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At least 991 records · Page 55Linked to original sources
USGS2016

Bayesian cross-validation for model evaluation and selection, with application to the North American Breeding Bird Survey

The analysis of ecological data has changed in two important ways over the last 15 years. The development and easy availability of Bayesian computational methods has allowed and encouraged the fitting of complex hierarchical models. At the same time, there has been increasing emphasis on acknowledging and accounting for model uncertainty. Unfortunately, the ability to fit complex models has outstripped the development of tools for model selection and model evaluation: familiar model selection tools such as Akaike's information criterion and the deviance information criterion are widely known to be inadequate for hierarchical models. In addition, little attention has been paid to the evaluation of model adequacy in context of hierarchical modeling, i.e., to the evaluation of fit for a single model. In this paper, we describe Bayesian cross-validation, which provides tools for model selection and evaluation. We describe the Bayesian predictive information criterion and a Bayesian approximation to the BPIC known as the Watanabe-Akaike information criterion. We illustrate the use of these tools for model selection, and the use of Bayesian cross-validation as a tool for model evaluation, using three large data sets from the North American Breeding Bird Survey.

Ecology↗
USGS2023

Mapping landslide susceptibility over large regions with limited data

Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub-regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.

California, Kentucky, North Carolina, West Virgini↗
USGS2012

Scenarios of land use and land cover change in the conterminous United States: Utilizing the special report on emission scenarios at ecoregional scales

Global environmental change scenarios have typically provided projections of land use and land cover for a relatively small number of regions or using a relatively coarse resolution spatial grid, and for only a few major sectors. The coarseness of global projections, in both spatial and thematic dimensions, often limits their direct utility at scales useful for environmental management. This paper describes methods to downscale projections of land-use and land-cover change from the Intergovernmental Panel on Climate Change's Special Report on Emission Scenarios to ecological regions of the conterminous United States, using an integrated assessment model, land-use histories, and expert knowledge. Downscaled projections span a wide range of future potential conditions across sixteen land use/land cover sectors and 84 ecological regions, and are logically consistent with both historical measurements and SRES characteristics. Results appear to provide a credible solution for connecting regionalized projections of land use and land cover with existing downscaled climate scenarios, under a common set of scenario-based socioeconomic assumptions.

Global Environmental Change↗
USGS2019

Incorporating citizen science data in spatially explicit integrated population models

Information about population abundance, distribution, and demographic rates is critical for understanding a species’ ecology and for effective conservation and management. To collect data over large spatial and temporal extents for such inferences, especially for species with low densities or wide distributions, citizen science can be an efficient approach. Integrated models have also emerged as an important methodology to estimate population parameters by combining multiple types of data, including citizen science data. We developed a spatially explicit integrated model that combines opportunistically collected presence–absence (PA) data, commonly collected in citizen science efforts, with systematically collected spatial capture–recapture (SCR) data, which are often limited to small spatial and temporal extents. We conducted single and multi‐season simulations with parameters informed by North American black bear ( Ursus americanus ) populations, to evaluate the influence of varying amounts of opportunistic PA data collected at larger spatial and temporal extents on the estimation of population‐level parameters. Integrating opportunistic PA data increased the precision and accuracy of posterior estimates of abundance, and survival and recruitment rates. In some cases, adding PA locations improved abundance estimates more than increasing PA detection probability. Posterior estimates were as precise and unbiased as when higher quality, but sparse, SCR data were available. We also applied the integrated model to SCR and citizen science PA data collected on black bears in New York, with results consistent with our simulations. Our findings indicate that citizen science in integrated models can be a cost‐efficient way to improve estimates of population parameters and increase the spatiotemporal extent of inference. Continued developments with integrated models and citizen science data will offer additional ways to improve our understanding of population structure and demographics.

Ecology↗
USGS2017

Application of an unstructured 3D finite volume numerical model to flows and salinity dynamics in the San Francisco Bay-Delta

A linked modeling approach has been undertaken to understand the impacts of climate and infrastructure on aquatic ecology and water quality in the San Francisco Bay-Delta region. The Delft3D Flexible Mesh modeling suite is used in this effort for its 3D hydrodynamics, salinity, temperature and sediment dynamics, phytoplankton and water-quality coupling infrastructure, and linkage to a habitat suitability model. The hydrodynamic model component of the suite is D-Flow FM, a new 3D unstructured finite-volume model based on the Delft3D model. In this paper, D-Flow FM is applied to the San Francisco Bay-Delta to investigate tidal, seasonal and annual dynamics of water levels, river flows and salinity under historical environmental and infrastructural conditions. The model is driven by historical winds, tides, ocean salinity, and river flows, and includes federal, state, and local freshwater withdrawals, and regional gate and barrier operations. The model is calibrated over a 9-month period, and subsequently validated for water levels, flows, and 3D salinity dynamics over a 2 year period. Model performance was quantified using several model assessment metrics and visualized through target diagrams. These metrics indicate that the model accurately estimated water levels, flows, and salinity over wide-ranging tidal and fluvial conditions, and the model can be used to investigate detailed circulation and salinity patterns throughout the Bay-Delta. The hydrodynamics produced through this effort will be used to drive affiliated sediment, phytoplankton, and contaminant hindcast efforts and habitat suitability assessments for fish and bivalves. The modeling framework applied here will serve as a baseline to ultimately shed light on potential ecosystem change over the current century.

California↗
USGS2015

A predictive model to inform adaptive management of double-crested cormorants and fisheries in Michigan

The proliferation of double-crested cormorants (DCCOs; Phalacrocorax auritus ) in North America has raised concerns over their potential negative impacts on game, cultured and forage fishes, island and terrestrial resources, and other colonial water birds, leading to increased public demands to reduce their abundance. By combining fish surplus production and bird functional feeding response models, we developed a deterministic predictive model representing bird–fish interactions to inform an adaptive management process for the control of DCCOs in multiple colonies in Michigan. Comparisons of model predictions with observations of changes in DCCO numbers under management measures implemented from 2004 to 2012 suggested that our relatively simple model was able to accurately reconstruct past DCCO population dynamics. These comparisons helped discriminate among alternative parameterizations of demographic processes that were poorly known, especially site fidelity. Using sensitivity analysis, we also identified remaining critical uncertainties (mainly in the spatial distributions of fish vs. DCCO feeding areas) that can be used to prioritize future research and monitoring needs. Model forecasts suggested that continuation of existing control efforts would be sufficient to achieve long-term DCCO control targets in Michigan and that DCCO control may be necessary to achieve management goals for some DCCO-impacted fisheries in the state. Finally, our model can be extended by accounting for parametric or ecological uncertainty and including more complex assumptions on DCCO–fish interactions as part of the adaptive management process.

Natural Resource Modeling↗
USGS2021

Modeling effects of disturbance across life history strategies of stream fishes

A central goal of population ecology is to establish linkages between life history strategy, disturbance, and population dynamics. Globally, disturbance events such as drought and invasive species have dramatically impacted stream fish populations and contributed to sharp declines in freshwater biodiversity. Here, we used RAMAS-Metapop to construct stage-based demographic metapopulation models for stream fishes with periodic, opportunistic, and equilibrium life history strategies and assessed their responses to disturbance scenarios that approximated the effects of invasion, drought, and the additive effects of both disturbances. Our models indicated that populations respond differentially to disturbance based on life history strategy. Equilibrium strategists were best able to deal with simulated invasion. Periodic strategists did well under lower levels of drought, whereas opportunistic strategists outperformed other life histories under extreme seasonal drought. When we modeled additive effects scenarios, these disturbances interacted synergistically, dramatically increasing terminal extinction risk for all three life history strategies. Modeling exercises that examine broad life history categories can help to answer fundamental ecological questions about the relationship between disturbance resilience and life history, as well as help managers to develop generalized conservation strategies when species-specific data are lacking. Our results indicate that life history strategy is a fundamental determinant of population trajectories, and that disturbances can interact synergistically to dramatically impact extinction outcomes.

Oecologia↗
USGS2006

The fitting of general force-of-infection models to wildlife disease prevalence data

Researchers and wildlife managers increasingly find themselves in situations where they must deal with infectious wildlife diseases such as chronic wasting disease, brucellosis, tuberculosis, and West Nile virus. Managers are often charged with designing and implementing control strategies, and researchers often seek to determine factors that influence and control the disease process. All of these activities require the ability to measure some indication of a disease's foothold in a population and evaluate factors affecting that foothold. The most common type of data available to managers and researchers is apparent prevalence data. Apparent disease prevalence, the proportion of animals in a sample that are positive for the disease, might seem like a natural measure of disease's foothold, but several properties, in particular, its dependency on age structure and the biasing effects of disease-associated mortality, make it less than ideal. In quantitative epidemiology, the a??force of infection,a?? or infection hazard, is generally the preferred parameter for measuring a disease's foothold, and it can be viewed as the most appropriate way to a??adjusta?? apparent prevalence for age structure. The typical ecology curriculum includes little exposure to quantitative epidemiological concepts such as cumulative incidence, apparent prevalence, and the force of infection. The goal of this paper is to present these basic epidemiological concepts and resulting models in an ecological context and to illustrate how they can be applied to understand and address basic epidemiological questions. We demonstrate a practical approach to solving the heretofore intractable problem of fitting general force-of-infection models to wildlife prevalence data using a generalized regression approach. We apply the procedures to Mycobacterium bovis (bovine tuberculosis) prevalence in bison (Bison bison) in Wood Buffalo National Park, Canada, and demonstrate strong age dependency in the force of infection as well as an increased mortality hazard in positive animals.

Ecology↗
USGS2023

Integrated science strategy for assessing and monitoring water availability and migratory birds for terminal lakes across the Great Basin, United States

Executive Summary In 2022, the U.S. Geological Survey (USGS) established the Saline Lake Ecosystems Integrated Water Availability Assessment (IWAAs) to monitor and assess the hydrology of terminal lakes in the Great Basin and the migratory birds and other wildlife dependent on those habitats. Scientists from across the USGS (with specialties in water quantity, water quality, limnology, avian biology, data science, landscape ecology, and science communication) formed the Saline Lake Ecosystems IWAAs Team. The team has developed this regional strategic science plan to guide data collection and assessment activities at terminal lakes in the Great Basin. The U.S. Congress requested the USGS to establish the Saline Lake Ecosystems IWAAs in response to historically low water levels at terminal lakes and associated wetlands across the Great Basin. Not all Great Basin terminal lakes have high salinity; however, all terminal lakes occur in endorheic, closed, basins with no surface-water outflow. Low lake levels across the Great Basin are the result of increased water use for agriculture and municipalities, drought conditions, and a warming climate. Great Basin terminal lake water extents have decreased by as much as 90 percent over the last 150 years, and terminal lake wetlands have decreased in area by as much as 47 percent since 1984. Lake elevations and wetland areas are primarily supported by freshwater inputs from snowmelt feeding upgradient rivers, streams, and springs. These freshwater inputs have been severely reduced because of continued and increased surface-water diversions and surface-water capture through groundwater pumping for agriculture, mining, and public supply as well as unprecedented drought conditions and warming temperatures related to climate change. Water quality, specifically salinity, is highly variable for terminal lakes of the Great Basin, and this variability is a result of the balance between freshwater inflow and evaporation. Variability of salinity at each of the terminal lakes can be affected by lake morphology, hydrogeologic features of the basin, annual variability in weather patterns, and changes in upgradient water use. Hypersaline terminal lakes provide abundant food resources such as brine shrimp and brine flies that support nesting and migrating birds. The density and composition of invertebrates are closely tied to lake salinity. Increased salinity can exceed the tolerance of invertebrates, severely limiting their biomass. In contrast, decreased salinity can lead to altered invertebrate community composition, reducing the abundance of optimal avian prey resources. Great Basin terminal lake ecosystems, including open-water and adjacent aquatic and terrestrial environments, provide resources necessary to sustain many animal populations throughout the year. Although a variety of taxa use terminal lakes, these ecosystems are of acute importance for the millions of migratory waterbirds (for example, shorebirds, wading birds, and waterfowl) dependent on the network of terminal lakes and their associated wetlands. Migratory birds transiting the Pacific and Central Flyways use Great Basin terminal lake ecosystems throughout the year to feed, nest, and transit between wintering and breeding ranges. As such, successful conservation of birds and their habitats requires coordinated management of water and habitats across the Great Basin network of terminal lakes and wetlands. The linkages between water availability and ecosystem vulnerability of terminal lakes in the Great Basin are not well understood. The vulnerability of terminal lakes is related to the factors driving change and adaptive capacity of the lake ecosystem. Saline lake ecosystems are vulnerable when changes in water quantity affect ecosystem function. Water quantity affects salinity, which affects food webs and habitat; these linkages can be investigated with water-quality and food web monitoring. Water quantity also affects inundated habitat, which can be quantified through remote sensing. It is necessary to quantify hydroclimatic and water use controls on water availability to terminal lakes to assess the response of the ecosystems. Remotely sensed data can provide a broad-scale and long-term synoptic view of terminal lake hydrologic characteristics, but ground observations are required to interpret changes in water quality and ecological functions. Some terminal lake basins have ongoing monitoring and modeling efforts within the Great Basin (for example, Great Salt Lake, Carson River Basin), yet most monitoring locations are hydrologically upgradient and too far away from lake inflows to provide an accurate assessment of hydrological trends for the lake ecosystems. Other terminal lakes have no long-term hydrological monitoring in their respective watersheds (for example, Lake Abert). Ecological data collection in the Great Basin is also insufficient to understand how many birds exist on the landscape, how birds use the mosaic of terminal-lake habitats as an interconnected system, and how Great Basin terminal lakes are linked to the larger continental system of the Pacific and Central Flyways. Across agencies and organizations, tracking bird movement, abundance, and diversity is inconsistent, with some lakes having once- or twice-a-year bird survey efforts and a few locations having more intensive ecological data-gathering efforts (for example, Great Salt Lake, Lake Abert). Bridging hydrological and ecological information gaps will improve understanding of the trends in water supply and water quality, habitat availability and usage, and impacts on vulnerable waterbird species, all of which would be used by managers in coordinated conservation of this unique network of terminal-lake habitats. The terminal lakes of the Great Basin are part of the Basin and Range physiographic province that extends from the Colorado Plateau on the east to the Sierra Nevada on the west, and from the Snake River Plain on the north to the Garlock fault and the Mojave block on the south. The Great Basin is larger than 650,000 square kilometers and encompasses most of the State of Nevada but also extends to western Utah, eastern California, southeastern Idaho, southwestern Wyoming, and southeastern Oregon. The climate is arid to semiarid with a hydrologic regime that is snowmelt dominated, providing as much as 75 percent of total annual runoff for the region. Terminal lakes of the Great Basin occupy the lowest areas of closed (endorheic) drainage basins, such that lake levels and water quality respond rapidly to surface-water inflow. Terminal lakes provide local and regional economic value to the States in the Great Basin, including mineral extraction, aquaculture, public works, and recreational uses. As an example, assessments of Great Salt Lake’s ecological health and economic impact find hemispheric importance for the former and regional importance for the latter. Great Salt Lake creates about 7,000 jobs and $2 billion of economic output per year, most of which would be lost with further declines in lake level. The objectives of this Science Strategy are threefold: (1) to identify how changing water availability affects the quality, diversity, and abundance of habitats supporting continental waterbird populations; (2) to highlight the scientific monitoring and assessment needs of Great Basin terminal lakes; and (3) to support coordinated management and conservation actions to benefit those ecosystems, migratory birds, and other wildlife. There are long-term hydrological, ecological, and societal challenges associated with terminal lakes ecosystems in the Great Basin. This Science Strategy benefits partners by providing a conceptual model, nested at different spatial extents, that identifies key scientific information needs to inform coordinated implementation of management and conservation plans within and among hydrologic basins to address these complex challenges.

California, Idaho, Nevada, Oregon, Utah, Wyoming↗
USGS2012

Linking urbanization to the Biological Condition Gradient (BCG) for stream ecosystems in the Northeastern United States using a Bayesian network approach

Urban development alters important physical, chemical, and biological processes that define urban stream ecosystems. An approach was developed for quantifying the effects of these processes on aquatic biota, and then linking those effects to endpoints that can be used for environmental management. These complex, interacting systems are challenging to model from a scientific standpoint. A desirable model clearly shows the system, simulates the interactions, and ultimately predicts results of management actions. Traditional regression techniques that calculate empirical relations between pairs of environmental factors do not capture the interconnected web of multiple stressors, but urban development effects are not yet understood at the detailed scales required to make mechanistic modeling approaches feasible. Therefore, in contrast to a fully deterministic or fully statistical modeling approach, a Bayesian network model provides a hybrid approach that can be used to represent known general associations between variables while acknowledging uncertainty in predicted outcomes. It does so by quantifying an expert-elicited network of probabilistic relations between variables. Advantages of this modeling approach include (1) flexibility in accommodating many model specifications and information types; (2) efficiency in storing and manipulating complex information, and to parameterize; and (3) transparency in describing the relations using nodes and arrows and in describing uncertainties with discrete probability distributions for each variable. In realization of the aforementioned advantages, a Bayesian network model was constructed to characterize the effect of urban development on aquatic macroinvertebrate stream communities through three simultaneous, interacting ecological pathways affecting stream hydrology, habitat, and water quality across watersheds in the Northeastern United States. This model incorporates both empirical data and expert knowledge to calculate the probabilities of attaining desired aquatic ecosystem conditions under different urban stress levels, environmental conditions, and management options. Ecosystem conditions are characterized in terms of standardized Biological Condition Gradient (BCG) management endpoints. This approach to evaluating urban development-induced perturbations in watersheds integrates statistical and mechanistic perspectives, different information sources, and several ecological processes into a comprehensive description of the system that can be used to support decision making. The completed model can be used to infer which management actions would lead to the highest likelihood of desired BCG tier achievement. For example, if best management practices (BMP) were implemented in a highly urbanized watershed to reduce flashiness to medium levels and specific conductance to low levels, the stream would have a 70-percent chance of achieving BCG Tier 3 or better, relative to a 24-percent achievement likelihood for unmanaged high urban land cover. Results are reported probabilistically to account for modeling uncertainty that is inherent in sources such as natural variability and model simplification error.

Scientific Investigations Report↗
USGS1988

Guidance for modeling causes and effects in environmental problem solving

Environmental problems are difficult to solve because their causes and effects are not easily understood. When attempts are made to analyze causes and effects, the principal challenge is organization of information into a framework that is logical, technically defensible, and easy to understand and communicate. When decisionmakers attempt to solve complex problems before an adequate cause and effect analysis is performed there are serious risks. These risks include: greater reliance on subjective reasoning, lessened chance for scoping an effective problem solving approach, impaired recognition of the need for supplemental information to attain understanding, increased chance for making unsound decisions, and lessened chance for gaining approval and financial support for a program/ Cause and effect relationships can be modeled. This type of modeling has been applied to various environmental problems, including cumulative impact assessment (Dames and Moore 1981; Meehan and Weber 1985; Williamson et al. 1987; Raley et al. 1988) and evaluation of effects of quarrying (Sheate 1986). This guidance for field users was written because of the current interest in documenting cause-effect logic as a part of ecological problem solving. Principal literature sources relating to the modeling approach are: Riggs and Inouye (1975a, b), Erickson (1981), and United States Office of Personnel Management (1986).

Report↗
USGS2018

Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements

Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground‐based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi‐state mark–recapture methods using a hierarchical N ‐mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state‐space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk ( Cervus elaphus nelsoni ) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N‐mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.

Ecological Applications↗
USGS2000

The Kyoto protocol and payments for tropical forest: An interdisciplinary method for estimating carbon-offset supply and increasing the feasibility of a carbon market under the CDM

Protecting tropical forests under the Clean Development Mechanism (CDM) could reduce the cost of emissions limitations set in Kyoto. However, while society must soon decide whether or not to use tropical forest-based offsets, evidence regarding tropical carbon sinks is sparse. This paper presents a general method for constructing an integrated model (based on detailed historical, remote sensing and field data) that can produce land-use and carbon baselines, predict carbon sequestration supply to a carbon-offsets market and also help to evaluate optimal market rules. Creating such integrated models requires close collaboration between social and natural scientists. Our project combines varied disciplinary expertise (in economics, ecology and geography) with local knowledge in order to create high-quality, empirically grounded, integrated models for Costa Rica.

Ecological Economics↗
USGS2017

Greater sage-grouse population trends across Wyoming

The scale at which analyses are performed can have an effect on model results and often one scale does not accurately describe the ecological phenomena of interest (e.g., population trends) for wide-ranging species: yet, most ecological studies are performed at a single, arbitrary scale. To best determine local and regional trends for greater sage-grouse ( Centrocercus urophasianus ) in Wyoming, USA, we modeled density-independent and -dependent population growth across multiple spatial scales relevant to management and conservation (Core Areas [habitat encompassing approximately 83% of the sage-grouse population on ∼24% of surface area in Wyoming], local Working Groups [7 regional areas for which groups of local experts are tasked with implementing Wyoming's statewide sage-grouse conservation plan at the local level], Core Area status (Core Area vs. Non-Core Area) by Working Groups, and Core Areas by Working Groups). Our goal was to determine the influence of fine-scale population trends (Core Areas) on larger-scale populations (Working Group Areas). We modeled the natural log of change in population size ( peak M lek counts) by time to calculate the finite rate of population growth ( λ ) for each population of interest from 1993 to 2015. We found that in general when Core Area status (Core Area vs. Non-Core Area) was investigated by Working Group Area, the 2 populations trended similarly and agreed with the overall trend of the Working Group Area. However, at the finer scale where Core Areas were analyzed separately, Core Areas within the same Working Group Area often trended differently and a few large Core Areas could influence the overall Working Group Area trend and mask trends occurring in smaller Core Areas. Relatively close fine-scale populations of sage-grouse can trend differently, indicating that large-scale trends may not accurately depict what is occurring across the landscape (e.g., local effects of gas and oil fields may be masked by increasing larger populations).

Wyoming↗
USGS2002

Analysis and Management of Animal Populations: Modeling, Estimation and Decision Making

This book deals with the processes involved in making informed decisions about the management of animal populations. It covers the modeling of population responses to management actions, the estimation of quantities needed in the modeling effort, and the application of these estimates and models to the development of sound management decisions. The book synthesizes and integrates in a single volume the methods associated with these themes, as they apply to ecological assessment and conservation of animal populations. KEY FEATURES * Integrates population modeling, parameter estimation and * decision-theoretic approaches to management in a single, cohesive framework * Provides authoritative, state-of-the-art descriptions of quantitative * approaches to modeling, estimation and decision-making * Emphasizes the role of mathematical modeling in the conduct of science * and management * Utilizes a unifying biological context, consistent mathematical notation, * and numerous biological examples

Book↗
USGS2021

Extensibility of U-net neural network model for hydrographic feature extraction and implications for hydrologic modeling

Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically extract surface water features from airborne interferometric synthetic aperture radar (IfSAR) data to update and validate Alaska hydrographic databases. U-net artificial neural networks (ANN) and high-performance computing (HPC) are used for supervised hydrographic feature extraction within a study area comprised of 50 contiguous watersheds in Alaska. Surface water features derived from elevation through automated flow-routing and manual editing are used as training data. Model extensibility is tested with a series of 16 U-net models trained with increasing percentages of the study area, from about 3 to 35 percent. Hydrography is predicted by each of the models for all watersheds not used in training. Input raster layers are derived from digital terrain models, digital surface models, and intensity images from the IfSAR data. Results indicate about 15 percent of the study area is required to optimally train the ANN to extract hydrography when F1-scores for tested watersheds average between 66 and 68. Little benefit is gained by training beyond 15 percent of the study area. Fully connected hydrographic networks are generated for the U-net predictions using a novel approach that constrains a D-8 flow-routing approach to follow U-net predictions. This work demonstrates the ability of deep learning to derive surface water feature maps from complex terrain over a broad area.

Alaska↗
USGS2010

Two-dimensional hydrodynamic modeling to quantify effects of peak-flow management on channel morphology and salmon-spawning habitat in the Cedar River, Washington

The Cedar River in Washington State originates on the western slope of the Cascade Range and provides the City of Seattle with most of its drinking water, while also supporting a productive salmon habitat. Water-resource managers require detailed information on how best to manage high-flow releases from Chester Morse Lake, a large reservoir on the Cedar River, during periods of heavy precipitation to minimize flooding, while mitigating negative effects on fish populations. Instream flow-management practices include provisions for adaptive management to promote and maintain healthy aquatic habitat in the river system. The current study is designed to understand the linkages between peak flow characteristics, geomorphic processes, riverine habitat, and biological responses. Specifically, two-dimensional hydrodynamic modeling is used to simulate and quantify the effects of the peak-flow magnitude, duration, and frequency on the channel morphology and salmon-spawning habitat. Two study reaches, representative of the typical geomorphic and ecologic characteristics of the Cedar River, were selected for the modeling. Detailed bathymetric data, collected with a real-time kinematic global positioning system and an acoustic Doppler current profiler, were combined with a LiDAR-derived digital elevation model in the overbank area to develop a computational mesh. The model is used to simulate water velocity, benthic shear stress, flood inundation, and morphologic changes in the gravel-bedded river under the current and alternative flood-release strategies. Simulations of morphologic change and salmon-redd scour by floods of differing magnitude and duration enable water-resource managers to incorporate model simulation results into adaptive management of peak flows in the Cedar River. PDF version of a presentation on hydrodynamic modelling in the Cedar River in Washington state. Presented at the American Geophysical Union Fall Meeting 2010.

Washington↗
USGS2022

Forecasting species distributions: Correlation does not equal causation

Aim Identifying the mechanisms influencing species' distributions is critical for accurate climate change forecasts. However, current approaches are limited by correlative models that cannot distinguish between direct and indirect effects. Location New Hampshire and Vermont, USA. Methods Using causal and correlational models and new theory on range limits, we compared current (2014–2019) and future (2080s) distributions of ecologically important mammalian carnivores and competitors along range limits in the northeastern US under two global climate models (GCMs) and a high-emission scenario (RCP8.5) of projected snow and forest biomass change. Results Our hypothesis that causal models of climate-mediated competition would result in different distribution predictions than correlational models, both in the current and future periods, was well-supported by our results; however, these patterns were prominent only for species pairs that exhibited strong interactions. The causal model predicted the current distribution of Canada lynx ( Lynx canadensis ) more accurately, likely because it incorporated the influence of competitive interactions mediated by snow with the closely related bobcat ( Lynx rufus ). Both modeling frameworks predicted an overall decline in lynx occurrence in the central high-elevation regions and increased occurrence in the northeastern region in the 2080s due to changes in land use that provided optimal habitat. However, these losses and gains were less substantial in the causal model due to the inclusion of an indirect buffering effect of snow on lynx. Main conclusions Our comparative analysis indicates that a causal framework, steeped in ecological theory, can be used to generate spatially explicit predictions of species distributions. This approach can be used to disentangle correlated predictors that have previously hampered understanding of range limits and species' response to climate change.

New Hampshire, Vermont↗