Making use of “citizen science” to maximise the effectiveness of tree

pest and disease surveys

N. Brown , S. Parnell, S. Denman,

and F. van den Bosch

• Introduction Public reports Our project Interesting questions

• Interpreting survey data

Statistical Process based

Outline

The case study

1) Stem “bleeds”

• Dark liquid runs from cracks between

bark plates.

• Necrotic tissue below bark

• Lab tests to confirm presence of bacteria

2) Agrilus biguttatus

• Larval development cryptic and may

take 1-2 years.

• Exit holes first external sign of presence

• Adults present briefly in a single

summer.

Acute Oak Decline (AOD) is a distinct syndrome that falls within the

wider context of oak decline.

Potential for lots of new discoveries.

Can be used for:

• First detection of new threats

• To define a distribution?

• To monitor control programs

How can official surveys make the most of available data?

Potential for lots of new discoveries.

Can be used for:

• First detection of new threats

• To define a distribution?

• To monitor control programs

How can official surveys make the most of available data?

How can we use public reports?

The good and bad sides of volunteer data

Introduce bias or maximise discoveries ?

• Can an independent survey cover enough ground?

• 192,800 ha of oak in England and Wales (in 1,039,000 ha of broadleaf) • Only sample 120 sites each year

• Does the public report accurately? • 436 reports, 208 positive

• Does the public report evenly?

National distribution

Observations by both foresters and entomologists are broadly consistent, but do not account for surveyor effort. Is this representative of the actual distribution? Where are the unaffected woodlands?

A designed survey for AOD

Locations from Forestry Research (DDAS) April 2013

Questions: Can we detect AOD in new areas? Does the public report sites in the same areas? Can we see spatial bias in the data? Can we use all data? How does a process based model compare?

N ational F orest I nventory

Selected for 2014 survey

Maximum nearest neighbour buffer around all known reports gives area to survey

N ational F orest I nventory

Selected for 2014 survey

We stratify the survey, putting the same amount of effort in each 50 km square

N ational F orest I nventory

Selected for 2014 survey

We select 20% of 10km squares, sending surveyors to oak woods within them

Squares containing public reports

We also select squares known to have AOD and keep these as a sub-sample to use in analysis

2014 Survey results: prevalence

• 38 contained AOD positives already, so not surveyed

Approximately 20% of selected area

• First detection of AOD in 22 squares

• 3 survey negatives had AOD reported from other woods

Approximately 32% of selected area

2014 Survey results: new discoveries

New discoveries are shown in red. Earlier AOD reports are shown as black dots.

• Both methods find AOD in the same areas

• But public reports are clustered within the overall pattern

There may be localised bias in reporting…

or reflect disease prevalence

Do reports occur evenly across England and Wales?

* Now using 2013 and 2014 surveys

Are statistical predictions affected by bias?

Very similar trends, but spatial autocorrelation is greater with all data included

* Now using 2013 and 2014 surveys

Only selected squares All reports

Surveys take place in some squares, but what happens in between?

Predicting the distribution from a sample

Disease Incidence

(cell 1)

Host Area

(cell 1)

Distance

(cell 1 to cell 2)

Host Area

(cell 2)

Disease Incidence

(cell 2)

0 2 4 6 8 10

Distance

Dis

pe

rsa

l

X X X =

Principles used to calculate disease incidence:

Initial predictions for AOD

Current distribution 2016

Additional reports continue to arrive. Including a first finding in south wales – reported by an Observatree volunteer. This is in an area where AOD had not been found, but where the temperature was predicted to be suitable for A. biguttatus.

Summary

• The effectiveness of disease surveys can be greatly increase by including public reports

• Sub-sampling can reduce spatial bias

• All data can be used if autocorrelation taken into account

• Process based models will limit the extent of over prediction

Acknowledgements

• TSU Staff: Mark Oram, Emyr Algieri, Lee Cooper, Steve Coventry, Craig Griffiths, Justin Hardy, Mark Hilleard, John Manning, Joe McMinn, Liz Richardson, Nicola Rivett, Tony Reeves, Zac Sibthorpe, Paul Turner and Stephen Whall.

• Forest Research Labs: Susan Kirk, Sarah Plummer and Dr Glyn Barrett

• Management of site data collection: Rory Vereker (FR)

• Locating oak woodland: National Forest Inventory (Alan Brewer and Robyn McAlister at IFOS) , Woodland Trust, National Trust, Wildlife Trusts, SSSI sites (Natural England, Countryside Council for Wales and Dr Scott Wilson)

This work was supported by the Forestry Commission and Defra project TH0108