Integration of Ecological Models and Bioinformatics Tools for Risk Estimation of Spodoptera frugiperda in Ecuadorian Agricultural Zones
DOI:
https://doi.org/10.28940/terralatinoamericana.v44i.2440Keywords:
precision agriculture, spatial analysis, performance metrics, agricultural pest, distribution predictionAbstract
The fall armyworm (Spodoptera frugiperda) is a polyphagous pest that af fects several economically important crops worldwide, with maize (Zea mays L.) as its principal host. This study evaluated the ef fectiveness of six statistical methods of varying complexity to model the potential distribution of S. frugiperda and identify high-risk agricultural areas in continental Ecuador. Geographic information system (GIS) tools, statistical packages, bioclimatic variables, and occurrence records were used to model the ecological niche of the species within its environmental space.Probability-of-presence maps generated in RStudio were reclassified in ArcGIS using the prevalence threshold of each model to produce potential distribution maps of the species. Model performance was evaluated using a set of metrics, including the Area Under the Curve (AUC), True Skill Statistic (TSS), Miller Calibration Slope, and cross-validation. High-risk agricultural areas in mainland Ecuador were delineated through spatial analyses of land cover and maps of the species’ potential distribution. The results confirmed the value of GLM, GAM, and GBM models, used individually or in combination, as robust tools for mapping S. frugiperda risk in Ecuador. Priority agricultural areas were identified along the Coastal region and inter-Andean valleys of the Sierra. The results also support the hypothesis that high temperatures are crucial for the development and spread of the pest, whereas excessive rainfall acts as a limiting factor. Currently, the potential distribution of S. frugiperda is concentrated mainly in the Coastal region of the country. The information generated provides insight into the invasion potential of the species and establishes a baseline for future studies on pest dynamics in economically important crops. This contributes to the development of preventive integrated pest management strategies aimed at reducing risks to food security and minimizing negative impacts on agroecosystems.
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Acharya, R., Malekera, M. J., Dhungana, S. K., Sharma, S. R., & Lee, K. Y. (2022). Impact of rice and potato host plants is higher on the reproduction than growth of corn strain fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae). Insects, 13(3), 256. https://doi.org/10.3390/insects13030256
Allouche, O., Tsoar, A., & Kadmon, R. (2006). Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43(6), 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x
Alphey, N., & Bonsall, M. B. (2018). Genetics-based methods for agricultural insect pest management. Agricultural and Forest Entomology, 20(2), 131–140. https://doi.org/10.1111/afe.12241
Araújo, M. B., Anderson, R. P., Barbosa, A. M., Beale, C. M., Dormann, C. F., Early, R., Garcia, R. A., Guisan, A., Maiorano, L., Naimi, B., O’Hara, R. B., Zimmermann, N. E., & Rahbek, C. (2019). Standards for distribution models in biodiversity assessments. Science Advances, 5(1), 1–12. https://doi.org/10.1126/sciadv.aat4858
Araújo, M. B., Pearson, R. G., Thuiller, W., & Erhard, M. (2005). Validation of species-climate impact models under climate change. Global Change Biology, 11(9), 1504–1513. https://doi.org/10.1111/j.1365-2486.2005.01000.x
Baloch, M. N., Fan, J., Haseeb, M., & Zhang, R. (2020). Mapping potential distribution of Spodoptera frugiperda (Lepidoptera: Noctuidae) in central Asia. Insects, 11(3), 172. https://doi.org/10.3390/insects11030172
Baquero, R. A., Barbosa, A. M., Ayllón, D., Guerra, C., Sánchez, E., Araújo, M. B., & Nicola, G. G. (2021). Potential distributions of invasive vertebrates in the Iberian Peninsula under projected changes in climate extreme events. Diversity and Distributions, 27(11), 2262–2276. https://doi.org/10.1111/ddi.13401
Barbosa, A. (2015). fuzzySim: Applying fuzzy logic to binary similarity indices in ecology. Methods in Ecology and Evolution, 6(7), 853–858. https://doi.org/10.1111/2041-210X.12372
Barbosa, A., Real, R., Muñoz, A. R., & Brown, J. A. (2013). New measures for assessing model equilibrium and prediction mismatch in species distribution models. Diversity and Distributions, 19(10), 1333–1338. https://doi.org/10.1111/ddi.12100
Bradie, J., & Leung, B. (2017). A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. Journal of Biogeography, 44(6), 1344–1361. https://doi.org/10.1111/jbi.12894
Carlson, C. J. (2020). embarcadero: Species distribution modelling with Bayesian additive regression trees in R. Methods in Ecology and Evolution, 11(7), 850–858. https://doi.org/10.1111/2041-210X.13389
Casmuz, A., & Juárez, M. (2010). Revisión de los hospederos del gusano cogollero del maíz, Spodoptera frugiperda (Lepidoptera: Noctuidae). Revista de la Sociedad Entomológica Argentina, 69(3-4), 209-231. http://www.scielo.org.ar/scielo.php?script=sci_arttext&pid=S0373-56802010000200007&lng=es&tlng=es.
Cokola, M. C., Mugumaarhahama, Y., Noël, G., Bisimwa, E. B., Bugeme, D. M., Chuma, G. B., Ndeko, A. B., & Francis, F. (2020). Bioclimatic zonation and potential distribution of Spodoptera frugiperda (Lepidoptera: Noctuidae) in South Kivu Province, DR Congo. BMC Ecology, 20(1), 1–13. https://doi.org/10.1186/s12898-020-00335-1
Early, R., González-Moreno, P., Murphy, S. T., & Day, R. (2018). Forecasting the global extent of invasion of the cereal pest Spodoptera frugiperda, the fall armyworm. NeoBiota 40: 25–50. https://doi.org/10.1101/391847
Elith, J., H. Graham, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., Li, J., G. Lohmann, L., A. Loiselle, B., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., McC. M. Overton, J., Townsend Peterson, A., … E. Zimmermann, N. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129–151. https://doi.org/10.1111/J.2006.0906-7590.04596.X
Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
Ferrer-Sánchez, Y., Mafaldo-Sajami, A. A., Plasencia-Vázquez, A. H., & Urdánigo-Zambrano, J. P. (2022). Riesgo para el cultivo de cacao por los cambios en la distribución potencial del fitopatógeno Moniliophthora perniciosa bajo escenarios de cambio climático en Ecuador continental. Revista Terra Latinoamericana, 40, 1–10. https://doi.org/10.28940/terra.v40i0.1338
Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/https://doi.org/10.1002/joc.5086
Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24(1), 38–49. https://doi.org/10.1017/S0376892997000088
Fourcade, Y., Besnard, A. G., & Secondi, J. (2018). Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Global Ecology and Biogeography, 27(2), 245–256. https://doi.org/10.1111/geb.12684
Gaibor, R., Rodríguez, S., Guevara, C., Reyes, J., & Plaza, P. (2023). Variation over time of bioinsecticides effect on Spodoptera frugiperda incidence in corn (Zea mays) in dry season in Mocache, Los Ríos, Ecuador. Tropical and Subtropical Agroecosystems, 26, 1–12. http://dx.doi.org/10.56369/tsaes.3958
Hastie, M. T. (2023). gam: Generalized Additive Models. R package version 1.22-2.
He, L., Wang, T., Chen, Y., Ge, S., Wyckhuys, K. A. G., & Wu, K. (2021). Larval diet affects development and reproduction of East Asian strain of the fall armyworm, Spodoptera frugiperda. Journal of Integrative Agriculture, 20(3), 736–744. https://doi.org/10.1016/S2095-3119(19)62879-0
Hentschel, R., Möller, K., Wenning, A., Degenhardt, A., & Schröder, J. (2018). Importance of ecological variables in explaining population dynamics of three important pine pest insects. Frontiers in Plant Science, 9, 1667 https://doi.org/10.3389/fpls.2018.01667
Huang, Y., Dong, Y., Huang, W., Ren, B., Deng, Q., Shi, Y., Bai, J., Ren, Y., Geng, Y., & Ma, H. (2020). Overwintering Distribution of Fall Armyworm (Spodoptera frugiperda) in Yunnan, China, and Influencing Environmental Factors. Insects, 11(11), 805. https://doi.org/10.3390/insects11110805
INIAP. (2019). Informe técnico anual. Sección: Entomología. In Estación Experimental Portoviejo. Departamento Nacional de Protección Vegetal (DNPV). https://repositorio.iniap.gob.ec/bitstream/41000/5631/1/iniapeep2019ENTOMOLOGIA.pdf
Jeger, M., Bragard, C., Caffier, D., Candresse, T., Chatzivassiliou, E., Dehnen-Schmutz, K., Gilioli, G., Gregoire, J. C., Jaques Miret, J. A., Navarro, M. N., Niere, B., Parnell, S., Potting, R., Rafoss, T., Rossi, V., Urek, G., Van Bruggen, A., Van der Werf, W., West, J., … MacLeod, A. (2017). Pest categorisation of Spodoptera frugiperda. EFSA Journal, 15(7). https://doi.org/10.2903/j.efsa.2017.4927
Junttila, S., Näsi, R., Koivumäki, N., Imangholiloo, M., Saarinen, N., Raisio, J., Holopainen, M., Hyyppä, H., Hyyppä, J., Lyytikäinen-Saarenmaa, P., Vastaranta, M., & Honkavaara, E. (2022). Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season. Remote Sensing, 14(4). https://doi.org/10.3390/rs14040909
Lake, T. A., Briscoe Runquist, R. D., & Moeller, D. A. (2020). Predicting range expansion of invasive species: Pitfalls and best practices for obtaining biologically realistic projections. Diversity and Distributions, 26(12), 1767–1779. https://doi.org/10.1111/ddi.13161
Li, X., & Wang, Y. (2013). Applying various algorithms for species distribution modelling. Integrative Zoology, 8(2), 124–135. https://doi.org/10.1111/1749-4877.12000
Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3), 18–22.
Lissovsky, A. A., & Dudov, S. V. (2021). Species-Distribution Modeling: Advantages and Limitations of Its Application. 2. MaxEnt. Biology Bulletin Reviews, 11(3), 265–275. https://doi.org/10.1134/s2079086421030087
Mcinerney, D. O., & Nieuwenhuis, M. (2009). A comparative analysis of kNN and decision tree methods for the Irish National Forest Inventory. International Journal of Remote Sensing, 30(19), 4937–4955. https://doi.org/10.1080/01431160903022936
Miller, M. E., Hui, S. L., & Tierney, W. M. (1991). Validation techniques for logistic regression models. Statistics in Medicine, 10(8), 1213–1226. https://doi.org/10.1002/sim.4780100805
Morales-Barbero, J., & Vega-Álvarez, J. (2019). Input matters matter: Bioclimatic consistency to map more reliable species distribution models. Methods in Ecology and Evolution, 10(2), 212–224. https://doi.org/10.1111/2041-210X.13124
Munro, H., Montes, C., Gandhi, K., & Poisson, M. (2022). A comparison of presence-only analytical techniques and their application in forest pest modeling. Ecological Informatics, 68, 101525. https://doi.org/https://doi.org/10.1016/j.ecoinf.2021.101525.
Muscarella, R., Galante, P. J., Soley-Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M., & Anderson, R. P. (2014). ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution, 5(11), 1198–1205. https://doi.org/10.1111/2041-210X.12261
Nagoshi, R., Nagoshi, B., Cañarte, E., Navarrete, B., Solórzano, R., & Garcés, S. (2019). Genetic characterization of fall armyworm (Spodoptera frugiperda) in Ecuador and comparisons with regional populations identify likely migratory relationships. PLoS ONE, 14(9), 1–17. https://doi.org/10.1371/journal.pone.0222332
Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7, 21. https://doi.org/10.3389/fnbot.2013.00021
Norberg, A., Abrego, N., Blanchet, F. G., Adler, F. R., Anderson, B. J., Anttila, J., Araújo, M. B., Dallas, T., Dunson, D., Elith, J., Foster, S. D., Fox, R., Franklin, J., Godsoe, W., Guisan, A., O’Hara, B., Hill, N. A., Holt, R. D., Hui, F. K. C., … Ovaskainen, O. (2019). A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecological Monographs, 89(3), 1–24. https://doi.org/10.1002/ecm.1370
Osorio-Olvera, L., Lira-Noriega, A., Soberón, J., Peterson, A. T., Falconi, M., Contreras-Díaz, R. G., Martínez-Meyer, E., Barve, V., & Barve, N. (2020). ntbox: An r package with graphical user interface for modelling and evaluating multidimensional ecological niches. Methods in Ecology and Evolution, 11(10), 1199–1206. https://doi.org/10.1111/2041-210X.13452
Owens, H. L., & Rahbek, C. (2023). voluModel: Modelling species distributions in three-dimensional space. Methods in Ecology and Evolution, 14(3), 841–847. https://doi.org/10.1111/2041-210X.14064
Pearce, J., & Ferrier, S. (2000). Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling, 133(3), 225–245. https://doi.org/10.1016/S0304-3800(00)00322-7
Peterson, A. T., Soberón, J., Pearson, R. G., Anderson, R. P., Martínez-Meyer, E., Nakamura, M., & Araújo, M. B. (2011). Ecological niches and geographic distributions. Princeton University Press.
Phillips, S. J., Dudík, M., & Schapire, R. E. (2004). A maximum entropy approach to species distribution modeling. Proceedings of the Twenty-First International Conference on Machine Learning, ICML 2004, 655–662. https://doi.org/10.1145/1015330.1015412
Portilla, F. (2018). Introducción. In Agroclimatología del Ecuador (pp. 17–40). Ecuador, Quito: Abya Yala https://doi.org/10.7476/9789978104927.0001
Rapacciuolo, G., Roy, D. B., Gillings, S., Fox, R., Walker, K., & Purvis, A. (2012). Climatic associations of British species distributions show good transferability in time but low predictive accuracy for range change. PLoS ONE, 7(7). https://doi.org/10.1371/journal.pone.0040212
Real, R., Barbosa, A. M., & Bull, J. W. (2017). Species distributions, quantum theory, and the enhancement of biodiversity measures. Systematic Biology, 66(3), 453–462. https://doi.org/10.1093/sysbio/syw072
Real, R., Barbosa, A. M., & Vargas, J. M. (2006). Obtaining environmental favorability functions from logistic regression. Environmental and Ecological Statistics, 13(2), 237–245. https://doi.org/10.1007/s10651-005-0003-3
Ren-Yan, D., Xiao-Quan, K., Min-Yi, H., Wei-Yi, F., & Zhi-Gao, W. (2014). The predictive performance and stability of six species distribution models. PLoS ONE, 9(11). https://doi.org/10.1371/journal.pone.0112764
Ridgeway, G. (2004). The GBM Package: Generalized Boosted Regression Models. R Version 1.6-3. Foundation for Statistical Computing, 5, 11.
Steen, V. A., Tingley, M. W., Paton, P. W. C., & Elphick, C. S. (2021). Spatial thinning and class balancing: Key choices lead to variation in the performance of species distribution models with citizen science data. Methods in Ecology and Evolution, 12(2), 216–226. https://doi.org/10.1111/2041-210X.13525
Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9, 1–11. https://doi.org/10.1186/1471-2105-9-307
Valavi, R., Elith, J., Lahoz-Monfort, J. J., & Guillera-Arroita, G. (2019). blockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods in Ecology and Evolution, 10(2), 225–232. https://doi.org/10.1111/2041-210X.13107
van Oijen, M. (2020). Linear Modelling: LM, GLM, GAM and Mixed Models. In: M. van Oijen (ed.). Bayesian Compendium (pp. 137–140). Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-55897-0_19
Vélez, M., Betancourt, C., & Mendoza, J. (2021). Evaluación de diferentes momentos de aplicación de insecticida Metomil 90% para el control del gusano cogollero (Spodoptera frugiperda) en el cultivo de maíz. Ciencia y Tecnología, 14(2), 33–40. https://doi.org/10.18779/cyt.v14i2.500
Watling, J. I., Brandt, L. A., Bucklin, D. N., Fujisaki, I., Mazzotti, F. J., Romanach, S. S., & Speroterra, C. (2015). Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models. Ecological Modelling, 309, 48-59. https://doi.org/10.1016/j.ecolmodel.2015.03.017
Xiang-Ning, S., Chuan-Ying, L., Yi-Jua, X., Shao-Huai, H., Wei-Ling, L., Zhang-Xuan, L., & Yu-Ping, Z. (2023). Feeding preference and adaptability of fall armyworm Spodoptera frugiperda on five species of host plants and six weeds. Journal of Environmental Entomology, 44, 263–272. https://www.cabidigitallibrary.org/doi/full/10.5555/20230028482
XiangHui, R., JianXin, C., Yang, M., Peng, L., XinYue, N., & WenJie, L. (2017). Radom forest model analysis on environment factors of Phyllotreta striolata occurrence in Brassica chinensis field. Journal of Agricultural Science and Technology, 19, 117–123.
Zambrano, M., Yanez, C., Sangoquiza, C., Alarcon, F., Zambrano, E., & Villafuerte, M. (2019). Situación del cultivo de maíz en Ecuador: investigación y desarrollo de tecnologías en el Iniap. XXIII Reunión Latinoamericana Del Maíz y IV Congreso de Semillas, 1–3. https://repositorio.iniap.gob.ec/bitstream/41000/5457/1/iniapeeppdf62.pdf
Zhang, D., Xiao, Y., Xu, P., Yang, X., Wu, Q., & Wu, K. (2021). Insecticide resistance monitoring for the invasive populations of fall armyworm, Spodoptera frugiperda in China. Journal of Integrative Agriculture, 20(3), 783–791. https://doi.org/10.1016/S2095-3119(20)63392-5
Zhao, Z., Xiao, N., Shen, M., & Li, J. (2022). Comparison between optimized MaxEnt and random forest modeling in predicting potential distribution: A case study with Quasipaa boulengeri in China. Science of The Total Environment, 842, 156867. https://doi.org/https://doi.org/10.1016/j.scitotenv.2022.156867
