Skip to main content
NCBI home page
Conservation Physiology logoLink to Conservation Physiology
. 2024 Feb 27;12(1):coae008. doi: 10.1093/conphys/coae008

Kibble diet is associated with higher faecal glucocorticoid metabolite concentrations in zoo-managed red wolves (Canis rufus)

Morgan Bragg 1,2,3,✉, Carly R Muletz-Wolz 4, Nucharin Songsasen 5, Elizabeth W Freeman 6
Editor: Frank Seebacher
PMCID: PMC10898788  PMID: 38414659

Suboptimal health is a main challenge in managing wildlife populations under human care. We found that kibble-only diets are linked to higher faecal glucocorticoid metabolite concentrations in zoo-managed red wolves. A deficient diet can likely act as a stressor and risk factor for poor health.

Keywords: Red wolf, fecal glucocorticoid metabolites, diet, zoo management, animal welfare

Abstract

The red wolf (Canis rufus) is a critically endangered canid that exists solely because of the establishment of the ex situ population in the late 1980s. Yet, the population under human care suffers from gastrointestinal (GI) disease in captivity. While the cause of GI disease is unknown, it is speculated that environmental factors can influence GI health of zoo-managed red wolves. The goal of the present study was to investigate the relationship between faecal glucocorticoid metabolite (FGM) concentrations, a biomarker for stress, and environmental factors for zoo-managed red wolves. Faecal samples were collected from 14 adult wolves three times a week for 5 to 12 months. Using a single-antibody cortisol enzyme immunoassay, FGM concentrations were quantified. Environmental factors were collected for each participating wolf on dietary type, sex, type of public access to enclosure, density (enclosure size [ft2]/number of wolves living in enclosure) and a monthly average status of GI health. Red wolves that ate a commercial kibble diet had both higher FGM concentrations over time and higher baseline FGM concentrations compared to individuals that received commercial kibble mixed with commercial meat. Density, public access or GI health were not related to FGM concentration; however, males had higher baseline FGM concentrations compared to female red wolves. Our findings suggest that management conditions, particularly diet, can strongly influence FGM concentration in the zoo-managed red wolf population. Findings from this study highlight the importance of management choices on individual welfare. Maintaining a healthy captive population of red wolves is imperative for the persistence of the species, including successful future reintroductions.

Introduction

The American red wolf (Canis rufus) is a critically endangered canid that once was nearly extirpated from its historic range in the southeastern United States due to persecution by humans (Hinton et al., 2013). Today, there are ~250 red wolves left in existence; 90% of those individuals live under human care (U.S. Fish and Wildlife Service, 2022), which emphasizes the importance of ex situ populations. Recently, United States Fish and Wildlife Services (USFWS) resumed reintroduction of red wolves into Alligator River National Wildlife Refuge in Manteo, North Carolina. From 2021 to 2023, USFWS released 16 adults and plan for more in the future (U.S. Fish and Wildlife Service, 2023). Therefore, maintaining sustainable and healthy ex situ populations is the key to the success of the reintroduction programme.

In mammals, the ex situ environment can trigger an adrenal response and increased glucocorticoid production due to enclosure type and size, diet and proximity to conspecifics or heterospecifics, including humans (Keay et al., 2006; Cavigelli and Chaudhry, 2012; van Kesteren et al., 2012; McKenzie et al., 2017). Although short-term elevation of glucocorticoids is necessary for an individual to survive a stressor (Keay et al., 2006), prolonged activation of the hypothalamic pituitary adrenal (HPA) axis (chronic stress) can compromise health and reproduction (de Weerth, 2017; Lattin and Kelly, 2020). No standard hormone profile of a chronically stressed individual exists. However, consistently elevated glucocorticoid concentrations are generally regarded as being indicative of a stressed animal (Palme, 2019; Romero and Gormally, 2019). Previous studies in wild animals reported impacts to reproduction and health (Creel, 2005; Young et al., 2006; Sheriff et al., 2011; Lattin and Kelly, 2020) in individuals with consistent heightened glucocorticoid, which is why we are interested in how management conditions impact faecal glucocorticoid metabolite (FGM) concentrations, a biomarker for stress, in the zoo-housed red wolves.

Diet also can influence glucocorticoid production and metabolism. Stetz et al. (2013) found that wild bears that have eaten a diet with higher nutritional value have lower FGM concentrations than bears eating vegetation with lower nutritional value, indicating that diet composition and quality can impact glucocorticoid production. A high-fibre diet could make transit time faster in the gut that, in turn, could decrease the amount of time for glucocorticoids to be reabsorbed by the intestines, leading to increased FGM excretion (von der Ohe et al., 2004; Keay et al., 2006). Furthermore, differences in diet composition can produce different glucocorticoid metabolites and the antibody cross-reactivity of each metabolite can vary, possibly influencing detectability (Goymann, 2012). In the wild, the red wolf diet consists of white tail deer (Odocoileus virginianus), rodents (Rodentia spp.), rabbits (Lagomorpha spp.), raccoon (Procyon lotor) and other small mammals (McVey et al., 2013). In zoos, the daily diet consists of 90–95% meat-based dry kibble for domestic dog and 5–10% supplemental items (e.g. bones, commercial meat and carcass) (Association of Zoos and Aquariums Canid Taxonomic Advisory Group, 2012). The kibble diet contains a large amount (30–60%) of starch (Fortes et al., 2010) that wolves are not adapted to digesting (Axelsson et al., 2013). In addition, there are some facilities that feed a mixed-diet daily—a mix of kibble and commercial meat. As it has been shown that diet can influence glucocorticoid production, we hypothesized that the differences in diet offered among institutions would result in variations in HPA axis activation and FGM concentrations in zoo-managed red wolves.

The amount of available space for individuals within an enclosure, or density, and public access to enclosures also can influence HPA axis activation. Several studies reported a relationship between FGM concentrations and enclosure size and height in felids (Wielebnowski et al., 2002; Quirke et al., 2012; Vaz et al., 2017). Furthermore, FGM concentrations have been observed to increase as the number of zoo visitors increased in the blackbuck (Antilope cervicapra), spider monkey (Ateles geoffroyii rufiventris) and clouded leopard (Neofelis nebulosa) (Wielebnowski et al., 2002; Davis et al., 2005; Rajagopal et al., 2011). In contrast, a previous study of 13 red wolves did not find differences in FGM concentrations between wolves on and off exhibit (Franklin et al., 2020). However, based on the wide-ranging and elusive nature of the species, we hypothesized that higher density and higher frequency of visitors would be linked to higher FGM in the red wolf.

Even though management of red wolves in zoos has been critical to their long-term survival, it is tied to health disorders not described in wild populations (Acton et al., 2000; Association of Zoos and Aquariums Canid Taxonomic Advisory Group, 2012). Unfortunately, gastrointestinal (GI) disease caused death in 21% of adult captive red wolves from 1992 through 2012 (Acton et al., 2000; Seeley et al., 2016). Common signs of GI disease in red wolves include chronic diarrhoea and intestinal inflammation, making it difficult to absorb nutrients (Association of Zoos and Aquariums Canid Taxonomic Advisory Group, 2012). The cause of GI disease is unknown, but there is evidence that alteration in the composition of the gut bacterial community can lead to GI health issues, which could be caused by factors like diet, infection, antibiotics or genetics (Petersen and Round, 2014; Jandhyala et al., 2015; Bragg et al., 2020). Furthermore, shifts in gut bacterial community composition and intestinal barrier permeability can be influenced by glucocorticoid production (Davidson et al., 2018). Understanding the relationship between environmental factors and FGM concentrations could illuminate possible contributors to GI disease, which could be observed non-invasively through faecal consistency score, in the ex situ red wolf population. Due to the specialized equipment and expertise needed to diagnosis GI disease, faecal consistency score has been used as a proxy for GI health status in the domestic dog (Niina et al., 2019; Bermingham et al., 2017; Jergens et al., 2003) and human (Falony et al., 2019) but has not been validated in the red wolf, thus making it a potential parameter for estimating GI disease status for this species.

The objective of this study is to understand if there is a link between FGM concentrations and environmental factors in the zoo-managed red wolves. We hypothesized that poor GI health status, a kibble diet, higher density and self-guided public access will be associated with increased glucocorticoid production in zoo-managed red wolves. Overactivation of the HPA axis from environmental stressors can support the evolution of GI pathologies (O’Mahony et al., 2009; Brzozowski,et al., 2016; de Weerth, 2017; Sylvia and Demas, 2018; Cryan et al., 2019). Findings from this study will help decipher the relationship between FGM concentrations and environmental factors, aiding in the identification of stressors that could be acting as risk factors for GI disease in red wolves.

Materials and Methods

Animals and sample collection

We collected data from 14 red wolves across seven zoological institutions in the USA (Table 1). Faecal samples were opportunistically collected three times a week for 5 to 12 months. Samples were collected by facility staff and kept at −20°C until shipment to Smithsonian National Zoo & Conservation Biology Institute. For wolves housed in groups, identification of samples from individual wolves was done by feeding markers such as plastic beads or food dye (depending on each institution’s preference).

Table 1.

Fourteen participating red wolves, including their facility, sex, diet type, type of public access, density (enclosure size [ft2]/number of wolves living in enclosure) and average canine inflammatory bowel disease activity index (CIBDAI) score of each wolf

Animal Facility Sex Diet type Public access Density Average CIBDAI (±SEM)
RW1 A F Mixed Staff 20 984 0 ± 0
RW2 B F Kibble Staff 5500 1.1 ± 0.13
RW3 B M Kibble Staff 5500 1.1 ± 0.12
RW4 C F Mixed Self 3550 0 ± 0
RW5 D M Mixed Self 4500 0.5 ± 0.14
RW6 E M Mixed Self 8100 2 ± 0
RW7 F F Mixed None 2500 1.2 ± 0.49
RW8 F F Kibble None 2500 0 ± 0
RW9 F F Kibble None 2500 0 ± 0
RW10 F F Kibble None 2500 0.3 ± 0.16
RW11 G F Mixed Staff 8712 0 ± 0
RW12 G F Mixed Staff 10 890 0 ± 0
RW13 G M Mixed Staff 8712 0 ± 0
RW14 G M Mixed Staff 10 890 0 ± 0
Open in a new tab

Environmental information including dietary type (kibble or mixed), relation of housing group (single, breeding pair or family group), type of public access to enclosure (none, self or staff guided), density (enclosure size [ft2]/number of wolves living in enclosure) and monthly canine inflammatory bowel disease activity index (CIBDAI) was gathered for each participating facility (Supplementary Material). Individuals were fed diets composed of a meat-based dry kibble approved for domestic dogs (kibble) or a mix of kibble and commercial meat (mixed). Density was measured, rather than enclosure or group size, because it is a better measure of the amount of space that is available for each wolf to use. The CIBDAI is a standardized assessment for clinical signs of GI disease in domestic dogs (Jergens et al., 2003), and scores attitude/activity, appetite, vomiting, stool consistency, stool frequency and weight loss on a scale of zero to three, with three being the most severe. The ratings of the six signs are added up to give a total CIBDAI score; 0–3 implies clinically miniscule disease, 4–5 implies mild presence of disease, 6–8 indicates moderate presence of disease, and 9 or higher indicates severe presence of disease.

Glucocorticoid extraction

Faecal samples were lyophilized, crushed and sifted prior to FGM extraction. Steroid extraction was performed using a modified method of that published by Young et al. (2004). Briefly, 0.2 g (±0.02 g) of lyophilized faecal powder was shaken for 30 min in 90% ethanol. Samples were centrifuged at 1500 rpm for 20 min and the first supernatant was recovered. The remaining pellet was resuspended in 5 mL of 90% ethanol, centrifuged again at 1500 rpm for 15 min and the second supernatant was recovered, combined with the first supernatant and dried down under air. Once dried down, it was resuspended in 100% methanol and allowed to air dry and then suspended in phosphate buffer saline (0.2 M NaH2PO4, #S8282; 0.2 M Na2HPO4, #S7907, Sigma Aldrich; 0.15 M NaCl, #S271, Fisherbrand; pH 7.0) and stored at −20°C until utilized for hormone assays. Steroid extraction efficiencies were determined with the addition of radiolabeled hormone (3H-cortisol; 4000–8000 dpm) and average recovery after extraction was 81% for all samples.

Enzyme immunoassay

We used an in-house cortisol enzyme immunoassay (EIA; R4866, Munro, University of California, Davis, CA; 1:85 [C.J. Munro, University of California, Davis]) as previously described by our laboratory (Young et al., 2004; Maly et al., 2018; Putman et al., 2019; Fazio et al., 2020; Maly et al., 2021; Yu et al., 2021). All samples were run in duplicates at the same time. Inter-assay variation was <15% and intra-assay variation was <10%. The coefficient of variation was calculated by dividing standard deviation of the percent binding by the mean of the percent binding then multiplying by 100 (Brown et al., 2008). Assay sensitivity was 0.039 ng/g faeces. Serial dilutions of faecal extracts yielded a displacement curve parallel to the standard curve (y = 1.261x − 15.559, R2 = 0.975, F1,5 = 161.905, P < 0.001).

Statistical analyses

We conducted all statistical analyses in R (version 4.1.2) (R Core Team, 2021). Using the function ‘hormBaseline’ in the package ‘hormLong’ (Fanson and Fanson, 2015), baseline and peak FGM concentrations (+1.5SD above mean) (Wielebnowski et al., 2002; Edwards et al., 2015; Franklin et al., 2020) of FGM were identified for each individual. Baseline is defined as the concentration of glucocorticoids required for normal physiological function (Bonier et al., 2009). Average FGM concentrations were calculated each week for each wolf to account for uneven sampling frequency among individuals (Jones et al., 2018).

Linear mixed models were used to assess relationships between FGM concentrations and environmental factors utilizing the function ‘lmer’ in the ‘lme4’ package (Bates et al., 2015). We included weekly averages of FGM concentrations as the response variable and sex, public access, diet type, density (enclosure size [ft2]/number of wolves living in enclosure) and monthly CIBDAI score as explanatory variables. Sex was included to evaluate if it was a factor driving variation seen in FGM concentration. We used backwards model selection using the ‘step’ function in the lmerTest package (Kuznetsova et al., 2017). First, we determined the appropriate random effect of facility, animal or animal nested within facility with restricted maximum likelihood (REML) on the full model and compared the models with AIC using the ‘step’ function in the ‘lmerTest’ package. Those results indicated that animal and facility were the appropriate random effects to account for repeated sampling from the same individuals at different zoos. Then, a full model with the appropriate random effects and all fixed effects of interest (sex, public access, diet type, density and monthly CIBDAI) was run with REML set to false to determine the significance of the explanatory variables. Linear mixed models fit by REML log-likelihood t-test used Satterthwaite approximations to degrees of freedom. We used ‘VIF’ function in the ‘regclass’ package (Petrie, 2020) to assess correlations between explanatory variables. Variables with a VIF value of greater than 5 are considered correlated and excluded from being in the same model. None of the explanatory variables were correlated in this study.

We ran an additional linear mixed effect model to assess the relationship between the baseline FGM concentration of each individual and environmental factors using the ‘lmer’ function in the ‘lmerTest’ package. We included baseline FGM concentrations as the response variable and sex, public access, diet type, density and monthly CIBDAI score as explanatory variables with REML set to false to determine the significance of the explanatory variables. We included facility as a random effect to account for variation across the different institutions. Similarly, we used backwards model selection using the ‘step’ function in the ‘lmerTest’ package. Statistical significance was set at alpha equals 0.05.

Results

A total of 899 faecal samples were collected from 14 wolves (9 females, 5 males) across 7 facilities. The median FGM concentration was 258 ng/g and ranged from 2 to 8089 ng/g (Table 2). The average baseline sample ranged from 71 to 791 ng/g and average peak samples (+1.5SD) ranged from 223 to 2031 ng/g (Table 2).

Table 2.

Faecal glucocorticoid metabolite (FGM) results from 14 red wolves managed in zoos, including the sample size (n), mean plus/minus standard error (SEM), median, minimum and maximum raw FGM concentrations (ng/g), as well as baseline samples and mean of the peak samples (+1.5SD; ng/g)

Animal n Mean ± SEM Median Min Max Baseline Peak mean
RW1 26 824 ± 131 640 176 2793 238 1085
RW2 143 202 ± 12 168 49 1127 71 223
RW3 132 230 ± 11 213 37 751 119 291
RW4 51 259 ± 37 206 38 1797 173 539
RW5 39 321 ± 65 187 35 2198 137 586
RW6 76 219 ± 29 165 29 1887 103 356
RW7 30 155 ± 18 129 35 447 117 309
RW8 82 1179 ± 85 1029 271 5877 791 1785
RW9 89 1475 ± 153 1003 34 8090 380 2031
RW10 62 1034 ± 79 952 157 3703 624 1532
RW11 49 203 ± 14 186 9 481 152 321
RW12 38 506 ± 86 353 30 2717 237 1085
RW13 39 468 ± 51 369 74 1322 230 720
RW14 43 318 ± 42 247 2 1665 198 593
Open in a new tab

The best model on the relationship between FGM and sex, public access, diet type, density and monthly CIBDAI score indicated that diet type was the sole explanatory factor linked to FGM concentrations (t20.33 = −6.34, P value < 0.0001; Table 3; Supplementary Table 1). We found that wolves that ate a mixed diet (mean ± SE = 331 ± 29.10 ng/g) had lower FGM concentrations than wolves that ate a kibble diet (mean ± SE = 691 ± 68.64 ng/g) (Fig. 1; Supplementary Fig. Fig. 1). The fixed effect in the model accounted for 38% of the variation while the fixed and random effects in the model accounted for 72% of the variation in weekly average FGM concentrations (Table 3). Density was not included in the best model; however, there was some indication of a positive relationship between the variable and weekly average FGM concentrations (Supplementary Table 1). No relationship was detected between weekly averages of FGM concentrations and the monthly CIBDAI score, sex or public access, respectively (Supplementary Table 1).

Table 3.

Estimates of coefficients of the best linear model investigating the impact of environmental variables on the log10-transformed weekly average FGM concentrations of red wolves (n = 14)

Log10 weekly average FGM concentrations (ng/g)
Predictors Estimates CI P
(Intercept) 2.96 2.66–3.26 <0.001
Diet type [mixed] −0.71 −0.93 to −0.49 <0.001
Random effects
σ 2 0.09
τ00 Animal 0.00
τ00 Facility 0.11
ICC 0.55
N Animal 14
N Facility 7
Observations 301
Marginal R2/conditional R2 0.378/0.723
Open in a new tab

Bold indicates P < 0.05. For diet type, kibble (intercept) is compared to mixed

Figure 1.

Figure 1

Open in a new tab

Longitudinal profiles of log10 weekly average faecal glucocorticoid metabolite (FGM) concentrations comparing a red wolf that ate kibble (A) versus mixed diet (B). Dashed line represents the mean log10 weekly average faecal glucocorticoid metabolite concentrations for each individual.

Diet type and sex had the strongest influence on baseline FGM concentrations in zoo-managed red wolves (Diet type: t300.04 = −15.1, P value < 0.001; Sex: t297.55 = 2.48, P value = 0.01; Table 4). We found that female wolves and wolves that ate a mixed diet had lower baseline FGM concentrations compared to male red wolves and wolves that ate a kibble diet (Fig. 2). The fixed effects accounted for 52% of the variation while the fixed and random effects in the model accounted for 91% of the variation in baseline FGM concentrations (Table 4). No relationship was detected between baseline FGM concentrations and public access, density and monthly CIBDAI score.

Table 4.

Estimates of coefficients of the best linear model investigating the impact of environmental variables on baseline FGM concentrations of red wolves (n = 14)

Baseline FGM concentrations (ng/g)
Predictors Estimates CI P
(Intercept) 531.41 379.73–683.09 <0.001
Diet type [mixed] −460.56 −520.60 to −400.52 <0.001
Sex [M] 37.31 7.64–66.98 0.014
Random effects
σ 2 8518.50
τ00 Facility 37 450.15
ICC 0.81
N Facility 7
Observations 301
Marginal R2/Conditional R2 0.524/0.912
Open in a new tab

Bold indicates P < 0.05. For diet type, kibble (intercept) is compared to mixed and for sex, female (intercept) is compared to male

Figure 2.

Figure 2

Open in a new tab

Effects of diet type (left) and sex (right) on baseline faecal glucocorticoid metabolite (FGM) concentrations in red wolves (n = 14). Means (± standard error) in each treatment combination calculated from generalized linear mixed-effects models, controlling for random effects.

Discussion

It is known that environmental stressors can initiate activation of the HPA axis and influence glucocorticoid production that is required to maintain homeostasis. However, inappropriate or overactivation of the HPA axis can negatively influence the GI health of an individual (O’Mahony et al., 2009; Brzozowski et al., 2016; de Weerth, 2017; Sylvia and Demas, 2018; Cryan et al., 2019). Thus, we wanted to investigate the link between environmental factors and HPA axis activation, detected via FGMs, that could negatively impact GI health in the red wolf. In the present study, we reported that dietary type (kibble or mixed) was the most influential predictor variable of FGM excretion. Specifically, red wolves that ate a daily diet of kibble had higher FGM concentrations than individuals that ate a daily diet of commercial meat mixed with kibble. Relationships between FGM concentrations and sex, public access and monthly CIBDAI score were not apparent. We did observe a potential effect of density on weekly average FGM concentrations. Moreover, we documented higher baseline FGM concentrations in males and in wolves that ate a daily diet of kibble. No relationships were detected between baseline FGM concentration and public access, density and monthly CIBDAI score. Our objective was to conduct a broad observational study testing for the relationship of multiple factors with FGM concentrations, and we suggest that these factors, particularly diet and density, warrant future studies with greater replication.

We documented a relationship between kibble diet and high FGM concentrations. The main diet of free ranging red wolves consists of white tail deer, small mammals and rodents (Dellinger et al., 2011). However, meat-based kibble formulated for domestic dog are commonly offered to zoo-managed red wolves (Association of Zoos and Aquariums Canid Taxonomic Advisory Group, 2012). Although the kibble is meat based, it also is 30–60% carbohydrates, which provides energy and fibre (Fortes et al., 2010). Unlike domestic dogs, wolves cannot digest starch, a type of carbohydrate, due to differences in the gene responsible for producing the enzyme required to break down starch (Axelsson et al., 2013). Dogs have had thousands of years to evolve the ability to digest a high-carbohydrate diet, but red wolves have only been in zoos for ~50 years (U.S. Fish and Wildlife Service, 2022), reducing their ability to effectively use carbohydrates. Recently, we reported zoo-managed red wolves that ate a kibble diet had different gut microbiome composition, or collection of bacteria in the gut, and increased abundance of two bacterial taxa associated with carbohydrate fermentation compared to wolves that ate a mixed, whole meat (carcass) or wild diet (Bragg et al., 2020). The kibble diet presented in this study contains 4–5% fibre while the mixed diet is a 50/50 combination of kibble and commercial meat that contains only 1% fibre, thus reducing the total amount of carbohydrates and fibre present. The impact of diet composition and quality on FGM concentrations has been established in other species like the black footed ferret (Mustela nigripes) (Santymire et al., 2020) and wild gull-billed tern chicks (Gelochelidon nilotica) (Albano et al., 2015).

Although the cause of increased FGM concentrations in red wolves fed kibble have not been elucidated, it could be that the rise in hormone concentration may partly be due to an increased defecation frequency that results from the higher amount of fibre in the diet (Palme, 2019), a difference in the type and detection of glucocorticoid metabolites between the two dietary types (Goymann, 2012) or it truly could be an increase in activation of the HPA axis. Higher FGM concentrations are associated with a diet high in fibre in wild Alaskan brown bears (Ursus arctos horribilis; von der Ohe et al., 2004), North American red squirrels (Tamiasciurus hudsonicus; Dantzer et al., 2011), zoo-managed spider (Ateles spp.) and woolly monkeys (Lagothrix ssp.; Ange-van Heugten et al., 2009). A diet high in fibre could decrease gut passage time, which decreases time for reabsorption of glucocorticoids by the intestines, thus increasing FGM concentrations (von der Ohe et al., 2004; Keay et al., 2006; Dantzer et al., 2011). It is possible that alterations in gut transit time can influence distribution of nutrients to gut bacteria (Carabotti et al., 2015), a risk factor for poor GI health. It is also possible that differences in diet content can influence gut microbiome composition, leading to the production of various glucocorticoid metabolites via different bacterial taxa present in the gut of wolves eating a kibble compared to a mixed dietary type (Goymann, 2012).

We documented a relationship between high baseline FGM concentrations and male red wolves. Our results agree with Escobar-Ibarra et al. (2017) who also reported higher FGM concentrations in male Mexican grey wolves (Canis lupus baileyi) compared to females. Red wolves in the wild form multigenerational packs where all members contribute to the maintenance and upbringing of the pack. Pack dynamics or social stress can invoke variations in glucocorticoid production (Cavigelli and Chaudhry, 2012). For example, higher FGM concentrations were observed in dominant individuals compared to subordinate individuals in large canids, suggesting that social status can impact glucocorticoid production (Creel, 2005; van Kesteren et al., 2012; Escobar-Ibarra et al., 2017). Also, social stress can influence factors like reproduction in canids, which is a top priority for a critically endangered species (Kleiman, 2011; Yordy and Mossotti, 2016). It is possible that the higher baseline FGM concentrations we documented in males are a result of social dynamics and/or biological differences between the sexes.

In the present study, we found no relationship between CIBDAI and FGM concentrations. The average monthly CIBDAI score of wolves in this study was 0.44, representing clinically miniscule disease, making it difficult to explore the relationship between CIBDAI and FGM concentrations. The CIBDAI was developed using histology and laboratory observations of intestinal inflammation and is used in domestic dogs as a scoring system to assess clinical activity of canine GI disease (Jergens et al., 2003). It is possible that CIBDAI may not be suitable as a proxy for GI health in red wolves as we know that red wolves in captivity are prone to GI disease via histopathology (Seeley et al., 2016; Acton et al., 2000). Thus, future studies will require greater sampling of animals known to have poor GI health and combining histopathology and various biomarkers for poor GI health in canids like faecal alpha-1 proteinase inhibitor, serum folate and cobalamin (Murphy et al., 2003; Heilmann and Steiner, 2018) to determine the relationship between chronic stress and GI health in zoo-managed red wolves.

Considering the importance of maintaining a sustainable and healthy ex situ population, future studies involving a larger sample size with equal distribution among explanatory variables are required to confirm the relationship between diet and glucocorticoids in the red wolf. Furthermore, we recognize that monitoring FGM concentrations is only one piece of information that is involved in the complex stress response. Therefore, additional studies should conduct longitudinal monitoring of multiple components concurrently, like FGM concentrations, behaviour (MacDougall-Shackleton,et al., 2019) and secretory immunoglobulin A (Sheriff et al., 2011), to fully assess the response to an environmental stressor. Moreover, it is possible that shifts in the gut microbiome that are related to poor GI health may also alter metabolism of hormones, further impacting FGM concentrations (De Palma et al., 2014). If the shift in gut microbiome composition due to poor GI health could alter metabolism of FGMs, then it would be expected that FGM concentrations would be correlated with biomarkers of GI health, potentially even CIBDAI.

In conclusion, the findings from the present study documented higher FGM concentrations in zoo-managed red wolves that ate a kibble diet compared to mixed diet. In addition, we documented higher baseline FGM concentrations in males and in wolves that ate a kibble diet. Density also showed a potential link to FGM concentrations that warrants future research. These data generated in the current study can be used to better understand general baseline and peak FGM concentrations and the impacts that husbandry practices have on HPA axis activation to provide targeted management recommendations for zoo-housed red wolves. We acknowledge the effort and limitations present in zoo facilities. Thus, it is suggested to reduce the amount of kibble and increase the amount of meat (commercial or whole carcass) in the daily diet of zoo-managed red wolves if possible. Chronic stress can predispose individuals to health issues, but additional studies are needed to confirm this relationship in the red wolf. A greater understanding of the relationship between chronic stress, environmental factors and GI health will help enhance the management of this critically endangered species to ensure its long-term survival.

Supplementary Material

Web_Material_coae008
web_material_coae008.zip (405.1KB, zip)

Acknowledgements

We thank Nicole Boisseau and Steve Paris for technical support in the laboratory. The authors thank North Carolina Museum of Life and Science, Wolf Haven International, US Fish and Wildlife Red Wolf Recovery Team, Roger Williams Park Zoo, Endangered Wolf Center, Fossil Rim Wildlife Center, Northeastern Wisconsin Zoo and the American Red Wolf SAFE.

Contributor Information

Morgan Bragg, Environmental Science and Policy Department, George Mason University, 4400 University Dr. Fairfax, VA 22030, USA; Center for Conservation Genetics, Smithsonian National Zoo & Conservation Biology Institute, 3001 Connecticut Ave. NW Washington, DC, 20008 USA; Center for Species Survival, Smithsonian National Zoo & Conservation Biology Institute, 1500 Remount Road, Front Royal, VA 22630, USA.

Carly R Muletz-Wolz, Center for Conservation Genetics, Smithsonian National Zoo & Conservation Biology Institute, 3001 Connecticut Ave. NW Washington, DC, 20008 USA.

Nucharin Songsasen, Center for Species Survival, Smithsonian National Zoo & Conservation Biology Institute, 1500 Remount Road, Front Royal, VA 22630, USA.

Elizabeth W Freeman, School of Integrative Studies, George Mason University, 4400 University Dr. Fairfax, VA 22030, USA.

Author Contributions

MB, CM-W, NS and EF planned the study and the experimental design. MB and NS facilitated sample collection from facilities. MB gathered environmental data, performed hormone extraction and enzyme immunoassays. MB and CM-W performed data analysis and interpreted results. MB wrote the manuscript and CM-W, NS and EF edited the manuscript.

Conflicts of Interest

The authors have no conflicts of interest to declare.

Funding

This work was supported by Point Defiance Zoo and Aquarium Dr Holly Reed Conservation and Volgenau Foundation.

Data Availability

The data underlying this article will be shared on reasonable request to the corresponding author.

Supplementary Material

Supplementary material is available at Conservation Physiology online.

References

  1. Acton AE, Munson L, Waddell WT (2000) Survey of necropsy results in captive red wolves (Canis rufus), 1992-1996. J Zoo Wildl Med 31: 2–8. 10.1638/1042-7260(2000)031[0002:SONRIC]2.0.CO;2. [DOI] [PubMed] [Google Scholar]
  2. Albano N, Santiago-Quesada F, Masero JA, Sánchez-Guzmán JM, Möstl E (2015) Immunoreactive cortisone in droppings reflect stress levels, diet and growth rate of gull-billed tern chicks. Gen Comp Endocrinol 213: 74–80. 10.1016/j.ygcen.2015.02.019. [DOI] [PubMed] [Google Scholar]
  3. Ange-van Heugten KD, Heugten E, Timmer S, Bosch G, Elias A, Whisnant S, Swarts HJM, Ferket P, Verstegen MWA (2009) Fecal and salivary cortisol concentrations in woolly (Lagothrix ssp.) and spider monkeys (Ateles spp.). Int J Zool 2009: 1–9. [Google Scholar]
  4. Association of Zoos and Aquariums Canid Taxonomic Advisory Group (2012) Large Canid (Canidae) Care Manual. Association of Zoos and Aquariums, Silver Spring, MD [Google Scholar]
  5. Axelsson E, Ratnakumar A, Arendt M-L, Maqbool K, Webster MT, Perloski M, Liberg O, Arnemo JM, Hedhammar Å, Lindblad-Toh K (2013) The genomic signature of dog domestication reveals adaptation to a starch-rich diet. Nature 495: 360–364. 10.1038/nature11837. [DOI] [PubMed] [Google Scholar]
  6. Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67: 1–48. 10.18637/jss.v067.i01. [DOI] [Google Scholar]
  7. Bermingham EN, Maclean P, Thomas DG, Cave NJ, Young W (2017) Key bacterial families (Clostridiaceae, Erysipelotrichaceae and Bacteroidaceae) are related to the digestion of protein and energy in dogs. PeerJ 5: 1–24. 10.7717/peerj.3019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bonier F, Martin PR, Moore IT, Wingfield JC (2009) Do baseline glucocorticoids predict fitness? Trends Ecol Evol 24: 634–642. 10.1016/j.tree.2009.04.013. [DOI] [PubMed] [Google Scholar]
  9. Bragg M, Freeman EW, Lim HC, Songsasen N, Muletz-Wolz CR (2020) Gut microbiomes differ among dietary types and stool consistency in the captive red wolf (Canis rufus). Front Microbiol 11: 1–13. 10.3389/fmicb.2020.590212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Brown J, Walker S, Steinman K (2008) Endocrine Manual for Reproductive Assessment of Domestic and Non-domestic Species Vol 2005. Smithsonian’s National Zoological Park, Conservation and Research Center, Virginia. Smithsonian Institution, Handbook, pp. 1–69 [Google Scholar]
  11. Brzozowski B, Mazur-Bialy A, Pajdo R, Kwiecien S, Bilski J, Zwolinska-Wcislo M, Mach T, Brzozowski T (2016) Mechanisms by which stress affects the experimental and clinical inflammatory bowel disease (IBD): role of brain-gut axis. Curr Neuropharmacol 14: 892–900. 10.2174/1570159X14666160404124127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Carabotti M, Scirocco A, Maselli MA, Severi C (2015) The gut-brain axis: interactions between enteric microbiota, central and enteric nervous systems. Ann Gastroenterol 28: 203–209. [PMC free article] [PubMed] [Google Scholar]
  13. Cavigelli SA, Chaudhry HS (2012) Social status, glucocorticoids, immune function, and health: can animal studies help us understand human socioeconomic-status-related health disparities? Horm Behav 62: 295–313. 10.1016/j.yhbeh.2012.07.006. [DOI] [PubMed] [Google Scholar]
  14. Creel S (2005) Dominance, aggression, and glucocorticoid levels in social carnivores. J Mammal 86: 255–264. 10.1644/BHE-002.1. [DOI] [Google Scholar]
  15. Cryan JF, O’Riordan KJ, Cowan CSM, Sandhu KV, Bastiaanssen TFS, Boehme M, Codagnone MG, Cussotto S, Fulling C, Golubeva AVet al. (2019) The microbiota-gut-brain axis. Physiol Rev 99: 1877–2013. 10.1152/physrev.00018.2018. [DOI] [PubMed] [Google Scholar]
  16. Dantzer B, McAdam AG, Palme R, Boutin S, Boonstra R (2011) How does diet affect fecal steroid hormone metabolite concentrations? An experimental examination in red squirrels. Gen Comp Endocrinol 174: 124–131. 10.1016/j.ygcen.2011.08.010. [DOI] [PubMed] [Google Scholar]
  17. Davidson GL, Cooke AC, Johnson CN, Quinn JL (2018) The gut microbiome as a driver of individual variation in cognition and functional behaviour. Philos Trans R Soc Lond B Biol Sci 373: 20170286. 10.1098/rstb.2017.0286. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Davis N, Schaffner CM, Smith TE (2005) Evidence that zoo visitors influence HPA activity in spider monkeys (Ateles geoffroyii rufiventris). Appl Anim Behav Sci 90: 131–141. 10.1016/j.applanim.2004.08.020. [DOI] [Google Scholar]
  19. De Palma G, Collins SM, Bercik P (2014) The microbiota-gut-brain axis in functional gastrointestinal disorders. Gut Microbes 5: 419–429. 10.4161/gmic.29417. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Dellinger JA, Ortman BL, Steury TD, Bohling J, Waits LP (2011) Food habits of red wolves during pup-rearing season. Southeast Nat 10: 731–740. 10.1656/058.010.0412. [DOI] [Google Scholar]
  21. Edwards KL, Shultz S, Pilgrim M, Walker SL (2015) Irregular ovarian activity, body condition and behavioural differences are associated with reproductive success in female eastern black rhinoceros (Diceros bicornis michaeli). Gen Comp Endocrinol 214: 186–194. 10.1016/j.ygcen.2014.07.026. [DOI] [PubMed] [Google Scholar]
  22. Escobar-Ibarra I, Mayagoitia-Novales L, Alcántara-Barrera A, Cerda-Molina AL, Mondragón-Ceballos R, Ramírez-Necoechea R, Alonso-Spilsbury M (2017) Long-term quantification of faecal glucocorticoid metabolite concentrations reveals that Mexican grey wolves may habituate to captivity. Eur Zool J 84: 311–320. 10.1080/24750263.2017.1332111. [DOI] [Google Scholar]
  23. Falony G, Vandeputte D, Caenepeel C, Vieira-Silva S, Daryoush T, Vermeire S, Raes J (2019) The human microbiome in health and disease: hype or hope. Acta Clin Belg 74: 53–64. 10.1080/17843286.2019.1583782. [DOI] [PubMed] [Google Scholar]
  24. Fanson B, Fanson K (2015) hormLong: An R package for longitudinal data analysis in wildlife endocrinology studies. PeerJ PrePrints3: 1–16. 10.7287/peerj.preprints.1546v1. [DOI] [Google Scholar]
  25. Fazio JM, Freeman EW, Bauer E, Rockwood L, Brown JL, Hope K, Siegal-Willott J, Parsons ECM (2020) Longitudinal fecal hormone monitoring of adrenocortical function in zoo housed fishing cats (Prionailurus viverrinus) during institutional transfers and breeding introductions. PloS One 15: 1–19. 10.1371/journal.pone.0230239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Fortes CMLS, Carciofi AC, Sakomura NK, Kawauchi IM, Vasconcellos RS (2010) Digestibility and metabolizable energy of some carbohydrate sources for dogs. Anim Feed Sci Technol 156: 121–125. 10.1016/j.anifeedsci.2010.01.009. [DOI] [Google Scholar]
  27. Franklin AD, Waddell WT, Behrns S, Goodrowe KL (2020) Estrous cyclicity and reproductive success are unaffected by translocation for the formation of new reproductive pairs in captive red wolves (Canis rufus). Zoo Biol 39: 230–238. 10.1002/zoo.21551. [DOI] [PubMed] [Google Scholar]
  28. Goymann W (2012) On the use of non-invasive hormone research in uncontrolled, natural environments: the problem with sex, diet, metabolic rate and the individual. Methods Ecol Evol 3: 757–765. 10.1111/j.2041-210X.2012.00203.x. [DOI] [Google Scholar]
  29. Heilmann RM, Steiner JM (2018) Clinical utility of currently available biomarkers in inflammatory enteropathies of dogs. J Vet Intern Med 32: 1495–1508. 10.1111/jvim.15247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Hinton JW, Chamberlain MJ, Rabon DR (2013) Red wolf (Canis rufus) recovery: a review with suggestions for future research. Animals 3: 722–744. 10.3390/ani3030722. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Jandhyala SM, Talukdar R, Subramanyam C, Vuyyuru H, Sasikala M, Reddy DN (2015) Role of the normal gut microbiota. World J Gastroenterol 21: 8787–8803. 10.3748/wjg.v21.i29.8787. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Jergens AE, Schreiner CA, Frank DE, Niyo Y, Ahrens FE, Eckersall PD, Benson TJ, Evans R (2003) A scoring index for disease activity in canine inflammatory bowel disease. J Vet Intern Med 17: 291–297. 10.1111/j.1939-1676.2003.tb02450.x. [DOI] [PubMed] [Google Scholar]
  33. Jones MK, Reiter LE, Gilmore MP, Freeman EW, Songsasen N (2018) Physiological impacts of housing maned wolves (Chrysocyon brachyurus) with female relatives or unrelated males. Gen Comp Endocrinol 267: 109–115. 10.1016/j.ygcen.2018.06.007. [DOI] [PubMed] [Google Scholar]
  34. Keay JM, Singh J, Gaunt MC, Kaur T (2006) Fecal glucocorticoids and their metabolites as indicators of stress in various mammalian species. J Zoo Wildl Med 37: 234–244. 10.1638/05-050.1. [DOI] [PubMed] [Google Scholar]
  35. Kesteren F, Sillero-Zubiri C, Millar R, Argaw K, Macdonald DW, Paris M (2012) Sex, stress and social status: patterns in fecal testosterone and glucocorticoid metabolites in male Ethiopian wolves. Gen Comp Endocrinol 179: 30–37. 10.1016/j.ygcen.2012.07.016. [DOI] [PubMed] [Google Scholar]
  36. Kleiman DG (2011) Canid mating systems, social behavior, parental care and ontogeny: are they flexible? Behav Genet 41: 803–809. 10.1007/s10519-011-9459-0. [DOI] [PubMed] [Google Scholar]
  37. Kuznetsova A, Brockhoff PB, Christensen RHB (2017) lmerTest package: tests in linear mixed effects models. J Stat Softw 82: 1–26. 10.18637/jss.v082.i13. [DOI] [Google Scholar]
  38. Lattin CR, Kelly TR (2020) Glucocorticoid negative feedback as a potential mediator of trade-offs between reproduction and survival. Gen Comp Endocrinol 286: 1–13. 10.1016/j.ygcen.2019.113301. [DOI] [PubMed] [Google Scholar]
  39. MacDougall-Shackleton SA, Bonier F, Romero LM, Moore IT (2019) Glucocorticoids and “stress” are not synonymous. Integr Org Biol 1: 1–9. 10.1093/iob/obz017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Maly MA, Edwards KL, Farin CE, Koester DC, Crosier AE (2018) Assessing puberty in ex situ male cheetahs (Acinonyx jubatus) via fecal hormone metabolites and body weights. Gen Comp Endocrinol 268: 22–33. 10.1016/j.ygcen.2018.07.011. [DOI] [PubMed] [Google Scholar]
  41. Maly MA, Edwards KL, Koester DC, Farin CE, Crosier AE, Martin G, Maly MA, Edwards KL, Koester DC, Farin CEet al. (2021) Assessing puberty in female cheetahs (Acinonyx jubatus) via faecal hormone metabolites and body weight. Reprod Fertil Dev 33: 841–854. 10.1071/RD21169. [DOI] [PubMed] [Google Scholar]
  42. McKenzie VJ, Song SJ, Delsuc F, Prest TL, Oliverio AM, Korpita TM, Alexiev A, Amato KR, Metcalf JL, Kowalewski Met al. (2017) The effects of captivity on the mammalian gut microbiome. Integr Comp Biol 57: 690–704. 10.1093/icb/icx090. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. McVey JM, Cobb DT, Powell RA, Stoskopf MK, Bohling JH, Waits LP, Moorman CE (2013) Diets of sympatric red wolves and coyotes in northeastern North Carolina. J Mammal 94: 1141–1148. 10.1644/13-MAMM-A-109.1. [DOI] [Google Scholar]
  44. Murphy KF, German AJ, Ruaux CG, Steiner JM, Williams DA, Hall EJ (2003) Fecal alpha1-proteinase inhibitor concentration in dogs with chronic gastrointestinal disease. Vet Clin Pathol 32: 67–72. 10.1111/j.1939-165X.2003.tb00316.x. [DOI] [PubMed] [Google Scholar]
  45. Niina A, Kibe R, Suzuki R, Yuchi Y, Teshima T, Matsumoto H, Kataoka Y, Koyama H (2019) Improvement in clinical symptoms and fecal microbiome after fecal microbiota transplantation in a dog with inflammatory bowel disease. Vet Med 10: 197–201. 10.2147/VMRR.S230862. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. O’Mahony SM, Marchesi JR, Scully P, Codling C, Ceolho A-M, Quigley EMM, Cryan JF, Dinan TG (2009) Early life stress alters behavior, immunity, and microbiota in rats: implications for irritable bowel syndrome and psychiatric illnesses. Biol Psychiatry 65: 263–267. 10.1016/j.biopsych.2008.06.026. [DOI] [PubMed] [Google Scholar]
  47. Ohe CG, Wasser SK, Hunt KE, Servheen C (2004) Factors associated with fecal glucocorticoids in Alaskan Brown bears (Ursus arctos horribilis). Physiol Biochem Zool 77: 313–320. 10.1086/378139. [DOI] [PubMed] [Google Scholar]
  48. Palme R (2019) Non-invasive measurement of glucocorticoids: advances and problems. Physiol Behav 199: 229–243. 10.1016/j.physbeh.2018.11.021. [DOI] [PubMed] [Google Scholar]
  49. Petersen C, Round JL (2014) Defining dysbiosis and its influence on host immunity and disease. Cell Microbiol 16: 1024–1033. 10.1111/cmi.12308. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Petrie A (2020). Regclass: tools for an introductory class in regression and modeling. R package version 1.6: 1–129. https://CRAN.R-project.org/package=regclass (last accessed 19 September 2022).
  51. Putman SB, Brown JL, Saffoe C, Franklin AD, Pukazhenthi BS (2019) Linkage between fecal androgen and glucocorticoid metabolites, spermaturia, body weight and onset of puberty in male African lions (Panthera leo). PloS One 14: 1–16. 10.1371/journal.pone.0217986. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Quirke T, O’Riordan RM, Zuur A (2012) Factors influencing the prevalence of stereotypical behaviour in captive cheetahs (Acinonyx jubatus). Appl Anim Behav Sci 142: 189–197. 10.1016/j.applanim.2012.09.007. [DOI] [Google Scholar]
  53. R Core Team (2021) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. [Google Scholar]
  54. Rajagopal T, Archunan G, Sekar M (2011) Impact of zoo visitors on the fecal cortisol levels and behavior of an endangered species: Indian blackbuck (Antelope cervicapra L.). J Appl Anim Welf Sci 14: 18–32. 10.1080/10888705.2011.527598. [DOI] [PubMed] [Google Scholar]
  55. Romero LM, Gormally BMG (2019) How truly conserved is the “well-conserved” vertebrate stress response? Integr Comp Biol 59: 273–281. 10.1093/icb/icz011. [DOI] [PubMed] [Google Scholar]
  56. Santymire RM, Lavin SR, Branvold-Faber H, Kreeger J, Che-Castaldo J, Rafacz M, Marinari P (2020) Influence of vitamin E and carcass feeding supplementation on fecal glucocorticoid and androgen metabolites in male black-footed ferrets (Mustela nigripes). PloS One 15: 1–13. 10.1371/journal.pone.0241085. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Seeley KE, Garner MM, Waddell WT, Wolf KN (2016) A survey of diseases in captive red wolves (Canis rufus), 1997–2012. J Zoo Wildl Med 47: 83–90. 10.1638/2014-0198.1. [DOI] [PubMed] [Google Scholar]
  58. Sheriff MJ, Dantzer B, Delehanty B, Palme R, Boonstra R (2011) Measuring stress in wildlife: techniques for quantifying glucocorticoids. Oecologia 166: 869–887. 10.1007/s00442-011-1943-y. [DOI] [PubMed] [Google Scholar]
  59. Stetz J, Hunt K, Kendall KC, Wasser SK (2013) Effects of exposure, diet, and thermoregulation on fecal glucocorticoid measures in wild bears. PloS One 8: 1–6. 10.1371/journal.pone.0055967. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Sylvia KE, Demas GE (2018) A gut feeling: microbiome-brain-immune interactions modulate social and affective behaviors. Horm Behav 99: 41–49. 10.1016/j.yhbeh.2018.02.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. U.S. Fish and Wildlife Service|Red Wolf Recovery Program (2022) FWS.gov. https://www.fws.gov/project/red-wolf-recovery-program (last accessed 12 July 2022).
  62. Vaz J, Narayan EJ, Dileep Kumar R, Thenmozhi K, Thiyagesan K, Baskaran N (2017) Prevalence and determinants of stereotypic behaviours and physiological stress among tigers and leopards in Indian zoos. PloS One 12: 1–27. 10.1371/journal.pone.0174711. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Weerth C (2017) Do bacteria shape our development? Crosstalk between intestinal microbiota and HPA axis. Neurosci Biobehav Rev 83: 458–471. 10.1016/j.neubiorev.2017.09.016. [DOI] [PubMed] [Google Scholar]
  64. Wielebnowski NC, Fletchall N, Carlstead K, Busso JM, Brown JL (2002) Noninvasive assessment of adrenal activity associated with husbandry and behavioral factors in the north American clouded leopard population. Zoo Biol 21: 77–98. 10.1002/zoo.10005. [DOI] [Google Scholar]
  65. Yordy J, Mossotti RH (2016) Kinship, maternal effects, and management: juvenile mortality and survival in captive African painted dogs, Lycaon pictus. Zoo Biol 35: 367–377. 10.1002/zoo.21306. [DOI] [PubMed] [Google Scholar]
  66. Young AJ, Carlson AA, Monfort SL, Russell AF, Bennett NC, Clutton-Brock T (2006) Stress and the suppression of subordinate reproduction in cooperatively breeding meerkats. Proc Natl Acad Sci U S A 103: 12005–12010. 10.1073/pnas.0510038103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Young KM, Walker SL, Lanthier C, Waddell WT, Monfort SL, Brown JL (2004) Noninvasive monitoring of adrenocortical activity in carnivores by fecal glucocorticoid analyses. Gen Comp Endocrinol 137: 148–165. 10.1016/j.ygcen.2004.02.016. [DOI] [PubMed] [Google Scholar]
  68. Yu JH, Brown J, Boisseau N, Barthel T, Murray S (2021) Effects of Lupron and surgical castration on fecal androgen metabolite concentrations and intermale aggression in capybaras (Hydrochoerus hydrochaeris). Zoo Biol 40: 135–141. 10.1002/zoo.21586. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Web_Material_coae008
web_material_coae008.zip (405.1KB, zip)

Data Availability Statement

The data underlying this article will be shared on reasonable request to the corresponding author.

Articles from Conservation Physiology are provided here courtesy of Oxford University Press

RESOURCES