Success of seabeach amaranth (Amaranthus pumilus Raf.) using habitat

selection based on light detection and ranging (LIDAR) data

Jon D. Sellars1,2

Claudia L. Jolls1

Cass A. Wigent1

1East Carolina University

Department of Biology

Greenville, NC

2National Geodetic Survey

National Oceanic and Atmospheric Administration

• fugitive • intolerant of

competition• requires disturbance• narrow elevation range

on non-eroding beaches (Bücher and Weakley 1990)

• habitat is relatively homogenous

Amaranthus pumilus

30 Degree Scan Angle

Scan Width

(~300 m)

Overlapping Swaths

Scan Direction

(parallel to beach)

Laser

LIght Detection And Ranging data (LIDAR)

Twin Engine Aircraft

Elevation ~700 m

Discriminant Function Analysis• Variables extracted from 2000 LIDAR were used to model

suitable habitat in 2001 for Shackleford Banks and Cape Lookout Spit, Cape Lookout National Seashore, NC– Elevation

• Elevation in the North American Vertical Datum of 1988 (m)

– Distance from shore• Distance from the mean high water line (m)

– Surface complexity• Standard deviation of the surface normal vectors in a 9 x 9 m2

neighborhood

– Slope• Slope of the surface in degrees

– Grey scale reflectance• Passive LIDAR data able to distinguish sand/water/vegetation

Discriminant Function Analysis• Plant locations were captured with a Differential Global

Positioning System (DGPS)– Estimated accuracy ± 1.2 m

• Plants (n = 168 represented by 126 unique DGPS locations) were compared to randomly generated points (n = 426) with in the study area– 3 x the number of random points were used to capture the greater

background variation

• Plant occurrences (n = 26) originally withheld from the model were used as a validation set– Every fifth point that represented a single plant location

• All statistical analyses were performed in SPSS 11.5.0– Variables were square root transformed to keep the DFA robust

despite outliers

Discriminant Function Analysis

Group Predicted Group Membership Total

Random Occurrence

CountRandom 355 65 420*

Occurrence 26 116 142

% Random 84.5 15.5 100.0

Occurrence 18.3 81.7 100.0

Contribution of Variable

Variable Correlation

Distance 0.859

Passive -0.757

Complexity 0.414

Slope 0.038

Elevation -0.027

Step 1. We analyzed all five variables to identify the most important as based on correlation to the discriminant function.

Discriminant Function Analysis

Group Predicted Group Membership Total

Random Occurrence

CountRandom 364 62 426

Occurrence 8 134 142

% Random 85.4 14.6 100.0

Occurrence 5.6 94.4 100.0

Contribution of Variable

Variable Correlation

Distance 0.955

Passive -0.849

Step 2. We analyzed just Distance and Passive

Discriminant Function Analysis

• Distance and reflectance were best able to distinguish suitable habitat based on their ability to differentiate between Random and Occurrence Points

• Variable coefficients from SPSS were used to model habitat in ArcView (3 m2 cells)– Using Map Calculator function

• Model correctly identified 24/26 (92 %) of validation points as occurring in suitable habitat– Overlay analysis in ArcView

Area Enlarged

Probability a cell contains Suitable Habitat

0.50 - 0.75 > 0.75

• Plant Location

Area Enlarged

Probability a cell contains Suitable Habitat

0.50 - 0.75 > 0.75

Mean High Water (MHW)

MHW + 0.77 m

MHW + 2.00 m

• Plant Location

Conclusions

• Habitat variables can be extracted from remote sensing data– LIDAR data can be used to delineate and model

suitable habitat for Amaranthus pumilus

• Distance from shore and passive data efficiently model suitable habitat– Potentially distance from shore is a surrogate

measure of disturbance

– Passive data delineate areas free of vegetation

Applications of Remote Sensing Data

to Rare Plant Conservation

• Covers large geographic areas

• Identify critical environmental variables and habitat

• Can aid rapid assessment of sites for species occurrence / re-introduction

ACKNOWLEDGMENTSACKNOWLEDGMENTSNorth Carolina Plant Conservation ProgramNorth Carolina Plant Conservation Program

National Park ServiceNational Park ServiceEast Carolina UniversityEast Carolina University

ALACE (NOAA, NASA, USGS)ALACE (NOAA, NASA, USGS)Mike Aslaksen, NGS-NOAA Mike Aslaksen, NGS-NOAA

Marj Boyer, NCDA-PCPMarj Boyer, NCDA-PCPJeff Colby, ECUJeff Colby, ECUKarl Faser, ECUKarl Faser, ECU

Cecil Frost, NCDA-PCPCecil Frost, NCDA-PCPMichael Hearne, NOAAMichael Hearne, NOAA

Mark Jansen, NOAAMark Jansen, NOAAMarcia Lyons, NPSMarcia Lyons, NPS

Karen Trueblood, ECUKaren Trueblood, ECUKeith Watson, USFWSKeith Watson, USFWS

Jason Woolard, NGS-NOAAJason Woolard, NGS-NOAA