William F. Fagan - US grants

William Fagan

DEB 0075667

ABSTRACT

Extinction is one of the most fundamental processes in ecology-it shapes evolution, community structure, and patterns of biodiversity. Yet studies of extinction are scarce, and studies involving hard data rather than theoretical models are almost totally lacking. We will take advantage of an extraordinary database concerning Sonoran Desert fishes to investigate relationships between extirpation and patterns of rarity and life-history traits. Key to our project is the development of new methods for quantifying patterns of rarity that are independent of the spatial scale of analyses. Investigations of rarity are important because most species are in some way rare. The details of how species differ in the "way in which they are rare" (e.g., in numbers, in spatial distribution) may be key to understanding a diversity of issues in ecology, including the assembly of communities and how species persist in the face of disturbances.
Our unique database includes over 25,000 locality records spanning 160 years of field research for all 50 known taxa of Sonoran Desert fishes. These fishes collectively constitute a gravely endangered biota for which data on the linkages among rarity, extinction-proneness, and life-history traits are of critical conservation importance. The methods we apply are modifications of Kunin's (1998) notion of "scale-area curves," which provide a means of quantifying rarity patterns across spatial scales. Specifically, we will examine how the patterns of rarity these fish exhibit relate to their extinction dynamics (e.g., frequency and spatial pattern of extirpation events) and current levels of endangerment. We will also determine the extent to which patterns of rarity and extinction can be predicted by life-history attributes.
Biodiversity databases are an untapped storehouse of information on distributions of species. They allow us to ask questions at different scales and with greater taxonomic breadth than any experiment or focused field study can. They also allow us to ask altogether different questions-such as questions about extinction. The research proposed here will explore linkages between rarity, extinction risk, and life-history traits, along the way defining limits on how existing databases can be used and clarifying how to improve development of new databases.

Identifying the mechanisms that structure local communities is a fundamental goal of community
ecology. Historically, ecologists invoked local processes to explain community structure, but
equivocal evidence has broadened debate to include the potential importance of regional
processes in structuring communities. Though experimentation has proven critical in resolving
issues in ecology, theoretical and observational studies have, thus far, been the primary tools used
to examine the effects of regional processes. I propose a series of novel experiments to test
theories that span the range between local and regional control of diversity. Local process,
metapopulation competition, and neutral community theories make predictions about the effects
of immigration that are qualitatively different, mutually exclusive, and testable in the same
immigration experiments. Using a laboratory system of Tribolium flour beetles and a field system
of Cecropia petiole beetles, I will experimentally test these theories by simultaneously
manipulating immigration regime and interaction strength (lab), and exposure to immigration and
distance from colonization source (field). Though recently developed theory suggests regional
processes can have a dominant effect on community structure, the theory must first be confirmed
empirically if community ecology is to gain insight from the regional process perspective.

PI: William Fagan, University of Maryland
CoPI: James Elser, Arizona State University
Sudhir Kumar, Arizona State University

Fast growing DNA and protein sequence databases, knowledge of the basic biological function of individual proteins, and availability of information characterizing relative gene expression intensities provide unique and exciting opportunities to construct eco-genomic hypotheses and test their predictions. One fundamental question involves how environmental constraints (such as nutrient limitation) shape protein sequences. In particular, this award explores how the availability of nutrients in the environment affects proteins by modulating their amino acid composition. This issue can be addressed because ecologists have demonstrated extensive interspecific variation in the elemental compositions and protein content of organisms and their tissues (e.g., plants are nitrogen-poor relative to insect herbivores; grasshoppers are nitrogen-rich relative to butterflies)., Amino acids, which are the building blocks of all proteins, can differ in their atomic compositions (e.g., carbon [C], nitrogen [N], sulfur [S], and oxygen [O]) because of differences in the "R-groups" (side-chains), which contribute to the unique chemical reactivity of individual amino acids and allow them to play individual structural and functional roles within protein sequences. Combining ecological and genomic perspectives allows examining directly whether the proteomes of N-rich and N-poor species differ with regard to the standardized N-content of the side chains of their proteins. These problems in "stoichio-proteomics" require the exploration of interspecific and intergenic variation in the elemental composition of proteins. To facilitate these explorations, this project will create a bioinformatics knowledgebase at the interface of ecology, evolution, and genomics. This database, the Genomics Resource Access for Stoichio-Proteomics (GRASP), will be implemented as a multi-tiered, data-driven web application. It will be a flexible and scalable system that will contain an integrated ecological dataset (characterizing key life history traits of insect species) and a database of protein composition. Some of the stoichioproteomic research hypotheses will be tested using these data. The Broader Impacts from this research extend beyond the immediate scientific interests in three ways. First, the GRASP-database and its query/retrieval system will provide an extensive, easy-to-use tool for the exploration and analysis of stoichiometric characteristics of proteins that will be freely available to the research community. GRASP will facilitate diverse stoichio-proteomic analyses far beyond our own research interests. The database technology will be made available to anyone who is interested in developing similar resources. Second, the proposed computing platform will be a useful teaching tool for undergraduate and graduate students at universities worldwide. Third, research and development conducted under the auspices of this proposal will make a significant, but focused, contribution to human resources development in interdisciplinary biology, especially at the interface of computational genomics and ecological evolution.

This research seeks to better understand the underlying behavioral mechanisms associated with the movement strategies of ungulates as they travel through different landscapes. Animal movements are usually motivated by a need for resources and among ungulates, some species (e.g., white-tailed deer) have generally abundant and well-dispersed resources and are considered range-residents, whereas other species (e.g., caribou, wildebeest), whose resources are predictably distributed in different parts of their range in different seasons, are migratory. However, large-scale, long-range movements that occur when resource distributions are fundamentally unpredictable in both time and space have so far received little attention. These movements could be called nomadism and one striking example can be observed in Mongolian gazelles, which are the most important wild ungulate in one of the last intact temperate grassland ecosystem on the planet. We will develop computer models that simulate and link behavioral movement mechanisms which can be either based on memory, perceptual cues or triggered by environmental factors. It explores their efficiency under different scenarios of resource distributions across time and space. Finally it tries to integrate empirical data on resource distributions as well as movements of moving animals, such as satellite data on primary productivity and satellite tracking data of Mongolian gazelles. New insights about the driving forces of ungulate movements will support conservation efforts for nomadic and migratory species in general and Mongolian gazelles specifically, which are constantly moving and difficult to manage with traditional static protected areas.

This project will test the hypothesis that insect herbivory is an unrecognized major factor influencing recovery of biological systems following the catastrophic 1980 eruption of Mount St. Helens (MSH). The work will quantify impacts of insect herbivores on two key "ecosystem engineering" plants: lupins, which facilitate soil development; and willows, the main source of three dimensional vegetation structure required by many animals. It will also evaluate the effects of herbivores on bird and mammal assemblages. The proposed work will extend the continuous study of insect herbivore impacts at MSH to 18 years, and of mammal community assembly and response to vegetation to 28 years, providing the most comprehensive data set on vertebrate response to catastrophic disturbance in the coniferous forest biome.

How biological systems recover from catastrophic disturbances such as volcanic eruptions is fundamental to a basic understanding of how communities of plants and animals assemble and function, and provides the theoretical basis for environmental restoration. MSH has provided a unique opportunity to test ecological understanding of recovery from disturbance. This work builds on this legacy by elucidating previously unrecognized mechanisms controlling recovery, and by providing long-term records accessible to future scientists. The project also enhances science education through student involvement and work with the National Volcanic Monument and its non-profit partner, the Mount St. Helens Institute.

Food web structure influences community stability, nutrient cycling, and primary productivity. Spatial ecology, on the other hand, has identified the primary roles that patch size and connectivity play in determining the presence and abundance of species in patchy habitats. This project aims to bridge the gap between these research programs, using stable isotope analyses to elucidate how food web structure depends on patch size and context. As the ratio of heavy to light isotopes of nitrogen and carbon measure trophic position and the ultimate sources of species' biomass, the isotopic signatures of all species in a community can provide a quantitative measure of functional food web structure. Using a well-studied salt marsh arthropod community, this project will quantify the effects of patch size and connectivity on the diversity and redundancy of realized trophic positions and determine whether species' trophic positions are themselves functions of patch spatial context.

This grant provides a much-needed synthesis of food web and spatial ecology that leverages a novel application of stable isotopic analysis to understand resource flow in a heterogeneous landscape. In doing so, it provides integrative training for a female graduate student, as well as basic ecological knowledge of a sensitive, economically important ecosystem.

This five-year project seeks to characterize decadal scale changes in penguin and seabird populations on the Antarctic Peninsula, and to identify the factors driving these long-term changes. Two interconnected research activities are proposed: 1. Continued, long-term monitoring and censusing of penguin and seabird populations at >117 sites throughout the Antarctic Peninsula via opportunistic ship-based data collection. 2. Synthesis and quantitative analyses of datasets detailing long-term changes in five penguin and seabird species from diverse sites throughout the Antarctic Peninsula. When complete, the penguin/seabird database will incorporate data from the Antarctic Site Inventory (ASI), the CCAMLR database, the US AMLR database, the LTER database from Palmer Station, data from British and Argentine researchers, historic census data compiled by the Scientific Committee on Antarctic Research (SCAR), and, when possible, additional privately held datasets. Additional data for temperature change, sea ice coverage, the seasonal timing and intensity of human visitation, and other factors have been gathered and will be analyzed together with population trajectories within a spatially explicit framework. The research will include hierarchical statistical analyses to characterize the long-term population dynamics of several key polar species across multiple spatial scales (sites, regions, and the Peninsula). Analyses also will focus on specific subsets of the overall database to contrast visitor impacts on paired colonies, sites, and regions that share similar environmental conditions but differ in the intensity of tourism.

The Broader Impacts include (1) research training and first-time Antarctic experiences for a postdoctoral researcher and several graduate students, all of whom will then be better positioned to bring their expertise in spatial and/or quantitative/theoretical ecology to bear on questions in polar research; (2) assembly and analysis of a large, multi-season database of penguin and seabird time series from the Antarctic Peninsula that will be publicly available, (3) assistance in distinguishing the impacts of tourism versus climate change on seabird populations. Under the Environmental Protocol to the Antarctic Treaty, Treaty Parties are charged with regular and effective monitoring to assess the impacts of human activities. This project will uniquely assist Parties in fulfilling this mandate.

Resource Predictability and Movement Strategies in Ungulates:
Does Temporal Uncertainty Lead to Nomadism?

The movements of all animals are affected by their need for resources such as food. Where and how quickly animals move often depends on where the best vegetation resources can be found and how predictable this food is from year to year. Some ungulate species with predictable environments migrate seasonally; caribou and wildebeest are examples of this. A few other species - gazelles, for example - appear to make large-scale, long-range movements that are seemingly unpredictable. This 'nomadism' likely occurs when the availability and location of resources varies considerably by season and by year. To make sense of these seasonal and annual movement strategies, researchers will combine: 1) theoretical computer models, 2) landscape-scale satellite images of vegetation, and 3) detailed movement data from individual gazelles in Mongolia (new field studies) and caribou in Alaska (historical data).
By understanding long-distance animal movements, we will be in a much better position to know how environmental variability affects animal behavior. The conservation of these species and their habitats depends on understanding their large-scale movement patterns and ecology. Our research will help identify the mechanisms by which animals 'read' their environment to know when and where to move over complex landscapes.

Biological Sciences (61). This project is refining, expanding and assessing a series of interactive, web-based instructional modules - the MathBench Biology Modules - which integrate mathematics more deeply into the undergraduate biological sciences curriculum in a way that reinforces biological concepts and increases quantitative literacy. The modules use humor, references to popular culture and interactive elements to engage students, but also build upon students' intuitive understanding to help them explore biological concepts using mathematical approaches. The modules focus on ten major quantitative skills identified by university faculty as being essential for a comprehensive understanding of modern biology. Twenty-six modules were previously developed and were piloted in five fundamental biology courses. Faculty from the partnering institutions are collaborating to modify a subset of the modules to address the specific needs of students enrolled at a nearby community college where increasingly large numbers of university graduates begin their education. The entire MathBench suite of modules is also being enriched with more robust interactive elements that fully capitalize on the advantages of technology-enhanced instruction. The effect of the modules on student learning outcomes and attitudes are being assessed, with the feedback being used to further refine the modules.

Intellectual Merit: This project addresses a pressing national need by enhancing the interdisciplinary training of biological sciences undergraduates. The MathBench Biology Modules are thoroughly grounded in pedagogical research and fully leverage the capabilities of modern instructional technology. Integration of the MathBench modules into fundamental courses across the biological sciences curriculum will allow students from diverse educational backgrounds to hone their quantitative skills, preparing them for more complex mathematical approaches in upper-division courses. The project team has strong expertise in interdisciplinary biological research, innovative use of technology in instruction, and the scholarship of teaching and learning.

Broader Impacts: The MathBench Biology Modules have significant broader impacts. First, they enhance the educational mission of institutions of higher education by exposing students to mathematical approaches essential for a deep understanding of biological concepts. Second, the adaptation of the MathBench modules for use at the community college level will help equilibrate the quantitative foundation of biological sciences majors at the university level regardless of where they receive their introduction to biology. Because community college enrollment includes large numbers of minorities and students who are the first in their families to attend college, this project's efforts benefit groups of students traditionally underrepresented in the sciences. Third, the project will have a lasting reach beyond the campuses where the modules are being developed because the modules will be freely available on the MathBench website and will be submitted for inclusion in national digital libraries. The modules' easy accessibility will make it possible for them to be disseminated widely in a variety of educational contexts at both the community college and university levels.

The project is integrating MathBench Biology Modules, a series of interactive, web-based instructional modules, into the undergraduate biological sciences curriculum of a variety of institutions of higher education. The modules use humor, references to popular culture, and interactive elements to engage students and build upon their intuitive understanding in order to help them explore biological concepts using fairly sophisticated mathematical approaches. Work under a prior NSF CCLI Phase 1 grant developed the 37 modules and piloted them in four fundamental biology courses at the University of Maryland (UM) and one introductory biology course at Montgomery College(MC), a nearby community college. Assessment data from Phase 1 indicates that students using MathBench have a greater appreciation for the importance of mathematics in modern biology and show gains in quantitative proficiency that are independent of previous and current math coursework. This project expands implementation of the modules to 10 diverse collaborating institutions (2-year/4-year, public/private, research/primarily undergraduate, and majority/minority-serving). Activities include workshops to help participating faculty create implementation plans,and to aid in collection of assessment data and in dissemination at professional conferences. Collaborating faculty serve as peer consultants at future workshops in order to establish teaching and learning communities on a local scale (University System of Maryland 2- and 4-year institutions) and a national/international scale (universities participating in the Howard Hughes Medical Institute Quantitative Biology Consortium, a group committed to improving undergraduate education in quantitative aspects of biology). The modules are being evaluated and refined with respect to learning outcomes, student attitudes, and ease of implementation in pursuit of the ultimate goal of creating a faculty development framework to support dissemination.

Intellectual Merit: The project addresses a pressing national need by enhancing the interdisciplinary experiences of biological sciences undergraduates. The design of the modules addresses diverse learning styles and educational backgrounds, and their easy accessibility assures dissemination to a variety of educational contexts.

Broader Impacts: The project is producing well-tested educational modules that allow students from diverse educational backgrounds to hone their quantitative skills, preparing them for more complex mathematical approaches in upper-division courses and increasing their appreciation for the role of mathematics in modern biology. Collaborative assessment efforts are providing rich comparative data on how the modules affect student learning and attitudes across educational contexts, and are providing valuable information on how learner-centered, technologically-based pedagogies can be used to augment traditional instruction in STEM disciplines.

This project is being co-funded by the Directorate for Biological Sciences.

A grant is awarded to University of Maryland, College Park to develop informatics tools that allow scientists and conservation managers to use animal relocation and tracking data to study movement processes at the population level. Technological advances such as GPS tracking devices have facilitated much recent progress in understanding the movements of individual animals, but scientists' understanding of the emergent spatial dynamics at the population level has not kept pace, in large part due to an absence of appropriate tools for data handling and statistical analysis. To bridge this key gap and study such processes as spatial learning, social interactions vis-à-vis aggregation, and population level movement patterns (e.g., migration, nomadism), detailed analyses of individual movement paths are not sufficient. Researchers must, in addition, attend to the relationships that exist between moving animals. This project will develop new and innovative data management and analysis tools focusing on the interrelationship of multiple moving individuals. These include measures that calculate 1) realized mobility (quantifying the relationship of individual to population ranges), 2) population dispersion (quantifying the spatial relationship among individuals), 3) movement coordination (quantifying the coordination of movements among individuals), and 4) intra-individual concordance (quantifying the spatial relationship of relocations of individuals over time). These innovative ways of treating animal movement data will allow researchers to investigate a broad range of new research questions. For example, by statistically analyzing the interrelationships of relocation data among individuals, it will be possible to distinguish and quantify population-level movement patterns such as migration, range residency, and nomadism. The same tools can be used to analyze interrelationships of relocation data among individuals but across time, thereby examining how animal movements change as individuals age and gain experience. Finally these same tools may be applied to analyze social networks and use animal relocations to understand fission-fusion dynamics of grouping behavior and characterize the timing and consistency of aggregations. Using existing data, they will develop and test these new tools using datasets on Mongolian gazelles, whooping cranes, and blacktip sharks. These species represent not only different types of movement (on land, in air, in water) but also different types of relocation data (from visual observations of individually marked animals to GPS relocations to relocations obtained from networked sensor arrays). They will focus on spatial learning and changes in migratory patterns in whooping cranes, nomadic long-distance movement in gazelles, and group formation in sharks.

The project will develop an analysis package in the open-source language R and complement it with a step-by-step hands-on manual to make tools available to a broad, international user community that includes academics, scientists working for governments and non-governmental organizations, and professionals directly engaged in conservation practice and land management. The software package will be made publicly available under http://www.clfs.umd.edu/biology/faganlab/movement/. Efforts will also include a major emphasis on graduate and undergraduate research and training, through assistantships for PhD students and undergraduates. Additional broader impacts will emerge from analyses of the whooping crane dataset. Through collaborations with endangered species biologists in the US Geological Survey, these analyses will have direct relevance to specific management actions for the whooping crane, such as the timing, group size, and composition of crane reintroductions and potentially their training with ultra-light aircraft.

Two significant challenges in ecology are to understand and accurately describe the spatial spread of species. Such spatial spread is important in a variety of ecological contexts, such as when non-native species invade new habitat and when species shift their spatial distributions in response to global change processes. Meeting these challenges requires population models that capture essential aspects of the dynamics of spatially spreading species, including demography and dispersal. Integro-difference equations will be used to describe the spread of populations with separate growth and dispersal stages wherein vital rates and dispersal abilities are determined by age, size, or developmental stage. Semi-discrete models (hybrid dynamical systems) involving reaction-diffusion equations and integro-differential equations will be employed to study the spread of populations in which different processes or different rates occur inside versus outside a species' reproductive period. Models with Allee effects will be developed for plant populations with pollination limitation, and for two-sex populations with reproductive asynchrony and imperfect mate-finding. Data from two well-studied field systems matching the structure of specific models will be used to parametrize key model components. The investigators will examine the existence of spreading speeds and traveling waves for the models, provide formulas for spreading speeds and traveling wave speeds, and calculate the sensitivity and elasticity of the speeds to changes in demographic and dispersal parameters. Methods from differential equations, integral equations, and dynamical systems will be used to investigate the spatial dynamics for the models. The outcomes of this research will also have broader impacts in other scientific disciplines where wave propagation is addressed. New rigorous mathematics will be integrated with extensive field and laboratory data to bridge the gulf between abstract mathematical results and ecological observations. To further broaden the impacts of this research, the investigators will also develop a MathBench module (.umd.edu) relating to ecological invasion dynamics. This module will feed into the larger, NSF-funded MathBench Initiative, which is designed to improve the quantitative literacy of undergraduate biology students and give them a deeper appreciation of the role of mathematics in understanding biological problems.

Through new research at the interface of mathematics and biology, this project will contribute to the growing body of information on the spatial spread of species. Research on the dynamics of species spatial spread is essential to understanding when and where resource managers can act to limit the spread and impacts of non-native, invasive species. Likewise, better understanding of the dynamics of spatial spread is essential for forecasting species responses to global change processes. In this project the investigators will develop and analyze mathematical models incorporating species birth, growth, death, and movement to identify points in species? life-cycles that are critical to the rates of population spatial spread. Models for plant species limited by pollen supply and for populations featuring imperfect mate finding will be explored, with a focus on understanding the effects that particular population processes have on the rate and nature of species spatial spread. By focusing on two ecological case-studies in addition to novel mathematics, this project will help to point out specific targets and opportunities for natural resources management.

An award is made to the University of Maryland to bring together a network of butterfly monitoring groups and experts in both informatics and statistics to develop a series of tools to greatly expand the access and use of butterfly data and knowledge. Citizen-scientists throughout North America perform thousands of surveys as part of a continent-wide network of butterfly monitoring programs, yet data from these surveys, are little known due to a lack of: 1) knowledge about and access to the data, 2) tools to visualize and share these critical data sets, and 3) appropriate models for analysis. Through this project, several new tools will be developed and launched, including a web interface and visualization tool for a continental-scale butterfly monitoring program, a framework for the distribution and visualization for data emerging from a large network of butterfly monitoring programs, and a web-enabled database of species traits, and a suite of statistical models to analyze data resulting from the most common types of butterfly monitoring protocols.

All efforts will be targeted toward developing tools that are broadly transferrable among butterfly monitoring programs as well as other insect monitoring programs, such as those focused on dragonflies, crickets, ladybeetles, and bees. Tools will be designed to be useful not just to the scientific and management communities, but also for the general public, the primary collectors of butterfly monitoring data. Throughout this project, undergraduates, graduates, and post-doctoral associates will be involved in all stages to foster education in information technology and statistical analysis.

This long-term project tests the hypothesis that insect herbivory is a major factor influencing recovery of natural communities following catastrophic disturbance, a process known as primary succession. Since Mount St. Helens erupted in 1980, field surveys and experiments have been used to quantify the impacts of herbivores on two key community members - lupins, which facilitate soil development, and willows, which provide the main source of three dimensional vegetation structure required by many animals. Over the next five years, these approaches will be continued in order to determine how effects of herbivores on host plants are linked to community development, if these effects attenuate as communities become more complex, and the factors that cause herbivore effects to vary across space.

Results from this study have revealed previously unrecognized mechanisms that influence the recovery of natural communities from catastrophic disturbances such as volcanic eruptions. The project provides long-term data essential to management and restoration efforts. The project also enhances science education through student involvement and work with the National Volcanic Monument and its non-profit partner, the Mount St. Helens Institute. The researchers will assist the Mount St. Helens Institute in developing resources that enable high school science classes to participate in research at the site.

This project aims to develop a statistically rigorous analytical foundation for the nascent field of movement ecology. An understanding of animal movement can inform a wide range of biological topics including population and community ecology, animal physiology, disease spread, gene flow, and wildlife management and conservation. Historically, a lack of movement data limited progress, but advances in tracking technology have facilitated the collection of high-quality movement data for an ever-growing number of species. Now, the key bottleneck is the dearth of good statistical tools for extracting information from these accumulating data sources. This research addresses this gap by combining movement-modeling techniques from physics with statistical methods for non-independent data from geostatistics. These next-generation analytical tools will allow biologists to tackle the four key analysis categories in movement ecology: 1) modeling movement paths, 2) estimating kinematic quantities such as velocity and distance travelled, 3) identifying driving relationships between resources and movement, and 4) quantifying animal space use. Outreach efforts will target the diverse array of biologists collecting and asking questions of animal movement data. Specifically, a series of tutorial papers will demonstrate methods covering the four analysis categories on conservation-focused case studies including African bush elephants in Kenya and the endangered khulan in the Mongolian Gobi desert. Additionally, an in-depth training course aimed at conservation practitioners and wildlife managers will be offered, and project results will be incorporated into the graduate and undergraduate curriculum at University of Maryland.

This work combines recent advances in modeling movement as a continuous space, continuous time stochastic process with kriging techniques from geostatistics. Kriging is a statistically optimal method of probabilistically "filling in the blanks" between a limited number of autocorrelated data points. Kriging revolutionized geostatistics and is now the gold standard for interpolating between autocorrelated spatial point observations. Analogously, adapting kriging to movement data will allow researchers to probabilistically reconstruct movement paths from a limited number of location observations. Knowledge of the continuous path an animal traversed is the critical nexus linking animal movement data to a wide range of ecologically-informative and conservation-relevant analyses. These transformative methods therefore have the potential to remove the key roadblock that is currently holding movement ecology back. To fully capitalize on that potential, this project will develop an integrated suite of freely available software packages for the R environment for statistical computing. These packages will enable users to answer movement related questions for a broad range of species with the powerful krige-based analytical tools the project develops. Project results will be available at http://biology.umd.edu/movement.html.

Evidence is mounting that students who are actively engaged in constructing knowledge achieve deeper conceptual understanding and more durable learning. Gateway Science, Technology, Engineering, and Mathematics (STEM) courses that utilize active learning have higher pass rates, which could ultimately lead to greater persistence of students on STEM career trajectories. Thus, the wider adoption of these active learning approaches is key to maintaining our scientific research enterprise and cultivating a diverse, scientifically literate workforce.

This study will use the Characteristics of Dissemination Success (CODS) model as a theoretical framework for changing the culture of teaching and learning in the biological sciences at the University of Maryland. Through engagement in a faculty learning community, instructors within the first four courses in the biological sciences curriculum will (1) develop progressive learning activities that employ evidence-based teaching approaches, then implement these in a coordinated fashion; (2) gather evidence for the effectiveness of their use of these approaches via an iterative process of monitoring student learning, then using this feedback to refine instructional activities; and (3) implement strategies that help students recognize evidence of their learning, to gain student buy-in for approaches that require greater effort and engagement. The success of the CODS model in fostering broader changes to the culture of teaching will be evaluated using a combination of quantitative and qualitative approaches.

This project aims to shift the culture of STEM course redesign from a solitary endeavor to a communal (and therefore sustainable) effort. It will foster new opportunities for interaction between like-minded colleagues in different STEM departments, creating a cadre of faculty dedicated to education reform. The project will also help a very diverse UMD student population, many of whom will become STEM professionals or K-12 science teachers, to develop metacognitive skills that enhance learning.

An urgent issue facing today's researchers in the life sciences is coping with the data explosion resulting from the advent of powerful new technologies. More data does not yield better information without the interdisciplinary tools required for such a transformation. This National Science Foundation Research Traineeship (NRT) award to the University of Maryland, College Park will build an innovative, cross-disciplinary model for graduate education that addresses this challenge by preparing students to pursue a range of STEM careers at the nexus of the computer, physical, and life sciences. Trainees will learn to combine physics-style quantitative modeling with data processing, analysis, and visualization methods from computer science to gain deeper insights into the principles governing living systems. The project anticipates training approximately sixty (60) PhD students, including thirty-five (35) funded trainees, from the physical, computer, and life sciences.

Understanding how data-derived interaction patterns can give insights into complex biological phenomena is the research focus of this program. Through an innovative combination of cross-disciplinary training, collaborative research, and outreach activities, NRT trainees will become experts in the process of transforming raw biological data into useful information from which new biological insights can be inferred. Participants will receive training in four different areas of network analysis: quantitative metrics for biological networks; mechanistic models of biological networks; network statistics and machine learning for biological applications; and visualization techniques for large, complex, biological datasets. This training will provide the foundation for research in one or more of three application areas, covering a wide range of biological scales: biomolecular networks; neuronal networks; and ecological/behavioral networks. Research experiences, interdisciplinary coursework, peer-to-peer tutorials, and internships with partners will provide graduate students with the skills needed to communicate complex scientific ideas to diverse audiences in order to maximize impact. Outreach activities will extend the benefits of the program to undergraduates, middle/high school students, and to the public at large.

The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.