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

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

Statistical associations between species and habitats The primary goal of habitat modeling is to provide spatially explicit estimation of species occurrences to: 1)Improve accuracy and precision of abundance estimates 2)Predict occurrence outside of surveyed times and areas 3)Improve evaluation of risks due to human activities 4)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?

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

Dec 1-15 Dec Jan 1-15 Jan Feb 1-14 Feb Mar 1-15 Mar Example outcome: Predicted Seasonal Variation in Right Whale Densities In the Southeast US Calving Area Right Whale DensitySea Surface Temperature Also: Bathymetry Effort Sightings

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

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

Effects of stormwater runoff on salmon

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

Predictive Model of Pre-spawn Mortality

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)

Identify alternative watershed, harvest, hatchery management strategies Compare Forecasted Effects of Strategies ABCDE F % Increase Chinook Coho Model Habitat Conditions & Fish Response GIS

Analytical approach  Landscape processes and land use affect in-stream habitat conditions Landscape Processes Land use Habitat Conditions Biological Response

Identifying peak-flow impaired sub-basins Impaired: >10% impervious area Functioning: <3% impervious area

Designing and evaluating recovery strategies with uncertain futures

GFDL modelHadley model Battin et al. 2007

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

Food webs and habitats: changes?

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

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 Intersect the shapefile with this density surface and summarize (including uncertainty) Outputs include estimates of numbers impacted, evaluation of “best” amongst options of areas and times for the activities

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

Primary causes of habitat destruction & degradation in Puget Sound

Spatial Aggregation of Data BathymetrySSTEffortSightings 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

VegetationGeologyClimate Light/heat inputs Nutrient/ chemical inputs Organic matter inputs Hydrologic regime Sediment supply Physical habitat characteristics Water quality and primary productivity Biological response