Computational Ecology: Environmental Problem Solving for the 21st Century Louis J. Gross The Institute for Environmental Modeling University of Tennessee Copyright 1999 L. J. Gross Overview of Talk: Environmental problems - an optimistic view Demos - individual-based approaches What is computational ecology? Regional ecological assessment Everglades Restoration What is multimodeling? ATLSS project overview Some early lessons from ATLSS The future - Individual-based approaches everywhere See: DeAngelis, D. L. et al. (1998) Landscape Modeling for Everglades Ecosystem Restoration. Ecosystems 1:64-75. Abstract: The availability of satellite-based remote sensing, computers capable of handling large databases, rapid communication networks, and small radio sensors able to transmit details on many aspects of individual animals has fostered the development of the new field of computational ecology. By combining mathematical and computer models of natural systems with geographically-explicit details of the biotic and abiotic components of the environment, we are developing the capability to rapidly compare alternative virtual futures to better plan sustainable ecosystems. This optimistic view of the potential for computational methodologies to aid our understanding of and ability to manage natural systems will be illustrated by the successful efforts of The Institute for Environmental Modeling to provide ecological assessments for long-term planning of one of the world's largest natural systems restoration projects, in the Everglades of South Florida. Further details are available through the author's home page at http://www.tiem.utk.edu/~gross/ What is Computational Ecology? Computational Ecology is "an interdisciplinary field devoted to the quantitative description and analysis of ecological systems using empirical data, mathematical models (including statistical models), and computational technology". Computational ecology offers the potential to address environmental issues across regions (e.g. county to state) for which standard models in mathematical ecology are inappropriate. This field did not exist ten years ago, and what makes it possible now are: * New computer modeling and mathematical approaches * Availability of satellite-based remote sensing * Computers capable of handling large databases * Rapid communication networks * Small radio transmitters and sensors Major Components of Computational Ecology Data Management: Ecological data are relatively sparse, irregular in character, contains a mixture of data types, and scales of measurement vary widely over time and space. The metadata, used to describe the data, are as diverse as the data itself. Mathematical modeling: To date this has focused on ascertaining general properties of natural systems from basic assumptions. Taking into account stochastic factors, the range of hierarchical levels from individual through ecosystem, and external forcing functions such as weather and human-controlled impacts represent a very small fraction of the modeling work done to date. Although sufficient computational power now exists to handle models taking these into account, it is not part of the culture of the field, which appreciates generality over precision and realism. Visualization: A wide variety of statistical techniques have been developed and/or applied to ecological data sets historically to aid in elucidating patterns in these data. Visualization methods have developed to the point where we can emphasize information with particular features in complex data sets. Not only are such methods important for observational data, but they are critical to analysis of model output and comparison of such output to observations. Regional ecological management and assessment One of the most important functions of the environmental sciences today is to analyze the impacts of human actions on ecosystems and to provide management recommendations to ameliorate these impacts. In all parts of the world ecosystems are affected by the shrinkage and dissection of natural areas, disruptions of natural cycles, and the input of pollutants. The spatial extent of the effects of these anthropogenic impacts range from very local to regional and therefore require assessments that can span these scales as well. Environmental scientists are increasingly using mathematical or computer modeling approaches for impact assessment. Some of these modeling approaches are tailored to deal with small spatial extent concerns such as effects of toxicants on local biological populations. Other approaches, such as analyses of potential land use changes, aim at the county spatial level, whereas a few address questions on much larger, regional levels; for example, the problems of northwestern forest management as it impacts the spotted owl. Because most cases of anthropogenic impact include specific problems on a number of different levels, it is appropriate to develop general methods for across-level coupling of models to provide input to the assessment of these impacts on natural systems. Ecological Assessment Ecological assessment refers to the determination of the impacts of various anthropogenic influences on a natural system. Common components of such an assessment would be: Changes in population densities of "important" species, either culturally or economically Biodiversity effects Non-native species introductions Changes in community structure (which may not necessarily be associated with biodiversity changes) Effects of pollutant inputs Direct effects of human actions on the system (e.g. hunting, deforestation, sewage/waste disposal) Indirect effects of human actions (e.g. habitat fragmentation, soil erosion, salinity changes) Coupled with the above for regional assessment would be taking account of the impacts on human systems as well, including: Human population density changes Economic impacts Land use changes and effects on urban/rural/commercial/residential percentages and the long term impact of these on future human needs Agricultural productivity Social/cultural changes Cultural attitudes towards conservation Regional Environmental Issues Over regional spatial extents (e.g. on order of 100-1000's of square km), environmental modeling requires taking account of smaller spatial heterogeneity in underlying habitats, trophic structures, and human impacts. The most important recent technological advance associated with regional modeling and assessment is the use and availability of Geographic Information Systems (GIS), allowing for the rapid visualization and analysis of two-dimensional images, such as those obtained from satellite or airplane remote sensors. GIS data are readily available for a variety of habitat characteristics, including basic vegetation maps, land-use maps, soil maps, road maps, population density, etc. GIS data, in addition to generally being static and thus providing only a "snapshot" of the system, do not readily allow one to track the animal components of a system, without using some proxy models. Although the technology is available to radio tag and track individual animals, except for a few large mammals and commercial species, this has been too expensive to apply in general. The above limitations of GIS has led TIEM to develop methods to link spatially explicit ecological models to GIS data, allowing for the potential to produce models that can analyze the effects of management systems on a variety of components of the natural system, not just those which can be observed remotely. ATLSS (Across Trophic Level System Simulation) Objective: Analyze the ecological impacts of hydrologic planning across the Everglades region of South Florida. ATLSS as a multimodel: a mixture of modeling approaches based upon the inherent temporal resolution and spatial extent of various trophic components, linked together by spatially-explicit information on underlying environmental (e.g. water, soil structure, etc.), biotic (e.g. vegetation), and anthropogenic factors (e.g. land-use). Current approaches: static spatially-explicit indices, compartment analysis, differential equations for structured populations and communities, and individual-based models. Linking models at very different spatial and temporal resolutions has been a major challenge, requiring a variety of spatial interpolation methods, and careful design of model interfaces and linkages with remote sensing data and GIS. Multimodeling is an essential tool to link together disparate data sets, aid monitoring programs, and produce relative assessments of the ecological impacts at landscape-levels of spatially-explicit management plans. Everglades Problems: Region has been greatly affected by many years of efforts to control the dominant environmental factor driving the system (water) Over 250 control points currently control water flow Effects of control have included severe changes in amount and pattern of hydrologic flows major declines in many species populations greatly enhanced fluxes of certain nutrients changes in plant and animal community composition the release of high levels of toxicants including mercury Complexity of hydrologic management is enhanced by agricultural demands, urban requirements from population growth, and concerns about environmental degradation Plans to restore the Everglades include significant $ for physical modifications (Restudy plan current estimate is $8 Billion not counting land purchases) Some Lessons from the ATLSS Experience to Date: Work closely with those with long experience in the system being modeled and use their experience to determine key species, guild and trophic functional groups on which to focus. Moderate the above based first on the availability of data to construct reasonable models, and secondly on the difficulty of constructing and calibrating the models. Don't try to do it all at once - start small - but have a long-term plan for what you wish to include overall, given time and funding. Leave room for multiple approaches: don't limit your options. In the face of limited or inappropriate data, use this as an opportunity to encourage further empirical investigations of key components of the system. Build flexibility in as much as possible. Be flexible about what counts as success. Be persistent, and have at least one member of the team who is totally dedicated to the project and willing to stake their future on it. Do whatever you can to maintain continuity in the source of long-term support for the project. Build a quality team who respect each others abilities and won't second guess each other, but who accept criticism in a collegial manner. Keep some part of the team out of the day-to-day political fray. Constantly communicate with stakeholders. Don't limit your approach because one stakeholder/funding agency wants you to. Be prepared for criticism based upon non-scientific criteria, including personal attacks. Ignore any of the stakeholders at your peril.