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

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

Study Motivation 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 We focused on the southern US because of key resource interactions with timber resources and declining bird trends Background “Proof of concept” study

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

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

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

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

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

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

Data Sources and Modeling Approach Two Nuisance Issues: 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 3. Karl Cottenie - limitations of species richness

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

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

Relative Predictive Ability of Variables Importance Value TE_40D SM_P_Y AM_P_N AM_T_N Main Effects Only

Main & Interaction Effects Model Accounts for 66.4% Annual mean temp (30-yr) Amount of forest 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

Importance Value SM_P_N A_AM_40 CV_PZ_40 AM_T_N ELEV_SD ASV_P_N DIF_AM_P CA_40P Main & Interaction Effects Relative Predictive Ability of Variables Model Specification & Performance

Accounts for 66.4%Accounts for 66.3% Annual mean temp (30-yr) Amount of forest 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

Importance Value A_AM_40 CV_PZ_40 AM_T_N CA_40P Main & Interaction Effects (simple) Relative Predictive Ability of Variables Model Specification & Performance

Evaluation on Independent Data (Simple Model) ◦ Recall: We held out 100 observations for testing Unadjusted Relative Error2.8% 95% CI0.06 to % to 4.88 Adjusted

Moran's I Max Moran's I Distance Units 2,0001,8001,6001,4001,2001, Moran's I Moran's I Max Moran's I Distance Units 2,0001,8001,6001,4001,2001, Moran's I Model Specification & Performance Evaluation on Independent Data (Simple Model) ◦ Why so little adjustment with residual interpolation? Unadjusted Adjusted

Model Specification & Performance Evaluation on Independent Data (Simple Model) Relative MAE10.6%10.4% Unadjusted Relative Error2.8% 95% CI0.06 to % to 4.88 Adjusted

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

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

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