1. Multi-Scale Modeling of Bird Diversity using Canopy Structure Metrics of Habitat Heterogeneity Scott Goetz Mindy Sun (WHRC) Ralph Dubayah Anu Swatatran Amanda Whitehurst (UMD) Andy Hansen Linda Phillips (MSU) Richard Pearson Ned Horning (AMNH) NASA Annual Biodiversity Meeting Seattle, April 2012 Magnolia warblerBlack throated blue warbler Collaborators: Matthew Betts (OSU) Richard Holmes (Dartmouth)

2. Objectives / Research Questions (1) How can patterns of ecosystem structure be observed and modeled at regional to continental scales? (2) What is the influence of satellite measurements of canopy structure on bird diversity (extent, richness and abundance)? (3) What are the relationships between bird diversity, vegetation structure and ecosystem productivity at regional to continental-scales? ~ Summer Tanager. Photo by Scott Somershoe, USGS.

3. LVIS RH100DRL Canopy Height UAVSAR Landsat NDVI difference 0 0.6 0 5 10 15 20 25 >30 m Synergistic use of lidar, moderate to high resolution optical & SAR.. 0 5 10 15 20 25 >30 m Hubbard Brook Experimental Forest What is the influence of satellite measurements of canopy structure on bird diversity (extent, richness and abundance) and habitat use (prevalence)? We made use of a unique bird observation data set across 371 sites at HBEF collected over 10+ years (1999-2009)

4. % variance explained Single versus multi-sensor predictions of the Bird Species Richness Hubbard Brook Experimental Forest Swatatran, Dubayah, Goetz, et al. (2012 PlosOne)

5. understory Lidar-derived canopy cover at different heights

6. midstory Lidar-derived canopy cover at different heights

7. overstory Lidar-derived canopy cover at different heights

8. cumulative

9. Species habitat use varies with cover across a range of heights Yellow-rumped warbler more prevalent in lower canopy Ovenbird more prevalent in upper canopy but also near surface (ground gleaner)

10. Predictions of Species Abundance at HBEF Boosted Regression Tree Hurdle Model Predictor Variables Laser Vegetation Imaging Sensor (LVIS) Total canopy cover Canopy cover at 15-20m height Canopy cover at 20-25m height Canopy cover at 25-30m height Energy return at 25% canopy height Energy return at 50% canopy height Energy return at 75% canopy height Canopy complexity Discrete Return Lidar (DRL)Elevation Canopy height Average crown diameter Average crown diameter*height Stem Density Crown area-weighted height LandsatNDVI NDVI Difference RadarHV backscatter HH/VV ratio HH/VV index Following Goetz et al. 2010 and Swatantran et al. 2012 Hurdle modeling approach links a presence-absence model with an abundance model to address the issue of inflated zero counts - predictions can be interpreted as being abundance given suitable habitat (after Strubbe et al., 2010)

11. Boosted Regression Tree Hurdle Model Predictions of Species Abundance at HBEF Magnolia Warbler, r 2 =0.756 Good prediction…Reasonable prediction…Poor prediction… Black-throated blue warbler, r 2 =0.497 Brown Creeper, r 2 =0.049 Mean r 2 for 16 species = 0.381, Max = 0.756, Min = 0.039.

12. Boosted Regression Tree Hurdle Model Predictions of Species Abundance at HBEF

13. National Scale Predictors of Bird Diversity Patterns Physical Environment: climate and topography Vegetation Properties: canopy density / percent cover, functional groups, biomass Vegetation Productivity: NPP, GPP (MODIS) Vegetation Structure: GLAS metrics What are the relationships between bird diversity, vegetation structure and ecosystem productivity at regional to continental-scales?

14. At least 10 GLAS shots within Breeding Bird Survey (BBS) routes

15. Robust Predictions of Bird Species Richness Forest Birds predicted well even in high Canopy Cover & Productivity areas High productivity routes (389) High Canopy Cover Explained = 63% High Productivity Explained = 68% All Forest Birds Explained Variance = 84% All 730 routes High Canopy Cover routes (259) Cross-validated with 10% reserved BBS routes

16. National Scale Predictions of Bird Guild Species Richness Biophysical Structure and Environmental predictors All Forest Woodland Grassland Models developed on BBS routes Goetz et al. (forthcoming)

17. Southeast US BBS sample locations, Segments, Routes Disturbance History and Land Use LVIS Canopy cover Canopy cover by height class Land cover Percent Ag Percent developed Percent Canopy Variety of cover types MODIS GPP VCF forest Soil fertility Geographic Location Three Analysis units Stratify Predictor variables Other biophysical Temperature Precipiation Elevation NDVI Regional Interactions among Ecosystem Productivity, Land Use and Canopy Structure

18. BBS stop locations PointSegmentRoute Three analysis units Southeast LVIS Transect Intersection of BBS routes with LVIS acquisitions

19. Stop locations and BBS route buffer LVIS transect overlap Collected GPS stop location data collected for 53 of 63 BBS routes from BBS Surveyor and/or driving the route GPS

20. Stop locations and BBS route buffer LVIS points in red BBS stop locations buffered (Red) BBS route buffered (Yellow)

21. Canopy cover at 10_15m Derivation of cover at multiple canopy heights / layers

22. Canopy cover at 25-30m 20-25m 15-20m 10-15m 5-10m 0-5m

23. Summary of Findings (thus far) 1. At local scale (e.g. HBEF) bird species richness and habitat use (multi-year prevalence) can be predicted well using lidar (and multi-sensor) canopy structure –Performance of abundance predictions is species specific and first requires identification of suitable habitat 2. At national scale bird species richness can be robustly predicted using a suite of environmental variables –Lidar canopy structure metrics are not selected as the most important predictors at this scale

24. Next Steps & in Progress 3. Regional scale work is ongoing using SE transect Extending analyses across productivity, land use and disturbance gradients Also: –Additional analysis of SE LVIS transect layers and analysis within BBS route observations –Extend work at HBEF including abundance modeling, vertical habitat use, breeding productivity