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USGS2012

A stakeholder project to model water temperature under future climate scenarios in the Satus and Toppenish watersheds of the Yakima River Basinin Washington, USA

The goal of this study was to support an assessment of the potential effects of climate change on select natural, social, and economic resources in the Yakima River Basin. A workshop with local stakeholders highlighted the usefulness of projecting climate change impacts on anadromous steelhead ( Oncorhynchus mykiss) , a fish species of importance to local tribes, fisherman, and conservationists. Stream temperature is an important environmental variable for the freshwater stages of steelhead. For this study, we developed water temperature models for the Satus and Toppenish watersheds, two of the key stronghold areas for steelhead in the Yakima River Basin. We constructed the models with the Stream Network Temperature Model (SNTEMP), a mechanistic approach to simulate water temperature in a stream network. The models were calibrated over the April 15, 2008 to September 30, 2008 period and validated over the April 15, 2009 to September 30, 2009 period using historic measurements of stream temperature and discharge provided by the Yakama Nation Fisheries Resource Management Program. Once validated, the models were run to simulate conditions during the spring and summer seasons over a baseline period (1981–2005) and two future climate scenarios with increased air temperature of 1°C and 2°C. The models simulated daily mean and maximum water temperatures at sites throughout the two watersheds under the baseline and future climate scenarios.

Washington↗

USGS2013

An integrated model of environmental effects on growth, carbohydrate balance, and mortality of Pinus ponderosa forests in the southern Rocky Mountains

Climate-induced tree mortality is an increasing concern for forest managers around the world. We used a coupled hydrologic and ecosystem carbon cycling model to assess temperature and precipitation impacts on productivity and survival of ponderosa pine ( Pinus ponderosa ). Model predictions were evaluated using observations of productivity and survival for three ponderosa pine stands located across an 800 m elevation gradient in the southern Rocky Mountains, USA, during a 10-year period that ended in a severe drought and extensive tree mortality at the lowest elevation site. We demonstrate the utility of a relatively simple representation of declines in non-structural carbohydrate (NSC) as an approach for estimating patterns of ponderosa pine vulnerability to drought and the likelihood of survival along an elevation gradient. We assess the sensitivity of simulated net primary production, NSC storage dynamics, and mortality to site climate and soil characteristics as well as uncertainty in the allocation of carbon to the NSC pool. For a fairly wide set of assumptions, the model estimates captured elevational gradients and temporal patterns in growth and biomass. Model results that best predict mortality risk also yield productivity, leaf area, and biomass estimates that are qualitatively consistent with observations across the sites. Using this constrained set of parameters, we found that productivity and likelihood of survival were equally dependent on elevation-driven variation in temperature and precipitation. Our results demonstrate the potential for a coupled hydrology-ecosystem carbon cycling model that includes a simple model of NSC dynamics to predict drought-related mortality. Given that increases in temperature and in the frequency and severity of drought are predicted for a broad range of ponderosa pine and other western North America conifer forest habitats, the model potentially has broad utility for assessing ecosystem vulnerabilities.

Jemez Mountains, Rocky Mountains↗

USGS2017

Development of a robust analytical framework for assessing landbird trends, dynamics and relationships with environmental covariates in the North Coast and Cascades Network

During 2015-2016, we completed development of a new analytical framework for landbird population monitoring data from the National Park Service (NPS) North Coast and Cascades Inventory and Monitoring Network (NCCN). This new tool for analysis combines several recent advances in modeling population status and trends using point-count data and is designed to supersede the approach previously slated for analysis of trends in the NCCN and other networks, including the Sierra Nevada Network (SIEN). Advances supported by the new model-based approach include 1) the use of combined data on distance and time of detection to estimate detection probability without assuming perfect detection at zero distance, 2) seamless accommodation of variation in sampling effort and missing data, and 3) straightforward estimation of the effects of downscaled climate and other local habitat characteristics on spatial and temporal trends in landbird populations. No changes in the current field protocol are necessary to facilitate the new analyses. We applied several versions of the new model to data from each of 39 species recorded in the three mountain parks of the NCCN, estimating trends and climate relationships for each species during 2005-2014. Our methods and results are also reported in a manuscript in revision for the journal Ecosphere (hereafter, Ray et al.). Here, we summarize the methods and results outlined in depth by Ray et al., discuss benefits of the new analytical framework, and provide recommendations for its application to synthetic analyses of long-term data from the NCCN and SIEN. All code necessary for implementing the new analyses is provided within the Appendices to this report, in the form of fully annotated scripts written in the open-access programming languages R and JAGS.

Natural Resource Report↗

USGS2015

Use of terrestrial field studies in the derivation of bioaccumulation potential of chemicals

Field-based studies are an essential component of research addressing the behavior of organic chemicals, and a unique line of evidence that can be used to assess bioaccumulation potential in chemical registration programs and aid in development of associated laboratory and modeling efforts. To aid scientific and regulatory discourse on the application of terrestrial field data in this manner, this article provides practical recommendations regarding the generation and interpretation of terrestrial field data. Currently, biota-to-soil-accumulation factors (BSAFs), biomagnification factors (BMFs), and bioaccumulation factors (BAFs) are the most suitable bioaccumulation metrics that are applicable to bioaccumulation assessment evaluations and able to be generated from terrestrial field studies with relatively low uncertainty. Biomagnification factors calculated from field-collected samples of terrestrial carnivores and their prey appear to be particularly robust indicators of bioaccumulation potential. The use of stable isotope ratios for quantification of trophic relationships in terrestrial ecosystems needs to be further developed to resolve uncertainties associated with the calculation of terrestrial trophic magnification factors (TMFs). Sampling efforts for terrestrial field studies should strive for efficiency, and advice on optimization of study sample sizes, practical considerations for obtaining samples, selection of tissues for analysis, and data interpretation is provided. Although there is still much to be learned regarding terrestrial bioaccumulation, these recommendations provide some initial guidance to the present application of terrestrial field data as a line of evidence in the assessment of chemical bioaccumulation potential and a resource to inform laboratory and modeling efforts.

Integrated Environmental Assessment and Management↗

USGS2026

Guidelines for producing integrated 210Pb and 14C age-models

Accurate reconstructions of past environmental changes are crucial in paleoecological research and require reliable chronologies of sedimentary archives. Establishing robust age-models and obtaining the most appropriate proxies for analysis is a complex scientific endeavor, requiring extensive resources and collaboration among specialists, including radiochronologists. Radiometric dating methods, such as 210 Pb and radiocarbon ( 14 C), are frequently employed to establish chronologies in aquatic sedimentary deposits and peat bogs. In this study, we review key aspects of sampling, analysis, and the principles underlying 210 Pb and 14 C age-models, focusing on methods for developing robust joint chronologies for paleoenvironmental research. Drawing largely from the authors' experiences and group discussions during and after a scientific workshop in 2022, we discuss important considerations for site selection, sampling strategies, and radiometric dating to construct integrated 210 Pb and 14 C age-models. Using expert consensus, this group – called Paleostats – aims to provide a set of best practices for other geochronologists with this methods paper. Among our conclusions, we emphasize the importance of accounting for site-specific factors such as prior information on sedimentation rates to establish appropriate sampling and analytical strategies. The use of appropriate coring devices can minimize disturbance to sediments and ensure the core surface remains intact and preserved until sectioning. Where excess 210 Pb is expected, sectioning at intervals of ≤1 cm provides an adequate sampling resolution for 210 Pb dating. Exceptions are possible, allowing for ~2–3 cm sections in areas with confirmed high sedimentation rates (e.g., > 1 cm yr −1 ). Recovering deeper core sections for 14 C dating with sufficient overlap allows for accounting errors in depth estimates made in the field. Special attention is advised during time intervals where validation proxies, such as the human-made radionuclides 137 Cs or post-bomb 14 C, are expected, and to determine the depth of secular equilibrium between 210 Pb and 226 Ra. Radiocarbon analyses are commonly performed by accelerator mass spectrometry, and age models are constructed mainly using Bayesian statistics with Markov Chain Monte Carlo techniques (e.g., Bacon ). A Bayesian approach ( Plum ) is now available for producing 210 Pb age-models, which infers the 210 Pb ex flux, eliminates the need for selecting an equilibrium depth, and allows dating cores with incomplete 210 Pb ex inventory. Plum offers improved chronologies by integrating raw 210 Pb and 14 C data, and these age-models can be enriched with other dating methodologies, such as identifying tephras and other well-recorded historical events. Harmonized reporting would contribute to making radiometric age-models reproducible, which would benefit from an international effort. Using 210 Pb and 14 C to produce integrated age-models may yield better insights into the interplay between natural and recent anthropogenic forcings on ecosystems. This can enhance our understanding of environmental processes and their impacts on climate change, ultimately supporting science-based assessments and decisions.

Earth-Science Reviews↗

USGS2019

Integrated assessment of wastewater reuse, exposure risk, and fish endocrine disruption in the Shenandoah River watershed

Reuse of municipal and industrial wastewater treatment plant (WWTP) effluent is an important component in augmenting global freshwater supplies. The Shenandoah River Watershed was selected to conduct on-site exposure experiments to assess endocrine disrupting characteristics of different source waters. This investigation of the Shenandoah River Watershed integrates WWTP wastewater reuse modeling, hydrological and chemical characterization, and in vivo endocrine disruption bioassessment to assess contaminant sources, exposure pathways, and biological effects. The percentage of accumulated WWTP effluent in each river reach (ACCWW) was used to predict environmental concentrations for consumer product chemicals (boron), pharmaceutical compounds (carbamazepine), and steroidal estrogens (estrone, 17-beta-estradiol, estriol, and 17-alpha-ethinylestradiol). Fish endocrine disruption was evaluated using vitellogenin induction in male or juvenile fathead minnows. Water samples were analyzed for >500 inorganic and organic constituents to characterize the complex contaminant mixtures. Municipal ACCWW at drinking water treatment plant surface-water intakes ranged from <0.01 to 2.1 % under mean-annual streamflow and up to 4.7 % under August streamflow. Measured and predicted environmental concentrations resulted in 17-beta-estradiol equivalency quotients ranging from <0.05 to 5.1 ng L-1 indicating low-to-moderate risk of fish endocrine disruption. Results from the fish exposure experiments also showed limited estrogenic effects as indicated by the low (0.5- to 3.2-fold) vitellogenin induction.

Shenandoah River watershed↗

USGS2019

Mapping a keystone shrub species, huckleberry (Vaccinium membranaceum), using seasonal colour change in the Rocky Mountains

Black huckleberries ( Vaccinium membranaceum ) provide a critical food resource to many wildlife species, including apex omnivores such as the grizzly bear ( Ursus arctos ), and play an important socioeconomic role for many communities in western North America, especially indigenous peoples. Remote sensing imagery offers the potential for accurate landscape-level mapping of huckleberries because the shrub changes colour seasonally. We developed two methods, for local and regional scales, to map a shrub species using leaf seasonal colour change from remote sensing imagery. We assessed accuracy with ground-based vegetation surveys. The high-resolution supervised random forest classification from one-meter resolution National Agricultural Imagery Program (NAIP) imagery achieved an overall accuracy of 75.31% (kappa = 0.26). The approach using multi-temporal 30-meter Landsat imagery similarly had an overall accuracy of 79.19% (kappa = .31). We found underprediction error was related to higher forest cover and a lack of visible colour change on the ground in some plots. Where forest cover was low, both models performed better. In areas with <10% forest cover, the high-resolution classification achieved an accuracy of 80.73% (kappa = 0.48), while the Landsat model had an accuracy of 82.55% (kappa = 0.47). Based on the fine-scale predictions, we found that 94% of huckleberry shrubs identified in our study area of Glacier National Park, Montana, USA are over 100 meters from human recreation trails. This information could be combined with productivity and phenology information to estimate the timing and availability of food resources for wildlife and to provide managers with a tool to identify and manage huckleberries. The development of the multi-temporal Landsat models sets the stage for assessment of impacts of disturbance at regional scales on this ecologically, culturally, and economically important shrub species. Our approach to map huckleberries is straightforward, efficient and accessible to wildlife and environmental managers and researchers in diverse fields.

Montana↗

USGS2020

Implanted satellite transmitters affect sea duck movement patterns at short- and long-term time scales

Studies of the effects of transmitters on wildlife often focus on survival. However, non-lethal behavioral changes resulting from radiomarking have the potential to affect inferences from telemetry data and may vary based on individual and environmental characteristics. We used a long-term, multi-species tracking study of sea ducks to assess behavioral patterns at multiple temporal scales following implantation of intracoelomic satellite transmitters. We applied state-space models to assess short-term behavioral patterns in individuals with implanted satellite transmitters, as well as comparing breeding site attendance and migratory phenology across multiple years after capture. In the short term, our results suggest an increase in dispersive behavior immediately following capture and transmitter implantation; however, behavior returned to seasonally-average patterns within approximately five days after release. Over multiple years, we found that breeding site attendance by both males and females was depressed during the first breeding season after radiomarking relative to subsequent years, with larger relative decreases in breeding site attendance among males than females. We also found that spring migration occurred later in the first year after radiomarking than in subsequent years. Across all behavioral effects, the severity of behavioral change often varied by species, sex, age, and capture season, suggesting heterogeneity in individual sensitivity. We conclude that, although individuals appear to adjust relatively quickly (i.e., within one week) to implanted satellite transmitters, changes in breeding phenology may occur over the longer term and should be considered when analyzing and reporting telemetry data.

Condor↗

USGS2022

Human and ecosystem health in coastal systems

U.S. coastal economies and communities are facing an unprecedented and growing number of impacts to coastal ecosystems including beach and fishery closures, harmful algal blooms, loss of critical habitat, as well as shoreline damage. This paper synthesizes our present understanding of the dynamics of human and ecosystem health in coastal systems with a focus on the need to better understand nearshore physical process interactions with coastal pollutants and ecosystems (e.g. fate and transport, circulation, depositional environment, climate change). It is organized around two major topical areas and six subtopic areas: 1) Identifying and mitigating coastal pollutants, including fecal pollution, nutrients and harmful algal blooms, and microplastics; and 2) Resilient coastal ecosystems, which focuses on coastal fisheries, shellfish and natural and nature-based features (NNBF). Societal needs and the tools and technologies needed to address them are discussed for each subtopic. Recommendations for scientific research, observations, community engagement, and policies aim to help prioritize future research and investments. A better understanding of coastal physical processes and interactions with coastal pollutants and resilient ecosystems (e.g. fate and transport, circulation, depositional environment, climate change) is a critical need. Other research recommendations include the need to quantify potential threats to human and ecosystem health through accurate risk assessments and to quantify the resulting hazard risk reduction of natural and nature-based features; improve pollutant and ecosystem impacts forecasting by integrating frequent and new data points into existing and novel models; collect environmental data to calibrate and validate models to predict future impacts on coastal ecosystems and their evolution due to anthropogenic stressors (land-based pollution, overfishing, coastal development), climate change, and sea level rise; and develop lower cost and rapid response tools to help coastal managers better respond to pollutant and ecosystem threats.

Shore and Beach↗

USGS2011

Evaluation of a present-day climate simulation with a new coupled atmosphere-ocean model GENMOM

We present a new, non-flux corrected AOGCM, GENMOM, that combines the GENESIS version 3 atmospheric GCM (Global Environmental and Ecological Simulation of Interactive Systems) and MOM2 (Modular Ocean Model version 2) nominally at T31 resolution. We evaluate GENMOM by comparison with reanalysis products (e.g., NCEP2) and three models used in the IPCC AR4 assessment. GENMOM produces a global temperature bias of 0.6 ◦C. Atmospheric features such as the jet stream structure and major semi-permanent sea level pressure centers are well simulated as is the mean planetary-scale wind structure that is needed to produce the correct position of stormtracks. Most ocean surface currents are reproduced except where they are not resolvable at T31 resolution. Overall, GENMOM captures reasonably well the observed gradients and spatial distributions of annual surface temperature and precipitation and the simulations are on par with other AOGCMs. Deficiencies in the GENMOM simulations include a warm bias in the surface temperature over the southern oceans, a split in the ITCZ and weaker-than-observed overturning circulation.

Geoscientific Model Development↗

USGS2011

Estimating water supply arsenic levels in the New England bladder cancer study

Background: Ingestion of inorganic arsenic in drinking water is recognized as a cause of bladder cancer when levels are relatively high (≥ 150 μg/L). The epidemiologic evidence is less clear at the low-to-moderate concentrations typically observed in the United States. Accurate retrospective exposure assessment over a long time period is a major challenge in conducting epidemiologic studies of environmental factors and diseases with long latency, such as cancer. Objective: We estimated arsenic concentrations in the water supplies of 2,611 participants in a population-based case–control study in northern New England. Methods: Estimates covered the lifetimes of most study participants and were based on a combination of arsenic measurements at the homes of the participants and statistical modeling of arsenic concentrations in the water supply of both past and current homes. We assigned a residential water supply arsenic concentration for 165,138 (95%) of the total 173,361 lifetime exposure years (EYs) and a workplace water supply arsenic level for 85,195 EYs (86% of reported occupational years). Results: Three methods accounted for 93% of the residential estimates of arsenic concentration: direct measurement of water samples (27%; median, 0.3 μg/L; range, 0.1–11.5), statistical models of water utility measurement data (49%; median, 0.4 μg/L; range, 0.3–3.3), and statistical models of arsenic concentrations in wells using aquifers in New England (17%; median, 1.6 μg/L; range, 0.6–22.4). Conclusions: We used a different validation procedure for each of the three methods, and found our estimated levels to be comparable with available measured concentrations. This methodology allowed us to calculate potential drinking water exposure over long periods.

Environmental Health Perspectives↗

USGS2016

Toward more realistic projections of soil carbon dynamics by Earth system models

Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.

Global Biogeochemical Cycles↗

USGS2025

Experimental evaluation of Eastern box turtle (Terrapene carolina carolina) detectability in visual search surveys

Understanding how detection probability varies over time, space, or in response to measurable covariates is important to inform the monitoring and assessment of many species. A standard model to understand detectability, the availability/perception model, admits that detection probability is the composite of two components: availability and ability of surveyors to detect the target. Availability is largely affected by environmental and behavioral factors, whereas perception is primarily affected by attributes of individual observers and survey protocols, and thus can potentially be partially controlled by survey design. We designed and implemented a field study to understand the perception component of detection for Eastern Box Turtles ( Terrapene carolina carolina ) using visual encounter surveys. We obtained and deployed museum specimens of Eastern Box Turtle shells and subjected them to visual search surveys by observers in realistic field situations. Overall, about 50% of the box turtle shells were detected by observers, including 41.5% in what we categorized as partially visible and 63.0% as fully visible. There were significant differences among observers, which may be due to observer-specific variation in search technique; the observers varied in how well they achieved the protocol guidance. Therefore, in visual search surveys, care in study design and analysis should be taken to account for variation in perception to determine detectability, as our study suggests 37% of perceptible targets are missed by surveyors.

Maryland↗

USGS2024

Environmental and geographical factors influence the occurrence and abundance of the southern house mosquito, Culex quinquefasciatus, in Hawai‘i

Hawaiian honeycreepers, a group of endemic Hawaiian forest birds, are being threatened by avian malaria, a non-native disease that is driving honeycreepers populations to extinction. Avian malaria is caused by the parasite Plasmodium relictum , which is transmitted by the invasive mosquito Culex quinquefasciatus . Environmental and geographical factors play an important role in shaping mosquito-borne disease transmission dynamics through their influence on the distribution and abundance of mosquitoes. We assessed the effects of environmental (temperature, precipitation), geographic (site, elevation, distance to anthropogenic features), and trap type (CDC light trap, CDC gravid trap) factors on mosquito occurrence and abundance. Occurrence was analyzed using classification and regression tree models (CART) and generalized linear models (GLM); abundance (count data) was analyzed using generalized linear mixed models (GLMMs). Models predicted highest mosquito occurrence at mid-elevation sites and between July and November. Occurrence increased with temperature and precipitation up to 580 mm. For abundance, the best model was a zero-inflated negative-binomial model that indicated higher abundance of mosquitoes at mid-elevation sites and peak abundance between August and October. Estimation of occurrence and abundance as well as understanding the factors that influence them are key for mosquito control, which may reduce the risk of forest bird extinction.

Hawaii↗

USGS2022

Divergent climate impacts on C3 versus C4 grasses imply widespread 21st century shifts in grassland functional composition

Aim Grasslands cover a third of Earth's landmass and provide critical ecosystem services. Anticipating how perennial C 3 (cool-season) and C 4 (warm-season) grasses respond to climate change will be key to predicting future composition and functioning of grasslands. Here, we evaluate environmental drivers of C 3 and C 4 perennial distributions and assess how C 3 and C 4 grass distributions shift in response to future climate change. Location Western United States. Methods We developed integrated species distribution models to identify climate and soil drivers of relative abundance of C 3 and C 4 perennial grasses. We then created projections of species abundances under future climate and evaluated when and where projected shifts in relative abundance were robust across climate models. Results Historically, C 3 grasses occupied areas with lower temperature and more variable precipitation regimes, while C 4 grasses occupied areas of higher temperature, greater temperature variability and greater warm-season precipitation. C 4 species also occupied narrower soil texture niches. In response to future climate change, C 3 grass abundance declined across 74% of areas, while C 4 abundance increased across 66% of areas. C 3 grasses expanded in mid- to higher-latitude areas with increasing temperature and decreasing seasonality of precipitation. In contrast, C 4 grasses increased in higher-latitude regions, but declined in lower-latitude, dryer regions. Results were surprisingly robust across climate scenarios, suggesting high confidence in the direction of these future changes. Main Conclusions Findings imply C 3 and C 4 perennial grasses will have highly divergent responses to climate change that may result in grassland functional compositional changes. Increasing temperatures and precipitation variability may favour some C 4 grasses, but C 4 habitat expansion may be constrained by soil conditions in western USA. Results provide actionable insights for anticipating the impacts of climate change on grass-dominated and co-dominated ecosystems and improving large-scale conservation and restoration efforts.

Diversity and Distributions↗

USGS2007

Empirical evaluation of decision support systems: Needs, definitions, potential methods, and an example pertaining to waterfowl management

Decision support systems are often not empirically evaluated, especially the underlying modelling components. This can be attributed to such systems necessarily being designed to handle complex and poorly structured problems and decision making. Nonetheless, evaluation is critical and should be focused on empirical testing whenever possible. Verification and validation, in combination, comprise such evaluation. Verification is ensuring that the system is internally complete, coherent, and logical from a modelling and programming perspective. Validation is examining whether the system is realistic and useful to the user or decision maker, and should answer the question: “Was the system successful at addressing its intended purpose?” A rich literature exists on verification and validation of expert systems and other artificial intelligence methods; however, no single evaluation methodology has emerged as preeminent. At least five approaches to validation are feasible. First, under some conditions, decision support system performance can be tested against a preselected gold standard. Second, real-time and historic data sets can be used for comparison with simulated output. Third, panels of experts can be judiciously used, but often are not an option in some ecological domains. Fourth, sensitivity analysis of system outputs in relation to inputs can be informative. Fifth, when validation of a complete system is impossible, examining major components can be substituted, recognizing the potential pitfalls. I provide an example of evaluation of a decision support system for trumpeter swan ( Cygnus buccinator ) management that I developed using interacting intelligent agents, expert systems, and a queuing system. Predicted swan distributions over a 13-year period were assessed against observed numbers. Population survey numbers and banding (ringing) studies may provide long term data useful in empirical evaluation of decision support.

Environmental Modelling and Software↗

USGS2020

Time-dependent accumulation of Cd, Co, Cu, Ni, and Zn in mayfly and caddisfly larvae in experimental streams: Metal sensitivity, uptake pathways, and mixture toxicity

Conceptual and quantitative models were developed to assess time-dependent processes in four sequential experimental stream studies that determined abundances of natural communities of mayfly and caddisfly larvae dosed with single metals (Cd, Co, Cu, Ni, Zn) or multiple metals (Cd+Zn, Co+Cu, Cu+Ni, Cu+Zn, Ni+Zn, Cd+Cu+Zn, Co+Cu+Ni, Cu+Ni+Zn). Metal mixtures contained environmentally relevant metal ratios found in mine drainage. Free metal ion concentrations, accumulation of metals by periphyton, and metal uptake by four families of aquatic insect larvae were either measured (Brachycentridae) or predicted (Ephemerellidae, Heptageniidae, Hydropsychidae) using equilibrium and biodynamic models. Toxicity functions, which included metal accumulations by larvae and metal potencies, were linked to abundances of the insect families. Model results indicated that mayflies accumulated more metal than caddisflies and the relative importance of metal uptake by larvae via dissolved or dietary pathways highly depended on metal uptake rate constants for each insect family and concentrations of metals in food and water. For solution compositions in the experimental streams, accumulations of Cd, Cu, and Zn in larvae occurred primarily through dietary uptake, whereas uptake of dissolved metal was more important for Co and Ni accumulations. Cd, Cu, and Ni were major contributors to toxicity in metal mixtures and for metal ratios examined. Our conceptual approach and quantitative results should aid in designing laboratory experiments and field studies that evaluate metal uptake pathways and metal mixture toxicity to aquatic biota.

Science of the Total Environment↗

USGS2016

The role of habitat filtering in the leaf economics spectrum and plant susceptibility to pathogen infection

1.The Leaf Economics Spectrum (LES) describes global covariation in the traits of plant leaves. The LES is thought to arise from biophysical constraints and habitat filtering (ecological selection against unfit trait combinations along environmental gradients). However, the role of habitat filtering in generating the LES has not been tested experimentally. 2.If the process of habitat filtering plays a role in generating the LES, the LES could weaken in communities that have yet to be filtered by the current environment, for example after abiotic environmental change. LES traits are commonly used to predict community and ecosystem processes, and if the LES weakens in unfiltered communities, LES-based models may no longer apply. 3.In the greenhouse, we experimentally simulated three stages of habitat filtering in response to abiotic change: from unfiltered, to semi-filtered, to completely filtered communities. In each stage, we quantified the strength of the LES and assessed the accuracy of trait-based models of an important ecological process, pathogen infection. 4.The strength of the LES increased with the completeness of habitat filtering, as did the accuracy of trait-based models of plant susceptibility to pathogen infection. 5. Synthesis . Our results suggest that habitat filtering plays a fundamental role in strengthening the trait correlations of the LES, and that trait-based models may be less accurate when communities have not been filtered by the current environment, for example, following rapid environmental change.

Journal of Ecology↗