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Sensitivity of Wildlife Habitat Capability Models to Spatial Resolution of Mapped Vegetation Data

This study examines the effects of spatial resolution on vegetation maps and habitat capability index scores for selected wildlife species. It compares different aggregation strategies and assesses the accuracy of the models. The study focuses on the Northern Spotted Owl and the Western Bluebird, two species sensitive to landscape patterns.

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Sensitivity of Wildlife Habitat Capability Models to Spatial Resolution of Mapped Vegetation Data

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  1. Sensitivity of wildlife habitat capability models to spatial resolution of underlying mapped vegetation data Matthew J. Gregory1Janet L. Ohmann2Brenda C. McComb3 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 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

  5. 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 Annual precipitation

  6. 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 Elevation

  7. 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

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

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

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

  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. Vegetation class Plot ID number Majority aggregation (3 x 3) Post-aggregation strategy

  13. “Biggest Gainers” inPost-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. “Biggest Gainers” in Post-Aggregation

  16. Pre-aggregation 90m 270m GNN Predicted Vegetation Class(using canopy cover, broadleaf proportion and average stand diameter) 30m Sparse/Open Lg. Mixed Sm. Broadleaf Sm. Conifer Lg. Broadleaf Md. Conifer 90m 270m Sm. Mixed Lg. Conifer Post-aggregation Md. Mixed 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 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

  21. Pre-aggregation 90m 270m Northern Spotted Owl Habitat Capability Index 30m Habitat Capacity Score (0 – 100) 0 - 10 40 - 50 10 - 20 50 - 60 90m 270m 20 - 30 > 60 Post-aggregation 30 - 40

  22. Area distribution of NSO HCI scores

  23. Predicted HCI scores at NSO nest sites

  24. HCI ModelWestern Bluebird (WBB) • 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: www.animalpicturesarchive.com

  25. Pre-aggregation 90m 270m Western Bluebird Habitat Capability Index 30m Habitat Capacity Score (0 – 100) 0 - 10 40 - 50 10 - 20 50 - 60 90m 270m 20 - 30 > 60 Post-aggregation 30 - 40

  26. Area distribution of WBB HCI scores

  27. HCI simple summary statistics 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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