Michael M. Fuller
My research focuses on three important topics in ecology: space and scale, population dynamics, and computational methods. To link these topics into an organic whole, I use a combination of data collection and management, modeling and simulation, and the development of statistical and analytical tools. My work on space and scale investigates the influence of spatial processes, such as dispersal, on species patterns including abundance, distribution, and association. I also study the interaction between environmental conditions, such as climate, and species traits, such as reproduction rate, and how this interaction influences the spatial structure of populations. Spatial patterns generally do not meet the assumptions of standard parametric statistical approaches, and therefore must be analyzed using a combination of geostatistical tools, such as variogram analysis, and computational approaches such as numerical and analytical modeling. Therefore part of my research involves developing and refining the computational techniques needed to analyze spatial patterns.
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 which is invading North America.
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. In a recent paper (in revision for Ecological Applications) we describe an approach called relative assessment. We use this approach to analyze the robustness of habitat management policy in the wetlands of Southern Florida. The technique uses random variation in input data to generatee empirically-based hypothetical climate scenarios. The effect of variation on the ranking of different management alternatives can then be compared using the criteria of interest.
For example, 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.
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 br/ing 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 review 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.
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. Click on the link above to learn more.
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 invertebr/ates and tropical dry-forest trees.
To analyze the invertebr/ate 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.
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.
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 well habitat conditions predict bird species composition. My long term goa 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.