ORIGINAL ARTICLE
Spatio-temporal risk assessment models for Lobesia botrana in uncolonized winegrowing areas
Guillermo Eugenio Heit 1, 2, A-B,D,F
,  
Walter Fabián Sione 3, C,E-F
,  
Pablo Gilberto Aceñolaza 4, 5, C,E-F  
 
 
More details
Hide details
1
Department of Plant Production, Faculty of Agronomy, University of Buenos Aires, Buenos Aires, Argentina
2
Bureau of Phytosanitary and Biological Compounds, National Animal Health and Agri-food Quality Service (SENASA), Buenos Aires, Argentina
3
Regional Center of Geomatics (CEREGEO), Autonomous University of Entre Ríos, Oro Verde, Entre Ríos, Argentina
4
CICyTTP – CONICET, España 149 (3105) Diamante, Entre Ríos, Argentina
5
Faculty of Agronomy, Entre Rios National University, Oro Verde (3100) Entre Ríos, Argentina
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
CORRESPONDING AUTHOR
Pablo Gilberto Aceñolaza   

Consejo Nacional de Investigaciones Científicas – CONICET/CICyTTP, Matteri y España s/n, Diamante, Entre Ríos (3105), Argentina
Online publish date: 2019-07-30
Submission date: 2018-10-26
Acceptance date: 2019-06-27
 
Journal of Plant Protection Research 2019;59(2):265–272
KEYWORDS
TOPICS
ABSTRACT
The objective of this work was to generate a series of equations to describe the voltinism of Lobesia botrana in the quarantine area of the main winemaking area of Argentina, Mendoza. To do this we considered an average climate scenario and extrapolated these equations to other winegrowing areas at risk of being invaded. A grid of 4 km2 was used to generate statistics on L. botrana captures and the mean temperature accumulation for the pixel. Four sets of logistic regression were constructed using the percentage of accumulated trap catches/grid/week and the degree-day accumulation above 7°C, from 1st July. By means of a habitat model, an extrapolation of the phenological model generated to other Argentine winemaking areas was evaluated. According to our results, it can be expected that 50% of male adult emergence for the first flight occurs at 248.79 ± 4 degree-days (DD), in the second flight at 860.18 ± 4.1 DD, while in the third and the fourth flights, 1671.34 ± 5.8 DD and 2335.64 ± 4.3 DD, respectively. Subsequent climatic comparison determined that climatic conditions of uncolonized areas of Cuyo Region have a similar suitability index to the quarantine area used to adjust the phenological model. The upper valley of Río Negro and Neuquén are environmentally similar. Valleys of the northwestern region of Argentina showed lower average suitability index and greater variability among SI estimated by the algorithm considered. The combination of two models for the estimation of adult emergence time and potential distribution, can provide greater certainties in decision-making and risk assessment of invasive species.
CONFLICT OF INTEREST
The authors have declared that no conflict of interests exist.
 
REFERENCES (31)
1.
Aalto J., Pirinen P., Heikkinen J., Venäläinen A. 2013. Spatial interpolation of monthly climate data for Finland, comparing the performance of kriging and generalized additive models. Theoretical and Applied Climatology 112 (1–2): 99–111.DOI: https://doi.org/10.1007/s00704....
 
2.
Ali M.H., Abustan I. 2014. A new novel index for evaluating model performance. Journal of Natural Resources and Development 4: 1–9. DOI: https://doi.org/10.5027/jnrd.v....
 
3.
Al Kandari N., Jolliffe I. 2012. Variable selection and interpretation of covariance principal components. Communications in Statistics-Simulation and Computation 30 (2): 339–354. DOI: https://doi.org/10.1081/SAC-10....
 
4.
Amo-Salas M., OrtegaLópez V., Harman R., Alonso-González A. 2011. A new model for predicting the flight activity of Lobesia botrana (Lepidoptera: Tortricidae). Crop Protection 30 (12): 1586–1593. DOI: https://doi.org/10.1016/j.crop....
 
5.
Andreadis S.S., Milonas P.FG., Savopoulou-Soultani M. 2005. Cold hardiness of diapausing and non-diapausing pupae of the European grapevine moth, Lobesia botrana Entomologia. Experimentalis et Applicata 117 (2): 113–118. DOI: https://doi.org/10.1111/j.1570....
 
6.
Armendáriz I., Pérez Sanz A., Capilla C., Juárez S., Miranda L., Nicolás J., Aparicio E. 2009. Cinco años de seguimiento de la polilla del racimo de la vid (Lobesia botrana) en la D.O. Arribes (Castilla y León, España [Five years of follow-up of the grape cluster moth (Lobesia botrana) in the D.O. Arribes (Castile and Leon, Spain)]. Boletín de Sanidad Vegetal – Plagas 35 (2): 193–204. (in Spanish).
 
7.
Damos P., Savopoulou-Soultani M. 2012. Temperaturedriven models for insect development and vital thermal requirements. Psyche: A Journal of Entomology 2012 :1–13. DOI: http://dx.doi.org/10.1155/2012....
 
8.
Di Rienzo J., Casanoves F., Balzarini M., González L., Tablada M., Robledo C. 2013.InfoStat versión 2013, Grupo InfoStat, FCA, Universidad Nacional de Córdoba, Argentina. Available on: http//www.infostat.com.ar. [Accessed: March 15, 2017].
 
9.
Dos Santos M., Porta Nova A. 2007. Estimating and Validating Nonlinear Regression Metamodels in Simulation. Communications in Statistics-Simulation and Computation 36 (1): 123–137. DOI: https://doi.org/10.1080/036109....
 
10.
Elith J., Graham C. 2009. Do they? How do they? Why do they differ? On finding reasons for differing performances of species distribution models. Ecography 32 (1): 66–77. DOI: https://doi.org/10.1111/j.1600....
 
11.
Fauvel M., Tarabalka Y., Benediktsson J., Chanussot J., Tilton J. 2013. Advances in spectral-spatial classification of hyperspectral images. Proceedings of the IEEE 101 (3): 652–675. DOI: https://doi.org/10.1109/JPROC.....
 
12.
Gallardo A., Ocete R., López M.A., Maistrello L., Ortega F., Semedo A., Soria F. 2009. Forecasting the flight activity of Lobesia botrana (Denis y Schiffermüller) (Lepidoptera: Tortricidae) in Southwestern Spain. Journal of Applied Entomology 133 (8): 626–632. DOI: https://doi.org/10.1111/j.1439....
 
13.
Gilioli G., Bodini A., Baumgärtner J. 2013. Metapopulation modeling and area-wide pest management strategies evaluation: an application to the Pine processionary moth. Ecological Modelling 260: 1–10. DOI: https://doi.org/10.1016/j.ecol....
 
14.
Gilioli G., Pasquali S., Marchesini E. 2016. A modelling framework for pest population dynamics and management: An application to the grape berry moth. Ecological Modelling 320: 348–357. DOI: https://doi.org/10.1016/j.ecol....
 
15.
González M. 2010. Lobesia botrana, grape moth (Lobesia botrana, polilla de la uva). Revista de Enología 2: 2–5.
 
16.
Haylock M., Hofstra N., Klein Tank A., Klok E., Jones P., New M. 2008. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research 113 (D20): 1–12. DOI: https://doi.org/10.1029/2008JD....
 
17.
Heit G., Sione W., Aceñolaza P., Zamboni L., Blanco P., Horak P., Cortese P. 2013. Potential distribution model of Lobesia botrana (Lepidoptera: Tortricidae). A planning tool for early detection at the regional level [Modelo de distribución potencial de Lobesia botrana (Lepidoptera: Tortricidae). Una herramienta de planificación para su detección temprana a nivel regional]. GeoFocus 13 (2): 179–194. (in Spanish, with English summary).
 
18.
Hlasny V., Livingston M. 2008. Economic determinants of invasion and discovery of nonindigenous insects. Journal of Agricultural and Applied Economics 40 (1): 37–52. DOI: https://doi.org/10.1017/S10740....
 
19.
FAO. 2015. International Standards for Phytosanitary Measures. Available on: https://www.ippc.int/en/core-a....
 
20.
Marmion M., Parviainen M., Luoto M., Heikkinen R., Thuiller W. 2009. Evaluation of consensus methods in predictive species distribution modelling. Diversity and Distributions 15 (1): 59–69. DOI: https://doi.org/10.1111/j.1472....
 
21.
Martín Vertedor D., Ferrero-García J., Torres-Vila L. 2010. Global warming affects phenology and voltinism of Lobesia botrana in Spain. Agricultural and Forest Entomology 12 (2): 169–176. DOI: https://doi.org/10.1111/j.1461....
 
22.
Mateo R., Felicisimo A., Munoz J. 2011. Modelos de distribución de especies: Una revisión sintética. Revista Chilena de Historia Natural 84 (2): 217–240. DOI: http://dx.doi.org/10.4067/S071....
 
23.
MDA Federal. 2004. Landsat GeoCover ETM+ 2000 Edition Mosaics Tile N-03-05.ETM-EarthSat-MrSID, 1.0, USGS, Sioux Falls, South Dakota, 2000.
 
24.
Narouei-Khandan H., Halbert S., Worner S., van Bruggen H. 2016. Global climate suitability of citrus huanglongbing and its vector, the Asian citrus psyllid, using two correlative species distribution modelling approaches, with emphasis on the USA. European Journal of Plant Pathology 144 (3): 655–670. DOI: https://doi.org/10.1007/s10658....
 
25.
Robertson M., Caithness N., Villet M. 2001. A PCA-based modelling technique for predicting environmental suitability for organisms from presence records. Diversity and Distributions 7 (1–2): 15–27. DOI: https://doi.org/10.1046/j.1472....
 
26.
Stahl K., Moore R., Floyer J., Asplin M., McKendry I. 2006. Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density. Agricultural and Forest Meteorology 139 (3): 224–236. DOI: http://dx.doi.org/10.1016/j.ag....
 
27.
Sutherst R. 2003. Prediction of species geographical ranges. Journal of Biogeography 30 (6): 805–816.
 
28.
Tognelli M., Roig-Juñent S., Marvaldi A., Flores G., Lobo J. 2009. An evaluation of methods for modelling distribution of Patagonian insects. Revista Chilena de Historia Natural 82 (3): 347–360. DOI: http://dx.doi.org/10.4067/S071....
 
29.
Urban M., Phillips B., Skelly D., Shine R. 2007. The cane toad’s (Chaunus [Bufo] marinus) increasing ability to invade Australia is revealed by a dynamically updated range model. Proceedings of the Royal Society. Series B, Biological Sciences 274 (1616): 1413–1419.
 
30.
Venette R., Kriticos D., Magarey R., Koch F., Baker R., Worner S., Gómez N., McKenney D., Dobesberger E., Yemshanov D., De Barro P., Hutchison W., Fowler G., Kalaris T., Pedlar J. 2010. Pest risk maps for invasive alien species: a roadmap for improvement. BioScience 60 (5): 349–362. DOI: https://doi.org/10.1525/bio.20....
 
31.
Zimmermann N., Thomas C., Graham C., Pearman P., Svenning J. 2010. New trends in species distribution modelling. Ecography 33 (6): 985–989. DOI: https://doi.org/10.1111/j.1600....
 
eISSN:1899-007X
ISSN:1427-4345