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

Why are there more kinds of specieshere compared to there?

Theoretical Focus Conservation Focus– Latitudinal Gradients

– Energy Theory

– Climate Attributes

– Faunal Integrity

– Human Footprint

– Habitat Attributes

The Relative Importance ofClimate and Broad-scale Habitat

for Predicting Regional Bird Richness

Curtis H. FlatherUSDA, Forest Service Rocky Mtn Research StnFort Collins, Colorado

Kevin J. Gutzwiller Department of BiologyBaylor UniversityWaco, 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 [2000-2002])

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


Candidate Predictors

Climate

HumanFootprint

Habitat

Long-term annual means (1971-2000)

Short-term annual means (2000-2002)

Deviation (Short from Long)

Seasonal means (breeding season)

Temperature / Precipitation

Elevation VariationForest AmountForest Arrangement

Patch size (mean and variance)Nearest neighbor (mean and variance)

Total edge

◦ PRISM Climate GroupOSU - Chris Daly

◦ 2000 Census◦ NLCD

◦ Bureau of Transportationas summarized by Ray Watts (2007)

Intensive Land Use

Human population

Roads

◦ National Land Cover Data (NLCD)USGS - 2001

◦ National Elevation Data (NED)USGS

Data are linked geographically by bufferingaround bird survey routes


Human footprint

Forest HabitatNLCD

Population


Forest Bird Richness

= f Habitat

HumanFootprint

Climate

Response Candidate Predictors

?

Data Sources and Modeling ApproachModel 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 ApproachModel Estimation

◦ Multivariate Adaptive Regression Splines (MARS)

Knot

Spline

Candidate Explanatory Variable

Res

pons

e Va

riabl

e

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


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 routesTrain = 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 & PerformanceRelative Predictive Ability of Variables

Impo

rtan

ce V

alue

0

20

40

60

80

100

TE_4

0D

SM_P

_Y

AM_P_N

AM_T_N

Main Effects Only

Main & Interaction Effects ModelAccounts for 66.4%


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)


Impo

rtan

ce V

alue

0

20

40

60

80

100

SM_P

_N

A_AM_4

0CV_

PZ_4

0

AM_T_N

ELEV

_SD

ASV_P

_NDI

F_AM

_P

CA_40P

Main & Interaction Effects

Relative Predictive Ability of VariablesModel Specification & Performance

Accounts for 66.4% Accounts for 66.3%


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 & PerformanceMain & Interaction Effects Model

Impo

rtan

ce V

alue

0

20

40

60

80

100

A_AM_4

0

CV_PZ

_40

AM_T_N

CA_40P

Main & Interaction Effects(simple)

Relative Predictive Ability of VariablesModel Specification & Performance

Model Specification & PerformanceEvaluation on Independent Data (Simple Model)

◦ Recall: We held out 100 observations for testing

Unadjusted

Relative Error 2.8%95% CI 0.06 to 5.52

2.3%-0.36 to 4.88

Adjusted

Moran's IMax Moran's I

Distance Units2,0001,8001,6001,4001,2001,000800600400200

Mor

an's

I0.8

0.6

0.4

0.2

0

-0.2

-0.4

Moran's IMax Moran's I

Distance Units2,0001,8001,6001,4001,2001,000800600400200

Mor

an's

I

0.40.30.20.1

0-0.1-0.2-0.3-0.4-0.5-0.6


◦ Why so little adjustment with residual interpolation?

Unadjusted

Adjusted


Relative MAE 10.6% 10.4%

Unadjusted

Relative Error 2.8%95% CI 0.06 to 5.52

2.3%-0.36 to 4.88

Adjusted


Distribution of absolute error(adjusted)

Freq

uenc

y

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

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

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