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Abstract Nutrient impacts on productivity in stream ecosystems can be obscured by light limitation imposed by canopy cover and water turbidity, thereby creating uncertainties in linking nutrient and productivity regimes. Evaluations of nutrient limitations are often based on a response ratio (RR) quantifying productivity stimulation above ambient levels given augmented nutrient supply. This metric neglects the primacy of light effects on productivity. We propose an alternative approach to quantify nutrient limitations using a “decline ratio” (DR), which quantifies the productivity decline from the maximum established by light availability. The DR treats light as the first‐order control and nutrient depletion as a disturbance causing productivity decline, allowing separation of nutrient and light influences. We used DR to assess nutrient diffusing substrate (NDS) experiments with three nutrients (nitrogen [N], phosphorus [P], iron [Fe]) from five Greenland streams during summer, where light is not limited due to the lack of canopy and low turbidity. We tested two hypotheses: (a) productivity maximum (i.e., highest chlorophyll‐aamong NDS treatments) is controlled by light and (b) DR depends on both light and nutrients. The productivity maximum was strongly predicted by light (R² = 0.60). The productivity decline induced by N limitation (i.e., DR_N) was best explained by light availability when parameterized with either dissolved inorganic nitrogen concentration (R² = 0.79) or N:Fe ratio (R² = 0.87). These predictions outperformed predictions of RR for which light was not a significant factor. Reversing the perspective on nutrient limitation from “stimulation above ambient” to “decline below maximum” provides insights into both light and nutrient impacts on stream productivity. 

Abstract The temporal structures of gross primary production (GPP) and ecosystem respiration (ER) vary across time scales in response to complex interactions among dynamic drivers (e.g., flow, light, temperature, organic matter supply). To explore emergent patterns of river metabolic variation, we applied frequency‐domain analysis to multiyear records of metabolism across 87 US rivers. We observed a dominant annual periodicity in metabolic variation and universal fractal scaling (i.e., power spectral density inversely correlated with frequency) at subannual frequencies, suggesting these are foundational temporal structures of river metabolic regimes. Frequency‐domain patterns of river metabolism aligned best with drivers related to energy inputs: benthic light for GPP and GPP for ER. Simple river metabolism models captured frequency‐domain patterns when parameterized with appropriate energy inputs but neglecting temperature controls. These results imply that temporal variation of energy supply imprints directly on metabolic signals and that frequency‐domain patterns provide benchmark properties to predict river metabolic regimes. 

Abstract Protecting surface water quality can be complicated by high spatiotemporal variability. Pollutant sources and transport pathways may be identified through sufficiently high‐density monitoring sites and high‐frequency sampling, but practical considerations necessitate tradeoffs between spatial and temporal resolution in water quality monitoring network design. We examined how tradeoffs in sampling density and frequency affect measures of spatiotemporal variability in water quality, emphasizing pattern stability over time. We quantified the spatial stability of stream water quality across >250 monitoring sites in the intensively monitored watershed draining to Lake Okeechobee, FL using Spearman's rank correlations between instantaneous observations and site long‐term means for each parameter. We found that water quality spatial patterns for geogenic, biogenic, and anthropogenic parameters were generally stable on decadal timescales for all solutes, and that sampling densely in space yields more information than sampling frequently in time. Variations in spatial stability decreased with increased sampling density but not with greater sampling frequency, attesting to the dominance of spatial variability over temporal variability. For nutrients, the spatial coefficient of variation (CV) was approximately double the temporal CV. Spatial stability of most solutes was similar across flow conditions, but high‐flow monitoring allows for more sites that effectively capture the long‐term spatial patterns of nutrient sources. Water quality monitoring regimes can be optimized for efficiency in capturing water quality patterns and should be adjusted to focus more on spatial variation. We discuss potential improvements for water quality monitoring, particularly in watersheds where scarce resources necessitate tradeoffs between sampling density and frequency. 

ABSTRACT The deterioration of stream water quality threatens ecosystems and human water security worldwide. Effective risk assessment and mitigation requires spatial and temporal data from water quality monitoring networks (WQMNs). However, it remains challenging to quantify how well current WQMNs capture the spatiotemporal variability of stream water quality, making their evaluation and optimisation an important task for water management. Here, we investigate the spatial and temporal variability of concentrations of three constituents, representing different input pathways: anthropogenic (NO₃⁻), geogenic (Ca²⁺) and biogenic (total organic carbon, TOC) at 1215 stations in three major river basins in Germany. We present a typology to classify each constituent on the basis of magnitude, range and dominance of spatial versus temporal variability. We found that mean measures of spatial variability dominated over those for temporal variability for NO₃⁻and Ca²⁺, while for TOC they were approximately equal. The observed spatiotemporal patterns were robustly explained by a combination of local landscape composition and network‐scale landscape heterogeneity, as well as the degree of spatial auto‐correlation of water quality. Our analysis suggests that river network position systematically influences the inference of spatial variability more than temporal variability. By employing a space–time variance framework, this study provides a step towards optimising WQMNs to create water quality data sets that are balanced in time and space, ultimately improving the efficiency of resource allocation and maximising the value of the information obtained. 

Abstract Sensitivity of ecosystem productivity to climate variability is a critical component of ecosystem resilience to climate change. Variation in ecosystem sensitivity is influenced by many variables. Here we investigate the effect of bedrock lithology and weathering products on the sensitivity of ecosystem productivity to variation in climate water deficit using Bayesian statistical models. Two thirds of terrestrial ecosystems exhibit negative sensitivity, where productivity decreases with increased climate water deficit, while the other third exhibit positive sensitivity. Variation in ecosystem sensitivity is significantly affected by regolith porosity and permeability and regolith and soil thickness, indicating that lithology, through its control on water holding capacity, exerts important controls on ecosystem sensitivity. After accounting for effects of these four variables, significant differences in sensitivity remain among ecosystems on different rock types, indicating the complexity of bedrock effects. Our analysis suggests that regolith affects ecosystem sensitivity to climate change worldwide and thus their resilience. 

Stream dissolved oxygen (DO) dynamics are an outcome of metabolic activity and subsequently regulate ecosystem functions such as in-stream solute and sediment reactions. The synchronization of DO signals in and across stream networks is both a cause and effect of the mode and timing of these functions, but there is limited empirical evidence for network patterns of DO synchrony. We used high frequency DO measurements at 42 sites spanning five catchments and stream orders to evaluate DO signal synchrony in response to variation in light (a driver of photosynthesis) and discharge (a control on DO signal spatial extent). We hypothesized that stream network DO synchrony arises when regional controls dominate: when light inputs are synchronous and when longitudinal hydrologic connectivity is high. By complement, we predicted that DO signal synchrony decreases as light becomes more asynchronous and stream flows decline or become discontinuous. Our results supported this hypothesis: greater DO signal synchrony arose with increasing light synchrony and flow connectivity. A model including these two controls explained 70% of variation in DO synchrony. We conclude that DO synchrony patterns within- and across-networks support the current paradigm of discharge and light control on stream metabolic activity. Finally, we propose that DO synchrony patterns are likely a useful prerequisite for scaling subdaily metabolism estimates to network and regional scales. 

Abstract Watershed resilience is the ability of a watershed to maintain its characteristic system state while concurrently resisting, adapting to, and reorganizing after hydrological (for example, drought, flooding) or biogeochemical (for example, excessive nutrient) disturbances. Vulnerable waters include non-floodplain wetlands and headwater streams, abundant watershed components representing the most distal extent of the freshwater aquatic network. Vulnerable waters are hydrologically dynamic and biogeochemically reactive aquatic systems, storing, processing, and releasing water and entrained (that is, dissolved and particulate) materials along expanding and contracting aquatic networks. The hydrological and biogeochemical functions emerging from these processes affect the magnitude, frequency, timing, duration, storage, and rate of change of material and energy fluxes among watershed components and to downstream waters, thereby maintaining watershed states and imparting watershed resilience. We present here a conceptual framework for understanding how vulnerable waters confer watershed resilience. We demonstrate how individual and cumulative vulnerable-water modifications (for example, reduced extent, altered connectivity) affect watershed-scale hydrological and biogeochemical disturbance response and recovery, which decreases watershed resilience and can trigger transitions across thresholds to alternative watershed states (for example, states conducive to increased flood frequency or nutrient concentrations). We subsequently describe how resilient watersheds require spatial heterogeneity and temporal variability in hydrological and biogeochemical interactions between terrestrial systems and down-gradient waters, which necessitates attention to the conservation and restoration of vulnerable waters and their downstream connectivity gradients. To conclude, we provide actionable principles for resilient watersheds and articulate research needs to further watershed resilience science and vulnerable-water management.