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At least 163 records · Page 9Linked to original sources
USGS2010

Persistent organic pollutants in fish tissue in the mid-continental great rivers of the United States

Great rivers of the central United States (Upper Mississippi, Missouri, and Ohio rivers) are valuable economic and cultural resources, yet until recently their ecological condition has not been well quantified. In 2004–2005, as part of the Environmental Monitoring and Assessment Program for Great River Ecosystems (EMAP-GRE), we measured legacy organochlorines (OCs) (pesticides and polychlorinated biphenyls, PCBs) and emerging compounds (polybrominated diphenyl ethers, PBDEs) in whole fish to estimate human and wildlife exposure risks from fish consumption. PCBs, PBDEs, chlordane, dieldrin and dichlorodiphenyltrichloroethane (DDT) were detected in most samples across all rivers, and hexachlorobenzene was detected in most Ohio River samples. Concentrations were highest in the Ohio River, followed by the Mississippi and Missouri Rivers, respectively. Dieldrin and PCBs posed the greatest risk to humans. Their concentrations exceeded human screening values for cancer risk in 27–54% and 16–98% of river km, respectively. Chlordane exceeded wildlife risk values for kingfisher in 11–96% of river km. PBDE concentrations were highest in large fish in the Missouri and Ohio Rivers (mean > 1000 ng g −1 lipid), with congener 47 most prevalent. OC and PBDE concentrations were positively related to fish size, lipid content, trophic guild, and proximity to urban areas. Contamination of fishes by OCs is widespread among great rivers, although exposure risks appear to be more localized and limited in scope. As an indicator of ecological condition, fish tissue contamination contributes to the overall assessment of great river ecosystems in the U.S.

Science of the Total Environment↗
USGS2014

Methodological developments in US state-level Genuine Progress Indicators: toward GPI 2.0

The Genuine Progress Indicator (GPI) has emerged as an important monetary measure of economic well-being. Unlike mainstream economic indicators, primarily Gross Domestic Product (GDP), the GPI accounts for both the benefits and costs of economic production across diverse economic, social, and environmental domains in a more comprehensive manner. Recently, the GPI has gained traction in subnational policy in the United States, with GPI studies being conducted in a number of states and with their formal adoption by several state governments. As the GPI is applied in different locations, new methods are developed, different data sources are available, and new issues of policy relevance are addressed using its component indicators. This has led to a divergence in methods, reducing comparability between studies and yielding results that are of varying methodological sophistication. In this study, we review the “state of the art” in recent US state-level GPI studies, focusing on those from Hawaii, Maryland, Ohio, Utah, and Vermont. Through adoption of a consistent approach, these and future GPI studies could utilize a framework that supports more uniform, comparable, and accurate measurements of progress. We also identify longer-term issues, particularly related to treatment of nonrenewable resource depletion, government spending, income inequality, and ecosystem services. As these issues are successfully addressed and disseminated, a “GPI 2.0” will emerge that better measures economic well-being and has greater accuracy and policy relevance than past GPI measurements. As the GPI expands further into mainstream policy analysis, a more formal process by which methods could be updated, standardized, and applied is needed.

Hawai'i;Maryl;Ohio;Utah;Vermont↗
USGS2022

The role of monitoring and research in the Greater Yellowstone Ecosystem in framing our understanding of the effects of disease on amphibians

Emerging infectious disease threatens amphibian biodiversity worldwide, including in landscapes that are protected from many anthropogenic stressors. We summarized data from studies in the Greater Yellowstone Ecosystem (GYE), one of the largest and most complete temperate-zone ecosystems on Earth, to assess the current state of knowledge about ranaviruses and the novel amphibian chytrid fungus (Bd) in this landscape, and to provide insight into future threats and conservation strategies. Our comprehension of these amphibian diseases in the GYE is based on >20 years of monitoring, surveys, population studies, and opportunistic observations of mortality events. Research indicates that local species are affected differently, depending on temperature, community structure, and location in the GYE. Bd has not been linked to die-offs in the GYE but evidence for ongoing reductions in survival contributes to foundational data about the effects of this pathogen in North America. Localized mortality events attributed to, or consistent with, disease from ranaviruses, are widespread in the GYE, but there is less information on how ranaviruses affect amphibian vital rates. The significance of disease in the long-term persistence of amphibians in the GYE is linked to anticipated changes in climate, especially drought. Additionally, expected increases in visitor use, and its associated impacts, have the potential to exacerbate the effects of disease. Long-term information from this large, intact landscape helps to frame our understanding of the effects of disease on amphibians and provides data that can contribute to management decisions, mitigation strategies, and forecasting efforts.

Wyoming↗
USGS2008

Assessing streamflow characteristics as limiting factors on benthic invertebrate assemblages in streams across the western United States

1. Human use of land and water resources modifies many streamflow characteristics, which can have significant ecological consequences. Streamflow and invertebrate data collected at 111 sites in the western U.S.A. were analysed to identify streamflow characteristics (magnitude, frequency, duration, timing and variation) that are probably to limit characteristics of benthic invertebrate assemblages (abundance, richness, diversity and evenness, functional feeding groups and individual taxa) and, thus, would be important for freshwater conservation and restoration. Our analysis investigated multiple metrics for each biological and hydrological characteristic, but focuses on 14 invertebrate metrics and 13 streamflow metrics representing the key associations between streamflow and invertebrates. 2. Streamflow is only one of many environmental and biotic factors that influence the characteristics of invertebrate assemblages. Although the central tendency of invertebrate assemblage characteristics may not respond to any one factor across a large region like the western U.S.A., we postulate that streamflow may limit some invertebrates. To assess streamflow characteristics as limiting factors on invertebrate assemblages, we developed a nonparametric screening procedure to identify upper (ceilings) or lower (floors) limits on invertebrate metrics associated with streamflow metrics. Ceilings and floors for selected metrics were then quantified using quantile regression. 3. Invertebrate assemblages had limits associated with all streamflow characteristics that we analysed. Metrics of streamflow variation at daily to inter-annual scales were among the most common characteristics associated with limits on invertebrate assemblages. Baseflow recession, daily variation and monthly variation, in streamflow were associated with the largest number of invertebrate metrics. Since changes in streamflow variation are often a consequence of hydrologic alteration, they may serve as useful indicators of ecologically significant changes in streamflow and as benchmarks for managing streamflow for ecological objectives. 4. Relative abundance of Plecoptera, richness of non-insect taxa and relative abundance of intolerant taxa were associated with multiple streamflow metrics. Metrics of sensitive taxa (Ephemeroptera, Plecoptera and Trichoptera), and intolerant taxa generally had ceilings associated with flow metrics while metrics of tolerant taxa, non-insects, dominance and chironomids generally had floors. Broader characteristics of invertebrate assemblages such as abundance and richness had fewer limits, but these limits were nonetheless associated with a broad range of streamflow characteristics.

Freshwater Biology↗
USGS2007

Species-area curves indicate the importance of habitats' contributions to regional biodiversity

We examined species-area curves, species composition and similarity (Jaccard's coefficients), and species richness in 17 vegetation types to develop a composite index of a vegetation type's contribution to regional species richness. We collected data from 1 to 1000 m2 scales in 147 nested plots in Rocky Mountain National Park, Colorado, USA to compare three species-area curve models' abilities to estimate the number of species observed in each vegetation type. The log(species)-log(area) curve had the largest adjusted coefficients of determination (r2 values) in 12 of the 17 types, followed by the species-log(area) curve with five of the highest values. When the slopes of the curves were corrected for species overlap among plots with Jaccard's coefficients, the species-log(area) curves estimated values closest to those observed. We combined information from species-area curves and measures of heterogeneity with information on the area covered by each vegetation type and found that the types making the greatest contributions to regional biodiversity covered the smallest areas. This approach may provide an accurate and relatively rapid way to rank hotspots of plant diversity within regions of interest.

Colorado↗
USGS2022

Using fish community and population indicators to assess the biological condition of streams and rivers of the Chesapeake Bay watershed, USA

The development of indicators to assess relative freshwater condition is critical for management and conservation. Predictive modeling can enhance the utility of indicators by providing estimates of condition for unsurveyed locations. Such approaches grant understanding of where “good” and “poor” conditions occur and provide insight into landscape contexts supporting such conditions. However, as assessments are conducted at large extents crossing jurisdictional boundaries, combined datasets are likely not suited for traditional assessment approaches which rely on jurisdictionally-specific reference sites. Here, we used a large dataset compiled from multiple providers to assess the condition of fish habitat for non-tidal streams and rivers in the Chesapeake Bay watershed (CBW), USA. We concurrently used community and species-level analyses to provide a more holistic view of habitat conditions by using random forest models to predict selected metrics and species occurrence with landscape data for inland CBW stream reaches. Community analyses included metrics describing composition, tolerances, habitat preferences, and functional traits of fish communities whereas species-level analyses consisted of distribution models for key sensitive and gamefish species. For community analyses, a final index was calculated as the average of selected metric deciles with higher scores inferring less biologically altered (i.e., better) conditions, providing an alternative to using reference sites. For species analyses, species occurrence was predicted for stream reaches, with presence indicating suitable habitat. Uncertainty was calculated for both approaches using model prediction intervals. Results indicated different numbers of suitable metrics for each region, with most in the Northern Appalachian (15) and least in the Southern Appalachian Piedmont (3). Four species (three sensitive) were suitable for modeling. At the CBW scale, predictions did not vary greatly among deciles for the community or species analyses for 2001, 2006, 2011, and 2016. Most stream reaches did not vary in mean decile rank or in species occurrence between 2001 and 2016; however, the largest community changes occurred in large rivers in the Coastal Plains ecoregion and the largest species occurrence changes occurred in Torrent Suckers in medium-sized rivers. When compared, results from community analyses agreed for one sensitive species (Brook Trout) but not the other three, potentially due to regionally inappropriate tolerance assignment. Comparisons also demonstrated substantial variation among approaches suggesting a lack of redundancy. While each approach traditionally has its targeted audience and respective strengths and weaknesses, concurrent use of these approaches permits direct comparisons and may assuage shortcomings of each approach when considered separately.

Chesapeake Bay watershed↗
USGS2013

An algorithmic and information-theoretic approach to multimetric index construction

The use of multimetric indices (MMIs), such as the widely used index of biological integrity (IBI), to measure, track, summarize and infer the overall impact of human disturbance on biological communities has been steadily growing in recent years. Initially, MMIs were developed for aquatic communities using pre-selected biological metrics as indicators of system integrity. As interest in these bioassessment tools has grown, so have the types of biological systems to which they are applied. For many ecosystem types the appropriate biological metrics to use as measures of biological integrity are not known a priori. As a result, a variety of ad hoc protocols for selecting metrics empirically has developed. However, the assumptions made by proposed protocols have not be explicitly described or justified, causing many investigators to call for a clear, repeatable methodology for developing empirically derived metrics and indices that can be applied to any biological system. An issue of particular importance that has not been sufficiently addressed is the way that individual metrics combine to produce an MMI that is a sensitive composite indicator of human disturbance. In this paper, we present and demonstrate an algorithm for constructing MMIs given a set of candidate metrics and a measure of human disturbance. The algorithm uses each metric to inform a candidate MMI, and then uses information-theoretic principles to select MMIs that capture the information in the multidimensional system response from among possible MMIs. Such an approach can be used to create purely empirical (data-based) MMIs or can, optionally, be influenced by expert opinion or biological theory through the use of a weighting vector to create value-weighted MMIs. We demonstrate the algorithm with simulated data to demonstrate the predictive capacity of the final MMIs and with real data from wetlands from Acadia and Rocky Mountain National Parks. For the Acadia wetland data, the algorithm identified 4 metrics that combined to produce a -0.88 correlation with the human disturbance index. When compared to other methods, we find this algorithmic approach resulted in MMIs that were more predictive and comprise fewer metrics.

Ecological Indicators↗
USGS2018

Variation in fish mercury concentrations in streams of the Adirondack region, New York: A simplified screening approach using chemical metrics

Simple screening approaches for the neurotoxicant methylmercury (MeHg) in aquatic ecosystems may be helpful in risk assessments of natural resources. We explored the development of such an approach in the Adirondack Mountains of New York, USA, a region with high levels of MeHg bioaccumulation. Thirty-six perennial streams broadly representative of 1st and 2nd order streams in the region were sampled during summer low flow and analyzed for several solutes and for Hg concentrations in fish. Several landscape and chemical metrics that are typically strongly related to MeHg concentrations in aquatic biota were explored for strength of association with fish Hg concentrations. Data analyses were based on site mean length-normalized and standardized Hg concentrations (assumed to be dominantly MeHg) in whole juvenile and adult Brook Trout Salvelinus fontinalis , Creek Chub Semotilus atromaculatus , Blacknose Dace Rhinichthys atratulus , and Central Mudminnow Umbra limi , as well as on multi-species z-scores. Surprisingly, none of the landscape metrics was related significantly to regional variation in fish Hg concentrations or to z-scores across the study streams. In contrast, several chemical metrics including dissolved organic carbon (DOC) concentrations, sulfate concentrations (SO 4 2− ), pH, ultra-violet absorbance (UV 254 ), and specific ultra-violet absorbance were significantly related to regional variation in fish Hg concentrations. A cluster analysis based on DOC, SO 4 2− , and pH identified three distinct groups of streams: (1) high DOC, acidic streams, (2) moderate DOC, slightly acidic streams, and (3) low DOC circum-neutral streams with relatively high SO 4 2− . Preliminary analysis indicated no significant difference in fish Hg z-scores between the moderate and high DOC groups, so these were combined for further analysis. The resulting two groups showed strong differences (p < 0.001) in DOC and SO 4 2− concentrations as well as in pH and UV 254 values. Median fish z-scores were significantly higher (p = 0.002) in the group of streams with higher DOC and UV 254 and lower pH and SO 4 2− . Screening values of DOC >6.9 mg/L, SO 4 2− <2.8 mg/L, pH <6.6, and UV 254 >0.31 cm −1 were tested as thresholds to identify Adirondack stream sites likely to have higher fish Hg concentrations. By applying a combined threshold of exceedance for either pH or UV 254 , sites with fish Hg concentrations that exceeded a wildlife guideline of 100 ng/g were correctly identified about 75% of the time among the 36 study streams. An estimate of Hg risk applied to a data set of 391 streams based on DOC concentrations showed that about 28% were likely to pose high risk to wildlife; most of these streams were located in the western Adirondacks.

New York↗
USGS2016

Using structural equation modeling to link human activities to wetland ecological integrity

The integrity of wetlands is of global concern. A common approach to evaluating ecological integrity involves bioassessment procedures that quantify the degree to which communities deviate from historical norms. While helpful, bioassessment provides little information about how altered conditions connect to community response. More detailed information is needed for conservation and restoration. We have illustrated an approach to addressing this challenge using structural equation modeling (SEM) and long-term monitoring data from Rocky Mountain National Park (RMNP). Wetlands in RMNP are threatened by a complex history of anthropogenic disturbance including direct alteration of hydrologic regimes; elimination of elk, wolves, and grizzly bears; reintroduction of elk (absent their primary predators); and the extirpation of beaver. More recently, nonnative moose were introduced to the region and have expanded into the park. Bioassessment suggests that up to half of the park's wetlands are not in reference condition. We developed and evaluated a general hypothesis about how human alterations influence wetland integrity and then develop a specific model using RMNP wetlands. Bioassessment revealed three bioindicators that appear to be highly sensitive to human disturbance (HD): (1) conservatism, (2) degree of invasion, and (3) cover of native forbs. SEM analyses suggest several ways human activities have impacted wetland integrity and the landscape of RMNP. First, degradation is highest where the combined effects of all types of direct HD have been the greatest (i.e., there is a general, overall effect). Second, specific HDs appear to create a “mixed-bag” of complex indirect effects, including reduced invasion and increased conservatism, but also reduced native forb cover. Some of these effects are associated with alterations to hydrologic regimes, while others are associated with altered shrub production. Third, landscape features created by historical beaver activity continue to influence wetland integrity years after beavers have abandoned sites via persistent landforms and reduced biomass of tall shrubs. Our model provides a system-level perspective on wetland integrity and provides a context for future evaluations and investigations. It also suggests scientifically supported natural resource management strategies that can assist in the National Park Service mission of maintaining or, when indicated, restoring ecological integrity “unimpaired for future generations.”

Colorado↗
USGS2021

Hierarchical models improve the use of alligator abundance as an indicator

Indicator species are species which can be monitored as an index to measure the overall health of an ecosystem. Crocodylians have been shown to be good indicators of wetland condition as they respond to changes in hydrology, can be efficiently monitored, and are a key part of ecosystem trophic relationships. Eye shine surveys at night are a standard method used to sample alligators, but because some individuals that are present in a study area may go undetected and the proportion of individuals counted is not constant over time, appropriate modeling is required to convert counts to estimates of abundance. We analyzed 13 years of American alligator ( Alligator mississippiensis ) survey count data from South Florida using an N -mixture model. Alligator abundance estimates were assigned to quartiles that were then represented as color coded categories of red, yellow, or green to provide a straightforward rating of Everglades restoration based on familiar stoplight coloring. These results were then compared to a previously used method in which unadjusted counts of these same data were assigned to color coded quartile categories. Water depth played a major role in the detection probability of alligators and the stoplight colors between the two methods matched 76% of the time. This suggests that the original stoplight score method provided a good overall snapshot of the trends in alligator abundance in the Everglades; however, the hierarchical models estimate abundance and trends of alligator abundance by incorporating detection probability thus providing unbiased estimates of abundance.

Florida↗
USGS2010

The impact of rare taxa on a fish index of biotic integrity

The index of biotic integrity (IBI) is a commonly used bioassessment tool that integrates abundance and richness measures to assess water quality. In developing IBIs that are both responsive to human disturbance and resistant to natural variability and sampling error, water managers must decide how to weigh information about rare and abundant taxa, which in turn requires an understanding of the sensitivity of indices to rare taxa. Herein, we investigated the influence of rare fish taxa (within the lower 5% of rank abundance curves) on IBI metric and total scores for stream sites in two of Minnesota's major river basins, the St. Croix (n = 293 site visits) and Upper Mississippi (n = 210 site visits). We artificially removed rare taxa from biological samples by (1) separately excluding each individual taxon that fell within the lower 5% of rank abundance curves; (2) simultaneously excluding all taxa that had an abundance of one (singletons) or two (doubletons); and (3) simultaneously excluding all taxa that fell within the lower 5% of rank abundance curves. We then compared IBI metric and total scores before and after removal of rare taxa using the normalized root mean square error (nRMSE) and regression analysis. The difference in IBI metric and total scores increased as more taxa were removed. Moreover, when multiple rare taxa were removed, the nRMSE was related to sample abundance and to total taxa richness, with greater nRMSE observed in samples with a larger number of taxa or sample abundance. Metrics based on relative abundance of fish taxa were less sensitive to the loss of rare taxa, whereas those based on taxa richness were more sensitive, because taxa richness metrics give more weight to rare taxa compared to the relative abundance metrics. ?? 2010 Elsevier Ltd.

Ecological Indicators↗
USGS2022

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.

Ecological Indicators↗
USGS2025

Stressor-driven changes in freshwater biological indicators inform spatial management strategies using expert knowledge, observational data, and hierarchical models

Stream ecosystems face continuous pressures from multiple anthropogenic stressors that reshape biological communities and impact ecosystem health and services. Managers can encounter challenges in stewarding ecosystems threatened by multiple stressors, in part because most multiple stressor studies are experimental and, while valuable, offer limited management relevance in targeting these stressors on the landscape. Recent advances in causal inference coupled with large biomonitoring data sets could further understanding of observational stressor-response relationships, aiding management. In this study, we use bioassessment data in the Chesapeake Bay watershed in the mid-Atlantic region of the United States to identify how water quality and physical habitat stressors influence key benthic macroinvertebrate response metrics, considering hierarchical relationships using Bayesian networks. Results suggest water temperature and specific conductivity were prevalent stressors in a mountainous region (northern Appalachians), whereas in an agriculturally dominated region (southern Appalachians) physical habitat alterations were the predominant stressor. In mixed-land use regions (Piedmont & Coastal Plains), specific conductivity was a key stressor, but habitat heterogeneity was important for macroinvertebrate metrics. To illustrate how these stressor-response relationships can be used to guide management decisions, we applied the resist-accept-direct (RAD) framework to develop a portfolio of management options based on predicted changes in macroinvertebrate metrics in response to physical habitat and water quality stressors. For example, accepting changes in areas with co-occurring stressors may be the most feasible option, whereas directing changes through stream restoration or water quality improvements may be effective in areas with single stressor groups. By leveraging observational bioassessment data and causal inference to identify key stressor-response relationships, this research supports decision making by building a simple, strategic management portfolio.

Delaware, Maryland, New York, Pennsylvania, Virgin↗
USGS2013

Influence of management and precipitation on carbon fluxes in greatplains grasslands

Suitable management and sufficient precipitation on grasslands can provide carbon sinks. The net carbon accumulation of a site from the atmosphere, modeled as the Net Ecosystem Productivity (NEP), is a useful means to gauge carbon balance. Previous research has developed methods to integrate flux tower data with satellite biophysical datasets to estimate NEP across large regions. A related method uses the Ecosystem Performance Anomaly (EPA) as a satellite-derived indicator of disturbance intensity (e.g., livestock stocking rate, fire, and insect damage). To better understand the interactions among management, climate, and carbon dynamics, we evaluated the relationship between EPA and NEP data at the 250 m scale for grasslands in the Central Great Plains, USA (ranging from semi-arid to mesic). We also used weekly estimates of NEP to evaluate the phenology of carbon dynamics, classified by EPA (i.e., by level of disturbance impact). Results show that the cumulative carbon balance over these grasslands from 2000 to 2008 was a weak net sink of 13.7 g C m −2 yr −1 . Overall, NEP increased with precipitation (R 2 = 0.39, P < 0.05) from west to east. Disturbance influenced NEP phenology; however, climate and biophysical conditions were usually more important. The NEP response to disturbance varies by ecoregion, and more generally by grassland type, where the shortgrass prairie NEP is most sensitive to disturbance, the mixed-grass prairie displays a moderate response, and tallgrass prairie is the least impacted by disturbance (as measured by EPA). Sustainable management practices in the tallgrass and mixed-grass prairie may potentially induce a period of average net carbon sink until a new equilibrium soil organic carbon is achieved. In the shortgrass prairie, management should be considered sustainable if carbon stocks are simply maintained. The consideration of site carbon balance adds to the already difficult task of managing grasslands appropriately to site conditions. Results clarify the seasonal and interannual dynamics of NEP, specifically the influence of disturbance and moisture availability.

Ecological Indicators↗
USGS2023

Intra-specific variation in responses to habitat restoration: Could artificial reefs increase spatiotemporal segregation between migratory phenotypes of lake sturgeon?

Habitat restoration is an important tool used to conserve biodiversity and restore species, but its effects are notoriously difficult to predict. Although outcomes of restoration projects are usually assessed using indices of species abundance and diversity, phenotypic differences among individuals within species are likely associated with differing responses to restored habitats. Here, we use lake sturgeon ( Acipenser fulvescens ) as a case study to illustrate how responses to habitat restoration can differ between phenotypes and potentially lead to unanticipated effects on populations. North America’s St. Clair River supports one of the largest remaining populations of lake sturgeon but has lost much spawning habitat due to its role as a major industrial corridor between the Laurentian Great Lakes Erie and Huron. Two artificial reefs were recently built in the lower and middle segments of the river to increase the available sturgeon spawning habitat. Interestingly, lake sturgeon in the St. Clair River express different migratory phenotypes that may be associated with different likelihoods of colonizing artificial reefs. Acoustic telemetry revealed that artificial reefs were more likely to be used by sturgeon that migrated downstream to overwinter in Lake St. Clair than those that migrated upstream to overwinter in Lake Huron. Furthermore, increasing time spent at the artificial reefs by Lake St. Clair migrants was associated with later arrival to and shorter occupancy of the river’s only natural spawning site, the primary location where the two phenotypes have opportunity to interbreed. Additional research is necessary to determine the ultimate impacts of the artificial reefs on lake sturgeon populations; nevertheless, our study showed phenotype-specific opportunity to colonize restored habitat and a mechanism through which this could lead to changes in gene flow. Our results illustrate the importance of considering intra-specific diversity when planning restoration projects and assessing the effects on populations.

Michigan, Ontario↗
USGS2024

Evaluation of metrics and thresholds for use in national-scale river harmful algal bloom assessments

The spatiotemporal distribution of harmful algal blooms (HABs) in rivers remains poorly understood, and there is an urgent need to develop a consistent set of metrics to better document HAB occurrences and forecast future events. Using data from seven sites in the Illinois River Basin, we computed metrics focused on HAB conditions related to excess algal growth and hypoxia. Daily mean chlorophyll and dissolved oxygen (DO) concentrations, gross primary productivity (GPP), and net ecosystem productivity (NEP) rates, focused on water quality status, identifying the timing of the transition from a clear-water to an algal dominated state. Early warning indicators (EWIs), the first-order autoregressive process (Ar1) and standard deviation (SD) of chlorophyll concentrations, focused on future events, forecasting blooms. Metrics were compared to either literature-derived or statistical-based thresholds and were normalized by total number of daily samples for an exceedance rate. Exceedances of a daily mean chlorophyll concentration averaged 50 % across all sites using a 10 µg L −1 threshold but increasing the threshold to 50 μg L −1 reduced the average exceedance rate to 5 %. The average exceedance rate for GPP (∼8 g O 2 m 2 d −1 threshold) was 15 %, similar to the daily amplitude DO concentration (∼3 mg L −1 threshold), but the average for NEP (0 g O 2 m 2 d −1 threshold) was higher, at 28 %. The number of days with at least 1 continuous DO concentration below the threshold of 5, 3, or 2 mg L −1 , had basin wide exceedance rates of 9 %, 3 %, and 2 %, respectively. Thresholds for EWIs, Ar1 and SD, were exceeded at 5 of the 7 sites with high chlorophyll concentrations and GPP rates. The correlation between proxies for algal biomass (chlorophyll concentration) and productivity (GPP) was strongest for sites in the middle region of the basin, with R 2 values between 0.54 and 0.74. Although, cyanotoxin concentrations are the most commonly used metrics by states to define an inland water HAB, there is a paucity of publicly available data. The wider availability of chlorophyll and oxygen data combined with the results from this study suggest that biomass and productivity state and event-based metrics may be a promising way to assess and predict the vulnerability of rivers to some of the deleterious effects of HABs at broad spatial scales.

Illinois, Indiana, Wisconsin↗
USGS2021

Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales

Cyanobacterial blooms can have negative effects on human health and local ecosystems. Field monitoring of cyanobacterial blooms can be costly, but satellite remote sensing has shown utility for more efficient spatial and temporal monitoring across the United States. Here, satellite imagery was used to assess the annual frequency of surface cyanobacterial blooms, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable cyanobacteria . Cyanobacterial frequency was assessed across 2,196 large lakes in 46 states across the continental United States (CONUS) using imagery from the European Space Agency’s Ocean and Land Colour Instrument for the years 2017 through 2019. In 2019, across all satellite pixels considered, annual bloom frequency had a median value of 4% and a maximum value of 100%, the latter indicating that for those satellite pixels, a cyanobacterial bloom was detected by the satellite sensor for every satellite image considered. In addition to annual pixel-scale cyanobacterial frequency, results were summarized at the lake- and state-scales by averaging annual pixel-scale results across each lake and state. For 2019, average annual lake-scale frequencies also had a maximum value of 100%, and Oregon and Ohio had the highest average annual state-scale frequencies at 65% and 52%. Pixel-scale frequency results can assist in identifying portions of a lake that are more prone to cyanobacterial blooms, while lake- and state-scale frequency results can assist in the prioritization of sampling resources and mitigation efforts. Satellite imagery is limited by the presence of snow and ice, as imagery collected in these conditions are quality flagged and discarded. Thus, annual bloom frequencies within nine climate regions were investigated to determine whether missing data biased results in climate regions more prone to snow and ice, given that their annual summaries would be weighted toward the summer months when cyanobacterial blooms tend to occur. Results were unbiased by the time period selected in most climate regions, but a large bias was observed for the Northwest Rockies and Plains climate region. Moderate biases were observed for the Ohio Valley and the Southeast climate regions. Finally, a clustering analysis was used to identify areas of high and low cyanobacterial frequency across CONUS based on average annual lake-scale cyanobacterial frequencies for 2019. Several clusters were identified that transcended state, watershed, and eco-regional boundaries. Combined with additional data, results from the clustering analysis may offer insight regarding large-scale drivers of cyanobacterial blooms.

Ecological Indicators↗
USGS2023

Broadening benefits and anticipating tradeoffs with a proposed ecosystem service analysis framework for the US Army Corps of Engineers

Would-be adopters of ecosystem service analysis frameworks might ask, ‘Do such frameworks improve ecosystem service provision or social benefits sufficiently to compensate for any extra effort?’ Here we explore that question by retrospectively applying an ecosystem goods and services (EGS) analysis framework to a large river restoration case study conducted by the US Army Corps of Engineers (USACE) and comparing potential time costs and outcomes of traditional versus EGS-informed planning. USACE analytic methods can have a large influence on which river and wetland restoration projects are implemented in the United States because they affect which projects or project elements are eligible for federal cost-share funding. A new framework is designed for the USACE and is primarily distinguished from current procedures by adding explicit steps to document and compare tradeoffs and complementarity among all affected EGS, rather than the subset that falls within project purposes. Further, it applies economic concepts to transform ecological performance indicators into social benefit indicators, even if changes cannot be valued. We conclude that, for large multi-partner restoration projects like our case study, using the framework provides novel information on social outcomes that could be used to enhance project design, without substantially increasing scoping costs. The primary benefits of using the framework in the case study appeared to stem from early comprehensive identification of stakeholder interests that might have prevented project delays late in the process, and improving the communication of social benefits and how tradeoffs among EGS benefits were weighed during planning.

Missouri↗