https://orcid.org/0000-0003-2177-6484
Figures
Abstract
Principles of allostasis and allostatic load have been widely applied in human research to assess the impacts of chronic stress on physiological dysregulation. Over the last few decades, researchers have also applied these concepts to non-human animals. However, there is a lack of uniformity in how the concept of allostasis is described and assessed in animals. The objectives of this review were to: 1) describe the extent to which the concepts of allostasis and allostatic load are applied theoretically to animals, with a focus on which taxa and species are represented; 2) identify when direct assessments of allostasis or allostatic load are made, which species and contexts are represented, what biomarkers are used, and if an allostatic load index was constructed; and 3) detect gaps in the literature and identify areas for future research. A search was conducted using CABI, PubMed, Agricola, and BIOSIS databases, in addition to a complementary hand-search of 14 peer-reviewed journals. Search results were screened, and articles that included non-human animals, as well as the terms “allostasis” or “allostatic” in the full text, were included. A total of 572 articles met the inclusion criteria (108 reviews and 464 peer-reviewed original research). Species were represented across all taxa. A subset of 63 publications made direct assessments of allostatic load. Glucocorticoids were the most commonly used biomarker, and were the only biomarker measured in 25 publications. Only six of 63 publications (9.5%) constructed an allostatic load index, which is the preferred methodology in human research. Although concepts of allostasis and allostatic load are being applied broadly across animal species, most publications use single biomarkers that are more likely indicative of short-term rather than chronic stress. Researchers are encouraged to adopt methodologies used in human research, including the construction of species-specific allostatic load indexes.
Citation: Seeley KE, Proudfoot KL, Edes AN (2022) The application of allostasis and allostatic load in animal species: A scoping review. PLoS ONE 17(8): e0273838. https://doi.org/10.1371/journal.pone.0273838
Editor: Sylvain Giroud, University of Veterinary Medicine Vienna: Veterinarmedizinische Universitat Wien, AUSTRIA
Received: April 20, 2022; Accepted: August 16, 2022; Published: August 30, 2022
Copyright: © 2022 Seeley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The link between stress and health is well documented across several taxa [1–3]. Acute, short-term stress is adaptive and essential for survival [3]; however, chronic or prolonged stress can have significant impacts on morbidity and mortality [4–6]. Most of the research evaluating the link between chronic stress and negative health outcomes has been done in humans [7–9] or laboratory animal models [10–12]. However, there is a growing body of work that evaluates the impact of chronic stress in non-human animals, both under managed care and in their native ranges [13–15].
Due to the important role of stress in human and animal health, many methods of measuring stress have been developed. These measurements include individual biomarkers such as leukocyte numbers and composition [16], leukocyte function [17], heart rate variability [18, 19], and glucocorticoids [20–22]. However, given that stressors cause a complex physiological and behavioral response in animals, there is no single measure that can fully quantify the stress response or its long-term effects; instead, it has been suggested that multiple biomarkers be used when making assessments about chronic stress [23–25].
In the last several decades, a concept known as “allostasis” has emerged as a framework for the complicated physiologic processes involved in the stress response [26, 27]. Allostasis is the idea of “stability through change” in which an organism makes physiological and behavioral adjustments in response to predictable and unpredictable stressors [26–28]. Allostasis is a complementary concept to homeostasis, which refers to the maintenance of certain physiological parameters within very narrow ranges [29]. Unlike homeostasis, allostatic parameters are not maintained within narrow ranges but instead fluctuate according to demand, such as an increase in heart rate and blood pressure during physical activity. Together, allostasis and homeostasis provide a holistic model for an organism’s response to stressors. Allostasis is maintained using the integrated responses of physiological axes such as the hypothalamic-pituitary-adrenal (HPA) and sympathetic-adrenal-medullary (SAM) axes, allowing for adaptation to both internal and external stressors [26].
Allostatic load (AL) is the cumulative cost incurred by somatic systems due to repeated or chronically activated allostasis [26, 30, 31]. All organisms experience daily and seasonal stressors with which they need to cope [26, 28, 31]. For instance, a prey animal will alter its behavior when faced with a predator, allowing it to evade predation and removing the stressor. However, when repeated or ongoing stressors overload an animal, there can be “wear and tear” across multiple somatic systems, which predisposes individuals to physiologic dysregulation and subsequent poor health [32, 33].
Allostatic load itself cannot be directly measured, instead it can only be inferred based on quantifiable biomarkers that change (either increase or decrease) from baseline levels due to the systemic dysregulation that occurs with chronically or repeatedly activated allostasis [23]. In 1997, Seeman et al. developed a model that estimated AL through an index (hereafter referred to as an “allostatic load index,” or ALI) that combined ten biomarkers to reflect the function of the neuroendocrine, cardiovascular, and metabolic systems. These initial biomarkers included norepinephrine, epinephrine, cortisol, dehydroepiandrosterone sulfate (DHEA-S), systolic blood pressure, diastolic blood pressure, high-density lipoprotein (HDL), total cholesterol-HDL ratio, waist-hip ratio, and glycosylated hemoglobin (HbA1c) [31]. This composite of biomarkers included both primary mediators of the stress response as well as the secondary mediators of allostasis. Since the original publication, over 50 different biomarkers have been used in various ALIs in the human literature [5, 23, 34].
ALIs are the primary means of evaluating AL in human populations, particularly in the fields of human health, anthropology, and sociology [34, 35]. There are hundreds of publications reporting associations between AL and chronic stressors in humans [4, 5, 36]. For example, it has been shown that people who disclose their sexual orientation have lower AL than those that do not [37], that caregiving and AL are predictive of future illness or disability [38], and that AL is impacted by ethnicity, gender, and educational attainment [39].
Several of the biomarkers incorporated into ALIs in human populations are well conserved across taxa. For instance, cortisol, glucose, DHEA-S, and interleukins are frequently incorporated into ALIs in humans [5] and are also measured in animal populations [40–42]. Despite the overlap in the individual biomarkers that are being measured in animals and humans, the application of ALIs to non-human animals has been limited, and individual biomarkers are sometimes used as proxies instead. In the last five years, our group has called for the application of a more rigorous AL methodology in animal populations [23, 43]. To better understand how the terms allostasis and AL have been used to date and where there are gaps in methodology, we conducted a scoping review of how the concepts of allostasis and AL are being applied in non-human animals.
Materials and methods
Review protocol and expertise
This scoping review was conducted using the Arksey and O’Malley framework [44]. The PRISMA-ScR checklist was utilized to ensure completeness (S1 Checklist). The protocol was created in advance of the literature review based on the input and expertise of the co-authors, which include veterinary medicine (KS), biological anthropology and primatology (AE), and animal welfare (KP). The repository of relevant articles and the resulting datasets are available upon request.
Review question and scope
The overall goal of this review was to describe how principles of allostasis are being used in research with non-human animals. We had three primary objectives: 1) describe the extent to which the concepts of allostasis and AL are applied theoretically to animal populations, with a focus on which taxa and species are represented; 2) identify when direct assessments of allostasis or AL are made, which species and contexts are represented, what biomarkers are used, and if an ALI was constructed; and 3) detect gaps in the literature and identify areas for future research.
Search strategy
A comprehensive search was conducted in CABI, PubMed®, Agricola, and BIOSIS™ databases on June 5, 2021, using the following algorithm: (allostasis OR allostatic AND (animal* OR wildlife* OR mammal* OR primate* OR avian* OR bird* OR reptile* OR snake* OR lizard* OR turtle* OR tortoise* OR amphibian* OR frog* OR fish*) NOT (human* OR hominidae OR “homo sapien”)). There were no constraints on the publication date for this search.
Databases do not always search the full text, so a complementary hand-search of 14 peer-reviewed journals was done. To select which journals were hand-searched, the results of the initial database search were categorized by publication and placed in rank order. Initially, the top 10 journals were going to be hand-searched. However, the 10th ranked journal had the same number of publications as the journals ranked 11 to 14, so all 14 were included in the review. The journals that were hand-searched included: General and Comparative Endocrinology; Hormones and Behavior; Physiology & Behavior; Comparative Biochemistry and Physiology Part A: Molecular and Integrative Physiology; Functional Ecology; PLOS One; Aquaculture; Integrative and Comparative Biology; Scientific Reports; Animals; Conservation Physiology; Journal of Experimental Biology; Oecologia; and Physiological and Biochemical Zoology. The website for each journal was used to conduct a full text search for the terms “allostasis” or “allostatic” and retrieve all relevant publications.
Relevance screening and inclusion criteria
The inclusion criteria for the scoping review were intentionally broad to allow for the inclusion of any relevant articles using non-human animal species. All search results were exported into Excel (Excel 2016, Microsoft Corporation, Redmond, WA). Duplicates were highlighted and removed using conditional formatting. An initial screening of the title and abstract was conducted by one author (KS). Articles written about non-human animals were included for full review, unless it was evident in the title or abstract that the animal was being used as a model for human disease. Only peer-reviewed research or review articles were included; editorials, commentaries, letters, conference proceedings, theses, and invited papers were excluded. Papers that were not available in English or could not be obtained via the Ohio State University Library system were also excluded.
After screening the title and abstract, all articles that were initially included were exported to a free citation manager (Zotero) and underwent full text review by one author (KS). Any article that pertained directly to a non-human animal population and included the terms “allostasis” or “allostatic” in the full text was included in the scoping review. Similar to the initial screening, any studies in which the animals were used as direct proxies for humans were excluded; however, laboratory animal studies were included if the research was aimed at learning about the welfare and response of the animal, and they were not used as models for humans. Articles where the search terms were listed in the literature cited or acknowledgments sections and not in other sections of the text were excluded. If the search terms were not found at all in the full text review, the publications were excluded.
To fulfill the second objective of this review, any publication that made direct assessments of AL by measuring biomarkers were coded by one author (KS). For the purpose of this review, only biomarkers that could be evaluated from ante-mortem samples (e.g., saliva, hair, feces, blood) or could be directly measured in living animals (e.g., heart rate, blood pressure) were evaluated.
Scoping review management, data charting, and analysis
All articles that met the inclusion criteria had the following data recorded: 1) taxa and species; 2) where in the article the search terms were mentioned (e.g., introduction, results); 3) whether the article was a review (including meta-analyses) or primary research; 4) which biomarkers were measured; 5) whether AL was inferred based on the biomarkers measured; and 6) if an ALI was constructed to evaluate AL.
Results
Descriptive statistics
Following the literature search and removal of duplicates, 2,460 publications were identified for title and abstract review, out of which 1,212 articles were identified for full text review (Fig 1). Reasons for exclusion after full text review included animals being used as models for human disease (n = 63), conceptual articles that did not specifically apply to animal populations (n = 14), the terms “allostasis” or “allostatic” being found in the bibliography only (n = 553), or the terms not being present in the full text (n = 10). A total of 572 articles met all inclusion criteria and were included in this scoping review (S1 Dataset). Of the included articles, 108 were review articles (including meta-analyses) and 464 were peer-reviewed original research publications.
This flowchart depicts article inclusion for allostasis and allostatic load in non-human animal species.
Articles were written between 2003 and 2021, with 84% (479/572) published within the last 10 years (Fig 2). Since the literature search was conducted in June of 2021, the bar in Fig 2 corresponding to the number of publications in 2021 only represents half of the year. Species across all five main vertebrate groups, as well as invertebrates, were represented (Fig 3): invertebrates (n = 10), fish (n = 143), amphibians (n = 12), reptiles (n = 38), birds (n = 134), and mammals (n = 177). There were also publications that referred to animal populations, but not a specific species or taxa (n = 32) or included multiple taxa (n = 26). Most species were discussed in only one or two publications, while some species were well represented in multiple publications (S1 Appendix). The most commonly studied fish species were Atlantic salmon (Salmo salar L.) (n = 21), gilthead seabream (Sparus aurata L.) (n = 20), and rainbow trout (Oncorhynchus mykiss) (n = 11). The most commonly studied reptile species were common lizards (Zootoca vivipara), and Eastern fence lizards (Sceloporus undulatus) with 6 publications each. The most commonly studied bird species were house sparrows (Passer domesticus) (n = 15), zebra finches (Taeniopygia guttata) (n = 7), and chickens (Gallus gallus domesticus) (n = 6). The most studied mammalian species were cows (Bos taurus) (n = 11), rats (Rattus spp.) (n = 11), and rhesus macaques (Macaca mulatta) (n = 8). An array of invertebrates and amphibian species were represented, but none that were studied in more than two publications.
A total of 572 articles were identified in the non-human animal literature using terms related to allostasis and/or allostatic load. *Search results for 2021 are through June and only represent half of the publications from this year.
A total of 572 articles were identified in the non-human animal literature using terms related to allostasis and/or allostatic load across all taxa. Of these, 63 directly assessed and made conclusions about allostatic load.
Biomarkers and assessment of allostatic load
Of the 572 publications, a total of 63 (11%) measured biomarkers to make assessments of allostasis or AL, 61 primary research publications (Table 1) and two meta-analyses [45, 46]. The two meta-analyses are not included in Table 1 due to the multiple species evaluated and the lack of primary data.
These 63 papers represented all taxa except amphibians (Fig 3) and numerous biomarkers were evaluated (Table 2). A vast majority of the publications (58/63; 92%) measured glucocorticoids (cortisol: n = 28, glucocorticoid metabolites: n = 16, corticosterone: n = 14) and in 25 papers (43%), glucocorticoids were the sole biomarker measured. Depending on the study, glucocorticoids were measured in a variety of tissues, including hair, feces, urine, plasma, and feathers.
Of the 63 publications that directly assessed allostasis or AL, only 6 (9.5%), many of which were from our group, constructed an ALI using published methodology [45, 86, 87, 89, 95].
Discussion
The overall goal of this scoping review was to evaluate the extent to which principles of allostasis and AL are being applied to non-human animals. Of the 572 articles included in this review, most were written within the last ten years. Over the last 5 years, there have been 40–60 peer-reviewed publications annually. This change reflects a growing application of allostasis and AL in animal populations.
The species diversity encompassed all taxonomic groups, with over 250 different species represented across the 572 articles. Seventy-nine percent of the publications discussed mammalian, fish, or avian species, with far fewer papers focusing on reptiles, amphibians or invertebrates. This gap in the literature presents opportunities for future research, particularly in taxa like amphibians, which are facing global population declines due to diseases like Batrachochytrium dendrobatidis (Bd) [103]. Since many disease states are diagnosed and/or monitored by measuring biomarkers (e.g., cholesterol and insulin resistance for metabolic syndrome in humans), ALIs encompassing multiple somatic systems have the potential to predict future health outcomes. The link between stress and risk of disease is well described in a wide variety of species, and ALIs have been used to characterize disease risks in human populations [104, 105]. Additionally, ALIs have the potential to be used to assess the impact of social and environmental stressors and how they drive animal movements and affect disease ecology on a broader scale [106].
Even within the largely represented groups, like fish, there is potential for expanded applications of AL. Many of the fish publications focused on the health and welfare of commercial species in aquaculture settings [54, 58, 107], with 61 publications looking at Atlantic salmon, gilthead seabream, rainbow trout or European seabass (Dicentrarchus labax). In contrast, only three papers explored allostasis in zebra fish (Danio rerio) [108–110], which are an important laboratory species that may benefit from studies of AL.
Despite 572 publications mentioning allostasis or AL, only a small proportion (63 publications, 11%) made direct assessments using physiological biomarkers. The other 509 articles only mentioned AL hypothetically as part of the introduction or discussion of other findings. Of this subset of 63 articles, 61 were primary research studies and two were meta-analyses that incorporated data from multiple publications to make their assessment. Within the primary research, 49 different species were studied across all taxa, with the exception of amphibians.
There were common themes amongst the 63 publications, including a focus on environmental challenges, social structure, and animals under managed care. Evaluating AL in the context of environmental challenges was a focus of several publications and encompassed different focus areas including the impacts of human activities (i.e., agriculture [51], urbanization [102], snow sports [14], and pollution [74]), and characterization of the effects of environmental parameters on AL (i.e., weather [70], fire [79], and salinity [47]). Several publications investigated the effects of social structure on AL in several species including hyena [100], Assamese macaques [75], bearded capuchins [81], cichlids [60], and rainbow trout [52]. Multiple researchers aimed to investigate the connection between animal management techniques and AL [53, 95], in some cases with an explicit emphasis on animal welfare [54, 92].
While each of the 63 publications drew direct conclusions about AL from their findings, the data need to be interpreted with caution. One of the biggest methodological challenges was that a high proportion (44%) of studies made conclusions about allostasis or AL based on glucocorticoids alone. This finding is unsurprising, as glucocorticoids have historically been considered the principle hormonal mediator for AL [26, 111, 112]. However, there are limitations to using glucocorticoids as the only measure of stress [113, 114]. For example, substantial inter- and intra-individual variation, as well as fluctuations due to temporary coping mechanisms associated with season or reproductive effort, complicate their interpretation [115–118]. Moreover, research in humans indicates that individual biomarkers are inadequate for estimating AL [119–123]. Therefore, given the advances in methodology, conclusions about AL cannot be made based on glucocorticoids alone.
A second consideration in interpreting the findings of many of these 63 publications is the use of AL to evaluate response to an acute stressor, often using a single-biomarker like glucocorticoids (e.g., [53, 70, 71, 85]). This deviates from the original purpose for the development of ALIs in humans, which was to estimate wear-and-tear as the result of chronic, long-term stress. Even when estimated using multiple biomarkers, AL should not be used to replace glucocorticoids as a measure of an acute stress response. For instance, Arlettaz et al. (2015) characterized the impact of free-riding snow sports had on black grouse (Tetrao tetrix) in an alpine habitat. To mimic the disturbance of sports, grouse were flushed on consecutive days and subsequently had elevated fecal glucocorticoids compared to baseline. Based on these findings it was concluded that repeated disturbances resulted in an increased AL and thereby presented a threat to wildlife populations. Similarly, Hing et al. (2016) investigated the effects of wildfires on brush-tailed bettongs (Bettongia penicillata) by measuring fecal glucocorticoids two days after a fire. When there was no significant elevation of glucocorticoids compared to baseline, it was concluded that this species adapts to these environmental challenges with no effect on AL. While these types of evaluations are essential to monitor high-risk populations, neither study adequately assessed AL, as the conclusions were based only on changes in cortisol levels occurring over a short time period.
Although ALIs have traditionally been used to evaluate the impact of chronic, long-term stressors in humans, there are challenges with this approach in non-human animal species, particularly wildlife, as it can be difficult to disentangle acute stress from chronic stress. For example, animals must be manually or chemically restrained to obtain a blood sample, which likely increases some of the biomarkers of interest, such as cortisol and glucose [124, 125]. Additionally, repeated sampling of animals can be challenging in wild settings, making it difficult to identify all the potential stressors an animal encounters over time. While it would not be an estimate of AL researchers may be able to adapt the methodology to try and consider using an index of biomarkers that would be expected to increase in the face of acute, short-term stress, such as glucose, cortisol, and catecholamines. This approach can allow us to gain a more robust understanding of the “cost” associated with acute stress in animals compared with single biomarkers such as cortisol [126].
Only six of the 63 publications that made direct assessments about AL used an ALI following the original method used in human research [31]. Western lowland gorillas (Gorilla gorilla gorilla) were the only non-human primate species in which an ALI was constructed and used to evaluate AL in four articles by the same research group [86–89]. Seven biomarkers were measured for the gorilla ALI, including albumin, cortisol, corticotropin releasing hormone (CRH), DHEA-S, glucose, IL-6, and tumor-necrosis factor (TNF)-α. Older animals, males, and gorillas with a higher number of stressful events over their lifetime had higher AL [86]. In a follow-up study, the authors found that wild-caught female gorillas had higher AL than mother-reared gorillas, although there was no difference in AL by rearing history for males [87]. Building upon their initial model, the authors found that the associations of AL with sex, age, stressful events, and rearing history remained when additional institutions were incorporated [88]. The authors later expanded the ALI and found that adding cholesterol and triglycerides improved predictions of morbidity and mortality risk in zoo housed gorillas [89].
In another publication that constructed an ALI, the authors proposed a measure of AL that they referred to as the “rat cumulative allostatic load measure (rCALM)” [95]. This study included the following biomarkers: cortisol, blood glucose, body weight, interleukin-1β (IL-1β), interleukin-2 (IL-2), IL-6, leptin, lactate, and creatine. The authors found that when evaluated individually the biomarkers were not predictive for neuronal deficits. However, when used as a comprehensive ALI, rCALM was an effective predictor of neurologic deficits. The authors concluded that the rCALM index estimated the effects of chronic stress and could potentially be used to indicate future disease risks.
The last publication that constructed an ALI used a meta-analysis. The aim of this publication was to evaluate hypotheses that explain variation in parasitism based on social status in vertebrate species [45]. The authors combined data from multiple studies and determined AL based on previously described methodology [46]. Authors concluded that AL was not correlated with relative parasitism in vertebrates [45]. However, the Goymann and Wingfield (2004) method used for calculating AL in this study deviates from the standards used in human populations. Instead of determining AL using an ALI comprised of multiple biomarkers, AL scores were assigned to individuals based on the assumed costs of becoming dominant within a social group; assessments were then made based on each individual’s assigned AL [46]. These assumed costs of dominance were based on cortisol levels in dominant vs. subordinate animals. However, cortisol alone is an insufficient proxy for AL, making this methodology problematic. Future meta-analyses that incorporate biomarkers measured in a species across multiple publications are encouraged.
Many of the papers reviewed here, both those that made direct assessments about AL and those that did not, measured multiple biomarkers, but assessed them individually and not as an index (e.g., [41, 49, 127]). Thus, there is an opportunity to re-assess previously collected data as an ALI, which may increase the power and impact of the data. For instance, Hudson et al (2020) measured six different biomarkers in Colorado checkered whiptail (Aspidoscelis neotesselata) that reflected reproductive status, energy metabolism and innate immunity. The authors assessed biomarkers individually to determine if there were AL changes based on season and reproductive status [62]. Instead, authors could have used these biomarkers to create an ALI to assess AL.
Since there is biological variation between taxa, there is likely not one single set of biomarkers that universally applies to all species, although it may be possible to determine a single ALI for a group of closely related species (e.g., great apes). Instead, we recommend that species-specific ALIs be constructed using biomarkers that are most reflective of long-term stressors. Biomarker discovery and advances in animal endocrinology are required to identify sufficient biomarkers to construct ALIs in many species. For example, we recently published a study that constructed an ALI to measure AL in ring-tailed lemurs (Lemur catta), and one of the largest challenges was finding biomarkers that could be measured in lemur serum with valid results [128]. In our case, several inflammatory cytokines were investigated as potential biomarkers for incorporation in the ALI but could not be reliably measured using the commercially available assays. It is important to note that one of the limitations of incorporating new biomarkers in an ALI is the lack of information about normal reference ranges in many species. Even when reference ranges are available, measured concentrations may be affected by the chemical restraint necessary for sample collection. However, researchers often uses sample-based cut-points instead of normal reference ranges to calculate AL; thus, an absence of reference ranges does not necessarily preclude the use of a biomarker for this type of research.
Future research using non-human animals should adopt a new way of thinking when assessing allostasis and AL. First, we encourage researchers to refrain from making conclusions about allostasis using glucocorticoids alone, and to focus on chronic rather than acute stressors. Second, we suggest that an ALI be constructed for each species using multiple relevant biomarkers that ideally reflect neuroendocrine, cardiovascular, immune, and metabolic systems as originally proposed for humans by Seeman et al. (1997). Using this approach, AL may become a useful measurement of stress and animal welfare. Indeed, several authors reflected on the importance of continuing to develop these measures as means of potentially evaluating chronic stress in animal populations [29, 82, 129] and acknowledged that AL may be an important tool in assessing animal welfare [43, 130]. The next step is to continue to refine approaches and methodologies to create practical and appropriate ALIs across species.
There are some limitations to the generalizability of this review. We only included manuscripts published in English, which excluded potentially relevant literature published in other languages. We also excluded studies that used laboratory rodents as models for humans, which may have limited the number of studies using common laboratory animals such as rodents and primates. Finally, “invertebrates” was not used as a specific search term; however, several invertebrate publications were found in the search results and included in the review. As a result, this review likely underestimated the number of papers that are applying allostasis or allostatic load to invertebrate species.
Conclusion
This review describes how principles of allostasis and allostatic load are being used in research with non-human animals. We identified a total of 572 peer-reviewed publications that mentioned allostasis and/or allostatic load published since 2003, covering a variety of non-human animal species. Of these, 63 made direct assessments about allostatic load in animals. However, many of these assessments were based on single biomarkers, such as glucocorticoids, and were focused on the effects of acute rather than chronic stressors. Future research in animals is encouraged in this area, with an emphasis on the creation of allostatic load indexes. Researchers should also use more consistent methodologies when assessing allostatic load, such as those already established in human research.
Supporting information
S1 Checklist. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist.
https://doi.org/10.1371/journal.pone.0273838.s001
(DOCX)
S1 Dataset. Full reference list of 572 articles included in the scoping review.
https://doi.org/10.1371/journal.pone.0273838.s002
(DOCX)
S1 Appendix. Taxonomic and species breakdown of literature applying the concepts of allostasis and allostatic load to animals.
https://doi.org/10.1371/journal.pone.0273838.s003
(DOCX)
References
- 1. Conti G, Hansman C, Heckman JJ, Suomif SJ. Primate evidence on the late health effects of early-life adversity. 2021; 7.
- 2. Kinnally EL, Martinez SJ, Chun K, Capitanio JP, Ceniceros LC. Early Social Stress Promotes Inflammation and Disease Risk in Rhesus Monkeys. Sci Rep. 2019;9. pmid:31110226
- 3.
Moberg GP, Mench JA, editors. The biology of animal stress: basic principles and implications for animal welfare. Wallingford, UK; New York, NY, USA: CABI Pub; 2000.
- 4. Beckie TM. A systematic review of allostatic load, health, and health disparities. Biol Res Nurs. 2012;14: 311–346. pmid:23007870
- 5. Juster R-P, McEwen BS, Lupien SJ. Allostatic load biomarkers of chronic stress and impact on health and cognition. Neurosci Biobehav Rev. 2010;35: 2–16. pmid:19822172
- 6. Stewart JA. The Detrimental Effects of Allostasis: Allostatic Load as a Measure of Cumulative Stress. J Physiol Anthropol. 2006;25: 133–145. pmid:16617218
- 7. Mcewen BS. Protection and Damage from Acute and Chronic Stress: Allostasis and Allostatic Overload and Relevance to the Pathophysiology of Psychiatric Disorders. Ann N Y Acad Sci. 2004;1032: 1–7. pmid:15677391
- 8. McEwen BS, Stellar E. Stress and the Individual: Mechanisms Leading to Disease. Arch Intern Med. 1993;153: 2093–2101.
- 9. Sterling P. Allostasis: A model of predictive regulation. Physiol Behav. 2012;106: 5–15. pmid:21684297
- 10. Meyer JS, Hamel AF. Models of Stress in Nonhuman Primates and Their Relevance for Human Psychopathology and Endocrine Dysfunction. ILAR J. 2014;55: 347–360. pmid:25225311
- 11. Nold V, Sweatman C, Karabatsiakis A, Böck C, Bretschneider T, Lawless N, et al. Activation of the kynurenine pathway and mitochondrial respiration to face allostatic load in a double-hit model of stress. Psychoneuroendocrinology. 2019;107: 148–159. pmid:31129488
- 12. Vodička M, Vavřínová A, Mikulecká A, Zicha J, Behuliak M. Hyper-reactivity of HPA axis in Fischer 344 rats is associated with impaired cardiovascular and behavioral adaptation to repeated restraint stress. Stress Amst Neth. 2020;23: 667–677. pmid:32543321
- 13. Anderson PA, Berzins IK, Fogarty F, Hamlin HJ, Guillette LJ. Sound, stress, and seahorses: The consequences of a noisy environment to animal health. Aquaculture. 2011;311: 129–138.
- 14. Arlettaz R, Nusslé S, Baltic M, Vogel P, Palme R, Jenni-Eiermann S, et al. Disturbance of wildlife by outdoor winter recreation: allostatic stress response and altered activity–energy budgets. Ecol Appl. 2015;25: 1197–1212. pmid:26485949
- 15. Sundrum A. Metabolic Disorders in the Transition Period Indicate that the Dairy Cows’ Ability to Adapt is Overstressed. Animals. 2015;5: 978–1020. pmid:26479480
- 16. Davis AK, Maney DL, Maerz JC. The use of leukocyte profiles to measure stress in vertebrates: a review for ecologists. Funct Ecol. 2008;22: 760–772.
- 17. Huber N, Marasco V, Painer J, Vetter SG, Göritz F, Kaczensky P, et al. Leukocyte Coping Capacity: An Integrative Parameter for Wildlife Welfare Within Conservation Interventions. Front Vet Sci. 2019;6: 105. pmid:31032265
- 18. Mohr E, Langbein J, Nurnberg G. Heart rate variability A noninvasive approach to measure stress in calves and cows. 2002; 9.
- 19. Pohlin F, Brabender K, Fluch G, Stalder G, Petit T, Walzer C. Seasonal Variations in Heart Rate Variability as an Indicator of Stress in Free-Ranging Pregnant Przewalski’s Horses (E. ferus przewalskii) within the Hortobágy National Park in Hungary. Front Physiol. 2017;8: 664. pmid:28936179
- 20. Malisch JL, Saltzman W, Gomes FR, Rezende EL, Jeske DR, Garland T. Jr. Baseline and Stress-Induced Plasma Corticosterone Concentrations of Mice Selectively Bred for High Voluntary Wheel Running. Physiol Biochem Zool. 2007;80: 146–156. pmid:17160887
- 21. Greggor AL, Spencer KA, Clayton NS, Thornton A. Wild jackdaws’ reproductive success and their offspring’s stress hormones are connected to provisioning rate and brood size, not to parental neophobia. Gen Comp Endocrinol. 2017;243: 70–77. pmid:27838379
- 22. Whipple AL, Ray C, Wasser M, Kitchens JN, Hove AA, Varner J, et al. Temporal vs. spatial variation in stress-associated metabolites within a population of climate-sensitive small mammals. Conserv Physiol. 2021;9. pmid:34026212
- 23. Edes AN, Crews DE. Allostatic load and biological anthropology. Am J Phys Anthropol. 2017;162: 44–70. pmid:28105719
- 24. Scheiber IBR, Sterenborg M, Komdeur J. Stress assessment in captive greylag geese (Anser anser)1. J Anim Sci. 2015;93: 2124–2133. pmid:26020308
- 25. Wielebnowski N. Stress and distress: evaluating their impact for the well-being of zoo animals. J Am Vet Med Assoc. 2003;223: 973–977. pmid:14552484
- 26. McEwen BS, Wingfield JC. The concept of allostasis in biology and biomedicine. Horm Behav. 2003;43: 2–15. pmid:12614627
- 27.
Sterling P, Eyer J. Allostasis: A new paradigm to explain arousal pathology. Handbook of life stress, cognition and health. Oxford, England: John Wiley & Sons; 1988. pp. 629–649.
- 28. Seeman TE, Crimmins E, Huang M-H, Singer B, Bucur A, Gruenewald T, et al. Cumulative biological risk and socio-economic differences in mortality: MacArthur Studies of Successful Aging. Soc Sci Med. 2004;58: 1985–1997. pmid:15020014
- 29. Hartigan P. A primer on stress-related pathology 1. Basic physiological considerations. Ir Vet J. 2004;57: 175–179.
- 30. Schulkin J. Allostasis: a neural behavioral perspective. Horm Behav. 2003;43: 21–27. pmid:12614630
- 31. Seeman TE, Singer BH, Rowe JW, Horwitz RI, McEwen BS. Price of adaptation—allostatic load and its health consequences. MacArthur studies of successful aging. Arch Intern Med. 1997;157: 2259–2268. pmid:9343003
- 32. Leahy R, Crews DE. Physiological dysregulation and somatic decline among elders: modeling, applying and re-interpreting allostatic load. Coll Antropol. 2012;36: 11–22. pmid:22816193
- 33. McEwen BS, Seeman T. Protective and damaging effects of mediators of stress. Elaborating and testing the concepts of allostasis and allostatic load. Ann N Y Acad Sci. 1999;896: 30–47. pmid:10681886
- 34. Duong MT, Bingham BA, Aldana PC, Chung ST, Sumner AE. Variation in the Calculation of Allostatic Load Score: 21 Examples from NHANES. J Racial Ethn Health Disparities. 2017;4: 455–461. pmid:27352114
- 35. Karlamangla AS, Singer BH, McEwen BS, Rowe JW, Seeman TE. Allostatic load as a predictor of functional decline: MacArthur studies of successful aging. J Clin Epidemiol. 2002;55: 696–710.
- 36. Guidi J, Lucente M, Sonino N, Fava GA. Allostatic Load and Its Impact on Health: A Systematic Review. Psychother Psychosom. 2021;90: 11–27. pmid:32799204
- 37. Juster R-P, Smith NG, Ouellet É, Sindi S, Lupien SJ. Sexual Orientation and Disclosure in Relation to Psychiatric Symptoms, Diurnal Cortisol, and Allostatic Load. Psychosom Med. 2013;75: 103–116. pmid:23362500
- 38. Stephen G, Kate M B. Caregiving and allostatic load predict future illness and disability: A population-based study. Brain Behav Immun—Health. 2021;16: 100295. pmid:34589788
- 39. Richardson LJ, Goodwin AN, Hummer RA. Social status differences in allostatic load among young adults in the United States. SSM—Popul Health. 2021;15: 100771. pmid:34584929
- 40. Firmino JP, Fernández-Alacid L, Vallejos-Vidal E, Salomón R, Sanahuja I, Tort L, et al. Carvacrol, Thymol, and Garlic Essential Oil Promote Skin Innate Immunity in Gilthead Seabream (Sparus aurata) Through the Multifactorial Modulation of the Secretory Pathway and Enhancement of Mucus Protective Capacity. Front Immunol. 2021;12: 633621. pmid:33777020
- 41. Hoffman CL, Higham JP, Heistermann M, Coe CL, Prendergast BJ, Maestripieri D. Immune function and HPA axis activity in free-ranging rhesus macaques. Physiol Behav. 2011;104: 507–514. pmid:21635909
- 42. Whitham JC, Bryant JL, Miller LJ. Beyond Glucocorticoids: Integrating Dehydroepiandrosterone (DHEA) into Animal Welfare Research. Animals. 2020;10: 1381. pmid:32784884
- 43. Edes AN, Wolfe BA, Crews DE. Evaluating allostatic load: A new approach to measuring long-term stress in wildlife. J Zoo Wildl Med. 2018;49: 272–282. pmid:29900795
- 44. Arksey H O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8: 19–32.
- 45. Habig B, Doellman MM, Woods K, Olansen J, Archie EA. Social status and parasitism in male and female vertebrates: A meta-analysis. Sci Rep. 2018;8: 3629. pmid:29483573
- 46. Goymann W, Wingfield JC. Allostatic load, social status and stress hormones: the costs of social status matter. Anim Behav. 2004;67: 591–602.
- 47. Freire CA, Cuenca ALR, Leite RD, Prado AC, Rios LP, Stakowian N, et al. Biomarkers of homeostasis, allostasis, and allostatic overload in decapod crustaceans of distinct habitats and osmoregulatory strategies: an empirical approach. Comp Biochem Physiol A Mol Integr Physiol. 2020;248: 110750. pmid:32592759
- 48. Steell SC, Cooke SJ, Eliason EJ. Artificial light at night does not alter heart rate or locomotor behaviour in Caribbean spiny lobster (Panulirus argus): insights into light pollution and physiological disturbance using biologgers. Conserv Physiol. 2020;8. pmid:33304586
- 49. Bermejo-Nogales A, Nederlof M, Benedito-Palos L, Ballester-Lozano GF, Folkedal O, Olsen RE, et al. Metabolic and transcriptional responses of gilthead sea bream (Sparus aurata L.) to environmental stress: new insights in fish mitochondrial phenotyping. Gen Comp Endocrinol. 2014;205: 305–315. pmid:24792819
- 50. Samaras A, Espírito Santo C, Papandroulakis N, Mitrizakis N, Pavlidis M, Höglund E, et al. Allostatic Load and Stress Physiology in European Seabass (Dicentrarchus labrax L.) and Gilthead Seabream (Sparus aurata L.). Front Endocrinol. 2018;9: 451. pmid:30158900
- 51. Blevins ZW, Wahl DH, Suski CD. Reach-Scale Land Use Drives the Stress Responses of a Resident Stream Fish. Physiol Biochem Zool. 2014;87: 113–124. pmid:24457926
- 52. Fernandes-de-Castilho M, Pottinger TG, Volpato GL. Chronic social stress in rainbow trout: Does it promote physiological habituation? Gen Comp Endocrinol. 2008;155: 141–147. pmid:17521651
- 53. Brijs J, Sandblom E, Rosengren M, Sundell K, Berg C, Axelsson M, et al. Prospects and pitfalls of using heart rate bio-loggers to assess the welfare of rainbow trout (Oncorhynchus mykiss) in aquaculture. Aquaculture. 2019;509: 188–197.
- 54. Calabrese S, Nilsen TO, Kolarevic J, Ebbesson LOE, Pedrosa C, Fivelstad S, et al. Stocking density limits for post-smolt Atlantic salmon (Salmo salar L.) with emphasis on production performance and welfare. Aquaculture. 2017;468: 363–370.
- 55. Grassie C, Braithwaite VA, Nilsson J, Nilsen TO, Teien H-C, Handeland SO, et al. Aluminum exposure impacts brain plasticity and behavior in Atlantic salmon (Salmo salar). J Exp Biol. 2013;216: 3148–3155. pmid:23661775
- 56. Hoglund E, Hogberget R, Atland A, Haraldstad T, Overli O, Vindas MA. Effects of repeated short episodes of environmental acidification on Atlantic salmon (Salmo salar) from a landlocked population. Sci Total Environ. 2021;753. pmid:32889313
- 57. Hoglund E, Korzan W, Atland A, Haraldstad T, Hogberget R, Mayer I, et al. Neuroendocrine indicators of allostatic load reveal the impact of environmental acidification in fish. Comp Biochem Physiol Part C Toxicol Pharmacol. 2020;229: 108679. pmid:31794875
- 58. Iversen MH, Eliassen RA. The effect of allostatic load on hypothalamic–pituitary–interrenal (HPI) axis before and after secondary vaccination in Atlantic salmon postsmolts (Salmo salar L.). Fish Physiol Biochem. 2014;40: 527–538. pmid:24045864
- 59. Lika K, Pavlidis M, Mitrizakis N, Samaras A, Papandroulakis N. Do experimental units of different scale affect the biological performance of European sea bass Dicentrarchus labrax larvae? J Fish Biol. 2015;86: 1271–1285. pmid:25846855
- 60. Mileva VR, Fitzpatrick JL, Marsh-Rollo S, Gilmour KM, Wood CM, Balshine S. The Stress Response of the Highly Social African Cichlid Neolamprologus pulcher. Physiol Biochem Zool. 2009;82: 720–729. pmid:19807269
- 61. Graham SP, Freidenfelds NA, Thawley CJ, Robbins TR, Langkilde T. Are invasive species stressful? The glucocorticoid profile of native lizards exposed to invasive fire ants depends on the context. Physiol Biochem Zool. 2017;90: 328–337. pmid:28384419
- 62. Hudson SB, Kluever BM, Webb AC, French SS. Steroid hormones, energetic state, and immunocompetence vary across reproductive contexts in a parthenogenetic lizard. Gen Comp Endocrinol. 2020;288: 113372. pmid:31866306
- 63. Hudson SB, Lidgard AD, French SS. Glucocorticoids, energy metabolites, and immunity vary across allostatic states for plateau side-blotched lizards (Uta stansburiana uniformis) residing in a heterogeneous thermal environment. J Exp Zool Part Ecol Integr Physiol. 2020;333: 732–743. pmid:32959993
- 64. Josserand R, Dupoué A, Agostini S, Haussy C, Le Galliard J-F, Meylan S. Habitat degradation increases stress-hormone levels during the breeding season, and decreases survival and reproduction in adult common lizards. Oecologia. 2017;184: 75–86. pmid:28364226
- 65. Lind C, Moore IT, Akçay Ç, Vernasco BJ, Lorch JM, Farrell TM. Patterns of Circulating Corticosterone in a Population of Rattlesnakes Afflicted with Snake Fungal Disease: Stress Hormones as a Potential Mediator of Seasonal Cycles in Disease Severity and Outcomes. Physiol Biochem Zool. 2017;91: 765–775. pmid:29286254
- 66. López‐Jiménez L, Blas J, Tanferna A, Cabezas S, Marchant T, Hiraldo F, et al. Ambient temperature, body condition and sibling rivalry explain feather corticosterone levels in developing black kites. Portugal S, editor. Funct Ecol. 2016;30: 605–613.
- 67. Lopez-Jimenez L, Blas J, Tanferna A, Cabezas S, Marchant T, Hiraldo F, et al. Lifetime variation in feather corticosterone levels in a long-lived raptor. Oecologia Berl. 2017;183: 315–326. pmid:27568027
- 68. Schultner J, Kitaysky AS, Welcker J, Hatch S. Fat or lean: adjustment of endogenous energy stores to predictable and unpredictable changes in allostatic load. Boonstra R, editor. Funct Ecol. 2013;27: 45–55.
- 69. Silva de Souza Matos L, Palme R, Silva Vasconcellos A. Behavioural and hormonal effects of member replacement in captive groups of blue-fronted amazon parrots (Amazona aestiva). Behav Processes. 2017;138: 160–169. pmid:28286082
- 70. de Bruijn R, Romero LM. Artificial rain and cold wind act as stressors to captive molting and non-molting European starlings (Sturnus vulgaris). Comp Biochem Physiol A Mol Integr Physiol. 2013;164: 512–519. pmid:23277223
- 71. Frongia GN, Peric T, Leoni G, Satta V, Berlinguer F, Muzzeddu M, et al. Assessment of Cortisol and DHEA Concentrations in Griffon Vulture (Gyps fulvus) Feathers to Evaluate its Allostatic Load. Ann Anim Sci. 2020;20: 85–96.
- 72. Johns DW, Marchant TA, Fairhurst GD, Speakman JR, Clark RG. Biomarker of burden: Feather corticosterone reflects energetic expenditure and allostatic overload in captive waterfowl. Williams T, editor. Funct Ecol. 2018;32: 345–357.
- 73. Cornelius JM, Zylberberg M, Breuner CW, Gleiss AC, Hahn TP. Assessing the role of reproduction and stress in the spring emergence of haematozoan parasites in birds. J Exp Biol. 2014;217: 841–849. pmid:24265426
- 74. Monclus L, Ballesteros-Cano R, de la Puente J, Lacorte S, Lopez-Bejar M. Influence of persistent organic pollutants on the endocrine stress response in free-living and captive red kites (Milvus milvus). Environ Pollut. 2018;242: 329–337. pmid:29990940
- 75. Fürtbauer I, Heistermann M, Schülke O, Ostner J. Low female stress hormone levels are predicted by same- or opposite-sex sociality depending on season in wild Assamese macaques. Psychoneuroendocrinology. 2014;48: 19–28. pmid:24980035
- 76. King JM, Bradshaw SD. Stress in an Island kangaroo? The Barrow Island euro, (Macropus robustus isabellinus). Gen Comp Endocrinol. 2010;167: 60–67. pmid:20178800
- 77. Moreira CM, Dos Santos LP, Sousa MBC, Izar P. Variation of glucocorticoid metabolite levels is associated with survival demands in immature and reproductive demands in adult wild black capuchins (Sapajus nigritus). Int J Psychol Res. 2016;9: 20–29.
- 78. Thompson NA. Understanding the links between social ties and fitness over the life cycle in primates. Behaviour. 2019;156: 859–908.
- 79. Hing S, Jones KL, Rafferty C, Thompson RCA, Narayan EJ, Godfrey SS. Wildlife in the line of fire: evaluating the stress physiology of a critically endangered Australian marsupial after bushfire. Aust J Zool. 2016;64: 385.
- 80. Warburton EM, Khokhlova IS, Palme R, Surkova EN, van der Mescht L, Krasnov BR. Flea infestation, social contact, and stress in a gregarious rodent species: minimizing the potential parasitic costs of group-living. Parasitology. 2020;147: 78–86. pmid:31452472
- 81. Mendonça-Furtado O, Edaes M, Palme R, Rodrigues A, Siqueira J, Izar P. Does hierarchy stability influence testosterone and cortisol levels of bearded capuchin monkeys (Sapajus libidinosus) adult males? A comparison between two wild groups. Behav Processes. 2014;109: 79–88. pmid:25239540
- 82. Ludwig C, Dehnhard M, Pribbenow S, Silinski-Mehr S, Hofer H, Wachter B. Asymmetric reproductive aging in cheetah (Acinonyx jubatus) females in European zoos. 2019; 7.
- 83. Heinrich SK, Hofer H, Courtiol A, Melzheimer J, Dehnhard M, Czirják GÁ, et al. Cheetahs have a stronger constitutive innate immunity than leopards. Sci Rep. 2017;7: 44837. pmid:28333126
- 84. Anestis SF. Urinary cortisol responses to unusual events in captive chimpanzees (Pan troglodytes). Stress. 2009;12: 49–57. pmid:18850493
- 85. Fusi J, Peric T, Probo M, Cotticelli A, Faustini M, Veronesi MC. How Stressful Is Maternity? Study about Cortisol and Dehydroepiandrosterone-Sulfate Coat and Claws Concentrations in Female Dogs from Mating to 60 Days Post-Partum. Animals. 2021;11: 1632. pmid:34072931
- 86. Edes AN, Wolfe BA, Crews DE. Assessing Stress in Zoo-Housed Western Lowland Gorillas (Gorilla gorilla gorilla) Using Allostatic Load. Int J Primatol. 2016;37: 241–259.
- 87. Edes AN, Wolfe BA, Crews DE. Rearing history and allostatic load in adult western lowland gorillas (Gorilla gorilla gorilla) in human care: Rearing History and Allostatic Load in Gorillas. Zoo Biol. 2016;35: 167–173. pmid:26881840
- 88. Edes AN, Wolfe BA, Crews DE. The first multi-zoo application of an allostatic load index to western lowland gorillas (Gorilla gorilla gorilla). Gen Comp Endocrinol. 2018;266: 135–149. pmid:29746855
- 89. Edes AN, Edwards KL, Wolfe BA, Brown JL, Crews DE. Allostatic Load Indices With Cholesterol and Triglycerides Predict Disease and Mortality Risk in Zoo-Housed Western Lowland Gorillas (Gorilla gorilla gorilla). Biomark Insights. 2020;15: 117727192091458. pmid:32425494
- 90. Hämäläinen A, Heistermann M, Kraus C. The stress of growing old: sex- and season-specific effects of age on allostatic load in wild grey mouse lemurs. Oecologia. 2015;178: 1063–1075. pmid:25847061
- 91. Setchell JM, Smith T, Wickings EJ, Knapp LA. Stress, social behaviour, and secondary sexual traits in a male primate. Horm Behav. 2010;58: 720–728. pmid:20688067
- 92. Montillo M, Rota Nodari S, Peric T, Polloni A, Corazzin M, Bergamin C, et al. Steroids in pig hair and welfare evaluation systems: combined approaches to improve management in pig breeding? Vet Ital. 2020;56: 177–184. pmid:33543913
- 93. Seeber PA, Franz M, Dehnhard M, Ganswindt A, Greenwood AD, East ML. Plains zebra (Equus quagga) adrenocortical activity increases during times of large aggregations in the Serengeti ecosystem. Horm Behav. 2018;102: 1–9. pmid:29630896
- 94. Marin MT, Cruz FC, Planeta CS. Chronic restraint or variable stresses differently affect the behavior, corticosterone secretion and body weight in rats. Physiol Behav. 2007;90: 29–35. pmid:17023009
- 95. McCreary JK, Erickson ZT, Paxman E, Kiss D, Montina T, Olson DM, et al. The rat cumulative allostatic load measure (rCALM): a new translational assessment of the burden of stress. Ng J, editor. Environ Epigenetics. 2019;5: dvz005. pmid:31065381
- 96. Caslini C, Comin A, Peric T, Prandi A, Pedrotti L, Mattiello S. Use of hair cortisol analysis for comparing population status in wild red deer (Cervus elaphus) living in areas with different characteristics. Eur J Wildl Res. 2016;62: 713–723.
- 97. Montillo Caslini, Peric Prandi, Netto Tubaro, et al. Analysis of 19 Minerals and Cortisol in Red Deer Hair in Two Different Areas of the Stelvio National Park: A Preliminary Study. Animals. 2019;9: 492. pmid:31357529
- 98. Verbeek E, Colditz I, Blache D, Lee C. Chronic stress influences attentional and judgement bias and the activity of the HPA axis in sheep. Homberg J, editor. PLOS ONE. 2019;14: e0211363. pmid:30699168
- 99. Benhaiem S, Hofer H, Dehnhard M, Helms J, East ML. Sibling competition and hunger increase allostatic load in spotted hyaenas. Biol Lett. 2013;9: 20130040. pmid:23616643
- 100. Davidian E, Wachter B, Heckmann I, Dehnhard M, Hofer H, Hoener OP. The interplay between social rank, physiological constraints and investment in courtship in male spotted hyenas. Funct Ecol.
- 101. Schoof VAM, Jack KM, Ziegler TE. Male Response to Female Ovulation in White-Faced Capuchins (Cebus capucinus): Variation in Fecal Testosterone, Dihydrotestosterone, and Glucocorticoids. Int J Primatol. 2014;35: 643–660.
- 102. Price K, Kittridge C, Damby Z, Hayes SG, Addis EA. Relaxing life of the city? Allostatic load in yellow-bellied marmots along a rural–urban continuum. Elizabeth A, editor. Conserv Physiol. 2018;6. pmid:30591838
- 103. Olson DH, Aanensen DM, Ronnenberg KL, Powell CI, Walker SF, Bielby J, et al. Mapping the Global Emergence of Batrachochytrium dendrobatidis, the Amphibian Chytrid Fungus. PLOS ONE. 2013;8: e56802. pmid:23463502
- 104. Aich P, Potter Griebel. Modern approaches to understanding stress and disease susceptibility: A review with special emphasis on respiratory disease. Int J Gen Med. 2009; 19. pmid:20360883
- 105. Sabbah W, Watt RG, Sheiham A, Tsakos G. Effects of allostatic load on the social gradient in ischaemic heart disease and periodontal disease: evidence from the Third National Health and Nutrition Examination Survey. J Epidemiol Community Health. 2008;62: 415–420. pmid:18413454
- 106. Creel S, Dantzer B, Goymann W, Rubenstein DR. The ecology of stress: effects of the social environment. Boonstra R, editor. Funct Ecol. 2013;27: 66–80.
- 107. Kolarevic J, Baeverfjord G, Takle H, Ytteborg E, Reiten BKM, Nergård S, et al. Performance and welfare of Atlantic salmon smolt reared in recirculating or flow through aquaculture systems. Aquaculture. 2014;432: 15–25.
- 108. Lara RA, Vasconcelos RO. Impact of noise on development, physiological stress and behavioural patterns in larval zebrafish. Sci Rep. 2021;11: 6615. pmid:33758247
- 109. Manuel R, Gorissen M, Zethof J, Ebbesson LOE, van de Vis H, Flik G. Unpredictable chronic stress decreases inhibitory avoidance learning in Tuebingen long-fin zebrafish: stronger effects in the resting phase than in the active phase. J Exp Biol. 2014;217: 3919–3928. pmid:25267842
- 110. Pavlidis M, Sundvik M, Chen Y-C, Panula P. Adaptive changes in zebrafish brain in dominant–subordinate behavioral context. Behav Brain Res. 2011;225: 529–537. pmid:21875623
- 111. Dallman MF, Strack AM, Akana SF, Bradbury MJ, Hanson ES, Scribner KA, et al. Feast and Famine: Critical Role of Glucocorticoids with Insulin in Daily Energy Flow. Front Neuroendocrinol. 1993;14: 303–347. pmid:8258378
- 112. Crossin GT, Trathan PN, Phillips RA, Gorman KB, Dawson A, Sakamoto KQ, et al. Corticosterone Predicts Foraging Behavior and Parental Care in Macaroni Penguins. Am Nat. 2012;180: E31–E41. pmid:22673661
- 113. McEwen BS. What Is the Confusion With Cortisol? Chronic Stress. 2019;3: 247054701983364. pmid:31608312
- 114. MacDougall-Shackleton SA, Bonier F, Romero LM, Moore IT. Glucocorticoids and “Stress” Are Not Synonymous. Integr Org Biol. 2019;1: obz017. pmid:33791532
- 115. Busch DS, Hayward LS. Stress in a conservation context: A discussion of glucocorticoid actions and how levels change with conservation-relevant variables. Biol Conserv. 2009;142: 2844–2853.
- 116. Cockrem JF. Individual variation in glucocorticoid stress responses in animals. Gen Comp Endocrinol. 2013;181: 45–58. pmid:23298571
- 117. Madliger CL, Love OP. The Need for a Predictive, Context-Dependent Approach to the Application of Stress Hormones in Conservation. Conserv Biol. 2014;28: 283–287. pmid:24283988
- 118. Dantzer B, Fletcher QE, Boonstra R, Sheriff MJ. Measures of physiological stress: a transparent or opaque window into the status, management and conservation of species? Conserv Physiol. 2014;2. pmid:27293644
- 119. Seeman TE, McEwen BS, Rowe JW, Singer BH. Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging. Proc Natl Acad Sci U S A. 2001;98: 4770–4775. pmid:11287659
- 120. Seplaki CL, Goldman N, Weinstein M, Lin Y-H. Measurement of cumulative physiological dysregulation in an older population. Demography. 2006;43: 165–183. pmid:16579213
- 121. Hwang A-C, Peng L-N, Wen Y-W, Tsai Y-W, Chang L-C, Chiou S-T, et al. Predicting All-Cause and Cause-Specific Mortality by Static and Dynamic Measurements of Allostatic Load: A 10-Year Population-Based Cohort Study in Taiwan. J Am Med Dir Assoc. 2014;15: 490–496. pmid:24631353
- 122. Castagné R, Garès V, Karimi M, Chadeau-Hyam M, Vineis P, Delpierre C, et al. Allostatic load and subsequent all-cause mortality: which biological markers drive the relationship? Findings from a UK birth cohort. Eur J Epidemiol. 2018;33: 441–458. pmid:29476357
- 123. Akinyemiju T, Wilson LE, Deveaux A, Aslibekyan S, Cushman M, Gilchrist S, et al. Association of Allostatic Load with All-Cause and Cancer Mortality by Race and Body Mass Index in the REGARDS Cohort. Cancers. 2020;12: 1695. pmid:32604717
- 124. Pavlova EV, Alekseeva GS, Erofeeva MN, Vasilieva NA, Tchabovsky AV, Naidenko SV. The method matters: The effect of handling time on cortisol level and blood parameters in wild cats. J Exp Zool Part Ecol Integr Physiol. 2018;329: 112–119. pmid:29893473
- 125. Leroy JLMR, Bossaert P, Opsomer G, Bols PEJ. The effect of animal handling procedures on the blood non-esterified fatty acid and glucose concentrations of lactating dairy cows. Vet J. 2011;187: 81–84. pmid:19889556
- 126. Gormally BMG, Romero LM. What are you actually measuring? A review of techniques that integrate the stress response on distinct time‐scales. Angelier F, editor. Funct Ecol. 2020;34: 2030–2044.
- 127. King GD, Chapman JM, Midwood JD, Cooke SJ, Suski CD. Watershed-Scale Land Use Activities Influence the Physiological Condition of Stream Fish. Physiol Biochem Zool. 2016;89: 10–25. pmid:27082521
- 128. Seeley KE, Proudfoot KL, Wolfe B, Crews DE. Assessing Allostatic Load in Ring-Tailed Lemurs (Lemur catta,). Animals. 2021;11: 3074. pmid:34827806
- 129. McEwen BS, Wingfield JC. What is in a name? Integrating homeostasis, allostasis and stress. Horm Behav. 2010; 7.
- 130. Korte SM, Olivier B, Koolhaas JM. A new animal welfare concept based on allostasis. Physiol Behav. 2007;92: 422–428. pmid:17174361