DOI: 10.5061/dryad.z08kprrrp
title: Physiological health of wintering glaucous-winged gulls in coastal British Columbia

description: We assessed physiological health of glaucous-winged gulls wintering in
 urban and natural habitats in the Salish Sea, British Columbia. Although
 ecological context differed among sites, based on stable isotope analysis,
 physiological health did not vary with location or habitat. Our study
 provides physiological reference values for future biomonitoring.
description: Methods Study area The
 Salish Sea (49° 20' 10.4", -123° 50' 21.6") comprises
 two areas of sheltered marine waters harbouring large human population
 centres: the Strait of Georgia in Southern British Columbia and Puget
 Sound in Washington State, which are connected to the open Pacific Ocean
 by the exposed Strait of Juan de Fuca. In January and February of 2020 and
 2021, we sampled adult GWGUs throughout the Canadian portion of the Salish
 Sea (Figure 1). We attempted to sample evenly between the following
 regions: ‘Lower Mainland’, ‘Greater Victoria’, ‘Southern Vancouver
 Island’, and the ‘Northern Salish Sea.’ Regions were categorized primarily
 by geographic proximity, but generally had similar beach substrate types
 and levels of anthropogenic influence throughout a given region
 (Supplemental Figures 1 and 2). Within each region, we
 sampled as evenly as possible among various habitat types including
 landfills, ‘urban,’ and ‘natural’ areas (Table 1). However, levels of
 human population density (Supplemental Figure 1), urbanization, and other
 types of land use varied among regions (Supplemental Figure 2), therefore
 urban habitat types are over-represented in some regions (Table 1). For
 example, urban areas were comprised of beaches near high human population
 densities, as well as city parks, whereas ‘natural’ habitats included
 beaches in areas with considerably lower human population densities, and
 less industrial activity. Gulls using landfills were also sampled in the
 Lower Mainland, Greater Victoria, Southern Vancouver Island, and the
 Northern Salish Sea. Satellite imagery from the North American Land Change
 Monitoring System database was used to guide categorization of capture
 locations into ‘urban’ versus ‘natural’ habitat types (250 x 250 m
 resolution, North American Land Change Monitoring System, 2021) while
 human population density (people/km<sup>2</sup>) was obtained
 using census data (Statistics Canada, 2017). All three habitat types and
 all four regions of the Salish Sea were sampled in both years of study.
 Additionally, in 2021, ten gulls were sampled on the west coast of
 Vancouver Island near the small towns of Ucluelet and Tofino as an
 ‘outlier’ group for comparison with Salish Sea birds.
 Data Collection Adult Gulls were
 live-captured primarily using baited noose-mats (Liu <em>et
 al</em>., 2017) and occasionally with pneumatic
 CO<sub>2</sub> net guns, when bait was not an effective
 attractant (Edwards and Gilchrist, 2011). As soon as possible after
 capture, we collected no more than 6 mL of blood (&lt;1% of body
 weight) from the brachial vein of one or both wings using a 27.5-gauge
 heparinized needle and syringe. We also collected approximately 20 µL of
 whole blood from tarsal veins using a non-heparinized lancet and capillary
 tube. Of this, half was stored in 95% ethanol for molecular sexing, and
 the rest was used to measure glucose levels (mmol/L) in the field using a
 handheld glucose meter (Accu-check Aviva; Roche, Basel, Switzerland). In
 the field, samples for haemoglobin analysis were prepared by adding 5 mL
 of fresh, whole blood to 1.25 mL of Drabkin’s reagent (D5941 Sigma-Aldrich
 Canada, Oakville, Ontario, Canada). Hematocrit was measured on two
 capillary tubes that were immediately filled with fresh, whole blood and
 stored at 4°C for up to 6 hours before centrifuging for three minutes at
 13,000 g (Microspin 24; Vulcon Technologies, Grandview, Missouri, USA).
 All remaining blood was stored in a heparinized vacutainer at 4°C for up
 to 6 hours before being centrifuged for 10 minutes at 5000 rpm. Plasma
 (without lipid extraction) and red blood cells were separated and stored
 at -20°C for up to 1 month and then at -80°C until assayed. Handling time
 was calculated for each individual and defined as the number of minutes
 from capture time until blood collection was finished. Before release, all
 gulls were photographed, aged, banded, and morphometric measurements were
 collected including mass (± 20 g) and tarsus length (± 1 mm).
 All research was conducted under Environment and Climate Change
 Canada (ECCC) Banding Permit #10667F, and ECCC Migratory Bird Sanctuary
 Permit #MM-BC-2020-0002. Animal use protocols were approved by ECCC’s
 Western and Northern Animal Care Committee (21MH03), as well as the Simon
 Fraser University Animal Care Committee (protocol no. 1318B-20). All
 personnel completed mandatory Animal Care training.
   <strong>Plasma
 analysis</strong> Plasma triglyceride levels
 (mmol L<sup>-1</sup>) were analyzed with a colorimetric assay
 according to the manufacturer’s instructions (Sigma-Aldrich Co.; also see
 Fowler and Williams, 2017). Hematocrit was measured using digital calipers
 (± 0.1 mm) and determined as a percentage of packed red cell volume to
 total column height (plasma plus packed red cell volume). Haemoglobin was
 measured using the cyanomethemoglobin method (Drabkin and Austin, 1932)
 modified for use with a microplate spectrophotometer and absorbance read
 at 540 nm (Wagner <em>et al</em>., 2008). Total antioxidant
 titres (OXY; µmol HClO mL<sup>-1</sup>) and reactive oxygen
 metabolites (dROMs; mg H<sub>2</sub>O<sub>2</sub>
 dL<sup>-1</sup>) in the plasma were measured using OXY and
 dROMs kits, respectively, from Diacron International (Grosseto, Italy).
 OXY absorbances were read at 490 nm, and dROMs at 546 nm, using protocols
 modified after Guindre‐Parker <em>et al</em>. (2013) and
 Casagrande et al. (2012), respectively. All assays were run using 96-well
 plates and a microplate spectrophotometer (BioTek Powerwave 340; BioTek
 Instruments, Winooski, Vermont). For quality control, samples with an
 intra-assay coefficient of variation (CV) &gt; 10% when assayed in
 triplicate (haemoglobin and OXY), or CV &gt; 12% if run in duplicate
 (triglycerides and dROMs), were re-assayed if no obvious outlier could be
 removed. Inter-assay variation was 4.11% (triglycerides), 2.20%
 (haemoglobin), 9.41% (OXY), 10.57% (dROMs), and intra-assay variation was
 6.74% (triglycerides), 1.60% (haemoglobin), 4.27% (OXY), and 6.69%
 (dROMs). <strong> </strong>
 <strong>Sexing</strong> To
 determine gull sex, DNA was extracted from blood stored in 95% ethanol
 using a modified Chelex protocol (Burg and Croxall, 2001; Walsh
 <em>et al</em>., 2013). Individuals were sexed using the
 Z43BF/Z43BR Primer Pair (Dawson <em>et al</em>., 2016); the
 forward primer was modified with M13 to allow incorporation of fluorescent
 marker to run on Licor gel. All PCR reactions were conducted in 10 µL
 reactions with 1 µL of genomic DNA. PCR cocktails contained 2.0 µL
 ClearFlexi Buffer 5x (Promega), 2.5 mM MgCl₂, 200 µM dNTP, 1 µM each
 primer, 0.05 µM M13 primer, 0.5 units GoTaq (Promega). We used the
 following Thermocycler Conditions: 1 cycle of 30 seconds at 94°C; 35
 cycles of 30 seconds at 94°C, and 45 sec at 55°C, and 45 seconds at 72°C,
 with a final extension for 5 minutes at 72°C, and 5 sec at 4°C. All PCR
 products were run on a 6% acrylamide gel. All gels included known
 positives (one male and one female) to maintain consistency across
 gels.   <strong>Stable
 Isotopes</strong> For carbon and nitrogen stable
 isotope analyses, we weighed 1 mg of freeze-dried whole blood into
 pre-combusted tin capsules. Encapsulated blood was combusted at 1030°C in
 a Carlo Erba NA1500 or Eurovector 3000 elemental analyser. The resulting
 N<sub>2</sub> and CO<sub>2</sub> were separated
 chromatographcally and introduced to an Elementar Isoprime (Elementar;
 Langenselbold, Germany) or a Nu Instruments Horizon (Nu Instruments Ltd.;
 Wrexham, United Kingdom) isotope ratio mass spectrometer. We used two
 reference materials to normalize the results to VPDB and AIR: BWBIII
 keratin (<em>δ</em><sup>13</sup>C = –20.18 ‰,
 <em>δ</em><sup>15</sup>N = +14.31 ‰, respectively)
 and PRCgel (<em>δ</em><sup>13</sup>C = –13.64 ‰,
 <em>δ</em><sup>15</sup>N= +5.07 ‰, respectively).
 Within run (n = 5) precisions as determined from both reference and sample
 duplicate analyses were ± 0.1 ‰ for both
 <em>δ</em><sup>13</sup>C and
 <em>δ</em><sup>15</sup>N.
 <br><strong>Data analysis</strong>
 For all physiological biomarkers measured, sample distributions
 were examined for normality and whether values were biologically plausible
 based on reference values for other gulls (e.g. Laranjeiro <em>et
 al</em>., 2020; Minias, 2015; Newman <em>et al</em>.,
 1997). Based on these reference values, biologically implausible outliers
 were removed for haemoglobin (n = 6; &gt; 24 g/dL) and OXY (n = 1;
 &lt; 110 µmol HClO/mL). Log transformations were used for
 triglycerides, glucose, and dROMs (Fowler and Williams, 2017). Statistical
 analyses were performed, with significance determined using an alpha level
 of 0.05, in <em>R version 4.3.3</em> (R Core Team, 2024).
 Pearson’s correlation coefficients were used to examine pairwise
 relationships between the six physiological biomarkers measured and to
 test the potential effect of handling time on each trait. To address
 potential bias due to sex differences in our data, we first determined
 whether the ratio of females to males sampled was significantly different,
 a) among capture years and b) between years, using the Chi-squared test.
 We also tested whether mass significantly varied with gull sex using
 ANOVA. Using linear mixed-effects models, we determined
 whether variation in any of the six biomarkers measured was explained by
 a) sex, b) mass, c) sex + mass, or d) sex*mass.  Free fatty acids in
 plasma can impact dROMs assay results (Pérez-Rodríguez <em>et
 al</em>., 2015), so we additionally tested whether triglycerides, or
 any combination of triglycerides, sex, and mass explained significant
 variation in dROMs measurements. All models were run with year as a random
 effect, except for haemoglobin which was only measured in 2021. We used
 Akaike Information Criterion for small sample sizes (AICc) to determine
 the model of best fit using the MuMIn package (<em>version
 1.48.4</em>; Bartoń, 2023). For a given trait, if the model with the
 lowest AICc score included mass as a significant effect, it was treated as
 a covariate, while a significant effect of sex was instead included as an
 interaction term with region or habitat in future models. This was to
 account for a likely skewed sex-ratio among regions and habitats, which
 could not be formally assessed due to uneven sample sizes. If neither mass
 nor sex were significant, no covariate or interaction term was
 included. Next, we assessed whether each trait varied
 significantly by a) region or b) habitat type at capture using ANOVA
 (<em>lme4 version 1.1-35.5</em>; Bates <em>et
 al.,</em> 2015) and post-hoc Tukey tests for pairwise comparisons.
 Least-squares means were calculated using the <em>emmeans</em>
 package (<em>version 1.10.4</em>; Lenth, 2023). To control for
 potential environmental variation between sampling years, year was
 included as a random effect for all models, excluding haemoglobin which
 was only measured in 2021. Covariates (mass, and/or triglycerides) and
 interaction terms (sex*region or sex*habitat, and sex*mass) determined
 previously by model selection (described above) were included as
 needed. Principal components analysis (PCA) was used to
 examine the pattern of correlations and distributions amongst GWGU
 physiological biomarkers, and to provide indices of overall ‘health’ for
 gulls wintering in the Salish Sea. Using the same approach as described
 above for individual biomarkers, we determined if any significant
 covariates (i.e. mass and/or sex) should be controlled for with PC
 variables. Additionally, PCA scores for individuals were used to compare
 physiological health among regions and habitat types of capture locations
 (R Core Team, 2024). Specifically, we tested whether individual scores
 from the first principal component (PC1) or the second principal component
 (PC2) varied significantly among region or habitat type using ANOVA and
 post-hoc Tukey tests for pairwise comparisons. For
 stable isotope data, we also determined whether to control for covariation
 due to sex and/or mass, and then tested if, a)
 <em>δ</em><sup>13</sup>C and
 <em>δ</em><sup>15</sup>N varied among sampled
 regions or habitats using ANOVAs with post-hoc Tukey tests, and then, b)
 if individual variation in
 <em>δ</em><sup>13</sup>C and
 <em>δ</em><sup>15</sup>N were correlated with each
 of the six physiological biomarkers we measured, as well as the two
 PCA-derived variables of overall ‘health’<em>.</em>
description: # Physiological health of wintering glaucous-winged gulls in coastal
 British Columbia Dataset DOI:
 [10.5061/dryad.z08kprrrp](10.5061/dryad.z08kprrrp) ## Description of the
 data and file structure We assessed variation in physiological health, sex
 ratio, and body mass of glaucous-winged gulls (GWGUs) wintering in the
 Salish Sea region of BC, in relation to anthropogenic activity (based on
 capture locations) and diet (via stable isotopes). In particular, we
 measured a suite of physiological markers commonly used in biomonitoring,
 which can be interpreted with respect to several aspects of health
 (Kophamel *et al.,* 2021; Whitehead and Dunphy, 2022). Since
 interpretation of multiple biomarkers can be strengthened within the
 broader context of an individual’s health (Mallory *et al.,* 2010; Warne
 *et al.,* 2015; Whitehead and Dunphy, 2022) these biomarkers were
 incorporated to generate indices of ‘overall health’ of individuals using
 principle components analysis. Specifically our objectives were to examine
 whether variation in, a) each of these individual physiological
 biomarkers, or b) the overall health of individuals was explained by
 geographical region or habitat type (i.e. categorical variables) of
 capture sites, and c) in an exploratory approach, whether variation in
 physiological metrics was explained by (continuous) variation in diet
 (trophic level) or foraging habitat type (marine vs terrestrial)
 potentially indicated by variation in *δ*15N and *δ*13C values of gull
 blood cells. ### Files and variables * CSV with data * units Values that
 were not collected for an individual or sample type are indicated with a
 blank cell.  | Field Name | Type or Unit | Description | |
 :--------------- | :-------------- |
 :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | band | numerical | individual unique identifier; metal band number issued by the Canadian Bird Banding Office | | site | character | local place names for capture sites | | region | categorical | region categories: LM = Lower Mainland aka Greater Vancouver area ; SC.= Northern Salish Sea including Sunshine Coast and sites on Vancouver Island north of Parksville; MVI = Mid Vancouver Island portion of Salish Sea including Nanaimo down to Cowichan Valley; GV = Greatver Victoria area including Sooke and Metchosin and Juan de Fuca sites; WC = west coast of Vancouver Island sites near Tofino and Ucluelet, BC. | | hab\_rest | categorical | habitat categories: URB = urban, NAT = natural, WC = west coast of Vancouver Island sites near Tofino and Ucluelet, BC | | landfill? | Y/N | Y = yes bird was captured at a landfill; N = not captured at a landfill | | sampling\_crew | character | to distinguish the two crews sampling simultaneously during the winter | | species | character | 4 letter bird species code | | month | date | full month name | | day | date | DD | | year | date | YYYY | | day\_mm\_yr | date | DD-MMM-YYYY | | recap? | Y/N | Y = recapture; N = bird not known to be previously banded | | latitude | decimal degrees | latitude in decimal degrees; WGS84 | | longitude | decimal degrees | longitude in decimal degrees; WGS84 | | GPS\_tag | numerical | unique GPS tag identifier | | mass\_bird\_bag | grams | mass of bird + mass of bag | | mass\_bag | grams | mass of bag | | mass\_bird | grams | mass of bird | | wing\_chord | millimeters | wing chord length in mm | | tarsus | millimeters | tarsus length | | culmen | millimeters | culmen length | | bill\_depth | millimeters | depth of bill at longest vertical axis | | mass\_tar | g/mm | mass divided by tarsus | | age\_a\_j | categorical | A = adult (age 4 or more years old); J = 1 to 3 years old | | sex | categorical | molecularly determined sex; M = male; F = female | | glucose | g/mmol | whole blood glucose measured using Accu-check arriva glucose meter; reading collected at 'bleed end time' for measure of time between capture and reading | | log(glucose) | log(g/mmol) | log transformed glucose values | | RBC1 (mm) | millimeters | length of red blood cells in centrifuged hematocrit tube #1 | | RBC2 (mm) | millimeters | length of red blood cells in centrifuged hematocrit tube #2 | | Plasma1 (mm) | millimeters | length of plasma column in centrifuged hematocrit tube #1 | | Plasma2 (mm) | millimeters | length of plasma column in centrifuged hematocrit tube #2 | | Total (mm) 1 | millimeters | total length of RBCs and plasma in hematocrit tube #1 | | Total (mm) 2 | millimeters | total length of RBCs and plasma in hematocrit tube #2 | | PCV\_1 | ratio | packed cell volume calculated from RBC1, Plasma1, and Total1 | | PCV\_2 | ratio | packed cell volume calculated from RBC2, Plasma2, and Total2 | | mean\_PCV% | % | average packed cell volume (PCV) from tube 1 and 2; converted to a percentage  | | stdev\_pcv | numerical | standard deviation between packed cell volume (PCV) from tube 1 and 2; converted to a percentage  | | cv\_pcv | % | coefficient of variation in packed cell volume (PCV) from tube 1 and 2; converted to a percentage  | | Hb\_1a | g/dL | hemoglobin reading from first well of the triplcate assayed | | Hb\_1b | g/dL | hemoglobin reading from second well of the triplcate assayed | | Hb\_1c | g/dL | hemoglobin reading from third well of the triplcate assayed | | Hb\_2a | g/dL | hemoglobin reading from first well of the triplcate assayed on a subsequent plate (repeated sample) | | Hb\_2b | g/dL | hemoglobin reading from second well of the triplcate assayed on a subsequent plate (repeated sample) | | Hb\_2c | g/dL | hemoglobin reading from third well of the triplcate assayed on a subsequent plate (repeated sample) | | hb\_avg | g/dL | average hemoglobin value for an individual | | hb\_std | numerical | standard deviation of hemoglobin value for an individual | | hb\_cv | % | coefficient of variation of hemoglobin sample  | | total\_trig\_1a | mmol/L | total triglyceride value from first well of duplicate assayed | | total\_trig\_1b | mmol/L | total triglyceride value from second well of duplicate assayed | | total\_trig\_2a | mmol/L | total triglyceride value from first well of duplicate assayed on a subsequent plate (repeated sample) | | total\_trig\_2b | mmol/L | total triglyceride value from second well of duplicate assayed on a subsequent plate (repeated sample) | | mean\_trig | mmol/L | average triglyeride value from all replicates of the sample | | log(trig) | log(mmol/L) | mean trigyceride value log transformed | | trig\_stdev | numerical | standard deviation of triglyceride value for an individual | | trig\_CV | % | coefficient of variation of triglyceride sample  | | capture\_time | HH:mm | Time an individual was captured | | bleed\_end\_time | HH:mm | Time blood sample collection commenced; time the glucose was measured in the blood | | release\_time | HH:mm | Time an individual was released | | bleed\_ht\_mins | minutes | Total minutes from time of capture until blood sample was collected; lucose was measured at this point | | total\_ht\_mins | minutes | Total minutes from time of capture until bird was released | | oxy | µmol HClO mL-1 | Total antioxidant titres measured in plasma | | oxy\_cv | % | coefficient of variation of oxy sample | | dROMs | mg H2O2 dL-1 | reactive oxygen metabolites  | | ISO\_ID | numerical | unique identifier for stable isotope samples | | d13C | δ13C ‰ | d13C stable isotope value measured in an individual's dried red blood cells | | d15N | δ15N ‰ | d15N stable isotope value measured in an individual's dried red blood cells | ## Code/software For all physiological biomarkers measured, sample distributions were examined for normality and whether values were biologically plausible based on reference values for other gulls (e.g. Laranjeiro *et al*., 2020; Minias, 2015; Newman *et al*., 1997). Based on these reference values, biologically implausible outliers were removed for haemoglobin (n = 6; &gt; 24 g/dL) and OXY (n = 1; &lt; 110 µmol HClO/mL). Log transformations were used for triglycerides, glucose, and dROMs (Fowler and Williams, 2017). Statistical analyses were performed, with significance determined using an alpha level of 0.05, in *R version 4.3.3* (R Core Team, 2024). Pearson’s correlation coefficients were used to examine pairwise relationships between the six physiological biomarkers measured and to test the potential effect of handling time on each trait. To address potential bias due to sex differences in our data, we first determined whether the ratio of females to males sampled was significantly different, a) among capture years and b) between years, using the Chi-squared test. We also tested whether mass significantly varied with gull sex using ANOVA. Using linear mixed-effects models, we determined whether variation in any of the six biomarkers measured was explained by a) sex, b) mass, c) sex + mass, or d) sex*mass.  Free fatty acids in plasma can impact dROMs assay results (Pérez-Rodríguez *et al*., 2015), so we additionally tested whether triglycerides, or any combination of triglycerides, sex, and mass explained significant variation in dROMs measurements. All models were run with year as a random effect, except for haemoglobin which was only measured in 2021. We used Akaike Information Criterion for small sample sizes (AICc) to determine the model of best fit using the MuMIn package (*version 1.48.4*; Bartoń, 2023). For a given trait, if the model with the lowest AICc score included mass as a significant effect, it was treated as a covariate, while a significant effect of sex was instead included as an interaction term with region or habitat in future models. This was to account for a likely skewed sex-ratio among regions and habitats, which could not be formally assessed due to uneven sample sizes. If neither mass nor sex were significant, no covariate or interaction term was included. Next, we assessed whether each trait varied significantly by a) region or b) habitat type at capture using ANOVA (*lme4 version 1.1-35.5*; Bates *et al.,* 2015) and post-hoc Tukey tests for pairwise comparisons. Least-squares means were calculated using the *emmeans* package (*version 1.10.4*; Lenth, 2023). To control for potential environmental variation between sampling years, year was included as a random effect for all models, excluding haemoglobin which was only measured in 2021. Covariates (mass, and/or triglycerides) and interaction terms (sex*region or sex*habitat, and sex*mass) determined previously by model selection (described above) were included as needed. Principal components analysis (PCA) was used to examine the pattern of correlations and distributions amongst GWGU physiological biomarkers, and to provide indices of overall ‘health’ for gulls wintering in the Salish Sea. Using the same approach as described above for individual biomarkers, we determined if any significant covariates (i.e. mass and/or sex) should be controlled for with PC variables. Additionally, PCA scores for individuals were used to compare physiological health among regions and habitat types of capture locations (R Core Team, 2024). Specifically, we tested whether individual scores from the first principal component (PC1) or the second principal component (PC2) varied significantly among region or habitat type using ANOVA and post-hoc Tukey tests for pairwise comparisons. For stable isotope data, we also determined whether to control for covariation due to sex and/or mass, and then tested if, a) *δ*13C and *δ*15N varied among sampled regions or habitats using ANOVAs with post-hoc Tukey tests, and then, b) if individual variation in *δ*13C and *δ*15N were correlated with each of the six physiological biomarkers we measured, as well as the two PCA-derived variables of overall ‘health’*.* ## Access information Data was derived from the following sources: * Data was collected through sampling wild Glaucous-winged gulls throughout the Salish Sea in January and February, 2020 and 2021. Laboratory analyses were conducted on biological samples to measure physiological biomarkers in blood and plasma samples, as well as blood stable isotopes, and to molecularly determine sex of individual gulls.  * North American Land Cover Database: [https://www.cec.org/north-american-environmental-atlas/land-cover-2010-modis-250m/](https://www.cec.org/north-american-environmental-atlas/land-cover-2010-modis-250m/) * Statistics Canada Population Density Census by census divisions (2016): [https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/hlt-fst/pd-pl/Table.cfm?Lang=Eng&T=703&SR=26&S=54&O=A&RPP=25&PR=0&CMA=0&CSD=0](https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/hlt-fst/pd-pl/Table.cfm?Lang=Eng&T=703&SR=26&S=54&O=A&RPP=25&PR=0&CMA=0&CSD=0)