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At least 1,243 records · Page 69Linked to original sources

USGS2024

First records distribution models to guide biosurveillance for non-native species

Quickly locating new populations of non-native species can reduce the ecological and economic costs of species invasions. However, the difficulty of predicting which new non-native species will establish, and where, has limited active post-border biosurveillance efforts. Because pathways of introduction underlie spatial patterns of establishment risk, an intuitive approach is to search for new non-native species in areas where many non-native species have first been detected in the past. We formalize this intuition via first records distribution models (FRDMs), which apply species distribution modeling methods to the collection of first occurrence records across species (i.e. one record per species). We define FRDMs as statistical models that quantify environmental conditions associated with species' first naturalized records to predict spatial patterns of establishment risk. We model the first records of non-native plants in the conterminous USA as a proof-of-concept. The novelty of FRDMs is that their inferences apply not just to the species that contributed data; they provide a rigorous framework for predicting hotspots of invasion for new non-native taxa that share a pathway of introduction with the modeled species. FRDMs can guide survey efforts for new non-native taxa at multiple scales and across ecosystems.

conterminous United States↗

USGS2011

Mitigating amphibian disease: strategies to maintain wild populations and control chytridiomycosis

Background Rescuing amphibian diversity is an achievable conservation challenge. Disease mitigation is one essential component of population management. Here we assess existing disease mitigation strategies, some in early experimental stages, which focus on the globally emerging chytrid fungus Batrachochytrium dendrobatidis . We discuss the precedent for each strategy in systems ranging from agriculture to human medicine, and the outlook for each strategy in terms of research needs and long-term potential. Results We find that the effects of exposure to Batrachochytrium dendrobatidis occur on a spectrum from transient commensal to lethal pathogen. Management priorities are divided between (1) halting pathogen spread and developing survival assurance colonies, and (2) prophylactic or remedial disease treatment. Epidemiological models of chytridiomycosis suggest that mitigation strategies can control disease without eliminating the pathogen. Ecological ethics guide wildlife disease research, but several ethical questions remain for managing disease in the field. Conclusions Because sustainable conservation of amphibians in nature is dependent on long-term population persistence and co-evolution with potentially lethal pathogens, we suggest that disease mitigation not focus exclusively on the elimination or containment of the pathogen, or on the captive breeding of amphibian hosts. Rather, successful disease mitigation must be context specific with epidemiologically informed strategies to manage already infected populations by decreasing pathogenicity and host susceptibility. We propose population level treatments based on three steps: first, identify mechanisms of disease suppression; second, parameterize epizootiological models of disease and population dynamics for testing under semi-natural conditions; and third, begin a process of adaptive management in field trials with natural populations.

Frontiers in Zoology↗

USGS2007

Developing methods to assess and predict the population and community level effects of environmental contaminants

The field of ecological toxicity seems largely to have drifted away from what its title implies—assessing and predicting the ecological consequences of environmental contaminants—moving instead toward an emphasis on individual effects and physiologic case studies. This paper elucidates how a relatively new ecological methodology, interaction assessment (INTASS), could be useful in addressing the field's initial goals. Specifically, INTASS is a model platform and methodology, applicable across a broad array of taxa and habitat types, that can be used to construct population dynamics models from field data. Information on environmental contaminants and multiple stressors can be incorporated into these models in a form that bypasses the problems inherent in assessing uptake, chemical interactions in the environment, and synergistic effects in the organism. INTASS can, therefore, be used to evaluate the effects of contaminants and other stressors at the population level and to predict how changes in stressor levels or composition of contaminant mixtures, as well as various mitigation measures, might affect population dynamics.

Integrated Environmental Assessment and Management↗

USGS2020

Wildfire and the ecological niche: Diminishing habitat suitability for an indicator species within semi‐arid ecosystems

Globally accelerating frequency and extent of wildfire threatens the persistence of specialist wildlife species through direct loss of habitat and indirect facilitation of exotic invasive species. Habitat specialists may be especially prone to rapidly changing environmental conditions because their ability to adapt lags behind the rate of habitat alteration. As a result, these populations may become increasingly susceptible to ecological traps by returning to suboptimal breeding habitats that were dramatically altered by disturbance. We demonstrate a multistage modeling approach that integrates habitat selection and survival during the key nesting life‐stage of a bird species of high conservation concern, the greater sage‐grouse ( Centrocercus urophasianus ; hereafter, sage‐grouse). We applied these spatially explicit models to a spatiotemporally robust dataset of sage‐grouse nest locations and fates across wildfire‐altered sagebrush ecosystems of the Great Basin ecoregion, western United States. Female sage‐grouse exhibited intricate habitat selection patterns that varied across regional gradients of ecological productivity among sagebrush communities, but often selected nest sites that disproportionately resulted in nest failure. For example, 23% of nests occurred in wildfire‐affected habitats characterized by reduced sagebrush cover and greater composition of invasive annual grasses. We found survival of nests was negatively associated with wildfire‐affected areas, but positively associated with higher elevations with increased ruggedness and overall shrub cover. Strong site fidelity likely drove sage‐grouse to continue nesting in habitats degraded by wildfire. Hence, increasing frequency and extent of wildfire may contribute disproportionately to reduced reproductive success by creating ecological traps that act as population sinks. Identifying such habitat mismatches between selection and survival facilitates deeper understanding of the mechanisms driving reduced geographic niche space and population decline at broad spatiotemporal scales, while guiding management actions to areas that would be most beneficial to the species.

Global Change Biology↗

USGS2014

Correspondence of biological condition models of California streams at statewide and regional scales

We used boosted regression trees (BRT) to model stream biological condition as measured by benthic macroinvertebrate taxonomic completeness, the ratio of observed to expected (O/E) taxa. Models were developed with and without exclusion of rare taxa at a site. BRT models are robust, requiring few assumptions compared with traditional modeling techniques such as multiple linear regression. The BRT models were constructed to provide baseline support to stressor delineation by identifying natural physiographic and human land use gradients affecting stream biological condition statewide and for eight ecological regions within the state, as part of the development of numerical biological objectives for California’s wadeable streams. Regions were defined on the basis of ecological, hydrologic, and jurisdictional factors and roughly corresponded with ecoregions. Physiographic and land use variables were derived from geographic information system coverages. The model for the entire state ( n  = 1,386) identified a composite measure of anthropogenic disturbance (the sum of urban, agricultural, and unmanaged roadside vegetation land cover) within the local watershed as the most important variable, explaining 56 % of the variance in O/E values. Models for individual regions explained between 51 and 84 % of the variance in O/E values. Measures of human disturbance were important in the three coastal regions. In the South Coast and Coastal Chaparral, local watershed measures of urbanization were the most important variables related to biological condition, while in the North Coast the composite measure of human disturbance at the watershed scale was most important. In the two mountain regions, natural gradients were most important, including slope, precipitation, and temperature. The remaining three regions had relatively small sample sizes ( n  ≤ 75 sites) and had models that gave mixed results. Understanding the spatial scale at which land use and land cover affect taxonomic completeness is imperative for sound management. Our results suggest that invertebrate taxonomic completeness is affected by human disturbance at the statewide and regional levels, with some differences among regions in the importance of natural gradients and types of human disturbance. The construction and application of models similar to the ones presented here could be useful in the planning and prioritization of actions for protection and conservation of biodiversity in California streams.

California↗

USGS2019

Predicting fish species richness and habitat relationships using Bayesian hierarchical multispecies occupancy models

Understanding how stream fishes respond to changes in habitat availability is complicated by low occurrence rates of many species, which in turn reduces the ability to quantify species–habitat relationships and account for imperfect detection in estimates of species richness. Multispecies occupancy models have been used sparingly in the analysis of fisheries data, but address the aforementioned deficiencies by allowing information to be shared among ecologically similar species, thereby enabling species–habitat relationships to be estimated for entire fish communities, including rare species. Here, we highlight the utility of hierarchical multispecies occupancy models for the analysis of fish community data and demonstrate the modeling framework on a stream fish community dataset collected in the Delaware Water Gap National Recreation Area, USA. In particular, we demonstrate the ability of the modeling framework to make inferences at the species-, guild-, and community-levels, thereby making it a powerful tool for understanding and predicting how environmental variables influence species occupancy probabilities and structure fish assemblages.

New York New Jersey, Pennsylvania↗

USGS2018

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Proceedings of the National Academy of Sciences of↗

USGS2016

Will changes in phenology track climate change? A study of growth initiation timing in coast Douglas-fir

Under climate change, the reduction of frost risk, onset of warm temperatures and depletion of soil moisture are all likely to occur earlier in the year in many temperate regions. The resilience of tree species will depend on their ability to track these changes in climate with shifts in phenology that lead to earlier growth initiation in the spring. Exposure to warm temperatures (“forcing”) typically triggers growth initiation, but many trees also require exposure to cool temperatures (“chilling”) while dormant to readily initiate growth in the spring. If warming increases forcing and decreases chilling, climate change could maintain, advance or delay growth initiation phenology relative to the onset of favorable conditions. We modeled the timing of height- and diameter-growth initiation in coast Douglas-fir (an ecologically and economically vital tree in western North America) to determine whether changes in phenology are likely to track changes in climate using data from field-based and controlled-environment studies, which included conditions warmer than those currently experienced in the tree's range. For high latitude and elevation portions of the tree's range, our models predicted that warming will lead to earlier growth initiation and allow trees to track changes in the onset of the warm but still moist conditions that favor growth, generally without substantially greater exposure to frost. In contrast, towards lower latitude and elevation range limits, the models predicted that warming will lead to delayed growth initiation relative to changes in climate due to reduced chilling, with trees failing to capture favorable conditions in the earlier parts of the spring. This maladaptive response to climate change was more prevalent for diameter-growth initiation than height-growth initiation. The decoupling of growth initiation with the onset of favorable climatic conditions could reduce the resilience of coast Douglas-fir to climate change at the warm edges of its distribution.

California, Oregon, Washington↗

USGS2023

Estimating lentic recreational fisheries catch and effort across the United States

Recreational fisheries represent a socially, ecologically, and economically significant component of global fisheries. The U.S. Inland Creel and Angler Survey Catalog (CreelCat) database includes inland recreational fisheries survey data across the United States to facilitate large-scale analyses. However, because survey methods differ, a statistical method capable of integrating these surveys is necessary to assess patterns and relationships across regions. Here, we developed a hierarchical generalized linear mixed modeling approach to estimate the relationship between daily recreational fisheries catch and effort based on waterbody, socio-economic, and ecological covariates. We applied this approach to CreelCat data on lentic waterbodies and found that recreational fisheries catch and effort were non-linearly related (i.e., catch per unit of effort declined as effort increased), where effort varied regionally and by waterbody area, median county age, and distance to nearest primary road. This modeling approach could be used to inform data-poor regions or waterbodies, make comparisons across spatial scales, and, with the inclusion of socio-economic and ecological factors, inform management techniques in an era of shifting demographics and landscapes.

Fisheries Management and Ecology↗

USGS2020

High‐resolution dynamically downscaled rainfall and temperature projections for ecological life zones within Puerto Rico and for the U.S. Virgin Islands

The weather research and forecasting (WRF) model and a combination of the regional spectral model (RSM) and the Japanese Meteorological Agency Non‐Hydrostatic Model (NHM) were used to dynamically downscale selected CMIP5 global climate models to provide 2‐km projections with hourly model output for Puerto Rico and the U.S. Virgin Islands. Two 20‐year time slices were downscaled for historical (1986–2005) and future (2041–2060) periods following RCP8.5. Projected changes to mean and extreme temperature and precipitation were quantified for Holdridge life zones within Puerto Rico and for the U.S. Virgin Islands. The evaluation reveals a persistent cold bias for all islands in the U.S. Caribbean, a dry bias across Puerto Rico, and a wet bias on the windward side of mountains within the U.S. Virgin Islands. Despite these biases, model simulations show a robust drying pattern for all islands that is generally larger for Puerto Rico (25% annual rainfall reduction for some life zones) than the U.S. Virgin Islands (12% island average). The largest precipitation reductions are found during the more convectively active afternoon and evening hours. Within Puerto Rico, the model uncertainty increases for the wetter life zones, especially for precipitation. Across the life zones, both models project unprecedented maximum and minimum temperatures that may exceed 200 days annually above the historical baseline with only small changes to the frequency of extreme rainfall. By contrast, in the U.S. Virgin Islands, there is no consensus on the location of the largest drying relative to the windward and leeward side of the islands. However, the models project the largest increases in maximum temperature on the southern side of St. Croix and in higher elevations of St. Thomas and St. John.

Puerto Rico↗

USGS2017

An expert elicitation process to project the frequency and magnitude of Florida manatee mortality events caused by red tide (Karenia brevis)

Red tides (blooms of the harmful alga Karenia brevis ) are one of the major sources of mortality for the Florida manatee ( Trichechus manatus latirostris ), especially in southwest Florida. It has been hypothesized that the frequency and severity of red tides may increase in the future because of global climate change and other factors. To improve our ecological forecast for the effects of red tides on manatee population dynamics and long-term persistence, we conducted a formal expert judgment process to estimate probability distributions for the frequency and relative magnitude of red-tide-related manatee mortality (RTMM) events over a 100-year time horizon in three of the four regions recognized as manatee management units in Florida. This information was used to update a population viability analysis for the Florida manatee (the Core Biological Model). We convened a panel of 12 experts in manatee biology or red-tide ecology; the panel met to frame, conduct, and discuss the elicitation. Each expert provided a best estimate and plausible low and high values (bounding a confidence level of 80 percent) for each parameter in each of three regions (Northwest, Southwest, and Atlantic) of the subspecies’ range (excluding the Upper St. Johns River region) for two time periods (0−40 and 41−100 years from present). We fitted probability distributions for each parameter, time period, and expert by using these three elicited values. We aggregated the parameter estimates elicited from individual experts and fitted a parametric distribution to the aggregated results. Across regions, the experts expected the future frequency of RTMM events to be higher than historical levels, which is consistent with the hypothesis that global climate change (among other factors) may increase the frequency of red-tide blooms. The experts articulated considerable uncertainty, however, about the future frequency of RTMM events. The historical frequency of moderate and intense RTMM (combined) in the Southwest region was 0.35 (80-percent confidence interval [CI]: 0.21−0.52), whereas the forecast probability was 0.48 (80-percent CI: 0.30−0.64) over a 40-year projected time horizon. Moderate and intense RTMM events are expected to continue to be most frequent in the Southwest region, to increase in mean frequency in the Northwest region (historical frequency of moderate and intense RTMM events [combined] in the Northwest region was 0, whereas the forecast probability was 0.12 [80-percent CI: 0.02−0.39] over a 40-year projected time horizon) and in the Atlantic region (historical frequency of moderate and intense RTMM events [combined] in the Atlantic region was 0.05 [80-percent CI: 0.005–0.18], whereas the forecast probability was 0.11 [80-percent CI: 0.03−0.25] over a 40-year projected time horizon), and to remain absent from the Upper St. Johns River region. The impact of red-tide blooms on manatee mortality has been measured for the Southwest region but not for the Northwest and Atlantic regions, where such events have been rare. The expert panel predicted that the median magnitude of RTMM events in the Atlantic and Northwest regions will be much smaller than that in the Southwest; given the large uncertainties, however, they acknowledged the possibility that these events could be larger in their mortality impacts than in the Southwest region. By its nature, forecasting requires expert judgment because it is impossible to have empirical evidence about the future. The large uncertainties in parameter estimates over a 100-year timeframe are to be expected and may also indicate that the training provided to panelists successfully minimized one common pitfall of expert judgment, that of overconfidence. This study has provided useful and needed inputs to the Florida manatee population viability analysis associated with an important and recurrent source of mortality from harmful algal blooms.

Open-File Report↗

USGS2010

Predicting performance for ecological restoration: A case study using Spartina altemiflora

The success of population-based ecological restoration relies on the growth and reproductive performance of selected donor materials, whether consisting of whole plants or seed. Accurately predicting performance requires an understanding of a variety of underlying processes, particularly gene flow and selection, which can be measured, at least in part, using surrogates such as neutral marker genetic distances and simple latitudinal effects. Here we apply a structural equation modeling approach to understanding and predicting performance in a widespread salt marsh grass, Spartina alterniflora, commonly used for ecological restoration throughout its native range in North America. We collected source materials from throughout this range, consisting of eight clones each from 23 populations, for transplantation to a common garden site in coastal Louisiana and monitored their performance. We modeled performance as a latent process described by multiple indicator variables (e.g., clone diameter, stem number) and estimated direct and indirect influences of geographic and genetic distances on performance. Genetic distances were determined by comparison of neutral molecular markers with those from a local population at the common garden site. Geographic distance metrics included dispersal distance (the minimum distance over water between donor and experimental sites) and latitude. Model results indicate direct effects of genetic distance and latitude on performance variation among the donor sites. Standardized effect strengths indicate that performance was roughly twice as sensitive to variation in genetic distance as to latitudinal variation. Dispersal distance had an indirect influence on performance through effects on genetic distance, indicating a typical pattern of genetic isolation by distance. Latitude also had an indirect effect on genetic distance through its linear relationship with dispersal distance. Three performance indicators had significant loadings on performance alone (mean clone diameter, mean number of stems, mean number of inflorescences), while the performance indicators mean stem height and mean stem width were also influenced by latitude. We suggest that dispersal distance and latitude should provide an adequate means of predicting performance in future S. alterniflora restorations and propose a maximum sampling distance of 300 km (holding latitude constant) to avoid the sampling of inappropriate ecotypes. ?? 2010 by the Ecological Society of America.

Ecological Applications↗

USGS2007

Concerns regarding a call for pluralism of information theory and hypothesis testing

1. Stephens et al. (2005) argue for 'pluralism' in statistical analysis, combining null hypothesis testing and information-theoretic (I-T) methods. We show that I-T methods are more informative even in single variable problems and we provide an ecological example. 2. I-T methods allow inferences to be made from multiple models simultaneously. We believe multimodel inference is the future of data analysis, which cannot be achieved with null hypothesis-testing approaches. 3. We argue for a stronger emphasis on critical thinking in science in general and less reliance on exploratory data analysis and data dredging. Deriving alternative hypotheses is central to science; deriving a single interesting science hypothesis and then comparing it to a default null hypothesis (e.g. 'no difference') is not an efficient strategy for gaining knowledge. We think this single-hypothesis strategy has been relied upon too often in the past. 4. We clarify misconceptions presented by Stephens et al. (2005) . 5. We think inference should be made about models, directly linked to scientific hypotheses, and their parameters conditioned on data, Prob(Hj| data). I-T methods provide a basis for this inference. Null hypothesis testing merely provides a probability statement about the data conditioned on a null model, Prob(data |H0). 6. Synthesis and applications . I-T methods provide a more informative approach to inference. I-T methods provide a direct measure of evidence for or against hypotheses and a means to consider simultaneously multiple hypotheses as a basis for rigorous inference. Progress in our science can be accelerated if modern methods can be used intelligently; this includes various I-T and Bayesian methods.

Journal of Applied Ecology↗

USGS2000

Water quality modeling in the systems impact assessment model for the Klamath River basin - Keno, Oregon to Seiad Valley, California

This report describes the water quality model developed for the Klamath River System Impact Assessment Model (SIAM). The Klamath River SIAM is a decision support system developed by the authors and other US Geological Survey (USGS), Midcontinent Ecological Science Center staff to study the effects of basin-wide water management decisions on anadromous fish in the Klamath River. The Army Corps of Engineersa?? HEC5Q water quality modeling software was used to simulate water temperature, dissolved oxygen and conductivity in 100 miles of the Klamath River Basin in Oregon and California. The water quality model simulated three reservoirs and the mainstem Klamath River influenced by the Shasta and Scott River tributaries. Model development, calibration and two validation exercises are described as well as the integration of the water quality model into the SIAM decision support system software. Within SIAM, data are exchanged between the water quantity model (MODSIM), the water quality model (HEC5Q), the salmon population model (SALMOD) and methods for evaluating ecosystem health. The overall predictive ability of the water quality model is described in the context of calibration and validation error statistics. Applications of SIAM and the water quality model are described.

California, Oregon↗

USGS2024

Geographic distribution of feather δ34S in Europe

Geographic distribution models of environmentally stable isotopes (the so-called “isoscapes”) are widely employed in animal ecology, and wildlife forensics and conservation. However, the application of isoscapes is limited to elements and regions for which the spatial patterns have been estimated. Here, we focused on the ubiquitous yet less commonly used stable sulfur isotopes (δ 34 S). To predict the European δ 34 S isoscape, we used 242 feather samples from Eurasian Reed Warbler ( Acrocephalus scirpaceus ) formed at 69 European wetland sites. We quantified the relationships between sample δ 34 S and environmental covariates using a random forest regression model and applied the model to predict the geographic distribution of δ 34 S. We also quantified within-site variation in δ 34 S and complementarity with other isotopes on both individual and isoscape levels. The predicted feather δ 34 S isoscape shows only slight differences between the central and southern parts of Europe while the coastal regions were most enriched in 34 S. The most important covariates of δ 34 S were distance to coastline, surface elevation, and atmospheric concentrations of SO 2 gases. The absence of a systematic spatial pattern impedes the application of the δ 34 S isoscape, but high complementarity with other isoscapes advocates the combination of multiple isoscapes to increase the precision of animal tracing. Feather δ 34 S compositions showed considerable within-site variation with highest values in inland parts of Europe, likely attributed to wetland anaerobic conditions and redox sensitivity of sulfur. The complex European geography and topography as well as using δ 34 S samples from wetlands may contribute to the absence of a systematic spatial gradient of δ 34 S values in Europe. We thus encourage future studies to focus on the geographic distribution of δ 34 S using tissues from diverse taxa collected in various habitats over large land masses in the world (i.e., Africa, South America, or East Asia).

Ecosphere↗

USGS2009

The ecological limits of hydrologic alteration (ELOHA): A new framework for developing regional environmental flow standards

The flow regime is a primary determinant of the structure and function of aquatic and riparian ecosystems for streams and rivers. Hydrologic alteration has impaired riverine ecosystems on a global scale, and the pace and intensity of human development greatly exceeds the ability of scientists to assess the effects on a river-by-river basis. Current scientific understanding of hydrologic controls on riverine ecosystems and experience gained from individual river studies support development of environmental flow standards at the regional scale. 2. This paper presents a consensus view from a group of international scientists on a new framework for assessing environmental flow needs for many streams and rivers simultaneously to foster development and implementation of environmental flow standards at the regional scale. This framework, the ecological limits of hydrologic alteration (ELOHA), is a synthesis of a number of existing hydrologic techniques and environmental flow methods that are currently being used to various degrees and that can support comprehensive regional flow management. The flexible approach allows scientists, water-resource managers and stakeholders to analyse and synthesise available scientific information into ecologically based and socially acceptable goals and standards for management of environmental flows. 3. The ELOHA framework includes the synthesis of existing hydrologic and ecological databases from many rivers within a user-defined region to develop scientifically defensible and empirically testable relationships between flow alteration and ecological responses. These relationships serve as the basis for the societally driven process of developing regional flow standards. This is to be achieved by first using hydrologic modelling to build a 'hydrologic foundation' of baseline and current hydrographs for stream and river segments throughout the region. Second, using a set of ecologically relevant flow variables, river segments within the region are classified into a few distinctive flow regime types that are expected to have different ecological characteristics. These river types can be further subclassified according to important geomorphic features that define hydraulic habitat features. Third, the deviation of current-condition flows from baseline-condition flow is determined. Fourth, flow alteration-ecological response relationships are developed for each river type, based on a combination of existing hydroecological literature, expert knowledge and field studies across gradients of hydrologic alteration. 4. Scientific uncertainty will exist in the flow alteration-ecological response relationships, in part because of the confounding of hydrologic alteration with other important environmental determinants of river ecosystem condition (e.g. temperature). Application of the ELOHA framework should therefore occur in a consensus context where stakeholders and decision-makers explicitly evaluate acceptable risk as a balance between the perceived value of the ecological goals, the economic costs involved and the scientific uncertainties in functional relationships between ecological responses and flow alteration. 5. The ELOHA framework also should proceed in an adaptive management context, where collection of monitoring data or targeted field sampling data allows for testing of the proposed flow alteration-ecological response relationships. This empirical validation process allows for a fine-tuning of environmental flow management targets. The ELOHA framework can be used both to guide basic research in hydroecology and to further implementation of more comprehensive environmental flow management of freshwater sustainability on a global scale. ?? 2009 Blackwell Publishing Ltd.

Freshwater Biology↗

USGS2024

Environmental drivers and spatial patterns of antibiotic-resistant, enteric coliforms across a forest–urban riverscape

Antibiotic resistant bacteria are prevalent environmental contaminants in freshwaters, and antibiotic resistance genes circulate throughout the urban water cycle. The increase of antibiotic resistant pathogens threatens public health through direct and indirect exposure, and natural resource managers need information on the spatial patterns of antibiotic resistant bacteria and environmental factors associated with their distribution to improve water quality monitoring and to better assess human, animal, and environmental health risks. We collected water and epilithic biofilm samples and measured physicochemical environmental variables at 29 sites distributed longitudinally in the Green-Duwamish River basin, Washington, USA. We characterized catchment-wide patterns of gram-negative fecal indicator bacteria and hypothesized that the presence of antibiotic resistance would be associated with environmental heterogeneity, bacterial primary ecology, stream compartment, and stream type. Antibiotic resistance was determined by microbial growth on selective media supplemented with 3 different antibiotics (ampicillin, chloramphenicol, or tetracycline). Phenotypic antibiotic resistance was positively associated with disturbance, but resistance to at least 1 antibiotic was also detected in undeveloped river segments, with an 83% overall detection rate (i.e., 24 out of 29 sites, 17 in the mainstem and 7 in tributaries). The most probable number of Escherichia coli was associated with higher levels of antibiotic resistance of non- E. coli coliforms across the basin (ρ = 0.38, p < 0.01) but was not associated with antibiotic resistance of E. coli . Phenotypic resistance was highest among non- E. coli coliforms in the water column of tributaries draining moderately to extensively developed subcatchments. Generalized linear mixed-effects model results showed that 18% of the variance in presence of antibiotic resistance was explained by the fixed effects (summed CV across environmental variables, stream type, primary ecology, and stream compartment), and when a spatial random effect was included, the model explained 27% of the variance. Our study provides new evidence that environmental factors and bacterial primary ecology are important underlying factors associated with spatial patterns of antibiotic resistant enteric coliforms. We used macroecological concepts and a riverscape approach to characterize the distribution of antibiotic resistance with methods applicable to municipalities.

Freshwater Science↗

USGS2004

Conceptual model and numerical simulation of the ground-water-flow system in the unconsolidated deposits of the Colville River Watershed, Stevens County, Washington

Increased use of ground- and surface-water supplies in watersheds of Washington State in recent years has created concern that insufficient instream flows remain for fish and other uses. Issuance of new ground-water rights in the Colville River Watershed was halted by the Washington Department of Ecology due to possible hydraulic continuity of the ground and surface waters. A ground-water-flow model was developed to aid in the understanding of the ground-water system and the regional effects of ground-water development alternatives on the water resources of the Colville River Watershed. The Colville River Watershed is underlain by unconsolidated deposits of glacial and non-glacial origin. The surficial geologic units and the deposits at depth were differentiated into aquifers and confining units on the basis of areal extent and general water-bearing characteristics. Five principal hydrogeologic units are recognized in the study area and form the basis of the ground-water-flow model. A steady-state ground-water-flow model of the Colville River Watershed was developed to simulate September 2001 conditions. The simulation period represented a period of below-average precipitation. The model was calibrated using nonlinear regression to minimize the weighted differences or residuals between simulated and measured hydraulic head and stream discharge. Simulated inflow to the model area was 53,000 acre-feet per year (acre-ft/yr) from precipitation and secondary recharge, and 36,000 acre-ft/yr from stream and lake leakage. Simulated outflow from the model was primarily through discharge to streams and lakes (71,000 acre-ft/yr), ground-water outflow (9,000 acre-ft/yr), and ground-water withdrawals (9,000 acre-ft/yr). Because the period of simulation, September 2001, was extremely dry, all components of the ground-water budget are presumably less than average flow conditions. The calibrated model was used to simulate the possible effects of increased ground-water pumping. Although the steady-state model cannot be used to predict how long it would take for effects to occur, it does simulate the ultimate response to such changes relative to September 2001 (relatively dry) conditions. Steady-state simulations indicated that increased pumping would result in decreased discharge to streams and lakes and decreased ground-water outflow. The location of the simulated increased ground-water pumping determined the primary source of the water withdrawn. Simulated pumping wells in the northern end of the main Colville River valley diverted a large percentage of the pumpage from ground-water outflow. Simulated pumping wells in the southern end of the main Colville River valley diverted a large percentage of the pumpage from flow to rivers and streams. The calibrated steady-state model also was used to simulate predevelopment conditions, during which no ground-water pumping, secondary recharge, or irrigation application occurred. Cumulative streamflow in the Colville River Watershed increased by 1.1 cubic feet per second, or about 36 percent of net ground-water pumping in 2001. The model is intended to simulate the regional ground-water-flow system of the Colville River Watershed and can be used as a tool for water-resource managers to assess the ultimate regional effects of changes in stresses. The regional scale of the model, coupled with relatively sparse data, must be considered when applying the model in areas of poorly understood hydrology, or examining hydrologic conditions at a larger scale than what is appropriate.

Scientific Investigations Report↗