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DOE OSTI2024

Cosmic neutrino decoupling and its observable imprints: insights from entropic-dual transport

Abstract Very different processes characterize the decoupling of neutrinos to form the cosmic neutrino background (CνB) and the much later decoupling of photons from thermal equilibrium to form the cosmic microwave background (CMB). The CνB emerges from the fuzzy, energy-dependent neutrinosphere and encodes the physics operating in the early universe in the temperature rangeT∼ 10 MeV toT∼ 10 keV. This is the epoch where beyond Standard Model (BSM) physics, especially in the neutrino sector, may be influential in setting the light element abundances, the necessarily distorted fossil neutrino energy spectra, and other light particle energy density contributions. Here we use techniques honed in extensive CMB studies to analyze the CνB as calculated in detailed neutrino energy transport and nuclear reaction simulations of the protracted weak decoupling and primordial nucleosynthesis epochs. Our moment method, relative entropy, and differential visibility approach can leverage future high precision CMB and light element primordial abundance measurements to provide new insights into the CνB and any BSM physics it encodes. We demonstrate that the evolution of the energy spectrum of the CνB throughout the weak decoupling epoch is accurately captured in the Standard Model by only three parameters per species, a non-trivial conclusion given the deviation from thermal equilibrium and the impact of the decrease of electron-positron pairs. Furthermore, we can interpret each of the three parameters as physical characteristics of a non-equilibrium system. Though the treatment presented here makes some simplifying assumptions including ignoring neutrino flavor oscillations, the success of our compact description within the Standard Model motivates its use also in BSM scenarios. We further demonstrate how observations of primordial light element abundances can be used to place constraints on the CνB energy spectrum, deriving response functions that can be applied for general deviations from a thermal spectrum. Combined with the description of those deviations that we develop here, our methods provide a convenient and powerful framework to constrain the impact of BSM physics on the CνB.

Astronomy & Astrophysics↗

DOE OSTI2020

Radiolytic and Thermolytic Bubble Gas Hydrogen Composition

The original version of this report described the development of a mathematical model for the estimation of the hydrogen composition of gas bubbles trapped in radioactive waste. The model described therein used a material balance approach to accurately incorporate the rates of hydrogen generation by several physical phenomena and scale the aforementioned rates in a manner that allows calculation of the final hydrogen composition. The proposed model accounted for the following physical phenomena: H 2 generation by primary radiolysis of water and salt solutions; H 2 generation by secondary radiolysis of formate and glycolate molecules in solution; Observation of negligible H 2 generation by thermolysis of formate at temperatures below 120 °C; H 2 generation by thermolysis of glycolate in caustic solutions; O 2 consumption by radiolysis and thermolysis of organic species. Additionally, the originally-proposed model conservatively excluded the following physical phenomena that are known to contribute slightly to the hydrogen composition in trapped gas bubbles: Equilibrium concentration of water vapor in trapped gas bubbles; Generation of N 2 O during radiolysis of nitrite solutions; Incomplete consumption of O 2 by organics. In this revision, the methodology for glycolate thermolysis has been improved to agree with recent findings published by Woodham and Martino. Furthermore, considerations for the thermolysis of organics other than glycolate have been made. In the case of non-glycolate organics, no evidence of non-flammable gas formation exists. Therefore, non-glycolate thermolysis is considered additive for conservatism.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1998

Use of an Existing Airborne Radon Data Base in the Verification of the NASA/AEAP Core Model

The primary objective of this project was to apply the tropospheric atmospheric radon (Rn222) measurements to the development and verification of the global 3-D atmospheric chemical transport model under development by NASA's Atmospheric Effects of Aviation Project (AEAP). The AEAP project had two principal components: (1) a modeling effort, whose goal was to create, test and apply an elaborate three-dimensional atmospheric chemical transport model (the NASA/AEAP Core model to an evaluation of the possible short and long-term effects of aircraft emissions on atmospheric chemistry and climate--and (2) a measurement effort, whose goal was to obtain a focused set of atmospheric measurements that would provide some of the observational data used in the modeling effort. My activity in this project was confined to the first of these components. Both atmospheric transport and atmospheric chemical reactions (as well the input and removal of chemical species) are accounted for in the NASA/AEAP Core model. Thus, for example, in assessing the effect of aircraft effluents on the chemistry of a given region of the upper troposphere, the model must keep track not only of the chemical reactions of the effluent species emitted by aircraft flying in this region, but also of the transport into the region of these (and other) species from other, remote sources--for example, via the vertical convection of boundary layer air to the upper troposphere. Radon, because of its known surface source and known radioactive half-life, and freedom from chemical production or loss, and from removal from the atmosphere by physical scavenging, is a recognized and valuable tool for testing the transport components of global transport and circulation models.

Kritz, Mark A.↗

NASA NTRS2003

Quantitative Species Measurements In Microgravity Combustion Flames

The capability of models and theories to accurately predict and describe the behavior of low gravity flames can only be verified by quantitative measurements. Although video imaging, simple temperature measurements, and velocimetry methods have provided useful information in many cases, there is still a need for quantitative species measurements. Over the past decade, we have been developing high sensitivity optical absorption techniques to permit in situ, non-intrusive, absolute concentration measurements for both major and minor flames species using diode lasers. This work has helped to establish wavelength modulation spectroscopy (WMS) as an important method for species detection within the restrictions of microgravity-based measurements. More recently, in collaboration with Prof. Dahm at the University of Michigan, a new methodology combining computed flame libraries with a single experimental measurement has allowed us to determine the concentration profiles for all species in a flame. This method, termed ITAC (Iterative Temperature with Assumed Chemistry) was demonstrated for a simple laminar nonpremixed methane-air flame at both 1-g and at 0-g in a vortex ring flame. In this paper, we report additional normal and microgravity experiments which further confirm the usefulness of this approach. We also present the development of a new type of laser. This is an external cavity diode laser (ECDL) which has the unique capability of high frequency modulation as well as a very wide tuning range. This will permit the detection of multiple species with one laser while using WMS detection.

Chen, Shin-Juh↗

NASA NTRSDate not supplied

Expanding SPoRT RGBs and Machine Learning Techniques to Enhance Air Quality Monitoring in Southern Asia

Air pollution poses significant environmental, public health, and societal concerns in the Hindu Kush Himalaya (HKH) region of south-central Asia, notably during the dry monsoon months (~November to May). Key contributors to poor air quality include dust from the Middle East and western India, persistent nocturnal fog/smog, and biomass burning. To address this issue, we established a robust air quality and chemistry observation and modeling product suite utilizing multi-spectral red-green-blue (RGB) composite satellite products from Korea’s GEO-KOMPSAT-2A satellite, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model for dust transport forecasts, and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model to predict aerosols and chemical species concentrations. Our team employed similar RGB recipes transitioned by the NASA Short-term Prediction Research and Transition (SPoRT) Center for the GOES-R era products over the Western Hemisphere, with significant success in depicting dust and nocturnal fog / low clouds, and to a lesser extent smoke and fire hot spots. We will extend these capabilities for the HKH region by applying an artificial intelligence (AI) model that objectively identifies dust from multi-spectral satellite data over the Southwestern United States. The AI model will be calibrated for automated dust detection over HKH from GEO-KOMPSAT-2A satellite data, with a goal of developing a similar AI model for objectively identifying smoke as well. The ultimate goal of this effort is to enhance dust and smoke predictions in the region by establishing improved emission initializations in HYSPLIT and/or WRF-Chem through the automated AI detection of dust and smoke.

Jonathan L. Case↗

NASA NTRS1981

The Voyager 1 EUV spectrum of the Io plasma torus

The Voyager 1 EUV spectrum of the hot Io plasma torus obtained before encounter in 1979 at a range of 4,800,000 km is analyzed in terms of a collisional model with improved collision strengths. Model calculations are based entirely on steady-state equilibrium between electron excitation-deactivation and radiative decay in the subspecies structure, and plasma temperature and species number densities are derived by fitting model spectra to the observations. Lines of S II, S III, S IV, O II, and O III are found to be dominant in the plasma torus, at torus half width densities of 44, 160, 220, 50, and 340/cu cm, respectively, and lines of S V and K III may also be present. An effective electron temperature of 80,000 K is estimated for the central dense region near 6 Jupiter radii. Model calculations are in substantial agreement with other spectroscopic observations of the hot torus; however, the observations exhibit strong morphological differences with recent ground-based observations of S II emission, and calculated ion densities differ from in situ Voyager 1 plasma science measurements.

Shemansky, D. E.↗

DOE OSTI2023

A Thermodynamic Model for Nd(III)–Sulfate Interaction at High Ionic Strengths and Elevated Temperatures: Applications to Rare Earth Element Extraction

Neodymium (Nd), a rare earth element (REE), is critical to numerous industries. Neodymium can be extracted from ore concentrates, waste materials, or recycled materials such as recycled Nd-Fe-B permanent magnets. In a standard process, concentrated sulfuric acid (H 2 SO 4 ) is used as an extraction/leaching agent. Therefore, knowledge of Nd(III)–sulfate interaction at high ionic strengths is important for optimization of the extraction process. In addition, sulfate is also a major species in natural surface waters and present in nuclear waste streams. Nd(III) has been used a chemical analog to trivalent actinides in nuclear waste research and development. Consequently, knowledge of Nd(III)-sulfate interactions is also impactful to the field of nuclear waste management. Here, in this study, we have developed a thermodynamic model that can describe the interaction of Nd(III) with sulfate to ionic strengths up to ~ 16.5 mol·kg –1 and to temperatures up to 100 °C. The model adopts the Pitzer formulation to describe activity coefficients of aqueous species. This model can be used to design and optimize a chemical process for REE recovery from ore concentrates, recycled materials, and acid mine drainage (AMD) and to understand the mobility of REEs and actinides in the environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Genome-wide identification and diversity of FAD2, FAD3 and FAE1 genes in terms of biotechnological importance in Camelina species

False flax, or gold-of-pleasure (Camelina sativa) is an oilseed that has received renewed research interest as a promising vegetable oil feedstock for liquid biofuel production and other non-food uses. This species has also emerged as a model for oilseed biotechnology research that aims to enhance seed oil content and fatty acid quality. To date, a number of genetic engineering and gene editing studies on C. sativa have been reported. Among the most common targets for this research are genes, encoding fatty acid desaturases, elongases, and diacylglycerol acyltransferases. However, the majority of these genes in C. sativa are present in multiple copies due to the allohexaploid nature of the species. Therefore, genetic manipulations require a comprehensive understanding of the diversity of such gene targets.

09 BIOMASS FUELS↗

NASA NTRS2008

Simplified Modeling of Oxidation of Hydrocarbons

A method of simplified computational modeling of oxidation of hydrocarbons is undergoing development. This is one of several developments needed to enable accurate computational simulation of turbulent, chemically reacting flows. At present, accurate computational simulation of such flows is difficult or impossible in most cases because (1) the numbers of grid points needed for adequate spatial resolution of turbulent flows in realistically complex geometries are beyond the capabilities of typical supercomputers now in use and (2) the combustion of typical hydrocarbons proceeds through decomposition into hundreds of molecular species interacting through thousands of reactions. Hence, the combination of detailed reaction- rate models with the fundamental flow equations yields flow models that are computationally prohibitive. Hence, further, a reduction of at least an order of magnitude in the dimension of reaction kinetics is one of the prerequisites for feasibility of computational simulation of turbulent, chemically reacting flows. In the present method of simplified modeling, all molecular species involved in the oxidation of hydrocarbons are classified as either light or heavy; heavy molecules are those having 3 or more carbon atoms. The light molecules are not subject to meaningful decomposition, and the heavy molecules are considered to decompose into only 13 specified constituent radicals, a few of which are listed in the table. One constructs a reduced-order model, suitable for use in estimating the release of heat and the evolution of temperature in combustion, from a base comprising the 13 constituent radicals plus a total of 26 other species that include the light molecules and related light free radicals. Then rather than following all possible species through their reaction coordinates, one follows only the reduced set of reaction coordinates of the base. The behavior of the base was examined in test computational simulations of the combustion of heptane in a stirred reactor at various initial pressures ranging from 0.1 to 6 MPa. Most of the simulations were performed for stoichiometric mixtures; some were performed for fuel/oxygen mole ratios of 1/2 and 2.

Bellan, Josette↗

NASA NTRS1992

Microflare heating of the galactic halo

Recent estimates of the strength of the random component of the galactic magnetic field suggest that magnetic reconnection is probably important in heating the galactic halo. A microflare model of impulsive energy release has been proposed for the solar corona by Sturrock and collaborators. When the solar microflare model is scaled to a heating rate and pressure appropriate to the galactic halo, it predicts soft X-ray background emission, column densities of highly ionized species, and diffuse UV line emission in reasonable agreement with observed values. Column densities and surface brightnesses of low-ionization species are more ambiguously predicted by the model, but magnetic heating could contribute to them as well.

Raymond, John C.↗

DOE OSTI2020

WA-IsoC_MSC1.1.0

The soil microbiome is central to the cycling of carbon and other nutrients and to the promotion of plant growth. Despite its importance, analysis of the soil microbiome is difficult due to its sheer complexity, with thousands of interacting species. Here, we reduced this complexity by developing model soil microbial consortia that are simpler and more amenable to experimental analysis but still represent important microbial functions of the native soil ecosystem. Samples were collected from an arid grassland soil and microbial communities (consisting mainly of bacterial species) were enriched on agar plates containing chitin as the main carbon source. Chitin was chosen because it is an abundant carbon and nitrogen polymer in soil that often requires the coordinated action of several microorganisms for complete metabolic degradation. Several soil consortia were derived that had tractable richness (30-50 OTUs) with diverse phyla representative of the native soil, including Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria and Verrucomicrobia. The resulting consortia can be stored as glycerol or lyophilized stocks at -80° C and revived while retaining community composition, greatly increasing their use as tools for the research community at large. One of the consortia that was particularly stable was chosen as a model soil consortium (MSC-1) for further analysis. MSC-1 species interactions were studied using both pairwise co-cultivation in liquid media and during growth in soil under several perturbations. Co-abundance analyses highlighted interspecies interactions and helped to define keystone species, including Mycobacterium, Rhodococcus, and Rhizobiales taxa. These experiments demonstrate the success of an approach based on naturally enriching a community of interacting species that can be stored, revived, and shared. The knowledge gained from querying these communities and their interactions will enable better understanding of the soil microbiome and the roles these interactions play in this environment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Direct numerical simulations of three-component Rayleigh–Taylor mixing and an improved model for multicomponent reacting mixtures

We present direct numerical simulations of a three-layer Rayleigh–Taylor instability (RTI) problem with a configuration based on the experiments of Suchandra & Ranjan ( J. Fluid Mech. , vol. 974, 2023, A35) and Jacobs & Dalziel ( J. Fluid Mech. , vol. 542, 2005, pp. 251–279). The problem consists of a layer of light fluid between two layers of heavy fluid with an Atwood number of 0.3. These simulations are first validated through comparison with available experimental data. The validated simulations are then utilized to analyse statistics in this three-component flow. First, length scales are examined utilizing spectra and two-point spatial correlations of velocity and species concentration fluctuations. Next, joint probability density functions (p.d.f.s) of species concentration are compared against several model p.d.f.s representing generalizations of the bivariate beta distribution. Notably, the joint p.d.f.s do not appear to be accurately described by a Dirichlet distribution, indicating the marginal distributions do not conform to a beta distribution. Finally, similarity of the present configuration to three-component mixing found in inertial confinement fusion (ICF) applications is exploited to develop and validate an improved model for the impact of multicomponent mixing on thermonuclear (TN) reaction rates. A single time instant from the present simulations is chosen for a TN burn calculation under the hypothetical assumption of ICF materials and temperatures. Total TN output from this second calculation is then compared against the prediction of the improved model. The new model is found to accurately predict TN reaction rates in both premixed and non-premixed configurations.

42 ENGINEERING↗

DOE OSTI2022

Modeling Combustion Reaction ODEs with Neural Networks

The chemistry of combustion reactions is complex as it involves many species and reactions. In practice, such a system is often modeled computationally using an empirically derived chemical mechanism. Given the large range of reaction rates, the system is then time evolved using a stiff ODE solver. However, even when reduced chemical mechanisms are employed, the system can become computationally expensive for two-dimensional or three-dimensional systems. Such systems can also face problems with instability. As such, it is desirable to find a cheaper, stable alternative to solving the reaction system. Neural networks offer the potential to learn these reaction ODEs and time evolve a combustion reaction in a more cost-efficient manner than stiff ODE solvers. In this study, a variety of neural networks are trained on zero-dimensional Cantera simulations of methane combustion with varying initial conditions. Several predictive approaches as well as several neural network architectures (artificial neural network with dropout, ResNet, and Neural ODE) are compared in their ability to predict combustion trajectories. Promising models are then identified.

combustion kinetics↗

DOE OSTI2026

Machine learning surrogates for ion energy–angle distributions in thermal and RF plasma sheaths

Ion energy–angle distributions (IEADs) at material surfaces are a critical input for plasma–material interaction (PMI) studies in fusion devices, yet they are computationally expensive to obtain using particle-in-cell (PIC) simulations. In this work, we develop a machine learning surrogate based on a deep deconvolutional neural network (DDeCNN) trained on large databases generated with the hPIC2 code. The surrogate is capable of reconstructing IEADs from sheath parameters for both thermal and radio-frequency (RF) plasmas, including cases with multiple ion species. Across thousands of test cases, the model achieves high accuracy, with over 97 % of predictions classified as good or average based on standard error metrics (MAE, MSE, L2). Even in the more challenging RF and multi-species regimes, the surrogate reliably captures the multi-peak structure of PIC results. Once trained, the surrogate produces IEADs in milliseconds on a common workstation, yielding speedups of six to seven orders of magnitude compared with running a full PIC simulation. This computational gain enables dense parameter scans and direct coupling of IEAD predictions with PMI and erosion models on whole-device scales in fusion-relevant conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2018

GMI-IPS: Processing & Visualization Software Used in ATom DC-8 Aircraft Studies

NASA's Atmospheric Tomography Mission (ATom) deployed in each of the four seasons during 2016-2018, the DC-8 aircraft in order to establish global-scale datasets intended to improve the representation of chemically reactive gases in global atmospheric chemistry models (ACMs). The Global Modeling Initiative (GMI) executed simulations for each ATom flight using the GMI Chemistry Transport Model (GMI-CTM) to provide species concentrations of chemical gases along the DC-8 flight transects. To solve the problem of translating the GMI-CTM simulation data to the unique spatial resolutions of each ATom flight, the GMI ICARTT Processing Software (GMI-IPS) was developed.The GMI-IPS is written in Python and provides data processing, flight extraction, and visualization support for aircraft research projects using ICARTT format, which is a standard format for airborne instrument data. Additionally, the GMI-IPS interpolates global gridded model data from Hierarchical Data Format (HDF) to ICARTT compatible flight transects. Software classes for instruments and collections provided by the ATom DC-8 aircraft such as MER10, MMS, etc. are derived from a common base class. Other functionality provided by the GMI-IPS are: deriving missing flight entries along a transect, reading ICARTT entries from file, and providing Python data structures for storing flight and model information, and more.The GMI-IPS is GIT source controlled, has approximately 30,000 lines of code, and supports parallelization across data collections. It delivered GMI-CTM data for more than forty distinct DC-8 aircraft flights that took place under ATom. The output ICARTT files adhere to format standard V1.1, and pass the scan utility provided by NASA LaRC Airborne Science Data for Atmospheric Composition. This presentation will include a software and methods overview, and results from ATom, including assessments using the GMI-CTM showing how well observations from ATom flight transects represent a broader region.

Damon, M. R.↗

DOE OSTI2022

Can we use antipredator behavior theory to predict wildlife responses to high-speed vehicles?

Animals seem to rely on antipredator behavior to avoid vehicle collisions. There is an extensive body of antipredator behavior theory that have been used to predict the distance/time animals should escape from predators. These models have also been used to guide empirical research on escape behavior from vehicles. However, little is known as to whether antipredator behavior models are appropriate to apply to an approaching high-speed vehicle scenario. We addressed this gap by (a) providing an overview of the main hypotheses and predictions of different antipredator behavior models via a literature review, (b) exploring whether these models can generate quantitative predictions on escape distance when parameterized with empirical data from the literature, and (c) evaluating their sensitivity to vehicle approach speed using a simulation approach wherein we assessed model performance based on changes in effect size with variations in the slope of the flight initiation distance (FID) vs. approach speed relationship. The slope of the FID vs. approach speed relationship was then related back to three different behavioral rules animals may rely on to avoid approaching threats: the spatial, temporal, or delayed margin of safety. We used literature on birds for goals (b) and (c). Our review considered the following eight models: the economic escape model, Blumstein’s economic escape model, the optimal escape model, the perceptual limit hypothesis, the visual cue model, the flush early and avoid the rush (FEAR) hypothesis, the looming stimulus hypothesis, and the Bayesian model of escape behavior. We were able to generate quantitative predictions about escape distance with the last five models. However, we were only able to assess sensitivity to vehicle approach speed for the last three models. The FEAR hypothesis is most sensitive to high-speed vehicles when the species follows the spatial (FID remains constant as speed increases) and the temporal margin of safety (FID increases with an increase in speed) rules of escape. The looming stimulus effect hypothesis reached small to intermediate levels of sensitivity to high-speed vehicles when a species follows the delayed margin of safety (FID decreases with an increase in speed). The Bayesian optimal escape model reached intermediate levels of sensitivity to approach speed across all escape rules (spatial, temporal, delayed margins of safety) but only for larger (> 1 kg) species, but was not sensitive to speed for smaller species. Overall, no single antipredator behavior model could characterize all different types of escape responses relative to vehicle approach speed but some models showed some levels of sensitivity for certain rules of escape behavior. We derive some applied applications of our findings by suggesting the estimation of critical vehicle approach speeds for managing populations that are especially susceptible to road mortality. Overall, we recommend that new escape behavior models specifically tailored to high-speeds vehicles should be developed to better predict quantitatively the responses of animals to an increase in the frequency of cars, airplanes, drones, etc. they will face in the next decade.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1994

Effect of lightning on the concentration of odd nitrogen species in the lower stratosphere: An update

Revised model estimates of the effect of lightning on the lower stratospheric NOy are presented. Several changes in the Atmospheric and Environmental Research Incorporated model were made since the last evaluation of the impact of lightning. Improvements were made in the model circulation and location of tropopause in the tropics, which is now calculated from the National Meteorological Center temperature data. Changes in model circulation reduced the mass flux from the troposphere to the tropical stratosphere. The calculated mass fluxes are found to agree better with some recent estimates. The circulation changes also reduced the advective mass flux from the tropical lower stratosphere to the midlatitudes. The change in circulation and the change in the tropopause height lead to increases in the calculated concentration of N2O, O3 and NOy in the tropical lower stratosphere. The effect of lightning is to increase the calculated concentration of NOy around 64 mbar by a factor of 2, compared to a factor of 10 enhancement in the previous calculations. Comparison with the Stratosphere Troposphere Exchange Project 1987 data indicates that the inclusion of a lightning source brings the model results in closer agreement with the observations.

Kotamarthi, V. R.↗

NASA NTRS2005

Space Based Ornithology: On the Wings of Migration and Biophysics

The study of bird migration on a global scale is one of the compelling and challenging problems of modern biology with major implications for human health and conservation biology. Migration and conservation efforts cross national boundaries and are subject to numerous international agreements and treaties. Space based technology offers new opportunities to shed understanding on the distribution and migration of organisms on the planet and their sensitivity to human disturbances and environmental changes. Migration is an incredibly diverse and complex behavior. A broad outline of space based research must address three fundamental questions: (1) where could birds be, i.e. what is their fundamental niche constrained by their biophysical limits? (2) where do we actually find birds, i.e. what is their realizable niche as modified by local or regional abiotic and biotic factors, and (3) how do they get there (and how do we know?), that is what are their migration patterns and associated mechanisms? Our working hypothesis is that individual organism biophysical models of energy and water balance, driven by satellite measurements of spatio-temporal gradients in climate and habitat, will help us to explain the variability in avian species richness and distribution. Dynamic state variable modeling provides one tool for studying bird migration across multiple scales and can be linked to mechanistic models describing the time and energy budget states of migrating birds. Such models yield an understanding of how a migratory flyway and its component habitats function as a whole and link stop-over ecology with biological conservation and management. Further these models provide an ecological forecasting tool for science and application users to address what are the possible consequences of loss of wetlands, flooding, drought or other natural disasters such as hurricanes on avian biodiversity and bird migration.

Smith, James A.↗