Scale-dependent population drivers inform avian management in a declining saline lake ecosystem.

Shrinking saline lakes provide irreplaceable habitat for waterbird species globally. Disentangling the effects of wetland habitat loss from other drivers of waterbird population dynamics is critical for protecting these species in the face of unprecedented changes to saline lake ecosystems, ideally through decision-making frameworks that identify effective management options and their potential outcomes. Here, we develop a framework to assess the effects of hypothesized population drivers and identify potential future outcomes of plausible management scenarios on a saline lake-reliant waterbird species. We use 36 years of monitoring data to quantify the effects of environmental conditions on the population size of a regionally important breeding colony of American white pelicans (Pelecanus erythrorhynchos) at Great Salt Lake, Utah, US, then forecast colony abundance under various management scenarios. We found that low lake levels, which allow terrestrial predators access to the colony, are probable drivers of recent colony declines. Without local management efforts, we predicted colony abundance could likely decline approximately 37.3% by 2040, although recent colony observations suggest population declines may be more extreme than predicted. Results from our population projection scenarios suggested that proactive approaches to preventing predator colony access and reversing saline lake declines are crucial for the persistence of the Great Salt Lake pelican colony. Increasing wetland habitat and preventing predator access to the colony together provided the most effective protection, increasing abundance 145.4% above projections where no management actions are taken, according to our population projection scenarios. Given the importance of water levels to the persistence of island-nesting colonial species, proactive approaches to reversing saline lake declines could likely benefit pelicans as well as other avian species reliant on these unique ecosystems.

Automated Seizure Detection Based on State-Space Model Identification.

In this study, we developed a machine learning model for automated seizure detection using system identification techniques on EEG recordings. System identification builds mathematical models from a time series signal and uses a small number of parameters to represent the entirety of time domain signal epochs. Such parameters were used as features for the classifiers in our study. We analyzed 69 seizure and 55 non-seizure recordings and an additional 10 continuous recordings from Thomas Jefferson University Hospital, alongside a larger dataset from the CHB-MIT database. By dividing EEGs into epochs (1 s, 2 s, 5 s, and 10 s) and employing fifth-order state-space dynamic systems for feature extraction, we tested various classifiers, with the decision tree and 1 s epochs achieving the highest performance: 96.0% accuracy, 92.7% sensitivity, and 97.6% specificity based on the Jefferson dataset. Moreover, as the epoch length increased, the accuracy dropped to 94.9%, with a decrease in sensitivity to 91.5% and specificity to 96.7%. Accuracy for the CHB-MIT dataset was 94.1%, with 87.6% sensitivity and 97.5% specificity. The subject-specific cases showed improved results, with an average of 98.3% accuracy, 97.4% sensitivity, and 98.4% specificity. The average false detection rate per hour was 0.5 ± 0.28 in the 10 continuous EEG recordings. This study suggests that using a system identification technique, specifically, state-space modeling, combined with machine learning classifiers, such as decision trees, is an effective and efficient approach to automated seizure detection.

Multi-Cluster Approaches to Radio Sensor Array Channel Modeling and Simulation.

In this paper, we explore the physical propagation environment of radio waves by describing it in terms of distant scattering clusters. Each cluster consists of numerous scattering objects that may exhibit certain statistical properties. By utilizing geometry-based methods, we can study the channel second-order statistics (CSOS), where each distant scattering cluster corresponds to a CSOS, contributes a portion to the Doppler spectrum, and is associated with a state-space multiple-input and multiple-output (MIMO) radio channel model. Consequently, the physical propagation environment of radio waves can be modeled by summing multiple state-space MIMO radio channel models. This approach offers three key advantages: simplicity, the ability to construct the entire Doppler power spectrum from multiple uncorrelated distant scattering clusters, and the capability to obtain the channels contributed by these clusters by summing the individual channels. This methodology enables the reconstruction of the radio wave propagation environment in a simulated manner and is crucial for developing massive MIMO channel models.

Demographic Basis of Spatially Structured Fluctuations in a Threespine Stickleback Metapopulation.

AbstractUncovering the demographic basis of population fluctuations is a central goal of population biology. This is particularly challenging for spatially structured populations, which require disentangling synchrony in demographic rates from coupling via movement between locations. In this study, we fit a stage-structured metapopulation model to a 29-year time series of threespine stickleback abundance in the heterogeneous and productive Lake Mývatn, Iceland. The lake comprises two basins (North and South) connected by a channel through which the stickleback disperse. The model includes time-varying demographic rates, allowing us to assess the potential contributions of recruitment and survival, spatial coupling via movement, and demographic transience to the population's large fluctuations in abundance. Our analyses indicate that recruitment was only modestly synchronized between the two basins, whereas survival probabilities of adults were more strongly synchronized, contributing to cyclic fluctuations in the lake-wide population size with a period of approximately 6 years. The analyses further show that the two basins were coupled through movement, with the North Basin subsidizing the South Basin and playing a dominant role in driving the lake-wide dynamics. Our results show that cyclic fluctuations of a metapopulation can be explained in terms of the combined effects of synchronized demographic rates and spatial coupling.

Forecasting emergency department waiting time using a state space representation.

The provision of waiting time information in emergency departments (ED) has become an increasingly popular practice due to its positive impact on patient experience and ED demand management. However, little scientific attention has been given to the quality and quantity of waiting time information presented to patients. To improve both aspects, we propose a set of state space models with flexible error structures to forecast ED waiting time for low acuity patients. Our approach utilizes a Bayesian framework to generate uncertainties associated with the forecasts. We find that the state-space models with flexible error structures significantly improve forecast accuracy of ED waiting time compared to the benchmark, which is the rolling average model. Specifically, incorporating time-varying and correlated error terms reduces the root mean squared errors of the benchmark by 10%. Furthermore, treating zero-recorded waiting times as unobserved values improves forecast performance. Our proposed model has the ability to provide patient-centric waiting time information. By offering more accurate and informative waiting time information, our model can help patients make better informed decisions and ultimately enhance their ED experience.

Killer whale presence drives bowhead whale selection for sea ice in Arctic seascapes of fear.

The effects of predator intimidation on habitat use and behavior of prey species are rarely quantified for large marine vertebrates over ecologically relevant scales. Using state space movement models followed by a series of step selection functions, we analyzed movement data of concurrently tracked prey, bowhead whales (Balaena mysticetus; n = 7), and predator, killer whales (Orcinus orca; n = 3), in a large (63,000 km2), partially ice-covered gulf in the Canadian Arctic. Our analysis revealed pronounced predator-mediated shifts in prey habitat use and behavior over much larger spatiotemporal scales than previously documented in any marine or terrestrial ecosystem. The striking shift from use of open water (predator-free) to dense sea ice and shorelines (predators present) was exhibited gulf-wide by all tracked bowheads during the entire 3-wk period killer whales were present, constituting a nonconsumptive effect (NCE) with unknown energetic or fitness costs. Sea ice is considered quintessential habitat for bowhead whales, and ice-covered areas have frequently been interpreted as preferred bowhead foraging habitat in analyses that have not assessed predator effects. Given the NCEs of apex predators demonstrated here, however, unbiased assessment of habitat use and distribution of bowhead whales and many marine species may not be possible without explicitly incorporating spatiotemporal distribution of predation risk. The apparent use of sea ice as a predator refuge also has implications for how bowhead whales, and likely other ice-associated Arctic marine mammals, will cope with changes in Arctic sea ice dynamics as historically ice-covered areas become increasingly ice-free during summer.

State Space Mixture Modeling: Finding People with Similar Patterns of Change.

Increasingly, behavioral scientists encounter data where several individuals were measured on multiple variables over numerous occasions. Many current methods combine these data, assuming all individuals are randomly equivalent. An extreme alternative assumes no one is randomly equivalent. We propose state space mixture modeling as one possible compromise. State space mixture modeling assumes that unknown groups of people exist who share the same parameters of a state space model, and simultaneously estimates both the state space parameters and group membership. The goal is to find people that are undergoing similar change processes over time. The present work demonstrates state space mixture modeling on a simulated data set, and summarizes the results from a large simulation study. The illustration shows how the analysis is conducted, whereas the simulation provides evidence of its general validity and applicability. In the simulation study, sample size had the greatest influence on parameter estimation and the dimension of the change process had the greatest impact on correctly grouping people together, likely due to the distinctiveness of their patterns of change. State space mixture modeling offers one of the best-performing methods for simultaneously drawing conclusions about individual change processes while also analyzing multiple people.

Verification of the Mode Decomposition Technique for Closely Distributed Modal Systems in the State Space Domain.

This study aims to propose and validate the state space mode decomposition technique for precise mode separation of non-classical damping systems and closely distributed modal systems. To assess the reliability and applicability of this technique, a 40-story building with a tuned mass damper is investigated, and acceleration responses measured by the building's health monitoring system are used for the verification of the technique. The mode separation results reveal that the separated modal power spectrum becomes distorted at neighboring natural frequency ranges when the performance index only considers the concentration of power spectral energy at the target natural frequency. However, by introducing an augmented performance index that includes a constraint condition to account for distortion, more accurate mode decomposition can be achieved.

A scaled-down model of a dairy barn to imitate a livestock building for modelling and control of environmental conditions.

Heat stress has vital importance in livestock farming due to the physiological changes on the animals so that meat and milk production is significantly degraded. Recently, the hereditary and mental effects of environmental conditions have also been discussed for future generations. Therefore, high-level automation solutions are required to keep the environmental conditions in the barns as optimal as possible. In this paper, a hangar-type scaled-down barn was experimentally designed for modelling and control of the environmental conditions. First, the temperature-humidity index (THI) which is a measure of the heat stress, was stabilized to its critical value in two regions of the barn by using proportional-integrative-derivative (PID) controller. The ventilation fans were controlled at variable speeds so that energy efficiency was also provided when compared to the on-off control. Second, we proposed the state-space modelling of the coupled temperature and humidity dynamics for the interior space of the barn so that the obtained model can be utilized for mathematical analysis and accurate control. Specifically, state monitoring and prediction, optimal control, and observer-based sensor-less control are to be applied based on its state-space model. The parameters of the state-space model were here estimated with an Extended-Kalman Filter (EKF). Performances are calculated in terms of mean square error (MSE), and the performance values were found to be less than 5% for stabilization and less than 2% for modelling, respectively. The proposed scaled-down barn model is a low-cost design that can be used as an example for those who work in this field to conduct experimental studies before making large investments. Barn design can also be modified for modelling, analysis, and control of new heat stress measures in the future.

State Space Modeling of Event Count Time Series.

This paper proposes a class of algorithms for analyzing event count time series, based on state space modeling and Kalman filtering. While the dynamics of the state space model is kept Gaussian and linear, a nonlinear observation function is chosen. In order to estimate the states, an iterated extended Kalman filter is employed. Positive definiteness of covariance matrices is preserved by a square-root filtering approach, based on singular value decomposition. Non-negativity of the count data is ensured, either by an exponential observation function, or by a newly introduced "affinely distorted hyperbolic" observation function. The resulting algorithm is applied to time series of the daily number of seizures of drug-resistant epilepsy patients. This number may depend on dosages of simultaneously administered anti-epileptic drugs, their superposition effects, delay effects, and unknown factors, making the objective analysis of seizure counts time series arduous. For the purpose of validation, a simulation study is performed. The results of the time series analysis by state space modeling, using the dosages of the anti-epileptic drugs as external control inputs, provide a decision on the effect of the drugs in a particular patient, with respect to reducing or increasing the number of seizures.

Contrasting seasonal effects of climate change influence density in a cold-adapted species.

Many ecological processes are profoundly influenced by abiotic factors, such as temperature and snow. However, despite strong evidence linking shifts in these ecological processes to corresponding shifts in abiotic factors driven by climate change, the mechanisms connecting population size to season-specific climate drivers are little understood. Using a 21-year dataset and a Bayesian state space model, we identified biologically informed seasonal climate covariates that influenced densities of snowshoe hares (Lepus americanus), a cold-adapted boreal herbivore. We found that snow and temperature had strong but conflicting season-dependent effects. Reduced snow duration in spring and fall and warmer summers were associated with lowered hare density, whereas warmer winters were associated with increased density. When modeled simultaneously and under two climate change scenarios, the negative effects of reduced fall and spring snow duration and warmer summers overwhelm the positive effect of warmer winters, producing projected population declines. Ultimately, the contrasting population-level impacts of climate change across seasons emphasize the critical need to examine the entire annual climate cycle to understand potential long-term population consequences of climate change.

Using Bayesian state-space models to understand the population dynamics of the dominant malaria vector, Anopheles funestus in rural Tanzania.

BACKGROUND: It is often assumed that the population dynamics of the malaria vector Anopheles funestus, its role in malaria transmission and the way it responds to interventions are similar to the more elaborately characterized Anopheles gambiae. However, An. funestus has several unique ecological features that could generate distinct transmission dynamics and responsiveness to interventions. The objectives of this work were to develop a model which will: (1) reconstruct the population dynamics, survival, and fecundity of wild An. funestus populations in southern Tanzania, (2) quantify impacts of density dependence on the dynamics, and (3) assess seasonal fluctuations in An. funestus demography. Through quantifying the population dynamics of An. funestus, this model will enable analysis of how their stability and response to interventions may differ from that of An. gambiae sensu lato. METHODS: A Bayesian State Space Model (SSM) based on mosquito life history was fit to time series data on the abundance of female An. funestus sensu stricto collected over 2 years in southern Tanzania. Prior values of fitness and demography were incorporated from empirical data on larval development, adult survival and fecundity from laboratory-reared first generation progeny of wild caught An. funestus. The model was structured to allow larval and adult fitness traits to vary seasonally in response to environmental covariates (i.e. temperature and rainfall), and for density dependency in larvae. The effects of density dependence and seasonality were measured through counterfactual examination of model fit with or without these covariates. RESULTS: The model accurately reconstructed the seasonal population dynamics of An. funestus and generated biologically-plausible values of their survival larval, development and fecundity in the wild. This model suggests that An. funestus survival and fecundity annual pattern was highly variable across the year, but did not show consistent seasonal trends either rainfall or temperature. While the model fit was somewhat improved by inclusion of density dependence, this was a relatively minor effect and suggests that this process is not as important for An. funestus as it is for An. gambiae populations. CONCLUSION: The model's ability to accurately reconstruct the dynamics and demography of An. funestus could potentially be useful in simulating the response of these populations to vector control techniques deployed separately or in combination. The observed and simulated dynamics also suggests that An. funestus could be playing a role in year-round malaria transmission, with any apparent seasonality attributed to other vector species.

Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density.

Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.

Predicting how environmental conditions and smolt body length when entering the marine environment impact individual Atlantic salmon Salmo salar adult return rates.

Populations of Atlantic salmon Salmo salar have experienced precipitous declines in abundance since the 1970s. This decline has been associated with reduced numbers of adult salmon returning to fresh water from their marine migration, i.e., their marine return rates (MRR). Thus, understanding the factors that affect MRR is of crucial conservation importance. The authors used a state-space model with a 13-year time series of individually tagged salmon mark-recapture histories on the River Frome, southern England, to test the effect of smolt body length on their MRR. In addition to smolt length, the model tested for the influence of environmental covariates that were representative of the conditions experienced by the smolts in the early stages of their seaward migration, i.e., from the lower river to the estuary exit. The model indicated that, even when accounting for environmental covariates, smolt body length was an important predictor of MRR. Although larger smolts have a higher probability of returning to their natal river as adults than smaller smolts, and one-sea-winter salmon have a survival rate twice as high as multi-sea-winter salmon, the actual biological mechanisms underpinning this phenomenon remain uncertain. These results have important applications for salmon conservation, as efforts to bolster salmon populations in the freshwater environment should consider methods to improve smolt quality (i.e., body size) as well as smolt quantity.

Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis-Hastings Method.

Extracting latent nonlinear dynamics from observed time-series data is important for understanding a dynamic system against the background of the observed data. A state space model is a probabilistic graphical model for time-series data, which describes the probabilistic dependence between latent variables at subsequent times and between latent variables and observations. Since, in many situations, the values of the parameters in the state space model are unknown, estimating the parameters from observations is an important task. The particle marginal Metropolis-Hastings (PMMH) method is a method for estimating the marginal posterior distribution of parameters obtained by marginalization over the distribution of latent variables in the state space model. Although, in principle, we can estimate the marginal posterior distribution of parameters by iterating this method infinitely, the estimated result depends on the initial values for a finite number of times in practice. In this paper, we propose a replica exchange particle marginal Metropolis-Hastings (REPMMH) method as a method to improve this problem by combining the PMMH method with the replica exchange method. By using the proposed method, we simultaneously realize a global search at a high temperature and a local fine search at a low temperature. We evaluate the proposed method using simulated data obtained from the Izhikevich neuron model and Lévy-driven stochastic volatility model, and we show that the proposed REPMMH method improves the problem of the initial value dependence in the PMMH method, and realizes efficient sampling of parameters in the state space models compared with existing methods.

Market prices, analysts' predictions, and Covid19.

This study employs a relatively new statistical method to analyze the time-series of US market prices. Specifically, it shows, that during Covid19, the strongest structural breaks happened. Moreover, since 1993 analysts were not able to predict market stock prices significantly at the 5% level. The new statistical method allows for a better analysis of market prices and analysts' recommendations.

Environmental variability directly affects the prevalence of divorce in monogamous albatrosses.

In many socially monogamous species, divorce is a strategy used to correct for sub-optimal partnerships and is informed by measures of previous breeding performance. The environment affects the productivity and survival of populations, thus indirectly affecting divorce via changes in demographic rates. However, whether environmental fluctuations directly modulate the prevalence of divorce in a population remains poorly understood. Here, using a longitudinal dataset on the long-lived black-browed albatross (Thalassarche melanophris) as a model organism, we test the hypothesis that environmental variability directly affects divorce. We found that divorce rate varied across years (1% to 8%). Individuals were more likely to divorce after breeding failures. However, regardless of previous breeding performance, the probability of divorce was directly affected by the environment, increasing in years with warm sea surface temperature anomalies (SSTA). Furthermore, our state-space models show that warm SSTA increased the probability of switching mates in females in successful relationships. For the first time, to our knowledge, we document the disruptive effects of challenging environmental conditions on the breeding processes of a monogamous population, potentially mediated by higher reproductive costs, changes in phenology and physiological stress. Environmentally driven divorce may therefore represent an overlooked consequence of global change.

Hierarchical Biometrical Genetic Analysis of Longitudinal Dynamics.

For many phenotypes, individual scores are obtained as the parameter estimates of person-level models fit to intensive repeated measures from physiological sensors or experience sampling studies. Biometrical genetic analysis of such phenotypes is often done in a 2-step sequence: first the phenotypic parameters are estimated for each individual, then classical twin modeling is used to partition their variance. This study demonstrates deficiencies in accuracy and statistical power of the two-step approach to estimate genetic signals and advocates for the use of hierarchical models to overcome both problems. Simulations are used to demonstrate the benefits to accuracy and statistical power from a hierarchical modeling approach. A model of heart rate fluctuations was applied to experimental data from twin pairs recorded in independent trials. Results of the data application reveal moderate but uncorrelated heritabilities for two parameters of heart rate: oscillation frequency and damping ratio. By merging biometrical genetic analysis with process models, hierarchical mixed-effects modeling has potential to assist with discovery and extraction of novel phenotypes from within-person data and to validate theoretical models of within-person processes.

Spatial optimization of invasive species control informed by management practices.

Optimization of spatial resource allocation is crucial for the successful control of invasive species under a limited budget but requires labor-intensive surveys to estimate population parameters. In this study, we devised a novel framework for the spatially explicit optimization of capture effort allocation using state-space population models from past capture records. We applied it to a control program for invasive snapping turtles to determine effort allocation strategies that minimize the population density over the whole area. We found that spatially heterogeneous density dependence and capture pressure limit the abundance of snapping turtles. Optimal effort allocation effectively improved the control effect, but the degree of improvement varied substantially depending on the total effort. The degree of improvement by the spatial optimization of allocation effort was only 3.21% when the total effort was maintained at the 2016 level. However, when the total effort was increased by two, four, and eight times, spatial optimization resulted in improvements of 4.65%, 8.33%, and 20.35%, respectively. To achieve the management goal for snapping turtles in our study area, increasing the current total effort by more than four times was necessary, in addition to optimizing the spatial effort. The snapping turtle population is expected to reach the target density one year after the optimal management strategy is implemented, and this rapid response can be explained by high population growth rate coupled with density-dependent feedback regulation. Our results demonstrated that combining a state-space model with optimization makes it possible to adaptively improve the management of invasive species and decision-making. The method used in this study, based on removal records from an invasive management program, can be easily applied to monitoring data for wildlife and pest control management using traps in a variety of ecosystems.

Monitoring tropical forest succession at landscape scales despite uncertainty in Landsat time series.

Forecasting rates of forest succession at landscape scales will aid global efforts to restore tree cover to millions of hectares of degraded land. While optical satellite remote sensing can detect regional land cover change, quantifying forest structural change is challenging. We developed a state-space modeling framework that applies Landsat satellite data to estimate variability in rates of natural regeneration between sites in a tropical landscape. Our models work by disentangling measurement error in Landsat-derived spectral reflectance from process error related to successional variability. We applied our modeling framework to rank rates of forest succession between 10 naturally regenerating sites in Southwestern Panama from about 2001 to 2015 and tested how different models for measurement error impacted forecast accuracy, ecological inference, and rankings of successional rates between sites. We achieved the greatest increase in forecasting accuracy by adding intra-annual phenological variation to a model based on Landsat-derived normalized difference vegetation index (NDVI). The best-performing model accounted for inter- and intra-annual noise in spectral reflectance and translated NDVI to canopy height via Landsat-lidar fusion. Modeling forest succession as a function of canopy height rather than NDVI also resulted in more realistic estimates of forest state during early succession, including greater confidence in rank order of successional rates between sites. These results establish the viability of state-space models to quantify ecological dynamics from time series of space-borne imagery. State-space models also provide a statistical approach well-suited to fusing high-resolution data, such as airborne lidar, with lower-resolution data that provides better temporal and spatial coverage, such as the Landsat satellite record. Monitoring forest succession using satellite imagery could play a key role in achieving global restoration targets, including identifying sites that will regain tree cover with minimal intervention.