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Rapid assessment of orangutan density
Biological Conservation 114 (2003) 103–113 www.elsevier.com/locate/biocon
Rapid assessment of orangutan density R. Buija, I. Singletonb, E. Krakauerc, C.P. van Schaikc,* a Behavioural Ecology, Utrecht University, PO Box 80086, 3508 TB, Utrecht, The Netherlands Sumatran Orang-utan Conservation Programme, PO Box 1472, Medan 20001, Sumatera Utara, Indonesia c Department of Biological Anthropology and Anatomy, Duke University, PO Box 90383, 3705 B Erwin Road, Durham NC 27708-0383, USA b
Received 12 January 2002; received in revised form 25 September 2002; accepted 13 December 2002
Abstract In order to design effective conservation measures for the orangutan, accurate information on their distribution and densities are needed. Nest counts along line transects allow for fairly accurate assessment of orangutan density. However, large sample sizes are needed to obtain accurate estimations of the decay rate of nests, preventing a rapid estimate. Moreover, due to the structure of tropical forests, nest numbers above and near the trail are underestimated. In this study, a rapid assessment method for the estimation of orangutan density is developed, allowing for the estimation of density with a single survey. Procedures are provided for estimating a correction factor which adjusts for underestimating nest numbers near the transect trail. Furthermore, we provide a shortcut to rapidly assess nest decay rate without the need for consecutive surveys. Finally, recommendations are made for determining orangutan density using two different techniques. These techniques incorporate correction factors and a shortcut to accurately assess orangutan density with only one or two visits to an area. # 2003 Elsevier Ltd. All rights reserved. Keywords: Orangutan; Pongo sp.; Nest census; Rapid density assessment; Shortcut nest decay rate estimate
1. Introduction The orangutan (Pongo sp.) occurs in increasingly small numbers on the islands of Borneo and Sumatra (e.g. Rijksen and Meijaard, 1999). In order to design effective conservation measures for the orangutan, accurate information on their distribution and densities is needed. By extrapolating known densities in different habitat types, estimates of orangutan numbers over wider areas can be obtained, provided other factors that may influence density are constant. Therefore, survey efforts have focused on estimating orangutan densities and identifying the ecological factors involved in the determination of these densities. Several techniques are available for estimating orangutan densities (van Schaik et al., 1995; Singleton, 2000). Ideally, a method should be used that requires only a single survey and produces reasonably accurate estimates. The line transect method employed by Brockelman and Ali (1987) is a relatively straightfor* Corresponding author. Tel.: +1-919-660-7390; fax: +1-919-6607348. E-mail address: [email protected] (C.P. van Schaik). 0006-3207/03/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0006-3207(03)00015-6
ward and quick technique that has been used to estimate forest primate densities. It relies on observing sufficient numbers of animals along a trail of known length. However, densities of secretive animals like orangutans may well be highly underestimated in this way, especially where they are hunted. Moreover, in areas with low orangutan densities, it would be very time-consuming to attempt to estimate orangutan densities by direct sightings, and reliable estimates would require very large sample sizes. Hence, techniques have been developed that rely on the sign left by animals, namely their nests and dung (Barnes and Jensen, 1987; Barnes, 1993; van Schaik et al., 1995). Rather than relying on encounters with the animals themselves, these techniques record the nests in conjunction with several parameters (e.g. decay rate, construction rate, proportion of nest builders in the population) that convert the estimated density from that of the sign to that of the animals themselves. Orangutan densities can be estimated from nest counts using the following equation: d ¼ ðCf NÞ=ðL 2w p r tÞ
ð1Þ
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in which: d=orangutan density (animals/km2); Cf=correction factor for N; N=number of nests observed along the transect; L=length of the transect covered (km); w=estimated width of the strip of habitat actually censused; p=proportion of nest builders in the population; r=rate at which nests are produced (n/day/ individual); and t=decay rate of nests: time during which a nest remains visible (in days). Density estimates based on nest counts gave reasonably accurate results where validated, at least for Sumatra (van Schaik et al., 1995), although the estimation of so many parameters inevitably leads to poor reliability. However, more recently, both minor and major problems have arisen in the use of this technique (Singleton, 2000). The aim of this paper is to examine the existing technique and its weaknesses, and to develop a reasonably accurate method to quickly determine orangutan density. The general procedures for the technique involved are provided at the end of the discussion. The proposed rapid assessment technique may be rather crude, but will allow for the estimation of orangutan density over large areas in relatively little time. Considering the extraordinary speed at which forests are being destroyed throughout the orangutan’s distribution on both islands, and the lack of density estimates for most parts of their range, such a technique is desperately needed for the development of effective conservation strategies.
2. Problems with the current method Several problems may occur in estimating orangutan density with the current method, most importantly the underestimation of nest numbers above or near the transect line, and the inaccurate estimation of t. Here, we explore these problems further. 2.1. Estimating the proportion of nest builders (p) and rate of nest production (r) The minor problems concern the estimation of the proportion of nest builders in the population (p) and the rate at which nests are produced (r). The value of these parameters may vary between populations, since they depend on the age and sex composition of populations. MacKinnon (1974) estimated that 14% of individuals in Sumatran and Bornean orangutan populations were young infants that do not construct nests, while more recent results (van Schaik et al., 1995; Singleton, 2000) suggested 10% young infants. Despite the fact that exact population composition is unknown in most sites, estimates on p do not vary greatly, and resulting errors in density estimates are likely to be low. Nest building rates may also vary due to differences in population composition. At Ketambe, adult females with infants produce
on average two nests per day, while adult males produce only 1.2 nests per day (Mitra Setia in van Schaik et al., 1995). Accounts of nest building indicate that an individual produces 1.7 nests per day, averaged across age/sex classes in a population in Sumatra (van Schaik et al., 1995). The value for r may also vary due to the re-use and rebuilding of old nests (Singleton, 2000). However, of a total of 1808 nests observed from March 1997 to June 1999 at Suaq, only 6.8% were rebuilt at some point during their lifespan, indicating that this is probably a rare event. Finally, r may differ significantly between localities in Sumatra and parts of Kalimantan. However, within Sumatra, the estimates of r vary only from 1.6 to 1.8 (e.g. van Schaik et al., 1995; Singleton, 2000), similar to the variation in p. Hence, again, the resulting error in the density estimation is similarly small. 2.2. Underestimating nest numbers near the transect trail More importantly, major problems have arisen in the estimation of other crucial parameters that have to be determined on every separate transect, especially the effective strip width of the transect (w) and the decay rate of the nests (t). These problems may yield inaccurate density estimates. To calculate the strip width (w) sampled along a transect, the perpendicular or shortest distance between the transect line and each nest is recorded (e.g. van Schaik et al., 1995). Missing nests above the trail may have two important consequences. First, strip width (w) may be overestimated, meaning that orangutan densities are underestimated. Second, density may be underestimated since total nest counts are biased. Even with the correct observer behaviour, nests are likely to be missed due to the multi-layered canopy of tropical forests. Nests are most likely to be seen at a certain distance from the trail, where the line of sight is least obstructed by foliage, and nest numbers are under-recorded both up to and beyond this distance. Consequently, density is underestimated regardless of the way w is estimated. This is particularly problematic if transects are surveyed only once, since repeat surveys in the opposite direction always yield new nests, even above the trail. Old nests in particular, which are more difficult to spot compared to new nests, are likely to be missed on first surveys, resulting in the under-recording of total nest numbers (R. Buij, unpublished data). Buckland et al. (1993) give several flexible and robust models for estimating w even when not all nests are discovered above and near the trail, but it is important to estimate the fraction f of true nest numbers N that goes undetected on a single survey to prevent underestimation of density. The correction factor needed to correct for N is Cf=1/(1f). In order to assess the extent of the underestimation of nest numbers, i.e. to calculate Cf, orangutan density can
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be estimated with the so-called new nest density technique (van Schaik et al., 1995). This technique overcomes the determination of decay rate (t), since it estimates orangutan density using only those nests built between two consecutive surveys. Assuming that the estimation of the other nest parameters is fairly accurate, Cf can be calculated by comparing new nest density estimates with true density in areas where this is known. Because of the long-term presence of researchers at Ketambe and Suaq Balimbing, true densities in both areas have been determined fairly accurately and can therefore be used for this purpose.
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and soil pH. It was suggested that wood is denser and thus stronger on poor soils with low pH (van Schaik and Mirmanto, 1985). In the same region, soil fertility is strongly correlated with soil pH (van Noordwijk and Hairiah, 1986). Thus, we expect that soil pH is negatively correlated with the strength of wood and, consequently, decay time. If correlations exist, they would allow for rapid assessments of the orangutan density in an area in a single visit, without the need for consecutive surveys.
3. Methods 2.3. Problems in estimating nest decay rate (t) 3.1. Study area Of all the parameters used in the density estimations, t is expected to vary most, as decay rates are likely to depend on (1) seasonally varying climatic factors (e.g. temperature, humidity, wind), (2) differences in the purpose for which nests were constructed, and (3) the wood density of the tree species concerned (van Schaik et al., 1995). Mean values for t have been estimated using nest monitoring and matrix techniques (Kemeny et al., 1956; van Schaik et al., 1995). Although the nest monitoring technique provides a relatively accurate estimation of t it requires a long period of data collection (Rijksen, 1978; Djojosudharmo in van Schaik et al., 1995). In contrast, only two surveys are needed to obtain decay rates with the matrix technique. However, this technique is more sensitive than the monitoring technique to the variation in nest firmness between night nests and day nests (van Schaik et al., 1995). In their estimation of orangutan density in various sites in the Leuser Ecosystem, Van Schaik et al. (1995) estimated t with an exponential relationship between altitude and t. However, these t values were estimated with a limited number of nests in the transition matrices, and t values were used from transects at only three different altitudes. Moreover, other factors such as vegetation type, which may differ at similar altitudes in the tropics due to different soil factors (BrockmannJerosch, 1919; Schro¨ter, 1926), are expected to have a much more direct influence on t than altitude. In this study, we calibrate the matrix technique against values obtained using the monitoring technique, and develop a correction factor. New nest density and all nest density estimates are also compared to test the accuracy of the correction factor. The correction factor can subsequently be applied to estimate t using the matrix technique on a large number of transects. However, two consecutive surveys are still necessary to estimate t using the matrix technique, we would much prefer to be able to assess t with a single survey. We therefore also explore the possibilities of a shortcut in the estimation of t, using correlates of t such as altitude
The sites sampled are all situated in the Gunung Leuser National Park, which is part of the Leuser Ecosystem in northern Sumatra, Indonesia. The Leuser Ecosystem lies predominantly within Aceh, and partly in North Sumatra, Sumatra, Indonesia. The larger Ketambe Area is located between latitudes 3 380 N and 3 420 N, and between longitudes 97 360 E and 97 400 E. The additional transect at Kemiri (3 490 N, 97 300 E) is located on the slopes of Gunung Kemiri. The dryland forests at Ketambe and Kemiri can be described as typical for this habitat type, with a very irregular canopy, a dense understorey and several strata of relatively small trees. The Suaq Balimbing Research Station (03 040 N, 97 260 E) is located in the Kluet region of the Leuser Ecosystem, which lies in the narrow coastal plain between the Barisan mountains and the coast (van Schaik, 1996; Singleton, 2000). Permanent transects were established in three different habitat types: backswamp, transit (peat) swamp, and hills. The transit and backswamp forests can be classified as being rather open, with a more or less continuous tree canopy and a relatively open understorey. Compared with the transit and backswamp forests, the hill forest at Suaq tends to have a higher canopy, although the understorey is similarly open. Total transect length and duration of monitoring of transects at Ketambe and Suaq Balimbing are presented in Table 1. Survey areas at Ketambe and Suaq Balimbing were shown to support relatively high densities of orangutans (Rijksen, 1978; van Schaik et al., 1995) and the habitat is considered to be generally representative for orangutan habitat in Sumatra and Borneo (Rijksen en Meijaard, 1999). 3.2. Field procedures Standard survey methods were used during the line transect surveys (Eberhardt, 1978; Burnham et al., 1980; van Schaik et al., 1995). All transects were located in
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Table 1 Data for 12 transects used for recording orangutan nests in the Larger Ketambe Area, Suaq Balimbing, and Kemiri areas Plot name
Start month
End month
Kemiri
December 1998
January 1999
Larger Ketambe Area Alas Lowland-1 Simpur Lowland-2 Sukarimbun Ketambe-25 Simpang kiri Upland
May 1998 May 1998 May 1998 May 1998 May 1998 May 1998 May 1998 May 1998
Suaq Balimbing Transit swamp Hills Backswamp
March 1997 March 1997 March 1997
Mean altitude (m)
Length (m)
pH soil (H2O)
2
1183
3000
5.30
July 1999 July 1999 July 1999 July 1999 July 1999 July 1999 July 1999 July 1999
8 8 8 8 8 8 8 8
277 366 622 660 673 788 1096 1281
1750 4050 4000 2500 3000 1200 2500 2500
6.30 4.85 – 4.29 – – – –
June 1999 June 1999 June 1999
28 28 28
5 100 5
1865 3085 1550
5.15 4.30 5.85
primary forest. Van Schaik et al. (1995) suggest that habitat variation may affect reliability when long transects go through different habitats with different densities. Hence, single transects were limited to one type of habitat at both Ketambe and Suaq as much as possible. Altitude was measured every 100 m along transects (Table 1). Data on the soil pH of the transects at Ketambe and Suaq were taken from previous studies (Ketambe: van Schaik and Mirmanto, 1985; Suaq: C.P. van Schaik, unpublished data; see Table 1). The pH data are mean figures, as transects were sometimes located on a complex of soil types with differing acidities. Measurements were made at 5–10 cm depth using a simple Hellige pH meter, whose values were highly correlated with pH–KCl values obtained from soil samples in the laboratory (van Noordwijk and Hairiah, 1986). Systematic nest counts from line transects were conducted by experienced nest counters only. Inter-observer tests showed that there were no differences in observer ability (P < 0.03; t-test). Two features were recorded to obtain the parameters needed to calculate orangutan densities: (1) the estimated perpendicular distance of the nest from the transect line, and (2) the stage of decay of the nest. Five classes were used to indicate decay stages (van Schaik et al., 1995): class 1: fresh, new nest; leaves are still green; class 2: older; leaves may still be attached, original shape is conserved, and no holes are visible in the nest; class 3: old; most leaves are gone and holes are visible in the nest; class 4: leaves are gone and holes visible in structure; and class 5: twigs and branches still present but no longer in original shape of nest.
No. surveys
The distinction between classes 4 and 5 was only made at Suaq. At Ketambe, class 4 included all nests that would have been classed as 4 or 5 at Suaq. This slight discrepancy in the methods at the two sites is not likely to affect decay rate estimation, since the calculation of the total time that nests were visible remains similar. Six additional attributes of each nest were also recorded to assist with their recognition on subsequent surveys. These were (1) the height of the nest, (2) the height of the nest tree, (3) the distance of the nest from the top of the tree, (4) the position of the nest in the tree relative to the main stem, (5) the angle between the transect line (direction of the trail) and the direction of the position of the nest, and (6) the local (Gayo) name of the nest tree. 3.3. Parameters p and r In both the Ketambe and Suaq populations, ca. 10% of individuals are infants, who do not construct nests (van Schaik et al., 1995). Consequently, the value of p adopted here is 0.9. The average value of 1.7 for r is also based on previous studies at Ketambe and Suaq (Rijksen, 1978; van Schaik et al., 1995). It was assumed that the values of these parameters at the Kemiri site are similar to those at Ketambe and Suaq. 3.4. Estimating strip width (w) To increase the accuracy of estimating w, transects with similar distributions of perpendicular distances were pooled. The distribution of the perpendicular distances of nests was compared for all transects with the Kolmogorov–Smirnov test. Four transects or groups of transects with similar distributions of perpendicular
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distances could be identified: Ketambe transects including Kemiri, backswamp, transit swamp, and hill transect at Suaq. Their w value was calculated using the computer program ‘‘Distance 4.0. Beta 3.0’’ (Thomas et al., 2001). In accordance with recommendations of Buckland et al. (1993), data were truncated and grouped before analysis. Up to 10% of highest perpendicular values were considered for truncation (Buckland et al., 1993). Detection intervals of perpendicular histograms were varied to ‘‘smear’’ possible heaping at favoured distances; histograms were analysed with cut-off points at 4, 5, 6, 7, 8, and 9 m intervals. Five recommended models were selected to fit the distance histograms (Buckland et al., 1993): uniform with cosine expansions, half-normal with cosine or hermite expansions, and hazard rate with either cosine or simple polynomial expansions. Model selection was based on Akaike’s information criterion (AIC) values; models that gave the lowest AIC value were used to estimate w (for explanation see Buckland et al., 1993). However, when a model with the lowest AIC value also gave significant goodness-of-fit values, the model with the second lowest AIC value was given priority, since significant goodness-of-fit statistics may indicate that the wrong model is being fitted to the detection histogram (Buckland et al., 1993). 3.5. Determining correction factor Cf In order to estimate Cf to determine true nest numbers, new nest densities calculated with new nests made between successive surveys were compared to true densities in areas where these were known. Cf was estimated separately for three different forest types: combined transit and backswamp forest at Suaq, hill forest at Suaq and lowland forest at Ketambe. Rijksen (1978) estimated the density at Ketambe at ca. 5 ind/km2, based on the presence and absence of individual orangutans in the study area there over a 4-year period. More recent estimates arrived at a similar density (e.g. T. Mitra Setia in van Schaik et al., 1995). Rijksen’s (1978) study area was situated in the lowland part of the Larger Ketambe Area, which is covered by our Alas, Lowland-1, Lowland-2 and Ketambe-25 transects. Similarly, true densities in the hills, transit and backswamp areas at Suaq were determined by Singleton (2000). His study, based on direct sightings, showed that the density in the transit and backswamp area was about 7 ind/km2 on average. True density in the hills around Suaq was estimated at 1–1.5 ind/km2 (Singleton, 2000). However, owing to the occurrence of a mast episode there, which attracted relatively high numbers of orangutans (Singleton, 2000), density at the time of the present study was estimated at 4 ind/km2 (C.P. van Schaik, unpublished data). This period lasted for 4 months, indicated by an increase in new nest counts
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from May until August 1997. Therefore, after weighting for this mast episode, we obtained an average figure of 1.64 ind/km2 over the whole of our 28 months of study at Suaq. 3.6. Estimating t with the monitoring technique Data from Suaq Balimbing were used to estimate nest decay rate (t) using the nest monitoring technique. For Ketambe, no nest monitoring data were available. The duration of survival of each nest at Suaq was calculated as the total number of months the nest was observed in the field, including the first and the last month of observation. Nests that were already existent at the onset of the study (i.e. March 1997) were removed from the analysis since their full duration of visibility could not be known. Nests that were still present at the end of the study were included in the analysis as censored nests. The overall survival of nests in each of the three forest types was assessed using the Kaplan–Meier estimate of survival in JMP1 statistical software. The JMP1 software deals with censored nests differently from regular nests. The Kaplan–Meier test is the preferred method of calculating survival and creating estimated survival curves (Motulsky, 1995). Data were analysed using the Wilcoxon test for homogeneity (Tschopp et al., 1999). Log-Rank tests are typically the preferred method of testing for similarity (Tschopp et al., 1999). This method, however, is only applicable if Kaplan–Meier survival curves do not cross, indicating departure from potential hazards, a requirement of the Log-Rank test (Peto et al., 1977). 3.7. Estimating t with the matrix technique Data from the Suaq, Ketambe and Kemiri transects were used to determine t with the transition matrix technique. Transects in the Larger Ketambe area were surveyed with bi-monthly intervals over a period of 15 months (seven transition matrices; Table 1). Transects at Suaq Balimbing were surveyed with monthly intervals over a period of 28 months (14 independent transition matrices; Table 1). For comparison with the Ketambe matrices, t values were also estimated from Suaq matrices with bi-monthly intervals. The transect at Kemiri was surveyed twice (December 1998 and January 1999) with an interval of 30 days. Since minor variations in the transition probabilities of a single matrix may cause large variations in estimating the duration of visibility, we investigated whether the transition matrices from each transect at Ketambe and Suaq could be summed to increase the accuracy of the estimation. Hence, matrices from each transect at Ketambe and Suaq were compared for similarity using goodness-of-fit statistics (Bishop et al., 1975).
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4. Results
4.3. Estimating t with the monitoring technique
4.1. Estimating w
Nest survival curves in the different habitats were analysed using the Wilcoxon test for homogeneity. The Log-Rank test was ruled out because survival curves crossed at several points, and because the error distribution was a better fit with the Wilcoxon test requirement of logistic distribution. The Wilcoxon test indicated that no significant difference occurred between nest survival times in backswamp and hills (P=0.08), backswamp and transit swamp survival (P =0.44) and transit swamp and hills survival (P=0.24). Survival curves were plotted for nests in all three forest types (Fig. 2). The mean survival time for nests was calculated; resulting values were 206.42 days for the transit swamp (S.D.=0.23), 227.70 days for the hills (S.D.=0.29) and 192.73 days for the backswamp (S.D.=0.23).
Models used for estimating w and analysis statistics are presented in Table 2. For the Ketambe, backswamp and hill transects, models that gave the lowest AIC values were selected; for the transit swamp transect, the model with the second lowest AIC value was selected, since this was the only model that gave a non-significant goodness-of-fit statistic. Only two values of w 2 are significant. Detection functions are presented in Fig. 1a–d. 4.2. Determining f and Cf New nest density estimates, true densities and resulting correction factors Cf for the three habitats surveyed are shown in Table 3. These correction factors indicate that more nests were missed in the lowland forest at Ketambe than in the transit and backswamp at Suaq. Relatively few nests were missed in the hill forest at Suaq.
4.4. Estimating t with the matrix technique No significant differences were found between transition matrices from each transect at Ketambe; similarly, no differences were found between matrices from the
Table 2 Models selected for estimating the strip width (w) on each transect (or group of pooled transects), with selection criteria (Akaike’s information criterion), and the resultant estimated strip widths Transects
Model
w2
AIC
d.f.
Cut-off points (m)
% Data truncated (truncation point in m)
CV%
n
Estimated w
Ketambe Ketambe Ketambe Backswamp Backswamp Backswamp Transit swamp Transit swamp Transit swamp Hills Hills Hills
Half-normala Half-normal/hermite polynomial Half-normal/hermite polynomial Hazard ratea Hazard rate Uniform/cosine Hazard ratea Hazard rate Half-normal/cosine Hazard ratea Hazard rate Hazard rate
0.550 0.636 0.726 0.140 0.415 0.463 0.083 0.024* 0.012* 0.521 0.198 0.165
3479.2 3848.7 4048.4 1670.1 1762.9 1880.5 2141.7 1849.9 2175.3 1451.1 1709.5 1828.7
3 3 3 2 2 3 2 1 2 3 5 6
5 5 5 8 6 6 8 8 8 8 5 5
5.49 1.86 0.40 1.66 5.49 3.49 1.50 6.94 1.63 5.74 8.51 5.74
2.84 4.82 4.68 3.21 3.62 5.95 2.87 3.20 8.37 3.21 3.13 3.48
1119 1162 1179 591 568 580 724 684 723 443 430 443
17.51 19.22 19.64 25.28 23.58 23.08 28.61 27.16 28.15 33.45 30.92 31.62
(25) (30) (40) (40) (30) (36) (40) (32) (48) (48) (40) (45)
The percentage of perpendicular values that were truncated is given. The models used to fit the distribution of perpendicular distances consist of a function (e.g. half-normal) with or without a series adjustment (e.g. hermite polynomial). Goodness-of-fit statistics indicate fit of model to detection histogram. a Models used for estimating w. * P <0.05. Table 3 New nest densities and true densities on transects at Ketambe lowland (Alas, Lowland-1, Lowland-2, Ketambe-25), hills at Suaq, and Suaq swamp forest (backswamp and transit swamp), and resulting correction factors Cf (see text) Plots
Average number new nests
Cumulative length (km)
w (m)
p
r
t (days)
Estimated new nest density (ind/km2)
True density (ind/km2)
Cf
Lowland Ketambe Hills Suaq Swamp Suaq
50.1 15.1 40.7
9.5 3.085 3.415
17.51 33.45 27.10
0.9 0.9 0.9
1.7 1.7 1.7
60 30.4 30.4
1.64 1.57 4.73
5.0 1.64 7.0
3.05 1.04 1.48
Note that for the swamp at Suaq, the average w value of the transit and backswamp transects was used. The mean duration of visibility of new nests is similar to the average intervals between surveys.
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Fig. 1. Detection functions (expected detection probability) fitted to distance histograms (observed perpendicular distribution) from transects at Kemiri, Ketambe and Suaq Balimbing. The models selected to fit the distance histograms were (a) a half-normal key without adjustments to truncated nest survey data from the eight pooled Ketambe transects and the Kemiri transect (w =17.51 m), (b) a hazard rate key without adjustments to truncated nest survey data from the hill transect at Suaq (w=33.45 m), (c) a hazard rate key without adjustments to truncated nest survey data from the backswamp transect at Suaq (w=25.28 m), (d) a hazard rate key without adjustments to truncated nest survey data from the transit swamp transect at Suaq (w=28.61 m).
Fig. 2. Survival curves for nests at backswamp, transit swamp and hill transects, Suaq Balimbing.
three transects at Suaq (goodness-of-fit test; P > 0.05 in all cases). A single transition matrix, which is the summed matrix of all those found for a single transect, was therefore used to calculate the mean duration of visibility for the nests on each of the three transects at Suaq,
and on the eight transects at Ketambe. For Kemiri only one transition matrix was available. For the transects at Suaq, t was calculated from transition matrices with monthly and bi-monthly intervals. The estimated t value for each transect is presented in Table 4.
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Table 4 Estimated t values for pooled transition matrices of transects in the Larger Ketambe Area, at Suaq Balimbing (transition matrices calculated with monthly and bi-monthly intervals), and the t value for the single transition matrix from Kemiri; the average interval between repeat surveys, total nest numbers and the number of nest transitions used to calculate the mean duration of visibility are given Plot name
No. of pooled Nest Average Estimated transition numbers interval (days) t (days) matrices
Alas Lowland-1 Simpur Lowland-2 Sukarimbun Ketambe-25 Simpang kiri Upland Transit swamp Hills Backswamp Transit swamp Hills Backswamp Kemiri
7 7 7 7 7 7 7 7 14 14 14 13 13 13 1
56 201 217 186 156 92 100 59 735 470 601 735 470 601 117
60.50 60.25 59.57 60.13 60.25 60.25 61.57 60.13 30.40 30.40 30.40 60.80 60.80 60.80 30.00
174.1 195.9 274.1 305.3 329.3 350.6 421.5 373.4 246.4 249.4 234.1 240.4 269.4 228.7 220.8
4.5. Correcting matrix technique values The t values for the transit swamp, hills and backswamp, estimated with the monitoring technique at Suaq, should yield the most precise estimates of actual nest decay rate. When comparing t values from the monitoring and the transition matrices techniques obtained at Suaq (Table 5), a reasonably good correspondence is apparent (rs=0.80, n=3, for matrix t values with monthly interval; rs=0.99, n =3, for matrix t values with bi-monthly interval). Moreover, the rank order of t values produced by both techniques are similar, with the hill plot producing the highest estimated t value, followed by the transit swamp and the backswamp, respectively. However, the values obtained with the matrix technique are slightly overestimated and must be corrected if they are to be useful. Table 5 presents the available monitoring and matrix values from Suaq and the resulting correction
factors, which should be used when estimating t with the matrix technique. The correction factors for the matrix t values obtained from the nest data at Suaq were tested for their accuracy on the sample of t values calculated with the matrix technique from the Ketambe transects. Table 6 presents the densities estimated from new nest and all nest techniques from these transects, as well as the parameter values involved. The similarity between the new and all nest density estimates from all transects was tested with a regression analysis, which showed that the densities calculated with the new nest technique corresponded significantly with the estimates from the all nest technique (rs=0.75, n=9, P< 0.01). Thus, t values obtained with the matrix technique would suffice after being adjusted with our correction factors. 4.6. Shortcut for t estimation In order to be able to rapidly assess t on transects in the future, we investigated whether corrected t values obtained with the matrix technique in this study could be correlated to altitude or pH of the soil. It was found that the relationship between t and altitude is not exponential: t values of nests from all three transects at Suaq were much higher than the lowest t value at Ketambe, where the transect was significantly higher. Moreover, t values from the highest transect at Ketambe, as well as from the Kemiri transect, indicate that beyond a certain altitude decay rates do not increase but rather level out or even decrease. Thus, altitude is not a major factor in determining t. We also investigated whether soil acidity influenced nest decay rates. This was tested with pH data available for seven transects from Ketambe, Kemiri and Suaq (Table 1). A significant relationship was found between soil acidity and corrected t values (rs=0.61, n=7, P=0.038; Fig. 3). As soil pH increased, the duration of nest visibility decreased. Although confidence limits are large, the aim of the present study is to provide a rapid assessment technique and the linear relationship appears to be strong enough to allow soil pH to be used to predict t on transects.
Table 5 Correction factors for nest decay rates (t) calculated with the matrix technique, using more accurate values from the monitoring technique at Suaq (see text) Plot
t (days) From matrices t (days) From matrices t (days) From Correction factor for matrix Correction factor for matrix monthly interval bi-monthly interval monitoring values monthly interval values bi-monthly interval
Transit swamp Hills Back-swamp
246.4 249.4 234.1
Mean correction factor
240.4 269.4 228.7
206.4 227.7 192.7
0.838 0.913 0.823
0.859 0.845 0.843
0.858
0.849
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Table 6 Comparison of mean all nest density values (dN) and new nest density values (dN new) from the Ketambe and Kemiri transects; total nest counts (‘‘N’’ for mean nest numbers counted on transects; ‘‘N new’’ for number of new nests made between consecutive surveys) are given, as well as variables to estimate density Plot
N
N new
L
w
r
p
t, int
t, cor
Correction factor
dN
dN new
Alas Lowland-1 Lowland-2 Ketambe-25 Simpur Simpang kiri Sukarimbun Upland Kemiri
15.38 69.38 53.63 33.50 83.00 45.88 65.88 25.63 100.50
6.14 12.00 20.57 11.43 24.71 12.57 15.71 7.57 31.00
1.75 4.05 2.50 1.20 4.00 2.50 3.00 2.50 3.00
17.51 17.51 17.51 17.51 17.51 17.51 17.51 17.51 17.51
1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7
0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9
60.50 60.25 60.13 60.25 59.57 61.57 60.25 60.13 30.00
147.83 166.36 259.19 297.67 232.69 357.84 279.59 317.01 189.49
0.849 0.849 0.849 0.849 0.849 0.849 0.849 0.849 0.858
1.11 1.92 1.54 1.75 1.66 0.96 1.47 0.60 3.30
1.08 0.92 2.55 2.95 1.94 1.52 1.62 0.94 6.43
New nest densities were estimated with the average intervals between surveys (t, int). In the all nest densities, corrected t values obtained with the matrix technique were used (t, cor); correction factors for the matrix values are provided. Densities calculated with the new nest technique corresponded significantly with the estimates from the all nest technique (rs=0.75, n=9, P=0.0025).
Fig. 3. The relationship between nest decay time, obtained with transition matrix technique at Suaq Balimbing and Ketambe, and soil acidity. Matrix decay rates were corrected with decay rates obtained from the monitoring technique from Suaq. The 95% confidence levels to the mean are indicated (–).
5. Discussion In this study, an attempt was made to provide an easy method for the rapid assessment of orangutan density. Two major problems are overcome: underestimation of nest numbers above and near the transect line and inaccurate estimation of the decay rate of nests (t). 5.1. Correcting for underestimation of nest numbers near the transect trail When a high percentage of nests is missed directly above the trail, the detection histogram lacks the clear shoulder pattern necessary to accurately estimate w (Buckland et al., 1993). This results in the overestimation of w and the underestimation of density. Ideally, most or all nests are detected above and near the centre line, and an increasing percentage of nests is likely to be missed further from the trail. However, when observers tend to look predominantly sideways
instead of upwards and sideways, detection of nests above the trail is likely to be much less than 100%. This is probably the principal explanation of the detection distribution of nests recorded on the hill and transit swamp transects at Suaq during this study. For the hill transect, the explanation for the lack of a clear shoulder pattern in the detection function may lie in the fact that looking down on slopes is much easier than looking upward. For the transit swamp transect, the relatively high canopy in this part of the swamp may have made looking upwards for longer periods physically difficult for the observers. It should be stressed, however, that missed nests are more likely to go undetected because of inadequate vigilance on the part of the observer and not because nests are actually hidden from view due to dense forests. This is underlined by distribution histograms from Ketambe and Suaq. In the open transit swamp and hill forests at Suaq, relatively more nests were missed above the trail than in the denser forest at Ketambe. Moreover, earlier surveys at Suaq by other
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observers, who strove to minimise missing nests above the trails, yielded distributions similar to those found at Ketambe in the current study (van Schaik et al., 1995). In most cases, non-ideal distributions such as at the transit swamp and hill transects can be solved by fitting one of the robust models recommended by Buckland et al. (1993), and appropriate truncation and grouping of data can limit the problems introduced by observer behaviour. In this study, estimates of w were fairly accurate (Table 2), despite problematic detection histograms from the hill and transit swamp transects. ‘‘Grouping’’, ‘‘truncation’’, and qualities of robust models largely overcame observer problems, including ‘‘heaping’’. However, as recommended by Buckland et al. (1993), it is better to test the effectiveness of survey design and data collection at meeting critical assumptions during a pilot study. Apart from the problems associated with inadequate observation techniques, some nests will be missed even with the best search procedures owing to the multilayered canopy of tropical forests. For the Ketambe transect, in particular, we found that a relatively high proportion of new nests had been missed on a single count (Table 3). The fraction of nests missed (f) in the more open transit and backswamp forests is clearly lower, while f in the hill forest is lowest (Table 3). These results clearly demonstrate the effect of habitat on f: in the dense forest at Ketambe, with multiple understoreys and an irregular canopy, nests are more easily missed than in the more open transit and backswamp and hill forests (note, however, that at least in the hill forest, f could have been slightly overestimated due to overestimating w). Therefore, the correction factor Cf is habitat specific and should be estimated separately for each habitat type. 5.2. Correctly estimating t The variation in estimates of w is relatively small when compared with variation in estimates of t. For example, t values recently determined with the monitoring technique and a large nest sample at Suaq Balimbing (Singleton, 2000) showed that those used earlier by van Schaik et al. (1995) for the transit and backswamp areas at Suaq Balimbing, generated by the matrix technique on a small sample, were underestimated by > 300% (largely due to attempts to reach values similar to values estimated at Ketambe, now known to be biased by over-representation of day nests). We found that estimates of t using the transition matrix technique required only minor correction, provided samples were large enough. However, because at least two visits to the same transect are required when using the matrix technique, we also devised a shortcut. The significant negative correlation of t with the pH of the soil is not unexpected since wood becomes denser
(van Schaik and Mirmanto, 1985), which will decrease the rate of nest decay. This relationship allows us to estimate t in a single visit to a transect, provided the pH of the soil is determined. Since data were collected in a variety of habitats with distinct plant communities, the relationship is likely to provide accurate estimates of nest decay rates throughout the distribution range of the orangutan.
6. Summary of procedures Although the rapid assessment method proposed here is relatively crude, its gains are twofold. First, nest decay rate can be estimated using soil pH, making one visit to a single transect site sufficient to acquire a density estimate. Second, no marking of nests along a permanent transect line is necessary, which reduces the time spent on surveying a single transect. The pH of the soil should first be determined, to estimate t based on the linear relationship between pH and t. A large sample of nests is necessary to be able to accurately estimate w (75–100 nests), and w should be estimated separately for each transect. To arrive at a minimum estimate of Cf, a transect needs to be walked again in the opposite direction. In this second count, every attempt should be made to spot all nests near or above the transect trail, which will allow for calculation of the fraction f of true nest numbers N that went undetected on the first count (Cf=1/(1f)). We expect Cf to vary between 1.25 in open forest to 3 in dense forest. In dense forests with much undergrowth, it may be necessary to leave the trail in locations where the canopy is not easily visible in order to search the centre line more carefully (but note that the measurements should always be taken from the centre line itself; Buckland et al., 1993). When two surveys of a single transect are performed, t may be calculated with the matrix technique by multiplying the matrix t values by 0.85 (Table 5). The density equation is similar to the rapid density assessment equation. When the transect is surveyed twice, density can also be estimated with the new nest technique. This overcomes the problems associated with the estimation of t. Both techniques require a large sample of nests to be able to make reasonably accurate estimates of t and w. Although this technique is less crude than the rapid assessment technique, it requires two visits to a single transect, and precise marking of nests along the transect line. This is not required when estimating density based on a single survey. Where time is limited the choice is between a series of ballpark estimates for many sites and no estimates for most sites, along with a very accurate estimate for one or two sites. In a species where densities vary by an order of magnitude in the same region, the first method is to be preferred. We therefore believe that, if used
R. Buij et al. / Biological Conservation 114 (2003) 103–113
carefully, the method proposed here can be a useful tool for rapid assessment of orangutan densities.
Acknowledgements We gratefully acknowledge the co-operation and support of the Indonesian Institute of Science (LIPI, Jakarta), the Indonesian Nature Conservation Service (PKA) in Jakarta, Medan, and Kutacane (Gunung Leuser National Park Office). RB thanks the Universitas National (UNAS, Jakarta) for sponsoring the research. We thank the Leuser Development Programme (LDP, Medan), especially M. Griffiths and Dr. K. Monk, for strong logistical support. We also thank the LDP staff in Ketambe for providing an excellent research environment. Financial support was generously provided by the Wildlife Conservation Society (Suaq), and the Netherlands Foundation for the Advancement of Tropical Research (WOTRO), the Dobberke Foundation and the Lucie Burgers Foundation for Comparative Behaviour Research (Ketambe). Financial support was very generously provided by the Netherlands Organisation for Scientific Research (NWO). We thank Han de Vries for advise on statistical matters. We thank the European Commission and the Government of Indonesia as the funding agencies for the Leuser Development Programme.
References Barnes, R.F.W., 1993. Indirect methods for counting elephants in forests. Pachyderm 16, 24–30. Barnes, R.F.W., Jensen, K.L., 1987. How to count elephants in forests. IUCN African Elephant and Rhino Specialist Group Technical Bulletin 1, 1–6. Bishop, Y.M.M., Fienberg, E., Holland, P.W., 1975. Discrete Multivariate Analysis. MIT Press, Cambridge. Brockelman, W.Y., Ali, R., 1987. Methods of surveying and sampling forest primate populations. In: Marsh, C.W., Mittermeier, R.A. (Eds.), Primate Conservation in the Tropical Rain Forest. Alan R. Liss, New York, pp. 23–62. Brockmann-Jerosch, H., 1919. Baumgrenze und Klimacharacter, Beitr. Z. Geobot. Landesaufnahme der Schweiz. Pflanzengeogr. Kommission der Schweizer Naturforsch Gesellschaft 48.
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Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.L., 1993. Distance Sampling: estimating Abundance of Biological Populations. Chapman and Hall, London. Burnham, K.P., Anderson, D.R., Laake, J.L., 1980. Estimation of densities from line transect sampling of biological populations. Wildlife Monographs 72. Eberhardt, L.L., 1978. Transect methods for population studies. Journal of Wildlife Management 42, 1–31. Kemeny, J.G., Snell, J.L., Thompson, G.L., 1956. Introduction to Finite Mathematics. Prentice-Hall Inc, Englewood Cliffs NJ. MacKinnon, J.R., 1974. The ecology and behaviour of wild orangutans (Pongo pygmaeus). Animal Behaviour 22, 3. Motulsky, H.J., 1995. Intuitive biostatistics: survival curves. In: Intuitive Biostatistics. Oxford University Press Inc, London. Peto, R., Pike, M.C., Armitage, P., Breslow, N.E., Cox, D.E., Howard, S.V., 1977. Design and analysis of randomized clinical trials requiring prolonged observation of each patient. II. Analysis and examples. British Journal of Cancer 35, 1–39. Rijksen, H.D., 1978. A Field Study of Sumatran Orang-utans (Pongo pygmaeus abelii Lesson 1827). PhD Thesis, Wageningen, H. Veenman and Zonen B.V. Rijksen, H.D., Meijaard, E., 1999. Our Vanishing Relative: The Status of Wild Orang-utans at the Close of the Twentieth Century. Kluwer Academic Publishers, Dordrecht. Schro¨ter, C., 1926. Das Pflanzenleben den Alpen. Albert Raustein, Zu¨rich. Singleton, I, 2000. Ranging Behaviour and Seasonal Movements of Sumatran Orangutans (Pongo pygmaeus abelii) in Swamp Forests. PhD Thesis, Durrell Institute of Conservation and Ecology, University of Kent at Canterbury, UK. Thomas, L., Laake, J.L., Strindberg, S., Marques, F.F.C., Borchers, D.L., Buckland, S.T., Anderson, D.R., Burnham, K.P., Hedley, S.L., Pollard, J.H., 2001. Distance 4.0. Beta 3. Research Unit for Wildlife Population Assessment, University of St. Andrews, UK. Tschopp, P.J., Wesley, J.T., Spekkens, A., Lohfeld, L., 1999. Collagen injections for urinary stress incontinence in a small urban urology practice: time to failure. Journal of Urology 162 (3), 779–783. van Noordwijk, M., Hairiah, K., 1986. Mycorrhizal infection in relation to soil pH and soil phosphorus content in a rain forest of northern Sumatra. Pl. Soil 96, 299–302. van Schaik, C.P., 1996. Strangling figs: their role in the forest. In: van Schaik, C.P., Supriatna, J. (Eds.), Leuser, A Sumatran Sanctuary. Yayasan Bina Sains Hayati, Indonesia, pp. 111–119. van Schaik, C.P., Mirmanto, E., 1985. Spatial variation in the structure and litterfall of a Sumatran rain forest. Biotropica 17, 196. van Schaik, C.P., Priatna, A., Priatna, A., 1995. Population estimates and habitat preferences of orangutans based on line transects of nests. In: Nadler, R.D., Galdikas, B.M.F., Sheeran, L.K., Rosen, N. (Eds.), The Neglected Ape. Plenum Press, New York, pp. 129–147.
Rapid assessment of orangutan density R. Buija, I. Singletonb, E. Krakauerc, C.P. van Schaikc,* a Behavioural Ecology, Utrecht University, PO Box 80086, 3508 TB, Utrecht, The Netherlands Sumatran Orang-utan Conservation Programme, PO Box 1472, Medan 20001, Sumatera Utara, Indonesia c Department of Biological Anthropology and Anatomy, Duke University, PO Box 90383, 3705 B Erwin Road, Durham NC 27708-0383, USA b
Received 12 January 2002; received in revised form 25 September 2002; accepted 13 December 2002
Abstract In order to design effective conservation measures for the orangutan, accurate information on their distribution and densities are needed. Nest counts along line transects allow for fairly accurate assessment of orangutan density. However, large sample sizes are needed to obtain accurate estimations of the decay rate of nests, preventing a rapid estimate. Moreover, due to the structure of tropical forests, nest numbers above and near the trail are underestimated. In this study, a rapid assessment method for the estimation of orangutan density is developed, allowing for the estimation of density with a single survey. Procedures are provided for estimating a correction factor which adjusts for underestimating nest numbers near the transect trail. Furthermore, we provide a shortcut to rapidly assess nest decay rate without the need for consecutive surveys. Finally, recommendations are made for determining orangutan density using two different techniques. These techniques incorporate correction factors and a shortcut to accurately assess orangutan density with only one or two visits to an area. # 2003 Elsevier Ltd. All rights reserved. Keywords: Orangutan; Pongo sp.; Nest census; Rapid density assessment; Shortcut nest decay rate estimate
1. Introduction The orangutan (Pongo sp.) occurs in increasingly small numbers on the islands of Borneo and Sumatra (e.g. Rijksen and Meijaard, 1999). In order to design effective conservation measures for the orangutan, accurate information on their distribution and densities is needed. By extrapolating known densities in different habitat types, estimates of orangutan numbers over wider areas can be obtained, provided other factors that may influence density are constant. Therefore, survey efforts have focused on estimating orangutan densities and identifying the ecological factors involved in the determination of these densities. Several techniques are available for estimating orangutan densities (van Schaik et al., 1995; Singleton, 2000). Ideally, a method should be used that requires only a single survey and produces reasonably accurate estimates. The line transect method employed by Brockelman and Ali (1987) is a relatively straightfor* Corresponding author. Tel.: +1-919-660-7390; fax: +1-919-6607348. E-mail address: [email protected] (C.P. van Schaik). 0006-3207/03/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0006-3207(03)00015-6
ward and quick technique that has been used to estimate forest primate densities. It relies on observing sufficient numbers of animals along a trail of known length. However, densities of secretive animals like orangutans may well be highly underestimated in this way, especially where they are hunted. Moreover, in areas with low orangutan densities, it would be very time-consuming to attempt to estimate orangutan densities by direct sightings, and reliable estimates would require very large sample sizes. Hence, techniques have been developed that rely on the sign left by animals, namely their nests and dung (Barnes and Jensen, 1987; Barnes, 1993; van Schaik et al., 1995). Rather than relying on encounters with the animals themselves, these techniques record the nests in conjunction with several parameters (e.g. decay rate, construction rate, proportion of nest builders in the population) that convert the estimated density from that of the sign to that of the animals themselves. Orangutan densities can be estimated from nest counts using the following equation: d ¼ ðCf NÞ=ðL 2w p r tÞ
ð1Þ
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in which: d=orangutan density (animals/km2); Cf=correction factor for N; N=number of nests observed along the transect; L=length of the transect covered (km); w=estimated width of the strip of habitat actually censused; p=proportion of nest builders in the population; r=rate at which nests are produced (n/day/ individual); and t=decay rate of nests: time during which a nest remains visible (in days). Density estimates based on nest counts gave reasonably accurate results where validated, at least for Sumatra (van Schaik et al., 1995), although the estimation of so many parameters inevitably leads to poor reliability. However, more recently, both minor and major problems have arisen in the use of this technique (Singleton, 2000). The aim of this paper is to examine the existing technique and its weaknesses, and to develop a reasonably accurate method to quickly determine orangutan density. The general procedures for the technique involved are provided at the end of the discussion. The proposed rapid assessment technique may be rather crude, but will allow for the estimation of orangutan density over large areas in relatively little time. Considering the extraordinary speed at which forests are being destroyed throughout the orangutan’s distribution on both islands, and the lack of density estimates for most parts of their range, such a technique is desperately needed for the development of effective conservation strategies.
2. Problems with the current method Several problems may occur in estimating orangutan density with the current method, most importantly the underestimation of nest numbers above or near the transect line, and the inaccurate estimation of t. Here, we explore these problems further. 2.1. Estimating the proportion of nest builders (p) and rate of nest production (r) The minor problems concern the estimation of the proportion of nest builders in the population (p) and the rate at which nests are produced (r). The value of these parameters may vary between populations, since they depend on the age and sex composition of populations. MacKinnon (1974) estimated that 14% of individuals in Sumatran and Bornean orangutan populations were young infants that do not construct nests, while more recent results (van Schaik et al., 1995; Singleton, 2000) suggested 10% young infants. Despite the fact that exact population composition is unknown in most sites, estimates on p do not vary greatly, and resulting errors in density estimates are likely to be low. Nest building rates may also vary due to differences in population composition. At Ketambe, adult females with infants produce
on average two nests per day, while adult males produce only 1.2 nests per day (Mitra Setia in van Schaik et al., 1995). Accounts of nest building indicate that an individual produces 1.7 nests per day, averaged across age/sex classes in a population in Sumatra (van Schaik et al., 1995). The value for r may also vary due to the re-use and rebuilding of old nests (Singleton, 2000). However, of a total of 1808 nests observed from March 1997 to June 1999 at Suaq, only 6.8% were rebuilt at some point during their lifespan, indicating that this is probably a rare event. Finally, r may differ significantly between localities in Sumatra and parts of Kalimantan. However, within Sumatra, the estimates of r vary only from 1.6 to 1.8 (e.g. van Schaik et al., 1995; Singleton, 2000), similar to the variation in p. Hence, again, the resulting error in the density estimation is similarly small. 2.2. Underestimating nest numbers near the transect trail More importantly, major problems have arisen in the estimation of other crucial parameters that have to be determined on every separate transect, especially the effective strip width of the transect (w) and the decay rate of the nests (t). These problems may yield inaccurate density estimates. To calculate the strip width (w) sampled along a transect, the perpendicular or shortest distance between the transect line and each nest is recorded (e.g. van Schaik et al., 1995). Missing nests above the trail may have two important consequences. First, strip width (w) may be overestimated, meaning that orangutan densities are underestimated. Second, density may be underestimated since total nest counts are biased. Even with the correct observer behaviour, nests are likely to be missed due to the multi-layered canopy of tropical forests. Nests are most likely to be seen at a certain distance from the trail, where the line of sight is least obstructed by foliage, and nest numbers are under-recorded both up to and beyond this distance. Consequently, density is underestimated regardless of the way w is estimated. This is particularly problematic if transects are surveyed only once, since repeat surveys in the opposite direction always yield new nests, even above the trail. Old nests in particular, which are more difficult to spot compared to new nests, are likely to be missed on first surveys, resulting in the under-recording of total nest numbers (R. Buij, unpublished data). Buckland et al. (1993) give several flexible and robust models for estimating w even when not all nests are discovered above and near the trail, but it is important to estimate the fraction f of true nest numbers N that goes undetected on a single survey to prevent underestimation of density. The correction factor needed to correct for N is Cf=1/(1f). In order to assess the extent of the underestimation of nest numbers, i.e. to calculate Cf, orangutan density can
R. Buij et al. / Biological Conservation 114 (2003) 103–113
be estimated with the so-called new nest density technique (van Schaik et al., 1995). This technique overcomes the determination of decay rate (t), since it estimates orangutan density using only those nests built between two consecutive surveys. Assuming that the estimation of the other nest parameters is fairly accurate, Cf can be calculated by comparing new nest density estimates with true density in areas where this is known. Because of the long-term presence of researchers at Ketambe and Suaq Balimbing, true densities in both areas have been determined fairly accurately and can therefore be used for this purpose.
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and soil pH. It was suggested that wood is denser and thus stronger on poor soils with low pH (van Schaik and Mirmanto, 1985). In the same region, soil fertility is strongly correlated with soil pH (van Noordwijk and Hairiah, 1986). Thus, we expect that soil pH is negatively correlated with the strength of wood and, consequently, decay time. If correlations exist, they would allow for rapid assessments of the orangutan density in an area in a single visit, without the need for consecutive surveys.
3. Methods 2.3. Problems in estimating nest decay rate (t) 3.1. Study area Of all the parameters used in the density estimations, t is expected to vary most, as decay rates are likely to depend on (1) seasonally varying climatic factors (e.g. temperature, humidity, wind), (2) differences in the purpose for which nests were constructed, and (3) the wood density of the tree species concerned (van Schaik et al., 1995). Mean values for t have been estimated using nest monitoring and matrix techniques (Kemeny et al., 1956; van Schaik et al., 1995). Although the nest monitoring technique provides a relatively accurate estimation of t it requires a long period of data collection (Rijksen, 1978; Djojosudharmo in van Schaik et al., 1995). In contrast, only two surveys are needed to obtain decay rates with the matrix technique. However, this technique is more sensitive than the monitoring technique to the variation in nest firmness between night nests and day nests (van Schaik et al., 1995). In their estimation of orangutan density in various sites in the Leuser Ecosystem, Van Schaik et al. (1995) estimated t with an exponential relationship between altitude and t. However, these t values were estimated with a limited number of nests in the transition matrices, and t values were used from transects at only three different altitudes. Moreover, other factors such as vegetation type, which may differ at similar altitudes in the tropics due to different soil factors (BrockmannJerosch, 1919; Schro¨ter, 1926), are expected to have a much more direct influence on t than altitude. In this study, we calibrate the matrix technique against values obtained using the monitoring technique, and develop a correction factor. New nest density and all nest density estimates are also compared to test the accuracy of the correction factor. The correction factor can subsequently be applied to estimate t using the matrix technique on a large number of transects. However, two consecutive surveys are still necessary to estimate t using the matrix technique, we would much prefer to be able to assess t with a single survey. We therefore also explore the possibilities of a shortcut in the estimation of t, using correlates of t such as altitude
The sites sampled are all situated in the Gunung Leuser National Park, which is part of the Leuser Ecosystem in northern Sumatra, Indonesia. The Leuser Ecosystem lies predominantly within Aceh, and partly in North Sumatra, Sumatra, Indonesia. The larger Ketambe Area is located between latitudes 3 380 N and 3 420 N, and between longitudes 97 360 E and 97 400 E. The additional transect at Kemiri (3 490 N, 97 300 E) is located on the slopes of Gunung Kemiri. The dryland forests at Ketambe and Kemiri can be described as typical for this habitat type, with a very irregular canopy, a dense understorey and several strata of relatively small trees. The Suaq Balimbing Research Station (03 040 N, 97 260 E) is located in the Kluet region of the Leuser Ecosystem, which lies in the narrow coastal plain between the Barisan mountains and the coast (van Schaik, 1996; Singleton, 2000). Permanent transects were established in three different habitat types: backswamp, transit (peat) swamp, and hills. The transit and backswamp forests can be classified as being rather open, with a more or less continuous tree canopy and a relatively open understorey. Compared with the transit and backswamp forests, the hill forest at Suaq tends to have a higher canopy, although the understorey is similarly open. Total transect length and duration of monitoring of transects at Ketambe and Suaq Balimbing are presented in Table 1. Survey areas at Ketambe and Suaq Balimbing were shown to support relatively high densities of orangutans (Rijksen, 1978; van Schaik et al., 1995) and the habitat is considered to be generally representative for orangutan habitat in Sumatra and Borneo (Rijksen en Meijaard, 1999). 3.2. Field procedures Standard survey methods were used during the line transect surveys (Eberhardt, 1978; Burnham et al., 1980; van Schaik et al., 1995). All transects were located in
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Table 1 Data for 12 transects used for recording orangutan nests in the Larger Ketambe Area, Suaq Balimbing, and Kemiri areas Plot name
Start month
End month
Kemiri
December 1998
January 1999
Larger Ketambe Area Alas Lowland-1 Simpur Lowland-2 Sukarimbun Ketambe-25 Simpang kiri Upland
May 1998 May 1998 May 1998 May 1998 May 1998 May 1998 May 1998 May 1998
Suaq Balimbing Transit swamp Hills Backswamp
March 1997 March 1997 March 1997
Mean altitude (m)
Length (m)
pH soil (H2O)
2
1183
3000
5.30
July 1999 July 1999 July 1999 July 1999 July 1999 July 1999 July 1999 July 1999
8 8 8 8 8 8 8 8
277 366 622 660 673 788 1096 1281
1750 4050 4000 2500 3000 1200 2500 2500
6.30 4.85 – 4.29 – – – –
June 1999 June 1999 June 1999
28 28 28
5 100 5
1865 3085 1550
5.15 4.30 5.85
primary forest. Van Schaik et al. (1995) suggest that habitat variation may affect reliability when long transects go through different habitats with different densities. Hence, single transects were limited to one type of habitat at both Ketambe and Suaq as much as possible. Altitude was measured every 100 m along transects (Table 1). Data on the soil pH of the transects at Ketambe and Suaq were taken from previous studies (Ketambe: van Schaik and Mirmanto, 1985; Suaq: C.P. van Schaik, unpublished data; see Table 1). The pH data are mean figures, as transects were sometimes located on a complex of soil types with differing acidities. Measurements were made at 5–10 cm depth using a simple Hellige pH meter, whose values were highly correlated with pH–KCl values obtained from soil samples in the laboratory (van Noordwijk and Hairiah, 1986). Systematic nest counts from line transects were conducted by experienced nest counters only. Inter-observer tests showed that there were no differences in observer ability (P < 0.03; t-test). Two features were recorded to obtain the parameters needed to calculate orangutan densities: (1) the estimated perpendicular distance of the nest from the transect line, and (2) the stage of decay of the nest. Five classes were used to indicate decay stages (van Schaik et al., 1995): class 1: fresh, new nest; leaves are still green; class 2: older; leaves may still be attached, original shape is conserved, and no holes are visible in the nest; class 3: old; most leaves are gone and holes are visible in the nest; class 4: leaves are gone and holes visible in structure; and class 5: twigs and branches still present but no longer in original shape of nest.
No. surveys
The distinction between classes 4 and 5 was only made at Suaq. At Ketambe, class 4 included all nests that would have been classed as 4 or 5 at Suaq. This slight discrepancy in the methods at the two sites is not likely to affect decay rate estimation, since the calculation of the total time that nests were visible remains similar. Six additional attributes of each nest were also recorded to assist with their recognition on subsequent surveys. These were (1) the height of the nest, (2) the height of the nest tree, (3) the distance of the nest from the top of the tree, (4) the position of the nest in the tree relative to the main stem, (5) the angle between the transect line (direction of the trail) and the direction of the position of the nest, and (6) the local (Gayo) name of the nest tree. 3.3. Parameters p and r In both the Ketambe and Suaq populations, ca. 10% of individuals are infants, who do not construct nests (van Schaik et al., 1995). Consequently, the value of p adopted here is 0.9. The average value of 1.7 for r is also based on previous studies at Ketambe and Suaq (Rijksen, 1978; van Schaik et al., 1995). It was assumed that the values of these parameters at the Kemiri site are similar to those at Ketambe and Suaq. 3.4. Estimating strip width (w) To increase the accuracy of estimating w, transects with similar distributions of perpendicular distances were pooled. The distribution of the perpendicular distances of nests was compared for all transects with the Kolmogorov–Smirnov test. Four transects or groups of transects with similar distributions of perpendicular
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distances could be identified: Ketambe transects including Kemiri, backswamp, transit swamp, and hill transect at Suaq. Their w value was calculated using the computer program ‘‘Distance 4.0. Beta 3.0’’ (Thomas et al., 2001). In accordance with recommendations of Buckland et al. (1993), data were truncated and grouped before analysis. Up to 10% of highest perpendicular values were considered for truncation (Buckland et al., 1993). Detection intervals of perpendicular histograms were varied to ‘‘smear’’ possible heaping at favoured distances; histograms were analysed with cut-off points at 4, 5, 6, 7, 8, and 9 m intervals. Five recommended models were selected to fit the distance histograms (Buckland et al., 1993): uniform with cosine expansions, half-normal with cosine or hermite expansions, and hazard rate with either cosine or simple polynomial expansions. Model selection was based on Akaike’s information criterion (AIC) values; models that gave the lowest AIC value were used to estimate w (for explanation see Buckland et al., 1993). However, when a model with the lowest AIC value also gave significant goodness-of-fit values, the model with the second lowest AIC value was given priority, since significant goodness-of-fit statistics may indicate that the wrong model is being fitted to the detection histogram (Buckland et al., 1993). 3.5. Determining correction factor Cf In order to estimate Cf to determine true nest numbers, new nest densities calculated with new nests made between successive surveys were compared to true densities in areas where these were known. Cf was estimated separately for three different forest types: combined transit and backswamp forest at Suaq, hill forest at Suaq and lowland forest at Ketambe. Rijksen (1978) estimated the density at Ketambe at ca. 5 ind/km2, based on the presence and absence of individual orangutans in the study area there over a 4-year period. More recent estimates arrived at a similar density (e.g. T. Mitra Setia in van Schaik et al., 1995). Rijksen’s (1978) study area was situated in the lowland part of the Larger Ketambe Area, which is covered by our Alas, Lowland-1, Lowland-2 and Ketambe-25 transects. Similarly, true densities in the hills, transit and backswamp areas at Suaq were determined by Singleton (2000). His study, based on direct sightings, showed that the density in the transit and backswamp area was about 7 ind/km2 on average. True density in the hills around Suaq was estimated at 1–1.5 ind/km2 (Singleton, 2000). However, owing to the occurrence of a mast episode there, which attracted relatively high numbers of orangutans (Singleton, 2000), density at the time of the present study was estimated at 4 ind/km2 (C.P. van Schaik, unpublished data). This period lasted for 4 months, indicated by an increase in new nest counts
107
from May until August 1997. Therefore, after weighting for this mast episode, we obtained an average figure of 1.64 ind/km2 over the whole of our 28 months of study at Suaq. 3.6. Estimating t with the monitoring technique Data from Suaq Balimbing were used to estimate nest decay rate (t) using the nest monitoring technique. For Ketambe, no nest monitoring data were available. The duration of survival of each nest at Suaq was calculated as the total number of months the nest was observed in the field, including the first and the last month of observation. Nests that were already existent at the onset of the study (i.e. March 1997) were removed from the analysis since their full duration of visibility could not be known. Nests that were still present at the end of the study were included in the analysis as censored nests. The overall survival of nests in each of the three forest types was assessed using the Kaplan–Meier estimate of survival in JMP1 statistical software. The JMP1 software deals with censored nests differently from regular nests. The Kaplan–Meier test is the preferred method of calculating survival and creating estimated survival curves (Motulsky, 1995). Data were analysed using the Wilcoxon test for homogeneity (Tschopp et al., 1999). Log-Rank tests are typically the preferred method of testing for similarity (Tschopp et al., 1999). This method, however, is only applicable if Kaplan–Meier survival curves do not cross, indicating departure from potential hazards, a requirement of the Log-Rank test (Peto et al., 1977). 3.7. Estimating t with the matrix technique Data from the Suaq, Ketambe and Kemiri transects were used to determine t with the transition matrix technique. Transects in the Larger Ketambe area were surveyed with bi-monthly intervals over a period of 15 months (seven transition matrices; Table 1). Transects at Suaq Balimbing were surveyed with monthly intervals over a period of 28 months (14 independent transition matrices; Table 1). For comparison with the Ketambe matrices, t values were also estimated from Suaq matrices with bi-monthly intervals. The transect at Kemiri was surveyed twice (December 1998 and January 1999) with an interval of 30 days. Since minor variations in the transition probabilities of a single matrix may cause large variations in estimating the duration of visibility, we investigated whether the transition matrices from each transect at Ketambe and Suaq could be summed to increase the accuracy of the estimation. Hence, matrices from each transect at Ketambe and Suaq were compared for similarity using goodness-of-fit statistics (Bishop et al., 1975).
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4. Results
4.3. Estimating t with the monitoring technique
4.1. Estimating w
Nest survival curves in the different habitats were analysed using the Wilcoxon test for homogeneity. The Log-Rank test was ruled out because survival curves crossed at several points, and because the error distribution was a better fit with the Wilcoxon test requirement of logistic distribution. The Wilcoxon test indicated that no significant difference occurred between nest survival times in backswamp and hills (P=0.08), backswamp and transit swamp survival (P =0.44) and transit swamp and hills survival (P=0.24). Survival curves were plotted for nests in all three forest types (Fig. 2). The mean survival time for nests was calculated; resulting values were 206.42 days for the transit swamp (S.D.=0.23), 227.70 days for the hills (S.D.=0.29) and 192.73 days for the backswamp (S.D.=0.23).
Models used for estimating w and analysis statistics are presented in Table 2. For the Ketambe, backswamp and hill transects, models that gave the lowest AIC values were selected; for the transit swamp transect, the model with the second lowest AIC value was selected, since this was the only model that gave a non-significant goodness-of-fit statistic. Only two values of w 2 are significant. Detection functions are presented in Fig. 1a–d. 4.2. Determining f and Cf New nest density estimates, true densities and resulting correction factors Cf for the three habitats surveyed are shown in Table 3. These correction factors indicate that more nests were missed in the lowland forest at Ketambe than in the transit and backswamp at Suaq. Relatively few nests were missed in the hill forest at Suaq.
4.4. Estimating t with the matrix technique No significant differences were found between transition matrices from each transect at Ketambe; similarly, no differences were found between matrices from the
Table 2 Models selected for estimating the strip width (w) on each transect (or group of pooled transects), with selection criteria (Akaike’s information criterion), and the resultant estimated strip widths Transects
Model
w2
AIC
d.f.
Cut-off points (m)
% Data truncated (truncation point in m)
CV%
n
Estimated w
Ketambe Ketambe Ketambe Backswamp Backswamp Backswamp Transit swamp Transit swamp Transit swamp Hills Hills Hills
Half-normala Half-normal/hermite polynomial Half-normal/hermite polynomial Hazard ratea Hazard rate Uniform/cosine Hazard ratea Hazard rate Half-normal/cosine Hazard ratea Hazard rate Hazard rate
0.550 0.636 0.726 0.140 0.415 0.463 0.083 0.024* 0.012* 0.521 0.198 0.165
3479.2 3848.7 4048.4 1670.1 1762.9 1880.5 2141.7 1849.9 2175.3 1451.1 1709.5 1828.7
3 3 3 2 2 3 2 1 2 3 5 6
5 5 5 8 6 6 8 8 8 8 5 5
5.49 1.86 0.40 1.66 5.49 3.49 1.50 6.94 1.63 5.74 8.51 5.74
2.84 4.82 4.68 3.21 3.62 5.95 2.87 3.20 8.37 3.21 3.13 3.48
1119 1162 1179 591 568 580 724 684 723 443 430 443
17.51 19.22 19.64 25.28 23.58 23.08 28.61 27.16 28.15 33.45 30.92 31.62
(25) (30) (40) (40) (30) (36) (40) (32) (48) (48) (40) (45)
The percentage of perpendicular values that were truncated is given. The models used to fit the distribution of perpendicular distances consist of a function (e.g. half-normal) with or without a series adjustment (e.g. hermite polynomial). Goodness-of-fit statistics indicate fit of model to detection histogram. a Models used for estimating w. * P <0.05. Table 3 New nest densities and true densities on transects at Ketambe lowland (Alas, Lowland-1, Lowland-2, Ketambe-25), hills at Suaq, and Suaq swamp forest (backswamp and transit swamp), and resulting correction factors Cf (see text) Plots
Average number new nests
Cumulative length (km)
w (m)
p
r
t (days)
Estimated new nest density (ind/km2)
True density (ind/km2)
Cf
Lowland Ketambe Hills Suaq Swamp Suaq
50.1 15.1 40.7
9.5 3.085 3.415
17.51 33.45 27.10
0.9 0.9 0.9
1.7 1.7 1.7
60 30.4 30.4
1.64 1.57 4.73
5.0 1.64 7.0
3.05 1.04 1.48
Note that for the swamp at Suaq, the average w value of the transit and backswamp transects was used. The mean duration of visibility of new nests is similar to the average intervals between surveys.
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Fig. 1. Detection functions (expected detection probability) fitted to distance histograms (observed perpendicular distribution) from transects at Kemiri, Ketambe and Suaq Balimbing. The models selected to fit the distance histograms were (a) a half-normal key without adjustments to truncated nest survey data from the eight pooled Ketambe transects and the Kemiri transect (w =17.51 m), (b) a hazard rate key without adjustments to truncated nest survey data from the hill transect at Suaq (w=33.45 m), (c) a hazard rate key without adjustments to truncated nest survey data from the backswamp transect at Suaq (w=25.28 m), (d) a hazard rate key without adjustments to truncated nest survey data from the transit swamp transect at Suaq (w=28.61 m).
Fig. 2. Survival curves for nests at backswamp, transit swamp and hill transects, Suaq Balimbing.
three transects at Suaq (goodness-of-fit test; P > 0.05 in all cases). A single transition matrix, which is the summed matrix of all those found for a single transect, was therefore used to calculate the mean duration of visibility for the nests on each of the three transects at Suaq,
and on the eight transects at Ketambe. For Kemiri only one transition matrix was available. For the transects at Suaq, t was calculated from transition matrices with monthly and bi-monthly intervals. The estimated t value for each transect is presented in Table 4.
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Table 4 Estimated t values for pooled transition matrices of transects in the Larger Ketambe Area, at Suaq Balimbing (transition matrices calculated with monthly and bi-monthly intervals), and the t value for the single transition matrix from Kemiri; the average interval between repeat surveys, total nest numbers and the number of nest transitions used to calculate the mean duration of visibility are given Plot name
No. of pooled Nest Average Estimated transition numbers interval (days) t (days) matrices
Alas Lowland-1 Simpur Lowland-2 Sukarimbun Ketambe-25 Simpang kiri Upland Transit swamp Hills Backswamp Transit swamp Hills Backswamp Kemiri
7 7 7 7 7 7 7 7 14 14 14 13 13 13 1
56 201 217 186 156 92 100 59 735 470 601 735 470 601 117
60.50 60.25 59.57 60.13 60.25 60.25 61.57 60.13 30.40 30.40 30.40 60.80 60.80 60.80 30.00
174.1 195.9 274.1 305.3 329.3 350.6 421.5 373.4 246.4 249.4 234.1 240.4 269.4 228.7 220.8
4.5. Correcting matrix technique values The t values for the transit swamp, hills and backswamp, estimated with the monitoring technique at Suaq, should yield the most precise estimates of actual nest decay rate. When comparing t values from the monitoring and the transition matrices techniques obtained at Suaq (Table 5), a reasonably good correspondence is apparent (rs=0.80, n=3, for matrix t values with monthly interval; rs=0.99, n =3, for matrix t values with bi-monthly interval). Moreover, the rank order of t values produced by both techniques are similar, with the hill plot producing the highest estimated t value, followed by the transit swamp and the backswamp, respectively. However, the values obtained with the matrix technique are slightly overestimated and must be corrected if they are to be useful. Table 5 presents the available monitoring and matrix values from Suaq and the resulting correction
factors, which should be used when estimating t with the matrix technique. The correction factors for the matrix t values obtained from the nest data at Suaq were tested for their accuracy on the sample of t values calculated with the matrix technique from the Ketambe transects. Table 6 presents the densities estimated from new nest and all nest techniques from these transects, as well as the parameter values involved. The similarity between the new and all nest density estimates from all transects was tested with a regression analysis, which showed that the densities calculated with the new nest technique corresponded significantly with the estimates from the all nest technique (rs=0.75, n=9, P< 0.01). Thus, t values obtained with the matrix technique would suffice after being adjusted with our correction factors. 4.6. Shortcut for t estimation In order to be able to rapidly assess t on transects in the future, we investigated whether corrected t values obtained with the matrix technique in this study could be correlated to altitude or pH of the soil. It was found that the relationship between t and altitude is not exponential: t values of nests from all three transects at Suaq were much higher than the lowest t value at Ketambe, where the transect was significantly higher. Moreover, t values from the highest transect at Ketambe, as well as from the Kemiri transect, indicate that beyond a certain altitude decay rates do not increase but rather level out or even decrease. Thus, altitude is not a major factor in determining t. We also investigated whether soil acidity influenced nest decay rates. This was tested with pH data available for seven transects from Ketambe, Kemiri and Suaq (Table 1). A significant relationship was found between soil acidity and corrected t values (rs=0.61, n=7, P=0.038; Fig. 3). As soil pH increased, the duration of nest visibility decreased. Although confidence limits are large, the aim of the present study is to provide a rapid assessment technique and the linear relationship appears to be strong enough to allow soil pH to be used to predict t on transects.
Table 5 Correction factors for nest decay rates (t) calculated with the matrix technique, using more accurate values from the monitoring technique at Suaq (see text) Plot
t (days) From matrices t (days) From matrices t (days) From Correction factor for matrix Correction factor for matrix monthly interval bi-monthly interval monitoring values monthly interval values bi-monthly interval
Transit swamp Hills Back-swamp
246.4 249.4 234.1
Mean correction factor
240.4 269.4 228.7
206.4 227.7 192.7
0.838 0.913 0.823
0.859 0.845 0.843
0.858
0.849
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Table 6 Comparison of mean all nest density values (dN) and new nest density values (dN new) from the Ketambe and Kemiri transects; total nest counts (‘‘N’’ for mean nest numbers counted on transects; ‘‘N new’’ for number of new nests made between consecutive surveys) are given, as well as variables to estimate density Plot
N
N new
L
w
r
p
t, int
t, cor
Correction factor
dN
dN new
Alas Lowland-1 Lowland-2 Ketambe-25 Simpur Simpang kiri Sukarimbun Upland Kemiri
15.38 69.38 53.63 33.50 83.00 45.88 65.88 25.63 100.50
6.14 12.00 20.57 11.43 24.71 12.57 15.71 7.57 31.00
1.75 4.05 2.50 1.20 4.00 2.50 3.00 2.50 3.00
17.51 17.51 17.51 17.51 17.51 17.51 17.51 17.51 17.51
1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7
0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9
60.50 60.25 60.13 60.25 59.57 61.57 60.25 60.13 30.00
147.83 166.36 259.19 297.67 232.69 357.84 279.59 317.01 189.49
0.849 0.849 0.849 0.849 0.849 0.849 0.849 0.849 0.858
1.11 1.92 1.54 1.75 1.66 0.96 1.47 0.60 3.30
1.08 0.92 2.55 2.95 1.94 1.52 1.62 0.94 6.43
New nest densities were estimated with the average intervals between surveys (t, int). In the all nest densities, corrected t values obtained with the matrix technique were used (t, cor); correction factors for the matrix values are provided. Densities calculated with the new nest technique corresponded significantly with the estimates from the all nest technique (rs=0.75, n=9, P=0.0025).
Fig. 3. The relationship between nest decay time, obtained with transition matrix technique at Suaq Balimbing and Ketambe, and soil acidity. Matrix decay rates were corrected with decay rates obtained from the monitoring technique from Suaq. The 95% confidence levels to the mean are indicated (–).
5. Discussion In this study, an attempt was made to provide an easy method for the rapid assessment of orangutan density. Two major problems are overcome: underestimation of nest numbers above and near the transect line and inaccurate estimation of the decay rate of nests (t). 5.1. Correcting for underestimation of nest numbers near the transect trail When a high percentage of nests is missed directly above the trail, the detection histogram lacks the clear shoulder pattern necessary to accurately estimate w (Buckland et al., 1993). This results in the overestimation of w and the underestimation of density. Ideally, most or all nests are detected above and near the centre line, and an increasing percentage of nests is likely to be missed further from the trail. However, when observers tend to look predominantly sideways
instead of upwards and sideways, detection of nests above the trail is likely to be much less than 100%. This is probably the principal explanation of the detection distribution of nests recorded on the hill and transit swamp transects at Suaq during this study. For the hill transect, the explanation for the lack of a clear shoulder pattern in the detection function may lie in the fact that looking down on slopes is much easier than looking upward. For the transit swamp transect, the relatively high canopy in this part of the swamp may have made looking upwards for longer periods physically difficult for the observers. It should be stressed, however, that missed nests are more likely to go undetected because of inadequate vigilance on the part of the observer and not because nests are actually hidden from view due to dense forests. This is underlined by distribution histograms from Ketambe and Suaq. In the open transit swamp and hill forests at Suaq, relatively more nests were missed above the trail than in the denser forest at Ketambe. Moreover, earlier surveys at Suaq by other
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observers, who strove to minimise missing nests above the trails, yielded distributions similar to those found at Ketambe in the current study (van Schaik et al., 1995). In most cases, non-ideal distributions such as at the transit swamp and hill transects can be solved by fitting one of the robust models recommended by Buckland et al. (1993), and appropriate truncation and grouping of data can limit the problems introduced by observer behaviour. In this study, estimates of w were fairly accurate (Table 2), despite problematic detection histograms from the hill and transit swamp transects. ‘‘Grouping’’, ‘‘truncation’’, and qualities of robust models largely overcame observer problems, including ‘‘heaping’’. However, as recommended by Buckland et al. (1993), it is better to test the effectiveness of survey design and data collection at meeting critical assumptions during a pilot study. Apart from the problems associated with inadequate observation techniques, some nests will be missed even with the best search procedures owing to the multilayered canopy of tropical forests. For the Ketambe transect, in particular, we found that a relatively high proportion of new nests had been missed on a single count (Table 3). The fraction of nests missed (f) in the more open transit and backswamp forests is clearly lower, while f in the hill forest is lowest (Table 3). These results clearly demonstrate the effect of habitat on f: in the dense forest at Ketambe, with multiple understoreys and an irregular canopy, nests are more easily missed than in the more open transit and backswamp and hill forests (note, however, that at least in the hill forest, f could have been slightly overestimated due to overestimating w). Therefore, the correction factor Cf is habitat specific and should be estimated separately for each habitat type. 5.2. Correctly estimating t The variation in estimates of w is relatively small when compared with variation in estimates of t. For example, t values recently determined with the monitoring technique and a large nest sample at Suaq Balimbing (Singleton, 2000) showed that those used earlier by van Schaik et al. (1995) for the transit and backswamp areas at Suaq Balimbing, generated by the matrix technique on a small sample, were underestimated by > 300% (largely due to attempts to reach values similar to values estimated at Ketambe, now known to be biased by over-representation of day nests). We found that estimates of t using the transition matrix technique required only minor correction, provided samples were large enough. However, because at least two visits to the same transect are required when using the matrix technique, we also devised a shortcut. The significant negative correlation of t with the pH of the soil is not unexpected since wood becomes denser
(van Schaik and Mirmanto, 1985), which will decrease the rate of nest decay. This relationship allows us to estimate t in a single visit to a transect, provided the pH of the soil is determined. Since data were collected in a variety of habitats with distinct plant communities, the relationship is likely to provide accurate estimates of nest decay rates throughout the distribution range of the orangutan.
6. Summary of procedures Although the rapid assessment method proposed here is relatively crude, its gains are twofold. First, nest decay rate can be estimated using soil pH, making one visit to a single transect site sufficient to acquire a density estimate. Second, no marking of nests along a permanent transect line is necessary, which reduces the time spent on surveying a single transect. The pH of the soil should first be determined, to estimate t based on the linear relationship between pH and t. A large sample of nests is necessary to be able to accurately estimate w (75–100 nests), and w should be estimated separately for each transect. To arrive at a minimum estimate of Cf, a transect needs to be walked again in the opposite direction. In this second count, every attempt should be made to spot all nests near or above the transect trail, which will allow for calculation of the fraction f of true nest numbers N that went undetected on the first count (Cf=1/(1f)). We expect Cf to vary between 1.25 in open forest to 3 in dense forest. In dense forests with much undergrowth, it may be necessary to leave the trail in locations where the canopy is not easily visible in order to search the centre line more carefully (but note that the measurements should always be taken from the centre line itself; Buckland et al., 1993). When two surveys of a single transect are performed, t may be calculated with the matrix technique by multiplying the matrix t values by 0.85 (Table 5). The density equation is similar to the rapid density assessment equation. When the transect is surveyed twice, density can also be estimated with the new nest technique. This overcomes the problems associated with the estimation of t. Both techniques require a large sample of nests to be able to make reasonably accurate estimates of t and w. Although this technique is less crude than the rapid assessment technique, it requires two visits to a single transect, and precise marking of nests along the transect line. This is not required when estimating density based on a single survey. Where time is limited the choice is between a series of ballpark estimates for many sites and no estimates for most sites, along with a very accurate estimate for one or two sites. In a species where densities vary by an order of magnitude in the same region, the first method is to be preferred. We therefore believe that, if used
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carefully, the method proposed here can be a useful tool for rapid assessment of orangutan densities.
Acknowledgements We gratefully acknowledge the co-operation and support of the Indonesian Institute of Science (LIPI, Jakarta), the Indonesian Nature Conservation Service (PKA) in Jakarta, Medan, and Kutacane (Gunung Leuser National Park Office). RB thanks the Universitas National (UNAS, Jakarta) for sponsoring the research. We thank the Leuser Development Programme (LDP, Medan), especially M. Griffiths and Dr. K. Monk, for strong logistical support. We also thank the LDP staff in Ketambe for providing an excellent research environment. Financial support was generously provided by the Wildlife Conservation Society (Suaq), and the Netherlands Foundation for the Advancement of Tropical Research (WOTRO), the Dobberke Foundation and the Lucie Burgers Foundation for Comparative Behaviour Research (Ketambe). Financial support was very generously provided by the Netherlands Organisation for Scientific Research (NWO). We thank Han de Vries for advise on statistical matters. We thank the European Commission and the Government of Indonesia as the funding agencies for the Leuser Development Programme.
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