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Transcript of 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