the effect of residential properties on breeding bird diversity in urban forest patches

The Effect of Residential Properties on Breeding Bird

Diversity in UrbanForest Patches

Chrissa Carlson1, Mary Cadenasso2, Gary Barrett1

1. Institute of Ecology, University of Georgia2. Department of Plant Sciences, University of California, Davis

Submitted to Ecological Applications

Urban Habitats:

•Not all are created equal How is

biodiversity is impacted by surrounding

urban development?

•Residential areas: top-down vs. bottom-up management (Melles 2005)

•Interaction between landscape elements under different management regimes

Urban Habitats:

Birds and urban biodiversity

• Quality of life (Clergeau et al. 2001)

• Biological poverty (Turner et al. 2004)

• Respond to environmental variables at multiple scales (Hostetler 2001)

• Conservation of metapopulations (Fernandez-Juricic 2004)

Major research question

• Can homeowners modify avian diversity in forest fragments from the outside-in?

• Is there a relationship between breeding bird community structure within forest habitat fragments and the resources located in the surrounding residential matrix, managed by humans at the scale of the individual land parcel?– Resources=tree and other vegetation cover,

bird feeders, baths, etc.

Major research question

Birds in urban forest fragments:

• Island biogeography theory (Robbins et al. 1989) but…– Patches of equal area often differ in species

richness (Dawson et al. 1993)– Isolation not as important as in agricultural

landscapes (Watson et al. 2005)

• Landscape context matters (Bennett et al. 2004, Park and Lee 2000, Nilon and Pais 1997, Andren 1994)

• Land-use is a poor predictor of bird occurrence (Hostetler and Knowles-Yanez 2003)

Total species richness is best explained by patch-level features (area, vegetation structure), but additional variation may be explained by:•Neighborhood age (+)•Variation in the distribution of landcover types in the surrounding neighborhood

Hypotheses:

Methods: Site Selection• 2-10 ha forest patches in Gwynns Falls Watershed• Surrounding landuse primarily residential• National Landcover Dataset (NLCD) 2000 and EMERGE 2004 aerial imageryn=15

Methods: Bird Surveys

• Territory mapping method

A

A

• Territory mapping method– Each site visited six times– Clusters of detections used to estimate

relative density of breeding territories

Methods: Bird Surveys

Patch 12: Northern Cardinal

B

C

D

Methods: Forest Patch Characterization

• Patch area• Modified UFORE surveys on 5 m radius

plots centered at random points– tree species richness– tree density– max DBH– foliage height diversity– stem size diversity

• Principle Components Analysis performed on vegetation structure variables to capture variation in forest structure among sites

Methods: Neighborhood Characterization

• HERCULES classification (Cadenasso et al. in press) applied to surrounding neighborhoods within 100 m buffer

Methods: Neighborhood Characterization

Bldg type: 1Bldg cover: 3CV cover: 1FV cover: 3Pavement cover: 2Bare soil cover: 0

Bldg type: 2Bldg cover: 3CV cover: 1FV cover: 3Pavement cover: 2Bare soil cover: 0

Bldg type: 1Bldg cover: 3CV cover: 1FV cover: 3Pavement cover: 2Bare soil cover: 0

Bldg type: 0Bldg cover: 0CV cover: 0FV cover: 2Pavement cover: 1Bare soil cover: 1

Bldg type: 2Bldg cover: 3CV cover: 1FV cover: 1Pavement cover: 2Bare soil cover: 0

Bldg type: 1Bldg cover: 3CV cover: 1FV cover: 2Pavement cover: 2Bare soil cover: 0

Methods: Neighborhood Characterization

Bldg type: 1Bldg cover: 3CV cover: 1FV cover: 3Pavement cover: 2Bare soil cover: 0

Bldg type: 2Bldg cover: 3CV cover: 1FV cover: 3Pavement cover: 2Bare soil cover: 0

Bldg type: 1Bldg cover: 3CV cover: 1FV cover: 3Pavement cover: 2Bare soil cover: 0

Bldg type: 0Bldg cover: 0CV cover: 0FV cover: 2Pavement cover: 1Bare soil cover: 1

Bldg type: 2Bldg cover: 3CV cover: 1FV cover: 1Pavement cover: 2Bare soil cover: 0

Bldg type: 1Bldg cover: 3CV cover: 1FV cover: 2Pavement cover: 2Bare soil cover: 0

• Area-weighted mean calculated for each cover variable in each buffer

• PCA performed on area-weighted means to capture variation in distribution of landcovers

Methods: Neighborhood Age• Used HERCULES classification to

calculate area-weighted median neighborhood age

Methods: Data Analysis• Information-Theoretic Model

Selection using Akaike’s Information Criterion

• Linear regression models

Multiple working hypotheses:

Total Species Richness (TSR)

– TSR=ßo+ß1(Area)+ß2(PCAveg)+ß3(Age)

– TSR=ßo+ß1(Area)+ß2(PCAveg)+ß3(Age)-ß4(Age2)

– TSR=ßo+ß1(Area)+ß2(PCAveg)+ß3(PCAHERC1)

– TSR=ßo+ß1(Area)+ß2(PCAveg)+ß3(PCAHERC1)+ ß4(PCAHERC2)

– TSR=ßo+ß1(Area)+ß2(Age)

– TSR=ßo+ß1(Area)+ß2(Age)-ß3(Age2)

– TSR=ßo+ß1(Area)+ß2(PCAHERC1)

– TSR=ßo+ß1(Area)+ß2(PCAHERC1)+ß3(PCAHERC2)

– TSR=ßo+ß1(Area)+ß2(PCAveg)

– TSR=ßo+ß1(Area)

Total species richness is best explained by patch-level features (area, vegetation structure), but additional variation may be explained by:•Neighborhood age (+)•Variation in the distribution of landcover types in the surrounding neighborhood

Results: Bird Species Detected

Results: Neighborhood Characterization

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PCAHERC1

PCAHERC2

Results: Neighborhood Characterization

Plenty o’ pavement

Tons o’ trees

Bunch o’ bare soil

Gobs o’ grass

Results: Neighborhood Characterization

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Site number

Area-weighted median age (years)

6.46 - 60.02 years

32.49 [mean] ± 14.73 [S.D.]

TSR=ßo+ß1(Area)-ß2(PCAHERC1)0.47

TSR VI

TSR VII

Results: Model Selection

TSR VIII

TSR V

TSR IV

TSR III

TSR II

TSR I

0.0366.56

61.11

0.1363.60

0.0266.97

0.0168.02

0.1263.76

0.0267.90

0.0169.45

ωiAICcModel

TSR IX

0.1164.08

TSR X 0.0864.62

TSR=ßo+ß1(Area)+ß2(PCAveg)-ß3(PCAHERC1)

TSR=ßo+ß1(Area)+ß2(Age)-ß3(Age2)

TSR=ßo+ß1(Area)-ß2(PCAHERC1)

TSR VI

Results: Model Selection

TSR VIII

TSR V

TSR IV

TSR III

TSR II

TSR I

0.03

0.13

0.02

0.01

0.12

0.02

0.01ωiModel

TSR IX

0.11

TSR X 0.08

R2=0.69

R2=0.69

R2=0.68

66.56

63.60

66.97

68.02

63.76

67.90

69.45

AICc

64.08

64.62

Conclusions•Breaking news: Forest birds like trees

•The real kicker: they like them in their habitat, but also surrounding it. Evidence from the analysis…

•PCAHERC1 (negative) appeared in two of three best models, this axis represented the tendency for tree cover in the neighborhood to decrease as pavement increases

•The quadratic transformation of neighborhood age (AGE-AGE2) appeared in the second best species richness model; neighborhood canopy cover has an inverse parabolic relationship with neighborhood age (Grove et al. 2006)C

oars

e ve

g co

ver

Neighborhood Age

R2=0.60

~40 years

Conclusions•Neighborhood age alone did not appear in any of the top models; time since disturbance does not allow more species to colonize

•The structure of the forest itself explained less variation in breeding bird diversity than the structure of the neighborhood surrounding the forest

•Diverse household land-management practices collectively shape urban landscapes•Relationships were observed at very narrow buffer width individual landowners can impact habitats from the outside-in•Neighborhoods can be managed as a buffers to forest habitat (Watson et al. 2005)

Conclusions

• Biodiversity can be used as a tool to inform land management practices that effect multiple environmental issues

• Confronting the culture of status associated with big green lawns is a challenge for many aspects of conservation

• Emphasize aesthetic/ecological value of a complex yard habitat, particularly adjacent to forest

Conservation value?

Further research

• Different buffer widths

• Change across years

• Habitat quality: productivity/survivorship

• Test models in different landscapes

Acknowledgements• Graduate Committee: Gary Barrett,

Roarke Donnelly, Robert Cooper

• BES: Paige Warren, Charlie Nilon, Morgan Grove, Jarlath O’Neil-Dunne, Mike McGuire, Kirsten Schwartz

• Field Assistance: Andy Flies

• NSF Graduate Research Fellowship

Questions?