Michael M. Fuller

Research Interests

I am interested in the processes that govern the assembly, structure, and function of species populations and communities. My research combines field studies with computational approaches (analytical and numerical models) to understand the influence of random and deterministic processes on ecological patterns at multiple scales. Areas of interest include: forest ecology, spatial pattern analysis, invasive species control, community dynamics, graph-theoretic (network) analysis, computational ecology, and individual-based ecology. The subjects of my studies include tropical forest trees, invasive plants, aquatic micro-invertebrates, and the Eurasian collared dove (an exotic species in North America).


Current Research

I am currently working on several projects at the Institute for Environmental Modeling. These include developing a general model for the control of invasive plants and a specific model for the spread of the Eurasian collared-dove (Streptopelia decaocto), an exotic bird that is invading North America. A map of the distribution of the collared dove in the US is shown below. Notice that although there are more Summer (BBS) survey locations, the winter range is more extensive. This observation may have several causes, including reporter bias in the Christmas Bird Counts and the fact that the doves aggregate in flocks during the winter, making them easier to detect.
Map of Collared Dove Density in USA

We are using integro-difference equations to predict the rate of spread and ultimate northern limit of the population in North America. Our dove model incorporates biological parameters of population growth (i.e. length of reproductive period, number of offspring per brood, etc) as well as latitudinal variation in the population growth rate. In a separate but related project, we are analyzing the effect of the collared-dove on populations of native dove species in North America. The above projects involve collaborations with several people, including Erika Asano, Dr. Andrew Whittle, Dr. Louis Gross, and Dr. Suzanne Lenhart.

Relative Assessment of Everglades Management Scenarios In addition to the dove projects, I am working with Dr. Louis Gross to document the modeling approach used in the the Florida Everglades Restoration project. We recently submitted a paper to the journal Ecological Applications that details a new approaches to scenario analysis with respect to habitat management in the wetlands of Southern Florida. One technique is to use the 30-year record of water levels in the park to generate empirically-based hypothetical climate scenarios.

The climate scenarios can be used as inputs to habitat quality models to project the effect of climate shifts on different species in the park. Because these species differ in their habitat needs, management scenarios must balance the positive and negative impacts of water management for species that may have conflicting needs. For example, the endangered Cape Sable seaside sparrow requires upland grassland whereas the endangered snail kite depends on wetlands (see figure at right). Relative assessment involves quantifying differences in the suitability of different management options according to some criteria.

Computational Science and Natural Resource Management
The increased sophistication and application of models to ecological problems is just one example of how computers are changing the science of ecology. Increasingly, advances in miniaturization, computing power, remote sensing, and modeling are revolutionizing the field of natural resource management. But these advances also bring many challenges. The need for information management and communication, dynamic models, and real-time monitoring places increasing demands on legacy data structures and over-burdened networking infrastructures. To meet these demands, natural resource managers require access to high-performance computing tools and improvements in data storage, communication, and analysis (see example, below). Computer scientists are needed who can collaborate with natural resource managers and modelers to develop novel solutions. In a recent paper in Computing in Science and Engineering we highlight several key problems in resource management that represent exciting opportunities for computer scientists and engineers in search of challenging practical problems.


Spatial Analysis in Community Ecology. Other projects I'm currently working on include the development of techniques for the spatial analysis of bird communities and forest ecosystems. Much of my work involves developing computer programs in C++ as an aid to data transformation, statistical analysis, and simulation.



Previous Research

My doctoral dissertation tested the predictions of the neutral theory of biodiversity and biogeography. The neutral theory emphasizes the role of random demographic change on species diversity and abundance. Proponents of the neutral theory have shown that simple neutral models, in which individuals have an equal probability of birth, death, and dispersal, can reproduce several observed community patterns, such as species relative abundance. This result suggests that random processes alone can explain the structure of communities. However, many empirical studies have shown that community patterns are also influenced by variation among species in their ability to survive and reproduce. To understand the extent to which neutral models can predict species distribution and abundance, I compared the variation inspecies abundances in natural communities with that predicted by neutral models. My dissertation analyzed the variation in species abundances of two very different kinds of organism: tropical aquatic invertebrates and tropical dry-forest trees.


Community-Metacommunity Dynamics of Aquatic Invertebrates

To analyze the invertebrate community patterns, I collaborated with Tamara Romanuk and Jurek Kolasa. We used a 9-year dataset collected by Jurek and his collaborators that represented 50 rock pool communities and 72 species (middle figure on the right). We used neutral models to predict the relative abundance of each species based on their metacommunity proportions. We then compared the predictions to empirical species proportions at the community (i.e. individual pool) and metacommunity scales. We found that at the community scale, common species were far more variable in abundance than predicted by neutral models. At the metacommunity scale, rare species were more common than predicted. In addition, variation in species diversity and abundance was strongly influenced by the relative density of predators. Trophic interactions influenced both community and metacommunity patterns. The findings of our metacommunity study are now published (Fuller et al. 2005) and will be cited in a review article on the neutral theory, written by Brian McGill for the journal Ecology.


Species Association Networks of Tropical Trees
Tropical forest is often cited as a community that may be governed by neutral dynamics (e.g. Hubbell 2001). Trees are structurally and ecologically similar, and therefore may be more similar ecologically. In two projects, I collaborated with Brian Enquist and Andreas Wagner to analyze patterns of species association in a tropical forest. We used data on the geographic coordinates of over 19,000 tropical dry- forest trees (106 species) to determine whether species niche differences influence community structure. In an unprecedented approach, we used the principles of graph theory to analyze the effect of tree crown overlap and body size on community structure. We constructed networks representing the spatial association of species (see bottom figure in the frame to the right). This approach revealed how species interactions in local neighborhoods influence the structure of the community.

An important goal of the project was to determine the randomness of species distributions on the forest plot. We compared species networks constructed from the empirical distribution of species to those in which the geographic coordinates of individual trees had been randomized (see figure, above). We found that the networks which represented the empirical community often differed strikingly from those of randomized communities. However, our ability to detect the effect of niche differences on community structure was sensitive to how network complexity was measured. In the future, I want to examine the effect of intraspecific spatial autocorrelation on network structure in this forest. You can learn more about spatial autocorrelation here: Lichstein et al. 2002.


Future Goals

The above results illustrate that combining models with empirical data is a powerful approach for uncovering the influence of different factors on species patterns. In future, I will be exploring the use of network analysis to address a wide range of questions in community and landscape ecology, including how cross-trophic species interactions influence species relative abundance within communities. My long term goal is to understand how positive, negative, and neutral interactions influence the stability and structure of communities, and whether communities evolve such that opposing forces are in held in a dynamic balance.

References Cited

Fuller, M.M., T.N. Romanuk, and J. Kolasa. 2005. Effects of predation and variation in species relative abundance on the parameters of neutral models. Community Ecology 6:229-240.

Hubbell, Stephen P. 2001. The Unified Theory of Biodiversity and Biogeography. Princeton, NJ. Princeton Univ. Press.


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