Ecosystem Models

Many types and classes of models have been developed and applied to parts or all of the Salish Sea ecosystem including efforts to model impacts of climate change (e.g., Kairis 2010, Casola 2009), assess the implications of alternative urban growth patterns (e.g., Alberti et al. 2004), predict impacts of future seismic events (e.g. Hyndman 2003, Hartzell 2002), predict weather patterns (e.g., Grell et al. 1995, Colle 1998), understand water circulation patterns (e.g., Hamilton 1985, Babson et al. 2006), evaluate residency time of toxic chemicals and effects on biota (e.g. Spromberg 2006), assess food web dynamics (e.g., Harvey et al. 2010), predict biological invasions (e.g., Cordell 2010, Colnar 2007), etc. Because of immediate information needs, we focus our efforts on the models that identify and compare threats to the Salish Sea ecosystem and identify indicators of threats or ecosystem condition in this Science Update. Secondarily and incompletely, we focus on models that help identify mechanistic linkages between threats (Drivers) and changes in ecosystem states but only for the “high” and “very high” ranked threats that we identified in the Introduction of this chapter. Finally, we present water circulation models because they are focused on identifying the cause of an event (low oxygen levels in Hood Canal) of concern to many because of the negative effects on biota. This event may be associated with one or more of the high level threats but until the cause is better understood, we will not know.

For our purposes, a “model” is a mathematical representation of the ecosystem or components of the ecosystem including human impacts. For the models described we identify model inputs, their primary findings, their utility to management and conservation, and their ability to identify ecosystem threats and indicators. In addition, when information is available, we provide information on model reliability, which is usually assessed by comparing simulated results to empirical data or correlations between derived indices and biological data. Finally, we identify information gaps.

1. Models Identifying Ecosystem Threats and Indicators

For the ecosystem threats and indicator models that follow we compare their potential to be used to identify /evaluate threats in Table 5 below. We also assess their ability to identify indicators or for the model outputs to be used as indicators.

Relative Risk Models

Relative risk models were developed to characterize relative risks to an ecosystem and have been used for a variety of purposes (see Landis and Wiegers 2007). These models have been applied to very large estuaries to evaluate the relative influence of different stressors (threats) and their sources (e.g., Iannuzzi et al. 2009). In Puget Sound, this modeling approach has been used to investigate the causes of the Cherry Point Pacific Herring run (Landis et al. 2004) and to identify, rank and assess their combined impacts of stressors to the near shore environment at Cherry Point (Hayes and Landis 2004). The Cherry Point near shore analysis, analyzed cumulative impacts from multiple sources of chemical and non-chemical stressors (e.g., ballast water, piers, point source pollution, recreational activities) to assess risk to multiple species that use the near shore environment (Hayes and Landis 2004). This approach allows researchers to compare threats spatially and quantitatively and to identify: (1) the most threatened geographical subregions, (2) the sources contributing the most risk, and (3) the habitats and speices most at risk. To date, this model has only been applied at small scales but could be applied to the entire Salish Sea ecosystem. Results from this modeling effort suggest that the major contributors of risk to the Cherry Point near shore marine environment are vessel traffic, upland urban and agricultural land use, and shoreline recreational activities (Hayes and Landis 2004). For the Cherry Point Pacific herring stock, exploitation, habitat alteration and climate change were the risk factors that contributed to the decline. The retrospective assessment identified the warm Pacific Decadal Oscillation (PDO) as the primary factor altering herring population dynamics (Landis et al. 2004).

Mass-balance Model for evaluating Food Web Structure and Community Scale Indicators

Harvey et al. (2010) developed a mass-balance model of the Puget Sound Central Basin food web with the goal of identifying indicators for assessing the effectiveness of various management activities. The model consists of 65 functional groups that range from primary producers to top order consumers that live in nearshore, offshore, pelagic, and demersal environments. It also includes several fisheries. Their model indicates that the system is dominated by demersal species and that most of the biomass is aggregated in seven functional groups. Bottom-up dynamics appear to strongly influence trophic flows but there are examples of top-down control with bald eagles apparently able to cause trophic cascades. Model simulations indicate that current commercial fishing mortality appears to be sustainable and below maximum sustained yields due, in part, to declines in commercial fisheries in recent years. Their model has not yet been used to test the ecosystem-level impacts of past levels of fishing effort on previously heavily exploited fishes such as rockfish and gadoids. Finally, their model has significant implications on which species or functional groups are good indicators of changes in management activities (e.g., Samhouri et al. 2009) but their technical memo does not contain recommended indicators. Other than fisheries, this model does not include the impacts of human activities on the food web and is currently focused on central Puget Sound but will include other basins in future iterations and will eventually be replaced by an Atlantis model (Horne et al. 2010, Fulton et al. 2004, 2007). The Atlantis model will add several features that the current model is lacking, most notably: tighter coupling between functional groups and abiotic features like temperature, circulation, nutrients and dissolved oxygen; spatial dynamics that allow simulation of multiple basins of Puget Sound; species-habitat interactions; and more realistic representation of life history features such as age structure, migrations, and prey switching. Atlantis also enables simulation of monitoring and assessment programs designed to evaluate the effectiveness of management policies.

Mapping Cumulative Impacts to the California Current Marine Ecosystems

Halpern et al. (2008) developed an ecosystem-specific, multiscale spatial model in a GIS environment that combined multiple drivers (e.g., sea temperature, shipping, and species invasion) into a single estimate of cumulative human impact for the world’s oceans. In a second paper, Halpern et al. (2009) focused on mapping these same cumulative impacts to California Current marine ecosystems with the goal of identifying the most and least impacted areas and the top threats to the region - this analysis included Puget Sound. However, results for Puget Sound proper were not discussed.

The highest impact scores were concentrated around areas of large human populations including Puget Sound. Climate change drivers (SST, UV, and ocean acidification) exhibited the greatest ecosystem impacts across the region because of their widespread distribution and high vulnerability of many ecosystems to these stressors. Other important drivers included atmospheric deposition of pollution, ocean-based pollution, and commercial shipping. Intertidal and nearshore ecosystems were the most heavily impacted because of exposure to stressors from both land- and ocean-based human activities. The two top impacted ecosystems by human activities were mudflats and oyster reefs. The authors attribute the impacts to these systems from historic overharvesting of oysters and subsequent disease outbreaks that accompanied the introduction of non-native and invasive species and to the expansion of non-native species like cordgrass (Spartina alterniflora) into mudflats (Callaway and Josselyn 1992, Ruesink et al. 2005). Other highly impacted ecosystems identified by Halpern et al. (2009) included salt marsh, beach, seagrass, and rocky intertidal.

Mapping the Terrestrial Anthropogenic Impacts to the Western U.S. – Human Footprint

In the terrestrial enviornament, researchers have evaluated the cumulative impact of human activities in a GIS environment at global (e.g., Sanderson et al. 2002), national (Theobald 2010) and regional (e.g., Leu et al. 2008) scales. The regional effort by Leu et al. (2008), involved calculating the physical human footprint, defined as the actual space occupied by human features for the western U.S. including Washington. Recognizing that human features influence ecological processes beyond the physical space occupied by those features they also mapped the effect area, or the ecological human footprint. To accomplish this, they derived an index that combines 14 landscape structural and anthropogenic features in a GIS environment: human habitation, interstate highways, federal and state highways, secondary roads, railroads, irrigation canals, power lines, linear feature densities, agricultural lands, campgrounds, highway rest stops, landfills, oil and gas developments, and human-induced fires.

They estimated that 13% of the western U.S. was dominated by human features with agricultural land, human population areas, and roads covering the majority of this area. In addition, they found that low elevation areas with deeper soils were disproportionally affected (43% vs. 7%) by the human footprint and so were ecoregions dominated by urbanized areas like the Puget Trough - Willamette Valley - Georgia Basin ecoregion.

To test the footprint model, they correlated bird abundance patterns with human footprint patterns and found that synanthropic species increased with greater human footprint scores and species sensitive to habitat fragmentation generally decreased in abundance with increasing human footprint scores (Leu et al. 2008, Johnson et al. 2010, Knick and Hanser 2010). In addition, the presence of a deadly fungal disease (Batrachochytrium dendrobatidis) in native frogs of the Pacific Northwest was strongly correlated with human footprint scores (Adams et al. 2010) – this disease has been associated with rapid global decline and extinction of amphibians in several regions around the world (Skerratt et al. 2007). Like the California Current model above, the human footprint model can be used to identify areas for conservation activities, areas for restoration and areas appropriate for human activity. In addition, the authors of this model developed a theoretical approach to using human footprint data to monitor the effectiveness of landscape level conservation efforts (Haines et al. 2008).

2. Models Associated with the Threat Climate Change

Many models have been developed to assess climate change impacts on plants and animals, hydrology, sea surface temperature, weather patterns, sea level, ocean acidification, UV radiation, etc. In addition there have been several efforts to summarize and synthesize the findings of these models (e.g., Climate Impacts Group 2009, and IPCC 2007). The Climate Change section of this chapter focuses on the outcomes of climate change models and rather than repeat this information here, we refer readers to that section or to the reports that summarize and explain the various modeling efforts.

3. Models Associated with the threat Residential, Commercial and Industrial Development

The Distributed Hydrology Soil Vegetation Model (DHSVM) is a spatially explicit, biophysically-driven hydrologic model (Wigmosta et al. 1994, 2002; Cuo et al. 2008, 2009). DHSVM uses GIS-derived representations of elevation, soil type, soil thickness, vegetation, and meteorological data to simulate water and energy fluxes at and below the land surface. The model has been used to evaluate effects of forest management on land surface hydrologic response, especially flooding, of upland forested basins (e.g., Bowling and Lettenmaier 2001; La Marche and Lettenmaier 2001; Whitaker et al. 2003). The model represents the effects of topography on incident and reflected solar radiation, and on downslope redistribution of moisture in the saturated zone, which in turn controls both fast and slow runoff response. DHSVM has been recently modified to predict the hydrologic response of partially urbanized watersheds by altering the treatment of precipitation on impervious surfaces, adding water detention, and spatially varying the surface runoff depending on land cover (Cuo et al. 2008, 2009). The model’s output, which closely matches empirically observed trends in flux rates and volumes, illustrates important linkages between landscape pattern and hydrology, with more extreme, episodic flux rates and volumes in urbanizing, highly impervious landscapes (Bowling and Lettenmaier 2001; La Marche and Lettenmaier 2001; Whitaker et al. 2003; Cuo et al. 2008, 2009).

The Land Cover Change Model (LCCM) was developed to forecast potential trends in land cover change in the central Puget Sound, in conjunction with landscape-based models of bird species richness and abundance (Hepinstall et al. 2008, 2009). LCCM uses a set of spatially explicit multinomial logit models of site-based land-cover transitions. LCCM is fully integrated with the UrbanSim model (Waddell et al. 2003), a spatially explicit socioeconomic model of land use decision-making that predicts changes in the spatial distribution of households, jobs, and real estate quantities, types, and prices. Coupling the LCCM with UrbanSim allows for simulation of multiple interacting aspects of urban development, via UrbanSim’s interfaces with external macroeconomic and transportation models. The LCCM explicitly models human decisions responsible for land cover change including interactions among humans and between socioeconomic and environmental variables, and dynamic shifts in land use/land cover resulting from such interactions (Hepinstall et al. 2008, 2009). Model results suggest that, under current development trends, urban land cover is expected to increase over the next 20 years by 68-73 percent of its 1999 extent, with resultant shifts (based on linkages to avian diversity models) in the dominance of synanthropic and early successional guilds over forest interior bird guilds (Hepinstall et al. 2008, 2009).

Envisioning Puget Sound Alternative Futures (Bolte and Vache). See Models Associated with the Threat Shoreline Development below.

4. Models Associated with the Threat Shoreline Development

Historic change and impairment of Puget Sound shorelines (Simenstad et al. 2009) – this change analysis modeled changes in the spatial arrangement of dominant ecosystem processes along Puget Sound’s beaches, estuaries and river deltas. The outcomes of this change analysis are described in detail under Shoreline Development.

Envisioning Puget Sound Alternative Futures (Bolte and Vache 2010). This effort models changes in landscape composition based on alternative trajectories: 1) status quo: continuation of current trends, 2) Managed growth: concentrates growth within urban growth areas and near regional growth centers, and 3) unmanged growth: relaxation of land use restrictions. Scenarios were created using a spatially and temporally explicit alternative futures model and created a set of spatial coverages reflecting different scenario outcomes for a variety of landscape variables (land use/land cover, shoreline modifications, and population projections). The model also generated a set of summary statistics describing landscape change variables. This modeling effort is being used to project future impairment of ecosystem functions, goods and services. Results are presented by sub-basins. The results are presented in 12 maps and 57 graphs that generally demonstrate greater loss of forests, wetlands and an increase in development associated with the unmanged growth scenario but with considerable variation among sub-basins. In addition, graphs indicate an increase in docks, impervious survaces, marinas, and shoreline armoring associated with unmanged growth.

5. Models Associated with the Threat Pollution

Placeholder – this section needs to be developed.

6. Models Associated with the Threat Invasive and Non-native Species

Physico-chemical factors affecting copepod occurrences

Cordell et al. (2010) modeled the physio-chemical factors affecting occurrences of non-indigenous planktonic copepod in the northeast Pacific estuaries. They characterized estuaries with and without populations of the copepod Pseudodiaptomus inopinus and identified relatively low salinity and stratification of water column temperature and salinity as important predictors of copepod occurrence. This type of modeling can be used to predict species invasions and environmental susceptibility and potentially identify methods to reduce invasion potential.

Please see Invasive and Non-native Species section for other modeling efforts.

7. Puget Sound Water Circulation and Water Quality Models

In some cases we don’t know the threats but observe events that cause concern and then attempt to identify the cause (usually viewed as a threat). The following modeling efforts attempt to identify the causes of ecological events such as low oxygen events in Hood Canal that cause negative effects on the biota. Modeling efforts are particularly useful for these types of investigations because of complex interactions among a variety of factors contributing to the event including bathymetry, water circulation, and water chemistry. We recommend expanding this section to include additional models especially by those involved in these efforts.

Hood Canal Dissolved Oxygen Program

The deep waters of the southern Hood Canal have historically had low dissolved oxygen concentration. However, in recent years the severity of the hypoxia has increased and is having negative effects on biota. In response, the Hood Canal Dissolved Oxygen Program was initiated to investigate the sources of low oxygen events and their effects on marine life. Researchers are using the Regional Ocean Modeling System of Haidvogel et al. (2008) to achieve this goal and publications are expected in the next year and should be available for future editions of this publication.

Estuarine circulation model for Puget Sound, Georgia Basin

Researchers with the Puget Sound Regional Synthesis Model initiative (PRISM 2010) developed an estuarine circulation model for Puget Sound, Georgia Basin and linkages to Pacific currents, adapted from the Princeton Ocean Model (POM; Edwards et al. 2007). The model is designed to examine hydrodynamic factors including three-dimensional patterns of water column circulation, tidal and riparian fluxes, water temperature, and salt water/freshwater exchange patterns and rates within the Puget Sound/Georgia Basin system. Simulation results have been favorably compared with empirical measurements for Carr Inlet (Edwards et al. 2007), and are being used to demonstrate that surface current patterns and other hydrodynamic factors potentially play a significant role in driving hypoxic conditions in Hood Canal (Hood Canal Dissolved Oxygen Program, unpublished results). The model is being used more broadly by PRISM to understand temporal dynamics in salt/freshwater exchanges, differences in subbasin residence times, and contributions of freshwater fluxes from Puget Sound/Georgia Basin to oceanic currents.

Summary and Conclusions

Although incomplete, we found that ecosystem modeling efforts are being broadly applied to the Salish Sea ecosystem to help us understand everything from the relative magnitude of ecosystem threats to the causes of low oxygen events. In this Update we identify models that identify and rank threats to the Salish Sea ecosystem and that can be used as indicators or can be used to identify a potential suite of indicators and provide a summary of those efforts in Table 5.

During this model identification and review process, we identify the following research needs:

1. In Table 5, we assess the use of various models to identify and rank threats and identify indicators. The approaches described here or similar approaches could be applied at the scale of the Salish Sea ecosystem. Such an effort would identify the primary threats, help quantify the extent of ecosystem threats, and identify the most threatened ecosystems. This type of information is critical for spatially explicit and effect conservation planning. Ideally, the terrestrial and aquatic modeling efforts would be combined into a single seamless model of the marine, terrestrial and freshwater ecosystems because of the interacting and synergistic effects of threats originating and moving between these ecosystems.
2. Expand the mass balance model to the entire Salish Sea and eventually replace it with the Atlantis model. This effort will allow managers to identify effective indicators at the scale of the Salish Sea and the use of the Atlantis model will allow better coupling between functional groups and abiotic features like temperature, circulation, nutrients and dissolved oxygen; spatial dynamics that allow simulation of multiple basins of Puget Sound; species-habitat interactions; and more realistic representation of life history features such as age structure, migrations, and prey switching. Atlantis also enables simulation of monitoring and assessment programs designed to evaluate the effectiveness of management policies
3. Continue to link modeling efforts as demonstrated by the linking of cycling and circulation models to investigate causes of low oxygen events. Such links allow researchers to expand the scope and scale of inference and take advantage of existing efforts.
4. When causes of ecosystem change are not well understood, as is the case with low oxygen levels in Hood Canal, models can be used to understand the causes of these types of events.

Table 5. Models identifying ecosystem threats and indicators we assess their intended or potential use to identify/evaluate threats or management alternatives and their ability to identify or be used as indicators.

Adams, M. J., N. D. Chelgren, D. Reinitz, R. A. Cole, L. J. Rachowicz, S. Galvan, B. McCreary, C. A. Pearl, L. L. Bailey, J. Bettaso, E. L. Bull, and M. Leu. 2010. Using occupancy models to understand the distribution of an amphibian pathogen, Batrachochytrium dendrobatidis. Ecological Applications 20:289-302.

Alberti, M., R. Weeks, and S. Coe. 2004. Urban land-cover change analysis in Central Puget Sound. Photogrammetric Engineering and Remote Sensing 70:1043-1052.

Babson, A. L., A. Kawase, and P. MacCready. 2006. Seasonal and interannual variability in the circulation of Puget Sound, Washington: A box model study. Atmosphere-Ocean 44:29-45.

Bolte, J. and K. Vache. 2010. Envisioning Puget Sound Alternatives Futures. Prepared for Washington Department of Fish and Wildlife in support of the Puget Sound Nearshore Ecosystem Restoration Project. Prepared by Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR.

Bowling, L. C. and D. P. Lettenmaier. 2001. The effects of forest roads and harvest on catchment hydrology in a mountainous maritime environment. Pages 145-164 in M. S. Wigmosta and S. J. Burges, editors. Land Use and Watersheds: Human Influence on Hydrology and Geomorphology in Urban and Forest Areas. AGU Water Science and Application, Volume 2.

Callaway, J. C. and M. N. Josselyn. 1992. The introduction and spread of smooth cordgrass (Spartina alterniflora) in south San Francisco Bay. Estuaries 15:218-226.

Casola, J. H., L. Cuo, B. Livneh, D. P. Lettenmaier, M. T. Stoelinga, P. W. Mote, and J. M. Wallace. 2009. Assessing the Impacts of Global Warming on Snowpack in the Washington Cascades. JOURNAL OF CLIMATE 22:2758-2772.

Climate Impacts Group. 2009. The Washington climate change impacts assessment evaluating Washington's future in a changing climate. Climate Impacts Group, [Seattle, Wash.].

Colle, B. A. and C. F. Mass. 1998. Windstorms along the western side of the Washington Cascade Mountains. Part I: A high-resolution observational and modeling study of the 12 February 1995 event. Monthly Weather Review 126:28-52.

Colnar, A. M. and W. G. Landis. 2007. Conceptual model development for invasive species and a regional risk assessment case study: The European green crab, Carcinus maenas, at Cherry Point, Washington, USA. Human and Ecological Risk Assessment 13:120-155.

Cordell, J. R., L. M. Tear, and S. M. Bollens. 2010. Modelling physico-chemical factors affecting occurrences of a non-indigenous planktonic copepod in northeast Pacific estuaries. Biological Invasions 12:1427-1445.

Cuo, L., D. P. Lettenmaier, B. V. Mattheussen, P. Storck, and M. Wiley. 2008. Hydrologic prediction for urban watersheds with the Distributed Hydrology-Soil-Vegetation Model. Hydrological Processes 22:4205-4213.

Cuo, L., D. P. Lettenmaier, M. Alberti, and J. E. Richey. 2009. Effects of a century of land cover and climate change on the hydrology of the Puget Sound basin. Hydrological Processes 23:907-933.

Edwards, K. A., M. Kawase, and C. P. Sarason. 2007. Circulation in Carr Inlet, Puget Sound, during spring 2003. Estuaries and Coasts 30:945-958.

Fulton, E. A., J. S. Parslow, A. D. M. Smith, and C. R. Johnson. 2004. Biogeochemical marine ecosystem models II: the effect of physiological detail on model performance. Ecological Modelling 173:371-406.

Fulton, E. A., A. D. M. Smith, and C. R. Johnson. 2004. Biogeochemical marine ecosystem models I: IGBEM - a model of marine bay ecosystems. Ecological Modelling 174:267-307.

Fulton, E. A., A. D. M. Smith, and D. C. Smith. 2007. Alternative management strategies for southeast Australian Commonwealth fisheries: Stage 2: quantitative management strategy evaluation. CSIRO Australia, Tasmania.

Grell, G. A., J. Dudhia, and D. R. Stauffer. 1995. A Description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5). NCAR Technical Note NCAR/TN-398 + STR, Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research, Boulder, CO.

Haidvogel, D. B., H. Arango, W. P. Budgell, B. D. Cornuelle, E. Curchitser, E. Di Lorenzo, K. Fennel, W. R. Geyer, A. J. Hermann, L. Lanerolle, J. Levin, J. C. McWilliams, A. J. Miller, A. M. Moore, T. M. Powell, A. F. Shchepetkin, C. R. Sherwood, R. P. Signell, J. C. Warner, and J. Wilkin. 2008. Ocean forecasting in terrain‐following coordinates: Formulation and skill assessment of the Regional Ocean Modeling System. Journal of Computational Physics 227:3595-3624.

Haines, A. M., M. Leu, L. K. Svancara, J. M. Scott, and K. P. Reese. 2008. A theoretical approach to using human footprint data to assess landscape level conservation efforts. Conservation Letters 1:165-172.

Halpern, B. S., C. V. Kappel, K. A. Selkoe, F. Micheli, C. M. Ebert, C. Kontgis, C. M. Crain, R. G. Martone, C. Shearer, and S. J. Teck. 2009. Mapping cumulative human impacts to California Current marine ecosystems. Conservation Letters 2:138-148.

Halpern, B. S., S. Walbridge, K. A. Selkoe, C. V. Kappel, F. Micheli, C. D'Agrosa, J. F. Bruno, K. S. Casey, C. Ebert, H. E. Fox, R. Fujita, D. Heinemann, H. S. Lenihan, E. M. P. Madin, M. T. Perry, E. R. Selig, M. Spalding, R. Steneck, and R. Watson. 2008. A global map of human impact on marine ecosystems. Science 319:948-952.

Hamilton, P., J. T. Gunn, and G. A. Cannon. 1985. A BOX MODEL OF PUGET-SOUND. Estuarine Coastal and Shelf Science 20:673-692.

Hartzell, S., A. Leeds, A. Frankel, R. A. Williams, J. Odum, W. Stephenson, and W. Silva. 2002. Simulation of broadband ground motion including nonlinear soil effects for a magnitude 6.5 earthquake on the Seattle fault, Seattle, Washington. Bulletin of the Seismological Society of America 92:831-853.

Harvey, C. J., K. K. Bartz, J. Davies, T. B. Francis, T. P. Good, A. D. Guerry, B. Hanson, K. K. Holsman, J. Miller, M. L. Plummer, J. C. P. Reum, L. D. Rhodes, C. A. Rice, J. F. Samhouri, G. D. Williams, N. Yoder, P. S. Levin, and M. H. Ruckelshaus. 2010. A Mass-balance Model for Evaluating Food Web Structure and Community-scale Indicators in the Central Basin of Puget Sound. NOAA Technical Memorandum NMFS-NWFSC-106, U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Seattle, WA.

Hayes, E. H. and W. G. Landis. 2004. Regional ecological risk assessment of a near shore marine environment: Cherry Point, WA. Human and Ecological Risk Assessment 10:299-325.

Hepinstall, J. A., M. Alberti, and J. M. Marzluff. 2008. Predicting land cover change and avian community responses in rapidly urbanizing environments. Landscape Ecology 23:1257-1276.

Hepinstall, J. A., J. M. Marzluff, and M. Alberti. 2009. Modeling Bird Responses to Predicted Changes in Land Cover in an Urbanizing Region. Pages 625-659 in J. Millspaugh and F. R. Thompson, editors. Models for Planning Wildlife Conservation in Large Landscapes. Academic Press, San Diego.

Horne, P. J., I. C. Kaplan, K. N. Marshall, P. S. Levin, C. J. Harvey, A. J. Hermann, and E. A. Fulton. 2010. Design and parameterization of a spatially explicit ecosystem model of the Central California Current. NOAA Tech. Memo. NMFS-NWFSC-104, U.S. Department of Commerce, National Oceanic and Atmospheric Administration.

Hyndman, R. D., S. Mazzotti, D. Weichert, and G. C. Rogers. 2003. Frequency of large crustal earthquakes in Puget Sound-Southern Georgia Strait predicted from geodetic and geological deformation rates. Journal of Geophysical Research-Solid Earth 108.

Iannuzzi, T. J., J. L. Durda, D. V. Preziosi, D. F. Ludwig, R. G. Stahl, Jr., A. A. DeSantis, and R. A. Hoke. 2009. Development of a preliminary relative risk model for evaluating regional ecological conditions in the Delaware River Estuary, USA. Integrated Environmental Assessment and Management 6:164-179.

IPCC. 2007. Climate change 2007 : the physical science basis : contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. In S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H. L. Miller, editors. Cambridge University Press, Cambridge; New York.

Johnson, D. H., M. J. Holloran, J. W. Connelly, S. E. Hanser, C. L. Amundson, and S. T. Knick. 2010. Influences of environmental and anthropogenic features on greater sage-grouse populations, 1997–2007. Studies in Avian Biology: in press.

Kairis, P. A. and J. M. Rybczyk. 2010. Sea level rise and eelgrass (Zostera marina) production: A spatially explicit relative elevation model for Padilla Bay, WA. Ecological Modelling 221:1005-1016.

Knick, S. T. and S. E. Hanser. 2010. Connecting pattern and process in greater sage-grouse populations and sagebrush landscapes. Studies in Avian Biology: in press.

La Marche, J. L. and D. P. Lettenmaier. 2001. Effects of forest roads on flood flows in the Deschutes River, Washington. Earth Surface Processes and Landforms 26:115-134.

Landis, W. G., P. B. Duncan, E. H. Hayes, and A. J. Markiewicz. 2004. A regional retrospective assessment of the potential stressors causing the decline of the Cherry Point Pacific herring run. Human and Ecological Risk Assessment 10:271-297.

Landis, W. G. and J. K. Wiegers. 2007. Ten years of the relative risk model and regional scale ecological risk assessment. Human and Ecological Risk Assessment 13:25-38.

Leu, M., S. E. Hanser, and S. T. Knick. 2008. The human footprint in the west: A large-scale analysis of anthropogenic impacts. Ecological Applications 18:1119-1139.

PRISM. 2010. The Puget Sound Regional Synthesis Model Initiative, http://www.prism.washington.edu/home/index. University of Washington, Seattle, WA.

Ruesink, J. L., H. S. Lenihan, A. C. Trimble, K. W. Heiman, F. Micheli, J. E. Byers, and M. C. Kay. 2005. Introduction of non-native oysters: Ecosystem effects and restoration implications. Annual Review of Ecology Evolution and Systematics 36:643-689.

Samhouri, J. F., P. S. Levin, and C. J. Harvey. 2009. Quantitative Evaluation of Marine Ecosystem Indicator Performance Using Food Web Models. Ecosystems 12:1283-1298.

Sanderson, E. W., M. Jaiteh, M. A. Levy, K. H. Redford, A. V. Wannebo, and G. Woolmer. 2002. The human footprint and the last of the wild. Bioscience 52:891-904.

Skerratt, L. F., L. Berger, R. Speare, S. Cashins, K. R. McDonald, A. D. Phillott, H. B. Hines, and N. Kenyon. 2007. Spread of chytridiomycosis has caused the rapid global decline and extinction of frogs. Ecohealth 4:125-134.

Simenstad, C. A., M. Ramirez, J. Burke, M. Logsdon, H. Shipman, C. Tanner, C. Davis, J. Fung, P. Bloch, K. Fresh, D. Myers, E. Iverson, A. Bailey, P. Schlenger, C. Kiblinger, P. Myre, W. Gerstel, and A. MacLennan. 2009. Historic Change of Puget Sound Shorelines: Puget Sound Nearshore Ecosystem Project Change Analysis. Puget Sound Nearshore Report No. 2009-XX, Published by Washington Department of Fish and Wildlife, Olympia, Washington, and U.S. Army Corps of Engineers, Seattle, Washington.

Spromberg, J. A. and J. P. Meador. 2006. Relating chronic toxicity responses to population-level effects: A comparison of population-level parameters for three salmon species as a function of low-level toxicity. Ecological Modelling 199:240-252.

Theobald, D. M. 2010. Estimating natural landscape changes from 1992 to 2030 in the conterminous US. Landscape Ecology: published on-line.

Waddell, P., A. Borning, M. Noth, N. Freier, and M. Becke. 2003. Microsimulation of urban development and location choices: design and implementation of UrbanSim. Networks and Spatial Economics 3:43-67.

Whitaker, A., Y. Alila, J. Beckers, and D. Toews. 2003. Application of the distributed hydrology soil vegetation model to redfish creek, British columbia: model evaluation using internal catchment data. Hydrological Processes 17:199-224.

Wigmosta, M. S., B. Nijssen, and P. Storck. 2002. The Distributed Hydrology Soil Vegetation Model. Pages 7-42 in V. P. Singh and D. Frevert, editors. Mathematical Models of Small Watershed Hydrology and Applications. Water Resources Publications, LLC, Highlands Ranch, CO.

Wigmosta, M. S., L. W. Vail, and D. P. Lettenmaier. 1994. A distributed hydrology-vegetation model for complex terrain. Water Resources Research 30: