Using DNA metabarcoding to characterize national scale diatom-environment relationships and to develop indicators in streams and rivers of the United States

Recent advancements in DNA techniques, metabarcoding, and bioinformatics could help expand the use of benthic diatoms in monitoring and assessment programs by providing relatively quick and increasingly cost-effective ways to quantify diatom diversity in environmental samples. However, such applications of DNA-based approaches are relatively new, and in the United States, unknowns regarding their applications at large scales exist because only a few small-scale studies have been done. Here, we present results from the first nationwide survey to use DNA metabarcoding (rbcL) of benthic diatoms, which were collected from 1788 streams and rivers across nine ecoregions spanning the conterminous USA. At the national scale, we found that diatom assemblage structure (1) was strongly associated with total phosphorus and total nitrogen concentrations, conductivity, and pH and (2) had clear patterns that corresponded with differences in these variables among the nine ecoregions. These four variables were strong predictors of diatom assemblage structure in ecoregion-specific analyses, but our results also showed that diatom-environment relationships, the importance of environmental variables, and the ranges of these variables within which assemblage changes occurred differed among ecoregions. To further examine how assemblage data could be used for biomonitoring purposes, we used indicator species analysis to identify ecoregion-specific taxa that decreased or increased along each environmental gradient, and we used their relative abundances of gene reads in samples as metrics. These metrics were strongly correlated with their corresponding variable of interest (e.g., low phosphorus diatoms with total phosphorus concentrations), and generalized additive models showed how their relationships compared among ecoregions. These large-scale national patterns and nine sets of ecoregional results demonstrated that diatom DNA metabarcoding is a robust approach that could be useful to monitoring and assessment programs spanning the variety of conditions that exist throughout the conterminous United States.

Improving the performance of macroinvertebrate based multi-metric indices by incorporating functional traits and an index performance-driven approach

Human-driven multiple pressures impact freshwater ecosystems worldwide, reducing biodiversity, and impacting ecosystem functioning and services provided to human societies. Multi-metric indices (MMIs) are suitable tools for tracking the effects of anthropogenic pressures on freshwater ecosystems because they incorporate various biological metrics responding to multiple pressures at different levels of biological organization. However, the performance and applicability of MMIs depend on their metrics' selection and their calibration against natural environmental gradients. In this study, we aimed to unravel i) how incorporating functional trait-based metrics affects the performance of MMIs, ii) how disentangling the natural environmental gradients from anthropogenic pressures effects affects the performance of MMIs, and iii) how the performance of MMIs developed using a metric performance-driven approach compares with MMIs developed using an index performance-driven approach. We carried out a field survey measuring abiotic and biotic variables at 53 sites in the Karun River basin (Iran) in 2018. For functional trait-based metrics, we used 15 macroinvertebrate traits and calculated community-weighted mean trait values and functional diversity indices. We used random forest modeling to account for the effect of natural environmental gradients on each metric. Based on our results, incorporating functional traits increased the MMI performance significantly and facilitated ecological interpretation of MMIs. Both taxonomic and functional components of macroinvertebrate assemblages co-varied strongly with natural environmental gradients, and accounting for these covariations improved the performance of MMIs. Finally, we found that index performance-driven MMIs performed better in terms of precision, bias, sensitivity, and responsiveness than metric performance-driven MMIs.

Development of a macroinvertebrate-based biotic index to assess water quality of rivers in Niger State, North Central Ecoregion of Nigeria

The increasing pollution of lotic ecosystems in sub-Saharan Africa, particularly in Nigeria, poses a threat to water quality, public health and biodiversity. It is therefore essential to develop appropriate tools and methods for monitoring these rivers, particularly in heavily affected areas, where these water resources are vital to the surrounding communities that are heavily dependent on them. To fill this gap, we propose to develop a multimetric index based on macroinvertebrates for the assessment of ecological quality of rivers in Niger State (NSRBI). Eighty-eight metrics were evaluated through a step-by-step statistical process (namely, range test and stability, redundancy test and relationship with abiotic variables), in which metrics that did not meet the conditions were excluded. At the end of this process, only four metrics (%Hemiptera, Diptera richness, Pielou equitability and % of very large individuals (size > 40 mm)) fulfilling all criteria were included in the index. These metrics were then scored on a continuous scale and divided into four water quality classes: "very poor", "poor", "fair" and "good". Evaluation of the performance of the index on test sites showed a correspondence of 90% between index result and environmental-based classification. Therefore, the NSRBI could be a valuable tool for monitoring and assessing the ecological conditions of rivers in Niger State and the North Central Nigeria ecoregion predominantly in urban and agricultural landscapes.

Omernik's Ecoregion Framework: a Legacy for Understanding Regional Patterns in Attainable Resource Quality

An initial and comprehensive map of ecological regions across the conterminous United States was provided by Omernik in 1987. Because that paper was the most-cited published by the Annals of the American Association of Geographers, we sought to assess and quantify its contribution to science. To do so, we conducted a scientometric analysis to address the following main questions: 1) What are the temporal and spatial citation trends? We expected that Omernik's paper would still be employed 36 years after its publication, and mostly in the United States of America. 2) For what types of environments and organisms has it been applied? Based on its generality, we expected that it had been applied to both terrestrial and aquatic ecosystems. 3) What are the main applications of Omernik's article? We predicted that it would mostly be used for describing and delineating study sites and management areas, as well as for selecting regional reference sites. The number of citations presented a positive temporal increase, indicating its continued applicability. Most papers dealt with aquatic environments, mainly in streams carried out predominantly in the United States of America, as was one of its earliest applications. The usefulness of ecoregions for assessing and managing biotic and abiotic patterns and distributions were the main topics addressed by scientists. Ecoregions have offered a general framework for developing regional expectations and rational regional management policies across large areas, as was their original intent. In addition, ecoregion maps were used for communicating patterns-or the lack of them-to interested scientists, citizens, and decision-makers. That comprehensiveness of Omernik's ecoregion approach has led to its widespread applicability and continued usefulness to a diverse set of scientific and management disciplines.

Striving for consistent bioassessment across diverse landscapes: Using land use matters for classifying reference and disturbed sites for index development

Consistency in ecological assessments is challenging across large diverse landscapes because natural geological, climatic, and hydrological factors vary greatly. As a result, large landscapes are often subdivided into ecoregions and assessments are based on ecoregion specific indices. In the present study, we developed and compared multimetric indices (MMIs) using benthic diatom data from the 2008-2009 dataset from the United States (US) National Rivers and Streams Assessment. Nationwide and separate ecoregion specific MMIs were developed with reference, moderately disturbed, and highly disturbed sites selected using criteria based on physicochemical condition of the habitat or based on watershed land use (% agriculture and % urban). Metrics were adjusted to account for variation in natural conditions when needed. We found only land use criteria selected reference sites with consistently low median % watershed disturbance (%WD) and large differences in %WD between reference and highly disturbed sites. <38 % of sites were identified as reference or highly disturbed by both physicochemical and land use criteria. All MMIs displayed substantial discrimination ability between reference and highly disturbed sites. At the national scale, MMIs based on land use outperformed MMIs based on physicochemical conditions for all performance attributes tested. When national scale MMIs were applied to ecoregions, MMIs based on land use were again better than MMIs based on physicochemical conditions for most performance attributes and even had better or comparable performance to the land use MMIs developed separately for each ecoregion. Our findings show that developing MMIs using land use criteria and adjusting metrics for natural variation could improve assessment consistency without losing MMI performance across large, diverse landscapes as in the US National Rivers and Streams Assessment.

A review of the application of the macroinvertebrate-based multimetric indices (MMIs) for water quality monitoring in lakes

The increasing stress on lake ecosystems is affecting their functioning such as providing goods and services to inhabiting organisms and riparian communities. Monitoring of water quality is important for sustainable management and restoration of lake ecosystems. However, the costs associated with traditional approaches have become prohibitive, while not giving reliable early warning signals on resource conditions. Thus, the current shift in the use of bioindicators and multimetric indices (MMIs) in the monitoring of water quality is currently gaining global recognition with more emphasis on its application in lotic ecosystems. Therefore, this paper provides an elaborated insight into the application of macroinvertebrate-based MMIs in lentic ecosystems and the successes achieved so far. The various metrics and indices, the development strategies, application challenges, the use of macroinvertebrates as bioindicators, and the future projection of enhancing MMI usage in lentic environment monitoring, particularly in developing countries, are extensively covered. The use of MMI as a rapid lake biomonitoring tool needs to be adopted for sustainable applications in lake ecosystem management and as an integrated approach to monitoring human-induced stress especially in developing countries where there is a paucity of information.

Genus-level, trait-based multimetric diatom indices for assessing the ecological condition of rivers and streams across the conterminous United States

Taxonomic inconsistency in species-level identifications has constrained use of diatoms as biological indicators in aquatic assessments. We addressed this problem by developing diatom multimetric indices (MMIs) of ecological condition using genus-level taxonomy and trait-based autecological information. The MMIs were designed to assess river and stream chemical, physical and biological condition across the conterminous United States. Trait-based approaches have the advantage of using both species-level and genus-level data, which require less effort and expense to acquire than traditional species-based approaches and eliminate the persistent taxonomic biases introduced over vast geographic extents. For large-extent assessment programs that require multiple taxonomic laboratories to process samples, such as the United States Environmental Protection Agency's (U.S. EPA's) National Rivers and Streams Assessment (NRSA), the trait approach can eliminate discrepancies in species-level identification or nomenclature that hinder diatom data interpretation. We developed trait-based MMIs using NRSA data for each of the three large ecoregions across the U.S. - the East, Plains, and West. All three MMIs performed well in discriminating least-disturbed from most-disturbed sites. The MMI for the East had the greatest discrimination ability, followed by MMIs for the Plains and West, respectively. The performance of the MMIs was comparable to that observed in existing NRSA fish and macroinvertebrate MMIs. Our research shows that trait-based diatom indices constructed on genus-level taxonomy can be effective for large-scale assessments, and may also allow programs such as NRSA to assess trends in freshwater condition retrospectively, by revisiting older diatom datasets. Moreover, our genus-based approach facilitates including of diatoms into other assessment programs that have limited monitoring resources.

A web-based tool for assessing the condition of benthic diatom assemblages in streams and rivers of the conterminous United States

Benthic diatom assemblages are known to be indicative of water quality but have yet to be widely adopted in biological assessments in the United States due to several limitations. Our goal was to address some of these limitations by developing regional multi-metric indices (MMIs) that are robust to inter-laboratory taxonomic inconsistency, adjusted for natural covariates, and sensitive to a wide range of anthropogenic stressors. We aggregated bioassessment data from two national-scale federal programs and used a data-driven analysis in which all-possible combinations of 2-7 metrics were compared for three measures of performance. After ranking the best-performing MMIs, we selected the final MMIs by evaluating stress-response relations in independent regional datasets of diatom samples paired with measures of several water-quality stressors, including herbicides and streamflow flashiness. Each regional MMI performed well at calibration sites and represented diverse aspects of the structure and function of diatom communities. Most metrics included in the best MMIs were modeled to account for natural variation including climate, topography, soil characteristics, lithology, and groundwater influence on streamflow. MMI performance improved with higher numbers of component metrics, but this effect diminished beyond six metrics. Component metrics of MMIs were associated with a broad suite of measured stressors in every region, including salinity, nutrients, herbicides, and streamflow flashiness. We provide a web-based software application that allows users in the conterminous United States to apply our MMIs to their own datasets and compare MMI scores from their sites to a broader regional context.

Improving biological condition assessment accuracy by multimetric index approach with microalgae in streams and lakes

Multimetric index (MMI) approach is a broadly used in ecological assessment because it can integrate information of various kinds of ecologically related metrics of freshwater ecosystems and provide an easily understandable score for purpose of further evaluation and managements. Accounting for natural variation and disentangling covariation between natural environmental factors and human disturbance factors are imperative for an accurate assessment. Lots of progress has been made recently on the aforementioned two aspects. Three approaches, a priori classification of sites by regions or typologies, site-specific modeling of expected reference condition and varying metrics in site groups, have been tested in lakes and streams to improve assessment accuracy. All existed studies support that site-specific modeling can efficiently account for natural variation and generate a MMI with good performance. However, until now, no strong evidence has shown that diatom/blue-algae typologies are better than regionalization frameworks on accounting for natural variation either in lakes or in streams. To separate the natural variation explained by site specific modeling from that of varying metrics is necessary for a thorough and accurate evaluation on the valuableness of site-grouping by typologies. Different performance of varying metrics among site groups of streams and lakes was most probably caused by the lack of representativeness of diatom metrics on biological condition rather than the complex multi-stressor gradients in streams and rivers. A recent study showed that blue-green algae enhanced performance of diatom-based MMI on defining lake condition under high level of human disturbance. On the other hand, with more and more extensive and intensive use of statistics techniques in developing MMI, we also discussed some statistical challenges faced by scientists in field of ecological assessment, especially on setting significance level of a statistical test and multiple comparison issue in MMI performance comparison.

Resources and Practices to Improve Diatom Data Quality

Environmental programs in the United States face technical challenges that inhibit the ability to use diatoms in water quality monitoring and assessment projects. Specifically, inconsistent taxonomy can obscure diatom responses to environmental variables. Problems are the result of (1) limited access to a common set of taxonomic references, especially those that are geographically relevant, (2) inefficient enumeration protocols, (3) lack of complete and transparent documentation of taxa, and (4) limited opportunities for continued education, training, and knowledge sharing. However, robust resources and practices are available to improve diatom data quality and interpretation. Several resources improve diatom data quality, including a publicly accessible taxonomic reference (diatoms.org) and recommended practices. These practices include adoption of the voucher floras, random sample assignment, replicate microscope slides, and improved quality control. Finally, the Society for Freshwater Science Diatom Taxonomic Certification Committee is developing educational materials and certification exams to support practitioner training and to increase the diatom research knowledge base. The resources and practices in this article are broadly applicable to improving basic and applied research on diatoms worldwide.

The importance of natural versus human factors for ecological conditions of streams and rivers

Streams are influenced by watershed-scale factors, such as climate, geology, topography, hydrology, and soils, which mostly vary naturally among sites, as well as human factors, agriculture and urban development. Thus, natural factors could complicate assessment of human disturbance. In the present study, we use structural equation modeling and data from the 2008-2009 United States National Rivers and Streams Assessment to quantify the relative importance of watershed-scale natural and human factors for in-stream conditions. We hypothesized that biological condition, represented using a diatom multimetric index (MMI), is directly affected by in-stream physicochemical environment, which in turn is regulated by natural and human factors. We evaluated this hypothesis at both national and ecoregion scales to understand how influences vary among regions. We found that direct influences of in-stream environment on diatom MMIs were greater than natural and human factors at the national scale and in all but one ecoregion. Meanwhile, in-stream environments were jointly explained by natural variations in precipitation, base flow index, hydrological stability, % volcanic rock, soil water table depth, and soil depth and by human factors measured as % crops, % other agriculture, and % urban land use. The explained variance of in-stream environment by natural and human factors ranged from 0.30 to 0.75, for which natural factors independently accounted for the largest proportion of explained variance at the national scale and in seven ecoregions. Covariation between natural and human factors accounted for a higher proportion of explained variance of in-stream environment than unique effects of human factors in most ecoregions. Ecoregions with relatively weak effects by human factors had relatively high levels of covariance, high levels of human disturbance, or small ranges in human disturbance. We conclude that accounting for effects of natural factors and their covariation with human factors will be important for accurate ecological assessments.

Reduction of taxonomic bias in diatom species data

Inconsistency in taxonomic identification and analyst bias impede the effective use of diatom data in regional and national stream and lake surveys. In this study, we evaluated the effect of existing protocols and a revised protocol on the precision of diatom species counts. The revised protocol adjusts five elements of sample preparation, taxon identification and enumeration, and quality control (QC) samples. We used six independent datasets to assess the effect of the adjustments on analytical outcomes. The first dataset was produced by five analysts from three laboratories following a standard protocol (Charles et al. 2002). The remaining datasets were produced by 2-3 analysts in 1-3 laboratories following a revised protocol. The revised protocol included the following modifications: 1) use of Battarbee settling chambers to prepare coverslips, 2) development of coordinated pre-count voucher floras based on morphological operational taxonomic units (mOTUs), 3) random assignment of samples to analysts, 4) post-count identification and documentation of taxa, and 5) increased QC samples. The revised protocol reduced taxonomic bias, as measured by reduction in analyst signal, and improved similarity among QC samples. Reduced taxonomic bias improves the performance of biological assessments, facilitates transparency across studies, and refines estimates of diatom species distributions.

Incorporating functional traits to enhance multimetric index performance and assess land use gradients

Taxonomic-based multimetric indices (MMIs) have been widely employed for assessing ecosystem status, particularly through the use of stream macroinvertebrate assemblages. However, the functional diversity and composition of assemblages is also important for maintaining stream ecosystem condition. Nonetheless, aquatic insect functional diversity and composition have not commonly been included in MMIs. Our goal was to advance our understanding of the performance and ecological interpretation of an MMI that potentially combined functional and taxonomic metrics. We sampled aquatic insects and natural and land-use variables at 74 temperate Chinese streams. We selected a candidate set of 36 functional and 20 taxonomic metrics that were screened by range tests, natural variation, responsiveness to anthropogenic disturbance, and redundancy for subsequent inclusion in MMIs. We determined if natural variation adjustments improved the performance of a functional-taxonomic MMI. Finally, we evaluated the degree to which the functional-taxonomic MMI served as an early-warning indicator of land use intensity. Natural variation explained between 19.62% and 71.02% of metric variability, indicating that functional metrics changed systematically along natural gradients. The final functional-taxonomic MMI adjusted for natural variation incorporated multiple aspects of assemblage characteristics: functional richness, Rao's quadratic entropy, abundance-weighted frequency of soft bodies, abundance-weighted frequency of predators, and number of Diptera taxa. In contrast to the natural variation unadjusted MMI, the functional-taxonomic adjusted MMI clearly distinguished least-disturbed sites from most-disturbed sites, exhibited high precision and low bias, and showed a significant negative response to land uses. The slope of a linear regression relative to 0-10% urban and 0-20% agriculture was significantly steeper for the functional-taxonomic adjusted MMI than that of the taxonomic adjusted MMI. We conclude that functional-taxonomic adjusted MMIs are more effective indicators of ecological condition and risks to biota from human pressures than are purely taxonomic unadjusted MMIs because functional-taxonomic MMIs are more sensitive to subtle anthropogenic pressures.

A national-scale vegetation multimetric index (VMMI) as an indicator of wetland condition across the conterminous United States

In 2011, the US Environmental Protection Agency and its partners conducted the first National Wetland Condition Assessment at the continental-scale of the conterminous United States. A probability design for site selection was used to allow an unbiased assessment of wetland condition. We developed a vegetation multimetric index (VMMI) as a parsimonious biological indicator of ecological condition applicable to diverse wetland types at national and regional scales. Vegetation data (species presence and cover) were collected from 1138 sites that represented seven broad estuarine intertidal and inland wetland types. Using field collected data and plant species trait information, we developed 405 candidate metrics with potential for distinguishing least disturbed (reference) from most disturbed sites. Thirty-five of the metrics passed range, repeatability, and responsiveness screens and were considered as potential component metrics for the VMMI. A permutation approach was used to calculate thousands of randomly constructed potential national-scale VMMIs with 4, 6, 8, or 10 metrics. The best performing VMMI was identified based on limited redundancy among constituent metrics, sensitivity, repeatability, and precision. This final VMMI had four broadly applicable metrics (floristic quality index, relative importance of native species, richness of disturbance-tolerant species, and relative cover of native monocots). VMMI values and weights from the survey design for probability sites (n = 967) were used to estimate wetland area in good, fair, and poor condition, nationally and for each of 10 ecoregion by wetland type reporting groups. Strengths and limitations of the national VMMI for describing ecological condition are highlighted.

Striving for consistency in the National Wetland Condition Assessment: developing a reference condition approach for assessing wetlands at a continental scale

One of the biggest challenges when conducting a continental-scale assessment of wetlands is setting appropriate expectations for the assessed sites. The challenge occurs for two reasons: (1) tremendous natural environmental heterogeneity exists within a continental landscape and (2) reference sites vary in quality both across and within major regions of the continent. We describe the process used to set reference expectations and define a disturbance gradient for the United States (US) Environmental Protection Agency's National Wetland Condition Assessment (NWCA). The NWCA employed a probability design and sampled 1138 wetland sites across the conterminous US to make an unbiased assessment of wetland condition. NWCA vegetation data were used to define 10 reporting groups based on ecoregion and wetland type that reduced the naturally occurring variation in wetland vegetation associated with continent-wide differences in biogeography. These reporting groups were used as a basis for defining quantitative criteria for least disturbed and most disturbed conditions and developing indices and thresholds for categories of ecological condition and disturbance. The NWCA vegetation assessment was based on a reference site approach, in which the least disturbed reference sites were used to establish benchmarks for assessing the condition of vegetation at other sites. Reference sites for each reporting group were identified by filtering NWCA sample data for disturbance using a series of abiotic variables. Ultimately, 277 least disturbed sites were used to set reference expectations for the NWCA. The NWCA provided a unique opportunity to improve our conceptual and technical understanding of how to best apply a reference condition approach to assessing wetlands across the US. These results will enhance the technical quality of future national assessments.

Advancing evaluation of bioassessment methods: A reply to Liu and Cao

A series of three papers was written about the development of multimetric indices (MMIs) using diatoms in rivers, streams and lakes for transcontinental surveys conducted by the United States Environmental Protection Agency. Stevenson et al. (2013) used the surface sediment diatom data from the 2007 National Lake Assessment to develop national scale site specific models for MMIs to account for natural variation in condition among sites. Liu and Stevenson (2017) also used the 2007 lakes data to evaluate performance of MMIs by grouping sites by ecoregions or typologies (naturally similar types of lakes defined by similarity in diatom species composition) with site specific metric models (SSMMs) that adjust metrics for natural variability among sites. Tang et al. (2016) used benthic diatom data from the 2008-2009 National River and Stream Assessment to develop SSMMs and MMIs by ecoregion and typology. All three studies showed that SSMMs improved performance of diatom MMIs by accounting for natural variation among sites. None of the studies provided consistent evidence that grouping sites by typologies produced better MMI performance than grouping sites by ecoregions. Liu and Cao (2018) criticized the Tang et al. (2016) paper for using means and standard errors to evaluate relative performance of MMI calculation methods at the site group scale, however, their criticism is incorrect. Actually, Tang et al. (2016) only used means to summarize and report relative performance of MMI calculation methods in the body of the paper. Tang et al. (2016) appropriately used non-parametric rank sum approaches to evaluate the probability that the multiple MMI calculations for separate site groups were the same for ecoregion (n = 9) and typology (n = 7) site groups. Liu and Stevenson (2017) used this same non-parametric approach for tests of lake diatom MMIs. Liu and Cao's (2018) concerns can be addressed by distinguishing between the goals and methods used for testing and evaluation of MMI calculation methods at the national and site-group scales. Tang et al. (2016) did not aggregate data across site groups to test MMI performance at the national scale because they were following standard EPA methods that develop separate MMIs for each site group. In conclusion, Liu and Cao (2018) misunderstood the MMI evaluation in Tang et al. (2016) and added no new information to this body of work, because all the concerns they raised were discussed in Liu and Stevenson (2017).

Development of a diatom-based multimetric index for acid mine drainage impacted depressional wetlands

Acid mine drainage (AMD) from coal mining in the Mpumalanga Highveld region of South Africa has caused severe chemical and biological degradation of aquatic habitats, specifically depressional wetlands, as mines use these wetlands for storage of AMD. Diatom-based multimetric indices (MMIs) to assess wetland condition have mostly been developed to assess agricultural and urban land use impacts. No diatom MMI of wetland condition has been developed to assess AMD impacts related to mining activities. Previous approaches to diatom-based MMI development in wetlands have not accounted for natural variability. Natural variability among depressional wetlands may influence the accuracy of MMIs. Epiphytic diatom MMIs sensitive to AMD were developed for a range of depressional wetland types to account for natural variation in biological metrics. For this, we classified wetland types based on diatom typologies. A range of 4-15 final metrics were selected from a pool of ~140 candidate metrics to develop the MMIs based on their: (1) broad range, (2) high separation power and (3) low correlation among metrics. Final metrics were selected from three categories: similarity to reference sites, functional groups, and taxonomic composition, which represent different aspects of diatom assemblage structure and function. MMI performances were evaluated according to their precision in distinguishing reference sites, responsiveness to discriminate reference and disturbed sites, sensitivity to human disturbances and relevancy to AMD-related stressors. Each MMI showed excellent discriminatory power, whether or not it accounted for natural variation. However, accounting for variation by grouping sites based on diatom typologies improved overall performance of MMIs. Our study highlights the usefulness of diatom-based metrics and provides a model for the biological assessment of depressional wetland condition in South Africa and elsewhere.

Improving assessment accuracy for lake biological condition by classifying lakes with diatom typology, varying metrics and modeling multimetric indices

Site grouping by regions or typologies, site-specific modeling and varying metrics among site groups are four approaches that account for natural variation, which can be a major source of error in ecological assessments. Using a data set from the 2007 National Lakes Assessment project of the USEPA, we compared performances of multimetric indices (MMI) of biological condition that were developed: (1) with different lake grouping methods, ecoregions or diatom typologies; (2) by varying or not varying metrics among site groups; and (3) with different statistical techniques for modeling diatom metric values expected for minimally disturbed condition for each lake. Hierarchical modeling of MMIs, i.e. grouping sites by ecoregions or typologies and then modeling natural variability in metrics among lakes within groups, substantially improved MMI performance compared to using either ecoregions or site-specific modeling alone. Compared with MMIs based on ecoregion site groups, MMI precision and sensitivity to human disturbance were better when sites were grouped by diatom typologies and assessing performance nationwide. However, when MMI performance was evaluated at site group levels, as some government agencies often do, there was little difference in MMI performance between the two site grouping methods. Low numbers of reference and highly impacted sites in some typology groups likely limited MMI performance at the group level of analysis. Varying metrics among site groups did not improve MMI performance. Random forest models for site-specific expected metric values performed better than classification and regression tree and multiple linear regression, except when numbers of reference sites were small in site groups. Then classification and regression tree models were most precise. Based on our results, we recommend hierarchical modeling in future large scale lake assessments where lakes are grouped by ecoregions or diatom typologies and site-specific metric models are used to establish expected metric values.

Predictive mapping of the biotic condition of conterminous U.S. rivers and streams

Understanding and mapping the spatial variation in stream biological condition could provide an important tool for conservation, assessment, and restoration of stream ecosystems. The USEPA's 2008-2009 National Rivers and Streams Assessment (NRSA) summarizes the percentage of stream lengths within the conterminous United States that are in good, fair, or poor biological condition based on a multimetric index of benthic invertebrate assemblages. However, condition is usually summarized at regional or national scales, and these assessments do not provide substantial insight into the spatial distribution of conditions at unsampled locations. We used random forests to model and predict the probable condition of several million kilometers of streams across the conterminous United States based on nearby and upstream landscape features, including human-related alterations to watersheds. To do so, we linked NRSA sample sites to the USEPA's StreamCat Dataset; a database of several hundred landscape metrics for all 1:100,000-scale streams and their associated watersheds within the conterminous United States. The StreamCat data provided geospatial indicators of nearby and upstream land use, land cover, climate, and other landscape features for modeling. Nationally, the model correctly predicted the biological condition class of 75% of NRSA sites. Although model evaluations suggested good discrimination among condition classes, we present maps as predicted probabilities of good condition, given upstream and nearby landscape settings. Inversely, the maps can be interpreted as the probability of a stream being in poor condition, given human-related watershed alterations. These predictions are available for download from the USEPA's StreamCat website. Finally, we illustrate how these predictions could be used to prioritize streams for conservation or restoration.

Evaluating biochemical response of filamentous algae integrated with different water bodies

The present study prompted the second attempts to evaluate biochemical responses of filamentous algae under different physico-chemical variables in various water bodies in Turkey. These variables were investigated by use of multivariate approach in the years of 2013 (May and November) and 2014 (May and October). Studied ecoregions had the different geographic position, climate, land-use, and anthropogenic activities, could strongly affect physico-chemical variables of water bodies, which caused to change or regulate in algal biomass composition due to the different response of filamentous species. Besides, biochemical responses of species changed at different sampling times and stations. Multivariate analyses indicated that temperature, heavy metals, and nutrient contents of aquatic systems were found to be major variables driving the spatial and temporal occurrence and biochemical contents of filamentous species. Total protein and pigment production by filamentous algae were high in water bodies having high nutrients, whereas they were low in high heavy metal contents. Amount of malondialdehyde (MDA), H₂O₂, total thiol group, total phenolic compounds, proline, total carbohydrate, and bioaccumulation of metals by filamentous algae were closely related with heavy metal contents of water bodies, indicated by the multivariate approach. Significant increase in aforementioned biochemical compounds with a distinct range of habitats and sensitive-tolerance to environmental conditions could make them highly valuable indicators.

A Large-Scale, Multiagency Approach to Defining a Reference Network for Pacific Northwest Streams

Aquatic monitoring programs vary widely in objectives and design. However, each program faces the unifying challenge of assessing conditions and quantifying reasonable expectations for measured indicators. A common approach for setting resource expectations is to define reference conditions that represent areas of least human disturbance or most natural state of a resource characterized by the range of natural variability across a region of interest. Identification of reference sites often relies heavily on professional judgment, resulting in varying and unrepeatable methods. Standardized methods for data collection, site characterization, and reference site selection facilitate greater cooperation among assessment programs and development of assessment tools that are readily shareable and comparable. We illustrate an example that can serve the broader global monitoring community on how to create a consistent and transparent reference network for multiple stream resource agencies. We provide a case study that offers a simple example of how reference sites can be used, at the landscape level, to link upslope management practices to a specific in-channel response. We found management practices, particularly areas with high road densities, have more fine sediments than areas with fewer roads. While this example uses data from only one of the partner agencies, if data were collected in a similar manner they can be combined and create a larger, more robust dataset. We hope that this starts a dialog regarding more standardized ways through inter-agency collaborations to evaluate data. Creating more consistency in physical and biological field protocols will increase the ability to share data.

Biochemical responses of filamentous algae in different aquatic ecosystems in South East Turkey and associated water quality parameters

To the best of our knowledge, any study about biochemical response of filamentous algae in the complex freshwater ecosystems has not been found in the literature. This study was designed to explore biochemical response of filamentous algae in different water bodies from May 2013 to October 2014, using multivariate approach in the South East of Turkey. Environmental variables were measured in situ: water temperature, oxygen concentration, saturation, conductivity, salinity, pH, redox potential, and total dissolved solid. Chemical variables of aqueous samples and biochemical compounds of filamentous algae were also measured. It was found that geographic position and anthropogenic activities had strong effect on physico-chemical variables of water bodies. Variation in environmental conditions caused change in algal biomass composition due to the different response of filamentous species, also indicated by FTIR analysis. Biochemical responses not only changed from species to species, but also varied for the same species at different sampling time and sampling stations. Multivariate analyses showed that heavy metals, nutrients, and water hardness were found as the important variables governing the temporal and spatial succession and biochemical compounds. Nutrients, especially nitrate, could stimulate pigment and total protein production, whereas high metal content had adverse effects. Amount of malondialdehyde (MDA), H2O2, total thiol groups, total phenolic compounds, proline, total carbohydrate, and metal bioaccumulation by filamentous algae could be closely related with heavy metals in the ecosystems. Significant increase in MDA, H2O2, total thiol group, total phenolic compounds, and proline productions by filamentous algae and chlorosis phenomenon seemed to be an important strategy for alleviating environmental factors-induced oxidative stress as biomarkers.

Accounting for regional variation in both natural environment and human disturbance to improve performance of multimetric indices of lotic benthic diatoms

Regional variation in both natural environment and human disturbance can influence performance of ecological assessments. In this study we calculated 5 types of benthic diatom multimetric indices (MMIs) with 3 different approaches to account for variation in ecological assessments. We used: site groups defined by ecoregions or diatom typologies; the same or different sets of metrics among site groups; and unmodeled or modeled MMIs, where models accounted for natural variation in metrics within site groups by calculating an expected reference condition for each metric and each site. We used data from the USEPA's National Rivers and Streams Assessment to calculate the MMIs and evaluate changes in MMI performance. MMI performance was evaluated with indices of precision, bias, responsiveness, sensitivity and relevancy which were respectively measured as MMI variation among reference sites, effects of natural variables on MMIs, difference between MMIs at reference and highly disturbed sites, percent of highly disturbed sites properly classified, and relation of MMIs to human disturbance and stressors. All 5 types of MMIs showed considerable discrimination ability. Using different metrics among ecoregions sometimes reduced precision, but it consistently increased responsiveness, sensitivity, and relevancy. Site specific metric modeling reduced bias and increased responsiveness. Combined use of different metrics among site groups and site specific modeling significantly improved MMI performance irrespective of site grouping approach. Compared to ecoregion site classification, grouping sites based on diatom typologies improved precision, but did not improve overall performance of MMIs if we accounted for natural variation in metrics with site specific models. We conclude that using different metrics among ecoregions and site specific metric modeling improve MMI performance, particularly when used together. Applications of these MMI approaches in ecological assessments introduced a tradeoff with assessment consistency when metrics differed across site groups, but they justified the convenient and consistent use of ecoregions.

Quantifying Variability in Four U.S. Streams Using a Long-Term Dataset: Patterns in Biotic Endpoints

Effective water resources assessment and management requires quantitative information on the variability of ambient and biological conditions in aquatic communities. Although it is understood that natural systems are variable, robust estimates of long-term variation in community-based structure and function metrics are rare in U.S. waters. We used a multi-year, seasonally sampled dataset from multiple sites (n = 5-6) in four streams (Codorus Creek, PA; Leaf River, MS; McKenzie and Willamette Rivers, OR) to examine spatial and temporal variation in periphyton chlorophyll a, and fish and macroinvertebrate metrics commonly used in bioassessment programs. Within-site variation of macroinvertebrate metrics and benthic chlorophyll a concentration showed coefficient of variation ranging from 16 to 136%. Scale-specific variability patterns (stream-wide, season, site, and site-season patterns) in standardized biotic endpoints showed that within-site variability patterns extended across sites with variability greatest in chlorophyll a and lowest in Hilsenhoff's Biotic Index. Across streams, variance components models showed that variance attributed to the interaction of space and time and sample variance accounted for the majority of variation in macroinvertebrate metrics and chlorophyll a, while most variation in fish metrics was attributed to sample variance. Clear temporal patterns in measured endpoints were rare and not specific to any one stream or assemblage, while apparent shifts in metric variability related to point source discharges were seen only in McKenzie River macroinvertebrate metrics in the fall. Results from this study demonstrate the need to consider and understand spatial, seasonal, and longer term variability in the development of bioassessment programs and subsequent decisions.

Ecological assessments with algae: a review and synthesis

Algae have been used for a century in environmental assessments of water bodies and are now used in countries around the world. This review synthesizes recent advances in the field around a framework for environmental assessment and management that can guide design of assessments, applications of phycology in assessments, and refinements of those applications to better support management decisions. Algae are critical parts of aquatic ecosystems that power food webs and biogeochemical cycling. Algae are also major sources of problems that threaten many ecosystems goods and services when abundances of nuisance and toxic taxa are high. Thus, algae can be used to indicate ecosystem goods and services, which complements how algal indicators are also used to assess levels of contaminants and habitat alterations (stressors). Understanding environmental managers' use of algal ecology, taxonomy, and physiology can guide our research and improve its application. Environmental assessments involve characterizing ecological condition and diagnosing causes and threats to ecosystems goods and services. Recent advances in characterizing condition include site-specific models that account for natural variability among habitats to better estimate effects of humans. Relationships between algal assemblages and stressors caused by humans help diagnose stressors and establish targets for protection and restoration. Many algal responses to stressors have thresholds that are particularly important for developing stakeholder consensus for stressor management targets. Future research on the regional-scale resilience of algal assemblages, the ecosystem goods and services they provide, and methods for monitoring and forecasting change will improve water resource management.

Delineation and validation of river network spatial scales for water resources and fisheries management

Identifying appropriate spatial scales is critically important for assessing health, attributing data, and guiding management actions for rivers. We describe a process for identifying a three-level hierarchy of spatial scales for Michigan rivers. Additionally, we conduct a variance decomposition of fish occurrence, abundance, and assemblage metric data to evaluate how much observed variability can be explained by the three spatial scales as a gage of their utility for water resources and fisheries management. The process involved the development of geographic information system programs, statistical models, modification by experienced biologists, and simplification to meet the needs of policy makers. Altogether, 28,889 reaches, 6,198 multiple-reach segments, and 11 segment classes were identified from Michigan river networks. The segment scale explained the greatest amount of variation in fish abundance and occurrence, followed by segment class, and reach. Segment scale also explained the greatest amount of variation in 13 of the 19 analyzed fish assemblage metrics, with segment class explaining the greatest amount of variation in the other six fish metrics. Segments appear to be a useful spatial scale/unit for measuring and synthesizing information for managing rivers and streams. Additionally, segment classes provide a useful typology for summarizing the numerous segments into a few categories. Reaches are the foundation for the identification of segments and segment classes and thus are integral elements of the overall spatial scale hierarchy despite reaches not explaining significant variation in fish assemblage data.