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1. Predictive Runtime Monitoring of Vehicle Models Using Bayesian Estimation and Reachability Analysis

   https://doi.org/10.1109/IROS45743.2020.9340755  

   Chou, Yi ; Yoon, Hansol ; Sankaranarayanan, Sriram ( October 2020 , 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   null (Ed.)

   We present a predictive runtime monitoring technique for estimating future vehicle positions and the probability of collisions with obstacles. Vehicle dynamics model how the position and velocity change over time as a function of external inputs. They are commonly described by discrete-time stochastic models. Whereas positions and velocities can be measured, the inputs (steering and throttle) are not directly measurable in these models. In our paper, we apply Bayesian inference techniques for real-time estimation, given prior distribution over the unknowns and noisy state measurements. Next, we pre-compute the set-valued reachability analysis to approximate future positions of a vehicle. The pre-computed reachability sets are combined with the posterior probabilities computed through Bayesian estimation to provided a predictive verification framework that can be used to detect impending collisions with obstacles. Our approach is evaluated using the coordinated-turn vehicle model for a UAV using on-board measurement data obtained from a flight test of a Talon UAV. We also compare the results with sampling-based approaches. We find that precomputed reachability analysis can provide accurate warnings up to 6 seconds in advance and the accuracy of the warnings improve as the time horizon is narrowed from 6 to 2 seconds. The approach also outperforms sampling in terms of on-board computation cost and accuracy measures. 

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2. Deep echo state networks with uncertainty quantification for spatio‐temporal forecasting

   https://doi.org/10.1002/env.2553  

   McDermott, Patrick L. ; Wikle, Christopher K. ( December 2018 , Environmetrics)

   Long‐lead forecasting for spatio‐temporal systems can entail complex nonlinear dynamics that are difficult to specify a priori. Current statistical methodologies for modeling these processes are often highly parameterized and, thus, challenging to implement from a computational perspective. One potential parsimonious solution to this problem is a method from the dynamical systems and engineering literature referred to as an echo state network (ESN). ESN models usereservoir computingto efficiently compute recurrent neural network forecasts. Moreover, multilevel (deep) hierarchical models have recently been shown to be successful at predicting high‐dimensional complex nonlinear processes, particularly those with multiple spatial and temporal scales of variability (such as those we often find in spatio‐temporal environmental data). Here, we introduce a deep ensemble ESN (D‐EESN) model. Despite the incorporation of a deep structure, the presented model is computationally efficient. We present two versions of this model for spatio‐temporal processes that produce forecasts and associated measures of uncertainty. The first approach utilizes a bootstrap ensemble framework, and the second is developed within a hierarchical Bayesian framework (BD‐EESN). This more general hierarchical Bayesian framework naturally accommodates non‐Gaussian data types and multiple levels of uncertainties. The methodology is first applied to a data set simulated from a novel non‐Gaussian multiscale Lorenz‐96 dynamical system simulation model and, then, to a long‐lead United States (U.S.) soil moisture forecasting application. Across both applications, the proposed methodology improves upon existing methods in terms of both forecast accuracy and quantifying uncertainty.

    

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3. Integrated community occupancy models: A framework to assess occurrence and biodiversity dynamics using multiple data sources

   https://doi.org/10.1111/2041-210X.13811  

   Doser, Jeffrey W. ; Leuenberger, Wendy ; Sillett, T. Scott ; Hallworth, Michael T. ; Zipkin, Elise F. ( February 2022 , Methods in Ecology and Evolution)

   1. The occurrence and distributions of wildlife populations and communities are shifting as a result of global changes. To evaluate whether these shifts are negatively impacting biodiversity processes, it is critical to monitor the status, trends and effects of environmental variables on entire communities. However, modelling the dynamics of multiple species simultaneously can require large amounts of diverse data, and few modelling approaches exist to simultaneously provide species and community‐level inferences.

   2. We present an ‘integrated community occupancy model’ (ICOM) that unites principles of data integration and hierarchical community modelling in a single framework to provide inferences on species‐specific and community occurrence dynamics using multiple data sources. The ICOM combines replicated and nonreplicated detection–nondetection data sources using a hierarchical framework that explicitly accounts for different detection and sampling processes across data sources. We use simulations to compare the ICOM to previously developed hierarchical community occupancy models and single species integrated distribution models. We then apply our model to assess the occurrence and biodiversity dynamics of foliage‐gleaning birds in the White Mountain National Forest in the northeastern USA from 2010 to 2018 using three independent data sources.

   3. Simulations reveal that integrating multiple data sources in the ICOM increased precision and accuracy of species and community‐level inferences compared to single data source models, although benefits of integration were dependent on the information content of individual data sources (e.g. amount of replication). Compared to single species models, the ICOM yielded more precise species‐level estimates. Within our case study, the ICOM had the highest out‐of‐sample predictive performance compared to single species models and models that used only a subset of the three data sources.

   4. The ICOM provides more precise estimates of occurrence dynamics compared to multi‐species models using single data sources or integrated single‐species models. We further found that the ICOM had improved predictive performance across a broad region of interest with an empirical case study of forest birds. The ICOM offers an attractive approach to estimate species and biodiversity dynamics, which is additionally valuable to inform management objectives of both individual species and their broader communities.

    

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4. Resilience and alternative stable states after desert wildfires

   https://doi.org/10.1002/ecm.1432  

   Abella, Scott R. ; Gentilcore, Dominic M. ; Chiquoine, Lindsay P. ( November 2020 , Ecological Monographs)

   Improving models of community change is a fundamental goal in ecology and has renewed importance during global change and increasing human disturbance of the biosphere. Using the Mojave Desert (southwestern United States) as a model system, invaded by nonnative plants and subject to wildfire disturbances, we examined models of resilience, alternative stable states, and convergent‐divergent trajectories for 36 yr of plant community change after 31 wildfires in communities dominated by the native shrubsLarrea tridentataorColeogyne ramosissima. Perennial species richness on average was fully resilient within 23 yr after disturbance in both community types. Perennial cover was fully resilient within 25 yr in theLarreacommunity, but recovery was projected to require 52 yr in theColeogynecommunity. Species composition shifts were persistent, and in theColeogynecommunity, the projected compositional recovery time of 550 yr and increasing resembled a deflected trajectory toward potential alternative states. Disturbed sites contained a perennial species composition of predominately short‐statured forbs, subshrubs, and grasses, contrasting with the larger‐statured shrub and tree structure of undisturbed sites. Auxiliary data sets characterizing species recruitment, annual plants including nonnative grasses, biocrust communities, and soils showed persistent differences between disturbed and undisturbed sites consistent with positive feedbacks potentially contributing to alternative stable states. Resprouting produced limited resilience for the large shrubsL. tridentataandYuccaspp. important to population persistence but did not forestall long‐term reduced abundance of the species. The nonnative annual grassBromus rubensincreased on disturbed sites over time, suggesting persistently abundant nonnative plant fuels and reburn potential. Biocrust cover on disturbed sites was half and species richness a third of amounts on undisturbed sites. Soil nitrogen was 30% greater on disturbed sites and no significant trend was evident for it to decline on even the oldest burns. Disturbed desert plant communities simultaneously supported all three models of resilience, alternative stable states, and convergent‐divergent trajectories among community measures (e.g., species richness, composition), timeframes since disturbance, and spatial resolutions. Accommodating expression within ecosystems of multiple models, including those opposing each other, may help broaden theoretical models of ecosystem change.

    

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5. The Future of Developed Barrier Systems: 1. Pathways Toward Uninhabitability, Drowning, and Rebound

   https://doi.org/10.1029/2023EF003672  

   Anarde, K. A. ; Moore, L. J. ; Murray, A. B. ; Reeves, I. R. B. ( April 2024 , Earth's Future)

   Many barrier islands and spits (collectively, “barriers”) throughout the world are highly developed. As low‐lying, sandy coastal landforms, barrier systems are naturally reshaped by processes associated with storms and sea‐level rise (SLR). The resulting landscape changes threaten development, and in response, humans employ defensive measures that physically modify barrier geometry to reduce relatively short‐term risk. These measures include the construction of large dunes, emplacement of beach nourishment, and removal of washover. Simulations conducted using a new coupled modeling framework show that, over decades to centuries, measures to protect roadways and communities alter the physical characteristics of barrier systems in ways that ultimately limit their habitability. We find that the pathway toward uninhabitability (via roadway drowning or community narrowing) and future system states (drowning or rebound) depends largely on dune management—because building dunes blocks overwash delivery to the barrier interior—and on initial conditions (barrier elevation and width). In the model, barriers can become lower and narrower with SLR to the point of drowning. The timing and occurrence of barrier drowning depends on randomness in the timing and intensity of storms and dune recovery processes. We find that under a constant rate of SLR, negative feedbacks involving storms can allow barriers that do not drown to rebound toward steady‐state geometries within decades after management practices cease.

    

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