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USGS2019

Developing an expert elicited simulation model to evaluate invasive species and fire management alternatives

Invasive species can alter ecosystem properties and cause state shifts in landscapes. Resource managers charged with maintaining landscapes require tools to understand implications of alternative actions (or inactions) on landscape structure and function. Simulation models can serve as a virtual laboratory to explore these alternatives and their potential impacts on a landscape. To be useful, however, managers need to participate in model development to ensure that model structure can evaluate the response of key resources to plausible actions. Here, we detail development of a state‐and‐transition simulation model (STSM) to evaluate buffelgrass ( Cenchrus ciliaris L. syn Pennisetum ciliare (L.) Link) in Saguaro National Park (SNP), Arizona, USA, through collaboration between managers and researchers. We integrate expert knowledge and research to create and parameterize a stochastic, spatially explicit STSM to evaluate specific management objectives. We also develop a dynamic link between the STSM and a fire behavior model to allow exploration of potential novel processes introduced to the ecosystem by buffelgrass invasion. Our projections show that buffelgrass can be expected to increase on the landscape and that the integration of fire into the model accelerates the projected rate of invasion and increases degradation of resources of management concern. We highlight the benefit of engaging end users in the modeling process so that the model is targeted to evaluate management objectives, in this case retention of saguaro cacti ( Carnegiea gigantea (Engelm.) Britton & Rose) on the landscape. Being able to integrate an external model that can help address the unique characteristics of a problem such as the introduction of fire into the SNP desert ecosystem increases the ability of simulations to address complex ecological and management questions.

Ecosphere↗

USGS2020

Is your ad hoc model selection strategy affecting your multimodel inference?

(Yackulic) 1. Ecologists routinely fit complex models with multiple parameters of interest, where hundreds or more competing models are plausible. To limit the number of fitted models, ecologists often define a model selection strategy composed of a series of stages in which certain features of a model are compared while other features are held constant. Defining these multi-stage strategies requires making a series of decisions, which may potentially impact inferences, but have not been critically evaluated. 2. We begin by identifying key features of strategies, introducing descriptive terms when they did not already exist in the literature. Strategies differ in how they define and order model building stages. Sequential-by-sub-model strategies focus on one sub-model (parameter) at a time with modeling of subsequent sub-models dependent on the selected model structures from the previous stages. Secondary candidate set strategies model sub-models independently and combine the top set of models from each sub-model for selection in a final stage. Build-up approaches define stages across sub-models and increase in complexity at each stage. Strategies also differ in how the top set of models is selected in each stage and whether they use null or more complex model structures for non-target sub-models. 3. We tested the performance of different model selection strategies using four datasets and three model types. For each dataset, we determined the “true” distribution of AIC weights by fitting all plausible models. Then, we calculated the number of models that would have been fitted and the portion of “true” AIC weight we recovered under different model selection strategies. 4. Sequential-by-sub-model strategies often performed poorly. Build-up or secondary candidate sets were more reliable, provided all models within 5 AIC of the top model were carried forward to subsequent stages. The structure of non-target sub-models was less important. 5. Multi-stage approaches cannot compensate for a lack of critical thought in selecting covariates and building models to represent competing a priori hypotheses. However, even when competing hypotheses for different sub-models are limited, thousands or more models may be possible so strategies to explore candidate model space reliably and efficiently will be necessary.

Ecosphere↗

USGS2022

Geologic, geomorphic, and edaphic underpinnings of dryland ecosystems: Colorado Plateau landscapes in a changing world

Drylands represent more than 41% of the global land surface and are at degradation risk due to land use and climate change. Developing strategies to mitigate degradation and restore drylands in the face of these threats requires an understanding of how drylands are shaped by not only soils and climate, but also geology and geomorphology. However, few studies have completed such a comprehensive analysis that relates spatial variation in plant communities to all aspects of the geologic–geomorphic–edaphic–plant–climate system. The focus of this study is the Colorado Plateau, a high-elevation dryland in the southwestern United States, which is particularly sensitive to future change due to climate vulnerability and increasing land-use pressure. Here, we examined 135 long-term vegetation-monitoring sites in three national parks and characterized connections between geology, geomorphology, soils, climate, and dryland plant communities. To first understand the geologic and geomorphic influences on soil formation and characteristics, we explore associations between soil pedons, bedrock geology, and geomorphology. Then, we characterize principal axes of variation in plant communities and ascertain controls and linkages between components of the edaphic–geomorphic system and plant community ordinations. Geologic and geomorphic substrate exerted controls on important properties of the soil profile, particularly depth, water-holding capacity, rockiness, salinity, and fine sands. Ordination identified five distinct plant communities and three primary axes of variation, representing gradients of woody- to herbaceous-dominated communities (Axis 1), saline scrublands to C 3 grasslands (Axis 2), and annual to perennial communities (Axis 3). Geology, geomorphology, and soil explained a large proportion of variation in Axis 1 (74%), while climate variables largely explained Axis 2 (68%), and Axis 3 was not well explained by the random forest models. The variables identified as most influential to each axis were, respectively: (1) soil depth; (2) aridity, lithology, and soil salinity; and (3) temperature and precipitation. We posit that Axis 3 represents a land degradation gradient due to historic grazing, likely exacerbated by dry conditions. Results provide a novel framework that links the geologic and geomorphic evolution of landscapes, with the distribution of soils and plant communities that can guide ecosystem management, exemplifying an approach applicable to drylands globally.

Arizona, Colorado, New Mexico, Utah↗

USGS2024

Phenology forecasting models for detection and management of invasive annual grasses

Non-native annual grasses can dramatically alter fire frequency and reduce forage quality and biodiversity in the ecosystems they invade. Effective management techniques are needed to reduce these undesirable invasive species and maintain ecosystem services. Well-timed management strategies, such as grazing, that are applied when invasive grasses are active prior to native plants can control invasive species spread and reduce their impact; however, anticipating the timing of key phenological stages that are susceptible to management over vast landscapes is difficult, as the phenology of these species can vary greatly over time and space. To address this challenge, we created range-wide phenology forecasts for two problematic invasive annual grasses: cheatgrass ( Bromus tectorum ), and red brome ( Bromus rubens ). We tested a suite of 18 mechanistic phenology models using observations from monitoring experiments, volunteer science, herbarium records, timelapse camera imagery, and downscaled gridded climate data to identify the models that best predicted the dates of flowering and senescence of the two invasive grass species. We found that the timing of flowering and senescence of cheatgrass and red brome were best predicted by photothermal time models that had been adjusted for topography using gridded continuous heat-insolation load index values. Phenology forecasts based on these models can help managers make decisions about when to schedule management actions such as grazing to reduce undesirable invasive grasses and promote forage production, quality, and biodiversity in grasslands; to predict the timing of greatest fire risk after annual grasses dry out; and to select remote sensing imagery to accurately map invasive grasses across topographic and latitudinal gradients. These phenology models also have the potential to be operationalized for within-season or within-year decision support.

Ecosphere↗

USGS2025

Unlocking ecological insights from sub-seasonal visible-to-shortwave infrared imaging spectroscopy: The SHIFT campaign

We stand at the threshold of a transformative era in Earth observation, marked by space-borne visible-to-shortwave infrared (VSWIR) imaging spectrometers that promise consistent global observations of ecosystem function, phenology, and inter- and intra-annual change. However, the full value of repeat spectroscopy, the information embedded within different temporal scales, and the reliability of existing algorithms across diverse ecosystem types and vegetation phenophases have remained elusive due to the absence of suitable sub-seasonal spectroscopy data. In response, the Surface Biology and Geology (SBG) High-Frequency Time Series (SHIFT) campaign was initiated during late February 2022 in Santa Barbara County, California. SHIFT, designed to support NASA's SBG mission, addressed mission scoping, scientific advancement, applications development, and community building. This ambitious endeavor included weekly Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery acquisitions for 13 weeks (spanning February 24 to May 29, 2022), accompanied by coordinated terrestrial vegetation and coastal aquatic data collection. We describe the rich datasets collected and illustrate how the complex sub-seasonal patterns of change can be linked to biological science and applications, surpassing insights from multispectral observations. Leveraging open-source processing methods and cloud-based analysis tools, the SHIFT campaign showcases the readiness of the scientific community to harness ecological insights from remotely sensed hyperspectral time series. We provide an overview of SHIFT's goals, data collections, preliminary results, and the collaborative efforts of early career scientists committed to unlocking the transformative potential of high-frequency time series data from space-borne VSWIR imaging spectrometers.

Ecosphere↗

USGS2025

Using imaging spectroscopy and elevation in machine learning to estimate soil salinity in intermittently tidal wetlands

Coastal soil salinization patterns are changing due to drought, sea level rise (SLR), and changing freshwater inflow. These changes are expected to impact coastal wetland plant health and ecosystem function, such as changes to biomass and productivity. These impacts have led to greater interest in how we monitor soil salinization across spatial and temporal scales. Remote sensing is a promising tool for estimating soil salinity at the spatial scales required for decision making by land managers. However, the development of a remote sensing estimation approach for wetland soil salinity must account for two factors: (1) the high spatial and temporal heterogeneity of coastal wetlands and (2) the fact that soil salinity is the result of multiple historical land use, hydrological, and geomorphic processes. In spring 2022, a combined airborne-field campaign, known as SHIFT, collected a weekly time series of airborne visible to shortwave infrared (VSWIR) image spectroscopy data. This dataset provides a unique opportunity to assess the application of fine spatial (5 m) and temporal (weekly) resolution VSWIR data to estimate root zone soil salinity; when combined with environmental variables such as elevation, these data can account for some of these factors. In this study, we utilized VSWIR and elevation datasets in a random forest regression to predict and map soil salinity in an intermittently tidal estuary, Devereux Slough, located in Santa Barbara County, California. The final model combined spectral indices with elevation to better capture soil salinity dynamics despite lower correlation ( r = 0.85) than solely using elevation ( r = 0.92). This research demonstrates the utility of remote sensing datasets, namely, elevation and the modified Anthocyanin Reflectance Index (mARI), for predicting root zone soil salinity in intermittently tidal coastal wetlands. These findings are an important step in advancing coastal remote sensing by creating a gridded salinity dataset that can be used for salinity monitoring and other coastal applications, such as modeling change in vegetation communities or ecosystems facing the impacts of climatic variability and change.

California↗

USGS2017

Decadal declines in avian herbivore reproduction: density-dependent nutrition and phenological mismatch in the Arctic

A full understanding of population dynamics depends not only on estimation of mechanistic contributions of recruitment and survival, but also knowledge about the ecological processes that drive each of these vital rates. The process of recruitment in particular may be protracted over several years, and can depend on numerous ecological complexities until sexually mature adulthood is attained. We addressed long-term declines (23 breeding seasons, 1992–2014) in the per capita production of young by both Ross's Geese ( Chen rossii ) and Lesser Snow Geese ( Chen caerulescens caerulescens ) nesting at Karrak Lake in Canada's central Arctic. During this period, there was a contemporaneous increase from 0.4 to 1.1 million adults nesting at this colony. We evaluated whether (1) density-dependent nutritional deficiencies of pre-breeding females or (2) phenological mismatch between peak gosling hatch and peak forage quality, inferred from NDVI on the brood-rearing areas, may have been behind decadal declines in the per capita production of goslings. We found that, in years when pre-breeding females arrived to the nesting grounds with diminished nutrient reserves, the proportional composition of young during brood-rearing was reduced for both species. Furthermore, increased mismatch between peak gosling hatch and peak forage quality contributed additively to further declines in gosling production, in addition to declines caused by delayed nesting with associated subsequent negative effects on clutch size and nest success. The degree of mismatch increased over the course of our study because of advanced vegetation phenology without a corresponding advance in Goose nesting phenology. Vegetation phenology was significantly earlier in years with warm surface air temperatures measured in spring (i.e., 25 May–30 June). We suggest that both increased phenological mismatch and reduced nutritional condition of arriving females were behind declines in population-level recruitment, leading to the recent attenuation in population growth of Snow Geese.

Ecology↗

USGS2018

Herbivory and eutrophication mediate grassland plant nutrient responses across a global climatic gradient

Plant stoichiometry, the relative concentration of elements, is a key regulator of ecosystem functioning and is also being altered by human activities. In this paper we sought to understand the global drivers of plant stoichiometry and compare the relative contribution of climatic vs. anthropogenic effects. We addressed this goal by measuring plant elemental (C, N, P and K) responses to eutrophication and vertebrate herbivore exclusion at eighteen sites on six continents. Across sites, climate and atmospheric N deposition emerged as strong predictors of plot‐level tissue nutrients, mediated by biomass and plant chemistry. Within sites, fertilization increased total plant nutrient pools, but results were contingent on soil fertility and the proportion of grass biomass relative to other functional types. Total plant nutrient pools diverged strongly in response to herbivore exclusion when fertilized; responses were largest in ungrazed plots at low rainfall, whereas herbivore grazing dampened the plant community nutrient responses to fertilization. Our study highlights (1) the importance of climate in determining plant nutrient concentrations mediated through effects on plant biomass, (2) that eutrophication affects grassland nutrient pools via both soil and atmospheric pathways and (3) that interactions among soils, herbivores and eutrophication drive plant nutrient responses at small scales, especially at water‐limited sites.

Ecology↗

USGS2024

Sources of bias in applying close-kin mark–recapture to terrestrial game species with different life histories

Close-kin mark–recapture (CKMR) is a method analogous to traditional mark–recapture but without requiring recapture of individuals. Instead, multilocus genotypes (genetic marks) are used to identify related individuals in one or more sampling occasions, which enables the opportunistic use of samples from harvested wildlife. To apply the method accurately, it is important to build appropriate CKMR models that do not violate assumptions linked to the species’ and population's biology and sampling methods. In this study, we evaluated the implications of fitting overly simplistic CKMR models to populations with complex reproductive success dynamics or selective sampling. We used forward-in-time, individual-based simulations to evaluate the accuracy and precision of CKMR abundance and survival estimates in species with different longevities, mating systems, and sampling strategies. Simulated populations approximated a range of life histories among game species of North America with lethal sampling to evaluate the potential of using harvested samples to estimate population size. Our simulations show that CKMR can yield nontrivial biases in both survival and abundance estimates, unless influential life history traits and selective sampling are explicitly accounted for in the modeling framework. The number of kin pairs observed in the sample, in combination with the type of kinship used in the model (parent–offspring pairs and/or half-sibling pairs), can affect the precision and/or accuracy of the estimates. CKMR is a promising method that will likely see an increasing number of applications in the field as costs of genetic analysis continue to decline. Our work highlights the importance of applying population-specific CKMR models that consider relevant demographic parameters, individual covariates, and the protocol through which individuals were sampled.

Ecology↗

USGS2014

Integrated assessment of runoff from livestock farming operations: analytical chemistry, in vitro bioassays, and in vivo fish exposures

Animal waste from livestock farming operations can contain varying levels of natural and synthetic androgens and/or estrogens, which can contaminate surrounding waterways. In the present study, surface stream water was collected from 6 basins containing livestock farming operations. Aqueous concentrations of 12 hormones were determined via chemical analyses. Relative androgenic and estrogenic activity was measured using in vitro cell assays (MDA-kb2 and T47D-Kbluc assays, respectively). In parallel, 48-h static-renewal in vivo exposures were conducted to examine potential endocrine-disrupting effects in fathead minnows. Mature fish were exposed to surface water dilutions (0%, 25%, 50%, and 100%) and 10-ng/L of 17α-ethynylestradiol or 50-ng/L of 17β-trenbolone as positive controls. Hepatic expression of vitellogenin and estrogen receptor α mRNA, gonadal ex vivo testosterone and 17β-estradiol production, and plasma vitellogenin concentrations were examined. Potentially estrogenic and androgenic steroids were detected at low nanogram per liter concentrations. In vitro estrogenic activity was detected in all samples, whereas androgenic activity was detected in only 1 sample. In vivo exposures to the surface water had no significant dose-dependent effect on any of the biological endpoints, with the exception of increased male testosterone production in 1 exposure. The present study, which combines analytical chemistry measurements, in vitro bioassays, and in vivo fish exposures, highlights the integrated value and future use of a combination of techniques to obtain a comprehensive characterization of an environmental chemical mixture.

Arkansas, Iowa, Kentucky, New York, Wisconsin↗

USGS2014

Sources of endocrine-disrupting compounds in North Carolina waterways: a geographic information systems approach

The presence of endocrine-disrupting compounds (EDCs), particularly estrogenic compounds, in the environment has drawn public attention across the globe, yet a clear understanding of the extent and distribution of estrogenic EDCs in surface waters and their relationship to potential sources is lacking. The objective of the present study was to identify and examine the potential input of estrogenic EDC sources in North Carolina water bodies using a geographic information system (GIS) mapping and analysis approach. Existing data from state and federal agencies were used to create point and nonpoint source maps depicting the cumulative contribution of potential sources of estrogenic EDCs to North Carolina surface waters. Water was collected from 33 sites (12 associated with potential point sources, 12 associated with potential nonpoint sources, and 9 reference), to validate the predictive results of the GIS analysis. Estrogenicity (measured as 17β-estradiol equivalence) ranged from 0.06 ng/L to 56.9 ng/L. However, the majority of sites (88%) had water 17β-estradiol concentrations below 1 ng/L. Sites associated with point and nonpoint sources had significantly higher 17β-estradiol levels than reference sites. The results suggested that water 17β-estradiol was reflective of GIS predictions, confirming the relevance of landscape-level influences on water quality and validating the GIS approach to characterize such relationships.

Environmental Toxicology and Chemistry↗

USGS2016

Complex mixtures, complex responses: Assessing pharmaceutical mixtures using field and laboratory approaches

Pharmaceuticals are present in low concentrations (<100 ng/L) in most municipal wastewater effluents but may be elevated locally because of factors such as input from pharmaceutical formulation facilities. Using existing concentration data, the authors assessed pharmaceuticals in laboratory exposures of fathead minnows ( Pimephales promelas ) and added environmental complexity through effluent exposures. In the laboratory, larval and mature minnows were exposed to a simple opioid mixture (hydrocodone, methadone, and oxycodone), an opioid agonist (tramadol), a muscle relaxant (methocarbamol), a simple antidepressant mixture (fluoxetine, paroxetine, venlafaxine), a sleep aid (temazepam), or a complex mixture of all compounds. Larval minnow response to effluent exposure was not consistent. The 2010 exposures resulted in shorter exposed minnow larvae, whereas the larvae exposed in 2012 exhibited altered escape behavior. Mature minnows exhibited altered hepatosomatic indices, with the strongest effects in females and in mixture exposures. In addition, laboratory-exposed, mature male minnows exposed to all pharmaceuticals (except the selective serotonin reuptake inhibitor mixture) defended nest sites less rigorously than fish in the control group. Tramadol or antidepressant mixture exposure resulted in increased splenic T lymphocytes. Only male minnows exposed to whole effluent responded with increased plasma vitellogenin concentrations. Female minnows exposed to pharmaceuticals (except the opioid mixture) had larger livers, likely as a compensatory result of greater prominence of vacuoles in liver hepatocytes. The observed alteration of apical endpoints central to sustaining fish populations confirms that effluents containing waste streams from pharmaceutical formulation facilities can adversely impact fish populations but that the effects may not be temporally consistent. The present study highlights the importance of including diverse biological endpoints spanning levels of biological organization and life stages when assessing contaminant interactions.

Environmental Toxicology and Chemistry↗

USGS2016

Landfill leachate as a mirror of today's disposable society: Pharmaceuticals and other contaminants of emerging concern in final leachate from landfills in the conterminous United States

Final leachates (leachate after storage or treatment processes) from 22 landfills in 12 states were analyzed for 190 pharmaceuticals and other contaminants of emerging concern (CECs), which were detected in every sample, with the number of CECs ranging from 1 to 58 (median = 22). In total, 101 different CECs were detected in leachate samples, including 43 prescription pharmaceuticals, 22 industrial chemicals, 15 household chemicals, 12 nonprescription pharmaceuticals, 5 steroid hormones, and 4 animal/plant sterols. The most frequently detected CECs were lidocaine (91%, local anesthetic), cotinine (86%, nicotine degradate), carisoprodol (82%, muscle relaxant), bisphenol A (77%, component of plastics and thermal paper), carbamazepine (77%, anticonvulsant), and N,N-diethyltoluamide (68%, insect repellent). Concentrations of CECs spanned 7 orders of magnitude, ranging from 2.0 ng/L (estrone) to 17 200 000 ng/L (bisphenol A). Concentrations of household and industrial chemicals were the greatest (&sim;1000-1 000 000 ng/L), followed by plant/animal sterols (&sim;1000-100 000 ng/L), nonprescription pharmaceuticals (&sim;100-10 000 ng/L), prescription pharmaceuticals (&sim;10-10 000 ng/L), and steroid hormones (&sim;10-100 ng/L). The CEC concentrations in leachate from active landfills were significantly greater than those in leachate from closed, unlined landfills (p = 0.05). The CEC concentrations were significantly greater (p < 0.01) in untreated leachate compared with treated leachate. The CEC concentrations were significantly greater in leachate disposed to wastewater treatment plants from modern lined landfills than in leachate released to groundwater from closed, unlined landfills (p = 0.04). The CEC concentrations were significantly greater (p = 0.06) in the fresh leachate (leachate before storage or treatment) reported in a previous study compared with the final leachate sampled for the present study.

Arkansas, Florida, Iowa, Kentucky, Maine, Minnesot↗

USGS2016

Spatial and temporal variation in microcystins occurrence in wadeable streams in the southeastern USA

Despite historical observations of potential microcystin-producing cyanobacteria (including Leptolyngbya , Phormidium , Pseudoanabaena , and Anabaena species) in 74% of headwater streams in Alabama, Georgia, South Carolina, and North Carolina (USA) from 1993 to 2011, fluvial cyanotoxin occurrence has not been systematically assessed in the southeastern United States. To begin to address this data gap, a spatial reconnaissance of fluvial microcystin concentrations was conducted in 75 wadeable streams in the Piedmont region (southeastern USA) during June 2014. Microcystins were detected using enzyme-linked immunosorbent assay (limit = 0.10 µg/L) in 39% of the streams with mean, median, and maximum detected concentrations of 0.29 µg/L, 0.11 µg/L, and 3.2 µg/L, respectively. Significant (α = 0.05) correlations were observed between June 2014 microcystin concentrations and stream flow, total nitrogen to total phosphorus ratio, and water temperature; but each of these factors explained 38% or less of the variability in fluvial microcystins across the region. Temporal microcystin variability was assessed monthly through October 2014 in 5 of the streams where microcystins were observed in June and in 1 reference location; microcystins were repeatedly detected in all but the reference stream. Although microcystin concentrations in the present study did not exceed World Health Organization recreational guidance thresholds, their widespread occurrence demonstrates the need for further investigation of possible in-stream environmental health effects as well as potential impacts on downstream lakes and reservoirs. Environ Toxicol Chem 2016;9999:1–7. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US Government work and, as such, is in the public domain in the United States of America.

Alabama, Georgia, Maryland, North Carolina, South ↗

USGS2017

Are exposure predictions, used for the prioritization of pharmaceuticals in the environment, fit for purpose?

Prioritization methodologies are often used for identifying those pharmaceuticals that pose the greatest risk to the natural environment and to focus laboratory testing or environmental monitoring toward pharmaceuticals of greatest concern. Risk-based prioritization approaches, employing models to derive exposure concentrations, are commonly used, but the reliability of these models is unclear. The present study evaluated the accuracy of exposure models commonly used for pharmaceutical prioritization. Targeted monitoring was conducted for 95 pharmaceuticals in the Rivers Foss and Ouse in the City of York (UK). Predicted environmental concentration (PEC) ranges were estimated based on localized prescription, hydrological data, reported metabolism, and wastewater treatment plant (WWTP) removal rates, and were compared with measured environmental concentrations (MECs). For the River Foss, PECs, obtained using highest metabolism and lowest WWTP removal, were similar to MECs. In contrast, this trend was not observed for the River Ouse, possibly because of pharmaceutical inputs unaccounted for by our modeling. Pharmaceuticals were ranked by risk based on either MECs or PECs. With 2 exceptions (dextromethorphan and diphenhydramine), risk ranking based on both MECs and PECs produced similar results in the River Foss. Overall, these findings indicate that PECs may well be appropriate for prioritization of pharmaceuticals in the environment when robust and local data on the system of interest are available and reflective of most source inputs.

City of York↗

USGS2017

Tools to minimize interlaboratory variability in vitellogenin gene expression monitoring programs

The egg yolk precursor protein vitellogenin is widely used as a biomarker of estrogen exposure in male fish. However, standardized methodology is lacking and little is known regarding the reproducibility of results among laboratories using different equipment, reagents, protocols, and data analysis programs. To address this data gap we tested the reproducibility across laboratories to evaluate vitellogenin gene ( vtg ) expression and assessed the value of using a freely available software data analysis program. Samples collected from studies of male fathead minnows ( Pimephales promelas ) exposed to 17α-ethinylestradiol (EE2) and minnows exposed to processed wastewater effluent were evaluated for vtg expression in 4 laboratories. Our results indicate reasonable consistency among laboratories if the free software for expression analysis LinRegPCR is used, with 3 of 4 laboratories detecting vtg in fish exposed to 5 ng/L EE2 ( n = 5). All 4 laboratories detected significantly increased vtg levels in 15 male fish exposed to wastewater effluent compared with 15 male fish held in a control stream. Finally, we were able to determine that the source of high interlaboratory variability from complementary deoxyribonucleic acid (cDNA) to quantitative polymerase chain reaction (qPCR) analyses was the expression analysis software unique to each real-time qPCR machine. We successfully eliminated the interlaboratory variability by reanalyzing raw fluorescence data with independent freeware, which yielded cycle thresholds and polymerase chain reaction (PCR) efficiencies that calculated results independently of proprietary software. Our results suggest that laboratories engaged in monitoring programs should validate their PCR protocols and analyze their gene expression data following the guidelines established in the present study for all gene expression biomarkers.

Environmental Toxicology and Chemistry↗

USGS2023

Pesticide prioritization by potential biological effects in tributaries of the Laurentian Great Lakes

Watersheds of the Great Lakes Basin (USA/Canada) are highly modified and impacted by human activities including pesticide use. Despite labeling restrictions intended to minimize risks to nontarget organisms, concerns remain that environmental exposures to pesticides may be occurring at levels negatively impacting nontarget organisms. We used a combination of organismal-level toxicity estimates (in vivo aquatic life benchmarks) and data from high-throughput screening (HTS) assays (in vitro benchmarks) to prioritize pesticides and sites of concern in streams at 16 tributaries to the Great Lakes Basin. In vivo or in vitro benchmark values were exceeded at 15 sites, 10 of which had exceedances throughout the year. Pesticides had the greatest potential biological impact at the site with the greatest proportion of agricultural land use in its basin (the Maumee River, Toledo, OH, USA), with 72 parent compounds or transformation products being detected, 47 of which exceeded at least one benchmark value. Our risk-based screening approach identified multiple pesticide parent compounds of concern in tributaries of the Great Lakes; these compounds included: eight herbicides (metolachlor, acetochlor, 2,4-dichlorophenoxyacetic acid, diuron, atrazine, alachlor, triclopyr, and simazine), three fungicides (chlorothalonil, propiconazole, and carbendazim), and four insecticides (diazinon, fipronil, imidacloprid, and clothianidin). We present methods for reducing the volume and complexity of potential biological effects data that result from combining contaminant surveillance with HTS (in vitro) and traditional (in vivo) toxicity estimates. Environ Toxicol Chem 2022;00:1–18. Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Great Lakes↗

USGS2023

Identifying hydrologic signatures associated with streamflow depletion caused by groundwater pumping

Groundwater pumping can reduce streamflow in nearby waterways (‘streamflow depletion’), a process which must be accounted for in integrated management of surface and groundwater resources. However, causal identification of streamflow depletion from hydrographs alone is challenging because pumping impacts are masked by other drivers of hydrologic variability. To identify potential indicators of streamflow depletion, we used synthetic hydrographs and an analytical streamflow depletion model to assess potential pumping impacts on specific hydrograph characteristics (‘hydrologic signatures’) for 215 streamgages spanning the conterminous United States (CONUS). We found that streamflow depletion commonly impacts signatures associated with seasonal and annual low flows and low flow recessions. The largest impacts occurred during dry years, suggesting streamflow depletion may be evident in dry years even where impacts are unmeasurable in wet years. Random forest models indicated that streamflow depletion could significantly impact Annual, Summer, and Fall signatures in most streams. Our finding that multiple hydrologic signatures are consistently responsive to streamflow depletion across CONUS suggests that the underlying hydrological processes linking pumping to streamflow reductions are consistent across diverse settings, information that will aid in identifying indicators of streamflow depletion from streamflow hydrographs.

Hydrological Processes↗