Pacific Northwest Species Change

 

Focus species for the study, their codes and proportion of plots where the species is present or absence is shown in the table below.

For more information on the plot locations click on the species latin name.

 

Species

Common name

American species code

Presence

Absence

% presence

% absence

Total sites (N)

Pseudotsuga menziesii

Douglas-fir

202

6643

16128

29.2

70.8

22771

Thuja plicata

Western redcedar

242

3107

19664

13.6

86.4

22771

Tsuga heterophylla

Western hemlock

263

2106

20665

9.3

90.8

22771

Tsuga mertensiana

Mountain hemlock

264

473

22298

2.1

97.9

22771

Pinus contorta

Lodgepole pine

108

4742

18029

20.8

79.2

22771

Pinus ponderosa

Ponderosa pine

122

2393

20378

10.5

89.5

22771

Picea sitchensis

Sitka spruce

98

364

22407

1.6

98.4

22771

Pinus albicaulis

Whitebark pine

101

644

22127

2.8

97.2

22771

Chamaecyparis nootkatensis

Alaska yellow cedar

42

887

21884

3.9

96.1

22771

Abies procera

Noble fir

22

82

22689

0.4

99.6

22771

Picea breweriana

Brewer's spruce

92

6

22765

0.0

100

22771

Chamecyparis lawsoniana

Port Orford-cedar

41

23

22748

0.1

99.9

22771

Pinus jeffreyi

Jeffrey pine

116

350

22421

1.5

98.5

22771

Picea engelmannii

Engelmann spruce

93

1365

21406

6

94

22771

Larix occidentalis

Western larch

73

670

22101

2.9

97.1

22771

Juniperus occidentalis

Western juniper

64

456

22315

2.0

98.0

22771

Juniperus scopulorum

Rocky Mt. Juniper

66

301

9217

3.2

96.8

9518

Quecus garryana

Garry Oak

815

118

9400

1.2

98.8

9518

Acer macrophyllum

Big leaf maple

312

480

22291

2.1

97.9

22771

Calocedrus decurrens

Incense cedar

81

561

22210

2.5

97.5

22771

Abies lasiocarpa

Subapline fir

19

2369

20402

10.4

89.6

22771

Abies amabilis

Pacific silver fir

11

764

22007

3.4

96.6

22771

Abies grandis

Grand fir

17

598

22173

2.6

97.4

22771

Alnus rubra

Red alder

351

879

21892

3.9

96.1

22771

 

 

FIA:42 BC:Cy

FIA:312 BC:Mb

FIA:92

FIA:202 BC:Fd

FIA:93 BC:Se

FIA:815 BC:Qg

FIA:17 BC:Bg

FIA:81

FIA:116

FIA:108 BC:Pl

FIA:264 BC:Hm

FIA:22 BC:Bp

FIA:122 BC:Py

FIA:41

FIA:351 BC:Dr

FIA:66 BC:Jr

FIA:11 BC:Ba

FIA:98 BC:Ss

FIA:19 BC:Bl

FIA:263 BC:Hw

FIA:242 BC:Cw

FIA:64

FIA:73 BC:Lw

FIA:101 BC:Pa

Field Datasets
 
 
We have developed an innovative approach to modeling tree species distribution by taking advantage of the strengths of both the empirical and process-modeling approaches. Rather than utilizing climatic data directly in mapping species distribution, we interpret the physiological implications of seasonal variation in water availability, humidity deficits, excess or suboptimal temperatures, and the impact of frost on a generic tree species. By extracting information on how these environmental variables differentially affect the performance of selected species across their current ranges, we are able to predict where a species might be stressed under a changing climate.
 
To model the distributions, we utilize regression-tree modeling technique to rank dependent variables in order of importance based on least squares analysis (Melendez et al. 2006). This approach can be considered as a sequence of yes/no queries partitioning dependent variables for a given species into homogeneous sets of responses. Regression tree analyses are increasingly applied in ecological research (e.g., De’ath et al. 2000; Schwalm et al. 2006) as they are independent of statistical distributions and have proved to be particularly suited for dealing with collinear datasets, potentially insignificant predictors and outliers (Schwalm et al. 2006). A prerequisite of our generic modeling approach is that it must be able to predict, with reasonable accuracy, the current distribution of individual species, and to do so automatically.
 

The 3-PG model has generally proven able to predict maximum canopy LAI over the full range of forested environments in Oregon with reasonable

predictions in other Pacific NW states (Waring et al. 2002, Waring et al. 2005). There has been considerable effort to use remote sensing techniques

to estimate tree mortality caused by insects, disease, and fire. Quickbird, Landsat, and video imagery have all served to estimate mortality from bark

beetles and defoliating insects (Franklin et al. 1995).  In this work, we will take advantage of a variety of remote sensing techniques, including

 U.S. Forest Service aerial mapping and a MODIS-derived disturbance index, to confirm the extent of insect damage across the westernUnited States

 to validate his and our model predictions of where climatic variation could induce stress sufficient to support a major outbreak (Nemani 2008).

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