Brown Treesnake Eradication, Suppression & Surveillance planner
An independent browser reimplementation of the Eradication, Suppression and Surveillance planning model for the brown treesnake on Guam, which was developed by Melia Nafus and colleagues at the U.S. Geological Survey and placed in the public domain, and which this page rebuilds from that public release so that the model can be used without a spreadsheet. It is offered as a draft for discussion, it is not a USGS product, and it carries no endorsement by the USGS or its authors.
Status. This reproduces the published worked example exactly on cost, tool coverage, eradication probability and detection probability. The residual density it reports sits a little below the figure from the original Excel workbook, which reflects a known sensitivity in how Excel evaluates the model rather than a difference in the model itself, so the density figures should be read as provisional until checked side by side with the original. The capture‑probability averaging and the prey‑density term follow the definitions confirmed by the model’s author.
You describe a control plan on the left, the model projects the snake population forward year by year, and the predicted result appears on the right. Only the two blocks on the left take input.
YOUR PLAN · INPUTS
Start from
Treatment parameters
2.32
Removal tools
Tool
Use
Season
App. days
Placement
Transects
Spacing m
Units
Length m
Cost
Aerial Delivery System (ADS) requires only the number of application days; the model derives the rest from the EPA label. Cost is per application day.
PREDICTED RESULT · OUTPUTS
Against your goal
—
Area coverage by tool
Outcome probabilities
Eradication probability (Pε)complete removal in the treated area—
Residual total densitysnakes/ha after treatment and rebound—
Residual density ≥950 mm (Fe)large snakes governing recovery—
Functional eradication achieved?large‑snake density below threshold—
Expected individuals encounteredtotal removed over the period—
Surveillance & detection (indicative)
Detection probabilitychance a snake would be seen if present—
Minimum recommended site visitsto support the absence target—
Estimated total costacross all tools and years—
Projected population trajectory
■ total density ■ density ≥950 mm – – recovery threshold. Treatment ends after the chosen period; later years show post‑treatment rebound.
Per‑class removal probability is the running average across individuals in each size class, per tool, as confirmed by the model author.
Plain-language summary
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Long-run tendency (if this annual effort were sustained)
— your treatment window – – continuation if effort were held – – recovery threshold
—
This holds the current annual effort beyond the treatment window to show whether the residual is a step toward zero or a plateau, which is the question an end-of-treatment figure alone cannot answer. It assumes immigration continues at the modelled rate and is a direction of travel rather than a year-by-year prediction, since forecasts past roughly fifteen years are unreliable.
Suppress forever, or clear once (decision economics)
horizon yrsfence cost $/mmonitoring $/yr
— cumulative cost of suppressing forever — cumulative cost of clearing once behind a fence, then monitoring
—
This monetises the separation choice. It assumes the suppression plan must run every year to hold its plateau, and that a fenced area is cleared once and then only monitored. The fence length is taken from the treated area as a square perimeter, and the fence cost, monitoring cost and horizon are editable assumptions of yours rather than figures from the model, so set them to your own estimates. It is a decision aid, not an accounting statement.
Sensitivity (how much the answer depends on the inputs)
Residual density one year after treatment, recomputed when the least certain inputs are varied, so you can see which assumptions actually move the answer. The bounded-versus-open switch is usually the one that matters most, which is the same lesson the barrier and goal panels give from other angles.
What separation buys (barrier comparison)
— no barrier (open) — partial barrier or buffer — full snake fence
—
The same plan is projected under three degrees of separation, modelled by reducing the immigration that reaches the treated area, with a partial barrier taken to cut most but not all of it. It shows why a bounded area can be cleared while an open one cannot, and it is a direction of travel rather than a year-by-year prediction.
Tool placement height (from behavioural data)
6.0prey index
trap height 1.0 mbait height 0.5 m
— prey-poor reference — prey-rich reference — current site, shaded band is uncertainty. Dots mark a large susceptible snake (~1000 mm) and a small snake (~550 mm).
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Derived from Nafus et al. (2025), Behavioural plasticity in detection height of an invasive, arboreal snake, Wildlife Research, using the released height dataset. It reflects a cross-site relationship between size, prey and detection height. With the toggle off, or with the tools at their default heights (traps 1.0 m, bait tubes 0.5 m), the forecast is identical to the validated model; raising or lowering a tool away from those defaults then changes its capture in proportion to how well its height matches where snakes of each size are predicted to be. The prey index here is the behavioural-study measure, which combines lizards, birds and mammals and differs from the endotherm count the planner uses, so set it to match your site using the reference points, where a prey-depleted forest is about 1.3 and a prey-rich site about 19. Snake condition is held at the study average.
Compare two plans (A / B)
Set up a plan, save it as A, change it, then save as B to compare.