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USGS2024

Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses

RiverWare is a river system modeling tool developed by CADSWES (Center of Advanced Decision Support for Water and Environmental Systems) that allows the user to simulate complex reservoir operations and perform period-of-record analyses for different scenarios. For the InFRM hydrology studies, RiverWare is used to generate a homogeneous regulated POR by simulating the basin as if the reservoirs and their current rule sets had been present in the basin for the entire time period. Statistical analyses can then be performed on the extended records at the gages. This report summarizes the RiverWare portion of the hydrologic analysis being completed for the InFRM Hydrology study of the Colorado River Basin. The RiverWare model described in this chapter presents development of the Colorado River Basin hydrology, which mimics current operational conditions. The use of the RiverWare program allows for data extension to periods prior to dam construction. The utilization of longer gage record improves discharge frequency results and increases the confidence of the analysis being performed. The modeling evaluation criteria are: (1) evaluate output based on validating policies and functions, and (2) prioritize operation based on surcharge and flood control. A detailed explanation of the Colorado River Basin POR hydrology will be in a later section. Calibration results will also be shown that illustrate the overall model performance for the POR. The time window simulation run is for January 01, 1930 – September 30, 2019. This time window captures all big events occurred over the Colorado River basin. Each simulated water year was inspected individually to better validate the results. Historical pool elevations along with observed inflows and outflows were compared against the model simulated results.

Texas↗

USGS2014

Improvement of the R-SWAT-FME framework to support multiple variables and multi-objective functions

Application of numerical models is a common practice in the environmental field for investigation and prediction of natural and anthropogenic processes. However, process knowledge, parameter identifiability, sensitivity, and uncertainty analyses are still a challenge for large and complex mathematical models such as the hydrological/water quality model, Soil and Water Assessment Tool (SWAT). In this study, the previously developed R program language-SWAT-Flexible Modeling Environment (R-SWAT-FME) was improved to support multiple model variables and objectives at multiple time steps (i.e., daily, monthly, and annually). This expansion is significant because there is usually more than one variable (e.g., water, nutrients, and pesticides) of interest for environmental models like SWAT. To further facilitate its easy use, we also simplified its application requirements without compromising its merits, such as the user-friendly interface. To evaluate the performance of the improved framework, we used a case study focusing on both streamflow and nitrate nitrogen in the Upper Iowa River Basin (above Marengo) in the United States. Results indicated that the R-SWAT-FME performs well and is comparable to the built-in auto-calibration tool in multi-objective model calibration. Overall, the enhanced R-SWAT-FME can be useful for the SWAT community, and the methods we used can also be valuable for wrapping potential R packages with other environmental models.

Science of the Total Environment↗

USGS2015

Linking degradation status with ecosystem vulnerability to environmental change

Environmental change can cause regime shifts in ecosystems, potentially threatening ecosystem services. It is unclear if the degradation status of ecosystems correlates with their vulnerability to environmental change, and thus the risk of future regime shifts. We assessed resilience in acidified (degraded) and circumneutral (undegraded) lakes with long-term data (1988–2012), using time series modeling. We identified temporal frequencies in invertebrate assemblages, which identifies groups of species whose population dynamics vary at particular temporal scales. We also assessed species with stochastic dynamics, those whose population dynamics vary irregularly and unpredictably over time. We determined the distribution of functional feeding groups of invertebrates within and across the temporal scales identified, and in those species with stochastic dynamics, and assessed attributes hypothesized to contribute to resilience. Three patterns of temporal dynamics, consistent across study lakes, were identified in the invertebrates. The first pattern was one of monotonic change associated with changing abiotic lake conditions. The second and third patterns appeared unrelated to the environmental changes we monitored. Acidified and the circumneutral lakes shared similar levels and patterns of functional richness, evenness, diversity, and redundancy for species within and across the observed temporal scales and for stochastic species groups. These similar resilience characteristics suggest that both lake types did not differ in vulnerability to the environmental changes observed here. Although both lake types appeared equally vulnerable in this study, our approach demonstrates how assessing systemic vulnerability by quantifying ecological resilience can help address uncertainty in predicting ecosystem responses to environmental change across ecosystems.

Oecologia↗

USGS2018

Landscape-scale variation in canopy water content of giant sequoias during drought

Recent drought (2012–2016) caused unprecedented foliage dieback in giant sequoias ( Sequoiadendron giganteum ), a species endemic to the western slope of the southern Sierra Nevada in central California. As part of an effort to understand and map sequoia response to droughts, we studied the patterns of remotely sensed canopy water content (CWC), both within and among sequoia groves in two successive years during the drought period (2015 and 2016). Our aims were: (1) to quantify giant sequoia responses to severe drought stress at a landscape scale using CWC as an indicator of crown foliage status, and (2) to estimate the effect of environmental correlates that mediate CWC change within and among giant sequoia groves. We utilized airborne high fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) data from the Carnegie Airborne Observatory to assess giant sequoia foliage status during 2015 and 2016 of the 2012–2016 droughts. A series of statistical models were generated to classify giant sequoias and to map their location in Sequoia and Kings Canyon National Parks (SEKI) and vicinity. We explored the environmental correlates and the spatial patterns of CWC change at the landscape scale. The mapped CWC was highly variable throughout the landscape during the two observation years, and proved to be most closely related to geological substrates, topography, and site-specific water balance. While there was an overall net gain in sequoia CWC between 2015 and 2016, certain locations (lower elevations, steeper slopes, areas more distant from surface water sources, and areas with greater climate water deficit) showed CWC losses. In addition, we found greater CWC loss in shorter sequoias and those growing in areas with lower sequoia stem densities. Our results suggest that CWC change indicates sequoia response to droughts across landscapes. Long-term monitoring of giant sequoia CWC will likely be useful for modeling and predicting their population-level response to future climate change.

California↗

USGS2003

Estimating the susceptibility of surface water in Texas to nonpoint-source contamination by use of logistic regression modeling

In the State of Texas, surface water (streams, canals, and reservoirs) and ground water are used as sources of public water supply. Surface-water sources of public water supply are susceptible to contamination from point and nonpoint sources. To help protect sources of drinking water and to aid water managers in designing protective yet cost-effective and risk-mitigated monitoring strategies, the Texas Commission on Environmental Quality and the U.S. Geological Survey developed procedures to assess the susceptibility of public water-supply source waters in Texas to the occurrence of 227 contaminants. One component of the assessments is the determination of susceptibility of surface-water sources to nonpoint-source contamination. To accomplish this, water-quality data at 323 monitoring sites were matched with geographic information system-derived watershed- characteristic data for the watersheds upstream from the sites. Logistic regression models then were developed to estimate the probability that a particular contaminant will exceed a threshold concentration specified by the Texas Commission on Environmental Quality. Logistic regression models were developed for 63 of the 227 contaminants. Of the remaining contaminants, 106 were not modeled because monitoring data were available at less than 10 percent of the monitoring sites; 29 were not modeled because there were less than 15 percent detections of the contaminant in the monitoring data; 27 were not modeled because of the lack of any monitoring data; and 2 were not modeled because threshold values were not specified.

Texas↗

USGS2010

Predicted effects of climate warming on the distribution of 50 stream fishes in Wisconsin, U.S.A.

Summer air and stream water temperatures are expected to rise in the state of Wisconsin, U.S.A., over the next 50 years. To assess potential climate warming effects on stream fishes, predictive models were developed for 50 common fish species using classification-tree analysis of 69 environmental variables in a geographic information system. Model accuracy was 56·0–93·5% in validation tests. Models were applied to all 86 898 km of stream in the state under four different climate scenarios: current conditions, limited climate warming (summer air temperatures increase 1° C and water 0·8° C), moderate warming (air 3° C and water 2·4° C) and major warming (air 5° C and water 4° C). With climate warming, 23 fishes were predicted to decline in distribution (three to extirpation under the major warming scenario), 23 to increase and four to have no change. Overall, declining species lost substantially more stream length than increasing species gained. All three cold-water and 16 cool-water fishes and four of 31 warm-water fishes were predicted to decline, four warm-water fishes to remain the same and 23 warm-water fishes to increase in distribution. Species changes were predicted to be most dramatic in small streams in northern Wisconsin that currently have cold to cool summer water temperatures and are dominated by cold-water and cool-water fishes, and least in larger and warmer streams and rivers in southern Wisconsin that are currently dominated by warm-water fishes. Results of this study suggest that even small increases in summer air and water temperatures owing to climate warming will have major effects on the distribution of stream fishes in Wisconsin.

Wisconsin↗

USGS2010

Public review draft: A method for assessing carbon stocks, carbon sequestration, and greenhouse-gas fluxes in ecosystems of the United States under present conditions and future scenarios

The Energy Independence and Security Act of 2007 (EISA), Section 712, authorizes the U.S. Department of the Interior to develop a methodology and conduct an assessment of the Nation's ecosystems focusing on carbon stocks, carbon sequestration, and emissions of three greenhouse gases (GHGs): carbon dioxide, methane, and nitrous oxide. The major requirements include (1) an assessment of all ecosystems (terrestrial systems, such as forests, croplands, wetlands, shrub and grasslands; and aquatic ecosystems, such as rivers, lakes, and estuaries), (2) an estimation of annual potential capacities of ecosystems to increase carbon sequestration and reduce net GHG emissions in the context of mitigation strategies (including management and restoration activities), and (3) an evaluation of the effects of controlling processes, such as climate change, land use and land cover, and wildlfires. The purpose of this draft methodology for public review is to propose a technical plan to conduct the assessment. Within the methodology, the concepts of ecosystems, carbon pools, and GHG fluxes used for the assessment follow conventional definitions in use by major national and international assessment or inventory efforts. In order to estimate current ecosystem carbon stocks and GHG fluxes and to understand the potential capacity and effects of mitigation strategies, the method will use two time periods for the assessment: 2001 through 2010, which establishes a current ecosystem GHG baseline and will be used to validate the models; and 2011 through 2050, which will be used to assess future potential conditions based on a set of projected scenarios. The scenario framework is constructed using storylines of the Intergovernmental Panel on Climate Change (IPCC) Special Report Emission Scenarios (SRES), along with initial reference land-use and land-cover (LULC) and land-management scenarios. An additional three LULC and land-management mitigation scenarios will be constructed for each storyline to enhance carbon sequestration and reduce GHG fluxes in ecosystems. Input from regional experts and stakeholders will be solicited to construct realistic and meaningful scenarios. The methods for mapping the current LULC and ecosystem disturbances will require the extensive use of both remote-sensing data and in-situ (for example, forest inventory data) to capture and characterize landscape-change events. For future potential LULC and ecosystem disturbances, key drivers such as socioeconomic, policy, and climate assumptions will be used in addition to biophysical data. The product of these analyses will be a series of maps for each future year for each scenario. These annual maps will form the basis for estimating carbon storage and GHG emissions. For terrestrial ecosystems, carbon storage, carbon-sequestration capacities, and GHG emissions under the current and projected future conditions will be assessed using the LULC and ecosystem-disturbance estimates in map format with a spatially explicit biogeochemical ensemble modeling system that incorporates properties of management activities (such as tillage or harvesting) and properties of individual ecosystems (such as elevation, vegetation characteristics, and soil attributes). For aquatic ecosystems, carbon burial in sediments and GHG fluxes are functions of the current and projected future stream flow and sediment transports, and therefore will be assessed using empirical modeling methods. Validation and uncertainty analysis methods described in the methodology will follow established guidelines to assess the quality of the assessment results. The U.S. Environmental Protection Agency's Level II ecoregions map (which delineates 24 ecoregions for the Nation) will be the practical instrument for developing and delivering assessment results. Consequently, the ecoregion will be the reporting unit of the assessment because the mitigation scenarios, assessment results, validation, and uncertainty analysis will be

Open-File Report↗

USGS2010

A method for assessing carbon stocks, carbon sequestration, and greenhouse-gas fluxes in ecosystems of the United States under present conditions and future scenarios

he Energy Independence and Security Act of 2007 (EISA), Section 712, mandates the U.S. Department of the Interior to develop a methodology and conduct an assessment of the Nation’s ecosystems, focusing on carbon stocks, carbon sequestration, and emissions of three greenhouse gases (GHGs): carbon dioxide, methane, and nitrous oxide. The major requirements include (1) an assessment of all ecosystems (terrestrial systems, such as forests, croplands, wetlands, grasslands/shrublands; and aquatic ecosystems, such as rivers, lakes, and estuaries); (2) an estimate of the annual potential capacities of ecosystems to increase carbon sequestration and reduce net GHG emissions in the context of mitigation strategies (including management and restoration activities); and (3) an evaluation of the effects of controlling processes, such as climate change, land-use and land-cover change, and disturbances such as wildfires. The concepts of ecosystems, carbon pools, and GHG fluxes follow conventional definitions in use by major national and international assessment or inventory efforts. In order to estimate current ecosystem carbon stocks and GHG fluxes and to understand the potential capacity and effects of mitigation strategies, the method will use two time periods for the assessment: 2001 through 2010, which establishes a current ecosystem carbon and GHG baseline and will be used to validate the models; and 2011 through 2050, which will be used to assess potential capacities based on a set of scenarios. The scenario framework will be constructed using storylines of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES), along with both reference and enhanced land-use and land-cover (LULC) and land-management parameters. Additional LULC and land-management mitigation scenarios will be constructed for each storyline to increase carbon sequestration and reduce GHG fluxes in ecosystems. Input from regional experts and stakeholders will be solicited to construct these scenarios. The methods for mapping the current LULC and ecosystem disturbances will require the extensive use of both remote-sensing data and field-survey data (for example, forest inventories) to capture and characterize landscape-changing events. For potential LULC changes and ecosystem disturbances, key drivers such as socioeconomic and climate changes will be used in addition to the biophysical data. The result of these analyses will be a series of maps for each future year for each scenario. These annual maps will form the basis for estimating carbon storage and GHG emissions. For terrestrial ecosystems, carbon storage, carbon-sequestration capacities, and GHG emissions under the present conditions and future scenarios will be assessed using the LULC-change and ecosystem-disturbance estimates in map format with a spatially explicit biogeochemical ensemble modeling system that incorporates properties of management activities (such as tillage or harvesting) and properties of individual ecosystems (such as energy exchange, vegetation characteristics, hydrological cycling, and soil attributes). For aquatic ecosystems, carbon burial in sediments and fluxes of GHG are functions of the present and future potential stream flow and sediment transport and will be assessed using empirical hydrological modeling methods. Validation and uncertainty analysis methods described in the methodology will follow established guidelines to assess the quality of the assessment results. The U.S. Environmental Protection Agency’s Level II ecoregions map will be the practical instrument for developing and delivering assessment results. Consequently, the ecoregion (there are 22 modified ecoregions) will be the reporting unit of the assessment because the scenarios, assessment results, validation, and uncertainty analysis will be produced at that scale. The implementation of these methods will require collaborations among various Federal agencies, State agencies, nongovernmental organizations, and the science community. Using the method described in this document, the assessment can be completed in approximately 3 to 4 years. The primary deliverables will be assessment reports containing tables, charts, and maps that will present the estimated GHG parameters annually for 2001 through 2050 by ecosystem, pool, and scenario. The results will permit the evaluation of a range of policies, mitigation options, and research topics, such as the demographic, LULC-change, or climate-change effects on carbon stocks, carbon sequestration, and GHG fluxes in ecosystems.

Scientific Investigations Report↗

USGS2016

Characterization of mean transit time at large springs in the Upper Colorado River Basin, USA: A tool for assessing groundwater discharge vulnerability

Environmental tracers (noble gases, tritium, industrial gases, stable isotopes, and radio-carbon) and hydrogeology were interpreted to determine groundwater transit-time distribution and calculate mean transit time (MTT) with lumped parameter modeling at 19 large springs distributed throughout the Upper Colorado River Basin (UCRB), USA. The predictive value of the MTT to evaluate the pattern and timing of groundwater response to hydraulic stress (i.e., vulnerability) is examined by a statistical analysis of MTT, historical spring discharge records, and the Palmer Hydrological Drought Index. MTTs of the springs range from 10 to 15,000 years and 90 % of the cumulative discharge-weighted travel-time distribution falls within the range of 2−10,000 years. Historical variability in discharge was assessed as the ratio of 10–90 % flow-exceedance ( R 10/90% ) and ranged from 2.8 to 1.1 for select springs with available discharge data. The lag-time (i.e., delay in discharge response to drought conditions) was determined by cross-correlation analysis and ranged from 0.5 to 6 years for the same select springs. Springs with shorter MTTs (<80 years) statistically correlate with larger discharge variations and faster responses to drought, indicating MTT can be used for estimating the relative magnitude and timing of groundwater response. Results indicate that groundwater discharge to streams in the UCRB will likely respond on the order of years to climate variation and increasing groundwater withdrawals.

Upper Colorado River Basin↗

USGS2020

Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling

This study employed a novel combination of data (winter cover crop cost-share enrollment records, satellite remote sensing of wintertime vegetation, and results of Soil and Water Assessment Tool (SWAT) water quality simulations) to estimate the environmental performance of winter cover crops (WCC) at the watershed scale, from 2008 through 2017, within the Tuckahoe sub-watershed of the Choptank River. The Choptank is a river basin within the Chesapeake Bay watershed and, as a focus watershed for the U.S. Department of Agriculture’s Conservation Effects Assessment Project (CEAP), has been the subject of considerable study assessing linkages between land use and water quality. Farm enrollment data from the Maryland Agricultural Cost Share (MACS) program documented a strong increase in the use of WCC within the Tuckahoe watershed during the study period, from 27% of corn fields and 9% of soybean fields in 2008 to 89% of corn fields and 46% of soybean fields in 2016. Satellite remote sensing of wintertime ground cover detected increased wintertime vegetation following corn crops, in comparison to full season and double cropped soybean, consistent with patterns of cover crop implementation. Although inter-annual variation in climate strongly affected observed levels of vegetation, with warm winters resulting in increased vegetative cover, a 30-year analysis of wintertime greenness revealed significant increases in wintertime vegetation associated increased adoption of WCC. The predominant WCC species recorded by the MACS program as planted in the Tuckahoe watershed were wheat (68.1%), barley (16.1%), and rye (7.2%). The MACS WCC enrollment data were combined with output from the SWAT model, calibrated to streamflow and nutrient loading from the Tuckahoe watershed, to estimate water quality impacts based on known distribution of cover crop species and planting dates (2008 to 2017). Results indicated a 25% overall 10-year reduction in nitrate leaching from cropland resulting from cover crop adoption, rising to an estimated 38% load reduction in 2016 when 64% of fields were planted to cover crops. A large portion of WCC (39.3%) were planted late (after October 15) and planted to wheat (68.1%). Increased environmental benefits would be achieved by shifting agronomic methods away from late-planted wheat.

Delaware, Maryland↗

USGS1999

Environmental models of mineral deposits- A state of the art

Although mineral deposits have been classified by their geologic and mineralogical characteristics for decades, the recognition that mineral deposits also could be classified by their environmental characteristics is relatively new. In the past 5 years, numerous advancements have been made in this subject area, building on the earlier work of economic geologists who classified geologic characteristics. Several different approaches to understanding the environmental behavior of mineral deposits and associated altered areas have been taken, ranging from wholesale assessments of large areas (millions of km 2 ) to detailed assessments of individual watersheds or individual mines. While these first attempts have succeeded in describing some of the environmental characteristics of ore deposits in a number of ways, many important “environmental variables” are not included in present descriptive models. For example, the models should be expanded in scope to include a more thorough treatment of climatic and ecoregional effects- embodying such physical environmental characteristics as precipitation, evaporation, temperature, and ground water-surface water interactions. More complete model descriptions will have applications to the determination of baselines and natural backgrounds in mined and unmined areas, as well as possible anticipated effects of new mining in a given area, and mitigation and remediation strategies. The challenge to geologists and geochemists is to incorporate a widely disparate set of physical and chemical characteristics of mineralized and altered zones at scales ranging from microscopic (sub-millimeter) to macroscopic (10’s to 100’s of kilometers). This paper presents an overview of the development of mineral deposit environmental models, the current state of the art, an evaluation of needed improvements, and expected advancements in this field.

Geologica Hungarica. Series geologica↗

USGS2010

Seasonal groundwater contribution to crop-water use assessed with lysimeter observations and model simulations

Groundwater evaporation can play an important role in crop-water use where the water table is shallow. Lysimeters are often used to quantify the groundwater evaporation contribution influenced by a broad range of environmental factors. However, it is difficult for such field facilities, which are operated under limited conditions within limited time, to capture the whole spectrum of capillary upflow with regard to the inter-seasonal variability of climate, especially rainfall. Therefore, in this work, the method of combining lysimeter and numerical experiments was implemented to investigate seasonal groundwater contribution to crop-water use. Groundwater evaporation experiments were conducted through a weighing lysimeter at an agricultural experiment station located within an irrigation district in the lower Yellow River Basin for two winter wheat growth seasons. A HYDRUS-1D model was first calibrated and validated with weighing lysimeter data, and then was employed to perform scenario simulations of groundwater evaporation under different depths to water table (DTW) and water input (rainfall plus irrigation) driven by long term meteorological data. The scenario simulations revealed that the seasonally averaged groundwater evaporation amount was linearly correlated to water input for different values of DTW. The linear regression could explain more than 70% of the variability. The seasonally averaged ratio of the groundwater contribution to crop-water use varied with the seasonal water input and DTW. The ratio reached as high as 75% in the case of DTW=1.0. m and no irrigation, and as low as 3% in the case of DTW=3.0. m and three irrigation applications. The results also revealed that the ratio of seasonal groundwater evaporation to potential evapotranspiration could be fitted to an exponential function of the DTW that may be applied to estimate seasonal groundwater evaporation. In this case study of multilayered soil profile, the depth at which groundwater may evaporate at potential rate was 0.60-0.65. m, and the extinction depth of groundwater evaporation was approximately 3.8. m. ?? 2010 Elsevier B.V.

Journal of Hydrology↗

USGS2012

Representing the acquisition and use of energy by individuals in agent-based models of animal populations

1. Agent-based models (ABMs) are widely used to predict how populations respond to changing environments. As the availability of food varies in space and time, individuals should have their own energy budgets, but there is no consensus as to how these should be modelled. Here, we use knowledge of physiological ecology to identify major issues confronting the modeller and to make recommendations about how energy budgets for use in ABMs should be constructed. 2. Our proposal is that modelled animals forage as necessary to supply their energy needs for maintenance, growth and reproduction. If there is sufficient energy intake, an animal allocates the energy obtained in the order: maintenance, growth, reproduction, energy storage, until its energy stores reach an optimal level. If there is a shortfall, the priorities for maintenance and growth/reproduction remain the same until reserves fall to a critical threshold below which all are allocated to maintenance. Rates of ingestion and allocation depend on body mass and temperature. We make suggestions for how each of these processes should be modelled mathematically. 3. Mortality rates vary with body mass and temperature according to known relationships, and these can be used to obtain estimates of background mortality rate. 4. If parameter values cannot be obtained directly, then values may provisionally be obtained by parameter borrowing, pattern-oriented modelling, artificial evolution or from allometric equations. 5. The development of ABMs incorporating individual energy budgets is essential for realistic modelling of populations affected by food availability. Such ABMs are already being used to guide conservation planning of nature reserves and shell fisheries, to assess environmental impacts of building proposals including wind farms and highways and to assess the effects on nontarget organisms of chemicals for the control of agricultural pests.

Methods in Ecology and Evolution↗

USGS2014

Population viability and connectivity of the Louisiana black bear ( Ursus americanus luteolus )

In 1992, the U.S. Fish and Wildlife Service (USFWS) granted Ursus americanus luteolus (Louisiana black bear) threatened status under the U.S. Endangered Species Act of 1973, listing loss and fragmentation of habitat as the primary threats. A study was developed by the U.S. Geological Survey in cooperation with the University of Tennessee, the Louisiana Department of Wildlife and Fisheries, and the USFWS to estimate demographic rates and genetic structure of Louisiana black bear populations; evaluate relations between environmental and anthropogenic factors and demographic, genetic, and movement characteristics of Louisiana black bear populations; and develop data-driven stochastic population projection models to assess long-term persistence of individual subpopulations and the overall black bear population in Louisiana. Data were collected with non-invasive DNA sampling, live capture, winter den visits, and radio monitoring from 2002 to 2012 in the four areas supporting breeding subpopulations in Louisiana: Tensas River Basin (TRB), Upper Atchafalaya River Basin (UARB), Lower Atchafalaya River Basin (LARB), and Three Rivers Complex (TRC). Bears were live trapped and radio collared in the TRB and TRC to estimate survival and reproductive rates, deterministic matrix models were used to estimate asymptotic growth rates, and stochastic population models were used to estimate long-term viability. DNA extracted from hair collected at baited, barbed-wire enclosures in the TRB, UARB, and LARB and capture-mark-recapture (CMR) analysis based on Bayesian hierarchical modeling methods were used to estimate apparent survival ( φ ), per capita recruitment ( γ ), abundance ( N ), realized growth rate ( λ ), and long-term viability. From 2002 to 2012, we radio monitored 86 adult females greater than (>) 2 years old within the TRB, and 43 adult females were monitored in the TRC. The mean annual survival rate estimate ranged from 0.97 to 0.99 for the TRB and from 0.93 to 0.97 for the TRC. Fecundity and yearling recruitment in the TRB were 0.47 and 0.15, respectively, whereas estimates for the TRC were 0.37 and 0.18. Depending on estimated carrying capacity, the strength of the density dependence, level of uncertainty, and the treatment of unresolved signals, persistence probabilities for the TRC subpopulation ranged from 0.295 to 0.999. Estimates of N for females in the TRB ranged from 140 to 163 during 2006–12 when detection heterogeneity was assumed to follow a logistic-normal distribution (Model 1) and from 133 to 158 when a 2-point finite mixture distribution was assumed (Model 2). Annual estimates of γ ranged from 0.00 to 0.16 and from 0 to 0.22, depending on the model, and estimates of φ ranged from 0.87 to 0.93 during that period. In the UARB, estimates of N for females ranged from 25 to 44 during the study period, regardless of heterogeneity model. Estimated γ ranged from 0.00 to 0.41, and φ ranged from 0.88 to 0.90 during that period. Estimated N for females in the LARB was from 78 to 97 from 2010 to 2012 based on Model 1 and from 68 to 84 based on Model 2. Estimates of γ were 0.00 for 2010–11 regardless of heterogeneity model and ranged from 0.24 to 0.31 for 2011–12, depending on the model assumptions. We estimated φ as 0.81 for 2010–11, and from 0.84 to 0.85 for 2011–12, depending on model assumptions. We estimated Φ as 0.81 for 2010–11, ranging and from 0.84 to 0.85 for 2011–12, depending on model assumptions. On the basis of vital rate estimates from Model 1 of the CMR analysis, probability of persistence over 100 years for the TRB population was >0.999, 0.975, and 0.958 for process-only, 50-percent (%) credible interval (CI), and 95% CI projections, respectively. Similarly, the probability of persistence based on Model 2 was >0.999, 0.982, and 0.958. For the UARB, probabilities of persistence based on Model 1 were >0.999, 0.971, and 0.958 for process-only, 50% CI, and 95% CI projections, respectively, and 0.993, 0.929, and 0.849 for Model 2. Using the telemetry and reproductive data from the TRC, probabilities of persistence were greater than or equal to 0.95 only for projections based on the most optimistic set of assumptions. Assuming that the dynamics of the TRB, TRC, and UARB populations were independent and using the most pessimistic population-specific persistence probabilities (that is, 0.958, 0.295, and 0.849, respectively), the overall probability of persistence for bears in that population system was 0.996. Genetic methods were used to estimate interchange and structure between subpopulations in Louisiana and in Minnesota (MINN); Mississippi (MISS); and the White River Basin (WRB), Arkansas. Results from the all-population and the WRB–TRB clustering analyses indicate at least five genetically distinct populations. The genetic clustering and migrant analyses combined with capture data provided direct evidence that interchange has occurred from the WRB to the TRB and MISS, from the TRB to MISS, from the UARB to the TRC, and from the TRC to the TRB. Indirect evidence that interchange occurred from the UARB to the TRC and from the UARB to the TRB by way of the TRC was documented. No evidence was found of interchange from any of the subpopulations to the WRB, UARB, or LARB. From April 2010 to April 2012, global positioning system (GPS) radio collars were placed on 8 female and 23 male bears ranging from 1 to 11 years of age to develop a step-selection function model to predict routes and rates of interchange. For both males and females, the probability of a step being selected increased as the distance to natural land cover and agriculture at the end of the step decreased and as distance from roads at the end of a step increased. Of 4,000 correlated random walks, the least potential interchange was between TRB and TRC and between UARB and LARB, but the relative potential for natural interchange between UARB and TRC was high. The step-selection model predicted that dispersals between the LARB and UARB populations were infrequent but possible for males and nearly nonexistent for females. No evidence of natural female dispersal between subpopulations has been documented thus far, which is also consistent with model predictions.

Louisiana↗

USGS2002

Using Christmas Bird Count data in analysis of population change

The scientific credibility of Christmas Bird Count (CBC) results depend on the development and implementation of appropriate methods of statistical analysis. The key to any successful analysis of CBC data is to begin with a careful review of how the limitations of the data are likely to influence the results of the analysis, then to choose methods of analysis that accommodate as much as possible the limitations of the survey. For our analyses of CBC data, we develop a flexible model for effort adjustment and use information from the data to guide the selection of the best model. We include geographic structuring to accommodate the regional variation in number of samples, use a model that allows for overdispersed poisson data appropriate for counts, and employ empirical Bayes procedures to accommodate differences in quality of information in regional summaries. This generalized linear model approach is very flexible, and can be applied to a variety of studies focused on factors influencing wintering bird populations. In particular, the model can be easily modified to contain covariates, allowing for assessment of associations between CBC counts and winter weather, disturbance, and a variety of other environmental factors. These new survey analysis methods have added value in that they provide insights into changes in survey design that can enhance the value of the information. The CBC has been extremely successful as a tool for increasing public interest in birding and bird conservation. Use of the information for bird conservation creates new demands on quality of information, and it is important to maintain a dialogue between users of the information, information needs for the analyses, and survey coordinators and participants. Our work as survey analysts emphasizes the value and limitations of existing data, and provides some indications of what features of the survey could be modified to make the survey a more reliable source of bird population data. Surveys only remain useful if they adapt to current needs, while still maintaining consistency with historical goals.

American Birds↗

USGS2024

Climate change

Amphibian ecology and distribution are strongly correlated with climate. Regional patterns of amphibian biodiversity are intimately linked to temperature, evapotranspiration rate, and clines in humidity. While amphibians are and will continue to be adversely affected by recent and projected changes in climate, research suggests that adaptation may happen more slowly than the expected rate of environmental shifts. Here, we review conservation-relevant aspects of both realised and potential impacts of climate change, and outline options for amphibian conservation planning and management. Recent advances in our understanding of climate change impacts on amphibians have primarily stemmed from ecological modelling and direct assessment of climatic tolerances and dispersal capacities through physiological assays, landscape genetics, and dispersal tracking. Anthropogenic climate change has already altered amphibian assemblages and their impacts on ecosystem functioning and services. Because of known and hypothesised ecological tolerances, many amphibians might have reached or exceeded most limits in their ability to adapt to or tolerate further climate change, however the uncertainties are substantial. Implementation of conservation planning and action can help to forestall severe impacts of environmental shifts. Scientific research and science-based decision-making and policy development have already lagged; the current pace of conservation planning and action may not allow for effective identification of threats and mitigation. An increased response rate could help to avert further loss of amphibian biodiversity and decay of ecosystem services. The lack of basic field research in natural habitats continues to be an underlying challenge. We suggest priority areas of research to include the development of biologically realistic predictive models of amphibian response to climate change, field verification of model estimates and key parameters, population monitoring across multiple sites and taxa, and a combination of efforts within and across ecosystems to understand how impacts of climate change can be better mitigated.

Book chapter↗

USGS2020

Simulation of discharge, water-surface elevations, and water temperatures for the St. Louis River estuary, Minnesota-Wisconsin, 2016–17

The St. Louis River estuary is a large freshwater estuary, next to Duluth, Minnesota, that encompasses the headwaters of Lake Superior. The St. Louis River estuary is one of the most complex and compromised near-shore systems in the upper Great Lakes with a long history of environmental contamination caused by logging, mining, paper mills, and other heavy industrial activities. Presently (2020), a widely available, science-based assessment tool capable of evaluating ecosystem-level responses to remediation and restoration projects has not existed for the estuary. To address this need, the U.S. Geological Survey (USGS) built a predictive, mechanistic, three-dimensional hydrodynamic model for the estuary using the Environmental Fluid Dynamics Code framework. In the current version, the model can simulate continuous discharge, water-surface elevations, water temperature, and flow velocity, although the modular framework allows for future additions of water-quality modeling. The model was calibrated using data collected from April 2016 through November 2016 and validated with data collected from April 2017 through November 2017. The four types of data used to evaluate model performance were water-surface elevations, discharge, water temperature, and flow velocities. Streamflow and temperature boundary condition data included a mixture of USGS streamgage data, Minnesota Department of Natural Resources gage data, and estimates derived from the gage data. The model was able to simulate the water-surface elevations with generally good agreement between the simulated and measured values for both years at the daily time step. Specifically, the model was able to demonstrate excellent agreement with the measured data with Nash-Sutcliffe efficiency coefficients greater than 0.8 for all three locations; however, the model was unable to produce hourly water-surface elevations with such accuracy for 2016–17. Discharge was more dynamic than the water-surface elevations, both for the measured and simulated data. Generally, most of the discharge ranged from −650 to 1,200 cubic meters per second, but the constantly changing flux exiting the estuary into Lake Superior (positive flows) and entering the estuary from Lake Superior (negative flows) occurred throughout the year. Even upstream at the St. Louis River at Oliver, Wisconsin, gage (USGS station 0402403250), the effect of flows into the estuary from Lake Superior did occur, demonstrating the strong effect of the Lake Superior seiche on flows for the estuary. From a performance standpoint, the model was able to simulate discharge with generally good agreement in both years, although the 2017 validation was better than the 2016 calibration period. For the daily Nash-Sutcliffe efficiency coefficients, the simulated values were 0.98, 0.62, 0.49, and 0.71 for the Oliver gage; the Superior Bay entry channel at Superior, Wisc., (USGS station 464226092005600); the Superior Bay Duluth Ship Canal at Duluth, Minn., (USGS station 464646092052900); and total entries (combination of the Superior entry and Duluth entry), respectively. For the hourly evaluation criteria, the model performed poorly, with Nash-Sutcliffe efficiency coefficients less than 0 for the two entries into Lake Superior; therefore, as a predictor of discharge at the hourly scale, the model performed worse than using the measured data average. Similar to discharge, the model was a good predictor of flow velocity at the daily time scale but had difficulty matching the measured data at the hourly scale. For discharge and flow velocity, matching at subdaily time steps for a system as complicated as the St. Louis River estuary is considered difficult because the match is highly sensitive to coordinating the exact measurement location to the simulated value. The final calibration target was water temperature, calibrated for the Oliver gage and the Duluth entry. For calibration purposes, the Duluth entry was the more important water temperature target because the Oliver gage was more of an internal check on the model. The Nash-Sutcliffe efficiency coefficients for the Duluth entry were high; hourly Nash-Sutcliffe efficiency coefficients at the Duluth entry were either at or greater than 0.7 for both years, and daily values were 0.84 and 0.82 for 2016 and 2017, respectively.

Minnesota, Wisconsin↗

USGS2011

Building on crossvalidation for increasing the quality of geostatistical modeling

The random function is a mathematical model commonly used in the assessment of uncertainty associated with a spatially correlated attribute that has been partially sampled. There are multiple algorithms for modeling such random functions, all sharing the requirement of specifying various parameters that have critical influence on the results. The importance of finding ways to compare the methods and setting parameters to obtain results that better model uncertainty has increased as these algorithms have grown in number and complexity. Crossvalidation has been used in spatial statistics, mostly in kriging, for the analysis of mean square errors. An appeal of this approach is its ability to work with the same empirical sample available for running the algorithms. This paper goes beyond checking estimates by formulating a function sensitive to conditional bias. Under ideal conditions, such function turns into a straight line, which can be used as a reference for preparing measures of performance. Applied to kriging, deviations from the ideal line provide sensitivity to the semivariogram lacking in crossvalidation of kriging errors and are more sensitive to conditional bias than analyses of errors. In terms of stochastic simulation, in addition to finding better parameters, the deviations allow comparison of the realizations resulting from the applications of different methods. Examples show improvements of about 30% in the deviations and approximately 10% in the square root of mean square errors between reasonable starting modelling and the solutions according to the new criteria.

Stochastic Environmental Research and Risk Assessm↗