Optimization of Maggot mass rearing via topological statistics approach

Thiago de Melo; Rodrigo Rosa; Jamil Viana Pereira; Washington Mio; Claudio José von Zuben; Alice Libardi

doi:10.5269/bspm.76009

Thiago de Melo Unesp
Rodrigo Rosa Department of Mathematics, Institute of Biosciences, Letters and Exact Sciences, São Paulo State University (Unesp) https://orcid.org/0009-0003-1912-6121
Jamil Viana Pereira Department of Mathematics, Institute of Geosciences and Exact Sciences, São Paulo State University (Unesp) https://orcid.org/0000-0003-3268-6628
Washington Mio Department of Mathematics, Florida State University, USA. https://orcid.org/0000-0003-1106-8089
Claudio José von Zuben Department of Biodiversity, Biosciences Institute, São Paulo State University (Unesp), Brazil. https://orcid.org/0000-0002-9622-3254
Alice Libardi Department of Mathematics, Institute of Geosciences and Exact Sciences, São Paulo State University (Unesp) https://orcid.org/0000-0002-7183-2635

Resumen

In this work we present an investigation of mass rearing, via topological data analysis and statistical methods. We study the optimization of mass production and develop methods for interpretation and visualization of contrasts in mass rearing results under different combinations of larval densities and availability of food resources. The approach considers receiver operating characteristic (ROC) curves to define distances between histograms It is used to compare outcomes under different conditions based on survival rates and properties of the distributions of pupal weight and size. The method is effective and practical for optimizing mass rearing and identification of cost-effective approaches.

Descargas

La descarga de datos todavía no está disponible.

Citas

H. Assemi, M. Rezapanah, R. Vafaei-Shoushtari, and A. Mehrvar. Modified artificial diet for rearing of tobacco budworm, Helicoverpa armigera, using the Taguchi method and Derringer’s desirability function. J. Insect Sci., 12(100), 2012.

R. C. Bautista, N. Mochizuki, J. P. Spencer, E. J. Harris, and D. M. Ichimura. Mass-rearing of the tephritid fruit fly parasitoid Fopius arisanus (Hymenoptera: Braconidae). Biol. Control, 15:137–144, 1999.

A. Bianconi, C. J. Von Zuben, A. B. S. Serapi˜ao, and J. S. Govone. Artificial neural networks: A novel approach to analyzing the nutritional ecology of a blowfly species, Chrysomya megacephala. Journal of Insect Science, 10, 2010.

C. Calı and M. Longobardi. Some mathematical properties of the ROC curve and their applications. Ricerche di Matematica, 64(2):391–402, 2015.

G. Carlsson. Topology and data. Bull. Amer. Math. Soc., 46:255–308, 2009.

S H. Cha and S. N. Srihari. On measuring the distance between histograms. Pattern Recognition, (35):1355–1370, 2002.

H Chen. Artificial diets used in mass production of the New World screwworm, Cochliomyia hominivorax. J. Appl. Entomol, 138:708–714, 2014.

A. C. Cohen. Formalizing insect rearing and artificial diet technology. Am. Entomol., 47(4):198–206, 2001.

E. S. Dalmaijer, C. L. Nord, and D. E. Astle. Statistical power for cluster analysis. BMC Bioinformatics, 23(1):205, 2022.

J. C. Fabricius. Entomologia systematica emendata et aucta, volume 4. Hafniae, 1794.

T. Fawcett. An introduction to ROC analysis. Pattern Recognition Letters, 27(8):861–874, 2006.

F. Firoozfar, H. Moosa-Kazemi, M. Baniardalani, M. Abolhassani, M. Khoobdel, and J. Rafinejd. Mass rearing of Lucilia sericata Meigen (Diptera: Calliphoridae). Asian Pac. J. Trop. Biomed., 1(1):54–56, 2011.

B. Greenberg. Flies as forensic indicators. J. Med. Entomol., 28:565–577, 1991.

J. D. Hunter. Matplotlib: A 2D graphics environment. Computing In Science & Engineering, 9(3):90–95, 2007.

E. Jones, T. Oliphant, P. Peterson, et al. SciPy: Open source scientific tools for Python, 2001–. [Online; accessed February 13, 2018].

E. F. Knipling. The basic principles of insect population suppression and management. USDA Agric. Handb., 1979.

S. L. Lapointe, T. J. Evens, and R. P. Niedz. Insect diets as mixtures: Optimization for a polyphagous weevili. J. of Insect Physiol., 54:1157–1167, 2008.

S. L. Lapointe, T. J. Evens, R. P. Niedz, and D. J. Hall. Artificial diet optimized to produce normative adults of Diaprepes abbreviatus (Coleoptera: Curculionidae). Environ. Entomol., 39(2):670–677, 2010a.

S. L. Lapointe, R. P. Niedz, and T. J. Evens. An artificial diet for Diaprepes abbreviatus (Coleoptera: Curculionidae) optimized for larval survival. Florida Entomol., 93(1):56–62, 2010b.

T. T. S. Leal, A. P. Prado, and A. J. Antunes. Rearing the larvae of the blowfly Chrysomya chloropyga (Wiedemann) (Diptera, Calliphoridae) on oligidic diets. Rev. Bras. Zool., 1(1):41–44, 1982.

A. R. Olsen. Regulatory action criteria for filth and other extraneous materials. III. review of flies and foodborne enteric disease. Regulatory Toxicology and Pharmacology, 28(3):199–211, December 1998.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.

S. van der Walt, S. Chris Colbert, and G. Varoquaux. The NumPy array: A structure for efficient numerical computation. Computing in Science & Engineering, 13:22–30, 2011.

C. J. Von Zuben, F. J. Von Zuben, and W. A. C. Godoy. Larval competition for patchy resources in Chrysomya megacephala (Dipt., Calliphoridae): implications of the spatial distribution of immatures. J. Appl. Entomol., 125(1):537–541, 2001.