Funded Grants

An integrated, hierarchical framework for modeling biocomplexity

The most fertile ground for advances in science often resides at the interface of disciplinary boundaries, in regions where boundaries of existing knowledge overlap. The synergistic potential of interdisciplinary research is a mainstay in the analysis of complex systems. Finding common threads and emergent patterns in seemingly disparate fields is an e ective means to advance theory, increase applicability, and thus expand knowledge. Consider the following disciplines and associated issues:

  • A main theme in ecology is how species deal with habitat loss and fragmentation. This focus is warranted due to the extent and cost associated with habitat destruction. Indeed, habitat loss and fragmentation are considered by most to be the greatest threats to conservation of biodiversity. Increasingly, landscape structure and function are altered or even dominated by humans. Loss of natural habitat is occurring worldwide and often results not only in a reduced amount of native habitat, but also in a change in its configuration in a landscape. Habitat fragmentation refers to the subdivision of native habitat into smaller pieces. This destructive process transforms natural landscapes into quiltwork series of disjunct patches or corridors of remaining natural habitat and a matrix of human landscape elements (e.g., agricultural fields, residential developments). Species persist in these structured environments as metapopulations, i.e., assemblages of local populations inhabiting habitat patches that persist collectively through a balance of colonization and extinction events. The Granville fritillary butterfly is one species that has been studied extensively in this context. But how much habitat loss can such a species tolerate and still persist?
  • Epidemiology is the study of transmission and control of disease. This discipline has been highlighted recently due to the threat of bioterrorism, the possible reemergence of smallpox, and the spread of the SARS virus. Consider the SARS virus as an illustrative example. The illness spreads as a result of the “basic reproduction ratio” being greater than one, or roughly speaking, the transmission rate being greater than the recovery rate. It also is clear that transmission has an inherently spatial component, as evidenced by the use of control measures including restriction of travel between urban centers and quarantine of probable SARS patients. There is also heterogeneity in the reaction of the host to this virus. What if a vaccine were available? How many people would have to be vaccinated in order to eradicate SARS?
  • The concept of selfish DNA is relatively new in the field of Genetics. This curious phrase refers to a non-coding sequence of DNA that seems to serve no purpose to the host, but is maintained through the ability to replicate. Transposons or transposable elements (TEs) are often classified as selfish DNA, yet they are further distinguished by their ability to insert copies of themselves into new genomic locations in their host, a process called transposition. Transposition is an inherently spatial process, with chromosomal regions differing in their susceptibility to transposition events. Random insertion of nonfunctioning DNA into a host’s genome doubtless can have detrimental effects on the host, often giving rise to mutations in functional genes. As a result, TEs often are viewed as molecular parasites. What mediates a TEs eradication from a host or a lineage?

The dynamics of the scenarios described above for ecology, epidemiology, and genetics can be captured in a general sense with the same structured population model, which in ecology is referred to as a metapopulation model.

Because of the structural parallels, these 3 systems have the same threshold condition. In ecology this is called the extinction threshold, and it maintains that the extinction of a species will occur if its habitat is reduced beyond a certain critical fraction determined by the species’ life history characteristics. In a similar fashion, it is not necessary to vaccinate all individuals or close all DNA to transposition in order to eradicate a disease or a transposable element. But there are several critical features of these systems that the simple metapopulation model fails to incorporate. All of these systems have an associated spatial landscape, whether it is comprised of habitat patches, a network of cities, or chromosomes. Yet, the structure of the landscape is not included in the simple metapopulation model. How can this model incorporate the intricacies of the landscape, and what predictive benefit is gained by adding such complexity to the model? Moreover, the simple model monitors the system at a metapopulation level operating at a slow time frame (colonization, extinction) but fails to incorporate the local patch dynamics occurring at a faster time scale (birth, death). What can be gained by adding this additional complexity, and does it help quantify these critical thresholds?

Our research will focus primarily on ecological extensions of this structured population model. Our goal is to develop a theoretical framework for studying questions associated with landscape structure and change, in terms of their consequences for species persistence. This framework will be hierarchical in nature, with each new tier adding another level of complexity to model. Each model extension will be linked to a simulation study and then an empirical study. Simulation studies are helpful in evaluating analytic performance and the sensitivity of model parameters. Empirical studies will explore both experimental and natural landscapes. We believe this step is critical for validation of model performance. As we progress from simple to more complex models, we will ask: Does this added complexity capture an emerging property of the system? Is the added complexity justified in terms of gaining more predictive value? How does the model compare with simulation studies and empirical evidence? And ultimately, does this extension of the metapopulation model benefit our understanding of how species persist in complex landscapes? Once these questions are answered, we will transfer this knowledge to further our understanding of parallel problems in other fields, such as epidemiology and genetics.