uspest.org home   

Home page and directory of selected Degree-Day, establishment Risk, and Pest event maps (DDRP)

Summary An improved understanding of where an invasive species could potentially establish as well as when developmental stages are expected to occur have the potential to support and dramatically improve strategic and tactical pest surveillance and management decisions. We have developed a new spatial modeling platform that integrates mapping of phenology and climatic suitability in real-time to provide timely and comprehensive guidance on where and when invasive insect species could potentially invade the conterminous United States. The Degree-Days, Risk, and Phenological event mapping (DDRP) platform serves as an open-source and relatively easy-to-parameterize decision support tool that can predict the potential distribution, number of generations, life stages present, and dates of phenological events of a target species. DDRP is written entirely in R, making it flexible and extensible, and capitalizes on multiple R packages to generate gridded and graphical outputs. Currently we are using DDRP to model 15 high-priority invasive insect species (see below), but its process-based modeling approach may be adapted for a broad spectrum of organisms with temperature-dependent development. The DDRP platform will enhance efforts to prevent, monitor, and manage new and emerging invasive pests in the United States.

Full paper available 12/31/2020 at: Barker et al. (2020)   NEW publication "Phenological mapping of invasive insects: Decision support for surveillance and management" 12/22/2023 at: Barker et al. (2023) Open source code at: https://github.com/bbarker505/ddrp_v2   Guide for users including platform requirements (updated 10/28/2020)

Species directory link
documentation
First spring adults
First egg hatch
Current stages
and generations
No. of generations
per year
1. ALB
asian longhorned beetle
Anoplophora glabripennis
model spreadsheet
ALB_cohorts/ALB_Earliest_PEMp0Excl1_20241231.png ALB_cohorts/ALB_Earliest_PEMe1Excl1_20241231.png ALB_cohorts/ALB_StageCount_Excl1_20240327.png ALB_cohorts/ALB_NumGen_Excl1_20241231.png
2. ASRB
asiatic rice borer
Chilo suppressalis
model spreadsheet
ASRB_cohorts/ASRB_Earliest_PEMp0Excl1_20241231.png ASRB_cohorts/ASRB_Earliest_PEMe1Excl1_20241231.png ASRB_cohorts/ASRB_StageCount_Excl1_20240326.png ASRB_cohorts/ASRB_NumGen_Excl1_20241231.png
3. CGN
honeydew moth
Cryptoblabes gnidiella
model spreadsheet
white paper
CGN_cohorts/CGN_Earliest_PEMp0Excl1_20241231.png CGN_cohorts/CGN_Earliest_PEMe1Excl1_20241231.png CGN_cohorts/CGN_StageCount_Excl1_20240326.png CGN_cohorts/CGN_NumGen_Excl1_20241231.png
4. EAB2
emerald ash borer
Agrilus planipennis
model spreadsheet
peer-review pub.
EAB2_cohorts/EAB2_Earliest_PEMp0Excl1_20241231.png EAB2_cohorts/EAB2_Earliest_PEMe1Excl1_20241231.png EAB2_cohorts/EAB2_StageCount_Excl1_20240326.png EAB2_cohorts/EAB2_NumGen_Excl1_20241231.png
5. ECW
egyptian cottonworm
Spodoptera littoralis
model spreadsheet
white paper
ECW_cohorts/ECW_Earliest_PEMp0Excl1_20241231.png ECW_cohorts/ECW_Earliest_PEMe1Excl1_20241231.png ECW_cohorts/ECW_StageCount_Excl1_20240325.png ECW_cohorts/ECW_NumGen_Excl1_20241231.png
6. FCM
false codling moth
Thaumatotibia leucotreta
model spreadsheet
white paper
FCM_cohorts/FCM_Earliest_PEMa0Excl1_20241231.png FCM_cohorts/FCM_Earliest_PEMe1Excl1_20241231.png FCM_cohorts/FCM_StageCount_Excl1_20240327.png FCM_cohorts/FCM_NumGen_Excl1_20241231.png
7. JPSB
Japanese pine sawyer beetle
Monochamis alternatus
model spreadsheet
white paper
JPSB_cohorts/JPSB_Earliest_PEMp0Excl1_20241231.png JPSB_cohorts/JPSB_Earliest_PEMe1Excl1_20241231.png JPSB_cohorts/JPSB_StageCount_Excl1_20240328.png JPSB_cohorts/JPSB_NumGen_Excl1_20241231.png
8. LBAM
light brown apple moth
Epiphyas postvittana
model spreadsheet
peer-review pub.
LBAM_cohorts/LBAM_Earliest_PEMp0Excl1_20241231.png LBAM_cohorts/LBAM_Earliest_PEMe1Excl1_20241231.png LBAM_cohorts/LBAM_StageCount_Excl1_20240326.png LBAM_cohorts/LBAM_NumGen_Excl1_20241231.png
9. OAB
oak ambrosia beetle
Platypus quercivorus
model spreadsheet
white paper
OAB_cohorts/OAB_Earliest_PEMp0Excl1_20241231.png OAB_cohorts/OAB_Earliest_PEMe1Excl1_20241231.png OAB_cohorts/OAB_StageCount_Excl1_20240328.png OAB_cohorts/OAB_NumGen_Excl1_20241231.png
10. OWBW
old world bollworm
Helicoverpa armigera
model spreadsheet
OWBW_cohorts/OWBW_Earliest_PEMp0Excl1_20241231.png OWBW_cohorts/OWBW_Earliest_PEMe1Excl1_20241231.png OWBW_cohorts/OWBW_StageCount_Excl1_20240327.png OWBW_cohorts/OWBW_NumGen_Excl1_20241231.png
11. PTLM
pine tree lappet moth
Dendrolimus pini
model spreadsheet
white paper
PTLM_cohorts/PTLM_Earliest_PEMp0Excl1_20241231.png PTLM_cohorts/PTLM_Earliest_PEMe1Excl1_20241231.png PTLM_cohorts/PTLM_StageCount_Excl1_20240326.png PTLM_cohorts/PTLM_NumGen_Excl1_20241231.png
12. SLI
cotton cutworm
Spodoptera litura
model spreadsheet
SLI_cohorts/SLI_Earliest_PEMp0Excl1_20241231.png SLI_cohorts/SLI_Earliest_PEMe1Excl1_20241231.png SLI_cohorts/SLI_StageCount_Excl1_20240326.png SLI_cohorts/SLI_NumGen_Excl1_20241231.png
13. SLYM
silver Y moth
Autographa gamma
model spreadsheet
white paper
SLYM_cohorts/SLYM_Earliest_PEMp0Excl1_20241231.png SLYM_cohorts/SLYM_Earliest_PEMe1Excl1_20241231.png SLYM_cohorts/SLYM_StageCount_Excl1_20240326.png SLYM_cohorts/SLYM_NumGen_Excl1_20241231.png
14. STB
small tomato borer
Neoleucinodes elegantalis
model spreadsheet
white paper
peer-review pub.
STB_cohorts/STB_Earliest_PEMa0Excl1_20241231.png STB_cohorts/STB_Earliest_PEMe1Excl1_20241231.png STB_cohorts/STB_StageCount_Excl1_20240326.png STB_cohorts/STB_NumGen_Excl1_20241231.png
15. SUNP
Sunn pest
Eurygaster integriceps
model spreadsheet
white paper
SUNP_cohorts/SUNP_Earliest_PEMa0Excl1_20241231.png SUNP_cohorts/SUNP_Earliest_PEMe1Excl1_20241231.png SUNP_cohorts/SUNP_StageCount_Excl1_20240327.png SUNP_cohorts/SUNP_NumGen_Excl1_20241231.png
16. TABS
tomato leafminer
Tuta absoluta
model spreadsheet
white paper
TABS_cohorts/TABS_Earliest_PEMa0Excl1_20241231.png TABS_cohorts/TABS_Earliest_PEMe1Excl1_20241231.png TABS_cohorts/TABS_StageCount_Excl1_20240328.png TABS_cohorts/TABS_NumGen_Excl1_20241231.png

Acknowledgements This work was funded by grants including the USDA APHIS PPQ Cooperative Agricultural Pest Survey (CAPS) and Science and Technology programs, the USDA National Institute of Food and Agriculture, Crop Protection and Pest Management, Applied Research and Development Program (NIFA-CPPM-ARDP), grant no. 2014-70006-22631, the Western Region IPM Center as a Signature program, and the Department of Defense Strategic Environmental Research and Development Program (SERDP), project no. RC01-035. Dan Upper provided spatial weather data processing and systems administration for the project.

Oregon IPM Center at Oregon State University USDA APHIS 
 PPQ USDA APHIS PPQ Cooperative Agricultural Pest Survey R scientific programming The PRISM Group at OSU National Institute
   for Food and Agriculture

Last updated on Mar 28 2024