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Abstract of Talks on Bayesian Network
and Decision Support ModelingBruce G. Marcot
updated 02 July 2009(a list of my presentations on the topic, with selected abstracts)
Amstrup, S. C., B. G. Marcot, and D. C. Douglas. 2008. Forecasting the 21st century world-wide status of polar bears using a Bayesian network modeling approach. Second USGS Modeling Conference. 11-15 February 2008, Orange Beach, Alabama.
Beever, E. A., and B. G. Marcot. 2008. Bayesian network models as a framework for forecasting wildlife response to GCC. Presented 18 November 2008 at the WildREACH Workshop, USDI Fish and Wildlife Service, Fairbanks, Alaska.
Jay, C. V., B. G. Marcot, and A. S. Fischbach. 2009. Forecasting Pacific walrus status: modeling key factors with Bayesian networks. For presentation at: Society for Marine Mammalogy - Biennial Conference on the Biology of Marine Mammals, 12-16 October 2009.
Abstract:
Sea ice over the Chukchi and Bering Seas is important to many Arctic marine mammals, including the Pacific walrus (Odobenus rosmarus divergens). Sea ice provides walruses with substrates to rest, molt, and give birth in relative seclusion of humans and predators. Sea ice also provides walruses access to large areas over the continental shelf to forage on productive patches of benthic invertebrates. Changes in seasonal sea ice conditions from climate warming will likely impact walruses in complex ways. For example, in the Chukchi Sea, walruses responded to recent extreme reductions in summer ice extent by occupying previously unused coastal haul-outs, which led to increased(?) calf mortalities from crowding and may have imposed increased physical stress on adults from reduced foraging opportunities. However, reduced sea ice in the Arctic also has led to increased levels of primary production by creating a longer growing season and larger areas of open water, which could potentially provide more food for walrus prey on the seafloor. The combined impact of these and other factors on the Pacific walrus population is difficult to forecast. To evaluate the implications of climate change on walruses, we are developing a Bayesian network model that will integrate key environmental correlates and potential anthropogenic stressors to the walrus population. With the model, we will estimate probabilities of future persistence of Pacific walrus populations, quantify uncertainties, and evaluate the sensitivity of model outcomes to climate model uncertainties, environmental conditions, and stressors. Results will provide policy makers and regulatory agencies with new information to address emerging concerns about the species’ status under changing environmental conditions in the Arctic.
Marcot, B. G. 1999. The DecAID advisory system for managing snags and down wood for wildlife habitat; the Old Forest Remnants Study; and use of Bayesian belief networks for modeling species viability for the Interior Columbia Basin Ecosystem Management Project. Presented 27 October 1999 to the Survey and Manage Committee, USDA Forest Service, Corvallis Forestry Sciences Lab (invited presentation).
Marcot, B. G. 1999. Use of Bayesian belief network models for evaluating Final EIS alternatives for wildlife viability. Presented 10 March 1999 at Annual Northwest Section Conference, The Wildlife Society, Bozeman MT. (invited presentation).
Abstract:
The Terrestrial Staff of the Science Advisory Group has developed “causal web” models relating key environmental correlates (KECs) of wildlife species, to potential population response under several Final EIS alternatives for the Interior Columbia Basin Ecosystem Management Project. The models involve use of Bayesian belief networks (BBNs), which represent conditional probabilities of population response given environmental conditions at two scales of spatial resolution. The KECs were identified by use of literature and expert panels and formalized into a Species-Environment Relations database. The probabilities and BBN model structures were derived from literature and, where needed, expert judgment. The BBN models provide a consistent, testable framework by which to represent simple habitat relations of a wide array of species. Sensitivity analyses using entropy-reduction metrics identify controlling KECs that may be worthy of further study or monitoring. BBN species modeling represents a major step beyond using expert panels to evaluate population viability; it opens the “black box” of expert opinion by formally modeling the subjacent ecological relations.
Marcot, B. G. 1999. Use of Bayesian belief networks for modeling wildlife population viability: a sing-along. Presented 27 October 1999 to Wildlife Seminar, Department of Fisheries and Wildlife, Oregon State University. (invited presentation). Corvallis OR.
Marcot, B. G. 2001. Decision support models for plant and animal conservation. In: Restoration and recovery: beyond good intentions. Society for Ecological Restoration Northwest Chapter Conference, 2-6 April 2001. Bellevue WA.
Abstract:
A major challenge in conservation is managing species and ecosystems with scant scientific knowledge and great uncertainty. Decision support models (DSMs) can aid this by (1) evaluating the implications of uncertainty in meeting management goals, (2) combining empirical data with expert judgment, and (3) through sensitivity testing and validation steps, identifying key habitat elements as a basis for prioritizing inventory and monitoring. DSMs include a wide range of tools and include Bayesian analyses and belief network modeling, data and text mining, decision modeling such as decision tree analysis, expert systems, fuzzy logic and fuzzy set theory models, genetic algorithms, rule and network induction, neural networks, reliability analyses, quantitative (environmental) risk analysis, simulation and scenario modeling, and other approaches. Successful use of DSMs for plant and animal conservation depends largely on the availability of data or experts, and the willingness of decision-makers to articulate their risk attitudes and decision criteria. These are no small hurdles. Many DSMs can aid in merging scientific data with expert knowledge, although no model can replace empirical field studies. Several examples of DSMs are demonstrated to illustrate the 3 objectives listed above.
Marcot, B. G. 2001. Functional assessments and Bayesian belief network modeling. Presented 9 May 2001 to the INLAS modeling team, USDA Forest Service and others, The Dalles, Oregon. Invited presentation.
Marcot, B. G. 2002. I believe, therefore I model: Modeling wildlife-habitat relations with Bayesian belief networks. Presented 20 January 2002 to Faculty and Graduate Seminar, Department of Wildlife, Utah State University, Logan, Utah.
Marcot, B. G. 2002. Modeling rare species with Bayesian belief networks. Seminar presented 31 January 2002 to graduate class on species modeling, Department of Wildlife, Utah State University, Logan, Utah.
Marcot, B. G. 2002. Modeling species and decision with Bayesian belief networks. Presented 17 April 2002 at: Workshop on species modeling. Ministry of Forests, Victoria, British Columbia, Canada.
Marcot, B. G. 2003. Characterizing species at risk: experience in species and decision modeling under the Northwest Forest Plan. In: September 2003 Annual National Conference of The Wildlife Society, Session on "Assessing Risks to Wildlife Populations From Multiple Stressors". Burlington VT.
Abstract:
A "Survey and Manage Species Program" has been established under the Northwest Forest Plan (USFS, BLM) that, in part, entails site surveys and annual reviews of rare and little-known species of fungi, lichens, bryophytes, vascular plants, mollusks, and vertebrates in late-successional and old-growth forest reserves. Under this program, colleagues and I have developed 12 species-habitat models using Bayesian belief networks (BBN) in a rigorous procedure involving expert knowledge, peer review and model revision, testing with known site data, and validation and updating with new, random-site data. These species-habitat risk analysis models are intended to help managers predict likelihoods of species presence based on microsite characteristics, and to prioritize sites for expensive and intensive pre-disturbance survey. Thus, their sensitivity to site conditions, and their accuracy of predicting species presence (more so than absence), are paramount and being tested. A second set of BBN models represents risk management in the decision framework for annual species reviews conducted by managers and specialists under the program. These decision models were crafted strictly from the guidelines in the Record of Decision and are helping to ensure consistency in the species reviews, as well as helping to identify hidden inconsistencies in the guidelines. Overall, the species risk analysis models and the decision risk management models are best used to guide thinking and evaluation, not to substitute for human decision-making.
Marcot, B. G. 2003. ROD criteria as Bayesian belief network decision models. In: Step 3 Workshop, 2003 Survey & Manage Annual Species Review. Pacific Northwest Research Station Director's Office, USDA Forest Service, Portland, Oregon, 29 May 2003. [ Invited.]
Marcot, B. G. 2006. Modeling with Bayesian belief networks. Presented 27 October 2006 to USDI Fish and Wildlife Service shortcourse on Principles of Modeling for Conservation Planning and Analysis, NCTC Course No. ECS 3149, Portland Oregon [invited].
Marcot, B. G. 2007. Workshop on building Bayesian belief network models. 22 March 2007 for the Landscape Level Wildlife Assessment Project, Washington Department of Fish and Wildlife, Olympia, Washington. [Invited.]
Marcot, B. G. 2008. Bayesian network modeling. Presented 20 February 2008 to USDI Fish and Wildlife Service, Regional Office, Portland, Oregon. [ Invited.]
Marcot, B. G. 2008. Hosted workshop, presented talk on use of Bayesian networks for modeling species-habitat relationships of Mardon skipper (Polites mardon) in the Washington Cascades. 01 April 2008, Gifford Pinchot National Forest, Vancouver, Washington.
Marcot, B. G. 2008. I believe, therefore I model: Evaluating species at risk with with Bayesian belief networks and other tools. Presented 10 September 2008 to Seminar Series, Alaska Science Center, USDI Geological Survey, Anchorage, Alaska. [ Invited.]
Marcot, B. G., and S. C. Amstrup. 2009. Warm times ahead: modeling the future of polar bear global populations. In: Annual Meeting, Oregon Chapter of The Wildlife Society. 12 February 2009. Glen Eden, Oregon.
Abstract:
As part of a larger Polar Bear Science Team effort with USDI Geological Survey, analyses were made of historic, current, and future (next century) habitat and populations of polar bears in four ecoregions throughout their global distribution. Habitat carrying capacity was modeled using a simple analytic framework, projecting future capacity as a function of present polar bear crude and ecological densities. Population outcomes were modeled based on potential effects of environmental conditions, prey and foraging habitat availability, and anthropogenic stressors, in a Bayesian network. All future projections were made using minimum, maximum, and ensemble mean values of sea ice conditions summarized from a suite of 10 general circulation models. Polar bear populations were projected to decline during the 21st century throughout their range, and severity of decline depended on future ice floe availability. In 2 ecoregions, most likely outcomes were extirpation within 45-75 years, resulting in overall potential loss of about two-thirds of the world’s current polar bear population by mid-century. The main factor accounting for declines is loss of ice sea habitat.
Marcot, B. G., and K. Mellen. 2002. Ways to locate, map, and model high priority sites, including use of GIS and Bayesian belief network modeling. Presented 05 April 2002 at: Taxa team work session, high priority site selection. Survey and Manage Species Program, Portland, Oregon.