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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.

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Why habitat water quality models l.jpg

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 l.jpg

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 l.jpg

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?


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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


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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


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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


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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


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Effects of stormwater runoff on salmon


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ModelOverview

Pre-spawn

Mortality Data

GIS (Habitat)

Datalayers

Overlay GIS

Datalayers with

Drainage Basins

  • Significant Variables

  • Predictive Model of Pre-spawn Mortality

Statistical

Analysis


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Predictive Model of

Pre-spawn Mortality


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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)


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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


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Analytical approach

  • Landscape processes and land use affect in-stream habitat conditions

Landscape

Processes

Landuse

Habitat

Conditions

Biological

Response


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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


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Designing and evaluating recovery strategies with uncertain futures


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GFDL model

Hadley model

Battin et al. 2007


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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


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Food webs and habitats: changes?


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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)


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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)


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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)


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Primary causes of habitat destruction & degradation in Puget Sound


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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


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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


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