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At least 541 records · Page 30Linked to original sources

A sagebrush conservation design to proactively restore America’s sagebrush biome

A working group of experts with diverse professional backgrounds and disciplinary expertise was assembled to conceptualize a spatially explicit conservation design to support and inform the Sagebrush Conservation Strategy Part 2. The goal was to leverage recent advancements in remotely sensed landcover products to develop spatially and temporally explicit maps of sagebrush rangeland condition and landscape threats. In addition, the group sought to provide a common basis for understanding the state of sagebrush rangelands through time. First, the study team developed a spatially explicit model to assess geographic patterns in sagebrush ecological integrity and used this model to identify core sagebrush areas (CSAs), growth opportunity areas (GOAs), and other rangeland areas (ORAs) across the biome. Among the identified rangelands, 33.4 million acres were classified as CSAs; 84.3 million acres as GOAs; and 127.2 million acres as ORAs as of 2020. Second, the team sought to demonstrate the ecological relevance of the identified CSAs and GOAs by comparing these data with independent datasets for sagebrush obligate species of conservation concern. Geographical patterns in sagebrush ecological integrity were strongly associated with the occurrence of high-priority species and also displayed clear links to population performance for greater sage-grouse. Third, the team parsed out the type, location, and acres of primary threats within the different categories (CSAs, GOAs, and ORAs) to help focus active management by identifying places where multiagency and organization efforts can protect CSAs and GOAs that have higher levels of integrity with lower cumulative threats. The assessment of the condition of the sagebrush biome (that is, the location, amount, and conservation status) indicated that complex ecosystem function problems are driving ~73 percent of the demonstrated threats within the CSAs and GOAs (rather than point-source problems, such as human development). Fourth, the team developed trend estimates for the identified CSAs and GOAs and three selected primary threats (invasive annual grasses, conifer encroachment, and human modification) to the sagebrush biome from 2001 to 2020. Results showed that an average of 1.3 million acres per year have transitioned to ORAs at an annual rate of −1.34 percent. Fifth, the team developed an approach to integrate climate change effects into the threat-based landscape conservation design and conducted an initial assessment on the magnitude of near-term climate effects in the context of observed historical trends. The team’s analysis suggests that climate change alone is unlikely to be the dominant threat to sagebrush ecological integrity in the next few decades, although interactions of climate with wildfire and invasive annual grasses may be an important threat, especially in the longer term. A spatial overlap analysis was performed and highlighted 45.8 million acres of shared priorities among existing conservation frameworks to help anchor and guide collaborative landscape-scale conservation of areas that still have no to low threats. This information is critical to provide context for decisions about the volume and nature of conservation actions and funding requirements.

western United States↗

A decision support system for prioritizing forested wetland restoration in the Yazoo Backwater Area, Mississippi

A working prototype decision support system (DSS) was developed for the Yazoo Backwater Area, Mississippi, to help planners and managers prioritize, plan, conduct, and optimize forested wetland restoration activities. The DSS comprises geographic information system (GIS) spatial data themes, application programs that provide a cumulative analysis of the relative ability of sites to function as wetlands, and output data that are specific to a given restoration analysis scenario. The DSS input includes GIS data themes such as geomorphology, soils, land use, elevation, farmed wetlands, flood frequency, topographic depressions, streams, public lands, roads, and permanent water bodies, which can be used as spatial templates to define areal hydrologic settings. These GIS data themes can then be ranked and combined to estimate the relative suitability of a potential wetland restoration site, thereby, determining relative wetland equivalence on the landscape. The GIS applications used in this DSS perform the following three functions: assess the ecology (the Eco-Assessor); reclassify land-use in areas selected for restoration (the Tree-Translator); and generate output data to compare restoration scenarios (the Parameter-Generator). Areas selected for reforestation are translated (in the GIS) into ?forested? land use, and the tree species that are ?planted? on the landscape (in the DSS) either compose an ecologically optimal or an economically optimal community of tree species. Output from the DSS can be compared and analyzed by using economic, statistical, graphical, and tabular methods. Output data for seven selected scenarios were generated for the Yazoo Backwater Area and are presented as examples to illustrate the flexibility of the DSS to identify areas that meet restoration objectives.

Water-Resources Investigations Report↗

The influence of tree canopy cover data choices on urban ecosystem accounting

Because urban landscapes are heterogeneous, the methods and spatial resolution used to depict the land surface greatly influence the representation of urban features. Land cover products such as tree canopy cover (TCC) are particularly sensitive to the methodology and resolution used in their creation. Differences in TCC mapping have implications on the outcomes of ecosystem service (ES) models, including those underlying natural capital accounting. Here, we quantify the sensitivity of physical rainfall interception and local climate regulation ES models for 189 U.S. cities to TCC inputs from four TCC products: a) National Land Cover Database (NLCD), b) Enhanced NLCD TCC, c) aggregated city-specific composite, and d) global tree canopy height dataset. We find both city-level and aggregate differences in TCC estimates, from a 38% decrease to a 3% increase relative to an aggregated high-resolution product. These differences result in up to 3% overestimations and 27% underestimation of rainfall interception and 2–56% underestimation of local climate regulation ES. City size, population, and greenness in addition to climatic variation drove differences between TCC products, and this variation requires users to carefully consider the choice of input data for any planned analysis. Though high-resolution data can offer greater nuance and accuracy, more limited spatiotemporal availability can hinder their usefulness for long-term monitoring applications such as natural capital accounting. The differences found in this study provide valuable insights for making informed decisions on data inputs for use in urban ecosystem research and for contextualizing model outcomes.

Contiguous United States↗

A Bayesian random effects discrete-choice model for resource selection: Population-level selection inference

Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a Bayesian hierarchical discrete-choice model for resource selection can provide managers with 2 components of population-level inference: average population selection and variability of selection. Both components are necessary to make sound management decisions based on animal selection.

Journal of Wildlife Management↗

Optimal management strategies in variable environments: Stochastic optimal control methods

Dynamic optimization was used to investigate the optimal defoliation of salt desert shrubs in north-western Utah. Management was formulated in the context of optimal stochastic control theory, with objective functions composed of discounted or time-averaged biomass yields. Climatic variability and community patterns of salt desert shrublands make the application of stochastic optimal control both feasible and necessary. A primary production model was used to simulate shrub responses and harvest yields under a variety of climatic regimes and defoliation patterns. The simulation results then were used in an optimization model to determine optimal defoliation strategies. The latter model encodes an algorithm for finite state, finite action, infinite discrete time horizon Markov decision processes. Three questions were addressed: (i) What effect do changes in weather patterns have on optimal management strategies? (ii) What effect does the discounting of future returns have? (iii) How do the optimal strategies perform relative to certain fixed defoliation strategies? An analysis was performed for the three shrub species, winterfat (Ceratoides lanata), shadscale (Atriplex confertifolia) and big sagebrush (Artemisia tridentata). In general, the results indicate substantial differences among species in optimal control strategies, which are associated with differences in physiological and morphological characteristics. Optimal policies for big sagebrush varied less with variation in climate, reserve levels and discount rates than did either shadscale or winterfat. This was attributed primarily to the overwintering of photosynthetically active tissue and to metabolic activity early in the growing season. Optimal defoliation of shadscale and winterfat generally was more responsive to differences in plant vigor and climate, reflecting the sensitivity of these species to utilization and replenishment of carbohydrate reserves. Similarities could be seen in the influence of both the discount rate and the climatic patterns on optimal harvest strategics. In general, decreases in either the discount rate or in the frequency of favorable weather patterns lcd to a more conservative defoliation policy. This did not hold, however, for plants in states of low vigor. Optimal control for shadscale and winterfat tended to stabilize on a policy of heavy defoliation stress, followed by one or more seasons of rest. Big sagebrush required a policy of heavy summer defoliation when sufficient active shoot material is present at the beginning of the growing season. The comparison of fixed and optimal strategies indicated considerable improvement in defoliation yields when optimal strategies are followed. The superior performance was attributable to increased defoliation of plants in states of high vigor. Improvements were found for both discounted and undiscounted yields.

Journal of Environmental Management↗

Coastal barrier resilience and resistance: Analysis and metrics for characterizing coastal state

Barrier islands are shaped by a variety of short- and long-term environmental processes such as storms and relative sea-level rise. These islands, found along the estuarine-marine interface, provide ecosystem services including storm surge and wave attenuation, erosion protection to inland marshes, habitat for fish and wildlife, and recreation. Natural resource managers require actionable information on how barrier island resilience and resistance changes over time to understand how an island’s current state relates to past conditions and to inform restoration prioritization and implementation. The U.S. Geological Survey and The Water Institute collaborated on a study to develop indicators of resilience and resistance for barrier islands in Louisiana. Here, resilience captures island persistence on yearly to decadal time scales, and resistance captures persistence on event time scales of days to weeks. The indicators fall in two categories: Tier 1 Screening Metrics, that can be readily calculated from available data, are easily interpretable as an evaluation of barrier condition, and provide a high-level snapshot of overall barrier resilience and resistance; and Tier 2 Analysis Metrics, which are detailed metrics that required specialized analysis and interpretation and are more applicable to answering specific questions managers may have about barrier state. The research team derived Tier 1 resilience indicators from subaerial land and vegetation cover calculated from publicly available maps and products based on satellite imagery. By benchmarking the total land and vegetation extent against their respective historical maxima, this metric provides a snapshot of an island’s current state in the context of its long-term trajectory. The research team developed Tier 1 resistance indicators based on subaerial island configuration and water level recurrence as a proxy for evaluating island resistance to storms, which are the primary driver of short-term change. These Tier 1 metrics can be analyzed over time to provide a high-level assessment of how an island’s resistance decreases because of elevation loss or sea-level rise or increases due to restoration or natural recovery. The research team developed Tier 2 resilience and resistance indicators and associated analyses to provide detailed information for specific time periods or applications (e.g., wildlife management). These metrics include habitat coverage from high-resolution maps, which show composition changes over time to capture the evolving resilience of specific habitat types; high tide flooding analysis, which evaluate island area relative to specified flooding thresholds to characterize resistance in the short-term or, if analyzed over time, indicate changes in resilience; and hypsometric curve analysis, which allows managers to evaluate island area changes above their own elevation benchmarks of interest and similarly characterize resistance in the short-term or indicate changes in resilience if assessed over time. The research team calculated Tier 1 metrics of the barrier islands and headlands along the coast of Louisiana for the period of 1984 through 2021 and Tier 2 metrics for select times during that period depending on data available and quality. The results were captured in a report card for each barrier, which also includes an overview of the metrics and their interpretation; a restoration and storm history; and Tier 1 and Tier 2 metric analysis, including benchmarking against coastwide and regional values as well as to an island’s pre-restoration trajectory. These report cards provide a readily digestible synthesis of barrier condition and trajectory that coastal managers can use to support restoration prioritization and other decisions.

Louisiana↗

Utilizing hydrophones to detect streambed mobilization in the Wild and Scenic reach of the Rio Chama

This paper describes a study that deployed underwater acoustic sensors (hydrophones) to detect streambed mobilization in the Wild and Scenic reach of the Rio Chama, New Mexico. Fine sediment accumulation in the gravel‑cobble bed affects brown trout spawning habitat, and understanding incipient motion thresholds is essential for planning effective environmental flushing flows released from El Vado Dam. Two hydrophone stations were operated during 2021–2022, capturing audio data during a high‑flow pulse in late 2021. Acoustic processing techniques, including spectral analysis and threshold‑based impact detection, were used to identify sediment‑generated noise associated with coarse sediment transport. Results indicate detectable sediment mobilization during the high‑flow pulse, demonstrating hydrophones’ potential as a passive monitoring tool for environmental flow planning and sediment‑management decision support.

Conference Paper↗

How "simple" methodological decisions affect interpretation of population structure based on reduced representation library DNA sequencing: A case study using the lake whitefish

Reduced representation (RRL) sequencing approaches (e.g., RADSeq, genotyping by sequencing) require decisions about how much to invest in genome coverage and sequencing depth, as well as choices of values for adjustable bioinformatics parameters. To empirically explore the importance of these “simple” methodological decisions, we generated two independent sequencing libraries for the same 142 individual lake whitefish (Coregonus clupeaformis) using a nextRAD RRL approach: (1) a larger number of loci at low sequencing depth based on a 9mer (library A); and (2) fewer loci at higher sequencing depth based on a 10mer (library B). The fish were selected from populations with different levels of expected genetic subdivision. Each library was analyzed using the STACKS pipeline followed by three types of population structure assessment (FST, DAPC and ADMIXTURE) with iterative increases in the stringency of sequencing depth and missing data requirements, as well as more specific a priori population maps. Library B was always able to resolve strong population differentiation in all three types of assessment regardless of the selected parameters, largely due to retention of more loci in analyses. In contrast, library A produced more variable results; increasing the minimum sequencing depth threshold (-m) resulted in a reduced number of retained loci, and therefore lost resolution at high -m values for FST and ADMIXTURE, but not DAPC. When detecting fine population differentiation, the population map influenced the number of loci and missing data, which generated artefacts in all downstream analyses tested. Similarly, when examining fine scale population subdivision, library B was robust to changing parameters but library A lost resolution depending on the parameter set. We used library B to examine actual subdivision in our study populations. All three types of analysis found complete subdivision among populations in Lake Huron, ON and Dore Lake, SK, Canada using 10,640 SNP loci. Weak population subdivision was detected in Lake Huron with fish from sites in the north-west, Search Bay, North Point and Hammond Bay,showing slight differentiation. Overall, we show that apparently simple decisions about library construction and bioinformatics parameters can have important impacts on the interpretation of population subdivision. Although potentially more costly on a per-locus basis, early investment in striking a balance between the number of loci and sequencing effort is well worth the reduced genomic coverage for population genetics studies. More conservative stringency settings on STACKS parameters lead to a final dataset that was more consistent and robust when examining both weak and strong population differentiation. Overall, we recommend that researchers approach “simple” methodological decisions with caution, especially when working on non-model species for the first time.

Michigan, Ontario, Saskatchewan↗

Testing model parameters for wave‐induced dune erosion using observations from Hurricane Sandy

Models of dune erosion depend on a set of assumptions that dictate the predicted evolution of dunes throughout the duration of a storm. Lidar observations made before and after Hurricane Sandy at over 800 profiles with diverse dune elevations, widths, and volumes are used to quantify specific dune erosion model parameters including the dune face slope, which controls dune avalanching, and the trajectory of the dune toe, which controls dune migration. Wave‐impact models of dune erosion assume a vertical dune face and erosion of the dune toe along the foreshore beach slope. Observations presented here show that these assumptions are not always valid and require additional testing if these models are to be used to predict coastal vulnerability for decision‐making purposes. Observed dune face slopes steepened by 43% yet did not become vertical faces, and only 50% of the dunes evolved along a trajectory similar to the foreshore beach slope. Observations also indicate that dune crests were lowered during dune erosion. Moreover, analysis showed a correspondence between dune lowering and narrower beaches, smaller dune volumes, and/or longer wave impact.

Maryland, New Jersey, New York↗

Selecting Great Lakes streams for lampricide treatment based on larval sea lamprey surveys

The Empiric Stream Treatment Ranking (ESTR) system is a data-driven, model-based, decision tool for selecting Great Lakes streams for treatment with lampricide, based on estimates from larval sea lamprey ( Petromyzon marinus ) surveys conducted throughout the basin. The 2000 ESTR system was described and applied to larval assessment surveys conducted from 1996 to 1999. A comparative analysis of stream survey and selection data was conducted and improvements to the stream selection process were recommended. Streams were selected for treatment based on treatment cost, predicted treatment effectiveness, and the projected number of juvenile sea lampreys produced. On average, lampricide treatments were applied annually to 49 streams with 1,075 ha of larval habitat, killing 15 million larval and 514,000 juvenile sea lampreys at a total cost of $5.3 million, and marginal and mean costs of $85 and $10 per juvenile killed. The numbers of juvenile sea lampreys killed for given treatment costs showed a pattern of diminishing returns with increasing investment. Of the streams selected for treatment, those with > 14 ha of larval habitat targeted 73% of the juvenile sea lampreys for 60% of the treatment cost. Suggested improvements to the ESTR system were to improve accuracy and precision of model estimates, account for uncertainty in estimates, include all potentially productive streams in the process (not just those surveyed in the current year), consider the value of all larvae killed during treatment (not just those predicted to metamorphose the following year), use lake-specific estimates of damage, and establish formal suppression targets.

Journal of Great Lakes Research↗

Ecosystem ecology meets adaptive management: food web response to a controlled flood on the Colorado River, Glen Canyon

Large dams have been constructed on rivers to meet human demands for water, electricity, navigation, and recreation. As a consequence, flow and temperature regimes have been altered, strongly affecting river food webs and ecosystem processes. Experimental high‐flow dam releases, i.e., controlled floods, have been implemented on the Colorado River, USA, in an effort to reestablish pulsed flood events, redistribute sediments, improve conditions for native fishes, and increase understanding of how dam operations affect physical and biological processes. We quantified secondary production and organic matter flows in the food web below Glen Canyon dam for two years prior and one year after an experimental controlled flood in March 2008. Invertebrate biomass and secondary production declined significantly following the flood (total biomass, 55% decline; total production, 56% decline), with most of the decline driven by reductions in two nonnative invertebrate taxa, Potamopyrgus antipodarum and Gammarus lacustris . Diatoms dominated the trophic basis of invertebrate production before and after the controlled flood, and the largest organic matter flows were from diatoms to the three most productive invertebrate taxa ( P. antipodarum , G. lacustris , and Tubificida). In contrast to invertebrates, production of rainbow trout ( Oncorhynchus mykiss ) increased substantially (194%) following the flood, despite the large decline in total secondary production of the invertebrate assemblage. This counterintuitive result is reconciled by a post‐flood increase in production and drift concentrations of select invertebrate prey (i.e., Chironomidae and Simuliidae) that supported a large proportion of trout production but had relatively low secondary production. In addition, interaction strengths, measured as species impact values, were strongest between rainbow trout and these two taxa before and after the flood, demonstrating that the dominant consumer–resource interactions were not necessarily congruent with the dominant organic matter flows. Our study illustrates the value of detailed food web analysis for elucidating pathways by which dam management may alter production and strengths of species interactions in river food webs. We suggest that controlled floods may increase production of nonnative rainbow trout, and this information can be used to help guide future dam management decisions.

Arizona↗

Genetic population structure of Shoal Bass within their native range

Endemic to the Apalachicola River basin of the southeastern USA, the Shoal Bass Micropterus cataractae is a fluvial‐specialist sport fish that is imperiled because of anthropogenic habitat alteration. To counter population declines, restorative stocking efforts are becoming an increasingly relevant management strategy. However, population genetic structure within the species is currently unknown, but it could influence management decisions, such as brood source location. Leveraging a collaborative effort to collect and genotype specimens with 16 microsatellite loci, our objective was to characterize hierarchical population structure and genetic differentiation of the Shoal Bass across its native range, including an examination of structuring mechanisms, such as relatedness and inbreeding levels. Specimens identified as Shoal Bass were collected from 13 distinct sites ( N ranged from 17 to 209 per location) and were then taxonomically screened to remove nonnative congeners and hybrids (pure Shoal Bass N ranged from 13 to 183 per location). Our results revealed appreciable population structure, with five distinct Shoal Bass populations identifiable at the uppermost hierarchical level that generally corresponded with natural geographic features and anthropogenic barriers. Substructure was recovered within several of these populations, wherein differences appeared related to spatial isolation and local population dynamics. An analysis of molecular variance revealed that 3.6% of the variation in our data set was accounted for among three larger river drainages, but substructure within each river drainage also explained an additional 8.9% of genetic variation, demonstrating that management at a scale lower than the river drainage level would likely best conserve genetic diversity. Results provide a population genetic framework that can inform future management decisions, such as brood source location, so that genetic diversity within and among populations is conserved and overall adaptability of the species is maintained.

Apalachicola–Chattahoochee–Flint River Basin↗

Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?

The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub-daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state-of-the art applications of ML for water quality models and discuss opportunities to improve the use of ML for emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model-data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge-guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision-relevant predictions of riverine water quality.

Hydrological Processes↗

Grazing-induced changes to biological soil crust cover mediate hillslope erosion in a long-term exclosure experiment

Dryland ecosystems are particularly vulnerable to erosion generated by livestock grazing. Quantifying this risk across a variety of landscape settings is essential for successful adaptive management, particularly in light of a changing climate. In the Upper Colorado River Basin, there are nearly 25 000 km 2 of rangelands with underlying soils derived from Mancos Shale, an erodible and saline geologic parent material. Salinity is a major concern within the Colorado River watershed, much of which is attributed to runoff and leaching from Mancos Shale deposits. In a 60-yr paired-watershed experiment in western Colorado, we used silt fences to measure differences in saline hillslope erosion, including both total sediment yield and concentrations of primary saline constituents (Na and Se), in watersheds that were either exposed to grazing or where livestock was excluded. After accounting for the strong effects of soil type, slope, and antecedent precipitation, we found that grazing increased sediment loss by ≈50% across our 8-yr time series (0.1–1.5 tn ha −1 ), consistent with levels reported at the watershed scale in early published work from studies at the same location. Eroded sediment Se levels were low and unaffected by grazing history, but Na concentrations were significantly reduced on grazed hillslopes, likely due to depletion of surface Na in soils exposed to chronic soil disturbance by livestock. Variable selection and path analysis identified that biological soil crust (BSC) cover, more than any other variable, explained the differences in sediment yields between grazed and ungrazed watersheds, partially through the enhancement of soil aggregate stability. Our results suggest that BSC cover should be granted heightened consideration in rangeland decision support tools (e.g., state-and-transition models) and that measures to reduce surface disturbance from livestock such as altering the timing or intensity of grazing may be effective for reducing downstream impacts.

Colorado↗

Studies by the U.S. Geological Survey on sources, transport, and fate of agricultural chemicals

The U.S. Geological Survey's National Water-Quality Assessment (NAWQA) Program is assessing the sources, transport, and fate of chemicals applied to crops in agricultural basins across the Nation (referred to as "study units," see map). Chemicals selected for study include nutrients (nitrogen and phosphorus) and about 50 commonly used pesticides and their transformation products, including triazine and acetanilide herbicides such as atrazine and metolachlor, and organophosphorus insecticides such as chlorpyrifos and diazinon. The basins in the studies represent a range of agricultural settings—with varying crop types and agricultural practices related to tillage, irrigation, artificial drainage, and chemical use—as well as a range of landscapes with different geology, soils, topography, climate, and hydrology. Consistent methodology and analysis allow comparisons among the different basins. This study design leads to an improved understanding of the many factors that can affect the movement of water and chemicals in different agricultural settings (see "Complex factors," next page). Information from these studies will help with decision-making related to chemical use, conservation, and other farming practices that are used to reduce runoff of agricultural chemicals and sediment from fields. This information also will benefit the U.S. Environmental Protection Agency, the Department of Agriculture, local and regional water managers, and agricultural chemical manufacturers who are involved in managing chemical use and pesticide registration.

Fact Sheet↗

USGS Illinois River monitoring and evaluation

Asian carp monitoring and contract removal will continue throughout the Upper Illinois Waterway system as needed for adaptive management to mitigate, control, and contain Asian carp. Compiling data from monitoring and removal efforts into a centralized database (Illinois River Catch Database application) facilitates data standardization, quality, accessibility, sharing, and analysis to aid in Asian carp removal efforts, evaluations of management actions, and modeling efforts (e.g., SEACarP model). Data summarization, visualization, and modeling supports a better understanding of bigheaded carp life history, behavior, and habitat use. Integrating Asian carp-related data and analyses into decision support tools and products aids in applying control and containment methods in an informed and transparent manner (e.g., improved efficiencies in implementations of the Unified Method, inform targeted removal efforts or deterrent deployments in key locations based on preferential benthic characteristics and environmental conditions).

Illinois↗

Hydrologic models and analysis of water availability in Cuyama Valley, California

Changes in population, agricultural development practices (including shifts to more water-intensive crops), and climate variability are placing increasingly larger demands on available water resources, particularly groundwater, in the Cuyama Valley, one of the most productive agricultural regions in Santa Barbara County. The goal of this study was to produce a model capable of being accurate at scales relevant to water management decisions that could be considered in the evaluation of the sustainable water supply. The Cuyama Valley Hydrologic Model (CUVHM) was designed to simulate the most important natural and human components of the hydrologic system, including components dependent on variations in climate, thereby providing a reliable assessment of groundwater conditions and processes that can inform water users and help to improve planning for future conditions. Model development included a revision of the conceptual model of the flow system, construction of a precipitation-runoff model using the Basin Characterization Model (BCM), and construction of an integrated hydrologic flow model with MODFLOW-One-Water Hydrologic Flow Model (MF-OWHM). The hydrologic models were calibrated to historical conditions of water and land use and, then, used to assess the use and movement of water throughout the Valley. These tools provide a means to understand the evolution of water use in the Valley, its availability, and the limits of sustainability. The conceptual model identified inflows and outflows that include the movement and use of water in both natural and anthropogenic systems. The groundwater flow system is characterized by a layered geologic sedimentary sequence that—in combination with the effects of groundwater pumping, natural recharge, and the application of irrigation water at the land surface—displays vertical hydraulic-head gradients. Overall, most of the agricultural demand for water in the Cuyama Valley in the initial part of the growing season is supplied by groundwater, which is augmented by precipitation during wet winter and spring seasons. In addition, the amount of groundwater used for irrigation varies from year to year in response to climate variation and can increase dramatically in dry years. Model simulation results, however, also indicated that irrigation may have been less efficient during wet years. Agricultural pumpage is a major component to simulated outflow that is often poorly recorded. Therefore, an integrated, coupled farm-process model is used to estimate historical pumpage for water-balance subregions that evolved with the development of groundwater in the Valley from 1949 through 2010. The integrated hydrologic model includes these water-balance subregions and delineates natural, municipal, and agricultural land use; streamflow networks; and groundwater flow systems. The redefinition of the geohydrologic framework (including the internal architecture of the sedimentary units) and incorporation of these units into the simulation of the regional groundwater flow system indicated that faults have compartmentalized the alluvial deposits into subregions, which have responded differently to regional groundwater flow, locations of recharge, and the effects of development. The Cuyama Valley comprises nine subregions grouped into three regional zones, the Main, Ventucopa Uplands, and Sierra Madre Foothills, which are fault bounded, represent different proportions of the three alluvial aquifers, and have different water quality. The CUVHM uses MF-OWHM to simulate and assess the use and movement of water, including the evolution of land use and related water-balance regions. The model is capable of being accurate at annual to interannual time frames and at subregional to valley-wide spatial scales, which allows for analysis of the groundwater hydrologic budget for the water years 1950–2010, as well as potential assessment of the sustainable use of groundwater. Simulated changes in storage over time showed that significant withdrawals from storage generally occurred not only during drought years (1976–77 and 1988–92) but also during the early stages of industrial agriculture, which was initially dominated by alfalfa production. Since the 1990s, agriculture has shifted to more water-intensive crops. Measured and simulated groundwater levels indicated substantial declines in selected subregions, mining of groundwater that is thousands to tens of thousands of years old, increased groundwater storage depletion, and land subsidence. Most of the recharge occurs in the upland regions of Ventucopa and Sierra Madre Foothills, and the largest fractions of pumpage and storage depletion occur in the Main subregion. The long-term imbalance between inflows and outflows resulted in simulated overdraft (groundwater withdrawals in excess of natural recharge) of the groundwater basin over the 61-year period of 1949–2010. Changes in storage varied considerably from year to year, depending on land use, pumpage, and climate conditions. Climatically driven factors can greatly affect inflows, outflows, and water use by more than a factor of two between wet and dry years. Although precipitation during inter-decadal wet years previously replenished the basin, the water use and storage depletion have lessened the effects of these major recharge events. Simulated and measured water-level altitudes indicated the presence of large areas where depressed water levels have resulted in large desaturated zones in the younger and Older Alluvium layers in the Main-zone subregions. The results of modeled projection of the base-case scenario 61 years into the future indicated that current supply-and-demand are unsustainable and will result in additional groundwater-level declines and related storage depletion and land subsidence. The reduced-supply and reduced-demand projections reduced groundwater storage depletion but may not allow for sustainable agriculture under current demands, agricultural practices, and land use.

California↗

River otter distribution in Nebraska

The river otter ( Lontra canadensis ) was extirpated from Nebraska, USA, in the early 1900s and reintroduced starting in 1986. Information is needed regarding the distribution of river otters in Nebraska before decisions can be made regarding its conservation status. Understanding distribution of a species is critically important for effective management. We investigated river otter distribution in Nebraska with occupancy modeling and maximum entropy (Maxent) modeling using 190 otter sign observations on Nebraska's navigable rivers and 380 historical otter records from November 1977 to April 2014. Both methods identified the Platte River, Elkhorn River, central and eastern Niobrara River, and southern Loup River system as core areas within the distribution of otters in Nebraska. The Maxent model provided more liberal estimates of site occupancy and identified some smaller rivers as being within the distribution of otters in Nebraska, which were not identified using occupancy modeling. We recommend that multiple data sets and analysis methods be used to estimate species distribution because this allows for the broadest geographical coverage and decreases the likelihood of overlooking areas with fewer animal records. If further reintroduction efforts or translocation efforts are to take place in the future, we recommend focusing on areas with high modeled occupancy but few historical and survey records

Nebraska↗