We summarize the foundational elements of a new area of research we call soundscape ecology. The study of sound in landscapes is based on an understanding of how sound, from various sources—biological, geophysical and anthropogenic—can be used to understand coupled natural-human dynamics across different spatial and temporal scales. Useful terms, such as soundscapes, biophony, geophony and anthrophony, are introduced and defined. The intellectual foundations of soundscape ecology are described—those of spatial ecology, bioacoustics, urban environmental acoustics and acoustic ecology. We argue that soundscape ecology differs from the humanities driven focus of acoustic ecology although soundscape ecology will likely need its rich vocabulary and conservation ethic. An integrative framework is presented that describes how climate, land transformations, biodiversity patterns, timing of life history events and human activities create the dynamic soundscape. We also summarize what is currently known about factors that control temporal soundscape dynamics and variability across spatial gradients. Several different phonic interactions (e.g., how anthrophony affects biophony) are also described. Soundscape ecology tools that will be needed are also discussed along with the several ways in which soundscapes need to be managed. This summary article helps frame the other more application-oriented papers that appear in this special issue.

What is soundscape ecology? An introduction and overview of an emerging new science

Innovative uses of advanced sensors and sensor networks are starting to be translated into new ecological knowledge. These sensors are providing a new set of “eyes” through which researchers may observe the world in new ways, extend spatial and temporal scales of observation, more accurately estimate what cannot be observed, and, most important, obtain unexpected results or develop new paradigms. Automated sensors are widely deployed by members of the Organization of Biological Field Stations, yet some needs—particularly for chemical and biological sensors—are not currently being met. There are additional opportunities for developing sensor networks at synoptic, regional, continental, and global scales. Although we are seeing more uses of sensor systems and, in particular, sensor networks, the opportunities for these systems are just beginning to be realized, with much more work to be done, including formulation of new questions, development of new sensors, better software, and new ways for researchers to...

https://www.jstor.org/stable/10.1525/bio.2009.59.5.6

New Eyes on the World: Advanced Sensors for Ecology

Network centric approach to complex systems has been attracting a lot of attention in the recent past. With specific applications in space, military and other information enterprises, such an approach is capable of yielding autonomy at the system level. This article describes a distributed approach to information processing in sensor networks. The network architecture entails a hierarchy of capabilities, information and control, where nodes in the network are thoroughly or partially autonomous. The network also consists of slave nodes dedicated to sensing and gathering information. Individual nodes are expected to possess resources for networking and computing and presume autonomy through multi functional modules for sensory processing and situation assessment. Data association, registration and fusion are formulated into a joint process, and an expectation maximization (EM) approach is developed to solve the three problems simultaneously. A fuzzy cognitive map (FCM) is proposed to perform situation assessment, while a genetic algorithm (GA) is applied for learning, to identify an optimal structure. The distributed architecture is applied to coastal surveillance with simulations on the west coast of Canada.

Distributed sensing based on intelligent sensor networks

Monitoring the environment with acoustic sensors is an effective method for understanding changes in ecosystems. Through extensive monitoring, large-scale, ecologically relevant, datasets can be produced that can inform environmental policy. The collection of acoustic sensor data is a solved problem, the current challenge is the management and analysis of raw audio data to produce useful datasets for ecologists. This paper presents the applied research we use to analyze big acoustic datasets. Its core contribution is the presentation of practical large-scale acoustic data analysis methodologies. We describe details of the data workflows we use to provide both citizen scientists and researchers practical access to large volumes of ecoacoustic data. Finally, we propose a work in progress large-scale architecture for analysis driven by a hybrid cloud-and-local production-grade website.

Practical Analysis of Big Acoustic Sensor Data for Environmental Monitoring

Wireless sensor networks (WSNs) offer a powerful feasible integration of distributed sensing capability, real-time data analysis, and remote surveillance due to the combined result of miniaturization of electronic devices and availability of powerful computational capability, larger information storage, and ubiquitous Internet connection. With these advances, WSNs are starting to be translated into a new ecological knowledge. They are providing a new insight into the observation of the world in new ways of extended spatial and temporal scales. Through WSNs, more unexpected phenomena can be obtained, and new paradigms can be developed. Recently, more and more ecological WSNs have been established, and lagre WSNs are deployed to monitor habitats with different scales. The research in the temporal scale ranges from the evaluation of soil moisture dynamics at several minutes to daily precipitation. Spatial measurements, on the other hand, range from the evaluation of global climate change to those related to the monitoring of forest and riparian environments in the range of a few meters. Although we are seeing more use of ecological WSNs, opportunities and challenges begin to be realized, including newly better design of software and hardware, formulation of new questions, discovery of previously unobservable phenomena, and development of new sensors, etc.

Ecological Monitoring Using Wireless Sensor Networks-—Overview, Challenges, and Opportunities

The LTER Grid Pilot Study was conducted by the National Center for Supercomputing Applications, the University of New Mexico, and Michigan State University, to design and build a prototype grid for the ecological community. The featured grid application, the Biophony Grid Portal, manages acoustic data from field sensors and allows researchers to conduct real-time digital signal processing analysis on high-performance systems via a web-based portal. Important characteristics addressed during the study include the management, access, and analysis of a large set of field collected acoustic observations from microphone sensors, single signon, and data provenance. During the development phase of this project, new features were added to standard grid middleware software and have already been successfully leveraged by other, unrelated grid projects. This paper provides an overview of the Biophony Grid Portal application and requirements, discusses considerations regarding grid architecture and design, details the technical implementation, and summarizes key experiences and lessons learned that are generally applicable to all developers and administrators in a grid environment.

/pdf/cyberinfrastructure-for-the-analysis-of-ecological-acoustic-7h7brc5men.pdf

Cyberinfrastructure for the analysis of ecological acoustic sensor data: a use case study in grid deployment

The LTER grid pilot study was conducted by the National Center for Supercomputing Applications, the University of New Mexico, and Michigan State University, to design and build a prototype grid for the ecological community. The featured grid application, the Biophony Grid Portal, manages acoustic data from field sensors and allows researchers to conduct real-time digital signal processing analysis on high-performance systems via a Web-based portal. Important characteristics addressed during the study include the management, access, and analysis of a large set of field collected acoustic observations from microphone sensors, single signon, and data provenance. During the development phase of this project new features were added to standard grid middleware software and have already been successfully leveraged by other, unrelated grid projects. This paper provides an overview of the Biophony Grid Portal application and requirements, discusses considerations regarding grid architecture and design, details the technical implementation, and summarizes key experiences and lessons learned that are generally applicable to all developers and administrators in a grid environment.

/pdf/cyberinfrastructure-for-the-analysis-of-ecological-acoustic-dtql36k540.pdf

CyberInfrastructure for the analysis of ecological acoustic sensor data: a use case study in grid deployment

As grids move from prototypes to testbeds to production infrastructure, grid resource providers are faced with the challenge of de- livering reliable services to enable productive use of available resources. On high performance, distributed grids such as the TeraGrid, moving large data sets to, from, and between supercomputing resources requires reliable data management services. The Reliable File Transfer (RFT) Service in the Globus Toolkit Version 4 (GT4) provides this capability on the TeraGrid and other grids. We present modifications to RFT to support clustering to achieve high availability in the presence of server failures, based on a standard Web service tiered architecture, leveraging the capa- bilities of modern database systems. Clustering distributes the RFT service across multiple tightly coupled servers so that RFT can continue to provide service even when individual components fail.

/pdf/clustering-the-reliable-file-transfer-service-2nyoi5v9qr.pdf

Clustering the Reliable File Transfer Service

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Cyberinfrastructure for the analysis of ecological acoustic sensor data: a use case study in grid deployment

CyberInfrastructure for the analysis of ecological acoustic sensor data: a use case study in grid deployment

Clustering the Reliable File Transfer Service