Catchment-scale alder cover controls nitrogen fixation in boreal headwater streams

Hiatt, Daniel L.; Robbins, Caleb J.; Back, Jeffrey A.; Kostka, Pamela K.; Doyle, Robert D.; Walker, Coowe M.; Rains, Mark C.; Whigham, Dennis F.; King, Ryan S.

doi:10.1086/692944

Cited by 11 publications

(7 citation statements)

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“…Therefore, warm-water refugia in winter are also crucial, keeping some reaches unfrozen and available as overwintering habitats [86]. Lastly, much of this groundwater first passes through and interacts with nitrogen-fixing alder patches on adjacent hillslopes, delivering nitrogen-rich groundwater to riparian wetlands and these streams [14], where it enhances primary productivity in the riparian wetlands [14,87] and controls rates of in-stream nitrogen fixation and respiration [15,85]. The nutrient subsidies to these streams are then evident in the juvenile salmonids, who preferentially use abundant allochthonous sources, especially in the headwater settings [88].…”

Section: Discussionmentioning

confidence: 99%

“…Though regionally variable, estimates suggest that groundwater discharge provides 14-90% of all stream flow in the conterminous United States [5]. In addition to subsidizing stream flow, groundwater discharge to streams can also modulate stream temperature [11,12] and deliver nutrients and organic carbon [13,14], thereby playing important roles in structuring habitats from the benthos [15] to the fish [16]. Groundwater is also an important water supply component, with 321,000,000 m 3 of groundwater withdrawals comprising 26% of all water use in the United States in 2015 [17].…”

Section: Introductionmentioning

confidence: 99%

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Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

et al. 2021

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We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

et al. 2021

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“…Candidate models for NO 3 − included alder ( Alnus spp.) plant cover, which has been shown to influence NO 3 − concentrations in Alaska streams (Callahan et al, 2017; Hiatt et al, 2017; Shaftel et al, 2012). NO 3 − is also negatively correlated with DOC in some Alaska watersheds (Harms et al, 2016), with steeper basins having higher NO 3 − and lower DOC (Rodriguez‐Cardona et al, 2016; Walker et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Section: 1029/2020jg005851mentioning

confidence: 99%

Headwater Catchments Govern Biogeochemistry in America's Largest Free‐Flowing River Network

et al. 2020

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Riverine chemistry reflects both watershed conditions and instream processing, both of which vary across river networks, yet little is known about the scales at which watershed attributes regulate biogeochemical constituents. We used spatial stream network (SSN) models to quantify both watershed and instream effects on streamwater constituents in the Kuskokwim River (western Alaska), the largest free-flowing river in the United States. We assessed chemical constituents spanning from labile nutrients (nitrate [NO 3 − ] and orthophosphate [PO 4 3− ]) to biologically and chemically conservative tracers (calcium [Ca 2+ ], strontium [Sr 2+ ], and Sr isotopes [ 87 Sr/ 86 Sr]). We also examined the behavior of dissolved organic carbon (DOC) relative to these constituents to understand whether bulk DOC behaved conservatively in a large boreal river network, where future changes in DOC and nutrient loading are expected under a shifting climate. We derived watershed spatial covariates comprising land cover, geology, and geomorphic characteristics at 14 different spatial extents based on stream order and relative to position in the river network to understand the effect of distant and proximal watershed attributes on streamwater constituents. For all constituents but 87 Sr/ 86 Sr, watershed attributes in low-order headwater catchments yielded the best predictive ability for streamwater constituent concentrations across the entire network. Spatial patterning for conservative tracers and bulk DOC showed strong spatial autocorrelation, whereas PO 4 3− and NO 3 − exhibited spatial patchiness indicative of instream processing. Our work shows that headwater streams are disproportionately important contributors to network-wide biogeochemical constituents supporting aquatic food webs and that conservation and management of aquatic systems should include these small and often remote watershed areas. Plain Language Summary Streamwater chemistry is influenced by watershed attributes like geology, gradient, and land cover and biochemical changes occurring within the stream channel, but how the relative importance of these factors changes as water moves through a river network is not known. We used a statistical model that accounts for water flows and the spatial layout of the river to study how stream chemistry is influenced by both watershed attributes and instream conditions at local to watershed-wide scales. We studied the Kuskokwim River in Alaska, America's largest free-flowing river, and found that conditions in small, headwater streams play a disproportionately important role in shaping streamwater chemistry throughout the river network. We also found that chemicals that are rapidly used by algae and microbes are spatially more variable in the river network when compared to passively transported chemicals that have lower biological demand. This work is important because streamwater chemical patterns can influence how different organisms are distributed in rivers, and certain watershed attributes will influence how changing clima...

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“…For autotrophic cyanobacteria, N fixation tends to increase with light (Berrendero et al, 2016; Carmiggelt & Horne, 1975; Grimm & Petrone, 1997), P (Marcarelli & Wurtsbaugh, 2006, 2007, Kunza & Hall, 2013, but see Scott et al, 2009), temperature (Marcarelli & Wurtsbaugh, 2006; Welter et al, 2015) and substrate stability (Marcarelli & Wurtsbaugh, 2009). Dissolved inorganic N (DIN) frequently inhibits N fixation in cyanobacteria, because they can meet their nutritional needs with less energy (Eberhard et al, 2018; Hiatt et al, 2017; Kunza & Hall, 2013, 2014; Marcarelli & Wurtsbaugh, 2006, 2007; Scott et al, 2009). Likewise, heterotrophic N fixation rates increase with carbon (C; energy) availability, such as that provided by leaf litter (Tam et al, 1981) and fine sediment (Francis et al, 1985).…”

Section: Introductionmentioning

confidence: 99%

Nitrogen fixation rates in forested mountain streams: Are sediment microbes more important than previously thought?

et al. 2022

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Biological nitrogen (N) fixation, the microbial conversion of N2 gas to ammonia, makes N available to food webs. Low‐N streams often have a high relative abundance of N‐fixing taxa, suggesting that N fixation is an important N source in these systems. Despite this potential, stream N fixation has not been well‐characterised, particularly compared to lakes and marine environments. One unknown is the relative contributions of various N‐fixing organisms, particularly heterotrophic microbes. In low‐N streams in the Cascade Mountains (Washington, USA), three groups of N‐fixers predominate: cyanobacteria (Nostoc paramelioides) colonies that house a midge symbiont (Cricotopus spp.), cyanobacteria without a midge symbiont, and heterotrophic sediment microbes. In seven streams, we measured N fixation rates in each group with the acetylene reduction assay and a 15N2 calibration. Cyanobacteria N fixation rates were relatively low (7.9 ± 8.9 μg N m−2 hr−1, mean ± SD) compared to other mountain streams. Although rates were comparable among types of N‐fixers, our sediment conversion ratio (moles of ethylene produced:moles of N fixed) was 0.16:1, much lower than our cyanobacteria conversion ratio of 1.72:1 and the commonly used theoretical ratio of 3:1. Sediment N fixation rates (5.7 ± 4.0 μg N m−2 hr−1) were higher than previously reported rates measured only with acetylene reduction. The midge symbiosis did not greatly impact N fixation rates; however, owing to their prevalence, colonies with the midge probably contributed more total N to streams than colonies without the midge. Additionally, N fixation by sediment heterotrophs was comparable to that of cyanobacteria colonies on an areal basis. Our study demonstrated that the contribution of sediment heterotrophs previously may have been underestimated in streams, especially considering that sediment heterotrophs are probably present for a longer portion of the growing season than cyanobacteria in temperate and boreal ecosystems.

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Catchment-scale alder cover controls nitrogen fixation in boreal headwater streams

Cited by 11 publications

References 43 publications

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

Headwater Catchments Govern Biogeochemistry in America's Largest Free‐Flowing River Network

Nitrogen fixation rates in forested mountain streams: Are sediment microbes more important than previously thought?

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