sensitivity of wildlife habitat capability models to spatial resolution of underlying mapped...

Sensitivity of wildlife habitat capability models to spatial

resolution of underlying mapped vegetation dataMatthew J. Gregory1

Janet L. Ohmann2

Brenda C. McComb3

1 Department of Forest Science, Oregon State University, Corvallis, OR2 Pacific Northwest Research Station, USDA Forest Service, Corvallis, OR

3 Department of Natural Resources Conservation, University of Massachusetts-Amherst, Amherst, MA

Why aggregate maps?

Comparisons to coarser resolution products

Processing speed for spatially-explicit models

Displaying maps at more appropriate spatial scales“my backyard isn’t correct” syndrome

Finding appropriate scales for analysis

Project objectives

Examine effects of spatial resolution on vegetation mapsestimates of arealocal scale accuracy

Assess effects of spatial resolution on habitat capability index (HCI) scores for selected wildlife species

Methods Gradient Nearest Neighbor (GNN)

imputation at three resolutions 900 m2 (30m x 30m cells) 8100 m2 (90m x 90m cells) 72,900 m2 (270m x 270m cells)

Two different aggregation strategies Pre-aggregation: Aggregate → Impute Post-aggregation: Impute → Aggregate

Use GNN maps as input to HCI models Northern spotted owl and Western

bluebird considered sensitive to landscape pattern

Accuracy assessment for GNN and HCI models

Pre-aggregation strategy Aggregate each

spatial explanatory variable to a coarser resolution before ordination and imputation (GNN)

Mean aggregation for continuous variables, majority aggregation for categorical variables

30m

Annual precipitation

270m

90m

Pre-aggregation strategy Aggregate each

spatial explanatory variable to a coarser resolution before ordination and imputation (GNN)

Mean aggregation for continuous variables, majority aggregation for categorical variables

Elevation

30m

270m

90m

Pre-aggregation strategy Aggregate each

spatial explanatory variable to a coarser resolution before ordination and imputation (GNN)

Mean aggregation for continuous variables, majority aggregation for categorical variables

Tasseled-cap bands

30m

270m

90m

CCA ordinations are remarkably similar

Pre-aggregation ordination

Selected environmental variables at 30m

CCA axis 1C

CA

axi

s 2

CCA ordinations are remarkably similar

Pre-aggregation ordination

Selected environmental variables at 90m

CCA axis 1C

CA

axi

s 2

CCA ordinations are remarkably similar

Pre-aggregation ordination

Selected environmental variables at 270m

CCA axis 1C

CA

axi

s 2

Post-aggregation strategy

Find the majority plot neighbor from initial 30x30m resolution at coarser resolution

Maintains the imputation flavor of predictions at a pixel independent of scale, but …

Non-intuitive scaling is somewhat unique to imputation methods

An example …

Plot ID number

Vegetation class

Majority aggregation (3 x 3)

Post-aggregation strategy

“Biggest Gainers” inPost-Aggregation

Is this non-intuitive scaling a common occurrence?

Find plots with largest percent increases between resolutionstend to be “on the edge” of

gradient spaceunderrepresented or rare

conditions?

“Biggest Gainers” in Post-Aggregation

“Biggest Gainers” in Post-Aggregation

30m

90m 270m

90m 270m

Pre-aggregation

Post-aggregation

GNN Predicted Vegetation Class (using canopy cover, broadleaf proportion and average stand diameter)

Sparse/OpenSm. BroadleafLg. BroadleafSm. Mixed

Md. Mixed

Lg. Mixed

Sm. Conifer

Md. Conifer

Lg. Conifer

VLg. Conifer

GNN accuracy assessment (local)

GNN accuracy assessment (regional)

HCI Model History

Conceived as a framework for combining expert opinion and empirical studies (McComb et al., 2002)

Developed for a number of wildlife species in Western Oregon as part of the CLAMS project using GNN vegetation

Measures of sensitivityfocal window changesvegetative attributes and ranges

Have thus far not looked at spatial resolution of underlying vegetation models

HCI ModelNorthern Spotted Owl

(NSO) Habitat: Old

forest clumps suitable for nesting/foraging

HCI = weighted average of nesting and foraging indices

GNN variables Canopy cover Tree diameter

diversity Quadratic mean

diameter TPH (different

size classes)

Photo credit: www.animalpicturesarchive.com

30m

90m 270m

90m 270m

Pre-aggregation

Post-aggregation

Northern Spotted Owl Habitat Capability Index

0 - 10

10 - 20

20 - 30

30 - 40

40 - 50

50 - 60

> 60

Habitat Capacity Score (0 – 100)

Area distribution of NSO HCI scores

Predicted HCI scores at NSO nest sites

Habitat: Early successional specialist favoring snags for nesting

HCI score is predominantly a function of nest site

GNN variables: Canopy cover SPH 25-50cm

and >5m tall SPH >50cm and

>5m tallPhoto credit:

www.animalpicturesarchive.com

HCI ModelWestern Bluebird (WBB)

30m

90m 270m

90m 270m

Pre-aggregation

Post-aggregation

Western Bluebird Habitat Capability Index

0 - 10

10 - 20

20 - 30

30 - 40

40 - 50

50 - 60

> 60

Habitat Capacity Score (0 – 100)

Area distribution of WBB HCI scores

HCI simple summary statistics

30m Pre-90m Pre-270m Post-90mPost-270m

Mean

SDMea

nSD

Mean

SDMea

nSD

Mean

SD

WBB

0.979

6.143

1.004

7.288

0.980

7.911

0.970

7.144

0.964

7.847

NSO 16.334

15.343

15.198

16.758

13.141

18.073

14.689

16.567

11.777

17.943

Study area: 2.3 million ha

Conclusions

Scaling with imputation techniques provide unique opportunities for ancillary models

Aggregation using imputation spatial pattern and accuracy measures

maintained from 30m → 90m post-aggregation tends to accentuate sparse

vegetation (non-intuitive scaling) Effect on HCI models

spatial pattern can be unpredictable based on aggregation technique at coarser resolutions

can potentially bias HCI scores