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. 2026 Apr 30;12(3):e70966. doi: 10.1002/vms3.70966

Characterising Reference Intervals in Clinically Normal Red Wolves (Canis rufus): A Baseline for the Detection of Clinical Disease

Heather Broughton 1,✉, Karen Wolf 2, Kadie Anderson 2,3
PMCID: PMC13129490  PMID: 42057642

ABSTRACT

Background

The American red wolf (Canis rufus) is a critically endangered canid that has been extirpated throughout most of its historic range in the United States. To counter the decline of this charismatic species, captive breeding programs were established in the 1970s and 1980s and reservoirs for the red wolf population continue to be managed at zoological institutions around North America. Complicating management, red wolves suffer from a high prevalence of chronic illness in captivity, often contributing to reduced fecundity and survival in breeding programs.

Objectives

Given the above concerns, as well as the lack of a true species‐specific baseline to diagnose disease in general, this study sought to establish concise clinical reference intervals for commonly used haematological and biochemical parameters in red wolves.

Methods

Utilising exclusion criteria set by the National Committee for Clinical Laboratory Standards (NCCLS) and the American Society for Veterinary Clinical Pathology (ASVCP), this study established clinical reference intervals for 35 biochemical and haematological parameters from 31 healthy captive red wolves at the Red Wolf Propagation Facility in Graham, Washington.

Results and Conclusions

The clinical reference intervals developed by this study offer the first set of healthy, species‐specific baselines to diagnose disease that may better serve veterinary clinicians in earlier detection of illness and initiation of treatment, which is likely to improve long‐term clinical outcomes and disease management in red wolves.

Clinical Study Registration

This work was performed ad hoc alongside ongoing efforts for wolf management and is not associated with a registered clinical study.

Keywords: biochemical, haematological, reference intervals, veterinary

Using data and samples collected from 31 red wolves (Canis rufus) housed at the Red Wolf Propagation Facility in Graham, Washington, this study establishes a narrower and more accurate clinical baseline for 35 haematological and biochemical parameters used in the diagnosis of clinical disease in this critically endangered species.

Photo credit: Katie Cotterill, Point Defiance Zoo and Aquarium, 2025.

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1. Introduction

The red wolf (Canis rufus) is a free‐ranging canid species native to the southeastern United States, with historic populations ranging from Pennsylvania south to Florida and west to Texas (Rivenbark et al. 2018; Figure 1). Throughout the course of the 19th and 20th centuries, intensive predator control programs, coupled with significant habitat loss, led to a decline in red wolf numbers, and the species was declared functionally extinct in the wild as of 1980 (Rabon and Harrison 2014). As a result, the Endangered Species Act added red wolves to its list of protected species and initiated intensive management under the US Fish and Wildlife Services (USFWS) Red Wolf Recovery Program to establish reservoir populations in a captive breeding program at the Red Wolf Propagation Facility (RWPF) in Graham, Washington, which is also co‐managed by Point Defiance Zoo & Aquarium (PDZA) in Tacoma, Washington (Rabon and Harrison 2014). While initial efforts to repopulate the species targeted more than 400 animals as potential breeding pairs, genetic hybridisation with coyotes and grey wolves led to the inclusion of only 14 animals as the founding ancestors for all red wolves in captivity today (Rabon and Harrison 2014).

FIGURE 1.

FIGURE 1

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One of several critically endangered red wolves (Canis rufus) housed at the Red Wolf Propagation Facility in Graham, Washington, USA. Photo credit: Katie Cotterill, Point Defiance Zoo and Aquarium, 2025.

Critical reductions in genetic heterozygosity among bottlenecked animal populations can have profound impacts on animal health through the perpetuation of genetic abnormalities and reduced adaptability to biotic and abiotic threats (Bouzat 2010; Frankham et al. 2010). As such, USFWS and all participating institutions under the Association of Zoos and Aquariums (AZA) Red Wolf Species Survival Plan regularly track the health and breeding potential of all wolves living in captivity (Rabon and Harrison 2014). While captive breeding programs have achieved success in terms of preservation of the species, exceeding initial projections set forth by the USFWS, current morbidity and mortality from chronic illnesses, coupled with reproductive failure in captivity, have prompted concerns among veterinarians and wildlife management officials alike (Bragg et al. 2020; Fontaine et al. 2024; Henson et al. 2017; Hinton et al. 2013; Murray et al. 2015).

In a retrospective analysis conducted between 1997 and 2012 involving 378 red wolves, researchers found that the highest cause of mortality in adult animals was chronic illness that could have been prevented or managed if identified at earlier stages (Seeley et al. 2016). Current practice utilises comparable reference intervals from dogs, or reference intervals created by the Zoological Information Management System (ZIMS) and mostly based on grey wolves, as a mode of comparison for red wolf parameters (Bragg et al. 2020; Fontaine et al. 2024; Henson et al. 2017; ISIS 2002; Murray et al. 2015; Rivenbark et al. 2018; Seeley et al. 2016; AZA Canid TAG 2012). However, as red wolves (Canis rufus), grey wolves (Canis lupus) and domestic dogs (Canis familiaris) are distinct species, and biotic and abiotic variables are likely to vary because of their divergent living conditions and diet, this practice has likely led to cases of misdiagnosis, or a complete lack of detection, of early subclinical cases of disease due to the absence of species‐specific cutoff criteria.

Using standards established by the National Committee for Clinical Laboratory Standards (NCCLS) and the American Society for Veterinary Clinical Pathology (ASVCP), this study sought to rectify this issue by establishing an updated baseline against which abnormal and clinically relevant biochemical/haematological values could be compared for diagnosis of underlying health conditions in red wolves (Broughton et al. 2017; Friedrichs et al. 2012; Gunn‐Christie et al. 2012; Henson et al. 2017). Specifically, ASVCP and NCCLS selection criteria were applied within a captive population of 36 red wolves during annual fall physical examinations, allowing the identification of 31 clinically healthy individuals around which to base species‐specific cutoffs for blood biochemistry and haematology analytes commonly utilised by zoological veterinary practitioners.

2. Materials and Methods

For the preparation of reference intervals associated with this study, steps and selection criteria set forth by the NCCLS and ASVCP were closely followed as indicated below (Broughton et al. 2017; Friedrichs et al. 2012; Gunn‐Christie et al. 2012; Henson et al. 2017).

2.1. Preliminary Investigation

The sampling framework for this study was determined based on normal veterinary handling practices for red wolves in captivity at RWPF in Graham, Washington, one of the largest captive breeding strongholds in the United States. This study population was chosen to minimise biological variation (same diet, same location, same husbandry practices) while also providing a large enough sample group to allow extrapolation to the overall red wolf population—the 36 red wolves sampled at RWPF comprise 13% of the total 280 red wolves in captivity (USFWS 2026).

2.2. Selection of Reference Individuals

The initial sample population for this study included a captive population of 36 red wolves housed at RWPF. All wolves in this facility are descendants of 14 original captive breeding pairs and comprise a single pack from a genetic/familial standpoint, which is true for all red wolves currently in captivity. Wolves were captured in the fall of 2019 as part of the normally scheduled annual veterinary wellness examinations. Physical examinations and sample collection were facilitated via manual restraint using tarps and nets to guide animals into the den site, where they were restrained utilising a catch pole. No chemical restraint was used for the purposes of this study. Wolves captured included 18 females and 18 males, ranging in age from 3 to 12 years, as identified by external genitalia and recorded birthdates as kept in medical records. Because this study utilised samples and physical examination data already collected/conducted on a yearly basis as part of the veterinary examination process, all sampling was covered by an IACUC waiver (see disclosure statements).

2.3. Patient Preparation and Sample Collection

Wolves were evaluated for body condition score (ranked on a standard veterinary 9‐point scale); visual identification of lesions on the ears, nose or throat; heart and respiratory auscultation; urogenital lesions identified visually; and lymph node enlargement detected via palpation of the prescapular, axillary, inguinal and popliteal lymph nodes (American Animal Hospital Association 2010; Broughton et al. 2017). Blood for biochemical and haematological evaluations was collected via jugular venipuncture into red‐top (serum) and purple‐top (EDTA) tubes, respectively. Serum was separated on site, and all samples were sent immediately on ice to Zoetis Diagnostic Laboratories (Zoetis, Mukilteo, WA, USA) for evaluation. In addition, blood smears were made from EDTA whole blood evaluated at the same laboratory. These methods were selected to most closely mimic standard veterinary protocols at the park used to evaluate red wolves in captivity.

2.4. Exclusion Criteria for Patients

Wolves were excluded from the final evaluation if they were under current treatment for a known health issue; if lesions were identified on the ears, nose, throat, or urogenital region; if heart or lung auscultation was abnormal; if body condition was greater than 8/9 or less than 3/9; or if systemic lymph node enlargement was evident in more than one lymph chain. Medical records were used to exclude wolves currently receiving treatment for known disease, as wolves are closely monitored by veterinary staff and zookeepers. While the initial study started with 36 animals, creation of reference intervals was based on 31 healthy individuals after the above exclusion criteria were applied (five animals were excluded).

2.5. Analytical Quality and Procedures

Biochemical parameters were evaluated from serum and included metrics for glucose, blood urea nitrogen, creatinine, creatine kinase, total protein, globulin, albumin, albumin:globulin ratio, cholesterol, total bilirubin, alkaline phosphatase, aspartate aminotransferase, alanine aminotransferase and gamma‐glutamyltransferase, all of which are available via the ClinChem 25 panel through Zoetis (Laboratory Test Code # 1000; Zoetis, Mukilteo, WA, USA). Similarly, ions were evaluated using the above methods, as the ClinChem 25 profile also includes parameters for calcium, phosphate, sodium, chloride, potassium, anion gap and osmolarity. Finally, haematologic parameters were measured from EDTA whole blood using the electrical impedance available in Zoetis’ Basic CBC panel (Zoetis, Mukilteo, WA, USA; Laboratory Test Code # 2000). All samples were sent to the laboratory for processing the same day, negating the need for long‐term storage. The above methods and laboratories were chosen due to their rigorous quality control standards and to most closely mimic veterinary sampling protocols at RWPF and similar captive breeding institutions to minimise observational biases.

2.6. Postanalytical Considerations and Statistical Analysis of Reference Values

2.6.1. Graphical Presentation, Outlier Determination and Data Distribution

All data points were initially evaluated using standard statistical approaches to screen for normality, outliers, leverage points and linearity (R Core Team 2013). Specifically, data distribution was examined via histograms, plots of residuals versus fit values, standardised residuals, square root of standardised residuals, Cook's distance, Tukey's interquartile ranges (IQRs) and Shapiro–Wilk tests (Broughton et al. 2017; Friedrichs et al. 2012; Gunn‐Christie et al. 2012; Henson et al. 2017). No outliers or leverage points were identified for removal during this exploration, though some response variables were determined to be non‐normal, requiring log transformation and use of non‐parametric bootstrapping during analysis (Broughton et al. 2017; Friedrichs et al. 2012; Gunn‐Christie et al. 2012; Henson et al. 2017).

2.6.2. Determination of Reference Limits

Biochemistry and haematological values for reference intervals were determined using 90% confidence intervals calculated using MedCalc statistical software and modelled using non‐parametric bootstrapping techniques to account for small sample size (Friedrichs et al. 2012; Gunn‐Christie et al. 2012; MedCalc Statistical Software bvba 2014). Analyte minimums, maximums, mean, median and standard deviations were also provided by the MedCalc analyses and are provided for interpretation (Friedrichs et al. 2012; Gunn‐Christie et al. 2012; MedCalc Statistical Software bvba 2014). MedCalc compares all analytes on a Gaussian distribution with either identity or log transformation, as indicated by normality of the data structure.

2.6.3. Statistical Comparison to Demographic Parameters

In addition, all analytes were modelled against independent variables for age and sex to determine associations with demographic parameters. Models were constructed and evaluated in R statistical software using generalised linear models, which are useful for modelling normal, as well as non‐normal, biological data over traditional linear models (R Core Team 2013). Models for count variables employed the use of Poisson distributions with a log link as opposed to using a Gaussian distribution. While the NCCLS and ASVCP recommend Gaussian distributions for simplification, Poisson distributions offer a more statistically accepted methodology where count variables are measured due to the natural exclusion of negative values and the ability to handle discrete data (Fox et al. 2015). Continuous values were modelled using a Gaussian distribution with either an identity or a log link, depending on the data structure (R Core Team 2013). The dataset was also explored for overdispersion for count variables, which was not found to be a significant influence within the dataset. Final model selection was via AIC scores provided by stepwise comparison using the dredge function in the MuMIn package (Bartoń 2023). All logged values were back‐transformed prior to reporting for ease of interpretation. For complete model output, please refer to Table S1.

3. Results

3.1. Haematological Values and Demographic Associations

Reference intervals for haematological values established by this study, as determined via 90% confidence intervals, are listed above in Table 1. With regards to demographic trends in haematological parameters, both red and white blood cells showed strong or marginally statistically significant associations with age (Table 1). Older animals trended toward higher total leukocyte count, increased platelet counts, increased total polysegmented neutrophils, higher total lymphocyte count and increased total monocyte count (Table 1). Conversely, many parameters showed a decrease with advancing age, including red blood cells, haemoglobin concentration and haematocrit, as well as total counts of eosinophils (Table 1). In addition to age, sex was significantly associated with some haematological parameters, including polysegmented neutrophils and monocytes, which were decreased in males relative to females, and eosinophils, which were increased in males relative to females (Table 1; for the complete modelling approach to examine the above demographic trends, see Table S1).

TABLE 1.

Red wolf clinical reference ranges for haematological and biochemical parameters.

Category Analyte (units) Reference intervals (90% confidence) Minimum values Maximum values Mean/median values Standard deviation Demographic associations
Haematology White blood cells (K/µL) 5.1–15.7 5.1 21.7 9.7/9.2 3.2 Increases with age
Red blood cells (M/µL) 6.3–8.3 5.9 8.2 7.3/7.4 0.5 Decreases with age
Haemoglobin (g/dL) 16.5–21.8 15.1 20.7 18.8/19.0 1.2 Decreases with age
Haematocrit (%) 50.0–63.8 44.5 59.5 56.1/56.6 3.1 Decreases with age
Mean corpuscular volume (fL) 71.4–83.1 70.3 82.5 77.2/77.1 2.8 None
Mean corpuscular haemoglobin (pg) 23.6–28.0 23.9 28.0 25.8/25.8 1.1 None
Mean corpuscular haemoglobin concentration (%) 31.3–36.0 31.7 35.1 33.6/33.9 1.1 None
Platelet count (K/µL) 151.2–417.9 178.0 524.0 268.8/239.5 69.7 Increases with age
Polysegmented neutrophils (/µL) 3061.7–11,322.1 3596.0 16,535.0 6584.7/5848.0 2605.8 Increases with age; decreased in males versus females
Lymphocytes (/µL) 567.8–2836.1 1010.0 3081.0 1809.9/1684.0 532.0 Increases with age
Monocytes (/µL) 151.8–810.6 163.0 955.0 389.1/347.0 172.1 Increases with age; decreased in males versus females
Eosinophils (/µL) 189.7–3150.0 69.0 3192.0 891.6/786.0 594.7 Decreases with age; increased in males versus females
Basophils (/µL) 0–64.5 0.0 81.0 29.4/28.0 18.0 None
Biochemistry Glucose (mg/dL) 68.8–154.6 68.0 154.0 112.3/115.0 20.9 None
Blood urea nitrogen (mg/dL) 13.4–30.8 15.0 30.0 22.3/22.0 4.3 None
Creatinine (mg/dL) 0.6–2.9 0.4 2.0 1.4/1.5 0.4 None
Sodium (mEq/L) 142.2–151.8 143.0 153.0 147.2/147.0 2.4 Decreases with age
Potassium (mEq/L) 4.3–5.7 4.4 5.6 5.0/4.9 0.3 None
Na/K ratio 26.2–33.7 26.0 33.0 29.7/30.0 1.9 Decreases with age
Chloride (mEq/L) 110.1–118.8 111.0 121.0 114.4/114.0 2.3 Decreases with age
Carbon dioxide (mEq/L) 14.5–23.3 11.0 23.0 19.2/20.0 2.5 None
Anion gap 12.5–25.3 14.0 33.0 18.6/18.0 3.6 Decreased in males versus females
Calcium (mg/dL) 8.0–10.2 7.7 9.8 9.0/9.0 0.5 None
Phosphorus (mg/dL) 0.2–5.7 1.0 6.0 2.9/2.8 1.2 None
Osmolality 286.1–307.1 288.0 308.0 296.9/296.0 5.1 Decreases with age
Total protein (g/dL) 5.5–7.2 5.4 7.3 6.4/6.3 0.4 None
Albumin (g/dL) 2.8–4.1 2.7 4.0 3.4/3.4 0.3 Decreases with age
Globulin (g/dL) 2.2–3.7 2.4 4.3 3.0/2.9 0.4 Increases with age
Albumin:globulin ratio 0.8–1.6 0.7 1.6 1.2/1.2 0.2 Decreases with age
Total bilirubin (mg/dL) 0.1–0.2 0.1 0.2 0.1/0.1 0.0 None
Alkaline phosphatase (U/L) 0.0–42.6 0.0 49.0 21.7/20.0 10.9 None
Gamma‐glutamyl transferase (U/L) 1.1–8.5 0.0 5.0 2.6/3.0 1.4 None
Alanine aminotransferase (U/L) 29.9–185.2 40.0 230.0 82.5/71.0 44.1 Decreases with age
Aspartate aminotransferase (U/L) 19.8–95.2 27.0 105.0 57.9/57.0 18.1 None
Cholesterol (mg/dL) 59.7–217.5 86.0 226.0 141.7/138.0 37.5 Increases with age; increased in males versus females
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Note: The above table includes the 90% confidence (i.e., reference) intervals for biochemical/haematological parameters obtained during wellness checks performed in the fall of 2019 in a healthy population of 31 captive red wolves housed at the Red Wolf Propagation Facility in Graham, Washington. All analytes were measured via the ClinChem 25 panel (Test Code: 1000; biochemical parameters) and Basic CBC (Test Code: 2000; haematological parameters) at Zoetis Diagnostic Laboratories (Zoetis, Mukilteo, WA, USA). All reference range parameters were calculated in MedCalc statistical software using nonparametric bootstrapping techniques to account for small sample size as indicated by established clinical standards (Friedrichs et al. 2012; Gunn‐Christie et al. 2012; MedCalc Statistical Software bvba 2014). The 90% confidence intervals presented above encompass the range of expected biochemical/haematological values in 90% of healthy red wolves and do not necessarily fit within the confines of the measured minimum and maximum values for each parameter, as they are projected normal ranges. For comparison, the minimum, maximum, mean, median and standard deviation are also included for each parameter measured. Generalised linear models with covariates for wolf age and sex were also fit for each parameter of interest, and the directionality of statistically significant associations (p‐values <0.05) and marginally statistically significant associations (p‐values <0.09) between parameters of interest and wolf demographics is included (for full model output, see Table S1). Marginally statistically significant associations were included due to low sample size, as relevancy could not be fully excluded.

3.2. Biochemical Values and Demographic Associations

Reference intervals for biochemical parameters are also provided above in Table 1. Generally, advancing age showed strong associations with multiple biochemical parameters, including increases in globulin and cholesterol, and decreases in sodium, Na+/K+ ratio, chloride, osmolality, albumin, albumin:globulin ratio and alanine aminotransferase (Table 1). Of the biochemical parameters evaluated, only anion gap and cholesterol showed associations with sex, with anion gap being lower in males relative to females and cholesterol higher (Table 1; the modelling approach for biochemical trends with demographic parameters is also detailed in Table S1).

4. Discussion

The above findings establish clinical baselines within a healthy red wolf population, while considering age‐ and sex‐related variance in the interpretation of the data. As such, the baselines established here may serve as a more species‐specific resource for veterinarians when attempting to identify subclinical and early stages of disease in red wolves, allowing more accurate planning of therapeutic interventions.

In addition to establishing a more accurate reference range for this target species, the results presented here also demonstrate the importance of considering demographic parameters in the interpretation of results. For example, haematological parameters are heavily impacted by age and sex. Here, these differences are reflected as increases in neutrophils, monocytes, lymphocytes and platelets with increasing age, but decreases in other cell populations such as eosinophils and erythrocytes. While further work would be required to definitively determine the causes behind these changes in red wolves, some possibilities include increasing immunocompetence in adults, nonspecific inflammation due to immunosenescence in geriatric individuals, chronic stress and waning levels of sex steroids (Franceschi et al. 2000; Liu et al. 2023; Mahlknecht and Kaiser 2010; Weyand and Goronzy 2016). Similarly, differences in eosinophil, neutrophil and monocyte populations are seen between males and females, further supporting the role of sex steroids in driving these trends.

With regard to biochemical and electrolyte parameters, age‐driven increases in globulin and cholesterol are evident despite decreases in overall albumin and albumin:globulin ratio. Electrolyte ratios, including sodium, Na+/K+ ratio, chloride and osmolality, wane with age. Similarly, age‐related decreases are also noted with alanine aminotransferase, a biochemical analyte released by damaged hepatocytes. In combination, these changes might signal declining metabolic function with older age in red wolves like that noted in other species (McKenzie et al. 2025; Usui et al. 2014). These findings are more pronounced in males, which largely exhibit higher levels of cholesterol, but lower overall anion gap (Holven and Roeters van Lennep 2023).

While this study may better aid veterinarians and wildlife management officials in the early diagnosis and treatment of morbidity in captive and free‐ranging red wolves, it does have limitations. First, early subclinical stages of disease extending past those pathologies that could be easily recognised and excluded via preformed criteria might have been missed, meaning that some subclinical animals may have been included in the normal group. Second, as red wolf populations are highly fragmented across a range of geographic areas within the United States, biotic and abiotic variables of each new location should be considered when applying these findings to other captive red wolf populations. Despite these limitations, this study serves to establish a more concise baseline for the detection and diagnosis of morbidities within captive red wolves over those baselines currently available when detecting subclinical and early stages of disease.

5. Conclusion

Captive and free‐ranging red wolves continue to face significant hurdles in their fecundity and survival throughout the continental United States. While this study does not solve all pressing concerns for red wolf conservation, it does lend substantially to the management of red wolf health in captive breeding populations by establishing baseline clinical reference criteria specific to this charismatic species, thereby facilitating earlier diagnosis and treatment of common diseases important to the continued survival of the species.

Author Contributions

Heather Broughton: conceptualisation, investigation, writing – original draft, methodology, validation, visualisation, writing – review and editing, formal analysis, data curation, supervision, resources. Karen Wolf: conceptualisation, investigation, funding acquisition, methodology, validation, writing – review and editing, project administration, data curation, supervision. Kadie Anderson: conceptualisation, investigation, funding acquisition, methodology, validation, writing – review and editing, project administration, data curation, supervision.

Funding

Work for this project was made possible by funding and support from Point Defiance Zoo and Aquarium in Tacoma, Washington, Northwest Trek in Eatonville, Washington, and the Institute for Integrative Conservation at William and Mary in Williamsburg, Virginia. The Red Wolf Propagation Facility in Graham, Washington, provided the supportive infrastructure necessary to carry out this study.

Ethics Statement

All samples and data used in this study were previously approved by an IACUC waiver (EFIR19‐05), as all collected samples were procured during standard yearly health examinations at the Red Wolf Propagation Facility in Graham, Washington, without the need for additional handling or sampling.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1: Red wolf demographic trends in association with haematological and biochemical parameters.

VMS3-12-e70966-s001.docx (116.2KB, docx)

Acknowledgements

Work for this project was made possible by funding and support from Point Defiance Zoo and Aquarium in Tacoma, Washington, Northwest Trek in Eatonville, Washington, and the Institute for Integrative Conservation at William and Mary in Williamsburg, Virginia. The Red Wolf Propagation Facility in Graham, Washington, provided the supportive infrastructure necessary to carry out this study. In addition to serving as bastions for red wolf conservation and providing day‐to‐day care for the wolves included in the study, zoological and veterinary staff associated with these institutions also helped in the capture of wolves and collection of samples and health evaluations presented in this article as part of annual health monitoring. All samples used in this study were covered by an IACUC waiver (EFIR19‐05).

Data Availability Statement

All data used for this study are available immediately upon request.

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Associated Data

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

Supplementary Materials

Table S1: Red wolf demographic trends in association with haematological and biochemical parameters.

VMS3-12-e70966-s001.docx (116.2KB, docx)

Data Availability Statement

All data used for this study are available immediately upon request.

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