Pest Risk Analysis Research in Europe

:Developments from EU project PRATIQUE

Alan MacLeod

Pest Risk Analyst

PRA Workshop

Brasilia

March 2012

Outline

• Introduction to Fera

• PRATIQUE

– Datasets

– Consistency

– Mapping

– Factors that ease eradication

– Computer Assisted PRA (CAPRA)

• Work in China & Russia

What is Fera?

• A government science agency which provides the UK’s food and environment sectors with:-

• expert scientific advice

• regulatory services

• applied research facilities and

• emergency responsiveness

National Assembly for Wales, Agriculture Department

Forestry Commission

- Forest Research

UK

London

•York

Contingency Response

Policy

Plant health and Seeds

Inspections

Bee Disease

Pest Risk Analysis

Diagnosis and

Taxonomy

Containment and

Eradication

Seed Listing and

marketing

Pollinator Research

National reference laboratory

Phytophthera

Plant Clinic

National Bee Unit

Plant Breeders

Rights

Plant Protection

Pesticide Usage

Biorefining

Operator Exposure

Pesticide Fate

Environmental Risk

Ecotoxicology Usage Surveys

Wildlife Poisoning

Natural products

Ecochemistry Nano

materials

Risk Assessment

Environmental Risk

Badgers and TB

Bird Strike Control

NonNative Species

Secretariat

Rabies in Wildlife

Vaccine Deployment

Wildl ife Damage

Control Methods

Fertility Control

Welfare

Bird Radar

Wind Farm EIA

Disease Dynamics

Invasive Species

Population Monitoring

Eradication Programmes

Wildlife Management

Proficiency testing

Food Authenticity

Food Contaminants

Environmental Contaminants

Pesticides

Veterinary medicines

Packaging

Testing Standards

Mycotoxins National

Reference Laboratory

Chemical residues

Food Safety

Advice from “farm to fork”

Animal feed

Genetically modified

organisms

Plant Health Plant Protection

Animal welfare &

wildlife diseases Microbiology

food chain hazards

Food chain contaminants Food additives

Food authenticity,

Novel foods

PRATIQUE: Permission granted to a ship or boat to

use a port on satisfying the local quarantine

regulations or on producing a clean bill of health

Acknowledgments 6 European research institutes (Fera, CIRAD, INRA, JKI, LEI, PPI)

5 European universities (IBOT, Imperial, UNIFR, UPAD, WU)

2 international organisations (CABI & EPPO)

2 partners from outside Europe (CRCNPB & Bio-Protection)

Food and Environment Research Agency

PRATIQUE: Key partner skills

Natural scientists

• Entomologists

• Plant pathologists

• Ecologists

• Phytosanitary experts

• Plant protection

managers

Social scientists

• Economists

Engineering

• Risk analysts

• Computer

scientists

• To enhance PRA techniques for the EU / EPPO

(i) by assembling datasets required for PRA for

the whole EU (27 countries)

(ii) by conducting multi-disciplinary research to

enhance techniques

(iii) by providing a user friendly decision support

system

PRATIQUE Aims

What is the PRA area?

Why did PRA in Europe need

enhancing?

1. PRA is a young area of study (first schemes developed only in 1990s)

2. Lack of data to analyse the risks posed by pests to countries in the EU or EPPO

3. Developments outside of PRA can be applied in PRA

4. PRA procedures are complex* for the risk analysts and the decision makers. Tools needed which brings all factors together

*EPPO PRA scheme (2009): over 50 questions, 5 level risk rating, 3 levels

of uncertainty - no mechanisms to combine ratings and derive risk

• Whilst there is a history of plant health*, formal

pest risk analysis is relatively young

• ISPM No. 2 Guidelines for PRA (1996)

• ISPM No. 11 PRA (more detail)(2004)

– tells us what to do but not how

– “Climatic modelling systems may be used…” (2.2.2.2)

– “There are analytical techniques which can be used in

consultation with experts in economics….” (2.3.2.3)

• As well as standards, need tools and resources

1. Young discipline

* MacLeod et al. (2010) Food Security, 2, 49-70

2. Lack of Data for PRA

• PRA quality is highly dependent on data

• EU and EPPO need to produce PRAs relevant for all member states

• Data from some member states difficult to obtain

• Language barriers

• Crop, pathway, and impacts-related data often very difficult to obtain

Wrote to EU Member States

• Collected electronic / web accessible data

sources (e.g. Crop / pest distribution)

• Import data, other economic datasets, yields…

• PRA area data e.g. land use, climate data, soil

types, …

• Pest management data

• Reviewed datasets

Datasets on imports, production, &

economics

Datasets relating to climate, soils…

Dataset quality and usefulness evaluations Dataset

Categories

Total

evaluated

Data rating

(overall)

Total

retained

A B C D U

Pests in the current area

of distribution 236 50 61 53 70 2 166

Pathways and economic

datasets 118 5 37 38 16 22 96

Area under consideration

for the PRA (land use

etc)

266 30 105 91 27 13 239

Pest management 155 24 66 28 8 29 147

Score Definition

A Essential, high quality and widely applicable

B Good quality but applicable to specific regions

C Narrow or very limited usefulness or overlap with categories A or B.

D Unreliable, contain too many errors or are generally irrelevant

U Cannot currently be assessed due to a language barrier

Data sets linked to computer

assisted PRA (CAPRA)

3. To enhance techniques

• Consistency

• Mapping

• Spread

• Economic impact

Consistency

• Reviewed 43 schemes & guidelines seeking best practice on ensuring consistency: – Biosecurity and plant health standards

– PRA schemes

– Weed risk analysis schemes

– Animal health schemes

• Consistency in risk rating more likely if: – use a clear and structured framework – ask unambiguous questions – obtain responses from groups of assessors – provide examples to help guide risk rating, e.g. CFIA – mechanism to combine risk elements (risk matrices)

EPPO (2009) PRA Scheme - Format

• Series of questions: Categorisation (19)

Entry (14)

Establishment (15)

Spread (3)

Impacts (16)

Risk management (44)

• Explanatory Notes

• Responses required: 5 level risk rating

3 level uncertainty score

Written justification

• No method for summarising

each section or overall risk

and uncertainty

Consistency

Revised EPPO scheme • To improve structure

• Reword some questions = clearer meaning

• Provide biological examples for rating guidance at 5 levels for each question

• A visualiser developed to review questions

• Mechanism to combine risk elements

• Matrix models provided to summarise risk and

uncertainty from many questions and sub-questions

PRAs can be long

documents

Qualitative Impact Assessment Methods: Visualiser to

review responses to questions

• Each question’s risk

rating from very low (1)

to very high risk (5) is

put on the graph as a

bubble

• The larger the size of

the bubble, the greater

the uncertainty

• Each cluster of

questions has the same

colour

• A bar marks the

summarised rating

(here for entry) of the

expert(s)

• Visualisation of the

author’s judgment, no

modelling!

Qualitative Impact Assessment Methods: Visualiser to

review responses to questions

• Each question’s risk

rating from very low (1)

to very high risk (5) is

put on the graph as a

bubble

• The larger the size of

the bubble, the greater

the uncertainty

• Each cluster of

questions has the same

colour

• A bar marks the

summarised rating

(here for entry) of the

expert(s)

• Visualisation of the

author’s judgment, no

modelling!

Consistency

Was no mechanism to combine factors that contributed to risk (risk elements)

Examined the concept of risk matrix

Used in USA & Australia

Risk matrix

Likelihood of introduction

Establishment

Low Medium High

Entry

Low Low Low Medium

Medium Low Medium High

High Medium High High

Matrix model for Entry (does not show uncertainty)

Risk matrix with uncertainty

Likelihood of introduction

Establishment

Low Medium High

Entry

Low Low Low Medium

Medium Low Medium High

High Medium High High

High

Low

Med

ium

Hig

h

Low Medium

Entr

y

Establishment

Uncertainty distributions

Very Unlikely

Unlikely

Moderately likely

Likely

Very likely

The distributed scores/ratings corresponding to the three levels of uncertainty

Very Unlikely / Minimal (Score / rating of 1)

Low Medium High

Unlikely / Minor (Score/ rating of 2)

Low Medium High

Moderately Likely / Moderate (Score / rating of 3)

Low Medium High

Likely / Major (Score / rating of 4)

Low Medium High

Very Likely / Massive (Score / rating of 5)

Low Medium High

The distributed scores/ratings corresponding to the three levels of uncertainty

Very Unlikely / Minimal (Score / rating of 1)

Low Medium High

Unlikely / Minor (Score/ rating of 2)

Low Medium High

Moderately Likely / Moderate (Score / rating of 3)

Low Medium High

The distributed scores/ratings corresponding to the three levels of uncertainty

Very Unlikely / Minimal (Score / rating of 1)

Low Medium High

Unlikely / Minor (Score/ rating of 2)

Low Medium High

Moderately Likely / Moderate (Score / rating of 3)

Low Medium High

Likely / Major (Score / rating of 4)

Low Medium High

Very Likely / Massive (Score / rating of 5)

Low Medium High

Uncertainty rating

Qu

estio

n/

risk e

lem

en

t sco

re

Low uncertainty: 90%

confidence that rating is

correct

Medium: 50% confidence

that rating is correct

High uncertainty: 35%

confidence that rating is

correct

(after Intergovernmental

Panel on Climate Change,

2005)

Assignment based on the

beta & truncated normal

distribution

Matrix model with uncertainty

Matrix models

Have generic models for

• Entry

• Establishment

• Spread

• Impact

Could combine likelihood of entry, establishment, spread and impact to show overall pest risk

Loss of detail when combine all elements

Can be difficult to agree how to combine elements (low likelihood : high impact)

Risk mapping

Global Annual Degree Days base 10°C (from Baker, 2002)

Maps can help risk assessors

World Potato Production (from Monfreda et al., 2008)

Why do we need a DSS for risk

mapping?

• General maps of climate, current pest distribution, crop

distribution or other factors do not directly indicate pest risk

• Risk maps can be very useful in PRA but guidance is

needed :

– To advise when appropriate to map (may not be needed)

– May be inappropriate to map predictions (data problems)

– Mapping requires significant modelling and mapping

skills, resources and time

– Maps can be created by a confusingly wide variety of

methods

– Maps can produce misleading results

Climatic mapping: Models

• Inductive techniques – Maxent

– Diva-GIS (BIOCLIM / DOMAIN)

– OpenModeller (8 algorithms)

– DK-GARP

– OM-GARP

– BIOCLIM

– Environmental Distance (~ DOMAIN)

– Envelope Score

– Support Vector Machine (SVM)

– Climate Space Model (CSM)

– Artificial Neural Network (ANN)

– CLIMEX match climates

• Deductive techniques NAPPFAST: Phenology and

Generic Infection Models

Diva-GIS (Ecocrop) [Based on species’ physiological characteristics]

• Integrated techniques CLIMEX compare locations

Climatic Mapping DSS

Asks questions to help decide if should map, and is so what

technique to use

Is it appropriate to map climatic suitability? (sub questions)

What type of organism is being assessed and what are the key

climatic factors affecting distribution?

How much information is available on the climatic responses of

the pest?

What category of location data is available?

Based on the type of organism, the information available on its

climatic responses and the category of location data, how

well is each climatic mapping method likely to perform?

Pest location data category

N Pest location data category Availability

1 Native range locations only

2 Native plus exotic range locations

3 Locations biased to the periphery of the range

4 Locations biased to the centre of the range

5 Few location data points

6 Very few location data points

7 Erroneous locations included

8 Locations influenced by natural barriers

9 Locations influenced by seasonal invasion

10 Distribution constrained by hosts

11 Regional distribution data only

12 Locations influenced by climate change

13 Location category unknown

Climate

Response

Information

Availability

Location Data Category

Methods + ++ +++ 1 2 3 4 5 6 7 8 9 10 11 12 13

Phenology

models

CLIMEX

match

CLIMEX

compare

Regression

models

KEY

Climatic response rating or location data category irrelevant to model

functioning Method poorly adapted to climatic response or location data category - results

very difficult to interpret Method moderately well adapted to climatic response or location data

category - results moderately difficult to interpret Method well adapted to climatic response or location data category - results

relatively straightforward to interpret

“Traffic Lights” to summarise performance of

different model based on availability of data on

the pest’s distribution and responses to climate

Area of potential establishment for

Diabrotica virgifera virgifera

Hosts Climatic suitability Area of potential

establishment & =

Area at highest risk

Sandy soils Host distribution Maize output not on

sandy soils Total maize output

Climate suitability Maize output not on

sandy soils Area at highest risk

& =

& =

Climatic Mapping: Tutorials and

manuals

• How to run several models,

e.g. Diva-GIS, Maxent,

Openmodeller Desktop and

CLIMEX,

• How to compare model

outputs

• How to interpret the results

Risk Mapping Conclusions

• The PRATIQUE DSS enables assessors to create

and combine maps to display:

– the area of potential establishment

– the area where plants are at highest risk (i.e areas

most suitable for the pest and of highest "value")

• useful for prioritising surveillance programmes

• Link to spread models

• Link to economic models

Spread: generic spread models

created

• Spatial process (spatial explicit) models

Radial rate expansion

Radial rate expansion (random entry point)

Dispersal kernel

Spread models for Diabrotica

Radial expansion model

Dispersal kernel model

Diabrotica v. virgifera spread 1992-2011

Spread – example result

Dispersal kernel model

Showing A. glabripennis spread

from 4 outbreak sites over 30

years.

Based on Climex model

Colours: % population

abundance

white < 10-6 %,

yellow,

orange

red > 10%

2

1

2

2

2

11

1

2

1

2

1

1

p

u

r

p

p

p

purf

Establishment: A. glabripennis

(Years for development, Climex)

4+ or not possible 4 years 3 years 2 years 1 year Years for development

Economic modelling

• Simple qualitative approach

• More complex quantitative approach

– Partial budgeting

– markets based (partial equilibrium)

Analysis of previous eradication

efforts

• > 170 campaigns (102 species)

(41 invertebrate species, 26 pathogens, 27 plant species)

• For each campaign ask 96 questions

• Seek to identify factors for eradication success

• Linear mixed effect models (LMMS) & classification and

regression trees (CART) applied

FINDINGS

• Small infestations (< 4,000 ha) are easier to eradicate

• Eradications in man-made habitats are more successful

• Natural habitats provide a major challenge

• Fungi most difficult to eradicate

Pluess et al. 2012. Biological Invasions, DOI 10.1007/s10530-011-0160-

4. Provide a user friendly DSS

• Previous EPPO Scheme (2009) difficult to use

• For the analyst – Many questions (most detailed system)

– Some seem repetitive

– Difficult interface

– Difficult to make consistent judgements

– Difficult to summarise

• For the decision maker – Lengthy documents produced

– Difficult to focus on key elements

User friendly DSS

• PRATIQUE provided

• a computerised EPPO PRA scheme incorporating PRATIQUE outputs

• Revised structure

• Reworded questions

• Rating guidance

• Links to datasets

• Guidance documents

• Can share PRA document (for group work)

EPPO Computerised PRA Scheme

(CAPRA)

Experimental studies

Sentinel trees in Asia

• To produce a dataset of potential Asian pests of

selected woody plants not yet introduced into Europe

Beijing suburban area

Continental conditions

Fuyang, nr. Hangzhou

Warm and humid climate

(Dr. Fan Jian-tin;

Zhejiang Forestry University)

Sentinel trees in Asia

Beijing

• 400 seedlings of 4 species exported

• 177 seedlings survived after a long stay in customs

• planted in a semi-urban nursery 5th May 2007

Abies alba- 60

Quercus suber- 50

Quercus ilex- 48

Cupressus sempervirens- 19

•Monthly survey

•No serious insect damage

observed until June 2008

•Alternaria sp. found on Abies

•Unidentified fungi on

Quercus and Cupressus

Sentinel trees in Asia

Hangzhou

• 598 young trees of 7 species planted

• Each 1m – 1.5m tall

• planted in a forestry region May 2008 •More than 50 species of

insects during summer 2000

Most yet unidentified

Some highly damaging

e.g. tussock moth on oaks

Arboreta Surveys

• Far East Russia (Siberia): surveys of pests on

European trees and shrubs in arboreta

Harsh Siberian climate

not suitable for many

European plants

Maritime climate

Novel method to obtain lists of potential

plant pests before introduction

• Sentinels in China colonised by:

97 insect species

24 symptomatic infections

• Russian arboreta

Of the many insect species, 30 high risk species

identified

106 symptomatic infections and 75 fungal species

on 56 woody plants

• BUT significant identification problems

• Future International Plant Sentinel Network?

Comparison of methods

Arboreta Sentinel trees

Logistics - “Simple” - Complicated

No. of plant species - Many - Few

Statistics - Poor - Robust

Weaknesses - No seedling pests - No mature tree pests

- Mostly foliage pests

- Lethal pests - Travel and plantation

difficult to assess stress

Complementary methods

Both require strong local links !

Thank you Obrigado

Food and Environment Research Agency, Sand Hutton, York, YO41 1LZ

Food and Environment Research Agency

Ecological activities occur across various temporal and spatial scales

Millennia

Centuries

Years

Months

Days

Hours

cm m km 100 km 1,000 km

Landscape evolution

Impacts of Invasive species

Forests develop

El Nino events

Adapted from Turner, Dale & Gardner (1989) Landscape Ecology 3 (3/4) 245-252

Climate change

Local land use change

Trees grow

Annual crops Where risk assessors

aim to inform All year round crops

The scale at which much field research is performed

Infection