1. Plant Epidemiology and Risk Analysis Laboratory, Center for Plant Health Science and Technology (CPHST), USDA-APHIS-PPQ Integrating Technology to Assist the Weed Risk Assessment Process Larry Fowler, Anthony Koop, Brian Spears, and Barney Caton

2. Overview PERAL Weed Team PERAL Weed Team Technologies to support the WRA process Technologies to support the WRA process Caveat: Invasive plant = weed Caveat: Invasive plant = weed

3. Mission statement: To provide scientific and analytical support for PPQ regulatory programs requiring decisions associated with invasive plants. PERAL Weed Team

4. Importation: Help complete WRAs to support decision making related to imports Exportation: Provide technical assistance to guard against US exports being unduly restricted by trading partners Domestic: Support surveying, monitoring, and management Activity areas

5. 1. Identify invasive plant problems/issues 2. Set priorities 3. Gather data (not research) 4. Perform weed risk assessments (WRAs) 5. Recommend risk management strategies Weed Team activities We work cooperatively with ● Other CPHST labs ● National APHIS Weed Team

6. Technologies to assist WRAs Prioritization Prioritization Pathway monitoring/verification Pathway monitoring/verification Potential for geographic spread Potential for geographic spread

7. 1. Prioritization Project with Weed Science Society America Project with Weed Science Society America Model ranking of global weeds not known to occur in U.S. Model ranking of global weeds not known to occur in U.S. 550+ weeds scored comprehensively 550+ weeds scored comprehensively Includes spp. Includes spp. present but present but not naturalized not naturalized # Scientific name Common name 1 Echinochloa pyramidalis antelope grass 2 Ludwigia hyssopifolia seedbox, primrose willow 3 Rubus alceifolius giant bramble 4 Lygodium flexuosum maidenhair creeper 5 Actinoscirpus grossus giant bulrush 6 Sagittaria pygmaea pygmy arrowhead 7 Hakea salicifolia willow-leaved hakea 8 Ligustrum robustum tree privet 9 Wikstroemia indica tiebush, Indian stringbush 10 Senecio inaequidens narrow-leaved ragwort

8. Prioritization: applications Choose best candidates for possible listing as noxious weeds (WRAs) Choose best candidates for possible listing as noxious weeds (WRAs) Rapid scoring (relatively) for newly emerging/introduced species Rapid scoring (relatively) for newly emerging/introduced species Identify likely target species for surveying, especially species in cultivation Identify likely target species for surveying, especially species in cultivation

9. 2. Pathway monitoring/verification Problem: Problem:

10. Pathway monitoring/verification AIMS: Agricultural Internet Monitoring System AIMS: Agricultural Internet Monitoring System NC State: Center for Integrated Pest Mgt.NC State: Center for Integrated Pest Mgt. Identify regulated species of interestIdentify regulated species of interest Secure web applicationSecure web application Semi-automates: Semi-automates: WebcrawlingWebcrawling Evaluating sites for riskEvaluating sites for risk Sending information lettersSending information letters Archiving and retrieving informationArchiving and retrieving information

11. Pathway monitoring/verification Monitored organisms Monitored organisms APHIS-regulatedAPHIS-regulated Insects/mollusks/weeds/fruits and vegetables Insects/mollusks/weeds/fruits and vegetables Animal products & byproducts Animal products & byproducts APHIS-nonregulated organisms (esp. invasive plants)APHIS-nonregulated organisms (esp. invasive plants) Application to WRAs Application to WRAs Identify presence in the U.S.Identify presence in the U.S. Identify trade pathways from overseasIdentify trade pathways from overseas

12. AIMS and FAST technology 1496 names = 599 species + 897 common/syns 1496 names = 599 species + 897 common/syns Manual search Manual search 2 min × 10 hits × 2 min processing = 549 person hr2 min × 10 hits × 2 min processing = 549 person hr FAST FAST 3 d to search the net and build index (software)3 d to search the net and build index (software) 12.5 min to process index list12.5 min to process index list Simultaneous search for commerce keywordsSimultaneous search for commerce keywords Thus, person hr only for processing very likely hitsThus, person hr only for processing very likely hits

13. 3. Potential geographic spread Model to predict extent of plant invasions in U.S. Model to predict extent of plant invasions in U.S. NAPPFAST: NCSU/APHIS Plant Pest ForecASTNAPPFAST: NCSU/APHIS Plant Pest ForecAST 10 year climate database (1994-2004)10 year climate database (1994-2004) Typical parameters Typical parameters Optimal growing daysOptimal growing days High T growth inhibitionHigh T growth inhibition Cold T exclusion areaCold T exclusion area Output = Probability map Output = Probability map

14. Example application Preliminary assessment for kikuyugrass (Pennisetum clandestinum) Preliminary assessment for kikuyugrass (Pennisetum clandestinum) D. Borchert, B. Nietschke, C. Thayer and L. Fowler YOU ARE HERE

15. Potential spread: applications Assess consequences of introduction Assess consequences of introduction Higher risk = greater, contiguous, or special areasHigher risk = greater, contiguous, or special areas Lower risk = lesser or non-contiguous areasLower risk = lesser or non-contiguous areas Factor in invasiveness scoring Factor in invasiveness scoring Survey targeting (if WRA-related) Survey targeting (if WRA-related) Identification of weed-free areas Identification of weed-free areas But, often lack relevant biological data But, often lack relevant biological data

16. Technologies to assist WRAs Prioritization – Prioritization – Plant invasiveness Plant invasiveness ranking model ranking model Pathway monitoring – Pathway monitoring – AIMS searches AIMS searches Potential spread – Potential spread – NAPPFAST geographic NAPPFAST geographic model model