Skip to main content
Dryad logo

Physiological status is a stronger predictor of nutrient selection than ambient plant nutrient content for a wild herbivore

Citation

Le Gall, Marion (2022), Physiological status is a stronger predictor of nutrient selection than ambient plant nutrient content for a wild herbivore, Dryad, Dataset, https://doi.org/10.5061/dryad.5dv41ns3w

Abstract

There is generally a close relationship between a consumer's food and its optimal nutrients. When there is a mismatch, it is hypothesized that mobile herbivores switch between food items to balance nutrients, however, there are limited data for field populations. In this study, we measured ambient plant nutrient content at two time points and contrasted our results with the nutrient ratio selected by wild female and male grasshoppers (Oedaleus senegalensis). Few plants were near O. senegalensis’ optimal protein:carbohydrate ratio (P:C), nor were plants complementary. Grasshoppers collected earlier all regulated for a carbohydrate-biased ratio but females ate slightly more protein. We hypothesized that the long migration undertaken by this species may explain its carbohydrate needs. In contrast to most laboratory studies, grasshoppers collected later did not tightly regulate their P:C. These results suggest that field populations are not shifting their P:C to match seasonal plant nutrient shifts and that mobile herbivores rely on post-ingestive mechanisms in the face of environmental variation. Because this is among the first studies to examine the relationship between ambient nutrient landscape and physiological state our data are a key step in bridging knowledge acquired from lab studies to hypotheses regarding the role ecological factors play in foraging strategies.

Methods

2. Material and methods

2.1. O. senegalensis nutritional preferences and physiological variables over time

2.1.1. The Senegalese grasshopper

Oedaleus senegalensis is a grass-feeder and a major pest of millet and other cereal crops of subsistence agriculture in the Sahel zone of West Africa. The field station where we ran the experiment belongs to the Direction de la Protection des Vegetaux (DPV), 13°49′23.8″N 15°24′19.4″W. It is located in the village of Nganda, in the West Central Agricultural Region of Senegal where most millet and groundnut production takes place.

We estimated grasshopper abundance in various agricultural fields (millet, groundnut, and fallow fields) with sweep net surveys within 20 m of each plot where vegetation samples were taken. The same researcher conducted all surveys by evenly sweeping 20 times, each a 180° arc approximately one m apart along a straight line.

2.1.2. Nutrient selection, mass gain, and body lipid content

We collected 5th instar grasshoppers at two time points in August 2017 around the DPV field station. We initiated the experiments on 04-Aug-2017 (early) and 17-Aug-2017, 2017 (late). We weighed and put grasshoppers in individual aerated plastic containers (15 × 10 cm). Each cage contained a water tube, a perch for roosting, and two dishes containing artificial diet. We prepared and dried the food in our laboratory at Arizona State University (United States) following the method developed by Simpson and Abisgold (1985). In total, we made three diets varying in protein to carbohydrate ratios but otherwise isocaloric: p35:c7, p28:c14, and p7:c35. These diets were chosen because they span extreme protein:carbohydrate ratios, therefore ensuring that the IT is not outside of the range offered to the grasshoppers. For each diet, “p” stands for percent of protein in the diet, and “c” stands for percent of carbohydrates in the diet. The protein component of all foods was a 3:1:1 mix of casein, peptone, and albumen, while the digestible carbohydrate (henceforth carbohydrates) component was a 1:1 mix of sucrose and dextrin. All foods contained similar amounts of Wesson's salt (2.4%), cholesterol (0.5%), linoleic acid (0.5%), ascorbic acid (0.3%) and vitamin mix (0.2%) (Dadd, 1961). The remainder of the diet was cellulose, a non-nutritive bulking agent.

We gave the grasshoppers one of two treatments of pre-weighed diet pairings: p7:c35 & p35:c7 or p7:c35 & p28:c14 and used 20–25 grasshoppers per treatment (approximately half males and half females). After three days, the diets were removed and dried for 24–36 h at 60°C and then re-weighed to record consumption at the nearest 0.1 mg. Grasshoppers were weighed at the beginning and the end of the experiment to calculate mass gain. We extracted lipids from each individual grasshopper by using a series of three 24 h chloroform washes on the dried carcasses. Lipid content was calculated by subtracting post-chloroform wash dry mass from pre-chloroform wash dry mass.

2.2. Plant survey and chemical analysis

2.2.1. Plant survey

Plant surveys and collection took place on 5-Aug-2017 (early) and on 17-Aug-2017 (late). We randomly selected 5 quadrats of 10 m by 10 m within a fallow and millet field. We selected fallow and millet fields because these are where grasshoppers have been shown to be the most abundant (Le Gall et al., 2020bToure et al., 2013Word et al., 2019). For each quadrat, we recorded the three most abundant plant species and presented the data for grasses (O. senegalensis is a grass-feeder). For each plant species, we collected enough plant material to obtain 20 mg of dry sample (different specimens of the same species were pooled together) (Fig. ESM2). We chose the most abundant species because grass diversity and overall plant biomass were low (Fig ESM2) but grasshopper density high (Fig. ESM1), therefore the most abundant grass were likely to be representative of the plants consumed by the grasshoppers.

2.2.2. Chemical analysis

Upon collection, we dried the plants in an oven at 60°C for 48 h and brought back the samples to our laboratory at Arizona State University (United States). We ground plant samples for 30 s at 200 rpm using a Retsch MM 400 ball mill. We measured plant protein content with a Bradford assay and the non-structural carbohydrate content using the phenol-sulfuric acid method (Deans et al., 2018).

2.3. Contrasting nutrient regulation with nutrient availability

For animals to meet their optimal nutrient intake without relying on post-ingestive mechanisms there are two possibilities: 1) plant protein:carbohydrate ratio closely resembles the optimal protein:carbohydrate ratio (aka Intake Target, thereafter IT) measured in the choice experiment or 2) unbalanced but complementary foods are available so that by mixing among plants to achieve an optimal ratio. To test the first hypothesis, we plotted the self-selected protein:carbohydrate ratios from the choice experiment as nutritional rails (y = mx, m=Intake Target) and calculated the Euclidian distance separating plant nutritional content from that optimal rail in the nutrient space for the two time points. The longer the Euclidian Distance, the further an animal is from the optimal P:C ratio.

Euclidian distance = |mxplantprotein - yplantcarbohydrates|/√(1 + m2) where (xplantprotein, ycarbohydrates) are the coordinates of each plant in the nutrient landscape.

To test the second hypothesis, we compared the median protein:carbohydrate ratio of plants (50% of plants fall on either side of this number) with the self-selected protein:carbohydrate ratio measured in the choice experiment. If the median plant protein:carbohydrate ratio and the self-selected number of plants are different, it means there are more plants on one side of the nutrient space relative to the IT, making diet mixing for an optimal balance challenging.

Methodological consideration: all the plants presented are acceptable host plants for O. senegalensis. We acknowledge that we did not account for non-nutritional factors (e.g., plant structure or plant defenses) that could skew host plant selection. However, measuring and ranking these non-nutritional factors was not feasible in a single study, so for the purpose of this study, we only compare variation in total plant nutrients.

2.4. Statistical analysis

2.4.1. Grasshopper density in the field

Grasshopper density in fallow, millet, and groundnut fields at the two time points was compared using ANOVA.

2.4.2. Choice experiment

The amount of protein and carbohydrate eaten for each treatment was compared using MANCOVA techniques with start mass as a covariate to correct for size differences among individuals. We used a Pillai's test statistic. In all grasshopper-related analyses, we included sex as an independent variable. Protein, carbohydrate, and total food intakes were compared using ANCOVA techniques. Grasshopper wet start masses were compared using ANOVA techniques. Mass gain and lipid content were analyzed with ANCOVA techniques. We reported the effects of protein and carbohydrate intake on mass gain and lipid content. To account for size differences protein and carbohydrate intakes were plotted as mass corrected ratios. Temperatures were analyzed using an ANOVA.

2.4.3. Plant nutrient analyses and relationship to the ITs

Plant P:C ratio was analyzed with a MANOVA and plant protein and carbohydrate content with ANOVAs. Finally, we compared the Euclidian distances of plant p:c to the ITs using ANOVA techniques; median plant p:c ratio and median ITs were compared using a median test score.