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Almost 50 years ago, Simmons26 called gyrocotylideans a “century-old enigma” and this status still persists despite the advent of more advanced identification methods3. The poor understanding of the group (e.g., the complete life cycle of none of the species is known) is linked with the scarcity of available data and the biological peculiarities of these tapeworms and their holocephalan hosts. In particular, most of the host species are rarely available deep-sea dwellers, which often could not be examined fresh or were frozen with their parasites prior to examination. If isolated alive, gyrocotylideans exhibit an unusual morphological variability due to the contraction of their large bodies and as a result of different fixative procedures which were tested to ensure their relaxation (e.g.27). Despite these issues, several comprehensive studies have been conducted, e.g.15,16,21,28, which provided deep insight into the biology, ecology and taxonomy of these enigmatic tapeworms. Nevertheless, the poor quality of the specimens studied and the use of different, not always appropriate, methods of parasite fixation, unintentionally affected the quality of morphological descriptions of most gyrocotylidean species, which prevented the establishing of clear morphological borders to delimit individual species. As a result, the informative value of morphological traits used for species delimitation should be re-assessed, based on the simultaneous use of molecular data, i.e., the use of hologenophores to match morphology and molecular data. Existing problems with species delimitation and morphological variability even led to complete omission of morphological characterisation of two new species described just recently6.
Herein, the genotyping of the Gyrocotyle spp. specimens acquired in Taiwan revealed four distinct genotypes, each one more related to the North Atlantic isolates identified as “Gyrocotyle urna” off Ireland (the isolate is genetically diverse from G. urna off Norway), “G. rugosa” off Alaska (probably misidentified, see below), G. discoveryi off Ireland and G. confusa off Norway, respectively, than to each other.
In addition to casting doubts on the restriction of gyrocotylideans to individual oceans, our data also question the proclaimed strict host specificity3,7, because specimens of Gyrocotyle sp. genotype 3 were found in two hosts species, which are not the closest relatives to one another—C. phantasma and C. cf. argiloba (Fig. 4). Broader host specificity was also reported for G. fimbriata, which was found in Hydrolagus colliei and Chimaera phantasma, and for G. rugosa, recorded in Callorhinchus callorynchus and C. milii14,15,24,29. Gyrocotyle urna was also found in several holocephalans, including Chimaera monstrosa, Callorhinchus callorynchus, Hydrolagus ogilbyi Waite and H. colliei24,29,30. In contrast, Bandoni & Brooks16 revised the host spectrum of this parasite, considering C. monstrosa as the only host of G. urna.
The suitability of the molecular markers employed for this group also requires attention, because a considerable amount of phylogenetic information was also lost in the un-rooted dataset due to treatment of the numerous gaps in the 28S rRNA alignment. The involvement of partial COI gene sequences seemed to be informative for estimating gyrocotylidean phylogeny, because we obtained a no-gap COI alignment and improved support for some nodes in the three-gene network. The suitability of this marker requires assessment employing further taxa, because except for our isolates off Taiwan and Argentina, only a single sequence of the COI gene (i.e., that of G. urna off Norway; GenBank acc. no. JQ268546) is currently available.
A single specimen of Gyrocotyle sp. genotype 4 was conspicuously different morphologically from the remaining ones by having few folds on the lateral margins, many acetabular spines, a narrow funnel and a small rosette. However, its formal description as a new species would be premature, because only a single specimen was found. Morphological differences among the specimens of the other genotypes were not so obvious, even though a careful examination of the hologenophores allowed us to find several morphological traits that were characteristic for particular genotypes (see “Results” section). Among them, the number of acetabular spines and the distribution of the body spines and their size may be potentially useful for species differentiation, especially because the body contraction can hardly affect them. Since body contraction cannot be absolutely excluded even when live specimens are properly fixed, its effect could be overcome to some degree by an evaluation of ratios related to the main body dimensions (e.g., length of uterine sac/total body length) rather than comparison of total measurements of internal structures.
The specimens off Taiwan most probably represent several new species, but we decided not to describe them formally as new taxa, mainly because of the shortage of comparative data. In addition to these specimens, two hologenophores of Gyrocotyle rugosa off Argentina were examined, which made it possible to characterise the type species of the genus. The host of G. rugosa described by Diesing10 was questionable until Callorhynchus antarcticus (= C. callorynchus—see31) off New Zealand was finally established as its currently accepted type host3,32. Gyrocotyle rugosa was found in coastal waters of South America, South Africa and New Zealand as a parasite of C. callorynchus and C. milii, suggesting its broader host specificity16,24. Our specimens from C. callorynchus off Argentina were identified as G. rugosa based on crenulated (i.e., without any folds) lateral margins, a tiny uterine sac, a branched uterus and embryonated eggs in the uterine sac; the latter two traits are unique to this species21. Genetically, it clustered with an unspecified isolate of Gyrocotyle from C. milii off Australia, and these specimens seem to be conspecific.
In contrast, an isolate from Hydrolagus colliei off Alaska identified as G. rugosa (GenBank acc. nos. AF286925 and AF124455) was apparently misidentified, because (i) it was found in an unrelated definitive host (H. colliei belongs to the family Chimaeridae, whereas the type host to the family Callorhinchidae), (ii) its distant geographic origin (the type locality of G. rugosa is unclear, but it is definitely in the Southern hemisphere), and (iii) its genetic divergence from our isolate of G. rugosa from the type host off Argentina. The isolate from H. colliei may represent Gyrocotyle fimbriata or G. parvispinosa, which have been reported from this host off the Pacific coast of North America, but its identification was not possible because morphological vouchers were not available to the present authors.
Gyrocotylideans were generally considered to be oioxenous, i.e. strictly specific parasites sensu Euzet and Combes33, with each gyrocotylidean species parasitising a single holocephalan species. Although several species were reported from two or more hosts species16,24, these findings are usually considered as misidentifications due to the unclear taxonomy of the order. Moreover, some holocephalans, such as Ch. monstrosa, H. colliei, H. affinis, and Ca. callorynchus, were often found to harbour two or more gyrocotylidean species, one common and the other rare9,10,21,22,23. Our findings of Gyrocotyle sp. genotypes 1 and 3 in Ch. phantasma and Gyrocotyle sp. genotypes 2, 3 and 4 in Ch. cf. argiloba suggested stenoxenous host specificity (i.e., the occurrence in a few closely related hosts) of gyrocotylideans, because the specimens of genotype 3 were found in both species of Chimaera. The obvious genetic similarity of our G. rugosa specimen from Ca. callorynchus and the isolate of Gyrocotyle sp. from Ca. milii also questions the strict specificity of this group, but morphological vouchers of the latter, which are necessary for the confirmation of their conspecificity, are not available.
Our genetic analyses provided insight into the interrelationships among the gyrocotylideans, even though the absence of a suitable outgroup did not enable us to broadly assess the possible evolutionary scenario of this earliest evolving group of tapeworms. Moreover, genetic data on only half of the nominal species of Gyrocotyle are available, not considering the possibility of misidentifications of previously sequenced specimens, for which hologenophores are not available. However, some clues of host-parasite coevolution can be inferred from the network. The mutual genetic distance of species/genotypes from the same host species suggests multiple colonisation events rather than co-speciation with their hosts within the order. It seems that G. phantasma might have been colonised by Gyrocotyle sp. genotype 1 or genotype 3, because these two genotypes are not the closest relatives in our analyses. The same pattern is obvious for C. cf. argiloba parasitised by Gyrocotyle sp. genotype 2, 3 and 4, and also for C. monstrosa, which harbours G. urna, G. confusa and G. nybelini. Indeed, Colin et al.27 considered these species from C. monstrosa to be conspecific, but our genetic data support the validity of three separate and genetically distant species. Moreover, G. nybelini formed by far the most distant lineage among all isolates, which may suggest the validity of the genus Gyrocotyloides Furhmann, 1931.
Genetic divergence of congeneric tapeworms from the same host species was also observed in several elasmobranch/teleost-cestode assemblages, e.g., Acanthobothrium spp. (Onchoproteocephalidea) and the mumburarr whipray Urogymnus acanthobothrium Last, White & Kyne; Echeneibothrium spp. (Rhinebothriidea) and the yellownose skate Dipturus chilensis (Guichenot); and Pseudoendorchis spp. (Onchoproteocephalidea) and the catfish Pimelodus maculatus Lacepède34,35,36.
The aim of this paper was to provide new insight into the phylogenetic relationships within the enigmatic order Gyrocotylidea, but, in particular, to demonstrate the lack of geographical patterns in the distribution of most its species and the limited suitability of current morphological characteristics for species circumscription. Herein, we have outlined a methodology (fixation of live specimens with hot fixative and the exclusive use of hologenophores) that should be used in future taxonomic, ecological and biogeographical studies of gyrocotylideans in order to reliably circumscribe their actual species diversity and to unravel associations with their hosts, a relict group of marine vertebrates. Gyrocotylideans represent one of the key groups of parasitic flatworms (Neodermata) in terms of a better understanding of their evolutionary history and the switch of free-living flatworms to parasitism. More

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Antibiotic duration experiment
Twenty-eight-week-old female C57BL/6J mice from the same birth cohort were co-housed (5–6 mice per cage) prior to beginning the experiment, and then separated into individual cages 1 week prior to antibiotic treatment. Singly housed mice were exposed to 0.5 mg/mL33 cefoperazone in their drinking water for 0, 2, 4, 8, or 16 days (Fig. 1A). Based on the literature, we calculated the minimum dose of cefoperazone based on the mean and standard deviation of water consumption by C57BL/6J mice ((7.7, mp 0.3,{mathrm{mL}}) per 30 g of body weight)44. If the heaviest mouse in our study (~22 g) consistently consumed water at 2 SD below the mean (i.e. 5.5 of 0.5 mg/mL cefoperazone), they would still receive 125 mg/kg/day of cefoperazone, which is within the therapeutic dosing range for humans (100–150 mg/kg/day; although cefoperazone is administered to humans via intravenous injection)45.
Fig. 1: Effect of antibiotic exposure duration on non-responder phenotype.

The table in the center denotes the number of non-responder and responder mice in each treatment duration group. A Experimental design for the duration experiment. Circles denote sampled time points. Time points were considered sampled “during” antibiotic treatment between day 0 and day 2, 4, 8, and 16, respectively, as denoted by orange shades. B Relative abundance of phyla on the last day of antibiotics treatment. The control panel is an average over all untreated controls from all time points. Only phyla with a relative abundance of at least 0.1% are shown. Each barchart denotes means from at least two samples and white insets are the sample size used for each barchart. C Percentage of mitochondria and chloroplast sequences in 16S amplicon data relative to antibiotic treatment. Colors: red—controls not treated with antibiotics, green—non-responders, blue—responders. D Principal coordinate analysis (PCoA) of samples during and after antibiotic exposure (n = 143 samples with >10,000 reads per sample, day ≥ 0). Ellipses denote 95% confidence intervals from a Student t-distribution. Each point denotes a sample. ASV abundances were rarefied to 10,000 reads for each sample and percentages in brackets denote the explained variance. Samples with less than 10,000 reads per sample were not included in the analysis. E Dynamics of amplicon sequence variants (ASVs). Gained ASVs are variants that were not present before antibiotics treatment but are present after. Similarly, lost ASVs were present before treatment but not after, and persistent ASVs were present before and after. Stars denote significance under a Mann–Whitney U test: *p  More

Specimens
We used brains obtained from three cetacean species (harbour porpoise—Phocoena phocoena, minke whale—Balaenoptera acutorostrata, and humpback whale—Megaptera novaeangliae) and 11 artiodactyl species (sand gazelle—Gazella marica, domestic pig—Sus scrofa, Nubian ibex—Capra nubiana, springbok—Antidorcas marsupialis, blesbok—Damaliscus pygargus, greater kudu—Tragelaphus strepsiceros, blue wildebeest—Connochaetes taurinus, dromedary camel—Camelus dromedarius, nyala—Tragelaphus angasii, river hippopotamus—Hippopotamus amphibius, and African buffalo—Syncerus caffer) (Table 1). All artiodactyl brains were perfusion fixed with 4% paraformaldehyde in 0.1 M phosphate buffer through the carotid arteries following euthanasia45. The harbour porpoise specimens were perfusion fixed through the heart following euthanasia, while the minke whale and humpback whale brains were immersion fixed in 4% paraformaldehyde in 0.1 M phosphate buffer. All brains were then stored in an antifreeze solution at – 20 °C until use45. All specimens were taken under appropriate governmental permissions, with ethical clearance provided by the University of the Witwatersrand Animal Ethics Committee (Clearance number 2008/36/1), which uses guidelines similar to those of the National Institutes of Health regarding the use of animals in scientific research and is compliant with ARRIVE guidelines.
Immunohistochemical staining
Blocks of tissue from the anterior cingulate (dorsal to the rostrum of the corpus callosum, in all species apart from the humpback whale where we did not have this tissue block) and occipital cortex (presumably primary visual cortex, from all species) with underlying white matter were taken from each of the specimens. These were placed in a 30% sucrose in 0.1 M phosphate buffer solution at 4 °C until equilibrated. The blocks were frozen in crushed dry ice, mounted on an aluminium stage and sectioned at 50 µm orthogonal to the pial surface. Alternate sections were stained for Nissl (with 1% cresyl violet), UCP1, UCP2, UCP3, UCP4, UCP5, dopamine-ß-hydroxylase (DBH) and tyrosine hydroxylase (TH). To investigate the presence of neural structures immunolocalizing uncoupling proteins, DBH and TH, we used standard immunohistochemical procedures with antibodies directed against UCP1, UCP2, UCP3, UCP4, UCP5, DBH and TH. While immunolocalization for UCP1, UCP4, UCP5, DBH and TH were clear, only occasional cortical neurons were immunopositive for UCP2, and no immunolocalization could be detected for UCP3 in the species studied. It should be noted here that immunostaining for DBH and TH did not work in the humpback whale specimen, perhaps due to the fixation procedure or the conformation of the targeted proteins in this species preventing recognition of the DBH and TH proteins by the antibodies used. Sections used for the Nissl series were mounted on 0.5% gelatine-coated glass slides, cleared in a solution of 1:1 chloroform and absolute alcohol, then stained with 1% cresyl violet to reveal cell bodies. For the immunohistochemical staining, each section was treated with endogenous peroxidase inhibitor (49.2% methanol:49.2% 0.1 M PB:1.6% of 30% H2O2) for 30 min and subsequently subjected to three 10 min 0.1 M PB rinses. Sections were then incubated for 2 h, at room temperature, in blocking buffer (containing 3% normal rabbit serum, NRS, for the UCP1-5 sections/3% normal horse serum, NHS, for the DBH sections/3% normal goat serum, NGS, for the TH sections, plus 2% bovine serum albumin and 0.25% Triton-X in 0.1 M PB). This was followed by three 10 min rinses in 0.1 M PB. The sections were then placed in the primary antibody solution that contained the appropriately diluted primary antibody in blocking buffer for 48 h at 4°C under gentle shacking. The optimal dilutions for the UCP primary antibodies were determined with a series of stains in which the dilution of the primary antibodies ranged from 1:300 through to 1:9600, with any staining in all species being absent at a dilution of 1:4800 irrespective of fixation method. We used antibodies directed against UCP1 (Santa Cruz Biotechnology, C-17, sc-6528, Lot# D0411, goat polyclonal IgG, dilution 1:300, RRID:AB_2304265), UCP2 (Santa Cruz Biotechnology, C-20, sc-6525, Lot# E0211, goat polyclonal IgG, dilution 1:300, RRID:AB_2213585), UCP3 (Santa Cruz Biotechnology, C-20, sc-7756, Lot# A2511, goat polyclonal IgG, dilution 1:300, RRID:AB_2213922), UCP4 (Santa Cruz Biotechnology, N-16, sc-17582, Lot# E2004, goat polyclonal IgG, dilution 1:300, RRID:AB_793648), UCP5 (Santa Cruz Biotechnology, Q-16, sc-50540, Lot# B1207, goat polyclonal IgG, dilution 1:300, RRID:AB_2286101), DBH (Merck-Millipore, MAB308, mouse monoclonal IgG, dilution 1:4000, RRID:AB_2245740) and TH (Merck-Millipore, AB151, rabbit polyclonal IgG, dilution 1:3000, RRID:AB_10000323). This incubation was followed by three 10 min rinses in 0.1 M PB and the sections were then incubated in a secondary antibody solution (1:1000 dilution of biotinylated anti-goat IgG, BA-5000, Vector Labs, for UCP1-5 sections/1:1000 dilution of biotinylated anti-mouse IgG, BA 2001, Vector labs, for DBH sections/1:1000 dilution of biotinylated anti-rabbit IgG, BA-1000, Vector Labs, for TH sections, in a blocking buffer containing 3% NRS/NHS/NGS and 2% BSA in 0.1 M PB) for 2 h at room temperature. This was followed by three 10 min rinses in 0.1 M PB, after which sections were incubated for 1 h in avidin-biotin solution (at a dilution of 1:125, Vector Labs), followed by three 10 min rinses in 0.1 M PB. Sections were then placed in a solution of 0.05% 3,3′-diaminobenzidine (DAB) in 0.1 M PB for 5 min, followed by the addition of 3 ml of 3% hydrogen peroxide to each 1 ml of solution in which each section was immersed. Chromatic precipitation was visually monitored and verified under a low power stereomicroscope. Staining was allowed to continue until such time as the background stain was at a level that would assist architectural reconstruction and matching without obscuring the immunopositive neurons. Development was halted by placing the sections in 0.1 M PB, followed by two more rinses in 0.1M PB. To test for non-specific staining of the immunohistochemical protocol, in selected sections the primary antibody or the secondary antibody were omitted, which resulted in no staining of the tissue. The immunostained sections were then mounted on 0.5% gelatine coated glass slides, dried overnight, dehydrated in a graded series of alcohols, cleared in xylene and coverslipped with Depex. Digital photomicrographs were captured using Zeiss Axioshop and Axiovision software. No pixilation adjustments, or manipulation of the captured images were undertaken, except for the adjustment of contrast, brightness, and levels using Adobe Photoshop 7.
Western immunoblotting
Protein expression for UCP1 and UCP4 was assayed using standard qualitative Western immunoblotting techniques. To verify the specificity of the UCP1 antibody for the UCP1 protein, we tested this antibody with rat brown fat. For the UCP4 antibody protein samples were extracted from the paraformaldehyde fixed tissue using the Qproteome FFPE Tissue Kit (Qiagen, Germany). The tissue blocks analysed here were taken from the anterior cingulate and occipital cortex (as described above) and contained both gray and white matter. 30–40 mg of the sample were incubated in 100 µl of Extraction Buffer EXB Plus (Qiagen, Germany) containing 6% β-mercaptoethanol on ice for 5 min and mixed by vortexing. The samples were boiled for 20 min at 100°C and subsequently incubated at 50°C overnight with agitation at 300 rpm. The samples were then placed on ice for 1 min and centrifuged for 15 min at 14 000g at 4°C. The supernatant was transferred into clean tubes and the protein concentration was determined using the Bradford protein assay kit (Bio-Rad Laboratories, USA). The protein extracts (20 µg) were made soluble in sample buffer comprised of 0.0625 M Tris–HCl, pH 6.8, 10% glycerol, 2% SDS, 2.5% β-mercaptoethanol and 0.001% bromophenol blue, boiled at 95°C for 5 min and subjected to 12% SDS-polyacrylamide gel electrophoresis and transferred to polyvinylidene difluoride (PVDF) (Millipore) at 20 V/cm for 1h. Electrophoresis and protein transfer was achieved using Mini Trans-Blot Electrophoretic Transfer Cell (Bio-Rad Laboratories, Inc. USA). After the transfer the blots were blocked for 2 h in 1 × Animal-Free Blocker (SP-5030 Vector Labs, USA). The blots were incubated over night at 4°C under gentle agitation in the primary antibody solutions (1:300 goat anti-UCP1, Santa Cruz Biotechnology, sc-6528 or 1:300 goat anti-UCP4, Santa Cruz Biotechnology, sc-17582). The blots were washed for 3 × 10 min in 1 × Animal-Free Blocker and incubated for 1 h at room temperature in HRP-conjugated rabbit anti-goat secondary antibody (1:1000, Dako, USA) for 1 h. This was followed by 3 × 10 min washes with 50 mM Tris buffer, pH 7.2. The protein bands were detected using 3,3′-diaminobenzidine tetrahydrochloride hydrate (DAB) (Sigma, D5637). The blots were incubated in a solution containing 1mg/ml DAB in 50 mM Tris, pH 7.2 for 5 min at room temperature, followed by the addition of an equal amount of 0.02% hydrogen peroxide solution. Development was arrested by placing the blots in 50 mM Tris (pH 7.2) for 10 min, followed by two more 10 min rinses in distilled water.
Stereological analysis
Using a design-based stereological approach we analysed immunohistochemically stained sections in the grey matter of the anterior cingulate and occipital cortex, as well as the underlying white matter from these regions of 14 cetartiodactyl species. Regions of interest (ROI) were drawn from similar locations across species as supported by published anatomical descriptions of the cetacean and artiodactyl brain. Using a light microscope equipped with a motorized stage, digital camera, MicroBrightfield system (MBF Bioscience, USA) system and StereoInvestigator software (MBF Bioscience, version 2018.1.1; 64-bit), we quantified UCP1-immunoreactive neuron densities in the grey matter, UCP4-immunoreactive glia densities in the grey and white matter, and DBH- and TH-immunoreactive bouton densities in the grey and white matter of these cortical regions. Separate pilot studies for each immunohistochemical stain was conducted to optimise sampling parameters, such as the counting frame and sampling grid sizes, and achieve a coefficient of error (CE) below 0.127,46,47,48,49. In addition, we measured the tissue section thickness at every sampling site, and the vertical guard zone was determined according to tissue thickness to avoid errors/biases due to sectioning artefacts27,46,47,48,49. Supplementary Tables S1–S4 provide details of the parameters used for each neuroanatomical region and stain and between the species in the current study. To estimate the ROI total number, we used the ‘Optical Fractionator’ probe.
UCP1- and UCP4-immunoreactive neuron and glia densities were obtained by sampling the cortical areas of interest and subjacent white matter with the aid of an optical disector. The cortex and white matter were outlined separately at low magnification (2X), and the optical disector was performed at 40X. UCP-immunoreactive neuron and glia density was calculated as the total number of UCP-immunoreactive neurons and glia divided by the product of surface area (x, y), the tissue sampling fraction, and the sectioned thickness (50 µm). The tissue sampling fraction was calculated as the ratio of the optical disector height to mean measured section thickness. Given that overall cell density per unit volume is known to vary with differences in brain size, we calculated the percentage of UCP-immunoreactive neurons or glia, expressed as the ratio of UCP-immunoreactive neurons or glia to total neuronal or glial density for each region of interest, to standardize the data for cross species comparison. Using Nissl-stained sections we obtained estimates of neuronal and glial densities within the cortex and glial density within the white matter using optical disector probes combined with a fractionator sampling scheme46. A pilot study determined the optimal sampling parameters and grid dimensions to place disector frames in a systematic-random manner. For DBH and TH bouton densities, ‘spot’ densities were calculated by multiplying the ROI area by the cut section thickness, and then using the generated volume as the denominator to the ROI estimated number. For all tissue sampled the optical fractionator was used while maintaining strict criteria, e.g. only complete boutons were counted, 63 X oil immersion, and obeying all commonly known stereological rules. The stereologic analyses presented here resulted in sampling an average of 118 counting frames per region of interest with a total of 13,053 counting frames investigated.
Statistical analyses
We hypothesized that the percentage of cortical neurons immunoreactive to UCP1 were significantly different between artiodactyls and cetaceans. To test this hypothesis, we compared the proportion of UCP1 expression in the anterior cingulate and occipital cortex of 16 cetartiodactyls. For the anterior cingulate cortex, we sampled a total of 1109 sampling sites (~ 100 sites per species) within the artiodactyl group and found that 36.83% of sampled cortical neurons were immunoreactive to UCP1. In comparison our cetacean sample consisted of 723 sampling sites (~ 145 sites per species), with 87.28% of the sampled cortical neurons immunoreactive to UCP1. For the occipital cortex, we sampled a total of 1 038 sites (~ 94 sites per species) within the artiodactyl group and found that 34% of sampled cortical neurons within the occipital cortex were immunoreactive to UCP1. The cetacean sample consisted of 723 sampling sites (~ 145 sites per species), and we found that 92.36% of the sampled cortical neurons were immunoreactive to UCP1.
To test if the respective underlying proportions were different between the sample groups, we conducted statistical hypothesis testing using the Two-Proportions Z-test as implemented in the R Programming language. Our Null hypothesis (Ho) stated that there is no significant difference between the proportions of artiodactyl immunoreactive UCP1 sampled cortical neurons (π1) and the proportions of cetacean UCP1 sampled cortical neurons (π2)—that is, π1 − π2 = 0. The alternate hypothesis (H1) stated that there is a significant difference in these proportions such that π1 − π2 ≠ 0, with one of the proportions being either less than or greater than the other. We thus conducted a two-sided hypothesis test, with the significance level (α) set at 0.05 (i.e., P-values less than, or equal to, α, would reject the null hypothesis in favour of the alternate hypothesis). Based on these analyses the proportion of immunolabelled UCP1 cortical neurons were found to be significantly different between the groups, with cetaceans having a significantly higher proportion of UCP1-immunoreactive neurons in the anterior cingulate cortex (χ2 = 51.69; df =1, P = 6.49 × 10−13, 95% confidence interval = − 0.122; − 0.067) and occipital cortex (χ2 = 56.30; P = 6.21 × 10−14, 95% confidence interval = − 0.114; − 0.060).
We used a two sample T-test (as implemented in R) to test for significant differences in noradrenergic bouton density between cetaceans and artiodactyls. Cetaceans were found to have significantly higher mean DBH-immunoreactive bouton densities in the anterior cingulate cortex as compared to artiodactyls (t = − 3.595; df =15, P = 0.011). Cetaceans were also found to have significantly higher mean DBH-immunoreactive bouton densities in the occipital cortex as compared to artiodactyls (t = − 4.546; df =15, P = 0.002). Similarly, we tested for significant differences in mean DBH bouton density in the underlying cortical white matter of cetaceans and artiodactyls. We did not find any significant differences in DBH-immunoreactive bouton density for the anterior cingulate (t =− 0.597; df =15, P = 0.585) or occipital cortex (t = − 0.08; df =15, P = 0.941).
To test for the effect of confounding variables on the significant differences observed in DBH bouton density in the cortex, we used an analysis of covariance controlling sequentially for the effect of cortical neuron density, cortical glia density and brain mass. Our analyses revealed that after adjusting for the density of cortical neurons cetaceans still had significantly higher DBH-immunoreactive bouton density in the anterior cingulate cortex (adjusted mean = 10.176) in comparison to artiodactyls (adjusted mean = 8.176) (F = 5.222; df =13, P = 0.041). Adjusting for the covariate cortical neuron density, resulted in a similar result for the occipital cortex (adjusted mean = 14.678) in comparison to artiodactyls (adjusted mean = 10.395) (F = 14.05; df =13, P = 0.00278). When controlling for the density of cortical glia, cetaceans also had significantly higher DBH-immunoreactive bouton densities in the anterior cingulate cortex (adjusted mean = 10.62) in comparison to artiodactyls (adjusted mean = 8.01) (F = 9.72; df =13, P = 0.00889). Similar results were found for the occipital cortex, with cetaceans having significantly higher DBH-immunoreactive bouton density (adjusted mean = 14.471) compared to artiodactyls (adjusted mean = 10.395) (F = 11.2; df =13, P = 0.00581). When controlling for brain mass, cetaceans were also found to have a significantly higher DBH-immunoreactive bouton densities in the anterior cingulate (adjusted mean = 11.36) in comparison to artiodactyls (adjusted mean = 7.75) (F = 11.06; df = 13, P = 0.00604) as well as in the occipital cortex (cetacean adjusted mean = 15.406, artiodactyls adjusted mean = 10.055) (F = 11.85; df = 13, P = 0.00488). More

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Experiment 1: proof of principle
As a proof of principle, we designed the first experiment to test whether appendages may help to conceal the outline. We created an image of a uniformly light grey coloured circular object with a size of 2950 pixels (px)/250.0 mm circumference and 470 px/39.8 mm radius on a dark grey background using Adobe InDesign CS6 version 8.030. The initial setup started with no appendages added to the outline (Fig. 3a, ‘0’). We then added object-coloured appendages (i.e. lines of 1 Pt/4 px/0.4 mm thickness and 118 px/10.0 mm length) with regular intervals resembling protruding neoptile chick feathers orthogonally to the object outline (‘Basic Scenario’, Fig. 3a). The first image with appendages had 32 appendages added to the outline (Fig. 3a, ‘32′). We then doubled the number of appendages stepwise creating denser spaced appendages to the outline until the extended outline was completely filled (Fig. 3a, ‘full circle’). For the vision of a simulated predator, we used the spatial acuity from humans (Homo sapiens, 72 cycles per degree, cpd)36,45,46 in the basic scenario. The full details for the parameters are provided in Supplementary Table S1 (a–g).
Figure 3

(a) Basic Scenario: Seven stages of the artificial chick setup with varying number of thin, non-transparent appendages having all the same length. (b) Scenario 1: varying appendage thickness applied to the Basic Scenario. (c) Scenario 2: varying appendage transparency applied to the Basic Scenario. (d) Scenario 3: varying appendage length heterogeneity applied to the Basic Scenario. (e) Scenario 4: varying background complexity with chessboard backgrounds. (f) Scenario 5: high, medium and low spatial acuity applied to the Basic Scenario. (a–f) The analysed region of interest (ROI) is highlighted in red for clarification only. The figure was produced in Adobe Photoshop29 and InDesign30.

Full size image

To further explore the mechanism, we altered appendage characteristics, background and the spatial acuity of the predator. First, we increased appendage thickness to 2 Pt/8 pixels/ 0.7 mm (Scenario 1a) and 3 Pt/12 pixels/1.1 mm (Scenario 1b) resulting in decreased inter-appendage intervals (Fig. 3b and Supplementary Table S1, h–u). Second, we changed appendage transparency to 25% (Scenario 2a) and 50% transparency (Scenario 2b) (Fig. 3c and Supplementary Table S1, v to ai). Third, we varied the appendage length heterogeneity; half of the appendages having 50% of the length (Scenario 3a), and half of the appendages at 25% and one quarter at 50% of the original appendage length (Scenario 3b) (Fig. 3d and Supplementary Table S1, aj to aw). Fourth, we investigated the effect of background complexity on the detectability of the outline. As background, we used a chessboard pattern with large squares (346 pixels/29.3 mm, Scenario 4a) and with small squares (86 pixels/7.3 mm, Scenario 4b) (Fig. 3e and Supplementary Table S1, ax to bk). Fifth, we altered the spatial acuity to test whether or how the visual systems of different predators would affect detectability. We simulated the spatial acuity of a corvid predator (30 cpd, Scenario 5a) and canid predator (10 cpd, Scenario 5b) (Fig. 3f and Supplementary Table S1, bl to by), the two most common predators of ground-nesting plovers16,47,48. This range also covered other potential predators (Supplementary Table S2).
We did not account for differences in colour vision between different predators as the setup mostly consists of greyscale images that predominantly differ in luminance. Note that in many animals, visual acuity is greater for achromatic than chromatic stimuli34,49.
We conducted visual modelling and visual analysis using the Quantitative Colour Pattern Analysis (QCPA) framework27 integrated into the Multispectral Image Analysis and Calibration (MICA) toolbox50 for ImageJ version 1.52a51. We converted the generated images into multispectral images containing the red, green and blue channel in a stack and transformed them further into 32-bits/channel cone-catch images based on the human visual system, which are required by the framework. To create the luminance channel, we averaged the long and medium wave channel, which is thought to be representative of human vision52. We modelled the spatial acuity with Gaussian Acuity Control at a viewing distance of 1300 mm and a minimum resolvable angle (MRA) of 0.01389. To increase biological accuracy, we applied a Receptor Noise Limited (RNL) filter that reduces noise and reconstructs edges in the image. The RNL filter used the Weber fractions “Human 0.05” provided by the framework (longwave 0.05, mediumwave 0.07071, shortwave 0.1657), luminance 0.1, 5 iterations, a radius of 5 pixels and a falloff of 3 pixels as specified in van den Berg et al.27 (Supplementary Fig. S1).
To test for the detectability of the outline, we used LEIA27, which is conceptually similar to the boundary strength analysis34. Boundary strength analysis requires an image with clearly delineated (clustered) colour and luminance pattern elements. However, a large degree of subthreshold details, which may be still perceived by the viewer gets lost in the clustering process. LEIA has the advantage of not requiring such a clustered input and therefore can be directly applied to RNL filtered images. LEIA measures the edge intensity (i.e. the luminance contrast) locally at each position in the image. The output image displays ΔS values in a 32-bit stack of four slices, where each slice shows the values measured in different angles (horizontal, vertical and the two diagonals, for more details, see van den Berg et al.27).
We ran LEIA on the chosen region of interest (ROI) with the same Weber fractions used for the RNL filter. The ROI was a 180 pixel-wide band that included the area of the appendages extended by 30 pixels towards the object inside and towards the outside (Fig. 3a). We log-transformed the ΔS values as recommended for natural scenes53 to make the results comparable to the natural background images used in Experiment 2 (see below). To test whether the size of the ROI affected our results, we ran an additional analysis using a 1500 × 1500 pixel-wide rectangle surrounding the object as the ROI, which included a bigger area of the background and the full object inside (Supplementary Fig. S2).
We extracted the luminance ΔS values from the four slices of the output image stack in ImageJ and stored them in separate matrices for further analysis using R version 3.5.328. ImageJ generally assigned values outside the chosen ROI to zero. Thus, we first discarded all values of zero. We then set all negative values that arose as artefacts in areas without any edges to zero, in order to make them biologically meaningful. We then identified the parallel maximum (R function pmax ()) of the four interrelated direction matrices and transferred this value to a new matrix.
High luminance and colour contrasts imply high conspicuousness34. Consequently, a lower luminance contrast leads to lower conspicuousness and therefore, better camouflage. As the outline is an important cue for predators locating and identifying a prey item7, we assumed that especially low contrasts in the outline of an object improve camouflage. Thus, a reduction of edge intensity in the object outline by the appendages indicates a camouflage improvement. To test whether the object outline became less detectable we compared the edge intensity of the outline pixels in the basic scenario without appendages (Supplementary Table S1, a) with corresponding pixels from other scenarios. The outline pixels were characterised by high edge intensity and constituted a prominent peak. They comprised 1.59% of all pixels in the analysis focused on the contour region (see “Results”, Fig. 1a). For all scenarios, we calculated the mean edge intensity of the high edge intensity pixels (HEI pixels) and identified the changes with parameter variation. Unless otherwise stated we used R28 to produce graphs and panels.
As an alternative mechanism, we tested whether appendages create a transition zone with intermediate luminance around the object (Mean Luminance Comparison (MLC), Supplementary material). We calculated the mean luminance of the object inside up to the border (object region), the area covered by appendages (appendage region) and the background (background region). We predicted that the appendage region would be characterised by intermediate luminance between object and background and therefore provide a luminance transition zone to conceal the object outline.
Experiment 2: chick photographs
Using pictures of young snowy plover chicks hiding when approached by a simulated predator, we tested if protruding neoptile feathers helped to conceal the chicks’ outline and therefore improve their camouflage.
We studied snowy plovers in their natural environment at Bahía de Ceuta, Sinaloa, Mexico. Fieldwork permits were granted by the Secretaría de Medio Ambiente y Recursos Naturales (SEMARNAT). All field activities were performed in accordance with the approved ethical guidelines outlined by SEMARNAT. The breeding site consists of salt flats that are sparsely vegetated and surrounded by mangroves54. The predators of chicks are not well described but likely similar to the egg predator community that includes several mammalian predators such as racoon, opossum, coyote, bob cat, avian predators such as crested caracara Caracara cheriway and reptiles17. General field methodology is provided elsewhere55,56. In 2017, we took photographs of young (one to 3 days old) chicks hiding on the ground, that had already left the nest scrape. To photograph the chicks, two observers approached free-roaming families with two mobile hides within the period one hour after sunrise and one hour before sunset. At a distance of 100–200 m, one observer acted as ‘predator’, left the hide and openly approached the brood while the second observer kept watching the chicks. The chicks responded by crouching to the ground and staying motionless while the parents were alarming. The second observer directed the ‘predator’ to the approximate hiding place. When searching for the chicks, we took great care to reduce the number of steps to avoid modification of the ground through our tracks.
Once the first chick had been found, the second observer joined the ‘predator’ and took the chick photographs. We used a Nikon D7000 camera converted to full spectrum including the UV range (Optic Makario GmbH, Germany) and a Nikkor macro 105 mm lens that allows transmission of light at low wavebands. The equipment was chosen because calibration data were available for this combination50. Each hiding background was photographed with and without the chick using a UV pass filter for the UV spectrum and a UV/IR blocking filter (“IR-Neutralisationsfilter NG”, Optic Makario GmbH, Germany) for the visible spectrum. The camera was set to an aperture of f/8, ISO 400 and the pictures were stored in “RAW” file format. We used exposure bracketing to produce three images to ensure that at least one picture was not over or underexposed. A 25% reflectance standard (Zenith Polymer Diffuse Reflectance Standard provided by SPHEREOPTICS, Germany) placed in the corner of each picture enabled a subsequent standardizing of light conditions.
In total, we took pictures of 32 chicks from 15 families. For 21 chicks we obtained photographs suitable for further analyses with an unobstructed view to the entire chick and only one chick per photograph. Of these, we randomly selected pictures of 15 chicks. Unfortunately, it was not possible to obtain proper alignment of visual and UV pictures in ImageJ as either chick or camera moved slightly in the break between changing filters for the two settings. Therefore, we restricted our analyses to human colour vision and discarded the UV pictures for further analysis.
In each picture, we manually selected the chick outline and the feather-boundary as a basis for the ROIs (Fig. 2a–c). The chick outline included bill, legs, rings and all areas densely covered by feathers without background shining through. We then marked the feather-boundary, i.e., the smoothened line created by the protruding neoptile feather tips. In the next step, we transferred images of chicks with or without protruding feathers, i.e. cropped at feather-boundary or chick outline, respectively, and inserted them into a uniform or the natural background. First, we cropped the chick without protruding feathers and transferred it into a uniform black background. Second, we cropped the chick including all feathers and inserted it into exactly the same hiding spot on the picture of the natural background (Fig. 2b). Third, we cropped the chick excluding the protruding feathers and transferred it into the natural background (Fig. 2c).
We then proceeded with LEIA following the protocol of experiment 1 with the following changes. Again, the selected ROI was the contour region ranging from the chick outline extended by 30 pixels towards the chick inside to the feather-boundary extended by 30 pixels towards the outside. We excluded all areas of the ROI that showed a shadow of the chick as the chicks’ shadow was missing on the empty natural background images to which the cropped chicks were transferred to (Fig. 2a–c). We used the images of the cropped chicks on the black background to determine the threshold of the HEI pixels according to the protocol of experiment 1 for each chick separately. For each cropped chick that was transferred to the picture with the natural background, we compared the mean edge intensity of the HEI pixels provided by LEIA with and without protruding feathers (Fig. 2b,c) using a two-sided paired t-test.
We also calculated mean luminance differences for chick photographs. Details for this MLC are given in the supplementary material. More

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