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Do line-transect surveys systematically underestimate primate densities in logged forests.

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Lmerican Journal of Primatology 13:l-9 (1987)
RESEARCH ARTICLES
Do Line-Transect Surveys Systematically Underestimate
Primate Densities in Logged Forests?
JOSEPH P. SKORUPA
Graduate Group in Ecology, Department of Anthropology, University of California, Davis,
California
The rate at which social groups of primates are encountered in disturbed
rain forest may be biased relative to undisturbed rain forest. A recently
reported case study revealed a 25%reduction in postlogging raw encounter
rates even though the true density of primates remained at the prelogging
level. If biased raw encounter rates are typical of disturbed forests, and if
they translate into equally biased line-transect density estimates, results of
many comparative surveys might prove misleading (ie, apparent declines of
primates in disturbed forest may not be real). Here a set of line-transect
density estimates from logged forest are tested for systematic bias by comparing them to range-mapping density estimates, and the response of a
Fourier series detectability function to several hypothetical patterns of bias
in raw encounter rates is illustrated. Tests of line-transect density estimates
from logged forest provide no evidence of systematic bias. The Fourier series
results suggest that biased raw encounter rates may often be ameliorated
by line-transect density estimators. Available evidence suggests that linetransect density estimates or similarly transformed encounter rates usually
provide reliable comparative results within the limits of a particular study’s
resolution. In contrast, conclusions drawn directly from comparative raw
encounter rates (without transforming them into density estimates) are
more prone to error.
Key words: census techniques, disturbed rain forest, bias, Hylobates lar, Presbytis
melalophos, Presbytis obscum, Colobus guereza
INTRODUCTION
As human influence on tropical rain forests continues to expand at increasing
rates (eg, Myers, 1984; Caufield, 19851, it has become apparent that assessments of
the capacity of primate species to survive in disturbed rain forest should be an
integral component of long-term conservation plans [Johns, 1985a; Skorupa, 1986;
Johns & Skorupa, in press]. Forests that are managed for sustainable selective
logging represent one category of disturbed forest that may hold particular promise
for primate conservation [eg, Johns, 1983al. To accurately assess the conservation
value of selectively logged forests, it is essential that strictly comparable estimates
of primate densities in unlogged and logged forest be obtainable.
Johns [1985b] recently reported that changes in primate behavior induced by
logging disturbance resulted in reduced detectability of primates in logged forest.
Received July 8,1986; revision accepted November 28,1986.
Address reprint requests to Joseph P. Skorupa, Dept. of Anthropology, Univ. of California, Davis, CA
95616.
0 1987 Alan R. Liss, Inc.
2 I Skorupa
Johns compared matched seasonal samples collected before and after logging at a
West Malaysian study site and found that encounter rates with primate social
groups declined by 25% after logging, even though primate densities were verified
to have remained unchanged. More importantly, Johns [1985b] argued that although
raw encounter rates after logging were being reduced by an increased propensity for
primates to “freeze,” by more time spent resting, and by a decreased propensity to
vocalize, line-transect estimates of effective census strip width should not be expected to change (eg, “detectability functions [of line-transect estimators] . . . cannot
consider differences in certain behavioral parameters between habitats. . . . the
cumulative effect is to reduce the proportion of groups (sighted) within the (same)
effective sample strip width”). Consequently, Johns implied that the biased (reduced)
raw encounter rates he observed could be expected to translate into equally biased
(reduced) line-transect density estimates. This implication is not trivial, because the
detectability functions that some line-transect density estimators are based on were
formulated to be responsive to changes in detectability [ie, altered encounter rates;
cf Burhnam et al, 19801. Although Johns [1985b] tenders no such claim, his paper
does leave one wondering whether line-transect surveys in logged forest might not
systematically underestimate primate densities-a possibility with significant implications for conservation planning (eg, are reported declines of primates in logged
rain forest merely sampling artifacts?).
In this study data from Johns’s West Malaysian study area were used t o test
whether line-transect estimates of primate density in logged forest show a consistent
negative bias (ie, underestimation). The results of these tests do not indicate a
consistent negative bias. “herefore, census data from Kibale Forest, Uganda were
used t o simulate a 25%reduction in encounter rates [as Johns, 1985b, reported]; it
was found that under reasonable selection criteria (for dropping 25%of the original
data points), a Fourier series line-transect density estimator yields closely comparable “before” and “after-logging” density estimates. Contrary to Johns’s inference,
estimates of effective strip width can be altered by biased encounter rates. This
result provides at least one set of conditions consistent with the failure to detect
systematic negative bias in density estimates for logged forest. There is, however, at
least one selection criterion that doesn’t ameliorate the effects of biased encounter
rates, and that is when bias is uniformly distributed with respect to distance from
the census line.
MATERIALS AND METHODS
Johns [1983c, 19861 spent two- to three-week observation periods surveying
primate densities in each of three logged hill dipterocarp forest plots in the Sungai
Tekam Forestry Concession, Pahang, West Malaysia. Primate densities were estimated by two methods: (1)mapping the approximate home ranges of primate social
groups and (2) using a Fourier series line-transect estimator [Johns,1983bl. Range
mapping is generally considered to provide the most accurate approximation of true
density for rain forest primates [eg, Struhsaker 1975; National Research Council,
19811, and therefore John’s line-transect density estimates for logged forest can be
tested for systematic bias by comparing them to the range-mapping estimates of
true density. This test does not depend on the absolute accuracy of the rangemapping estimates, but it does assume that the range-mapping estimates consistently will be closer to the true density than will the line-transect estimates. This
assumption is particularly likely to be valid when line-transect estimates are based
on minimal sampling effort, as is the case here [cf Johns,1983b].
To test for systematic bias the frequencies of apparent overestimation (positive
bias) and underestimation (negative bias) were tested against the null hypothesis of
random error (ie, equal frequencies of positive and negative bias) using the binomial
Problems of Censusing Primates / 3
test [Siegel, 19561. Second, differences in the average magnitude of apparent positive
vs negative bias were assessed by using the Mann-Whitney U test [Siegel, 19561.
To examine the response (or nonresponse) of a Fourier series detectability function to biased (reduced)encounter rates, a set of census data was used that had been
generated from sightings of red colobus monkeys (Colobus badius) in unlogged
portions of Kibale Forest, Uganda, during a recent two-year field study [Skorupa,
19861. First, a Fourier series density estimate was calculated for the baseline data
set to serve as a hypothetical “before-logging” standard. Then, a 25% reduction in
encounter rates was simulated by removing 25% of the baseline data points according to the selection criteria described below.
Case I
It is assumed here that all primates within 10 m of the census line will flush
and therefore be detected regardless of whether the observer is in logged or unlogged
forest. It is also assumed that primates sufficiently removed from the census line
will not react (ie, fi-eeze,hide, etc) to the observer in either type of forest. Therefore,
the 25% reduction in data points is achieved by assuming that any point between 10
and 60 m from the census line has an equal probability of being deleted. The specific
probability is set by the number of points to be deleted in order to reduce the entire
data set by 25%. In this case there are 74 points between 10 and 60 m and 25% of
the total sample (n=112)equals 28; therefore the probability of deletion equals 281
74 (0.378). This approach explicitly solves for the theoretically expected mean outcome of the process whereby intermediate points are randomly deleted.
Case I1
Here it is assumed that all primates within 10 m of the census line will flush
but that behaviors such as resting or vocalizing will otherwise be unaltered at all
other distances. Therefore all data points beyond 10 m from the census line are
assumed to have equal probability of deletion.
Case I11
Here the 25% reduction in data points is achieved by assuming that all points
have an equal probability (0.25)of deletion.
For each of the above hypothetical patterns of bias, Fourier series density
estimates (ie, theoretically expected mean values) were calculated for comparison
against the previously established nonbiased standard.
RESULTS
Tests for Systematic Bias
Data for the three most common primate species at Sungai Tekam- Hylobates
lac Presbytis melalophlos, and I! obscura-provide nine pairwise comparisons of
range-mapping and line-transect density estimates for logged forest (Table I). Linetransect density estimates appear t o overestimate true density in four cases while
underestimating true density in four cases (with one case showing no apparent bias);
a 1:l ratio of overestimates to underestimates is exactly the ratio expected from
random error: Therefore the null hypothesis cannot be rejected (binomial test, N =8,
x =4, P> .60, one-tailed probability).
The magnitudes of overestimates range from +8% to +50% while the magnitudes of underestimates range from -7% to -46%. There is no significant difference
in the average magnitudes of apparent positive and negative bias (nl=4, n2=4,
U=7, P> .40, one-tailed probability). Thus, both the frequency and the magnitude of
negative bias exhibited by line-transed density estimates for logged forest seem
4 I Skorupa
TABLE I. Comparison of Line-Transect Estimates of Primate Densities (Groupsh') to
Range-Mapping Estimates in 3 Logged Study Areas*
Species
Hylobates lar
Range-mapping estimates
Line-transect estimates
Relative bias
Presbytis melalophos
Range-mapping estimates
Line-transect estimates
Relative bias
Presbytis obscura
Range-mapping estimates
Line-transect estimates
Relative bias
1-2-year-old
logged
3-4-year-old
logged
5-6-year-old
logged
2.75
1.76
- 36%
2.50
3.76
+50%
2.75
1.51
2.26
3.01
+33%
4.52
4.89
+8%
3.39
3.39
0%
1.90
1.76
- 7%
2.37
3.02
+27%
1.42
0.76
-46%
-45%
*
*Line-transect estimates of primate densities were calculated by using the Fourier series estimator
presented by Burnham et a1 [1980]. Data i n this table are from Johns [1983b: Table 4.19; see also Johns,
1986: 2091.
consistent with the hypothesis that, just as expected in unlogged forest, error is
random (ie, nonbiased).
Fourier Series Detectability Function Responses
The baseline data set (Table II) results in a Fourier estimate of effective stri
width (ESW) that equals 100.0 m; this leads to a density estimate of 10.4 groupskm
(Table 110. The absolute accuracy of the above density estimate is not an issue that
is relevant to this paper: This paper is concerned with the accuracy of betweenhabitat comparisons.
Under the assumptions of case I (see Materials and Methods), the theoretically
expected Fourier estimate of ESW is 79 m, which largely compensates for the
simulated 25% negative bias (reduction) in encounters, leading to a n expected
density estimate of 9.8 groups/km2 (Tables 11,111).Thus, while the bias in encounter
rates is 25%, the bias in the expected line-transect density estimate is only 5%. In
case I1 the expected line-transect density estimate is again substantially less biased
than the raw encounter rate that the density estimate is based on. The completely
random selection criterion of case I11 results in an unaltered expected estimate of
strip width (ESW) and therefore a 25% underestimate of true density (Tables 11,
111).
!l
DISCUSSION
Failure to detect systematic negative bias in Johns' [1983b, 19861 line-transect
density estimates for primates inhabiting logged forest is consistent with several
possible explanations. It could indicate that the assumptions of the hypothetical
cases I and I1 are generally valid in logged forest, and therefore that biased raw
encounter rates are ameliorated by line-transect density estimators to a point that
the bias is insignificant relative to the coarse resolution (ie, high random sampling
error) associated with most primate surveys in rain forest. Even with intensive
sampling, resolution only approaches +25% for common primate species and is more
likely to be around +45% for species of typical rarity [Skorupa, 19861. Alternatively,
Problems of Censusing Primates / 5
the results may simply indicate that the effect Johns observed is temporary, because
Johns [1985b] recorded biased encounter rates shortly following logging, and the
data tested here come from older logged forest in which encounter rates could
already have returned to normal. As a third alternative, the biased encounter rates
in Johns’s main study plot may have been an artifact of having habituated the study
groups of primates prior to logging (with the groups regaining normal wariness after
the trauma of logging). For surveys of unhabituated primates this plausible source
of bias would not be a concern (A.D. Johns, personal communication). Finally, the
results may only reflect the low power of tests based on small sample sizes.
The “low-power” alternative seems unlikely since there is not the slightest hint
of deviation from expectations based on random error. Furthermore, another data
point that can be added from Kibale Forest also shows no obvious negative bias.
Despite the fact that black-and-white colobus monkeys (Colobus guereza) are extremely fearful of humans in logged forest, the line-transect density estimate of 5.9
groups/km2 fits well with the range-mapping estimate of 5.0-6.2 groups/km2 (Skorupa, unpublished data). Unfortunately, none of the other Kibale primates concentrate their activities within small, distinct core areas to the extent that C. guereza
does [Oates, 1974; cf. Struhsaker Leland, 19791. Therefore, obtaining range-mapping
density estimates for unmarked social groups of the other species occupying logged
forest is problematic.
Regardless of which explanation best applies in this particular case, the hypothetical test cases indicate that biased raw encounter rates need not necessarily lead
to an expectation of equally biased line-transect density estimates (cases I, II,Table
111).Most methods for transforming raw encounter rates into density estimates (ie,
line-transect density estimators)assume that there is some region around the census
line in which the detection probability equals 1.0 [eg, Burnham et al, 19801. Furthermore, most methods assume that a plot of detection frequencies (as a function of
distance from the census line) should show a shoulder roughly corresponding to the
1.0 detection probability region [eg, Burnham et al, 19801.
Ultimately, it is the height of the shoulder that determinesthe density estimate.
Thus, for the entire class of what are referred to here as “shoulder density estimators,” biased raw encounter rates will only influence density estimates to the
extent that they alter the estimated height of the frequency distribution’sshoulder.
The estimated height of the shoulder is determined primarily by the frequency of
encounters very close to the census line (ie, inside the 1.0 detection probability
region). This explains why the patterns of bias in cases I and I1 had so little affect
on density estimates. The hypothesized “flush response” of primates near the census
line insured that bias within the shoulder region was lower than the general level
of bias.
Estimates of ESW, by contrast, are measuring how far the shoulder can be
extended by compressing the tail of the detection frequency curve back toward the
origin and to the height of the shoulder. Clearly, estimates of ESW should decline
as points are preferentially deleted from the tail (ie, the region beyond the shoulder).
In fact, if all deleted points came from the tail, the new ESW estimate should be
expected to exactly compensate for the deletions (bias) and lead to unbiased density
estimates. Depending on the specific density estimator, the width of the shoulder
region, the width of the flush response region, and the magnitude of encounter rate
bias, line-transect density estimates can range from showing no bias to being nearly
as biased as the underlying encounter rates. However, as long as any flush region
exists, density estimators will be less biased than raw encounter rates.
Apparently, Johns [1985133 expected biased raw encounter rates to translate into
equally biased line-transect density estimates because he was assuming that the
6 / Skorupa
TABLE 11. Distributions of Perpendicular Sighting Distances Used to Calculate the
Fourier Series Density Estimate Statistics Presented in Table III*
No. sightings
Perpendicular
distance (m)
0
1
2
3
5
6
7
8
10
11
12
13
15
16
17
18
19
20
21
22
23
25
26
27
28
30
31
32
33
35
36
37
38
40
41
42
43
45
46
47
49
50
51
54
56
57
58
60
61
62
64
Baseline
data set
3
4
4
2
2
1
1
3
3
2
2
3
3
2
2
4
1
4
3
2
3
3
3
2
2
2
2
1
2
2
1
1
1
2
1
1
3
2
2
1
1
1
1
1
2
1
1
1
1
1
1
Case I
Case I1
3
4
4
2
2
1
1
3
3
1.243
1.243
1.865
1.865
1.243
1.243
2.486
0.622
2.486
1.865
1.243
1.865
1.865
1.865
1.243
1.243
1.243
1.243
0.622
1.243
1.243
0.622
0.622
0.622
1.243
0.622
0.622
1.865
1.243
1.243
0.622
0.622
0.622
0.622
0.622
1.243
0.622
0.622
1
1
1
1
3
4
4
2
2
1
1
3
3
1.371
1.371
2.056
2.056
1.371
1.371
2.741
0.685
2.741
2.056
1.371
2.056
2.056
2.056
1.371
1.371
1.371
1.371
0.685
1.371
1.371
0.685
0.685
0.685
1.371
0.685
0.685
2.056
1.371
1.371
0.685
0.685
0.685
0.685
0.685
1.371
0.685
0.685
0.685
0.685
0.685
0.685
Case I11
2.25
3
3
1.50
1.50
0.75
0.75
2.25
2.25
1.50
1.50
2.25
2.25
1.50
1.50
3
0.75
3
2.25
1.50
2.25
2.25
2.25
1.50
1.50
1.50
1.50
0.75
1.50
1.50
0.75
0.75
0.75
1.50
0.75
0.75
2.25
1.50
1.50
0.75
0.75
0.75
0.75
0.75
1.50
0.75
0.75
0.75
0.75
0.75
0.75
Problems of Censusing Primates / 7
TABLE 11. Distributions of Perpendicular Sighting Distances Used to Calculate the
Fourier Series Densitv Estimate Statistics Presented in Table III* (continued)
No. sightings
Perpendicular
distance (m)
66
68
69
71
75
83
84
89
90
Samde size
Baseline
data set
2
1
1
1
1
1
2
1
1
112
Case I
Case I1
Case III
2
1
1
1
1
1
2
1
1
84
1.371
0.685
0.685
0.685
0.685
0.685
1.371
0.685
0.685
84
1.50
0.75
0.75
0.75
0.75
0.75
1.50
0.75
0.75
84
*The baseline data are generated from actual observations of red colobus monkeys in unlogged portions
of Kibale Forest. See Materials and Methods for a discussion of how the other distributions were derived.
TABLE 111. Parameter Values for Fourier Series Line-Transect Density Estimates
Correspondingto Several Hypothetical Patterns of Bias in Raw Encounter Rates+
Parameter*
n
W" (m)
1/W*
2/nW*
1/W*(2/n+1P2
a1
a2
a3
a4
lla
ESW (m)
L (km)
Density (groups/km2)
% bias
Baseline
112
90
0.01111
0.00020
0.00148
0.00890
0.00115
-
0.02001
100.0
108.0
10.4
NIA
Case
I1
I
84
90
0.01111
0.00026
0.00170
0.00848
0.00348
0.00222
0.00078
0.02529
79.0
108.0
9.8
-5.2b
84
89.08
0.01122
0.00027
0.00172
0.00996
0.00285
0.00156
-
0.02403
83.2
108.0
9.3
-9.9b
III
84
89.35
0.01119
0.00027
0.00172
0.00884
0.00115
-
0.02003
99.9"
108.0
7.8
-24.gb
+See Burnham et a1 [1980]for definition of parameters and for the details of calculating Fourier series
line-transect density estimates. Parameters calculated from data presented in Table II. Effective strip
width (ESW) and density values are rounded to the nearest tenth.
aThe difference between this value and the baseline value is due to rounding error.
bBias calculated from precise density estimates, not from rounded estimates.
*W*= maximum value of perpendicular distance; n = sample size; ak = kth term of Fourier series; a =
l/Z ESW; L = census strip length.
bias in detectability would be uniformly distributed with respect to distance from
the census line [as in case III; Johns, personal communication]. The "flush" assumption of test cases I and I1 probably leads to more realistic and generally applicable
expectations than does case III. Johns [1985b] reported that even freezing (hiding)
primates flushed when approached closely, and there is no reason to doubt that
resting and nonvocalizing primates would behave similarly. Accordingly, patterns
of bias should tend to be concentrated away from the census line.
8 I Skorupa
Johns [1985b] rightfully cautions that comparative census data from undisturbed and disturbed forest must be interpreted very carefully; however, any doubts
about the validity of reported declines of primates in disturbed rain forest that may
have been engendered by his paper should be tempered by the knowledge that
biased raw encounter rates in disturbed forest do not automatically translate into
equally biased line-transect density estimates. If Johns’ results don’t represent a
temporary effect or an artifact of habituation, then the full weight of his cautionary
advice does apply to those who attempt to draw conclusions directly from comparative raw encounter rates.
In summary, between-habitat variation of primate densities is best assessed via
intensive long-term studies that use a combination of censusing techniques as crosschecks on results [eg, Johns 1983b], but, when short-term surveys are all that can
be afforded, available evidence (n= 10) suggests that line-transect density estimates
or similarly transformed encounter rates provide reasonably reliable comparative
results within typically coarse limits of resolution.
CONCLUSIONS
1. The effect that biased raw encounter rates with primate social groups in
disturbed rain forest will have on line-transect density estimates depends on the
distribution of bias relative to distance from the census line and on the type of
density estimator being employed. When bias is unequally distributed because of a
“flush response” (ie, when normally detectable groups that go undetected due to
biasing behaviors are concentrated away from the census line), and when a “shoulder density estimator” is being employed, line-transect density estimates ameliorate
the biased raw encounter rates.
2. Although it is not clear that biased encounter rates are typical of censuses in
disturbed rain forest, if they are, the sparse available data (n= 10) appear consistent
with the expectations derived from “flush response” test cases and inconsistent with
the assumption that bias is uniformly random relative to distance from the census
line.
3. Between-habitat variation of primate densities is best assessed via intensive
long-term studies that utilize a combination of censusing techniques as cross-checks,
but, when short-term surveys are all that can be afforded, line-transect density
estimates or similarly transformed encounter rates usually provide reasonably reliable comparative results within coarse limits of resolution.
4.Conclusions about habitat suitability that are drawn directly from comparative raw encounter rates are more prone to error than similar conclusions based on
density estimates.
ACKNOWLEDGMENTS
The baseline data for the test cases were collected during a field study supported
by a grant from World Wildlife Fund-US (project No. 1969) and by the New York
Zoological Society (both through Dr. Thomas T. Struhsaker). The author is also
grateful to the Ugandan National Research Council, Uganda Department of Forests,
and the Department of Zoology, Makerere University, for local sponsorship. This
paper benefited from comments on earlier drafts offered by L. Berenstain, T.M.
Butynski, A.D. Johns, J.F. Oates, and B.O. Manullang. I am particularly indebted
to Dr. Andrew D. Johns for freely sharing numerous insights relevant to this paper.
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RUPPELL) IN EAST AFRICA. PhD Dissertation, University of London, England, 1974.
Siegel, S. NONPARAMETRIC STATISTICS.
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Skorupa, J.P. Responses of rainforest primates to selective logging in Kibale Forest,
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