
NSF Org: |
DBI Division of Biological Infrastructure |
Recipient: |
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Initial Amendment Date: | June 1, 2011 |
Latest Amendment Date: | May 23, 2014 |
Award Number: | 1062411 |
Award Instrument: | Continuing Grant |
Program Manager: |
Anne Maglia
DBI Division of Biological Infrastructure BIO Directorate for Biological Sciences |
Start Date: | July 1, 2011 |
End Date: | June 30, 2015 (Estimated) |
Total Intended Award Amount: | $829,870.00 |
Total Awarded Amount to Date: | $834,320.00 |
Funds Obligated to Date: |
FY 2012 = $300,504.00 FY 2013 = $300,340.00 FY 2014 = $4,450.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
3112 LEE BUILDING COLLEGE PARK MD US 20742-5100 (301)405-6269 |
Sponsor Congressional District: |
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Primary Place of Performance: |
3112 LEE BUILDING COLLEGE PARK MD US 20742-5100 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | ADVANCES IN BIO INFORMATICS |
Primary Program Source: |
01001213DB NSF RESEARCH & RELATED ACTIVIT 01001314DB NSF RESEARCH & RELATED ACTIVIT 01001415DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.074 |
ABSTRACT
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.
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
In recent years, technological advances in animal tracking technologies have dramatically increased our abilities to tag individual animals and follow their movements. Ever increasing detail from high-resolution relocation data of individual animals allows researchers not only to examine in greater detail traditional questions related to home range behaviors or habitat use but also to follow new inquiries about behavioral processes such as spatial learning and social interactions. However, analytical methods to analyze these data have not kept pace with the increase in data quantity and quality. This grant provides outcomes with regard to methods and model development for analyzing high resolution tracking data. It also provides outcomes that apply innovative methods to gain new insights from animal relocation data.
In particular, we developed mathematical methods that go beyond traditional approaches that analyze movement data from one movement step to the next, i.e. one recorded location to the next. Instead we developed methods that allow analyzing movement data in a continuous time framework. This approach is advantageous since it incorporates and analyses the correlations among all locations and thus allows extracting more information from movement data. This is particular important for the increasingly high resolution of novel tracking devices where the correlation among locations is particularly high. We have demonstrated how these methods allow much better estimations of animals' home ranges and reconstruction of movement paths than was previously possible. Using these methods on a data set of Mongolian gazelle movements we could demonstrate how their ranging areas may be vastly greater than previously assumed. A second theme of method development related to analyzing interrelations of movement paths among different individuals rather than analyzing movements of only single individuals. We developed methods that not only can detect whether animal movement is correlated among individuals, but that also can detect different types of correlations. For example, animal movements could be correlated because they move towards a common food source or they could be correlated because different individuals interact socially and thus coordinate their movements. Our tools allow differentiating among these different types of correlations and can detect important ecological processes in populations such as the onset of migrations, simply by analyzing changes in the correlation among moving individuals. To allow wide access of these tools to the broad user community of ecologists and conservation biologists, we made these tools available as libraries for the open-source programming environment R.
Finally this grant provided not only outcomes in methods. Applications of new methodologies revealed also new insights into important ecological and behavioral processes. For example, by analyzing the interrelations of animal relocation data among individuals, we could demonstrate that learning of migratory patterns of reintroduced whooping cranes in the eastern United States happened over several years and was socially transmitted. These findings have important implication for the conservation and management of this reintroduced population. In another example, we could demonstrate how high-resolution animal movement data can be used to link animal movement directly to ecosystem functions. We analyzed tracking data of frugivorous hornbills in South Africa. By combining movement data of birds with gut passage times of plant seeds, we could spatially map seed dispersal pathways of plants in fragmented forest landscapes. These methods allowed us to identify critical stepping-stones that are important for the connectivity of plant species in fragmented landscapes.
Last Modified: 09/28/2015
Modified by: Tho...
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