Non-human primates (NHPs) are known to be important reservoirs of diseases that can be pathogenic to humans, and vice versa (Daszak et al., 2000; Leendertz et al., 2006; Liu et al., 2014; Wallis and Lee, 1999; Walsh et al., 2003). One such primate species is Macaca fascicularis, known to carry various diseases, including Plasmodium knowlesi – the fifth malaria (Imai et al., 2014). Currently, P. knowlesi is not fully adapted for human-to-human transmission through an anthropophilic vector, and so traditional malaria control methods have significantly reduced effectiveness in controlling transmission (Imai et al., 2014; Shearer et al., 2016; Viana et al., 2014; Vythilingam et al., 2006). P. knowlesi can be highly pathogenic in humans however, with symptoms akin to Plasmodium falciparum in the severity of infection (Cox-Singh, 2012).
The P. knowlesi malaria parasite currently circulates in non-human populations only, and is thought to be transmitted by primarily exophagic, forest dwelling mosquito species in the Anopheles leucosphyrus group (Barber et al., 2012; Fornace et al., 2016; Imai et al., 2014; Vythilingam, 2010). Zoonotic spillover events result in naturally acquired human infections, and while it is unlikely that humans are a dead-end host, fully-fledged human-to-human transmission does not seem to occur (Barber et al., 2012). “Stuttering chains” are more likely to represent current human-to-human transmission patterns (Viana et al., 2014, p. 270). With increasing incidence of P. knowlesi infections in human populations, the risk of mutation to allow human-to-human transmission via an anthropophilic vector increases (Imai et al., 2014).
Macaca fascicularis is the primary reservoir host for P. knowlesi on Palawan (Imai et al., 2014). 75% of human diseases have a zoonotic origin (Patz et al., 2004), including Plasmodium vivax and P. falciparum (Cox-Singh, 2012; Liu et al., 2014). Cox-Singh (2012) emphasises the importance of surveillance and monitoring to identify host-switch events, and with the uncertainty surrounding the effectiveness of human-to-human transmission, the question of whether spillover events are frequent or infrequent becomes far more important. Understanding the behaviour and ranging patterns of the macaque reservoir is therefore crucial to understanding the potential for transmission of P. knowlesi and other infections to humans.
Both the macaque reservoir and the mosquito vector are required for a spillover event to occur (Moyes et al., 2014). Imai et al. (2014) have linked increasing numbers of cases with deforestation and the disruption of the macaque and mosquito habitats. With increasing road building, forest clearances for farming or livestock grazing and logging practices – be it large scale or for specific trees – forests become more fragmented, the forest edge increases in size, and the deeper less disturbed areas of the forest become more accessible to humans, and the reservoirs and vectors can change the way they use the different forest types (Paige et al., 2016; Patz et al., 2004; Vythilingam, 2010). Various mosquito species have been incriminated as the vector of P. knowlesi, and mosquitoes carrying the pathogen have been identified at sampling sites in habitats likely visited by humans and macaques in Malaysian Borneo, often in habitats not typically associated with that vector species (Vythilingam, 2010).
Fornace et al. (2016) examine the relationship between P. knowlesi infection in Sabah and the proportion of forest surrounding a village, finding that clearance in the previous year was correlated with increased infection, but not clearance that year. This would imply that longer term changes and adaptation to the forest clearance – with some forest regrowth – is important to understanding the changes in macaque movement, mosquito populations and the species interface. In Malaysia, some species in the An. leucosphyrus group have been found away from their usual closed forest habitat, in the forest fragments, where NHPs are increasingly found, and even in villages in the case of Anopheles cracens (Vythilingam, 2010).
Figure 1: showing the distribution of the P. knowlesi reservoir, taken from Moyes et al. (2014)
P. knowlesi infections are prevalent in M. fascicularis populations in multiple South East Asian countries as seen in Figure 1 above (from Moyes et al. 2014), and the first non-human infection in Laos was detected recently (Cox-Singh, 2012; Zhang et al., 2016). In each area the human-macaque-mosquito interface is different, and the local situation must be considered (Paige et al., 2016).
The MONKEYBAR group (LSHTM Malaria Centre) are currently undertaking a long-term study to collect and analyse data relating to the similarities and differences in the natural history of P. knowlesi in Sabah, Malaysia, and the Island of Palawan, The Philippines. Despite being ecologically similar and geographically proximate, there are large numbers of P. knowlesi cases in Sabah, but very few on Palawan. When understanding P. knowlesi risk it is necessary to understand the ecology of the reservoir, vectors, host of interest and their overlap within the environment.
Much of the macaque daily time budget is given over to foraging (Md-Zain, n.d.), and the movement of primates in particular is shaped by preferential return to previously visited sites and heterogeneity of resource distribution (Boyer et al., 2011; Boyer and Walsh, 2010). Macaques typically prefer secondary forest in proximity to rivers and coastline (Fooden, 2006), but with increasing contact with humans, are becoming increasingly gregarious. M. fascicularis are considered to be a weed species – so named because of their ability to adapt to living in close proximity to humans, to flourish in urban environments, and to depend on farmland for a substantial portion of their diet (Richard et al., 1989). Macaques are primarily frugivorous, but in the dry and early wet season when fruit is not abundant, are generally known to focus on other foods as a fall-back (Fooden, 2006). Therefore understanding the distribution of resources in the study site will likely shed light on macaque distributions.
P. knowlesi represented 62% of malaria cases in in 2013 in Sabah, Malaysia, so represents a significant health threat (Fornace et al., 2016). At this time the incidence of P. falciparum was decreasing as the incidence of P. knowlesi increased, implying that different control methods are required for the different strains, presenting a new control challenge for malaria elimination in Malaysia. With increasing forest fragmentation, bringing macaques into closer contact with humans, there are concerns that a similar increase in P. knowlesi cases could be seen in Palawan. Palawan is a very popular destination for domestic and international tourists, and an increase in P. knowlesi cases, particularly if cases are then transported to countries with little experience of malaria, represents a real human health threat. Macaques carry various diseases, including other malaria species with zoonotic potential, and other bacterial and viral pathogens which could be harmful to human health with increased population overlap (Bailey and Mansfield, 2010; Paige et al., 2016; Zhang et al., 2016).
This paper uses data collected by the MONKEYBAR group to examine the macaque-human interface, in order to examine the risk of P. knowlesi to the local human population of Palawan and visiting tourists. Data on the changes in macaque presence/absence with changing human forest use patterns is considered alongside survey data of the human population in the study site, building a picture of the macaque behaviour and movement. Data were collected from three 2km transect walks between February 2013 and May 2014. The transect sites were chosen for their varied environments, including mixed higher elevation secondary forest and forest edge (Site I), undisturbed lower elevation forest (Site II), and forest heavily disturbed by agriculture (Site III). Once a month at each of the three sites, a macaque census was conducted and phenology data collected.
Specific Objectives
The overall aim of the project is to describe macaque behaviour and movement, with reference to the risk of disease transmission, in particular P. knowlesi.
Interface hypothesis – if macaque-human interactions are generally aggressive, I expect macaque density to increase with distance from roads and houses, and with decreasing levels of human disturbance.
Crop raiding hypothesis – given reported higher levels of hunting in Palawan than Sabah, I expect crop raiding events to be linked to a seasonal drop in food availability, reducing the cost of potential interactions with humans at farms if food is less available in the forest.
Primatology hypothesis – macaques are expected to be found more frequently in areas with higher densities of feeding trees, more feeding species available, and higher fruit availability.
The forest composition hypothesis provides essential context for the interface and crop raiding hypotheses; as the interface hypotheses proposes a push factor away from settlements, the crop raiding hypothesis proposes a pull factor towards settlements, and the forest composition hypothesis a pull factor towards the more diverse forest with a higher density of feeding trees. The strength of these influences on the macaques – level of acclimatisation to humans, fruit availability in the forest – will change their behaviour when encountered, the likelihood of proximity to humans, and risk of P. knowlesi spillover events.
Within the Palawan study area, seen below in Figure 1, houses were surveyed and three sites were chosen to conduct the macaque census and investigate forest composition. The sites were chosen for their differences in forest composition and levels of disturbance.
Figure 1: the study site, showing houses, the transect points at each site in black, and the botanic plots in yellow.
A survey of each household in the study site (locations shown in Figure 1) was conducted in November 2014 by MONKEYBAR researchers based at The Royal Institute of Tropical Medicine in Manila. Demographic data and perceptions of macaques was gathered at a household level, data on macaque sightings in the previous four weeks was gathered at an individual level. There are no other similar primates on Palawan, so it is very unlikely that the survey participants gave answers about a species other than Macaca fascicularis, particularly as the local name was also used (Kühl et al., 2008; Meijaard et al., 2011).
Data sets relating to forest composition and macaque presence/absence were received, summarised in Table 1. There was 52 weeks of data that had been collected between February 2013 and May 2014. On occasion sites were inaccessible due to typhoons, and the macaque census delayed by a month. At each site there were 24 censuses; each transect walked in the morning and the evening.
Line and point transect observations were recorded, with methodology (line or point) not identified. GPS points for each observation were taken at the point of detection on each transect, therefore the point transect data should have a GPS point that corresponds to one of the point transect points. The Garmin GPS units used are accurate to 10m (“Garmin – aboutGPS,” n.d.), doubled for the difference between two points, and doubled again for inaccuracies introduced by forest cover and elevation changes. I am therefore assuming that all of the presence points within 40m of a recorded point transect point were detected using the point transect methodology, rather than opportunistic line sampling. The data are not comparable due to different sampling efforts and detectabilities (Buckland and Handel, 2006; Kühl et al., 2008; Thomas et al., 2010). This is an important assumption and limitation of the data (see Appendix 1 for a further explanation of line and point transect methodologies).
Data on forest disturbance and type was collected at 20m intervals along each transect. This was separated into ‘Disturbed’, ‘Moderately Disturbed’ and ‘Undisturbed’ forest (Appendix 1, Table 1). Bishop et al. (1981) categorise disturbance of habitat using home range, level of harassment, habituation, and presence of predators. We are unable to define home range, however on Palawan macaques are only predated by humans, and with increasing human encroachment on the forest and lack of macaque habituation to humans (PM Kim 2017, personal communication, …) we can assume as forest disturbance increases, the negative impact on macaques increases (Richard et al., 1989). Forest was therefore classified as disturbed if there was evidence of human activity, and moderate if no overt evidence of human activity, but was still described as disturbed by the field team. Once visualised in ArcGIS, these classifications corresponded well with the descriptions of the sites given by the field team.
As seen in Figure 1, each site had 6 transects, 1km in length, spaced 200m apart, then divided in 200m segments – forming a grid of 36 points 200m apart. These points were used to conduct the point transect macaque census. Botanic plots were used to gather forest composition data at the beginning of the study. The plots have a width of 10m either side of the transect, and are 100m in length. Some transects in sites II and III have more than one plot, as it was randomised. Plots in site I were chosen for a good tree density, so this may overestimate the tree density. Sites II and III were randomised, then the locations the team were able to access were selected.
Figure 2: site I, showing the level of forest disturbance at 20m intervals, locations of houses, and macaque sightings
Figure 3: showing site II, with levels of disturbance at 20m intervals shown, locations of houses, and macaque sightings
Figure 4: showing site III, with level of forest disturbance at 20m intervals, house locations, and macaque sightings
Data on trees present in each botanic plot at the beginning of the study was cleaned, and tree density and feeding tree density was calculated for each plot (plot locations are shown in yellow in Figure 1). A combined list of suspected and verified feeding trees was used to define feeding species. Plots were assigned a forest disturbance level based on maximum disturbance. The plot data were then used to describe the study area, and determine whether the forest type varied between site, and whether the same forest classification between sites had the same composition. Plots were assessed for abundance/presence of tree species (availability) and for the number of trees in a plot (density).
In order to combine the botanic plot (tree density etc.) data with the macaque census data, the plot specific data was entered where a transect point was found inside a plot. Where transect points did not overlap with a plot, the average for that forest type, within that site was calculated and used.
A monthly phenology survey considered flowers, young leaves, unripe fruits, ripe fruits and vines. Each tree was classified as having none, few, some, or many. The survey also recorded instances of tree death through the study. All trees with a Diameter at Breast Height (DBH) of 10cm or above were recorded and used in the monthly phenology surveys. Where possible, the same person did the phenology plots, to try and eliminate observer/recorder bias between surveys. The data for each plot were combined by month to give an average proportion of trees in each site with no, few, some and many ripe and unripe fruits. The data on young leaves, flowers and vines were not used.
When relating the phenology data to the macaque census data, monthly values were used for the transect points that overlapped with the plots. The data on ripe and unripe fruit was therefore month and plot specific.
I intended to use the Distance Sampling programme to calculate the density of macaques in each forest type (Buckland et al., n.d.; Buckland and Handel, 2006; Thomas et al., 2010). With 52 observations however, there were insufficient presence points for this methodology to work for point transect data (Kühl et al., 2008). Therefore, logistic regression was used to calculate odds ratios for macaque sightings across the different variables.
Data were collated in order to conduct further analyses. Transect points form the data structure; at each transect point, for each census, there is either a presence or an absence point, and environmental variables for each transect point. Distance to the nearest house (gathered from human survey data) was plotted in ArcGIS and extracted using the near tool for point data. The distance to the road was extracted from a raster data set, using the ‘near’ tool.
Plots are situated at various points within the transects, however there are many transect points without a corresponding plot. In order to determine forest composition data for each transect point, the phenology and botanic plot data were averaged by forest type within a site. Where there was a point transect within a plot, the specific plot data was used, rather than an average.
Bivariate analyses were performed to determine which variables to enter into the model using Chi-square and independent sample t-tests, and the variables identified were then assessed for multicollinearity using multiple linear regression.
Logistic regression analyses were performed in R to identify characteristics of the study site that were associated with the presence or absence of macaques.
To investigate the interface, crop raiding and primatology hypotheses, the results from the human survey, macaque census, and phenology surveys are considered below.
Between the 11th and 14th November 2014, households within the study site were asked whether macaques they had seen in the past four weeks. Out of 489 respondents, 120 had seen macaques in the past four weeks, adding to a total of 523 macaque observations between October and November 12th 2014. This data was used to investigate the interface and crop raiding hypotheses.
Table …: locations of the macaque encounters, as reported in the human survey
| Encounter location | Forest | River | Beach | Farm | Other |
| Percentage | 62.3 | 8.2 | 9.8 | 12.3 | 7.4 |
As seen in Table … , the vast majority of encounters happened in the forest, with the next largest proportion of encounters at farms. Of the people who saw macaques, 13.4% were farmers, 3.5% drivers, 6.4% housewives, 11.3% students, 5% fishermen, 4.3% worked in construction, 5% sawali/buho gathering, 5.7% made charcoal, with 46.8% reporting ‘other’. Of the famers who saw macaques, 20% reported some
Table …: showing the ways the survey respondents reported using the forest, includes people who have and have not seen macaques
| Forest use | Wood | Hunting | Food | Do not use | Other |
| Percentage | 34.7 | 2.5 | 3.4 | 22 | 37.3 |
Using the forest for wood is the most common reported reason for entering the forest, apart from ‘other’. Only 2.5% report using the forest to hunt, and hunting is now reported to be infrequent, although it was common in previous years (PM Kim, personal communication, …). The knowledge of how to hunt is still alive, but there is one instance of hunting recorded in the macaque census notes and three survey respondents reported hunting, so there is evidence that some hunting still occurs at the site.
The crop raiding and primatology hypotheses – that crop raiding will be linked to seasonal fruit availability, and that macaques will prefer areas with denser forest and higher availability of feeding species – require information on macaque movement and detailed data about the forest composition of the three sites.
In order to maintain sample size, I included both auditory and visual detections of the macaques. It is highly likely that some auditory detections are false positives, particularly where the detection is based upon moving branches and the sound of an animal moving through the canopy. While experienced fieldworkers can often detect the type of animal that is present from the sound of the movement, even the most experienced trackers can be incorrect. Where the auditory detections are based upon calls, this is likely to be more reliable, however there are a number of bird species which can mimic macaques. At the study site the Ashy Fronted Drongo and the Spangled Drongo are reported to mimic macaques with high fidelity, and it is possible that the Hill Myna may also mimic macaque calls, this is not yet documented at the study site however (PM Kim 2017, personal communication, ).
Thus far, the data supports the hypothesis that macaques preferentially spend time in undisturbed places that are far from humans. There is a clear trend in the observation data and number of macaques, as seen in Tables 5 and 6 below. The average group size is higher in disturbed areas than in moderately disturbed or undisturbed areas, but were this a true effect we would expect higher group sizes in site III than are seen. The apparent relationship between group size and forest type may be caused by a lower tree density in disturbed areas, resulting in higher detectability of the macaques.
Table 5: macaque observations, stratified by site
| Macaques by site | Site 1 | Site 2 | Site 3 |
| Number of observations | 28 | 19 | 5 |
| Number of macaques | 111 | 105 | 11 |
| Estimated size of troop | 3.9 | 5.5 | 2.2 |
Table 6: macaque observations, stratified by forest type
| Macaques by forest type | Disturbed | Moderately | Undisturbed |
| Number of observations | 6 | 12 | 34 |
| Number of macaques | 47 | 45 | 179 |
| Estimated size of troop | 7.83 | 3.75 | 5.26 |
These results seem to correspond with the reports from the field team that the macaques produced predator calls when people were seen, and consistently ran away from people at all sites. There appears to be no habituation, even in the disturbed sites, shown by the lack of any evidence of macaques in the disturbed areas of site III (PM Kim 2017, personal communication, ).
It is important to note that the number of the tree species at the study site is exceptionally high. At all sites the number of tree species and families continued to increase with each botanic plot. It is likely that had more plots been done, further species and families would have been identified. As in focus groups, we would ideally continue to sample until we no longer received new species. Therefore despite having a significant proportion of the tree species, we may not have a complete picture of the species and diversity at and between each site.
This project examines hypotheses which require an understanding of the forest composition, macaque, and human behaviour. In order to combine these varying sources and carry out further analyses, the macaque census and forest composition data must be combined into one data set. The following section examines the differences in the sites and in the disturbance levels, in order to understand the heterogeneities between the sites and decide on the best way to split and group the forest composition data.
We expected evidence of difference in fruit abundance between the forest types, however there was no evidence against the null hypothesis of no difference in the mean proportion of trees with ripe fruit between the forest types (F = 0.632, p = 0.539). Similarly there was no evidence against the null when examining the proportion of trees with unripe fruits (F = 1.362, p=0.273).
Proportions of trees with few, some and many ripe and unripe fruits were plotted by month (Appendix 2, graphs 1 to 6). Despite the proportion with any fruit being very low, the graphs showed clear seasonal trends. Therefore when comparing the phenology data with macaque sightings, seasonality should be taken into account. It would not be appropriate to average the phenology data across time.
In order to investigate the primatology hypothesis, it was necessary to determine whether there was a true difference in the disturbance levels and forest composition between the sites, the tree species, tree density, and disturbance levels between the sites.
There are 150 unique tree species identified in the study, heterogeneously distributed through the sites with all sites sharing 30 common species, descriptive statistics shown below in Table 2. The sites can be differentiated when looking at forest type and abundance of feeding trees within each site. Overall sites I and II were more similar to each other than to site III.
Table …: descriptive statistics by site, only the data estimated to be point transect data was used for the macaque detections
| Measure | Site I | Site II | Site III |
| Tree density (per hectare) | 804 | 1000 | 834 |
| No. of species (total) | 85 | 69 | 98 |
| No. of feeding species unique to the site | 20 | 18 | 40 |
| No. of feeding trees (combined) | 436 | 545 | 409 |
| Prop. of feeding species available at site – total (verified, suspected) | 0.7568 |
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