session 6: citizen science to surveillance: estimating reporting probabilities of exotic insect...

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Project 1029 Citizen science to surveillance: Estimating reporting probabilities of exotic insect pests

Peter Caley, Marijke Welvaert & Simon BarryCSIRO

Plant Biosecurity Cooperative Research Centre

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Problem being addressed

Project aim – To clarify how data collected through citizen science activities have the potential to be useful to biosecurity surveillance …

Specific talk objective – What biosecurity surveillance information is contained within the ‘unstructured’ data streams

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Control and intention within data streams

Structured citizen science

Unstructured citizen science

Crowd sourcing

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Example: Bowerbird sighting & identification

• Reported April 2014

• Identified Nov. 2015

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Bowerbird record: Amarusa australis

• Black spittlebug in same family as the glassy-winged sharp shooter (GWSS)

• Two citizen sightings uploaded to ALA as of 30-06-2016

• Relevance to GWSS reporting?

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Methods

Case-control experimental design- Cases = citizen species observations uploaded thru

Atlas of Living Australia (ALA) portal up until 30 June 2016.

- Controls = weighted (by no. obs) sample of species within ALA not reported by citizens up until 30 June 2016.

- Coleoptera & Hemiptera only considered

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Features (covariates)

Size (mm) Colour (0—4) Pattern (0—4) Morphology (0—4) Range size (km2 – all ALA records) Observer density (all CS reports for orders) Pest status (naïve)

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Analysis

)(...

Sampled) Covariates|eportedPr(logit

321*0

FeatureslpxPatternColourSize

R

nn

Logistic regression

Predicting requires explicit formulation that accounts for proportion of ‘cases’ sampled (P1) and ‘controls’ sampled (P0)

0

1

0

1

log)(exp1

log)(expFeatures)|dPr(Reporte

PPFeatureslp

PPFeatureslp

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Factors influencing reporting probabilityFeature Odds ratio 95% C.I.

Order 1.9 (Beetles) 1.0 – 3.7Size 1.1 (per mm) 1.06 – 1.14Colour 1.9 (per unit score) 1.3 – 2.7 Pattern 4.0 (per unit score) 2.6 – 6.3 Morphology 2.1 (per unit score) 1.5 – 3.0

Range 1.001 (per km2) 0.999 – 1.002 Pest 21.9 7.9 – 60.1

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Inferred reporting probs. for High Priority Pests

Using ‘old’ Plant Health Australia cross-sectorial HPP species list

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Lychee longicorn beetle (Aristobia testudo)

Source: www.yellowman.cn

• Large (c.35 mm)• Colourful• Patterned• Interesting

morphology• Predicted 2-year

(Reported sighting) = 0.99

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Colorado potato beetle (Leptinotarsa decemlineata)

Source: United States Department of Agriculture

• Moderate size (c.10 mm)

• Colourful• Racing stripes• Predicted 2-year

P(Upload) = 0.98

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Glassy winged sharp shooter (Homalodisca vitripennis)

Source: Don Pace

• Moderate size (c.12 mm)

• Colourful• Some pattern• 2-year predicted

P(Upload) = 0.83

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Asian citrus psyllid (Diaphorina citri)

• Small size (c. 2 mm)

• Little colour• Little pattern• 2-year

predicted P(Upload) = 0.22

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Russian wheat aphid (Diuraphis noxia)

Source: Frank Peairs, Colorado State University, Bugwood.org

• Small (c.3 mm)• Plain• Boring• Predicted

P(Upload) = 0.04

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Conclusions

Physical features drive reporting probabilities within unstructured citizen science data streams.

Reporting probabilities for exotic HPPs can be inferred- relative probabilities most robust- absolute probabilities less clear

Can identify for which species unstructured citizen science reporting probability is insufficient

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Thank you

For more information, please email peter.caley@csiro.au | simon.barry@csiro.au

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Logistic regression

nnxxPP

YY

...log)Covariates|1Pr(1

)Covariates|1Pr(log 1101

0

We often don’t know P0 and P1, and besides, the estimates of Odds Ratios (= exp(’s)) stay the same:

nn xx

...sampled Covariates|1Pr(Y1

sampled) Covariates|1Pr(Ylog 11*0

However, we can no longer estimate Pr(Y=1 | Covariates) – sometimes we want to (e.g. screening models)

Explicit formulation that accounts for proportion of cases sampled (P1) and controls sampled (P0)

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0

1

0

1

log)(exp1

log)(expFeatures)|asePr(

PPFeatureslp

PPFeatureslp

C

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Talk outline

Problem being addressed Quantifying factors influencing citizen

reporting of endemic insect species Application to High Priority Pests Conclusions