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USGS2025

The role of bison (Bison bison) herbivory in the function of semi-arid grasslands of Arizona

The American Plains Bison ( Bison bison ) is recognized as a grassland keystone species; however, their effect on grassland ecosystem function can vary widely among different plant communities, ranging from degradation to enhancement. Grazing dynamics, environmental conditions, and the capacity for plants to compensate for losses due to herbivory largely govern the degree and magnitude of herbivory-induced ecosystem change, making it challenging to manage grazing ecosystems. Here, we examine how different grazing intensities and environmental conditions affect aboveground herbaceous production and herbaceous nitrogen yield within high-elevation, semi-arid grasslands of the Grand Canyon ecosystem in northern Arizona, United States. We conducted a replicate herbivore exclusion experiment in grassland meadows using both long-term exclosures (0.40 ha) and temporary grazing exclusion cages (1 m 2 ) to quantify herbaceous production and nitrogen yield in sites with high bison density (Grand Canyon National Park) and low bison density (Kaibab National Forest). Our goal was to assess the influence of bison grazing on aboveground herbaceous production, the relationship between herbaceous consumption (offtake) and production, and evaluate potential differences in herbaceous nitrogen yield. We found that bison grazing enhanced herbaceous production 1.32-fold and nitrogen yield 1.61-fold, regardless of grazing intensity, availability of soil nutrients, or spatiotemporal variation in climate during our study. Although we expected herbaceous production to decline at the highest levels of herbaceous offtake, we observed a linear positive relationship between offtake and production in Grand Canyon. Over the 2-year study, bison grazing was the primary influential factor explaining variation in grassland production compared to other environmental variables (i.e., temperature, precipitation, and soil nutrients). Our results show no evidence of degradation in aboveground plant productivity, which is an important metric of ecosystem function, from the current dynamics of bison herbivory in the grasslands of the North Rim of Grand Canyon National Park.

Arizona↗

USGS2026

Application of close-kin mark–recapture to an American black bear population using harvest samples

Close-kin mark–recapture (CKMR) offers a promising alternative to traditional mark–recapture methods for estimating wildlife population parameters. CKMR estimates demographic parameters by identifying genetic relationships between individuals, effectively “recapturing” parental genotypes through their offspring's genetic profiles. One key advantage of CKMR over traditional methods is the ability to estimate abundance without recapture, facilitating the use of lethally collected biological samples. Here, we present an application of CKMR to a terrestrial species using samples collected from harvested individuals. We analyzed genetic data from 1767 American black bears ( Ursus americanus ) harvested over two consecutive years in Michigan's Upper Peninsula. Using Genotyping-in-Thousands by sequencing (GT-seq), we identified parent-offspring pairs and developed sex-specific models tailored to black bear reproductive biology and management strategies. Female population estimates aligned with our expectations, based on prior knowledge of the population. In contrast, male population estimates deviated from expectations, potentially due to an unmodeled combination of factors pertaining to male reproductive biology and sampling bias. Our study not only demonstrates the viability of using harvest-based CKMR for monitoring of terrestrial wildlife populations, but also highlights the need for careful model specification. By providing a novel method to estimate demographic parameters and population size, CKMR using harvest samples can inform management decisions and harvest quotas. This approach leverages existing management practices involving collection of genetic samples, such as mandatory harvest registrations and health monitoring programs. Our findings contribute to the growing body of CKMR applications and expand its use to terrestrial species management.

Michigan↗

USGS2019

An analysis of autocorrelation and bias in home range estimation

Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function AKDE, Silvermans rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ( N̂ area ) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the hold-out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂ area . To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animals movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂ area . While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N̂ area >1,000, where 30% had an N̂ area <30. In this frequently encountered scenario of small N̂ area , AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.

Ecological Monographs↗

USGS2022

Parasites in kelp-forest food webs increase food-chain length, complexity, and specialization, but reduce connectance

We explored whether parasites are important in kelp forests by examining their effects on a high-quality, high-resolution kelp-forest food web. After controlling for generic effects of network size, parasites affected kelp-forest food web structure in some ways consistent with other systems. Parasites increased the trophic span of the web, increasing top predator vulnerability and the longest chain length. Unique links associated with parasites, such as concomitant predation (consumption of parasites along with their hosts by predators) increased the frequency of network motifs involving mutual consumption and decreased niche contiguity of free-living species. However, parasites also affected kelp-forest food web structure in ways not seen in other systems. Kelp-forest parasites are richer and more specialized than other systems. As a result, parasites reduced diet generality and decreased connectance in the kelp forest. Although mutual consumption motifs increased in frequency, this motif type was still a small fraction of all possible motifs, so their increase in frequency was not enough to compensate for the decrease in connectance caused by adding many specialist parasite species.

California↗

USGS2016

Effects of climate and water balance across grasslands of varying C 3 and C 4 grass cover

Climate change in grassland ecosystems may lead to divergent shifts in the abundance and distribution of C 3 and C 4 grasses. Many studies relate mean climate conditions over relatively long time periods to plant cover, but there is still much uncertainty about how the balance of C 3 and C 4 species will be affected by climate at a finer temporal scale than season (individual events to months). We monitored cover at five grassland sites with co-dominant C 3 and C 4 grass species or only dominant C 3 grass species for 6 yr in national parks across the Colorado Plateau region to assess the influence of specific months of climate and water balance on changes in grass cover. C 4 grass cover increased and decreased to a larger degree than C 3 grass cover with extremely dry and wet consecutive years, but this response varied by ecological site. Climate and water balance explained 10–49% of the inter-annual variability of cover of C 3 and C 4 grasses at all sites. High precipitation in the spring and in previous year monsoon storms influenced changes in cover of C 4 grasses, with measures of water balance in the same months explaining additional variability. C 3 grasses in grasslands where they were dominant were influenced primarily by longer periods of climate, while C 3 grasses in grasslands where they were co-dominant with C 4 grasses were influenced little by climate anomalies at either short or long periods of time. Our results suggest that future changes in spring and summer climate and water balance are likely to affect cover of both C 3 and C 4 grasses, but cover of C 4 grasses may be affected more strongly, and the degree of change will depend on soils and topography where they are growing and the timing of the growing season.

Ecosphere↗

USGS2017

Model-based estimators of density and connectivity to inform conservation of spatially structured populations

Conservation and management of spatially structured populations is challenging because solutions must consider where individuals are located, but also differential individual space use as a result of landscape heterogeneity. A recent extension of spatial capture–recapture (SCR) models, the ecological distance model, uses spatial encounter histories of individuals (e.g., a record of where individuals are detected across space, often sequenced over multiple sampling occasions), to estimate the relationship between space use and characteristics of a landscape, allowing simultaneous estimation of both local densities of individuals across space and connectivity at the scale of individual movement. We developed two model-based estimators derived from the SCR ecological distance model to quantify connectivity over a continuous surface: (1) potential connectivity—a metric of the connectivity of areas based on resistance to individual movement; and (2) density-weighted connectivity (DWC)—potential connectivity weighted by estimated density. Estimates of potential connectivity and DWC can provide spatial representations of areas that are most important for the conservation of threatened species, or management of abundant populations (i.e., areas with high density and landscape connectivity), and thus generate predictions that have great potential to inform conservation and management actions. We used a simulation study with a stationary trap design across a range of landscape resistance scenarios to evaluate how well our model estimates resistance, potential connectivity, and DWC. Correlation between true and estimated potential connectivity was high, and there was positive correlation and high spatial accuracy between estimated DWC and true DWC. We applied our approach to data collected from a population of black bears in New York, and found that forested areas represented low levels of resistance for black bears. We demonstrate that formal inference about measures of landscape connectivity can be achieved from standard methods of studying animal populations which yield individual encounter history data such as camera trapping. Resulting biological parameters including resistance, potential connectivity, and DWC estimate the spatial distribution and connectivity of the population within a statistical framework, and we outline applications to many possible conservation and management problems.

Ecosphere↗

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↗