Nutritional physiology of life history trade-offs: how food protein-carbohydrate content influences life-history traits in the wing-polymorphic cricketGryllus firmus

Abstract

Although life-history trade-offs result from the differential acquisition and allocation of nutritional resources to competing physiological functions, many aspects of this topic remain poorly understood. Wingpolymorphic insects, which possess alternative morphs that trade off allocation to flight capability versus early reproduction, provide a good model system for exploring this topic. In this study, we used the wingpolymorphic cricket Gryllus firmus to test how expression of the flight capability versus reproduction trade-off was modified across a heterogeneous protein-carbohydrate nutritional landscape. Newly molted adult female long-and short-winged crickets were given one of 13 diets with different concentrations and ratios of protein and digestible carbohydrate; for each cricket, we measured consumption patterns, growth and allocation to reproduction (ovary mass) versus flight muscle maintenance (flight muscle mass and somatic lipid stores). Feeding responses in both morphs were influenced more by total macronutrient concentration than by protein-carbohydrate ratio, except at high-macronutrient concentration, where proteincarbohydrate balance was important. Mass gain tended to be greatest on protein-biased diets for both morphs, but was consistently lower across all diets for long-winged females. When long-winged females were fed high-carbohydrate foods, they accumulated greater somatic lipid stores; on high-protein foods, they accumulated greater somatic protein stores. Food protein-carbohydrate content also affected shortwinged females (selected for early reproductive onset), which showed dramatic increases in ovary size, including ovarian stores of lipid and protein, on protein-biased foods. This is the first study to show how the concentration and ratio of dietary protein and carbohydrate affects consumption and allocation to key physiological features associated with the reproduction-dispersal life-history trade-off.

Experimental Manipulation Shows a Greater Influence of Population than Dietary Perturbation on the Microbiome of Tyrophagus putrescentiae

“…In insects, different nutritive factors, such as the proteincarbohydrate balance, influence the animal's life history, including reproduction (30,31). Perturbation can lead to dysbiosis, i.e., a disruption of the proportions of gut core bacteria, and homeostasis, i.e., changes in the host physiology (32).…”

Metabolic rate is canalized in the face of variable life history and nutritional environment

“…The cricket morphs differ in features that should influence metabolic rates, including feeding strategy (Clark et al . ; Clark, Zera & Behmer ) and numerous aspects of physiology and intermediary metabolism. For example, flight muscles of LW(f) individuals exhibit 10 times greater resting metabolic rate than the underdeveloped flight muscles of the SW morph or ovaries of that morph (Zera, Sall & Grudzinski ).…”

“…We expected that diet quality and morph‐specific physiological differences would influence whole‐animal standard metabolic rates, mass gain and body composition. Based on prior findings, we also predicted that crickets would practice partial compensatory feeding when eating imbalanced diets or diets with low total macronutrient content (Clark, Zera & Behmer ). We discuss our results in the context of the evolution and physiological ecology of metabolic rates.…”

How to get the most bang for your buck: the evolution and physiology of nutrition-dependent resource allocation strategies

“…protein, carbohydrate and lipid content), as well as a wider range of the timing of resource level changes across an organism's lifetime [32][33][34]. These efforts provide a much more complete picture of how an organism responds to diet, in that they distinguish between allocation changes owing to limitation in specific nutrient classes versus effects owing to more general caloric restriction [35,36]. However, this approach increases complexity, which can make interpreting the results in an evolutionary context a challenge when patterns are highly dynamic.…”