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Animal Ecology & Physiology
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Biology
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body condition index; composite variable; fitness component; latent variable; multiple regression; multiple-indicator multiple-cause model; path analysis; principle component analysisAbstract
Choice, description and data collection of biometric and physiological measures included in the worked examples
Blood samples (sex, haematocrit, buffy coat, cholesterol, uric acid)
Buffy coat, the fraction of white blood cells, is elevated if the body needs to fight against infections (Campbell et al., 2008). Haematocrit, the proportion of red blood cells with the main function of oxygen transportation, has been shown to be positively related to survival in Eurasian oystercatcher (Haematopus ostralegus) (Verhulst et al., 2004) and crimson fiches (Neochmia phaeton) (Milenkaya et al., 2015). Uric acid is a nitrogenous waste product of birds (Tsahar et al., 2006). The amount of nitrogenous waste produced by an individual can be linked to energy budget and diet (Campbell et al., 2008) and has been shown to be related to body mass loss in Yellow-legged gulls (Larus cachinnans) (Alonso-Alvarez et al., 2002). Cholesterol, a lipid, has been shown to best reflect body mass change in Yellow-legged gulls (Larus cachinnans) (Alonso-Alvarez et al., 2002). Griminger (1986) also suggested an influence of diet composition on cholesterol levels in birds.
Blood samples (approximately 0.35 ml per bird) were taken from the brachial vein. Sex was determined using molecular techniques from blood stored in cell lysis buffer at room temperature.
Two blood collection tubes of approximately 65 µL each were taken per individual bird and centrifuged 10 minutes at 9503g between 2 to 6 hours (mean±se=3.6±0.96hrs) after blood extraction. Since this time delay did not significantly affect buffy coat and haematocrit, it was not included as a variable in the analysis. Buffy coat and haematocrit were measured by taking standardized pictures of the blood collection tube in a specific holder constructed for this purpose (Fig. S21), and then measuring the length of the portion with red blood cells, white blood cells and plasma in pixels with the program Paint.NET. The haematocrit and buffy coat were calculated by taking the proportion of the length of red blood cells and white blood cells, respectively, to the total length (red blood cells + white blood cells + plasma). The reason for using pictures and measuring pixels instead of measuring proportions directly in the field was to minimize measuring bias due to more accurate measurements compared to a ruler/calliper. Repeatability (calculated by estimating the intra-class correlation coefficients (ICC) (and its 95% confidence interval) using the variance components from a one-way ANOVA with the r-package ICC (Wolak et al., 2012) between the two collection tubes of the same individual of both haematocrit and buffy coat samples was high. Haematocrit: ICC (lower & upper confidence interval) = 0.984 (0.982, 0.986), n=758 duplicate samples. Buffy coat: ICC (lower & upper confidence interval) = 0.943 (0.931, 0.953), n = 390 duplicate samples). Number of observations for the buffy coat was smaller because samples were removed when edges were not sharp and/or diagonal.
The blood of two capillaries was transferred to tubes with heparin buffer and centrifuged at 6082g for 8 minutes. After centrifugation, the plasma (at least 50 µL) was extracted and stored in a -80°C freezer. Cholesterol (mmol/L blood) and uric acid (µmol/L blood) was determined (cholesterol: enzymatic method on discrete automatic analyser; uric acid: automatic and colorimetric method using uricase) by the University Veterinary Diagnostic Laboratory (UVDL) in Utrecht, the Netherlands, with Olympus AU-680 from the Beckman Coulter company.
Biometry
Biometric parameters, namely length of tarsus to toe (mm), wing length (mm), head length (mm), bill tip height (mm) and mass, were measured following the standard techniques described in Durell et al. (1993). Bill tip height (measured 3 mm from the bill tip using a calliper) was used as a proxy for the type of individuals’ feeding specialization (individual characteristics; Fig. 1), ranging from worm specialists (indicated by a pointed bill and a low bill tip height) to shellfish specialists (indicated by a blunt bill and high bill tip height) (Van de Pol et al., 2009). Birds were aged on basis of their plumage, bill and leg characteristics (Cramp & Simmons, 1983) and the age was classified in 1st, 2nd, 3rd calendar year and adults (>3rd calendar year). We focused only on sub-adults (2nd and 3rd calendar year) and adults (>3rd calendar year) in the case study because number of caught and sampled juveniles (1st calendar year) was small (n=28; Table S1). Handling time (confounding variable; Fig. 1) was recorded to correct for time-dependent mass loss as well as possible effects on other physiological measures. Handling time (as a proportion of 24 hours) was defined as the time between capture and measuring (Fig. 1) to correct for seasonal effects on energy reserves and physiological parameters (Norte et al., 2009).
Feather sample (corticosterone)
Corticosterone, a steroid stress hormone, fulfils its main functions by mobilizing stored resources and up-regulating metabolism for coping with increased energetic challenges, resulting in higher corticosterone secretion in birds coping with harsh environments (Jimeno, Briga, et al., 2017; Jimeno et al., 2018; Jimeno, Hau, et al., 2017; Marra & Holberton, 1998).
When a bird was caught, we took the 5th tail/flight feather from the left side of the bird and stored it in a paper envelope until it was sent to the laboratory of the Department of Evolutionary Ecology at the National Museum for natural Science (CSIS) in Spain for corticosterone extraction. We followed the methodology for steroid extraction from feathers described in Bortolotti et al. (2008). Feather samples were prepared by selecting the most proximal part of the feather, next to the calamus, which was least abraded, and cutting around 3.5 cm from there, taking white feather only. Feather samples were weighed to the nearest mg with an analytical scale (Sartorius). Average mass of feather material per sample was 32.4 mg (SD = 4.24). The vane plus rachis were cut in small pieces (<5 mm) with scissors. We added 6 ml of methanol to the tube with the feather particles, and left the tubes for 30 min in an ultrasound water bath. Then, tubes were capped and left overnight (around 19 hours) in a shaking water bath at 50°C. Samples were decanted in a clean tube and filtered using a nylon plug filter (0.45µ). Tubes were washed with 2 ml of methanol, which was added to the previous extract after a similar filtering. Samples were then placed in a heated tube rack (50°C) under a stream of nitrogen until evaporation (Techne, Germany). Dried extracts were suspended in 150 µl of steroid free buffer and vigorously vortexed for 10 min. Extracted samples were then assayed following kit inserts using a commercial corticosterone ELISAs (DRG, Germany), and optical density measured with a plate spectrophotometer (BioTek, USA).
Bill colour
Oystercatchers have orange bills, and presumably this is due to carotenoids. Carotenoids have been linked to antioxidant and immune status signalling (Perez-Rodriguez, 2009; Simons, Cohen, et al., 2012; Von Schantz et al., 1999) and may therefore be a signal of individuals’ phenotypic quality. In male zebra finches, for instance, bill redness reflects recent environmental (Eraud et al., 2007) and immunological challenges (Cote et al., 2009), and has been shown to be positively correlated to immune functioning (Birkhead et al., 2006) and survival and reproduction (Simons, Briga, et al., 2012).
Bill colour measurements were performed using digital photography (Panasonic Lumix GX8) from each individuals’ right and left side, which has been shown to be a valid method in ecology to study animal coloration (Simons, Briga, et al., 2012; Stevens et al., 2007; Villafuerte & Negro, 1998). The colour of an object is greatly influenced by the colour and brightness of the light source used to illuminate it. To minimise variation in light colour and brightness, photographs were taken in a purpose-built photo-box to ensure standardized light conditions (Fig. S22). To further compensate for variations in light source colour and brightness and allow captured colour and brightness information to be standardised in order to compare different pictures from different individuals, the colours white, grey and black were visible from a photo reference card on each picture (Fig. S22). This allowed post-production colour and exposure balancing of the images. Camera settings were also standardized for all pictures. We used the program, Pixel Grabber (Nienhuis, 2015) (Fig. S22) to quantify the colours in a continuous scale of the conventional colour model developed for humans, amenable for statistical analyses. Colour measurements may be defined according to different systems (e.g. Munsell, Lab, HCL) but one of the most widely used is the HCL colour system, which provides independent values of hue, chroma and luminance which are the parameters generally used to define a colour. Hue corresponds to wavelength of light, chroma refers to spectral variance and therefore to colour purity so that the more monochromatic a colour is, the higher its chroma value. Luminance is correlated with physical light intensity and refers to the position on a grey-scale between black and white (Quesada & Senar, 2006).
This gives for each part of the bill measured for each individual three axes of the colour: Hue, Chroma and Luminance, where a higher “hue” indicates a more yellowish colour compared to a more orange colour (Fig. S7). Pictures where the measuring points were damaged, dirty or blurry (3%) were excluded from analysis. Correlations of the right and left side of the bill as well as different parts of bill were correlated (Table S10), so that we focused on only one of the several bill measuring points for analysis.
For a random subset (n=86) of the total dataset (n=598), we repeated the measurement of the bill colour
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