MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,

Coupling Scientific and Humanistic Approaches to Address Wicked Environmental Problems of the Twenty-first Century: Collaborating in an Acoustic Community Nexus

Addressing serious environmental challenges, or wicked problems, locally and globally, we argue here that working collaboratively as scientist and humanist we are in a strategic position to help address biodiversity crises. We outline synergies that combine the strengths, tools, and fresh perspectives of soundscape ecology and sound studies in ethnomusicology. Our unique collaboration places sound at the core of our process but utilizes a community acoustics lens to bring both the sounds of nature and those of people together to couple our epistemologies, methodologies, and deep commitment to addressing the ecological needs today.Pour aborder les graves défis, ou sévères problèmes, environnementaux, aux niveaux local et global, nous avançons ici qu’en travaillant en collaboration en tant que scientifique et humaniste, nous nous plaçons en position stratégique pour contribuer à répondre aux crises de la biodiversité. Nous soulignons les synergies qui associent les forces, les outils et les nouvelles perspectives sur l’écologie des paysages sonores et les études sur le son en ethnomusicologie. Notre collaboration unique place le son au coeur du processus, mais a recours au prisme de la communauté acoustique pour rassembler tant les sons de la nature que ceux produits par les gens ensemble pour apparier nos épistémologies, nos méthodologies et notre profond engagement pour répondre aux besoins écologiques d’aujourd’hui

Soundscape Conservation in U.S. National Parks: Implications for Adjacent Land Use Planning

Humans have altered the Earth’s ecosystems and biodiversity significantly. With the conversion of land and the loss of biodiversity, the world loses its natural sounds. The loss of natural sounds is compounded by the growing intrusions of motorized noise. Noise pollution is a ubiquitous problem in cities around the world, but the issue is spreading to more remote areas due to expanding transportation networks, motorized recreation and urban sprawl. The U.S. National Park Service (NPS) recognizes park soundscapes, or entire acoustic environment of a given area, as resources just as air and water are resources. However, national park resources are only provided protection within a legally defined boundary separating it from surrounding land uses. To better understand the acoustic resources and noise issues in parks, the U.S. NPS Natural Sounds Program sent a survey to each of the park units (n=391) in 2009. There were 149 respondents representing 141 different park units. We analyzed the data using qualitative theme identification and quantitative analyses. The primary noise impacts for parks were from motorized noise sources (n=97), and specifically road noise was reported by 36 respondents. Adjacent land uses were identified as causing specific impacts by 15 respondents. We demonstrate how Geographic Information Systems can be used to quantify the noise impacts from surrounding development mentioned by park respondents. We buffered urban land use of responding park units using ArcGIS. The total urban area of each park unit was compared to survey results to determine if urban area correlated to parks conducting noise mitigation measures. Respondents (n=14) mentioned adjacent land use planning as a measure that they were using to mitigate noise impacts. The research findings from this study will help guide future soundscape conservation efforts by NPS

Tooting the Latin American horn: advances in the scholarship of ecoacoustics and soundscape ecology is occurring with vigor

Soundscape ecology (Pijanowski et al., 2011a,b; Pijanowski & Farina, 2011), ecoacoustics (Sueur & Farina, 2015) and bioacoustics (Laiolo, 2010; Aide et al., 2013) Soundscape ecology (Pijanowski et al., 2011a,b; Pijanowski & Farina, 2011), ecoacoustics (Sueur & Farina, 2015) and bioacoustics (Laiolo, 2010; Aide et al., 2013) have witnessed an explosion of work over the last decade due to the advances in passive acoustic recorders, new conceptual frameworks that integrate the study of sound into environmental change research, abilities to store and analyze massive data, and the growing need to understand how the rapid declines in species richness and abundances can be addressed with intervention policies at the landscape level. Also of note are the tremendous contributions to these acoustic-based fields of research that are occurring from all across the global academy, with advances being made by researchers working in just about every continent and country. The contributions from Latin America are impressive and play an important and unique role in our understanding of how a variety of sound sources – from biological organisms (i.e., the biophonies), to the geophysical world (i.e., geophonies) and those from humans (i.e., anthrophonies or technophonies) – are used to assess how important Latin American ecosystems are being altered. The purpose of this paper is to provide a summary of the multi-faceted work being conducted in Latin America as represented in the exceptional papers of this special issue on ecoacoustics and soundscape ecology, placing these in the context of the growing scholarship in these acoustics-based areas of study

Urban Growth Modelling with Artificial Neural Network and Logistic Regression. Case Study: Sanandaj City, Iran

Cities have shown remarkable growth due to attraction, economic, social and facilities centralization in the past few decades. Population and urban expansion especially in developing countries, led to lack of resources, land use change from appropriate agricultural land to urban land use and marginalization. Under these circumstances, land use activity is a major issue and challenge for town and country planners. Different approaches have been attempted in urban expansion modelling. Artificial Neural network (ANN) models are among knowledge-based models which have been used for urban growth modelling. ANNs are powerful tools that use a machine learning approach to quantify and model complex behaviour and patterns. In this research, ANN and logistic regression have been employed for interpreting urban growth modelling. Our case study is Sanandaj city and we used Landsat TM and ETM+ imageries acquired at 2000 and 2006. The dataset used includes distance to main roads, distance to the residence region, elevation, slope, and distance to green space. Percent Area Match (PAM) obtained from modelling of these changes with ANN is equal to 90.47% and the accuracy achieved for urban growth modelling with Logistic Regression (LR) is equal to 88.91%. Percent Correct Match (PCM) and Figure of Merit for ANN method were 91.33% and 59.07% and then for LR were 90.84% and 57.07%, respectively

Acoustic Signatures of Habitat Types in the Miombo Woodlands of Western Tanzania

The Miombo Woodlands of Tanzania comprise several habitat types that are home to a great number of flora and fauna. Understanding their responses to increasing human disturbance is important for conservation, especially in places where people depend so directly on their local ecosystem services to survive. Soundscapes are a powerful approach to study complex biomes undergoing change. The sounds emitted by soniferous fauna characterize the acoustic profile of the landscapes they inhabit such that habitats with the highest acoustic abundance are considered as the most diverse and possibly more ecologically resilient. However, acoustic variability within similar habitat types may pose a challenge not only in determining their pristine condition but also in acoustically detecting their ecological change. The study presented here attempts to assess the soundscapes across habitat types in the Miombo Woodlands of the Issa Valley, Tanzania. A set of 24-hour audio recordings was collected using passive acoustic recorders over an area of 18000 km2. Sites were clustered based on acoustic indices that quantified sound diversity, and this cluster was compared to a similar cluster based on habitat physical attributes calculated using MODIS imagery and geographic information systems. The culminating analysis is a statistical correlation between habitats as defined by their soundscapes and by their landscape attributes. Results indicate acoustic variability was greater in dense canopy habitats and that wetland soundscapes are strongly correlated to the landscape attributes. More analysis need to be done to detect further ecological factors affecting the soundscape in places where the variability was high

Biological invasion hotspots: a trait-based perspective reveals new sub-continental patterns

Invader traits (including plant growth form) may play an important, and perhaps overlooked, role in determining macroscale patterns of biological invasions and therefore warrant greater consideration in future investigations aimed at understanding these patterns. To assess this need, we used empirical data from a national-level survey of forest in the contiguous 48 states of the USA to identify geographic hotspots of forest plant invasion for three distinct invasion characteristics: invasive species richness, trait richness (defined as the number of the five following plant growth forms represented by the invasive plants present at a given location: forbs, grasses, shrubs, trees, and vines), and species richness within each growth form. Three key findings emerged. 1) Th e hotspots identified encompassed from 9 to 23% of the total area of our study region, thereby revealing many forests to be not only invaded, but highly invaded. 2) Substantial spatial disagreement among hotspots of invasive species richness, invasive trait richness, and species richness of invasive plants within each growth form revealed many locations to be hotspots for invader traits, or for particular growth forms of invasive plants, rather than for invasive plants in general. 3) Despite eastern forests exhibiting higher levels of plant invasion than western forests, species richness for invasive forbs and grasses in the west were respectively greater than and equivalent to levels found in the east. Contrasting patterns between eastern and western forests in the number of invasive species detected for each growth form combined with the spatial disagreement found among hotspot types suggests trait-based variability in invasion drivers. Our findings reveal invader traits to be an important contributor to macroscale invasion patterns

Animation by Computer. a Tool for Understanding the Dynamical Behaviour of Ancient Machines

The datasets in this article are associated with the research article \u27Assessing biological and environmental effects of a total solar eclipse with passive multimodal technologies\u27 (Brinley Buckley et al., 2018). We documented biotic and abiotic changed during a total solar eclipse on 21 August 2017, in south-central Nebraska, USA, with a multimodel suite of tools, including time-lapse camera systems, data loggers, and sound recording devices. Time-lapse images were used to approximate changes in light, data loggers were used to record temperature and humidity, and sound recordings were used to calculate acoustic indices characterizing variation in the soundscape, as well as to manually identify and estimate avian vocalization activity

Iterative Near-Term Ecological Forecasting: Needs, Opportunities, And Challenges

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward