Balancing ecological complexity in predictive models: a reassessment of risk models in the mountain pine beetle system

1. The nature of ecological risk assessment is to predict the probability of an event, such as extinction or invasion, in a location where the event has rarely occurred. This typically requires developing risk models from data on events in different locations. One perplexing challenge in developing these models is to find the optimal balance of model complexity that reflects the tactical details of a system, but is sufficiently strategic to be applicable under a wide range of situations. 2. Here we address the balance of complexity in risk models for the mountain pine beetle system. Mountain pine beetles (Dendroctonus ponderosae Hopkins) are destructive pests of pine forests in western North America. Much effort has gone into collecting empirical evidence and developing mechanistic models of infestation dynamics, which has resulted in a wealth of process-based information. Current risk models, however, are based solely on indices of stand susceptibility that do not incorporate much of this ecological understanding. In practice, current risk models have proven ineffective at predicting the risk or extent of an infestation. 3. We assemble an ecological framework of the beetle-host interaction that allows us to compare across phenomenological and mechanistic models. We demonstrate that current risk models predict only ranked risk among forest stands, as opposed to absolute risk, and thereby provide an explanation for their limited ability to predict risk in practice. By comparing existing models with the ecological framework, we identify the primary factors determining risk, and propose which dynamical processes should be modelled explicitly, and which might be strategically abstracted. 4. Synthesis and applications. Balancing model complexity in predictive risk models is challenging for systems with complex ecology and imperfect information. Here we draw together a wide range of empirical and modelling work in the mountain pine beetle system to develop a strategic framework of the ecological interactions. Through this framework, we demonstrate why current risk models have been ineffective in predicting risk, and suggest a starting point for future risk models that explicitly describe the dynamical processes necessary to predict absolute risk.

[1]  L. Safranyik,et al.  Susceptibility of lodgepole pine stands to the mountain pine beetle : testing of a rating system , 2000 .

[2]  J. Negrón,et al.  Probability of ponderosa pine infestation by mountain pine beetle in the Colorado Front Range , 2004 .

[3]  N. Stenseth,et al.  A theoretical basis for understanding and managing biological populations with particular reference to the spruce bark beetle , 1989 .

[4]  A. Carroll,et al.  The biology and epidemiology of the mountain pine beetle in lodgepole pine forests. , 2006 .

[5]  G. D. Amman,et al.  Guidelines for reducing losses of lodgepole pine to the mountain pine beetle in unmanaged stands in the Rocky Mountains. , 1977 .

[6]  J. Borden,et al.  Evaluation of the push-pull tactic against the mountain pine beetle using verbenone and non-host volatiles in combination with pheromone-baited trees , 2006 .

[7]  R. Lavigne,et al.  The applicability of available hazard rating systems for mountain pine beetle in lodgepole pine stands of southeastern Wyoming , 1986 .

[8]  Simon N. Wood,et al.  Super–sensitivity to structure in biological models , 1999, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[9]  R. Waring,et al.  Physiological stress in lodgepole pine as a precursor for mountain pine beetle attack1 , 2009 .

[10]  C. Kolar,et al.  Progress in invasion biology: predicting invaders. , 2001, Trends in ecology & evolution.

[11]  W. E. Cole Interpreting Some Mortality Factor Interactions Within Mountain Pine Beetle Broods , 1975 .

[12]  B. Bentz,et al.  TEMPERATURE-DEPENDENT DEVELOPMENT OF THE MOUNTAIN PINE BEETLE (COLEOPTERA: SCOLYTIDAE) AND SIMULATION OF ITS PHENOLOGY , 1991, The Canadian Entomologist.

[13]  R. May,et al.  Modelling vaccination strategies against foot-and-mouth disease , 2003, Nature.

[14]  A. Berryman Biological Control, Thresholds, and Pest Outbreaks , 1982 .

[15]  Uta Berger,et al.  Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology , 2005, Science.

[16]  B. Bentz,et al.  Low seasonal temperatures promote life cycle synchronization , 2001, Bulletin of mathematical biology.

[17]  Jesse A. Logan,et al.  A critical assessment of risk classification systems for the mountain pine beetle , 1993 .

[18]  A. Berryman,et al.  Interacting Selective Pressures in Conifer-Bark Beetle Systems: A Basis for Reciprocal Adaptations? , 1987, The American Naturalist.

[19]  David W. Roberts,et al.  Predictive models of whitebark pine mortality from mountain pine beetle , 2003 .

[20]  A. Berryman,et al.  The role of host plant resistance in the colonization behavior and ecology of bark beetles (Coleoptera: Scolytidae) , 1983 .

[21]  J. D. Stuart Hazard rating of lodgepole pine stands to mountain pine beetle outbreaks in southcentral Oregon , 1984 .

[22]  John Harwood,et al.  Risk assessment and decision analysis in conservation , 2000 .

[23]  R. Waring,et al.  Resistance of conifers to bark beetle attack: Searching for general relationships , 1987 .

[24]  Steven F. Railsback,et al.  Individual-based modeling and ecology , 2005 .

[25]  Nils Chr. Stenseth,et al.  Interaction dynamics of bark beetle aggregation and conifer defense rates , 1989 .

[26]  P. White,et al.  Mathematical elements of attack risk analysis for mountain pine beetles. , 2000, Journal of theoretical biology.

[27]  D. Geiszler,et al.  Modeling the dynamics of mountain pine beetle aggregation in a lodgepole pine stand , 2004, Oecologia.

[28]  Kenneth F. Raffa,et al.  Mixed messages across multiple trophic levels: the ecology of bark beetle chemical communication systems , 2001, CHEMOECOLOGY.

[29]  William Gurney,et al.  Modelling fluctuating populations , 1982 .

[30]  P. White,et al.  Phase transition from environmental to dynamic determinism in mountain pine beetle attack , 1997 .

[31]  A. Berryman Dynamics of Bark Beetle Populations: Towards a General Productivity Model , 1974 .

[32]  Kenneth F. Raffa,et al.  FEEDBACK BETWEEN INDIVIDUAL HOST SELECTION BEHAVIOR AND POPULATION DYNAMICS IN AN ERUPTIVE HERBIVORE , 2004 .

[33]  A. A. Berryman,et al.  A mechanistic computer model of mountain pine beetle populations interacting with lodgepole pine stands and its implications for forest managers , 1986 .

[34]  N. Stenseth,et al.  Metastability of forest ecosystems infested by bark beetles , 1984, Researches on Population Ecology.

[35]  William H. Emmingham,et al.  Thinning Alternatives for Ponderosa Pine: Tools and Strategies for Family Forest Owners , 2005 .

[36]  A. Berryman Towards a theory of insect epidemiology , 1978, Researches on Population Ecology.

[37]  C. Elkin,et al.  Attack and Reproductive Success of Mountain Pine Beetles (Coleoptera: Scolytidae) in Fire-Damaged Lodgepole Pines , 2004 .

[38]  Glenn W. Suter,et al.  Ecological risk assessment , 2006 .

[39]  David L. Adams,et al.  A model for hazard rating lodgepole pine stands for mortality by mountain pine beetle , 1980 .

[40]  V. Kaitala,et al.  Predicting the risk of extinction from shared ecological characteristics. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[41]  K. Raffa,et al.  Interactions among conifer terpenoids and bark beetles across multiple levels of scale: An attempt to understand links between population patterns and physiological processes , 2005 .

[42]  H. Godfray,et al.  Population growth rates: issues and an application. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[43]  James T. Carlton,et al.  Pattern, process, and prediction in marine invasion ecology , 1996 .

[44]  Robert N. Coulson,et al.  Population Dynamics of Bark Beetles , 1979 .

[45]  Jesse A. Logan,et al.  Ghost Forests, Global Warming and the Mountain Pine Beetle , 2001 .

[46]  D. Cummings,et al.  Strategies for containing an emerging influenza pandemic in Southeast Asia , 2005, Nature.

[47]  John R. Krebs,et al.  Predicting population responses to resource management , 2001 .

[48]  B. Bentz,et al.  Local Projections for a Global Model of Mountain Pine Beetle Attacks , 1996 .