated by a few micro-endemics. The narrowest-ranging species, the Austin blind salamander (Eurycea waterlooensis), occupies 138.0 km² against roughly 3.08 million km² for the widest, and all top 20 by weight are lungless salamanders from the Texas Edwards Plateau, southern Appalachians, and California.
  • Field effort follows directly from the detection arithmetic: 66 swabs per site give a 95.21% chance of detecting Bsal at 5% design prevalence with a 90%-sensitive assay, totalling 1,650 swabs across 25 sites. Weaker assumptions get expensive fast — at 1% prevalence and 80% sensitivity, 373 swabs per site would be needed.
  • Reconciling names between the pathogen survey and the range maps matched 10,409 of 10,451 salamander records (99.60%), including 136 records recovered from a misspelt epithet in the range index; only the 42 records identified to genus alone were dropped.

How it was done

Three USGS products were combined in an equal-area projection: a national Bd/Bsal pathogen survey (11,189 records, 10,451 of them salamanders), GAP species range maps filtered from 1,719 species down to the 184 native salamander species, and the Watershed Boundary Dataset's 12-digit subwatersheds. Scientific names were reconciled between survey and range map through a cascade of exact, synonym, subspecies, and spelling matches. Each species then received a priority weight equal to the smallest range area divided by its own, multiplied by the share of its watersheds never sampled, and a maximum covering location problem was solved both greedily and exactly to pick 25 subwatersheds. A binomial imperfect-test detection model set the swab count per site, and the results were operationalized into a field standard operating procedure covering habitat scouting, swabbing technique, between-site decontamination, sample preservation, and diagnostic controls.

Data sources

  • USGS national Bd/Bsal survey v2.0, November 2024 — 11,189 records including 10,451 salamander swabs at 603 unique coordinates
  • USGS GAP Species Range Maps, CONUS 2001 v1 — 1,719 species filtered to 184 native salamanders spanning 76,156 subwatersheds
  • USGS Watershed Boundary Dataset — 103,068 national 12-digit subwatershed polygons
  • AmphibiaWeb, ITIS, and Amphibian Species of the World as taxonomic authorities

Limitations

GAP range maps are presence priors rather than verified occupancy, so the protocol instructs field teams to scout micro-habitat by focal-species ecology and record documented absences instead of assuming the target species is present. The 66-swab figure also assumes a large host population, making it conservative for micro-endemics confined to small spring or cave systems.

Figures from this analysis

How this research was produced

K-Dense Web planned and ran this environment investigation end to end — gathering the sources, carrying out the analysis, producing the figures, and drafting the report. The full session transcript, including every intermediate step, is available to view.

Share:
Environment

Air Quality Health Impact Analysis

Analyze EPA AQI data to predict air quality categories and explore respiratory health impacts using XGBoost classification.

Environment

Aravali Mountains Impact Prediction

Model environmental impacts of policy changes on India's Aravali ranges with climate, air quality, and groundwater projections to 2050.

Environment

India Urban Air Pollution Tipping Points

Detect pollution tipping points in Indian metros using Mann-Kendall tests and forecast PM2.5 to 2026 with Holt-Winters.

Run this kind of analysis on your own question

Try K-Dense Web free and see how an AI co-scientist accelerates your research.