Searching to eradicate:What to do after jumps to unknown locations?

Daniel Springa, Oscar Cachob, Luke Crofta, Tom Kompasc, Nhu Che

a Monash University, b University of New Englandc Crawford School of Economics & AC-BEE, ANU

Some examples of successful and not-yet-successful eradications

Kochia scopariaArea: largeeradicated ~15 yrs

Black-striped musselArea: smalleradicated <15 yrs

Successes

Mainly treatment, little search

Mainly search (passive & active)

• Over 100,000 ha searched• Spatial extent & no of colonies increasing• >$230 million spent

Not yet successful: Red Imported Fire Ants in Queensland

Kochia Skeleton weed

Kochia and skeleton weed: Similar areas, different outcomes

• Eradicated within 15 yrs• Good passive surveillance• Escaped jumps probably rare

• Not eradicated after 35 yrs• Poor passive surveillance• Escaped jumps prob. common

FailureSuccess

•Larger area infested

Kochia scoparia Skeleton weed

Kochia and skeleton weed: Similar areas, different outcomes

Eradicated within 15 yr Not eradicated within 35 yr

Both invasions large, both had jumps:What biological & surveillance factors could explain

different outcomes?

Discovery of new invasion: Three questions

1. Can it be eradicated?

2. Should it be eradicated?

3. If so, how?

KochiaBlack-striped mussel RIFA

? ? ?

Interdependent

Sometimes answers obvious, sometimes not

KochiaBlack-striped mussel

• Eradicable? Yes. • Eradicate? Yes:

- B > C• Method?“brute-force treatment”

RIFA

• Eradicable? Maybe• Eradicate? Maybe

- Near threshold?• Method?

- Passive, active S

• Eradicable? Maybe. • Eradicate? Maybe:

- B & C both highMethod?

- Passive, active S

obvious not

KochiaBlack-striped mussel

• No jumps• Costs known

- “Nuke” marina• Eradicate?

- Yes: B > C

RIFA

• Some jumps but abundance low- Needles in haystack

• Eradicate? - Depends on search costs &

effectiveness

Importance of jumps

No jumps:• Spatial extent easy to estimate

(eg Leung Cacho Spring 2010)

• Can accurately estimate costs if treatment works (eg mussels)

Jumps:• Spatial extent, density?• Costs?•Bigger role for bioecon

First pests

RIFA search & treatment 2001-2009

treatment (T)search (S)both

Heavy treatment when spatial extent believed known & “small”, then S/T grew

What we usually don’t know

• Current locations of all individuals

• Reproduction and spread dynamics

Undetected individuals- didn’t search there

or missed them

Limits to prediction

• Best predictive model won’t predict all individuals

• Eg est. 600 undetected RIFA nests

• If missed individuals cause irretrievable spread, prediction alone not enough to eradicate

• Need surveillance methods that don’t depend too much on predictive accuracy

How to find pests in poorly predictable locations over large area?

• Search bigger area (eg remote sensing)• Optimal placement of search less important

If search sensitivity < 1:Use two sensors, one to search big area,

one to “mop up” afterwards

• Mopping up is often pretty easy - so placement not so important

Importance of search placement: RIFA results

• Ran RIFA spread model with “probability search”

• Some gains compared to proximity search

– Increased detections but not eradication probability– Could not find “outlier nests” with probability search

• Then increased remote sensing sensitivity– Big increase in eradication probability

even when less active search

Predictive Models - A Weather Analogy• Identify current rain locations

• Predict future rain locations based on past locations & past movement

• RIFA: predict new locations based on recent detections

• But only some locations searched & not all colonies detectable

– prediction is hard!

Importance of search placement: More results

• Eradication probability with “probability search” and no remote sensing

– low unless search really large area

– Is that probability search or exhaustive search?

– Search placement less important than searching a large area

Summary of main findings

1. How to find “needles in haystack”?– Search strategies

2. What matters most for eradication feasibility?– Search sensitivity, biology

3. Two roles for bioeconomists:

– Determine decision thresholds (eg eradicate/control)

– Improving passive surveillance & reducing human-assisted movement of pests

• Requires incentive compatible tools

Do people need bounties to report fire ants?

• Depends on private costs of removal• Private cost of government removal

Effective search strategy

• Use cheap, low-sensitivity search of large area then expensive high-sensitivity sensor

– Eg: remote sensing then people search near remote detections

Why it works well

• Clustering & search cost-sensitivity relationship:

– Most pests form clusters

– Easier to find ≥1 individual if cluster larger• Find most clusters with cheap, low-sensitivity search

– Use expensive sensor to “mop up”• ie finds individuals missed by cheap sensor

RIFA clusterHigh probability of detecting ≥1 nest, but also high probability of missing some nests. Active search can find

them because more sensitive.

Assumes that nest detection probabilities are independent and nests not less detectable in larger clusters (eg not smaller)

Hypothetical example

XFind red nest b/c large or one of many

Finds green nests because more sensitive

Remote sensing

May have found no nests without remote sensing b/c don’t know where to look

Don’t need people search here so save $$

Improving remote sensing more important than improving ground search

X

Improved ground search finds few extra nests in known clusters

New detections (important)

More nests detected per cluster (not very important)

What matters for search effectiveness

• Sensitivity• Delimitation

• Probability of “escaped jumps”: - jumps that occur before you find parents

Sensitivity = ? Sensitivity = 0.95

Community involvement important

1. Reducing jump probability – Truncate maximum dispersal distance or

change shape of dispersal curve

– caused big increase in eradication probability

2. Increasing citizen detection probability – With information campaign, bounties

– caused big increase in eradication probability

What happens to missed individuals?

X

• Helicopters & people miss small isolated infestations

• Must wait until they become bigger

• Any escaped jumps by then?Need decision threshold analysis

Catching up with invasion

• As long as jumps are rare, can “catch up” with invasion even if search large areas with low sensitivity

Eradicating with imperfect surveillance

t=1 t=2 t=3 t=5

0 100 200 300 400 500 600 700 800 9000

100

200

300

400

500

600

0 100 200 300 400 500 600 700 800 9000

100

200

300

400

500

600

0 100 200 300 400 500 600 700 800 9000

100

200

300

400

500

600

0 100 200 300 400 500 600 700 800 9000

100

200

300

400

500

600

• Some RIFA clusters decline then get larger due to incomplete removal and delay before rediscovery

• Most clusters eventually removed– “Real world” example: Kochia

0 100 200 300 400 500 600 700 800 9000

100

200

300

400

500

600

0 100 200 300 400 500 600 700 800 9000

100

200

300

400

500

600

t=3 t=5

Declining abundance and spatial extent

Model to estimate whether “catch up” to invasion with imperfect surveillance

1. Distribution of colony ages within each cluster2. Spread kernel3. Reproduction rate = fn(age of colony)4. Detectability of colony5. passive detection probability6. active detection probability• What combination of escape rate and jump rate

prevents catch up?• What radius of active search keeps invasion in decline?