Dynamic Stream Network Intermittence Explains Emergent Dissolved Organic Carbon Chemostasis in Headwaters

Received: 20 February 2019 Accepted: 3 April 2019 DOI: 10.1002/hyp.13455

SCIENTIFIC BRIEFING

Dynamic stream network intermittence explains emergent dissolved organic carbon chemostasis in headwaters

Rebecca L. Hale1 | Sarah E. Godsey2

1 Department of Biological Sciences, Idaho State University, Pocatello, Idaho Abstract 2 Department of Geosciences, Idaho State Dissolved organic carbon (DOC) concentrations vary among headwaters, with varia- University, Pocatello, Idaho tion typically decreasing with watershed area. We hypothesized that streamflow

Correspondence intermittence could be an important source of variation in DOC concentrations Rebecca L. Hale, Department of Biological across a small watershed, through (a) temporal legacies of drying on organic matter Sciences, Idaho State University, Pocatello, ID 83209‐8007. accumulation and biotic communities and (b) spatial patterns of connectivity with Email: [email protected] DOC sources. To test these hypotheses, we conducted three synoptic water chemis- 2 Funding information try sampling campaigns across a 25.5‐km watershed in south‐eastern Idaho during National Science Foundation, Grant/Award early spring, late summer, and late fall. Using changepoint analysis, we found that Number: 1653998; Office of Experimental Program to Stimulate Competitive Research, DOC variability collapsed at a consistent location (watershed areas ~1.3 to National Science Foundation, Grant/Award ~1.8 km2) across seasons, which coincided with the watershed area where variability Number: IIA 1301792 in streamflow intermittence collapsed (~1.5 km2). To test hypothesized mechanisms through which intermittence may affect DOC, we developed temporal, spatial, and spatio‐temporal metrics of streamflow intermittence and related these to DOC concentrations. Streamflow intermittence was a strong predictor of DOC across seasons, but different metrics predicted DOC depending on season. Seasonal changes in the effects of intermittence on DOC reflected seasonal changes from instream to flowpath controls. A metric that captured spatial connectivity to sources significantly predicted DOC during high flows, when DOC is typically controlled by transport. In contrast, a reach‐scale temporal metric of intermittence predicted DOC during the late growing season, when DOC is typically controlled by instream processes and when legacy effects of drying (e.g., diminished biological communities) would likely affect DOC. The effects of intermittence on DOC extend beyond temporal legacies at a point. Our results suggest that legacy effects of intermittence do not propagate downstream in this system. Instead, snapshots of spatial patterns of intermittence upstream of a reach are critical for understanding spatial patterns of DOC through connectivity to DOC sources, and these processes drive patterns of DOC even in perennial reaches.

KEYWORDS

watershed, chemostasis, dissolved organic carbon, intermittence, legacies, landscape, network, stream drying, headwater catchment

1 | INTRODUCTION are connected within networks, and it is not just the drying and wetting that occurs at a point, but the network context of a reach Variability in dissolved organic carbon (DOC) concentrations usually that matters for biogeochemical processes. Although both classic dampens in larger streams (Creed et al., 2015; Temnerud & Bishop, (e.g., Vannote et al., 1980) and recent research (e.g., Bertuzzo, Helton, 2005; Zimmer, Bailey, McGuire, & Bullen, 2013). This emergent Hall Robert, & Battin, 2017) on carbon in streams highlight the DOC chemostasis is typically attributed to higher variation in importance of the upstream network in controlling reach‐scale terrestrial‐aquatic connectivity in headwaters (Creed et al., 2015; patterns of DOC, these ideas have not been incorporated into work Hotchkiss et al., 2015) and increased instream processing with scale on intermittence. (Creed et al., 2015; Vannote, Minshall, Cummins, Sedell, & Cushing, There is therefore a need to conceptually link our understanding 1980). One potential, but unassessed, source of variation in DOC and predictions about the effects of intermittence with our broader across headwaters is variation in streamflow intermittence. Although understanding of C cycling in stream systems. Both instream and streamflow intermittence can occur throughout stream networks watershed‐scale processes control DOC concentrations and fluxes, (González‐Ferreras & Barquín, 2017), headwater streams are more and the relative importance of these processes can vary seasonally likely to experience periodic drying (Fritz et al., 2013; González‐ and across flow conditions (Mulholland & Hill, 1997; Raymond, Ferreras & Barquín, 2017; Jaeger et al., 2019). Streamflow intermit- Saiers, & Sobczak, 2016). As a result, we might expect intermittence tence varies across headwaters (e.g., Godsey & Kirchner, 2014; to affect DOC at network scales by controlling connectivity to Jensen, McGuire, & Prince, 2017) and reach‐scale patterns of stream upstream and upland DOC sources, as well as through legacy effects drying have been linked to variation in carbon cycling and transport of wetting and drying on those sources (Figure 1). Models with a (e.g., Acuña, Giorgi, Muñoz, Sabater, & Sabater, 2007; Shumilova spatial component (i.e., either spatial or spatio‐temporal metrics) do et al., 2019). DOC sources and transformation rates vary within inter- not replace but instead complement temporal dimensions of intermittent reaches through wetting and drying (Acuña et al., 2005, mittence, which have been well studied. At a point, there is strong Acuña et al., 2007; Datry, Corti, Claret, & Philippe, 2011; Dieter evidence that temporal legacies of intermittence strongly affect C et al., 2011; Abril, Muñoz, & Menéndez, 2016; Harjung, Sabater, & cycling through material accumulation (Acuña et al., 2007; von Butturini, 2017), and legacies of drying can impact C cycling through Schiller et al., 2015; Shumilova et al., 2019) and altered rates of pro- the accumulation of organic matter and drying impacts on biotic cesses such as decomposition (Feminella, 1996; Bonada et al., 2007; communities (von Schiller et al., 2015). Although most work on the Arscott et al., 2010; Bogan et al., 2013; Datry et al., 2016; legacies of drying on biota have focused on macroinvertebrates (Leigh D'Ambrosio et al., 2017; Figure 1). A key gap in our understanding et al., 2016), metabolism studies have shown that gross primary of intermittency effects on C cycling is how spatial and temporal productivity is also depressed following drying in intermittent aspects of intermittence interact to control biogeochemical patterns reaches compared with years when stream drying does not occur and processes. (Acuña et al., 2007). Therefore, spatial patterns of streamflow inter- A major limitation in our understanding of intermittency controls mittence may affect spatial variation in DOC concentrations across on C has been that metrics of intermittence have been developed headwaters. opportunistically, based on available data, rather than to test a priori Although the effects of drying and rewetting on C cycling within hypotheses about the mechanisms linking the hydrology of intermit- reaches has been well studied (e.g., Acuña et al., 2007; Dieter et al., tence and biogeochemical processes. Stream intermittence is most 2011; Harjung et al., 2017), it is unclear how intermittence affects pat- familiar in its categorical designations of perennial, intermittent, and terns of DOC across stream networks (but see Zimmer & McGlynn, ephemeral. These categories imply that the important distinctions 2018). Landscape approaches to understanding stream intermittence among sites are (a) whether streams are flowing or not (perennial vs. focus on heterogeneity within a reach (e.g., pools; Datry, Pella, Leigh, not) and (b) the frequency and duration of that drying relative to pre- Bonada, & Hugueny, 2016) but fail to take this further to consider cipitation inputs (intermittent vs. ephemeral). Moving beyond categor- the landscape context of intermittence at network scales (Dent, ical metrics requires sufficient continuous flow measurements or flow Grimm, & Fisher, 2001; Godsey & Kirchner, 2014; Stanley, Fisher, & presence/absence observations to employ continuous rather than cat- Grimm, 1997). Despite conceptual recognition of the network egorical temporally integrated metrics. These metrics include median context of intermittence (Larned, Datry, Arscott, & Tockner, 2010; and minimum flows, flow duration, drying frequency or recurrence Stanley et al., 1997), most studies of intermittence focus on temporal interval, number of drying events, and flow permanence (usually at‐a‐point metrics of intermittence (Arscott, Larned, Scarsbrook, & evaluated as a percent of time with nonzero flows over a variety of Lambert, 2010; Bogan, Boersma, & Lytle, 2013; Bonada, Rieradevall, evaluation periods; Feminella, 1996; Arscott et al., 2010; Bogan & Prat, 2007; D'Ambrosio, De Girolamo, Barca, Ielpo, & Rulli, 2017; et al., 2013; King et al., 2015; Schriever et al., 2015). However, the Datry et al., 2016; Feminella, 1996) and assess wetting and drying current focus on temporal metrics necessarily excludes a spatial per- effects on biogeochemistry at the reach scale over time alone. Fur- spective of intermittence and limits our understanding of additional thermore, conceptual models of intermittent stream biogeochemistry ways that intermittence affects C cycling. that draw comparisons with soils (Arce et al., 2018; Larned et al., Spatial and spatio‐temporal metrics of stream intermittence are 2010) miss a critical difference between soils and dry streams: Streams uncommon. Spatial distributions of flow presence and absence have HALE AND GODSEY 3

FIGURE 1 Illustration of the spatial and temporal dimensions of intermittency, metrics, and hypothesized effects on DOC. Metrics in bold are used in this study. If used in other studies, examples of use are cited as follows: (1) Arscott et al., 2010; (2) King, Scoggins, & Porras, 2015; (3) Datry et al., 2011; (4) Bogan et al., 2013; (5) Schriever et al., 2015; (6) Gallart et al., 2016. Many other studies have used perennial versus intermittent designations and are not cited here. We propose the spatial and spatio‐temporal metrics, which except for the Arscott et al. “distance to nearest perennial” have not been used elsewhere. DOC, dissolved organic carbon

been reported in some recent studies, typically by mapping surface 1. What is the role of streamflow intermittence in structuring DOC flow (e.g., Godsey & Kirchner, 2014; Goulsbra, Evans, & Lindsay, concentration variability across a small watershed?

2014; Jensen et al., 2017; Whiting & Godsey, 2016; Zimmer & 2. What metrics of streamflow intermittence best predict variation in McGlynn, 2018), but in many studies, network effects of spatial drying DOC concentrations? patterns are not assessed or are implicitly assumed to be small compared with local conditions. The challenges associated with measuring spatial and spatio‐temporal aspects of intermittence are large. Although it is relatively easy to calculate the distribution of perennial 2 | METHODS and intermittent streams in a network from NHD+, the accuracy of this dataset with respect to flow permanence is poor (Fritz et al., 2.1 | Site description 2013), though progress is being made in this area (Jaeger et al., 2019). Even at smaller scales, leading models of stream drying are We conducted this work in the Gibson Jack stream, which drains unable to reproduce the spatial patterns of drying (Ward, Schmadel, ~25.5 km2 of a steep (~20°), high relief (~500 m) watershed in & Wondzell, 2018). Despite these challenges, there are strong concep- south‐eastern Idaho, USA. The stream flows predominantly towards tual reasons to expect spatio‐temporal aspects of stream drying to the east, and the watershed's north‐facing slopes are dominated by affect DOC through effects on source connection and drying legacies Douglas fir, whereas the south‐facing slopes are dominated by (Figure 1). sagebrush, grasses, and juniper (Kline, 1978). Soils in the watershed Here, we test the hypothesis that streamflow intermittence are strongly influenced by aeolian dust deposition with silt loams over- explains variation in DOC concentration across a small watershed. lying quartz and shale in the northern part of the watershed and lime- We compare a variety of spatial, spatio‐temporal, and temporal stone in the south, and the mainstem follows a mapped fault through metrics of intermittence to test hypothesized mechanisms through lower Gibson Jack (Kline, 1978). Limited grazing occurs within the which intermittence could influence DOC (Figure 1). Specifically, we watershed boundaries, and the U.S. Forest Service manages the ask the following: watershed for mixed uses, including research, recreation, grazing, 4 HALE AND GODSEY and water supplies. Mean annual precipitation and temperatures in the 2.4 | Statistical analysis basin vary strongly with elevation. Precipitation ranges from 0.38 to 0.76 m/year (Welhan, 2006), falling predominantly as rain at lower 2.4.1 | Metrics of streamflow permanence elevations (~1,500 m at the outlet) and falling as a mixture of rain and snow to form a small seasonal snowpack at upper elevations We used data from the repeated streamflow mapping and synoptic (~2,100 m at the highest divide; rain–snow partitioning derived from sampling to construct four metrics of streamflow intermittence elevational trends observed in nearby watersheds based on Tennant, (Figure 1). First, we relied on a temporal metric of intermittence that Crosby, Godsey, VanKirk, & Derryberry, 2015). Mean annual tempera- represented the dynamics of flow intermittence at a given sampling tures near the divide are ~6.6 °C based on weather station data location as in Section 2.2: this metric is categorical and referred to collected in WY2016, with maximum temperature of 30.8 °C and as SiteFlowPermanence. Sites were categorized as intermittent if they minimum temperature of −17.0 °C. were observed to dry at any time during the study period and perennial if surface flow was always observed. Given additional continuous time series data, we could replace this categorical metric with other 2.2 | Mapping streamflow metrics such as the number or duration of dry periods or the percent of time with surface flow, but these data are not available throughout ‐ Streamflow patterns across the Gibson Jack stream network were the watershed during the study period (Figure 1). Spatial and spatio assessed by manually mapping streamflow across a range of temporal metrics were also developed to describe the streamflow streamflow conditions occasionally from November 2015 to August intermittence of the stream network upstream of each sampling loca- 2017, referred to as “the study period.” Manual mapping was con- tion. To assess purely spatial effects, for each sampling event and each ducted by walking the stream and noting any transitions between sampling location, we calculated the number of upstream sampling flowing and nonflowing sections (excluding brief transitions less than points connected by surface flow (Nupstream connected). This metric ‐ ‐ 10 m long). Mapping of the stream network (~15 km) was typically reflects the length of upstream surface flow connected network. We conducted within a 2‐week period and was conducted throughout normalized this value by the total number of upstream sites to calcu- the snow‐free season to cover a range of flow conditions in November late another spatial metric: the percent of upstream sites that were 2015, April–June and November 2016, and May and August 2017. surface flow connected (%upstream connected; Figure 1). Finally, we evalu- ‐ One subwatershed was not mapped during the initial year of mapping, ated a spatio temporal metric that was based on the repeated and therefore, data for these sites are based on data from November streamflow mapping and resulting classification. For each synoptic 2016 to August 2017, which includes three observations that spanned sampling location, we calculated the percent of the upstream network high spring melt flows to low fall flows and the synoptic campaigns that was classified as perennial (%upstream perennial; Figure 1). detailed below. Stream sections that were flowing during all observations (N =3–7 depending on site) were classified as perennial, whereas 2.4.2 | Change point analysis those with at least one dry observation were classified as intermittent. We introduce additional metrics based on these observations in We used the pruned exact linear time (PELT) method to identify the Section 2.4.1. watershed area threshold where variation in streamflow intermittence and water chemistry collapses (Abbott et al., 2018). Originally developed for time series analysis, PELT has been adapted to assess 2.3 | Synoptic sampling changes in variation in spatial data as well (Abbott et al., 2018). We conducted PELT analysis separately for DOC for each sampling event, To characterize network‐scale biogeochemical patterns, we collected as well as for two metrics of intermittence: a spatio‐temporal metric water samples every 200 m across the Gibson Jack watershed (%upstream perennial) and a spatial metric (%upstream connected). Analysis was (N =69–97 samples depending on streamflow) three times during implemented in R (R Core Team, 2018) using the “changepoint” pack- different flow conditions and seasons to measure spatial patterns of age (Killick & Eckley, 2014). water chemistry (Figure 2a). Samples from the entire network were collected within a 4‐hr period on three dates: November 6, 2016 (very 2.4.3 | Multiple linear regression low flow, dormant); May 6, 2017 (snowmelt, high flow, early growing season); and August 14, 2017 (low flow, late growing season). Samples To assess whether streamflow intermittence was associated with were transported back to the lab on ice, where they were filtered variation in DOC concentrations, we used simple and multiple linear through ashed 0.7‐μm GF/F filters within 24 hr and frozen for later regression to test relationships between DOC, watershed area, and analysis. Samples were analysed for DOC on a Shimadzu TOC/TN four metrics of streamflow intermittence: SiteFlowPermanence, analyser at Brigham Young University's Environmental Analysis Lab. %upstream connected, Nupstream connected, and %upstream perennial (Figure 1). Samples collected in August 2017 were also analysed for specific UV We also tested for the effect of area (Area [km2]) given previous find- absorbance (SUVA254) on a spectrophotometer at the Center for ings that area drives changes in DOC (Creed et al., 2015). In this por- Ecological Research and Education at Idaho State University. tion of the analysis, we only included sites with watershed areas less HALE AND GODSEY 5

FIGURE 2 (a) Discharge record for Gibson Jack with synoptic sampling indicated by grey shading. (b) Change point analysis of spatio‐ temporal measure of intermittence, %upstream perennial. (c) Change point analysis of spatial measure of intermittence across three sample dates, %upstream connected. (d) Change point analysis of DOC concentration indicates that DOC variability decreases at watershed areas from 1.3 to 1.8 km2 across sampling dates. (e)

Change point analysis of August SUVA254 indicates that SUVA254 variability decreased at 1.5 km2 watershed area. DOC, dissolved organic carbon than 1.46 km2 because this was the area of variance collapse indicated scale intermittence converged to 50% at ~1.46 km2. The variance by the PELT analysis (see Section 3.1). We tested four groups of threshold was consistent for both the spatio‐temporal (%upstream peren- potential models (Area alone, intermittence alone, Area + intermittence, nial; Figure 3b) and spatial (%upstream connected; Figure 3c) metrics. The and Area × intermittence) for each sampling period, testing each of the threshold was also consistent across sampling periods (i.e., %upstream four metrics of intermittence outlined above. We used Akaike's infor- connected in August, November, and May), despite variation in flows mation criteria (AIC) to identify the best models; models with ΔAIC < 2 and mean concentrations (Figure 2a,c). were considered equivalent. Models were developed using the “lm” The mean and variance of DOC concentrations varied across syn- function in R (R Core Team, 2018). Area, DOC concentration, and optic sampling dates. Across the whole watershed, mean DOC con-

Nupstream connected were all log‐transformed to meet assumptions of centrations were significantly lower in November (1.13 ± 0.98 mg/L) normality. than May (1.85 ± 1.24 mg/L) and August (1.58 ± 0.35 mg/L; analysis of variance, df = 237, p < .01, Tukey's HSD p < .01). Coefficients of

variation in DOC were lower in November (CVDOC = 0.22) than May | 3 RESULTS (CVDOC = 0.67) and August (CVDOC = 0.65). However, seasonal differ- ences in means and variance were pronounced across sites with drain- 3.1 | Changepoint analysis age areas smaller than the 1.46 km2 variance collapse point. For these headwaters, the highest variation was observed in November during

Half (49%) of the Gibson Jack stream network flowed intermittently the lowest flows (CVDOC = 1.15), midrange variation was observed in during the study period (fall 2016 to summer 2017), and network‐ May during the highest flows (CVDOC = 0.80), and the lowest variation 6 HALE AND GODSEY

FIGURE 3 (a) Variability in DOC collapses at same spatial scale (i) across seasons, but the range of variability (ii) changes across seasons. (b) DOC concentrations in August were significantly related to the interaction between watershed area and site flow permanence (intermittent/perennial). (c) DOC was predicted by metrics of streamflow permanence rather than watershed area across flow conditions, but the significant metric varied between growing and dormant periods. DOC, dissolved organic carbon

was observed during moderate flows in August (CVDOC = 0.27). streamflow intermittence explained up to 61% of variation in DOC. DOC concentrations were significantly lower during November Area only improved model power beyond streamflow intermittence (0.83 ± 0.95 mg/L) than August (1.79 ± 0.49 mg/L) and May alone for August, during the growing season. The explanatory power (2.15 ± 1.73 mg/L; analysis of variance, df = 79, p < .05, Tukey's of streamflow intermittence varied across sampling periods (R2 =.1 HSD p < .05). to .6) and was the highest during the growing season under moderate Despite wide ranges in variance and mean across sampling dates, flow conditions (Table 1; Figure 3). variance in DOC concentrations and SUVA254 converged over space Different metrics of streamflow intermittence were better predic- at similar locations for all sampling dates. Variance in DOC concentra- tors of DOC under different conditions. Spatial metrics were the best tions collapsed at 1.3 km2 in May and 1.8 km2 in August. No signifi- predictors of DOC in May and November, whereas a temporal metric cant changepoint in DOC was detected statistically for November, was the best predictor of DOC in August. DOC was best predicted in but there was a visible decrease in variability at 1.7 km2 (Figure 2d). August, during the growing season, and the best‐fit model included 2 2 Variance in SUVA254 collapsed at 1.5 km . These collapse points the interaction between SiteFlowPermanence and Area (R = .61, matched the location at which intermittence converged: 1.46 km2 p < .05, n = 21). DOC during November was best predicted by the spa- 2 (Figure 2b–e). The colocation of a threshold of intermittence and a tial metric %upstream connected (R = .34, p < .05, n = 17). Equivalent collapse in DOC concentration and quality variation suggests that models for November DOC included Area, which explained no or min- streamflow intermittence may be related to the emergence of imal additional variation (R2 = .34–.37; Table 1). DOC was significantly, 2 chemostasis at the network scale in this watershed. but only weakly related to Nupstream connected in May (R = .10, p < .05).

The integrated spatio‐temporal metric, %upstream perennial, was never the best predictor of DOC concentrations. 3.2 | Relationships between streamflow Across seasons, increased streamflow intermittence tended to intermittence and DOC decrease DOC concentrations. However, we cannot compare the effect of intermittence across seasons because the relevant metric DOC concentrations in these small watersheds (<1.46 km2) were varied across dates. In May, DOC increased with the number of significantly related to streamflow intermittence. However, the upstream connected sites flowing (+0.13 mg/L DOC per additional explanatory power of those relationships and the relevant metric of site). A similar effect was seen in November with the %upstream connected streamflow intermittence varied seasonally. At least one metric of metric, where a fully flowing network would increase DOC by streamflow intermittence was a better predictor of DOC than Area 1.16 mg/L over a network with no connected flow. In August, peren- at all times. Although DOC is expected to change with watershed area nial sites had DOC concentrations 1.30 mg/L greater than intermittent (Abbott et al., 2018; Creed et al., 2015), Area explained only 1% to 4% sites overall, and DOC concentrations from intermittent and perennial of the variance in DOC across seasons, whereas models including sites converged as watershed area increased (Figure 4b). HALE AND GODSEY 7

TABLE 1 Coefficient of determination for each model that includes scale (Area), temporal (StreamFlowPermanence), spatial (both Nupstream connected and %upstream connected), and spatio‐temporal metrics of intermittence (%upstream perennial)

Flow Month (season) N Variable type Metric Univariate Additive Interactive

High May (spring) 43 Area Area 0.01 NA NA Med August (late growing) 21 Area Area 0.00 NA NA Low November (fall) 17 Area Area 0.04 NA NA SUVA (August) 17 Area Area 0.00 NA NA

High May (spring) 43 Spatial Nupstream connected 0.10** 0.12* 0.13

Med August (late growing) 21 Spatial Nupstream connected 0.01 0.01 0.04

Low November (fall) 17 Spatial Nupstream connected 0.18* 0.37** 0.40*

SUVA (August) 17 Spatial Nupstream connected 0.01 0.02 0.05

High May (spring) 43 Spatial %upstream connected 0.02 0.03 0.13

Med August (late growing) 21 Spatial %upstream connected 0.18* 0.23 0.24

Low November (fall) 17 Spatial %upstream connected 0.34** 0.34** 0.36

SUVA (August) 17 Spatial %upstream connected 0.14 0.19 0.19

High May (spring) 43 Spatio‐temporal %upstream perennial 0.05 0.06 0.10

Med August (late growing) 21 Spatio‐temporal %upstream perennial 0.52** 0.53** 0.53**

Low November (fall) 17 Spatio‐temporal %upstream perennial 0.02 0.11 0.11

SUVA (August) 17 Spatio‐temporal %upstream perennial 0.44** 0.45** 0.45** High May (spring) 43 Temporal SiteFlowPermanence 0.00 0.02 0.05 Med August (late growing) 21 Temporal SiteFlowPermanence 0.37** 0.42** 0.61** Low November (fall) 17 Temporal SiteFlowPermanence 0.18 0.29 0.30 SUVA (August) 17 Temporal SiteFlowPermanence 0.30** 0.35* 0.58**

Note. N refers to the number of samples included in the model, which is smaller than the overall watershed numbers because it includes only sites with watershed areas smaller than the variance collapse point. The univariate column presents results for the given variable and month. The additive column includes area and the given variable, and the interactive column builds off the additive model to include an interaction term between area and the given variable. Bold indicates the best model for the stated month based on Akaike's information criteria (see Section 2 for details). *p < .1. **p < .05.

3.3 | Relationships between streamflow emergence of DOC chemostasis has been observed at continental intermittence and SUVA254 (Creed et al., 2015) and local scales (Zimmer et al., 2013). In larger streams and rivers, the emergence of chemostasis has been attributed SUVA across all sites during August ranged from 0.45 to 4.31 L/mg‐ 254 to an increased influence of groundwater (Creed et al., 2015) and the C m with a mean value of 3.16 L/mg‐C m. Across small watersheds averaging of DOC sources and increased influence of instream pro- (<1.46 km2), SUVA averaged 2.81 L/mg‐C m and ranged from 0.45 254 cesses (Creed et al., 2015; Ejarque et al., 2017). In the Gibson Jack to 4.11 L/mg‐Cm.SiteFlowPermanence by itself was a significant pre- watershed, DOC concentrations were more variable in headwater dictor of SUVA within small watersheds, as was the spatio‐temporal 254 streams, with chemostasis emerging at ~1.46 km2. DOC changepoints metric % in an equivalent model (Table 1). Intermittent upstream perennial were spatially consistent with changepoints for network‐scale inter- sites had higher SUVA values, indicating more degraded DOC, likely 254 mittence metrics, suggesting that streamflow permanence was related from terrestrial sources, whereas perennial sites had lower SUVA , 254 to the emergence of chemostasis. The mechanism for this association indicating “fresher” DOC, possibly from instream production. is likely the same as that for the emergence of chemostasis in other watersheds: the mixing of heterogeneous sources (Creed et al., 4 | DISCUSSION 2015; Zimmer et al., 2013). Our multiple linear regression results suggest that, at this scale, intermittence is the primary control on both DOC concentrations and quality (SUVA ); therefore, it makes sense 4.1 | DOC concentrations stabilize where flows are 254 that a decrease in variation in DOC concentration and quality is more permanent associated with the loss of heterogeneity in streamflow intermittence In this watershed, streamflow intermittence aligned spatially with and the mixing of the distinctive concentrations and sources of DOC emergence of DOC chemostasis at the stream network scale. The in intermittent and perennial streams. Importantly, the scale of 8 HALE AND GODSEY chemostasis can vary greatly depending on the extent and resolution Hill, 1997; Raymond et al., 2016), but controls during lower flows vary of sampling, and that the mechanisms producing chemostasis may depending on instream and watershed productivity (Mulholland & Hill, vary with scale (Zimmer et al., 2013). Therefore, intermittence‐driven 1997). Under low‐flow conditions, DOC is controlled by instream pro- chemostasis is likely scale‐dependent, consistent with the observation cesses when instream productivity is high and by flowpath controls that the representative elementary area associated with hydro- during dormant periods or when riparian canopy is closed. chemical patterns is dynamic and variable (Temnerud & Bishop, Our results suggest that the effects of intermittence parallel overall 2005; Zimmer et al., 2013). Although DOC concentrations and the controls on DOC and seasonal changes from instream to flowpath flowing network varied significantly over time, the spatial patterns of controls. The Gibson Jack canopy is relatively open, promoting high chemostasis were consistent. Others have found that spatial patterns summer instream productivity. During these conditions (low flow in water chemistry can be stable over time (Abbott et al., 2018). Our and high instream process rates), we would expect instream controls results highlight that intermittence affects spatial patterns of DOC to dominate DOC (Mulholland & Hill, 1997). This is reflected in the regardless of season and flow conditions. strong effects of local‐scale legacies of stream drying on DOC in August, when the local biological community both reflected the legacies of intermittence and drove spatial variation in DOC concen-

4.2 | Seasonal variation in intermittence controls trations and SUVA254. SUVA254 values were significantly lower for parallel variation in instream vs. flowpath controls on perennial streams than intermittent streams, supporting the conclu- DOC sion that legacies of drying in intermittent reaches hindered the devel- opment of primary producer communities and instream production of Intermittence predicted variation in DOC concentrations in headwa- DOC. In contrast, during the fall, when productivity and flow were ters smaller than the variance collapse point, but the metrics, and both low, we would expect flowpath controls to dominate DOC therefore the mechanisms responsible for these patterns, varied sea- (Mulholland & Hill, 1997; Raymond et al., 2016). Indeed, in November, sonally. Spatial and spatio‐temporal metrics of streamflow intermit- these expectations were supported when we found spatial metrics of tence predicted DOC concentrations during high‐ and low‐flow intermittence best predicted DOC concentrations, suggesting that the conditions in the spring and fall, whereas a temporal metric predicted effect of intermittence during this period was due to connections to DOC concentrations during the late growing season. Here, we suggest DOC sources. We also found that expected flowpath controls during that this seasonal variation is predictable based on our broader under- high flows were reflected by the spatial intermittence metric standing of seasonal controls on watershed organic carbon exports. predicting DOC during May. Interestingly, the integrated spatio‐

Even with relatively coarse metrics of intermittence, based on 2 years temporal metric, %upstream perennial, was never the best predictor of of streamflow mapping, our regression models were able to explain a DOC concentrations. This suggests that source connection alone or substantial proportion of the variation in DOC concentrations and instream legacy effects alone most strongly affect DOC in this system, quality. Higher resolution streamflow data, more resolved metrics of and that the legacy effects of drying at the reach scale do not propa- intermittence, and a longer study period would likely shed additional gate strongly downstream (Figure 1). More resolved spatio‐temporal light on the mechanisms linking DOC and intermittence at network metrics, more sampling during different conditions, and additional scales. For example, some intermittent reaches do not dry every year, DOC quality data in future work may reveal integrated spatio‐ and therefore, we may have incorrectly categorized some streams as temporal controls. perennial. More detailed temporal metrics (e.g., measures of flow Instream legacies of drying in Gibson Jack appeared to manifest duration) would likely suggest additional mechanisms linking intermit- through effects on instream primary productivity rather than accumu- tence and DOC. lation of organic matter. In contrast to previous studies where storage Although temporal patterns of intermittence can explain variation of organic matter led to elevated DOC concentrations in intermittent reach‐scale carbon cycling between intermittent and perennial reaches (Acuña et al., 2005; Brooks & Lemon, 2007; von Schiller streams (Brooks & Lemon, 2007) or between wet and dry years in et al., 2015; Zimmer & McGlynn, 2018), we found that perennial an intermittent stream (e.g., Acuña et al., 2005, 2007), instream pro- streams supported higher concentrations of DOC and, in August, cess rates are not the only control on DOC concentrations and fluxes. DOC with a “fresher” SUVA254 signature. This suggests that perennial Controls on DOC concentrations and fluxes can vary from instream connection to sources as well as higher instream production sup- processes to watershed‐scale flowpath controls (Creed et al., 2015; ported by perennial flow during the growing season are more impor- Mulholland & Hill, 1997 ; Raymond et al., 2016), and the relative tant than accumulation and flushing in Gibson Jack, though these importance of these two sets of controls vary seasonally and across findings may only apply to baseflow conditions. Increases in DOC fol- flow conditions (McGuire et al., 2014;Mulholland & Hill, 1997 ; lowing rewetting may have been too transient for us to have captured Raymond et al., 2016). Importantly, it is not just flow that controls with infrequent sampling; the substantial effects of initial rewetting on DOC but the interactions between flow conditions, watershed and DOC concentrations can be as short as 1 week at both reach (von instream phenology, and temperature (Mulholland & Hill, 1997; Schiller et al., 2015) and network scales (Zimmer & McGlynn, 2018). Raymond et al., 2016). Across studies, DOC is consistently controlled Importantly, the seasonal patterns of instream and flowpath controls by flowpaths during high flows (Casas‐Ruiz et al., 2017; Mulholland & likely vary regionally depending on seasonal patterns of flow and HALE AND GODSEY 9 productivity (Bernhardt et al., 2018). For example, deciduous forested ORCID ‐ streams have the highest in stream productivity during spring and fall Rebecca L. Hale https://orcid.org/0000-0002-3552-3691 when the riparian canopy is open, and instream controls on DOC Sarah E. Godsey https://orcid.org/0000-0001-6529-7886 dominate during these seasons (Mulholland & Hill, 1997), whereas ‐ productivity is the highest in the relatively open canopy Gibson Jack REFERENCES – Creek during the summer months (May September; Cornell, 2013). Abbott, B. W., Gruau, G., Zarnetske, J. P., Moatar, F., Barbe, L., Thomas, Z., Thus, seasonal patterns of legacy versus network controls of intermit- … Pinay, G. (2018). Unexpected spatial stability of water chemistry in tence on DOC would be expected to vary across sites. headwater stream networks. Ecology Letters, 21(2), 296–308. https:// doi.org/10.1111/ele.12897 Abril, M., Muñoz, I., & Menéndez, M. (2016). Heterogeneity in leaf litter decomposition in a temporary Mediterranean stream during flow frag- 4.3 | Implications mentation. 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