Estimating Abundance of African Wildlife: An Aid to Adaptive Management

297 Pages·2001·8.36 MB·English

by  Hugo Jachmann (auth.)

ESTIMATING ABUNDANCE OF
AFRICAN WILDLIFE
An Aid to Adaptive Management
ESTIMATING ABUNDANCE OF
AFRICAN WILDLIFE
An Aid to Adaptive Management
by
Hugo Jachmaon
Environmental Council of Zambia
Wildlife Resource Monitoring Unit
Lusaka, Zambia
SPRINGER SCIENCE+BUSINESS MEDIA, LLC
Library of Congress Cataloging-in-Publication Data
Jachmann, Hugo.
Estimating abundance of African Wildlife : an aid to adaptive management
/ by Hugo Jachmann
p.cm.
IncIudes bibliographical references (p. ).
ISBN 978-1-4613-5525-0 ISBN 978-1-4615-1381-0 (eBook)
DOI 10.1007/978-1-4615-1381-0
1. WiIdlife management-Africa. 2. Animal populations-Africa-Measurement. 1.
TitIe.
SK571 1J33 2001
639.9'3'096--dc21
00-67107
Copyright © 2001 Springer Science+Business Media New York
Origina11y published by Kluwer Academic Publishers in 2001
Softcover reprint of the hardcover 1s t edition 2001
AII rights reserved. No part of this publication may be reproduced, stored in a
retrieval system or transmitted in any form or by any means, mechanical, photo
copying, recording, or otherwise, without the prior written permission of the
publisher, Kluwer Academic Publishers, 101 Philip Drive, Assinippi Park, Norwell,
Massachusetts 02061
Printed on acid-free paper.
TABLE OF CONTENTS
List of Figures ix
List of Tables xvii
List of Boxes xx
Foreword xxi
Preface xxiii
PART I: INTRODUCTION 1
Chapter 1: Introduction to Estimating Wildlife
Abundance 3
1.1 A Brief Overview of Techniques 4
1.2 Selection of a Technique 10
Chapter 2: Interpretation of Population Estimates 13
2.1 Population Estimate 13
2.2 Variance, Standard Error and Confidence Limits 15
2.3 Bias and Accuracy 21
2.4 Stratification 22
2.5 Random Sampling versus Systematic Sampling 24
2.6 Sample Count Theory 25
2.7 Categorisation of Data 32
PART II: DIRECT COUNTS 37
PART IIa: GROUND COUNT TECHNIQUES 37
Chapter 3: Ground Count Techniques; An Introduction 39
3.1 Total Ground Counts 39
3.1.1 Total Ground Counts of Hippos and Crocodiles 40
3.1.2 Total Ground Counts Using Individual Recognition 41
3.2 Sample Ground Counts 42
Chapter 4: Line Transect Counts 45
4.1 Theoretical Background 45
4.1.1 Conceptual Background 47
4.1.2 Different Shapes of the Detection Function 48
vi
4.2 Software to Analyse Line Transect Data 53
4.3 Survey Design 54
4.3.1 Stratification of the Survey Area 55
4.4 Field Procedures 57
4.5 Precision and Accuracy of Line Transect Results 62
4.6 Data Analysis with Program Distance 65
4.6.1 Analysing the Lupande Warthog Data with
Program Distance 67
4.6.2 Analysing the Lupande Elephant Data with
Program Distance 73
4.7 The Mean Perpendicular Distance Method 77
4.7.1 Data Analysis using the Mean Perpendicular
Distance Method 78
4.7.2 Analysing the Lupande Warthog Data with
the MPD Method 82
4.7.3 Analysing the Lupande Elephant Data with
the MPD Method 83
4.8 Vehicle Counts 86
4.8.1 Road Counts 86
4.9 Comparative Costs of Sample Counts on the Ground 91
PART lIb: AERIAL COUNT TECHNIQUES 93
Chapter 5: Aerial Total Counts 95
5.1 Sources of Bias in Aerial Total Counts 96
5.2 Census Design 98
5.3 Aerial Total Counts of Hippos and Crocodiles 102
5.4 The Double Count Method 103
5.5 Field Example of Observer Bias Experiments 107
5.6 Field Example of Visibility Bias and Sighting Probability
Bias Experiments 111
Chapter 6: Aerial Sample Counts 113
6.1 Choice of Sample Unit 113
6.1.1 Blocks 113
6.1.2 Transects 116
6.2 Survey Design 117
6.3 Positioning the Streamers 121
6.4 Analysis 123
6.4.1 Jolly's Method 1 for Equal Sized Sample Units 124
vii
6.4.2 Jolly's Method 2 for Unequal Sized Sample Units 125
6.5 Precision in Relation to Density and Sampling Intensity 126
6.5.1 Density and Precision 127
6.5.2 Sample Size and Precision 130
6.6 Field Example of an Aerial Survey 131
6.6.1 Analysis of a Stratified Design 136
6.7 Comparative Costs of Aerial Counts 137
PART III: INDIRECT COUNTS 139
PART IlIa: SPECIAL AND INDEX TECHNIQUES 139
Chapter 7: Special Techniques 141
7.1 The Mark/Recapture Method 141
7.2 Field Example 142
7.3 Estimating Abundance through Radio-Tracking 145
7.4 Field Example 151
7.5 Estimating Abundance using DNA Analysis 153
Chapter 8: Index Counts 155
8.1 Field Example of a Patrol Index Count 162
8.2 Monitoring of Sex and Age Ratios, and Juvenile Mortality 165
PART I1Ib: INDICATOR TECHNIQUES 167
Chapter 9: Dropping Counts 169
9.1 Theoretical Concept 171
9.2 Dropping Counts in Woodland Savanna 173
9.2.1 Defecation Rate 173
9.2.2 Decay Rate 175
9.2.3 Analysis 176
9.3 Dropping Counts in Forests 178
9.3.1 Defecation Rate 179
9.3.2 Decay Rate and Analysis 179
9.4 Dropping Counts on Roads 180
9.4.1 Validity ofthe Method 181
9.4.2 Sample Design and Analysis 183
9.4.3 Field Examples 183
9.5 Sources of Error 188
Vlll
9.6 Faecal Counts of Other Species 189
Chapter 10: Footprint Measurements 191
10.1 Concept, Sample Design and Analysis 191
10.2 Field Example 192
10.3 Costs of Indicator Counts 194
PART IV: ANALYSING DISTRIBUTION DATA
AND POPULATION TRENDS 195
Chapter 11: Analysing Animal Distribution, Movements
and Population Trends 197
11.1 Analysis of Animal Distribution and Movements 199
11.2 Field Example 201
11.3 Analysing Population Trends 205
11.4 Field Example 209
PART V: SYNTHESIS 213
Chapter 12: Deciding on a Technique 215
12.1 The Reasons for Counting 216
12.2 The Animal Species 217
12.3 The Habitat Type 220
12.4 The Budget 221
12.5 Making a Decision 223
References 231
APPENDICES 239
Appendix I: A BRIEF MANUAL TO PROGRAM
'AERIAL' 241
Appendix II: PROGRAM 'AERIAL' IN
QUICKBASIC 4.5 245
Index 277
LIST OF FIGURES
Fig. 1.1 Techniques for estimating wildlife abundance.
Fig. 1.2 Techniques for counting wildlife on the ground (S=Stratified
and N=Non-Stratified).
Fig. 1.3 Techniques for counting wildlife from the air (S=Stratified
and N=Non-Stratified).
Fig. 1.4 Indirect techniques for counting wildlife.
Fig. 2.1 Diagram of hypothetical example of a small survey area with
10 elephants. Five transects placed at regular intervals to
cover the entire survey area with a 5% sampling intensity
(left), and 5 transects placed at regular intervals in the western
half of the survey area with a 10% sampling intensity (right),
both return one elephant sighting (black dot). The estimate for
the 10% sample gives the true number, whereas the estimate
for the 5% sample is twice the true number.
Fig. 2.2 A simple illustration of variance. In the first sample (light
grey) the variance is low with the estimates clustered around
the mean. In the second sample (dark grey) the variance is
high, with the estimates widely spread around the mean. The
mean is the same in each case.
Fig. 2.3 Frequency distribution of 100 observations. About 68% of the
data lie within the interval mean - SE to mean + SE, about
95% within the interval mean -2SE to mean + 2SE, and most
data within the interval mean -3SE to mean + 3SE.
Fig. 2.4 Three examples of the results of an aerial survey carried out
in the South Luangwa National Park in 1998. The Poisson
distributions for elephants, zebra and hartebeest observed in
43 parallel transects.
Fig. 2.5 Diagram of hypothetical survey area of 20 km x 5 km,
showing 15 sample units (indicating line of travel) out of a
total population of 200 sample units. The sample area is the
percentage of the survey area covered by the sample units or
x
strips. Duikers counted are indicated with white stars, and
elephants with black stars.
Fig. 3.1 Hippos are easy to count from the river bank.
Fig. 3.2 Total counts through individual recognition use tusk size and
shape, in combination with ear patterns in elephants, facial
characteristics, size and colouring of mane in lions, and
different stripe patterns in zebras.
Fig. 4.1 Diagrammatic representation of a line-transect sample, where
the centre-line is the transect line, with 13 observations of the
target species, indicated by perpendicular distances. The thick
lines show the data that should be collected for a single
observation, where: 0 is the position of the observer, A is the
position of the group of animals, S is the sighting distance and
G is the sighting angle (P is the perpendicular distance).
Fig. 4.2 Hypothetical example of a frequency diagram of
perpendicular distance data for groups of animals observed
(#). The detection function g(x) is indicated.
Fig. 4.3 Hypothetical example of a frequency diagram of
perpendicular distance data from an underlying negative
exponential or reverse logistic detection function g(x).
Fig. 4.4 Hypothetical example of a frequency diagram of
perpendicular distance data from an underlying uniform
detection function g(x).
Fig. 4.5 Hypothetical example of a frequency diagram of
perpendicular distance data from an underlying half normal
detection function g(x).
Fig. 4.6 Frequency diagrams of perpendicular distance data for 6 large
ungulate species, collected in the Lupande GMA in 1996 and
1997. a) Grysbok, b) Common Duiker, c)Warthog, d) Impala,
e) Bushbuck, and f) Elephant. With the exception of
bushbuck, 1996 and 1997 data are combined. Outliers were
omitted.
Xl
Fig. 4.7 Diagram of broad landscape units found in the Lower
Lupande Game Management Area in the central Luangwa
Valley, Zambia.
Fig. 4.8 Diagrammatic representation of the measurements required to
calculate the perpendicular distance of an animal from the
transect line.
Fig. 4.9 Diagram of a block transect starting at a perimeter road and
consisting of 3 segments. Segment la is away from the road,
Ib is across and lc is back to the road.
Fig. 4.10 Diagram illustrating magnetic variation (V).
Fig. 4.11 Standard error in relation to population estimate for 10 large
ungulate species counted during six line-transect surveys in
the Lupande GMA in 1996 and 1997.
Fig.4.12 The precision of the population estimate (% Standard Error)
in relation to the density of 10 large ungulate species, counted
during six line-transect surveys in the Lupande GMA in 1996
and 1997.
Fig. 4.13 The precision of the population estimate (% Standard Error)
in relation to the number of sightings of 10 large ungulate
species, counted during six line-transect surveys in the
Lupande GMA in 1996 and 1997.
Fig. 4.14 The perpendicular distance data for warthog, including two
outliers, obtained through a series of 6 line-transect surveys in
the Lupande GMA in 1996 and 1997. The data probably come
from an underlying negative exponential detection function.
Fig. 4.15 The perpendicular distance data for warthog, omitting two
outliers, obtained through a series of 6 line-transect surveys in
the Lupande GMA in 1996 and 1997. The data probably come
from an underlying negative exponential detection function.
Fig. 4.16 The perpendicular distance data for elephants, collected in the
Lupande GMA in 1997, probably come from an underlying
negative exponential detection function.