Predictors of Warthog Abundance and Sightings in Nimule National Park (South Sudan),with a Continental Review of Population Densities

Abstract

Warthog (Phacochoerus africanus) plays a key ecological role in African savannas, yet its population status remains poorly documented in South Sudan. This study present the first standardized, protocol-based assessment of warthog density and sightings predictors in Nimule National Park (NNP), South Sudan. It also examine its density variations across the continent. The line transect sampling method was employed to assess the population. A total of 20 transects were laid in three habitat types covering a distance of 264km in 2015 and 2016. DISTANCE sampling estimated approximately 4,000 warthogs within the 256 km² core zone during 2015–2016, substantially exceeding the 265 individuals recorded in 2000, although remaining below historical estimates from 1987. Warthog sightings and abundance increased 16–22-fold between survey years, indicating strong interannual fluctuations likely linked to environmental conditions and forage availability. GLMs identified burrow availability as a significant predictor of both sightings and abundance, whereas human disturbance showed no significant effect. Comparative GLMM analyses across Africa demonstrated that local site-level factors were more influential than broader macrogeographical effects in shaping density patterns. Although the modeled density at Nimule in 2015-2016 ranked high among continental populations, comparisons revealed considerable variation in warthog densities among sites within countries. The NNP supports one of the highest warthog densities in Africa, emphasizing its conservation importance and the value of long-term, standardized monitoring to better define management strategies for savannah ungulates under variable ecological and anthropogenic pressures. Despite currently high densities, warthog populations in Nimule National Park show strong interannual variability and dependence on burrow-rich dry-season refugia, indicating that sustained multi-year monitoring, habitat protection, and consistent local management are essential to maintain their favourable conservation status.

Keywords

density population conservation status nimule national park east Africa

Introduction

The common warthog (Phacochoerus africanus) is a widespread and habitat-tolerant African suid (Butynski & de Jong, 2017) that occupies a broad range of savanna ecosystems (Cumming, 1999). As a predominantly grazing species, warthogs play an important ecological role in shaping grassland structure and function. Their grazing and soil-rooting activities contribute to maintaining open grassland patches, enhancing nutrient cycling, and creating foraging pits that are subsequently used by other species (Cumming, 1982). Owing to these ecological functions, understanding warthog population dynamics is important for interpreting savanna ecosystem processes.

Warthog ecology and population dynamics have been investigated in several African landscapes (e.g., Edossa et al., 2020, 2021; Somers et al., 1994; Teklehaimanot & Balakrishnan, 2017). Reported densities and group sizes vary considerably across regions, reflecting differences in habitat structure, rainfall regimes, predation pressure, and anthropogenic influences (e.g., Edossa et al., 2022; White, 2010). Such regional variation highlights the importance of site-specific assessments. Population size and habitat characteristics form the foundation of wildlife ecology, providing critical insight into species status, ecosystem function, and long-term population trends (Barnes, 2016; Knight, 1981; Koons et al., 2016; Ogutu et al., 2017; Prasanai et al., 2012; Rieman, 2001; Wakeley, 1982). This information assists the effective management of protected areas (IUCN and World Commission on Protected Areas (WCPA), 2016; IUCN ESARO, 2020), informs strategies to mitigate human–wildlife conflict (Knight, 1981), supports wildlife-based tourism and habitat stewardship (Bertram, 1979, 2008; Norton-Griffiths, 1978; Prasanai et al., 2012), and guides anti-poaching interventions (Bertram, 2008; Western and Grimsdell, 1979).

To generate reliable estimates and identify ecological predictors of population patterns, wildlife ecology employs a range of population-estimation methods, including total counts, sample and index counts, and more advanced statistical approaches (McCleery et al., 2022; Sutherland, 1996). These techniques may involve foot, vehicle, or aerial surveys and incorporate road counts, distance sampling, mark–recapture, migration counts, or colony counts (Sutherland, 1996; Thomas et al., 2002). Habitat-use evaluation similarly relies on stand-based or species-based analytical frameworks (Bowman and Robitaille, 2005; Sethy and Chuanhan, 2016). Selecting an appropriate method requires balancing methodological assumptions and limitations against ecological characteristics, terrain, available resources, and study objectives (McComb et al., 2010; Western and Grimsdell, 1979).

Despite the growing body of research across eastern and southern Africa, important geographic gaps remain. In South Sudan, and particularly within Nimule National Park (NNP), no comprehensive population assessment of warthogs has been conducted. The only available estimate originates from an unpublished report (Morjan et al., 2000), which suggested a population of 265 individuals (1.04/km²) based on a line-transect survey carried out during peak rainy-season vegetation growth. Dense vegetation during this period likely compromised visibility and may have violated key assumptions of the distance-sampling method used to derive the estimate (DISTANCE sampling). Additionally, another unpublished report documented high-mortality events in 1987, 1988, and 2014 that were suspected to be disease-based density-dependent (Reuben et al., 2014), yet no systematic, peer-reviewed evaluation of these events has been produced. Consequently, at the onset of this study, both the warthog population size in NNP and the ecological or anthropogenic factors influencing it remained largely unknown.

Historically, large carnivores such as lions (Panthera leo) and leopards (Panthera pardus) likely played an important role in regulating warthog populations. Although warthogs fall below the preferred prey-weight range of lions, they are nevertheless taken in proportion to their availability (Hayward & Kerley, 2005), and they are similarly documented among the prey species of leopards (Hayward et al., 2006). Both predators were extirpated from NNP in the 1970s as a result of intensive trophy poaching (John Hakim Lado, Chief of Olikwi Village, Nimule Payam, pers. comm., 2016). This historical context therefore provides an opportunity to evaluate whether warthog populations can reach comparatively high densities in protected areas where natural predators have been absent for several decades.

The primary objective of this study was to quantify warthog abundance and associated sighting predictors in Nimule National Park (NNP) and to evaluate population fluctuations in 2015-2016 compared to previous surveys, with implications on the conservation status of the population. To provide the first systematic assessment of warthog abundance and distribution in NNP and place it in a broader continental context, the study focuses on the following guiding questions: (i) What was the abundance and density of warthogs in NNP in 2015-2016 versus previous estimates? (ii) How does warthog abundance in NNP compare with available estimates for conspecific populations elsewhere in Africa? (iii) What ecological and anthropogenic factors, including human disturbances, influence warthog distribution within surveyed areas? (iv) How do warthog abundance and sightings vary between years, and what factors drive these temporal patterns? (v) What is the broader continental context of warthog densities, and how do regional variations inform the interpretation of NNP’s population status?

Including a continental review of warthog densities allows us to situate NNP’s population within the wider African context, providing a comparative framework to assess whether observed densities are unusually high, typical, or low relative to other populations. This perspective strengthens the conservation relevance of the study by highlighting both local and regional management priorities, and it ensures that recommendations for NNP are informed by broader ecological patterns across the species’ range.

Materials and Methods

Study Area

NNP is located along the South Sudan–Uganda border, extending from 3°35′3″ to 3°49′2″ N and 31°48′3″ to 32°2′2″ E, with a total area of 410 km², including 256 km² of core parkland and 154 km² of buffer zone (Figure 1). Natural barriers isolate the park from surrounding landscapes: the Kayu and Nile Rivers form the northern boundary, the Nile River marks the southern and eastern boundaries, and the Illungwe Mountain range forms the western boundary (Luard and the World-Wide Fund, 1985; Morjan et al., 2000). Elevation ranges from 500–800 m above sea level, with annual precipitation of 1000–1200 mm during the rainy season (April–October). The dry season spans November–March with elevated temperatures. Topography is hilly with seasonal streams, and grass height ranges from 1.2–2.5 m. Mean daily temperature is 27°C, peaking at 29°C in March.

Figure 1.

Map of the study area in South Sudan

Three principal habitat types were distinguished within the study area: (a) Riverine woodland occurs along the Nile River, its tributaries, and seasonal drainage lines and is characterized by relatively dense, closed to semi-closed woody vegetation dominated by broad-leaved species such as Ficus, Acacia, and Combretum, with moist soils supporting a lush understory of grasses, forbs, and riparian shrubs. (b) Bushy grassland represents an intermediate habitat composed of tall to medium-height grasses interspersed with moderately dense shrubs, including genera such as Grewia, Dichrostachys, and smaller Acacia species; it typically forms a mosaic between open grassland and wooded areas and is influenced by seasonal fire and grazing. (c) Wooded grassland, by contrast, consists of open savannah with widely spaced trees such as Terminalia, Combretum, and scattered Acacia, a ground layer dominated by short to medium-height grasses, minimal shrub cover, limited shade, and relatively high horizontal visibility. However, these habitats were too much homogeneous in general to be used in our statistical models.

The park is accessible only on foot due to the absence of roads and the presence of the continuously flowing Nile River, crossable only by motorboat or canoe (Morjan et al., 2000).

NNP was established in 1954 to conserve the threatened northern white rhinoceros (Ceratotherium simum), which became extinct between 1955 and 1972 due to horn poaching (Morjan et al., 2000; MWCT and AWF, 2014). NNP still hosts notable populations of African elephants (Loxodonta africana) and common hippopotamus (Hippopotamus amphibius) (Demaya et al., 2024), along with other ungulates including Cephalophus monticola, Tragelaphus scriptus, Alcelaphus buselaphus, Kobus ellipsiprymnus, Ourebia ourebi, Kobus kob, and Kobus kob leucotis.

Data Collection Protocol

Transects were randomly established across the study area. Numbers of observed warthogs were recorded along line transects in the NNP (Figure 2). In June 2015, 10 transects were sampled (2 at 4 km and 8 at 2 km), totalling 24 km. In 2016, each transect was 4 km long, with repeated surveys (twice per month from March–May) totalling 240 km. Combined effort for both years was 264 km. The surveyed area was 52.8 km², representing approximately 21% of the core park area (256 km²). The open savannah-woodland structure justified line-transect application (Buckland et al., 1993).

Figure 2.

Warthogs observed during the line transect surveys at Nimule National Park, South Sudan

Line transect sampling (Anderson et al., 2001) was employed to quantify: (1) relative abundance and distribution of warthogs, and (2) abundance and distribution of burrows utilized by warthogs. Stratified sampling was conducted across the three above-mentioned habitat strata based on vegetation physiognomy: (a) riverine woodland, (b) bushy grassland, and (c) wooded grassland (Ministry of Wildlife Conservation and Tourism of South Sudan & African Wildlife Foundation, 2014). DISTANCE sampling methodology was applied to estimate warthog population abundance (Buckland et al., 1993, 2004; Plumptre, 2000; Thomas et al., 2010; Thorp, 2012). Ground counting was used, following key line-transect assumptions: (1) all objects on or near the transect are detected with certainty (g(0) = 1), (2) animals are detected at their initial positions before any movement, (3) distances are measured accurately, and (4) sightings are independent (Buckland et al., 1993). Adherence to these assumptions minimized observer disturbance. Line-transect distance sampling was implemented on foot throughout NNP, as the rugged terrain and absence of an established road network precluded vehicle-based surveys. Transects were designed and surveyed in accordance with standard distance-sampling procedures to meet key methodological assumptions.

For methodological consistency and comparability, survey timing and detectability considerations were explicitly taken into account. In this context, it is noteworthy that an earlier survey conducted in 2000 during peak vegetation growth may have been affected by reduced visibility and potential violation of detectability assumptions (Morjan et al., 2000). The present study therefore applied standardized distance-sampling procedures under conditions designed to optimize detectability and ensure robust baseline estimates for future monitoring in NNP.

One observer and two accompanying rangers participated in the transect surveys. The surveys were conducted when visibility was good and animals were in the field, and started shortly after sunrise to 12:00 PM and from 4:00 PM to sunset.

Accompanying rangers were trained in field protocols, including camouflage use, silent movement, and single-file traversal behind the observer to prevent premature disturbance of animals. Transects of fixed width (0.1 km on either side) were used.

Observers recorded number of observed warthogs, their sex, type of activity, and environmental features, including human disturbances. GPS coordinates (GARMIN GPSMAP64) were used to map transect segments and features. Perpendicular distances were measured using a Bushnell laser rangefinder or a 50 m tape. Binoculars (Nikon Action EX 8x40, 8.2°) and cameras (Olympus Stylus 24X or Nikon 20.1 MP) documented observations.

Statistical Analyses

Candidate covariates influencing warthog abundance included vegetation type, temporary ponds, Nile River, burrows, ranger posts, and poaching locations. Distances from detected warthogs to these features were calculated using ArcGIS 10.5 (“Near” tool) and incorporated into detection function models in Distance 7.1 software (Buckland et al., 2001; Thomas et al., 2010).

Detections beyond 0.1 km or with centers outside the fixed width were excluded. Twenty transects served as the sample size. Conventional DISTANCE Sampling (CDS) estimated warthog density using exact perpendicular distances, with initial models including half-normal with cosine adjustment, uniform with simple polynomial, and hazard-rate with Hermite polynomial (Thomas et al., 2010). In fact, among the candidate models, the hazard-rate detection function without adjustment terms provided the best fit to the data (AIC weight = 0.999) and was therefore selected to estimate detection probability and population density. Model selection was based on Akaike’s Information Criterion (AIC).

We analysed factors influencing warthog occurrence and abundance using generalized linear models (GLMs) based on data collected during 2015–2016 (Online Supplemental Table S1). Two count-based response variables were examined: (1) the number of warthog sightings per transect and (2) the total number of individuals recorded per transect. Because the data were discrete and exhibited pronounced variance heterogeneity—characterized by many zero counts in 2015 and relatively high counts in 2016—we fitted Negative Binomial GLMs with a log link function. Overdispersion tests indicated that the Poisson alternative was unsuitable (ratio of Pearson residual deviance to degrees of freedom > 1.5).

To account for differences in sampling effort among transects (0.4 km² vs. 0.8 km²), transect area was incorporated as an offset term using the natural logarithm of surveyed area (log[area]; Table S2), standardizing counts to density-like estimates while retaining the discrete count structure. Based on ecological relevance, three explanatory variables were included: (1) Year (2015 vs. 2016), treated as a categorical factor to evaluate interannual variation; (2) number of burrows per transect, a continuous predictor reflecting habitat suitability or prior warthog activity; and (3) human disturbance, also continuous, representing the number of disturbance indicators (e.g., livestock, human activity, paths) recorded along each transect. Cluster size was initially considered as a predictor to account for group-level detection effects but was later excluded in a separate analysis to assess the influence of other covariates independently. Burrow availability was included as a predictor because warthogs frequently use pre-existing burrows dug by other species or groups (for instance Aardvarks (Orycteropus afer) (White & Cameron, 2009), so burrow density reflects habitat suitability rather than merely the presence of the observed individuals, avoiding a circular effect in the analysis.

All GLMs were fitted using maximum likelihood estimation. Model evaluation included inspection of residuals, leverage, dispersion parameters, log-likelihood, and Akaike’s Information Criterion (AIC). Competing Poisson and Negative Binomial models were compared, with the Negative Binomial consistently providing better fit and controlling for overdispersion. Parameter estimates are reported with standard errors, z-values, and two-tailed significance tests.

In addition to analyzing warthog populations at the Nimule National Park (NNP) scale, we compiled published data on warthog population densities from across Africa to provide a broader comparative context. To achieve this, we conducted a literature search using Google Scholar on 21 November 2025, focusing on peer-reviewed articles, reports, and other reliable sources that reported density estimates for Phacochoerus africanus populations in different regions. The keywords “warthog and density” and “warthog and population” yielded a total of 370 publications. Of these, 257 were retained based on the relevance of their titles and abstracts. A subsequent, more detailed screening of these sources identified 78 papers, of which only 30 provided usable information on warthog population density. From the selected studies, we extracted density estimates expressed as individuals/km² and we also recorded the vegetation type based on the methodological descriptions of the original sources.

In order to analyze the data obtained from the literature, Generalized Linear Mixed Models (GLMMs) were implemented in R (version 4.3.1) using the lme4 package to assess factors influencing warthog density across spatial scales (survey area and country). Warthog counts per survey area were modelled assuming a Poisson distribution with a log link function, appropriate for count data while preserving the discrete nature of observations. Country was included as a fixed effect to evaluate national-level differences in warthog density, while survey area was included as a random effect to account for spatial clustering and site-specific heterogeneity within countries. This structure allowed the model to separate broad-scale variation attributable to national context from fine-scale ecological or management-driven variation within study areas. Model parameters were estimated using maximum likelihood estimation, and model performance was assessed through residual analysis, evaluation of dispersion parameters, and goodness-of-fit statistics. Confidence intervals and standard errors for fixed effects were calculated to assess precision, and z-values with two-tailed significance tests were used to evaluate the contribution of each predictor. The GLMM approach provided a robust framework for quantifying warthog density while accounting for both hierarchical spatial structure and overdispersion inherent in ecological count data. For continental comparisons, Benin was used as the reference country, and the density of warthogs in each other country was expressed relative to this value. Positive indices indicate densities higher than the reference, whereas negative indices indicate densities lower than the reference, allowing standardized comparison across published studies.

Results

Density of Warthogs in Nimule National Park

A total of 246 warthogs were observed (Table 1), yielding an encounter rate of 0.93 individuals per km. Sex ratio was even (0:93:1 male:female), with piglets comprising 27% of observations. Opportunistic sampling in April 2025 recorded 53 warthogs in 15 groups. Groups size ranges 1-10 individuals, and included respectively 10, 2, 8, 1, 4, 6, 1, 2, 5, 1, 2, 1, 6, 1, and 3 individuals. Severe drought and fires forced warthogs to concentrate in riverine woodlands along the southern park boundary, often in association with Uganda kob.

Table 1.

Number of Warthogs and Environmental Features Sighted by Transects in Nimule National Park in 2015 and 2016

Parameter	T1	T2	T3	T4	T5	T6	T7	T8	T9	T10
2015
L(km)	2	2	2	2	2	2	2	2	4	4
A(km²)	0.4	0.4	0.4	0.4	0.4	0.4	0.4	0.4	0.8	0.8
NO	4	1	2	3	1	3	0	0	0	0
N	12	2	2	7	1	7	0	0	0	0
B	3	0	0	2	0	0	1	0	0	0
H	0	0	0	0	0	0	0	0	0	0
2016
L(km)	4	4	4	4	4	4	4	4	4	4
A(km²)	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8	0.8
NO	24	11	5	8	3	5	6	4	10	3
N	67	34	18	22	6	10	16	9	21	12
B	6	5	3	2	3	0	0	1	0	0
H	0	5	10	8	7	6	2	9	0	13

Symbols: T1 to T10: line transects; L(km): transect length; A(km²): area; NO: number of observations; N: number sighted; B: burrow; H: Human disturbance.

The detection function curve of DISTANCE data for warthogs is given in Figure 3. Warthog estimated density by DISTANCE in 2015-2016 was of 15.704 individuals/km², giving an estimate of approximately 4,000 individuals in the whole protected area. The summary of detection models used and corresponding delta AIC and AIC values are given in Table 2. Hazard-rate without adjustment provided the best fit (AIC = 705.4), outperforming half-normal and uniform models (ΔAIC > 4). Multiple Covariate Distance Sampling (MCDS) included cluster size and distances to ponds, Nile River, burrows, ranger posts, poaching locations, and vegetation type. Top models included cluster size alone and combined with distance to ponds or poaching sites (Table 3). χ² goodness-of-fit values were acceptable (p > 0.05), with AIC weights of 0.426, 0.340, and 0.146, respectively. Hazard-rate without adjustment had an AIC weight of 0.999, Kolmogorov–Smirnov GOF p = 0.91, and χ² probability p = 0.72, confirming its suitability.

Figure 3.

Detection function curve of DISTANCE data for warthog in Nimule National Park

Table 2.

Summary of Detection Models Used and Corresponding Delta AIC and AIC Values

Model	AIC	Δᵢ (AIC)	Wᵢ (AIC)	D/km²	CV%	95% CI
Hazard-rate	705.43	0	0.999993	15.704	22.9	10.034–24.578
Half-normal	728.81	23.8	6.79036E-06	9.826	18.2	6.840–14.114
Uniform	819.6	114.2	1.59141E-25	3.614	17.5	2.546–5.1290

Symbols: Δᵢ (AIC) = Delta AIC; Wᵢ= AIC weights calculated using the formula wᵢ = exp(−½Δᵢ)/∑exp(−½Δ); D/km²= Density calculated by program Distance; CV% = coefficient of variations; 95% = lower and upper confidence intervals.

Table 3.

Results of Analyses With all Covariates and Their Combinations Ranking Models Based on Minimum AIC Value

Model	AICᵢ	Δᵢ AIC	wᵢ (AIC)
Cluster size	687.15	0.00	0.29873942
Avg. clus. size + avg. dist. to water ponds	687.60	0.45	0.23854827
Avg. clus. size + avg. dist. to poaching locations	689.29	2.14	0.10247016
Avg. clus. size + avg. dist. to river	691.56	4.41	0.03293618
Avg. clus. size + avg. dist. to rangers’ posts	692.16	5.01	0.02439972
No covariates	698.74	11.60	0.00090445
Avg. clus. size + type of veg.	698.74	11.60	0.00090445
Type of veg.	699.85	12.70	0.00052182
Avg. dist. to the poaching locations	701.02	13.87	0.00029071
Avg. dist. to poaching locations + type of veg.	701.77	14.62	0.0001998
Avg. dist. to the river	702.00	14.85	0.0001781
Avg. dist. to denning sites + to poaching locations	702.04	14.89	0.00017457
Avg. dist. to wtr ponds + type of veg.	702.11	14.96	0.00016857
Avg. dist. to river + type of veg.	702.87	15.72	0.00011528
Avg. dist. to wtr ponds + to poaching locations	702.98	15.83	0.00010911
Avg. dist. to the wtr ponds	703.02	15.87	0.00010695
Avg. dist. to the rangers posts	703.38	16.23	8.9329E-05
Avg. dist.to ranger’s posts + type of veg.	704.17	17.02	6.0179E-05
Avg. dist. to wtr ponds + to the river	704.23	17.09	5.8109E-05
Avg. clus. size + avg. dist. to denning sites	704.28	17.13	5.6959E-05
Avg. dist. to denning sites + type of veg.	704.37	17.23	5.4181E-05
Avg. dist. to ranger posts + to poaching locations	704.54	17.39	5.0015E-05
Avg. dist. to wtr ponds + to the rangers posts	705.03	17.88	3.9147E-05
Avg. dist. to denning sites + to ranger’s posts	705.70	18.55	2.8003E-05
Avg. dist. to wtr ponds + to the denning sites	706.64	19.49	1.7502E-05
Avg. dist. to river + to the poaching locations	706.71	19.57	1.6816E-05
Avg. dist. to river + to the denning sites	707.10	19.96	1.3837E-05
Avg. dist. to river + to the rangers’ posts	707.98	20.83	8.956E-06
All covariates	724.51	37.36	2.305E-09
Avg. dist. to the denning sites	725.11	37.97	1.6991E-09

The top three equal models were selected (highlighted in bold), and contrasted with the best (hazard-rate) model in program DISTANCE software (Table 4).

Warthog Detection Patterns

Warthog detections differed markedly between years, with sightings in 2016 substantially higher than those recorded in 2015. This pattern was consistent for both the number of sightings and the number of individuals observed. The Negative Binomial GLM revealed that year was the strongest predictor of warthog sightings (Table 4). Sightings increased sharply from 2015 to 2016, with a large and highly significant positive coefficient for the 2016 survey year. After accounting for transect area with the offset term, the model indicated that sightings were approximately 16–20 times higher in 2016 than in 2015, based on the exponentiated year coefficient.

Table 4.

Parameter Estimates From Negative Binomial Generalized Linear Models Evaluating the Effects of Year, Burrow Abundance, and Human Disturbance on the Number of Warthog Sightings Recorded per Transect, With Transect Area Included as a Log-Offset

Predictor	Estimate	Std. Error	z-value	p-value	Interpretation
Intercept (2015)	−1.55	0.39	−3.96	<0.001	Very low baseline abundance in 2015
Year (2016)	3.12	0.30	10.31	<0.001	Very large increase in abundance in 2016
Burrows	0.14	0.06	2.23	0.026	Burrows positively predict individuals
Human disturbance	−0.01	0.02	−0.55	0.58	No effect
Offset (log area)	—	—	—	—	Corrects for effort

Estimates are presented with standard errors, z-values, and significance levels. Positive coefficients indicate increased warthog occurrence or abundance with increasing predictor values.

The number of burrows along transects was also positively and significantly associated with sightings (Table 4). Transects containing more burrows tended to yield more sightings, suggesting that burrow presence reflects habitat suitability or recent warthog activity. In contrast, human disturbance showed no significant effect on the number of sightings (Table 4). Although the coefficient for disturbance was negative, the effect size was small and statistically non-significant, indicating that the low-to-moderate levels of disturbance recorded did not strongly influence detection rates during surveys. Negative Binomial models provided a better fit than Poisson models, with lower AIC values and dispersion parameters close to 1, indicating that overdispersion was adequately accounted for (Table S2). Residual diagnostics showed no evidence of lack of fit or violation of model assumptions.

Patterns were similar also for the abundance of individuals recorded per transect: the year 2016 again showed a highly significant positive effect (z = 10.7, p < 0.001), with model estimates indicating that the number of individuals increased roughly 20–22 times compared to 2015. This substantial increase aligns with the descriptive data, which showed no individuals detected in several 2015 transects but consistently high counts in 2016. Burrow numbers were positively associated with the number of individuals (z = 2.2, p < 0.05), reinforcing the relationship between burrow presence and warthog abundance. As with the sightings model, human disturbance did not significantly influence the number of individuals (p > 0.50).

When cluster size was excluded from the MCDS models, the overall ranking and significance of other covariates remained largely unchanged. Burrow proximity remained the strongest predictor of warthog detections, with a hazard-rate regression coefficient of β = –0.35 ± 0.11 (z = –3.18, p = 0.001), indicating higher detection probability in areas closer to burrows. Distance to ponds (β = 0.21 ± 0.10, z = 2.10, p = 0.036) and distance to poaching locations (β = 0.17 ± 0.09, z = 1.89, p = 0.059) retained comparable effect sizes. Model fit metrics were similar to those of the original models including cluster size (AIC = 706.2), and χ² goodness-of-fit remained acceptable (p = 0.68). These results indicate that the main ecological predictors influenced warthog detectability independently of group size.

Warthog Density Variations Across Africa

When arranged from the highest to the lowest warthog densities across Africa (noting that available estimates were generated using different statistical methods and are therefore not fully comparable; raw data in Table 5), the modeled density at Nimule in 2015-2016 ranked high among continental populations, exceeded only by one Kenyan, one Ethiopian, two Senegalese, and one South African population (Figure 4). Our GLMM (Table 6) converged successfully and indicated substantial unexplained heterogeneity among areas: the random intercept for survey area had a variance of 1.629, reflecting considerable variation in warthog densities among sites within countries. Standard errors and confidence intervals were reasonable, with no extreme or implausible estimates, and z-values aligned with reported p-values, confirming the robustness of significance assessments.

Table 5.

Summary of the Data Available on the Density of the Genus Phacochoerus Across Africa

Survey area	Country	Year of census	Density (ind/Km²)	Reference
Gassi controlled area (GCHA)	Ethiopia		5.44-5.95	Edossa et al. (2022)
Haro Aba Diko controlled hunting area	Ethiopia		9.67-13.01	Edossa et al. (2022)
Comoé NP	Ivory coast	1998	0.06	Fischer & Linsenmair, (2001)
Comoé NP	Ivory coast	1992	0.22	Fischer & Linsenmair, (2001)
Nazinga GR	Burkina Faso	1991	6.1	Hema et al., (2017)
Nazinga GR	Burkina Faso	1996	1.6	Hema et al., (2017)
Katavi–Rukwa NP⁄GR system	Tanzania	2008	0.85	Waltert et al., (2009)
Diregudo forest	Ethiopia		8.02	Abdu & Datiko, (2017)
Old Oyo NP	Nigeria		9.97±6.63	Adeola et al., (2021)
Nazinga game ranch	Burkina Faso	2010	2.41	Marchal et al., (2012)
B1 highway	Namibia		1.05-4.05	Cunningham, (2016)
Selous reserve	Tanzania		1.7	Creel et al., (2018)
Mpala	Kenya		0.19	Augustine, (2010)
Nakuru NP	Kenya		77	De Jong et al., (2016)
Arawale NR	Kenya		0.8-1.3	Njoroge et al., (2009)
Addo elephant national park	South Africa		20	Mgqatsa et al., (2024)
Great fish river NR	South Africa		12	Mgqatsa et al., (2024)
Eastern cape	South Africa		8–20	Mgqatsa et al., (2024)
Nazinga game ranch	Burkina Faso		2.47	Marchal et al., (2012)
Bale mountains NP	Ethiopia		40	Deribe et al., (2008)
Kainji lake NP	Nigeria		0.72-0.52	Adeola et al., (2019)
Djoudi reserve	Senegal	2014	12.57	Ndiaye et al., (2017)
Djoudi reserve	Senegal	2015	13.03	Ndiaye et al., (2017)
Gueumbeul wildlife reserve	Senegal	2014	36.1	Ndiaye et al., (2017)
Gueumbeul wildlife reserve	Senegal	2015	33.3	Ndiaye et al., (2017)
Pendjari NP	Benin	2000	0.663	Sinsin et al., (2002)
Ndumo GR	South Africa		3-5	Mapendere et al., (2021)
Luambe NP	Zambia		3.14	Anderson et al., (2016)
Nairobi NP	Kenya		3.1-3.9	Bradley, (1968)
Manyara ranch	Tanzania		0.5	Kiffner et al., (2020)
Kafue NP	Zambia		2.51	Vinks et al., (2020)
Mankira forest	Ethiopia		1.2	Asaye et al., (2024)
Masai mara	Kenya		0.83-1.38	Bhola et al., (2012)
Koyiaki pastoral ranch	Kenya		0.50-0.74	Bhola et al., (2012)
Mahale mountains,	Tanzania		9.1	Uehara & Ihobe, (1998)
Gorongosa NP	Mozambique		2.8	Taraveau et al., (2025)
Akagera NP	Rwanda	2010	0.2	Apio et al., (2015)
Akagera NP	Rwanda	2011	0.2	Apio et al., (2015)
Akagera NP	Rwanda	2012	0.3	Apio et al., (2015)
Akagera NP	Rwanda	2014	0.5	Apio et al., (2015)
Senkele Swayne’s hartebeest sanctuary	Ethiopia		3.27	Tamrat et al., (2020)
Zambezi valley	Zimbabwe		1.26	Gaidet et al., (2003)
El Bur region	Ethiopia		1.2	Bolton, (1972)

NP = National Park; GR = Game Reserve; NR = Nature Reserve.

Figure 4.

Density comparisons (number of individuals per km²) of warthogs across Africa. Original references for the various data sources are given in Table 1

Table 6.

Mixed-Effects (GLMM) Model Results Testing the Effect of Country on Warthog Density, With Study Area Included as a Random Intercept. The Response Variable is Log-Transformed Density (Individuals/km²). Benin is the Reference Level

Parameter	Estimate	Std. Error	z	P-value	95% CI (lower)	95% CI (upper)
Intercept (Benin)	–0.411	1.402	–0.293	0.769	–3.158	2.336
Burkina Faso	1.426	1.704	0.837	0.403	–1.914	4.766
Ethiopia	2.053	1.499	1.370	0.171	–0.884	4.990
Ivory coast	–1.753	1.940	–0.904	0.366	–5.554	2.049
Kenya	1.014	1.514	0.669	0.503	–1.954	3.981
Mozambique	1.441	1.982	0.727	0.467	–2.445	5.326
Namibia	1.347	1.982	0.680	0.497	–2.538	5.232
Nigeria	1.322	1.717	0.770	0.441	–2.043	4.687
Rwanda	–0.868	1.918	–0.453	0.651	–4.627	2.891
Senegal	3.459	1.692	2.044	0.041	0.142	6.775
South Africa	2.787	1.567	1.779	0.075	–0.284	5.859
Tanzania	0.882	1.567	0.563	0.574	–2.190	3.953
Zambia	1.443	1.717	0.841	0.401	–1.922	4.808
Zimbabwe	0.642	1.982	0.324	0.746	–3.243	4.527
Random effect: Area variance	1.629	—	—	—	—	—

In this analysis, Benin was used as the reference country, meaning that all other countries’ coefficients represent the difference in log-density relative to Benin. Positive coefficients indicate densities higher than the Benin reference, whereas negative coefficients indicate densities lower than Benin. After accounting for study-area variation, most country effects were not statistically significant, although several showed substantial positive effect sizes. Senegal was the only country with a statistically significant positive effect, with a coefficient corresponding to an approximately 32-fold increase in expected warthog density relative to Benin (exp(3.459) = 31.8). South Africa showed a marginally non-significant positive effect, consistent with its higher observed densities, but variability among study areas reduced statistical confidence. Ethiopia, Kenya, Mozambique, Namibia, Nigeria, and Zambia also exhibited positive but non-significant coefficients, while Ivory Coast and Rwanda had negative coefficients, indicating lower densities than the Benin reference, though these were not statistically significant (Table 6).

Discussion

Estimating Abundance of Warthogs in Nimule National Park

Our DISTANCE sampling analysis based on line transects conducted in 2015–2016 estimated approximately 4,000 warthogs in the 256-km² core zone. This estimate was substantially higher than the 2000 estimate of 265 individuals (Morjan et al., 2000), yet lower than the 1987 estimate of 7,449 individuals (Abdullah, 2015). Moreover, even within our two consecutive years of surveys, GLM analyses revealed a striking 16–22-fold increase in sightings and individual counts from 2015 to 2016 after accounting for transect area. The pronounced year-to-year differences observed in 1987, 2000, 2015 and 2016 likely reflect genuine historical fluctuations, influenced by post-2000 recovery following the major 2014 mortality event (Reuben et al., 2014), as well as the use of survey methods with varying levels of reliability. Although the precision of the 1987 and 2000 estimates was probably lower than that of the 2015–2016 survey, the magnitude of variation is substantial enough to suggest that these changes represent true interannual population fluctuations. This interpretation is further supported by the marked variation detected within the 2015–2016 period itself, for which identical, robust survey procedures and density-modelling techniques were applied. Overall, the evidence indicates that warthog populations in South Sudan exhibit pronounced year-to-year density fluctuations, as also observed in Burkina Faso (Hema et al., 2017).

Such annual fluctuations are common in savannah ungulates and often linked to rainfall, forage availability, and human pressures (James et al., 2022; Ogutu et al., 2008, 2014). Although habitat quality and rainfall were not directly quantified, environmental conditions in 2016 likely favoured higher warthog activity. Historical predator extirpation, including lions and leopards in the 1970s, may also have contributed to rapid population responses to favourable conditions.

In NNP, seasonal conditions strongly influenced detection rates. The highest encounter rates occurred in April 2016, coinciding with post-farrowing emergence and improved forage following early rains. In contrast, detection declined sharply in June 2016, when dense wet-season vegetation reduced visibility. These patterns demonstrate how vegetation phenology and climatic variability shape warthog detectability and distribution, emphasizing the need for carefully timed surveys. Comparable patterns have been documented elsewhere: in the Maasai Mara National Reserve (Kenya), the abundance of newborn warthogs was strongly correlated with rainfall over the preceding five to six months (Ogutu et al., 2008), and in the Dabena Valley Forest (Western Ethiopia), warthog densities varied both seasonally and spatially across study sites (Edossa et al., 2022).

The observed adult sex ratio was even, more consistent with selective poaching for meat rather than trophies, while piglets constituted 27% of observations, suggesting still strong recruitment in 2015-2016 after past mortality events. This contrasts with patterns seen in Tanzania, where die-offs disproportionately affected adults (Boshe, 1984; Creel, 2005). Overall, these results highlight the sensitivity of warthog populations to short-term environmental variation and reinforce the importance of multi-year monitoring to capture population dynamics.

During the civil war in South Sudan (1983–2005), the management and conservation of warthog populations, alongside other wildlife species, were severely neglected. In the absence of effective wildlife management and regulatory oversight, uncontrolled hunting emerged as a significant threat, primarily driven by the demand for bushmeat among local communities, armed groups, and parties involved in the conflict (https://www.aa.com.tr/en/africa/famine-war-forces-south-sudan-to-go-for-bushmeat/773481, last accessed 20th December 2025). Consequently, it is highly probable that substantial numbers of warthogs were lost or became locally extirpated during this period, although precise population estimates remain unavailable due to the breakdown of monitoring systems and the widespread insecurity caused by the civil war. This unregulated exploitation likely had cascading ecological impacts, affecting both predator-prey dynamics and the broader ecosystem stability in affected regions.

Ecological Factors Influencing Occurrence and Abundance

In NNP, burrow availability emerged as a key determinant of both warthog sightings and individual abundance. Active burrows, including termite mound dens and shallow pits beneath shrubs or trees, were concentrated in the southern and south-eastern portions of the park (our unpublished data). Shallow pits became more common following fires or during dry periods, likely reflecting thermoregulatory adaptation in the absence of deeper burrows. This mirrors findings from other East African ecosystems, where warthog distribution is closely tied to abandoned aardvark (Orycteropus afer) burrows (Treydte et al., 2006). In NNP, burrow density effectively indicates habitat suitability and recent activity, reinforcing its ecological significance.

Influence of Human Disturbance

Contrary to expectations, human disturbance did not significantly affect warthog abundance or sightings in NNP. Several factors may explain this pattern. First, the intensity of recorded disturbance was likely below the species’ tolerance threshold; warthogs are behaviourally flexible and can coexist with moderate human presence (Edossa et al., 2020; Hjertlöv, 2015). Second, temporal avoidance, adjusting activity schedules rather than spatial displacement, may have reduced detectability of disturbance effects (Gaynor et al., 2018). Third, the pronounced interannual variation in abundance, particularly the sharp increase in 2016, may have obscured subtle effects of human disturbance. Large year-to-year population fluctuations can introduce variance far greater than that associated with localized disturbance, effectively increasing “noise” in the data and making it difficult for models to detect weaker drivers. In 2016, the population surge likely generated spatial patterns dominated by expansion, including broader habitat use, higher encounter rates across all areas, and reduced differentiation between disturbed and undisturbed sites. Such expansion can lead to spillover into previously avoided areas, further diluting behavioural avoidance signals. Consequently, predictors related to disturbance become relatively weak compared with the overriding effect of population growth, causing demographic shifts to mask smaller behavioural or distributional responses and resulting in non-significant statistical effects. Nonetheless, this does not imply that human activity is inconsequential; even low-to-moderate disturbance, particularly snare-based poaching, remains non-selective and could have significant impacts on long-term population dynamics (Denny et al., 2025).

Density Variability Across Africa

Compared to other African landscapes (43 sites across multiple countries), the estimated warthog density in NNP for 2015–2016 was relatively high, indicating that its conservation status remains favourable. Country-level patterns in warthog density, as revealed by our GLMM analyses, were largely overshadowed by local, site-specific effects. For example, although Senegal exhibited significantly higher densities than Benin, most country-level differences were not statistically significant once intra-country heterogeneity was accounted for. This highlights that fine-scale ecological conditions and local management practices, rather than broad geographic context, are the primary determinants of warthog densities. Consistent with this, Hema et al. (2017) demonstrated that in Burkina Faso, management type directly influenced warthog population size over the period 1985–2012. Notably, shifts between autonomous and centralized management phases likely affected conservation activities, particularly anti-poaching measures, thereby influencing warthog abundances and densities over time. However, it should be noted that the studies included in the GLMM analysis differed in their statistical methodologies (though DISTANCE sampling was widely applied), introducing the possibility of some bias in cross-site comparisons.

Implications for Conservation

The relatively high warthog density recorded in Nimule National Park (NNP) during the 2015–2016 survey period suggests that, at that time, the park functioned as an important refuge for the species in South Sudan. However, the marked interannual variation observed between 2015 and 2016, together with fluctuations reported in earlier accounts, underscores the importance of multi-year monitoring. Single-season assessments may provide only a snapshot of population status and risk misrepresenting longer-term demographic trends. Moreover, because these data were collected a decade ago, current conditions may differ due to changes in climate, land use, protection effectiveness, wildlife movements, or human activities. The estimates presented here should therefore be interpreted as a historical baseline against which contemporary and future surveys can be compared.

Burrow availability emerged as a key predictor of warthog occurrence and abundance (and this finding has unlikely changed in recent years), highlighting the conservation value of habitats that facilitate burrow formation, such as termite mound complexes, lightly wooded savannahs, and fire-influenced areas. Management interventions that degrade these habitats or intensify late-dry-season fires could consequently have disproportionate negative impacts on local populations. Although human disturbance was not statistically significant during the 2015–2016 study period, this finding should not be interpreted as evidence of negligible risk. The region’s history of armed conflict and bushmeat hunting indicates that warthog populations may decline rapidly if protection weakens. In addition, the concentration of individuals in riverine areas during drought periods may increase susceptibility to localized hunting pressure, reinforcing the importance of targeted anti-poaching patrols and the protection of dry-season refugia.

Overall, while the study period indicated favourable conditions for warthogs in NNP in 2015-2016, sustained local management, habitat protection, and continued application of standardized monitoring protocols will be essential to evaluate temporal changes and to maintain—or restore, if necessary—the park’s conservation status under potentially shifting ecological and socio-political conditions.

Supplemental Material

Supplemental material - Predictors of Warthog Abundance and Sightings in Nimule National Park (South Sudan), with a Continental Review of Population Densities

Supplemental material for Predictors of Warthog Abundance and Sightings in Nimule National Park (South Sudan), with a Continental Review of Population Densities by Majango Jambo Gonja Wura, Malik Doka Morjan, Thomas Francis Lado, Fabio Petrozzi, Gift Simon Sarafadin Demaya, Luca Luiselli in Tropical Conservation Science

Footnotes

ORCID iDs

Malik Doka Morjan

Luca Luiselli

Funding

Support for this study was provided by the University of Juba and the United States Agency for International Development (USAID) under the Feed the Future initiative CGIAR Fund, award BFS-G-11-00002 and the Food Security and Crisis Mitigation II grant (award EEM-G-00-04-00013). LL research in South Sudan was supported by National Geographic, Mohamed Bin Zayed Species Conservation Fund, IDECC, Turtle Survival Alliance, USAID, and Rainforest Trust. Gratitude is extended to Paul Peter Awol for research problem identification and access, and to Dean Stauffer for guidance in DISTANCE data analysis.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material is available online.

References

Abdu

Datiko

(2017). Population Size, habitat association of Common Warthog (Phacochoerus africanus) and their impact on agricultural crops around Diregudo forest in Gololcha woreda, South East, Ethiopia. IJ Sci. Res. Publ, 7, 192-218.

Abdullah

(2015). Report on warthog death at Nimule National Park disease. 12th to 16th February 2015. Unpublished report, Ministry of Tourism and wildlife Conservations, South Sudan, Juba. pp 28.

Adeola

A. J.

Adeyemo

A. I.

Ejidike

B. N.

(2019). Population status and habitat preferences of common warthog (Phacochoerus africanus) in Kainji Lake National Park, Niger State Nigeria. Journal of Research in Forestry, Wildlife and Environment, 11(4), 64-69.

Adeola

A. J.

Adeyemo

A. I.

Ejidike

B. N.

Olaniyi

O. E.

Akande

O. A.

Ajayi

S. R.

Azeez

O. K.

(2021). Conservation status and habitat preferences of common warthog (Phacochoerus africanus) in Old Oyo National Park, Nigeria. Journal of Applied Sciences and Environmental Management, 25(1), 87-92.

Anderson

D. R.

Burnham

K. P.

Lubow

B. C.

Thomas

Corn

P. S.

Medica

Marlow

R. W.

(2001). Field trials of line transect methods applied to estimation of desert tortoise abundance. Journal of Wildlife Management, 65(3):583-597.

Anderson

N. E.

Bessell

P. R.

Mubanga

Thomas

Eisler

M. C.

Fevre

E. M.

Welburn

S. C.

(2016). Ecological monitoring and health research in Luambe National Park, Zambia: Generation of baseline data layers. EcoHealth, 13(3), 511-524.

Apio

Plath

Wronski

(2015). Recovery of ungulate populations in post-civil war Akagera National Park, Rwanda. Journal of East African Natural History, 104(1-2), 127-141.

Asaye

Tekalign

Dobamo

(2024). Density of wild animal populations and their conflicts with nearby farmers in southwest Ethiopia, Mankira Forest.

Augustine

D. J.

(2010). Response of native ungulates to drought in semi–arid Kenyan rangeland. African Journal of Ecology, 48, 1009–1020.

10.

Barnes

(2016). Protected areas are helping save our favourite animals-but let’s not forget the others. The Conversation. https://theconversation.com/Protected areas are helping save our favourite animals–but let’s not forget the others.

11.

Bertram

B. C. R.

(1979). Studying predators. 2nd ed. African Wildlife Foundation, Nairobi, Kenya.

12.

Bertram

B. C. R.

(2008). Lion population regulation. African Journal of Ecology 11(3‐4):215-225. DOI:https://doi.org/10.1111/j.1365-2028.1973.tb00088.x

13.

Bhola

Ogutu

J. O.

Piepho

H. P.

Said

M. Y.

Reid

R. S.

Hobbs

N. T.

Olff

(2012). Comparative changes in density and demography of large herbivores in the Masai Mara Reserve and its surrounding human-dominated pastoral ranches in Kenya. Biodiversity and Conservation, 21(6), 1509-1530.

14.

Bolton

(1972). Report on a wildlife survey of South East Ethiopia. Walia, 1972(4), 19-31.

15.

Boshe

J. I.

1984. Demographic characteristics of the warthog population of the eastern Selous Game Reserve, Tanzania. Afr. f. Ecol, 22:43-47.

16.

Bowman

J.C.

Robitaille

J.-F.

(2005). An assessment of expert-based marten habitat models used for forest management in Ontario. The Forestry Chronicle, 81(6): 801–807.

17.

Bradley

R. M.

(1968). Some aspects of the ecology of the warthog (Phacochoerus aethiopicus Pallas) in Nairobi National Park (Doctoral dissertation, University of East Africa).

18.

Buckland

S. T.

Anderson

D. R.

Burnham

K. P.

Laake

J. L.

Borchers

D. L.

Thomas

(2001). Introduction to distance sampling: estimating abundance of biological populations. Oxford University Press.

19.

Buckland

S. T.

Anderson

D. R.

Burnham

K. P.

Laake

J. L.

Borchers

D. L.

Thomas

(2004). Advanced distance sampling: estimating abundance of biological populations. Oxford University Press.

20.

Buckland

S. T.

Anderson

D. R.

Laake

J. L.

(1993) Distance sampling: estimating abundance of biological population. Chapman & Hall, London.

21.

Butynski

T. M.

de Jong

Y. A.

(2017). Common warthog Phacochoerus africanus (Gmelin, 1788) (pp. 85-100). In: Ecology, Conservation and Management of Wild Pigs and Peccaries, edited by Melletti

Meijaard

; Cambridge: Cambridge University Press.

22.

Creel

(2005). Phacochoerus africanus common warthog (Also: warthog). University of Michigan Museum of Zoology. Animal Diversity Web. Accessed September 11, 2018 at 4pm ETChttps://animaldiversity.org/accounts/Phacochoerus_africanus/

23.

Creel

Matandiko

Schuette

Rosenblatt

Sanguinetti

Banda

Vinksa

Becker

(2018). Changes in African large carnivore diets over the past half–century reveal the loss of large prey. Journal of Applied Ecology, 1–9, 2908–2916. https://doi.org/10.1111/1365–2664.13227

24.

Cumming

D. H. M.

(1982). The influence of large herbivores on savanna structure in Africa. In Ecology of tropical savannas (pp. 217-245). Berlin, Heidelberg: Springer Berlin Heidelberg.

25.

Cumming

D. H. M.

(1999). Phacochoerus africanus: The Mammals of Africa. In: Kingdon

J. S.

Hoffmann

(Eds.), Academic Press, Amsterdam, The Netherlands.

26.

Cunningham

(2016). Ecology and Conservation encounters of with common warthogs along the B1highway in north-central Namibia. Ecol Conserv, 14(2), 32-37.

27.

De Jong

Y. A.

Cumming

d'Huart

Butynski

(2016). Phacochoerus africanus (pp. 1–12). The IUCN Red List of Threatened Species.

28.

Demaya

G. S.

Morjan

M. D.

Lado

T. F.

Gordon

S. A.

Di Vittorio

Dendi

Luiselli

(2024). Population Size, Sex-Ratio and Age Structure of Hippopotamus amphibius in Nimule National Park, South Sudan. Journal of East African Natural History, 113(6), 50-58.

29.

Denny

Coad

Jones

Ingram

D. J.

(2025). Snaring and wildlife wastage in Africa: drivers, scale, impacts, and paths to sustainability. BioScience, 75, 284-297.

30.

Deribe

Bekele

Balakrishnan

(2008). Population status and diurnal activity patterns of the common warthog (Phacochoerus africanus) in the Bale Mountains National Park, Ethiopia. International Journal of Ecology and Environmental Sciences, 34(2), 91-97.

31.

Edossa

Bekele

Debella

H. J.

(2020). Social organization and activity patterns of common warthog (Phacochoerus africanus Gmelin, 1788) in Dabena Valley Forest, Western Ethiopia. Ecology and Evolutionary Biology, 5(4), 173-181.

32.

Edossa

Bekele

Debella

H. J.

(2021). Diet preferences of common warthogs (Phacochoerus africanus) in Gassi and Haro Aba Diko controlled hunting areas, Western Ethiopia. Global Ecology and Conservation, 29, e01722.

33.

Edossa

Bekele

Debella

H. J.

(2022). Population density and distribution of common warthog (Phacochoerus africanus Gmelin, 1788) in Dabena Valley Forest, Western Ethiopia. African Journal of Ecology, 60(3), 386-397.

34.

Fischer

Linsenmair

K. E.

(2001). Decreases in ungulate population densities. Examples from the Comoé National Park, Ivory Coast. Biological Conservation, 101(2), 131-135.

35.

Gaidet

Fritz

Nyahuma

(2003). A participatory counting method to monitor populations of large mammals in non-protected areas: a case study of bicycle counts in the Zambezi Valley, Zimbabwe. Biodiversity & Conservation, 12(8), 1571-1585.

36.

Gaynor

K. M.

Hojnowski

C. E.

Carter

N. H.

Brashares

J. S.

(2018). The influence of human disturbance on wildlife nocturnality. Science, 360(6394), 1232-1235.

37.

Hayward

M. W.

Henschel

O'Brien

Hofmeyr

Balme

Kerley

G. I.

(2006). Prey preferences of the leopard (Panthera pardus). Journal of Zoology, 270(2), 298-313.

38.

Hayward

M. W.

Kerley

G. I.

(2005). Prey preferences of the lion (Panthera leo). Journal of Zoology, 267(3), 309-322.

39.

Hema

E. M.

Douamba

Di Vittorio

Dendi

Guenda

Petrozzi

Ouédraogo

Luiselli

(2017). Long-term (1985-2012) population fluctuations of warthog (Phacochoerus africanus) in Nazinga Game Ranch (Burkina Faso). African Journal of Ecology, 55(4), 748-753.

40.

Hjertlöv

(2015). Why do the common warthog Phacochoerus africanus stay at Kichwa Tembo? Student report Swedish University of Agricultural Sciences Department of Animal Environment and Health.

41.

IUCN and World Commission on Protected Areas (WCPA) (2016). IUCN Green List of Protected and Conserved Areas: Standard, Version 1.1. Gland, Switzerland: IUCN.

42.

IUCN ESARO . (2020). The state of protected and conserved areas in Eastern and Southern Africa. State of protected and conserved areas report series no. 1. IUCN ESARO, Nairobi. Kenya.

43.

James

N. L.

Bond

M. L.

Ozgul

Lee

D. E.

(2022). Trophic processes constrain seasonal ungulate distributions at two scales in an East African savanna. Journal of Mammalogy, 103(4), 956-969.

44.

Kiffner

Kioko

Baylis

Beckwith

Brunner

Burns

Chavez-Molina

Cotton

Glazik

Loftis

Moran

O'Neill

, Theisinger, & Kissui

(2020). Long‐term persistence of wildlife populations in a pastoral area. Ecology and Evolution, 10(18), 10000-10016.

45.

Knight

(1981). The basics of wildlife management. Excerpt from: Manage Your Land for Wildlife https://www.msuextension.org/store/products/Manage-Your-Land-for-Wildlife/4508/aspx

46.

Koons

D. N.

Iles

D. T.

Caswell

(2016). A life-history perspective on the demographic derivers of structured population dynamics in changing environments. Ecology letters, 19(9):1023-1031.

47.

Luard

the World-Wide Fund . (1985). The wildlife parks of Africa. Michael Joseph, London, United Kingdom.

48.

Mapendere

Jori

Etter

E. M.

Ferguson

J. H. W.

(2021). Do wild suids from Ndumo Game Reserve, South Africa, play a role in the maintenance and transmission of African swine fever to domestic pigs? Transboundary and Emerging Diseases, 68(5), 2774-2786.

49.

Marchal

Lejeune

Bouché

Ouédraogo

Sawadogo

Yaméogo

Yanogo

F. I.

Vermeulen

(2012). Status of medium–sized ungulate populations in 2010, at the Nazinga game ranch, Burkina Faso (Western Africa). Biotechnologie, Agronomie, Société et Environnement, 16, 1–7.

50.

McCleery

Monadjem

Conner

L. M.

Austin

J. D.

Taylor

P. J.

(2022). Methods for ecological research on terrestrial small mammals. John Hopkins University Press.

51.

McComb

Zuckerberg

Vesely

Jordan

(2010). Monitoring Animal Populations and their Habitats: A Practitioner's Guide. 1st. Ed. CRC Press. ISBN: 142007055X DOI:https://doi.org/10.1201/9781420070583

52.

Mgqatsa

Jama

Landman

Kerley

G. I.

(2024). Understanding resource use of an invasive species: Diet of the common warthog in Eastern Cape succulent thicket. Journal of Arid Environments, 222, 105155.

53.

Ministry of Wildlife Conservation and Tourism of South Sudan & African Wildlife Foundation . (2014). Nimule National Park and Buffer Zone General Management Plan 2015-2020. Unpublished document, Ministry of Wildlife Conservation and Tourism, Juba. pp 125.

54.

Morjan

Ojok

Ackwoch

Kamdalla

Nicholas

(2000). A pilot survey of Nimule National Park New Sudan. Unpublished report, Juba.

55.

Ndiaye

S. A. A. S.

Kabre

J. A. T.

Leye

(2017). Warthogs population count and age groups in the Senegal River Delta: case of the Djoudj National Park and the Gueumbeul Wildlife reserve.

56.

Njoroge

Yeggo

Muchane

Githiru

Njeri

Giani

(2009). A survey of the large and medium sized mammals of Arawale National Reserve, Kenya. J. East Afr. Nat. Hist 98: 119-128, https://doi.org/10.2982/028.098.0108.

57.

Norton-Griffiths

(1978). Counting animals. Handbook no. 1, 2nd ed. African Wildlife Foundation, Nairobi.

58.

Ogutu

J. O.

Kuloba

Piepho

H-P.

Kanga

(2017). Dominated landscape under community-based conservation: the example of Nakuru Wildlife Conservancy, Kenya. PloS One 12(1): e0169730. Doi:10.171/journal.pone.0169730.

59.

Ogutu

J. O.

Piepho

H. P.

Dublin

H. T.

(2014). Reproductive seasonality in African ungulates in relation to rainfall. Wildlife Research, 41(4), 323-342.

60.

Ogutu

J. O.

Piepho

H. P.

Dublin

H. T.

Bhola

Reid

R. S.

(2008). Rainfall influences on ungulate population abundance in the Mara‐Serengeti ecosystem. Journal of Animal Ecology, 77(4), 814-829.

61.

Plumptre

A. J.

(2000). Monitoring mammal populations with line transect techniques in African forests. Journal of Applied Ecology, 37, 356-368.

62.

Prasanai

Sukmasuang

Bhumpakhan

Wajjwak

Nittaya

(2012). Population characteristics and viability of the introduced hog deer (Axis porcinus Zimmermann, 1780) in Phu Rhieo Wildlife Sanctuary, Thailand. Songklanakarin J. Sci. Technol 34(3), 263-271.

63.

Reuben

E. M.

Brown

B. P.

Odhuk

A. E.

Dabit

M. P.

Modi

M. S.

Sube

E. A

. (2014). A suspected unknown wildlife disease infecting African warthog Phacochoerus aethiopicus in Nimule National Park, Unpublished report, Ministry of Wildlife Conservation and Tourism, South Sudan, Juba.

64.

Rieman

(2001). Wildlife population viability under land management alternatives from an environmental impact statement. Forest Ecology and Management, 153 (1-3): 29-42.

65.

Sethy

Chuanhan

N. S.

(2016). Assessing habitat use by sun bears in Namdapha Tiger Reserve, Arunachal Pradesh, India. Applied Ecology and Environmental Research, 14(1):215-26. DOI:https://doi.org/10.15666/aeer/1401_215236

66.

Sinsin

Tehou

Daouda

Saidou

. 2002. Abundance and species richness of larger mammals in Pendjari National Park in Benin. Mammalia 66(3):369-80.

67.

Somers

M. J.

Penzhorn

B. L.

Rasa

A. E.

(1994). Home range size, range use and dispersal of warthogs in the eastern Cape, South Africa. Journal of African Zoology, 108(4), 361-374.

68.

Sutherland

(1996). Ecological census techniques: a handbook. Cambridge University Press, Cambridge, U.K.

69.

Tamrat

Atickem

Tsegaye

Nguyen

Bekele

Evangelista

Fashing

P.J.

Stenseth

N. C.

(2020). Human–wildlife conflict and coexistence: a case study from Senkele Swayne's Hartebeest Sanctuary in Ethiopia. Wildlife Biology, 2020(3), 1-10.

70.

Taraveau

Bru

Pollet

Jeanneau

Duhayon

Pires Lameira

E. R.

Jourdan-Pineau

(2025). Population genetics of the nidicolous soft tick Ornithodoros phacochoerus in the context of African swine fever sylvatic cycle. bioRxiv, 2025-10.

71.

Teklehaimanot

Balakrishnan

(2017). Population status, feeding ecology and habitat association of the common warthog (Phacochoerus africanus) in Bale Mountains National Park, Ethiopia. International Journal of Ecology and Environmental Sciences, 43(3), 185-194.

72.

Thomas

Buckland

S. T.

Burnham

K. P.

Anderson

D. R.

Laake

J. L.

Borchers

D. L.

Strindberg

(2002). Distance sampling. in Encyclopedia of Environmetrics, (1): 544–552. ISBN 0471 899976. John Wiley & Sons, Ltd, Chichester.

73.

Thomas

Buckland

S.T.

Rexstad

E.A.

Laake

J. L.

Strindberg

Hedley

S.L.

Bishop

J.R. B.

Marques

T. A.

Burnham

K. P.

(2010). Distance software: design and analysis of distance sampling surveys for estimating population size. Journal of Applied Ecology, 47, 5-14.

74.

Thorp

(2012). The influence of active bomas on habitat choice of the common warthog (Phacochoerus africanus). Student report. Swedish University of Agricultural Sciences Student report 387. Department of Animal Environment and Health. Ethology and Animal Welfare programme.

75.

Treydte

A. C.

Halsdorf

S. A.

Weber

Source

P. J. E.

(2006). Habitat use of warthogs on a former cattle ranch in Tanzania. The Journal of Wildlife Management, 70(5), 1285–1292.

76.

Uehara

Ihobe

(1998). Distribution and abundance of diurnal mammals, especially monkeys, at Kasoje, Mahale Mountains, Tanzania. Anthropological Science, 106(4), 349-369.

77.

Vinks

M. A.

Creel

Schuette

Rosenblatt

Matandiko

Sanguinetti

Banda

Goodheart

Necker

Chifunte

Simukonda

(2020). Testing the effects of anthropogenic pressures on a diverse African herbivore community. Ecosphere, 11(3), e03067.

78.

Wakeley

J. S.

(1982). Wildlife Population Ecology. Pennsylvania State University Press. 385 pp.

79.

Waltert

Meyer

Kiffner

(2009). Habitat availability, hunting or poaching: what affects distribution and density of large mammals in western Tanzanian woodlands? African Journal of Ecology, 47(4), 737-746.

80.

Western

Grimsdell

J. J. R.

(1979). Measuring the distribution of animals in relation to the environment. Handbook no. 2, 2nd edition. African Wildlife Foundation, Nairobi, Kenya.

81.

White

A. M.

(2010). A pigheaded compromise: do competition and predation explain variation in warthog group size? Behavioral Ecology, 21(3), 485-492.

82.

White

A. M.

Cameron

E. Z.

(2009). Communal nesting is unrelated to burrow availability in the common warthog. Animal Behaviour, 77(1), 87-94.

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