Conceived and designed the experiments: ML HW JS S NB. Performed the experiments: ML HW JS S WP AA PB YD EG DG IH HH IK D.Kiswayadi D.Kristiantono DM HK TM MM AN KP DP ER WR GR ER DS A.Sarimunid A. Salampessy ES A.Sumnatri SS IT TT KY MY ZZ. Analyzed the data: GG-A JL-M ML HW JS. Wrote the paper: ML GG-A HW JS NL-M S JL-M TS.
During manuscript preparation, one co-author (JS) took up a position with one of the funders (Panthera). However, this does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.
Large carnivores living in tropical rainforests are under immense pressure from the rapid conversion of their habitat. In response, millions of dollars are spent on conserving these species. However, the cost-effectiveness of such investments is poorly understood and this is largely because the requisite population estimates are difficult to achieve at appropriate spatial scales for these secretive species. Here, we apply a robust detection/non-detection sampling technique to produce the first reliable population metric (occupancy) for a critically endangered large carnivore; the Sumatran tiger (
Setting conservation priorities for top predators requires repeatable and robust estimates of abundance or distribution over large areas. These assessments should be conducted at a meaningful scale for the species in question, such as a landscape, sub-species distribution, or overall species range
The main strategy followed to conserve Sumatran wildlife and its habitats has been to establish large protected areas, including the mountainous national parks of Kerinci Seblat (13,971 km2) and Gunung Leuser (7,927 km2). However, extensive tracts of lower elevation forests were excised during their designation to allow for commercial logging. These lowland forests can support relatively high densities of Sumatran tigers and act as important corridors that maintain landscape integrity and therefore population viability
Reliable information is required on the conservation status of flagship species to better understand the impact of deforestation on Sumatra's wildlife. For the Sumatran tiger, previous population assessments have fixated on estimating the total number of individuals across the island or within several protected areas
We would like to thank the Indonesian Ministry of Forestry for their permission to conduct this work and for the support of the Director of Biodiversity Conservation in its implementation.
From 2007–2009, eight organisations (Wildlife Conservation Society, Fauna & Flora International, Durrell Institute of Conservation and Ecology, World Wildlife Fund, Zoological Society of London, Sumatran Tiger Conservation and Protection, Leuser International Foundation, Rhino Foundation of Indonesia and the Sumatran Tiger Protection and Conservation Foundation) partnered with the Indonesian Ministry of Forestry to conduct simultaneous field surveys across Sumatran rainforest under different management regimes. The survey protocol was developed from a detection/non-detection sampling framework proposed by MacKenzie
Within each cell, a team of 4–5 people surveyed locations considered likely to contain tiger pugmarks, e.g. ridge trails. The locations of tiger detections were recorded with a GPS along transects within 17×17 km grid cells. Cell size was based on the putative home range size of an adult male Sumatran tiger to allow changes in the distribution of resident tigers to be reflected as changes in the proportion of the grid cells occupied. Survey teams aimed to achieve wide spatial coverage of each cell, but this was influenced by the prevailing topography. For example, such coverage was less easy to achieve in rugged mountainous cells where deviations from pronounced ridge trails required descending steep slopes, often for hundreds of metres. Surveys were conducted in all habitat types likely to support tigers, from sea-level peat swamp to forests around the volcanic peak of Mount Kerinci, the highest point on Sumatra (3,805 m asl). In total, 13,511 km of transects were surveyed in 394 cells that covered seven landscapes across all eight mainland Sumatran provinces (
Study area | TCLstatus |
SurveyDates | Average yearly forest loss (%) | # grid cells | |
Surveyed | with tiger sign | ||||
Kerinci Seblat-Batang Hari |
I | 09/01/07–10/09/09 | 0.8 | 110 | 76 |
Southern Sumatra |
II+III | 24/03/07–25/06/08 | 1.2 | 51 | 21 |
Way Kambas National Park | - | 06/01/08–11/03/08 | 2.3 | 10 | 2 |
Leuser-Ulu Masen | I | 02/05/07–01/03/09 | 0.8 | 159 | 76 |
Northern Riau |
n/a | 09/06/09–22/12/09 | 9.8 | 18 | 0 |
Central Sumatra |
I+II+III | 09/04/07–15/10/09 | 1.9 | 31 | 21 |
Eastern Sumatra |
n/a | 26/04/07–21/11/09 | 2.2 | 15 | 10 |
*I = global priority; II = regional priority; III = long-term priority.
Kerinci Seblat National Park and Batang Hari Protection Forest and their surrounding forests.
Bukit Barisan Selatan National Park and Bukit Balai Rejang Selatan.
Pasir Pangaraian, Giam Siak, Duri, Balaraja, Tapung.
Tesso Nilo, Bukit Bungkuk, Bukit Rimbang-Baling, Bukit Batabuh, Bukit Tigapuluh, Kerumutan.
Dangku, Bukit Duabelas, Berbak.
To match the discrete sampling protocol assumed by the models used, in which a number of replicate surveys are conducted within each sampling site, transects were divided into segments, assigning ‘1’ to those containing at least one detection and ‘0’ otherwise. In order to account for variation in terrain ruggedness, distances were determined by overlaying the two-dimensional tracklogs from GPS handsets carried by field teams onto a three-dimensional digital elevation model.
Tiger site occupancy was considered to vary across Sumatra, given the island's diverse topographic composition, ranging from prey-rich lowland forests to less productive and rugged montane forests. Furthermore, the influence of anthropogenic threats on habitat quality was expected to negatively affect tiger occupancy. Deforestation was considered to be important because Sumatra has one of Southeast Asia's highest rates of conversion from intact forest to non-forest (WWF-Indonesia 2010). To explore the influence of biophysical and anthropogenic threat covariates on tiger occupancy, a spatial dataset of nine potential explanatory variables was constructed within ArcGIS v9.3 software (ESRI). Information was obtained from several sources: elevation and slope
Tiger detection/non-detection data were analyzed to estimate site occupancy (ψ) using models that explicitly account for imperfect species detection: the basic occupancy model
Candidate explanatory variables for tiger site occupancy/density were standardized using a z-transformation and assessed for collinearity. Two pairs of variables showed strong significant correlation (Pearson's r = 0.80 for elevation and slope; r = −0.78 for forest cover and distance to forest) and were not included together within the same models. Tiger detection history was constructed by defining the survey replicates as 5 km transect segments. This was chosen to mitigate the dependence between consecutive replicates that, given tiger movement patterns, could be expected at smaller scales, but without compromising the results by the loss of data that would result when choosing a very coarse replicate length. To assess the robustness of the results to moderate changes in the definition of replicates, models were also run using different segment lengths (4 and 6 km).
The analysis was performed obtaining maximum-likelihood estimates by numerical maximization, using RMark 2.0.1 for the basic occupancy and abundance (Poisson and negative binomial) models and M
Tiger signs were detected in 206 of 394 cells, corresponding to a naïve occupancy estimate of 0.52. The model that best explained the observed data was a Poisson abundance model dependent on average distance to forest, elevation, recent deforestation and protected area status. This model had much stronger support than the constant model (ΔAICc = 61) and was considerably better than the best competing model with one covariate less (ΔAICc = 7.5). Adding one extra covariate only marginally improved model fit and the confidence interval of the corresponding regression coefficient included zero. There was no support for zero-inflation in the abundance distribution while models that allowed for overdispersion did not provide better fit or failed to converge. The basic, clustering and beta-binomial models were poor fits to the data in comparison to the abundance model. Moderate variations in the segment length used to define the spatial replicates did not lead to substantial changes in the results. The same model provided the best explanation for the data and the support of the next highest ranked models remained consistent.
Tiger occupancy estimates varied considerably within the different landscapes (
Study area |
|
|
|
|
Kerinci Seblat-Batang Hari | 0.83 (0.037) | 2.0 (0.28) | 0.88 (0.021) | 2.2 (0.27) |
Southern Sumatra | 0.64 (0.048) | 1.2 (0.16) | 0.63 (0.041) | 1.0 (0.13) |
Way Kambas National Park | 0.52 (0.069) | 0.8 (0.15) | 0.45 (0.055) | 0.6 (0.10) |
Leuser Ecosystem-Ulu Masen | 0.70 (0.042) | 1.4 (0.19) | 0.69 (0.035) | 1.3 (0.17) |
Northern Riau | 0.33 (0.055) | 0.5 (0.09) | 0.16 (0.038) | 0.2 (0.05) |
Central Sumatra | 0.78 (0.048) | 1.7 (0.27) | 0.80 (0.027) | 1.8 (0.23) |
Eastern Sumatra | 0.77 (0.041) | 1.9 (0.30) | 0.67 (0.025) | 1.8 (0.25) |
Overall | 0.72 (0.039) | 1.5 (0.20) | 0.71 (0.030) | 1.5 (0.19) |
The two right-most columns show the estimates conditional to the data observed. Standard errors are shown in brackets.
The occupancy estimate of Sumatran tigers across the entire island was 0.72(±0.039), with an average estimated
Managers require population estimates that cover meaningful units, whether at landscape, sub-species or species scales. However, gaining such information for cryptic species living at low densities across large areas has previously proved difficult. This study provides the first comprehensive assessment of Sumatran tigers using site-specific surveys and modelling detection probability. We believe there is cause for optimism for the long-term survival of Sumatran tigers because from the major landscapes surveyed, the species still has a reasonably good conservation status. However, surveys tended to be conducted in prime habitat and with poorer habitat, with presumably lower occupancy, generally being excluded. Our results reveal the insidious effects of deforestation, especially in the patchier forests that had occupancy levels which were 20% lower than the island average. Thus, maintaining forest integrity is critical for the long-term survival of tigers, especially given the road expansions planned through the core tiger areas of Kerinci Seblat National and the Leuser Ecosystem. For landscapes that are already fragmented, such as those in Riau, reconnecting forest blocks is recommended. However, given the rapid conversion of remaining forests in this province, stopping further fragmentation and maximizing chances for tiger dispersal between remaining forest blocks would be considered a significant achievement in itself.
The sampling protocol implemented in this study has wide application to other difficult to detect species. However, there are several caveats associated with the use of the abundance model that ranked top in our analysis. Understanding how underlying model assumptions are met is essential to the correct interpretation of estimates obtained. The model assumes that differences in abundance are the only source of heterogeneity in site-specific detection probabilities; otherwise bias may be induced in the estimators. In this study, a well-defined protocol was developed and implemented to minimize heterogeneity in detection probability. Nevertheless, some residual, unmodelled heterogeneity may still have remained, e.g. pugmark detections tended to be easier in wetter substrates and surveys were conducted over both wet and dry seasons. The model also assumes that tiger site abundance closely fits a Poisson distribution. Tigers are territorial so it could be expected that their distribution exhibits some degree of under-dispersion. Finally, the model is based on a functional dependence between species detectability
This study overcame three limitations associated with previous assessments of Sumatran tiger; unmodelled detection probability, uncontrolled confounding variables and lack of site-specific survey data. Thus, the state variable (occupancy) was estimated from tiger sign data while accounting for detection probabilities and the influence of several biophysical and anthropogenic threat covariates. Next, the rapid survey technique, which only required spatial replicates within a sampling unit rather than temporal replicates that would have required multiple visits to the cell, enabled the majority (58%) of presumed tiger habitat to be covered
To complete the Sumatra-wide survey, 13,511 km of transects were walked. To put this effort into context, Sumatra is 1,790 km in length. Whilst our island-wide survey provides the first baseline data and first monitoring system that have been urgently requested by the Global Tiger Initiative for Indonesia
The surveys conducted in this study primarily focussed on protected areas that are recognised as being priority (or the most important) landscapes where tiger occupancy should therefore be highest. Expanding the surveys to cover the 42% remaining tiger landscapes, where deforestation and fragmentation is higher
Whilst the status of Sumatran tigers was good in the major landscapes, the province of Riau, where there were fewer tigers detections, provides a sobering reminder of what can happen if high deforestation rates, and the associated conflicts between people and tigers, are not mitigated
The authors wish to thank the following for supporting the Sumatra-wide survey initiative: the Indonesian Ministry of Forestry for giving permission, Ullas Karanth, Darryl MacKenzie, Jim Nichols, Arjun Gopalaswamy and Samba Kumar for advice on the initial survey design, Martin Ridout and Byron Morgan for advice on the statistical analysis and WWF, CI and WCS for providing the forest cover data. For the respective survey areas, we would like to thank the national park head and/or BKSDA head for supporting our fieldwork, as well as Bambang Novianto. Finally, we would like to thank the following for contributing to the initiative in some form: Frankfurt Zoological Society, Ikue Sri Rejeki, Koesdianto, A. Suprianto, F. Panjaitan, H. Gebog, L. Subali, E. Tugio, Nursamsu, David Gaveau, Bonnie, Noviar Andayani, Simon Hedges, Martin Tyson, Riza Sukriana, Maju Bintang Hutajulu, A. Budiman, Heri Irawan and Rony Faslah.