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USGS2010

A simple solar radiation index for wildlife habitat studies

Solar radiation is a potentially important covariate in many wildlife habitat studies, but it is typically addressed only indirectly, using problematic surrogates like aspect or hillshade. We devised a simple solar radiation index (SRI) that combines readily available information about aspect, slope, and latitude. Our SRI is proportional to the amount of extraterrestrial solar radiation theoretically striking an arbitrarily oriented surface during the hour surrounding solar noon on the equinox. Because it derives from first geometric principles and is linearly distributed, SRI offers clear advantages over aspect-based surrogates. The SRI also is superior to hillshade, which we found to be sometimes imprecise and ill-behaved. To illustrate application of our SRI, we assessed niche separation among 3 ungulate species along a single environmental axis, solar radiation, on the northern Yellowstone winter range. We detected no difference between the niches occupied by bighorn sheep ( Ovis canadensis ) and elk ( Cervus elaphus; P = 0.104), but found that mule deer ( Odocoileus hemionus ) tended to use areas receiving more solar radiation than either of the other species ( P < 0.001). Overall, our SRI provides a useful metric that can reduce noise, improve interpretability, and increase parsimony in wildlife habitat models containing a solar radiation component.

Wyoming↗

USGS2006

Models for predicting recreational water quality at Lake Erie beaches

Data collected from four Lake Erie beaches during the recreational seasons of 2004-05 and from one Lake Erie beach during 2000-2005 were used to develop predictive models for recreational water quality by means of multiple linear regression. The best model for each beach was based on a unique combination of environmental and water-quality explanatory variables including turbidity, rainfall, wave height, water temperature, day of the year, wind direction, and lake level. Two types of outputs were produced from the models: the predicted Escherichia coli concentration and the probability that the bathing-water standard will be exceeded. The model for one of beaches, Huntington Reservation (Huntington), was validated in 2005. For 2005, the Huntington model yielded more correct responses and better predicted exceedance of the standard than did current methods for assessing recreational water quality, which are based on the previous day's E. coli concentration. Predictions based on the Huntington model have been available to the public through an Internet-based 'nowcasting' system since May 30, 2006. The other beach models are being validated for the first time in 2006. The methods used in this study to develop and test predictive models can be applied at other similar coastal beaches.

Scientific Investigations Report↗

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

Influences of potential oil and gas development and future climate on Sage-grouse declines and redistribution

Multiple environmental stressors impact wildlife populations, but we often know little about their cumulative and combined influences on population outcomes. We generally know more about past effects than potential future impacts, and direct influences such as changes of habitat footprints than indirect, long-term responses in behavior, distribution, or abundance. Yet, an understanding of all these components is needed to plan for future landscapes that include human activities and wildlife. We developed a case study to assess how spatially explicit individual-based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater Sage-grouse in southwest Wyoming, USA, we projected oil and gas development footprints and climate-induced vegetation changes 50 years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we recalculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term Sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered a useful means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. The inclusion of movement and demographic responses to oil and gas infrastructure resulted in substantive changes in distribution and abundance when cumulated over several decades and throughout the regional population. When combined, additive development and climate-induced vegetation changes reduced abundance by up to half of the original size. In our example, the consideration of only a single population stressor the final possible population size by as much as 50%. Multiple stressors and their cumulative impacts need to be broadly considered through space and time to avoid underestimating the impacts of multiple gradual changes and overestimating the ability of populations to withstand change.

Wyoming↗

USGS2017

Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure

Measuring post-fire effects at landscape scales is critical to an ecological understanding of wildfire effects. Predominantly this is accomplished with either multi-spectral remote sensing data or through ground-based field sampling plots. While these methods are important, field data is usually limited to opportunistic post-fire observations, and spectral data often lacks validation with specific variables of change. Additional uncertainty remains regarding how best to account for environmental variables influencing fire effects (e.g., weather) for which observational data cannot easily be acquired, and whether pre-fire agents of change such as bark beetle and timber harvest impact model accuracy. This study quantifies wildfire effects by correlating changes in forest structure derived from multi-temporal Light Detection and Ranging (LiDAR) acquisitions to multi-temporal spectral changes captured by the Landsat Thematic Mapper and Operational Land Imager for the 2012 Pole Creek Fire in central Oregon. Spatial regression modeling was assessed as a methodology to account for spatial autocorrelation, and model consistency was quantified across areas impacted by pre-fire mountain pine beetle and timber harvest. The strongest relationship (pseudo-r 2 = 0.86, p < 0.0001) was observed between the ratio of shortwave infrared and near infrared reflectance (d74) and LiDAR-derived estimate of canopy cover change. Relationships between percentage of LiDAR returns in forest strata and spectral indices generally increased in strength with strata height. Structural measurements made closer to the ground were not well correlated. The spatial regression approach improved all relationships, demonstrating its utility, but model performance declined across pre-fire agents of change, suggesting that such studies should stratify by pre-fire forest condition. This study establishes that spectral indices such as d74 and dNBR are most sensitive to wildfire-caused structural changes such as reduction in canopy cover and perform best when that structure has not been reduced pre-fire.

Oregon↗

USGS2018

Linking the agricultural landscape of the Midwest to stream health with structural equation modeling

Multiple physical and chemical stressors can simultaneously affect the biological condition of streams. To better understand the complex interactions of land-use practices, water quality, and ecological integrity of streams, the U.S. Geological Survey National Water Quality Assessment Project is conducting regional-scale assessments of stream condition across the United States. In the summer of 2013, weekly water samples were collected from 100 streams in the Midwestern United States. Employing watershed theory, we used structural equation modeling (SEM) to represent a general hypothesis for how 16 variables (previously identified to be important to stream condition) might be inter-related. Again, using SEM, we evaluated the ability of this “stressor network” to explain variations in multimetrics of algal, invertebrate, and fish community health, trimming away any environmental variables not contributing to an explanation of the ecological responses. Seven environmental variables—agricultural and urban land use, sand content of soils, basin area, percent riparian area as forest, channel erosion, and relative bed stability—were found to be important for all three-community metrics. The algal and invertebrate models included water-chemistry variables not included in the fish model. Results suggest that ecological integrity of Midwest streams are affected by both agricultural and urban land uses and by the natural geologic setting, as indicated by the sand content of soils. Chemicals related to crops (pesticides and nutrients) and residential uses (pyrethroids) were found to be more strongly related to ecological integrity than were natural factors (riparian forest, watershed soil character).

Environmental Science & Technology↗

USGS2024

Accuracy, accessibility, and institutional capacity shape the utility of habitat models for managing and conserving rare plants on western public lands

Public lands are often managed for multiple uses ranging from energy development to rare plant conservation. Habitat models can help land managers assess and mitigate potential effects of projects on rare plants, but it is unclear how models are currently being used. Our goal was to better understand how staff in the Bureau of Land Management currently use habitat models to inform their decisions, and perceived challenges and benefits associated with that use. We first examined litigation documents to determine whether the agency has been challenged on its use of data for rare plants and found no relevant legal challenges. Second, we analyzed model use in National Environmental Policy Act (NEPA) documents and found no clear citations of habitat models. Finally, we conducted interviews with agency staff who analyze potential effects of proposed actions on rare plants in NEPA documents. The primary challenges interviewees faced in using models related to data organization and access, model quality and accuracy, and institutional capacity. Interviewees believed models could be used more to inform decisions and actions to conserve rare plants and rare plant habitat on public lands and recommended improving staff access to models, creating models for additional species, and addressing staffing limitations.

Arizona, California, Colorado, New Mexico, Utah, W↗

USGS2011

Marine and terrestrial factors affecting Adélie penguin Pygoscelis adeliae chick growth and recruitment off the western Antarctic Peninsula

An individual-based bioenergetics model that simulates the growth of an Adélie penguin Pygoscelis adeliae chick from hatching to fledging was used to assess marine and terrestrial factors that affect chick growth and fledging mass off the western Antarctic Peninsula. Simulations considered the effects on Adélie penguin fledging mass of (1) modification of chick diet through the addition of Antarctic silverfish Pleuragramma antarcticum to an all-Antarctic krill Euphausia superba diet, (2) reduction of provisioning rate which may occur as a result of an environmental stress such as reduced prey availability, and (3) increased thermoregulatory costs due to wetting of chicks which may result from increased precipitation or snow-melt in colonies. Addition of 17% Antarctic silverfish of Age-Class 3 yr (AC3) to a penguin chick diet composed of Antarctic krill increased chick fledging mass by 5%. Environmental stress that results in >4% reduction in provisioning rate or wetting of just 10% of the chick’s surface area decreased fledging mass enough to reduce the chick’s probability of successful recruitment. The negative effects of reduced provisioning and wetting on chick growth can be compensated for by inclusion of Antarctic silverfish of AC3 and older in the chick diet. Results provide insight into climate-driven processes that influence chick growth and highlight a need for field research designed to investigate factors that determine the availability of AC3 and older Antarctic silverfish to foraging Adélie penguins and the influence of snowfall on chick wetting, thermoregulation and adult provisioning rate.

Marine Ecology Progress Series↗

USGS2022

Environmental DNA metabarcoding read numbers and their variability predict species abundance, but weakly in non-dominant species

Metabarcoding of environmental DNA (eDNA) provides more comprehensive, efficient, and non-invasive sampling of biological communities than conventional methods. However, limitations of metabarcoding include taxon-specific biases in amplification and sequencing that distort assessments of community composition. Further, hyper-abundant species may disproportionately affect community composition assessments and impair the detection of rare species (i.e., “species masking”). In this study, we examine methodological approaches to improve eDNA metabarcoding assessments of community structure using fish community diversity in a pond in south Florida using MiFish primers modified to improve cichlid detection. Mitochondrial 12S eDNA amplicon sequencing via Illumina NovaSeq was analyzed using the DADA2 model-based exact sequence inference. The fish species and abundances in the system were recorded during piscicide treatment and subsequent native species restocking. Our results demonstrate that (1) ultra-high-throughput sequencing on the newer NovaSeq patterned flow cell provided reliable detection of very rare taxa—with detections of a single individual. (2) Read numbers were significantly correlated to the total surface area of the fish population, and numerical abundance to a lesser degree; however, dominant taxa largely drove those correlations, and simulations showed that biases in the most abundant taxa will have disproportionate effects on the strength of the correlation. (3) The read number coefficient of variation for each species across spatially separated replicate samples may provide less biased abundance estimates compared with estimates based on average read counts. Finally, (4) exact sequence inference detected multiple haplotypes and population genetic diversity within a species. Our results demonstrate the real-world metabarcoding capacity to reveal community structure and reliably detect rare species and unique haplotypes and shows that read numbers can, to a limited degree, be used to infer the size of fish populations. Careful examination of detection biases among dominant taxa and spatial variation among samples are required for rigorous eDNA-based estimates of community structure. Our results demonstrate the capacity of NovaSeq metabarcoding to reveal freshwater fish community structure and reliably detect rare species and unique haplotypes. Metabarcoding read numbers were significantly correlated to the total surface area of the fish species' populations, allowing for conditional inferences of population sizes. However, dominant taxa largely drove those correlations, and simulations indicated that biases toward the most abundant taxa will have disproportionate effects on the strength of the correlation.

Florida↗

USGS2015

Landscape genomics of Sphaeralcea ambigua in the Mojave Desert: a multivariate, spatially-explicit approach to guide ecological restoration

Local adaptation influences plant species’ responses to climate change and their performance in ecological restoration. Fine-scale physiological or phenological adaptations that direct demographic processes may drive intraspecific variability when baseline environmental conditions change. Landscape genomics characterize adaptive differentiation by identifying environmental drivers of adaptive genetic variability and mapping the associated landscape patterns. We applied such an approach to Sphaeralcea ambigua , an important restoration plant in the arid southwestern United States, by analyzing variation at 153 amplified fragment length polymorphism loci in the context of environmental gradients separating 47 Mojave Desert populations. We identified 37 potentially adaptive loci through a combination of genome scan approaches. We then used a generalized dissimilarity model (GDM) to relate variability in potentially adaptive loci with spatial gradients in temperature, precipitation, and topography. We identified non-linear thresholds in loci frequencies driven by summer maximum temperature and water stress, along with continuous variation corresponding to temperature seasonality. Two GDM-based approaches for mapping predicted patterns of local adaptation are compared. Additionally, we assess uncertainty in spatial interpolations through a novel spatial bootstrapping approach. Our study presents robust, accessible methods for deriving spatially-explicit models of adaptive genetic variability in non-model species that will inform climate change modelling and ecological restoration.

California, Nevada↗

USGS2003

Estimating rates of local extinction and colonization in colonial species and an extension to the metapopulation and community levels

Coloniality has mainly been studied from an evolutionary perspective, but relatively few studies have developed methods for modelling colony dynamics. Changes in number of colonies over time provide a useful tool for predicting and evaluating the responses of colonial species to management and to environmental disturbance. Probabilistic Markov process models have been recently used to estimate colony site dynamics using presence–absence data when all colonies are detected in sampling efforts. Here, we define and develop two general approaches for the modelling and analysis of colony dynamics for sampling situations in which all colonies are, and are not, detected. For both approaches, we develop a general probabilistic model for the data and then constrain model parameters based on various hypotheses about colony dynamics. We use Akaike's Information Criterion (AIC) to assess the adequacy of the constrained models. The models are parameterised with conditional probabilities of local colony site extinction and colonization. Presence–absence data arising from Pollock's robust capture–recapture design provide the basis for obtaining unbiased estimates of extinction, colonization, and detection probabilities when not all colonies are detected. This second approach should be particularly useful in situations where detection probabilities are heterogeneous among colony sites. The general methodology is illustrated using presence–absence data on two species of herons. Estimates of the extinction and colonization rates showed interspecific differences and strong temporal and spatial variations. We were also able to test specific predictions about colony dynamics based on ideas about habitat change and metapopulation dynamics. We recommend estimators based on probabilistic modelling for future work on colony dynamics. We also believe that this methodological framework has wide application to problems in animal ecology concerning metapopulation and community dynamics.

Oikos↗

USGS2026

What is the (real) rate of soil health practice adoption? Making sense of three data sources

Conservation stakeholders looking to quantify the impact of their investments to increase soil health practice adoption over time often face challenges in interpreting practice adoption data due to discrepancies in language and results among data sources. Similarly, efforts to estimate environmental outcomes of practice adoption, such as water quality and greenhouse gas emissions, can vary depending on different practice adoption input data. To help make sense of different adoption data sources, we compared county-level adoption data for winter cover crops (WCC), no-till (NT), and reduced tillage (RT) in three areas of the United States with contrasting climates and production systems: central Illinois (CIL), southern Illinois (SIL), and western New York (WNY). We analyzed data available during 2015 through 2022 from the Operational Tillage Information System (OpTIS, remote sensing), US Census of Agriculture (AgCensus, a farmer survey), and, specifically in Illinois, the Illinois Soil Conservation Transect Survey (Transect, a roadside survey). The magnitude of differences between the datasets depended on the practice and geographic location. For example, OpTIS and AgCensus tillage data were much more similar in Illinois (average difference of less than 4 percentage points) compared to New York (average differences of 20 percentage points). Similarly, there was less variability and smaller differences between OpTIS and AgCensus WCC data in Illinois compared to WNY. AgCensus tended to report lower WCC adoption for Illinois and greater adoption in WNY compared to OpTIS. All data sources agreed that the rate of change in tillage practices is slow (mainly –1% to 1%) and that adoption of WCC is low (assuming linear growth, it could take nearly a century to reach 50% WCC adoption in CIL). Differences among the datasets were attributed to definitional inconsistencies for RT and NT and how WCC data were acquired. For example, the AgCensus asks if a WCC was planted, whereas OpTIS and Transect evaluate the presence of a standing WCC. Data sources also reflect different time periods (calendar years or crop years) and types of cropland assessed (corn [ Zea mays L.], soybean [ Glycine max {L.} Merr.], or all cropland). We propose two recommendations to improve interpretation and consistency: (1) a working group to harmonize definitions and protocols and develop educational materials for data users, and (2) a research effort that integrates different adoption data types and produces publicly available adoption data at HUC-10 and county scales. Such activities could help improve data access and utility for evidence-based conservation decision-making and enhance the accuracy of environmental models that rely on adoption data as input.

Journal of Soil and Water Conservation↗

USGS1981

Assessment of impacts of proposed coal-resource and related economic development on water resources, Yampa River basin, Colorado and Wyoming: A summary

Expanded mining and use of coal resources in the Rocky Mountain region of the western United States will have substantial impacts on water resources, environmental amenities, and social and economic conditions. The U.S. Geological Survey has completed a 3-year assessment of the Yampa River basin, Colorado and Wyoming, where increased coal-resource development has begun to affect the environment and quality of life. Economic projections of the overall effects of coal-resource development were used to estimate water use and the types and amounts of waste residuals that need to be assimilated into the environment. Based in part upon these projections, several physical-based models and other semiquantitative assessment methods were used to determine possible effects upon the basin's water resources. Depending on the magnitude of mining and use of coal resources in the basin, an estimated 0.7 to 2.7 million tons (0.6 to 2.4 million metric tons) of waste residuals may be discharged annually into the environment by coal-resource development and associated economic activities. If the assumed development of coal resources in the basin occurs, annual consumptive use of water, which was approximately 142,000 acre-feet (175 million cubic meters) during 1975, may almost double by 1990. In a related analysis of alternative cooling systems for coal-conversion facilities, four to five times as much water may be used consumptively in a wet-tower, cooling-pond recycling system as in once-through cooling. An equivalent amount of coal transported by slurry pipeline would require about one-third the water used consumptively by once-through cooling for in-basin conversion. Current conditions and a variety of possible changes in the water resources of the basin resulting from coal-resource development were assessed. Basin population may increase by as much as threefold between 1975 and 1990. Volumes of wastes requiring treatment will increase accordingly. Potential problems associated with ammonia-nitrogen concentrations in the Yampa River downstream from Steamboat Springs were evaluated using a waste-load assimilative-capacity model. Changes in sediment loads carried by streams due to increased coal mining and construction of roads and buildings may be apparent only locally; projected increases in sediment loads relative to historic loads from the basin are estimated to be 2 to 7 percent. Solid-waste residuals generated by coal-conversion processes and disposed of into old mine pits may cause widely dispersed ground-water contamination, based on simulation-modeling results. Projected increases in year-round water use will probably result in the construction of several proposed reservoirs. Current seasonal patterns of streamflow and of dissolvedsolids concentrations in streamflow will be altered appreciably by these reservoirs. Decreases in time-weighted mean-annual dissolved-solids concentrations of as much as 34 percent are anticipated, based upon model simulations of several configurations of proposed reservoirs. Detailed statistical analyses of water-quality conditions in the Yampa River basin were made. Regionalized maximum waterquality concentrations were estimated for possible comparison with future conditions. Using Landsat imagery and aerial photographs, potential remote-sensing applications were evaluated to monitor land-use changes and to assess both snow cover and turbidity levels in streams. The technical information provided by the several studies of the Yampa River basin assessment should be useful to regional planners and resource managers in evaluating the possible impacts of development on the basin's water resources.

Colorado, Wyoming↗

USGS1995

Water budgets, water quality, and analysis of nutrient loading of the Winter Park Chain of Lakes, central Florida, 1989-92, with a section on littoral vegetation

The Winter Park chain of lakes (Lakes Maitland, Virginia, Osceola, and Mizell) has a combined area of about 900 acres, an immediate drainage area of about 3,100 acres, and mean depths ranging from 11 to 15 feet. The lakes are an important recreational resource for the surrounding communities, but there is concern about the possible effects of stormwater runoff and seepage of nutrient-enriched ground water on the quality of water in the lakes. The lakes receive water from several sources: rainfall on lake surfaces, inflow from other surface-water bodies, stormflow that enters the lakes through storm drains or by direct runoff from land adjacent to the lakes, and ground-water seepage. Water leaves the lakes by evaporation, surface outflow, and ground-water outflow. Of the three, only surface outflow can be measured directly. Rainfall, surface inflow and outflow, and lake-stage data were collected from October 1, 1989, to September 30, 1992. Stormflow, evaporation and ground-water inflow and outflow were estimated for the 3 years of the study. Ground-water outflow was calculated by evaluating the rate of lake-stage decline during dry periods. Estimated ground-water outflow was compared to downward leakage rates estimated by ground-water flow models. Lateral ground-water inflow from surficial sediments was calculated as the residual of the flow budget. Flow budgets were calculated for the 3 years of the study. In water year 1992 (a year with about average rainfall), inflow consisted of rainfall, 48 inches; stormflow, 15 inches; surface inflow, 67 inches; and ground water, 40 inches. The calculated outflows were evaporation, 47 inches; surface outflow, 90 inches; and ground water, 33 inches. Water-quality data also were used to calculate nutrient budgets for the lakes. Bimonthly water samples were collected from the lakes and at surface inflow and outflow sites, and were analyzed for physical characteristics, dissolved oxygen, pH, specific conductance, major ions, the nutrients nitrogen and phosphorus, and chlorophyll (collected at lake sites only). Specific conductance ranged from about 190 to 230 microsiemens per centimeter at 25 degrees Celsius in Lakes Maitland, Virginia and Osceola and from about 226 to 260 microsiemens per centimeter at 25 degrees Celsius in Lake Mizell. The median concentrations of total ammonia-plus-organic nitrogen in all the lakes ranged from 0.79 to 0.99 milligrams per liter. Median total phosphorus concentrations ranged from less than 0.02 to 0.20 milligrams per liter. Stormwater samples were collected for 17 storms at one storm-drain site and 16 storms at another storm-drain site on Lake Osceola. Median total nitrogen concentrations at the sites were 2.23 and 3.06 milligrams per liter and median total phosphorus concentrations were 0.34 and 0.40 milligrams per liter. The water quality in the Winter Park lakes generally is fair to good, based on a trophic-state index used by the Florida Department of Environmental Protection for assessing the tropic state of Florida lakes. This index was determined from median total nitrogen, total phosphorus, and chlorophyll-a concentrations, and median Secchi-disk transparency for all lakes for the period September 1989 to June 1992. Based on a one-time sampling of 20 sites around the lakes, surficial ground-water quality is highly variable. Nutrient concentrations were highly variable and could not be correlated to the proximity of septic tanks. Fertilizer probably is the primary source of nutrients in the surficial ground water. Nutrient budgets were calculated for the lakes for the 3 years of the study. The most variable source of nutrient loading to the lakes is stormwater. Nutrient-loading modeling indicates that reduction of nutrients in stormflow probably would improve lake-water quality. However, even with complete removal of nitrogen and phosphorus from stormwater, the lakes might still be mesotrophic with respect to both nutrients during periods of below average rainfall because of the input from the other sources of inflow to the lakes. Littoral vegetation in the lakes was surveyed in March 1992. The length of shoreline containing vegetation was 44 percent in Lake Maitland, 62 percent in Lake Virginia, 46 percent in Lake Osceola, and 76 percent in Lake Mizell. The types of vegetation present generally were similar for all four lakes.

Florida↗

USGS2024

Great Lakes mallard population dynamics

Breeding mallard ( Anas platyrhynchos ) populations in the Great Lakes region (Michigan, Minnesota, Wisconsin, USA) declined by >40% between 2000–2022 based on abundance data collected during spring aerial surveys. Mallards are an important waterfowl species in this region, where an estimated 60–80% of the mallard harvest is composed of locally banded birds. Extensive population monitoring datasets are available for mallards, presenting an opportunity to address complex questions such as estimating productivity at large spatial and temporal scales, identifying the effects of harvest on mallard demography, quantifying mechanisms for harvest compensation, and integrating multiple datasets to quantify the demographic drivers of population change. Our objective was to simultaneously examine factors affecting demographic parameters and their relative contribution to Great Lakes mallard population dynamics. We used 32 years of banding, band recovery, and aerial survey data collected for mallards from Michigan and Wisconsin to develop an integrated population model (IPM). We used age ratios at banding to estimate productivity, band recoveries from hunter-harvested birds to estimate annual survival and cause-specific mortality (i.e., harvest or non-hunting), and modeled abundance using aerial survey and demographic parameter estimates from 1991–2022. The IPM results indicated the decline in Great Lakes mallard abundance was caused by increased non-hunting mortality and a decline in productivity. Productivity varied spatially but temporally declined with the loss of Conservation Reserve Program area. Moreover, our productivity assessment provided evidence of density dependence in reproduction. Non-hunting mortality was 3.5–6.7 times and 1.3–4.2 times greater than harvest mortality for adult and juvenile female mallards, respectively, indicating environmental factors during spring and summer, not harvest, most greatly influenced annual mortality for female mallards. Our IPM reduced uncertainty in the factors affecting Great Lakes mallard population dynamics and indicated management actions that address non-hunting mortality and productivity would be most effective in increasing Great Lakes mallard abundance.

Michigan, Wisconsin↗

USGS2020

Assessment of multi-stressors on compositional turnover of diatom, invertebrate and fish assemblages along an urban gradient in Pacific Northwest streams (USA)

This study is part of the regional stream-quality assessment (RSQA) conducted by the U.S. Geological Survey (USGS) National Water Quality Assessment (NAWQA) project. The purpose of this study is to examine small streams along land-use and stressor gradients at the regional scale and to evaluate the relative importance of instream stressors on diatom, macroinvertebrate, and fish assemblages. In 2015, the RSQA project assessed stream quality in 82 wadeable streams that were selected along an urban land-use gradient in the Pacific Northwest Region (PNW) of the United States. This study evaluates the effects of four major categories of measured instream stressors – flow (i.e. alteration), water quality, habitat, and contaminants (in water and sediment) – on stream biota. We used gradient forest (GF) models to evaluate taxon specific responses to the various stressors for the three biotic assemblages. Results for diatom, invertebrate and fish assemblages showed that several environmental variables including substrate size, dissolved oxygen, and two or more different contaminants were selected in each of the GF models. In general, all three assemblages were negatively associated with any contaminant measures above zero, except the more tolerant taxa in each assemblage, which responded positively to contaminants. Total nitrogen (TN) and total phosphorus (TP) were important in both the diatom and invertebrate GF models but not in the fish models, which were related to temperature and stream flow. TP and TN were the top two variables for diatom GF models and various taxa responded at a range of nutrient concentrations; however, some taxa responded at low concentrations, for example around 0.02 for TP and 0.5 mg/L for TN. In general, the three biotic assemblages responded to multiple stressors following general patterns of known sensitive versus tolerant taxa for each of the biotic groups studied, yet the GF models allow us to explore taxon specific responses. For example, most of the sensitive Ephemeroptera, Plecoptera, Trichoptera invertebrate taxa (EPT) responded negatively when any contaminant increased above zero; yet some taxa such as the tolerant Trichoptera Cheumatopsyche responded positively to contaminants and many of the other stressors. The findings of this study demonstrate the value of using multiple assemblages to monitoring stressor gradients associated with urban stream systems and the importance of evaluating the responses of individual taxa to stressors.

Washington, Oregon↗

USGS2017

Putting flow-ecology relationships into practice: A decision-support system to assess fish community response to water-management scenarios

This paper presents a conceptual framework to operationalize flow–ecology relationships into decision-support systems of practical use to water-resource managers, who are commonly tasked with balancing multiple competing socioeconomic and environmental priorities. We illustrate this framework with a case study, whereby fish community responses to various water-management scenarios were predicted in a partially regulated river system at a local watershed scale. This case study simulates management scenarios based on interactive effects of dam operation protocols, withdrawals for municipal water supply, effluent discharges from wastewater treatment, and inter-basin water transfers. Modeled streamflow was integrated with flow–ecology relationships relating hydrologic departure from reference conditions to fish species richness, stratified by trophic, reproductive, and habitat characteristics. Adding a hypothetical new water-withdrawal site was predicted to increase the frequency of low-flow conditions with adverse effects for several fish groups. Imposition of new reservoir release requirements was predicted to enhance flow and fish species richness immediately downstream of the reservoir, but these effects were dissipated further downstream. The framework presented here can be used to translate flow–ecology relationships into evidence-based management by developing decision-support systems for conservation of riverine biodiversity while optimizing water availability for human use.

Water↗

USGS2004

Global land ice measurements from space (GLIMS): remote sensing and GIS investigations of the Earth's cryosphere

Concerns over greenhouse‐gas forcing and global temperatures have initiated research into understanding climate forcing and associated Earth‐system responses. A significant component is the Earth's cryosphere, as glacier‐related, feedback mechanisms govern atmospheric, hydrospheric and lithospheric response. Predicting the human and natural dimensions of climate‐induced environmental change requires global, regional and local information about ice‐mass distribution, volumes, and fluctuations. The Global Land‐Ice Measurements from Space (GLIMS) project is specifically designed to produce and augment baseline information to facilitate glacier‐change studies. This requires addressing numerous issues, including the generation of topographic information, anisotropic‐reflectance correction of satellite imagery, data fusion and spatial analysis, and GIS‐based modeling. Field and satellite investigations indicate that many small glaciers and glaciers in temperate regions are downwasting and retreating, although detailed mapping and assessment are still required to ascertain regional and global patterns of ice‐mass variations. Such remote sensing/GIS studies, coupled with field investigations, are vital for producing baseline information on glacier changes, and improving our understanding of the complex linkages between atmospheric, lithospheric, and glaciological processes.

Geocarto International↗