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1 April 2010 Artificial Diet Optimized to Produce Normative Adults of Diaprepes abbreviatus (Coleoptera: Curculionidae)
Stephen L. Lapointe, Terence J. Evens, Randall P. Niedz, David G. Hall
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Abstract

The root weevil Diaprepes abbreviatus L. has been continuously reared since 1992 on an artificial diet (F1675) first reported in 1982. The weight of weevils reared on F1675 was more than twice as great as field-collected individuals. Recently, several ingredients included in the original F1675 diet were shown to have little or no effect on insect performance. We examined the effects of varying three ingredients of a simplified diet on the weight of adult D. abbreviatus. We used a geometric design combined with response surface models to evaluate the effects of the amount and proportion of diet ingredients to identify optimal diet recipes for normative weight gain defined in relation to field-collected D. abbreviatus from locations in Florida. Our results showed that it is possible to produce adults of normative weight or, for that matter, any mean weight within a wide range by varying the proportions of cottonseed meal, wheat germ and cellulose, a non-nutritive filler. Although wheat germ contributed to greater weight gain, survival of larvae to adult was lower on diets containing only wheat germ compared with diets containing only cottonseed meal as the principal nutritive components. The analyses of all the variables measured indicate that cottonseed meal is the only major nutritive component, in addition to standard vitamin and salt mixes, required to produce adult D. abbreviatus of normative weight.

Stephen L. Lapointe, Terence J. Evens, Randall P. Niedz, and David G. Hall "Artificial Diet Optimized to Produce Normative Adults of Diaprepes abbreviatus (Coleoptera: Curculionidae)," Environmental Entomology 39(2), 670-677, (1 April 2010). https://doi.org/10.1603/EN09304
Received: 15 October 2009; Accepted: 1 December 2009; Published: 1 April 2010
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KEYWORDS
Diaprepes abbreviatus
diet formulation
mixture optimization
response surface model
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