Characterization of the Spatial Distribution of the Pepper Weevil, Anthonomus eugenii Cano (Col.: Curculionidae), in Pepper Fields in South Florida
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Within Field Distribution of Pepper Weevil on Long Hot and Jalapeño Peppers
2.2. Collection and Fruit Dissection
2.3. Statistics Used to Determine Distribution Patterns
2.4. Moran’s I and Geary’s C
2.5. SADIE Index
2.6. Data Analysis
3. Results
3.1. Moran’s I and Geary’s C Indices
3.2. SADIE Index
4. Discussion
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Elmore, J.C.; Davis, A.C.; Campbell, R.E. The Pepper Weevil; Technical Bulletin; USDA: Washington, DC, USA, 1934; p. 27. [Google Scholar]
- Rodriguez-Leyva, E. Life History of Triaspis eugenii Wharton and Lopez-Martinez (Hymenoptera: Braconidae) and Evaluation of Its Potential for Biological Control of Pepper Weevil Anthonomus eugenii Cano (Coleoptera: Curculionidae). Ph.D. Thesis, University of Florida, Gainesville, FL, USA, 2006. [Google Scholar]
- Addesso, K.M.; McAuslane, H.J.; Alborn, H.T. Attraction of pepper weevil to volatiles from damaged pepper plants. Entomol. Exp. Appl. 2011, 138, 1–11. [Google Scholar] [CrossRef]
- Russell, C.J. Improved Detection and Monitoring of Pepper Weevil (Anthonomus eugenii) in Ontario Peppers. Master’s Thesis, University of Guelph, Guelph, ON, Canada, 2021. [Google Scholar]
- Andrews, K.L.; Rueda, A.; Gandini, G.; Evans, S.; Arango, A.; Avedillo, M. A supervised control program for the pepper weevil, Anthonomus eugenii Cano, in Honduras, Central America. Int. J. Pest Manag. 1986, 32, 1–4. [Google Scholar]
- Riley, D.G.; Schuster, D.J.; Barfield, C.S. Refined action threshold for pepper weevil adults (Coleoptera: Curculionidae) in bell peppers. J. Econ. Entomol. 1992, 85, 1919–1925. [Google Scholar] [CrossRef]
- Ingerson-Mahar, J.; Eichinger, B.; Holmstrom, K. How does pepper weevil (Coleoptera: Curculionidae) become an important pepper pest in New Jersey? J. Integr. Pest Manag. 2015, 6, 23. [Google Scholar] [CrossRef]
- Segarra-Carmona, A.E.; Pantoja, A. Sequential sampling plan, yield loss components and economic thresholds for the pepper weevil, Anthonomus eugenii Cano (Coleoptera: Curculionidae). J. Agric. Univ. Puerto Rico 1988, 72, 375–385. [Google Scholar]
- Cartwright, B.; Teague, T.G.; Chandler, L.D.; Edelson, J.V.; Bentsen, G. An action threshold for management of the pepper weevil (Coleoptera: Curculionidae) on bell peppers. J. Econ. Entomol. 1990, 83, 2003–2007. [Google Scholar] [CrossRef]
- Riley, D.G.; Schuster, D.J.; Barfield, C.S. Sampling and dispersion of pepper weevil (Coleoptera: Curculionidae) adults. Environ. Entomol. 1992, 21, 1013–1021. [Google Scholar] [CrossRef]
- Wantuch, H.; Kuhar, T. Pepper Weevil; Department of Entomology, Virginia Tech: Blacksburg, VR, USA, 2014. [Google Scholar]
- Southwood, T.R.E. Ecological Methods; John Wiley & Sons, Chapman and Hall: London, UK, 1978. [Google Scholar]
- Khan, R.A.; Seal, D.R.; Zhang, S.; Liburd, O.E.; Srinivasan, R.; Evans, E. Distribution pattern of thrips (Thysanoptera: Thripidae) and tomato chlorotic spot virus in south Florida tomato fields. Environ. Entomol. 2020, 49, 73–87. [Google Scholar] [CrossRef] [PubMed]
- Seal, D.R.; McSorley, R.; Chalfant, R.B. Seasonal abundance and spatial distribution of wireworms (Coleoptera: Elateridae) in Georgia sweet potato fields. J. Econ. Entomol. 1992, 85, 1802–1808. [Google Scholar] [CrossRef]
- Pieters, E.P. Spatial Distributions and Sampling of Cotton Arthropods with Special Reference to Sequential Sampling. Ph.D. Thesis, Texas A&M University, College Station, TX, USA, 1973. [Google Scholar]
- Rózsa, L.; Reiczigel, J.; Majoros, G. Quantifying parasites in samples of hosts. J. Parasitol. 2000, 86, 228–232. [Google Scholar] [CrossRef] [PubMed]
- Rabinovich, J.E. Introducción a la Ecología de Poblaciones Animales; Compañía Editorial Continental: Mexico City, Mexico, 1980. [Google Scholar]
- Green, R.H. Measurement of non-randomness in spatial distributions. Res. Popul. Ecol. 1966, 8, 1–7. [Google Scholar] [CrossRef]
- Winder, L.; Alexander, C.; Griffiths, G.; Holland, J.; Woolley, C.; Perry, J. Twenty years and counting with SADIE: Spatial analysis by distance indices software and review of its adoption and use. Rethink. Ecol. 2019, 4, 1–16. [Google Scholar] [CrossRef]
- Maruyama, Y. An alternative to Moran’s I for spatial autocorrelation. arXiv 2015, arXiv:1501.06260. [Google Scholar]
- Peret, M.; Dittmar, P.; Agehara, S.; Smith, H. Vegetable Production Handbook of Florida, 2021–2022; The University of Florida (UF) Institute of Food and Agricultural Sciences (IFAS): Gainesville, FL, USA, 2021. [Google Scholar]
- Getis, A. Reflections on spatial autocorrelation. Reg. Sci. Urban Econ. 2007, 37, 491–496. [Google Scholar] [CrossRef]
- Aswi, A.; Cramb, S.; Duncan, E.; Mengersen, K. Detecting spatial autocorrelation for a small number of areas: A practical example. J. Phys. 2021, 1899, 012098. [Google Scholar] [CrossRef]
- Mathur, M. Spatial autocorrelation analysis in plant population: An overview. J. Appl. Nat. Sci. 2015, 1, 501–513. [Google Scholar] [CrossRef]
- Wong, D.W.S.; Lee, J. Statistical Analysis of Geographic Information with ArcView GIS and ArcGIS; Wiley: Hoboken, NJ, USA, 2005. [Google Scholar]
- Zhang, D.; Mao, X.; Meng, L. A method using ESDA to analyze the spatial distribution patterns of cultural resource. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2010, 38, 273–278. [Google Scholar]
- Cliff, A.; Ord, J.K. Spatial Processes: Models and Applications; Pion: London, UK, 1981. [Google Scholar]
- Perry, J.N.; Bell, E.D.; Smith, R.H.; Woiwod, I.P. SADIE: Software to measure and model spatial pattern. Asp. Appl. Biol. 1996, 46, 95–103. [Google Scholar]
- Perry, J.N. Measures of spatial pattern for counts. Ecology 1998, 79, 1008–1017. [Google Scholar] [CrossRef]
- Park, Y.; Tollefson, J.J. Characterization of the spatial dispersion of corn root injury by corn rootworms (Coleoptera: Chrysomelidae). J. Econ. Entomol. 2005, 98, 378–383. [Google Scholar] [CrossRef]
- Martins, J.C.; Picanco, M.C.; Silva, R.S.; Gonring, A.H.R.; Galdino, T.V.S.; Guedes, R.N.C. Assessing the spatial distribution of Tuta absoluta (Lepidoptera: Gelechiidae) eggs in open-field tomato cultivation through geostatistical analysis. Pest Manag. Sci. 2017, 74, 30–36. [Google Scholar] [CrossRef] [PubMed]
- Shrestha, G.; Rijal, J.P.; Reddy, G.V.P. Characterization of the spatial distribution of alfalfa weevil, Hypera postica, and its natural enemies, using geospatial models. Pest Manag. Sci. 2020, 77, 906–918. [Google Scholar] [CrossRef] [PubMed]
- Rijal, J.P.; Brewster, C.C.; Bergh, J.C. Spatial distribution of grape root borer (Lepidoptera: Sesiidae) infestations in Virginia vineyards and implications for sampling. Environ. Entomol. 2014, 43, 716–728. [Google Scholar] [CrossRef] [PubMed]
- Perry, J.N. Spatial analysis by distance indices. J. Anim. Ecol. 1995, 64, 303–314. [Google Scholar] [CrossRef]
- Perry, J.N. Spatial association for counts of two species. Acta Jutl. 1997, 72, 149–169. [Google Scholar]
- Wickham, H.; Hester, J.; Chang, W.; Bryan, J. Devtools-package: Devtools: Tools to Make Developing R Packages Easier. R Package Version 2.4.5. 2022. Available online: https://CRAN.R-project.org/package=devtools (accessed on 15 July 2024).
- Gigot, C. Analyzing Plant Disease Epidemics with the R Package Epiphy. R Package Version 0.5.0.9000. 2023. Available online: https://CRAN.R-project.org/package=epiphy (accessed on 15 July 2024).
- SAS Institute. SAS/STAT 9.4 User’s Guide; SAS Institute: Cary, NC, USA, 2013. [Google Scholar]
- Eller, F.J.; Bartelt, R.J.; Shasha, B.S.; Schuster, D.J.; Riley, D.G.; Stansly, P.A.; Mueller, T.F.; Shuler, K.D.; Davis, J.H.; Sutherland, C.A. Aggregation pheromone for the pepper weevil, Anthonomus eugenii Cano (Coleoptera: Curculionidae): Identification and field activity. J. Chem. Ecol. 1994, 20, 1537–1555. [Google Scholar] [CrossRef] [PubMed]
- Eller, F.J.; Palmquist, D.E. Factors Affecting Pheromone Production by the Pepper Weevil, Anthonomus eugenii Cano (Coleoptera: Curculionidae) and collection efficiency. Insects 2014, 5, 909–920. [Google Scholar] [CrossRef] [PubMed]
- Bandeira, P.T.; Favaro, C.F.; Francke, W.; Bergmann, J.; Zarbin, P.H.G. Aggregation pheromones of weevils (Coleoptera: Curculionidae): Advances in the identification and potential uses in semiochemical-based pest management strategies. J. Chem. Ecol. 2021, 47, 968–986. [Google Scholar] [CrossRef] [PubMed]
- Addesso, K.M.; McAuslane, H.J. Pepper weevil attraction to volatiles from host and nonhost plants. Environ. Entomol. 2009, 38, 216–224. [Google Scholar] [CrossRef] [PubMed]
- Giblin-Davis, R.M.; Oehlschlager, A.C.; Perez, A.; Gries, G.; Gries, R.; Weissling, T.J.; Chinchilla, C.M.; Peña, J.E.; Hallett, R.H.; Pierce, H.D.; et al. Chemical and behavioral ecology of palm weevils (Curculionidae: Rhynchophorinae). Fla. Entomol. 1996, 79, 153–167. [Google Scholar] [CrossRef]
- Addesso, K.M.; Alborn, H.T.; Bruton, R.R.; McAuslane, H.J. A multicomponent marking pheromone produced by the pepper weevil, Anthonomus eugenii (Coleoptera: Curculionidae). Chemoecology 2021, 31, 247–258. [Google Scholar] [CrossRef]
- Dominguez, A.; Lopez, S.; Bernabe, A.; Guerrero, A.; Quero, C. Influence of age, host plant and mating status in pheromone production and new insights on perception plasticity in Tuta absoluta. Insects 2019, 10, 256. [Google Scholar] [CrossRef] [PubMed]
- Faleiro, J.R.; Kumar, J.A.; Rangnekar, P.A. Spatial distribution of red palm weevil Rhynchophorus ferrugineus Oliv. (Coleoptera: Curculionidae) in coconut plantations. Crop Prot. 2002, 21, 171–176. [Google Scholar] [CrossRef]
- Peng, C.; Brewer, G.J. Distribution of the red sunflower seed weevil (Coleoptera: Curculionidae) on sunflower. Environ. Entomol. 1994, 23, 1101–1105. [Google Scholar] [CrossRef]
- Schotzko, D.J.; Quisenberry, S.S. Pea leaf weevil (Coleoptera: Curculionidae) spatial distribution in peas. Environ. Entomol. 1999, 28, 477–484. [Google Scholar] [CrossRef]
Year | Field | Pepper Type | GPS Location | Planting Time |
---|---|---|---|---|
2021 | 1 | Jalapeño | 25.62°, −80.47° | October 2020 |
2021 | 2 | Long hot | 25.62°, −80.47° | October 2020 |
2021 | 3 | Jalapeño | 25.58°, −80.52° | November 2020 |
2021 | 4 | Long hot | 25.58°, −80.52° | November 2020 |
2022 | 5 | Jalapeño | 25.62°, −80.49° | October 2021 |
2022 | 6 | Long hot | 25.62°, −80.49° | October 2021 |
2023 | 7 | Long hot | 25.43°, −80.52° | October 2022 |
Indices | Random | Aggregation | Uniform/Regular |
---|---|---|---|
Moran’s I | Insignificant p value | Significant p value and positive Z | Significant p value and negative Z |
Geary’s C | Values near 1 | Values = 0 | Values near 2 |
SADIE analysis | Ia = 1 | Ia > 1 | Ia < 1 |
Field X/Pepper Type | Index | Date 1 (9 WAP) | Date 2 (10 WAP) | Date 3 (11 WAP) | Date 4 (12 WAP) | Date 5 (13 WAP) | Date 6 (14 WAP) |
---|---|---|---|---|---|---|---|
Field 1 Jalapeño | Moran’s I | 0.009, Z = 3.23, p = 0.0012 | 0.020, Z = 6.16, p < 0.0001 | 0.018, Z = 4.88, p < 0.0001 | 0.009, Z = 3.12, p = 0.0018 | 0.001, Z = 1.606, p = 0.11 | |
Geary’s C | 1.030, Z = 2.04, p = 0.04 | 0.961, Z = −3.14, p = 0.0017 | 0.972, Z = −2.21, p = 0.03 | 0.9865, Z = −1.07, p = 0.28 | 0.987, Z = −0.996, p = 0.32 | ||
Field 2 Long Hot | Moran’s I | 0.017, Z = 4.73, p < 0.0001 | 0.013, Z = 3.93, p < 0.0001 | 0.017, Z = 4.8, p < 0.0001 | 0.017, Z = 4.84, p < 0.0001 | ||
Geary’s C | 0.878, Z = −9.67, p < 0.0001 | 0.976, Z = −1.88, p = 0.0606 | 0.960, Z = −3.16, p = 0.0016 | 0.968, Z = −2.51, p = 0.0120 | |||
Field 3 Jalapeño | Moran’s I | 0.0006, Z = 1.52, p = 0.13 | 0.005, Z = 2.41, p = 0.02 | 0.006, Z = 2.59, p = 0.0096 | 0.002, Z = 1.811, p = 0.07 | ||
Geary’s C | 0.975, Z = −2.02, p = 0.04 | 0.96, Z = −3.50, p = 0.0005 | 0.980, Z = −1.56, p = 0.12 | 1.000, Z = 0.785, p = 0.43 | |||
Field 4 Long Hot | Moran’s I | 0.042, Z = 9.83, p < 0.0001 | 0.028, Z = 6.94, p < 0.0001 | 0.011, Z = 3.67, p = 0.0002 | 0.002, Z = 1.72, p = 0.08 | ||
Geary’s C | 0.927, Z = −5.79, p < 0.0001 | 0.960, Z = −3.17, p = 0.0015 | 1.000, Z = 0.035, p = 0.97 | 0.977, Z = −1.81, p = 0.07 | |||
Field 5 Jalapeño | Moran’s I | −0.003, Z = 0.7, p = 0.48 | 0.003, Z = 2.07, p = 0.04 | 0.060, Z = 13.32, p < 0.0001 | 0.005, Z = 2.36, p = 0.02 | 0.040, Z = 9.61, p < 0.0001 | −0.002, Z = 0.985, p = 0.32 |
Geary’s C | 0.999, Z = −0.0736, p = 0.94 | 0.980, Z = −1.06, p = 0.29 | 0.890, Z = −8.58, p < 0.0001 | 0.990, Z = −0.978, p = 0.33 | 0.960, Z = −3.40, p = 0.0007 | 0.986, Z = −1.1, p = 0.27 | |
Field 6 Long Hot | Moran’s I | 0.008, Z = 2.97, p = 0.0030 | 0.037, Z = 8.86, p < 0.0001 | 0.040, Z = 9.39, p < 0.0001 | 0.013, Z = 3.95, p < 0.0001 | 0.020, Z = 4.61, p < 0.0001 | |
Geary’s C | 0.950, Z = −4.02, p < 0.0001 | 0.920, Z = −5.96, p < 0.0001 | 0.933, Z = −5.27, p < 0.0001 | 0.960, Z = −3.53, p = 0.0004 | 0.980, Z = −1.31, p = 0.19 | ||
Field 7 Long Hot | Moran’s I | 0.016, Z = 10.92, p < 0.0001 | |||||
Geary’s C | 0.980, Z = −3.26, p = 0.0011 |
Field/Year | Pepper Type | Sampling Week | Ia | Pa | Dis Pattern |
---|---|---|---|---|---|
1/Year 1 | Jalapeño | 1 (9 WAP) | 1.34 | 0.04 | AGG |
2 (10 WAP) | 1.39 | 0.01 | AGG | ||
3 (11 WAP) | 1.69 | p < 2.22 × 10−16 | AGG | ||
4 (12 WAP) | 0.98 | 0.49 | RAN | ||
5 (13 WAP) | 0.91 | 0.61 | REG | ||
2/Year 1 | Long hot | 1 (9 WAP) | 1.4 | 0.04 | AGG |
2 (10 WAP) | 1.3 | 0.08 | AGG | ||
3 (11 WAP) | 0.9 | 0.44 | REG | ||
4 (12 WAP) | 1.07 | 0.31 | RAN | ||
3/Year 1 | Jalapeño | 1 (9 WAP) | 1.39 | 0.03 | AGG |
2 (10 WAP) | 1.22 | 0.18 | RAN | ||
3 (11 WAP) | 1.43 | 0.06 | AGG | ||
4 (12 WAP) | 1.28 | 0.07 | RAN | ||
4/Year 1 | Long hot | 1 (9 WAP) | 2.19 | p < 2.22 × 10−16 | AGG |
2 (10 WAP) | 2.0 | p < 2.22 × 10−16 | AGG | ||
3 (11 WAP) | 1.24 | 0.15 | RAN | ||
4 (12 WAP) | 1.05 | 0.3 | RAN | ||
5/Year 2 | Jalapeño | 1 (9 WAP) | 1.36 | 0.06 | AGG |
2 (10 WAP) | 0.94 | 0.57 | RAN | ||
3 (11 WAP) | 2.06 | p < 2.22 × 10−16 | AGG | ||
4 (12 WAP) | 1.09 | 0.32 | RAN | ||
5 (13 WAP) | 1.65 | 0.02 | AGG | ||
6 (14 WAP) | 1.29 | 0.08 | RAN | ||
6/Year 2 | Long hot | 1 (9 WAP) | 1.4 | 0.02 | AGG |
2 (10 WAP) | 1.46 | 0.02 | AGG | ||
3 (11 WAP) | 1.96 | p < 2.22 × 10−16 | AGG | ||
4 (12 WAP) | 1.38 | 0.07 | AGG | ||
5 (13 WAP) | 1.59 | p < 2.22 × 10−16 | AGG | ||
7/Year 3 | Long hot | 1 (9 WAP) | 2.09 | p < 2.22 × 10−16 | AGG |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Adeleye, V.O.; Seal, D.R.; Martini, X.; Meru, G.; Liburd, O.E. Characterization of the Spatial Distribution of the Pepper Weevil, Anthonomus eugenii Cano (Col.: Curculionidae), in Pepper Fields in South Florida. Insects 2024, 15, 579. https://doi.org/10.3390/insects15080579
Adeleye VO, Seal DR, Martini X, Meru G, Liburd OE. Characterization of the Spatial Distribution of the Pepper Weevil, Anthonomus eugenii Cano (Col.: Curculionidae), in Pepper Fields in South Florida. Insects. 2024; 15(8):579. https://doi.org/10.3390/insects15080579
Chicago/Turabian StyleAdeleye, Victoria O., Dakshina R. Seal, Xavier Martini, Geoffrey Meru, and Oscar E. Liburd. 2024. "Characterization of the Spatial Distribution of the Pepper Weevil, Anthonomus eugenii Cano (Col.: Curculionidae), in Pepper Fields in South Florida" Insects 15, no. 8: 579. https://doi.org/10.3390/insects15080579