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There is increasing interest among ecologists in the physiological processes that underlie individual differences in traits that influence fitness. This is because disentangling the mechanisms that mediate life histories may allow a more realistic perspective on the origin and evolution of trade-offs, the ecological conditions that mediate those trade-offs, and thus how individual variation in life-history trajectories is maintained (e.g., Lessells 2008; Monaghan et al. 2009; Isaksson et al. 2011a,b).
In recent years, the mechanism that has received the most interest in this context is arguably oxidative stress, that is, a surplus of the reactive oxidants (such as reactive oxygen species [ROS]) to the protective antioxidants (such as glutathione and α-tocopherol). The focus on oxidative stress arises from the paradox that oxygen and ROS are crucial for cellular life, but that ROS are at the same time detrimental to lipids, proteins, and DNA unless they are detoxified. For example, ROS are actively produced and released during the initial phases of infection (Sorci and Faivre 2009), but they are also produced as a by-product during aerobic respiration (Cadenas and Davies 2000). In addition, ROS are also used as signal molecules between cells, crucial for triggering, for example, female reproduction (Agarwal et al. 2008). It has been shown that experimentally increased reproduction increases the level of oxidative damage and decreases resistance to an oxidative attack (Alonso-Alvarez et al. 2004; Wiersma et al. 2004; Christe et al. 2012), suggesting that a metabolically demanding trait like reproduction may indeed increase ROS leakage (but see Selman et al. 2008). Furthermore, oxidative stress is also predicted to accumulate and/or increase with age (Finkel and Holbrook 2000), providing a physiological memory and a link between current and future performance (reviewed in Monaghan et al. 2009; Isaksson et al. 2011a,b). However, studies investigating the relationship between longevity and oxidative stress are not conclusive and more research is needed in this area (e.g., Robert and Bronikowski 2010; Speakman and Selman 2011).
In addition, environmental factors can directly or indirectly influence the oxidative stress status of wild animals (Isaksson et al. 2005; Isaksson and Andersson 2007; Nussey et al. 2009; Costantini 2010; van de Crommenacker et al. 2011, 2012). Diet quality and quantity such as intake of dietary antioxidants or essential building blocks for endogenously synthesized antioxidants, along with intake of toxins that are active as oxidants are examples of how environmental differences can influence the oxidative stress status directly (see Isaksson et al. 2011aa). Other environmental factors may be proxies for linked differences in, for example, diet, behaviour, reproduction, or infections (e.g., Christe et al. 2012; Isaksson et al. 2013).
The lack of comprehensive studies of variation in extracellular and endogenously synthesized antioxidants and oxidants in ecological contexts leaves us poorly equipped to interpret the functional significance of laboratory studies, where phenotypic and environmental variation typically is experimentally kept to a minimum. The lack of data on natural variation is also a problem with respect to the use of measures of oxidative stress as biomarkers in the wild (Hõrak and Cohen 2010).
To address these issues, a large study of individual variation in the endogenously synthesized antioxidant system – glutathione, and extracellular measures of oxidative stress was conducted using a wild population of great tits (Parus major). The population is particularly useful for testing how environmental variation affects oxidative stress as nest boxes had been set up to create a factorial design with two different habitat types (deciduous- vs. evergreen-dominated habitat) crossed with two different breeding densities. The two habitat types correspond to a large difference in caterpillar abundance (i.e., deciduous habitat has higher abundance compared with the evergreen area, see Data S1, Figs. S1 and S2). The factor of habitat type is likely to affect food intake, investment in reproduction, and time spent foraging, all of which are known to influence oxidative stress physiology (e.g., Isaksson and Andersson 2007; Slos et al. 2009; Costantini 2010; van de Crommenacker et al. 2011). The experimentally manipulated breeding density is likely to increase intraspecific competition and interactions, and many life-history traits in great tits show density dependence (e.g., Wilkin et al. 2006). High breeding density in this population has also recently been shown to increase avian malaria prevalence (Isaksson et al. 2013), which has been shown to increase plasma-reactive oxygen metabolites (ROM; van de Crommenacker et al. 2012; see also Bertrand et al. 2006). Thus, increased breeding density is predicted to increase oxidative stress. In addition to habitat type and breeding density, spring date (within year variation in timing of breeding), clutch size, sex, age, and body condition were investigated.
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The main results in this study reveal that sex, habitat type, and spring date are factors that explain most natural variation in tGSH, GSSG, OXY, and ROM. The experimentally manipulated breeding density did not show any significant effect.
First, adult great tits that are breeding in deciduous-dominated habitats (i.e., higher abundance of caterpillars) have higher levels of ROM compared with birds that breed in evergreen habitats. However, the random effect of habitat plot explained the variation better than the fixed factor of habitat type, thus habitat type did not reach significance in the model (Fig. 2). Perhaps, the most likely explanation of the habitat effect is the difference in food availability between the environments and the plots (see Figs. S1 and S2). The OXY and ROM assays capture several dietary macromolecules such as OXY measures dietary antioxidants (e.g., ascorbic acid and α-tocopherol) and ROM peroxidized dietary amino acids and lipids. Thus, both assays are likely to be influenced by food quantity and/or quality, thereby influencing the circulating levels of ROM and OXY (e.g., Costantini 2010; van de Crommenacker et al. 2011; see also Costantini 2011). However, neither habitat type nor the plot explained any significant variation in OXY. Perhaps the lack of an effect on OXY is due to the low across-assay repeatability compared with ROM. Alternatively, it is less sensitive to environmental changes, because the endogenously synthesized antioxidants can counterbalance the dietary ones (Vertuani et al. 2004; see also van de Crommenacker et al. 2011). Regarding behavior, the difference in habitat type may influence birds’ individual decision in how much to invest in reproduction, with an increased investment when the opportunity arises via high food availability. This may push individuals over their optimal clutch size, thus an increased physiological challenge despite the higher caterpillar abundance needed to raise a larger brood in the deciduous habitat. Given that the females in the deciduous area lay a larger clutch, but then fail to raise the whole brood, this may be a likely scenario. In contrast to the present results, the direction of the habitat effect on ROM and OXY differs from a recent study on Seychelles warbler Acrocephalus sechellensis. In that study, birds that were holding a high-quality territory (i.e., high insect abundance corrected for territory size) had significantly lower absolute and relative ROM (van de Crommenacker et al. 2011). The authors interpreted their results as a consequence of higher oxygen metabolism (i.e., higher release of ROS) in low-quality territories due to higher foraging activities to find insects to feed their chicks with. However, the direct link between ROS production and oxidative metabolism has been questioned (Speakman and Selman 2011). Possibly, the difference in results is a consequence of difference in habitat structure, breeding opportunities, and family structures (i.e., Seychelles warblers have helpers) between great tits and Seychelles warblers which makes the habitat effect context specific (see Komdeur et al. 2002; van de Crommenacker et al. 2011, 2012).
Furthermore, habitat type also influenced the endogenously synthesized antioxidant – glutathione. However, the effect was in the opposite direction to ROM, with birds in the deciduous habitats having lower tGSH compared with birds in the evergreen habitat, suggesting that birds in the evergreen habitat may have an increased demand of cellular activity and protection, whereas birds in the deciduous habitat can keep cellular homeostasis with a low tGSH. Alternatively, the biosynthesis of glutathione is rate limited by the dietary precursor cysteine, but whether cysteine is limited for birds in the wild and in a deciduous habitat is unknown and requires further investigations (Isaksson et al. 2011a,b).
In contrast to habitat quality, the experimentally manipulated breeding density did not significantly influence any of the physiological variables. Previously we have shown that high bird densities result in higher prevalence (i.e., presence/absence) of Plasmodium circumflexum (a type of avian malaria) which was related to lower oxidation of GSH (Isaksson et al. 2013). Thus, it was surprising to not find an effect of breeding density on the GSSG. However, as birds in the high-density areas started to breed later than birds in low-density areas, it is possible that the effect of spring date on GSSG overrides the effect of breeding density (see Fig. 3). Alternatively, yearly spatial and/or individual variation in malaria prevalence overrides the effect of breeding density on GSSG.
Linear and quadratic function of spring date was overall of high importance for explaining variation in all physiological variables except tGSH. Great tits breeding later in the season had higher ROM and OXY, but lower GSSG. Overall, the linear function was a better fit than the quadratic function of spring date. Possible variables that are linked and could contribute to the high importance of spring date or/and timing of breeding (i.e., date for sampling was standardized for hatching of offspring) are temperature, food abundance (i.e., low food abundance early and late in spring, see Data S1), and individual quality (i.e., high-quality individuals breed early, Perrins and McCleery 1989).
Furthermore, sex was found to be an overall important explanatory factor. Females had significantly lower extracellular oxidative metabolites and antioxidant defenses, but higher tGSH compared with males. Previously, it has been shown that spotted snow skink females (Niveoscincus ocellatus) also have lower levels of ROM compared with males, but no sex difference in OXY was detected (Isaksson et al. 2011a,b). The sex difference in redox physiology during the breeding season is likely to be mediated via sex steroids, with testosterone increasing and estrogens reducing ROS production (Gupta and Thapliyal 1985; Viña et al. 2005; Tobler and Sandell 2009). Regarding glutathione, it is the most important intracellular antioxidant and the higher levels in females may reflect a higher investment in cellular maintenance or alternatively an upregulation in response to higher cellular ROS during chick feeding (see also Alonso-Alvarez et al. 2004; Wiersma et al. 2004; Christe et al. 2011). However, the lower ROM levels in females may suggest that they are better protected against oxidative stress than males (e.g., Gupta and Thapliyal 1985). Overall, regardless of the mechanism, the finding is likely to reflect a sex difference in behavior, food intake, and/or reproductive physiology that influences antioxidant and oxygen metabolite levels in great tits.
Total GSH is predicted to decline and GSSG and ROM are predicted to increase with age due to cellular senescence (Rebrin and Sohal 2008), but here age had no significant effect on physiology. Possibly, this is due to the cross-sectional approach, but more likely is due to the crude pooling of all the individuals that are older than 2 years. In a study of captive partridges (Alectoris rufa) of known ages (i.e., 1–8 years old), old partridges had higher GSSG, total OXY, and lipid peroxidation compared with middle-aged partridges (Alonso-Álvarez et al. 2010). Unfortunately, these types of analyses were not possible in this data set.
The last parameter tested was clutch size, which was significantly negatively associated with ROM (independent of sex). Similarly, a negative association between ROM and litter size was revealed in female mice that had just given birth; however, after reproduction (during weaning) the association was instead positive (Stier et al. 2012). This could indicate that those individuals that have a high reproductive output are the individuals that can initially avoid generation of ROM (high-quality individuals) despite the higher investment, but that the physiological costs are paid later at least in mammals (Stier et al. 2012). When reproductive investment is artificially increased in birds, it is linked to decreased resistance to an oxidative attack (Alonso-Alvarez et al. 2004), lower antioxidant protection (Wiersma et al. 2004), or a higher ROM (Christe et al. 2011). However, evidence for a linear cost of reproduction in terms of increased oxidative stress or damage in correlative data from natural populations is more difficult to detect (Nussey et al. 2009; Isaksson et al. 2011a,b; Stier et al. 2012; Metcalfe and Monaghan 2013; but see also Bize et al. 2008).
Finally, the random effect of nest box nested within habitat type had a great influence on all the markers of oxidative stress explaining approximately 7–10% of the variation. This is supported by the very strong positive correlation between social partners’ physiology (of all markers), suggesting that males and females respond equally to environmental influences in their territory, despite a large sex difference in ROM, OXY, and tGSH. Alternatively, great tits mate assortatively with regard to physiology. Whether this is an adaptive response or a consequence of territory quality requires further investigation.