John H. Porter

Big data and the future of ecology

The need for sound ecological science has escalated alongside the rise of the information age and “big data” across all sectors of society. Big data generally refer to massive volumes of data not readily handled by the usual data tools and practices and present unprecedented opportunities for advancing science and inform- ing resource management through data-intensive approaches. The era of big data need not be propelled only by “big science” – the term used to describe large-scale efforts that have had mixed success in the individual-driven culture of ecology. Collectively, ecologists already have big data to bolster the scientific effort – a large volume of distributed, high-value information – but many simply fail to contribute. We encourage ecologists to join the larger scientific community in global initiatives to address major scientific and societal problems by bringing their distributed data to the table and harnessing its collective power. The scientists who contribute such information will be at the forefront of socially relevant science – but will they be ecologists?

Wireless Sensor Networks for Ecology

Abstract Field biologists and ecologists are starting to open new avenues of inquiry at greater spatial and temporal resolution, allowing them to “observe the unobservable” through the use of wireless sensor networks. Sensor networks facilitate the collection of diverse types of data (from temperature to imagery and sound) at frequent intervals—even multiple times per second—over large areas, allowing ecologists and field biologists to engage in intensive and expansive sampling and to unobtrusively collect new types of data. Moreover, real-time data flows allow researchers to react rapidly to events, thus extending the laboratory to the field. We review some existing uses of wireless sensor networks, identify possible areas of application, and review the underlying technologies in the hope of stimulating additional use of this promising technology to address the grand challenges of environmental science.

New Eyes on the World: Advanced Sensors for Ecology

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 work together across large distances.

Evolution of a multisite network information system: the LTER information management paradigm.

urban watershed, coastal estuary, eastern deciduous forest, tropical rain forest, tallgrass prairie—these are just a few of the ecosystems represented in the 24 sites of the Long-Term Ecological Research (LTER) Network (Franklin et al. 1990). By combining information from the diverse ecosystems represented in the LTER network, participants have a unique opportunity for large-scale investigations of complex phenomena like climate change, biodiversity, soil dynamics, and environmental policy. In 1996, to facilitate data exchange and synthesis from its multiple sites, LTER launched the LTER Network Information System (NIS), based on an independent site and central office organizational infrastructure. Other organizational partnerships provide examples of earlier efforts also focused on communications and data sharing: the Worm Community System, the Flora of North America Project (FNAP), and the Organization of Biological Field Stations (OBFS). The Worm Community System was developed—before Internet connectivity became available—as a collaborative software environment through which its 1400 widely dispersed researchers could share information on the genetics, behavior, and biology of the soil nematode species Caenorhabditis elegans. Insight into the complexity of a network structure was gained through attention to the design and analysis of both the system’s structure and usability (Star and Ruhleder 1996). The FNAP system, in contrast, was developed with Internet technology. The FNAP, with a goal of identifying and cataloging all plant species, uses online technology to create

Quantity is Nothing without Quality: Automated QA/QC for Streaming Environmental Sensor Data

Sensor networks are revolutionizing environmental monitoring by producing massive quantities of data that are being made publically available in near real time. These data streams pose a challenge for ecologists because traditional approaches to quality assurance and quality control are no longer practical when confronted with the size of these data sets and the demands of real-time processing. Automated methods for rapidly identifying and (ideally) correcting problematic data are essential. However, advances in sensor hardware have outpaced those in software, creating a need for tools to implement automated quality assurance and quality control procedures, produce graphical and statistical summaries for review, and track the provenance of the data. Use of automated tools would enhance data integrity and reliability and would reduce delays in releasing data products. Development of community-wide standards for quality assurance and quality control would instill confidence in sensor data and would improve interoperability across environmental sensor networks.

Cross-Scale Patterns in Shrub Thicket Dynamics in the Virginia Barrier Complex

A bstractTo interpret broad-scale erosion and accretion patterns and the expansion and contraction of shrub thickets in response to sea level rise for a coastal barrier system, we examined the fine-scale processes of shrub recruitment and mortality within the context of the influence of ocean current and sediment transport processes on variations in island size and location. We focused on Myrica cerifera shrub thickets, the dominant woody community on most barrier islands along the coastline of the southeastern USA. Observations suggest that M. cerifera, a salt-intolerant species, is increasing in cover throughout the Virginia barrier islands, yet rising sea level in response to climate change is increasing erosion and reducing island area. Our objective was to explain this apparent paradox using pattern–process relationships across a range of scales with a focus on ocean currents and sediment transport interacting with island characteristics at intermediate scales. Multi-decadal comparisons across scales showed a complex pattern. At the scale of the entire Virginia barrier complex, modest decreases in upland area were accompanied by large increases in shrub area. Responses were more variable for individual islands, reflecting inter-island variations in erosion and accretion due to differences in sediment transport via ocean currents. Several islands underwent dramatic shrub expansion. Only for within-island responses were there similarities in the pattern of change, with a lag-phase after initial shrub colonization followed by development of linear, closed canopy thickets. Understanding the fine-scale processes of shrub seedling establishment and thicket development, in conjunction with the influence of ocean currents and sediment transport, provides a framework for interpreting island accretion and erosion patterns and subsequent effects on shrub thicket expansion or contraction across scales of time and space.

The Ethics of Data Sharing and Reuse in Biology

Recent increases in capabilities for gathering, storing, accessing, and sharing data are creating corresponding opportunities for scientists to use data generated by others in their own research. Although sharing data and crediting sources are among the most basic of scientific ethical principles, formal ethical guidelines for data reuse have not been articulated in the biological sciences community. This article offers a framework for developing ethical principles on data reuse, addressing issues such as citation and coauthorship, with the aim of stimulating a conversation in the science community and with the goal of having professional societies formally incorporate considerations of data reuse into their codes of ethics.

Fostering ecological data sharing: collaborations in the International Long Term Ecological Research Network

The International Long Term Ecological Research (ILTER) Network was established in 1993 and is now composed of thirty-eight national networks representing a diversity of ecosystems around the globe. Data generated by the ILTER Network are valuable for scientists addressing broad spatial and temporal scale research questions, but only if these data can be easily discovered, accessed, and understood. Challenges to publishing ILTER data have included unequal distribution among networks of information management expertise, user-friendly tools, and resources. Language and translation have also been issues. Despite these significant obstacles, ILTER information managers have formed grassroots partnerships and collaborated to provide information management training, adopt a common metadata standard, develop information management tools useful throughout the network, and organize scientist/information manager workshops that encourage scientists to share and integrate data. Throughout this article, we share lessons learned from the successes of these grassroots international partnerships to inform others who wish to collaborate internationally on projects that depend on data sharing entailing similar management challenges.

Ecological image databases: From the webcam to the researcher

Abstract Imagery of ecological systems can be used to observe organisms, to observe rare events and to document long-term changes in ecological systems. Here we describe two systems used for archiving and sharing imagery with ecological researchers in the United States and Taiwan, discuss the database design and interface issues and how they were resolved and present some examples of their use. The Shan-Ping Bee Camera System monitors a bee colony at the Shan-Ping Forest Ecological Garden in south-central Taiwan. The Virginia Ecocam System uses wirelessly-connected web cameras to capture imagery from remote barrier islands of the Virginia Coast Reserve. Both systems provide multiple tools to retrieve and display images for research use using a relational database to store and query image metadata, but store the images in either a file system or using a Storage Resource Broker (SRB).

A Metadata-based Framework for Multilingual Ecological Information Management

Herein, we introduce a framework of an ecological information management prototype of tools based on a metadata standard. The framework was developed by the Ecological Informatics Working Group of the Taiwan Forestry Research Institute (TFRI) to aid with editing, storing, and using documents in the multiple languages of Asian cultures that comprise the East-Asia Pacific International Long-Term Ecological Research (EAP-ILTER) Network. The conceptual framework of the system can be divided into three tiers. The first tier deals with datasets and related information. The second tier relates to information management. Once datasets and other related information have been described, they are stored in a schema-independent database. The third tier is comprised of the full web-based interfaces that allow easy access to the second tier. Results of the application of this framework consist of an Ecological Metadata Language (EML) document database module, a data analysis function module, and a collection of 58 EML documents.