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Diet overlap among non-native trout species and native Cutthroat Trout (Oncorhynchus clarkii) in two U.S. ecoregions

Citation

Minder, Mario et al. (2022), Diet overlap among non-native trout species and native Cutthroat Trout (Oncorhynchus clarkii) in two U.S. ecoregions, Dryad, Dataset, https://doi.org/10.5061/dryad.8931zcrpz

Abstract

The invasion of freshwater ecosystems by non-native species can constitute a significant threat to native species and ecosystem health. Non-native trouts have long been stocked in areas where native trouts occur and have negatively impacted native trouts through predation, competition, and hybridization. This study encompassed two seasons of sampling efforts across two ecoregions of the western United States: The Great Basin in summer 2016 and the Yellowstone River Basin in summer 2017. We found significant dietary overlaps among native and non-native trouts within the Great Basin and Yellowstone River Basin ecoregions. Three orders of invertebrates (Ephemeroptera, Trichoptera and Diptera) composed the majority of stomach contents and were responsible for driving the observed patterns. Great Basin trout had higher body conditions (k) and non-native Great Basin trout had higher gut fullness values than Yellowstone River Basin trout, indicating a possible limitation of food in the Yellowstone River Basin. Native fishes were the least abundant and had the lowest body condition in each ecoregion. These findings may indicate a negative impact on native trouts by non-native trouts. We recommend additional monitoring of native and non-native trout diets, regular invertebrate surveys to identify the availability of diet items, and reconsidering stocking efforts that can result in overlap of non-native fishes with native cutthroat trout.

Methods

We sampled 23 sites in three rivers of the Great Basin: the Bear, Carson, and Humboldt Rivers, and 20 sites in three rivers of the Yellowstone River Basin: the Bighorn, Powder, and Tongue Rivers (Figure 1). Sites were selected as part of a larger macrosystems project (MACRO macrorivers.ku.edu) using the GIS-based tool RESonate to characterizes river segments using valley-scale hydrogeomorphic variables (Williams et al., 2013). Sites were chosen to maximize variability in hydrogeomorphology and ensure that sites accurately represent the broad geographic ecoregions that we sampled. Details for Great Basin site characteristics and designations can be found in Maasri et al. (2019).

Diet Collections

Fish collections were performed during July and August 2016 in the Great Basin and July 2017 in the Yellowstone River Basin. At each site, fishes were collected from reaches measuring 20 times the average wetted width of the stream (Patton, Hubert, Rahel, & Gerow, 2000). Fishes were collected with one-pass backpack electrofishing supplemented with hook and line and seining, following American Fisheries Society standard collection protocols and local states collecting regulations (Bonar, Hubert, & Willis, 2009). All fishes collected were identified to species, weighed (g), and measured for standard length (mm). When available, ten fish from each species at each site were randomly selected and sacrificed for gut content analysis (Ball State University IACUC #126193). Stomachs were removed for preservation in 10 % formalin. For all fish, only the stomach was examined to minimize bias caused by digestibility of diet items (Sutela & Huusko, 2000). A quantitative survey (abundance per m2) of benthic invertebrates that was conducted at all study sites at the time of fish collection (Unpublished dataset, Erdenee 2016) was used to determine the proportional environmental abundance of each diet item for selectivity analyses.

Diet Analysis

Gut content analysis was based on methods used in (Minder, Arsenault, Erdenee, & Pyron, 2020). Guts (esophagus to pyloric valve) were evacuated of all contents, weighed, and contents were examined under a dissecting scope. All invertebrates were identified to family using Merritt and Cummins (1996). We grouped invertebrates to order, and orders that represented < 1% of the total number of diet items were grouped into a single ‘other’ category. To reduce bias caused by moisture trapped in samples, contents of each gut were dried at 50° for 48 hours and weighed after identification to determine gut fullness (FI) (Parker, 1963). FI was only calculated for fish that had non-empty guts.

FI was measured using the dry weight of the stomach content approach:

FI =FWdWwx 100

Where FI is the percentage of total weight contributed by the gut contents, FWd is the dry weight of the gut contents and Ww is the wet weight of the fish (Schleuter, 2007).

Body condition of fish was calculated using Fulton’s Condition Factor (K)(Nash, Valencia, & Geffen, 2006)

K=100WL3

Where K is Fulton’s condition factor, L is the length of the fish in centimeters and W is the wet weight in grams. For salmonids, K > 1.4 is considered good condition and K < 1 is considered poor condition (Barnham & Baxter, 2003).

Calculations of frequency of occurrence (FO), and mean prey abundance (Ni) were used to quantify diets of individual fishes. FO was calculated as:

FO=FiP× 100

 where FO is the occurrence of a prey item Fi divided by the number of non-empty guts (P). The metric FO describes the percentage of individuals that have consumed a specific food item. While this metric does not provide details on amounts of items consumed, it is robust to limitations of other diet analysis challenges such as differences in prey condition and presence of unidentifiable tissues. (Baker, Buckland, & Sheaves, 2014; Buckland, Baker, Loneragan, & Sheaves, 2017).

Mean prey abundance (Ni) was used to compare feeding behavior and diet composition among fishes (Macdonald & Green, 1983). Niwas calculated as:

Ni=1P×ΣNijΣNij

Where Ni is the mean number of prey i consumed, Nij is the number of prey i in a single predator j, and ΣNij is the sum of all the prey in a single predator gut j.

Dietary behavior was quantified with the Chesson’s α electivity index (Chesson, 1978):

α=(ri/pi)ri/pi

where ri is the proportion of the diet item consumed by an individual fish, pi is the proportional environmental abundance of the diet item at the capture site, and n is the number of prey item categories. If α = 1/n, the item in the diet is equal to its proportion in the environment, and we can assume that the item has been randomly selected. If α > 1/n, then the diet item has been positively selected for, and if α < 1/n, then that diet item has been avoided. Environmental abundances for diet items were calculated for each sample site and then averaged for each fish species to ensure that site-specific selectivity was maintained.

Finally, we calculated the degree of diet overlap to assess diet similarities among fish species at a site using numerical gut content abundances. Mean proportional abundances were compared among species using Schoener’s similarity index:

C=1-12ΣPx,i-Py,i

Where C is the Schoener’s similarity index metric, and Px,i and Py,i are the proportions of diet item i in the gut of species x and y, respectively (Schoener, 1970). This index ranges from 0 to 1 with values of 0 indicating no diet overlap and values of 1 indicating a complete overlap of diet items. Schoener’s index values higher than 0.6 or lower than 0.4 are generally considered ecologically relevant (Childs, Clarkson, & Robinson, 1998; Muth & Snyder, 1995; Wallace Jr, 1981).

Statistical Analysis

We used one-way ANOVA and Tukey’s post-hoc tests to compare mean variation for gut fullness among fishes. Statistics were calculated using R version 3.4.3 (R Core Team, 2017). We used non-metric multidimensional scaling (NMDS) with Bray-Curtis distance to examine relationships among fish diet contents by species. NMDS generates an ordination based on a specified number of dimensions and attempts to meet the conditions of a rank similarity matrix (Clarke, 1993). NMDS also produces stress values to quantify the effectiveness of an ordination for pattern analysis, with values below 0.2 considered to be compliant (Clarke, 1993). This method uses ranked distances and is therefore useful for data that fail to meet the assumptions of normality (Clarke & Warwick, 2001; McCune, Grace, & Urban, 2002). Pearson’s correlations were conducted using NMDS scores from fish diets and the abundance of invertebrate orders and these coefficients were plotted to show the degree of association between fish species and diet items (West et al., 2003).

We used ANOSIM to test the null hypothesis that there is no difference in fish gut contents among the assemblage of species (Clarke, 1993). To ensure diets from fish collected by angling did not differ from other methods, we also tested for differences within species by each sampling method. ANOSIM produces a test statistic (R) that quantifies the observed differences between test variables. R is expressed as a number between 1 and -1, which can be interpreted as maximum dissimilarity between groups and maximum similarity between groups, respectively (Clarke, 1993). An R value of 1 indicates complete dissimilarity between two groups, an R value of 0 is interpreted as complete similarity among groups, and a negative R value suggests that there is more similarity between groups than within groups (Clarke, 1993).

Funding

National Science Foundation, Award: 1442595

German Federal Ministry of Education and Research, Award: 01LN1320A