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Dietary composition and fatty acid content of giant salmonflies (Pteronarcys californica) in two Rocky Mountain rivers

Cite this dataset

Briggs, Michelle et al. (2021). Dietary composition and fatty acid content of giant salmonflies (Pteronarcys californica) in two Rocky Mountain rivers [Dataset]. Dryad.


Many aquatic invertebrates are declining or facing extinction from stressors that compromise physiology, resource consumption, reproduction, and phenology. However, the influence of these common stressors specifically on consumer-resource interactions for aquatic invertebrate consumers is only beginning to be understood. We conducted a field study to investigate Pteronarcys californica (i.e., the ‘giant salmonfly’), a large-bodied insect that is ecologically and culturally significant to rivers throughout the western U.S. We sampled gut contents and polyunsaturated fatty acid (PUFA) composition of salmonflies to compare resource consumption across river (Madison or Gallatin, Montana), sex (male or female), and habitat (rock or woody debris). We found that allochthonous detritus comprised the majority of salmonfly diets in the Gallatin and Madison Rivers, making up 68% of the gut contents on average, followed by amorphous detritus, diatoms, and filamentous algae. Diets showed little variation across river, sex, or length. Minor differences in diets were detected by habitat type, with a higher proportion of diatoms in the diets of salmonflies collected from rocky habitat compared to woody debris. Fatty acid composition generally supported the results of gut content analysis but highlighted the importance of primary producers. The presence of eicosapentaenoic acid (EPA; 20:5n-3) and alpha linolenic acid (ALA; 18:3n-3) suggest consumption of diatoms and filamentous green algae, respectively. Our research underscores the importance of a healthy riparian zone that provides allochthonous detritus for invertebrate nutrition as well as the role of algae as an important source of fatty acids.


Study area

This study was conducted in the Madison and Gallatin Rivers, located in Southwest Montana. The Madison and Gallatin Rivers originate in Yellowstone National Park, Wyoming, and flow north for 295 and 193 river kilometers, respectively. Both rivers join the Jefferson River near Three Forks, Montana to form the Missouri River.

Study sites on the Madison River were located between Hebgen and Ennis Reservoirs, which were created by dams constructed in 1914 and 1906, respectively. In this area, the Madison River flows through a broad valley, with riparian vegetation dominated by willow and grasses. The Madison watershed (6,620 km2) is 39% woodlands and 48% grass and shrublands (Anderson et al. 2019). Near our study sties, mean July water temperature and discharge were 14.7 ºC and 1,130 cfs, respectively, in 2020 (USGS gage 06038800). Because the Gallatin River does not have a present-day USGS water temperature sensor, we evaluated historical temperature at the same location as the USGS gage from NorWeST (Isaak et al. 2017) to compare water temperature between rivers. NorWeST reported mean August water temperature of 16.4 ºC from 1993-2011.

Study sites on the Gallatin River were located between the towns of Big Sky and Gallatin Gateway. The Gallatin River in the study area flows through both valley and canyon topography. The Gallatin River is free flowing, with riparian vegetation dominated by willow and conifer forest. The Gallatin watershed (4,784 km2) is 70% woodlands and 18% grass and shrublands (Anderson et al. 2019). Mean July discharge was 975 cfs in 2020 (USGS gage 06043120). Because the Gallatin River does not have a present-day USGS water temperature sensor, we collected historical temperature from NorWeST (Isaak et al. 2017) at the same location as the USGS gage. NorWeST reported mean August water temperature of 10.7 ºC from 1993-2011.

Salmonfly collection

Salmonflies were collected from three sites each on the Madison and Gallatin Rivers on July 9 – 10, 2020. Sites were chosen to span the length of each river known to support relatively dense salmonfly populations and were spaced at minimum approximately 12.5 river kilometers apart. We collected salmonflies in the evening between the hours of 17:00 – 21:00 to increase the likelihood of sampling a full gut and to maintain a consistent sampling window across all sites. At each site, we collected three male and three female late instars from two habitat substrate types: rock surfaces and woody debris (n = 12 individuals per site; 36 per river). Individuals were collected directly from substrates using forceps. Salmonflies were immediately preserved in Kahle’s solution in individual vials and transported to the laboratory.

Gut content analysis

Gut contents were identified and analyzed in the laboratory following the methods described in Rosi-Marshall et al. (2016b). We measured salmonfly body length (mean = 34.86; s.e. = 0.46 mm) and dissected each individual to remove the gut tract. Gut contents from the upper portion of the gut tract were emptied into water, the gut wall was removed, and contents were placed in a beaker. We placed each beaker in a water bath sonicator for 30 seconds to disperse clumps. Gut contents were then filtered onto Metricel membrane filters (25-mm diameter, 0.45 µm pore size). Filters were dried at 60ºC for ten minutes and then mounted onto slides using standard immersion oil. We photographed particles at 100x to 400x magnification depending on the size of item and the resolution required to accurately identify the item type. We identified and measured the area of up to 150 particles per slide using NIS-Elements imaging software (Nikon, 2019) and estimated the proportion of area comprised of each diet item type as our response variable. Diet item types identified included wood and leaf debris combined, amorphous detritus, animal material, fungi, diatoms, and filamentous algae (Figure 2).

Fatty acid analysis

Late-instar larval salmonflies were collected from two sites on the Madison River, Palisades and Varney Bridge, on November 27, 2019. We collected three females and three males from each site for fatty acid composition analyses. Salmonflies were frozen, shipped overnight, and stored at -80ºC upon arrival at the processing facility. We freeze-dried and then weighed salmonflies prior to extracting fatty acid methyl esters (FAMEs) for composition analyses. Freeze-dried salmonflies were deposited in 7 mL dichloromethane:methanol (2:1, v:v), crushed using a glass rod, vigorously sonicated, and stored over night at −20°C. Total lipids were extracted three times from tissues with dichloromethane:methanol (2:1, v:v). Pooled cell-free lipid extracts were evaporated to dryness under N2-atmosphere and transesterified with methanolic HCl (3 mol L-1, 60 °C, 15 min, Sigma-Aldrich 33050-U). FAMEs were extracted three times with isohexane (2 mL). Pooled FAME-containing fractions were evaporated to dryness under N2 and resuspended in 50 µL of isohexane. FAMEs were analyzed by gas chromatography (GC) using a HP 6890 gas chromatograph (Agilent Technologies) equipped with a flame ionization detector (FID) and a DB-225 (J&W Scientific, 30 m × 0.25 mm inner diameter (id) × 0.25 μm film) capillary column. Configuration details are given elsewhere (Martin-Creuzburg et al., 2010; Martin-Creuzburg et al., 2017). FAMEs were quantified by comparison to internal standards (C17:0, C23:0 ME) of known concentrations (adapted to dry mass in the sample), using multipoint calibration curves generated using FAME standards (Sigma). FAMEs were identified by their retention times and their mass spectra, which were recorded with a quadrupole gas chromatograph-mass spectrometer (GC-MS; Agilent Technologies, 5975C inert MSD) equipped with a DB-225MS fused-silica capillary column (J&W Scientific, 30 m × 0.25 mm id × 0.25 μm film); gas chromatographic settings as for FID. Mass spectra were recorded between 50 and 600 m/z in the electron ionization (EI) mode. The limit of quantitation was 10 ng of fatty acid.

Data analysis

We used both multivariate and univariate approaches to analyze diet proportions for differences by sex, habitat, and river. We used non-metric multidimensional scaling (NMDS) based on Bray-Curtis dissimilarities to visualize how salmonfly gut contents differed by sex, habitat, and river. To test whether the multivariate position of diets differed by these variables, we used a mixed effects PERMANOVA including sex, habitat, and river as fixed effects and site as a random effect. Testing for differences in multivariate position provides information on the composition of the diet items. We used a PERMDISP to test whether multivariate dispersion of diets differed across the same three covariates. Testing for differences in multivariate dispersion, or spread, provides information about how variable the diet items were (Anderson 2006). Multivariate analysis was conducted using the vegan package (Oksanen et al. 2019).

We used a univariate approach to determine if the proportions either of wood and leaf material or of diatoms, two of the most abundant diet groups, differed by length, sex, habitat, and river. We constructed linear mixed effects models with either proportion of wood and leaf or proportion of diatoms as the response variable. We included site as a random effect and each combination of length, river, habitat, and sex as fixed effects. We chose not to include any interaction terms as we had no a priori reason to do so. We constructed a total of 16 models for each response variable, ranging from a model including all four fixed effects to a null model which included only the random effect. We used Akaike’s Information Criterion adjusted for small sample sizes (AICc) for model selection, and we selected the models with the lowest AICc scores for each response variable as our final models. Linear mixed effects models were fit using the lme4 package (Bates et al. 2015), and we used the MuMIn package for AICc model selection (Barton 2020). We used two-sided Student’s t tests to determine if the percent composition of fatty acids differed by site. All statistical analysis was completed in R version 4.0.4 (R Core Team 2019).


Montana State University

National Park Service

Northwestern Energy

Northwestern Energy