# Presentation: 672587

## 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

**Notes:**
These are outcomes

## 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

**Notes:**
Thus far, most existing approaches we are using are focused on single species or few specie  s.  Eva’s review highlighted the last bullet as a gap in existing modeling approaches

## 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?

## Habitat Modeling Approach

**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

**Notes:**
This slide is just to describe the basic approach.  We use sightings from systematic surveys to develop empirical models of the species-area relationships, and then project these out spatially to derive density surfaces, and predictions of mammal occurrence outside of surveyed times/areas. 

## Also: 

- Example outcome:  Predicted Seasonal Variation in Right Whale Densities
- In the Southeast US Calving Area

- Right Whale Density

- Sea Surface Temperature

- Also:
- Bathymetry
- Effort
- Sightings

**Notes:**
Using predictive surfaces--This slide uses powerpoint animation to show the predicted movements of calving right whales as a function of SST in their calving grounds off of Florida and Georgia.  The first click should cycle through images to mid-January, showing the movement south and offshore of animals as SSTs of the “preferred” 13-15 degrees C make their way into the habitat.  The next click should take you through the end of the season as the habitat, and the whales, make their way out of the area.  This model has very good predictive capability and seems to track the movements of whales effectively.  It has been used to re-evaluate critical habitat boundaries and assess the probability of whale-vessel interactions under various proposed vessel traffic management schemes. 

## Example outcome:  Spatial distribution of bottlenose dolphins in the 

- 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

**Notes:**
This is the most recent thing we are working on.  These are bottlenose dolphin sightings data from an aerial survey conducted in winter 2007.  The model fit to this data included the variables listed above. The fitted (predicted) densities match the observations extremely well.

## Example outcome:  Spatial distribution of bottlenose dolphins in the 

- 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

**Notes:**
This is the projected density surface based on Chlorophyll and SST for January.  The goal of this project is to provide density surfaces of this type to support planning of military operations (e.g., ordinance testing, traning exercises, ship trials, etc.).  This area encompasess the Eastern Gulf Testing and Training Range (EGTTR) used by the air force, marines, and Navy for this type of thing.  The finer scale resolution and prediction of marine mammal (and turtle) densities is intended to allow planners to both develop more effective environmental assessments and (hopefully) reduce the impacts on protected species  

## Effects of stormwater runoff on salmon

## ModelOverview

**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

## 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

- 100

- Impaired: >10% impervious area

- Functioning: <3% impervious area

**Notes:**
High Density Development has 72% Effective Impervious Area
Medium Density Development has 36% Effective Impervious Area
Would like to find the % Impervious area in other categories (limitation of Analysis)

## Designing and evaluating recovery strategies with uncertain futures 

**Notes:**
1 key source of uncertainty in future conditions—climate change

## GFDL model

**GFDL model**

**Hadley 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 Category

- Provides. . .

- Shoreline protection/stabilization

- Assimilative capacity

**Notes:**
I’ve categorized the servcies into 3 categories and highlighted the kinds of things within that category that we intend to examine.  Walk through fig.

## 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

**Notes:**
This is where we are going (probably) with all of this.  Basically a decision support framework where a user or planner can evaluate amongst various options using real-time or near real-time remotely sensed data and an existing habitat model to generate predictions of impacts at small scales.  This is all done (primarily) within a GIS framework, so much of the technical side is automating the acquisition, processing and generation of prediction surfaces.  

## 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 = 0.0012;  y = -0.042 + 19.286x

- Fortson
- Creek

- Fauntleroy

- Piper’s

- Des Moines

- Longfellow

- Thornton

## Slide 23

## Primary causes of habitat destruction & degradation in Puget Sound

## 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

**Notes:**
Some more on process.  Just that we generally define a relatively high resolution spatial grid and aggregate remotely sensed data, bathymetry, spatial variables like distance from shore, survey effort, and sightings
data into this grid to both develop the empirical species-habitat relationships and produce the predictive surfaces.

## Vegetation

- Geology

- Climate

- Light/heat inputs

- Nutrient/chemical inputs

- Organic matter inputs

- Hydrologic regime

- Sediment supply

- Physical habitat characteristics

- Water quality and primary productivity

- Biological response