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Pest risk analysis: tools, resources and key challenges
Dr Alan MacLeod
Pest risk analyst
United Kingdom
Typical structure of PRA
Devorshak, C. (2012) Plant Pest Risk Analysis, Concepts and Application, CAB International, Wallingford, UK 2
INITIATION
• Identity of pest
• Reason for PRA
• Identify PRA area
RISK MANAGEMENT
• Management options
(Consultation)
• Monitoring and review of measures
RISK ASSESSMENT
• Host Plants
• Geographical Distribution
(biology of the pest)
• Capable of introduction ? (entry & establishment)
• Spread & potential impact?
• Overall risk
Interpreting evidence
Gathering evidence
PRA complexity
3
Type of PRA Principal Applications Assessment methods
Rapid PRA Following new interceptions, new outbreaks,
Rapid qualitative evaluation of the literature, online datasets and other evidence
Comprehensive (detailed) PRA
To justify phytosanitary measures on trade / import requirements
Detailed qualitative evaluationof the literature, online datasets and other evidence, could have quantitative aspects
Detailed analysis of particular elements of risk (e.g. focus on entry or establishment or impact or management)
To resolve major uncertainties in PRA
To defend challenges to a PRA (WTO disputes)
Detailed quantitativeassessment; could include modelling and mapping, e.g. interception data and climatic suitability, cost: benefit analysis, systems approach
Burgman et al. (2014) The Role of Pest Risk Analysis in Plant Biosecurity. In: The Handbook of Plant Biosecurity, Springer
Tools & Resource needs
• Entry: data to assess pathways
• Establishment: data for mapping
Venette et al. (2010) Pest Risk Maps for Invasive Alien Species: A Roadmap for Improvement, Bioscience 60, 349-362 4
• Spread: data for modelling
• Impacts: data for analysis
Potential distribution of Lycorma delicatulaJung et al. (2017) Journal of Asia-Pacific Biodiversity 10 (4) 532-538
Relationships between economic techniquesSoliman et al. (2010) Crop Protection 29 (6), 517-524
Robinet et al. (2012) PLoS One, 7(10), e43366
Case study: Thrips palmi
• A polyphagous pest feeding on leaves, flowers and fruit of many horticultural plants including Cucurbitaceae, Solanaceae and Orchidaceae
• Native to southern Asia. Now widespread mostly throughout tropical and sub-tropical countries
• Not established in Europe
Images from EPPO Global database http://gd.eppo.int MacLeod et al. (2004) Crop Protection 23 (7), 601-610. 5
MacLeod (2015) The relationship between biosecurity surveillance and risk analysis. In: Biosecurity surveillance: quantitative approaches, CABI 6
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1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Orc
hid
imp
ort
s ('
000
to
nn
es)
Thri
ps p
alm
i int
erce
pti
on
s
T. palmi on Thai orchids Thousands of Tonnes imported
Case study: Thrips palmi on Thai orchids
Sudden rise in interceptions of T. palmiPRA conducted, • identified Thai orchids as principle
pathwayPRA concluded:• could establish in glasshouses• could impact cucumbers, aubergines &
peppers• Consult with Thai Ministry of Agriculture• New phytosanitary measures developed
(inspections at production sites, chemical treatment of orchids)
• Trade continued
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1
1.5
2
2.5
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3.5
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4.5
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40
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Orc
hid
imp
ort
s ('
000
to
nn
es)
Thri
ps p
alm
i int
erce
pti
on
s
T. palmi on Thai orchids Thousands of Tonnes imported
Case study: Thrips palmi on Thai orchids
As a result of PRA, phytosanitary measures strengthened in 1998
MacLeod (2015) The relationship between biosecurity surveillance and risk analysis. In: Biosecurity surveillance: quantitative approaches, CABI
Case study: Acidovorax citrulli
• Bacterial pathogen of cucurbit fruit (e.g. blotches on watermelons)
• Can cause significant losses
• Assess risk to EU
• Could enter via imported seed & seedlings
• Could establish
• Most harmful when warm & humid
• Potential measures:
• seed testing
• inspect seedlings
MacLeod et al. (2012) Pest risk assessment for European Community plant health: A comparative approach with case studies. http://www.efsa.europa.eu/fr/supporting/doc/319e.pdf
8
Global distribution of watermelon production (green/ yellow) with approximate occurrence of Acidovorax citrulli (ellipses)
Mechanism for combining components
• PRA method developed in EFSA project (BBN)
• Questions on entry (6), establishment (1), spread (1), consequences (2)
• Apportion degree of belief across 5 categories (VL, L, M, H, VH) (defined)
• Combine risk elements via conditional probability tables
• Perform assessment without and with measures in place
9
3.06 Pest Risk
3.05 Potential Impact2.08 Overall potential for Entry
Entry: Pathway 1, Seeds Entry: Pathway 2, Seedlings
3.06 Pest Risk
3.05 Potential Impact2.08 Overall potential for Entry
Entry: Pathway 1, Seeds Entry: Pathway 2, Seedlings
Macleod et al. (2012) Pest risk assessment for European Community plant health: A comparative approach with case studies. http://www.efsa.europa.eu/fr/supporting/doc/319e.pdf
Acidovorax citrulli risk without RROs
Expressing uncertainty & assessing measures
• Method captures uncertainty
• Indicates effectiveness of measures
• seed testing & visual inspection not very effective
• Challenge to adopt method
Risk With RROs
0
0.1
0.2
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0.9
1
very low low medium high very high
Comparison
Macleod et al. (2012) Pest risk assessment for European Community plant health: A comparative approach with case studies. http://www.efsa.europa.eu/fr/supporting/doc/319e.pdf
Risk element Tools & resources ChallengesEntry Customs import data Lack of data on species of plants for planting
(aggregated HS data
Establishment Species distribution models (e.g.
CLIMEX, MaxEnt)
Lack detailed pest distribution data
Pest adaptation to new environment
Responses to climate change (hosts & pests)Spread Generic models of spread (natural &
via humans)
Mixed mechanisms for spread
Lab studies to real world (e.g. flight mills)
Repeated introductions?Impact Economic tools (e.g. partial
budgeting, partial equilibrium
(supply & demand))
Quantifying all ecosystem service impacts
Impacts over time (e.g industry resilience)
Apply knowledge of pest to PRA areaPest adaptation to new hosts
Overall risk and
uncertainty
Qualitative methods (e.g. matrices)
Quantitative methods (e.g.Monte
Carlo)
Revealing uncertainty
Resources required
Management Efficacy data / trials
If multiple pests - Prioritisation
systems (lack detail)
Consistency in ALOP/ ALR
Allocation of resources to deal with multiple
pestsRisk
communication
IPPC guide
EFSA Guide
Identifying stakeholders
Common understanding of linguistic terms? 11
Challenges within the PRA process
Disconnect challenge
MacLeod & Lloyd (2020) Emerging Topics in Life Sciences 4 (5), 463–471. 12
Millennia
Centuries
Years
Months
Days
Hours
cm m km 100 km 1,000 km
Landscape evolution
Direct impacts of Invasive species
Forests develop
El Nino events
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
The disconnect between where PRA aims to inform (red), and the scale at which field data informing the analysis is often collected (green).
Tem
po
ral s
cale
Spatial scale
Challenges to the process
• PRA is reactive (Brasier, 2008) i.e. pests must be known knowns
• PRA standards cannot assess risks from unknown organisms (known unknowns) Some ‘newly escaped’ organisms were previously unknown to science before they escaped and were found to be pests
• Brockerhoff & Bulman (2014): we have a good understanding of the risks posed by many [known] pests and pathogens, experience indicates that there are many pests that are unknown,
• their behaviour in a new environment differs from that in their native range
• they are unknown to science
• Call to manage risk in a more general way to protect plant resources
• PRA focuses on genetically stable species – how to take adaptation into account (wider environmental tolerances / broaden hosts)? (e.g. de Vienne et al., 2009)
• Multiple stressors (multiple pests on same host)Brasier, 2008, Plant Pathology 57 (5), 792–808. Brockerhoff & Bulman (2014) New Zealand Journal of Forestry, 59 (2), 3-8 de Vienne et al. (2009) J of Evolutionary Biol 22, 2532-2541
13