Why habitat/water quality models?

1. Why habitat/water quality models? • To map/predict current and future species assemblages, status • To project future status of species and habitats under alternative management strategies, environmental conditions • To explore linkages between habitat condition, water quality, and species status Mary Ruckelshaus, Tim Beechie, Lance Garrison, Josh Nowlis

2. Habitat approaches to ecosystem(ish) modeling • Statistical associations between species and habitats • Spatial modeling of habitat-forming process functioning and potential impacts of toxics • Linked mechanistic models of climate -->land use/hydrology-->species dynamics • Under development: full ecosystem models including effects habitat change on other ecosystem components; linking watershed models to marine

3. Statistical associations between species and habitats The primary goal of habitat modeling is to provide spatially explicit estimation of species occurrences to: • Improve accuracy and precision of abundance estimates • Predict occurrence outside of surveyed times and areas • Improve evaluation of risks due to human activities • Define habitat boundaries for the purposes of designation under ESA Some applications of these approaches: What are the localized effects of military operations ? What are the risks of vessel-whale interactions in different areas ? Where are the areas of overlap between fisheries and mammals ? Where should protected areas be located?

4. Habitat Modeling Approach The goal is to develop spatially explicit predictions of animal density and abundance Empirical models of the species-environment relationship Sightings from Surveys Project these into space as a density surface

5. Example outcome: Predicted Seasonal Variation in Right Whale Densities In the Southeast US Calving Area Mar 16-30 Jan 1-15 Dec 1-15 Dec 16-31 Mar 1-15 Feb 1-14 Feb 15-28 Jan 16-31 Also: Bathymetry Effort Sightings Right Whale Density Sea Surface Temperature

6. Example outcome: Spatial distribution of bottlenose dolphins in the Eastern Gulf of Mexico Sightings (dots) and modeled densities of bottlenose dolphins from a GAM model based on sea surface temperature, chlorophyll concentration, depth, and distance from shore

7. Example outcome: Spatial distribution of bottlenose dolphins in the Eastern Gulf of Mexico The resulting density surface may be used to support environmental assessments and planning of military operations in the Eastern Gulf Testing and Training Range

8. Effects of stormwater runoff on salmon

9. ModelOverview Pre-spawn Mortality Data GIS (Habitat) Datalayers Overlay GIS Datalayers with Drainage Basins • Significant Variables • Predictive Model of Pre-spawn Mortality Statistical Analysis

10. Predictive Model of Pre-spawn Mortality

11. Habitat approaches to ecosystem(ish) modeling • Statistical associations between species and habitat quantity/quality (data intensive, some extrapolation possible, limited generality) • Spatial modeling of habitat-forming process functioning (remotely sensed data, relationships theoretically derived) • Linked mechanistic models of climate -->land use/hydrology-->species dynamics (time, computer-intensive, validation not realistic)

12. Identify alternative watershed, harvest, hatchery management strategies GIS Model Habitat Conditions & Fish Response 0.25 Chinook Compare Forecasted Effects of Strategies 0.20 Coho 0.15 % Increase 0.10 0.05 0.00 A B C D E F

13. Analytical approach • Landscape processes and land use affect in-stream habitat conditions Landscape Processes Landuse Habitat Conditions Biological Response

14. 100 100 100 100 94 97 96 95 100 100 98 99 100 100 100 100 100 100 101 Identifying peak-flow impaired sub-basins 100 Impaired: >10% impervious area Functioning: <3% impervious area

15. Designing and evaluating recovery strategies with uncertain futures

16. GFDL model Hadley model Battin et al. 2007

17. Habitat-water quality models: next steps? • Estimates of full suite of ecosystem services and tradeoffs • Dynamic drivers or set habitat-based capacity, survival in ecosystem models

19. Food webs and habitats: changes?

20. 1) Nearshore ecosystem services Service Category Provides. . . Species directly important to humans (commercial and/or recreational harvest) Food-web support & Habitat provisioning Species important in food-webs Carbon sequestration Supporting/Regulating Assimilative capacity Shoreline protection/stabilization Non-consumptive use (in situ) Cultural/Aesthetic Non-use (ex situ)

21. Decision Support Tool in Concept User identifies time and spatial options for a given activity Download and process remotely sensed data Create corresponding shapefile for the area Generate updated Density Surface Outputs include estimates of numbers impacted, evaluation of “best” amongst options of areas and times for the activities Intersect the shapefile with this density surface and summarize (including uncertainty)

22. Heavily Used Roads (Arterials) 100% Thornton 80% Longfellow Des Moines 60% Piper’s Mean Pre-spawn Mortality Rate 40% Fauntleroy 20% 2 r = 0.943;p = 0.0012; y = -0.042 + 19.286x Fortson Creek 0% 0% 1% 2% 3% 4% 5% All Arterials (PSRC)

24. Primary causes of habitat destruction & degradation in Puget Sound

25. Spatial Aggregation of Data Bathymetry SST Effort Sightings Habitat information derived from remotely sensed data: Sea surface temperature, ocean color, SS height anomaly, winds All data aggregated into spatial cells (~ 5 x 5 km square cells) Predictive surfaces generated based on additional remotely sensed data

26. Vegetation Geology Climate Nutrient/chemical inputs Organic matter inputs Hydrologic regime Light/heat inputs Sediment supply Physical habitat characteristics Water quality and primary productivity Biological response