Caesar Kleberg Wildlife Research Institute Texas A&M - - PowerPoint PPT Presentation

A Computerized, Distance-Sampling System for Use During Aerial Surveys of Wildlife Populations Matthew J. Schnupp Caesar Kleberg Wildlife Research Institute Texas A&M University-Kingsville

Introduction

• Estimation of wildlife abundance is an important

aspect of wildlife conservation and management

• Distance sampling is a recommended technique for

estimating wildlife abundance but requires a large number of detections

• Distance sampling with helicopter surveys has been

used to estimate ungulates, carnivores, and Galliformes

• Distance sampling involves traversing a linear

transect and measuring perpendicular distances to

• bservations
• Major assumptions are:
• distances are measured accurately
• observations directly on the transect are detected
• no movement of animals prior to detection
• Using distance sampling with helicopter surveys

introduces unique challenges

Distance Sampling

Objectives

• I. Develop an electronic system that could be used

to collect distance-sampling data during helicopter surveys to provide:

• instantaneous guidance
• accurate distances & transect length
• record of cluster size
• II. To evaluate the precision and accuracy of the

system in a: a) Controlled test b) Mock survey in the field c) Actual field surveys of northern bobwhite

Study Area

Control Test TAMUK, Kleberg County Field Tests Rolling Plains (RPQRR, Fisher County) South Texas Plains (King Ranch, Kenedy & Kleberg Counties)

Methods

Modified System for Electronic Surveys (MSES)

• 2 tablet personal computers (PCs)
• 1 Raven Cruizer lightbar and DGPS
• 2 MDL LaserAce 300 Laser rangefinders
• 2 custom 17-key keypads
• Custom ArcPAD Applet

Objective I. System Development

Methods

MSES

• System weight: 3.5 kg
• Assembly time: 5–10 min
• Data Processing: 1–2 hrs
• Original Cost: $16,445.00
• Current Cost: $ 6,399.00

Pilot Guidance Observer Guidance System Control

Methods

PRE-encounter

Methods

POST-encounter

Methods

Methods

Objective IIa. Accuracy & Precision (Control Test)

• A controlled test was conducted

from a building rooftop (10m) to test the accuracy of the system in the absence of rotor turbulence and human error

• Eight targets were painted on

the ground below the building at 10-m intervals extending to 130 m

• The laser rangefinder was fired

(10 /target) from a tripod

Methods

Objective IIb. Accuracy & Precision (Mock Survey)

• A mock field trial was conducted in an

agricultural field to test the accuracy of the system in the presence of rotor turbulence and human error

• Helicopter flights (n = 3 obs/flight) occurred

along a 3-km transect containing 16 targets

• Targets were distributed within 10-m intervals

from 10–70m

Used MSES during actual field surveys to estimate northern bobwhite during Dec-Jan 2008-09:

• 18 pastures in Norias and Santa Gertrudis

Divisions of King Ranch ranging in area from 1,705 to 3,327 ha. (42,242 ha.)

• 8-10 transects/pasture (80 total) were designed

using ArcMap 9.3 and were spaced 600 m apart

• Average survey effort was 40 km/pasture (total

effort = 707 km)

Methods

Objective IIc. Field Evaluation (Case Study)

Statistical Analyses

• Calculated Euclidean-distance

error

• No error would indicate

complete overlap

• Regressed estimated distance

versus actual distance using SAS 9.1

• No error would be

indicated by a slope = 1 and r2 = 1

Target Electronic estimation location Transect

Accuracy & Precision

Statistical Analyses

• Program DISTANCE 6.0 was

used to estimate bobwhite density

• Evaluated detection function

models

• Half-normal + cosine
• Uniform + cosine
• Evaluated density estimates
• 95% Confidence Intervals
• Coefficient of Variation (CV)

Density Estimation

Results

Table 1. Euclidean distance error (m) of MSES during a controlled test and field test, Kleberg County and Fisher County, 2007 Test Error SE Controlled Test 1.77 0.12 Field Test 5.55 1.33

Results

y = 1.001x - 1.514 r² = 0.99

20 40 60 80 100 120 140 20 40 60 80 100 120 Estimated distance (m)

Figure 1. Linear regression of estimated distance and actual distance using MSES during a controlled test, Kleberg County, 2007

Results

Actual distance (m)

y = 0.987x + 0.259 r² = 0.98

10 20 30 40 50 60 70 10 20 30 40 50 60 70 Estimated distance (m) Actual distance (m)

Figure 2. Linear regression of estimated distance and actual distance using MSES during a field test, Fisher County, 2007

Results

Actual distance (m)

Figure 3. Histogram of perpendicular distances

• btained during northern-bobwhite surveys (n = 313

detections), Kenedy & Kleberg Counties, 2008–2009

Results

Detection Probability Perpendicular distance in meters

Site n D 95% CI CV(D) Santa Gertrudis 86 0.13 0.09 0.19 18.23 East-Norias 72 0.12 0.08 0.19 20.03 West-Norias 155 0.11 0.09 0.14 12.11 Pooled 313 0.12 0.10 0.15 9.52 Table 2. Northern bobwhite density (no/ha) estimated using helicopter-based distance sampling and MSES, Kenedy & Kleberg Counties, 2008–2009

Results

Conclusions

• MSES was a practical electronic system that could

be used with distance sampling during helicopter surveys

• MSES provided accurate and precise distances in a

controlled setting and field evaluation

• MSES produced precise estimates of northern

bobwhite density

Conclusions

• Methods used for aerial surveys prior to the MSES:
• Angle of declination
• Visual estimation (Strut Marker System)
• Geographical position (Leave the Transect)
• These options can be biased, inaccurate, and tedious

Management Implications

• MSES addresses prior limitations of using distance

sampling with aerial surveys (i.e., navigating a centerline, accurate perpendicular distance estimation, geo-referenced encounter locations)

• MSES is a promising system for estimating

density of terrestrial species in which aerial-based distance sampling is appropriate

• MSES merges the reliability and benefits of

distance sampling with the efficiency of helicopter surveys to achieve accurate and precise results

Management Implications

Acknowledgments

• Fidel Hernandez, Steve DeMaso, Lenny Brennan, Fred

Bryant, Dale Rollins, Robert Perez, and Eric Redeker

• Joseph Sands and Trent Teinert
• King Ranch, Inc.
• Rolling Plains Quail Research Ranch
• Texas Parks and Wildlife Department
• Quail Associates
• Texas Quail Coalition
• Hamman, Kleberg, McNutt, and Smith Foundation