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Plant Pathogens of Hawaii
##This checklist database is a collection of reports from 16 sources referenced in the "data_set" parameter. They consist of: 1. bishop museum: Data from the Bishop museum fungal database 2. board report: From National Agricultural Pest Information System (NAPIS) board reports 3. [bugwood](https://www.bugwood.org/plantdiseases.cfm): Widely prevalent Fungi, Bacteria, and Virues in Hawaii from bugwood database 4. CAPS survey data: Cooperative Agriculture Pest Survey databases (negative data) 5. [crop knowledge master](http://www.extento.hawaii.edu/kbase/crop/cropmenu.htm): crop knowledge master website 6. CTAHR New Pest reports: Collected by Dr. Michael Melzer 7. [Don Gardner](http://www.hear.org/pph/database/#dongardnerlegacydatabase): Data from Don Gardner’s legacy database 8. First report: First reports found from literature search of scientific journals (Google Scholar, Web of Science, PubMed) 9. [GBIF](https://www.gbif.org/): Global Biodiversity Information Facility 10. [hawaii checklist](https://scholarspace.manoa.hawaii.edu/items/43c51984-f009-40ff-851c-107f0269a6d5): The “2009 Checklist of Plant Diseases in Hawaii” 11. Hawaii NPDN data: National Plant Diagnostic Data from Hawaii (detections from Hawaii and dignosed at labs in AZ, FL, HI, NC, NY, OR, SC, and WI). 12. [HDOA new pest advisory](https://hdoa.hawaii.gov/pi/ppc/new-pest-advisories/): Hawaii Department of Agriculture new pest advisory 13. miscellaneous: personal literature search 14. NAPIS data: National Agricultural Pest Information System database 15. [UH extension articles](https://www.ctahr.hawaii.edu/site/PubList.aspx?key=Plant%20Disease): University of Hawaii extension articles 16. USDA_confirmations: USDA National Agricultural Statistics Service This is a compile of detections and not negative data. The exception would be the CAPS data, in which negative data would represent the entire state. Negative CAPS data are represented by zero values in the island columns. ##The database consist of 23 columns. Below is a discription of each column. Number of unique values are in parentheses. 1. host_family (186): Scientific family name of plant host 2. host_genus (675): Scientific genus name of plant host 3. host_species (839): Scientific species name of plant host 4. host_scientific_name (1361): Scientfic binomial noenclature of plant host 5. host_common_name (921): Plant host common name from [Forest & Kim Starr](http://www.starrenvironmental.com/images/) and USDA PLANTS Database 6. pathogen_genus (3378): Scientific genus name of plant pathogen genus name of plant pathogen 7. pathogen_species (3397): Scientific species name of plant pathogen 8. pathogen_scientific_name (7007): Scientfic binomial noenclature of pathogen host 9. pathogen_group (16): Seperates organisms in pathogen name columns into nematode, entomopathogenic fungi, saprophytic fungi, bacteria, amoeba, unknown, virus, fungi, and parasitic plant 10. pathogen_status (2): Consit of sources that determine organism in pathogen name columns as a plant-pathogen or not (manual research, hawaii checklist pathogen, fungus-host database pathogen, based on name - for viruses only, and NPDN database pathogen) 11. data_set (16): Source of report (details above) 12. status: The status of pathogen present in Hawaii (absent or present) 13. year: Date of report (117 years) 14. Hawaii: Presence of pathogen in the island of Hawaii (1=present, 0=absent, blank=unknown) 15. Maui: Presence of pathogen in the island of Maui (1=present, 0=absent, blank=unknown) 16. Lanai: Presence of pathogen in the island of Lanai (1=present, 0=absent, blank=unknown) 17. Molokai: Presence of pathogen in the island of Molokai (1=present, 0=absent, blank=unknown) 18. Oahu: Presence of pathogen in the island of Oahu (1=present, 0=absent, blank=unknown) 19. Kauai: Presence of pathogen in the island of Kauai (1=present, 0=absent, blank=unknown) 20. Kahoolawe: Presence of pathogen in the island of Kahoolawe (1=present, 0=absent, blank=unknown)
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Using Evironmental Samplers to Detect Ceratocystis Fungi in Orchidland 2016
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Rapid ʽŌhiʽa Death (ROD) currently threatens ōhiʽa lehua (Metrosideros polymorpha) on Hawaiʽi Island. First identified in Puna in 2014, the disease has now spread island wide. Besides direct sampling of trees, environmental sampling could serve as an easier and broader strategy to detect Ceratocystis spp., the fungi causing Rapid Ohia Death (ROD). Envrionmental sampling could also help monitor the effect of felling ROD infected trees. We developed Passive and Active Environmental Samplers for collecting airborne particulates and deployed them at a property in Puna, where both C. lukuohia, and C. huliohia had been detected, and where the land owner practiced the management method of felling infected trees. We set up 2 Active Environmental Samplers (modified mosquito traps connected to a battery that uses a fan to continuously draw in air) and 3 Passive Environmental Samplers (uses a vane to move in the direction of the wind without the use of electricity) from July 12th to October 25th, 2016. The Active Traps contained one slide (1 replicate) each, while the Passive Traps contained 4 slides (4 replicates) each. Wind and precipitation data from a National Oceanic and Atmospheric Agency (NOAA) weather station at the Hilo airport was used in analysis. The dataset contains a list of sampling weeks, their start and end dates, and whether or not tree felling occurred during that week.
Phytophthora Database
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,NOTE: This resource, previously available at www.phytophthoradb.org, is no longer publicly accessible.,This database aims to include Phytophthora collections throughout the world in order to create a global atlas of the diversity and distribution of Phytophthora species.,Phytophthora, an oomycete plant pathogen, is more closely related to brown algae and diatoms than true fungi and has been placed in a separate kingdom, the Stramenopiles (Gunderson et al., 1987; Förster et al., 1990; Leipe et al., 1994). Due to their high virulence and ability to spread rapidly throughout the world, Phytophthora is one of the most important groups of plant pathogens. The destructive potential of Phytophthora diseases is well illustrated by late blight (P. infestans), which was responsible for the Irish potato famine and has again become globally problematic due to the introduction of new, fungicide-resistant lineages (Fry and Goodwin, 1997). Sudden oak death in the US (Rizzo et al., 2002) and diseases on ornamental plants in Europe (Werres et al., 2001), caused by P. ramorum, are examples of the threat to forest ecosystems and the nursery industry. Toward the goal of enhancing our ability to detect, diagnose, monitor, and manage Phytophthora diseases, we have been systematically cataloging genotypic and phenotypic data of Phytophthora spp. in a web-based database that can be easily accessed and utilized by the global community of plant health professionals. Although we are currently focusing on the genotypic characterization of the isolates archived in the World Phytophthora Collection (WPC; Phytophthora.ucr.edu) at UC-Riverside and in the Pennsylvania Department of Agriculture (PDA), we plan to include Phytophthora collections throughout the world in order to create a global atlas of the diversity and distribution of Phytophthora species. This project has been mainly supported by the NRI-Plant Biosecurity program (2005-35605-15393 and 2008-55605-18773) and currently involves the following principal investigators, including Seogchan Kang, David Geiser, and Scott Isard (Penn State), Mike Coffey (UC-Riverside), Joe Russo (ZedX, Inc.), Kelly Ivors (NC State), Frank Martin and Nik Grunwald (USDA-ARS). Grants from the Pennsylvania Department of Agriculture (ME442316 and ME 445580) and a cooperative agreement with USDA-ARS (59-1920-3-304) have also been used to support parts of this project.,Partially supported by Grant from the USDA-AFRI Plant Biosecurity program supported the establishment and improvement of the PD (2005-35605-15393 and 2008-55605-18773).,
라온피플 - 노지 작물 질병 진단 이미지
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노지 작물의 질병 진단을 위한 주요 노지 작물(10종) 질병 이미지 데이터
Pacific Island Network Established Invasive Plant Species Monitoring Dataset 2010-2023
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The Pacific Island Inventory and Monitoring Network (PACN) collected data on the status of established invasive plant species in the mangrove community of American Memorial Park (AMME), in the wet forest and subalpine plant communities of Hawaiʻi Volcanoes National Park (HAVO) and Haleakalā National Park (HALE), and in the wet forests and coastal community of Kalaupapa National Historical Park (KALA). Specifically, crews collected data on nonnative species richness, frequency and cover along 250, 500 and 1,000 meter long belt transects.
Pacific Island Network Established Invasive Plant Species Monitoring Dataset 2010-2023
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The Pacific Island Inventory and Monitoring Network (PACN) collected data on the status of established invasive plant species in the mangrove community of American Memorial Park (AMME), in the wet forest and subalpine plant communities of Hawaiʻi Volcanoes National Park (HAVO) and Haleakalā National Park (HALE), and in the wet forests and coastal community of Kalaupapa National Historical Park (KALA). Specifically, crews collected data on nonnative species richness, frequency and cover along 250, 500 and 1,000 meter long belt transects.
Study Site Information
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These data contain the study site locations and elevation information where bolts (tree stem sections) infected with Ceratocystis lukuohia and/or C. huliohia were collected on Hawai'i Island.
Orchidlands Estates Ceratocystis DNA Detection 2016
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Rapid ʽŌhiʽa Death (ROD) currently threatens ōhiʽa lehua (Metrosideros polymorpha) on Hawaiʽi Island. First identified in Puna in 2014, the disease has now spread island wide. Besides direct sampling of trees, environmental sampling could serve as an easier and broader strategy to detect Ceratocystis spp., the fungi causing ROD. Environmental sampling could also help monitor the effect of felling ROD infected trees. We developed Passive and Active Environmental Samplers and deployed them at a property in Puna, where both C. lukuohia, and C. huliohia had been detected, and where the land owner practiced the management method of felling infected trees. We set up 2 Active Environmental Samplers (modified mosquito traps connected to a battery that uses a fan to continuously draw in air) and 3 Passive Environmental Samplers (uses a vane to move in the direction of the wind without the use of electricity) from July 12th to October 25th, 2016. The Active Samplers contained one slide (1 replicate) each, while the Passive Samplers contained 4 slides (4 replicates) each. Samplers were located in the lawn (2 Active, 1 Passive), next to a rainwater catchment tank (1 Passive), and next to a small shed (1 Passive). The dataset contains a list of sampling weeks and their start and end dates, and quantitative polymerase chain reaction (qPCR) results for individual slides that were collected from Active and Passive Samplers located at the Lawn, Tank and Shed sites. Samples were tested twice for Ceratocystis lukuohia, Ceratocystis huliohia, and Metrosideros polymorpha DNA after extraction with a Machery Nagel Plant II Extraction Kit and again after DNA was concentrated by ethanol precipitation. Positive qPCR test results are presented as quantitation cycle (Cq) in which fluorescence is detected for Ceratocystis lukuohia, Ceratocystis huliohia, and Metrosideros polymorpha DNA for each individual slide and number of replicates that were positive out of three for C. lukuohia and C. huliohia and out of six for M. polymorpha. Lines that are blank in columns for Cq values reflect negative test results. When Cq values have more than one replicate for a test, the reported Cq value represents the average of all positive replicates.
Nestucca Bay NWR and Siletz Bay NWR: Invasive Plant Inventory and Early Detection Prioritization Tool (IPIEDT) - Area, Species, Area-Species Link Tables, 2019
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The IPIEDT is an Access Database file. The user responds to questions in a “yes/no/unknown” format or chooses from a list of states or conditions that best describe the situation. Each descriptive response corresponds to a numerical value which is then used to calculate species and area scores and generate ranked lists. A higher species or area score correlates to a higher priority for inventory. The workshop's resulting data tables are archived so that the results of the workshop are preserved if the master database receives changes based on new threats or areas. Each Invasive Plant Species and Area Prioritization Workshop will have an IPIEDT Access database and 2-3 stand-alone CSV outputs. Each table is offered as an open source CSV and the Access Database tool zipped and is also available for download.
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