a model for predicting bird abundance

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A Model for Predicting Bird Abundance Adaptive Management Bank Rehabilitation Site Design Effectiveness Monitoring -- Meeting Program Objectives?

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A Model for Predicting Bird Abundance. Adaptive Management Bank Rehabilitation Site Design Effectiveness Monitoring -- Meeting Program Objectives?. OBJECTIVES. Use Bird abundance patterns and Riparian characteristics - PowerPoint PPT Presentation

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Page 1: A Model for Predicting  Bird Abundance

A Model for Predicting Bird Abundance

•Adaptive Management

•Bank Rehabilitation Site Design

•Effectiveness Monitoring

-- Meeting Program Objectives?

Page 2: A Model for Predicting  Bird Abundance
Page 3: A Model for Predicting  Bird Abundance

OBJECTIVES

• Use Bird abundance patterns and Riparian characteristics

• Predict bird abundance following construction and as riparian habitat develops

-- Rehabilitation sites -- Program area

Page 4: A Model for Predicting  Bird Abundance

Unit t+1

Study areaRehab siteReach

Unit t

Study areaRehab siteReach

Habitat Arrangement

Hydrograph tHydrograph t+1

RestorationVeg metricst+1

Veg Class / Association

Veg metrics t

Veg Class / Association

Bird metrics t+1

Abundance Density Productivity

Bird metrics t

Abundance Density Productivity

Bird Behavior

Timing

Flow level

Area of Disturbance

Heterogeneity

Wildlife Model For Bird Response

Riparian removal

Side channel formation

Replanting

Time

I II III

ConstructionDisturbance

Emmigration

Food availability

Flow level

Page 5: A Model for Predicting  Bird Abundance

Riparian Mapping and Inventory

McBain and Trush, Redwood Sciences Laboratory

Page 6: A Model for Predicting  Bird Abundance

Most Abundant Vegetation Types used for Regression Tree Model

• White Alder

• Narrowleaf Willow

• Mixed Willow

• Black Cottonwood

• Mixed conifer – White Oak

• Himalaya Berry

• Calif. Grape

• Canyon Live Oak

• Grasses

Page 7: A Model for Predicting  Bird Abundance

Variables for our Regression Tree Example

• Vegetation Type

• River Mile

• Patch Size

• Vegetation Type within

200 m of each Survey Station

Page 8: A Model for Predicting  Bird Abundance
Page 9: A Model for Predicting  Bird Abundance
Page 10: A Model for Predicting  Bird Abundance

|0.95

0.49

0.13 0.63

1.08

0.82

0.97

0.78 1.30

0.63

1.30

1.17

2.10 1.10

1.20

1.30 0.40

0.72

0.07 1.10

1.62

2.00 1.30

YELLOW WARBLER

White Alder- Narrow Willow- Black Cottonwood-

Vegetation Type

Willow-Oak-Pines n=243n=67

Veg. Type

River Mile> 89n=128n=115

River Mile < 89

River Mi.< 83

Patch Size

< .07 >

River Mi.> 83 River Mi. >

105River Mi. < 105

< 108 > River Mi.

River Mi.> 90

River Mi.<90

Various Veg. Types

Page 11: A Model for Predicting  Bird Abundance

Predicting Yellow Warbler Abundance

0101L White Alder 111.7 1.32 0.1780102R FoothillPine-WO 111.7 0.62 0.0680104L Narrowleaf Willow 111.6 1.32 0.1780105R Mixed Conifer -WO 111.3 1.32 0.1780106L White Alder 111.2 1.32 0.178HOFL13 Narrowleaf Willow 78.1 1.26 0.096VAGU1 White Alder 75.6 0.78 0.111VAGU4 Tree of Heaven 75.3 0.13 0.178VAGU5 Narrowleaf Willow 74.8 0.78 0.111

Patch Veg. Assoc. RvrMi Pred x SE

Page 12: A Model for Predicting  Bird Abundance

Predicted Mean/ Survey Area = Density;

Density X Patch Area =Predicted Number of YWAR

Total Predicted Number for All Patches = Estimated Population

How Many Yellow Warblers

in the Study Area?

Page 13: A Model for Predicting  Bird Abundance
Page 14: A Model for Predicting  Bird Abundance

|1.30

0.71 1.40

1.20

1.14

0.92 1.30

1.50

0.77 1.60

0.90

1.80

1.60

1.60

1.80

2.40 1.70

0.90 1.90

1.40

1.20 1.80

2.50

SONG SPARROW

n=37 n=274

Vegetation Type

White Alder- Narrow Willow- Black Cottonwood-

River Mile

Willow-Oak-Pines

River Mile < 90

River Mile> 90

Veg. Type River Mile

Patch Size

Patch Size

Various Vegetation Types

Page 15: A Model for Predicting  Bird Abundance