Journal of Threatened Taxa | www.threatenedtaxa.org | 26 February 2018 | 10(2): 11245–11253

 

 

 

Observations of occurrence and daily activity patterns of ungulates in the Endau Rompin Landscape, peninsular Malaysia

 

Win Sim Tan1, Norazmi bin Amir Hamzah2, Salman Saaban3, Nurul Aida Zawakhir4, Yugees Rao5, Norolhuda Jamaluddin6, Francis Cheong7, Norhidayati binti Khalid8, Nur Iadiah Mohd Saat9, Eka Nadia binti Zaidee Ee10, Azwan bin Hamdan11, Mei Mei Chow12, Chee Pheng Low13, Mufeng Voon14, Song Horng Liang15, Martin Tyson16 & Melvin Gumal17

 

1,4,5,6,7,8,9,10,11,13,17 Wildlife Conservation Society - Malaysia Program, 7 Jalan Ridgeway, 93200 Kuching, Sarawak, Malaysia

2 Johor National Parks Corporation, Aras 1, Bangunan DatoMohamad Saleh Perang, Kota Iskandar, 79576 Iskandar Puteri, Johor, Malaysia

3 Department of Wildlife and National Parks of Peninsular Malaysia, Km 10, Jalan Cheras, 56100 Kuala Lumpur, Malaysia

12 Penang Green Council, Tingkat 46, KOMTAR, 10503 Penang, Malaysia

14 Sarawak Forestry Corporation, Lot 218, Jalan Tapang, Kota Sentosa, 93250 Kuching Sarawak, Malaysia

15 Pertubuhan Pelindung Alam Malaysia, B-06-06 BLOK B, CGC, Jalan Casa Green, 43200 Cheras, Selangor, Malaysia

16 Wildlife Conservation Society, 132 Bloomingdale Ave #2, Saranac Lake, NY 12983, United States of America.

1wtan@wcs.org (corresponding author), 2norazmi.ah@johor.gov.my, 3salman@wildlife.gov.my,

4nzawakhir@wcs.org, 5yrao@wcs.org, 6njamaluddin@wcs.org, 7fcheong@wcs.org, 8knorhidayati@wcs.org, 9nmohdsaat@wcs.org, 10ekanadia@wcs.org, 11azwan@wcs.org, 12 bavrielchow@gmail.com, 13cplow@wcs.org, 14mfvoon@gmail.com, 15director@pelindungalam.org, 16mtyson@wcs.org, 17mgumal@wcs.org

 

 

 

 

doi: http://doi.org/10.11609/jott.3519.10.2.11245-11253  |  ZooBank: urn:lsid:zoobank.org:pub:F2D79DCF-2B2F-42AE-A70D-91AC7D844FAF

 

Editor: David Mallon, Manchester Metropolitan University, UK.        Date of publication: 26 February 2018 (online & print)

 

Manuscript details: Ms # 3519 | Received 27 May 2017 | Final received 15 January 2018 | Finally accepted 25 January 2018

 

Citation: Tan, W.S., N.B.A. Hamzah, S. Saaban, N.A. Zawakhir, Y. Rao, N. Jamaluddin, F. Cheong, N.B. Khalid, N.L. Mohdsaat, E.N.B. Zaidee Ee, A.B. Hamdan, M.M. Chow, C.P. Low, M. Voon, S.H. Liang, M. Tyson, & M. Gumal.  (2018). Observations of occurrence and daily activity patterns of ungulates in the Endau Rompin Landscape, peninsular Malaysia. Journal of Threatened Taxa 10(2): 11245–11253; http://doi.org/10.11609/jott.3519.10.2.11245-11253

 

Copyright: © Tan et al. 2018. Creative Commons Attribution 4.0 International License. JoTT allows unrestricted use of this article in any medium, reproduction and distribution by providing adequate credit to the authors and the source of publication.

 

Funding: Liz Claiborne Art Ortenberg Foundation (LCAOF); Panthera Corporation; United States Fish and Wildlife Service (USFWS).

 

Competing interests: The authors declare no competing interests.

 

Acknowledgements: The permission to conduct this research was granted by the State Government of Johor through the memorandum of understanding signed with the Johor National Parks Corporation for the Johor Wildlife Conservation Project. The Malaysian Department of Wildlife and National Parks, State Forestry Department of Johor and State Forestry Department of Pahang are partners to the project. The authors thank the Liz Claiborne Art Ortenberg Foundation, Panthera Corporation, and United States Fish and Wildlife Service for funding this work. In addition, the authors thank Johor National Parks Corporation for their assistance with our camera trapping works, Mike Meredith and Ngumbang Juat of Biodiversity Conservation Society Sarawak for their advice with the data analysis and Daniel Kong and Sylvia Ng of Wildlife Conservation Society - Malaysia Program for their help with the data audit. The field surveys would have been impossible without the hard work from all members of the field team who spent time out in the rain and sun, and also in the office, poring over the images, creating maps and analyzing these data.

 

Author Details: Win Sim Tan is a senior researcher for WCS - Malaysia Program. He has an honours degree in Wildlife Ecology from University of Wisconsin – Madison. He has been involved in a tiger conservation project in the Endau Rompin landscape of Johor and Pahang, Peninsular Malaysia since 2013.  Norazmi bin Amir Hamzah is currently the director of Johor National Parks Corporation.  Salman Saaban is currently the director of the Enforcement Division for the Department of Wildlife and National Parks of Peninsular Malaysia. Nurul Aida completed a degree in Ecology and Biodiversity at University of Malaya, Malaysia. She is now an Assistant Coordinator managing the Pahang Endau-Rompin Landscape tiger team. She was previously involved in the wildlife monitoring surveys including camera trapping of tigers for population estimation.  Yugees Anandarao is currently one of the three Assistant Coordinators working on a tiger conservation project in the Endau Rompin landscape of Johor and Pahang, Malaysia. She has been working for WCS - Malaysia since 2012.  Norolhuda Jamaluddin has a degree in Biology from University of Technology Mara, Malaysia. She has been working with WCS - Malaysia in a tiger project which focusing on research and law enforcement to save the precious animal and its environment since 2010.  Francis Cheong is currently the assistant director for WCS – Malaysia Program, managing a tiger and elephant conservation project in the Endau Rompin landscape of Johor and Pahang, Peninsular Malaysia.  Norhidayati binti Khalid is currently one of the senior researchers for WCS – Malaysia Program, working on an elephant conservation project in the Endau Rompin landscape of Johor and Pahang, Peninsular Malaysia.  Nur Iadiah completed her degree in Conservation and Management of Biodiversity in 2010. She joined WCS Malaysia as an intern and subsequently a researcher to work on a human-elephant conflict and mitigation project in 2012. She is currently a senior researcher, working on a tiger conservation project in the Endau Rompin landscape of Johor and Pahang, Peninsular Malaysia.  Eka Nadia has a degree in Zoology from the faculty of Science and Technology, Universiti Kebangsaan Malaysia. She worked on a turtle conservation project for WWF-Malaysia for six months before joining WCS - Malaysia as a tiger researcher project in the Endau Rompin landscape. She is familiar with camera trapping to estimate tiger population and monitor tiger hotspots.  Azwan Hamdan is a senior researcher at WCS – Malaysia Program, involved in camera trapping, wildlife surveys, threat monitoring and human-wildlife conflict data collection. He is an administrator for SMART (Spatial Monitoring and Reporting Tools) database for a tiger research project in the Endau Rompin Landscape of Johor and Pahang, Malaysia.  Mei Mei Chow is an environmental research assistant at Penang Green Council based in Penang, Malaysia. She was previously a wildlife researcher for WCS-Malaysia Program, involved in wildlife surveys and patrol data management. Low Chee Pheng is the project coordinator of the tiger program for Wildlife Conservation Society - Malaysia Program. She coordinates the research program to monitor the Tiger and prey population in the Endau-Rompin Landscape in Malaysia. Voon Mufeng was with the Wildlife Conservation Society as a senior researcher for four years handling the tiger conservation projects in Rompin, Pahang. Currently she works with the Sarawak Forestry Corporation (SFC) as an Executive in the GeoDrone Unit, managing the geospatial information for five main core functions of SFC. She holds a MSc in Conservation Biology from University of Kent, United Kingdom. Song Horng Liang has been working in wildlife conservation for 15 years; and is currently the executive director of a local Malaysian NGO, Pelindung Alam Malaysia, working on large scale wildlife surveys that cover from elephants, tigers to gibbons and hornbills. He previously managed the elephant and tiger units of Wildlife Conservation Society Malaysia for about 8 years; worked for TRAFFIC Southeast Asia on undercover wildlife trade investigation on Saiga antelopes, tigers and their prey in Malaysia and Singapore for 2 years; and assisted the Rainforest Wolf Project of Raincoast Conservation Foundation in western Canada for 2 years. He received degrees from University of Victoria, Canada (Bachelor) and National University of Singapore (MSc).  Martin Tyson is currently a technical advisor for Asian elephants at the Wildlife Conservation Society. He has worked in several SE Asian countries, focusing on elephant population survey, mitigation of human–elephant conflict, and assisting range states in developing elephant conservation plans.  Melvin Gumal is the Country Director for Wildlife Conservation Society Malaysia.  He has been in conservation science since 1988 and studied in the universities of Melbourne and Cambridge. He currently works with colleagues on conservation of tigers and their prey, elephants, orang-utans, sharks, rays and their habitats.

 

Author Contribution: Win Sim Tan and Melvin Terry Gumal conceived the original idea and drafted the manuscript with inputs from all authors. Win Sim Tan analyzed the data. Norazmi bin Amir Hamzah, Salman Saaban, Francis Cheong, Martin Tyson, Song Horng Liang, Mufeng Voon, Chee Pheng Low and Azwan Hamdan provided critical feedback and helped to shape the manuscript. Francis Cheong, Song Horng Liang, Mufeng Voon, Chee Pheng Low, Azwan Hamdan, Nurul Aida Zawakhir, Yugees Rao, Norolhuda Jamaluddin, Norhidayati binti Khalid, Nur Iadiah Mohd Saat, Eka Nadia binti Zaidee Ee, Mei Mei Chow and Win Sim Tan contributed to data collection. Melvin Terry Gumal supervised the project.

 

 

 

 

 

Abstract: Camera trap data was used to study occurrence and daily activity patterns in the Endau Rompin Landscape of peninsular Malaysia during 2011, 2013 and 2015 to estimate Malayan Tiger Panthera tigris jacksoni population densities.  By-catch data were also collected for seven ungulate species: Barking Deer Muntiacus muntjak, Bearded Pig Sus barbatus, Wild Boar Sus scrofa, Greater Mousedeer Tragulus napu, Lesser Mousedeer Tragulus kanchil, Malayan Tapir Tapirus indicus and Sambar Deer Rusa unicolor.  Of these, Bayesian single-season occupancy analysis suggested that Barking Deer were the most widespread and Mousedeer spp. the least widespread during the study period.  Bearded Pig, Malayan Tapir and Wild Boar were recorded in more than half of the camera trap area (Sambar Deer was excluded due to small sample size).  Daily activity patterns based on independent captures in 2015 suggest that Barking Deer, Bearded Pig and Wild Boar are mostly diurnal, mousedeer species are crepuscular and Malayan Tapir strongly nocturnal.

 

Keywords: Bayesian single-season occupancy, by-catch, camera trap, daily activity pattern, Endau Rompin Landscape, occurrence, peninsular Malaysia, ungulate.

 

 

 

 

INTRODUCTION

 

Of the 11 species of ungulates reported from Peninsular Malaysia (Francis 2008), 10 have been reported in the southern Endau Rompin Landscape (ERL).  Banteng Bos javanicus, Gaur Bos gaurus and Sumatran Rhinoceros Dicerorhinus sumatrensis were recorded in the past century (Milton 1963; Davison & Kiew 1987; Burhanuddin et al. 1995).  The other recently reported ungulate species are Barking Deer Muntiacus muntjak, Bearded Pig Sus barbatus, Greater Mousedeer Tragulus napu, Lesser Mousedeer Tragulus kanchil, Malayan Tapir Tapirus indicus, Sambar Deer Rusa unicolor and Wild Boar Sus scrofa (Aihara et al. 2016; WCS - Malaysia Program unpub.). Conservation of wildlife in peninsular Malaysia is regulated by the Wildlife Conservation Act (2010), including harvesting for commercial purposes. Bearded Pig and Malayan Tapir are listed as Totally Protected, and Barking Deer, Greater Mousedeer, Lesser Mousedeer, Sambar Deer and Wild Boar as Protected under the Wildlife Conservation Act (2010).  Their status on the IUCN Red List of Threatened Species (IUCN 2017) and Red List of Mammals for Peninsular Malaysia (DWNP 2010) are shown in Table 1.

These ungulates are likely the major prey base for the Critically Endangered Malayan Tiger in peninsular Malaysia (Kawanishi 2002; Goldthorpe & Neo 2011; Kawanishi et al. 2013; Rayan & Linkie 2015).  Karanth & Sunquist (1995) found that larger carnivores selectively hunt larger prey when available.  A decline in large ungulate prey has been reported to be linked to a decline in a tiger population (Ramakrishnan et al. 1999).  Understanding the ecology of large ungulate prey is, therefore, important to predator conservation.  Collecting information about these ungulates can be useful to tiger conservation in the ERL.

Camera trapping is an effective non-invasive method to study shy and reclusive wild animals (see O’Connell et al. 2011; Ancrenaz et al. 2012; Sunarto et al. 2013; Trolliet et al. 2014).  Detection/non-detection information captured by camera traps can be used to study species occurrence (O’Connell & Bailey 2011; Shannon et al. 2014) and activity pattern (Ridout & Linkie 2009).  There are, however, some limitations on the use of these data as indicated by Liang (2015), including the fact that setting cameras at certain heights for large mammals sometimes misses smaller animals that pass by undetected.

In 2011, 2013 and 2015, Wildlife Conservation Society (WCS) - Malaysia Program conducted intensive camera trapping to estimate Malayan Tiger population densities in the ERL.  By-catch data exist from the camera trapping from those three years and is used to understand the occurrence and activity patterns of ungulate species.  To estimate species occurrence, Bayesian single-season occupancy framework is used.

 This paper will provide the first published baseline data of occurrence and activity patterns of ungulates in the ERL. Bayesian statistics offer advantages over the conventional/Frequentist statistics (Dennis 1996; Ellison 1996; Wade 2001; Dorazio 2016), and have been regularly used in wildlife data analysis in recent years (see Royle & Dorazio 2008; Parent & Rivot 2012; Kery & Royle 2015; Dorazio 2016).  One of the advantages of Bayesian statistics is incorporating pre-existing data (see Dennis 1996) or prior knowledge into the analysis.  The baseline data from this paper can therefore be incorporated into Bayesian analysis in future ungulate occurrence or occupancy studies in the ERL.

 

 

MATERIAL AND METHODS

 

Study area

The ~4,186km2 forested ERL (Fig. 1) is managed by three main agencies.  Endau Rompin State Park Pahang, (approximately 578km2) and Pahang Permanent Reserved Forests (PRFs; approximately 624km2) are administered by the State Forestry Department of Pahang, while Johor PRFs (approximately 2,506km2) are managed by the State Forestry Department of Johor.  Endau Rompin Johor National Park (approximately 478km²) is overseen by the Johor National Parks Corporation.  The national park is mainly lowland and hill dipterocarp forest while the PRFs are predominantly lowland dipterocarp forest (Gumal et al. 2014).

The conservation status of the seven species of ungulates for the states of Johor and Pahang are different (Table 1). In the former, due to the Sultan of Johor’s decree, there have been no approvals for permits for hunting of protected ungulates since 2010, except in cases where these animals have been shown to harm humans or their property.  In such instances, applications still have to be approved by the Department of Wildlife and National Parks (DWNP).  In Pahang, permits to hunt are only extended to the Greater Mousedeer, Lesser Mousedeer and Wild Boar.

 

 

 

 

Data collection

The camera trapping exercise was carried out during the years 2011, 2013 and 2015 in the ERL.  For each camera trapping year, camera traps were set up from early June to December and each camera trap station operated for an average of 70 trap nights (Appendix 1).  Average spacing between camera trap stations, calculated based on the distance of a camera trap station to the nearest camera trap station, using the R package secr (Efford 2016), was approximately 2–3 km (Appendix 1).

Camera traps were placed on animal trails and logging roads to increase wildlife detection probability (see Karanth et al. 2002; Karanth & Nichols 2002; Sunarto et al. 2013).  Two camera traps, positioned about 7m apart, were set up approximately 45cm above ground level at each station (see Karanth et al. 2002; Karanth & Nichols 2002).

The camera traps used were Bushnell Trophy Cam With Viewscreen, Bushnell Trophy Cam Aggressor Brown, Panthera V3, Panthera V4 and Panthera V5.  Bushnell camera traps were configured to capture videos, while Panthera camera traps were configured to capture photos.  No difference in probability of detection between camera modes was assumed because video footage and still-photography seem to share similar capture success rates (Glen et al. 2013).  At the end of deployment, all the images and videos were reviewed and audited or counter-checked by WCS - Malaysia Program researchers.  Wildlife images of uncertain identification, particularly of Bearded Pig and Wild Boar, were sent to Daniel Kong who is experienced in wildlife identification for review.  Images that could not be positively identified were excluded from the analyses.  Due to difficulty in distinguishing Greater Mousedeer from Lesser Mousedeer from camera trap photos, the two species were grouped as Mousedeer spp.

 

 

Table 1. Conservation status of ungulates in the Endau Rompin landscape, Peninsular Malaysia from various sources.

 

Common name

Scientific name

Protection of Wildlife Conservation Act 2010

Red List of mammals for peninsular Malaysia 2010

IUCN Red List of Threatened Species 2017

Barking Deer

Muntiacus muntjak

Protected, but moratorium on hunting them

Near Threatened

Least Concern

Bearded Pig

Sus barbatus

Totally Protected

Near Threatened

Vulnerable

Greater Mousedeer

Tragulus napu

Protected but can be hunted with a permit in Pahang only

-

Least Concern

Lesser Mousedeer

Tragulus kanchil

Protected but can be hunted with a permit in Pahang only

-

Least Concern

Malayan Tapir

Tapirus indicus

Totally Protected

Near Threatened

Endangered

Sambar Deer

Rusa unicolor

Protected, but moratorium on hunting them

Vulnerable

Vulnerable

Wild Boar

Sus scrofa

Protected but can be hunted with a permit in Pahang only

-

Least Concern

 

 

 

Occupancy analysis

To estimate species occurrence, detection and non-detection data of ungulates from years 2011, 2013 and 2015 from the ~2,471km2 occurrence study area (Fig. 1) were analysed for Bayesian single-season occupancy.   Camera trap stations set up in the study area increased from 131 camera trap stations in 2011 to 138 camera trap stations in 2013 and to 155 camera trap stations in 2015 (Appendix 1).

A sampling occasion was defined as a 24-hour period (Shannon et al. 2014).  A species was recorded as detected (1) or not detected (0) on each occasion for each camera trap station, generating a species-specific detection history. Periods that were less than 24 hours, for example when camera traps were inactive or malfunctioning were recorded as not available (NA).  Stolen camera trap stations that yielded no data were excluded.

BoccSS” function of the R package wiqid (Meredith 2016) was used to estimate the detection and occupancy probabilities (see MacKenzie et al. 2002; 2006) for each species for a season in a Bayesian framework based on species-specific detection histories.  Uninformative priors were used because there was no recent published occupancy papers or occupancy study in the landscape to provide such information.  To ensure convergence, a total of 45,000 iterations were used after a discarded burn-in of 1,000 iterations.

Each camera trap station represented a sampling point (Efford & Dawson 2012) instead of a fixed-size plot, allowing estimation of the true “proportion of area used/occupied” by a species (MacKenzie & Royle 2005; Efford & Dawson 2012).  The conventional occupancy definition is not applicable in this paper because the animals do not remain in front of the camera traps all the time as opposed to the Efford & Dawson (2012) definition that the animal uses or is always found in the occupied area.  Therefore, instead of occupancy, the authors opted to use the term occurrence to represent the probability of a camera trap station used by at least one individual.  Due to the by-catch nature of the data, however, modelling with site-specific variables is not explored in this paper.  This is because the original study is not designed to investigate how site-specific variables will affect occurrence.  The authors do not wish to mislead readers to biased estimates.

 

Daily activity pattern analysis

A total of 238 camera trap stations in the ~3,454km2 study area (Fig. 1) in 2015 produced a total of 18,254 trap nights.  Each camera trap station operated for an average of 76.7 trap nights. Camera trap data from the year 2015 was used for this analysis because of potential variation in activity patterns from one year to another (McDonough & Loughry 1997; Blake et al. 2012).  Activity patterns of terrestrial animals may change in response to food availability (Schnurr et al. 2004), hunting (Kitchen et al. 2000; Gray & Phan 2011), habitat conversion (Presley et al. 2009) and habitat fragmentation (Norris et al. 2010).

To ensure independence, detections of a species that were captured within 30 minutes of previous triggers of the same species at the same location were excluded (Ridout & Linkie 2009; Linkie & Ridout 2011).  After conversion of capture times into radians, density Plot function of the R package overlap (Meredith & Ridout 2016) was used to fit a kernel density function to the radian data (see Fernández-Durán 2004) and plot a probability density distribution of a photo or video being captured within any particular interval of the day, also known as daily activity pattern (Linkie & Ridout 2011).  In the ERL, from July to December 2015, sunrise and sunset times were approximately 07:00 and 19:00 hours respectively (Time and Date AS 2015).  From this analysis, camera-trapped species are classed as diurnal (active during daytime), nocturnal (active during night), crepuscular (active during twilight and dawn) and cathemeral (irregular active hours).

 

 

RESULTS

 

Occupancy

With an average occurrence of 85% (Table 2), Barking Deer appeared to be the most widespread ungulate in the camera trap area.  Bearded Pig, Malayan Tapir and Wild Boar, on the other hand, with an average occurrence of 59%, 67% and 67% (Table 2) respectively, were found in more than half of the study area.  Mousedeer spp., with an average occurrence of 67% (Table 2), was the least widespread ungulate in the camera trap area.

 

Activity pattern

This study provided insights into the daily activity pattern of ungulates on old logging roads and animal trails (Images 1–6).  Barking Deer (81.7% of observations between 07:00 and 19:00 hours), Bearded Pig (69.6% of observations between 07:00 and 19:00 hours) and Wild Boar (78.7% of observations between 07:00 and 19:00 hours) were mostly diurnal (Table 3).  Malayan Tapir (15.9% of observations between 0700 and 1900 hours) was strongly nocturnal (Table 3).  Mousedeer spp. (51.0% of observations between 07:00 and 19:00 hours) appeared to be crepuscular (Fig. 2) and Sambar Deer (58.1% of observations between 07:00 and 19:00 hours) appeared to be cathemeral (Fig. 2).  Through simulations, Rowcliffe et al. (2014) found that with a sample size of 20, the kernel model described by Ridout & Linkie (2009) consistently produced <20% median bias. Due to the small sample size of Sambar Deer (n = 15), we made no conclusion about its activity pattern.

 

 

Table 2. Bayesian single-season occupancy estimates of each ungulate species for each camera trapping year, with 95% highest density intervals in parentheses. Number of camera trap stations (n) for each year was also included. Sambar Deer was excluded because of small sample size.

 

Species

2011

(n = 131)

2013

(n = 138)

2015

(n = 155)

Mean occurrence

Barking Deer

0.83 (0.76–0.90)

0.82 (0.75–0.88)

0.89 (0.84–0.93)

0.85

Bearded Pig

0.56 (0.46–0.66)

0.65 (0.56–0.72)

0.57 (0.50–0.63)

0.59

Malayan Tapir

0.73 (0.63–0.83)

0.71 (0.62–0.80)

0.57 (0.49–0.64)

0.67

Mousedeer spp.

0.45 (0.36–0.54)

0.41 (0.33–0.49)

0.28 (0.23–0.34)

0.38

Wild Boar

0.69 (0.61–0.78)

0.75 (0.67–0.83)

0.50 (0.42–0.59)

0.67

 

 

 

Table 3. Number of independent captures, and percentage of captures from sunrise (07:00 hours) to sunset (19:00 hours), for each ungulate species.

 

Species

Number of independent captures

Percentage of captures from 07:00–19:00 hours (%)

Barking Deer

1393

81.7

Bearded Pig

714

69.6

Malayan Tapir

361

15.9

Mousedeer spp.

387

51.0

Sambar Deer

15

58.1

Wild Boar

222

78.7

 

 

 

 

 

 

 

DISCUSSION

 

Occupancy

Proxy of vegetation cover using normalized difference vegetation index, measure of terrain ruggedness using digital elevation models, distance to closest roads as a proxy of human disturbance and habitat classification based on high-resolution satellite images are a few of the site-specific variables which can be incorporated into the occupancy analysis.  Modelling with site-specific variables, however, is not encouraged due to the by-catch nature of the data.  A different sampling strategy and greater sampling efforts to include data on variables will be required to study the correlation between site-specific variables and occurrence.

Bearded Pig and Wild Boar are known to forage in oil palm plantations that lie adjacent to forests (Hone 1995; Maddox et al. 2007; Luskin et al. 2013).  Considered as agricultural pests, they can be eradicated (with permits) under the Wildlife Conservation Act 2010.  The effect of hunting pressure along forest-plantation edge habitats on the occurrence of bearded pig and wild boar is unknown.  This is because data is not easily available for firearms and frequency of hunting incidents, and terrain and food availability, which in turn affect Bearded Pig and Wild Boar occurrence along forest-plantation edge habitats.  And it is difficult to analyse this problem via an occupancy design or camera trap by-catch data.

A second reason for the difficulty to analyse is the three management systems in the landscape, i.e. protection and law enforcement effort is different between the national parks and PRFs.  Johor National Parks Corporation maintains a 24-hour presence at the national park entrances of Peta and Selai.  Patrols are conducted in the national park sporadically.  The state forestry department of Johor mainly maintains daytime monitoring of PRFs where there is ongoing logging activity.  Logging road gates are placed at most of the PRF entrances to deny unauthorized vehicle access but not intruders on foot or by motorbikes.  Finally, the DWNP is supposed to be patrolling both areas as they have jurisdiction.  It is not clear how these odd differences in protection and enforcement effort can affect ungulate occupancy in the landscape as the study area is a contiguous forested habitat that allows unimpeded wildlife movement.

 

Activity pattern

Mostly diurnal, the Barking Deer, Bearded Pig and Wild Boar exhibited two activity peaks, in the morning after sunrise and in the late afternoon around sunset (Fig. 2).  There appears to be a reduction in activity in the afternoon. In the tropical rainforest, primates and flying foxes have long been observed with twin activity peaks (Chivers 1980; Bennett & Caldecott 1989; Gumal 2004) when they were observed using scan sampling. The lull in activity tended to be around mid-day when the sun was strongest.  During these periods, these guilds of animals were often seen resting and in the case of flying foxes, fanning to cool their dark bodies (Gumal 2004).  The lull for the ungulates could be driven by a similar biological requirement to cool their bodies during the hottest part of the day, thus resting in shade or reducing their foraging activity.  The lull could also be exaggerated as the camera traps tended to be set up at old logging roads and animal trails where shade was limited. Harsh sunlight in the afternoon could have deterred ungulates from using the roads or trails.  In conjunction with activity pattern analysis, a forest canopy cover study (see Korhonen et al. 2006) should reveal if ungulate avoidance of logging roads and animal trails depends on the amount of sunlight in the afternoon.

Daily activity pattern can also be potentially used as a proxy to monitor the status of ungulates in the ERL.  Activity patterns of mammals were affected by human disturbance and hunting (Gray & Phan 2011).  In the Kaeng Krachan National Park, Ngoprasert et al. (2017) found that leopards became more diurnal in the absence of tourist activity. Several studies further noted that poached species became more nocturnal in response to high hunting pressure (Di Bitetti et al. 2008; Ohashi et al. 2013; van Doormaal et al. 2015).  A change in daily activity pattern, particularly an increase in nocturnal activity, therefore, can serve as a potential indicator of human disturbance and hunting. If such a change is observed in conservation area, we recommend that immediate studies be undertaken to investigate the cause.

 

 

REFERENCES

 

Aihara, Y., T. Hosaka, M. Yasuda, M. Hashim & S. Numata (2016). Mammalian wildlife tourism in South-East Asian tropical rainforests: The case of Endau Rompin National Park, Malaysia. Journal of Tropical Forest Science 28(2): 167–181.

Ancrenaz, M., A.J. Hearn, J. Ross, R. Sollmann & A. Wilting (2012). Handbook for Wildlife Monitoring Using Camera-traps. BBEC II Secretariat, Sabah, 71pp.

Bennett, E.L. & J.O. Caldecott (1989). Primates of Peninsular Malaysia, pp. 355–363. In: Lieth, H. & M.J.A. Werger (eds.). Ecosystems of the World Volume 14B: Tropical Rain Forest Ecosystems. Elsevier, Amsterdam, xvii+713pp.

Blake, J.G., D. Mosquera, B.A. Loiselle, K. Swing, J. Guerra & D. Romo (2012). Temporal activity patterns of terrestrial mammals in lowland rainforest of Eastern Ecuador. Ecotropica 18: 137–146.

Burhanuddin, H.M.N., K. Hassan, S. Samsuddin, O. Shahruddin & A.M. Tajuddin (1995). A survey on the distribution of large mammals in Endau Rompin State Park, Johor. The Journal of Wildlife and Parks (Malaysia) 14: 16–25.

Chivers, D.J. (1980). Malayan Forest Primates: Ten Years’ Study in Tropical Rain Forest. Springer, New York, 364pp.

Davison, G.W.H. & B.H. Kiew (1987). Mammals of Ulu Endau, Johore, Malaysia. Malayan Nature Journal 41: 435–439.

Dennis, B. (1996). Discussion: Should ecologists become bayesians? Ecological Applications 6(4): 1095–1103; http://doi.org/10.2307/2269594

Di Bitetti, M.S., A. Paviolo, C.A. Ferrari, C.D. Angelo & Y. Blanco (2008). Differential responses to hunting in two sympatric species of Brocket Deer (Mazama americana and M. nana). Biotropica 40(5): 636–645; http://doi.org/10.1111/j.1744-7429.2008.00413.x

Dorazio, R.M. (2016). Bayesian data analysis in population ecology: motivations, methods, and benefits. Population Ecology 58: 31–44; http://doi.org/10.1007/s10144-015-0503-4

DWNP (2010). Red List of Mammals for Peninsular Malaysia. Department of Wildlife and National Parks, Kuala Lumpur, 150pp.

Efford, M.G. (2016). secr: Spatially Explicit Capture-recapture Models. In: R Development Core Team, R Package. 3.3.1. Edition. R Foundation for Statistical Computing, Vienna, Austria.

Efford, M.G. & D.K. Dawson (2012). Occupancy in continuous habitat. Ecosphere 3(4): 32; http://doi.org/10.1890/ES11-00308.1

Ellison, A.M. (1996). An introduction to bayesian inference for ecological research and environmental decision-making. Ecological Applications 6(4): 1036–1046; http://doi.org/10.2307/2269588

Francis, C.M. (2008). A Guide to the Mammals of Southeast Asia. Princeton University Press, New Jersey, 392pp.

Fernández-Durán, J.J. (2004). Circular distributions based on nonnegative trigonometric sums. Biometrics 60(2): 499–503; http://doi.org/10.1111/j.0006-341X.2004.00195.x

Gray, T.N.E. & C. Phan (2011). Habitat preferences and activity patterns of the larger mammal community in Phnom Prich Wildlife Sanctuary, Cambodia. Raffles Bulletin of Zoology 59(2): 311–318.

Glen, A.S., S. Cockburn, M. Nichols, J. Ekanayake & B. Warburton (2013). Optimising camera traps for monitoring small mammals. PLoS One 8(6): e67940; http://doi.org/10.1371/journal.pone.0067940

Goldthorpe, G. & S.H. Neo (2011). A preliminary investigation into the effects of hunting on three large ungulate species in Peninsular Malaysia, with implications for tiger conservation. Malayan Nature Journal 63(3): 549–560.

Gumal, M. (2004). Diurnal home range and roosting trees of a maternity colony of Pteropus vampyrus natunae (Chiroptera: Pteropodidae) in Sedilu, Sarawak. Journal of Tropical Ecology 20: 247–258; http://doi.org/10.1017/S0266467403001275

Gumal, M., B.M.S. Abu Bakar, Mohd Nawayai Yasak, S.H. Liang, BPY-H. Lee, C.P. Low, Hasnizam Hamzah, D. Kong, D. Magintan, D.T.Y Ten, B.Z. Ahmad Zulfi, B.A. Azmi, B.K. Norhidayati, P.Y. Thai, M.F. Voon, F.F.M. Cheong & S. Ng (2014). Small-medium wild cats of Endau Rompin Landscape in Johor, Peninsular Malaysia. Cat News Special Issue 8: 10–18.

Hone, J. (1995). Spatial and temporal aspects of vertebrate pest damage with emphasis on feral pigs. Journal of Applied Ecology 32: 311–319; http://doi.org/10.2307/2405098

IUCN (2017). IUCN Red List of Threatened Species. Version 2016.2. <www.iucnredlist.org>. Accessed on 19 March 2017.

Karanth, K.U., N.S. Kumar & J.D. Nichols (2002). Field surveys: Estimating absolute densities of tigers using capture-recapture sampling, pp. 139–152. In: Karanth, K.U. & J.D. Nichols (eds.). Monitoring Tigers and Their Prey: A Manual for Researchers, Managers and Conservationists in Tropical Asia. Centre for Wildlife Studies, Bangalore, 193pp.

Karanth, K.U. & J.D. Nichols (2002). Monitoring Tigers and Their Prey: A Manual for Researchers, Managers and Conservationists in Tropical Asia. Centre for Wildlife Studies, Bangalore, 193pp.

Karanth, K.U. & M.E. Sunquist (1995). Prey selection by tiger, leopard and dhole in tropical forests. Journal of Animal Ecology 64(4): 439-450; http://doi.org/10.2307/5647

Kawanishi, K. (2002). Population status of Tigers (Panthera tigris) in a primary rainforest of Peninsular Malaysia. PhD Thesis. Department of Wildlife Ecology and Conservation, University of Florida, Florida, 126pp.

Kawanishi, K., G.R. Clements, M. Gumal, G. Goldthorpe, M.N. Yasak & D.S.K. Sharma (2013). Using BAD for good: How best available data facilitated a precautionary policy change to improve protection of the prey of the Tiger Panthera tigris in Malaysia. Oryx 47(3): 420–426; http://doi.org/10.1017/S0030605312000294

Kery, M. & J.A. Royle (2015). Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS. Elsevier, Amsterdam, 808pp.

Kitchen, A.M., E.M. Gese & E.R. Schauster (2000). Changes in coyote activity patterns due to reduced exposure to human persecution. Canadian Journal of Zoology 78: 853–857; http://doi.org/10.1139/z00-003

Korhonen, L., K.T. Korhonen, M. Rautiainen & P. Stenberg (2006). Estimation of forest canopy cover: A comparison of field measurement techniques. Silva Fennica 40(4): 577–588; http://doi.org/10.14214/sf.315

Liang, S.H. (2015). Refining camera-trapping set up for smaller-sized animals. Unpublished MSc Thesis. National University of Singapore, Singapore, 135pp.

Luskin, M.S., E.D. Christina, L.C. Kelley & M.D. Potts (2013). Modern hunting practices and wild meat trade in the oil palm plantation-dominated landscapes of Sumatra, Indonesia. Human Ecology 2014: 1–11; http://doi.org/10.1007/s10745-013-9606-8

MacKenzie, D.I., J.D. Nichols, G.B. Lachman, S. Droege, J.A. Royle & C.A. Langtimm (2002). Estimating site occupancy rates when detection probabilities are less than one. Ecology 83(8): 2248–2255; http://doi.org/10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2

MacKenzie, D.I. & J.A. Royle (2005). Designing occupancy studies: General advice and allocating survey effort. Journal of Applied Ecology 42: 1105–1114; http://doi.org/10.1111/j.1365-2664.2005.01098.x

MacKenzie, D.I., J.D. Nichols, J.A. Royle, K.H. Pollock, L.L. Bailey & J.E. Hines (2006). Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence. Academic Press, Massachusetts, 344pp.

Maddox, T., D. Priatna, E. Gemita & A. Salampessy (2007). The Conservation of Tigers and Other Wildlife in Oil Palm Plantations. Jambi Province, Sumatra, Indonesia. Zoological Society of London, London, 62pp.

McDonough, C.M. & W.J. Loughry (1997). Influences on activity patterns in a population of nine-banded armadillos. Journal of Mammalogy 78(3): 932–941; http://doi.org/10.2307/1382953

Meredith, M. (2016). wiqid: Estimation Functions for Wildlife Population Models. In: R Development Core Team, R Package. 3.3.1. Edition. R Foundation for Statistical Computing, Vienna, Austria.

Meredith, M. & M. Ridout (2016). overlap: Estimates of Coefficient of Overlapping for Animal Activity Patterns. In: R Development Core Team, R Package. 3.3.1. Edition. R Foundation for Statistical Computing, Vienna, Austria.

Milton, O. (1963). Field Notes on Wildlife Conservation in Malaya. American Committee for International Wildlife Protection, New York, 28pp.

Ngoprasert, D., A.J. Lynam & G.A. Gale (2017). Effects of temporary closure of a national park on leopard movement and behaviour in tropical Asia. Mammalian Biology 82: 66-73; http://doi.org/10.1016/j.mambio.2016.11.004

Norris, D., F. Michalski & C.A. Peres (2010). Habitat patch size modulates terrestrial mammal activity patterns in Amazonian forest fragments. Journal of Mammalogy 91(3): 551–560; http://doi.org/10.1644/09-MAMM-A-199.1

O’Connell, A.F. & L.L. Bailey (2011). Inference for occupancy and occupancy dynamics, pp. 191–205. In: O’Connell, A.F., J.D. Nichols & K.U. Karanth (eds.). Camera Traps in Animal Ecology: Method and Analyses. Springer, Berlin, 271pp.

O’Connell, A.F., J.D. Nichols & K.U. Karanth (2011). Camera Traps in Animal Ecology: Method and Analyses. Springer, Berlin, 271pp.

Ohashi, H., M. Saito, R. Horie, H. Tsunoda, H. Noba, H. Ishii, T. Kuwabara, Y. Hiroshige, S. Koike, Y. Hoshino, H. Toda & K. Kaji (2013). Differences in the activity pattern of the Wild Boar Sus scrofa related to human disturbance. European Journal of Wildlife Research 59(2): 167–177; http://doi.org/10.1007/s10344-012-0661-z

Parent, E. & E. Rivot (2012). Introduction to Hierarchical Bayesian Modeling for Ecological Data. CRC Press, Florida, 427pp.

Presley, S.J., M.R. Willig, I. Castro-Arellano & S.C. Weaver (2009). Effects of habitat conversion on temporal activity patterns of phyllostomid bats in lowland Amazonian rain forest. Journal of Mammalogy, 90(1): 210–221; http://doi.org/10.1644/08-MAMM-A-089.1

Linkie, M. & M.S. Ridout (2011). Assessing tiger-prey interactions in Sumatran rainforests. Journal of Zoology 284: 224–229; http://doi.org/10.1111/j.1469-7998.2011.00801.x

Ramakrishnan, U., R.G. Cocc & N.W Pelkey (1999). Tiger decline caused by reduction of large ungulate prey: evidence from a study of leopard diets in southern India. Biological Conservation 89:113-120; http://doi.org/10.1016/S0006-3207(98)00159-1

Rayan, D.M. & M. Linkie (2015). Conserving tigers in Malaysia: A science-driven approach for eliciting conservation policy change. Biological Conservation 184: 18–26; http://doi.org/10.1016/j.biocon.2014.12.024

Ridout, M.S. & M. Linkie (2009). Estimating overlap of daily activity patterns from camera trap data. Journal of Agricultural, Biological and Environmental Statistics 14(3): 322–337; http://doi.org/10.1198/jabes.2009.08038

Rowcliffe, J.M., R. Kays, B. Kranstauber, C. Carbone & P.A. Jansen (2014). Quantifying levels of animal activity using camera trap data. Methods in Ecology and Evolution 5:1170-1179; http://doi.org/10.1111/2041-210X.12278

Royle, J.A. & R.M. Dorazio (2008). Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, metapopulations and communities. Academic Press, California, 444pp.

Schnurr, J.L., C.D. Canham, R.S. Ostfeld & R.S. Inouye (2004). Neighborhood analyses of small-mammal dynamics: Impacts on seed predation and seedling establishment. Ecology 85(3): 741–755; http://doi.org/10.1890/02-0644

Shannon, G., J.S. Lewis & B.D. Gerber (2014). Recommended survey designs for occupancy modeling using motion-activated cameras: insights from empirical wildlife data. PeerJ 2:e532; http://doi.org/10.7717/peerj.532

Sunarto, R. Sollmann, Azlan Mohamed & M.J. Kelly (2013). Camera trapping for the study and conservation of tropical carnivores. Raffles Bulletin of Zoology 28: 21–42.

Time and Date AS (2015). Johor Bahru, Malaysia - Sunrise, Sunset, and Daylength. June to December 2015. <www.timeanddate.com/sun/malaysia/johor-bahru>. Accessed on 5 October 2016.

Trolliet, F., M. Huynen, C. Vermeulen & A. Hambuckers (2014). Use of camera traps for wildlife studies. A review. Biotechnology, Agronomy, Society and Environment 18(3): 446–454.

van Doormaal, N., H. Ohashi, S. Koike & K. Kaji (2015). Influence of human activities on the activity patterns of Japanese sika deer (Cervus nippon) and wild boar (Sus scrofa) in Central Japan. European Journal of Wildlife Research 61(4): 517–527; http://doi.org/10.1007/s10344-015-0922-8

Wade, P.R. (2001). The conservation of exploited species in an uncertain world: novel methods and the failure of traditional techniques, pp. 119–167. In: Reynolds, J., G.M. Mace, K.H. Redford & J.G. Robinson (eds.). Conservation of Exploited Species. Cambridge University Press, Cambridge, 548pp.

 

 

 

Appendix 1. Camera trapping details and efforts.

 

Year

Average spacing between camera trap stations (km)

Total camera trap stations for occupancy analysis

Total trap nights

Average trap nights per camera trap

2011

3.2

131

9401

71.8

2013

3.2

138

10160

73.6

2015

2.5

155

11399

73.5