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At least 289 records · Page 16Linked to original sources

USGS2016

A stochastic bioenergetics model based approach to translating large river flow and temperature in to fish population responses: The pallid sturgeon example

In managing fish populations, especially at-risk species, realistic mathematical models are needed to help predict population response to potential management actions in the context of environmental conditions and changing climate while effectively incorporating the stochastic nature of real world conditions. We provide a key component of such a model for the endangered pallid sturgeon ( Scaphirhynchus albus ) in the form of an individual-based bioenergetics model influenced not only by temperature but also by flow. This component is based on modification of a known individual-based bioenergetics model through incorporation of: the observed ontogenetic shift in pallid sturgeon diet from marcroinvertebrates to fish; the energetic costs of swimming under flowing-water conditions; and stochasticity. We provide an assessment of how differences in environmental conditions could potentially alter pallid sturgeon growth estimates, using observed temperature and velocity from channelized portions of the Lower Missouri River mainstem. We do this using separate relationships between the proportion of maximum consumption and fork length and swimming cost standard error estimates for fish captured above and below the Kansas River in the Lower Missouri River. Critical to our matching observed growth in the field with predicted growth based on observed environmental conditions was a two-step shift in diet from macroinvertebrates to fish.

Missouri River↗

USGS2021

Improved wetland soil organic carbon stocks of the conterminous U.S. through data harmonization

Wetland soil stocks are important global repositories of carbon (C) but are difficult to quantify and model due to varying sampling protocols, and geomorphic/spatio-temporal discontinuity. Merging scales of soil-survey spatial extents with wetland-specific point-based data offers an explicit, empirical and updatable improvement for regional and continental scale soil C stock assessments. Agency-collected (U.S. Department of Agriculture, U.S. Environmental Protection Agency) and community-contributed soil datasets were compared for representativeness and bias, with the goal of producing a harmonized national map of wetland soil C stocks with error quantification for wetland areas of the conterminous United States (CONUS) identified by the USGS National Landcover Change Dataset (NLCD). This allowed application of an empirical predictive model of SOC density to be applied across the entire CONUS using relational %OC distribution alone. A broken-stick quantile-regression model identified %OC with its relatively high analytical confidence as a key predictor of SOC density in soil segments; soils less than 6%OC (hereafter, mineral wetland soils, 85% of the dataset) had a strong linear relationship of %OC to SOC density (RMSE = 0.0059, ~4% mean RMSE) and soils greater than 6%OC (organic wetland soils, 15% of the dataset) had virtually no predictive relationship of %OC to SOC density (RMSE = 0.0348 g C cm-3, ~56% mean RMSE). Disaggregation by vegetation type (woody v. emergent herbaceous), or region did not alter the breakpoint significantly (6% OC) nor improve model accuracies for inland and tidal wetlands. Similarly, SOC stocks in tidal wetlands were related to %OC, but without a mappable product for disaggregation to improve accuracy by soil class, region or depth. Our layered, harmonized CONUS wetland soil maps have now revised wetland SOC stock estimates downward by 24% (9.5 vs. 12.5Pg C) with the overestimation being entirely an issue of inland, organic wetland soils, (35% lower than SSURGO-derived SOC stocks). Further, SSURGO underestimated soil carbon stocks at depth, as modeled wetland SOC stocks for organic-rich soils showed significant preservation downcore in the NWCA dataset (<3% loss between 0-30 cm and 30-100 cm depths) in contrast to mineral-rich soils (37% downcore stock loss). Future CONUS wetland soil C assessments will benefit from focused attention on improved organic wetland soil measurements, land history, and spatial representativeness.

Frontiers in Soil Science↗

USGS2020

Landscape and climatic influences on actual evapotranspiration and available water using the Operational Simplified Surface Energy Balance (SSEBop) Model in eastern Bernalillo County, New Mexico, 2015

The U.S. Geological Survey, in cooperation with the Bernalillo County Public Works Division, conducted a 1-year study in 2015 to assess the spatial and temporal distribution of evapotranspiration (ET) and available water within the East Mountain area in Bernalillo County, New Mexico. ET and available water vary spatiotemporally because of complex interactions among environmental factors, including vegetation characteristics, soil characteristics, topography, and climate. Precipitation data from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) ( P ) were used in conjunction with actual ET ( ETa ) data from the Operational Simplified Surface Energy Balance (SSEBop) model to estimate available water ( P – ETa ) at 100-meter (m) resolution in the study area. Maps, descriptive statistics, boxplots, regression analyses (continuous data), and multiple comparison tests (categorical data) were used to characterize P , ETa , and available water and their relations to topographic, soil, and vegetation datasets in the East Mountain area. Five categories of the natural land-cover type (evergreen forest, shrub, herbaceous, deciduous forest, and mixed forest) and four categories of developed land-cover type specific to residential intensity (developed open, developed low, developed medium, and developed high) were analyzed individually and in interaction with multiple elevation, tree canopy, and soil texture classes. Annual mean P in 2015 in the East Mountain area was 608 millimeters (mm), and annual mean ETa was 543 mm (89 percent of annual P in 2015), indicating that in 2015, a spatial mean of about 65 mm of water was available for runoff, soil moisture replenishment, or groundwater recharge. Monthly ETa was greatest in July and smallest in January. The intervening months did not show smooth temporal or consistent spatial changes from month to month. Months with lower ETa (January to March, October to December) also tended to have greater available water, indicating that soil moisture (water supply) and potential ET (water demand) may have been out of phase. Regression analyses showed that monthly ETa data had the highest correlation with annual ETa among the atmospheric, topographic, soil, or vegetation datasets, particularly during the early and late growing season (March, April, May, and September). In contrast, monthly P was highly variable and not as highly correlated with annual ETa . Among landscape variables, correlations with annual ETa were highest for tree canopy cover (coefficient of determination [R 2 ] = 0.46). Correlations between ETa and other landscape variables were lower (R 2 = 0.06–0.19): available soil water in the top 100 centimeters, soil bulk density of layer 1, slope, sand content of soil layer 1, soil depth, available soil water in the top 25 centimeters, leaf area index, aspect eastness, and elevation. Evergreen forest areas had the highest annual median ETa , followed by mixed forest, open residential areas, and deciduous forest. Available water typically was higher in landcover types with lower ETa : herbaceous cover, followed by deciduous forest, high-intensity developed areas, and shrub. Deciduous forest had the second highest median available water, despite having the fourth highest ETa , because deciduous forest had greater P than most other areas. Annual median ETa typically was greatest in the second highest elevation band (2,401–2,800 m above the North American Vertical Datum of 1988 [NAVD 88]), and lower in the highest elevation band (2,801–3,254 m above NAVD 88), despite having greater P , likely because of decreased tree canopy cover or a shift from evergreen to deciduous trees at the highest elevations. Annual median ETa increased with tree canopy cover, regardless of landcover type. ETa correlation was higher with tree canopy than with leaf area index or normalized difference vegetation index. This result indicates that it is important to include the thermal band (from satellite multispectral data) in vegetation indices used to describe ETa , perhaps to account for the influence of energy limitation or water limitation on ET. Of all natural landcover types, finer soils had the most available water, whereas coarser soils had the least available water. Relations of soil type with P – ETa were different than with ETa , indicating ET and available water have a complex response to differences in soil type. Further modeling would be useful in determining soils’ infiltration, storage, conductivity, and plant-water availability relations to individual storms for each position in the landscape, as well as the corresponding effects of these processes on ET and available water. The best multivariate linear model for annual ETa had an R 2 value of 0.62. Monthly ETa models had R 2 values between 0.16 and 0.65. Models usually, but not always, performed best during the growing season. These results indicate that even the best multivariate linear models cannot explain a notable amount of the variability in ET. The monthly ETa models with the highest correlations (August and September) followed a July having almost twice the mean precipitation for July (1981–2010), which indicates that a soil-moisture variable is needed to more accurately model monthly ETa . Further study is needed to better characterize this system, the variables that affect ET and available water, and the partitioning of available water into runoff, soil moisture storage, and groundwater recharge.

New Mexico↗

USGS1981

From ecological test site to geographic information system: lessons for the 1980's

Geographic information systems were common elements in two kinds of interdisciplinary regional demonstration projects in the 1970's. Ecological test sits attempted to provide for more efficient remote-sensing data delivery for regional environmental management. Regional environmental systems analysis attempted to formally describe and model the interacting regional social and environmental processes, including the resource-use decision making process. Lessons for the 1980's are drawn from recent evaluations and assessments of these programs, focusing on cost, rates of system development and technology transfer, program coordination, integrative analysis capability, and the involvement of system users and decision makers.

Pecora VII Symposium↗

USGS2003

Use of the Legal-Institutional Analysis Model to assess hydropower licensing negotiations

In the United States, the Federal Energy Regulatory Commission (FERC) is responsible for issuing or renewing licenses for hydropower projects owned and operated by power companies. During the licensing process, these companies are required to consult with agencies and other parties that are affected by project operating regimes. Typical participants include state and federal fish and wildlife agencies, environmental interest groups, and the FERC. One of the most difficult tasks facing participants is to reach agreement about what kinds of environmental conditions should be placed on license. Researchers at the United States Geological Survey developed a model to analyze the institutional context of natural resource disputes. The Legal-Institutional Analysis Model (LIAM) is a computerized model that allows an analyst to determine the likely behavior of each organization in a conflict. The model also analyzes the types and levels of negotiating power held by each organization. Researchers at the USGS have used the model in several cases involving hydropower license applications. To use the model, they facilitate workshops for stakeholder groups in order to develop a shared understanding of the likely obstacles and opportunities for successful resolution of the issues. This allows a systematic workshop analyses to develop strategies for successful negotiations, because they are able to better understand the negotiation problem and work more effectively with both their allies and their competitors.

Conference Paper↗

USGS2013

Framing scenarios of binational water policy with a tool to visualize, quantify and valuate changes in ecosystem services

In the Santa Cruz Watershed, located on the Arizona-Sonora portion of the U.S.-Mexico border, an international wastewater treatment plant treats wastewater from cities on both sides of the border, before discharging it into the river in Arizona. These artificial flows often subsidize important perennial surface water ecosystems in the region. An explicit understanding of the benefits of maintaining instream flow for present and future generations requires the ability to assess and understand the important trade-offs implicit in water-resource management decisions. In this paper, we outline an approach for modeling and visualizing impacts of management decisions in terms of rare terrestrial and aquatic wildlife, vegetation, surface water, groundwater recharge, real-estate values and socio-environmental vulnerable communities. We identify and quantify ecosystem services and model the potential reduction in effluent discharge to the U.S. that is under scrutiny by binational water policy makers and of concern to stakeholders. Results of service provisioning are presented, and implications for policy makers and resource managers are discussed. This paper presents a robust ecosystem services assessment of multiple scenarios of watershed management as a means to discern eco-hydrological responses and consider their potential values for future generations living in the borderlands.

Arizona;Sonora↗

USGS2024

Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA

Objective Long‐term standardized monitoring programs are fundamental to assessing how fish populations respond to anthropogenic stressors. Standardized monitoring programs may need to adopt new methods to adapt to rapid environmental changes that are associated with a changing climate. In the upper Yellowstone River, Montana, biologists have used a standardized, mark–recapture monitoring protocol to annually estimate the abundance of trout since 1978 to assess population status and trends. However, within the past two decades, climate change has caused changes in discharge timing that have prevented standardized monitoring from occurring annually. Methods We investigated the feasibility of using two analytical methods, N‐mixture models and mean capture probability, for estimating the abundance of three trout species in the upper Yellowstone River using the historical long‐term data set; these methods allow abundance to be estimated when a mark–recapture estimate cannot be obtained due to hydrologic conditions. Result When compared with abundance estimates from mark–recapture methods, N‐mixture models most often resulted in negatively biased abundance estimates, whereas mean capture probability analyses resulted in positively biased abundance estimates. Additionally, N‐mixture models produced negatively biased estimates when tested against true abundance values from simulated data sets. The bias in the N‐mixture model estimates was caused by poor model fit and variation in capture probability. The bias in the mean capture probability estimates was caused by heterogeneity in capture probability, likely caused by variable environmental conditions, which were not accounted for in the models. Conclusion N‐mixture models and mean capture probability are not viable alternatives for estimating abundance in the upper Yellowstone River. Thus, exploring additional adaptations to sampling methodologies and analytical approaches, including models that require individually marked fish, will be valuable for this system. Climate change will undoubtedly necessitate changes to standardized sampling methods throughout the world; thus, developing alternative sampling and analytical methods will be important for maintaining the utility of long‐term data sets.

Montana↗

USGS2005

Evaluating water management strategies with the Systems Impact Assessment Model: SIAM version 4

Water from many of California's coastal rivers has been used for a wide variety of development ventures, including major agricultural diversions, hydropower generation, and contaminant assimilation from industry, agriculture and logging. Anthropogenic impacts often degrade water quality and decrease the quantity and quality of aquatic habitat. Reallocating streamflow away from uses that degrade water quality to uses that foster higher water quality is a critical step in restoring riverine habitat and the anadromous fish that rely on that habitat for a portion of their life cycle. Reallocation always brings with it the need to examine the economic efficiency of the proposed changes. If the dollar benefits of improving water quality are greater than the costs, the criterion of improving economic efficiency is satisfied, a fact that can be highly persuasive to decision makers contemplating reallocation. Previous related studies have examined nonmarket benefits of the Trinity River in northern California (Douglas and Taylor, 1998; Douglas and Taylor, 1999abc) but nothing of this kind had been done on the Klamath River, another system with numerous uses for and competition over water in times of drought. An economic survey is nearing completion for the mid- to lower Klamath River, including the Scott, Shasta, and Salmon Rivers, but excluding the Trinity River. This survey provides valuable insights about the magnitude of the benefits and nature of the costs of reallocating water from market uses to instream flows that improve water quality and assist in the recovery of Klamath River fish stocks. Preliminary survey results (Douglas and Johnson, 2002; Douglas and Sleeper, In Prep.) indicate that about 234,000 California, Oregon, and Nevada households made recreation trips to the Klamath River Basin 1997-1998 and that these users spent about $372 million on trip related expenditures. Clearly the prosperity of the region is closely linked to the demand for mid- and lower Klamath River Basin recreation trips. Further, respondents indicated that they would make roughly 36% more recreational trips per annum to the Klamath if the water quality and the fishery were restored to an unspoiled condition. Using two distinct types of survey data, these additional trips would yield benefits with a present value of approximately $9.6 billion (at a discount rate of 7.5%). Calculating costs to restore the fishery and raise water quality involved five major hypothetical restoration activities: (1) purchasing Klamath project farmland and environmentally sensitive forest lands, (2) allocating more water down the Trinity River to enhance the quantity and quality of Klamath flows below the confluence, (3) removing four mainstem dams along the Klamath River and losing their associated hydropower production, (4) eliminating all harvest of Klamath-Trinity fish stocks for a 12-year period including the acquisition of fishing rights from both tribal and commercial marine fishermen, and (5) operating all Klamath-Trinity fish hatcheries to restore self-reproducing stocks. In total, restoration costs were estimated to be about $1.7 to $2.3 billion. If the assumptions used in this study are valid, it is clear that the benefits ($9.6B) outweigh the costs of restoring water quality and the fishery. The apparent disparity between restoration benefits and costs for the Klamath River may suggest to some that water resources on the Klamath be reallocated to environmentally friendly nonmarket uses. The economic analysis rests in part on the information made available to the survey designers by the biological, hydrologic, and water quality data incorporated in The System Impact Assessment Model (SIAM). It is our hope that SIAM can be used to improve the river's water quality and fishery, and strengthen the important regional economy.

Open-File Report↗

USGS2016

Spatial prediction of wheat Septoria leaf blotch (Septoria tritici) disease severity in central Ethiopia

A number of studies have reported the presence of wheat septoria leaf blotch ( Septoria tritici ; SLB) disease in Ethiopia. However, the environmental factors associated with SLB disease, and areas under risk of SLB disease, have not been studied. Here, we tested the hypothesis that environmental variables can adequately explain observed SLB disease severity levels in West Shewa, Central Ethiopia. Specifically, we identified 50 environmental variables and assessed their relationships with SLB disease severity. Geographically referenced disease severity data were obtained from the field, and linear regression and Boosted Regression Trees (BRT) modeling approaches were used for developing spatial models. Moderate-resolution imaging spectroradiometer (MODIS) derived vegetation indices and land surface temperature (LST) variables highly influenced SLB model predictions. Soil and topographic variables did not sufficiently explain observed SLB disease severity variation in this study. Our results show that wheat growing areas in Central Ethiopia, including highly productive districts, are at risk of SLB disease. The study demonstrates the integration of field data with modeling approaches such as BRT for predicting the spatial patterns of severity of a pathogenic wheat disease in Central Ethiopia. Our results can aid Ethiopia's wheat disease monitoring efforts, while our methods can be replicated for testing related hypotheses elsewhere.

Ecological Informatics↗

USGS2026

Assessing future hydrologic extremes using an integrated hydrology and river operations model in the Russian River watershed

Study region The Russian River watershed, situated in coastal, northern California, experiences hydrologic extremes, including periodic droughts and flooding. Water managers are working to maintain sustainable water supplies and environmental flows, while mitigating flood risks. Study focus This paper introduces an integrated hydrology and river operations model for the Russian River watershed. This model is distinct from models in previous studies because it represents surface-groundwater interactions and uses climate forcings to estimate dynamic water use demands that are superimposed onto both reservoir operations and water supply constraints. The model was used to examine three historical (1990–2015) and eight future (2016–2099) water use and climate change scenarios. New hydrological insights for the region The direct connection between streams and aquifers facilitated both annual aquifer replenishment by high winter streamflows and streamflow depletion by groundwater wells (19 % of pumped groundwater in alluvial aquifers from stream leakage) during critical low flow periods. Simulated streamflow changes included 59 % longer and 54 % more severe streamflow droughts, 26 % lower seasonal low streamflows, and up to 125 % higher peak streamflows, averaged over future climate and water use scenarios, suggesting increased future flood and water availability risks. Results showed the importance of reservoir operations for mitigating the impacts of increased hydroclimatic volatility, despite a decrease in reservoir reliability at Lake Mendocino, suggesting that reservoir management may be used to decrease future risks.

California↗

USGS1997

A SAS® code to correct for non-normality and non-constant variance in regression and anova models using the box-cox method of power transormation

A computer program written in SAS ® code for the Box–Cox family of power transformations is presented. The purpose of the program is to suggest a power transformation for the positive continuous response variables in only regression and ANOVA models. A brief overview of data transformation in regression and analysis of variance is given.An example using real data from the U.S. Environmental Protection Agency‘s Environmental Monitoring and Assessment Program/Estuaries (EMAP-E) illustrates the use of the program.

Environmental Monitoring and Assessment↗

USGS2025

Real-time oil spill concentration assessment through fluorescence imaging and deep learning

Oil spills may pose severe ecological and socioeconomic threats, necessitating rapid and accurate environmental assessment. Traditional assessment methods used to determine the extent of a spill including gas chromatography-mass spectrometry, satellite imaging, and visual surveys, are often time-consuming, expensive, and limited by weather conditions or sampling constraints. Furthermore, these methods frequently struggle to provide real-time data crucial for prompt decision-making during spill emergencies. This study addresses these limitations by combining fluorescence imaging, deep learning, a mobile application, and a data management system for automated and real-time oil spill assessment. Our approach leverages a convolutional neural network architecture for feature extraction coupled with a custom regression model, trained and evaluated on a self-curated comprehensive dataset of 1,530 fluorescence images from two distinct oil types, a napthalenic crude oil and an aromatic-napthalenic crude oil, at concentrations ranging from 0 to 500 mg/L. The proposed approach demonstrates superior performance compared to both traditional machine learning models and more complex deep learning architectures, achieving an R² score of 0.9958 and RMSE of 9.28. The application enables rapid, cost-effective field measurements with robust data tracking and analysis capabilities. This research advances oil spill monitoring technology with a scalable solution that balances accuracy, speed, and accessibility for real-time environmental assessment and emergency response.

Journal of Hazardous Materials↗

USGS2016

Topographic and fire weather controls of fire refugia in forested ecosystems of northwestern North America

Fire refugia, sometimes referred to as fire islands, shadows, skips, residuals, or fire remnants, are an important element of the burn mosaic, but we lack a quantitative framework that links observations of fire refugia from different environmental contexts. Here, we develop and test a conceptual model for how predictability of fire refugia varies according to topographic complexity and fire weather conditions. Refugia were quantified as areas unburned or burned at comparatively low severity based on remotely sensed burn severity data. We assessed the relationship between refugia and a suite of terrain-related explanatory metrics by fitting a collection of boosted regression tree models. The models were developed for seven study fires that burned in conifer-dominated forested landscapes of the Western Cordillera of Canada between 2001 and 2014. We fit nine models, each for distinct levels of fire weather and terrain ruggedness. Our framework revealed that the predictability and abundance of fire refugia varied among these environmental settings. We observed highest predictability under moderate fire weather conditions and moderate terrain ruggedness (ROC-AUC = 0.77), and lowest predictability in flatter landscapes and under high fire weather conditions (ROC-AUC = 0.63–0.68). Catchment slope, local aspect, relative position, topographic wetness, topographic convergence, and local slope all contributed to discriminating where refugia occur but the relative importance of these topographic controls differed among environments. Our framework allows us to characterize the predictability of contemporary fire refugia across multiple environmental settings and provides important insights for ecosystem resilience, wildfire management, conservation planning, and climate change adaptation.

Ecosphere↗

USGS2016

Estimating juvenile Chinook salmon ( Oncorhynchus tshawytscha ) abundance from beach seine data collected in the Sacramento–San Joaquin Delta and San Francisco Bay, California

Resource managers rely on abundance or density metrics derived from beach seine surveys to make vital decisions that affect fish population dynamics and assemblage structure. However, abundance and density metrics may be biased by imperfect capture and lack of geographic closure during sampling. Currently, there is considerable uncertainty about the capture efficiency of juvenile Chinook salmon ( Oncorhynchus tshawytscha ) by beach seines. Heterogeneity in capture can occur through unrealistic assumptions of closure and from variation in the probability of capture caused by environmental conditions. We evaluated the assumptions of closure and the influence of environmental conditions on capture efficiency and abundance estimates of Chinook salmon from beach seining within the Sacramento–San Joaquin Delta and the San Francisco Bay. Beach seine capture efficiency was measured using a stratified random sampling design combined with open and closed replicate depletion sampling. A total of 56 samples were collected during the spring of 2014. To assess variability in capture probability and the absolute abundance of juvenile Chinook salmon, beach seine capture efficiency data were fitted to the paired depletion design using modified N-mixture models. These models allowed us to explicitly test the closure assumption and estimate environmental effects on the probability of capture. We determined that our updated method allowing for lack of closure between depletion samples drastically outperformed traditional data analysis that assumes closure among replicate samples. The best-fit model (lowest-valued Akaike Information Criterion model) included the probability of fish being available for capture (relaxed closure assumption), capture probability modeled as a function of water velocity and percent coverage of fine sediment, and abundance modeled as a function of sample area, temperature, and water velocity. Given that beach seining is a ubiquitous sampling technique for many species, our improved sampling design and analysis could provide significant improvements in density and abundance estimation.

California↗

USGS2011

Land cover change, biogeochemical modelling of carbon stocks, and climate change in West Africa

The carbon in ecosystems exists in dynamic soil and vegetation pools which vary in amounts and cycle with the global atmosphere at varying rates. These stocks and fluxes play important roles in global carbon regulation and in the maintenance of goods and services. Changes in land cover or ecosystems result in increased or decreased fluxes to the atmosphere and play a major role in climate regulation. Carbon in soil is closely coupled to soil nitrogen and the continued mining of soil for crops or fuel without replenishment of nutrients results in decreased productivity and impacts food security. The assessment of these processes across large areas, although difficult, is aided by the integration of simulation modelling (biogeochemical and ecosystem) and remote sensing. We acquired satellite imagery for four periods from the 1960s to 2000s, trained environmental scientists from 14 countries on image analysis and interpretation, and now report systematic analyses of land cover changes in select countries of West Africa and quantify potential impacts of climate change and management at specific sites. Statistical changes and maps of land cover are documented for most countries. Senegal, for example, illustrates a 57 percent loss in dense forests between 1975 and 2000 with an even greater loss rate in the preceding 10 years. Bare soil increased 16.6 percent, often related to unproductive “badland” formation. Settlements increased 45.6 percent, and reforestation replaced bare sandy areas for sand dune stabilization. In some countries (Senegal and Ghana), the impact of these conversions and changes in land management and future projections has been incorporated into biogeochemical models to quantify carbon changes and project future carbon and crop scenarios. We present current assessments of carbon fluxes and the availability of data for these West African countries.

Conference Paper↗

USGS2024

Partitioning of chemicals in aquatic organisms

Human populations have utilized heavy metals and persistent organic pollutants for their physiochemical properties in industrial, agricultural, and consumer goods for decades. Limited knowledge on their persistence and toxicological effects has resulted in organisms being exposed to some of the most problematic compounds ever generated by humans. Although overlap in exposure paradigms exists for historical and emerging contaminants, the different physiochemical properties, sources into the environment, and bioactivity of contaminants of emerging concern (CECs) have highlighted the importance of characterizing their risk to aquatic wildlife under chronic low-dose exposure scenarios. This chapter defines the fundamental terminology associated with characterizing the exposure paradigm in ecological risk assessment. The different sources and fate, routes of exposure, and biotransformation of common contaminants are covered using model chemicals to emphasize important factors that affect their partitioning among different environmental matrices. Finally, this chapter concludes with a discussion about bioaccumulation models and an example of how two similar CECs demonstrate different clearance rates and bioaccumulation potentials in fish.

Book chapter↗

USGS2006

The relative roles of environment, history and local dispersal in controlling the distributions of common tree and shrub species in a tropical forest landscape, Panama

We used regression models and information-theoretic model selection to assess the relative importance of environment, local dispersal and historical contingency as controls of the distributions of 26 common plant species in tropical forest on Barro Colorado Island (BCI), Panama. We censused eighty-eight 0.09-ha plots scattered across the landscape. Environmental control, local dispersal and historical contingency were represented by environmental variables (soil moisture, slope, soil type, distance to shore, old-forest presence), a spatial autoregressive parameter (??), and four spatial trend variables, respectively. We built regression models, representing all combinations of the three hypotheses, for each species. The probability that the best model included the environmental variables, spatial trend variables and ?? averaged 33%, 64% and 50% across the study species, respectively. The environmental variables, spatial trend variables, ??, and a simple intercept model received the strongest support for 4, 15, 5 and 2 species, respectively. Comparing the model results to information on species traits showed that species with strong spatial trends produced few and heavy diaspores, while species with strong soil moisture relationships were particularly drought-sensitive. In conclusion, history and local dispersal appeared to be the dominant controls of the distributions of common plant species on BCI. Copyright ?? 2006 Cambridge University Press.

Journal of Tropical Ecology↗

USGS1995

Coastal ecosystem decision‐support GIS: Functions and methodology

To solve coastal resource and environmental issues and handle complex spatial data and information effectively and efficiently, a coastal ecosystem decision‐support geographic information system (GIS) is being developed at the Southern Science Center of the U.S. National Biological Service. With three subsystems, natural resource management, environmental impact assessment, and data and information handling, respectively, the multifunctional system is designed to provide decision makers with tools to integrate and organize various environmental data and information for analytical modeling, spatial query, and graphic visualization. The multiple integration approach was employed in this system's development based on a commercial GIS, which includes subsystems integration, environmental data integration, decision‐support functions integration, and analytical model integration.

Marine Geodesy↗