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Sensitivity of wildlife habitat capability models to spatial resolution of underlying mapped vegetation data Matthew J. Gregory 1 Janet L. Ohmann 2 Brenda.

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Presentation on theme: "Sensitivity of wildlife habitat capability models to spatial resolution of underlying mapped vegetation data Matthew J. Gregory 1 Janet L. Ohmann 2 Brenda."— Presentation transcript:

1 Sensitivity of wildlife habitat capability models to spatial resolution of underlying mapped vegetation data Matthew J. Gregory 1 Janet L. Ohmann 2 Brenda C. McComb 3 1 Department of Forest Science, Oregon State University, Corvallis, OR 2 Pacific Northwest Research Station, USDA Forest Service, Corvallis, OR 3 Department of Natural Resources Conservation, University of Massachusetts-Amherst, Amherst, MA

2 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

3 Project objectives  Examine effects of spatial resolution on vegetation maps  estimates of area  local scale accuracy  Assess effects of spatial resolution on habitat capability index (HCI) scores for selected wildlife species

4 Methods  Gradient Nearest Neighbor (GNN) imputation at three resolutions  900 m 2 (30m x 30m cells)  8100 m 2 (90m x 90m cells)  72,900 m 2 (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

5 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

6 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

7 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

8  CCA ordinations are remarkably similar Pre-aggregation ordination Selected environmental variables at 30m CCA axis 1 CCA axis 2

9  CCA ordinations are remarkably similar Pre-aggregation ordination Selected environmental variables at 90m CCA axis 1 CCA axis 2

10  CCA ordinations are remarkably similar Pre-aggregation ordination Selected environmental variables at 270m CCA axis 1 CCA axis 2

11 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 …

12 Plot ID numberVegetation class Majority aggregation (3 x 3) Post-aggregation strategy

13 “Biggest Gainers” in Post-Aggregation  Is this non-intuitive scaling a common occurrence?  Find plots with largest percent increases between resolutions  tend to be “on the edge” of gradient space  underrepresented or rare conditions?

14 “Biggest Gainers” in Post-Aggregation

15

16 30m 90m270m 90m270m Pre-aggregation Post-aggregation GNN Predicted Vegetation Class (using canopy cover, broadleaf proportion and average stand diameter) Sparse/Open Sm. Broadleaf Lg. Broadleaf Sm. Mixed Md. Mixed Lg. Mixed Sm. Conifer Md. Conifer Lg. Conifer VLg. Conifer

17 GNN accuracy assessment (local)

18 GNN accuracy assessment (regional)

19 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 sensitivity  focal window changes  vegetative attributes and ranges  Have thus far not looked at spatial resolution of underlying vegetation models

20 HCI Model Northern 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:

21 30m 90m270m 90m270m Pre-aggregation Post-aggregation Northern Spotted Owl Habitat Capability Index > 60 Habitat Capacity Score (0 – 100)

22 Area distribution of NSO HCI scores

23 Predicted HCI scores at NSO nest sites

24  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 tall Photo credit: HCI Model Western Bluebird (WBB)

25 30m 90m270m 90m270m Pre-aggregation Post-aggregation Western Bluebird Habitat Capability Index > 60 Habitat Capacity Score (0 – 100)

26 Area distribution of WBB HCI scores

27 HCI simple summary statistics 30mPre-90mPre-270mPost-90mPost-270m MeanSDMeanSDMeanSDMeanSDMeanSD WBB NSO Study area: 2.3 million ha

28 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


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