DOI: 10.5061/dryad.p5hqbzkvz
title: Blood and muscle O2 storage capacity in North American diving ducks

description: Breath-hold diving presents air-breathing vertebrates with the challenge
 of maintaining aerobic respiration while exercising underwater. Adaptive
 increases in the oxygen (O2) storage capacity in the lungs, blood, or
 muscle tissues can enhance these reserves and greatly extend aerobic
 foraging time underwater. Here, we report blood- and muscle-O2 storage
 parameters (blood hemoglobin concentration ([Hb]), hematocrit, and
 myoglobin concentration ([Mb]) in the pectoralis and gastrocnemius) for 16
 species of diving and dabbling ducks found in North America, and
 investigate which parameters are correlated with the diving behaviors
 reported in both the sea ducks (Mergini) and the pochards (Aythini). Both
 [Hb] in the blood and [Mb] in the gastrocnemius, a major leg muscle used
 in propulsion for these predominantly leg-propelled divers, were
 significantly higher in the sea ducks compared to the dabblers (Anatini).
 The pochards also showed a significant increase in [Hb] and were
 intermediate between the sea ducks and the dabblers in hematocrit and [Mb]
 in the gastrocnemius. Among these four variables and total body mass, [Mb]
 in the gastrocnemius was the most significant predictor of mean species
 dive time, and these two variables were correlated across the phylogeny.
 Our results indicate that the observed changes in O2 storage capacity in
 the blood and muscles are positively correlated with diving behavior in
 two clades of ducks, such that larger increases are correlated with longer
 dive times.
description: <em>Hemoglobin and Hematocrit</em>
 Blood was obtained via cardiac puncture from freshly collected
 ducks.Immediately after extraction, [Hb] of the whole blood was analyzed
 using a HemoCue 201+ Analyzer (HemoCue America, Brea, CA, USA), which
 chemically converts Hb to azidemethemoglobin before measuring absorbance
 at 570 and 880 nm. All values were corrected using an avian correction
 factor of –1 g/dL (Simmons and Lill 2006). Packed red blood cell volume
 (Hct) was measured in quadruplicate using 75 mm heparinized capillary
 tubes. Tubes were spun for five minutes in a ZIPocrit centrifuge (LW
 Scientific, Lawrenceville, GA, USA), after which Hct was measured. All
 hematocrit measurements represent the mean of all four tubes, except in
 cases where one tube broke or was clearly faulty (e.g. 28% when three
 other tubes were 45%). Mean corpuscular hemoglobin concentration (MCHC),
 which shows the average [Hb] in red blood cells, was calculated using the
 following equation: MCHC = (Hb[g/dL] x 100)/Hct[%]). 6–27 individuals were
 used for hemoglobin and hematocrit measurements. 
 <em>Myoglobin</em> Frozen tissue
 samples from the pectoralis and gastrocnemius of 16 species of ducks were
 used for myoglobin assays. Myoglobin concentration was determined using
 the modified Reynafarje (Reynafarje 1963) method utilized by Dawson et al.
 (2016, 2020). Frozen tissue was homogenized in 19.25 volumes (1 mL buffer
 per 1 gram of frozen tissue) of ice-cold homogenization buffer (40 mM
 potassium phosphate; pH 6.6) and centrifuged for 99 minutes at 13,700x g
 and 4 °C (Dawson et al. 2016). The resulting supernatant was transferred
 to a 25 mL rotating boiling flask and exposed to pure carbon monoxide (CO)
 for 8 minutes. Sodium dithionite was added and CO was bubbled in for a
 further 2 minutes to ensure complete reduction of Mb. Samples were diluted
 a further 19.5x with homogenization buffer, transferred to a 1 ml cuvette
 (10mm optical path length). The optical density of each sample was
 measured in triplicate with a homogenization buffer blank at 538 and 568
 nm using a VWR V1200 Spectrophotometer (VWR, Radnor, PA, USA). Myoglobin
 concentration ([Mb]; mg/g) was then determined based on the following
 equation: [Mb] = (OD538 – OD568) x 112.6 (Dawson et al. 2016). Only 10
 individuals per species were included in myoglobin assays.
description: ## Data from: Blood- and muscle-oxygen storage capacity in North American
 diving ducks --- This dataset contains information on blood and oxygen
 storage parameters in 16 species of diving and non-diving ducks. Blood
 oxygen meaurements include hemoglobin concentration and hematocrit values.
 Myoglobin concentration was measured in the pectoralis (major flight
 muscle) and gastrocnemius (major leg muscle used for swimming and diving).
 ## Description of the data and file structure OxygenStorage_Raw.csv
 contains all data on blood and muscle oxygen storage measurements. Each
 individual bird and all its associated data are present in a single row.
 Species: Latin genus_species name for each species. ABA Code: The 4-letter
 code used by the American Birding Association, included to make coding
 species in R easier. Diver?: Y or N to indicate whether diving is the main
 mode of food acquisition for this species. Tribe: The taxonomic grouping
 used to split the two diving groups by their evolutions of diving.
 Mergini: sea ducks; Aythyini: pochards; Anatini: dabbling ducks. Sex: M or
 F. Age: AHY - after hatch year bird, at least 1 year of age. Body Mass:
 Total body mass of bird Hct: Hematocrit, also known as packed red cell
 volume (%) \[Hb]: Hemoglobin concentration (g/dL) \[Mb]: Myoglobin
 concentration (mg/g) Gastrocnemius: Major muscle in the leg used in
 swimming and diving Pectoralis: Breast muscle, major flight muscle Total
 mass of gastrocnemius and pectoralis is the summed weights for both the
 left and right sides of the muscle. DiveTimes_Literature.csv contains the
 mean dive times by species. For individual citations, please refer to the
 published article. Species: Latin genus_species name for each species.
 Short: The 4-letter code used by the American Birding Association,
 included to make coding species in R easier. Time: The mean dive time for
 that species. ## Suggested R Packages and Sample Code Ape, Phylobase,
 Phytools, Geiger, Caper, NLME, GGPlot2, Tidyverse, Dplyr, AICcmodavg \**
 Phylogenetic analyses require a data frame with only one line per species,
 so all variables were collapsed into a mean table (df.mean) PhylANOVA
 Analyses tribe &lt;- as.factor(df.mean$tribe) phylanova(tree, tribe,
 df.mean$variable, nsim = 1000, posthoc = TRUE, p.adj =
 "bonferroni") Ancestral Trait Mapping row.names(df.mean) &lt;-
 df.mean$species name &lt;- df.mean[,column#] name &lt;-
 setNames(df.mean$variable, rownames(df.mean)) name.contmap &lt;-
 contMap(tree, name[tree$tip.label], res = 200, plot = FALSE) name.contmap
 &lt;- setMap(name.contmap,
 c("white","#FFFFB2","#FECC5C","#FD8D3C", "#E31A1C")) plot(name.contmap,fsize=c(1.3,1), lwd = c(7,7), leg.txt="Variable", sig = 1, legend = 10) Model Selection For Phylogenetic GLS: \##specify order of taxa in dataframe spp &lt;- rownames((df)) bm &lt;- corBrownian(phy = tree, form = ~spp) Save all the models of interest, e.g.: m.global &lt;- gls(time ~ hb + hct + mb.g + mb.p + mass, data = df, correlation = bm, method = "ML") cand.models &lt;- list("global" = m.global, etc.) selectiontable &lt;- aictab(cand.set = cand.models) write.csv(selectiontable, "computer location")