Why are there more kinds of species here compared to there?

1. Why are there more kinds of species here compared to there? Theoretical Focus Conservation Focus – Latitudinal Gradients – Faunal Integrity – Energy Theory – Human Footprint – Climate Attributes – Habitat Attributes

2. The Relative Importance of Climate and Broad-scale Habitat for Predicting Regional Bird Richness • Background Curtis H. Flather USDA, Forest Service Rocky Mtn Research Stn Fort Collins, Colorado Kevin J. Gutzwiller Department of Biology Baylor University Waco, Texas • Data sources and modeling approach • Model specification and performance • Future work Outline

3. Forest Service is engaged in a national study looking at natural resource responses (including biodiversity) to changes in socioeconomic, human population, climate change, land use, and habitat conditions • • “Proof of concept” study • We focused on the southern US because of key resource interactions with timber resources and declining bird trends Background Study Motivation

4. ◦ annual survey (1966-present) ◦ > 4,000 routes are surveyed ◦ survey routes are ~40 km long ◦ 50, 3-min point counts Data Sources and Modeling Approach Response Variable Data Source Forest Bird Richness (3-year mean [2000-2002]) North American Breeding Bird Survey (BBS)

5. Temperature / Precipitation ◦ NLCD Long-term annual means (1971-2000) ◦ 2000 Census ◦ PRISM Climate Group Short-term annual means (2000-2002) ◦ Bureau of Transportation OSU - Chris Daly Deviation (Short from Long) as summarized by Ray Watts (2007) Seasonal means (breeding season) Elevation Variation Forest Amount Forest Arrangement ◦ National Land Cover Data (NLCD) ◦ National Elevation Data (NED) Total edge USGS - 2001 USGS Patch size (mean and variance) Nearest neighbor (mean and variance) Intensive Land Use Human population Roads Data Sources and Modeling Approach Candidate Predictors Data Source Climate Habitat Human Footprint

6. Forest Habitat NLCD Data Sources and Modeling Approach Data are linked geographically by buffering around bird survey routes Human footprint Population

7. Response Candidate Predictors Forest Bird Richness Climate = f Habitat Human Footprint ? Data Sources and Modeling Approach

8. Data Sources and Modeling Approach Model Estimation ◦ Multivariate Adaptive Regression Splines (MARS) - Highly flexible modeling approach - Nonparametric and will fit local / global relations - Found to perform well in recent ecological applications

9. MARS: • Derives optimal piece-wise functions • of the original predictors Spline Knot Response Variable Candidate Explanatory Variable Data Sources and Modeling Approach Model Estimation ◦ Multivariate Adaptive Regression Splines (MARS) • Knots determined by adaptive search • leading to the best fit with min # knots • Must guard against overspecification

10. 1. Bird detectability ◦ Raw counts from BBS are biased low ◦ Capture-recapture estimates were used (COMDYN) 2. Spatial autocorrelation ◦ Data is expected to show spatial pattern ◦ Some of that spatial dependency will be captured by predictors ◦ Spatial dependency that remains needs to be incorporated ◦ Residual Interpolation Data Sources and Modeling Approach Two Nuisance Issues: 3. Karl Cottenie - limitations of species richness

11. ◦ N = 426 routes Train = 326 Test = 100 ◦ Two stages in the analysis Main effects model Main effects + interactions Model Specification & Performance

12. Accounts for 59% Annual mean temp (30-yr) Annual mean precip (30-yr) Total forest edge density Seasonal mean precip (3-yr) • • • • • • • • Model Specification & Performance Main Effects Model

13. 100 Main Effects Only 80 60 Importance Value 40 20 0 TE_40D AM_T_N AM_P_N SM_P_Y Model Specification & Performance Relative Predictive Ability of Variables

14. Accounts for 66.4% Amount of forest Annual mean temp (30-yr) Average forest patch size Variation in forest patch size Season mean precip (30-yr) Elevation variation Spatial variation in precip (30-yr) Deviation ann mean precip (3-yr from 30-yr) Model Specification & Performance Main & Interaction Effects Model

15. 100 Main & Interaction Effects 80 60 Importance Value 40 20 0 CA_40P A_AM_40 AM_T_N SM_P_N ELEV_SD ASV_P_N DIF_AM_P CV_PZ_40 Model Specification & Performance Relative Predictive Ability of Variables

16. Model Specification & Performance Main & Interaction Effects Model Accounts for 66.4% Accounts for 66.3% Amount of forest Annual mean temp (30-yr) Average forest patch size Variation in forest patch size Season mean precip (30-yr) Elevation variation Spatial variation in precip (30-yr) Deviation ann mean precip (3-yr from 30-yr)

17. 100 80 Main & Interaction Effects (simple) 60 Importance Value 40 20 0 CA_40P A_AM_40 AM_T_N CV_PZ_40 Model Specification & Performance Relative Predictive Ability of Variables

18. Adjusted 2.3% -0.36 to 4.88 Relative Error 2.8% 95% CI 0.06 to 5.52 Model Specification & Performance Evaluation on Independent Data (Simple Model) ◦ Recall: We held out 100 observations for testing Unadjusted

19. 0.4 0.8 Moran's I Moran's I 0.3 Max Moran's I Max Moran's I 0.6 0.2 0.1 0.4 Moran's I Moran's I 0 0.2 -0.1 -0.2 0 -0.3 -0.2 -0.4 -0.5 -0.4 -0.6 200 200 400 400 600 600 800 800 1,000 1,000 1,200 1,200 1,400 1,400 1,600 1,600 1,800 1,800 2,000 2,000 Distance Units Distance Units Model Specification & Performance Evaluation on Independent Data (Simple Model) ◦ Why so little adjustment with residual interpolation? Unadjusted Adjusted

20. Relative Error 2.8% 95% CI 0.06 to 5.52 Relative MAE 10.6% 10.4% Model Specification & Performance Evaluation on Independent Data (Simple Model) Unadjusted Adjusted 2.3% -0.36 to 4.88

21. Distribution of absolute error (adjusted) Frequency Absolute Error (Percent) Model Specification & Performance Evaluation on Independent Data (Simple Model)

22. Conclusions ◦ Climate and habitat characteristics are both important in predicting forest bird richness ◦ Predictive strength was generally greater for habitat-related predictors ◦ Results suggest a tradeoff: parsimony versus complexity ◦ Models provided predictions that on average had little bias but a substantial amount of residual variation remains

23. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Future Work ◦ Lack within-stand characteristics of forest habitats Forest Inventory and Analysis (FIA) plot grid