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Predicting pest and disease establishment
Sue Worner Better Border Biosecurity (B3)
B3 Conference, May 2014
Predicting pest and disease establishment
B3 Theme: Risk Grouping
Stakeholders:
New Zealand Collaborators:
International collaborators:
Invasion eradication continuum
Introduction & establishment
Detection Investigation Response Eradication
Theme 1
Risk analysis
Theme 5
Eradication & response
Courtesy: John Kean
Predicting pest and disease establishment
Risk assessment:
Categorise potential pests: which species Assess likelihood of establishment: where Research Aim: Develop better methods to decide if an organism is likely/unlikely to establish
Pest risk analysis (from FAO, 1996) www.fao.org
Outcomes that are currently in use by
MPI
Quantitative methods for identifying emerging risks and improving pest risk assessment (PRA)
Self organising map( SOM) models: to determine
risk of establishment -> Which species
ARC GIS climate-matching software to evaluate establishment likelihood for new pests and diseases -> Where Detailed climate/habitat suitability maps -> Where
Which species are likely to establish?
An assemblage of species integrates: 1. The biological and climatic characteristics in a region. 2. An invasion history or pathway(s) shared by geographic areas
3. Species profiles are well used in ecology, oil exploration and
research on past climates
Big data?
459 geographic areas (vectors)
84
4 s
pe
cie
s (
de
scri
pto
rs) Armenia India Bihar Italy Jordan Taiwan - - - Barbados
AM INbh IT JO CNtw - - - BB
Abraxas grossulariata ABRXGR 0 0 0 0 0 - - - 0
Acanthocoris scabrator ACACS1 0 0 0 0 0 - - - 0
Acanthocoris scaber ACACSC 0 0 0 0 0 - - - 0
Acanthocoris sordidus ACACSO 0 0 0 0 1 - - - 0
Acaudaleyrodes rachipora ACADCI 0 0 0 0 0 - - - 0
Achaea serva ACAESE 0 0 0 0 0 - - - 0
Acanthiophilus helianthi ACAIHE 0 1 1 1 0 - - - 0
- - - - - - - - - - -
- - - - - - - - - - -
- - - - - - - - - - -
Zeiraphera diniana ZRPHDI 0 0 0 0 0 - - - 0
Zeiraphera rufimitrana ZRPHRU 0 0 0 0 0 - - - 0
Zulia charon ZULICH 0 0 0 0 0 - - - 0
Zulia colombiana ZULICO 0 0 0 0 0 - - - 0
Zygaena filipendulae ZYGNFI 0 0 1 0 0 - - - 0
Country names and codes
Sp
ecie
s n
am
es a
nd
cel
ls
Crop Compendium – Global Module, 5th
Edition © CAB International,
Wallingford, UK, 2007
Conventional clustering did not work
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Artificial intelligence: The Self Organising Feature Map (SOM)
SOM orders 459 pest profiles and projects them onto a 2D map Similar pest profiles are assigned to the same cell
459 844
Indicates donor and recipient regions
Worner SP & Gevrey M (2005) Journal of Applied Ecology 43, 858-867
Established
1
Not Established
0
Weights
Species Weights Aphis craccivora 1 0.7079
Bemisia tabaci 1 0.7033
Rhopalosiphum maidis 1 0.7025
Acyrthosiphon pisum 1 0.6746
Saissetia coffeae 1 0.6652
Thrips tabaci 1 0.6612
Pseudococcus longispinus 1 0.6567
Locusta migratoria 1 0.6488
Aphis spiraecola 1 0.6479
Agrotis segetum 0 0.6432
Aspidiotus nerii 1 0.6425
Ceratitis capitata 0 0.6266
Hyperomyzus lactucae 1 0.6263
Phyllocnistis citrella 0 0.6228
Rhopalosiphum padi 1 0.6168
Spodoptera exigua 0 0.6165
Pieris brassicae 0 0.6009
Which species are high risk?
Portion of New Zealand pest profile
ARC GIS climate-matching software for evaluating establishment likelihood for new pests and diseases
All of NZ climate match with rest of world based on a CLIMEX match
Craig Phillips, John Kean, Cor Vink
www.arcgis.com
World climate match with areas of New Zealand forestry
• Virginia Morroni and Suvi Viljanen- Rollinson report on Karnal bunt (Telletia indica)
• Climate Match Index (CLIMEX) to assess likelihood of establishment in NZ
Karnal bunt: climate match
Climate suitability for Puccinia psidii s.l. indicated by CLIMEX Ecoclimatic Index (EI)
Kriticos et al. (2013) Combining a climatic niche model of an invasive fungus with its host species distributions to Identify risks to natural assets: Puccinia psidii sensu lato in Australia. PLOS One DOI: 10.1371/journal.pone.0064479
Future developments: Consensus or ensemble models for current and changing climate
Psa Dwarf bunt Guava rust
Khandan, H (2014) Ensemble models to assess the risk of exotic plant pathogens in a changing climate, PhD thesis, Lincoln University 2014
Current climate
Climate change scenario A1B 2030
Future developments: Hybrid models combining physiological response with correlative models
Hybrid suitability prediction for
P. brassicae in New Zealand
Senay, S (2014) Modelling invasive species-landscape interactions using high resolution, spatially explicit models. PhD thesis, Lincoln University, 2014
Future developments: Traits analysis to determine which species and where?
Looking ahead - our vision for the future
Climate and habitat suitability assessments combined with trait analysis is key for improved: - detection eg., identification of hotspots - surveillance and response, eg. test different sampling and monitoring strategies in silico - eradication strategies – eg., test and evaluate different eradication procedures in silico - more accurate economic and environmental impact assessment
Thank you