آمار
| تعداد نشریات | 45 |
| تعداد شمارهها | 1,489 |
| تعداد مقالات | 18,175 |
| تعداد مشاهده مقاله | 58,780,702 |
| تعداد دریافت فایل اصل مقاله | 20,253,159 |
Importance of vegetation index in codling moth Cydia pomonella distribution modeling | ||
| پژوهش های کاربردی در گیاهپزشکی | ||
| دوره 12، شماره 1، فروردین 1402، صفحه 27-41 اصل مقاله (1.03 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/arpp.0621.16077 | ||
| نویسندگان | ||
| Hakimeh Shayestehmehr1؛ Roghaiyeh Karimzadeh* 1؛ Bakhtiar Feizizadeh2؛ Shahzad Iranipour1 | ||
| 1Department of Plant Protection, Faculty of Agriculture, University of Tabriz, Tabriz, Iran | ||
| 2Department of Remote Sensing & GIS, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran | ||
| چکیده | ||
| Codling moth, Cydia pomonella L. (Lepidoptera: Tortricidae) is the key insect pest of apple orchards in Iran. This study was conducted in the main apple-growing regions of East Azarbaijan Province to generate potential habitat suitability maps of C. pomonella using MaxEnt modeling and to determine the importance of vegetation index in improving the accuracy of these models. Field surveys for collecting the occurrence data of codling moth were conducted during three growing seasons, 2017 - 2019. The activity of codling moth adult males was monitored using delta-shaped traps baited with female sex pheromone. Fifteen environmental variables were considered as potential predictors for estimating codling moth distribution. These variables were categorized into topographic, climatic, and remote sensing variables. A MaxEnt modeling algorithm was used to predict the distribution of codling moth. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). By using the topographic, climatic, and topographic+climatic variables, the AUC values were 0.840, 0.951, and 0.938, respectively. The model including normalized difference vegetation index (NDVI) had the highest AUC value (0.99), which strongly supports model predictive power and indicates the importance of vegetation index in codling moth distribution modeling. NDVI was the most contributed variable in the model followed by precipitation of September, slope, minimum temperature of May, and mean temperature of April. The distribution map obtained in MaxEnt provides an important tool for identifying potential risk zones of codling moth. This map can assist managers in forecasting and planning control measures and therefore, effective management of current infestations of codling moth. | ||
| کلیدواژهها | ||
| Species distribution؛ niche modeling؛ risk map؛ pest management؛ forecasting | ||
| مراجع | ||
|
References Beers EH, Suckling DM, Prokopy RJ, Avilla J, 2003. Ecology and management of apple arthropod pests. In: Ferree D, Warrington I (eds). Apples: Botany, Production and Uses. CABI International,UK. Pp 489-519. Dminić I, Kozina A, Bažok R, Barčić JI, 2010. Geographic information systems (GIS) and entomological research: a review. Journal of Food, Agriculture & Environment 8: 1193-1198. Jiang D, Chen S, Hao M, Fu J, Ding F, 2018. Mapping the potential global codling moth (Cydia pomonella L.) distribution based on a machine learning method. Scientific reports 8: 1-8. Jones VP, Hilton R, Brunner JF, Bentley WJ, Alston DG, et al., 2013. Predicting the emergence of the codling moth, Cydia pomonella (Lepidoptera: Tortricidae), on a degree‐day scale in North America. Pest Management Science 69: 1393-1398. Kamangar S, Ranjbar Aghdam H, 2020. Determination of the best time to control the codling moth, Cydia pomonella L., 1758 (Lep: Tortricidae), based on the estimation of thermal units (GDH). Journal of Applied Research in Plant Protection 9:13-29. Karimzadeh R, Hejazi MJ, Helali H, Iranipour S, Mohammadi SA, 2014. Predicting the resting sites of Eurygaster integriceps Put. (Hemiptera: Scutelleridae) using a geographic information system. Precision Agriculture 15: 615-626. Kumar S, Neven LG, Zhu H, Zhang R, 2015. Assessing the global risk of establishment of Cydia pomonella (Lepidoptera: Tortricidae) using CLIMEX and MaxEnt niche models. Journal of economic entomology 108: 1708-1719. Leitão PJ, Santos MJ, 2019. Improving models of species ecological niches: a remote sensing overview. Frontiers in Ecology and Evolution 7: 9. doi: 10.3389/fevo.2019.00009. Lestina J, Cook M, Kumar S, Morisette J, Ode PJ, et al., 2016. MODIS imagery improves pest risk assessment: a case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA. Environmental entomology 45: 1343-1351. Makori DM, Fombong AT, Abdel-Rahman EM, Nkoba K, Ongus J, et al., 2017. Predicting spatial distribution of key honeybee pests in Kenya using remotely sensed and bioclimatic variables: Key honeybee pests distribution models. ISPRS International Journal of Geo-Information 6: 66. https://doi.org/10.3390/ijgi6030066. Merrill SC, Holtzer TO, Peairs FB, Lester PJ, 2009. Modeling spatial variation of Russian wheat aphid overwintering population densities in Colorado winter wheat. Journal of Economic Entomology 102: 533-541. Miller J, Rogan J, 2007. Using GIS and remote sensing for ecological mapping and monitoring. In: Mesev V (ed). Integration of GIS and Remote Sensing. Wiley, USA. Pp. 233-268. Peterson AT, Soberón J, 2012. Species distribution modeling and ecological niche modeling: getting the concepts right. Natureza & Conservação 10: 102-107. Phillips SJ, 2005. A brief tutorial on Maxent. AT&T Research 190: 231-259. Phillips SJ, Dudík M, Schapire RE, 2004. A maximum entropy approach to species distribution modeling. 21st International Conference on Machine learning. July 4 - 8, Banff Alberta, Canada. P. 83. Richard K, Abdel-Rahman EM, Mohamed SA, Ekesi S, Borgemeister C, et al., 2018. Importance of remotely-sensed vegetation variables for predicting the spatial distribution of African citrus triozid (Trioza erytreae) in Kenya. ISPRS International Journal of Geo-Information 7: 429. doi:10.3390/ijgi7110429. Rotenberry JT, Preston KL, Knick ST, 2006. GIS‐based niche modeling for mapping species' habitat. Ecology 87: 1458-1464. Sallam MF, Xue RD, Pereira RM, Koehler PG, 2016. Ecological niche modeling of mosquito vectors of West Nile virus in St. John’s County, Florida, USA. Parasites & Vectors 9: 1-14. Svobodová E, Trnka M, Žalud Z, Semeradova D, Dubrovský M, et al., 2014. Climate variability and potential distribution of selected pest species in south Moravia and north-east Austria in the past 200 years–lessons for the future. The Journal of Agricultural Science 152: 225-237. Urbani F, D’Alessandro P, Biondi M, 2017. Using Maximum Entropy Modeling (MaxEnt) to predict future trends in the distribution of high altitude endemic insects in response to climate change. Bulletin of Insectology 70: 189-200. Villordon A, Roussel C, Hardy T, 2006. Development of a GIS-based model for predicting sweetpotato weevil infestation risk in Louisiana: progress, problems, and prospects. HortScience 41: 1045B-1045. Warren DL, Seifert SN, 2011. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological applications 21: 335-342. West AM, Kumar S, Brown CS, Stohlgren TJ, Bromberg J, 2016. Field validation of an invasive species Maxent model. Ecological Informatics 36: 126-134. Zhu H, Kumar S, Neven LG, 2017. Codling moth (Lepidoptera: Tortricidae) establishment in China: stages of invasion and potential future distribution. Journal of Insect Science 17: 85. | ||
|
آمار تعداد مشاهده مقاله: 1,610 تعداد دریافت فایل اصل مقاله: 1,039 |
||