Characterizing temporal variability in streams supports nutrient indicator development using diatom and bacterial DNA metabarcoding

Abstract

Interest in developing periphytic diatom and bacterial indicators of nutrient effects continues to grow in support of the assessment and management of stream ecosystems and their watersheds. However, temporal variability could confound relationships between indicators and nutrients, subsequently affecting assessment outcomes. To document how temporal variability affects measures of diatom and bacterial assemblages obtained from DNA metabarcoding, we conducted weekly periphyton and nutrient sampling from July to October 2016 in 25 streams in a 1293 km² mixed land use watershed. Measures of both diatom and bacterial assemblages were strongly associated with the percent agriculture in upstream watersheds and total phosphorus (TP) and total nitrogen (TN) concentrations. Temporal variability in TP and TN concentrations increased with greater amounts of agriculture in watersheds, but overall diatom and bacterial assemblage variability within sites-measured as mean distance among samples to corresponding site centroids in ordination space-remained consistent. This consistency was due in part to offsets between decreasing variability in relative abundances of taxa typical of low nutrient conditions and increasing variability in those typical of high nutrient conditions as mean concentrations of TP and TN increased within sites. Weekly low and high nutrient diatom and bacterial metrics were more strongly correlated with site mean nutrient concentrations over the sampling period than with same day measurements and more strongly correlated with TP than with TN. Correlations with TP concentrations were consistently strong throughout the study except briefly following two major precipitation events. Following these events, biotic relationships with TP reestablished within one to three weeks. Collectively, these results can strengthen interpretations of survey results and inform monitoring strategies and decision making. These findings have direct applications for improving the use of diatoms and bacteria, and the use of DNA metabarcoding, in monitoring programs and stream site assessments.

Keywords: Agriculture; Algae; Bioassessment; Metrics; Nitrogen; Phosphorus.

Figure 1.

Nonmetric multidimensional scaling (NMDS) ordination for diatoms with site colors indicating gradients of low (cyan) to high (pink) watershed percent agriculture (a). NMDS for bacteria with site colors indicating gradients of low to high watershed percent agriculture (b).

Figure 2.

TP and TN variability plotted against watershed % agriculture (a, b) and within site variability plotted as site-specific z-scores by date (c, d). Regression lines are based on site means and are intended for descriptive visualizations of relationships between watershed % agriculture and (a) TP (y = 43.7739 × e(0.0272x), R² = 0.76, p < 0.01) and (b) TN (y = 505.4212 + 5.1405x, R² = 0.32, p < 0.01). Black circles = site means ± 1 SD for all dates and black circles = date means ± 10^th and 90^th percentiles for all sites. Gray circles = data from all dates at each site or from all sites on each date. Z-scores were calculated for each site individually. TP n = 281, TN n = 280.

Figure 3.

Site variability in metrics (standard deviations for each site using data from all weeks) and distance to centroids plotted against site mean TP and TN concentrations for diatoms (a, b) and for bacteria (c, d). HP = high phosphorus taxa, LP = low phosphorus taxa, HN = high nitrogen taxa, LN = low nitrogen taxa. Regression lines are based on site means and are intended for descriptive visualizations of relationships: LP diatoms y = 0.5157 − 0.0762 × ln(x), R² = 0.71, p < 0.001; HP diatoms y = 0.478 + 0.0194 × ln(x), R² = 0.12, p = 0.091; LN diatoms y = 0.9830 − 0.1279 × ln(x), R² = 0.42, p < 0.001; HN diatoms y = −0.3246 + 0.0744 × ln(x), R² = 0.28, p = 0.006; LP bacteria y = 0.2521 − 0.0319 × ln(x), R² = 0.64, p < 0.001; HP bacteria y = −0.0202 + 0.0177 × ln(x), R² = 0.23, p = 0.016; LN bacteria y = 0.4789 − 0.0585 × ln(x), R² = 0.42, p < 0.001; HN bacteria y = −0.269 + 0.0482 × ln(x), R² = 0.27, p = 0.008.

Figure 4.

Scatter plots for diatoms (a) and bacteria (b) comparing correlations between metrics and same week nutrient concentrations and correlations between metrics and site mean nutrient concentrations for each sampling week (linear regression R² values for each week). Mean R² values for each metric are shown as large symbols. HP = high phosphorus taxa, LP = low phosphorus taxa, HN = high nitrogen taxa, LN = low nitrogen taxa.

Figure 5.

Weekly R² values from diatom (a) and bacteria (b) regressions between metrics and site means of TP and TN concentrations plotted versus dates with daily precipitation overlaid. HP = high phosphorus taxa, LP = low phosphorus taxa, HN = high nitrogen taxa, LN = low nitrogen taxa.