Quantitative methods for plant parasitic nematode species

Page 1

A combined approach for better estimation of a species’ biosecurity risk Sunil Singh1,2,3 Supervisors: Mike Hodda1,2,3 and Gavin Ash 1,3 1Cooperative

Research Centre for National Plant Biosecurity Ecosystem Sciences 3Charles Sturt University

2CSIRO

biosecurity built on science Cooperative Research Centre for National Plant Biosecurity


Biosecurity problem  Nearly 600 PPN sp. cause economic damage  Losses of >$600 million/yr in Australia and $157 billion/yr worldwide  Many damaging species absent from Australia  Multiple pathways for entry  Quarantinable sp. impact market access biosecurity built on science


Approach 1  Analysis of distribution data - 355 regions x 250 PPN species SOM Map of regional clusters

PPN SOM Distribution Analysis Dataset biosecurity built on science


Outcomes  Clustering of regions with similar species assemblages  Identification of “donor” regions for invasive species  SOM Index = likelihood of sp. existing in a given region

biosecurity built on science


Limitations  Preliminary estimate of risk  Does not include a species’ impact  Taxonomic and geographic bias in distribution data

biosecurity built on science


Approach 2  Criteria based assessment  Expert risk estimate (0-1 probability value) E.g. Criteria: Pathways  Assoc. with propagative material p ≥ 0.6  Assoc. as contaminant p<0.6 >0.3  Not directly assoc. to trade < 0.3

 Weighted average  Sum of (p x weight) = estimate of overall risk

biosecurity built on science


Combining 1 & 2 Example: Pine wilt nematode (B. xylophilus)

Criteria

Probability

Weight

Distribution (SOM index from 1) 0.37

0.2

Pathways

0.80

0.15

Survival adaptations

0.65

0.1

Pathogenicity

0.85

0.1

Host range

0.55

0.1

Emerging pest

0.80

0.1

Taxonomy

0.60

0.1

Pathotypes

0.50

0.05

Disease complex

0.60

0.05

Knowledge

0.45

0.05

Sum (prob. x weight)

0.62 biosecurity built on science


Examples Species

SOM Index

B. xylophilus

0.37

Combined weighted average 0.62

H. carotae

0.10

0.47

H. glycines

0.40

0.63

H. oryzae

0.47

0.52

M. chitwoodi

0.20

0.62 biosecurity built on science


Other approaches  Detailed climatic assessment (Climex)  Expert opinion

biosecurity built on science


Conclusion  Combining SOM index from analysis of distribution data with a criteria based assessment can better estimate a species’ risks than either of the methods alone.

biosecurity built on science


What next?  Source regions fauna  Detailed analysis of high risk sp.  Surveillance and diagnostics

biosecurity built on science


Acknowledgements Dr Dean Paini (CSIRO) - for assistance with SOM analysis CRC National Plant Biosecurity for PhD scholarship

Thank you

 For more information, please email sunil.singh@csiro.au ph: 02 6246 4417 biosecurity built on science


Turn static files into dynamic content formats.

Create a flipbook
Issuu converts static files into: digital portfolios, online yearbooks, online catalogs, digital photo albums and more. Sign up and create your flipbook.