TY - JOUR
AU - Hall, Grant
AB - Abstract The Kruger National Park (KNP) is home to the last genetically viable, minimally managed population of African wild dogs (Lycaon pictus, wild dogs) in South Africa. Until 2004, this population remained stable, but since has been declining. In this study, we aimed to improve our understanding of the ecology of KNP wild dogs by estimating the relative contribution of different prey types to their diet across landscape types. Based on a Bayesian mixing model, we assessed wild dog diet and foraging preferences using stable isotope analysis. We sampled 73 individuals from 40 packs found in six different landscape types. In thickets, packs predominantly prey on small browsing and mixed-feeding species (accounting for ~73% of their diet), but occasionally hunt large grazers (~24%) and large browsers (~3%). In open landscape types where lions (Panthera leo) are more or less absent, such as in the Lowveld sour bushveld, wild dogs prey on large browsers and large grazers (~67%). Our results demonstrate that KNP wild dogs occupy a broader ecological niche than previously thought, with small browsers forming an integral part of their diet. We also present the first data describing differences in wild dog diet–tissue discrimination factors for tail hair and whiskers compared to respective stable nitrogen (δ15N) and carbon (δ13C) values obtained from feces of captive wild dogs, as well as from those of South Africa’s broader managed metapopulation. While these data should be considered preliminary, we suggest that until wild dog diet–tissue discrimination factors are calculated through a controlled feeding study, the discrimination factors calculated for the gray wolf (Canis lupus) should be used for wild dog-related isotope studies, rather than the often cited values for red foxes (Vulpes vulpes). African wild dog, diet, feces, hair, isotopic discrimination, Kruger National Park, South Africa, stable isotope analysis, trophic ecology, whiskers African wild dogs (Lycaon pictus), hereafter wild dogs, are the most endangered carnivore in South Africa, and the second most endangered carnivore on the African continent (Davies-Mostert et al. 2016). Due to an increasing human population, wild dogs have become patchily distributed across a fragmented landscape of conservation areas, with vast terrain previously used by wild dogs being transformed into agricultural, urban, and game-breeding areas (Mills et al. 1998; Creel and Creel 2002; Davies-Mostert et al. 2015). Major anthropogenic threats to the species include high rates of snaring by poachers, introduction of diseases from domesticated animals, road accidents, and direct persecution derived from conflict with wildlife farmers (Woodroffe et al. 2007a; Gusset et al. 2008; Woodroffe and Sillero-Zubiri 2020). Compounding these issues is that wild dogs naturally live in low densities, even when prey species are abundant, and are in direct competition with other intraguild predators such as lions (Panthera leo) and spotted hyenas (Crocuta crocuta; Creel and Creel 2002; Woodroffe et al. 2007a). This makes it difficult for packs to persist in most protected areas that also maintain high densities of other predator species. In these areas, wild dogs need to survive by resource partitioning and avoiding lions and spotted hyenas (Mills and Gorman 1997; Miller et al. 2013; Swanson et al. 2014), with the exception of Hluhluwe-iMfolozi Park (HiP) in South Africa, where both wild dogs and lions have maintained consistently high densities (Somers et al. 2017; Marneweck 2020). In South Africa, efforts to minimize the extinction risk to wild dogs resulted in the wild dog managed metapopulation program being implemented in 1998. This program is aimed at creating a second, genetically viable population of wild dogs outside of the last viable, relatively unmanaged population occurring within the Kruger National Park (KNP; Mills et al. 1998; Davies-Mostert et al. 2015; Tensen et al. 2019). Such management involves periodic translocation of wild dogs between game reserves that host packs to mimic natural dispersal events and gene flow as closely as possible, and avoid deleterious effects associated with a genetic bottleneck (Mills et al. 1998; Gusset et al. 2008). Overall, this approach has been successful in increasing the number of wild dogs in South Africa, with the managed metapopulation growing from 17 individuals in 1998 to 202 in 2005 (Davies-Mostert et al. 2015). To date, the South African population remains small but somewhat stable at ~500 individuals (Nicholson et al. 2020), while maintaining levels of genetic variability comparable with natural systems, with the current managed metapopulation maintaining ~95% of its original heterozygosity (Tensen et al. 2019). Of particular conservation concern is that wild dogs in the KNP, a population considered of global conservation and genetic significance (Creel et al. 2004; Tensen et al. 2016; Kuiper et al. 2018), are declining at ~3% per annum (Nicholson et al. 2020). Nicholson et al. (2020), indicating that management actions to assist the KNP wild dog population be considered. Information on foraging preferences can aid in understanding species-specific animal behavior and physiological processes, as well as assist in structuring and implementing relevant conservation measures (Webster et al. 2002; West et al. 2006; Crawford et al. 2008). For carnivores in particular, dietary information has traditionally been gathered by means of gut content analysis (Pezzo et al. 2003; Valdmann et al. 2005), from kill sites (Marucco et al. 2008; Webb et al. 2008), and the investigation of fecal contents for the remains of prey species (Darimont et al. 2004; Latham et al. 2013). Data derived from these methods have become the primary source of knowledge on the trophic ecology of African wild dogs (Creel and Creel 2002; van Dyk and Slotow 2003; Woodroffe et al. 2007b; Davies-Mostert et al. 2013). Such approaches are not always logistically feasible, because they require long-term field excursions (Avenant and Nel 2002), which become difficult when investigating animals that occupy large home ranges (Bothma and Coertze 2004), such as wild dogs (Creel and Creel 2002). The invasive nature of gut content analysis also is not appropriate when study species are of conservation concern (Darimont and Reimchen 2002), and observations of fecal contents may not always be reliable because prey items differ in their digestibility (Lockie 1959; Darimont and Reimchen 2002). Stable isotope analysis (SIA) is a useful tool in trophic ecology and has become increasingly popular as a means to complement traditional methods used to study community structure and predator–prey interactions (Roth and Hobson 2000; Codron et al. 2007; McLaren et al. 2015). Ratios of the stable isotopes of nitrogen (15N/14N or δ15N) and carbon (13C/12C or δ 13C) are used frequently in such studies. Stable nitrogen isotope ratios change predictably with trophic level (e.g., isotopically light 14N is excreted in urine; DeNiro and Epstein 1981; Peterson and Fry 1987), while stable carbon isotope ratios show very little change with trophic position and reflect sources of primary productivity (DeNiro and Epstein 1978; Vogel 1978). SIA is advantageous when examining metabolically inert tissue types (such as whiskers, hair, feathers, and nails), because dietary information from all assimilated food sources over the period of tissue growth is integrated (Roth and Hobson 2000; Voigt et al. 2014; McLaren et al. 2015). Correctly used, SIA can be used to construct relatively nonbiased links between primary and secondary consumers that otherwise are often challenging to demonstrate (Codron et al. 2007). A critical limitation to such analyses is that there currently is little information regarding species-specific isotopic discrimination factors, which account for shifts in stable isotope ratios once dietary sources are assimilated into different consumer tissue types (McLaren et al. 2015). Based on data derived from direct observations of hunts, as well as scat analysis, wild dogs in the KNP have been reported to act as rate-maximizing optimal foragers, specializing on impala (Aepyceros melampus) and kudu (Tragelaphus strepsiceros; Reich 1981; Mills 1992; Mills and Gorman 1997) and showing an affinity for thicket and woodland landscape types (Mills and Biggs 1993). Wild dog packs in another South African metapopulation managed reserve (HiP) use dense landscape types to ambush and trap prey, making smaller browser species among the most profitable to be killed (Krüger et al. 1999). Similarly, wild dogs in northern Kenya have been shown to feed predominantly on small browsing species such as Kirk’s dik-dik (Madoqua kirkii), with this species making up ~70% of their diet outside of protected areas (Woodroffe et al. 2007b). In this study, we aimed to improve our understanding of the trophic ecology of KNP wild dog packs by assessing the relative contribution of different prey groups to their diet across landscape types. These landscape types have been defined based on specific geomorphology, soil composition, climate, vegetation pattern, and associated fauna and form defined units for management practices in the KNP (Gertenbach 1983). This was undertaken using a Bayesian isotope mixing model based on the isotopic analysis of tail hair samples from 73 individuals across 40 packs collected between January 2009 and December 2018. To our knowledge, there currently is only one published study using SIA as it pertains to the trophic ecology of wild dogs (Crossey et al. 2020); in the current study, we thus present preliminary estimates of wild dog diet–tissue isotopic discrimination factors. These estimates, based on samples collected from captive wild dogs, as well as from animals forming part of South Africa’s managed metapopulation outside of the KNP, were calculated using feces as a proxy of dietary content not integrated by individuals (Crawford et al. 2008), and drawing comparisons between the fecal values obtained with both tail hair and whisker δ15N and δ13C values, respectively. We predicted that wild dog diet would differ between landscape types, as the profitability of hunting prey species varies with environmental factors, and that wild dog diet–tissue discrimination factor estimates would be similar to those calculated for gray wolves (Canis lupus; McLaren et al. 2015), the closest phylogenetic relative of wild dogs (Gopalakrishnan et al. 2018) for which we could find published data. Materials and Methods Ethical clearance and sampling permits. —This study was undertaken with the approval of the University of Pretoria Animal Ethics Committee (ethics clearance number: EC015-18), and followed the guidelines of the American Society of Mammalogists (ASM) pertaining to ethical research involving live animals (Sikes et al. 2016). Hair, whisker, and fecal samples from wild dogs within the managed metapopulation were obtained with permission from the Endangered Wildlife Trust (EWT; data sharing agreement number: 270). All hair samples obtained from the SANParks Biobank at Skukuza in KNP were acquired through a data sharing agreement, as well as with a Threatened or Protected Species Ordinary Permit (permit number: O 27732). Sampling sites and study animals. —The KNP (covering ~2,000,000 ha) is situated in the Lowveld semi-arid savanna in the north-eastern corner of South Africa (Fig. 1). At ~300 m above sea level, rainfall is highly seasonal and mainly occurs during the austral summer (October–March), with April–November being dry. Mean annual rainfall varies from between 500 and 700 mm in the south of the park, to between 300 and 500 mm in the north (Venter et al. 2003). We obtained tail hair samples from 73 wild dogs across 40 different packs (Table 1), which were stored at the SANParks Skukuza Biobank, KNP. These samples represent the period between January 2009 and December 2018 and were collected opportunistically throughout the park. Global positioning system (GPS) coordinates were recorded for each wild dog sampled. Wild dogs are cooperative hunters, hunting and feeding as a group (Creel and Creel 2002), and individuals within packs feed on the same prey items. We therefore considered packs, rather than individuals, as our sampling units. Packs were determined to be independently sampled based on the collection of samples from known individuals. Each wild dog pack sampled was assigned to a landscape type (Gertenbach 1983; Table 1) based on the GPS coordinates indicating their sampling location (Fig. 1). Table 1. African wild dog (Lycaon pictus) tail hair carbon (δ13CVPDB) and nitrogen (δ15NAir) stable isotope values (mean ± SD) calculated for 40 packs sampled in different landscape types (Gertenbach 1983) in the Kruger National Park, South Africa Landscape type . Wild dogs sampled (n) . Packs sampled (n) . Pack tissue data . . . . . . . δ13CVPDB (‰) . . δ15NAir (‰) . . . . . Mean . SD . Mean . SD . Mixed Combretum/Terminalia sericea woodland 25 12 −14.7 2.0 +11.4 0.6 Lowveld sour bushveld 7 5 −15.1 0.7 +9.2 0.9 Malelane mountain bushveld 10 8 −15.2 1.2 +10.6 1.2 Thickets of the Sabie and Crocodile River 27 13 −15.2 1.0 +11.5 0.7 Sclerocarya birrea caffra/Acacia nigrescens savanna 1 1 −16.5 — +11.7 — Phalaborwa sandveld 3 1 −16.3 0.2 +12.3 0.2 Landscape type . Wild dogs sampled (n) . Packs sampled (n) . Pack tissue data . . . . . . . δ13CVPDB (‰) . . δ15NAir (‰) . . . . . Mean . SD . Mean . SD . Mixed Combretum/Terminalia sericea woodland 25 12 −14.7 2.0 +11.4 0.6 Lowveld sour bushveld 7 5 −15.1 0.7 +9.2 0.9 Malelane mountain bushveld 10 8 −15.2 1.2 +10.6 1.2 Thickets of the Sabie and Crocodile River 27 13 −15.2 1.0 +11.5 0.7 Sclerocarya birrea caffra/Acacia nigrescens savanna 1 1 −16.5 — +11.7 — Phalaborwa sandveld 3 1 −16.3 0.2 +12.3 0.2 Open in new tab Table 1. African wild dog (Lycaon pictus) tail hair carbon (δ13CVPDB) and nitrogen (δ15NAir) stable isotope values (mean ± SD) calculated for 40 packs sampled in different landscape types (Gertenbach 1983) in the Kruger National Park, South Africa Landscape type . Wild dogs sampled (n) . Packs sampled (n) . Pack tissue data . . . . . . . δ13CVPDB (‰) . . δ15NAir (‰) . . . . . Mean . SD . Mean . SD . Mixed Combretum/Terminalia sericea woodland 25 12 −14.7 2.0 +11.4 0.6 Lowveld sour bushveld 7 5 −15.1 0.7 +9.2 0.9 Malelane mountain bushveld 10 8 −15.2 1.2 +10.6 1.2 Thickets of the Sabie and Crocodile River 27 13 −15.2 1.0 +11.5 0.7 Sclerocarya birrea caffra/Acacia nigrescens savanna 1 1 −16.5 — +11.7 — Phalaborwa sandveld 3 1 −16.3 0.2 +12.3 0.2 Landscape type . Wild dogs sampled (n) . Packs sampled (n) . Pack tissue data . . . . . . . δ13CVPDB (‰) . . δ15NAir (‰) . . . . . Mean . SD . Mean . SD . Mixed Combretum/Terminalia sericea woodland 25 12 −14.7 2.0 +11.4 0.6 Lowveld sour bushveld 7 5 −15.1 0.7 +9.2 0.9 Malelane mountain bushveld 10 8 −15.2 1.2 +10.6 1.2 Thickets of the Sabie and Crocodile River 27 13 −15.2 1.0 +11.5 0.7 Sclerocarya birrea caffra/Acacia nigrescens savanna 1 1 −16.5 — +11.7 — Phalaborwa sandveld 3 1 −16.3 0.2 +12.3 0.2 Open in new tab Fig. 1. Open in new tabDownload slide — Landscape types of the Kruger National Park, South Africa (Gertenbach 1983) in which African wild dog (Lycaon pictus) packs were sampled. Star symbols indicate the location where each pack was sampled and lines indicate the presence of tarred roads in the Park. Fig. 1. Open in new tabDownload slide — Landscape types of the Kruger National Park, South Africa (Gertenbach 1983) in which African wild dog (Lycaon pictus) packs were sampled. Star symbols indicate the location where each pack was sampled and lines indicate the presence of tarred roads in the Park. We carried out a preliminary investigation into wild dog diet–tissue discrimination factors using opportunistically collected whisker, tail hair, and fecal samples from 13 wild dogs immobilized either for translocation or routine veterinary purposes from April to July 2018. These individuals were all sampled in South Africa, and included captive wild dogs from the Johannesburg Zoo (n = 3), Gauteng Province; temporarily captive wild dogs being readied for release into larger reserves in short-term holding facilities at Zimanga Private Game Reserve (n = 4) and Tembe Elephant Park (n = 1; both in KwaZulu Natal Province); and free-ranging wild dogs from Madikwe Game Reserve (n = 1), North West Province, and HiP (n = 4) in KwaZulu Natal. To ensure a minimally invasive means of sampling, samples were collected by trimming the tail hair/whisker as close to the base of the follicle as possible. Approximately 5 g of fecal material from each of these wild dogs was collected and frozen within 24 h post-defecation at −20°C until further processing. Stable isotope analysis. —All frozen fecal samples were lyophilized, then ground and sieved using a metal strainer (mesh size: 20 μm). This was undertaken to separate fecal powder from undigested material, such as large pieces of bone, hair, and grass (Fiess et al. 1999), which potentially could interfere with fecal stable isotope values. Whisker and tail hair samples were cleaned by suspending them in a 2:1 chloroform: ethanol solution in an ultrasonic bath for 15 min. The solvent then was decanted, and samples dried overnight at 70°C. Fecal powder, tail hair, and whiskers (whiskers sampled from base to tip, up to a maximum of six segments) were weighed as aliquots of ~0.4–0.6 mg using a micro-balance (Mettler Toledo Mk5; Mettler Toledo, Columbus, Ohio), and placed in tin capsules precleaned in toluene. Samples were combusted at 1,020°C in an elemental analyzer (Flash EA1112 Series), coupled to a Delta V Plus stable light isotope ratio mass spectrometer via a ConFlo IV system (all equipment supplied by Thermo Fisher, Bremen, Germany). Two laboratory running standards and a blank sample were run after every 11 unknown samples (Merck Gel and DL-Valine). These running standards are calibrated against international standards (IAEA-CH-3, IAEA-CH-6, IAEA-CH-7, IAEA N-1, IAEA N-2, IAEA NO-3) produced by the International Atomic Energy Association (IAEA), and NBS22 (produced by the US National Bureau of Standards). The precision for δ15N was <0.05‰ and <0.06‰ for δ13C. All results are referenced to Vienna Pee Dee Belemnite for carbon isotope values, and to air for nitrogen isotope values (Bond and Hobson 2012). Results are expressed in delta notation using a per mille scale (‰) using the following standard equation (Coplen 2011): δX=[(Rsample/Rstandard)−1], where X =  15N or 13C and R represents 15N/14N or 13C/12C, respectively. Statistical analyses. —Values for δ15N and δ13C from tail hair and feces were obtained from samples run in duplicate. Whisker samples yielded three to six subsampled segments depending on the whisker length/density. We used a one-way ANOVA (Scheffe 1999), followed by Holm–Sidak’s pair wise t-tests (Wenge and Romano 2007) to test for differences in δ15N and δ 13C signatures between tail hair, whiskers, and fecal samples from the same individual. Preliminary indicators for wild dog diet–tissue discrimination factors were calculated using the mean ± SD of δ 13C and δ 15N values calculated for each sample type, with mean δ 13C and δ 15N for tail hair and whiskers subtracted from respective fecal values (as a proxy of diet; Crawford et al. 2008), to give isotopic diet–tissue discrimination estimates (Table 2). Differences in mean δ 15N and δ13C tail hair values from wild dog packs sampled in different landscape types in the KNP also were tested using a one-way ANOVA (Scheffe 1999), followed by Holm–Sidak’s pair wise t-tests (Wenge and Romano 2007) to isolate which values from specific landscape types significantly differed from others. Statistical significance was set at alpha (α) = 0.05 and inferred at P <0.05. We computed these statistical analyses using algorithms in SigmaPlot version 14.0 (Systat Software Inc. 2017). Table 2. Converted muscle estimate carbon (δ 13CVPDB) and nitrogen (δ 15NAir) stable isotope values (mean ± SD) calculated for presumptive prey species of African wild dog (Lycaon pictus) packs in the Kruger National Park, South Africa Prey species . Hair samples (n) . Converted muscle estimates . . . . . . δ13CVPDB (‰) . . δ15NAir (‰) . . . . Mean . SD . Mean . SD . Large browsers 26 −24.6 1.2 +5.8 2.0 Small browsers 8 −24.8 0.9 +7.2 2.4 Large grazers 100 −12.2 0.8 +6.7 1.1 Aepyceros melampus 42 −17.1 3.3 +8.0 2.1 Prey species . Hair samples (n) . Converted muscle estimates . . . . . . δ13CVPDB (‰) . . δ15NAir (‰) . . . . Mean . SD . Mean . SD . Large browsers 26 −24.6 1.2 +5.8 2.0 Small browsers 8 −24.8 0.9 +7.2 2.4 Large grazers 100 −12.2 0.8 +6.7 1.1 Aepyceros melampus 42 −17.1 3.3 +8.0 2.1 Data for prey species were obtained from hair samples collected and analyzed by Codron et al. (2007) and categorized into: large browsers (southern giraffe, Giraffa camelopardalis, and kudu, Tragelaphus strepsiceros); small browsers (bushbuck, Tragelaphus scriptus; steenbok, Raphicerus campestris; and gray duiker, Sylvicapra grimmia; and large grazers (Burchell’s zebra, Equus burchellii; African buffalo, Syncerus caffer; blue wildebeest, Connochaetes taurinus; waterbuck, Kobus ellipsiprymnus; reedbuck, Redunca arundinum; and sable antelope, Hippotragus niger). Open in new tab Table 2. Converted muscle estimate carbon (δ 13CVPDB) and nitrogen (δ 15NAir) stable isotope values (mean ± SD) calculated for presumptive prey species of African wild dog (Lycaon pictus) packs in the Kruger National Park, South Africa Prey species . Hair samples (n) . Converted muscle estimates . . . . . . δ13CVPDB (‰) . . δ15NAir (‰) . . . . Mean . SD . Mean . SD . Large browsers 26 −24.6 1.2 +5.8 2.0 Small browsers 8 −24.8 0.9 +7.2 2.4 Large grazers 100 −12.2 0.8 +6.7 1.1 Aepyceros melampus 42 −17.1 3.3 +8.0 2.1 Prey species . Hair samples (n) . Converted muscle estimates . . . . . . δ13CVPDB (‰) . . δ15NAir (‰) . . . . Mean . SD . Mean . SD . Large browsers 26 −24.6 1.2 +5.8 2.0 Small browsers 8 −24.8 0.9 +7.2 2.4 Large grazers 100 −12.2 0.8 +6.7 1.1 Aepyceros melampus 42 −17.1 3.3 +8.0 2.1 Data for prey species were obtained from hair samples collected and analyzed by Codron et al. (2007) and categorized into: large browsers (southern giraffe, Giraffa camelopardalis, and kudu, Tragelaphus strepsiceros); small browsers (bushbuck, Tragelaphus scriptus; steenbok, Raphicerus campestris; and gray duiker, Sylvicapra grimmia; and large grazers (Burchell’s zebra, Equus burchellii; African buffalo, Syncerus caffer; blue wildebeest, Connochaetes taurinus; waterbuck, Kobus ellipsiprymnus; reedbuck, Redunca arundinum; and sable antelope, Hippotragus niger). Open in new tab Isotopic discrimination factor estimates. —Our preliminary isotopic discrimination results, calculated from mean δ13C and δ15N values for tail hair subtracted from respective fecal values, suggest that there is an ~ +4.4‰ difference in δ13C, and +1.6‰ difference in δ15N values between wild dog feces and tail hair. However, these results are not fully representative of total dietary information integrated over the period of tail hair growth, with feces only exhibiting a snapshot of short-term dietary information. In addition, the growth rates for wild dog whiskers and tail hair are currently unknown, and these data should therefore be interpreted with caution. We could also not account for differences in the isotopic composition of wild dog diet across geographic locations, as well as any variation in diet between individuals sampled in this portion of the study (eight of which we sampled in captivity and five of which were free-ranging). For this reason, we used isotopic discrimination factors from the closest phylogenetic relative of the wild dog (the gray wolf), for which published data were available (McLaren et al. 2015). Isotopic diet–tissue discrimination factors of +4.3‰ for δ13C and +3.1‰ for δ15N, as calculated for wolf hair over a 4-month controlled feeding study, were thus applied for respective tail hair values in our study (McLaren et al. 2015). Stable Isotope Analysis in R. —In order to assess the relative contribution of prey types to wild dog diet across landscape types in the KNP, we used the package Stable Isotope Analysis in R (SIAR) version 4.1.3 (Parnell and Jackson 2011) in the software program R (R Core Team 2016). The SIAR package fits data on animal isotopes to their dietary habits using a Bayesian isotope mixing model. This is based on a Gaussian likelihood, solving for the most likely set of dietary proportions when given the isotopic ratios in a set of possible food sources and a set of consumers (Parnell and Jackson 2011). Upper and lower estimates for the proportion of prey groups contributing to wild dog diet in the different landscape types are reported with a 95% confidence interval, and illustrations were produced using SigmaPlot version 14.0 (Systat Software, San Jose, California). Published data for different KNP prey species, classified into isotopically distinct (δ13C and δ15N) prey groups, characterizing the isotopic composition of a predator’s diet, were used in the development of our model (Table 2). These data were obtained from Codron et al. (2007), with prey groups from KNP categorized as follows: (i) C3 large-bodied browsers (>100 kg, comprising southern giraffe, Giraffa camelopardalis; and kudu, Tr. strepsiceros); (ii) C3 small-bodied browsers (<100 kg, including bushbuck, Tragelaphus scriptus; steenbok, Raphicerus campestris; and gray duiker, Sylvicapra grimmia); (iii) C4 large-bodied grazers (Burchell’s zebra, Equus burchellii; African buffalo, Syncerus caffer; blue wildebeest, Connochaetes taurinus; waterbuck, Kobus ellipsiprymnus; reedbuck, Redunca arundinum; and sable antelope, Hippotragus niger); and (iv) C3/C4 mixed-feeding impala, Ae. melampus, as the most abundant and predominant mixed-feeding herbivore found in the KNP. For the SIAR, we calculated mean and SD δ 13C and δ15N values for wild dog packs sampled in different landscape types of the KNP (Gertenbach 1983). This resulted in wild dog packs being grouped into one of the following six separate categories: (i) those sampled in Phalaborwa sandveld (n = 1); (ii) mixed Combretum/Terminalia sericea woodland (mixed woodlands; n = 12); (iii) Lowveld sour bushveld (n = 5); (iv) Malelane mountain bushveld (n = 8); (v) thickets of the Sabie and Crocodile River (thickets; n = 13); and (vi) Sclerocarya birrea caffra/Acacia nigrescens savanna (savanna; n = 1; Gertenbach 1983; Table 1; Fig. 1). Packs from the Phalaborwa sandveld and Sc. birrea caffra/Ac. nigrescens savanna were excluded from analyses in SIAR as the sample size was too small (n = 1 in each case) to estimate intra group variance. Results We found statistically significant differences between both the δ15N (F2,36 = 8.49; n = 13; P < 0.05) and δ13C (F2,36 = 50.49; n = 13; P < 0.05) values obtained from the different biological sample types. Tail hair and whiskers did not significantly differ either in their δ15N values (t11 = 0.33; n = 13; P > 0.05) or δ13C values (t11 = 0.22; n = 13; P > 0.05), but had significantly different δ13C (tail hair: t11 = 8.81; n = 13; P < 0.05; whisker: t11 = 8.59; n = 13; P < 0.05) and δ15N (tail hair: t11 = 3.72; n = 13; P < 0.05; whisker: t11 = 3.39; n = 13; P < 0.05) values compared to the respective values obtained from fecal matter. Our preliminary wild dog isotopic diet–tissue discrimination estimates were +4.4‰ and +4.3‰ for tail hair and whisker δ13C values, respectively, as well as +1.6‰ and +1.4‰ for respective δ15N values (Table 3). Table 3. Calculated carbon (δ 13CVPDB) and nitrogen (δ 15NAir) stable isotope values (mean ± SD) for different biological tissue types (feces, tail hair, and whiskers) and accompanying diet–tissue discrimination factor estimates for 13 African wild dogs (Lycaon pictus) from South Africa Biological tissue . n . δ 13CVPDB (‰) . . δ 15NAir (‰) . . δ 13C diet–tissue discrimination factor estimate . δ15N diet–tissue discrimination factor estimate . . . Mean . SD . Mean . SD . . . Feces 13 −17.6 1.6 +11.0 1.2 — — Tail hair 13 −13.2 1.3 +12.6 1.1 +4.4 +1.6 Whiskers 13 −13.3 0.8 +12.4 0.9 +4.3 +1.4 Biological tissue . n . δ 13CVPDB (‰) . . δ 15NAir (‰) . . δ 13C diet–tissue discrimination factor estimate . δ15N diet–tissue discrimination factor estimate . . . Mean . SD . Mean . SD . . . Feces 13 −17.6 1.6 +11.0 1.2 — — Tail hair 13 −13.2 1.3 +12.6 1.1 +4.4 +1.6 Whiskers 13 −13.3 0.8 +12.4 0.9 +4.3 +1.4 Open in new tab Table 3. Calculated carbon (δ 13CVPDB) and nitrogen (δ 15NAir) stable isotope values (mean ± SD) for different biological tissue types (feces, tail hair, and whiskers) and accompanying diet–tissue discrimination factor estimates for 13 African wild dogs (Lycaon pictus) from South Africa Biological tissue . n . δ 13CVPDB (‰) . . δ 15NAir (‰) . . δ 13C diet–tissue discrimination factor estimate . δ15N diet–tissue discrimination factor estimate . . . Mean . SD . Mean . SD . . . Feces 13 −17.6 1.6 +11.0 1.2 — — Tail hair 13 −13.2 1.3 +12.6 1.1 +4.4 +1.6 Whiskers 13 −13.3 0.8 +12.4 0.9 +4.3 +1.4 Biological tissue . n . δ 13CVPDB (‰) . . δ 15NAir (‰) . . δ 13C diet–tissue discrimination factor estimate . δ15N diet–tissue discrimination factor estimate . . . Mean . SD . Mean . SD . . . Feces 13 −17.6 1.6 +11.0 1.2 — — Tail hair 13 −13.2 1.3 +12.6 1.1 +4.4 +1.6 Whiskers 13 −13.3 0.8 +12.4 0.9 +4.3 +1.4 Open in new tab Stable isotope results (mean ± SD) obtained from wild dogs sampled in the different KNP landscape types are summarized in Table 1. Our results showed significant differences in δ15N (F3 = 11.62; n = 38; P < 0.01) values between wild dog packs found in the different landscape types of KNP, but δ13C values did not differ significantly for wild dog packs across landscape types (F3 = 0.41; n =38; P > 0.05). Differences in δ15N values were only significant for wild dogs from Lowveld sour bushveld versus those occurring in mixed woodlands (t4,11 = 5.13; n = 17; P < 0.05), thickets (t4,12 = 5.41; n = 18; P < 0.01), and Malelane mountain bushveld (t4,6 = 3.03; n = 12; P < 0.05), with all other pair wise comparisons yielding P >0.05. Estimates for the proportion of prey groups contributing to wild dog diet in the different landscape types are reported with a 95% confidence interval (Fig. 2). Our results show distinct differences in wild dog diet across KNP landscape types. Differences in the proportion of each prey group related to the landscape type within which wild dogs were sampled were largest for: (i) large browsers in thickets (~3%) and mixed woodlands (1–3%) versus Lowveld sour bushveld (31–33%); (ii) small browsers, which were lowest in the Lowveld sour bushveld (21–23%) and highest in thickets (39–40%); (iii) large grazers, which varied from 7–8% for wild dogs sampled in mixed woodland to 23–24% in both thicket and Malelane mountain bushveld, respectively; and (iv) impala, which was lowest for packs sampled in Lowveld sour bushveld (15–17%) and highest in mixed woodland (61–62%; Fig. 2). Fig. 2. Open in new tabDownload slide —Stacked bar plots reporting the upper and lower 95% confidence intervals of the estimated proportion of prey species consumed by African wild dog (Lycaon pictus) packs sampled in different landscape types in the Kruger National Park, South Africa (Gertenbach 1983). Fig. 2. Open in new tabDownload slide —Stacked bar plots reporting the upper and lower 95% confidence intervals of the estimated proportion of prey species consumed by African wild dog (Lycaon pictus) packs sampled in different landscape types in the Kruger National Park, South Africa (Gertenbach 1983). Discussion Diet–tissue isotopic discrimination estimates. —McLaren et al. (2015) demonstrated that gray wolf whiskers and guard hairs show stable carbon diet-to-tissue isotopic discrimination of +4.31‰ and + 4.25‰, with nitrogen diet–tissue isotopic discrimination factors of +3.05‰ and +3.09‰, respectively. Our preliminary wild dog diet–tissue discrimination estimates for carbon closely match those of gray wolves for both whiskers (+4.3‰) and hair (+4.4‰). The discrimination factors for nitrogen calculated in our study (+1.4 for whiskers and +1.6 for tail hair) are two times lower than those obtained for gray wolves. As diet composition ultimately determines the source of amino acids for tissue synthesis, and subsequent carbon and nitrogen discrimination factors (Martinez del Rio et al. 2009; Parng et al. 2014), we speculate that variation in lipid proportion and protein quality in our wild dogs’ diet may explain the differences in the respective nitrogen discrimination factors (Caut et al. 2009). We also consider that there are species-specific differences in the physiological processes that affect isotopic fractionation. In particular, variability in δ 15N values is known to be greater for individuals from different species raised on the same diet, than for individuals of the same species raised on a different diet (DeNiro and Epstein 1981). The selection of discrimination factors can influence estimated diet proportions when using Bayesian mixing models (Derbridge et al. 2012). As a result, taxon-, tissue-, and diet-specific discrimination factors for each study species should be determined through controlled feeding studies (Caut et al. 2009; McLaren et al. 2015). Based on the close phylogenetic relationship between wild dogs and gray wolves (Gopalakrishnan et al. 2018), we are confident in our choice of isotopic discrimination factors for gray wolves, over the commonly cited values for captive red foxes (Vulpes vulpes; Roth and Hobson 2000). We therefore suggest that gray wolf isotopic discrimination factors (McLaren et al. 2015) be used for future studies attempting to address wild dog-based SIA questions, at least until wild dog-specific diet–tissue discrimination factors can be more reliably calculated. Dietary differences. —It has been suggested that wild dogs in KNP mainly prey upon impala and kudu (the most abundant medium-to-large sized prey in the park; Reich 1981; Mills 1992; Mills and Gorman 1997). Our results suggest that prey preferences of wild dogs in KNP differ significantly across their used landscape types. Marneweck et al. (2019) calculated home range sizes for 19 of the packs included in our study over the same period as our sampling was undertaken. The data presented by these authors show that the packs we sampled maintain home ranges that include several different landscape types outlined by Gertenbach (1983). It may therefore be the case that the landscape type within which we sampled wild dog packs is not the only landscape type used by these packs while hunting. Despite the possibility for this overlap, and the high likelihood that packs spend an unequal amount of time either moving through or hunting in different landscape types, our results indicate significant variation in the diet of wild dogs depending on the landscape type where they were sampled. Growth and isotopic turnover rates for wild dog hair have not been investigated. However, McLaren et al. (2015) demonstrated that gray wolf hair does not grow continuously, growing more slowly in the period from 60 to 120 days than from days 0 to 60 days. In addition, these authors demonstrated that gray wolf hair grown over a 60-day period exhibited significantly different δ13C values compared to day 0, but a significant difference could not be detected between days 60 and 120. Assuming that wild dog hair grows in a similar fashion to that of gray wolves, the stable isotope values of wild dog hair represent a long-term dietary signal, and this is representative of the average cumulative diet of packs (thus elucidating their most commonly consumed prey species). The model we generated predicted that large browsers (predominantly kudu in the case of wild dogs; Mills 1992; Mills and Gorman 1997; Creel and Creel 2002) and impala combined contribute 50% or more to wild dog diet in both mixed woodland and Lowveld sour bushveld. Whereas a combination of small browsers and large grazers comprise up to 55% and 64% of wild dog diet in Malelane mountain bushveld and thicket, respectively. In our study, the highest number of packs sampled in a single landscape type was found in thicket (13), with mixed woodland a close second (12). In thicket, our model predicts that small browsers (~40%) contribute almost as much as impala (~33%) to wild dog diet. This is congruent with the results of Krüger et al. (1999), who found that in HiP, nyala (Tragelaphus angasii, a mixed-feeder similar to impala), as well as red duiker (Cephalophus natalensis), and bushbuck (both browsers), are the most profitable species to be targeted in dense habitat types. This comes as the profitability of prey types differs with various factors (such as the size and vulnerability of prey across different landscape types), with wild dogs adjusting their prey selection based on ease of prey-capture (Reich 1981). In dense habitats in HiP, wild dogs use ambush techniques to flush prey, and chases seldom exceed 1 km (Krüger et al. 1999; similar behavior has also been recorded for packs in the Selous Game Reserve in Tanzania; Creel and Creel 1995). The higher proportion of small browsers consumed by packs sampled in KNP thickets may also be a result of impala behavior, because they are known to avoid dense habitat types due to an increased risk of predation (Ford et al. 2014). Such a reliance of wild dogs on small browsers as a major part of their diet has not been reported previously for packs in the KNP. These results are rather similar to those for wild dogs in northern Kenya, where Woodroffe et al. (2007b) showed that packs living outside of protected areas feed predominantly (~70%) on Kirk’s dik-dik (a small browsing antelope ~15% that of a wild dog’s body weight), with impala estimated to be the second most consumed species in these areas. The hunting of small browsing prey species in thicket and dense vegetation types could also prove beneficial if KNP packs are adopting similar hunting strategies to those recorded by Hubel et al. (2016) for wild dog packs in dense habitat types in Botswana. These authors suggested that a hunting strategy that makes use of several simultaneous, opportunistic chases, where packs pursue multiple smaller prey items, may be the key to their hunting success in closed habitat types. We found that large grazers make up ~24% of wild dog diet for packs sampled in the dense thicket landscape type. Creel and Creel (2002) demonstrated that wild dogs in Selous Game Reserve exhibit prey preferences based on pack size, with smaller packs disinclined to hunt larger prey such as Burchell’s zebra and blue wildebeest, but larger packs hunting blue wildebeest proportionately to the rate at which they encountered them. These authors reported that in 85% of the hunting incidences they observed, there were successful kills when wild dogs chased their prey into dense vegetation. This most likely is because fleeing prey must make decisions as to the best route around an obstacle, in some cases freezing altogether, whereas wild dogs, which often are more agile, are able to follow the exact line taken by their target without needing to make independent decisions (Estes and Goddard 1967; Creel and Creel 2002). The use of dense vegetation as a potential obstacle for prey is likely a key factor assisting some KNP wild dog packs to hunt large grazers in these areas. This is not surprising because wild dogs have been known to kill prey species as large as eland (Taurotragus oryx; an animal weighing on average >400 kg) by using man-made fences and structures as a means of trapping prey (van Dyk and Slotow 2003; Davies-Mostert et al. 2013). Wild dogs hunt for~3.5 h per day but would need to increase their hunting activity to 12 h per day to meet energy requirements should they lose a quarter of their food to kleptoparasitism (Gorman et al. 1998). This poses a serious risk to wild dogs, which naturally live in low densities, and show a negative relationship in density as hyena and lion numbers increase (Fanshawe and Fitzgibbon 1993; Creel and Creel 1996; Mills et al. 1998). Wild dogs in KNP lose a large number of kills to kleptoparasitism by spotted hyenas and lions, particularly in open habitats where visibility is good (Kruuk 1972; Fanshawe and Fitzgibbon 1993). In densely wooded areas both in Selous Game Reserve and KNP, however, spotted hyenas are rarely able to take food from wild dog packs (Mills and Biggs 1993; Creel and Creel 1995). Hunting in thickets in the KNP likely plays the dual role of allowing wild dogs to manage their energy requirements through opportunistic and ambush hunting strategies, where chase durations are reduced, while simultaneously avoiding kleptoparasitism by spotted hyenas and lions (Davies et al. 2021). In mixed woodland, where visibility is greater than in thickets, but still poor enough to confer an advantage in kleptoparasitism avoidance, our model shows that wild dogs are acting as rate maximizers. Here, it appears that packs are trading-off the benefits of opportunistic hunting, and instead acting as foraging specialists, hunting the most abundant medium-to-large-sized prey species available (Ginsberg and Macdonald 1990), with impala comprising ~62% of their diet. As seen for the packs sampled in thicket, small browsers still make up a sizable portion of wild dog diet for packs sampled in the mixed woodland landscape type (26–27%). Approximately 39% of natural pup mortality and 43% of natural adult wild dog mortality in KNP are caused by lions (van Heerden et al. 1995). In addition, Mills and Gorman (1997) demonstrated that KNP wild dogs avoid habitats chosen by lions, and Swanson et al. (2014) showed declines in the wild dog population of the Serengeti National Park, Tanzania, as lion numbers tripled between 1966 and 1998. Lions tend to select their habitat based on the density of their favored prey (blue wildebeest, buffalo, and Burchell’s zebra), which has resulted in KNP lion prides showing a strong affinity for savanna, where these prey species are abundant (Gertenbach 1983; Mills and Gorman 1997; Marneweck et al. 2019). It therefore is not surprising that we only were able to collect a single sample from a wild dog in open savanna, with Marneweck et al. (2019) finding no wild dog home ranges along the eastern boundary of the KNP, and these areas being almost completely avoided by wild dog packs. As the name suggests, the Lowveld sourveld is characterized by the presence of sour grass species, such as Hyperthelia dissolute, and generally is avoided by buffalo (Gertenbach 1983). In this landscape type, where lion density is comparatively lower than that in savanna (Mills and Gorman 1997), packs that we sampled seem to predominantly feed on large browsers (31–33%; Mills 1992; Mills and Gorman 1997; Creel and Creel 2002) and large grazers (~34%). Small browsers still make up a substantial proportion of their diet (up to 23%), but it is likely that in the relative absence of lions, wild dogs find it more profitable to hunt larger prey species more regularly than smaller species, particularly if their pack size is large enough (Creel and Creel 1995; Courchamp and Macdonald 2001). Mills and Gorman (1997) demonstrated that Lowveld sour bushveld and Malelane mountain bushveld both are preferred by KNP wild dogs, despite impala showing no preference for these landscape types. They suggested that the absence of impala is countered by the presence of kudu (the second most conspicuous prey species) in both of these landscape types. Our model supports these findings for the Lowveld sour bushveld, indicating that kudu make up a sizeable portion of wild dog diet in this landscape type (31–33%), but also suggests that the presence of small browsers (29–31%) may make up for the fewer impala being present in the Malelane mountain bushveld. Rogers et al. (2020) showed that SIA modeling approaches that analyze the whole hair samples, and not serially sectioned tissue segments, are unlikely to overestimate dietary niche breadth, but have the potential to underestimate niche breadth estimates for species occupying broad isotopic niches, and which exhibit temporal variations in diet. Wild dogs exhibit temporal variation in diet, both seasonally and, particularly, during droughts (Creel and Creel 2002; Skinner and Chimimba 2005). We, therefore, are confident in the accuracy of our model’s prediction that KNP wild dogs occupy a broader dietary niche than previously reported. However, our results may represent an underestimate of the breadth of wild dog foraging preferences in the park. We suggest that such preferences may be better elucidated using wild dog-specific diet–tissue discrimination factors (as well as hair and whisker growth rates), whereafter SIA can be more readily integrated with other traditional sampling methodologies for monitoring the species. The results of our study show a higher level of adaptability in the foraging behavior of wild dogs in KNP than previously reported, with small browsers comprising a greater percentage of wild dog diet than was originally thought. These results may be considered preliminary, because investigations into specific wild dog diet–tissue discrimination factors could improve the robustness of the assumptions upon which our model was based. ACKNOWLEDGMENTS We thank Agnes Maluleke and Kresen Pillay (Johannesburg Zoo), David Marneweck and Grant Beverley (Endangered Wildlife Trust [EWT] and the Wild dog Advisory Group of South Africa), and Louis van Schalkwyk, Peter Buss, and Leanna Rossouw (SANParks) for assistance with sampling logistics. Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. LITERATURE CITED Avenant NL , Nel JAJ. 2002 . Among habitat variation in prey availability and use by caracal Felis caracal . Mamm Biol . 67 (1): 18 – 33 . Google Scholar Crossref Search ADS WorldCat Bond AL , Hobson KA. 2012 . Reporting stable-isotope ratios in ecology: recommended terminology, guidelines and best practices . Waterbirds . 35 (2): 324 – 331 . Google Scholar Crossref Search ADS WorldCat Bothma J , Coertze RJ. 2004 . Motherhood increases hunting success in southern Kalahari leopards . 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TI - African wild dogs (Lycaon pictus) show differences in diet composition across landscape types in Kruger National Park, South Africa
JO - Journal of Mammalogy
DO - 10.1093/jmammal/gyab087
DA - 2021-09-01
UR - https://www.deepdyve.com/lp/oxford-university-press/african-wild-dogs-lycaon-pictus-show-differences-in-diet-composition-WJ4M0WI5Ah
SP - 1
EP - 1
VL - Advance Article
IS - 
DP - DeepDyve
ER -