Habitat Characteristics and Their Effectson the Density of Groups of Western HoolockGibbon (Hoolock hoolock) in Namdapha NationalPark, Arunachal Pradesh, India

Parimal Chandra Ray1 & Awadhesh Kumar1 &

Ashalata Devi2 & Murali C. Krishna1 & M. L. Khan3&

W. Y. Brockelman4

Received: 7 November 2014 /Accepted: 4 February 2015# Springer Science+Business Media New York 2015

Abstract Understanding the relationship between a species and its habitat is crucial forconservation action planning. The Endangered western hoolock gibbon (Hoolockhoolock) has a fragmented distribution in northeast India, Bangladesh and parts ofwestern Myanmar. Namdapha National Park in Arunachal Pradesh contains the largestpopulation of western hoolock gibbons in India. We carried out an auditory samplingsurvey and habitat analysis of western hoolock gibbons in the park from September to

Int J PrimatolDOI 10.1007/s10764-015-9834-4

* Awadhesh Kumartpileatus@gmail.com

Parimal Chandra Rayparimalcray@gmail.com

Ashalata Devikh_asha@tezu.ernet.in

Murali C. Krishnamurali7mrali@gmail.com

M. L. Khankhanml61@gmail.com

W. Y. Brockelmanwybrock@cscoms.com

1 Department of Forestry, North Eastern Regional Institute of Science & Technology, Nirjuli,791 109 Itanagar, Arunachal Pradesh, India

2 Department of Environmental Science, Tezpur University, Napam, 784 028 Tezpur, Assam, India3 Department of Botany, Dr. Hari Singh Gour Central University, Sagar 470 003 Madhya

Pradesh, India4 Ecology Lab, Bioresources Technology Unit, Science Park, Klong Luang, Pathum

Thani 12120, Thailand

December 2012 in three potentially suitable forest types: tropical broad-leaved forest,tropical wet evergreen forest, and wet temperate forest. A new method of analysis ofsinging frequency revealed that only 21–23% of groups sing per day. Group detectabilitydeclined sharply beyond a listening radius of 600 m. Auditory sampling across 15 listeningareas revealed an estimated mean density of 3.65 groups km−2. We found no significantdifferences in density among forest types. The habitat study revealed a total of 122 speciesof trees (girth at breast height ≥30 cm) in the three forest types, representing 73 genera in 41families, with the highest number of tree species in wet evergreen forest (93) followed bytropical broad-leaved forest (52) and wet temperate forest (40). None of the vegetation traitswe measured (mean canopy cover, girth, density, and total basal area of all trees) and nohabitat disturbance factors correlated significantly with gibbon density. This lack of corre-lation may have been due to the prevailing anthropogenic effects that adversely affectedgibbons and their habitat, such as forest degradation, road widening, and hunting, overrid-ing the relatively smaller natural variation in vegetation. This study adds to our knowledgeof the habitat requirements of hoolock gibbons and indicates that Namdapha National Parkis more important to conservation of the western hoolock than previously thought.

Keywords Auditory sampling . Gibbon density . Habitat characteristics . NamdaphaNational Park .Western hoolock gibbon

Introduction

We need data on primate species density and population size (Hoeing et al. 2013), habitatrequirements, and factors affecting distribution (Hamard et al. 2010; Phoonjampa et al.2011) to develop suitable conservation strategies. Gibbons are highly arboreal and requireconnected tree canopies for dispersal. They are threatened by increased forest fragmenta-tion and discontinuity of canopy. The main source of energy for all gibbon species is ripesucculent fruits of trees and lianas, which involves them heavily in seed dispersalmutualisms (Elder 2009; McConkey 2009). Evidence for an effect of forest type orcondition on gibbon density is ambiguous. Some studies have found correlations betweendensity and major differences in forest height or degree of degradation (Cheyne 2010;Hamard et al. 2010; Phoonjampa et al. 2011), but others have found little effect of variationin forest quality within a forest type (Brockelman et al. 2009; Akers et al. 2013). However,anthropogenic disturbances are typically the most important causes of population decline(Hoeing et al. 2013; Phoonjampa et al. 2011; Phoonjampa & Brockelman 2008).

The western hoolock gibbon (Hoolock hoolock) is a highly threatened primatespecies that lives in tropical evergreen rainforest, tropical wet evergreen and semi-evergreen forests, and subtropical monsoon evergreen broadleaf forests in northeastIndia (Das et al. 2009; Walker et al. 2009). These habitats are highly disturbed byhumans. They live in small territorial groups (two to six individuals) in relatively smallranges (Das 2002; Das et al. 2009). Moreover, the species is distributed patchilythroughout the northeastern states of India except Sikkim (Srivastava 1999), in north-eastern Bangladesh, and in Myanmar east of the Ayeyarwady–Chindwin River system.At present the western hoolock gibbon is listed as Endangered in India and CriticallyEndangered in Bangladesh on the International Union for Conservation of Nature(IUCN) Red List of Threatened Species (Brockelman et al. 2008; Molur et al. 2003).

P. C. Ray et al.

They are also listed in Schedule I of the Indian Wildlife (Protection) Act, 1972, whichsignifies the highest conservation priority. The western hoolock gibbon suffered adrastic population decline of more than 90% from 80,000 to less than 5000 in the lastcentury (Chivers 1977). This trend is continuing in the species’ remaining strongholdsin northeast India (Biswas et al. 2013). VORTEX population simulation models predicthigh rates of extinction for the majority of the small, fragmented populations inBangladesh and Assam, India (Molur et al. 2005). There are estimated to be 2600–4450 western hoolock gibbons in India and about 200 in Bangladesh (Choudhury2006; Molur et al. 2005). The global population is uncertain mainly because ofinsufficient surveys in Myanmar. The main source of uncertainty in India is the lackof surveys in the remote forests of the northeasternmost state of Arunachal Pradesh.

We surveyed the population of the western hoolock gibbon in what may be itslargest remaining continuous forest patch, in Namdapha National Park of ArunachalPradesh, India (Das et al. 2009; Saikia 2014). The park lies within the Himalayan andIndo-Burma global biodiversity hotspots (Conservation International 2005; Myers et al.2000) at the junction of the Palearctic and Malayan biogeographic realms. Although thepark occupies a remote corner of the country, it is exploited by people hunting wildlifesuch as ungulates, primates, and birds and collecting forest products. Additionaldisturbances include road widening and deforestation to facilitate human settlementand jhoom cultivation for paddy and other cash crops within the core zone. Indigenouspeople reside in and around the park in 27 villages, with a total of 1420 households(9618 people), as reported by Arunachalam et al. (2004), who surveyed the area during2001–2003. These villages comprise Chakma, Nepali, Lisu, Singhpo, and Mishmiethnic groups. Thus, the park is the abode not only of a rich biodiversity of flora andfauna, but also of indigenous people who are dependent on its resources.

We aimed to estimate gibbon group density in the three potentiallymost suitable foresttypes present within Namdapha National Park and to analyze the relationship betweengibbon density and forest characteristics to understand better the habitat requirements forgibbons in the park and how disturbance of the forest might affect gibbon populations.

An additional objective developed during the study was to improve the auditorysurvey method commonly used for sampling gibbon populations (Brockelman and Ali1987; Brockelman and Srikosamatara 1993; Brockelman et al. 2009; Hamard et al.2010; Nijman and Menken 2005; Phoonjampa et al. 2011) by developing a new way ofestimating the probability of singing in a given number of days.

Methods

Study Site

Namdapha National Park (27°23′–39′N; 96°15′–58′E) is located in Changlang District ofArunachal Pradesh, and covers an area of 1985 km2 including a 177 km2 area under abuffer zone (Nath et al. 2005). With an altitudinal range of 200–4571 m asl, three majorbiomes occur in the park: tropical, temperate, and alpine (Champion and Seth 1968). Thetemperature varies with altitude and ranges 4–35°C at forested altitudes below 2000 m.At higher elevations the temperature often falls below freezing during winter (Sarmahand Arunachalam 2011). Monthly precipitation ranges from a minimum of 1400mm to a

Habitat Effects on Western Hoolock Gibbon

maximum of 2500 mm, 75% of which falls between April and October (Kumar et al.2009). As a result of the great altitudinal range, temperature, and high rainfall, there ishigh species diversity within the eight types of vegetation present, viz., alpine, subalpine,mixed coniferous forest, wet temperate forest, subtropical pine forest, tropical wetevergreen forest, tropical broad-leaved forest, and bamboo (WWF 2011).

Sampling Sites

We censused gibbons at 15 survey sites (Fig. 1) in the three broad-leaved forest typeswith tall trees and continuous canopy cover: tropical broad-leaved forest, tropical wetevergreen forest, and wet temperate forest. These forest types have a total altitudinalrange of 200–2000 m asl and occupy 68% of the total area of the park (WWF 2011).These forests represent the potential distributional range of hoolock gibbons andinclude the broad-leaved, tropical wet evergreen and semi-evergreen forests withaltitudinal range of 200–1400 m asl described by Walker et al. (2009).

We originally intended to sample the entire forest area of the park, but during thesurvey relations between Lisu tribal hunters and park personnel were belligerent and wehad to modify our plans. Our listening areas were scattered throughout most of thenorthern, western, and eastern parts of the park but we were prohibited from working inthe southern areas of the park over the mountain range south of the Noa Dehing River.

Fig. 1 Map showing study areas forwestern hoolock gibbons inNamdaphaNational Park,Arunachal Pradesh, India.

P. C. Ray et al.

Thus, approximately one-third of the suitable forest of the park remains to be surveyed.Four of our sample areas were in the buffer zone and the rest were placed in represen-tative and accessible locations in the western, northern, and eastern areas accessible fromthe road being constructed on the south bank of the Noa Dehing River (Fig. 1).

Estimation of Gibbon Density

Weused the auditory samplingmethod commonly used for gibbons (Brockelman andAli1987; Brockelman and Srikosamatara 1993; Brockelman et al. 2009; Cheyne et al. 2008;Hamard et al. 2010; Nijman and Menken 2005; O’Brien et al. 2004; Phoonjampa andBrockelman 2008). This method has been proven to be particularly successful wheresurvey areas are in mountainous or undulating, partly inaccessible terrain with featuressuch as steep river valleys and ridges (Brockelman and Srikosamatara 1993; Nijman2004). We located the 15 listening areas using hand-held global positioning systemdevices and contour maps, and chose them based on accessibility and for maximumcoverage of the available gibbon habitat. At each site, we established three listening postson high points, often along former logging tracks, on ridges or hills, positioned 328–459m (mean 395m) apart. The elevation of listening posts ranged 289–993 m asl. Before thestart of the survey team members underwent 2 weeks of training in field censustechniques and data analysis. We manned each listening post for 5 consecutive days toensure that we heard all or nearly all groups (Brockelman and Srikosamatara [1993]recommend a sampling period of at least 3 consecutive days). At least one person sat ateach post from 06:00 h to 12:00 h and recorded the details of all vocal bouts (primarilyduets of mated pairs), including the starting and finishing times, compass bearing, andestimated distance. We collected data only during the dry season from September toDecember 2012, as singing bouts were limited during the rainy season.

We estimated the density of gibbon groups in each listening area by mappinglocalities where gibbon groups were vocalizing using triangulation from two or threelistening posts. We counted only duets indicating mated pairs and excluded solo calls.Hoolock groups commonly sing more than once in the morning from different locations(Brockelman et al. 2009). We considered songs that mapped >500 m apart to originatefrom separate groups, a rule of thumb suggested by Brockelman and Ali (1987). Thisdistance is based on the approximate diameter of a group’s home range and determinesthe maximum distance that a gibbon might move between song bouts. A maximumcircular home range of 63 ha for Hoolock hoolock (Ahsan 2001; Kakati 2004) wouldhave a radius of 466 m. We thus considered that 500 m was a conservative separationfor calling groups in Namdapha. Group calls given simultaneously were attributed toseparate groups, but if they occurred during different time periods and were sufficientlyclose we considered them to have come from the same group unless acoustic evidencesuggested that they were different.

We calculated density (D) from the formula (Brockelman and Ali 1987)

D ¼ n

p mð Þ � A½ �where n is the number of groups mapped in the listening area, p(m) is the estimatedproportion of groups expected to be detected and mapped at least once during a sampleperiod of m days, and A is the listening area. The value of p(m), which is used as a

Habitat Effects on Western Hoolock Gibbon

correction factor, can be estimated using the formula (Brockelman and Srikosamatara1993) with assumption that calling on successive days is independent:

p mð Þ ¼ 1− 1− p 1ð Þ½ �m

with p(1) being the detection probability for a single day. The term on the right is oneminus the estimated probability that a group will not sing and be detected during aperiod of m days.

As we do not know the total number of groups (N) in the listening area, we must finda way of estimating the value of p(1). If the probability of singing per day is low (i.e.,much below 0.50), we may not detect a sufficiently high proportion of groups inm daysand may require the use of p(m) as a correction factor. This was the case in the presentstudy and thus we devised a method of estimating p(1) and p(5) from the numbers ofgroups actually detected. Consider n(1) as the mean number of groups detected per dayin a listening area and n(m) as the number detected within m days. Their ratio, R = n(1)/n(m), will vary with p(1) and should be equal to p(1)/p(m), the ratio of proportions of Nthat are expected to be detected. The formula p(m) = f [p(1)] given in the equationabove is substituted for p(m). Then, the equation R = p(1)/p(m) is solved for p(1), whichallows calculation of the correction factor. At least for m = 4 or 5, there is no closedanalytical solution for p(1), but its value is easily obtained by trial and error on aMicrosoft Excel worksheet. We obtained values for m = 5 for the three main foresttypes by pooling the n(1) and n(5) data within forest types. Generally, the number ofgroups in a single listening area does not allow reliable estimation of p(1), which canvary greatly among groups (Brockelman and Srikosamatara 1993).

As we know nothing about the frequency distribution of n(1)/n(5), we used thejackknife method to determine means, standard errors, and 95% confidence intervals onR estimates before converting them to the p(5) scale and estimating correction factorsfor the total estimate of density over all sites.

Assumptions about the listening area are critical to density estimation. This area can bedetermined by assuming a listening radius that defines the maximum distance withinwhich all, or nearly all, groups can be detected and mapped by triangulation (Brockelmanand Ali 1987). We made two estimates of density, based on listening areas that assumelistening radii of 0.6 km and 1 km (a 0.6-km radius gives a listening area roughly half thatof a 1-km radius). Comparing the densities resulting from these two assumptions allowsus to determine if there is a significant loss of audibility within 1 km. We made densityestimates using SuperMapGIS Pro software with radii r of 0.6 km and 1 km (Brockelmanet al. 2009; Nijman and Menken 2005). The total survey effort covered an aggregatelistening area of 29.4 km2 (r = 0.6 km) and 67.4 km2 (r = 1 km) during 75 survey days.

Measurements of Habitat Characteristics

We measured habitat characteristics to investigate the relationship between foreststructure and gibbon density using 10–21 10 m × 10 m quadrats per listening site(Hamard et al. 2010). We placed quadrats in relatively level representative forest areasaround each listening post at a regular spacing of 30 m. We collected the following foreach quadrat: 1) mean canopy cover at a height of 20 m, at each corner and in the middleof the quadrat, using the point intercept method (Canfield 1941); 2) girth at breast height

P. C. Ray et al.

(GBH) (1.37 m above ground) of all trees with GBH ≥ 30 cm; 3) herbarium specimensof trees for drying and further identification. We converted GBH into basal area (BA)using the formula BA= (GBH)2/4π and used as an indicator of tree biomass. Wesummarized all data into four vegetation variables for each quadrat and calculated 1)mean canopy cover; 2) mean GBH of all trees ≥30 cm; 3) density (trees ha–1) of all trees≥30 cm; and 4) total basal area (m2 ha–1) of all trees ≥30 cm.

We calculated community parameters including frequency, density, abundance, andImportance Value Indices (IVIs) (Curtis and McIntosh 1950). We calculated IVI valuesfor each species by summing the relative frequency, relative density, and relativedominance (Phillips 1959). We measured species diversity with the Shannon–Wienerindex H (Shannon and Weiner 1963), and the Simpson index of dominance D(Simpson 1949) using IVI values as suggested by Magurran (1988). We measuredthe evenness of relative abundance with the index J (Pielou 1975).

Habitat Disturbance Factors

In addition to general habitat characteristics and estimation of gibbon density, we alsomeasured the distance from the border of each listening area to the nearest humansettlement and paddy field using Super Map GIS Pro software.

Data Analysis

We tested for normality of each vegetation correlate and gibbon density using Kolmo-gorov–Smirnov tests and visual inspection of histograms, normal Q–Q plots, and boxplots. Nearly all of these variables were non-normal, so we used the nonparametricKruskal–Wallis test to compare vegetation variables among the three forest types. Weused Mann–Whitney U tests for pairwise comparisons of means for each of thecorrelates between forest types. Spearman’s correlation tests to investigate correlationsbetween gibbon density and each of the vegetation correlates as well as the distance fromthe border of each listening area to the nearest human settlement and paddy field. Weconducted all statistical analyses with SPSS 16.0.Vwith a significance level of P < 0.05.

Results

Gibbon Density and Population

We heard and mapped a total of 124 gibbon groups across the 15 listening sites inNamdapha National Park, within a total listening area of 67.4 km2 within 1 km oflistening posts (Table I). Tropical broad leaved forest occupies 55.7% of the gibbonhabitat in the park, wet tropical forest 31.6%, and tropical wet evergreen forest 12.6%(Table II). The tropical broad-leaved forest is somewhat underrepresented in the survey (4of 15 sample areas; 27.2% of the total listening area) because of its relative inaccessibility.

The estimates of p(1), the proportion of groups estimated to sing on a single day, wererelatively similar across forest types, with a mean of 0.212 for the 1-km radius and 0.233 forthe 0.6-km radius (Table III). These estimates lead to substantial corrections of gibbon groupdensity of +43.6% for the 1-km listening radius and +36.2% for the 0.6-km listening radius.

Habitat Effects on Western Hoolock Gibbon

The mean density estimates were 2.4–2.6 groups km−2 for the 1-km listening radiusand 3.3–5.0 groups km−2 for the 0.6-km radius (Table III). The differences amonghabitat types were not significant for the 1-km radius (ANOVA; F2,15 = 0.508, P =0.776) or for the 0.6-km radius (ANOVA; F2,15 = 2.381, P = 0.304). The overall densityestimate for all sample areas is 3.65 groups km−2 for the 0.6-km listening radius, and

Table I Areas and numbers of groups of western hoolock gibbon (Hoolock hoolock) mapped in all sites inNamdapha National Park, India, in 2012 for 1-km and 0.6-km listening radii (r)

Forest type and site r = 1.0 km r = 0.6 km

Area (km2) Mean n1 n5 Area (km2) Mean n1 n5

Tropical broad leaved

Site 1 4.3 2.6 8 1.9 1.6 5

Site 2 4.5 1.4 6 2.0 1.2 5

Site 3 4.8 3.8 12 2.1 3.4 11

Site 4 4.7 1.8 6 2.1 1.8 6

Tropical wet evergreen

Site 1 4.4 2.8 11 1.9 1.8 7

Site 2 4.3 3.2 7 2.2 1.8 3

Site 3 4.7 2.4 8 2.1 1.2 6

Site 4 4.2 2.2 9 1.8 1.4 5

Site 5 4.3 4.6 13 1.8 3.4 9

Site 6 4.6 3.2 8 2.0 0.8 2

Site 7 4.7 2.8 8 2.1 1.4 5

Wet temperate

Site 1 4.4 0.8 4 1.9 0.6 3

Site 2 4.3 2.4 10 1.9 1.6 7

Site 3 4.9 2.4 7 1.9 1.4 3

Site 4 4.3 2.6 7 1.8 0.6 2

Total 67.4 124 29.5 79

n1 = number of gibbon groups heard on average in one day of listening; n5 = number of gibbon groupsmapped in 5 days.

Table II Summary of western hoolock gibbon listening area characteristics for the three forest types inNamdapha National Park, India, during September–December, 2012

Forest type Total area(km2)

Elevation range(m msl)

No. ofsites

Total listening area (km2)

1-km radius 0.6-km radius

Tropical broad leaved 731 438–749 4 18.3 8.1

Tropical wet evergreen 166 289–993 7 31.2 13.9

Wet temperate 415 472–903 4 17.9 7.5

Total 1312 289–993 15 67.4 29.5

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2.60 groups km−2 for the 1-km radius (Table III). These estimates are corrected for thegroups estimated to have been missed in the 5-day listening samples.

Based on the 1-km radius areas we estimate the total number of gibbon groups in thepark, weighted by forest type, at 3360. Based on the 0.6-km radius area this figure is5160. These numbers do not have good estimates of the error because the numbers ofsites per forest type are too small for error estimation. For the overall unweightedestimate for the park, we estimate 3400 ± 380 (SE) groups for the 1-km listening radiusand 4800 ±750 (SE) groups for the 0.6-km listening radius. The number of individualgibbons can be estimated by multiplying the number of groups by mean group size,estimated to be 3.47 individuals (range = 2–5 individuals), based on counts of 36groups from Namdapha National Park (Ray et al. 2014). However, these projectionsshould be treated with caution because we did not sample a large portion of the habitatarea of the park.

Forest Composition and Habitat Characteristics

We recorded a total of 122 species of trees from the three forest types, representing 73genera in 41 families. Fourteen families were represented by a single species, whilenine families were represented by more than five species. The dominant families in thestudy area were Meliaceae, Moraceae, Euphorbiaceae, Fagaceae, and Lauraceae. Thetropical wet evergreen forest had the highest number of species at 93, compared with 52tree species in tropical broad-leaved forest and 40 tree species in wet temperate forest.Tropical wet evergreen forest had the highest species richness and evenness, with manyrelatively rare species, which resulted in high Shannon–Wiener index and low Simpsonindex, as compared to those in the other forest types (Table IV). We found significantdifferences only for mean GBH of all trees and density of all trees of girth ≥30 cmamong the forest types (Table IV).

Table III Summary of estimated western hoolock gibbon group detection probabilities, correction factors,and estimated corrected densities, using 1-km and 0.6-km listening radii, for three different forest typesseparately and combined, in Namdapha National Park, Arunachal Pradesh, India

Forest type 1-km radius 0.6-km radius

p1 p5 Mean density± SEa

p1 p5 Mean density± SEa

Tropical broad leaved 0.205 0.682 2.6 ± 0.44 0.198 0.668 5.0 ± 0.98

Tropical wet evergreen 0.264 0.784 2.6 ± 0.26 0.236 0.740 3.6 ± 0.70

Wet temperate 0.192 0.656 2.4 ± 0.44 0.170 0.606 3.3 ± 1.03

Total 0.212 0.696b 2.60 ± 0.29 0.233 0.735c 3.65 ± 0.57

Detection probabilities: p1 = probability of detecting a gibbon group in one day; p5= probability of detecting agibbon group at least once in 5 days, which is also equal to c, the correction factor for inability to detect allgroups within 5 days.a These estimates have been divided by the respective correction factors. Differences in density among foresttypes are not statistically significant (Mann Whitney U test, P > 0.05).b Jackknifed 95% confidence interval for the correction factor = 0.563–0.787.c Jackknifed 95% confidence interval for the correction factor = 0.533–0.848.

Habitat Effects on Western Hoolock Gibbon

Tree girth (U = 6437, P = 0.007) and tree density (U = 1900, P = 0.031) weresignificantly higher in tropical broad-leaved forest than in tropical wet evergreen forest.Tropical broad leaved forest also had significantly higher girth (U = 2504, P = 0.022)and tree density (U = 328, P < 0.001) than wet temperate forest. Converted todiameters, trees of the tropical broad-leaved forest had a mean diameter breast heightof 47.2 cm and those of the wet temperate forest had a mean diameter of 42.7 cm. Treedensity was higher in tropical wet evergreen than in wet temperate forest (U = 697, P <0.001). The girth of all trees, however, did not differ between the tropical wet and wettemperate forest types (U = 56,280, P = 0.662).

Relationship Between Habitat Characteristics and Disturbances with GibbonDensity

The mean gibbon density was not significantly correlated with any of the measuredvegetation variables or the habitat disturbance factors (Table V).

Discussion

Population Density and Habitat Characteristics

Our estimated gibbon densities using auditory methods are higher than virtually allprevious studies carried out in other parts of the range of the western hoolock

Table IV Mean ± SE habitat and vegetation characteristics for three forest types in Namdapha National Park,Arunachal Pradesh, India, September–December 2012

Variable Forest type Kruskal–WallisH and P

Tropical broadleaved(N = 94)

Tropical wetevergreen(N = 52)

Wet tropical(N = 39)

Canopy cover (%) 57.6 ± 2.2 60.4 ± 5.6 56.6 ± 3.0 H = 2.6; P = 0.269

Tree girth (cm) 148.2 ± 4.6 139.9 ± 4.2 134.3 ± 5.6 H = 7.7; P = 0.015

Basal area of trees (m2 ha−1) 121.5 ± 0.9 144.4 ± 0.3 102.8 ± 0.9 H = 4.0; P = 0.133

Density of trees (trees ha−1) 562.5 ± 2.9 636.0 ± 1.0 536.6 ± 2.9 H = 42.7; P < 0.001

Distance from nearest humansettlement (km) (N = 15 sites)

4.23 ± 0.6 2.4 ± 0.7 4.81 ± 0.8 H = 4.9; P = 0.082

Distance from nearest paddyfield (km) (N = 15 sites)

3.21 ± 0.8 2.22 ± 0.7 3.44 ± 0.8 H = 1.4; P = 0.474

Number of species 52 93 40

Number of genera 39 60 33

Number of families 27 36 18

Evenness Index (J’) 0.79 0.87 0.83

Shannon-Wiener (H’) 3.12 3.93 3.06

Simpson Index (D) 0.10 0.03 0.08

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(Table VI). However, they are in the same range as those estimated for more southernhoolock populations in Myanmar such as Mahamyaing wildlife sanctuary (Brockelmanet al. 2009) and in more northern areas (2–3.5 groups km–2: Saw Htun andBrockelman, unpubl. data). Estimated densities from studies that covered a total of26 listening areas in five forest complexes prioritized for conservation of the westernhoolock in Karbi Anglong, Assam, were lower than two groups km−2 (Biswas et al.2013). It is possible that habitat disruption and human encroachment differ significantlybetween Namdapha National Park in Arunachal Pradesh and the sites covered inAssam. However, a major cause of the difference was the use of a correction factorto compensate for the relatively low frequency of singing in our study, whichsubstantially elevated our estimates of density. The relatively low probability ofsinging per day may be the result of gibbons becoming more secretive and singingless because of the presence of hunters who shoot at them, as found for the Borneangray gibbon and the Moloch gibbon in Indonesia (Nijman 2001).

Our estimates of density are also considerably higher than previous estimates madeusing line transect methods in Namdapha National Park (Chetry et al. 2003) (Table VI).Line transect methods, which depend on visual encounters and not singing frequency,are difficult to apply in steep rugged terrain and may detect relatively fewer groups thanare detected by auditory methods. However, in other studies, transect methods havebeen found to yield density estimates similar to those obtained using auditory methods(Hoeing et al. 2013; Nijman and Menken 2005; Waltert et al. 2008). Chetry andcolleagues’ survey was designed to record all primate species across the buffer regionsof the park. Thus, some of the difference between our estimates and those of Chetryet al. (2003) may also be due to differences in the area covered. The buffer regions aremore heavily disturbed by human settlements lying to the north of the Noa DehingRiver (Arunachalam et al. 2004). In our survey only four samples were in or very nearto the boundary of the buffer zone.

Use of a smaller 0.6-km listening radius yielded an overall mean density of groups40% higher than a 1-km listening radius. This is in accordance with other studies ongibbon density (Biswas et al. 2013; Brockelman et al. 2009; Geissmann et al. 2013;Lwin et al. 2011; Nijman and Menken 2005). The estimates based on the 1-km radius

Table V Correlations between western hoolock gibbon density (using two different listening radii) andvegetation characters in Namdapha National Park, Arunachal Pradesh, India, during September–December2012

Vegetation characters Spearman’s correlation coefficient and significance level

1.0-km radius 0.6-km radius

Canopy cover (%) −0.077 (P = 0.786) −0.184 (P = 0.511)

Girth of trees (cm) −0.22 (P = 0.938) −0.413 (P = 0.126)

Basal area of trees (m2 ha−1) 0.513(P = 0.051) 0.395 (P = 0.145)

Density of trees (trees ha−1) 0.356 (P = 0.139) 0.387 (P = 0.154)

Distance from nearest human settlement (km) 0.186 (P = 0.507) 0.048 (P = 0.864)

Distance from nearest paddy field (km) −0.401 (P = 0.139) 0.324 (P = 0.239)

N = 15 sites in all cases.

Habitat Effects on Western Hoolock Gibbon

are likely to be biased downward, as some groups between 0.6 km and 1 km from thelistening posts were probably missed because they were in ravines or behind hills.

The lack of correlation between gibbon density and forest vegetation variables (withthe exception of tree diversity), also noted in other studies (Akers et al. 2013;Brockelman et al. 2009; Hamard et al. 2010; Pacuilli 2010), may have been due toforest disturbance. To begin with, these variables did not vary greatly between sites andforest types in the region. Virtually all forests with continuous canopy and adequatefruit tree diversity are good habitats for gibbons (Das 2002; Muzaffar et al. 2007).Forest disturbance and huntingmay bemore significant factors than forest structure, andmayhave reduced gibbon densities at some sites, as found inMyanmar (Brockelman et al. 2009).

Our study has yielded several important conclusions: 1) the need for standardizationof survey methods, especially in the use of auditory methods; 2) the lack of majoreffects of forest type and habitat condition on gibbon density up to about 1000 m asl;and 3) the importance of Namdapha National Park in harbouring India’s largestpopulation of western hoolock gibbons, which survive at relatively high density despiteincreasing threats to their existence.

Conservation Issues

Namdapha National Park may be the largest dipterocarp forest (dominated by gianttrees of the Asian family Dipterocarpaceae) left in the region (Arunachalam et al. 2004)and may be the largest continuous forest patch left for the conservation of the westernhoolock gibbon in India (Das et al. 2009). However, the future of hoolock gibbons andother primates in this forest is uncertain and depends on improved conservation efforts.Lisu villagers living in and around the park have reported to us that they do not targetgibbons for bushmeat, and that they subsist primarily on meat from wild boar (Susscrofa), barking deer (Muntiacus muntjak), sambar (Rusa unicolor), and sometimesAssamese macaques (Macaca assamensis) and rhesus macaques (Macaca mulatta)

Table VI Comparative account of western hoolock gibbon density estimates reported from its distributionalrange

Study area Method Mean group density(km−2)

Reference

Listening radius Mean

1 km 0.6 km

Five priority forest complexes ofKarbi Anglong, Assam, India

Auditory sampling 1.41 1.77 Biswas et al. (2013)

Myanmar Auditory sampling 1.63 2.33 Geissmann et al. (2013)

Myanmar Auditory sampling 1.55 2.77 Lwin et al. (2011)

Namdapha National Park,Arunachal Pradesh, India

Line transect — — 0.74 Chetry et al. (2003)

Namdapha National Park,Arunachal Pradesh, India

Auditory sampling 2.60 3.65 This study

P. C. Ray et al.

(especially when they raid their crops). This observation, together with the findings thatgibbons appear to be relatively secretive and sing less frequently than those in other forests,may explain why gibbons are apparently surviving well in Namdapha National Park.

It is of the highest priority to better assess the effects of forest disturbance, vegetationcover changes, and human impacts such as hunting on gibbon population dynamics inprotected areas (Saikia 2014). From a conservation point of view, however, there is aneed to understand the economic dependency of the people on forest resources toformulate some sustainable and cost-effective solutions to minimize the current pres-sures, especially from forest clearance and hunting, on both the species and theirhabitats. This can be achieved up to a certain level through educating the indigenouspeoples to safeguard this natural wealth for their own well-being, but it must alsoinvolve alternative sources of income to remove their dependency on hunting andsubsistence activities in the forest.

Acknowledgments We thank the Arunachal Pradesh Forest Department, PCCF (Wildlife & BiodiversityConservation) and Field Director of Namdapha National Park for providing necessary permission and logisticsupport to carry out this work. Funding was kindly provided by the Council of Scientific and IndustrialResearch (CSIR) New Delhi, India and The Idea Wild Grant, USA. Our Research team also thanks theDirector of NERIST and HoD, Forestry, for administrative support. Special thanks to Mr. Tajum Yomcha,Research Officer of Namdapha National Park, and our field assistants Mr. Biranjay Basumatary, EreboChakma, Goleh Chakma, Tinku Chakma for their constant assistance during the field survey. We are verythankful for the valuable suggestions and comments on our manuscript by two reviewers.

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