Up until mid-2016, citizen science uploads to the Atlas of Living Australia (ALA) included c. 400 bug species, and c. 1,000 beetle species. Given the short time period (c. 3 years) over which most of these records have accumulated, this represents a considerable reporting effort. The key question from a plant biosecurity context is how this level of reporting translates to the detection and reporting of exotic insect pests in the event of an incursion.

1. biosecurity built on science
Project 1029 Citizen science to
surveillance: Estimating reporting
probabilities of exotic insect pests
Peter Caley, Marijke Welvaert & Simon Barry
CSIRO
Plant Biosecurity Cooperative Research Centre

2. biosecurity built on science
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

3. biosecurity built on science
Control and intention within data streams
Structured
citizen
science
Unstructured
citizen
science
Crowd
sourcing

4. biosecurity built on science
Example: Bowerbird sighting & identification
• Reported April
2014
• Identified Nov.
2015

5. biosecurity built on science
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?

6. biosecurity built on science
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

7. biosecurity built on science
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)

8. biosecurity built on science
Analysis
 
)(
...
Sampled)Covariates|eportedPr(logit
321
*
0
Featureslp
xPatternColourSize
R
nn

 

Logistic regression
Predicting requires explicit formulation that accounts
for proportion of ‘cases’ sampled (P1) and ‘controls’
sampled (P0)
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
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
0
1
0
1
log)(exp1
log)(exp
Features)|dPr(Reporte
P
P
Featureslp
P
P
Featureslp

9. biosecurity built on science
Factors influencing reporting probability
Feature Odds ratio 95% C.I.
Order 1.9 (Beetles) 1.0 – 3.7
Size 1.1 (per mm) 1.06 – 1.14
Colour 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

10. biosecurity built on science
Inferred reporting probs. for High Priority Pests
 Using ‘old’ Plant Health Australia cross-
sectorial HPP species list

11. biosecurity built on science
Lychee longicorn beetle (Aristobia testudo)
Source: www.yellowman.cn
• Large (c.35 mm)
• Colourful
• Patterned
• Interesting
morphology
• Predicted 2-year
(Reported
sighting) = 0.99

12. biosecurity built on science
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

13. biosecurity built on science
Glassy winged sharp shooter (Homalodisca vitripennis)
Source: Don Pace
• Moderate size
(c.12 mm)
• Colourful
• Some pattern
• 2-year predicted
P(Upload) = 0.83

14. biosecurity built on science
Asian citrus psyllid (Diaphorina citri)
• Small size (c. 2
mm)
• Little colour
• Little pattern
• 2-year
predicted
P(Upload) =
0.22

15. biosecurity built on science
Russian wheat aphid (Diuraphis noxia)
Source: Frank Peairs, Colorado State University,
Bugwood.org
• Small (c.3 mm)
• Plain
• Boring
• Predicted
P(Upload) = 0.04

16. biosecurity built on science
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

17. biosecurity built on science
Thank you
 For more information, please email
peter.caley@csiro.au | simon.barry@csiro.au

18. biosecurity built on science
Logistic regression
nn xx
P
P
Y
Y
 
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
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
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



...log
)Covariates|1Pr(1
)Covariates|1Pr(
log 110
1
0
We often don’t know P0 and P1, and besides, the
estimates of Odds Ratios (= exp(’s)) stay the same:
nn xx  







...
sampledCovariates|1Pr(Y1
sampled)Covariates|1Pr(Y
log 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)

19. biosecurity built on science
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0
1
0
1
log)(exp1
log)(exp
Features)|asePr(
P
P
Featureslp
P
P
Featureslp
C

20. biosecurity built on science
Talk outline
 Problem being addressed
 Quantifying factors influencing citizen
reporting of endemic insect species
 Application to High Priority Pests
 Conclusions