Session 6: Citizen science to surveillance: Estimating reporting probabilities of exotic insect...
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Transcript of 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 [email protected] | [email protected]
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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