Deana D. Pennington

An ontology for describing and synthesizing ecological observation data

Abstract Research in ecology increasingly relies on the integration of small, focused studies, to produce larger datasets that allow for more powerful, synthetic analyses. The results of these synthetic analyses are critical in guiding decisions about how to sustainably manage our natural environment, so it is important for researchers to effectively discover relevant data, and appropriately integrate these within their analyses. However, ecological data encompasses an extremely broad range of data types, structures, and semantic concepts. Moreover, ecological data is widely distributed, with few well-established repositories or standard protocols for their archiving and retrieval. These factors make the discovery and integration of ecological data sets a highly labor-intensive task. Metadata standards such as the Ecological Metadata Language and Darwin Core are important steps for improving our ability to discover and access ecological data, but are limited to describing only a few, relatively specific aspects of data content ( e.g. , data owner and contact information, variable “names”, keyword descriptions, etc. ). A more flexible and powerful way to capture the semantic subtleties of complex ecological data, its structure and contents, and the inter-relationships among data variables is needed. We present a formal ontology for capturing the semantics of generic scientific observation and measurement. The ontology provides a convenient basis for adding detailed semantic annotations to scientific data, which crystallize the inherent “meaning” of observational data. The ontology can be used to characterize the context of an observation ( e.g. , space and time), and clarify inter-observational relationships such as dependency hierarchies ( e.g. , nested experimental observations) and meaningful dimensions within the data ( e.g. , axes for cross-classified categorical summarization). It also enables the robust description of measurement units ( e.g. , grams of carbon per liter of seawater), and can facilitate automatic unit conversions ( e.g. , pounds to kilograms). The ontology can be easily extended with specialized domain vocabularies, making it both broadly applicable and highly customizable. Finally, we describe the utility of the ontology for enriching the capabilities of data discovery and integration processes.

Response of an aridland ecosystem to interannual climate variability and prolonged drought

Water is a key driver of ecosystem processes in aridland ecosystems. Thus, changes in climate could have significant impacts on ecosystem structure and function. In the southwestern US, interactions among regional climate drivers (e.g., El Niño Southern Oscillation) and topographically controlled convective storms create a spatially and temporally variable precipitation regime that governs the rate and magnitude of ecosystem processes. We quantified the spatial and temporal distribution of reduced grassland greenness in response to seasonal and annual variation in precipitation at two scales at the Sevilleta Long Term Ecological Research site in central New Mexico, using Normalized Difference Vegetation Index (NDVI) values from bi-weekly AVHRR data and seasonal ETM data from 1989 to 2005. We used spatially explicit NDVI Z-scores to identify times and places of significantly reduced greenness and related those to interactions between plant functional type, seasonal climate variation, and topography. Seasonal greenness was bimodal with a small peak in spring and a stronger peak following the summer monsoon. Greenness was generally spatially homogeneous in spring and more spatially variable in summer. From 2001 through spring 2002, drought effects were evidenced by a 4-fold increase in the number of pixels showing significantly low greenness. Spatial distribution of low greenness was initially modulated by topographic position, but as the drought intensified spread throughout the study area. Vegetation green up occurred rapidly when drought conditions ceased. We conclude that drought effects vary spatially over time, pervasive drought reduces broad-scale spatial heterogeneity, and greenness patterns recover rapidly when drought conditions end.

SCALE-DEPENDENT RESPONSES OF PLANT BIODIVERSITY TO NITROGEN ENRICHMENT

Experimental studies demonstrating that nitrogen (N) enrichment reduces plant diversity within individual plots have led to the conclusion that anthropogenic N enrichment is a threat to global biodiversity. These conclusions overlook the influence of spatial scale, however, as N enrichment may alter beta diversity (i.e., how similar plots are in their species composition), which would likely alter the degree to which N-induced changes in diversity within localities translate to changes in diversity at larger scales that are relevant to policy and management. Currently, it is unclear how N enrichment affects biodiversity at scales larger than a small plot. We synthesized data from 18 N-enrichment experiments across North America to examine the effects of N enrichment on plant species diversity at three spatial scales: small (within plots), intermediate (among plots), and large (within and among plots). We found that N enrichment reduced plant diversity within plots by an average of 25% (ranging from a reduction of 61% to an increase of 5%) and frequently enhanced beta diversity. The extent to which N enrichment altered beta diversity, however, varied substantially among sites (from a 22% increase to an 18% reduction) and was contingent on site productivity. Specifically, N enrichment enhanced beta diversity at low-productivity sites but reduced beta diversity at high-productivity sites. N-induced changes in beta diversity generally reduced the extent of species loss at larger scales to an average of 22% (ranging from a reduction of 54% to an increase of 18%). Our results demonstrate that N enrichment often reduces biodiversity at both local and regional scales, but that a focus on the effects of N enrichment on biodiversity at small spatial scales may often overestimate (and sometimes underestimate) declines in regional biodiversity by failing to recognize the effects of N on beta diversity.

A knowledge environment for the biodiversity and ecological sciences

The Science Environment for Ecological Knowledge (SEEK) is a knowledge environment that is being developed to address many of the current challenges associated with data accessibility and integration in the biodiversity and ecological sciences. The SEEK information technology infrastructure encompasses three integrated systems: (1) EcoGrid—an open architecture for data access; (2) a Semantic Mediation System based on domain-specific ontologies; and (3) an Analysis and Modeling System that supports semantically integrated analytical workflows. Multidisciplinary scientists and programmers from multiple institutions comprise the core development team. SEEK design and development are informed by three multidisciplinary teams of scientists organized in Working Groups. The Biodiversity and Ecological Analysis and Modeling Working Group informs development through evaluation of SEEK efficacy in addressing biodiversity and ecological questions. The Knowledge Representation Working Group provides knowledge representation requirements from the domain sciences and develops the corresponding knowledge representations (ontologies) to support the assembly of analytical workflows in the Analysis and Modeling System, and the intelligent data and service discovery in the EcoGrid. A Biological Classification and Nomenclature Working Group investigates solutions to mediating among multiple taxonomies for naming organisms. A multifaceted education, outreach and training program ensures that the SEEK research products, software, and information technology infrastructure optimally benefit the target communities.

Ecological Niche Modeling Using the Kepler Workflow System

Changes in biodiversity have been linked to variations in climate and human activities [295]. These changes have implications for a wide range of socially relevant processes, including the spread of infectious disease, invasive species dynamics, and vegetation productivity [27, 70, 203, 291, 294, 376, 426]. Our understanding of biodiversity patterns and processes through space and time, scaling from genes to continents, is limited by our ability to analyze and synthesize multidimensional data effectively from sources as wide-ranging as field and laboratory experiments, satellite imagery, and simulation models.

Data integration and workflow solutions for ecology

The Science Environment for Ecological Knowledge (SEEK) is designed to help ecologists overcome data integration and synthesis challenges. The SEEK environment enables ecologists to efficiently capture, organize, and search for data and analytical processes. We describe SEEK and discuss how it can benefit ecological niche modeling in which biodiversity scientists require access and integration of regional and global data as well as significant analytical resources.

The Dynamics of Material Artifacts in Collaborative Research Teams

Boundary objects are material artifacts that mediate the relationship between two or more disparate perspectives. The concept of boundary objects has been demonstrably useful in a variety of research areas; however, the meaning and function of boundary objects is contested. At issue is the relationship between boundary objects that negotiate between perspectives and those that specify across perspectives. In this study the changing nature of boundary objects in cooperative work is related to the dynamics of evolving problem conceptualization, system design, and enactment within cooperative work settings. Design based research on material artifacts produced by an incipient cross-disciplinary research team during their efforts towards negotiating integrated conceptualizations and specifying shared research agendas is used to generate a more comprehensive model of boundary objects through the life of a project.

GBD-Explorer: Extending open source java GIS for exploring ecoregion-based biodiversity data

Abstract Biodiversity and ecosystem data are both geo-referenced and “species-referenced”. Ecoregion classification systems are relevant to basic ecological research and have been increasingly used for making policy and management decisions. There are practical needs to integrate taxonomic data with ecoregion data in a GIS to visualize and explore species distribution conveniently. In this study, we represent the species distributed in an ecoregion as a taxonomic tree and extend the classic GIS data model to incorporate operations on taxonomic trees. A prototype called GBD-Explorer was developed on top of the open source JUMP GIS. We use the World Wildlife Fund (WWF) terrestrial ecoregion and WildFinder species databases as an example to demonstrate the rich capabilities implemented in the prototype.

Using web services and scientific workflow for species distribution prediction modeling

Species distribution prediction modeling plays a key role in biodiversity research. We propose to publish both species distribution data and modeling components as Web services and composite them into modeling systems using the scientific workflow approach. We build a prototype system using Kepler scientific workflow system and demonstrate the feasibility of the proposed approach. This study is the first step towards building a virtual e-science laboratory for ecologists to perform distributed and cooperative research on species distribution predictions.

Performance Evaluations of Geospatial Web Services Composition and Invocation

Geospatial data and analytical functions are essential to geospatial modeling. There are increasing interests in publishing both geospatial data and analytical functions as Web services and use them as the building blocks for domain specific geospatial modeling. While the advantages of using the Web services technologies have been well recognized and a number of prototype systems have been built to demonstrate the feasibility, very few performance evaluations have been reported in the previous studies. Compared with business data, geospatial data is rich in data types, large in data volumes and complex in semantics. On the other hand, the Web services technologies are known to have significant overheads with respects to deployment and invocation. The answers to how effective the Web services technologies can be, and, to what extent they are effective under the typical computation environments for geospatial modeling remain largely unknown. In this study we have set up an experimental system by deploying several geospatial Web services on top of popular commercial and open source spatial databases and geographical information systems (GIS). The Kepler scientific workflow system is used for geospatial Web services composition and invocation. We have conducted experiments to chain the geospatial Web services into a geospatial model under two data volume levels and two network settings. Our experiments show that the geospatial modeling using the Web services technologies remains effective in the wired LAN computation environment for data volume as large as 10000 points. However, the same data volume level incurs significant response lags under the wireless WAN computation environment. The experimental results may be used as a guideline for geospatial modeling using the Web services technologies when performances need to be taken into considerations.