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Penguins Sphenisciformes are a remarkable order of flightless wing-propelled diving seabirds distributed widely across the southern hemisphere. They share a volant common ancestor with Procellariiformes close to the Cretaceous-Paleogene boundary 66 million years ago and subsequently lost the ability to fly but enhanced their diving capabilities.
To inhabit such diverse and extreme environments, penguins evolved many physiological and morphological adaptations. However, they are also highly sensitive to climate change. Therefore, penguins provide an exciting target system for understanding the evolutionary processes of speciation, adaptation, and demography. Genomic data are an emerging resource for addressing questions about such processes.
Here we present a novel dataset of 19 high-coverage genomes that, together with 2 previously published genomes, encompass all extant penguin species. We also present a well-supported phylogeny to clarify the relationships among penguins. In contrast to recent studies, our results demonstrate that the genus Aptenodytes is basal and sister to all other extant penguin genera, providing intriguing new insights into the adaptation of penguins to Antarctica. As such, our dataset provides a novel resource for understanding the evolutionary history of penguins as a clade, as well as the fine-scale relationships of individual penguin lineages.
Against this background, we introduce a major consortium of international scientists dedicated to studying these genomes. Moreover, we highlight emerging issues regarding ensuring legal and respectful indigenous consultation, particularly for genomic data originating from New Zealand Taonga species.
We believe that our dataset and project will be important for understanding evolution, increasing cultural heritage and guiding the conservation of this iconic southern hemisphere species assemblage.
Penguins Sphenisciformes are a unique order of seabirds distributed widely across the southern hemisphere Fig. Approximately 20 extant penguin species are recognized across 6 well-defined genera Aptenodytes, Pygoscelis, Eudyptula, Spheniscus, Eudyptes , and Megadyptes [ 1—3 ]. Divisions between northern rockhopper Eudyptes moseleyi , western rockhopper Eudytes chrysocome , and eastern rockhopper penguins Eudyptes filholi [ 3 , 7 , 8 ].
Divisions between Fiordland crested Eudyptes pachyrhynchus and Snares crested penguins Eudyptes robustus [ 9 , 10 ]. Divisions between macaroni Eudyptes chrysolophus chrysolophus and royal penguins Eudyptes chrysolophus schlegeli [ 3 , 8 , 11 ]. Extant penguins span a modest range of sizes [ 14 , 15 ], with the emperor penguin Aptenodytes forsteri the largest 30 kg and Eudyptula penguins the smallest 1 kg.
In contrast, the fossil record reveals that many extinct penguin species were giants surpassing kg in body mass [ 13 ]. Locations of breeding colonies of penguins and sampling sites for the final genomes, adapted from Ksepka et al.
Sampling locations are shown with a small white ellipse. Note that the sampling location of the humboldt penguin Spheniscus humboldti is unclear because this individual was bred in the Copenhagen zoo, with ancestors imported from Peru and Chile in The radiation of penguins provides an excellent case study for researching biogeographic impacts on speciation processes.
Penguins inhabit every major coastline in the southern hemisphere, and almost every island archipelago in the Southern Ocean [ 16 ]. For this reason, penguins have evolved many unique adaptations, specific to the variety of ecological environments. Previous studies have suggested that global climate change during the Eocene [ 18 , 19 ], substantial oceanographic currents [ 7 ], and geological island uplift [ 3 ] were key drivers of penguin diversification.
Although the phylogenetic relationships within penguins are relatively well understood [ 1 , 3 , 18 , 20 ], it remains uncertain which lineage first diverged from other penguins. Both of these genera are endemic to coastal Antarctica and Antarctic and subantarctic islands, and thus a sequential branching pattern would suggest a polar ancestral area for extant penguins.
In contrast, morphological data and the fossil record suggest that the more temperate-adapted genus Spheniscus was the first to diverge [ 3 , 20 ]. Understanding the evolutionary diversification of penguins in respect to geological and climatic changes remains a substantial gap in understanding the biogeographic history of these iconic birds.
Although penguins are tied to landmasses for breeding and nesting [ 21 ], all species spend most of their lives at sea [ 22 ] and are therefore important components of terrestrial, coastal, and marine ecosystems [ 23 ].
While some taxa inhabit environments with strong winds and extreme cold temperatures, experiencing seasonal fluctuations in the length of daylight across the breeding and chick-rearing seasons [ 24 ], others inhabit relatively temperate or even tropical climates, with little variation in day length. The unique morphological and physiological adaptations that have evolved within penguins include the complete loss of aerial flight, where penguins instead use their flipper-like wings in wing-propelled diving [ 25 ], densely packed waterproof and insulating feathers [ 26 , 27 ], visual sensitivity of the eye lens for underwater predation [ 28—30 ], dense bones, stiff wing joints and reduced distal wing musculature to overcome buoyancy in water [ 31—33 ], enhanced thermoregulation for extreme low temperatures, long-term fasting, ability to digest secreted food, delayed digestion [ 34—40 ], different plumage [ 41 ] and crest ornaments [ 42 ], and catastrophic moult [ 43 ].
As such, penguins are an excellent system to study comparative evolution of adaptive traits. Penguins are also sentinels of the Southern Ocean [ 16 ], being particularly sensitive to human and environmental change [ 44 , 45 ]. Extensive demographic monitoring programs have indicated that many penguin species are declining in response to global warming [ 44—46 ], pollution, environmental degradation, and competition with fisheries, which are considered key drivers of these population declines [ 47—50 ].
Demographic coalescent models have demonstrated dramatic population declines during the Pleistocene ice ages, followed by rapid population expansions in response to global warming [ 51—54 ]. Future global warming is predicted to cause significant population declines [ 44 , 55—57 ].
Understanding past demographic histories and inferring future demographic trajectories therefore remain important steps for predicting ecosystem-wide changes in this rapidly warming part of the planet. Although penguins are a relatively well-studied group, previous evolutionary studies have been limited by the genetic markers used, such as short mitochondrial [ 2 , 10 , 58—60 ] or nuclear sequences [ 1 , 8 , 61 , 62 ], microsatellites [ 63 , 64 ], partial mitochondrial genomes [ 3 , 65 ], or single-nucleotide polymorphisms [ 11 , 53 , 54 , 66—68 ].
Several studies have hinted at associations between biological patterns and climate change [ 51—54 , 60 , 69 ]. These previous studies have created a basic framework to understand the timing of penguin diversification, identify population fluctuations during past climate cycles, and have hinted at the molecular basis for a range of physiological and morphological adaptations [ 51 ].
The molecular genomic basis for the unique morphological and physiological adaptations of penguins, compared to other aquatic and terrestrial birds, remains largely unknown. No previous study has attempted to explore the evolution of all penguins under a comparative genomic or evolutionary framework. In this Data Note, we present 19 new high-quality genomes that, together with the 2 previously reported genomes [ 51 ], encompass all extant penguin species.
We demonstrate the quality and application of this new dataset by constructing a well-supported phylogenomic tree of penguins. These data provide a critical resource for understanding the drivers of penguin evolution, the molecular basis of morphological and physiological adaptations, and demographic characteristics.
For species naming, we follow standard nomenclature; however, for Eudyptula we follow Grosser et al. Our project design see below relies on high-coverage genomes with little missing data see Li et al. Therefore, we designed our sample collection to include only high-quality blood samples. We collected 94 blood samples spanning 19 different penguin species 1—28 samples per species; Supplementary Table 1.
Samples were derived from the wild, zoological parks, or wildlife hospitals and were obtained according to strict permitting procedures, animal ethics, and consultation with indigenous representatives Supplementary Table 1. All downstream methods were conducted at BGI. Sample collection information for the 21 penguin genomes including 2 obtained in Li et al. We constructed 1 or more genomic libraries for each of the 19 penguin species depending on the DNA quality.
Following sequencing, we generated 3. Details of the sequencing platform used and the data statistics for 21 penguin genomes. HiSeq X ten was used for sequencing small insert size libraries; HiSeq was used for sequencing mate-pair libraries. Assembly statistics and BUSCO results for 21 penguin genomes within a total of 4, conserved avian orthologs. Sequences obtained from the bp insert size libraries and the 10x libraries were used to evaluate the genome size for each penguin using a k -mer approach [ 79 ].
Reads were scanned using a bp window with 1 bp sliding and the frequency of each 17 k -mer was recorded. The filtered reads for the 10x libraries were only used for estimating the genome size with 17 k -mer, while all reads were used for Supernova assembly.
Sequencing errors have a major effect on subsequent genome assembly because they both introduce mistakes in the assembly and also decrease the assembly continuities.
Several features can be linked to sequencing noise, including low-quality bases, adaptor contamination, and duplication [ 80 ]. Overall, we obtained a total of 2. Both SOAPdenovo v. For SOAPdenovo, paired-end reads from small insert size libraries were used to construct de Bruijn graphs, with various k -mer ranging from 23 to The best version, in terms of various k -mer in the graph construction step, was chosen as the SOAPdenovo representative for each species.
In addition, we also assembled genomic libraries from various insert sizes using Allpaths-LG following the default parameters. By comparing the assemblies from both SOAPdenovo and Allpaths-LG, according to both the scaffold N50 and the total length, we chose the best assembler as a representative for each of the 19 penguin species.
Supernova v. The optimal assembly strategy chosen for each penguin species is listed in Supplementary Table 2. For each assembly, we used GapCloser v. All penguins including those obtained in Li et al. Most assemblies have both a longer scaffold N50 and contig N50 than the Aptenodytes forsteri and Pygoscelis adeliae assemblies obtained by Li et al. The maximum contig N50 extends to kb for the macaroni penguin Eudyptes chrysolophus chrysolophus Fig. The highest-quality genome is Eudyptula novaehollandiae , encompassing a Therefore, our results demonstrate consistency and high quality among all 21 penguin genomes Fig.
Genome assembly statistics of all penguin species. A, Dot plot of the quality of each index showing contig N50 maximum is Eudyptes chrysolophus chrysolophus with , bp; minimum is Spheniscus humboldti with 19, bp and scaffold N50 maximum is Eudyptula novaehollandiae with 29,, bp; minimum is Eudyptes robustus with , bp. Each symbol indicates a penguin species, the x-axis indicates the scaffold N50, and the y-axis indicates the contig N50 for each species.
B, Genome size for each penguin species maximum is Eudyptula minor with 1,,, bp; minimum is Eudyptes sclateri with 1,,, bp. C, BUSCO assessments of all penguin genomes, showing the percentage of complete, duplicated, fragmented, or missing data. The symbols for each penguin species correspond to the symbols used in Fig. The genome assembly completeness provides an evaluation of the assembly quality.
This demonstrates that all 21 penguin genomes are near-complete, containing only a few gaps. Duplication rates among the 21 penguin genomes varied only between 0. Overall, we obtained almost-complete, high-quality genomes. Our genomic dataset including those obtained in Li et al. We used RepeatMasker v. We compared our genomes to 5 avian outgroups: wedge-rumped storm petrel Hydrobates tethys , Wilson's storm petrel Oceanites oceanicus , Atlantic yellow-nosed albatross Thalassarche chlororhynchos , zebra finch Taeniopygia guttata , and chicken Gallus gallus.
Genome sequences were aligned to RepBase In addition, we used RepeatModeler in a de novo repeat family identifying approach. All identified repeat elements were classified into 7 categories DNA, long interspersed nuclear element [LINE], short interspersed nuclear element [SINE], long terminal repeat [LTR], other, unknown, tandem repeat according to classification in repeat databases.
Repeat annotations using the 3 methods were combined into a non-redundant repeat annotation for each penguin genome and the 5 outgroups. Although all penguin genomes had similar repeat content, they varied in content for each category. In all penguins and outgroups, the most abundant repeat category was LINE.
Eudyptula minor minor had the most genome sequences identified as LTR 4.
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