Using nutritional geometry to define the fundamental macronutrient niche of the widespread invasive ant Monomorium pharaonis

Abstract

The emerging field of nutritional geometry (NG) provides powerful new approaches to test whether and how organisms prioritize specific nutritional blends when consuming chemically complex foods. NG approaches can thus help move beyond food-level estimates of diet breadth to predict invasive success, for instance by revealing narrow nutritional niches if broad diets are actually composed of nutritionally similar foods. We used two NG paradigms to provide different, but complementary insights into nutrient regulation strategies and test a hypothesis of extreme nutritional generalism in colony propagules of the globally distributed invasive ant Monomorium pharaonis . First, in two dimensions (protein:carbohydrates; P:C), M . pharaonis colonies consistently defended a slightly carbohydrate-biased intake target, while using a generalist equal-distance strategy of collectively overharvesting both protein and carbohydrates to reach this target when confined to imbalanced P:C diets. Second, a recently developed right-angled mixture triangle method enabled us to define the fundamental niche breadth in three dimensions (protein:carbohydrates:lipid, P:C:L). We found that colonies navigated the P:C:L landscape, in part, to mediate a tradeoff between worker survival (maximized on high-carbohydrate diets) and brood production (maximized on high-protein diets). Colonies further appeared unable to avoid this tradeoff by consuming extra lipids when the other nutrients were limiting. Colonies also did not rely on nutrient regulation inside their nests, as they did not hoard or scatter fractions of harvested diets to adjust the nutritional blends they consumed. These complementary NG approaches highlight that even the most successful invasive species with broad fundamental macronutrient niches must navigate complex multidimensional nutritional landscapes to acquire limiting macronutrients and overcome developmental constraints as small propagules.

Using Nutritional Geometry to Explore How Social Insects Navigate Nutritional Landscapes

“…NG now extends beyond studies of how and why foraging organisms prioritize multiple competing nutritional requirements [10] towards an integration with eco-evolutionary principles linking fundamental (FNN) and realized (RNN) nutritional niches to answer broad theoretical and applied ecological questions [22]. Researchers can first perform laboratory experiments defining an insect's FNN by confining them to a broad range of nutritionally defined diets [23,24], and then visualize how performance (e.g., growth, survival, egg-laying rate) varies across this landscape ( Figure 3A,B). The researcher can then perform field studies defining the insect's RNN by measuring the nutritional composition of resources they actually harvest when foraging in nature ( Figure 3C).…”

“…The researcher can then perform field studies defining the insect's RNN by measuring the nutritional composition of resources they actually harvest when foraging in nature ( Figure 3C). By overlaying RNNs upon FNNs, the researcher can test whether and how insects nutritionally target specific FNN dimensions, and the extent to which they face nutritional tradeoffs between different measures of performance [24]. Moreover, while the framework we have discussed so far builds upon diets varying in two dimensions (e.g., protein, carbohydrates), recent NG approaches have been developed to map food mixtures in three dimensions (e.g., protein, carbohydrates, lipids) across right-mixture-triangle landscapes [24,25].…”

“…By overlaying RNNs upon FNNs, the researcher can test whether and how insects nutritionally target specific FNN dimensions, and the extent to which they face nutritional tradeoffs between different measures of performance [24]. Moreover, while the framework we have discussed so far builds upon diets varying in two dimensions (e.g., protein, carbohydrates), recent NG approaches have been developed to map food mixtures in three dimensions (e.g., protein, carbohydrates, lipids) across right-mixture-triangle landscapes [24,25]. [26]) allows one to visualize the FNN of an insect under controlled conditions when confined to a range of nutritionally defined diets and under a range of social conditions.…”

“…While most ant species scavenge diverse foods, NG has enabled researchers to rigorously explore the nutritional foraging rules underlying this food-level omnivory. Specifically, all ant species tested to date collectively harvest specific ITs in diet-choice experiments, and no-choice experiments have shown diverse nutritional strategies ranging from prioritizing strict regulation of either carbohydrates [27] or protein [14], to letting both nutrients fluctuate freely around the IT [24]. NG has also showed that a colony's capacity for IT regulation can depend on the presence of developing brood [27], and that protein-biased diets can decrease both brood production and worker survival [27].…”

Bioenergy landscapes drive trophic shifts in generalist ants

“…Other ants are obligate predators, and many generalist species lie somewhere in between, opportunistically hunting, scavenging and feeding on honeydew (Clay et al., 2017; Gibb et al., 2015; Hölldobler & Wilson, 1990). Generalist ants—for whom fitness is often maximized on a mixed diet of honeydew and insect prey—maintain an optimal nutritional balance by adjusting their foraging preferences or overall food intake (Csata & Dussutour, 2019; Kaspari et al., 2020; Kay, 2004; Krabbe et al., 2019; Stahlschmidt & Johnson, 2018; Wilder et al., 2011). Trophic position in ants can thereby vary within and among species (Hanisch et al., 2019; Pfeiffer et al., 2014; Potapov et al., 2019; Roeder & Kaspari, 2017) and trophic variability helps modulate responses to the environment.…”