A COMPARISON OF APPROACHES FOR VERIFYING SOUTHWEST REGIONAL GAP VERTEBRATE-HABITAT

DISTRIBUTION MODELS

J. Judson Wynne, Charles A. Drost and Kathryn A. ThomasUSGS- Southwest Biological Science Center, Colorado Plateau Research Station, Flagstaff, Arizona

Southwest Regional Gap

• Data products: land cover, stewardship and wildlife-habitat maps

• 833 wildlife habitat maps

• Information to land managers, researchers, policy makers, and the general public

• AZ, CO, NM, NV, UT

- Habitat correlates identified via literature reviews

Objectives

• Compare and contrast the two approaches.

• Review approach to accuracy assessment used in previous GAP programs.

• Describe an alternative approach to GAP accuracy assessment.

• Identify best approach.

Mexican Jay Gap1

Predicted Nonhabitat

Predicted Habitat

30 0 30 Miles

N

Accuracy Assessment

• Agreement between the expected and observed

• Utility:- Evaluate model quality

- Identify and correct for error

- Compare techniques, algorithms, and model developers

- Assess relevance in decision making

Without Accuracy Assessment…

Models are untested hypotheses

Does =+ ?

= ?And does

Gap and “Accuracy Assessment”

Species list approach

- compiled list of species within given management areas

- overlaid on predictive distribution maps

- “measure of agreement” with omission and commission errors

Sample Size

Arizona Accuracy Assessment GAP1

- Source data: USDA FS, NPS, AGFD, TNC

10Mammals

15Birds

10Reptiles

10Amphibians

No. of AreasTaxonomic Group

From Drost et al. (1999)

1. Intensive, area wide for ≥ one taxon

- Criteria

2. Compilation of several surveys with ≥ one taxon

3. Compilation of secondary sources

Northern leopard frog Gap1

Predicted Habitat

20 0 20 MilesN

Predicted Nonhabitat

Collared Peccary GAP1

Predicted Nonhabitat

Predicted Habitat

30 0 30 Miles

N

Expansion

Range Issues

Require recent data to assess these issues

Contraction

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False Agreement

Incidental/ accidental occurrences

False Agreement

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Observations not within habitat

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1898

2001

1900

Issues of Temporal Scale

Time frame reflected in data

Issues of Spatial Scale

• For all groups, model accuracy <50% for areas <1000ha

• Accuracy increased as species list area increased

0.0120.56710Mammals

<0.01 0.78715Birds

0.0770.33910Reptiles

0.0260.48310Amphibians

P-valueR2 No. of Areas

Taxonomic Group

AREA (HA)

1000000

500000

100000

50000

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50

10

AC

CU

RA

CY

(%

)

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0

Birds

Arizona Accuracy Assessment GAP 1

• Approach: Use of species occurrence data

• Measure of habitat rather than indirect measure of range

• More appropriate scale

• Statistically meaningful accuracy metrics

A Higher Standard

- Presence only point data

- Grid-based data

- Presence/ absence point data

Data Sources for Model Verification

Presence Only Data

USDA Forest Service Arizona / New Mexico database

- Point data- Museum specimen, trapping and

other observations

- Standardized format

Presence/ Absence Data

AGFD Bat Data (Central Arizona)

- Systematic sampling protocols

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Mist Net Data_Bill Burger AGFD

Antrozous pallidus#

Corynorhinus townsendii#

Eptisicus fuscus#

Euderma maculatum#

Eumops perotis#

Idionycteris phyllotis#

Lasionycteris noctivagans#

Lasiurus blossevillii#

Lasiurus cinereus#

Lasiurus xanthinus#

Macrotis californicus#

Myotis auriculus#

Myotis californicus#

Myotis californicus or ciliolabrum#

Myotis ciliolabrum#

Myotis evotis#

Myotis species#

Myotis thysanodes#

Myotis velifer#

Myotis volans#

Myotis yumanensis#

Nytinomops femorosaccus#

Nytinomops macrotis#

Pipistrellus hesperus#

Tadarida brasiliensis#

unidentified bat#

50 0 50 Miles

N

- Seven year dataset- Multiple surveys/ year/ site

- Point data

Grid Data

Arizona Breeding Bird Atlas

- Appropriate scale (~1 km2 grid cell)

- Rigorous sampling design

- Recently completed

- Statewide coverage- Sampling- multiple years/ site- Trained/ experienced observers

SystematicTypically randomData Collection

Defined by sample designVariableReliability

Much largerVery smallSample Size

Limited to comprehensivePoorCoverage

Known for each datasetGenerally not addressedTemporal Issues

Close to map scaleVery coarseScale

Verification DataSpecies List

Comparison of Approaches

Conclusions

• Best approach: Occurrence/ verification data

• Verification data do exist

• Best available science

• Species lists inadequate for accuracy assessment