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13 May 2022 A review of spotted hyaena population estimates highlights the need for greater utilisation of spatial capture-recapture methods
Robert S. Davis, Louise K. Gentle, Emma L. Stone, Antonio Uzal, Richard W. Yarnell
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As apex predators with a regulating effect on interspecific competitors and prey demographics, monitoring of spotted hyaena (Crocuta crocuta) population trends can provide a reliable indicator of ecosystem health. However, the ability of current survey techniques to monitor carnivore densities effectively are increasingly questioned. This has led recent studies to advocate increased application of spatial capture-recapture (SCR) methods to estimate population density for large carnivores. We reviewed the literature regarding methods used to estimate population density for spotted hyaena since 2000. Our review found that SCR methods are underutilised for estimating spotted hyaena density, with only eight published studies (13% of articles assessed) using an SCR approach. Call-in surveys were the most frequently used method, featuring in 47% of studies. However, 63% of studies that used call-in surveys could not estimate a site-specific calibration index. The calibration index estimates the distance and rate at which the focal species responds to audio lures and, as response rates are impacted by site-specific ecological and environmental factors, studies that could not calibrate this index are likely inaccurate. Further application of SCR techniques will allow more robust estimation of spotted hyaena density, reducing uncertainty and potential overestimation that limit inference from existing survey methods.


Robust population estimates play a pivotal role in the implementation of effective conservation management strategies, reintroduction efforts and monitoring schemes (Hayward et al. 2015). As large carnivore populations continue to experience wide-scale declines (Ripple et al. 2014), robust methods for assessing density and population trends must be at the forefront of evidence-based conservation management (Hayward et al. 2015, Elliot & Gopalaswamy 2017). However, accurate data are often lacking for large carnivores, due to their cryptic behaviour and naturally low densities (Balme et al. 2009, Elliot & Gopalaswamy 2017). In addition, available estimates are often outdated, overestimated or subject to a high degree of uncertainty (Braczkowski et al. 2020a). The paucity of reliable data can impact the management of target species and, inadvertently, have a cascading effect on the management of other vulnerable species. For example, intraguild competition can have a detrimental effect on threatened or reintroduced carnivores, such as cheetah (Acinonyx jubatus) and African wild dog (Lycaon pictus), so reliable density estimates for sympatric large carnivores can provide a valuable metric for reintroduction success (Darnell et al. 2014, Weise et al. 2015).

Spotted hyaena: indicators of ecosystem health

Spotted hyaena (Crocuta crocuta) are widespread, social carnivores that occupy a broad range of habitats in sub-Saharan Africa, from sparse deserts to montane woodlands and suburban areas (Holekamp et al. 2012, Yirga et al. 2014). As the most abundant large carnivore in Africa (Watts & Holekamp 2008), spotted hyaena are routinely overlooked as a species of conservation concern. Instead, research has generally focused on spotted hyaena behavioural ecology (Dheer et al. 2022). However, the spotted hyaena is often maligned and subject to high levels of persecution, particularly outside protected areas (Bohm & Höner 2015). In addition, threats such as loss of natural prey, human-wildlife conflict and susceptibility to wire snaring and poisoning, are contributing to declines in spotted hyaena populations across Africa (Frank et al. 2011, Bohm & Höner 2015, Wolf & Ripple 2016, Loveridge et al. 2020). Loveridge et al. (2020) highlighted that wire-snaring is a particular conservation concern, with spotted hyaena representing 92% of large carnivore snaring records in the Zimbabwean region of the Kavango-Zambezi Transfrontier Conservation Area and, as such, the species' conservation status warrants further attention.

Spotted hyaena density varies greatly across their geographic range, from 0.85/100 km2 in arid environments (Fouché et al. 2020) up to 165/100 km2 in prey-rich East African savanna ecosystems (Watts & Holekamp 2008). As a dominant member of the large carnivore guild, spotted hyaena play an integral role in ecosystem services by regulating prey numbers, providing carrion for scavengers, and influencing carnivore dynamics through interspecific competition (Périquet et al. 2015, Green et al. 2018). Furthermore, spotted hyaena exhibit high levels of behavioural plasticity that enable them to persist in landscapes where other carnivores cannot compete (Holekamp & Dloniak 2010, Green et al. 2019). High behavioural plasticity makes spotted hyaena good models for assessing environmental change and monitoring wider ecosystem health (Trinkel 2009, Green et al. 2018, 2019). For example, increasing spotted hyaena population density can be an early indicator of competitive release from the regulating effect of competition with lions (Panthera leo) and signify declining trends in sympatric carnivores (M'soka et al. 2016, Green et al. 2018).

Current methods limit inference

Uncertainty regarding estimates of population size or density often stems from underlying issues with the survey methodologies employed for large carnivores. The challenges associated with surveying elusive, wide-ranging and often nocturnal large carnivores, combined with the need for rapid and cost-effective survey methods, has led to extensive use of index-calibrated methods to survey large carnivores (Mills et al. 2001, Funston et al. 2010, Winterbach et al. 2016). Index-calibrated methods assume a stable linear relationship between a measurable index (e.g. number of tracks or scats per km searched) and true population density (Funston et al. 2010) and, as these methods tend not to rely on direct observations, they are quicker and cheaper to conduct than more labour-intensive methods such as camera trapping or search-encounter techniques (Balme et al. 2009, Midlane et al. 2015).

In Africa, the use of two index-calibrated methods, track counts and call-in surveys, have frequently been employed to estimate density for spotted hyaena and other large carnivores (Croes et al. 2011, Aebischer et al. 2020, Henschel et al. 2020). However, the capacity of index-calibrated methods to account for variance in detection probability and spatial heterogeneity, whilst overestimating the precision of the putative index to successfully predict true density, has led to concerns that these methods produce spurious estimates and wide error margins (Gopalaswamy et al. 2015, Belant et al. 2019, Dröge et al. 2020). In some cases, confidence intervals do not include the true population size (Belant et al. 2019, Dröge et al. 2020) or are wide enough that inferences on population trends would be meaningless as a basis for conservation management decisions (e.g. Bouché et al. 2016, Bauer et al. 2017). Consequently, numerous authors have cautioned against the widespread application of index-calibrated methods to infer population trends and inform management and policy decisions (e.g. Rosenblatt et al. 2014, Gopalaswamy et al. 2015, Hayward et al. 2015, Dröge et al. 2020).

Call-in surveys, whereby acoustic lures (i.e. prey distress sounds or carnivore social calls) are played from a calling station and a calibration response index applied to estimate population size from the response rate (Mills et al. 2001), are commonly used to survey spotted hyaena and lion populations. Despite the popularity of this method, call-in surveys have several limitations that reduce the precision and inference of results (Elliot & Gopalaswamy 2017, Dröge et al. 2020). The calibration index is a key assumption in call-in surveys that determines the distance at which animals hear and respond to the acoustic lure (see Mills et al. 2001 for a description of the required experiment). Conducting call-in calibration experiments is difficult in dense habitats or areas where animals are not well habituated (Bauer 2007, Cozzi et al. 2013). As a result, researchers are often reliant on previous calibration estimates that may not accurately reflect their study site or population and, consequently, impact the precision of results (Kirsten et al. 2017). Furthermore, attempts to estimate response radius in areas of low density and restricted access can add further time constraints and financial costs, with potentially negative consequences for habituation (Midlane et al. 2015).

Emergence of spatial capture-recapture

In the last two decades, spatial capture-recapture (SCR) modelling has emerged as a reliable and robust technique from which to estimate population density for species that can be individually identified (Efford 2004, Borchers & Efford 2008, Royle et al. 2018). SCR methods utilise the spatial information associated with individual encounter history data to model the movement and distribution of individuals across a defined state space (Royle et al. 2014). The incorporation of a spatially explicit framework distinguishes SCR from conventional capture-recapture (CR) models, thereby addressing the challenges of buffering, heterogeneity in detection probability and trap-level variation that can limit inference from traditional CR studies (Royle et al. 2014). SCR methods are commonly associated with camera trap data, where individuals are often identified through their unique pelage patterns, although these models can also be applied to DNA sampling, acoustic surveys and search-encounter methods, and have been used to estimate density for a wide-range of global taxa (e.g. Sutherland et al. 2016, Sun et al. 2017, López-Bao et al. 2018, Balme et al. 2019). As SCR models have developed to incorporate additional covariates (e.g. age and sex; Sollmann et al. 2011) and supplementary data (e.g. movement data from radio/GPS collars; Royle et al. 2013), the use of SCR has become the standard method for obtaining reliable population estimates for many species with unique identification features (Royle et al. 2018). Despite the growing application and sophistication of SCR models, recent studies have highlighted that SCR approaches have been underutilised for multiple large carnivore species, including lions (Braczkowski et al. 2020a), snow leopards (Panthera uncia; Alexander et al. 2015, Sharma & Singh 2020) and wolves (Canis lupus; López-Bao et al. 2018).

In this study we review and critically assess the literature on spotted hyaena population estimates and survey methodologies by 1) evaluating the survey methods used to estimate spotted hyaena density and their geographical distribution; 2) discussing the limitations of current spotted hyaena survey methodology; and 3) highlighting the potential for future utilisation of SCR methods, whilst identifying possible survey considerations within the SCR framework for estimating spotted hyaena density.

Material and Methods

We followed the protocol of Braczkowski et al. (2020a) to conduct our literature review and searched for peer-reviewed articles on two comprehensive databases: Web of Science and Google Scholar. We used the following keyword combinations to search for peer-reviewed literature: “spotted hyaena” AND “density” OR “population size” OR “numbers”. We then repeated this process, replacing the keyword “spotted hyaena” with “Crocuta crocuta” (accounting for the English/US spelling hyaena/hyena) and the same density keyword variations. To remove bias in our search we limited the date range from 2000 to 2020, as SCR models were only developed towards the end of the 20th century (Royle et al. 2014). We checked all search pages for the Web of Science results but limited our Google Scholar results to the first 100 articles. All articles were inspected, and excluded when: 1) there were no population estimates, 2) only when previous or unpublished estimates of density or population size were cited; and/or 3) the survey method used was not explicitly stated. For studies that matched our criteria, we recorded the survey method used to estimate population size or density and calculated the total proportion of articles each method featured in. Table 1 provides definitions of survey methods documented in the literature to estimate spotted hyaena density. For studies that used call-in survey methods we also recorded if the study was able to calibrate a site-specific response rate.

Table 1.

Definitions of survey methods used to estimate spotted hyaena density and key literature that details each methodology.


We assessed the spatial coverage of published estimates to determine any geographical preference for individual survey techniques. We recorded the country of each study and calculated the total number of studies per country. We documented the survey method used in each study and, using the geographic regions documented by the African Union (African Union 2020), calculated the total number of times each method was used per region.


We reviewed 153 published studies on spotted hyaena from 19 African countries, in 49 different journals. We identified 60 studies, in 29 journals, that reported population estimates and detailed how these estimates were obtained (Table S1).

Preferred methods for population estimates

Overall, six survey methods were used to estimate spotted hyaena population density. Of the 60 studies assessed, 58 used one survey method (97% of studies) and two used multiple methods. Call-in surveys were the most frequently used method, featuring in 46.6% of the articles assessed (n = 28 studies; Fig. 1). Of the 27 studies (one study was removed that used N-mixture models and did not require a calibration index) that used call-in methods, over half (63%) were unable to undertake their own calibration experiments to estimate site-specific response distances of spotted hyaena. Index-calibrated methods (call-in surveys and track counts) were used in 63.3% of studies (n = 38 studies). SCR methods were used in eight studies, 13.3% of articles, with only one study published prior to 2019. Camera traps were used to estimate spotted hyaena density in seven of the eight SCR studies, with a search-encounter method, based on visual identification, used in one study. Of the eight studies that used SCR methods, seven of these studies had a multi-species focus, estimating density for spotted hyaena and at least one other species.

Fig. 1.

Survey methods used to obtain estimates of spotted hyaena density or population size and the proportion of reviewed articles that applied each survey method. Insert map shows the location of published studies from sub-Saharan Africa and the number of studies from each country where estimates were available. Hyaena silhouette: PhyloPic 2019.


Spatial coverage of survey methods

Density/abundance estimates were available for spotted hyaena populations in 16 African countries, representing 41% of spotted hyaena range states. Studies from East (50% of studies) and Southern (38.3% of studies) Africa accounted for the majority of available estimates (Table 2). There were six studies from the Central African region and one population estimate from West Africa. Call-in surveys or track counts were the most frequently used methods in all four regions. All population estimates using total counts and distance sampling were from East Africa, specifically in Kenya and Tanzania. Six of the eight population estimates derived from SCR methods were conducted in Southern Africa.


Here we found that SCR methods are currently underutilised for estimating spotted hyaena density, compared to other available methods. However, with seven of the eight studies using SCR methods published since 2019, this may indicate a growing change in preferred survey methodology. Despite the increase in SCR-derived estimates for spotted hyaena, the number of published studies utilising SCR methods is still relatively low when compared to sympatric carnivores. For example, 57% of published studies (n = 27) estimating leopard (Panthera pardus) density in sub-Saharan Africa, since 2000, used SCR methods (see Table S2). Spotted hyaena population estimates are also limited to only 16 out of 39 African countries where the species is resident (Bohm & Höner 2015), which evidences that just under two-thirds of range states lack baseline density estimates. Since spotted hyaena density varies considerably between habitats and with levels of anthropogenic disturbance (Yirga et al. 2017, Fouché et al. 2020), there is a need for increased reporting of population estimates from understudied regions to inform local conservation management.

Table 2.

Number of times individual survey methods were used to estimate spotted hyaena density and/or population size by region and country. Individual survey methods include call-ins, track counts (Track), total counts of individuals (Total), distance sampling (Distance), spatial capture-recapture (SCR) and capture-recapture (CR). Note that the total number of times methods were used here (n = 54) is larger than the number of published studies found in the review (n = 51 studies) as two studies used multiple methods.


Addressing issues with current methodologies

Our review indicates that call-in surveys are the most frequently used method for estimating spotted hyaena density. However, calculating a site-specific calibration index to estimate response radius remains a significant challenge. This is highlighted here as over half the published studies were unable to conduct site-specific calibration experiments. In addition, some authors acknowledged that their calibration indices were unreliable and subject to wide confidence limits, owing to small sample sizes (e.g. Ogutu et al. 2005). Studies that could not conduct their own calibration experiment often used the calibration estimates provided in Mills et al. (2001). Some studies (e.g. Kirsten et al. 2017, Mohammed et al. 2019) cited Bauer (2007) as the basis for their calibration response, despite this study not conducting independent calibration experiments. A lack of animal habituation and logistical feasibility were often cited as key reasons for not undertaking the calibration experiment. Site-specific differences in habitat structure, competing carnivore densities and anthropogenic disturbance are likely to affect the local response rate of spotted hyaena. Subsequently, it is unlikely these frequently cited calibration indices are widely applicable and corresponding estimates are likely to be inaccurate.

Call-in surveys can also suffer from issues with habituation. For example, Belant et al. (2016) found that lions quickly become habituated to audio lures and habituation levels are not reduced by temporal and spatial variation in calls. As a result, calibration experiments may lower species response rates during survey periods. Habituation from repeated call-in surveys could also impact response rates over multi-season surveys, with a lower response rate potentially leading to incorrect assumptions of population decline over time (Belant et al. 2016). In addition, response rate to acoustic lures can also be reduced in areas where competing carnivore densities are skewed, or human activity is prevalent (Midlane et al. 2015, Kirsten et al. 2017). For example, areas of high lion density can limit the response rate of spotted hyaena (Kiffner et al. 2007, Kirsten et al. 2017), whilst cautious behaviour in areas of increased human disturbance can mean responding individuals are still potentially missed (Bauer 2007). As such, call-in surveys are often of limited value for multi-species surveys and can be inaccurate in low density areas, where population estimates are often most urgently required.

Call-in surveys are an effective tool for confirming the presence of spotted hyaena, and other large carnivores, in understudied regions where conservation efforts have been restricted. For example, the presence of spotted hyaena and lion in Dinder National Park, Sudan, were recently confirmed through call-in surveys (Mohammed et al. 2019). Furthermore, we recognise that call-in surveys are beneficial for obtaining population estimates in areas that are logistically challenging for other survey methods, such as camera trapping. This is highlighted in our review by the extensive use of call-in surveys in Ethiopia, where studies were conducted in peri-urban areas that would make the use of other survey techniques difficult (Yirga et al. 2014, 2017). Where call-in surveys are conducted, we suggest efforts are made to identify responding individuals (Trinkel 2009) and this method has recently been trialled for lions at call-in surveys (Western et al. 2022). However, we appreciate that identifying and documenting individuals at call-in surveys can be difficult with cautious animals and low visibility habitats (Bauer 2007). The collection of individual encounter data at call-in surveys would allow these data to be analysed in an SCR framework (Elliot & Gopalaswamy 2017), if surveys were repeated, thereby improving precision, avoiding double counting, and accounting for imperfect detection. Going forward, we recommend that call-in surveys either adopt an SCR approach to data collection or the survey method is employed as an initial step to confirm species presence.

Track counts were the second most popular method for estimating spotted hyaena density and were represented in a fifth of all published articles. Despite the popularity of track counts, derived population estimates often have wide confidence intervals and overstated precision (Elliot & Gopalaswamy 2017, Belant et al. 2019, Dröge et al. 2020). Low precision stems from unmodelled detection probability and oversimplification of the variance in the relationship between track density and true population density in the initial linear equation (Gopalaswamy et al. 2015, Hayward et al. 2015, Dröge et al. 2020). Dröge et al. (2020) argued that track counts do not comply with IUCN guidelines for population monitoring, as estimates may not be accurate enough to monitor population trends over time. In addition, track counts are reliant on standardised methods and assumptions. A key assumption is that all animals in the surveyed region have the same probability of detection, regardless of environmental (e.g. prey availability, interspecific competition) or anthropogenic (increased human activity) variability (Elliot & Gopalaswamy 2017, Henschel et al. 2020). In the case of spotted hyaena, this assumption is difficult to meet, with spotted hyaena behaviour known to be influenced by human activity (Boydston et al. 2003, Belton et al. 2016), areas of increased prey availability (Davis et al. 2021b) and competition with lions (Périquet et al. 2015). Violating the assumption of equal detection results in underestimation of density (Henschel et al. 2020), with knock on effects for conservation management decisions. It is, therefore, difficult to make a strong case for the future use of track count surveys to monitor spotted hyaena populations.

When analysed in an occupancy framework, track counts are efficient and cost-effective for gathering large carnivore presence/absence data. The limitations of track count data are better incorporated into model inference within occupancy models, as they account for imperfect detection and allow the use of covariates to model heterogeneity in site-use estimates (MacKenzie et al. 2017). Track count data have provided valuable insights into the distribution and drivers of site use for multiple large carnivore species in Africa (e.g. Everatt et al. 2014, Henschel et al. 2016, Petracca et al. 2019) and we encourage further use of occupancy models over index-calibrated density estimates (Dröge et al. 2020). However, efforts to estimate density from occupancy models are cautioned against due to variability in spatial use and home-range utilisation (Link et al. 2018, Rogan et al. 2019).

Distance sampling and total count methods were used in 12 studies to estimate spotted hyaena density or population size, mainly from Kenya and Tanzania, notable for their wide-open grasslands and high visibility (Durant et al. 2011, Farr et al. 2019). Whilst these surveys were able to estimate spotted hyaena density, these models are reliant on open habitats and the study species being reasonably habituated to human presence (Durant et al. 2011). Furthermore, studies that conducted distance sampling in Kenya and Tanzania were able to observe spotted hyaena during daylight hours (Durant et al. 2011). Often spotted hyaena are more nocturnal in areas of anthropogenic disturbance (Kolowski et al. 2007). As such, the wider applicability of distance sampling and total counts appears limited, with low capture success in areas of reduced visibility (e.g. dense woodlands) and/or high levels of anthropogenic disturbance making robust estimates unlikely, or requiring intensive survey effort (e.g. Thorn et al. 2010, Burton et al. 2011). With ≥ 60 observations recommended for robust estimates from distance sampling (Buckland et al. 2015), and reliable data on observed distance and angle necessary, these methods are not applicable across a considerable area of the spotted hyaena's geographical range. Certainly, if the aim of conservation practitioners is to compare population trends between sympatric carnivores, total counts are not a replicable model as the survey method is not viable for more cryptic species.

Total counts were used in long-term studies in Kenya and Tanzania to assess temporal changes in demography and population size (Höner et al. 2005, Green et al. 2018). Whilst direct counts employed for spotted hyaena did incorporate individual identification, allowing for more robust estimates of population size and avoidance of double counting, the time and effort required for direct counts is better combined with long-term behavioural studies (Gese 2001). As these long-term studies often know all individual spotted hyaenas within the study area at any given time (Höner et al. 2005), these sites may provide a unique opportunity to compare the efficacy and accuracy of SCR methods and other survey techniques (as per Rafiq et al. 2019).

Towards robust estimates with SCR

The development of SCR models has overcome several of the issues that limit inference from conventional survey methods for spotted hyaena. Most notably, the incorporation of detection probability and survey effort into SCR models improves the precision of estimates, compared to the wide error margins associated with index-calibrated methods (Broekhuis & Gopalaswamy 2016, Braczkowski et al. 2020a). However, a recent review by Green et al. (2020) found that some SCR density estimates from camera trapping lacked the necessary precision for monitoring population trends over time, with precision increasing when more individuals from the study population were captured. Inference from large carnivore survey methods is often hampered by naturally low densities and small sample sizes, leading to inaccuracies or cautious estimation of population size (Bauer 2007, Winterbach et al. 2016). As small sample sizes are common in spotted hyaena studies (e.g. Mohammed et al. 2019, Fouché et al. 2020, Davis et al. 2021a), by extracting the individual and spatial information from encounter history data, SCR models can be used to make effective use of limited datasets and produce statistically robust estimates (Royle et al. 2014, 2018). Furthermore, by accounting for the spatial location of captures, SCR models allow for estimation of fine-scale variation in density across landscapes (Gopalaswamy et al. 2012). Covariates of interest (e.g. prey density, illegal activity) can also be included in SCR models to investigate potential drivers of spatial distribution, providing a more comprehensive understanding of species density and distribution, thereby informing conservation management (Broekhuis & Gopalaswamy 2016, Ramesh et al. 2017).

Current preferred survey methods for spotted hyaena fail to capitalise on the benefits of individual identification, which can provide additional metrics for assessing population health (Braczkowski et al. 2020a). Information on animal movement, sex ratios and survival rates are embedded within individual encounter history data (Karanth et al. 2006). Key indicators of population decline, or recovery, can be assessed by monitoring parameters derived from individual identification (Harmsen et al. 2017, Braczkowski et al. 2020a). For example, Duangchantrasiri et al. (2016) used survival rates from repeated SCR surveys to determine the efficacy of increased law enforcement efforts for tiger (Panthera tigris) population recovery. Using sex-specific movement parameters and calculated sex ratios derived from SCR estimates, Braczkowski et al. (2020b) highlighted increased home range movements and male-biased sex ratios as early indicators of potential collapse in lion population numbers.

In addition, the SCR approach is flexible, lending itself to direct (e.g. search-encounter; Broekhuis & Gopalaswamy 2016) and indirect (e.g. camera trapping; Rich et al. 2019) methods, allowing researchers to select appropriate methodologies for their study site and population. As spotted hyaena occupy a diverse array of habitats and display varying behavioural responses to anthropogenic disturbance (Belton et al. 2016, Yirga et al. 2017), the flexibility of applying SCR models to individual encounter history data provides a standardised framework to monitor the species throughout their range. For example, the open grassland habitats of East Africa would be appropriate for search-encounter methods, as spotted hyaena are regularly encountered in daylight hours and distance sampling techniques are a viable approach. Furthermore, the dense vegetation and high levels of human disturbance documented in countries, such as Cameroon (Croes et al. 2011, Kirsten et al. 2017), would benefit from applying SCR models to frequently used indirect methods, like camera trapping or DNA sampling.

SCR methods have been widely applied to estimate felid densities across Africa, with camera trap surveys routinely used to obtain encounter history data (e.g. Brassine & Parker 2015, Kane et al. 2015, Balme et al. 2019). Spotted hyaena are widely distributed across sub-Saharan Africa and are likely caught as bycatch on camera trap surveys undertaken for sympatric carnivores (e.g. Williams et al. 2020). However, spotted hyaena population estimates are rarely reported from these surveys, despite data occasionally being used as covariates to make inferences about the behaviour or density of the focal species (e.g. Ramesh et al. 2017, Balme et al. 2019). Of the eight studies using SCR methods to estimate spotted hyaena density, seven of these studies had a multi-species focus (e.g. O'Brien & Kinnaird 2011, Rich et al. 2019, Davis et al. 2021a, Vissia et al. 2021). Thereby highlighting that SCR estimates for spotted hyaena can be obtained from camera trap grids with a multi-species focus. A key requirement of SCR is that individuals are captured at multiple detectors, to estimate the spatial scale parameter (σ) reliably (Dupont et al. 2021). This has led to recommendations in trap spacing, whereby trap spacing should be approximately two times σ, thus ensuring multiple traps are placed within an individuals' home range (Sollmann et al. 2012, Sun et al. 2014). As spotted hyaena home ranges are often larger than competing carnivores of interest (e.g. leopard, cheetah), sampling arrays designed for other large carnivores should not violate this assumption for spotted hyaena (Davis et al. 2021a, Braczkowski et al. 2022). Increased reporting of spotted hyaena density, from studies where they may have been previously overlooked, would be beneficial for the conservation management of spotted hyaena and interspecific competitors.

One of the limitations of an SCR approach is the cost of equipment and/or survey effort (Balme et al. 2009, Rafiq et al. 2019, Braczkowski et al. 2020a). We acknowledge that call-in surveys and track counts are often cheaper to conduct (Balme et al. 2009). However, the improvements in precision and benefits of individual identification for long-term population monitoring means that SCR-derived estimates can provide a greater balance of accuracy and cost-effectiveness (Balme et al. 2009, Braczkowski et al. 2020a). In addition, the multi-species SCR approaches of both Rich et al. (2019), using camera traps, and Rafiq et al. (2019), using tourist photographic records, demonstrate the ability of SCR techniques to survey multiple large carnivore species simultaneously, thereby optimising survey costs. In areas where there is high tourism demand, the citizen science approach of Rafiq et al. (2019) has shown that SCR estimates are obtainable at considerably reduced costs. Where a citizen science approach is not possible, a viable option for reducing camera trap survey costs is the utilisation of spatial partial identity models (Augustine et al. 2018) which can produce robust SCR estimates from partial identity samples obtained using single camera trap stations, instead of the conventional dual camera survey design (Davis et al. 2021a).

Sexing spotted hyaena, particularly from camera trap images, could be a potential constraint of SCR methods for estimating spotted hyaena density. Sex-specific variation in space use and movement result in differences in detection probability and, where possible, should be incorporated into candidate models (Sollmann et al. 2011). However, movement patterns between male and female spotted hyaena are known to differ (Boydston et al. 2005, Kolowski et al. 2007) and the species is notoriously difficult to sex (Dheer et al. 2022). Therefore, incorrect classification could result in skewed sex ratios and reduced accountability for heterogeneity in the observation process. Consequently, any attempt to incorporate sex-specific variation should be reliant on agreement between multiple trained observers (see Dheer et al. 2022 for recommendations on sexing) or, in the case of long-term research projects, the incorporation of maintained identification databases to ascertain sex. Alternatively, aging spotted hyaena based on their spot patterns and coat wear is relatively easy (e.g. age groupings in M'soka et al. 2016). As movement patterns also vary between age groups in spotted hyaena (Boydston et al. 2005), the incorporation of age classes into SCR models could improve model inference whilst accounting for variation in detection probability.

A key assumption of SCR models is that individual activity centres are uniformly and independently distributed over the state space (a region that incorporates the study area and a defined buffer which includes all potential activity centres for sampled individuals; Royle et al. 2014). However, this assumption is often violated in social, group-living carnivores (e.g. lions, wolves), potentially influencing precision and affecting the underlying state process model (Bischof et al. 2020). As spotted hyaena are social carnivores, living in clans ranging from 5-90 individuals (Holekamp et al. 2012), these assumptions represent a possible source of bias in SCR-derived estimates. Despite their close-knit social groups, spotted hyaena display fission-fusion dynamics, whereby clan members are often found alone or in smaller subgroups that are subject to compositional change, and, as such, individual encounter history data is often collected. For example, Stratford et al. (2019) found that 62% of recorded camera trap images of spotted hyaena were lone individuals. As individual movements represent a large proportion of encounter history data, the impact on precision and interval coverage will likely be reduced (see Bischof et al. 2020). Indeed, simulations by López-Bao et al. (2018) have shown that SCR models can provide reliable outputs for species violating assumptions of dependence in activity centres. However, further development of SCR models that can incorporate fission-fusion dynamics and group association into the state point process are required (Elliot & Gopalaswamy 2017, Bischof et al. 2020).


Call-in surveys and track counts are currently the preferred methods for estimating spotted hyaena density. However, the efficacy of these methods has recently been questioned for long-term population monitoring (Gopalaswamy et al. 2015, Dröge et al. 2020, Elliot et al. 2020). In comparison, SCR methods have the potential to monitor population change and assess trends in survival (by including individual identification and movement parameters), whilst incorporating environmental attributes (e.g. prey density) and demographic covariates (Karanth et al. 2006, Braczkowski et al. 2020b). Index-calibrated methods account for almost two-thirds of available spotted hyaena estimates but often overestimate density or are subject to wide confidence intervals, creating uncertainty in population size and stability (Braczkowski et al. 2020a, Dröge et al. 2020). We argue that there should be greater concern for the status of spotted hyaena populations across Africa and increased survey efforts for understudied populations. Similar to recent calls for greater utilisation of SCR methods in the conservation management of lion (Braczkowski et al. 2020a) and snow leopard (Sharma & Singh 2020) populations. Here we recommend adoption of an SCR approach to estimate spotted hyaena density, providing a unified framework for population monitoring across the species' geographic range.


R. Davis thanks Nottingham Trent University who supported this research through a postgraduate fee waiver. The authors declare that they have no conflicts of interest.

Author Contributions

All authors conceived the study. R. Davis performed the literature review and led the writing of the manuscript with input from all authors.

Data Availability Statement

This manuscript is available online in the Nottingham Trent University repository (



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Supplementary online material

Table S1. Summary of the 60 publications found in the literature search that reported spotted hyaena (Crocuta crocuta) population estimates and the survey methods used. The survey method used is denoted as: call in (CI), track count (TC), total count (TO), spatial capture-recapture (SCR), capture-recapture (CR), distance sampling (DS). The country and region of Africa where the study was conducted is also presented.

Table S2. Literature reviewed on leopard (Panthera pardus) population estimates and methods used to estimate density in sub-Saharan Africa between 2000 and 2022.

This is an open access article under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits use, distribution and reproduction in any medium provided the original work is properly cited.
Robert S. Davis, Louise K. Gentle, Emma L. Stone, Antonio Uzal, and Richard W. Yarnell "A review of spotted hyaena population estimates highlights the need for greater utilisation of spatial capture-recapture methods," Journal of Vertebrate Biology 71(22017), 22017.1-15, (13 May 2022).
Received: 18 March 2022; Accepted: 22 April 2022; Published: 13 May 2022

call-in survey
population monitoring
survey methods
track counts
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