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