Patterns & trends in PH3 resistance of grain insects: Mining the Australian Grain Insect Resistance Database (AGIRD) Dr Matt Falk (Tame) Statistical Modeller Dr Sama Low-Choy
Dr Pat Collins
Dr Manoj Nayak
biosecurity built on science Cooperative Research Centre for National Plant Biosecurity
PH3
Problem
Year
 Questions - Why is this so? - Does this same trend apply everywhere, when, how? biosecurity built on science
Aims To reveal trends in AGIRD: how has strong resistance to phosphine developed over two decades (1992–2011) 4071 observations with 217 presences
Factors affecting incidence & trends: Biological and Environmental factors - Insect species - Commodity (host type) - Sub-region
Biosecurity practices - Storage type - Chemical treatments
BEBA factors
Agricultural context - Site Type biosecurity built on science
AGIRD
Rhyzopertha dominica Sitophilus oryzae Cryptolestes ferrugineus Oryzaephilus surinamensis Tribolium castaneum
• Developed by Rob Emery (DAFWA) • Three labs contribute • Supported by GRDC biosecurity built on science
Historical records
Natural language encoding
D95
D99
Clean, Consistent Dataset
Database joining
Mortality
Huh?? Bioassay lethal doses
Geocoding
Dose biosecurity built on science
QLD biosecurity regions
Expert-delineated regional boundaries aligned with major highways and topographic features
Aim to group similar biosecurity context, and hence practices and issues. biosecurity built on science
AGIRD Analysis – Bayesian Hurdle Model 1. Deal with excess zeros
2. Incorporate variable selection
Allow nonlinear trends
Bayesian methods to discover hidden strata and underlying BEBA factors delineating absence and potential presence of SR Trend models with a variable selector to reveal the relative
importance of BEBA factors
Strata
Incidence
in incidence of SR within each stratum
3. Inspect trends after adjusting for these strata
Trend
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Defining the strata: External Feeders
Resistance was found with 101 samples where it was unknown whether FEN was applied, in C, SEB, SEC or unspecified regions.
Hence, overall the key broad-scale factors affecting resistance in external feeders are related to (1) region - whether in the SE quadrants NESW; (2) biosecurity practice whether FEN was known to have been applied; and (3) TC is a special case in some regions.
No resistance was found in any of the 28 bioassays with FEN known to be applied in C, SEB, SEC or unspecified regions.
However resistance was found in 61 of the CRY and OS bioassays in SE quadrants NESW No resistance was found in any of the 460 of the TC bioassays in SE quadrants NESW biosecurity built on science
Incidence & Trends: External Feeders
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Incidence & Trends: External Feeders
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Key Results Summary Strata Region Treatment with fenitrothion (FEN) T. castaneum
Higher incidence for: Phosphine use C. ferrugineus (CRY) Central storages
Trend Generally increased, then plateaued, but may be currently increasing or decreasing, depending on the strata biosecurity built on science
Outputs Collins P, Falk M, Nayak M and Low-Choy S (in prep) Strong Resistance of Stored Grain Insects to Phosphine in Queensland, Australia - Tools for AGIRD harmonization
Falk M, Low-Choy S, O’Leary R, Nayak M and Collins P (in prep) A Bayesian Hurdle Model for Strong Resistance of Stored Grain Insects to Phosphine - MATLAB code for Bayesian decision trees
For more information, please email m.falk@qut.edu.au - Thanks to Sama Low-Choy, Pat Collins, Manoj Nayak, Hervoika Pavic and Rebecca O’Leary
CRC50177
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