Sensitivity of wildlife habitat capability models to spatial resolution of underlying mapped vegetation data Matthew J.
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Sensitivity of wildlife habitat capability models to spatial resolution of underlying mapped vegetation data Matthew J. Gregory1 Janet L. Ohmann2 Brenda C. McComb3 1 Department of Forest Science, Oregon State University, Corvallis, OR Northwest Research Station, USDA Forest Service, Corvallis, OR 3 Department of Natural Resources Conservation, University of Massachusetts-Amherst, Amherst, MA 2 Pacific 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 maps estimates of area local 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 Annual precipitation 30m 90m 270m 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 90m 270m 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 90m 270m Pre-aggregation ordination ordinations are remarkably similar CCA axis 2 CCA CCA axis 1 Selected environmental variables at 30m Pre-aggregation ordination ordinations are remarkably similar CCA axis 2 CCA CCA axis 1 Selected environmental variables at 90m Pre-aggregation ordination ordinations are remarkably similar CCA axis 2 CCA CCA axis 1 Selected environmental variables at 270m 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 … Post-aggregation strategy Vegetation class Plot ID number Majority aggregation (3 x 3) “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? “Biggest Gainers” in Post-Aggregation “Biggest Gainers” in Post-Aggregation GNN Predicted Vegetation Class 90m Pre-aggregation 270m (using canopy cover, broadleaf proportion and average stand diameter) 30m Sparse/Open Lg. Mixed Sm. Broadleaf Sm. Conifer Lg. Broadleaf Md. Conifer Sm. Mixed Lg. Conifer Md. Mixed VLg. Conifer 90m Post-aggregation 270m 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 sensitivity focal window changes vegetative attributes and ranges Have thus far not looked at spatial resolution of underlying vegetation models 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: www.animalpicturesarchive.com 90m Pre-aggregation 270m Northern Spotted Owl Habitat Capability Index 30m Habitat Capacity Score (0 – 100) 0 - 10 40 - 50 10 - 20 50 - 60 20 - 30 > 60 30 - 40 90m Post-aggregation 270m Area distribution of NSO HCI scores Predicted HCI scores at NSO nest sites HCI Model Western 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 90m Pre-aggregation 270m Western Bluebird Habitat Capability Index 30m Habitat Capacity Score (0 – 100) 0 - 10 40 - 50 10 - 20 50 - 60 20 - 30 > 60 30 - 40 90m Post-aggregation 270m Area distribution of WBB HCI scores HCI simple summary statistics Study area: 2.3 million ha 30m Pre-90m Pre-270m Post-90m Post-270m Mean SD Mean SD Mean SD Mean SD 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 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