Hasil untuk "Human ecology. Anthropogeography"

Ivana Živojinović, Stojan Ivanović, Oliver Tošković et al.

Abstract Access to (urban) nature is vital for people's wellbeing, but this accessibility is not evenly spread across socio‐demographic groups, nor across the European continent. This paper fills a research gap by exploring the use patterns and accessibility of forests and parks across European cities, based on a standardised online survey of 10,462 people from 33 European countries. The results highlight a complex relationship between accessibility, socio‐demographic factors and personal motivations in shaping the use of forests and parks. Key findings include variations in visiting patterns by age and gender, with younger individuals and women showing a higher propensity for frequent visits. Motivations for forest and park use varied widely, ranging from physical health and recreation to social interaction and mental well‐being. Importantly, the study identified accessibility challenges, particularly travel time, connectivity and limited amenities (e.g. safe walking/cycling routes, lighting, toilets), which limit park and forest use. The results also highlight the diversity in use patterns across different European regions and based on gender, revealing significant variations in how people value and use forests and parks. The study suggests that socio‐demographic factors, accessibility and personal motivations play crucial roles in determining forest and park use, underscoring the importance of inclusive urban planning to accommodate diverse needs and preferences. Read the free Plain Language Summary for this article on the Journal blog.

Jules Lecomte, Konrad Zinner, Michael Neumeier et al.

Human machine interaction is a huge source of inspiration in today's media art and digital design, as machines and humans merge together more and more. Its place in art reflects its growing applications in industry, such as robotics. However, those interactions often remains too technical and machine-driven for people to really engage into. On the artistic side, new technologies are often not explored in their full potential and lag a bit behind, so that state-of-the-art research does not make its way up to museums and exhibitions. Machines should support people's imagination and poetry in a seamless interface to their body or soul. We propose an artistic sound installation featuring neuromorphic body sensing to support a direct yet non intrusive interaction with the visitor with the purpose of creating sound scapes together with the machine. We design a neuromorphic multihead human pose estimation neural sensor that shapes sound scapes and visual output with fine body movement control. In particular, the feature extractor is a spiking neural network tailored for a dedicated neuromorphic chip. The visitor, immersed in a sound atmosphere and a neurally processed representation of themselves that they control, experience the dialogue with a machine that thinks neurally, similarly to them.

Debasmita Ghose, Oz Gitelson, Marynel Vazquez et al.

To collaborate with humans, robots must infer goals that are often ambiguous, difficult to articulate, or not drawn from a fixed set. Prior approaches restrict inference to a predefined goal set, rely only on observed actions, or depend exclusively on explicit instructions, making them brittle in real-world interactions. We present BALI (Bidirectional Action-Language Inference) for goal prediction, a method that integrates natural language preferences with observed human actions in a receding-horizon planning tree. BALI combines language and action cues from the human, asks clarifying questions only when the expected information gain from the answer outweighs the cost of interruption, and selects supportive actions that align with inferred goals. We evaluate the approach in collaborative cooking tasks, where goals may be novel to the robot and unbounded. Compared to baselines, BALI yields more stable goal predictions and significantly fewer mistakes.

Chengke Liu, Wei Xu

Developing human-controllable artificial intelligence (AI) and achieving meaningful human control (MHC) has become a vital principle to address these challenges, ensuring ethical alignment and effective governance in AI. MHC is also a critical focus in human-centered AI (HCAI) research and application. This chapter systematically examines MHC in AI, articulating its foundational principles and future trajectory. MHC is not simply the right to operate, but the unity of human understanding, intervention, and the traceablity of responsibility in AI decision-making, which requires technological design, AI governance, and humans to play a role together. MHC ensures AI autonomy serves humans without constraining technological progress. The mode of human control needs to match the levels of technology, and human supervision should balance the trust and doubt of AI. For future AI systems, MHC mandates human controllability as a prerequisite, requiring: (1) technical architectures with embedded mechanisms for human control; (2) human-AI interactions optimized for better access to human understanding; and (3) the evolution of AI systems harmonizing intelligence and human controllability. Governance must prioritize HCAI strategies: policies balancing innovation and risk mitigation, human-centered participatory frameworks transcending technical elite dominance, and global promotion of MHC as a universal governance paradigm to safeguard HCAI development. Looking ahead, there is a need to strengthen interdisciplinary research on the controllability of AI systems, enhance ethical and legal awareness among stakeholders, moving beyond simplistic technology design perspectives, focus on the knowledge construction, complexity interpretation, and influencing factors surrounding human control. By fostering MHC, the development of human-controllable AI can be further advanced, delivering HCAI systems.

Chengbo Zheng, Tim Miller, Alina Bialkowski et al.

High stakes decision-making often requires a continuous interplay between evolving evidence and shifting hypotheses, a dynamic that is not well supported by current AI decision support systems. In this paper, we introduce a mixed-initiative framework for AI assisted decision making that is grounded in the data-frame theory of sensemaking and the evaluative AI paradigm. Our approach enables both humans and AI to collaboratively construct, validate, and adapt hypotheses. We demonstrate our framework with an AI-assisted skin cancer diagnosis prototype that leverages a concept bottleneck model to facilitate interpretable interactions and dynamic updates to diagnostic hypotheses.

Matthew Andres Moreno, Santiago Rodriguez-Papa, Emily Dolson

Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution, and have been hypothesized to influence phylogenetic structure. Here, we set out to assess (1) if spatial structure, ecology, and selection pressure leave detectable signatures in phylogenetic structure, (2) the extent, in particular, to which ecology can be detected and discerned in the presence of spatial structure, and (3) the extent to which these phylogenetic signatures generalize across evolutionary systems. To this end, we analyze phylogenies generated by manipulating spatial structure, ecology, and selection pressure within three computational models of varied scope and sophistication. We find that selection pressure, spatial structure, and ecology have characteristic effects on phylogenetic metrics, although these effects are complex and not always intuitive. Signatures have some consistency across systems when using equivalent taxonomic unit definitions (e.g., individual, genotype, species). Further, we find that sufficiently strong ecology can be detected in the presence of spatial structure. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully. Although our results suggest potential for evolutionary inference of spatial structure, ecology, and selection pressure through phylogenetic analysis, further methods development is needed to distinguish these drivers' phylometric signatures from each other and to appropriately normalize phylogenetic metrics. With such work, phylogenetic analysis could provide a versatile toolkit to study large-scale evolving populations.

EM Wolkovich, T Jonathan Davies, William D Pearse et al.

Growing anthropogenic pressures have increased the need for robust predictive models. Meeting this demand requires approaches that can handle bigger data to yield forecasts that capture the variability and underlying uncertainty of ecological systems. Bayesian models are especially adept at this and are growing in use in ecology. Yet many ecologists today are not trained to take advantage of the bigger ecological data needed to generate more flexible robust models. Here we describe a broadly generalizable workflow for statistical analyses and show how it can enhance training in ecology. Building on the increasingly computational toolkit of many ecologists, this approach leverages simulation to integrate model building and testing for empirical data more fully with ecological theory. In turn this workflow can fit models that are more robust and well-suited to provide new ecological insights -- allowing us to refine where to put resources for better estimates, better models, and better forecasts.

Till Hielscher, Lukas Heuer, Frederik Wulle et al.

Predictive planning is a key capability for robots to efficiently and safely navigate populated environments. Particularly in densely crowded scenes, with uncertain human motion predictions, predictive path planning, and control can become expensive to compute in real time due to the curse of dimensionality. With the goal of achieving pro-active and legible robot motion in shared environments, in this paper we present HuMAN-MPC, a computationally efficient algorithm for Human Motion Aware Navigation using fast embedded Model Predictive Control. The approach consists of a novel model predictive control (MPC) formulation that leverages a fast state-of-the-art optimization backend based on a sequential quadratic programming real-time iteration scheme while also providing feasibility monitoring. Our experiments, in simulation and on a fully integrated ROS-based platform, show that the approach achieves great scalability with fast computation times without penalizing path quality and efficiency of the resulting avoidance behavior.

Byunghwee Lee, Rachith Aiyappa, Yong-Yeol Ahn et al.

Beliefs form the foundation of human cognition and decision-making, guiding our actions and social connections. A model encapsulating beliefs and their interrelationships is crucial for understanding their influence on our actions. However, research on belief interplay has often been limited to beliefs related to specific issues and relied heavily on surveys. We propose a method to study the nuanced interplay between thousands of beliefs by leveraging an online user debate data and mapping beliefs onto a neural embedding space constructed using a fine-tuned large language model (LLM). This belief space captures the interconnectedness and polarization of diverse beliefs across social issues. Our findings show that positions within this belief space predict new beliefs of individuals and estimate cognitive dissonance based on the distance between existing and new beliefs. This study demonstrates how LLMs, combined with collective online records of human beliefs, can offer insights into the fundamental principles that govern human belief formation.

M. Muñoz, Luke O. Frishkoff, Jenna E. Pruett et al.

With decades of intensive study, Anolis lizards have emerged as a biological model system. We review how new research on anoles has advanced our understanding of ecology and evolution, challenging long-standing paradigms and opening new areas of inquiry. Recent anole research reveals how changes in behavior can restructure ecological communities and can both stimulate and stymie evolution, sometimes simultaneously. Likewise, investigation of anoles as spatial or phylogenetic evolutionary experiments has documented evolutionary repeatability across spatiotemporal scales, while also illuminating its limits. Current research places anoles as an emerging model for Anthropocene biology, with recent work illustrating how species respond as humans reconfigure natural habitats, alter the climate, and create novel environments and communities through urbanization and species introduction. Combined with ongoing methodological developments in genomics, phylogenetics, and ecology, the growing foundational knowledge of Anolis positions them as a powerful model system in ecology and evolution for years to come. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 54 is November 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Shreyas Bhat, Joseph B. Lyons, Cong Shi et al.

This paper examines the effect of real-time, personalized alignment of a robot's reward function to the human's values on trust and team performance. We present and compare three distinct robot interaction strategies: a non-learner strategy where the robot presumes the human's reward function mirrors its own, a non-adaptive-learner strategy in which the robot learns the human's reward function for trust estimation and human behavior modeling, but still optimizes its own reward function, and an adaptive-learner strategy in which the robot learns the human's reward function and adopts it as its own. Two human-subject experiments with a total number of 54 participants were conducted. In both experiments, the human-robot team searches for potential threats in a town. The team sequentially goes through search sites to look for threats. We model the interaction between the human and the robot as a trust-aware Markov Decision Process (trust-aware MDP) and use Bayesian Inverse Reinforcement Learning (IRL) to estimate the reward weights of the human as they interact with the robot. In Experiment 1, we start our learning algorithm with an informed prior of the human's values/goals. In Experiment 2, we start the learning algorithm with an uninformed prior. Results indicate that when starting with a good informed prior, personalized value alignment does not seem to benefit trust or team performance. On the other hand, when an informed prior is unavailable, alignment to the human's values leads to high trust and higher perceived performance while maintaining the same objective team performance.

Can Chen, Xu-Wen Wang, Yang-Yu Liu

The stability of ecological systems is a fundamental concept in ecology, which offers profound insights into species coexistence, biodiversity, and community persistence. In this article, we provide a systematic and comprehensive review on the theoretical frameworks for analyzing the stability of ecological systems. Notably, we survey various stability notions, including linear stability, sign stability, diagonal stability, D-stability, total stability, sector stability, structural stability, and higher-order stability. For each of these stability notions, we examine necessary or sufficient conditions for achieving such stability and demonstrate the intricate interplay of these conditions on the network structures of ecological systems. Finally, we explore the future prospects of these stability notions.

N. Hammerschlag, Lfg Gutowsky, MJ Rider et al.

Understanding and ultimately predicting how marine organisms will respond to urbanization is central for effective wildlife conservation and management in the Anthropocene. Sharks are upper trophic level predators in virtually all marine environments, but if and how their behaviors are influenced by coastal urbanization remains understudied. Here, we examined space use and residency patterns of 14 great hammerheads Sphyrna mokarran, 13 bull sharks Carcharhinus leucas, and 25 nurse sharks Ginglymostoma cirratum in proximity to the coastal metropolis of Miami, Florida, using passive acoustic telemetry. Based on the terrestrial urban carnivore literature, we predicted sharks would exhibit avoidance behaviors of areas close to Miami, with residency patterns in these urban areas increasing during periods of lower human activity, such as during nocturnal hours and weekdays, and that dietary specialists (great hammerhead) would exhibit comparatively lower affinity towards highly urbanized areas relative to dietary generalists (bull and nurse shark). However, we did not find empirical support for these predictions. Space use patterns of tracked sharks were consistent with that of ‘urban adapters’ (species that exhibit partial use of urban areas). Modeling also revealed that an unmeasured spatial variable was driving considerable shark residency in areas exposed to high urbanization. We propose several hypotheses that could explain our findings, including food provisioning from shore-based activities that could be attracting sharks to urban areas. Ultimately, the lack of avoidance of urban areas by sharks documented here, as compared to terrestrial carnivores, should motivate future research in the growing field of urban ecology. OPEN ACCESS Researchers release an acoustically tagged nurse shark into waters off Miami, Florida, to investigate shark residency patterns in relation to coastal urbanization. Photo: R. Roemer

Rok Sturm, Juan José López Díez, J. Polajnar et al.

Our awareness of air-borne sounds in natural and urban habitats has led to the recent recognition of soundscape ecology and ecoacoustics as interdisciplinary fields of research that can help us better understand ecological processes and ecosystem dynamics. Because the vibroscape (i.e., the substrate-borne vibrations occurring in a given environment) is hidden to the human senses, we have largely overlooked its ecological significance. Substrate vibrations provide information crucial to the reproduction and survival of most animals, especially arthropods, which are essential to ecosystem functioning. Thus, vibroscape is an important component of the environment perceived by the majority of animals. Nowadays, when the environment is rapidly changing due to human activities, climate change, and invasive species, this hidden vibratory world is also likely to change without our notice, with potentially crucial effects on arthropod communities. Here, we introduce ecotremology, a discipline that mainly aims at studying substrate-borne vibrations for unraveling ecological processes and biological conservation. As biotremology follows the main research concepts of bioacoustics, ecotremology is consistent with the paradigms of ecoacoustics. We argue that information extracted from substrate vibrations present in the environment can be used to comprehensively assess and reliably predict ecosystem changes. We identify key research questions and discuss the technical challenges associated with ecotremology studies.

Debal Deb

The decimation of biodiversity at the species, genetic, and ecosystem levels as a direct consequence of the industrial resource use mode is well documented in human ecology and conservation literature. Not only wild biota but also domesticated crop landraces have been pushed to extinction by industrial land-use systems. The process of biodiversity erosion impinges on, and is augmented by, the decimation of local cultural elements, such as food cultures, the vocabularies of local languages, house architecture, and an inchoate appreciation of the non-use value of biodiversity, i.e., beyond its instrumental value. This process of biocultural erosion is evident in the district of Bankura, West Bengal, India, and this article collates evidence from over two decades of my research on the biodiversity and cultural elements of the region. The replacement of a traditional eco-centric ethic with an industrial ethic, and its consequent impacts on biodiversity and local cultural traditions in this region, is illustrative of the global process of biodiversity loss.

Jeba Rezwana, Mary Lou Maher

Human-AI co-creativity involves both humans and AI collaborating on a shared creative product as partners. In a creative collaboration, interaction dynamics, such as turn-taking, contribution type, and communication, are the driving forces of the co-creative process. Therefore the interaction model is a critical and essential component for effective co-creative systems. There is relatively little research about interaction design in the co-creativity field, which is reflected in a lack of focus on interaction design in many existing co-creative systems. The primary focus of co-creativity research has been on the abilities of the AI. This paper focuses on the importance of interaction design in co-creative systems with the development of the Co-Creative Framework for Interaction design (COFI) that describes the broad scope of possibilities for interaction design in co-creative systems. Researchers can use COFI for modeling interaction in co-creative systems by exploring alternatives in this design space of interaction. COFI can also be beneficial while investigating and interpreting the interaction design of existing co-creative systems. We coded a dataset of existing 92 co-creative systems using COFI and analyzed the data to show how COFI provides a basis to categorize the interaction models of existing co-creative systems. We identify opportunities to shift the focus of interaction models in co-creativity to enable more communication between the user and AI leading to human-AI partnerships.

Ada Altieri

In this report, I will review some of the most used models in theoretical ecology along with appealing reformulations and recent results in terms of diversity, stability, and functioning of large well-mixed ecological communities.

Sue Jackson, Elizabeth P. Anderson, Natalia C. Piland et al.

Abstract River rhythmicity refers to the periodic, recurrent phenomena of a riverscape that are synchronized with the rise and fall of river water, creating regimes of river time. River rhythmicity can serve as a lens into the temporal dimension of river formation and socio‐ecological dynamics that are of great interest to many disciplines. In this paper, we introduce river rhythmicity as a conceptual and analytical framework to unify riparian human communities, academic disciplines and water agencies in approaching research and management of rivers. We also explore how the disruptions to riverine rhythms that are experienced by river‐dwelling communities, and are often visible in river discharge data through time, reconfigure, hinder or sever relationships between people and rivers. To ground our discussion in practical, lived experience, we provide brief descriptions of regimes of river time to demonstrate how rhythmic patterns established with rivers in north‐central Canada and Amazonian Colombia shape the lives of two of our co‐authors. By prioritizing holistic accounts of river rhythms, we can elucidate a fuller range of phenomena and their dynamic interactions, revealing riverscape features that are highly valued by local communities yet not often visible to any one discipline. Rhythmicity provides a conceptual framework to help address several challenges facing river conservation and water allocation dilemmas. By emphasizing relationality, it serves to (a) move beyond a biophysical framing of human‐nature connectedness by demonstrating that dynamic processes and relationships are constitutive of rivers, not derivative of them; (b) enhance understanding of how the temporal dimensions of riverine relationships and river dwelling are experienced; (c) highlight the socio‐cultural consequences of changes to river time and (d) centre socially embedded relationships with rivers forged from generations of observations of care and reciprocity. Read the free Plain Language Summary for this article on the Journal blog.

Daniela ZAMFIR, Cristian TĂLÂNGĂ, linca Valentina STOICA

Romanian small towns - urban settlements of less than 20000 inhabitants, having a polarizing function with respect to the socio-economic activities in the deeply rural areas - are considered an interface between rural and urban communities. Determining the identity of small towns is rather difficult, because complex and varied political, social and economic changes occurred in the previous century. Thus, three distinct phases have been established: before 1950 the towns had a rather strong rural character; in 1950-1989 their identity was completely changed under the communist regime; after that, they somehow re gained their initial identity (the one before 1950), or promoted it at higher levels. There is a discrepancy between the present stage and that before 1989: the previous identity was conventional and constrained whereas today it develops in a natural process conditioned only by the town itself and by the choice of its inhabitants.