Conserving tigers Panthera tigris in selectively logged Sumatran forests

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Short communication

Conserving tigers Panthera tigris in selectively logged Sumatran forests Matthew Linkiea,*,1, Iding Achmad Haidirb, Agung Nugrohoc, Yoan Dinatac a

Durrell Institute of Conservation and Ecology, University of Kent, Canterbury, Kent CT2 7NR, United Kingdom Indonesian Department of Forestry, Kerinci Seblat National Park, Kerinci, Jambi, Indonesia c Fauna and Flora International-Indonesia Programme, Kerinci, Jambi, Indonesia b

A R T I C L E I N F O

A B S T R A C T

Article history:

The response of most large carnivores to selective logging is poorly understood. On the one

Received 8 May 2008

hand, selective logging may represent loss of important habitat, yet, on the other hand,

Received in revised form

selective logging may increase browse availability for a terrestrial ungulate prey base,

29 June 2008

thereby indirectly benefiting large carnivores. Using a camera trap-based sampling method,

Accepted 6 July 2008

we estimate tiger density in two primary-selectively logged forest areas that straddle Ker-

Available online 19 August 2008

inci Seblat National Park, Sumatra. We then investigate potential differences between the habitat use of tigers: within these study areas and forest types; and, within the finer-scale

Keywords:

landscape features associated with these covariates. Across the mixed forest study areas,

Deforestation

tiger density estimates (adult individuals/100 km2 ± S.E.) of 2.95 ± 0.56 and 1.55 ± 0.34 were

Detection

produced. However, within these areas, tigers showed a preference for primary over

Habitat use

degraded forest, and this was related to the greater accessibility of degraded forest sites

Large carnivore

to people, e.g., through their proximity to roads. Presently, the majority of Sumatran tigers

Logging

occur within large tracts of primary forest, but these extend outside of the island’s pro-

Presence

tected area borders, and these unprotected forests are especially at risk from the high levels of deforestation in Sumatra. As forest is cleared, previously remote, and therefore safer, tracts of primary forest become accessible and, eventually, degraded. Yet, from our study, degraded forest in combination with primary forest supported sufficiently high tiger densities and can, therefore, make an important contribution to tiger conservation. It is therefore essential to lessen the detrimental effects of accessibility through increasing law enforcement and destroying ex-logging roads. Crown Copyright  2008 Published by Elsevier Ltd. All rights reserved.

1.

Introduction

Throughout the tropics, rates of deforestation show little sign of abating (Achard et al., 2002). The resulting loss and fragmentation of habitat is a key threat to carnivores, especially those that require a large home range relative to their body

size (Woodroffe and Ginsberg, 1998). However, when considered at a finer-scale, tropical deforestation can take several different forms, from complete forest clearance by smallholder farmers such as for coffee, to disturbance by commercial timber operations through selective logging that leaves the forest canopy relatively intact. Whilst the former may

* Corresponding author: Tel.: +44 0 1227 823455; fax: +44 0 1227 827289. E-mail address: [email protected] (M. Linkie). 1 Present address: Fauna and Flora International-Indonesia Programme, Banda Aceh, Nanggroe Aceh Darussalam, Indonesia. 0006-3207/$ - see front matter Crown Copyright  2008 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2008.07.002

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represent an irreversible and principal threat for large carnivores, the latter may not. The response of most large carnivores to selective logging is poorly understood. On the one hand, forest degradation may be detrimental to this taxon as it is typically the first step for complete clearance of forest habitats, either through illegal logging or large-scale commercial agriculture (Linkie et al., 2004). Selective logging also opens up previously remote areas to hunters who kill both the carnivores and their ungulate prey (Peres, 2001; Azlan, 2006). On the other hand, selective logging may indirectly benefit large carnivores because more sunlight reaches the forest floor through the newly opened canopy, thereby increasing terrestrial primary productivity and food availability, and subsequently local densities of the ungulate prey base (Eisenberg, 1980). This dichotomy is particularly relevant to the Kerinci Seblat (KS) region in west-central Sumatra, which contains one of the largest populations of the critically endangered Sumatran tiger (Linkie et al., 2006). The KS National Park (NP) contains large tracts of closed canopy, primary evergreen rainforest that continue outside the KSNP border into commercial logging concessions that were selectively logged between 1980 and 2005. Focussing on two study sites that both contain a mosaic of primaryselectively logged forest, this study aims to examine the conservation value of these forests for tigers. We estimate absolute tiger densities and then compare tiger habitat use between study areas and forest types and then the landscape features associated with these covariates. Finally, we discuss the implications of maintaining selectively logged forests as part of the conservation landscape.

method that has been modified for tigers (Karanth et al., 2002). Individual tigers were identified from their unique stripe patterns and their capture histories constructed within the standard ‘X-matrix format’ using one-week sampling occasions (Otis et al., 1978). Closure tests were performed within CAPTURE software to verify that each tiger population was closed (i.e. no births, deaths, immigrations or emigrations) during the duration of the surveys within each respective study area (Rexstad and Burnham, 1991). In CAPTURE, the model selection procedure was used to identify the most appropriate population estimator. From seven available models, Model Mh is considered to be the most robust for tigers because it incorporates heterogeneous capture probabilities that produce more realistic estimates than the six other available models (Karanth et al., 2002). So, if Model Mh is ranked as a close second to the top model, then it is often used, instead, ^) to estimate tiger capture probability (^p) and abundance (N (O’Brien et al., 2003; Karanth et al., 2004). To estimate tiger density, the strip width buffer method was used. For this, information on the effective sampling area ^ was calculated within ArcView v3.2 GIS software (ESRI Inc., (A) Redlands, CA, USA) as the convex polygon connecting the ^ ) was then added outermost camera placements. A buffer (W to this polygon through estimating half the home range length, averaged for tigers in the sampled area. This width was estimated as the mean maximum distance moved (MMDM) for each individual tiger between camera placements (Wilson and Anderson, 1985; Karanth et al., 2002).

2.

Methods

2.1.

Field methods

At both study areas, tiger habitat use, defined as the probability of a tiger using a camera trap sampling unit (w), was estimated using camera trap data within a likelihood-based method (Mackenzie et al., 2005; Linkie et al., 2007). Tiger detection histories (H) were constructed for each camera trap placement (site) over one-week sampling occasions (Bungo = 19 occasions and Ipuh = 15). For each site and for each occasion, ‘1’ indicated the detection (photograph) of a tiger, whilst ‘0’ indicated the non-detection of a tiger. Tiger detection histories were then used to produce probabilities. For example, a detection history for site i (Hi) of 100011 would represent tiger detections on the first, fifth and sixth occasions over a single season and the probability of recording history Hi would be,

Camera trapping was conducted in two primary-selectively logged forest sites that both straddle the KSNP border: (i) Bungo, a 237 km2 area ranging from 363 to 1745 m asl located in Jambi province; and, (ii) Ipuh, a 569 km2 area ranging from 194 to 1064 m asl located in Bengkulu province. Camera trap surveys were conducted in two phases which overlapped a rainy season and a dry season: 2 · 4.5 months in Bungo and 2 · 3.5 months in Ipuh. Within each site, Photoscout passive infrared camera traps were deployed in these two consecutive phases whereby all traps were moved to an adjacent area after the first phase. This resulted in a total of 32 camera placements in Bungo (primary forest = 15 placements and degraded forest = 17) and 40 in Ipuh (primary forest = 31 and degraded forest = 9). Cameras, which were checked every two weeks, were set along ridge trails and medium–large bodied animal trails, as identified through the presence of tiger sign. To reduce the likelihood of tigers moving in and out of the trapping area undetected, a recommended minimum trap spacing of 1 km and a maximum trap spacing of 4 km was used (Karanth et al., 2002).

2.2.

Tiger density

Tiger density in Bungo and Ipuh was estimated by following the standard closed population capture-mark recapture

2.3.

Tiger habitat use

PrðHi ¼ 100011Þ ¼ wp1 ð1  p2 Þð1–p3 Þð1  p4 Þp5 p6

ð1Þ

where pj is the probability of detecting the species during sampling period j (=1,. . .,6), conditioned upon the species being present. The sites for both study areas were combined (n = 72), their detection histories produced and entered into PRESENCE v2.1 software (Proteus Wildlife Research Consultants, New Zealand). To investigate the relationship between tiger habitat use and environmental covariates, a spatial dataset was constructed. Within the GIS, all sites were firstly classified within categorical broad-scale covariates for forest type (primary = 1 and degraded = 0) and study area (Ipuh = 1 and Bungo = 0). Next, a second set of GIS data was extracted for the finer-scale

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continuous covariates of distance to logging roads and to public roads, elevation and slope from a regional spatial dataset, described in detailed by Linkie et al. (2006). To test for collinearity between the continuous covariates, these data were imported into SPSS v14.0 software (SPSS Inc., Chicago, IL, USA) and a Spearman’s rank correlation performed. This test identified significant (p < 0.05) non-independence between distance to logging roads, elevation and slope. So, a datareduction technique (principal component analysis) was used to identify the components explaining most of the variance observed between the three indicators. From the final component matrix (logging roads = 0.827, elevation = 0.865 and slope = 0.745), a single covariate was created that explained 66.3% of this variance. This covariate and a standardised distance to public roads covariate were then imported into PRESENCE for use in the final analysis. It was not possible to construct a human activity site covariate because too few data were recorded (e.g., only 11 out of 72 cameras recorded human presence). Next, a two stage statistical analysis was performed: (i) to investigate whether broad differences existed with tiger habitat use across study areas and across forest (primary and degraded) types; and, if so, (ii) to investigate the finer-scale environmental covariates associated with the study areas and/or the forest types (Mann-Whitney U) and that best explained tiger habitat use. Within PRESENCE, the importance of covariates on tiger habitat use was explored by setting the detection probability as constant, p(.), and then allowing the habitat use parameter (w) to vary with covariates either individually, e.g., w(forest type), or in combination, e.g., w(forest type + study area). Comparisons of the explanatory power of these candidate models were based on their delta Akaike information criterion (DAIC) values, adjusted for small sample sizes (DAICc), and their Akaike weights (wi) (Burnham and Anderson, 2002). The influence of covariates from models that were within two DAICc units of the top ranked model, and exhibited a satisfactory goodness of fit (p > 0.05), were discussed. Presence of spatial autocorrelation in the final models was then tested by calculating Moran’s I statistic (Cliff and Ord, 1981) using the CrimeStat III software (N Levine & Associates, Houston, TX).

3.

Results

3.1.

Tiger density

A total sampling effort of 2750 and 3255 trap-nights was recorded from Bungo and Ipuh, respectively. From Bungo and Ipuh, closure tests did not reject the null hypothesis that the population was closed during the camera trapping period (z = 1.28 and 1.05, p = 0.10 and 0.15). Ten individual tigers were identified from 63 tiger photographs in Bungo, whilst 15 individuals were identified from 64 tiger photographs in Ipuh, with estimated capture probabilities of 0.12 and 0.17, respectively. Model Mh in CAPTURE was ranked first in Ipuh (1.00) and a close second (0.99) to Model Mo in Bungo. For both areas, Model Mh was used and produced tiger abundance estimates, ^  S:E:ðN ^ Þ, of 13 ± 2.48 for Bungo and 19 ± 4.21 for Ipuh. N Following the strip width boundary method, respective buffer widths of 2.74 km for Bungo and 5.28 km for Ipuh produced

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Table 1 – Estimated tiger habitat use within 72 camera trap sites from two study areas located in and around Kerinci Seblat National Park, Sumatra Model Stage 1.1 1.2 1.3 1.4

1 – broad-scale analysis w(forest type)p(.) w(forest type + study area)p(.) w(.)p(.) w(study area)p(.)

Stage 2 – fine-scale analysis 2.1 w(dist. public roads)p(.) 2.2 w(slope/elev/dist. logging roads + dist. public roads)p(.) 2.3 w(.)p(.) 2.4 w(slope/elev/dist. logging roads)p(.)

DAICc

K

wi

0.00 2.00 2.12 3.27

3 4 2 3

0.5238 0.1927 0.1815 0.1021

0.00 1.12

3 4

0.4732 0.2703

2.20 3.13

2 3

0.1575 0.0989

Notes: w is the probability a site is used by tiger and p is the probability of detecting tiger in the jth survey where w(.)p(.) assumes that tiger habitat use and detection probability are constant across sites, K is the number of parameters in the model, DAICc is the difference in AICc values between each model with the low AICc model, wi is the AICc model weight. The top models (#1.1 and 2.1) were tested and found not to be affected by spatial autocorrelation (Moran’s I = 0.01, 0.02, respectively, p > 0.1).

^ W ^ Þ, of 441.00 km2 and 1227.18 km2, effective sampling areas, Að ^ ^ of respectively. In turn, these yielded tiger densities, DðSEj DjÞ, 2 2.95 ± 0.56 adult individuals/100 km (2.49  4.99, 95% CIs, Bungo) and 1.55 ± 0.34 adult individuals/100 km2 (1.30  2.93, 95% CIs, Ipuh).

3.2.

Tiger habitat use

Tigers were detected in 49 of the 72 sampling sites. From the broad-scale analysis, tiger habitat use was mainly found to be influenced by forest type (Table 1, Model 1.1). This model showed that tigers were more likely to select sites that were located in primary forest. These primary forest sites were found to be less accessible than degraded forest sites, which were related to the combination of proximity to logging roads, elevation and slope (n = 72, U = 238.00, Z = 4.22, p < 0.01), although not to the position of public roads (n = 72, U = 469.00, Z = 1.51, p = 0.13). The constant model (#1.3) also received support, which suggests that other covariates might be important. From investigating the finer-scale environmental covariates within the different study areas and forest types, two models were found to most influence tiger habitat selection (Table 1, Models 2.1–2.2). From the summed model weights for each covariate with respect to habitat use, these were predominantly related to distance from public roads (74.4%) and then slope, elevation and distance to logging roads (36.9%). Tigers showed a preference for habitat that was further from public roads and logging roads, at higher elevations and on steeper slopes.

4.

Discussion

This study presents the first estimates of tiger density from two primary-selectively logged forest mosaics using the capture–recapture sampling based framework. Whilst these

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study areas were found to maintain reasonably high Sumatran tiger densities, our results showed that tigers tended to select primary forest habitat over degraded forest habitat. For tigers, degraded forest per se does not necessarily represent poor quality, but rather the factors that relate to accessibility within this habitat, as shown in this study. Furthermore, these degraded sites, in combination with primary forest, formed part of our mixed forest study areas that supported tiger populations with sufficiently large abundance for Sumatran tigers. Previous studies in Indonesia and Malaysia have highlighted the important contribution that degraded forest can make to the conservation of threatened populations of mammals, including tigers (Kawanishi et al., 2003; Azlan, 2006; Azlan and Sharma, 2006; Linkie et al., 2007). Whilst, the conservation value of degraded Southeast Asian forests over the long-term is poorly understood (Sodhi and Brook, 2008), these habitats should not be written off as having no conservation value, because accessibility can be controlled for in these areas through, for example, destroying ex-logging roads and increasing law enforcement effort. If connected to primary forest, these habitats have the potential to make an important contribution to increasing the long-term survival prospects of the critically endangered Sumatran tiger.

4.1.

Camera trapping low density tiger populations

Sumatran tigers occur at naturally low densities and the estimates recorded from our mixed forest study areas were similar to those recorded from studies conducted in primary forest areas. From our study, we were unable to identify clear differences between the Bungo and Ipuh tiger density estimates because of the overlap in confidence intervals. Thus, for estimating tiger density, confidence is required in being able to reliably detect tigers and record a sufficiently large number of individuals. This therefore presents a challenge for obtaining reliable abundance estimates from low density populations, such as those living in tropical evergreen rainforests or heavily hunted forests. To minimise this potential problem, we set camera placements in consecutive phases in two adjacent areas to increase the number of individual tigers photographed. If our study had just used a single phase, then only three individual tigers would have been identified from Bungo and seven from Ipuh; with the former amount

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precluding a capture-recapture analysis. It is also important to achieve sufficient precision in density estimates and those with a coefficient of variation of less than 20% are considered as ‘fair’ (White et al., 1982; Wilson and Anderson, 1985). Comparing estimates from ten low density tiger populations from Sumatra, Malaysia and Thailand, shows that recording a greater number of individual tigers, i.e. 10–15, tends to improve density estimate precision (Table 2). By contrast, a study of six tiger populations that contained between 1 and 3 individuals was unable to estimate a measure of precision to accompany respective naive tiger density estimates because there were too few tiger detections (Lynam et al., 2007).

4.2.

Managing tigers in selectively logged forests

Our comparison across primary and selectively logged forest types found that no obvious benefits were derived for tigers from selective logging, but that factors relating to accessibility were important for explaining tiger habitat selection. Similarly, across the wider KS landscape, public roads negatively affected tigers (Linkie et al., 2006), whilst public roads and secondary roads, including logging roads, negatively effected tiger survivorship and reproduction in Russia (Kerley et al., 2002). The effect of logging operations on other species of large carnivores appears somewhat contradictory. For example, one study on pumas Felis concolor found that residents tended to avoid those areas with increased human activity and road development (Van Dyke et al., 1986) and subsequently underwent a population decline (Smallwood, 1994). Yet, in contrast, another study found that pumas were unaffected by logging activities (Gagliuso, 1991). The relationship between tropical forest loss and logging roads in facilitating access to potential farmland is well documented (Laurance, 2001; Linkie et al., 2004). Selective logging around KSNP often has an adverse long-term impact on tigers, because selectively logged forests are often considered as having low conservation value for wildlife and, consequently, legally cleared for large-scale oil palm plantations or small-scale farming, which is often illegal. This is also the case around many other Sumatran protected areas (Loucks et al., 2004) and the complete conversion of these forest landscapes reduces them to the poorest quality tiger habitat. From central Sumatra, a study from an oil palm

Table 2 – Capture-mark recapture analyses of low density tiger populations Adults/100 km2 (95% C.I.s)

Study

Location

Forest type

Mt+1

^  S:E:ðN ^Þ N

Kawanishi and Sunquist (2004)

Malaysia Malaysia Malaysia

Primary Primary Primary

5 5 6

7 ± 1.92 5 ± 2.35 6 ± 2.44

2.0 (1.7–4.0) 1.1 (1.1–4.4) 1.9 (1.9–6.6)

27.4 47.0 40.7

Simcharoen et al. (2007)

Thailand

Primary

15

19 ± 3.87

4.0 (3.4–7.1)

20.4

O’Brien et al. (2003)

Sumatra

Primary

9

13 ± 3.66

1.6 (1.2–3.2)

28.2

Linkie et al. (2006)

Sumatra Sumatra Sumatra

Primary Primary Primary

6 5 5

7 ± 2.65 6 ± 1.28 6 ± 1.87

3.3 (3.3–9.9) 2.0 (2.0–4.1) 1.5 (1.5–4.0)

37.9 21.3 31.2

This study

Sumatra Sumatra

Primary–secondary Primary–secondary

10 15

13 ± 2.48 19 ± 4.21

3.0 (2.5–5.0) 1.6 (1.3–2.9)

19.1 22.2

CV (%)

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plantation, scrub and secondary forest landscape, found that tigers avoided the oil palm section and showed a preference for the other two areas (Maddox et al., 2007). However, these scrub and secondary forest areas then became the focus of local land disputes, which resulted in their clearance for small-scale agriculture and, consequently, a dramatic decline in the tiger population. Our study indicates the importance of maintaining a primary forest refuge for tigers, such as that provided by KSNP, which in many places is far enough from human settlements and their associated threats (Linkie et al., 2006). Furthermore, forest within KSNP falls under the jurisdiction of the Department of Forestry, which is able to protect its tigers through a law enforcement programme. Similarly, from Russia, strong law enforcement and a large PA network are considered to have helped stabilise the Amur tiger population. However, this population is also considered to have benefited from being situated among adjacent areas containing low human population densities and in forest that has been selectively logged, rather than clear-felled (Miquelle et al., 1999).

5.

Conclusion

Indonesia has a well-designed and biogeographically representative protected areas system (Jepson and Whittaker, 2002). Sumatran tigers are currently located within some of the largest protected areas in Asia. As long as these protected areas are effectively managed, through habitat protection and anti-poaching measures, then their respective tiger subpopulations are predicted to remain viable (Linkie et al., 2006; Dinerstein et al., 2007). Whilst, it might be desirable for tiger conservation to expand the Sumatran protected area boundaries into adjacent agricultural or logging concessions where tigers may be found, this might be difficult to justify at the expense of national economic development or, where farming is illegal, difficult to enforce. Thus, in order to counter competing economic interests for this land, the value of these forests might therefore be considered for their contribution to ‘avoided deforestation’ schemes (Santilli et al., 2005), thereby simultaneously providing benefits to tigers, the wider biodiversity and national economic growth.

Acknowledgements We are grateful to the US Fish and Wildlife Service, 21st Century Tiger, Rufford Small Grants and the Peoples Trust for Endangered Species for funding our study. We are grateful to Ir. Soewartono, Dr Sugardjito and the Indonesian Department of Forestry and Nature Protection for assisting us in our Indonesian work. We would like to thank Nigel LeaderWilliams, Marcus Rowcliffe and Rachel Borysiewicz for useful comments and discussions on an earlier manuscript. R E F E R E N C E S

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