Introduction
Anthropogenic threats imperil global biodiversity (Johnson et al., Reference Johnson, Balmford, Brook, Buettel, Galetti, Guangchun and Wilmshurst2017), yet amongst the most widespread and insidious of these is armed conflict, having occurred in > 90% of high-biodiversity regions and up to 70% of protected areas in Africa since the 1940s (Daskin & Pringle, Reference Daskin and Pringle2018). During war and political instability, environmental concerns often wane, with conservation activities being suspended by both the state and private sectors in the face of more immediate military or humanitarian concerns (Hart et al., Reference Hart, Hart, Fimbel, Fimbel, Laurance and Oren1997; Hanson et al., Reference Hanson, Brooks, Da Fonseca, Hoffmann, Lamoreux and Machlis2009). Subsequent reduction in management and law enforcement within protected areas may facilitate overexploitation of wildlife and natural resources for subsistence or commercial use (Hatton et al., Reference Hatton, Couto and Oglethorpe2001). Although environmental policies may be re-established post-conflict, this is rarely prioritized immediately, and displaced people may settle within or near protected areas (Hatton et al., Reference Hatton, Couto and Oglethorpe2001; Gaynor et al., Reference Gaynor, Fiorella, Gregory, Kurz, Seto, Withey and Brashares2016; Daskin & Pringle, Reference Daskin and Pringle2018). When wildlife population declines have been driven by localized exploitation (Johnson et al., Reference Johnson, Balmford, Brook, Buettel, Galetti, Guangchun and Wilmshurst2017), regardless of whether armed conflict safeguards wildlife through anthropogenic exclusion (Dudley et al., Reference Dudley, Ginsberg, Plumptre, Hart and Campos2002), post-war recovery of wildlife populations is possible where intervention strategies are proactive and have consistent support and evaluation, as evidenced by the local recovery of ungulate and large carnivore populations in parts of Africa (Pringle, Reference Pringle2017; Bouley et al., Reference Bouley, Poulos, Branco and Carter2018; Braga-Pereira et al., Reference Braga-Pereira, Peres, Campos-Silva, Santos and Alves2020).
Large carnivores are ecologically important (Estes et al., Reference Estes, Terborgh, Brashares, Power, Berger and Bond2011) and have socio-economic benefits (Ripple et al., Reference Ripple, Estes, Beschta, Wilmers, Ritchie and Hebblewhite2014). Yet these species are amongst the most globally threatened, as their relatively slow generational turnover, low densities and large spatial and energetic requirements make them prone to extinction (Ripple et al., Reference Ripple, Estes, Beschta, Wilmers, Ritchie and Hebblewhite2014). The global decline of large carnivores is driven by anthropogenic pressures, such as bushmeat poaching and loss of suitable habitats and prey, leading to the fragmentation of rangelands and resulting in a conservation crisis for most of these species (Ripple et al., Reference Ripple, Estes, Beschta, Wilmers, Ritchie and Hebblewhite2014). This is of concern, and the majority of protected areas in Africa have populations of large carnivores that are below estimated carrying capacities (Strampelli et al., Reference Strampelli, Campbell, Henschel, Nicholson, Macdonald and Dickman2022). Consequently, robust population density assessments, which are a fundamental precursor for effective wildlife management (e.g. population viability assessment and offtake quota evaluation) and conservation policy development (e.g. conservation status evaluation and regional-level strategic planning), are imperative for the identification and management of threatened populations (Balme et al., Reference Balme, Hunter and Slotow2009; Sollmann et al., Reference Sollmann, Furtado, Gardner, Hofer, Jácomo, Tôrres and Silveira2011; Jacobson et al., Reference Jacobson, Gerngross, Lemeris, Schoonover, Anco and Breitenmoser-Würsten2016). Yet such baseline estimates are lacking for most large carnivore species.
The spotted hyaena Crocuta crocuta (hereafter hyaena) is widely distributed in Africa, with an estimated global population of 27,000–47,000 (Bohm & Höner, Reference Bohm and Höner2015). Although the species is categorized as Least Concern on the IUCN Red List (Bohm & Höner, Reference Bohm and Höner2015), there is a paucity of baseline data on ranging behaviour and population densities throughout its range (Dheer et al., Reference Dheer, Samarasinghe, Dloniak and Braczkowski2022b), despite purported declines across the continent (Ripple et al., Reference Ripple, Estes, Beschta, Wilmers, Ritchie and Hebblewhite2014; Wolf & Ripple, Reference Wolf and Ripple2016). Estimating population densities, particularly in understudied landscapes, is thus critical for improved regional conservation management and international policy development. Historically, hyaenas were considered widespread and abundant throughout Mozambique (Smithers & Tello, Reference Smithers and Tello1976), but following decades of war, both independence (1964–1975) and civil (1977–1992), wildlife management became compromised by poverty, food insecurity, insufficient legislation and poor law enforcement. In addition to combatant groups allegedly using bushmeat to feed soldiers, many people who had settled within protected areas have not been resettled. The subsequent widespread use of snares and gin traps has affected carnivores, with evidence suggesting that large carnivores are more widely depleted in Mozambique than in many other countries (Hatton et al., Reference Hatton, Couto and Oglethorpe2001; Beilfuss et al., Reference Beilfuss, Dutton and Moore2010). Since the 1992 ceasefire, there has been an improved national policy and framework for conservation, and better wildlife management and law enforcement (Hatton et al., Reference Hatton, Couto and Oglethorpe2001).
Despite these advances, anthropogenic pressures, largely through widespread bushmeat poaching, continue to drive extirpations of large carnivore populations in many protected (Bouley et al., Reference Bouley, Poulos, Branco and Carter2018; Everatt et al., Reference Everatt, Moore and Kerley2019b) and wildlife management areas (Lindsey & Bento, Reference Lindsey and Bento2012; Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024). Hyaenas are legally hunted in several wildlife management areas across Mozambique, and although sustainable trophy hunting could fund anti-poaching efforts and foster conservation (Lindsey et al., Reference Lindsey, Roulet and Romañach2007), offtake quotas rarely consider the additive pressures of illegal poaching, which could result in unsustainable harvest (Jorge et al., Reference Jorge, Vanak, Thaker, Begg and Slotow2013; Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024). Reliable baseline ecological data are crucial for developing such quotas, and recent studies indicate that robust estimates of leopard Panthera pardus densities are well below the outdated estimates used to derive hunting quotas (Strampelli et al., Reference Strampelli, Andresen, Everatt, Somers and Rowcliffe2020; Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024). Despite this, there is currently no robust, spatially explicit capture–recapture estimate of hyaena densities in Mozambique that could be used to set sustainable hunting quotas.
We used remote camera trapping within a spatial capture–recapture framework to determine the baseline population density of a hyaena population in the post-war wildlife management area Coutada 11, within the large, unfenced Zambezi Delta landscape of central Mozambique. We contextualize this estimate relative to range-wide hyaena density estimates to provide a better understanding of the status of this population globally and to suggest regional management recommendations for improved species conservation.
Study area
The 9,754 km2 Marromeu–Coutada Complex in the southern Zambezi Delta (hereafter, the Delta) of central Mozambique (Fig. 1), is partitioned into the Marromeu National Reserve and four wildlife management areas (Coutadas 10, 11, 12 and 14). The climate is tropical, with distinct dry (May–October) and wet (November–April) seasons and a mean annual rainfall of 1,200 mm (Beilfuss, Reference Beilfuss2001). The Delta comprises several threatened ecoregions (IUCN Red List of Ecosystems; Lötter et al., Reference Lötter, Burrows, Jones, Duarte, Costa and McCleland2023) supporting a range of vegetation types, including grasslands, papyrus swamps, miombo woodland and sand forest (Beilfuss, Reference Beilfuss2001). This diverse landscape supports abundant large ungulate populations that continue to recover post-war (Beilfuss et al., Reference Beilfuss, Dutton and Moore2010; Macandza et al., Reference Macandza, Ntumi, Mamugy, Bento, Nhambe, Monjane and Amrósio2022) and a large carnivore community, including resident hyaenas, leopards, African wild dogs Lycaon pictus and reintroduced lions Panthera leo and cheetahs Acinonyx jubatus (Briers-Louw et al., Reference Briers-Louw, Kendon, Naude and Gaynor2023).
Fig. 1 Camera-trap locations and detection frequencies of spotted hyaenas Crocuta crocuta across the two pilot surveys in (a) 2019 and (b) 2020 and (c) in the comprehensive survey in 2021, in wildlife management area Coutada 11 in the Zambezi Delta, central Mozambique. Camera-trap sites are indicated by crosses (no hyaena captures) and circles (hyaena captures), with the number of individual hyaenas photographed indicated by circle size.
Methods
Sampling design
We conducted camera-trapping surveys in Coutada 11 in 2019 (60 days, 48 stations) and 2020 (64 days, 48 stations). These pilot surveys provided insights for appropriate camera-trap placement for the hyaena population; however, hyaena detections were too low to estimate density accurately. Subsequently, we conducted a more comprehensive survey (140 days, 76 stations) in 2021 (Fig. 1; Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024), in which we optimized site coverage through adjacent block sampling (Karanth & Nichols, Reference Karanth and Nichols2002). We considered camera coverage sufficiently expansive to encompass the hyaena home range and sufficiently intensive to ensure multiple recaptures of individuals (Darnell et al., Reference Darnell, Graf, Somers, Slotow and Gunther2014), thus meeting spatial capture–recapture assumptions (Efford, Reference Efford2004). The 140-day sampling window accepted marginal violation of the population closure assumption for increased precision for a species with a slow life history (Dupont et al., Reference Dupont, Milleret, Gimenez and Bischof2019; Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024). The mean inter-trap distance was 2.09 km (0.99–3.60 km), which facilitated comprehensive sampling of hyaenas, based on minimum clan home ranges of 30–52 km2 in comparable landscapes (M'soka et al., Reference M'soka, Creel, Becker and Droge2016; Braczkowski et al., Reference Braczkowski, Gopalaswamy, Fattebert, Isoke, Bezzina and Maron2022). Stations comprised paired infrared cameras (Cuddeback model 1453, Cuddeback, USA) across roads or trails at a distance of c. 2 m from the path, mounted on trees or wooden poles 40–60 cm above the ground.
Data preparation
We classified camera-trap images to species and processed them using the camtrapR (Niedballa et al., Reference Niedballa, Sollman, Courtiol and Wilting2016) package in R 4.2.1 (R Core Team, 2022). We identified individual hyaenas from photographic captures by their unique, asymmetrical pelage patterns (O'Brien & Kinnaird, Reference O'Brien and Kinnaird2011), using Hotspotter (Crall et al., Reference Crall, Stewart, Berger-Wolf, Rubenstein and Sundaresan2013) pattern recognition software. Four observers independently assigned individual identities to hyaena photographs (i.e. authors WDB-L, TAK, DB, EE and VNN) and we only included those for which we reached a consensus in subsequent density analyses. We excluded images from further analyses in which individuals were unidentifiable or for which there was no consensus amongst observers (Braczkowski et al., Reference Braczkowski, Gopalaswamy, Fattebert, Isoke, Bezzina and Maron2022). We maintained a record of all identified individuals with complete (i.e. both flanks) and partial (i.e. right or left flank only) evidence. For partially identified individuals we selected the flank with the greatest number of captures to avoid mismatching flanks and mistakenly double-counting individuals (Henschel et al., Reference Henschel, Malanda and Hunter2014). Although we acknowledge this introduces individual heterogeneity into capture probabilities and thus negative bias, resulting in underestimation of abundance (Augustine et al., Reference Augustine, Royle, Kelly, Satter, Alonso, Boydston and Crooks2018), precautionary undercounting is less of a risk to conservation management than overestimating abundance (Palmero et al., Reference Palmero, Premier, Kramer-Schadt, Monterosso and Heurich2023). The presence of pseudo-scrotums in female hyaenas makes sex identification notoriously unreliable (Muller & Wrangham, Reference Muller and Wrangham2002) and thus we did not consider this useful for identification purposes. We selected sampling occasions of 24 h (00.00–23.59) to ensure independence of unique hyaena photographic capture events (Vissia et al., Reference Vissia, Wadhwa and van Langevelde2021). We also recorded any signs of poaching injuries (e.g. scars, or wounds around the neck).
Density estimation
We estimated hyaena density using a closed-population maximum-likelihood spatial capture–recapture model (Borchers & Efford, Reference Borchers and Efford2008) implemented in the R package secr 4.5.5 (Efford, Reference Efford2022; Supplementary Material 1). We modelled density as an inhomogeneous Poisson point process representing the intensity of activity centres within the state space, a standard approach in spatial capture–recapture analyses to facilitate computation as the process intensity varies over space and time (Efford & Fewster, Reference Efford and Fewster2013). We modelled the expected number of independent observations of individual i at trap j over k occasions as a binomial process with k trials and a detection probability p estimated according to a half-normal function of the distance between trap j and the latent activity centre of individual i with a spatial decay parameter σ and a baseline detection probability g0 (Efford, Reference Efford2022).
A 1 km grid extending 25 km around the trap array defined the modelling state-space area and accounted for individuals whose activity centres extended beyond the trapping area (Borchers & Efford, Reference Borchers and Efford2008; Efford, Reference Efford2022). We identified a starting buffer width of 25 km using the suggest.buffer function in secr. We tested larger buffer widths but density estimates remained stable and the estimated relative bias was tolerable at < 0.05 per 100 km2, and thus we used the smaller buffer width for computational efficiency (Efford, Reference Efford2022). We fitted all models by maximizing the full likelihood using the Nelder–Mead optimizer (Borchers & Efford, Reference Borchers and Efford2008). To ensure model convergence, we implemented parameter estimates from a model with homogeneous density as starting values for more complex models with inhomogeneous density (Efford & Fewster, Reference Efford and Fewster2013).
Based on past research we postulated that hyaena density would be influenced by the relative availability of suitable habitat and the intensity of anthropogenic activity (Supplementary Table 1). We used the ESPACCI 20 m resolution land-cover dataset for Africa (ESA, Reference ESA2017) to define habitat as grassland, shrubland, tree cover or community/cropland. We extracted the mean proportion of each land-cover type from a 7 km buffer (i.e. approximate core use area) around each point in the habitat mask (Pitman et al., Reference Pitman, Fattebert, Williams, Hill, Hunter and Pringle2017). However, after testing the proportion of each land-cover type for multicollinearity, we used proportion of tree cover to categorize landscape-level habitat (Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024). We log-transformed distance to the nearest community and used this to measure relative human activity. We scaled these continuous predictor variables to a mean of 0 and a standard deviation of 1 before including them as predictor variables in the density process.
A finite-mixture model approach (Efford & Fewster, Reference Efford and Fewster2013) accounted for variation in detection probability as individuals could not be reliably grouped into sex or age classes. We also included site-level habitat (i.e. cover of trees or open vegetation around each camera trap), a human activity index (i.e. number of independent human captures per trap effort) and a prey relative abundance index (i.e. number of independent suitable prey captures per trap effort) as predictors for g0 (Supplementary Table 1). We fitted an initial set of candidate models as single-session spatial capture–recapture models with a two-class latent mixture as a covariate for σ and g0 (Supplementary Table 2). This revealed a rare class (i.e. c. 5%, equivalent to a single individual in the observed sample) with 14-fold greater detectability than the more common class according to the area under the detection curve. This model did not adequately fit the data (goodness of fit P = 0.99), which suggested that the individual outlier could be masking other sources of detection heterogeneity within the population (Supplementary Table 3). To investigate this, we fitted a second set of models to a capture history that excluded the outlier, which estimated substantial variation in the remaining c. 95% of the population (Supplementary Tables 4 & 5). We therefore fitted models to the complete dataset (i.e. including the outlier) with a three-class latent mixture as covariates for σ and g0. The robustness of three-class mixtures has not yet been established, but they are known to converge at local maxima (Efford, Reference Efford2022). Thus, we are confident this represents an appropriate specification of the detection process for this population as the parameter estimates for the three classes were equivalent to those suggested by the two classes in the models both with and without the outlier using the robust two-class mixtures. We evaluated the subsequent candidate models using the Akaike information criterion corrected for small sample sizes (AICc; Burnham & Anderson, Reference Burnham and Anderson2003). We selected top-performing models on the parsimony principle to prevent overfitting.
To contextualize this density estimate, in December 2023 we conducted an informal review of academic and peer-reviewed literature on hyaena densities, using the keywords ‘spotted hyaena’ OR ‘spotted hyena’ OR ‘Crocuta crocuta’ AND ‘density’ in Google Scholar (Google, 2023), with searches limited to 25 standard pages. Where meta-analyses were available, we used the snowball approach to capture all relevant studies represented therein. We calculated ecological carrying capacity estimates for hyaenas based on the Hayward et al. (Reference Hayward, O'Brien and Kerley2007) model, which incorporates preferred prey species and preferred prey weight ranges of hyaenas. The prey abundance data required for these calculations were derived from regular aerial surveys (Macandza et al. Reference Macandza, Ntumi, Mamugy, Bento, Nhambe, Monjane and Amrósio2022). Given the lack of hyaena dietary information in the Delta we derived prey preferences from a nearby protected area with relatively similar prey composition and vegetation (Briers-Louw & Leslie, Reference Briers-Louw and Leslie2020; Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024). We also compiled hyaena trophy hunting quotas and offtake data for the Delta for 2017–2021.
Results
Sampling effort
Overall effort comprised 5,238 trap-nights across 76 stations covering an area of 619 km2 during the single-session dry-season annual survey (12 July–16 December 2021), resulting in 517 hyaena images. A total of 435 (84%) of these were suitable for individual identification, from which we derived 294 independent capture events and identified 23 individuals (Fig. 2). We recorded hyaenas at 54 camera-trap stations (naïve occupancy 71%), with at least one recapture for every individual (146 recaptures in total) and a mean of 6.3 ± SE 1.0 recaptures per individual.
Fig. 2 Detection frequencies of individually identified spotted hyaenas across the two pilot surveys (2019 and 2020) and comprehensive camera-trapping survey (2021) in Coutada 11 (Fig. 1). Twenty-three individuals were identified in 2021 and a total of 30 individuals in the three surveys.
Density estimation
The single-session spatial capture–recapture model, D~1, g0~(h3 + HumanIndex), σ~h3, was the highest-ranking model and had significantly more support (ΔAICc < 2) than alternative models (Table 1). The hyaena density estimate following this best-fit model was 1.3 ± SE 0.3 hyaenas/100 km2 (95% CI 0.8–2.1), which indicates a population of 23–37 individuals in Coutada 11 (Table 2). Based on this model, hyaena detection was positively correlated with human activity.
Table 1 Latent three-class spatial capture–recapture models for estimating density of the spotted hyaena Crocuta crocuta in the wildlife management area Coutada 11 in the Zambezi Delta, central Mozambique (Fig. 1), in 2021, ranked according to Akaike information criterion corrected for small sample sizes (AICc, with ΔAICc denoting the difference to the best-performing model). We included potential covariates for density (D) and probability of detection (g0) in the models, and a spatial scale parameter (σ) in the detection function. Covariates are detailed in Supplementary Table 1.
1HumanIndex, relative abundance index; Habitat type, tree cover or open vegetation; PreyIndex, prey relative abundance index; Comm_log, distance to the nearest community; TreeCover, proportion of tree cover.
Table 2 Specifications of the highest-ranking three-class spatial capture–recapture model used to estimate spotted hyaena density (D) in Coutada 11 in 2021. Model parameters are covariate coefficient (β), probability of detection (g0), spatial scale parameter (σ) and mixing proportion parameter (pmix) for each of the three latent classes (h3). We also calculated hyaena abundance (N) based on suitable habitat (Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024).
During the 2021 camera-trap survey, two (9%) individuals were photographed with visible signs of injuries caused by snaring (Plate 1). One individual (no. 5) had a snare wound around the neck and the other (no. 11) was missing a back foot, presumably sustained from a steel gin trap. The only visible evidence of snaring in the 2019 and 2020 pilot surveys was an individual (no. 4) with a deep snare wound to the neck in 2020; this individual was legally hunted in 2021.
Plate 1 Photographic evidence of spotted hyaenas Crocuta crocuta affected by snaring in the wildlife management area Coutada 11 (Fig. 1). A1 and A2 are adult hyaena no. 11 with a missing foot presumably from a gin trap; B1 and B2 are adult hyaena no. 5 with a snare wound around the neck.
This baseline density assessment for the hyaena population in the Delta falls within the lowest 10% of all 101 available range-wide density estimates and in the bottom 22% of all 18 spatial capture–recapture estimates (Fig. 3, Supplementary Table 6). Prey-based carrying capacity estimates indicate that hyaena density should be almost an order of magnitude higher (9.3–12.4 hyaenas/100 km2; Supplementary Table 7). During the survey periods (2019–2021), four adult hyaenas were trophy hunted in Coutada 11, and no more than four hyaenas per year were legally hunted throughout the Delta since 2017 (Supplementary Table 8).
Fig. 3 Spotted hyaena population density estimates (n = 101) across the species’ range (Supplementary Table 6). Inset indicates density estimates derived from a spatially explicit capture–recapture framework (n = 18). Dots indicate mean density estimates and bars indicate confidence intervals. Colours of dots and bars indicate land-use type, and black arrows indicate density estimates from this study. (Readers of the printed journal are referred to the online article for a colour version of this figure.)
Discussion
Reliable density estimates are fundamental for assessing the status of large carnivore populations and facilitating their recovery (Ripple et al., Reference Ripple, Estes, Beschta, Wilmers, Ritchie and Hebblewhite2014). This is especially important in the context of carnivore conservation in Africa, where anthropogenic threats often affect these ecologically and economically significant species (Harris et al., Reference Harris, Murphy, Green, Gámez, Mwamidi and Nunez-Mir2023). Yet hyaena populations remain comparatively understudied amongst large carnivore species (Davis et al., Reference Davis, Gentle, Stone, Uzal and Yarnell2022; Wilkinson et al., Reference Wilkinson, Dheer, Zett, Torrents-Ticó, Yarnell and Bar Ziv2023). Our baseline estimate of 0.8–2.1 hyaenas/100 km2 in Coutada 11 in central Mozambique is the first robust spatial capture–recapture density estimate for the species in the country.
The hyaena density we recorded in this study is relatively low compared to elsewhere, in the lower 25% of 18 range-wide spatial capture–recapture density estimates for the species (Supplementary Table 5). Our estimate is also substantially lower than recent spatial capture–recapture-based estimates for wildlife management areas in Tanzania (5.1–5.8 hyaenas/100 km2; Searle et al., Reference Searle, Strampelli, Smit, Mkuburo, Mathews and Kiwango2023), and, similar to leopard estimates in our study area (Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024), hyaena density appears to be well below the expected carrying capacity of 9.3–12.4 hyaenas/100 km2. Our hyaena density estimate was comparable to estimates for Limpopo National Park in southern Mozambique (1.49 hyaenas/100 km2; Everatt et al., Reference Everatt, Kokes and Lopez Pereira2019a), human-impacted miombo woodland-dominated protected areas in Malawi (1.15 hyaenas/100 km2, Davis et al., Reference Davis, Stone, Gentle, Mgoola, Uzal and Yarnell2021; 2.62 hyaenas/100 km2, Briers-Louw, Reference Briers-Louw2017) and arid savannah environments (0.85 hyaenas/100 km2, Fouché et al., Reference Fouché, Reilly, de Crom, Baeumchen and Forberger2020; 2.1 hyaenas/100 km2, Trinkel, Reference Trinkel2009), despite the Delta being a largely mesic landscape with relatively high prey availability (Macandza et al., Reference Macandza, Ntumi, Mamugy, Bento, Nhambe, Monjane and Amrósio2022). Our estimate was also similar to estimates from environmentally comparable post-war southern Angola and adjacent protected areas in northern Namibia (0.9–1.4 hyaenas/100 km2; Funston et al., Reference Funston, Henschel, Petracca, MacLennan, Whitesell, Fabiano and Castro2017; Hanssen et al., Reference Hanssen, Funston, Alfred and Alfred2017).
Large carnivore density is influenced by ecological factors such as intraguild competition, habitat type and prey density (Carbone & Gittleman, Reference Carbone and Gittleman2002; Caro & Stoner, Reference Caro and Stoner2003). Competition generally has a negligible influence on hyaena populations (Jones et al., Reference Jones, Blockley, Schreve and Carbone2021), and other large carnivore densities in the Delta are relatively low (Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024), suggesting that intraguild competition plays a minor role in hyaena density. Habitat suitability and availability are also improbable explanations for the low density as hyaena density in a similar floodplain–woodland habitat is almost an order of magnitude higher than in the Delta (e.g. 10.1 hyaenas/100 km2 in the Okavango Delta, Botswana; Rich et al., Reference Rich, Miller, Muñoz, Robinson, McNutt and Kelly2019). Furthermore, although the Delta was subject to decades of armed conflict and sustained bushmeat poaching, improved protection of the landscape has resulted in substantial recovery and growth of prey populations (Beilfuss et al., Reference Beilfuss, Dutton and Moore2010; Macandza et al., Reference Macandza, Ntumi, Mamugy, Bento, Nhambe, Monjane and Amrósio2022).
Anthropogenic disturbance may be a strong determinant of hyaena density, distribution and behaviour (Croes et al., Reference Croes, Funston, Rasmussen, Buij, Saleh, Tumenta and De Iongh2011; Schuette et al., Reference Schuette, Wagner, Wagner and Creel2013; Green & Holekamp, Reference Green and Holekamp2019). However, the relationship between carnivorous scavengers and human density can be highly variable and is generally poorly understood as it is often scale-dependent and linked to the relative opportunity costs and risks associated with navigating transformed anthropogenic landscapes. For example, detectability of scavenging predators may increase with relatively small-scale human impacts (Green et al., Reference Green, Johnson-Ulrich, Couraud and Holekamp2018), whereas large-scale human impacts often decrease detectability as a result of depleted prey and increased levels of human activity (Mwampeta et al., Reference Mwampeta, Wilton, Mkasanga, Masinde, Ranke and Røskaft2021). In Kruger National Park, South Africa, human infrastructure and activity offered favourable hunting opportunities for hyaenas at night and were linked to smaller home range sizes (Belton et al., Reference Belton, Cameron and Dalerum2016). The positive influence of human activity on hyaena detectability in the Delta could thus be explained by hyaenas being almost exclusively nocturnal and having little conflict with local communities because of low livestock densities.
Bushmeat poaching is widespread throughout Africa and is a significant threat to large carnivore populations (Lindsey et al., Reference Lindsey, Balme, Becker, Begg, Bento and Bocchino2013; Everatt et al., Reference Everatt, Moore and Kerley2019b; Naude et al., Reference Naude, Balme, O'Riain, Hunter, Fattebert, Dickerson and Bishop2020; Rogan et al., Reference Rogan, Distiller, Balme, Pitman, Mann and Dubay2022). In central Mozambique, wire snares and gin traps are the most frequently used poaching tools, and their use is highly unsustainable because of their indiscriminate nature (Lindsey et al., Reference Lindsey, Balme, Becker, Begg, Bento and Bocchino2013). Although we recorded only two hyaenas (9% of the individuals identified) visibly affected by poaching, this is probably an undercount of the true impact as these are the individuals that escaped the traps (Lindsey et al., Reference Lindsey, Balme, Becker, Begg, Bento and Bocchino2013; Loveridge et al., Reference Loveridge, Sousa, Seymour-Smith, Hunt, Coals and O'Donnell2020; Kendon et al., Reference Kendon, Comley, Wilkinson, Grobler, Nieman and Leslie2022; Searle et al., Reference Searle, Strampelli, Smit, Mkuburo, Mathews and Kiwango2023; Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024). In the Ruaha-Rungwa landscape in Tanzania only two of 256 individuals (< 1%) had snare injuries (Searle et al., Reference Searle, Strampelli, Smit, Mkuburo, Mathews and Kiwango2023), and in the Zimbabwean section of the Kavango–Zambezi Transfrontier Conservation Area 85 of 2,037 individuals (4%) had snare injuries (Loveridge et al., Reference Loveridge, Sousa, Seymour-Smith, Hunt, Coals and O'Donnell2020). However, our findings are comparable to the Serengeti National Park in Tanzania where 8% of breeding females died annually as a result of bushmeat snares (Hofer et al., Reference Hofer, East and Campbell1993). As high-ranking females play a significant role in maintaining clan persistence and accelerating population recovery, the loss of such individuals or their reduced fitness from snare-related injuries can have detrimental demographic effects (Benhaiem et al., Reference Benhaiem, Marescot, East, Kramer-Schadt, Gimenez, Lebreton and Hofer2018, Reference Benhaiem, Kaidatzi, Hofer and East2023; Dheer et al., Reference Dheer, Davidian, Courtiol, Bailey, Wauters and Naman2022a). Snaring survival rates of hyaenas (0.25–0.62; Loveridge et al., Reference Loveridge, Sousa, Seymour-Smith, Hunt, Coals and O'Donnell2020) suggest that 1–6 hyaenas could have died undetected in snares during our study. It is thus plausible that bushmeat poaching has limited the post-war recovery of hyaenas, as with other large carnivores across Mozambique (Lindsey & Bento, Reference Lindsey and Bento2012; Lindsey et al., Reference Lindsey, Balme, Becker, Begg, Bento and Bocchino2013; Bouley et al., Reference Bouley, Poulos, Branco and Carter2018; Everatt et al., Reference Everatt, Moore and Kerley2019b). We recommend that future studies include information on snared individuals, to help assess snaring trends and highlight hotspots where it may be a significant threat to sustaining viable large carnivore populations (Becker et al., Reference Becker, Creel, Sichande, Merkle, Goodheart and Mweetwa2024). Our findings suggest hyaenas may be less resilient to anthropogenic pressures than previously thought and emphasizes the need for population assessments and improved protection across the range of this species.
Hyaenas can display behavioural plasticity in response to disturbance. For example, in the Serengeti and Ngorongoro Crater hyaena populations increased rapidly following increases in prey (Hofer & East, Reference Hofer and East2003; Höner et al., Reference Höner, Wachter, East, Runyoro and Hofer2005). However, hyaenas are generally slow to recover post-war, especially with sustained pressure from surrounding communities, such as that experienced in south-west Africa, where transboundary animal movement and variable conservation practices and policies further complicate management (Braga-Pereira et al., Reference Braga-Pereira, Peres, Campos-Silva, Santos and Alves2020). Even following moderate disturbance hyaena populations may require > 15 years to recover (Benhaiem et al., Reference Benhaiem, Marescot, East, Kramer-Schadt, Gimenez, Lebreton and Hofer2018) as the relatively low fecundity rates and high levels of parental investment in their young confound population recovery (Becker et al., Reference Becker, Creel, Sichande, Merkle, Goodheart and Mweetwa2024). In Majete Wildlife Reserve and Kasungu National Park in Malawi, where there was intensive poaching followed by improved protection, hyaena densities have remained low (Briers-Louw, Reference Briers-Louw2017; Davis et al., Reference Davis, Stone, Gentle, Mgoola, Uzal and Yarnell2021). Hyaena density is largely dependent on prey availability and protection (Searle et al., Reference Searle, Strampelli, Smit, Mkuburo, Mathews and Kiwango2023). Thus, there is scope for population recovery given recovering prey populations and provided that improvements in protection are prioritized. Regionally, hyaenas are absent or occur in low numbers outside the Delta (Lindsey & Bento, Reference Lindsey and Bento2012), although reintroduction of hyaenas into neighbouring Gorongosa National Park following their post-war extirpation (Pringle, Reference Pringle2017; Bouley et al., Reference Bouley, Poulos, Branco and Carter2018) increases the likelihood of population connectivity and recovery in central Mozambique.
Monitoring is essential for informing sustainable trophy hunting quotas. This is especially important for hyaenas as they are difficult to sex (Dheer et al., Reference Dheer, Samarasinghe, Dloniak and Braczkowski2022b) and adult females tend to be slightly larger than males (McCormick et al., Reference McCormick, Holekamp, Smale, Weldele, Glickman and Place2022). Trophy hunters, who generally target larger individuals, could primarily be harvesting females, thereby reducing reproductive output and suppressing population growth. However, the regular protection activities supported by hunting operators can also be key determinants of large carnivore persistence (Strampelli et al., Reference Strampelli, Campbell, Henschel, Nicholson, Macdonald and Dickman2022). In the Ruaha-Rungwa landscape of Tanzania large carnivore occurrence was influenced more by management and law enforcement levels than by whether an area was used for photographic or trophy hunting tourism (Strampelli et al., Reference Strampelli, Campbell, Henschel, Nicholson, Macdonald and Dickman2022). The comparatively well-managed, low-volume and consistent trophy hunting in the Zambezi Delta could be justified as a mixed land-use system, primarily financed and secured by hunting, maximizes conservation value compared to alternative and currently infeasible or unsustainable protection models for the region. Encroachment of human activity within and around these wildlife management areas is regulated, and bushmeat poaching has only recently been reduced to a manageable level (an 87% reduction in bushmeat snares and traps during 2017–2021) through effective anti-poaching efforts (Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024). Nevertheless, to facilitate population recovery, a more conservative quota should be considered as cryptic bushmeat poaching also contributes to offtake. Future quotas and the possibility of hunting offtake should be dependent on continued monitoring of the population using consecutive and comparable surveys and analytical frameworks, such as those used in our survey, to identify and account for discrepancies between modelled quota effects and reality (Strampelli et al., Reference Strampelli, Andresen, Everatt, Somers and Rowcliffe2020; Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024).
Although law enforcement efforts have dramatically reduced bushmeat poaching post-war (Briers-Louw et al., Reference Briers-Louw, Kendon, Rogan, Naude, Leslie and Gaynor2024), the comparatively low hyaena density and evidence of snares suggest that poaching is probably suppressing the inherently slow population recovery of hyaenas in the Delta. To ensure the long-term viability and growth of this hyaena population, we recommend management prioritizes anti-poaching efforts and considers demographic augmentation to promote population growth and genetic diversity.
Author contributions
Study design: WDB-L; fieldwork: WDB-L, TAK; data analysis: WDB-L, TAK, MSR, DB, EE, VNN; writing: all authors.
Acknowledgements
We thank the Cabela Family Foundation and the Wildlife Conservation Alliance for supporting this project and the ongoing restoration efforts in the Zambezi Delta; Mark Haldane and the Zambeze Delta Safari team for their contributions; Andres Hayes for assisting with fieldwork; and the Administração Nacional das Áreas de Conservação for their support. This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
Conflicts of interest
None.
Ethical standards
This study abided by the Oryx guidelines on ethical standards, and was conducted under a research permit (ANAC RP# 06/10/23) from the Administração Nacional das Áreas de Conservação in Mozambique. Permission was obtained from the concessionaire to conduct camera trapping in Coutada 11. In accordance with the ethical criteria of Stellenbosch University, this non-invasive research did not require ethical approval. Where people were incidentally and unintentionally photographed by camera traps, these photographs were securely stored in an access-controlled database, and any metadata collected were anonymized, in compliance with standard ethical practices for the collection of personal images and information without consent in camera-trap research.
Data availability
Open-access supporting data is available at github.com/WillemBriersLouw/ZD_SpottedHyaenaDensity.
Open access
