Effects of supplemental feeding on nesting success and physiological metrics in Eastern Bluebirds (Sialia sialis)
Perryman, Danielle et al. (2022), Effects of supplemental feeding on nesting success and physiological metrics in Eastern Bluebirds (Sialia sialis), Dryad, Dataset, https://doi.org/10.6078/D1142Q
Supplemental feeding is a common anthropogenic influence on wildlife which, dependent on natural food availability, can have positive or negative effects on physiological condition. Animals may respond negatively to supplemental feeding if the artificial food source increases disease risk due to aggregation. We manipulated supplemental food availability in a wild population of Eastern bluebirds, Sialia sialis, to examine the influence on physiological metrics and nesting success without causing birds to aggregate to access food. Adult and nestling bluebirds were randomly assigned to one of three feeding groups. The first treatment group received mealworms (Tenebrio molitor larvae) inside nest boxes throughout the breeding attempt, the second treatment group received mealworms from nest completion until nestlings hatched, and the third treatment group received no supplementation but was disturbed at the same frequency as the other two treatment groups. We collected blood samples from adults and nestlings to quantify bacterial killing ability, corticosterone levels, and heterophil to lymphocyte ratios. As measures of nesting success, we quantified hatching success and fledging success. Neither the physiological stress metrics nor the nesting success metrics were significantly different across the experimental groups with regard to treatment. This indicates Eastern bluebirds are not significantly impacted by supplemental mealworm feeding during breeding, at least when natural food sources are abundant. Removal of a supplemental food source during breeding also did not influence bluebird productivity. Bird feeding by hobbyists may attract birds to locations with available nesting sites without demonstrably negative or positive effects.
Field methods: The range of Eastern bluebirds extends from the central United States to the Atlantic Coast (Sibley et al., 2014). Bluebirds prefer to nest in edge habitats, including edges created by human development, which brings them into close contact with humans (Jones et al., 2014). Eastern bluebirds are secondary cavity nesters, and breed in artificial nest cavities. For this study, nest boxes (2015: n = 187, 2016: n = 176) located along walking trails and roads in Stillwater, Oklahoma (36°7′18″N 97°4′7″W) were utilized to manipulate the food available to breeding Eastern bluebirds. Nest boxes were checked 2-3 times weekly throughout the breeding season during March-August in 2015 and 2016. Individual boxes were monitored daily when complete nests were detected to determine first egg dates. Boxes were again monitored daily when clutches were within two days of their expected hatch date (clutch completion date + 13 days). Then nest boxes were monitored during regular nest checks to determine if fledging occurred. This frequency of monitoring generally follows methods used previously in this population (Author et al., 2012).
Experimental manipulation of food availability: Small feeding cups (2 oz., plastic) were mounted in all nest boxes prior to the breeding season. In these cups, supplemental food was provided by adding fifteen mealworms (1.909 ± 0.177g) per individual bluebird in the nest box, including both adults and nestlings, three times per week (~11.4 – 40 g weekly) (Smith, 2021). Each time the nest boxes were visited, it was noted whether the cups were empty or contained mealworms to track if the birds had consumed them. Any nest boxes with consistently full cups, likely neophobic individuals or recently failed nests, were removed from all analyses (9.53% of 63 boxes total for both years which received PART/FULL treatments and survived to day 5). All boxes included in the analyses consumed greater than half of all mealworms provided to them.
The experimental groups were randomly allocated to FULL (2015: n = 35, 2016: n = 22), PART (2015: n = 26, 2016: n = 20) or CONT (2015: n = 36, 2016: n = 35) treatment groups. An additional subset of boxes was included as a control for potential effects of additional foot traffic at the boxes. The boxes in this second control group did not receive supplemental food and were visited only 2-3 times per week during regular nest monitoring. All other boxes, regardless of experimental treatment, were visited and opened the same number of times to control for any effects of disturbance. Because the two control groups did not differ significantly in any of the main effects (all p > 0.058), these groups were combined in the analyses described below.
Morphology: All adult birds in the study were captured for banding and weighing using a nest box trap no sooner than two days after nestlings hatched (Friedman et al., 2008). Mass for each individual was determined using either a digital balance (accuracy=0.01 g) or Pesola (accuracy=0.1 g). Nestlings were weighed on day 14 post-hatch (day 0=hatch day). Nestlings were banded with an aluminum USFWS band on day 11 post-hatch, and were examined to determine sex based on plumage coloration on day 14 (Pyle 1997).
Nest Success: A clutch was considered complete when the number of eggs did not increase for more than one day. Hatching success was quantified as the proportion of eggs in the clutch that hatched. Fledging success was determined as the proportion of nestlings that left the nest after the day 14 measurement. The typical time to fledging in Eastern bluebirds is 17-19 days (Gowaty and Plissner, 2015) with nestlings capable of weak flight at day 14. Nestlings absent from the nest after day 14 were considered successfully fledged, unless there was evidence of death or predation.
Blood Sampling and Laboratory Methods: Blood samples (50-100 µL) were collected from all nestlings and adults within three minutes of removal from the nest box to determine CORT levels (Owen, 2011). Compared to other studies on this population in which CORT has been measured, CORT levels in this study were elevated (Author et al. 2012). In this study, we collected blood samples within three minutes of removal from the nest box, rather than within three minutes of initial disturbance, as in previous studies. All blood samples were taken between 0700-1100 hours to minimize variation due to circadian rhythms in hormone levels (Remage-Healey and Romero, 2000). Approximately 5-10 µl of each whole blood sample was used to prepare blood smears in the field. The remainder of each whole blood sample was kept cool on ice until it was brought to the laboratory for plasma separation with a centrifuge at 1846 xg for seven minutes, which took place within 4 hours of collection. Plasma was stored at -20°C until it was used in BKA assays, or to measure plasma CORT. Average storage length prior to BKA analysis was 158±3.37 days. Storage length was not significantly related to BKA; therefore, we did not include this as a covariate in our analyses (t(76)=2.28, p = 0.99). Storage length also was not significantly related to CORT levels; therefore, we did not include this as a covariate in our analyses (t(276) =0.055, p = 0.956).
Slides were stained with Differential Quik Stain following kit instructions (Triangle Biomedical Sciences; Davis et al., 2008). A complete blood cell count was performed following the techniques described in Avian and Exotic Animal Hematology and Cytology (Campbell and Ellis, 2013, Clark et al., 2009) by an expert in white blood cell identification (IK) who was blind to the identity of the treatment groups. Heterophil to lymphocyte (H:L) ratios were calculated by dividing the total number of heterophils by the total number of lymphocytes.
Enzyme immunoassays (Corticosterone ELISA Kit ADI-901-097, Enzo Life Sciences) were used to quantify circulating plasma CORT levels. Based on previous optimization of the assay for Eastern bluebirds (unpubl. data), plasma samples were diluted 1:40 in 1.5% steroid displacement reagent (SDR). All samples were run in duplicate and the corticosterone standards (20,000, 4,000, 800, 500, 160, and 32 pg/mL) were run in triplicate. The optical density of the plates was then read at 405 nm on a BioTek ELx808 microplate reader. Samples with intra-assay coefficients of variation (CVs) higher than 15% were re-run (average intra-assay CV: 6.25%). The average inter-assay CV was 14.57%.
Tryptic soy agar (Fisher Scientific, DF0369-17-6) plates were made under sterile conditions and stored at 4°C the day prior to conducting BKA assays. Escherichia coli (ATCC #8739, Microbiologics) stocks and dilutions were prepared the day before assays were conducted. E. coli stock solutions were prepared by adding one lyophilized bacteria pellet (5.6 x 107 CFU) to 40 mL of phosphate-buffered saline (Sigma Aldrich, P-5368), which was then incubated at 37°C for 30 minutes and stored at 4°C. Plasma and bacteria were incubated in carbon dioxide independent media (Invitrogen Inc, Gibco media #18045). Additionally, 200 µL of 200 mM L-glutamine (Life Technologies, 25030-149) was added to the solution prior to incubation. To perform the assay, 5 µL of plasma was combined with 100 µL of the media solution and 10 µL of bacterial working solution. The bacteria and plasma were incubated together for 30 minutes at 41°C. The samples were then plated on agar plates and incubated for a minimum of 12 hours at 37°C. Control plates were prepared in the same manner as experimental plates; however, control plates did not contain plasma. The number of colonies on each plate was counted after the 12-hour incubation. Finally, the BKA of each plasma sample was quantified as the percent difference in the number of colonies on plasma treated plates compared to the number of colonies on control plates.
Statistical Methods: We compared masses of nestlings on day 14 among supplement groups to determine if variation in supplemental feeding schedules affected fledging mass. We used R (v. 4.1.0) to create linear mixed-effects models from the lme4 package to account for non-independence of nestlings and mated pairs from the same nest. NestID, a concatenate variable created with nest box names and start dates, was included as a random effect to account for non-independence and month was included as a fixed effect to account for seasonal variation. Masses of adults were collected at the time of capture and, while we did not anticipate an effect of our experiment on adult mass, we analyzed adult body mass separately using the same methods as for nestlings.
We conducted separate analyses on adults and nestlings for CORT, H:L ratios, and BKA. We again used linear mixed-effects models from the lme4 package to account for non-independence of nestlings and included NestID, as a random effect. Month was also included in these analyses as a fixed effect. For CORT, we evaluated 56 adults and 220 nestlings. For H:L ratios, we analyzed 48 adults and 44 nestlings, and for BKA, we analyzed 49 adults and 69 nestlings. For nestling measures of H:L ratio, we selected one nestling at random from each brood prior to data collection due to constraints on supplies and personnel. We acknowledge that nestlings might exhibit high variability in physiological traits within the same nest, but we opted to capture population-level variation, rather than within nest variation. CORT and H:L ratios were not normally distributed; therefore, we normalized these values by log10 transformation (Lobato et al., 2009).
To evaluate the effect of supplemental feeding on nesting success, we compared hatching and fledging success among experimental groups with month included as a fixed effect. Finally, to assess whether adult physiology was predictive of nesting success, we also determined whether CORT, BKA, or H:L ratios were associated with measures of nesting success across treatment groups. We used logistic regressions with a quasibinomial distribution for these analyses because fledging and hatching success varied between 0 and 1, with intermediate values representing portions of broods that successfully hatched or fledged. To ensure independence of measurements in instances when we had data from two adults associated with a nest, we removed one individual using a random number generator in Python (v. 3.6). Thus, only one adult was associated with success at each nest. We also included month as a factor due to the potential for reproductive success to vary during the season.
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