Decades later: revisiting the physiological markers of avian dehydration

Decades later: revisiting the physiological markers of avian dehydration | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 12 January 2026 V1 Latest version Share on Decades later: revisiting the physiological markers of avian dehydration Authors : Adrien Levillain 0009-0002-7643-8503 [email protected] , Astolfo Mata , Sylvie Massemin , and Sophie Reichert Authors Info & Affiliations https://doi.org/10.22541/au.176826076.64657877/v1 312 views 93 downloads Contents Abstract Decades later: revisiting the physiological markers of avian dehydration Introduction Methods Physiological markers of avian dehydration Plasma osmolarity and ions Nitrogen excretion Alternative markers Dehydration diagnostic and tolerance Glossary Supplementary material Supplementary tables Supplementary analysis References Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Understanding physiological responses is necessary to predict populations fate in a changing environment. Dehydration risk is expected to increase with climate change for several wild bird populations, especially in desert environments, and potentially leading to population collapses. Yet, very few field studies directly assess hydration state of wild birds. In this article, we provide ways to do so as we present a systematic review of the physiological markers of avian dehydration. We find that plasma osmolarity and plasma concentrations of sodium and chloride are the most reliable blood markers of dehydration, and seem to scale linearly with dehydration severity. Hematocrit appears to predict dehydration but multiple concerns impede its suitability as a marker. Alternatively, characteristics of the excreta -- such as urine osmolarity -- may represent less-invasive means to assess hydration state. There is currently no consensus on dehydration thresholds and tolerance, and further research is need to refine our dehydration diagnostic, representing crucial perspectives for future studies. Decades later: revisiting the physiological markers of avian dehydration Abstract Understanding physiological responses is necessary to predict populations fate in a changing environment. Dehydration risk is expected to increase with climate change for several wild bird populations, especially in desert environments, and potentially leading to population collapses. Yet, very few field studies directly assess hydration state of wild birds. In this article, we provide ways to do so as we present a systematic review of the physiological markers of avian dehydration. We find that plasma osmolarity and plasma concentrations of sodium and chloride are the most reliable blood markers of dehydration, and seem to scale linearly with dehydration severity. Hematocrit appears to predict dehydration but multiple concerns impede its suitability as a marker. Alternatively, characteristics of the excreta – such as urine osmolarity – may represent less-invasive means to assess hydration state. There is currently no consensus on dehydration thresholds and tolerance, and further research is need to refine our dehydration diagnostic, representing crucial perspectives for future studies. Keywords: avian, dehydration, ecophysiology, osmoregulation, water restriction List of symbols and abbreviations P [compound] : concentration of a specific compound in the plasma or serum. P osm : Plasma and serum osmolality (mOsm.kg -1 ) or osmolarity (in mOsm.L -1 ). TBW: total body water UA: uric acid U osm : Urine osmolarity or osmolality Introduction Several field studies have highlighted the importance of water availability on avian survival or breeding success, especially during heat events (e.g. Bourne et al., 2020; Conrey et al., 2016; Londe et al., 2021; Pattinson et al., 2022). Moreover, avian cooling costs and dehydration risk are expected to increase with climate warming, most notably for desert birds ( e.g. Albright et al., 2017; Conradie et al., 2019, 2020; Cook et al., 2020; McKechnie and Wolf, 2010; Riddell et al., 2019, 2021). Understanding how dehydration risk may affect populations is therefore of main importance when predicting their fate in a changing climate, emphasizing the need to directly assess dehydration and its consequences in the wild. While research addressing the physiological markers of avian dehydration appears to have peaked during the 1980s, we are unaware of any comprehensive published reviews of this topic, and these markers found very little application in field studies (Figure 1; Goldstein and Zahedi, 1990; Salaberria et al., 2014). As the majority of the published studies focus on poultry models, this research may not be well known to researchers in the fields of ecophysiology, ecology and conservation biology. Under laboratory conditions, water restriction affects many aspects of avian performance (reviewed by El Sabry et al., 2023), including fertility, reproduction (Koerth and Guthery 1991, Giuliano et al. 1995, 1998, Niranjan and Srivastava 2019) and growth ( e.g. Goldstein and Ellis, 1991; Herr Viola et al., 2009; Mhmoud et al., 2023). Dehydration also upregulates physiological markers of stress in birds ( e.g. corticosterone or heterophil to lymphocyte ratio; Cain and Lien, 1985; Iheukwumere and Herbert, 2003; Toghyani et al., 2011). In this Review, we discuss potential means for assessing the hydration state, including measurement of total body water, body mass loss under controlled conditions, or blood and urine characteristics (e.g. osmolarity, ions concentrations; Cheuvront et al., 2010; Cheuvront et al., 2013; Shirreffs, 2003). Osmoregulatory systems in birds have been extensively reviewed and we do not cover this topic (Braun, 1982; Goldstein, 2006; Hughes, 2003; Laverty and Skadhauge, 2008; Orosz and Echols, 2020; Skadhauge, 1981; Takei, 2000). Urine production in the kidney and post-renal urine handling are briefly summarized in Figure 2. Finally, we discuss dehydration diagnostic and perspectives for future studies. Figure 1. Histogram and density curve of the publication dates of articles investigating the physiological effect of dehydration on various physiological markers in birds (n=35). Median, 1991. Mean, 1995. Dehydration is defined as a significant loss of body mass or total body water during water restriction. Figure 2. (a) Renal and (b) post-renal modification of the urine in birds. Regulation of urine flow rate and concentration are crucial processes in the regulation of body fluids homeostasis in birds. These processes have been extensively reviewed elsewhere (Skadhauge 1981, Braun 1982, Goldstein 2006, Orosz and Echols 2020), and we recommend Orosz and Echols (2020) for a recent review of avian nephron morphology and renal physiology. Briefly, urine flow rate (UFR) is determined by the glomerular filtration rate (GFR) and the rate of tubular water reabsorption. Concentration of the excreted ureteral urine is dependent on the sum of the filtration, reabsorption and secretion processes (see Glossary). Water reabsorption in the renal tubules depends mainly on sodium and chloride reabsorption. Dehydrated individuals have a reduced GFR and increased rate of tubular water reabsorption, reducing the UFR (anti-diuresis) and producing a hyperosmolar ureteral urine, ultimately conserving water. Avian species are also capable of post-renal urine modification (Figure 2b adapted from Skadhauge (1981) with permission; reviewed by Hughes, 2003; Laverty and Skadhauge, 2008). Retrograde peristalsis (orange arrows) allows the movement of ureteral urine towards the upper part of the cloaca (coprodeum) and lower part of the gut (colon, ceca). Active sodium and chloride reabsorption during this process can induce water reabsorption by osmosis in the colon and ceca. The fluid produced following post-renal modification is sometimes referred to as ‘cloacal urine’ or ‘cloacal fluid’. Methods To review potential physiological markers of avian dehydration, we searched in ISI Web of Science on 4th December 2023, using the terms “(TS=”bird$” OR TS=”avian”) AND (TS=”dehydration” OR (TS=”Water” AND (TS=”restriction” OR TS=”deprivation”)))”, which yielded 610 articles (Dataset 1). Only studies that met our four eligibility criteria were retained for further analysis. Firstly, we only considered studies that induced dehydration through water restriction or deprivation, including thermal dehydration (i.e. the combination of water restriction and heat exposure). Studies that induced dehydration through salt exposure or by using restriction of both water and food were rejected, as fasting or salt exposure can influence many physiological variables including potential dehydration markers. Secondly, we specified that the study should report a significant decrease in either body mass or total body water mass during the experiment. We rejected studies on growing individuals because we could not assess body mass loss. Since studies did not always report whether the decrease in body mass was significant, we retained articles reporting a mean body mass loss of at least 11% during water restriction, representing a severe dehydration according to McKechnie and Wolf, 2010; Wolf, 2000). Thirdly, in order for a study to be included in our analysis, we also specified that it should measure some potential markers of dehydration within plasma or excreted body fluids (urine/faeces, salt gland excreta, tears, saliva) and report statistics. Note that we do not distinguish plasma from serum because it is likely that their content of potential dehydration markers ( e.g. electrolytes, nitrogenous compounds; Kiseleva et al., 2022) is similar; both are referred to as plasma hereafter. Since we did not compare values between studies, and only assessed the within-study effect, we did not dissociate osmolality (in mOsm.kg -1 ) from osmolarity (in mOsm.L -1 ). We only use the term osmolarity hereafter for clarity. Finally, we only retained papers that were written in English. When an article investigated multiple species, we considered the analyses for each species to be independent, and we counted them as separate ‘studies’. The initial literature search on Web of Science yielded 610 results (Dataset 1). We retained 140 articles after reading the abstracts, and 22 after reading the full text to assess whether the eligibility criteria were met. We added 13 articles from parallel reading; these were mainly articles published before 1990 (since 1970) and thus not indexed in Web of Science. This resulted in a total of 35 articles or 37 studies (Dataset 2). Summary findings of these studies are presented in Table 1. Data on body mass loss, hematocrit, plasma osmolarity and plasma ionic concentration (sodium, chloride and potassium) were extracted from studies using a longitudinal design ( i.e. repeated measure before and during water restriction; Dataset 3). Results from these analyses are presented in Table 2. When data was not available, we extracted the data from plots using the PlotDigitizer software (Aydin and Yassikaya 2021). We used linear mixed models to take into account pseudo replication, as well as bird species and order [model formula: measure ~ predictor + (1|Study)+(1|Species/Order); Bates et al., 2015]. Do note that we had to account for pseudo-replication because some studies measured markers of dehydration multiple times during the protocol (e.g. after 8, 12 and 16 hours of water deprivation). The model intercept was fixed at 0 because of the longitudinal nature of these studies. We also conducted the analysis using phylogenetic generalized least-squares regression to account for phylogenetic distance between species (Supplementary material, Figure SI-1, Table SI-1, Table SI-2). Results were highly similar and interpretations identical. We report only our linear mixed models in this review to homogenize our statistics. We report statistics as follows: n=number of observations, N=number of studies, sp=number of species, ord=number of avian orders. We report marginal (R 2 m ) as measure of the variance explained by fixed effects (Nakagawa et al. 2017). Physiological markers of avian dehydration Overview of the studies Overall, the median year of publication for the selected articles was 1991 (ranging from 1970 to 2021; Figure 1). Among the seven bird orders studied, Galliformes was the most highly represented (n=19/37; 51.4%), followed by Passeriformes (n=8/37; 21.6%), Casuariiformes (which was represented only by the emu, Dromaius novaehollandiae ; n=4/37; 10.8%), Anseriformes ( represented only by the Pekin duck, Anas platyrhynchos domesticus ; n=3/37; 8.1%) and others (Columbiformes, Psittaciformes and Struthioniformes; n=1 per order). The relative over-representation of Galliformes was mainly owing to the high proportion of studies on the domestic fowl ( Gallus domesticus ; n=9/37; 24.3%) and Japanese quail ( Coturnix japonica ; n=5/37; 13.5%). Total body water Dehydration is defined as a deficiency in total body water (TBW; Lacey et al., 2019). Consequently, assessing dehydration should be best achieved by a direct measurement of TBW. There are several available methods for measuring TBW; here, we discuss isotopic methods and quantitative magnetic resonance (QMR), as these are the most applicable to field studies (reviewed by Guglielmo et al., 2011; McWilliams and Whitman, 2013). Isotopic methods have been the ‘gold standard’ for TBW estimation for decades (reviewed by Speakman, 1997; Speakman and Hambly, 2016; Westerterp, 2017). These methods rely on the injection or oral administration of labeled water, and later sampling of blood, excreta or even exhaled breath vapor (Mitchell et al. 2015, Bourne et al. 2019, Whiteman et al. 2019). This method is labor intensive, need to be calibrated for the system studied, is quite expensive and requires some specialized equipment. Triple-labelled water can also provide estimation of water influx and efflux, as well as energy expenditure, contrary to other methods (for field application see Bourne et al., 2019; Bourne et al., 2021; Cooper et al., 2019; Sabat et al., 2021; Smit and McKechnie, 2015). It has recently been used to dissociate metabolic water production from exogeneous water acquisition (i.e. water from drinking or in food), which opens interesting perspectives for field studies (Whiteman et al., 2019; Sabat et al., 2021). When compared to isotopic methods, QMR yields similar estimates of body composition for avian species (Guglielmo et al. 2011, Mitchell et al. 2015) and has been applied in field studies (e.g. Guglielmo et al., 2022; Kelsey and Bairlein, 2019). Coefficient of variation in QMR for TBW has been reported to be <3% (Guglielmo et al. 2011). QMR necessitates the contention of birds but measurements are rapid (~2min) and no sedation is required. The main drawbacks of QMR are the price and weight of the equipment. In contrast to isotopic methods, QMR measurements do not require any prior administration of a marker and subsequent biological sampling. QMR has been used to estimate body condition in water restricted rufous-collared sparrows ( Zonotrichia capensis ; Navarrete et al., 2021), for which body mass and TBW loss under water restriction were highly correlated. Hematocrit Dehydrated individuals are likely to suffer from a decrease in blood volume ( e.g. Dawson et al., 1983; Stewart, 1972; Takei et al., 1988), potentially explaining the observed increase in the concentration of blood cells (Chikumba et al. 2013) and hemoglobin (Arad 1983, Carmi et al. 1993) during water restriction. The results of the selected studies support this hypothesis, as water restriction had a positive effect on hematocrit in 72.2% of studies (Table 1). Moreover, body mass loss was positively correlated with an increasing hematocrit when compiling data from multiple studies (Figure 3a; Table 2, p-value<0.001, R 2 m =0.10). This was also the case for analyses restricted to the domestic fowl ( Gallus domesticus ; est=0.73, SE=0.30, t=2.44, p-value=0.029, R 2 m =0.06, n=16, N=4). However, low R 2 m suggests that hematocrit is a poor predictor of body mass loss under water restriction, and thus dehydration severity. This is probably because plasma volume loss during water restriction is compensated for by the movement of fluid from the intracellular or interstitial compartment to the blood compartment, meaning that birds are likely ‘plasma conservers’ (Arad et al., 1989; Carmi et al., 1993, 1994; Dawson et al., 1983). For instance, Carmi et al (1994) reported an increase in hematocrit for rock pigeons ( Columbia livia ) only during severe dehydration (≥19% body mass loss). Hematocrit can also be influenced by other events such as variation in body condition, flight activity or salt loading in addition to dehydration (Roberts 1992, Jenni et al. 2006, Fair et al. 2007, Bradley et al. 2020). Due the hematocrit likely decreasing with salt intake, hematocrit may be less reliable for seabirds (Hughes 1995). Finally, its measurement may have limited precision relative to the biological variations expected (Goldstein and Zahedi 1990, Cheuvront and Kenefick 2014). Table 1: Number and proportion of studies reporting significant effects of water restriction on blood and excreta characteristics in avian species. Blood characteristics Blood volume 4/4 100% (-) Hematocrit 13/18 72.2% (+) P osm 31/33 93.9% (+) P [Na] 14/17 82.4% (+) P [Cl] 8/9 88.9% (+) P [K] 3/14 21.4% Unclear* P [UA] 2/2 − (+) P [Urea] 1/1 − (+) P [Creatinine] 1/2 − (+) P [AVT] 7/7 100% (+) P [AII] 3/3 100% (+) Excreta characteristics U osm 8/8 100% (+) Excreta water content 3/3 100% (-) Defecation rate 1/1 − (-) The proportion of studies was calculated when the sample size was equal to at least three studies. The list of studies is available as a supplementary file (Dataset 1). AVT, vasotocin; AII, angiotensin II; Cl, chloride; K, potassium; n=number of studies with significant effect; N=total number of studies; Na, sodium; P [compound] , concentration of a specific compound in the plasma; P osm , plasma osmolarity; UA, uric acid; U osm , urine osmolarity. *P [K] was reported to significantly decrease in 2 studies, and increase in one, but was most often not related with water restriction. Plasma osmolarity and ions Plasma osmolarity (P osm ) consistently increased with water restriction in most of the studies retained (93.9%; Table 1). The change in P osm was also positively correlated with body mass loss during water restriction across multiple studies, meaning that P osm seems to increase linearly with dehydration severity (Figure 3b; Table 2; p-value<0.001, R 2 m =0.39). The relationship between body mass loss and the increase in P osm was also significant when analyses were restricted to the domestic fowl (est=0.64, SE=0.06, t=11.28, p-value<0.001, R 2 m =0.61, n=17, N=5). This is consistent with the literature apart from the birds, as plasma osmolarity seems to be the most suitable marker of hydration state for humans (Cheuvront and Kenefick 2014) and has been reported to increase with dehydration in other taxa (e.g. mouse, Bekkevold et al., 2013; snake, Brusch and DeNardo, 2017; lizard, Chabaud et al., 2023; tortoise, Peterson, 2002). Measurements of plasma osmolarity may therefore represent an important mean to assess avian dehydration in the field. These measurements, requiring little plasma volume, are relatively inexpensive and have previously been shown to be highly repeatable for birds under both laboratory and field conditions (Goldstein and Zahedi, 1990). However, P osm increases with salt intake, and therefore may be less reliable to predict dehydration in seabirds (Roberts 1992, Hughes 2003, Peña-Villalobos et al. 2013). There are a couple of physiological mechanisms that might explain the increase in P osm with dehydration. Firstly, this relationship might be partly explained by hemoconcentration resulting from reduced plasma volume during water restriction. However, as discussed above, plasma volume may not decrease linearly during dehydration. Moreover, P osm increase during water restriction was not significantly correlated with changes in hematocrit across multiple studies (Table 2; t=1.57, p-value=0.139). Another mechanism contributing to the relationship between P osm and dehydration was the retention of electrolytes during water loss. This is supported by our results, as both P [Na] and P [Cl] consistently increased with water restriction (Table 1), and variation in their levels during water restriction were correlated with changes in P osm (Figure S2; Table 2; P [Na] : p-value<0.001, R 2 m =0.26; P [Cl] : p-value<0.001, R 2 m =0.75). Conversely, there was no significant variation in the plasma concentration of potassium (P [K] ) with water restriction in most studies (Table 1), and P [K] variation during water restriction was not correlated with variation in P osm (Table 2; t=0.11, p-value=0.914). In birds, electrolytes represent ~95% of P osm , mainly owing to relatively high concentrations of sodium (P [Na] ) and chloride (P [Cl] ; Scanes, 2015). During periods of water restriction, increased sodium and chloride reabsorption in the nephron and in the cloaca-coprodeum allow for water reabsorption through osmosis, ultimately reducing excretory water loss (Figure 2; Laverty and Skadhauge, 2008; Skadhauge, 1981). Moreover, an increase in P osm may allow plasma volume to be conserved through the creation of an osmotic gradient between the intracellular or interstitial compartments and the blood ( e.g. Roberts, 1992). Figure 3. Relationship between body mass loss during water restriction and hematocrit or plasma osmolarity. (a) Hematocrit change is expressed as a percentage of the initial value; p-value<0.001, R 2 m =0.10, n=36, N=12, sp=9, ord=6. (b) Plasma osmolarity change is expressed as a percentage of the initial value; p-value<0.001, R 2 m =0.39, n=47, N=24, sp=16, ord=8. The relationship is still significant when outliers are removed. Regression lines and prediction intervals of the models are plotted. n: number of observations, N: number of studies, sp: number of species, ord: number of avian orders. Table 2. Summary of model statistics assessing the link between changes in plasma osmolytes, hematocrit and body mass during water restriction across multiple studies. ΔHct ~ BML 0.65±0.16 <0.001 4.04 0.10 36/12 9/6 ΔP osm ~ BML 0.73±0.06 <0.001 12.02 0.39 47/24 16/8 ΔP osm ~ ΔHct 0.17±0.11 0.139 1.57 0.02 34/11 8/5 ΔP osm ~ ΔP [Na] 0.65±0.05 <0.001 12.86 0.26 28/15 13/8 ΔP osm ~ ΔP [Cl] 0.77±0.04 <0.001 18.18 0.75 17/11 10/6 ΔP osm ~ ΔP [K] 0.03±0.25 0.914 0.11 0.00 24/15 13/8 Model formula: [y~x + (1|Study) + (1|Species/Order)]. Δ, changes during water restriction; BML, body mass loss; Cl, chloride; Hct: hematocrit; K, potassium; Na, sodium; n, number of observations; N, number of studies; ord, number of avian orders; P [compound] , concentration of a specific compound in the plasma; P osm , plasma osmolarity; R 2 m , marginal R 2 ; SE, standard error; sp, number of species. Nitrogen excretion Byproducts of metabolism, such as uric acid (UA), urea and creatinine (Table 1) in the plasma may represent potential markers of dehydration. Plasma concentrations of UA (P [UA] ; Arad and Marder, 1983; Chikumba et al., 2013; Gerson and Guglielmo, 2011; Lumeij, 1987; Radin et al., 1996), urea (P [Urea] ; Arad et al., 1987, 1989; Arad and Marder, 1983; Lumeij, 1987) and creatinine (P [Creat]; Lumeij, 1987; Vanderhasselt et al., 2013) increased with water restriction in several studies and across multiple species. Unfortunately, many of the studies cited above used a protocol involving food restriction in combination with water restriction and were therefore excluded from our review, explaining the fact that a smaller number of studies investigating concentrations of nitrogenous byproducts are reported in Table 1. UA, urea and creatinine are excreted in the kidney, and their plasmatic concentrations may increase during dehydration as a result of a decreased excretion rate. Although excretion of these compounds is partially dependent on glomerular filtration, mechanisms regulating their overall respective excretion are different (Figure 2a). Approximately 90% of the UA excretion is owed to tubular secretion, meaning that it is largely uncoupled from glomerular filtration (Dantzler 1978, Skadhauge 1981, Dudas et al. 2005). By contrast, urea is subject to tubular reabsorption, and its overall rate of excretion depends mainly on the urine flow rate (see Glossary). Urine flow rate is reduced during dehydration, causing a greater fraction of urea to be reabsorbed: when a bird is well hydrated, close to 100% of filtered urea is excreted, whereas ~99% can be reabsorbed during dehydration (Baum et al. 1975, Skadhauge 1981). Finally, creatinine excretion is mainly dependent on glomerular filtration; however, tubular secretion and reabsorption may play a secondary role (Gasthuys et al. 2019, Wani and Pasha 2021). Owing to the tubular reabsorption of urea during water restriction, the magnitude of increase in P [Urea] may greatly exceed the increase in P osm , P [Na] , P [Cl] , P [UA] or P [Creatinine] ; urea could therefore represent an adequate marker of dehydration (Arad et al. 1987, 1989, Lumeij 1987, Scope and Schwendenwein 2020). For instance, while P osm increased by 13.3% in water restricted rock pigeons ( Columbia livia ), P [Urea] increased by 603% (Lumeij, 1987). Due to their different excretion regulation mechanisms, urea:UA and urea:creatinine ratios would be a relevant way to assess renal function (Baum et al. 1975). Additionally, some authors have hypothesized that higher P [UA] or P [Urea] during water restriction may be linked to increased protein catabolism (Gerson and Guglielmo 2011, Rutkowska et al. 2016, Navarrete et al. 2021, Rogers and Gerson 2024). Although uric acid, urea and creatinine could all be considered as potential markers of dehydration, there are potential drawbacks to their use that should be noted. For example, factors other than hydration state – such as protein intake, fasting state or chronic stress – can affect P [UA] or P [Urea] , meaning that they are not strictly specific markers of hydration state (Beattie et al., 2022, 2023; Goldstein et al., 2001; Lumeij and Remple, 1991). Even though creatinine tends to be more stable during changes in feeding compared to urea or UA, its low concentration in the plasma of birds presents challenges for its detection (Lumeij and Remple 1991, Scope et al. 2013, Gasthuys et al. 2019). Alternative markers A number of physiological parameters, such as the glomerular filtration rate (Roberts, 1991a, 1991b; Roberts and Dantzler, 1989), urine flow rate (Dawson et al., 1991; Williams et al., 1991) and hormone levels (i.e. vasotocin: Goldstein and Braun, 1988; Koike et al., 1977; Seth et al., 2004; angiotensin II: Goldstein, 1995; Gray and Simon, 1987; Takei et al., 1988; aldosterone: Arad, 1985; Arnason et al., 1986) have been linked with avian hydration state in several studies. However, measuring the glomerular filtration rate or urine flow rate necessitate the injection of an exogenous marker and later sampling of either blood or urine to assess the clearance rate, which is invasive and labor intensive, and therefore not suited to studies in field environments (Levey et al. 2020). Moreover, these parameters may be interpreted as markers of anti-diuresis rather than dehydration per se . Alternatively, excreta characteristics – such as urine osmolarity (U osm ; e.g. Goldstein and Braun, 1988; Navarrete et al., 2021; Roberts and Dantzler, 1989; Skadhauge et al., 1991), excreta water content (Dawson et al., 1985; Goldstein and Braun, 1988; Moldenhauer and Wiens, 1970; Withers, 1983) or defecation rate (Brischoux et al. 2020) – may represent less-invasive markers of avian hydration state. The concentration of UA in the excreta may also increase during water restriction, but evidence for this is lacking (Gerson and Guglielmo 2011). U osm is expected to increase while excreta water content decreases during dehydration as a result of a decrease in renal plasma flow, glomerular filtration rate, urine flow rate and increasing renal and post-renal water reabsorption (Box 1; Skadhauge, 1981). Most importantly, we expect changes in U osm during dehydration to exceed changes in P osm (Goldstein and Zahedi 1990, Dawson et al. 1991, Navarrete et al. 2021). For instance, P osm increased by 12.7% while U osm increased by 153.8% with dehydration in captive house sparrows ( Passer domesticus ; Goldstein and Braun, 1988). However, changes in U osm are likely more tied to anti-diuresis rather than dehydration itself, potentially explaining why U osm may vary with overnight fasting whereas P osm does not ( Alberts et al., 1988; Goldstein and Bradshaw, 1998). It is important to note that the term ‘urine’ can be used to refer to different fluids in the literature, and urine can be sampled using various methods with different degrees of invasiveness, from the insertion of a cannula into the cloaca, centrifugation of whole fresh excreta or using a capillary to collect the liquid fraction of the fresh excreta ( e.g. Alberts et al., 1988; Lee and Schmidt-Nielsen, 1971; Navarrete et al., 2021; Skadhauge and Dawson, 1980). These methods may lead to varying results, especially when directly sampling urine in the cloaca, where the fluid obtained may be a mix of ureteral urine (urine excreted by the kidney) and cloacal urine (urine formed after post-renal modification; Figure 2, Alberts et al., 1988; Laverty and Skadhauge, 2008). Of course, one of the benefits of using excreta to assess hydration state is that they can easily be sampled and with a minimal degree of invasiveness (especially for some nestlings, which produce faecal sacs; see Glossary). It is possible that other body fluids (such as salt-gland fluid or saliva) could also be used to assess hydration state in birds; however, literature on alternative fluids currently is lacking. It is believed that the salt gland mediates most of the sodium and chloride excretion in species that possess one (Goldstein 2001, Hughes 2003), and some researchers have hypothesized that the osmolarity of this fluid may vary depending on the hydration state (Stewart 1972). Regarding the use of tears or saliva, we found no studies assessing the osmolarity of these fluids in birds during water restriction, but these markers have shown some potential to predict dehydration in humans and may be useful as less-invasive sampling methods in birds (Villiger et al. 2018). Finally, non-invasive methods, such as assessing skin turgor or capillary refill time (See glossary), may provide insight into hydration state, but we currently have very limited insight into their reliability (Vanderhasselt et al. 2013, Baker-Cook et al. 2021). Dehydration diagnostic and tolerance Dehydration is strictly defined as a deficiency in total body water (Lacey et al. 2019). Yet, very few of the studies included in our review actually measured total body water (TBW), and we therefore used body mass loss under water restriction as a proxy for TBW loss. Water restriction lead to a hypertonic dehydration, and markers of osmolarity (e.g. P osm , P [Na] , P [Cl] ) are consequently relevant for determining whether an individual is dehydrated. Although we have identified some markers of dehydration, dehydration diagnostic is not trivial. Homeostasis is a dynamic process and the levels of the markers discussed above are likely to vary (Billman 2020). For instance, does an increase in P osm testify of dehydration, or simply of a physiological adjustment to maintain water balance (ie. anti-diuresis)? In human physiology, a TBW loss of 2% exceeds its day-to-day variation, and body mass loss under water restriction exceeding 2% promotes thirst, induces variation in levels of physiological markers and impairs performance (Cheuvront et al. 2010, 2013, Cheuvront and Kenefick 2014, Villiger et al. 2018). Current consensus is that P osm exceeding 300 mOsm.kg -1 (i.e. a ~3.5% increase in baseline P osm ) in humans predicts a TBW loss exceeding 2%, and thus dehydration (Lacey et al. 2019). Such thresholds are yet to be identified in birds and represent crucial perspectives for future studies (Figure 5). Figure 5. Dehydration thresholds and theoretical relationships between total body water loss and (a) performance or (a) plasma osmolarity in birds. Dehydration is defined as a deficiency in total body water. However, improving our diagnostic of dehydration require to identify biologically meaningful thresholds (e.g. performance or survival thresholds). Several studies have used varying thresholds of body mass loss to predict ‘moderate’, ‘severe’ or ‘lethal’ dehydration in birds (e.g. body mass loss of 11%, 15% or 22%; Albright et al., 2017; Conradie et al., 2019, 2020; McKechnie and Wolf, 2010). However, there is little discussion of the reliability of such estimates. As far as we are aware, the 11% threshold was initially suggested by Wolf (2000) and is based on the observation that verdins ( Auriparus flaviceps ) can lose effectively (Wolf and Walsberg., 1996). To put that into perspective, captive water-deprived domestic fowls ( Gallus domesticus ) and mourning doves ( Zenaida macroura ) lose approximately 45% and 37% of their body mass respectively by the time of death (Bartholomew and MacMillen, 1960; Mulkey and Huston, 1967), and there are multiple reports of birds losing more than 15% of their body mass without direct effects on survival (e.g. Arad et al., 1987; Bartholomew and Macmillen, 1960; Mulkey and Huston, 1967; Skadhauge, 1974; Withers, 1983). Experimental studies are needed to identify water-deficit thresholds eliciting a physiological response (i.e. thirst or variation in levels of physiological markers), and thresholds associated with impaired performance (cognitive, locomotor) or survival. We expect tolerance to be lower in more suboptimal environments (such as in the wild), and we expect performance to be hindered at much lower thresholds than survival. Comparative studies on species dehydration tolerance represent great perspective for future studies, as we may expect dehydration tolerance to vary between taxa and species. Heat tolerance has been reported to be higher in desert birds (Freeman et al. 2022), and whether dehydration tolerance follow the same pattern is yet to be determined. More specifically, relationships between heat tolerance and dehydration tolerance would be of great interest to better understand thermal and osmoregulatory challenges of wild birds. For instance, it may help us understand the over-representation of some taxa during heatwave-related mortality events (McKechnie et al. 2021). Would also provide further insight on whether birds ultimately died of hyperthermia, dehydration or a combination of both. Overall, estimation of dehydration thresholds would allow us to refine the dehydration diagnostic and improve model predictions, especially in regard to the potential effect of climate change on bird populations. Research on the topic of avian hydration state would also benefit from a better understanding of osmoregulation and water requirements, especially as they relate to the life stage, the diet and/or drinking habits. For instance, birds exploiting marine habitats have been reported to be susceptible to heat exposure, but – as far as we are aware – studies on their water balance and hydration state remain scarce (Stewart 1972, Hughes 2003, Oswald and Arnold 2012, Cook et al. 2020). Even if we assume that they have unlimited access to water, there is still an energetic cost associated with drinking saline water (Peña-Villalobos et al. 2013, Sabat et al. 2021). Increased research efforts are necessary to shed light on this topic, especially with respect to water balance and salt gland excretion. We also have very limited insight into heat tolerance and osmoregulation in nestlings, which develop endothermy and osmoregulatory ability during growth (Grabowski 1967, Price and Dzialowski 2017), and may have limited heat tolerance compared to adults (Diehl et al. 2023). Consequently, even though parents buffer temperature variation within the nest (Du and Shine 2015), nestlings may be threatened by dehydration during extreme events (Salaberria et al. 2014, van de Ven et al. 2020, Bourne et al. 2021, Oswald et al. 2021). We encourage researchers to conduct laboratory experiments to better define the physiology and water requirements of nestlings, as well as assessing nestling hydration state in the wild. Glossary Capillary refill time: time taken for color to return to an external capillary bed after pressure is applied to cause blanching. Diuresis: increased urine production, as opposed to ‘anti-diuresis’ (decreased urine production). Excretion (renal): sum of the filtration, reabsorption and secretion process. Faecal sac: a mucous membrane surrounding excreted faeces. Filtration (renal): filtration of the blood from the vascular compartment to the tubule lumen at the renal corpuscle. Post-renal modification: modification of the ureteral urine (i.e. urine excreted by the kidney) within the cloaca (coprodeum), colon and ceca. The term ‘cloacal urine’ usually refers to the urine produced after post-renal modifications. Reabsorption (renal): movement of solutes and water from the tubule lumen to the blood compartment in the nephron. Salt gland: a nasal organ specialized for the excretion of salt in birds. Secretion (renal): movement of solute from the blood compartment to the tubule lumen in the nephron. Supplementary material Supplementary figures Figure SI-1. Consensus phylogenetic trees computed from 10,000 phylogenetic trees using either (A) Ericson or (B) Hackett backbone. Phylogenetic trees were generated from the Birdtree database. Figure SI-2. Relationship between plasma osmolarity change and (A) plasma sodium concentration change; p-value<0.001, R 2 m =0.26, n=28, N=15, sp=13, ord=8; (B) plasma chloride concentration change; p-value<0.001, R 2 m =0.75 n=17, N=11, sp=10, ord=6. Regression lines and prediction intervals of the models are plotted. n=number of observations, N=number of studies, sp=number of species, ord=number of avian orders. Supplementary tables Table SI-1. Models estimate and p -value for MCMCglmm and LMER models. Model Estimate [CI] p MCMC Estimate [CI] p -value ΔP osm ~ BML 0.764 [0.589, 0.909] <0.001 0.729 [0.574, 0.880] <0.001 ΔHct ~ BML 0.641 [0.286, 0.967] <0.001 0.651 [0.297, 0.978] <0.001 ΔP osm ~ ΔP [Na] 0.649 [0.524, 0.789] <0.001 0.648 [0.541, 0.783] <0.001 ΔP osm ~ ΔP [Cl] 0.761 [0.653, 0.877] <0.001 0.768 [0.652, 0.855] <0.001 ΔP osm ~ ΔP [K] 0.041 [-0.106, 0.180] 0.570 0.027 [-0.473, 0.555] 0.914 ΔP osm ~ ΔHct 0.135 [-0.098, 0.452] 0.330 0.167 [-0.110, 0.453] 0.139 LMER model formula: [y~x + (1|Study) + (1|Specie/Order)]. MCMCglmm models formula: [y~x + (1|Study) + (1|Specie)]. Δ, changes during water restriction. BML: body mass loss; CI: 95th percentile confidence interval; Cl, chloride; Hct: hematocrit; K, potassium; Na, sodium; P [compound] , concentration of a specific compound in the plasma; P osm , plasma osmolarity. Table SI-2. Summary statistics of MCMCglmm models. Model Estimate [CI] p MCMC Specie Study Unit Lambda P osm ~ BML 0.764 [0.589, 0.909] <0.001 0.045 [0.000, 21.236] 0.075 [0.000 26.325] 2.287 [1.271, 5.308] 0.003 [0.000, 0.877] Hct ~BML 0.641 [0.286, 0.967] <0.001 0.389 [0.000, 29.686] 33.468 [13.494, 124.010] 21.110 [11.592, 38.277] 0.001 [0.000, 0 0.571] P osm ~ P [Na] 0.649 [0.524, 0.789] <0.001 12.111 [0.001, 33.611] 0.094 [0.000, 5.997] 0.722 [0.338, 2.442] 0.948 [0.683, 0.996] P osm ~ P [Cl] 0.761 [0.653, 0.877] <0.001 0.043 [0.000, 4.402] 0.031 [0.000, 3.059] 1.881 [0.682, 5.540] 0.002 [0.000, 0.719] P osm ~ P [K] 0.041 [-0.106, 0.180] 0.570 54.154 [17.606, 154.875] -0.046 [0.000, 8.952] 6.168 [2.343, 15.769] 0.924 [0.725, 0.984] P osm ~Hct 0.135 [-0.098, 0.452] 0.330 0.127 [0.000, 160.982] 0.271 [0.000, 50.768] 12.405 [7.297, 24.258] 0.001 [0.000, 0.922] The variance explained by random effects (specie, study), the residual variance (unit) are represented by their posterior mode and confidence interval (CI: 95th percentile confidence interval). Cl, chloride; Hct: hematocrit; K, potassium; Lambda: phylogenetic signal; Na, sodium; P [compound] , concentration of a specific compound in the plasma; P osm , plasma osmolarity. Supplementary analysis We used generalized linear mixed model with Markov Chain Monte Carlo estimation methods (MCMC GLMM ) to take into account phylogenetical distance between species in our analysis, using the MCMCGLMM package (Hadfield 2010). We generated 10,000 phylogenetic trees from the database Birdtree using for both Hackett and Ericson backbones (Birdtree.org; Jetz et al. , 2012). Phylogenetic trees were restricted to our species of interest. We then computed consensus trees. Because both Hackett and Ericson consensus trees were similar, we decided to use the Hackett tree following (Tomasek et al. 2019). The MCMC GLMM models were set to run for 6.10 6 iterations, with a thinning interval of 1.10 3 and a burn‐in of 1.10 6 . We observed no autocorrelation between sampled iterations. 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