A. Townsend Peterson, PhD - US grants

The study consists of an analysis of patterns of distribution, diversity and endemism in the terrestrial vertebrate fauna (amphibians, reptiles, birds, and mammals) of humid montane forests in the state of Oaxaca in southern Mexico. Two years will be spent exploring the four mountain ranges in the state that are least well known faunistically: the Sierra de Miahuatlan, Sierra de Yucuyacua, Sierra de Huautla, and various mountains in the Chimalapas region. Information from other recent explorations, as well as historical records, will be added in a geographic information system (GIS) data base. The GIS will allow analysis of the faunistic data in relation to physical geographic features, economic characteristics, and political limits. This study is a collaborative effort between the Field Museum of Natural History and the Universidad Nacional Autonoma de Mexico, and will result in the training of seven young Mexican biologists in techniques for biological inventories, data management, specimen curation, and analysis of conservation priorities.

Peterson 97-11621 Studies of the Magnitude of climate changes expected resulting from buildup of atmospheric CO2 are predicted to affect many facets of the Earth's environments, although the magnitude of many of these effects is unknown. This study aims to integrate data bases regarding the biodiversity of Mexico, thematic geographic data, up-to-date information from satellite imagery, and simple models of global climate change into a series of predictions of the effects of these changes on species of birds, mammals, and butterflies in the region, a first assessment of the biodiversity consequences of global climate change. Multi-seasonal analysis of Advanced Very High Resolution Radiometer imagery will be used to produce a detailed region wide vegetation map, which will be combined with existing environmental maps and data bases to characterize habitat types. Simple models of the effects of elevated C02 and other agents of climate change will be used to mimic future shifts in distribution and extent of vegetation types and habitats. Geographic distributions of individual species will be predicted based on characteristics of sites of known occurrence; based on the future-shift models developed from climate change projections, species' future distributions will be modeled, and extinction's and colonization inferred. The overall result will be an assessment of the biodiversity consequences of anticipated climate change over the next several decades or centuries in Mexico, as well as the development of several methods and models that will be useful to other investigators interested in similar and related issues.

9801587 Peterson Spatial scale has been considered carefully in the study of fragmented ecosystems, but the effects of temporal scale have been largely overlooked. Most fragmentation studies focus on anthropogenically fragmented situations that were largely fragmented on a time scale of 101 - 102 yr. This is a time scale within which faunas are expected to still be in the dynamics of relaxation. Naturally fragmented systems, however, provide an opportunity to view older fragmentation schemes ( 103 - 104 yr), potentially allowing analysis of response patterns under equilibrium conditions. The Oaxaca Valley in southern Mexico represents a naturally fragmented ecosystem, ideal for the study of the long-term effects of habitat fragmentation. Twelve thousand years ago, humid pine-oak forest was the dominant vegetation type throughout the region. This forest type is now restricted to high elevations. In the proposed study, the resident bird communities in these relict patches will be inventoried, distribution patterns described and avifaunal variation related to patch characteristics. Distributional data will be compiled using existing historic specimen data, and inventories completed by field inventories. Fragments will be described based on direct field measurement, consultation of thematic maps, and data from remotely sensed imagery. Robust new statistical techniques, which we have tested in anthropogenically fragmented ecosystems, will be applied to understanding influences of temporal scale on effects of fragmentation on avifaunas. Additional analyses will focus on functional groups of taxa, such as feeding guilds, phylogenetic lineages, etc., to provide more detailed understanding of processes involved. Results will be applied to designing a reserve system for maximal preservation of the bird diversity of the ecosystem.

9808739

The aim of this project is to design. implement and optimize a system enabling taxono-mists, conservation biologists. and decision-makers to access large quantities of data regarding the distribution and diversity of birds based on data from systematic collections and observational data sets. It will be composed of three geographically separated func-tional subsystems The first subsystem at the University of Kansas (KU) still allows man-agement of and access to integrated data housed in five large. geographically separate databases in institutions in Canada, the United States and Mexico. The second subsystem at the San Diego Supercomputer Center, will permit user-designed analysis and visualiza-tion of results from the data subsystem using a GIS database. The third, Java-based sub-system, situated on users' machines, will perform GIS mapping using results from the first two subsystems. The system builds on an existing database and map server developed at KU and the Biodiversity Insight System developed at SDSC. Open protocols such as Z39.50 and CGI will be used throughout to ensure scalability to additional databases and other taxa. allowing the system to encompass many additional sources of data.

This project will support the rehousing of the entire dry bird specimen collection in the Natural History Museum and Biodiversity Research Center, University of Kansas. The collection requires rehousing for two reasons:

1. Collections crowding - Collections growth through systematic research has resulted in severe crowding of specimens in cases, with no new space available in the collections area. Moreover, physical constraints of the collections area makes it unsuitable for installation of mobile compactor units.

2. Collections conservation- Almost all of the cases are more than 30 years old, and many have degraded to the point that they no longer provide adequate protection for specimen material against pest infestations and environmental fluctuations.

The solution is to rehouse the collection in larger, taller, modern cases. This solution is far less costly than installation of mobile compactor units, and will provide more space and better protection.

Approximately 20% more space would be available for specimen storage, enough to solve critical space problems and to provide about 5 years of normal collections expansion.

The new cases, already designed and tested as prototypes, would provide state-of-the-art protection for this world-class ornithological collection.

Understanding and sustaining the natural world in the 21st century depends on improving our capacity to access ecological, earth science, and human-dimension data; mining these data for new knowledge; and conveying new insights to decision-makers and the general public. Computer science and information technology research can effectively address many of these issues and advance our ability to conduct ecological science. This multidisciplinary research investigation will create a "Science Environment for Ecological Knowledge" (SEEK)-an information technology framework and infrastructure that will be used to derive and extend ecological knowledge by facilitating the discovery, access, integration, interpretation, and analyses of distributed ecological information. SEEK will provide for the integration of local desktop data with a larger network of data and analytical tools, enabling ecologists and other researchers to tackle complex research problems that were hitherto intractable.
The SEEK initiative stands on the foundation of substantial and productive NSF investment in ecological and biodiversity informatics and it brings together four highly collaborative, forward-looking institutions in a partnership committed to inventing and supporting a global computing infrastructure for environmental biology. The project involves a multidisciplinary team of computer scientists, ecologists and technologists from the Partnership for Biodiversity Informatics (PBI), a consortium comprising the National Center for Ecological Analysis and Synthesis (NCEAS); the San Diego Supercomputer Center (SDSC); the University of Kansas (KU), and the University of New Mexico (UNM)) and partnering institutions (Arizona State University, University of North Carolina, University of Vermont, and Napier University in Scotland).
This five-year initiative will lead to fundamental improvements in how researchers can 1) gain global access to data and information, 2) rapidly locate and utilize distributed computational services, and 3) exercise powerful new methods for capturing, reproducing, and extending the analysis process itself. SEEK will also specifically provide ecologists and other researchers access to a large-scale network of information resources and computational services, via powerful data discovery and analysis tools that operate from desktop computers. These capabilities will significantly build research capacity to more effectively address global research, management and policy issues in environmental biology that increasingly require much more efficient, automated access to distributed and heterogeneous data.
A multi-faceted approach will be employed to insure that the research products, software, and information technology infrastructure resulting from SEEK optimally benefit science, education, and the public. Outreach includes community involvement, a WWW portal, informatics training, and an innovative annual symposium and training program that focuses on information technology transfer to young investigators and students, particularly those from underrepresented groups.
In the information economy, access to information for knowledge creation and decision-making is as valuable as the information itself. This project will enable bringing the intellectual content of biodiversity and ecological information into currency for science and society. Examples of significant project outcomes include: (i) revolutionizing discovery, access to and integration of ecological, earth, and human dimension data and information through the SEEK infrastructure; (ii) developing intelligent analytical tools and infrastructure to support the needs of scientists, decision-makers, and the general public; (iii) education and training of the next generation of ecologists in information technology skills; and (iv) improving the opportunities for scientists, resource managers, policy makers, and the public to make scientifically-informed decisions about the environment by expanding access to ecological data, information, and knowledge.

West Nile virus (WNV) is a flavivirus that is native to Europe, Asia and eastern Africa. It appeared in New York City in 1999 and has since spread to much of the eastern USA. Models of WNV spread in the New World are clarifying the relative roles of both the mosquito host and other vectors, especially migratory birds. Recent reports place WNV in the Caribbean, which has serious consequence for endemic bird species in the region, many of which are endanger. This project will support more detailed models and a program of sampling will further document the arrival of WNV in the Caribbean and help predict its spread and possible containment.

This project represents an integrated effort to document the terrestrial
vertebrate biodiversity of the southern borderlands of China, one of the
least well explored regions worldwide. Specialists working with birds,
mammals, reptiles, and amphibians will staff the expeditions, accompanied by
experts with various parasite groups (fleas, flies, ticks, lice, nematodes,
cestodes, blood parasites, coccidia, etc.). The 5 expeditions will each
focus on a different sector of the borderlands region, including the border
areas with Laos, Vietnam, Burma, and northeastern India. Each specialist
will use tools for inventory that are the 'state of the art' in his or her
own field. The end result will be extensive series of specimens and other
new biological material for detailed study, as well as numerous scientific
publications documenting local vertebrate and parasite communities,
taxonomic insights, and new species. Parasite specimens will be collected
and stabilized, with a view towards long-term curation and eventual study.
Project data will be served to the broader scientific community with maximum
efficiency via Internet-based distributed database technology.

This project is near-unique in its broad-spectrum assessment of vertebrates
and many groups of parasites. Such a view of vertebrates and the full (or at
least a broad sample of the) diversity of their parasites is available from
few places on Earth, making for a new view into the true biological richness
of an area. In the case of the Chinese southern borderlands, not only will
many of the parasites be unknown to science, but even some or many of the
vertebrate hosts as well. Many new insights into the conservation of
biological diversity will come from such detailed views of the nature of
this diversity. This information will go beyond the quick-and-easy
vertebrate-based conservation priorities that currently dominate
conservation biology to provide a detailed view of the true dimensions of
biological diversity in a complex and little-known region.

SGER BS&I: Accelerating discovery of new species in Madagascar using remotely sensed data and ecological niche modeling

Christopher Raxworthy and Townsend Peterson

Exploratory Work on a New Research Idea:- Raxworthy et al. (2003, Nature 426, 837-841) recently published results suggesting that remotely sensed data (collected by satellites and the Space Shuttle) and ecological niche modeling can be used to help identify areas with new species. Distribution modeling was used to successfully predict chameleon species occurrences in Madagascar. But some of these models also produced distribution error: they over-predicted occurrences of species in 3 intersecting areas, which actually yielded 7 species new to science. These new species exhibit characters supporting close relationships to the species originally targeted for distribution modeling, and they appear to share similar niche requirements. This novel result suggests ecological niche modeling offers innovative potential for the discovery of new species and thus can be used to direct survey efforts to areas of greatest potential. The objective of this proposal is to test ecological niche modeling using a broader range of animal groups. This work both is exploratory and high risk; this is the first attempt to apply remote sensing data and ecological niche modeling to accelerating the discovery of new species.

A Severe Urgency:- The Malagasy Government has recently announced a three-fold expansion of the reserve network within 5 years. This magnitude of increase is unprecedented, and unlikely to ever be repeated again owing to the rapid loss of primary forest outside reserves. Although major elements of the reserve program have been proposed (ANGAP, 2001), a small window of opportunity currently exists for biologists to propose additional recommendations. Discovery of important areas with unprotected new species, during the critical next two years of reserve planning, would have a profound influence on the final reserve network design.
Major objectives:- 1) Produce ecological niche models for at least 200 species of amphibians, reptiles, and small mammals, 2) identify intersecting areas of potential over-prediction and select sites to be surveyed, 3) survey sites and rapidly work up collections to identify/diagnose new species, 4) disseminate results concerning unprotected regional endemism to the Malagasy Government, and 5) compare these new species discoveries against surveyed sites selected blind to model results, to evaluate the utility of ecological niche modeling for accelerating species discovery.

Broader Impacts:- Surveys will produce important voucher specimens (including tissues), broaden international collaboration between AMNH and the University of Antananarivo to include GIS and remote sensing, involve the participation and training of at least four PhD students from the USA and Madagascar, and provide data for a web-based international teaching module on distribution modeling being developed at AMNH. Innovative biogeographic perspectives are expected to have a profound influence on the establishment of future reserves in Madagascar.

Intellectual Merit and Project Significance:- The results and conclusions have the potential to radically improve the way biologists and conservationists select survey sites to document new species diversity. Confirming the ability of ecological niche modeling to identify areas of unknown endemism would represent a major advance, and offers exciting potential for accelerating species discovery on a global scale. Obvious resulting benefits would include more inclusive and timely conservation programs, and the availability of greater biodiversity for ecological and evolutionary research.

Proposal # 0345448

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PI: Peterson

ORNIS: A Community Effort to Build an Integrated, Distributed, Enriched, and Error-checked ORNithological Information System

The University of Kansas has been granted an award to build an integrated database system for the rapid integration of specimen records (including vocal recordings, egg and nest holdings) of avian species archived in biocollections held in North American institutions. The system will use globally accepted data exchange standards and internet protocols to eventually make up to 3.9 million collections available to the global biodiversity community and to allow the data from those collections to be shared and combined with data from all over the world. The exceptional merits of the system lie in both the informatics and community aspects of the project. There is broad community buy-in from 29 institutions in North America. The use of globally compatible standards and protocols (e.g. DiGIR) will allow universal access (e.g. through GBIF & IT IS and other potals). There is also broad collaboration with similar projects based on other taxa (e.g. MaNIS, HerpNET & FishNET) in collaborative georeferencing as well as the integration and enhancement of advanced machine assisted georeferencing techniques. Finally, the project will be using innovative data mining and cleansing techniques to greatly improve the quality of the database, all in a high throughput context.

This study documents the evolutionary history of the emerald toucanet (genus Aulacorhynchus) and the montane jay (genus Cyanolyca) that inhabit tropical montane forests from Mexico south to Bolivia. Evolutionary relationships in these two genera will be studied using DNA sequencing data to construct phylogenetic trees, elucidate questions regarding their origin and geographical distribution, and compare to other vertebrate groups. This information will be key in generating a first understanding of the history of the diversification of the Neotropical montane biota, a diverse but poorly understood portion of world biodiversity.

The information generated in this project will provide important information for evaluating conservation status of species within Cyanolyca and Aulacorhynchus. DNA sequences and samples obtained will be available for future studies in bird and genome evolution.

The overall goal of this three-year project is to discover, describe, and document the distribution of vertebrates (small mammals, birds, amphibians, reptiles) and their parasites from an area of southern Mongolia before this area is completely disrupted by planned major natural resource extraction, development, and overgrazing. This project is area?based, focusing on species inventory and concomitant discovery of vertebrates and their parasites, and will yield substantial data for ultimate hypothesis?based questions of biogeography, host specificity, conservation, and co-evolution of vertebrate taxa and their parasitic associates. The project will focus on the Gobi Gurvan Saykhan National Park, the second largest and most critically endangered Mongolian national park. Mongolia is a vastly undeveloped and under-surveyed region with geographic extremes and few roads; hence the biological diversity of many areas of the country remains poorly documented, and there are many undiscovered species within the country. Collections of specimens will enable the research necessary to understand how these species evolved in the areas where they now occur.

This project will provide training for researchers and educators in Mongolia and give undergraduate and graduate students the opportunity to train and conduct research in parasite/vertebrate biodiversity and systematics. Workshops in vertebrate/parasite biodiversity in Mongolia will reach a broad cross section of students; while community presentations will disseminate information to the rural community and international visitors to the park. The outreach education portion of this program, comparing Mongolian parasite biodiversity and culture with that of Nebraska, will target Nebraskan elementary school students and their parents.

Proposal #: CNS 08-21625
PI(s): Clark, Terry W.
Kieweg, Sarah L.; Kuczera, Krzysztof; Lushington, Gerry H.; Peterson, A. Townsend
Institution: University of Kansas
Lawrence, KS 66045-7563
Title: MRI/Acq.: Acq. of an Advanced Computational Infrastructure for Modeling Biological Systems
Project Proposed:
This project, acquiring a parallel cluster for scientist to conduct modeling on biological systems, aims to
- Deploy the cluster and integrate it with an existing high-capacity storage facility for projects,
- Leverage the equipment through the group of investigators towards interdisciplinary projects, and
- Advance collective computing expertise.
The cluster will be managed to provide a minimal set of resources on demand to investigators participating on the proposal with additional resources allocated to the community for promoting computational growth for new users and educational programs. The resource enables qualitative advancements in critical areas in the computational biosciences. Species niche modeling and context-dependent evolution of DNA is fundamental to downstream management of environmental resources through broadening understanding of organism response to environmental factors at macroscopic and microscopic levels. Other work aims to engineer microbial delivery agents for use in combating the AIDS epidemic. Molecular studies advance methodologies for accurately modeling phenomenological and microscopic properties of molecular interactions of protein and DNS, with other work including development of high throughput predictive models for small molecule reactivity.

Broader Impacts:
This project increases opportunities for students to gain experience in parallel computing as part of course work, enhances research opportunities for graduate students, supports the career development of junior faculty, and establishes a potential for a broader high performance parallel computing base within the university and surrounding area. A faculty coordinator is specifically assigned to assist in recruitment and retention of students from underrepresented groups.

This Integrative Graduate Education and Research Training (IGERT) award supports the development of an interdisciplinary climate change studies program at the University of Kansas, in collaboration with Haskell Indian Nations University. The Climate Change, Humans & Nature in the Global Environment award addresses one of the National Academy of Sciences? "grand challenges" of the 21st century: climate change and its impact on the planet?s ecological and social systems. The program will provide Ph.D. students in the social and natural sciences and engineering access to the advanced scientific infrastructure at the University of Kansas?s remote Sensing of Ice Sheets, Biodiversity Institute, and Institute for Policy & Social Research, and the Haskell Environmental Studies Research Center. Trainees will acquire the conceptual and technical toolkit, including remote sensing, modeling, and scaling across disciplines, needed to study the human and natural dimensions of climate change, strategies for mitigating its trajectory and effects, and the role of policy in shaping the drivers of and responses to climate change. Broader impacts include multidisciplinary research training, collaboration with students and faculty at Haskell and a network of tribal colleges to assess the impacts of climate change in indigenous communities; participation in a climate policy internship; establishing a graduate certificate in climate change studies; developing a portable curriculum for interdisciplinary integrative research and education; and for trainees, learning the collaborative skills, problem-centered interdisciplinary training, and understanding of science policy needed by future leaders in climate change science, engineering, and policy. IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.

One problem that has vexed biogeographers and others interested in the distribution of different species is the difficulties that many species have in adapting when their habitats are changed by human activity or other forces. A related problem is the difficulty in monitoring the number and distribution of these species. One such species is Henslow's sparrow (Ammodramus henslowii), a grassland-nesting bird that is found in the central U.S. and nearby parts of Canada. This doctoral dissertation research project will take a multifaceted view of Henslow's sparrow distributional biology, focusing on improving understanding about how populations respond to broad-scale habitat changes and of the extent to which human activities affect the amount and distribution of its breeding habitat, particularly in Midwestern grasslands. Henslow's sparrows currently have a distribution that is patchy and local; although the limits of the species' range are perhaps well-known, existing surveys are largely confined to roads. Current information therefore may neither represent all available habitat, such as grasslands in airfields, military bases, and reclaimed surface mines, nor does it accurately estimate population trends. No detailed map of suitable habitat for the full breeding distribution currently is available. The doctoral student will use ecological niche models to produce a detailed distributional understanding by identifying key environmental variables and characterizing the amount of suitable breeding habitat and its spatiotemporal dynamics within a patch-matrix framework while taking into account landscape heterogeneity and habitat patch characteristics produced by year-to-year disturbance dynamics. Once these models have been validated through independent field surveys, year-to-year variation of the extent and arrangement of suitable patches will be analyzed. This analysis will provide insight and explanation for the broad-scale nomadic behavior documented in earlier studies of the breed. These models also will be used to evaluate how well current survey techniques function to sample Henslow's sparrow breeding habitat. The student will evaluate these models by summarizing the amount of suitable grasslands in patches immediately surrounding routes identified by the North American Breeding Bird Survey and by comparing it to the broader distribution of this habitat (produced by the niche models) and the proportion of habitat types within the landscape matrix (using land-cover maps).

Henslow's sparrows have very specific nesting habitat requirements that make them relatively good indicators of healthy tallgrass prairie ecosystems. Like many other obligate grassland species, they have suffered severe habitat loss as a result of fragmentation and conversion of grasslands, a loss estimated to have exceeded 99 percent across North America. Unlike a few other species that adapted easily to these changes, Henslow's sparrows have not responded well across their breeding range, and they have exhibited significant population declines over the last century. Recent studies have shown that this species does not return consistently to patches of habitat from year to year, which makes management of their habitat more difficult. This project will make significant contributions to the assessment of Henslow's sparrow populations and trends. The project also will provide a test of the capability of current survey techniques to effectively survey rare species and spatially limited habitat types. The project also will facilitate evaluation of recommendations for habitat management that more effectively accommodate the nomadic behavior as well as the amount, distribution, arrangement, or disturbance dynamics of this habitat at landscape scales. The results of this project therefore will potentially affect how species like Henslow's sparrows are managed and will prompt reevaluation of population trend estimates or habitat availability for other, similar species. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.

Proposal #: 13-37899
PI(s): Huan, Jun
Blumenstiel, Justin P.; Kuczera, Krzysztof; Peterson, A. Townsend; Vakser, Ilya A.
Institution: University of Kansas
Title: MRI/Acq.: Computing Equipment for Supporting Data-intensive Bioinformatics Research
Project Proposed:
This project, acquiring a multicore hybrid cyberinfrastructure instrument, aims to enable and empower several lines of research requiring processing and/or storage of big and complex data. The equipment services critical areas in data and computing intensive bioinformatics research, mainly high-throughput sequencing data analysis, molecular dynamics simulation, cell signaling and molecular recognition, computational chemical biology environmental and evolutionary biology. Providing an open, extensible, and scalable platform to support data-intensive science programs, the instrumentation serves as a testbed for computing and simulation methodology development utilizing advanced multi-core architecture as well as supporting large-scale data analysis needs. The project responds to the clear trend towards data driven science, in which large amounts of information coming from instruments for automated sequencing, high throughput screening systems, and other high volume analysis techniques, is mined and analyzed to look for patterns from which hypotheses can be developed. Along with data-driven science as a source of insights, detailed modeling and simulation of biological structures and processes are becoming indispensable tools to test models, to evaluate experimental methodologies, and to interpret the results. Evaluation and interpretation drive the need across a broad spectrum of research. This acquisition supports the current need and near-term research goals and aims to maintain a forward position in computationally intensive research.
Broader Impacts:
The instrumentation impacts the research it enables. It is also well aligned to the institution?s strategic initiative it enables and contributes in seeking other research opportunity initiatives such as BigData and XSEDE (eXtreme Science and Engineering Discovery Environment). The equipment enhances research opportunities for graduate students, increasing opportunities for student to gain experience in parallel computing as part of course work, and establishes a broader high-performance parallel computing base within the institution and the surrounding region. The School of Engineering?s Diversity Programs will help identify qualified students from underrepresented groups and assist with the recruitment and retention for women and minorities possibly with scholarships. The computing resource should have significant impact in the region. Working with the Kansas IDeA Network for Biomedical Research Excellence Bioinformatics core, bioinformatics expertise will be delivered to minority and undergraduate-serving schools.

This research award will use the unique event of the 2013 hyper-typhoon Haiya in the Philippines to learn about the impacts of massive, natural catastrophes on the survival and potential recovery of vertebrate animal populations and species. This is premised on the fact that the research team had been conducting comprehensive vertebrate species surveys for about five years in the Philippine archipelago immediately prior to the recent typhoon. This work is important because such studies have not been conducted before, and they can tell us a great deal about the processes of local extirpation, colonization, adaptation and recovery or extinction of both populations and species. In a time of global environmental change, such studies provide society and resource managers with valuable empirical examples of what can be expected regarding the responses of biological diversity to particular catastrophic environmental changes, and how planning for the future might best proceed.

The first research objective is to provide an immediate "after the typhoon" species and abundance survey dataset to complement the prior, fortuitous 5 year effort. The research team has over 10 well-studied sites for which comprehensive inventories are already developed, and where they will assess species community changes caused by the typhoon. All survey methods are based on a proven field research program in the Philippines. The research team scouts sites, establishes camps, blazes transects, constructs trapping arrays (snap traps and live traps for mammals; pit fall traps, funnel traps, and adhesive traps for mammals, amphibians, and reptiles; and mist nets and harp traps for birds and bats), collects and handles live animals, collects all relevant data, photographs specimens, screens vertebrates for parasites, preserves genetic samples, and prepares modern museum specimens including associated ecological and microhabitat data. Resurveys will be linked to analyses of vegetation change developed from satellite imagery at two spatial resolutions, permitting broad extension of the point-based survey results to infer effects of the typhoon across the entire Philippine archipelago. The second main goal is to train a cohort of collaborating scientists in standardized survey methodology and data analysis frameworks to set the stage for a sustainable series of follow-up studies 3, 5, and 10 years after the 2014 surveys. This will be accomplished using (1) an established collaborative network in the Philippines of participating researchers, students, local government units, and conservation NGOs; (2) currently valid research permissions that allow for collection and export of biological specimens; (3) established logistical infrastructure and host sponsorship at several major universities and the national museum of the Philippines; (4) equipment, supplies, and a 4-wheel drive field vehicle ready for immediate use; and (5) research-active in-country counterparts with a proven track record of working with the NSF PI awardees. By involving students from both the U.S. and the Philippines (and their advisors) the research ensures a multi-academic-generational collaboration that maximizes training and capacity building to achieve research objectives.

The oVert (openVertebrate) Thematic Collection Network (TCN) will generate and serve high-resolution digital three-dimensional (3D) data for internal anatomy across vertebrate diversity. Via a network of digitization centers across the US, more than 20,000 fluid-preserved specimens representing over 80% of the living genera of vertebrates will be CT-scanned. This will provide broad coverage for exploration and research on all major groups of vertebrates. Contrast-enhanced scans will be generated that reveal soft tissues and organs. This collection of digital imagery and three-dimensional volumes will be open for exploration, download, and use to address questions related to the discovery of new species, documenting patterns of anatomical diversity and growth, and testing hypotheses of function and evolution. The resource will provide unprecedented global access to valuable specimens in US museum collections and will develop best practices and guidelines for high-throughput CT-scanning, including efficient workflows, preferred resolutions, and archival formats that optimize the variety of downstream applications. Museum specialists will be trained on the generation, curation, and distribution of 3D data, researchers in using 3D anatomical data, and high school and undergraduate students in the tools for creating 3D anatomical models. To drive the use of these digital specimens by K-12 STEM educators, teacher-driven workshops that generate freely available lesson plans focused on specific science standards that are based on digital and printed 3D models of specimens in US museum collections.

Data generated by oVert will serve as a catalyst for diverse research projects focused on understanding the vertebrate morphological diversity and will dramatically increase the accessibility of specimens housed in US scientific collections. These anatomical phenotypes represent a common currency that facilitates integration across the fields of taxonomy, evolution, developmental biology, comparative physiology, functional anatomy, paleontology, and ecology. The x-ray computed tomography (CT) scanning gemerates high-resolution digital anatomical data, represented as both 2D image stacks and 3D volumes and surfaces. With these 3D digital specimens, US and international research communities will be able to (1) diagnose, describe, and infer patterns of relationships among both living and extinct vertebrates, (2) test hypotheses of morphological evolution such as patterns of disparity, modularity, and phenotype-environment correlations, (3) develop structure-function models for testing hypotheses about morphological adaptations related to, e.g., feeding and locomotion, and (4) explore relationships between brain and nervous system anatomy and both sensory and musculoskeletal function. The 3D data will be distributed globally through MorphoSource, an on-line data repository for 3D biological specimen data, which will capture standardized metadata, ingest legacy data from previous and existing projects, and will supply media information to data aggregators including iDigBio (www.idigbio.org). Training workshops, both on-site at participating institutions and national society meetings of scientists and educators are planned to foster innovation and capabilities for users of 3D image data.

Tick-borne diseases, including Lyme disease, Rocky Mountain spotted fever, and others, are increasingly appreciated as a significant public health concern worldwide, and an increasing concern in the Great Plains in particular. However, a detailed understanding of these diseases, how they are acquired, where are the high-risk areas, and how might they best be mitigated, has remained surprisingly opaque. This project, a collaboration between University of Kansas, Kansas State University, Pittsburgh State University, Oklahoma State University, the University of Oklahoma, the University of Oklahoma, Norman campus, and the University of Central Oklahoma, represents a broad-scope, highly interdisciplinary, integrated, and data-intensive effort to illuminate these questions across two states, Kansas and Oklahoma in the Great Plains. Major elements of the project include assembling detailed, large-scale datasets on the occurrences of different tick species, the genomes of the ticks and the pathogens, and environmental variation across the region, as well as marshaling new artificial-intelligence tools to permit rapid and accurate tick identifications by non-experts. Project scientists will use ecological niche modeling and mathematical population modeling approaches to assess and predict transmission of the major tick-borne pathogens, and create and test the automated identification tools. The project will foster what can be termed "big data literacy" via a series of workshops and courses, as well as online data resources. Perhaps most importantly, the project will involve numerous undergraduate and graduate students in many project tasks, giving them opportunities to learn and explore futures in these and related areas of science. Project students will be recruited as broadly as possible, to represent in particular populations that are not well-represented in science, including minorities, women, and those from families without a tradition of university-level education. Project outcomes will include online, interactive maps of tick-borne disease risk, and online facilities for identification of tick photographs taken by the general public. Junior members of the project team (younger faculty, postdocs, and students) will be mentored and guided by more senior individuals, so as to maximize the probability of their successful advancement in this field. The project team will be guided by an advisory board with broad and international expertise, as well as state-level public health policy experience. At the close of the project, we anticipate a much-improved and considerably more detailed understanding of the diversity and risk of tick-borne diseases across Kansas and Oklahoma.

Tick-borne diseases are increasingly recognized as an important public health concern across the United States, including Lyme disease, ehrlichiosis, Rocky Mountain spotted fever, southern tick-associated rash illness, human granulocytic anaplasmosis, babesiosis, and viral infections with Heartland and Bourbon viruses. Knowledge of the spatial distributions of ticks and pathogen species, and the associated spatial risk of transmission of tick-borne diseases in the Great Plains is quite limited. This project marshals several "big data" streams (tick occurrence data, tick and pathogen genomic data, remote sensing data to characterize environments) and novel scientific tools to shed light on geographic patterns and temporal dynamics of risk of infection with the pathogens that cause these diseases. Specifically, this project, a collaboration between University of Kansas, Kansas State University, Pittsburgh State University, Oklahoma State University, the University of Oklahoma, the University of Oklahoma, Norman campus, and the University of Central Oklahoma, will involve field collections of ticks and vertebrates hosting ticks from 12 sites in ecologically distinct regions across Kansas and Oklahoma, which will be tested using genomic tools for a diverse suite of pathogens; the resulting data on tick and pathogen distributions will be the basis for detailed modeling of transmission risk using complementary, cutting-edge tools (correlative niche models, mechanistic population models) to achieve new syntheses of population and range dynamics. This project will explore and develop a first automated tick identification system based on deep-learning approaches, which will feed much more information into the other analyses envisioned for this project in the form of detailed distributional data. This project will also have substantial implications for broadening participation in science, via linking senior and junior scientists (including minority faculty members) in a joint, collaborative, and integrative effort designed to mentor and build confidence and stature among junior team members (faculty, postdocs, graduate students, undergraduate students) and among faculty from teaching-focused institutions. This project will offer various educational opportunities in the areas of big data analytics, big data access, and disease risk mapping, which will be made broadly and openly available to the scientific community. Finally, towards mitigating effects of tick-borne diseases in communities across the southern Great Plains, this project will produce and make broadly available detailed risk maps for each tick-borne disease in the region, and create technology for automating tick identifications, to allow citizen scientists and stakeholders in the general public better access to information on ticks and disease risks that are personally and immediately relevant.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus. Since its discovery in Wuhan, China, in 2019, COVID-19 has already led to over 2 million cases globally. It has spread globally, including to many vulnerable countries without adequate healthcare infrastructures. Many different responses have been tried, including social distancing, school and event closings, and travel bans. This project will develop mathematical models to address three fundamental questions: 1) how much participation and coordinated control is needed for effective protection? 2) what independent control efforts can compensate for lack of coordination to achieve effective protection? and 3) how do community population demographics, socioeconomic conditions, and health care infrastructure impact outcomes? This project aims to inform coordination of disease control policies at all scales (local, regional, national, international) to aid in curtailing the ongoing and future outbreaks. This project will advance fundamental understanding of the impacts of control efforts via a new risk perception-driven infectious disease model, and predict which drivers of public demand for community-level control efforts might lead to potentially harmful long-term decisions. The project will involve training two doctoral students in techniques related to mathematical modeling of disease dynamics and spread.

Many mitigation options are being weighed and implemented for COVID-19, with different decisions made at different administrative levels, including alternative quarantine strategies and different degrees of ?lockdown?. All of these decisions come with different perceptions of risk. The PIs will develop and analyze disease transmission models that incorporate various factors including public perception of risk, age-structure with a hospitalized population, and spatial structure with different scales spanning local communities to an entire country. These models will be used to explore impacts of community population demographics, socioeconomic conditions, and health care infrastructures, and how these factors impact control efforts under different social and economic settings. With the results of these models, policy- and decision-makers can consider the impacts of specific features of the communities under their administration as contributors to a broader network of public health efforts and choose the optimal mitigation steps. This work will inform coordination of disease control policies to curtail the ongoing outbreak directly. The results of this project, while tailored specifically to inform COVID-19 virus control strategies, will be applicable to any novel infectious disease outbreak in the future.


This award is being funded by the CARES Act supplemental funds allocated to MPS.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Avian influenza, often referred to as bird flu, has had many outbreaks in Asia, Africa, Europe, and North America in the past decades, resulting in losses of billions of poultry, thousands of wild waterfowl, and hundreds of humans. As the 1918 Influenza Pandemic revealed the potential of influenza viruses that originate in birds to kill millions of humans, prediction and prevention of the next influenza pandemic is one of the grand challenges in global health. Although significant investment and research have been made in avian influenza virus (AIV) research over the past two decades, the capacity to predict AIV pandemic is still woefully inadequate. There is an urgent and imperative need to fortify the prediction capacity, as AIV risk factors in the human-animal-environment systems (HAES) have changed rapidly in the past few decades and are expected to change at a much faster pace across the world in the next few decades. This Predictive Intelligence for Pandemic Prevention (PIPP) Phase I: Development Grant assembles a multi-institutional team to explore the appropriate pathways for establishing an International Center for Avian Influenza Pandemic Prediction and Prevention (ICAIP3). The mission of the center is to tackle the grand challenges in global health with a focus on avian-influenza pandemic prediction and prevention. The broader impacts of this project include increased international partnerships between researchers, stakeholders, and decision makers; development of STEM workforce for international and convergent research and diverse career paths; increased public scientific literacy and public engagement; and increased capacity for avian influenza pandemic prediction and prevention. <br/><br/>Scientifically, this project focuses on four major problems in pandemic prediction and prevention. First, this project investigates the complex and dynamic data problem via the OneHealth (Human-Animal-Environmental Health) approach and the Big Data approach. Researchers review and assess diverse AIV and HAES datasets and identify the pathways and procedures for harnessing the disparate datasets into analysis-ready datasets for modeling and analysis. Second, this project tackles the multi-scale and interwoven model problem via the integrated modeling approach. Researchers review and assess various AIV models and artificial intelligence (AI) and machine learning (ML) algorithms, and then identify appropriate AIV models and AI/ML algorithms for developing new integrated models to predict AIV evolution, spillover, and transmission. Third, this project assesses the decision support system problem via the structure decision making approach. Researchers review and assess various AIV decision support systems and tools (DSST), and then identify appropriate AIV DSST for further development and practical use in the AIV surveillance, pandemic preparedness, and response. Finally, this project identifies appropriate pathways to develop cyberinfrastructure for data-intensive research, communication, and sharing among the community of zoonotic infectious diseases. The center will be focused on prediction and prevention of AIV pandemic in Eurasia and its potential linkage and risk with North America.<br/><br/>This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Biological diversity has been the subject of hundreds of years of work by botanists and zoologists, accumulating rich stores of specimens and associated data in museums and herbaria around the world. These rich information resources, however, too often remain in analog format only, and have not been digitized and “enabled” in the service of science. This project aims to digitize, enrich, and share openly the rich data resources held in United States herbaria that correspond to plants of tropical Africa. By the close of the project, it will have captured data from 1.1 million herbarium specimens, and will augment digital accessible data records for the African continent by more than 15-fold. It will also have created a broad, international, intercontinental network of scientists and students interested in and experienced with management and analysis of such data. This combination of information resources and human capacity will enrich and improve biodiversity conservation planning across Africa. <br/><br/>Herbarium specimens represent a rich source of data on plant diversity. This project will focus on the tropical African seed plant specimen holdings of 21 U.S. herbaria, which will be imaged, associated data captured, and data records georeferenced and quality-controlled. Imaging and data capture will be carried out at each of the herbaria, and data will be aggregated for efficient georeferencing. For most records, georeferencing will be performed automatically; however, a small proportion of records will be georeferenced manually by plant scientists in Ghana, Rwanda, Malawi, and Gabon. Finally, project data will be subjected to detailed quality-control assessment, and served openly to the scientific community via a dedicated “African Plants” portal on Symbiota, as well as integration into iDigBio.org and the Global Biodiversity Information Facility (GBIF.org). These rich data resources will be used to understand the conservation status of African plant species in much greater detail than has been possible to date.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.