|Hong, Seung Cheon - North Carolina State University|
|Magarey, Roger - North Carolina State University|
|Borchert, Daniel - Animal And Plant Health Inspection Service (APHIS)|
Submitted to: NeoBiota
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/13/2015
Publication Date: 9/15/2015
Citation: Hong, S., Magarey, R.D., Borchert, D.M., Vargas, R.I., Souder, S. 2015. Site-specific temporal and spatial validation of a generic plant pest forecast system with observations of Bactrocera dorsalis (oriental fruit fly). . NeoBiota. need citation information.
Interpretive Summary: The increase in international trade has exacerbated the problem of non-indigenous species moving between continents and causing economic damage. Phytosanitary regulatory agencies aim to prevent non-indigenous pest entry and establishment and attempt to mitigate their impact when they become established. Pest risk maps are used by phytosanitary agencies to support risk analysis, pest surveillance, and emergency programs. One of the most important types of risk maps are those that estimate potential distribution based on climate suitability, which are usually created with bioclimatic models. In order to address this problem, there is benefit in creating a simple generic model framework that is a compromise between ease of use and capabilities for additional phytosanitary and pest management applications. In this study, we introduce the Generic Pest Forecast System (GPFS), for simulating relative pest populations in space and time. The oriental fruit fly (OFF)(Bactrocera dorsalis) was chosen as a study pest to test the GPFS model because there is an extensive amount of literature data available for model development and validation. The objective of this study carried out by a collaboration of North Carolina State University, U.S. Department of Agriculture, APHIS-PPQ, Raliegh, NC, and USDA Agricultural Research Service (ARS), Hilo, Hawaii, was to introduce the GPFS model and validate it for B. dorsalis using site-specific observations and distribution data. In addition, we wished to use the GPFS model to investigate the potential for establishment in the United States. The model was shown to be able to simulate relative pest populations in some locations, which could have potential to estimate potential impacts of a pest when combined with other biological and management variables. This study also shows the potential for improving pest risk models by conducting spatial and site-specific temporal validations against published observations. Although these kinds of temporal validations will not be possible for every species, they can provide insight into the spatial domain by suggesting why a species might not persist or provide an indication of the risk. In conclusion, validating pest risk models with spatial and site-specific temporal data may provide more robust and reliable results than validations with spatial data alone.
Technical Abstract: This study introduces a simple generic model, the Generic Pest Forecast System (GPFS), for simulatingthe relative populations of non-indigenousarthropod pests in space and time. The model was designed to calculate the population index or relative population using hourly weather dataas influenced by developmental rate, high and low temperature mortalities and wet soil moisture mortality..Each module contains biological parameters derived from controlled experiments. The hourly weather data used for the model inputs wereobtained from the National Center of Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) at a 38km spatial resolution. A combination of spatial and site-specific temporal data was used to validate the GPFS models. The oriental fruit fly, Bactrocera dorsalis (Hendel), was selected as a case study for this research because it is climatically driven and a major pest offruit production. Results from the GPFS model were compared with field B. dorsalis survey data in three locations: 1) Bangalore, India; 2) Hawaii, USA; and 3) Wuhan, China. The GPFS captured the initial outbreaks and major population peaks of B. dorsalisreasonably well, although agreement varied between sites.An index of agreement test indicated that GPFSmodel simulations matched with field B. dorsalisobservation data with a range between 0.50 and 0.94 (1.0 as a perfect match).Of the three locations, Wuhanshowed the highest match between the observed and simulatedB. dorsalispopulations,with indices of agreement of 0.85.The site-specific temporal comparisons implied that the GPFS model is informative for prediction of relative abundance.Spatial results from the GPFS model were also compared with 161 published observations of B. dorsalis distribution,mostly from EastAsia. Since parameters for pupal overwintering and survival were unknown from the literature, these were inferred from the distribution data. The studyshowed that GPFS has promise forestimating suitable areas for B. dorsalis establishment and potentially other non-indigenous pests.It is concluded that calibrating prediction models with both spatial and site-specific temporal data may provide more robust and reliable results than validations with either data set alone.