Environmental Patch Dynamics: A Vertical Metacommunity Framework Emerging from Marine Caves

Chapter 4 - Effects of Metacommunity Networks on Local Community Structures: From Theoretical Predictions to Empirical Evaluations

Intersections with connected infrastructure and vehicle sensors allow observing vulnerable road users (VRU) longer and with less occlusion than from a moving vehicle. Furthermore, the connected sensors are providing continuous measurements of VRUs at the intersection. Thus, we propose a data-driven prediction model, which benefits of the continuous, local measurements. While most approaches in literature use the most probable path to predict road users, it does not represent the uncertainty in prediction and multiple maneuver options. We propose the use of Recurrent Neural Networks fed with measured trajectories and a variety of contextual information to output the prediction in a local occupancy grid map in polar coordinates. By using polar coordinates, a reliable movement model is learned as base model being insensitive against blind spots in the data. The model is further improved by considering input features containing information about the static and dynamic environment as well as local movement statistics. The model successfully predicts multiple movement options represented in a polar grid map. Besides, the model can continuously improve the prediction accuracy without re-training by updating local movement statistics. Finally, the trained model is providing reliable predictions if applied on a different intersection without data from this intersection.

In this paper we present a trajectory generation method for autonomous overtaking of static obstacles in a dynamic urban environment. In these settings, blind spots can arise from perception limitations. For example, the autonomous car may have to move slightly into the opposite lane in order to cleanly see in front of a car ahead. Once it has gathered enough information about the road ahead, then the autonomous car can safely overtake. We generate safe trajectories by solving, in real-time, a non-linear constrained optimization, formulated as a Receding Horizon planner. The planner is guided by a high-level state machine, which determines when the overtake maneuver should begin. Our main contribution is a method that can maximize visibility, prioritizes safety and respects the boundaries of the road while executing the maneuver. We present experimental results in simulation with data collected during real driving.

In this article we present a trajectory generation method for autonomous overtaking of unexpected obstacles in a dynamic urban environment. In these settings, blind spots can arise from perception limitations. For example when overtaking unexpected objects on the vehicle's ego lane on a two-way street. In this case, a human driver would first make sure that the opposite lane is free and that there is enough room to successfully execute the maneuver, and then it would cut into the opposite lane in order to execute the maneuver successfully. We consider the practical problem of autonomous overtaking when the coverage of the perception system is impaired due to occlusion. Safe trajectories are generated by solving, in real-time, a non-linear constrained optimization, formulated as a receding horizon planner that maximizes the ego vehicle's visibility. The planner is complemented by a high-level behavior planner, which takes into account the occupancy of other traffic participants, the information from the vehicle's perception system, and the risk associated with the overtaking maneuver, to determine when the overtake maneuver should happen. The approach is validated in simulation and in experiments in real world traffic.

Open woody formations provide critical dryland ecosystem services—fuelwood, charcoal, construction materials, and livestock fodder—yet remain systematically underestimated by global monitoring systems prioritizing dense forests. Conventional binary forest/non-forest classifications fail to effectively detect these formations, creating a disconnect between forest cover assessments and ground reality. This limitation highlights fundamental challenges in detecting sparse woody vegetation where deciduous phenology, spectral confusion with herbaceous vegetation, and shadows compromise traditional remote sensing.We mapped continuous woody cover (0-100%) across 36 years (1989-2025) using Landsat imagery and Random Forest regression calibrated on 505 photo-interpreted plots and validated on 41 field plots. Images acquired during rainy season/early dry season maximized detection when deciduous species retain foliage. We analyzed fire regimes (2000-2024) and human pressure via distance gradients from habitations (1-10 km) to identify degradation drivers.Intact woody thickets declined dramatically from 60% (1989) to 35% (2025), with accelerating loss after 2010. Fire is the primary degradation driver: burned areas lose four times more woody cover than unburned zones. Crucially, fire frequency—not single events—determines degradation severity. Natural recovery is very limited even after 10 years, insufficient to offset immediate post-fire losses. Human proximity also shows significant impacts: woody cover is approximately 20% lower near habitations than in remote areas.Continuous woody cover mapping successfully quantifies sparse woody vegetation dynamics in heterogeneous dryland environments. This approach is transferable to similar dryland ecosystems globally facing comparable detection challenges. Results reveal that fire frequency exceeds natural recovery capacity, providing scientific evidence to inform conservation and restoration strategies in drylands under increasing anthropogenic pressure.

In complex urban road conditions, stationary or illegally parked vehicles present a considerable risk to the overall traffic system. In safety-critical applications like autonomous driving, the detection of stopped vehicles is of utmost importance. Previous methods for detecting stopped vehicles have been designed for stationary viewpoints, such as security cameras, which consistently monitor fixed locations. However, these methods for detecting stopped vehicles based on stationary views cannot address blind spots and are not applicable from driving vehicles. To address these limitations, we propose a novel deep learning-based framework for detecting stopped vehicles in dynamic environments, particularly those recorded by dashcams. The proposed framework integrates a deep learning-based object detector and tracker, along with movement estimation using the dense optical flow method. We also introduced additional centerline detection and inter-vehicle distance measurement. The experimental results demonstrate that the proposed framework can effectively identify stopped vehicles under real-world road conditions.

Mercury deposition in the western tropical South Atlantic during the last 70 ka

This paper uses the Internet of Things and blockchain-related technologies to combine computer technology to develop a real-time monitoring system for cold chain logistics. The system can grasp the real-time dynamics of the cold chain environment of agricultural products and control them according to the dynamic conditions, which eliminates the blind spots in the cold chain logistics process and also guarantees the quality and safety of fresh agricultural products. This paper optimizes the internal resources of the blockchain system model, proposes an optimization algorithm, and combines the blockchain technology to construct a cold chain logistics model for agricultural products to obtain an embedded cold chain system. Finally, this paper designs an experiment to verify the performance of the system. According to the results of experimental research, the cold chain logistics system of agricultural products based on embedded system and blockchain constructed in this paper can effectively improve the transportation efficiency of agricultural products, and it also has a certain auxiliary effect on the monitoring of agricultural logistics transportation.

Revealing the neural networks associated with processing of natural social interaction and the related effects of actor-orientation and face-visibility

Summary 1. Reductions in resource availability, associated with land‐use change and agricultural intensification in the UK and Europe, have been linked with the widespread decline of many farmland bird species over recent decades. However, the underlying ecological processes which link resource availability and population trends are poorly understood. 2. We construct a spatial depletion model to investigate the relationship between the population persistence of granivorous birds within the agricultural landscape and the temporal dynamics of stubble field availability, an important source of winter food for many of those species. 3. The model is capable of accurately predicting the distribution of a given number of finches and buntings amongst patches of different stubble types in an agricultural landscape over the course of a winter and assessing the relative value of different landscapes in terms of resource availability. 4. Sensitivity analyses showed that the model is relatively robust to estimates of energetic requirements, search efficiency and handling time but that daily seed survival estimates have a strong influence on model fit. Understanding resource dynamics in agricultural landscapes is highlighted as a key area for further research. 5. There was a positive relationship between the predicted number of bird days supported by a landscape over‐winter and the breeding population trend for yellowhammer Emberiza citrinella, a species for which survival has been identified as the primary driver of population dynamics, but not for linnet Carduelis cannabina, a species for which productivity has been identified as the primary driver of population dynamics. 6. Synthesis and applications. We believe this model can be used to guide the effective delivery of over‐winter food resources under agri‐environment schemes and to assess the impacts on granivorous birds of changing resource availability associated with novel changes in land use. This could be very important in the future as farming adapts to an increasingly dynamic trading environment, in which demands for increased agricultural production must be reconciled with objectives for environmental protection, including biodiversity conservation.

Land use changes have profoundly influenced global environmental dynamics. The Yellow River (YR), as the world's fifth-longest river, significantly contributes to regional social and economic growth due to its extensive drainage area, making it a key global player. To ensure ecological stability and coordinate land use demand, modeling the future land allocation patterns of the Yellow River Basin (YRB) will assist in striking a balance between land use functions and the optimization of its spatial design, particularly in water and sand management. In this research, we used a multi-objective genetic algorithm (MOGA) with the PLUS model to simulate several different futures for the YRB's land use between 1990 and 2020 and predict its spatial pattern in 2030. An analysis of the spatiotemporal evolution of land use changes in the YRB indicated that construction land expansion is the primary driver of landscape pattern and structure changes and ecological degradation, with climate change also contributing to the expansion of the watershed area. On the other hand, the multi-scenario simulation, constrained by specific targets, revealed that economic development was mainly reflected in land expansion for construction. At the same time, grassland and woodland were essential pillars to support the region's ecological health, and increasing the development of unused land emerged as a potential pathway towards sustainable development in the region. This study could be used as a template for the long-term growth of other large river basins by elucidating the impacts of human activities on land use and rationalizing land resource allocation under various policy constraints.

Ecological conservation and restoration in the anthropocene must struggle with overlapping drivers of biodiversity and cultural loss; ruptures of the ecological environment mirror ruptures of human relationships with nature. And yet technology cannot remove humans from nature; technological and infrastructural reconfigurations of nature create new vulnerabilities and risks for humans and ecosystems alike. How can conservation and restoration science productively grapple with complex infrastructure systems and decision-making processes as biophysical and social drivers of ecosystem change? Using dam removals in the USA and in the Mid Columbia River region of the Pacific Northwest, this dissertation develops a conceptual framework for Social, Environmental, and Technological Systems (SETS), and applies it at three spatial and temporal scales to the practice of dam removal as a river restoration strategy. Drawing upon existing data sets, as well as biophysical, document, survey, and interview data this dissertation addresses how dam removals have functioned in the context of the social histories of river restoration programs, examines how these restoration programs must continue to renegotiate the human relationships with nature through the infrastructure systems that enable certain forms of existence while precluding others. Of particular interest is how restoration programs have increasingly functioned to deliver novel infrastructure solutions, while ignoring longer-term changes in ecological structure and function due to infrastructure development; in other words, the infrastructural work of restored ecosystems, and the infrastructural blind spots of restoration programs. How restoration planning considers, or does not consider, infrastructural blind spots, is indicative of not only the biophysical drivers of threatened and endangered species loss, but also the political dynamics of decision making at large, and the power-knowledge relationships constituting legitimate and relevant knowledge in the decision making space. In the Pacific Northwest, there appears to be a tipping point of social convention in centering treaty rights and obligations vis-a-vis ongoing processes of colonization and institutionalized scientific expertise. Ecological restoration will only be successful if it addresses both engineered infrastructures and social justice.