Occurrence and Distribution of Philippine Warty Pig (Sus philippensis Nehring, 1886) in Mt. Banahaw

(1)

Occurrence and Distribution of Philippine Warty Pig (Sus philippensis Nehring, 1886) in Mt. Banahaw

de Tayabas, Luzon Island, Philippines

Al John C. Cabanas^1*, Anna Pauline O. de Guia², Renato S.A. Vega³, and Judeline C. Dimalibot²

1Mathematics and Natural Sciences Department, Southern Luzon State University, Lucban, Quezon, Philippines

2Animal Biology Division, University of the Philippines Los Baños, College, Laguna, Philippines

3College of Agriculture and Food Science, University of the Philippines Los Baños, College, Laguna, Philippines

This study determined the occurrence and distribution of Philippine warty pig (Sus philippensis) in Mt. Banahaw de Tayabas using camera trapping and indirect signs. The Philippine warty pig is an endemic species of wild pig in the Philippines and is currently listed as Vulnerable on the IUCN Red List because of the presence of several threats such as hunting, habitat fragmentation, and the current outbreak of African swine fever (ASF). Camera trap stations were established with 10 camera traps functioning 24 h for 17 d along different elevations. Different species distribution models (BIOCLIM, DOMAIN, and MAXENT) were constructed using 19 bioclimatic predictors to determine the potential distribution of the species in Mt. Banahaw. Results from three different SDMs suggested that Philippine warty pigs prefer to occupy secondary growth forests, as a high probability of occurrence was observed within 600–800 m above sea level (masl). Models also predicted that Philippine warty pigs occupy large portions of Mt. Banahaw de Tayabas, although sparsely in the extreme southern and northern sections of the mountain. The most reliable model that predicted the distribution of the species was MAXENT, as it acquired the highest area under curve (AUC) among the three SDMs. This study confirmed the presence of Philippine warty pigs in Mt. Banahaw de Tayabas and its preferred habitat. The data and information generated here will be useful for the local community’s plans in conserving and managing this endemic species.

Additional recommendations also include investigating the population size within and outside the protected area and establishing baseline data to assess the impact of ASF.

Keywords: camera traps, distribution, habitat, species distribution model

*Corresponding author: [email protected]

INTRODUCTION

The Philippines is home to four endemic species of wild pigs, which is greater than any other country, next to Indonesia. Their distribution follows predictable lines

with divisions associated with the major faunal regions of late Pleistocene aggregate island complexes (Oliver 1995). The Visayan warty pig (Sus cebifrons) is found in the islands of Visayas that includes Panay, Negros, and Cebu. Endemic to the islands of Mindoro is the Oliver's warty pig (S. oliveri), whereas the Palawan bearded pig (S. ahoenobarbus) can only be found in the islands of Philippine Journal of Science

151 (5): 1605-1621, October 2022 ISSN 0031 - 7683

Date Received: 20 Jan 2022

(2)

Palawan and associated islands. The two subspecies of the Philippine warty pig (S. philippensis) are found in Luzon (S. philippensis philippensis) and Mindanao faunal regions (S. philippensis mindanensis).

Philippine warty pigs (S. philippensis) are large-sized mammals that play an important role as ecosystem engineers. According to Jones et al. (1997), ecosystem engineering by organisms is the physical modification or creation of habitat. Pigs control the growth of wild plants, act as seed dispersers, modify the structure of the soil, and help establish pioneer plants. The feeding activities of wild boars strongly influence the structure and function of forested ecosystems (Focardi et al. 2008; Hone 2002).

Despite their ecological importance, little is known about the ecology, behavior, and distribution of S. philippensis, and their presence or absence on many islands in the Philippines is still unsettled (Oliver and Heaney 2017). As forest-associated species, one indicator used to infer their presence is the extent of remaining forests where their populations may still occur. The Philippine warty pig's population is expected to be decreasing in most parts of its range where it was formerly common (Meijaard et al.

2011). These drastic declines are mainly due to over-hunting and poaching, threats of hybridization with free-ranging domestic and feral pigs, and habitat loss and fragmentation (Oliver and Heaney 2017; Scheffers et al. 2012; Meijaard et al. 2011; Griffin and Griffin 2000; Oliver 1995). The topic of human-wildlife conflict, crop-raiding, and communities’

attitudes towards pigs will be discussed elsewhere [Cabanas et al. (in prep.)]. The Philippine warty pig is currently listed as Vulnerable in the International Union for the Conservation of Nature (IUCN) Red List (Oliver and Heaney 2017) and in the Department of Environment and Natural Resources (DENR) administrative orders (DENR 2019) of the Philippine Wildlife Act (DENR-BMB 2019).

The recent outbreaks of ASF also pose a significant threat to endemic pig species in the country (Luskin et al. 2020).

ASF outbreak was first recorded in August 2019 in the Philippines (BAI 2019) and since then, ASF has been spreading throughout the country. Several mass mortalities are likely to occur once ASF spreads among wild pig populations, as reported for the Borneo population of Sunda bearded pigs (Sus barbatus) (Ewers et al. 2021).

It is, therefore, essential to conduct regular monitoring of Philippine wild pig populations. The Philippines has the highest annual forest loss of all Southeast Asian countries with 2.8% forest loss per year, and most of the forests in the Philippines are secondary and degraded (Stibig et al.

2007). The objective of this study was to determine the occurrence and distribution, as well as infer the habitat preferences, of the Philippine warty pig in Mt. Banahaw de Tayabas. With a total land area of 11,133 hectares, the Mt. Banahaw Protected Landscape covers a total of nine

municipalities of Laguna and Quezon. Mt. Banahaw is a protected area for a vast number of Philippine endemics, making this an ideal site to conduct biodiversity studies.

Data gathered in this study will serve as important baseline information, which focuses on the distribution and ecology of the Philippine warty pig (S. philippensis) in the region, and will be beneficial in assessing current threats of ASF in the Philippine wild pig population, as all four known species of Philippine wild pigs are at risk of ASF (Luskin et al. 2020).

MATERIALS AND METHODS

Study Site

Mt. Banahaw is an active volcano that rises steeply to 2,177 m. The study was conducted at 500–1500 masl of Mt.

Banahaw de Tayabas. It is characterized as having a rough terrain and moderate to steep slopes. Types of forest include lowland dipterocarp on the lower slopes, as well as montane and mossy types of forest above 900 masl. Occupying the lower slopes are coconut plantations and inter-cropped fruit trees, whereas cultivations are apparent in the surrounding area (Birdlife Data Zone 2001). The sites were selected during reconnaissance surveys in February and March 2018. Local knowledge, particularly from former hunters, was considered in determining locations where the species are usually observed. Anecdotal information from the local community was also obtained in selecting the study sites.

The distance of the sampling site from the community was approximately 5 km.

Camera Trapping

In determining the occurrence of wild pigs in Mt. Banahaw de Tayabas, the study was conducted during the months of October and November 2018, which coincided with the wet or rainy season in Quezon Province (June–November) according to the Modified Corona's Classification of Climate (Lantican 2001).

Camera stations (Figure 1a) were selected based on reconnaissance information, accessibility of the terrain, and indigenous knowledge of former hunters. A total of 10 camera traps (Browning Dark Ops HD Pro) were deployed for 17 days covering different elevations and forest types.

Camera traps were set to function for 24 h. Deployed camera traps functioned effectively and were able to capture different species of wildlife – including the target species, the Philippine warty pig. A 20 m x 20 m plot was established at each camera trap station, making the camera trap the center point of each plot. Used wallows and tracks (Figure 1b) identified by former hunters were noted and documented to plot the distribution of the species.

(3)

Figure 1a. Camera trap locations in Mt. Banahaw de Tayabas. Heat map denotes the elevation of camera trap locations.

Figure 1b. Traces (used wallows and tracks) of Philippine warty pig identified by former hunters and gatherers.

Philippine Journal of Science

Vol. 151 No. 5, October 2022 Cabanas et al.: Occurence and Distribution of

Philippine Warty Pig

(4)

Habitat Assessment

For this study, we used the habitat description data sheet for the Southeast Asian Mammal Project by Heaney (1986) to assess the habitat of each camera trap location (N = 10). Using a 20 m x 20 m plot, we gathered information on leaf litter cover, humus cover, presence of moss, height of emergent trees, diameter at breast height (DBH) of canopy trees, distance to the nearest water, presence of Pandan, canopy cover, distance to clearing, presence of Ficus, presence of other fruit trees, presence of disturbance, and presence of Musa. Results obtained were used to determine the occurrence of the Philippine warty pig in the area and to characterize each habitat of camera trap stations using principal component analysis (PCA).

Data Analysis

In order to examine whether environmental factors predict the occurrence of the Philippine warty pig, we selected variables to construct a plot from the habitat data. PCA examines associations between variables, summarizing them into components (Vernes 2003). A total of 13 variables (Heaney 1986) were recorded to characterize the habitat on each camera trap station.

To examine how the different habitat variables relate to the occurrence of warty pigs (camera trap captures, foot tracks, and wallows), we used a generalized linear model (GLM) function. Following the method of Burnham and Anderson (2002), the “best” model was selected by comparing the full models with null models that dropped one or more predictor variables with the selection criteria based on the Akaike information criterion (AIC).

To determine the potential distribution of S. philippensis, three SDMs were constructed using all signs of pigs – which include camera trap photos, tracks, and used wallows identified by local hunters. Distribution models have become a significant tool for reserve selection, as well as survey design and management of rare species (Carpenter et al. 1993).

SDMs, also known as ecological niche models, use environment data for sites of occurrence or presence of a species to predict a response variable for a site where the environmental circumstances are appropriate for that species to continue thriving or most likely to occur (Araujo and Peterson 2012). Models that determine species population distribution range and abundance are a substantial tools in ecology by offering key evidence for decision-making practices on conservation and biodiversity management (Guisan and Zimmermann 2000). This study utilized three ecological niche models – namely, BIOCLIM, DOMAIN, and MAXENT. These three SDMs are widely used in academic research and species conservation. The results of these models were

combined to come up with the model average. We utilized three models to arrive at more conclusive results and compare the outputs of the single different models using AUC. AUC is a threshold independent measure of predictive accuracy. It is construed as the probability that a randomly selected presence location is ranked higher than a randomly chosen background point (Merow et al. 2013).

Nineteen (19) bioclimatic variables in raster format were downloaded from WorldClim–Global Climate Database (http://www.worldclim.org). These data are a set of climate layers representing information related to temperature and precipitation (Hijmans and Graham 2006). Using the boundary shapefile of Mt. Banahaw as mask, the data layers from the WorldClim v.2.0 raster file were then clipped to cover only the area of interest, as these layers are presented on a global scale.

All prepared climatic and location files were then entered in the machine algorithm – BIOCLIM, DOMAIN, and MAXENT (v.3.4.1) – along with landcover data.

The resulting raster (ASC) file containing the modeled distribution for S. philippensis was projected in QGIS (v.3.0 Girona), along with a shapefile from ArcGIS containing habitat types identified in Mt. Banahaw and a shapefile of the broad habitat types.

RESULTS AND DISCUSSION

Habitat Characterization

The PCA (Figure 2) presented different environmental variables that characterized the different habitat types of the camera trap locations. High leaf litter, for instance, can be found in the upper montane forest (UP), which also recorded significant number of mosses. Habitat characteristics of UP are related to the mid-montane forest (MM) variables, which was further characterized by having greater DBH of trees, presence of Pandanus, and closed canopy cover. PCA also showed that secondary growth (SG) and plantation (P) are relatively related to each other in terms of habitat types. This was confirmed by the presence of fruit trees (e.g. Ficus, Musa) and significant distance to anthropological clearings being present in both types of forests.

Wetter conditions provide an abundance of food, whereas hot and dry climates hinder such abundance (Klein 1965; Mattson 1980; Guthrie 1984). The most important factors shaping wild boar density and distribution in the environment are food, shelter, and water (Fernández- Llario et al. 2003). Following the results of the study, we suspect that the effects of precipitation and temperature on the availability of root crops are probably the key factors

(5)

in determining the potential distribution of Philippine warty pig in Mt. Banahaw de Tayabas. In the study of Caruso et al. (2018), habitat use of wild boar in Argentina was mainly confined to forest or forest edges and they frequently occurred near natural habitats. While wild boars often use forests and shrublands, Fonseca (2007) reported that the species is able to use open areas but prefer trees or bush covers for shelter. Wild boars are more abundant in old mature deciduous forests and locations where high food and landscape diversity is accessible (Acevedo et al.

2006). The availability of water restricts its density and scope in hot dry climates (Abaigar et al. 1994; Massei et al. 1997).

Occurrence of Philippine Warty Pigs in Mt.

Banahaw de Tayabas

After 17 camera trap days, only one camera trap station was able to capture the target species (Figure 3). A total of three captures of S. philippensis representing five individuals was recorded: two solitary adults and an adult female with two young. These captures are the first photo records of Philippine warty pig in Mt. Banahaw.

Sus philippensis’ unique morphological characteristics consist of usually black with grey-colored fur and a pale snout band. A long full crown tuft and nuchal mane extend along the back male S. philippensis. Males also have two

pairs of warts and gonial hair tufts (Meijaard et al. 2011).

Habitat Preferences of Philippine Warty Pig in Mt.

Banahaw de Tayabas

Box plots (Figure 4) show the occurrence (n = 8) of Philippine warty pigs based on their camera trap captures and indirect signs such as foot tracks and used wallows located within the 20 m x 20 m plots. Box plots for canopy cover (A) show that they were mostly present in an open canopy than a closed one. Moreover, traces revealed that they were found to thrive in areas that were in close proximity to clearings such as plantations (B). For the height of emergence (C), the species were mostly present where emergents were shorter (D). Philippine warty pigs also moved in areas where the trees have smaller DBH.

They were also found to exist in areas abundant with fruit trees (E). Traces of the species indicate they occupy areas of Musa sp. plantation (F). The last box plot shows that the species prefer habitats with easy access to water since most of the tracks were observed in areas near streams (G).

Generalized Linear Model

The AIC (Akaike 1973) is presented on Table 1. Based on the AIC values, Model 5 (full model) is the “best‟

model for pig presence prediction. The logistic regression coefficients gave the change in the log odds of the outcome

Figure 2. Principal components of habitat on different elevations (MM – mid-montane, P – plantation, SG – secondary growth, TF – tropical forest, and UP – upper montane).

(6)

Figure 3. Philippine warty pig (S. philippensis) caught by camera trapping in Mt. Banahaw de Tayabas.

(7)

Figure 4. Box plots of occurrence of Philippine warty pig with each habitat variable in Mt. Banahaw de Tayabas: [A] canopy cover, [B] distance to clearing, [C] height of emergence, [D] DBH of trees, [E] presence of fruit trees, [F] presence of Musa, and [G] distance to nearest water source.

(8)

for a one unit increase in the predictor variable. The table shows that for every one-unit change in Musa, the log odds of pig presence increase by 0.09088 and for a one unit increase in canopy cover, the log odds of pig presence decrease by 0.07482.

For categorical variables, results show that plantation (VegP) and secondary growth (VegSG) (positive coefficients) are more likely to have a higher presence/occurrence of warty pigs than mid-montane (VegMM), whereas tropical forest (VegTF) and upper montane (VegUP) are less likely to have a higher presence/occurrence of pigs than mid- montane (VegMM) (negative coefficients). The individual components of habitat types had different influences on the occurrence of the warty pigs.

Wild boars are much more active under moist conditions.

Grounds are easier to root, given the right amount of moisture (Lemel et al. 2003; Welander 2000). Nest sites, therefore, are constantly situated in close proximity to water (Dardaillon 1986; Fernández-Llario 2004). Water availability and temperature are two significant features that can play an important role in the abundance and distribution of wild boar (Cuevas et al. 2013).

Studies in other countries found that Sulawesi warty pigs (S. celebensis) (NRC 1983; Mustari 2009) feed on a wide range of diet, which includes roots, foliage, fallen fruits (Corypha sp., Arenga pinata, Ficus sp.) as they visit these plantation areas during fruiting season. Our

Table 1. AIC, model selection based on AIC (summary of values based on the outputs).

Model Intercept VegPL VegSG VegTF VegUP Musa Canopy Dist.

Water Dist.

clearing Null de-

viance Residual deviance AIC PigPres

~ Veg + Musa + Canopy.

cover + Dist.water + Clear.

dist

-20.24086 13.36451 16.86753 -1.1378 -23.833 0.09088 -0.0748 -0.03164 0.0573 209.09 138.08 156.08

PigPres

~ Veg + Clear.dist + Musa + Dist.water

-24.26971 16.52108 17.50483 -0.8071 -22.883 0.10084 -0.03132 0.0541 209.09 140.50 156.5

PigPres

~ Veg + Clear.dist + Musa

-13.977816 2.827558 8.288746 2.21297 -7.8936 0.13159 0.0210 209.09 145.25 159.25

PigPres

~ Veg + Musa

-3.46574 -5.25144 0.84512 2.21297 2.61844 0.10756 209.09 152.21 164.21

PigPres ~

Musa -1.78165 0.02469 209.09 188.54 192.54

results concur with this study and further indicate that the combined presence of food, water, and cover encapsulated as elements of habitat had a positive relationship with the distribution of wild pigs in Mt. Banahaw de Tayabas. In the study of Danilov and Pachenko (2012), results showed that wild boar and feral pigs in Russia used a range of natural and anthropogenic habitats to access either food or cover.

Moreover, in terms of distance to the nearest water, the probability of pig occurrence decreased with an increasing average distance to water (McClure et al. 2015).

Our data show that as the elevation increases, the indices of presence of the species decreases (Figure 5). Plots of Camera Station 1 up to Camera Station 5 recorded the lowest number of tracks of Philippine warty pigs. The elevations that recorded the greatest number of tracks were within 600–800 masl. Numerous tracks were also recorded in the plantation area. Results of this research follow the previous study of Blouch (1984), wherein he discovered the pockets of Javan warty pig (S. verrucosus) at 600–800 masl at Mt. Penanggungan in Indonesia. In Mt. Argapura in Surabaya, S. verrucosus was found thriving at 500–800 masl. The preferred habitat for S. verrucosus was found to be extensive areas of lowland secondary vegetation, particularly teak (Tectona grandis) plantations and stretch of Imperata cylindrica grassland, both scattered with brush and forest clumps.

Modeling the Distribution of Philippine Warty Pig

(9)

Figure 5. Distribution of traces such as footprints and wallows of Philippine warty pig plotted on different habitat gradients in Mt. Banahaw de Tayabas.

in Mt. Banahaw de Tayabas

SDMs. This study utilized three ecological niche models – namely, BIOCLIM, DOMAIN, and MAXENT. These three SDMs are widely used in academic research and species conservation. The results of these models were combined to come up with the model average.

BIOCLIM. The distribution model ran in BIOCLIM generated a mean training AUC of 0.9946. The resulting heat map below shows the probability of the presence of Philippine warty pig along different habitat types. Yellow markings indicate the predicted presence of the species (Figure 6). It shows that S. philippensis is widely present in the mixed-secondary agro-forest. Results also show that as elevation increases, the possible presence of the Philippine warty pig decreases.

DOMAIN. The distribution model ran in DOMAIN generated a mean training AUC of 0.9937. It analyzes a continuous similarity function for all candidate sites.

DOMAIN measures the environmental similarity to the most similar training position.

The distribution was found to be dispersed and more

scattered than the model output of BIOCLIM (Figure 7). The yellow markings also imply the potential species distribution of S. philippensis in Mt. Banahaw de Tayabas.

Main difference between BIOCLIM and DOMAIN is that the latter considered other portions of the mountain, whereas the former did not. Figure also indicates that areas with high probability of occurrence can be found in the cultivated area. This may be due to considerable presence of food resources such as coconut and root crops.

MAXENT. The distribution model ran in MAXENT generated a mean training AUC of 0.9962 [maximum of 1; relevant starting at 0.75, as per Fielding and Bell (1997)]. MAXENT predicts that the Philippine warty pigs occur throughout the Mt. Banahaw de Tayabas, although sparsely in the extreme southern and northern portions of the mountain (Figure 8). A relatively high probability of occurrence was observed at 600–800 masl. The habitat in the area was characterized as secondary growth forest.

MAXENT acquired the highest AUC value among the three SDMs, which implies that MAXENT is the better model for predicting the distribution of the Philippine warty pig in Mt. Banahaw de Tayabas.

(10)

Figure 6. Distribution of Philippine warty pig in Mt. Banahaw de Tayabas using BIOCLIM.

(11)

Figure 7. Distribution of Philippine warty pig in Mt. Banahaw de Tayabas using DOMAIN.

(12)

Figure 8. Distribution of Philippine warty pig in Mt. Banahaw de Tayabas using MAXENT.

(13)

Model Average

The three SDMs tested generated different distribution maps using the same 19 bioclimatic variables. The model shown below presents the model average of the three SDMs used in the study (Figure 9).

Among the three ecological niche models used, MAXENT provides the most reliable result and more dependable predictive model. The result agrees well with other studies. In the study of Reiss et al. (2011), results showed that MAXENT had better predictive performance, and its AUC values are significantly higher than BIOCLIM.

Elith et al. (2006) also confirmed that MAXENT has significantly higher predictive performance than BIOCLIM and DOMAIN. Giovanelli et al. (2010) confirm that MAXENT is the most precise among four prediction models (BIOCLIM, SVM, DOMAIN, and MAXENT).

Together, these studies stipulate the superior predictive precision of MAXENT over other models, and these include the BIOCLIM and DOMAIN.

Other Species Recorded

Other than Philippine warty pig, camera traps were also able to capture photos of other wildlife species (Figure 10). This includes common palm civet (Paradoxurus philippinensis) (three independent photos). This species is abundant in the mountain based on the species’ indirect signs along trails (scats) consisting of undigested seeds, which they deposit on the ground along the trail. Photos of feral cats were also captured in one of the camera trap stations (two independent photos). Within the elevation of 1500 masl, this is the first information about their presence in this part of Mt. Banahaw de Tayabas. This poses threats to many populations of small-medium-sized wildlife of Mt. Banahaw. Feral cats are known wildlife predators.

Camera traps deployed at 500-900 masl captured the greatest number of species. Captured species also include the long-tailed macaque (Macaca fascicularis) (one independent photo), the southern Luzon cloudrat (Phloeomys cumingii) (one independent photo), and the wild jungle fowl (Gallus gallus) (one independent photo).

Figure 9. Model average on the distribution of Philippine warty pig in Mt. Banahaw de Tayabas using the three SDMs.

(14)

Figure 10. Other wildlife species captured through the camera traps in Mt. Banahaw de

Tayabas: [A] feral cat Felis catus, [B] unidentified owl, [C] wild jungle fowl Gallus gallus, [D] long-tailed macaque Macaca fascicularis, [E] unidentified rodent, and [F] common palm civet Paradoxurus philippinensis.

CONCLUSIONS AND RECOMMENDATIONS

Camera trapping, foot tracks, and wallows verified the presence of S. philippensis in Mt. Banahaw with individuals captured at 700 masl. This study also confirmed the species’ preference of secondary growth forest (600–800 masl). The GLM generated from the results suggest that S. philippensis prefer plantations

and areas near water sources such as streams, areas with fruit trees such as Musa, and area that is closer to an anthropological clearing. The average from the three SDMs predicts that Philippine warty pigs occur throughout Mt. Banahaw de Tayabas, although sparsely in the extreme southern and northern portions of the mountain. Hunting pressure can often modulate the occurrence of pig species.

Farmers resort to hunting and poaching wild pigs in order to reduce the crop-raiding behavior of the pigs. Hunting

(15)

also plays a major role in the lives of many communities (Melletti and Meijaard 2017); however, it was not included in this analysis as we discuss human-wildlife conflict such as crop-raiding and subsequent retaliatory hunting, as well as communities’ attitudes towards pigs elsewhere [Cabanas et al. (in prep.)].

The Philippine warty pig’s presence in Mt. Banahaw indicates that the area’s resources are still sufficient for the success of the species. However, more fieldwork is needed to determine their viable populations in the wild. Data from this study will be useful for the local community’s plans for conserving and managing this endemic species.

Additional recommendations also include investigating the population size within and outside the protected area, as well as establishing baseline data to assess the impact of ASF.

ACKNOWLEDGMENT

We would like to extend our deepest gratitude to the Rufford Foundation and the DOST-ASTHRDP (Department of Science and Technology–Accelerated Science and Technology Human Resource Development Program) for funding this research. We would also like to thank the ENRO (Environment and Natural Resources Office) Tayabas headed by Mr. Melvin Rada. We also give thanks to the following individuals: Neil Jun Lobite for the help in analyzing the data; Cristian Javin, Wilfredo Tutor, and Rendel Durante for assisting the whole survey team; as well as Camila Meneses, Kier Mitchel Pitogo, and Rafael Ryno Sanchez for their invaluable assistance during the fieldwork.

REFERENCES

ABAIGAR T, DEL BARRIO G, ERICAD JR. 1994.

Habitat preference of wild boar (Sus scrofa L. 1758) in a Mediterranean environment. Indirect evaluation by signs. Mammalia 58: 201–210.

ACEVEDO P, ESCUDERO MA, MUNOZ R. GOR- TAZAR C. 2006. Factors affecting wild boar abundance across an environmental gradient in Spain. Acta Theriologica 51: 327–336.

AKAIKE H. 1973. Information Theory and an Extension of the Maximum Likelihood Principle. In: International Symposium on Information Theory. Petrov BN, Csaki F eds. p. 267–281.

ARAUJO MB, PETERSON AT. 2012. Uses and misuses of bioclimatic envelope modelling. Ecology 93(7):

1527–1539.

BIRDLIFE DATA ZONE. 2001. Birdlife. Retrieved in March 2022 from http://datazone.birdlife.org/site/

factsheet/mounts-banahaw-san-cristobal-national-park-iba-philippines/text.

BLOUCH RA. 1984. Current status of the Sumatran rhino and other large mammals in Southern Sumatra. IUCN/

WWF Indonesia Programme, Bogor, Indonesia.

[BAI] Bureau of Animal Industry. 2019. Manila Bulletin news: Processed pork from China tests positive for African swine fever. Quezon City, Philippines.

BURNHAM KP, ANDERSON DR. 2002. Model Selec- tion and Inference: a Practical Information—Theoret- ical Approach, 2nd ed. New York: Springer.

CARPENTER G, GILLISON A, WINTER J. 1993. DO- MAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodi- versity Conservation 2(6): 667–680.

CARUSO N, VALENZUELA AE, BURDETT CL, VIDAL EM, BIROCHIO D, CASANAVE EB. 2018.

Summer habitat use and activity patterns of wild boar Sus scrofa in rangelands of central Argentina.

PLOS One 13: 10.

CUEVAS MF, MASTRANTONIO L, OJEDA RA, JAKSIC FM. 2013. Effects of wild boar disturbance on vegetation and soil properties in the Monte Desert, Argentina. Mammalian Biology für Säugetierkd 77:

299–306.

DANILOV PI, PACHENKO DV. 2012. Expansion and some ecological features of the wild boar beyond the northern boundary of its historical range in European Russia. Russian Journal Ecology 43: 45–51.

DARDAILLON M. 1986. Seasonal variations in habitat selection and spatial distribution of wild boar (Sus scrofa) in the Camargue, southern France. Behavioural Processes 13: 251–268.

[DENR] Department of Environment and Natural Resources. 2019. Administrative Order 2019-09.

Retrieved from http://www.bmb.gov.ph/index.php/e- library/laws-and-policies/denr-administrative-orders/

dao-2017-2019

[DENR-BMB] Department of Environment and Natural Resources–Biodiversity Management Bureau. 2019.

National List of Threatened Fauna. Retrieved on 29 Apr 2022 from https://bmb.gov.ph/index.php/facts- and-figures-wild/national-list-of-threatened-fauna ELITH JH, GRAHAM CP, ANDERSON R, DUDK M,

FERRIER S. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecog- raphy 29: 129–151.

(16)

EWERS RM, NATHAN SKSS, LEE PAK. 2021. African swine fever ravaging Borneo’s wild pigs. Nature 593:

37. https://doi.org/10.1038/d41586-021-01189-3 FERNÁNDEZ-LLARIO P. 2004. Environmental cor-

relates of nest site selection by wild boar Sus scrofa.

Acta Theriologica 49: 383–392.

FIELDING AH, BELL JF. 1997. A review of methods for the assessment of prediction errors in conservation presence/

absence models. Environmental Conservation 24: 38–39.

FOCARDI C. 2008. Winter habitat selection by wild boar Sus scrofa in southeastern Poland. European Journal of Wildlife Research 54(2): 361–366.

FONSECA CR. 2007. Winter habitat selection by wild boar Sus scrofa in southeastern Poland. European Journal of Wildlife Research 54: 361–366.

GIOVANELLI JG, SIQUIERA MF, HADDAD CF, AL- EXANDRINO J. 2010. Modeling a spatially restricted distribution in the Neotropics: how the size of calibra- tion area affects the performance of five presence-only methods. Ecological Model. 221: 215–224.

GRIFFIN PB, GRIFFIN MB. 2000. Agta hunting and sustainability of resource use in northeastern Luzon, Philippines. In: Hunting for sustainability in tropical forests. Robinson JG, Bennett EL eds. New York:

Columbia University Press. p. 325–335.

GUISAN A, ZIMMERMANN NE. 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135: 147–186.

GUTHRIE RD. 1984. Mosaics, allelochemics, and nutrients: an ecological theory of late Pleistocene megafaunal extinctions. In: Quaternary Extinctions:

a Prehistoric Revolution. Martin PS, Klein RG eds.

University of Arizona Press.

HEANEY LR. 1986. Biogeography of mammals in Southeast Asia: estimates of rates of colonization, extinction and speciation. Biological Journal of the Linnean Society 28(1–2): 127–165.

HIJMANS RJ, GRAHAM CH. 2006. Testing the ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biology 12: 2272–2281.

HONE J. 2002. Feral pigs in Namadgi National Park, Aus- tralia: dynamics, impacts, and management. Biological Conservation 105: 231–242.

[IUCN] International Union for the Conservation of Na- ture. 2017. The IUCN Red List of Threatened Species, Version 2017-3. Retrieved on 30 Apr 2020 from www.

iucnredlist.org

JONES CG, LAWTON JH, SHACHAK M. 1997. Positive and negative effects of organism as physical ecosystem engineers. Ecology 78: 1946–1957.

KLEIN DR. 1965. Ecology of deer range in Alaska. Eco- logical Monographs 35: 259–285.

LANTICAN RM. 2001. The science and practice of crop production. SEAMEO SEARCA and UPLB, Los Baños, Philippines.

LEMEL J, TRUVE J, SODERBERG B. 2003. Variation in ranging and activity behaviour of European wild boar Sus scrofa in Sweden. Wildlife Biology 9: 29–36.

LUSKIN MW, MEIJAARD E, SURYA S, SHEHEREZA- DE, WALZER C, LINKIER M. 2020. African swine fever threatens Southeast Asia's 11 endemic wild pig species. Society for Conservation Biology [ee12784].

MASSEI G, GENOV PV, STAINES BW, GORMAN ML.

1997. Factors influencing home range and activity of wild boar (Sus scrofa) in a Mediterranean coastal area.

Journal of Zoology. 242: 411–423.

MATTSON WJ. 1980. Herbivory in relation to plant nitrogen content. Annual Review of Ecology and Systematics. 11: 119–161.

MCCLURE ML, BURDETT CL, FARNSWORTH ML, LUTMAN MW, THEOBALD DM, RIGGS PD. 2015.

Modeling and Mapping the Probability of Occurrence of Invasive Wild Pigs across the Contiguous United States. PLoS ONE 10(8): e0133771.

MEIJAARD EJ, D’HUART JP, OLIVER WL. 2011. Fam- ily Suidae (pigs). In: Handbook of the mammals of the world. Wilson DE, Mittermeier RA eds. Lynx Edicions.

MELLETTI M, MEIJAARD E eds. 2017. Ecology, Conservation, and Management of Wild Pigs and Peccaries. Cambridge: Cambridge University Press.

doi:10.1017/9781316941232

MEROW C, SMITH MJ, SILANDER JA. 2013. A practical guide to MaxEnt for modeling species distributions: what it does, and why inputs and settings matter.

Ecography 36: 1058–1069.

MUSTARI AH. 2009. Population density of Sulawe- si’s forest ungulates in Tanjung Peropa and Tanjung Amoalengo Wildlife Reserves, southeast Sulawesi.

Media Konservasi 14(2): 89–94.

[NRC] National Research Council. 1983. Little-known Asian animals with a promising economic future.

Washington: National Academy Press. p. 75–79.

OLIVER WL, HEANEY L. 2017. Sus philippensis. Ac- cessed on 19 Feb 2021 from [URL]

(17)

OLIVER WL. 1995. The taxonomy, distribution and status of Philippine wild pigs. IUCN/SSC Pigs and Peccaries Specialist Group. IUCN.

REISS HS, CUNZE S, KOENIG K, NEUMANN H, KROENCKE I. 2011. Species distribution modelling of marine benthos: a North Sea case study. Marine Ecology Progress Series 442(71).

SCHEFFERS BR, CORLETTE RT, DIESMOS A, LAU- RANCE WF. 2012. Local demand drives a bushmeat industry in a Philippine forest preserve. Tropical Con- servation Science 5(2): 133–141.

STIBIG HJ, STOLLE F, DENNIS R, FELDKTTER C.

2007. Forest cover change in Southeast Asia. In: Joint Research Center ed. The regional pattern. JRC Scien- tific and Technical Reports.

VERNES K. 2003. Fine‐scale habitat preferences and habitat partitioning by three mycophagous mammals in tropical wet sclerophyll forest, north‐eastern Australia.

Austral Ecology 28: 471–479.

WELANDER J. 2000. Spatial and temporal dynamics of wild boar (Sus scrofa) rooting in a mosaic landscape.

Journal of Zoology 252: 263–271.