Biological assemblages in streams are influenced by hydrological dynamics, particularly in non-perennial systems. Although there has been increasing attention on how drying impacts stream organisms, few studies have investigated how specific characteristics of drying and subsequent wetting transitions influence biotic responses via resistance and resilience traits. Here, we characterized how hydrologic metrics, including those quantifying drying and wetting transitions as well as dry and wet phases, alter diversity and composition of three aquatic assemblages in non-perennial streams in southern California: benthic macroinvertebrates, soft-bodied algae and diatoms. We found that flow duration prior to sampling was correlated with variation in macroinvertebrate and soft-bodied algal assemblage composition. The composition and richness of diatom assemblages, however, were predominantly influenced by the drying start date prior to sampling. Contrary to other studies, the duration of the dry phase prior to sampling did not influence the composition or richness of any assemblage. Although our study was conducted within a region in which each assemblage experienced comparable environmental conditions, we found no single hydrologic metric that influenced all assemblages in the same way. The hot-summer Mediterranean climate of southern California likely acts as a strong environmental filter, with taxa in this region relying on resistance and resilience adaptations to survive and recolonize non-perennial streams following wetting. The different responses of algal and diatom assemblages to hydrologic metrics suggest greater resilience to drying and wetting events, particularly for primary producers. As drying and wetting patterns continue to change, understanding biodiversity responses to hydrologic metrics could inform management actions that enhance the ecological resilience of communities in non-perennial streams. In particular, the creation and enhancement of flow regimes in which natural timing and duration of dry and wet phases sustain refuges that support community persistence in a changing environment.

Statistical stressor-response models are a common approach to derive biologically relevant water quality criteria for the management of waterbody health. These types of models are typically derived at state-wide or ecoregional scales and consequently incorporate a large amount of natural and disturbance-related variability that may obscure the relationship that one is interested in quantifying. We demonstrate an alternative approach termed “reach-specific modeling” to identify potential biological response thresholds to eutrophication in the Santa Margarita River watershed (California, U.S.A.). Individual models of benthic invertebrate response to eutrophication stress were created for both bioassessment sampling sites and NHD + stream-segments in the watershed (46 sites, 832 segments). Each model was built using only data from environmentally similar sites from a state-wide dataset to minimize variation from natural environmental gradients, while allowing eutrophication stress to vary. Thresholds of potential biological impact were extracted from each stressor-response model. Across the whole watershed thresholds varied from location-to-location: total nitrogen (1.14–1.26 mg L-1 TN), total phosphorus (0.12–0.15 mg L-1 TP), benthic algal biomass (29–39 mg m−2 benthic chl-a), and benthic ash-free dry mass (2.5––3.0 mg cm−2 AFDM). Notably, nearly all of the thresholds derived from these reach-specific models were ∼10–90 % higher than those from a similar state-wide model. Furthermore, there were a number of spatial groupings of thresholds for each eutrophication indicator across the watershed, suggesting reach-scale natural gradients in hydrogeomorphology and natural land cover type may mediate the stressor-biology interaction. Reach-scale models tended to have better fits than their state-wide counterparts, but had equivalent or slightly worse accuracy. The reach-specific approach to threshold development illustrates that the biological response to stress is likely not uniform within a single system, much less between systems. As a consequence, this approach can allow managers to identify systems that are more sensitive or resistant to a given stressor across diverse landscapes and make better informed decisions on their management accordingly.

Low flows pose unique challenges for accurately quantifying streamflow. Current field methods are not optimized to measure these conditions, which in turn, limits research and management. In this essay, we argue that the lack of methods for measuring low streamflow is a fundamental challenge that must be addressed to ensure sustainable water management now and into the future, particularly as climate change shifts more streams to increasingly frequent low flows. We demonstrate the pervasive challenge of measuring low flows, present a decision support tool (DST) for navigating best practices in measuring low flows, and highlight important method developmental needs. Water resource management is facing mounting challenges associated with water scarcity, including interactive effects of a changing climate and increased water demand (Craig et al. 2017). Climate change is increasing drought severity in many regions (Cook et al. 2020), while demand for limited water supplies depletes water resources (de Graaf et al. 2019). Combined, these stressors result in lower and more variable flows in streams and rivers (Zipper et al. 2021), particularly in arid regions (Hammond et al. 2021). Despite challenges posed by low-flow conditions, the majority of resources (e.g., time, funding) for monitoring streamflow have historically focused on high-water concerns, such as ensuring navigation and predicting floods (Vörösmarty et al. 2001; Ruhi et al. 2018), in larger, perennially-flowing systems (Krabbenhoft et al. 2022). Low-flow conditions (Mauger et al. 2021), which we define as streams or rivers with little downstream surface water flow caused by small volumes or very low downstream velocities (i.e., slackwater), are increasingly prevalent and thus necessitate greater focus on quantification approaches. Streamflow is the underlying physical template structuring biotic and abiotic processes, biogeochemical cycling, and ecological communities in river systems; thus, inaccurate low-flow measurements can propagate to and hinder diverse analyses requiring accurate low-flow data, ranging from drought characterization (Hammond et al. 2022), environmental flow allocations (Neachell and Petts 2019), ecological function assessments (Leigh and Datry 2017), species conservation plans (Lopez et al. 2022), and streamflow forecasting (Forzieri et al. 2014). We posit that a lack of low-flow measurement techniques leaves monitoring networks ill-equipped to inform water management, which is a fundamental challenge that must be addressed to ensure sustainable water management in the future. Our objectives are to: (1) demonstrate the widespread challenges in low-flow measurement across an existing monitoring network in the United States, (2) discuss limitations of current streamflow measurement methods in low-flow conditions, (3) present a DST for choosing among existing measurement methods, and (4) highlight important methodological developments needed to improve low-flow measurement and monitoring. Such methodological progress is a prerequisite for understanding how low flows will respond to changing climate and human demands, thereby supporting management and policy actions seeking to avoid or minimize these impacts. Point measurements of streamflow are essential for short- and long-term studies and monitoring, and can be made using many different methods (Turnipseed and Sauer 2010). If conducted over a range of flow conditions, discrete streamflow measurements can be used to develop a rating curve which relates stage and discharge, allowing for long-term, continuous quantification of discharge via stage sensors (Turnipseed and Sauer 2010). We focus our analysis and discussion on methods for point measurements of streamflow, but emphasize that limitations in these approaches have implications for the accuracy of longer-term streamflow monitoring via rating curve development. To quantify the prevalence of substandard low-flow measurements, we examined manual point measurements of streamflow from 8008 U.S. Geological Survey (USGS) gages across the continental United States in the GAGES II dataset (Falcone 2011), which is a dataset of sites with either 20+ years of discharge since 1950 or that were operational as of 2009 (Appendix S1). For each manual streamflow measurement, we collected the quality code assigned by USGS hydrographers immediately after making the discharge measurement: “Poor” quality is assigned when uncertainty in the discharge measurement is estimated to be above 8%, “fair” when uncertainty is estimated to be less than 8%, good when uncertainty is estimated to be less than 5%, and excellent when uncertainty is estimated to be less than 2% (Turnipseed and Sauer 2010). These quality codes are a qualitative method for estimating the accuracy of individual discharge measurements based on suitability of the channel cross-section, flow state, and other flow conditions (Turnipseed and Sauer 2010). For each gage, we identified the minimum streamflow value associated with a “good” manual flow measurement and calculated the percent of each gage's daily streamflow record below the minimum “good” threshold. To ensure our results were not overly sensitive to the value of the minimum “good” threshold, we also compared the percentage of each gage's streamflow record below two additional thresholds: (1) streamflow value corresponding to the minimum “fair” measurement, and (2) average of minimum “fair” and minimum “good” thresholds (see Table S1 for details), and obtained comparable results. The “minimum good” metric provides a conservative estimate of the duration of flow measurements with high uncertainty for each site; it only considers uncertainty related to manual measurements and does not account for additional uncertainty in stage measurements stemming from low-flow conditions. We interrogated the USGS network because it represents a high standard that many individual investigators use as a benchmark, and because it provided a large dataset relating manual streamflow measurements with qualitative assessments of quality/uncertainty. We performed all analyses in R version 4.2.1 (R Core Team 2022) and obtained USGS data from the National Water Information System using the DataRetrieval Package (De Cicco et al. 2022). Across the GAGES II network, the average percentage of flow records below the minimum good measurement was 8.4%, indicating high overall quality of the streamflow measurements. However, we found that 393 gages (~ 5.5%) had at least 50% of flow records below the minimum good flow value, 68 of which had over 95% of flow records below the minimum good flow threshold (Fig. 1A). Sites with a high percentage of streamflow below the minimum “good” threshold are widely distributed across diverse climatic zones, land uses, and hydrologic settings, although the greatest density of high uncertainty records are concentrated in the arid southwestern United States where low flows and water management issues linked to scarcity are pervasive (Brown et al. 2019). To provide an example of the difficulties in making low-flow measurements, we focused on the gage for Kings Creek near Manhattan KS (USGS Gage 06879650), a well-studied, grassland stream with a long continuous record (1979–present). Only 73 of the 238 manual flow measurements (~ 31%) were considered “good” or “excellent” (Fig. 1B). The relatively low incidence of “good” manual flow measurements at Kings Creek resulted in over 58.6% of the daily flow record (from 1980 to 2021) being below the lowest “good” flow measurement, with the proportion below that threshold in a given water year ranging from 2.5% to 100%. This underscores that even for a given site, the relative importance of accurate low-flow measurements will vary from year-to-year, with greatest impact during dry years (Fig. 1C). Furthermore, uncertainties in low-flow measurements may propagate into subsequent estimates of nutrient export, which may lead to some annual load estimates to be much less certain than others. Systems with frequent low flows and flashy high flows may also face highly uncertain streamflow measurements at the high flow end of the rating curve, leading to additional sources of uncertainty. While a sensitivity analysis of uncertainty propagation in streamflow is beyond the scope of this paper, our analysis highlights many areas in the United States where current methods are poorly suited to capture low-flow conditions. Three general categories of methods comprise the toolbox available to most practitioners. These include: (1) velocity-area methods; (2) tracer-based methods using salt or dye; and (3) measuring stage at a known streambed geometry (e.g., flume or weir) or capturing flow at a channel constriction (WMO 2010). Most methods tend to be inaccurate or unusable under low-flow conditions (Hamilton 2008) for three reasons: (1) low water velocities and/or shallow water depths (Fig. 2A,B), (2) mobile streambeds and/or irregular channels (Fig. 2D,E), and (3) high proportions of flow in the subsurface (Fig. 2C–E). Many streams transition from visible surface water flow to very slow or imperceptible movement of water, which is sometimes spatially discontinuous or pooled. Low velocities can lead to poor tracer mixing and recovery when using dilution gaging methods (Fig. 2A). High channel width-to-depth ratios (i.e., very wide channels with shallow water) can also lead to poor tracer mixing and the inability to fully submerge velocimeters (Fig. 2E). Furthermore, highly variable bed elevations (e.g., rocks and boulders) or emergent vegetation can further reduce the accuracy of velocity measurements and even render them impossible (Fig. 2D). Finally, estimates of discharge based on velocity-area methods only measure surface-water flow and therefore are not directly comparable to tracer-based estimates, which capture some subsurface flow. This is particularly relevant in low-flow conditions which often exhibit a greater proportion of hyporheic flow. These general problems are not mutually exclusive; indeed, multiple issues can arise in low-flow settings, leaving practitioners unsure about which method to use and leading to considerable uncertainty in low-flow measurements. Given these challenges, we present a DST that reflects our collective experience working in low-flow systems, and describes how we approach applying existing discharge methods given the complicating factors that dominate low-flow systems (Fig. 3). The aim of the DST is to offer guidance on a systematic way to apply consistent methods to complex systems. This tool assumes the chosen location is the best available site (i.e., there are no better sites within a reasonable distance upstream or downstream) and highlights what conditions should be avoided in site selection. The DST is not intended to be a data-driven study on the optimal way to measure low flows, rather it is offering informed opinions on what methods tend to work best in specific contexts from experts who frequently attempt flow measurements under non-ideal conditions. In compiling the DST, we also highlight conditions where method development should be prioritized, which we hope catalyzes further discussion and method advances within the water resource community. The initial bifurcation in this DST separates sites by whether water is visibly flowing or not (Fig. 3). We define visible flow as whether material in the water (e.g., leaves) can be observed moving downstream. If there is no visible movement, fewer options exist to measure flow. If streamflow is visible, the DST prompts a series of questions regarding channel cross-section and water depth to help practitioners identify the most suitable flow measurement for their site (Fig. 3). We acknowledge that the pathways and nodes are not equally likely to be encountered. For example, very few locations have natural constriction points for which the bucket method is suitable (Fig. 2F), even though it appears twice (Fig. 3). Furthermore, three nodes terminate in “no widely used methods.” In our experience, the majority of sites where we work (numbering in the dozens, examples in Fig. 2) fall into nodes characterized by “no widely used method” for at least part of the year, leaving us unable to accurately measure hydrologic fluxes and limiting subsequent analyses like long-term nutrient flux estimates. While this DST can be used to help practitioners identify the best possible methods, we acknowledge that under many low-flow conditions, even a recommended method can lead to suboptimal discharge measurements with relatively high error. Selecting a method to measure discharge requires practitioners to identify the degree of precision needed for their study and consider trade-offs between precision and resource costs. For some studies, hydrologic parameters that are easier to measure—like depth, wetted width/area, or approximate flow state—may be sufficient (Jaeger et al. 2023). In contrast, biogeochemistry studies for which water movement is a key variable for calculating nutrient loads (Gómez-Gener et al. 2021) may require greater precision than studies focused on aquatic habitat. Other trade-offs, including personnel costs, measurement frequency, and available time to conduct a measurement may outweigh the scientific considerations given in Fig. 3. At low but visible flows, dilution gaging can be used but may take hours to days, rather than minutes to an hour required at moderate to high flow conditions. In addition, dilution gaging at low flows often results in non-optimal breakthrough curves from incomplete mixing that are not suitable for discharge estimates. Portable flumes/weirs are faster to implement but require modifying the channel, for example manually creating berms to concentrate flow through a flume (Fig. 2C), which may not be possible for many reasons. While the DST provides recommendations for general categories of measurement methods, further modifications of each method can help accommodate specific flow conditions (e.g., different variations on the application of dilution gaging; Table S1). We provide suggestions for situations where modifications of standard methods may be desirable, and further challenges in applying those modifications in Table S1. In streams and rivers, streamflow is the underlying physical template structuring biotic and abiotic processes, biogeochemical cycling, and ecological communities. Discharge is used to assess the degree of connectivity between tributaries and quantify movement of solutes through a stream network. Time series of discharge are key inputs for models of aquatic ecosystem function and biogeochemical cycling, and the desired output of hydrologic models identifying factors driving streamflow and predicting responses to anthropogenic change. All of these applications require accurate discharge measurements across the full range of flow variability. While there is no universal answer to the question of “what percent error is acceptable when measuring low flows,” we argue the general need for a high degree of accuracy is clear. Although absolute changes in streamflow in low-flow systems may be small (e.g., changes from 0.01 to 0.02 m3/s), this represents a large relative change within the system (100%). Small changes in discharge at low flows can have substantial consequences for habitat extent and suitability (Rolls et al. 2012). Detection of long-term trends is hampered by imprecise or uncertain data, which may cause trends in vulnerable low-flow systems to go unquantified (Whitfield and Hendrata 2006). Environmental flow regulations require precise data for enforcement (Neachell and Petts 2019), and uncertain low-flow data can complicate implementation and enforcement. Finally, there are many systems ranging from large, arid rivers to small streams where difficult-to-measure low-flow conditions are the norm and thus prevent accurate streamflow measurements across the flow-duration curve, leaving sites with minimal data for research and management purposes. Although low flows may represent a smaller component of annual water or solute fluxes than high flows in many systems, they are critical for understanding and predicting hydrological, ecological, and biogeochemical dynamics in river systems. This is not possible without robust low-flow discharge measurements. In addition to providing guidance for systematically deciding which methods to employ in determining low-flow discharge, our DST (Fig. 3) highlights areas of critical need for method development and uncertainty assessment. In some cases, further modification and optimization of existing methods may be sufficient (e.g., Table S1). However, there are conditions for which entirely new methods need to be developed or refined, such as: (1) slackwater pools (Fig. 2A); (2) wide, shallow, irregular, or threaded channels (Fig. 2E), particularly in locations with no opportunity for channel modification; (3) reaches with dense emergent vegetation; and (4) reaches where wind strongly affects water surface velocities. These conditions are commonly found in freshwaters but share similarities with coastal settings, opening up the potential for method transfer to/from coastal hydrology (e.g., Birgand et al. 2022). There are promising recent technological advances including micro velocity sensors (Osorno et al. 2018), time-lapse imagery analysis from trail cameras and videos (Birgand et al. 2022; Chapman et al. 2022; Dolcetti et al. 2022) or radar altimetry (Bandini et al. 2020), and presence/absence sensors for measuring water surface extent (Chapin et al. 2014;). Emerging tools like time-lapse imagery analysis and water presence/absence sensors may improve our understanding of the spatiotemporal variation in the hydrologic state of low-flow systems by providing an assessment of surface water presence at the time of streamflow measurements, or in the absence of suitable discharge measurement approaches. However, more work must be done to advance these methods because as of now they only estimate stage or water presence/absence, leaving the difficulties of estimating discharge unresolved. Finally, there may be settings in which modeling or mathematical relationship development may be the best option (Gao et al. 2021). We suggest a concentrated effort on uncertainty assessment and method development is urgently needed, as there are numerous settings for which there is no current viable method for measuring streamflow. Methods development for accurate low-flow measurements will be critical as environmental change accelerates, leading to increased hydrologic variability and shifts to low flows around the world. To better manage future trade-offs among water uses, managers will require accurate data on streamflow under low-flow conditions. To achieve this, we need methodological flexibility to capture extreme flow conditions, including at low flows. Without improvements, we will not be able to sustain existing long-term streamflow records that can help us predict the continuing trajectories of environmental change. Understanding and managing shifts in water resources will be critical for ensuring habitat integrity, promoting good water quality, and safeguarding sustainable water access. The first step is ensuring consistent high-quality flow measurements in these vulnerable systems. Data S1. Supplementary Information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. This work was supported by NSF-DEB Grant #1754389 to the Dry Rivers Research Coordination Network and NSF-IOA Grant #2019603 to the Aquatic Intermittency effects of Microbiomes in Streams (AIMS) project. We would like to thank the members in the Dry Rivers Research Coordination Network and the AIMS team for their thoughtful conversations and contributions that helped advance the ideas explored in this manuscript.

Trash impairment of watersheds has been recognized as a worldwide environmental problem. Trash monitoring in streams and rivers is necessary to enhance our understanding of its effects on freshwater habitats and the role of streams as a conduit for transport to marine environments. Southern California, with a population of over 22 million, is home to nearly 7,400 km of wadeable streams in watersheds spanning a variety of land uses, making it an ideal region to study the extent and magnitude of trash and trash types (plastic, metal, glass, etc.) and identify relationships between land use and the amount of trash. These data can be used to develop mitigation strategies and evaluate management successes. We found that 77% of Southern California’s coastal stream kilometers contained trash, with an estimated stock of 7 million pieces of trash. Of the types enumerated, plastic trash was the most ubiquitous, present in 69% of stream kilometers, and the most abundant, with an estimated stock of over 4.3 million pieces of plastic. The most common items were single-use plastic containers, wrappers, and plastic bags. Urban land use was associated with the greatest extent and magnitude of trash, with levels nearly double those found in open land uses. Trash was strongly associated with indicators of human activity and development in watersheds. Road density and proximity to roads and parking lots were strongly associated with increased trash in watersheds. This survey also suggested that management actions had a positive effect on trash count. After the previous trash survey in Southern California streams in 2011–2013, a statewide ban on plastic bags was implemented in 2016. We found a significant decrease in the number of plastic bags within streams in the present survey compared to the previous survey.

Assessments of the ecological health of algal assemblages in streams typically focus on measures of their local diversity and classify individuals by morphotaxonomy. Such assemblages are often connected through various ecological processes, such as dispersal, and may be more accurately assessed as components of regional-, rather than local-scale assemblages. With recent declines in the costs of sequencing and computation, it has also become increasingly feasible to use metabarcoding to more accurately classify algal species and perform regional-scale bioassessments. Recently, zeta diversity has been explored as a novel method of constructing regional bioassessments for groups of streams. Here, we model the use of zeta diversity to investigate whether stream health can be determined by the landscape diversity of algal assemblages. We also compare the use of DNA metabarcoding and morphotaxonomy classifications in these zeta diversity-based bioassessments of regional stream health. From 96 stream samples in California, we used various orders of zeta diversity to construct models of biotic integrity for multiple assemblages of diatoms, as well as hybrid assemblages of diatoms in combination with soft-bodied algae, using taxonomy data generated with both DNA sequencing as well as traditional morphotaxonomic approaches. We compared our ability to evaluate the ecological health of streams with the performance of multiple algal indices of biological condition. Our zeta diversity-based models of regional biotic integrity were more strongly correlated with existing indices for algal assemblages classified using metabarcoding compared to morphotaxonomy. Metabarcoding for diatoms and hybrid algal assemblages involved rbcL and 18S V9 primers, respectively. Importantly, we also found that these algal assemblages, independent of the classification method, are more likely to be assembled under a process of niche differentiation rather than stochastically. Taken together, these results suggest the potential for zeta diversity patterns of algal assemblages classified using metabarcoding to inform stream bioassessments.

A key challenge in managing flow alteration is determining the severity and pattern of alteration associated with the degradation of biological communities. Understanding these patterns helps managers prioritize locations for restoration and flow management actions. However, the choices made about how to use these flow-ecology relationships can have profound implications on management decisions (e.g., which biological endpoints, which thresholds, which seasonal flow components to use). We describe a process for using flow-ecology relationships to prioritize management actions that 1) Represents the most relevant components of the annual hydrograph, 2) Demonstrates an appropriate level of sensitivity in order to discriminate locations to inform decision making, 3) Aims to protect multiple biological assemblages, 4) Reduces misclassification of priority areas (i.e., error of omission). Our approach is based on the functional flows approach which uses multiple flow metrics that describe the frequency, timing, magnitude, duration, and rate of change of seasonal process-based components of the annual hydrograph. Using this approach, we performed a flow-ecology analysis of regional bioassessment data, through which we determined where flow alteration impacts biology and prioritized reaches for changes in flow management to protect aquatic resources in a highly urbanized region of southern California, where managing scarce water resources leads to difficult decisions about tradeoffs that require technical information. We identified three important functional flow metrics for each of two bioassessment indices, one based on benthic macroinvertebrates, and another based on benthic algae. Based on thresholds that describe levels of alteration as well as thresholds describing the probability of achieving a healthy biological condition, we compared nine biological threshold combinations for each index. We found instances of flow alteration that impact biological condition highly variable (0–100% of subbasins) between combinations and we present a method for finding the most appropriate combination for prioritizing locations for flow management. We apply the final thresholds to the study region and propose 16 subbasins of high priority for implementing flow management and restoration. Importantly, we show that focusing on a single biological group would result in biologically altered locations being effectively ignored.

Large state or regional environmental flow programs, such as the one based on the California Environmental Flows Framework, rely on broadly applicable relationships between flow and ecology to inform management decisions. California, despite having high flow and bioassessment data density, has not established relationships between specific elements of the annual hydrograph and biological stream condition. To address this, we spatially and temporally linked USGS gage stations and biological assessment sites in California to identify suitable paired sites for comparisons of streamflow alteration with biological condition at a statewide scale. Flows were assessed using a set of functional flow metrics that provide a comprehensive way to compare alteration and seasonal variation in streamflow across different locations. Biological response was evaluated using the California Stream Condition Index (CSCI) and Algal Stream Condition Index (ASCI), which quantify biological conditions by translating benthic invertebrate or algal resources and watershed-scale environmental data into an overall measure of stream health. These indices provide a consistent statewide standard for interpreting bioassessment data, and thus, a means of quantitatively comparing stream conditions throughout the state. The results indicate that indices of biological stream condition were most closely associated with flow alteration in seasonality and timing metrics, such as fall pulse timing, dry-season timing, and wet season timing. Magnitude metrics such as dry-season baseflow, wet season baseflow, and the fall pulse magnitude were also important in influencing biological stream conditions. Development of ecological flow needs in large-scale environmental programs should consider that alteration to any of the seasonal flow components (e.g., dry-season baseflow, fall pulse flow, wet-season baseflow, spring recession flow) may be important in restructuring biological communities.

Eutrophication is one of the most pervasive stressors impacting streams, often leading to loss of biodiversity or change in natural functions. To protect against these adverse effects, managers can set targets for environmental indicators to limit eutrophication that are likely to maintain high biological integrity. To identify protective management targets, we evaluated the responses of three bioassessment indices (one for benthic macroinvertebrates and two for benthic algae assemblages) to five eutrophication indicators (total nitrogen [TN], total phosphorus [TP], benthic chlorophyll-a [chl-a], benthic ash-free dry mass [AFDM], and percent macroalgal cover [% cover] of the streambed). First, we used a bioassessment data set of 1249 sites in California to create logistic regression models of the likelihood of achieving several biointegrity goals for each index along increasing gradients of each indicator. Then, we evaluated eutrophication thresholds at concentrations corresponding to several relative probabilities (from 50 to 95 %), reflecting a range of policy makers’ potential tolerance for risk of failing to meet biointegrity goals. Finally, we validated the thresholds with relative risk assessment, and identified the lowest validated threshold across the three indices. All eutrophication indicators were significantly associated with increased risks to biointegrity, resulting in a set of validated thresholds for each biointegrity goal. For example, thresholds of 0.24 mg/L TN, 0.05 mg/L TP, 44 mg/m2 benthic chl-a, 2.5 mg/cm2 AFDM, and 26 % cover would achieve index scores above the 10th percentile of reference (a biointegrity goal that has been used in certain regulatory applications) with an 80 % probability. If these thresholds were applied ambient monitoring statewide dataset, TN was the most pervasively exceeded threshold (37 % of sites), particularly within the highly agricultural Central Valley (76 %), although exceedances of TN and AFDM were also common in the urbanized South Coast region (68 % and 72 % of sites, respectively).

Streamflow duration information underpins many management decisions. However, hydrologic data are rarely available where needed. Rapid streamflow duration assessment methods (SDAMs) classify reaches based on indicators that are measured in a single brief visit. We evaluated a proposed framework for developing SDAMs to develop an SDAM for the Arid West United States that can classify reaches as perennial, intermittent, or ephemeral. We identified 41 candidate biological, geomorphological, and hydrological indicators of streamflow duration in a literature review, evaluated them for a number of desirable criteria (e.g., defensibility and consistency), and measured 21 of them at 89 reaches with known flow durations. We selected metrics for the SDAM based on their ability to discriminate among flow duration classes in analyses of variance, as well as their importance in a random forest model to predict streamflow duration. This approach resulted in a "beta" SDAM that uses five biological indicators. It could discriminate between ephemeral and non-ephemeral reaches with 81% accuracy, but only 56% accuracy when distinguishing 3 classes. A final method will be developed following expanded data collection. This Arid West study demonstrates the effectiveness of our approach and paves the way for more efficient development of scientifically informed SDAMs.

Streamflow duration is used to differentiate reaches into discrete classes (e.g., perennial, intermittent, and ephemeral) for water resource management. Because the depiction of the extent and flow duration of streams via existing maps, remote sensing, and gauging is constrained, field-based tools are needed for use by practitioners and to validate hydrography and modeling advances. Streamflow Duration Assessment Methods (SDAMs) are rapid, reach-scale indices or models that use physical and biological indicators to predict flow duration class. We review the scientific basis for indicators and present conceptual and operational frameworks for SDAM development. Indicators can be responses to or controls of flow duration. Aquatic and terrestrial responses can be integrated into SDAMs, reflecting concurrent increases and decreases along the flow duration gradient. The conceptual framework for data-driven SDAM development shows interrelationships among the key components: study reaches, hydrologic data, and indicators. We present a generalized operational framework for SDAM development that integrates the data-driven components through five process steps: preparation, data collection, data analysis, evaluation, and implementation. We highlight priorities for the advancement of SDAMs, including expansion of gauging of nonperennial reaches, use of citizen science data, adjusting for stressor gradients, and statistical and monitoring advances to improve indicator effectiveness.

The Biological Condition Gradient (BCG) is a conceptual model that describes changes in aquatic communities under increasing levels of anthropogenic stress. The BCG helps decision-makers connect narrative water quality goals (e.g., maintenance of natural structure and function) to quantitative measures of ecological condition by linking index thresholds based on statistical distributions (e.g., percentiles of reference distributions) to expert descriptions of changes in biological condition along disturbance gradients. As a result, the BCG may be more meaningful to managers and the public than indices alone. To develop a BCG model, biological response to stress is divided into 6 levels of condition, represented as changes in biological structure (abundance and diversity of pollution sensitive versus tolerant taxa) and function. We developed benthic macroinvertebrate (BMI) and algal BCG models for California perennial wadeable streams to support interpretation of percentiles of reference-based thresholds for bioassessment indices (i.e., the California Stream Condition Index [CSCI] for BMI and the Algal Stream Condition Index [ASCI] for diatoms and soft-bodied algae). Two panels (one of BMI ecologists and the other of algal ecologists) each calibrated a general BCG model to California wadeable streams by first assigning taxa to specific tolerance and sensitivity attributes, and then independently assigning test samples (264 BMI and 248 algae samples) to BCG Levels 1-6. Consensus on the assignments was developed within each assemblage panel using a modified Delphi method. Panels then developed detailed narratives of changes in BMI and algal taxa that correspond to the 6 BCG levels. Consensus among experts was high, with 81% and 82% expert agreement within 0.5 units of assigned BCG level for BMIs and algae, respectively. According to both BCG models, the 10th percentiles index scores at reference sites corresponded to a BCG Level 3, suggesting that this type of threshold would protect against moderate changes in structure and function while allowing loss of some sensitive taxa. The BCG provides a framework to interpret changes in aquatic biological condition along a gradient of stress. The resulting relationship between index scores and BCG levels and narratives can help de-cision-makers select thresholds and communicate how these values protect aquatic life use goals.

Predictive biological indices have transformed the bioassessment landscape by allowing universal indices to be applicable across diverse environments. The successful development of a predictive benthic macroinvertebrate index for California wadeable streams helped to demonstrate the power of these tools in complex geographic settings. However, previous efforts to develop predictive algal indices for California were limited by poor performance and were ultimately unsuccessful. For this study, we leveraged a robust statewide dataset to develop two different types of predictive algal indices for California wadeable streams: an index of observed-to-expected taxa (O/E) to measure taxonomic completeness and a multimetric index (MMI) to evaluate ecological structure. We developed multiple versions of each index, including one for diatoms, one for soft-bodied algae, and a hybrid index using both assemblages. We evaluated index performance using a series of screening criteria for precision, accuracy, responsiveness, and regional bias. We found that final index performance varied among all assemblages: the best performing O/E index was a diatom-only index, whereas the predictive diatom and hybrid MMIs out-performed all other indices with excellent responsiveness and precision. We found that in comparison to benthic macroinvertebrates, algal communities were characterized by high beta diversity across reference sites and low average species richness per site, resulting in disparate algal populations that were challenging to model with predictive approaches, particularly for soft-bodied algae assemblages. While all O/E indices were considered to have weak performance, the predictive diatom and hybrid MMIs are accurate, responsive, and precise indices that will provide a powerful assessment of biological condition for statewide applications.

Open science principles that seek to improve science can effectively bridge the gap between researchers and environmental managers. However, widespread adoption has yet to gain traction for the development and application of bioassessment products. At the core of this philosophy is the concept that research should be reproducible and transparent, in addition to having long-term value through effective data preservation and sharing. In this article, we review core open science concepts that have recently been adopted in the ecological sciences and emphasize how adoption can benefit the field of bioassessment for both prescriptive condition assessments and proactive applications that inform environmental management. An example from the state of California demonstrates effective adoption of open science principles through data stewardship, reproducible research, and engagement of stakeholders with multimedia applications. We also discuss technical, sociocultural, and institutional challenges for adopting open science, including practical approaches for overcoming these hurdles in bioassessment applications.

Abstract Ecological monitoring of streams has often focused on assessing the biotic integrity of individual benthic macroinvertebrate (BMI) communities through local measures of diversity, such as taxonomic or functional richness. However, as individual BMI communities are frequently linked by a variety of ecological processes at a regional scale, there is a need to assess biotic integrity of groups of communities at the scale of watersheds. Using 4,619 sampled communities of streambed BMIs, we investigate this question using co‐occurrence networks generated from groups of communities selected within California watersheds under different levels of stress due to upstream land use. Building on a number of arguments in theoretical ecology and network theory, we propose a framework for the assessment of the biotic integrity of watershed‐scale groupings of BMI communities using measures of their co‐occurrence network topology. We found significant correlations between stress, as described by a mean measure of upstream land use within a watershed, and topological measures of co‐occurrence networks such as network size (r = −.81, p < 10–4), connectance (r = .31, p < 10–4), mean co‐occurrence strength (r = .25, p < 10–4), degree heterogeneity (r = −.10, p < 10–4), and modularity (r = .11, p < 10–4). Using these five topological measures, we constructed a linear model of biotic integrity, here a composite of taxonomic and functional diversity known as the California Stream Condition Index, of groups of BMI communities within a watershed. This model can account for 66% of among‐watershed variation in the mean biotic integrity of communities. These observations imply a role for co‐occurrence networks in assessing the current status of biotic integrity for BMI communities, as well as their potential use in assessing other ecological communities.

The aim of this study is to test a spatially explicit statistical model to identify indicators of natural stream flow using readily available stream, climate and landscape data. Understanding flow behavior of unmonitored streams at different temporal scales using environmental indicators is of great interest considering the logistic constraints of providing comprehensive flow instrumentation for all stream reaches in a region. Our results have applications to assess human impact in watersheds, to study environmental changes in fresh water resources, and in the management of local ecosystem. This study uses classification and regression tree analysis to identify significant explanatory variables for a predictive model of stream flow in semi-arid watersheds of southern California, USA. The study collected 77 variables with 30 years record, for a set of 48 sites, interpolated to create raster files at 30m spatial resolution. After applying Pearson correlation analyses to eliminate redundant variables, nine variables were found to have strong positive predictive value for estimating stream flow at ungauged sites. Nine prediction rasters portraying spatial variation of stream flow for the study region at three key index months during wet, dry, and average rainfall conditions. The predictive power of the variables was tested and cross validated over a subset of data not included when building the model. Model validation by site at monthly temporal resolution showed mixed results. While some sites where accurately predicted others did not. The comparison of observed vs predicted values by month suggest that this statistically based approach is able to predict the general patterns of stream flow at the regional scale, however it may be inaccurate in estimating actual flow values by month since the models tends to under-predict monthly discharge.

Assessment of stream health is a function of the physical, chemical, and biological integrity of the water body. While monitoring of all three indicator types is common, combining them into a unified assessment of stream quality is rare. In this study, a unified index was developed that compares biological response to physical and chemical stressors for southern California wadeable streams using a scientifically rigorous, easy-to-understand tool intended to facilitate stream management. The Stream Quality Index (SQI) is based on a stressor-response empirical model that quantifies the expected likelihood that chemical and physical stressors will impact multiple components of biological condition. While the individual stressor and response components are quantitative and have similar meaning across a variety of environmental settings, the final SQI narrative assessment is categorical and designed to be directly actionable within a management context. The four narrative assessment categories are: (1) “healthy and unstressed” (i.e., unimpacted biology, no stressors); (2) “healthy and resilient” (i.e., stressed, but biological communities are healthy); (3) “impacted and stressed” (i.e., impacted biology from observed stressors); and (4) “impacted by unknown stress” (i.e., biology is impacted, but stressors are low). To facilitate adoption by managers, a web-based application was developed that not only maps overall SQI results, but also enables users to readily access underlying quantitative information for stressors and biological responses. This transparent design was intended; high-level output and foundational components of the SQI are relevant for different audiences and details are not sacrificed for accessibility.

Stream management goals for biological integrity may be difficult to achieve in developed landscapes where channel modification and other factors constrain in-stream conditions. To evaluate potential constraints on biological integrity, we developed a statewide landscape model for California that estimates ranges of likely scores for a macroinvertebrate-based index that are typical at a site with the observed level of landscape alteration. This context can support prioritization decisions for stream management, like identifying reaches for restoration or enhanced protection based on how observed scores relate to model predictions. Median scores were accurately predicted by the model for all sites in California with bioassessment data (Pearson correlation r = 0.75 between observed and predicted for calibration data, r = 0.72 for validation). The model also predicted that 15% of streams statewide are constrained for biological integrity within their present developed landscape, particularly for urban and agricultural areas in the South Coast, Central Valley, and Bay Area regions. We worked with a local stakeholder group from the San Gabriel River watershed (Los Angeles County, California) to evaluate how the statewide model could support local management decisions. To achieve this purpose, we created an interactive application, the Stream Classification and Priority Explorer, that compares observed scores with predictions from the landscape model to assign priorities. We observed model predictions consistent with the land-use gradient from the upper to lower watershed, where potential limits to achieving biological integrity were more common in the heavily-urbanized lower watershed. However, most of the sites in the lower watershed scored within their predicted ranges, and were therefore given a low priority for restoration. In contrast, 2 low-scoring sites in the undeveloped upper watershed were prioritized for causal assessment and possible future restoration, whereas 3 high-scoring sites were prioritized for protection. The availability of geospatial and bioassessment data at the national level suggests that these tools can easily be applied to inform management decisions at other locations where altered landscapes may limit biological integrity.

Sites in poor ecological condition often require causal assessment to determine appropriate follow-up actions. Site-specific causal assessments can be time consuming. To streamline the process, we describe a quantitative method that expedites a key component of causal assessment: identifying a group of ecologically similar (comparator) sites that are used to compare and contrast biological condition and stressor exposure at the site of interest. A good set of comparator sites should: 1. Be capable of supporting similar biota to the impaired site in the absence of disturbance; 2. Comprise a gradient of biotic condition; and 3. Contain enough sites to assess variability. We used expected biological similarity to select good sets of comparator sites from a large pool of potential sites. Expected biological similarity was measured as Bray-Curtis dissimilarity values (BC) calculated from the expected benthic macroinvertebrate taxa lists produced by a predictive biotic index of stream health. Sets of comparator sites were created for 15 demonstration sites across Southern California in poor condition. We examined the stressor and biological data collected at the 15 sites and their comparators to assess the likelihood that four example stressors - total nitrogen, ammonia, specific conductivity, and bifenthrin - contribute to the poor biotic conditions that were observed. We were able to select more than 100 comparator sites for all but 1 of the 15 demonstration sites at a BC <0.1. These sets of comparator sites were then used to evaluate the four example stressors using two commonly used causal assessment types of evidence. Elevated conductivity was the most frequently supported likely cause among the demonstration sites, though total nitrogen and bifenthrin were also indicated at some sites. Though our specific approach was tailored for application in California's stream bioassessment framework, the concepts could be adapted for any bioassessment program with a large amount of sample data and an associated predictive index of biotic condition. Furthermore, this approach lays the groundwork for developing a novel approach to causal assessment that begins with a rapid, screening-level evaluation of stressors common in a region using these data-rich groups of comparator sites, which then informs follow-up management actions.