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Data collectionData were collected in the Sunshine Coast region in Queensland, Australia (− 26.65° S, 153.07° E), from February to April 2019. All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols and methods were approved and carried out in compliance with the ARRIVE guidelines under the approval of the University of the Sunshine Coast (USC) Animal Ethics permit (ANA/16/109T); Human Ethics permit (A181114) and in conjunction with the Sunshine Coast Council (SCC) Local Law permit (OM18/19).
Animals used in the trialsWe recruited 10 domestic cats through an approved media release (males n = 6; females n = 4; weight 2.8–8.4 kg; age 1.5–12 years; body length 38–53 cm; foreleg length 16–19 cm). As per the Sunshine Coast Council local law requirements, all cats had to be neutered, registered and microchipped to participate in the study.
EquipmentWe fitted each cat with a retail harness, to which we attached a tri-axial accelerometer (AX3; Axivity, Newcastle University, UK; 23 × 32.5 × 8.9 mm; 11 g) using cable ties (Fig. 1a). The accelerometer was initialised using the Open Movement Graphical User Interaction application (OMGUI; V1.0.0.37). Because a trade-off exists between data resolution and battery life, we logged data at 50 Hz and with a dynamic range of ± 8 g, with a 13-bit resolution, similar to a previous study23. When combined with the in-built memory storage capacity of 512 MB, and battery limitations, this configuration resulted in a maximum of 8–14 days of data collection. The quartz Real Time Clock and calendar provided a timestamp with a frequency of 32.768 kHz and a precision of ± 50 ppm, with manufacturer specifications indicating a drift of 0.18 s per hour. To overcome this drift over the eight days, we calibrated devices by video recording the signals of five claps/taps on the device, at the start and end of each individual data collection period, and also at random times during the day.Figure 1(a) The anatomical position of the accelerometer (AX3) on the sternum of the cat. (b) The activity of swatting stimulated by the use of a feather. (c) The axis orientation of the accelerometer planes, which are represented in the accelerometer trace data in the MATLAB interface. Fore-aft (surge), lateral (sway) and dorso-ventral (heave) movement is reflected in the X, Y and Z signals.Full size imageWe positioned the accelerometer on the scapular brace-strap of the harness, inverted such that the accelerometer was on the sternum of the cat (Fig. 1a–c). Field trials over four months on four cats in the study determined that this position, in comparison with mounting on the dorsal cranial median plane, did not interfere with the animals’ balance; it also removed all of the abnormal movement behaviours and unnecessary discomfort to the cat2. The positioning of the logging device on the frontal anterior, median plane, resulted in the primary axis for fore-aft (surge), lateral (sway) and dorso-ventral (heave) movement to be reflected in the X, Y and Z signals, respectively (Fig. 1c).The accelerometer harness was used in conjunction with the CatBib for the relevant treatment periods. The total combined mass of the harness, accelerometer and Catbib was to 34.1 g, with a minimum cat mass of 2.8 kg, suggesting the equipment did not weigh above 1.2% of total body weight in any cat studied. The CatBib is a prey protector device, manufactured from a lightweight, washable neoprene material, that is attached to a cat’s safety collar (Fig. 1b). The dimensions of the bib are 17.5 mm × 17.5 mm × 6.5 mm, with a total mass of 23.1 g and it is purple in colour. All cats adjusted to the harness and CatBib within the first hour of deployment and no subsequent adjustments were required. All cats had unrestricted access to roam freely outside during the eight days of field trials.To capture training data, each cat was filmed with a GoPro + 3 Hero device (H.264—1920 × 1080; f/2.8; 60 fps), undertaking natural or stimulated active behaviours through play (Fig. 1b). These activities or behaviours were manually documented to track the activity, date and the timestamps. We conducted two treatments over the eight days: in the first, cats were fitted with CatBib, whereas in the other, bibs were not worn. Each treatment was conducted for four consecutive days, and the sequence of treatments for each cat was randomised. The accelerometer device on the harness was left on the cats for the entire field trial and recorded continuously for the eight days (~ 192 h per cat; total = 2304 h).Data analysisEach accelerometer trace file was exported as a raw binary file through OMIGUI and imported into a custom-built MATLAB GUI. To build our training dataset, the video file timestamp information, determined using Mediainfo (version 18.08, 2018), was used to define the start time for a subset of the accelerometer trace, and the video length to define the end point (Supp. Fig. 1). Offsets between the accelerometer trace and video files were determined using the closest calibrated tap signal trace for each day. We were able to watch each video file in synchrony with the accelerometer trace, and manually annotate each movement/activity from the video files to the accelerometer subset (Clemente et al.)24 (Supp. 1.1. Matlab interface instructions; Supp. Fig. 1).We grouped activities according to behaviour into three classes: Sedentary, Eating and Locomotive and Hunting. We further subdivided each group into behaviours. Sedentary included lying, sitting, grooming and watching; Eating and Locomotive included—eating/drinking, walking, trotting; and for Hunting—galloping, jumping, pouncing, swatting, biting/holding (Supp. Table 1).The accelerometer trace was then further divided into rolling epochs of 50 samples in length, using 1 s duration at 50 Hz to ensure intensive acceleratory bursts of short duration such as jumping and pouncing are captured. The behaviour with the maximum frequency within each epoch was assigned as that epoch’s label. Raw accelerometer data in each epoch was assigned as that epoch’s label. Raw accelerometer data in each epoch was summarized using 26 of the most effective variables for procedure accuracy identified by Tatler et al.25. We included: axial acceleration (X, Y, Z),mean acceleration (X, Y, Z); minimum acceleration.(X, Y, Z); maximum acceleration (X, Y, Z); standard deviation of acceleration (X, Y, Z); Signal Magnitude Area, minimum Overall Dynamic Body Acceleration (ODBA); maximum ODBA, minimum Vectorial Dynamic Body Acceleration VDBA; maximum VDBA, sum ODBA; sum VDBA; correlation (XY, YZ, XZ); skewness (X, Y, Z); and kurtosis (X, Y, Z)25 (See Supp. Table 2 for a detailed description of each variable). Finally, we coded the two treatments: BibON and BibOFF and included this information in the training data set.Classification modellingTo determine whether we could predict cat hunting behaviours, we analysed the training data sets using a Kohonen super Self Organising Map (SOM) in the R package ‘Kohonen’ version 2.0.1926,27.Machine learning procedures such as random forest and support vector machines each provide computationally powerful methods of data classification, however each method is not equal in how it visualises its output. SOMS have been used in behavioural studies10,13,14,15 for their ability to efficiently create easily interpreted maps and identify patterns of behaviour. Self-organizing maps differ from other artificial neural networks as they apply competitive learning as opposed to error-correction learning. In this study, a self-organising map algorithm was chosen for its efficiency in visualising multi-dimensional and complex data onto an easily interpreted two dimensional map output. SOMs also have the ability to visualise which variables are most influential with the use of component planes (Fig. 3b–e) and unlike other procedures mentioned, SOMs use cluster analysis which in this study aids in identifying similar behaviours and visualising them closer together (in clusters) on the map output.To prepare data for the SOM function a random sample of the classifiers for the trained data were extracted, along with their associated behaviour, and combined into a list with 2 elements (measurements and activity). This list was then input into the function supersom.R function, with the grid argument defined using the somgrid.R function [e.g. supersom(TrainingData, grid = somgrid(7, 7, “hexagonal”))]. The 7 × 7 grid function was chosen based on a sensitivity analysis exploring all combinations of grids between 4 to 9 units in length (n = 36, Supp. Fig. 2). The 7 × 7 grid represented the grid which produced the highest accuracy and map symmetry26,28,29. We further tested the effect of the number of times the complete data set is presented to the network by varying the rlen argument in the supersom.R function. We found no obvious increase in overall accuracy with increased iterations, and therefore used the default length of 100 times (Supp. Figure 3). Each supersom procedure created was then tested using the predict.R function, with the newdata argument directed to a testing data set, which was a similar 2 element list containing all samples not included in the training data set. The result of this test was then assembled into a confusion matrix using the table.R function with predictions compared with the known behaviours in the test data set [e.g. table(predictions = ssom.pred$predictions$activity, activity = testData$activity) ]. A confusion matrix is a table where each row represents the instances in a predicted class, while each column represents the instances in the observed class, allowing mislabelled epochs to be easily identified. The confusion matrix was then finally used to compute four specific accuracy metrics—sensitivity (or recall), precision, specificity, as well as overall accuracy.To identify relationships between the size of training dataset, we trained a randomised subset of the BibOFF training data, to predict the remaining BibOFF data from all cats. We tested 35 different subset sample sizes from 100 to 100,000, replicating each sample size ten times (with replacement) to determine variation at each sample size.We then tested the extent to which accelerometer traces are modified by the presence of the CatBib. This modification was indicated by a change in overall prediction accuracy of the SOM between BibOFF and BibON treatments. To do this, we trained the SOM using a subset of the trained data for BibOFF and tested it against annotated classified BibON samples. In order to statistically compare results from bootstrap resampling, we took the median among bootstrap samples as the estimate of performance and quantified uncertainty using the corresponding 2.5th and 97.5th percentiles to represent credible 95% confidence intervals (CIs). We chose the median as a measure of central tendency, because resampling distributions were truncated at 1, so were skewed. If CIs for any pair of estimates (medians) do not overlap, then this is evidence of a significant difference between the estimates. If, however, one estimated median fell within the confidence interval for another estimate, then this was used as evidence of a lack of significant difference. For all other outcomes, differences are equivocal, and we interpreted them tentatively on the basis of the relative overlap in CIs.Finally we compared the output of the SOM with the output from a decision tree classification method using a random forest (RF) approach from the randomForest.R package30. We chose random forest as a comparison as this method has previously been shown to perform better than other similar methods (e.g. k-nearest neighbour, support vector machine, and naïve Bayes) when classifying behavioural data on free moving animals25,31. We trained both the SOM and RF procedures using the same 20,000 randomly selected epochs, and compared the overall accuracy for predicting the behaviour for the remaining ~ 192,000 epochs. The SOM was built using a 7 × 7 grid patterns, with the rlen argument set to 100. The RF was built with the number of trees set to 100 and the number of variables randomly sampled as candidates at each split set to 4. More

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Mya Breitbart has hunted novel viruses in African termite mounds, Antarctic seals and water from the Red Sea. But to hit pay dirt, she has only to step into her back garden in Florida. Hanging around her swimming pool are spiny-backed orbweavers (Gasteracantha cancriformis) — striking spiders with bulbous white bodies, black speckles and six scarlet spikes that make them look like a piece of medieval weaponry. Even more striking for Breitbart, a viral ecologist at the University of South Florida in St Petersburg, was what was inside. When she and her colleagues collected a few spiders and ground them up, they found two viruses previously unknown to science1.Although we humans have been focused on one particularly nasty virus since early 2020, there are legions of other viruses out there waiting to be discovered. Scientists estimate that there are about 1031 individual viral particles inhabiting the oceans alone at any given time — 10 billion times the estimated number of stars in the known Universe.It’s becoming clear that ecosystems and organisms rely on viruses. Tiny but mighty, they have fuelled evolution for millions of years by shuttling genes between hosts. In the oceans, they slice open microorganisms, spilling their contents into the sea and flooding the food web with nutrients. “Without viruses,” says Curtis Suttle, a virologist at the University of British Columbia in Vancouver, Canada, “we would not be alive.”There are just 9,110 named species listed by the International Committee on Taxonomy of Viruses (ICTV), but that’s obviously a pitiful fraction of the total. In part, that’s because officially classifying a virus used to require scientists to culture a virus in its host or host cells — a time-consuming if not impossible process. It’s also because the search has been biased towards viruses that cause diseases in humans or organisms we care about, such as farm animals and crop plants. Yet, as the COVID-19 pandemic has reminded us, it’s important to understand viruses that might jump from one host to another, threatening us, our animals or our crops.
The new scope of virus taxonomy: partitioning the virosphere into 15 hierarchical ranks
Over the past ten years, the number of known and named viruses has exploded, owing to advances in the technology for finding them, plus a recent change to the rules for identifying new species, to allow naming without having to culture virus and host. One of the most influential techniques is metagenomics, which allows researchers to sample the genomes in an environment without having to culture individual viruses. Newer technologies, such as single-virus sequencing, are adding even more viruses to the list, including some that are surprisingly common yet remained hidden until now. It’s an exciting time to be doing this kind of research, says Breitbart. “I think, in many ways, now is the time of the virome.”In 2020 alone, the ICTV added 1,044 species to its official list, and thousands more await description and naming. This proliferation of genomes prompted virologists to rethink the way they classify viruses and helped to clarify their evolution. There is strong evidence that viruses emerged multiple times, rather than sprouting from a single origin.Even so, the true range of the viral world remains mostly uncharted, says Jens Kuhn, a virologist at the US National Institute of Allergy and Infectious Diseases facility at Fort Detrick, Maryland. “We really have absolutely no idea what’s out there.”Here, there and everywhereAll viruses have two things in common: each encases its genome in a protein-based shell, and each relies on its host — be it a person, spider or plant — to reproduce itself. But beyond that general pattern lie endless variations.There are minuscule circoviruses with only two or three genes, and massive mimiviruses that are bigger than some bacteria and carry hundreds of genes. There are lunar-lander-looking phage that infect bacteria and, of course, the killer spiky balls the world is now painfully familiar with. There are viruses that store their genes as DNA, and others that use RNA; there’s even a phage that uses an alternative genetic alphabet, replacing the chemical base A in the standard ACGT system with a different molecule, designated Z.

Studies of the spiny-backed orbweaver found two viruses previously unknown to science.Credit: Scott Leslie/Minden Pictures/Alamy

Viruses are so ubiquitous that they can turn up even when scientists aren’t looking for them. Frederik Schulz did not intend to study viruses as he pored over genome sequences from waste water. As a graduate student at the University of Vienna, in 2015 he was using metagenomics to hunt for bacteria. This involves isolating DNA from a whole mix of organisms, chopping it into bits and sequencing all of them. A computer program then assembles the bits into individual genomes; it’s like solving hundreds of jigsaw puzzles whose pieces have been jumbled up.Among the bacterial genomes, Schulz couldn’t help but notice a whopper of a virus genome — obvious because it carried genes for a viral shell — with a remarkable 1.57 million base pairs2. It turned out to be a giant virus, part of a group whose members are large in terms of both genome size and absolute size (typically, 200 nanometres or more across). These viruses infect amoebae, algae and other protists, putting them in a position to influence ecosystems both aquatic and terrestrial.
Profile of a killer: the complex biology powering the coronavirus pandemic
Schulz, now a microbiologist at the US Department of Energy Joint Genome Institute in Berkeley, California, decided to search for related viruses in metagenome data sets. In 2020, in a single paper3, he and his colleagues described more than 2,000 genomes from the group that contains giant viruses; before that, just 205 such genomes had been deposited in public databases.Virologists have also looked inwards to find new species. Viral bioinformatician Luis Camarillo-Guerrero worked with colleagues at the Wellcome Sanger Institute in Hinxton, UK, to analyse metagenomes from the human gut, and built a database containing more than 140,000 kinds of phage. More than half of these were new to science. Their study4, published in February, matched others’ findings that one of the most common viruses to infect the bacteria in our guts is a group known as crAssphage (named after the cross-assembly software that picked it up in 2014). Despite its abundance, not much is known about how it contributes to our microbiome, says Camarillo-Guerrero, who now works at DNA-sequencing company Illumina in Cambridge, UK.Metagenomics has turned up a wealth of viruses, but it ignores many, too. RNA viruses aren’t sequenced in typical metagenomes, so microbiologist Colin Hill at University College Cork, Ireland, and his colleagues looked for them in databases of RNAs, called metatranscriptomes. Scientists normally use these data to understand the genes in a population that are actively being turned into messenger RNA in to make proteins, but RNA virus genomes can show up, too. Using computational techniques to pull sequences out of the data, the team found 1,015 viral genomes in metatrancriptomes from sludge and water samples5. Again, they’d massively increased the number of known viruses with a single paper.

The giant tupanvirus,found in amoebae, is more than 1,000 nanometres long and has the largest set of protein-coding genes of any known virus.Credit: J. Abrahão et al./Nature Commun.

Although it’s possible for these techniques to accidentally assemble genomes that aren’t real, researchers have quality-control techniques to guard against this. But there are other blind spots. For instance, viral species whose members are very diverse are fiendishly difficult to find because it’s hard for computer programs to piece together the disparate sequences.The alternative is to sequence viral genomes one at a time, as microbiologist Manuel Martinez-Garcia does at the University of Alicante, Spain. He decided to try trickling seawater through a sorting machine to isolate single viruses, amplified their DNA, and got down to sequencing.On his first attempt, he found 44 genomes. One turned out to represent some of the most abundant viruses in the ocean6. This virus is so diverse — its genetic jigsaw pieces so varied from one virus particle to the next — that its genome had never popped up in metagenomics studies. The team calls it 37-F6, for its location on the original laboratory dish, but Martinez-Garcia jokes that, given its ability to hide in plain sight, it should have been named 007, after fictional superspy James Bond.Virus family treesThe James Bond of ocean viruses lacks an official Latin species name, and so do most of the thousands of viral genomes discovered by metagenomics over the past decade. Those sequences presented the ICTV with a dilemma: is a genome enough to name a virus? Until 2016, proposing a new virus or taxonomic group to the ICTV required scientists to have that virus and its host in culture, with rare exceptions. But that year, after a contentious but cordial debate, virologists agreed that a genome was sufficient7.Proposals for new viruses and groups poured in (see ‘Adding to the family’). But the evolutionary relationships between these viruses were often unclear. Virologists usually categorize viruses on the basis of their shapes (long and thin, say, or a head with a tail) or their genomes (DNA or RNA, single- or double-stranded), but this says surprisingly little about shared ancestry. For example, viruses with double-stranded DNA genomes seem to have arisen on at least four separate occasions.

Source: ICTV

The original ICTV viral classification, which is entirely separate from the tree of cellular life, included only the lower rungs of the evolutionary hierarchy, from species and genus up to the order level — a tier equivalent to primates or trees with cones in the classification of multicellular life. There were no higher levels. And many viral families floated alone, with no links to other kinds of virus. So in 2018, the ICTV added higher-order levels: classes, phyla and kingdoms8.At the very top, it invented ‘realms’, intended as counterparts to the ‘domains’ of cellular life — Bacteria, Archaea and Eukaryota — but using a different word to differentiate between the two trees. (Several years ago, some scientists suggested that certain viruses might fit into the cell-based evolutionary tree, but that idea has not gained widespread favour.)The ICTV outlined the branches of the tree, and grouped RNA-based viruses into a realm called Riboviria. SARS-CoV-2 and other coronaviruses, which have single-stranded RNA genomes, are part of this realm. But then it was up to the broader community of virologists to propose further taxonomic groups. As it happened, Eugene Koonin, an evolutionary biologist at the National Center for Biotechnology Information in Bethesda, Maryland, had assembled a team to analyse all the viral genomes, as well as the latest research on viral proteins, to create a first-draft taxonomy9.They reorganized Riboviria and proposed three more realms (see ‘Virus realms’). There was some quibbling over the details, Koonin says, but the taxonomy was ratified without much trouble by ICTV members in 2020. Two further realms got the green light in 2021, but the original four realms will probably remain the largest, he says. Eventually, Koonin speculates, the realms might number up to 25.

Source: ICTV (talk.ictvonline.org/taxonomy); ICTV Coronaviridae Study Group. Nature Microbiol. 5, 536–544 (2020)

That number supports many scientists’ suspicion that there’s no one common ancestor for virus-kind. “There is no single root for all viruses,” says Koonin. “It simply does not exist.” That means that viruses probably arose several times in the history of life on Earth — and there’s no reason to think such emergence can’t happen again. “The de novo origin of new viruses, it’s still ongoing,” says Mart Krupovic, a virologist at the Pasteur Institute in Paris who was involved in both the ICTV decisions and Koonin’s taxonomy team.As to how the realms arose, virologists have several ideas. Perhaps they descended from independent genetic elements at the dawn of life on Earth, before cells even took shape. Maybe they escaped or ‘devolved’ from whole cells, ditching most of the cellular machinery for a minimal lifestyle. Koonin and Krupovic favour a hybrid hypothesis in which those primordial genetic elements stole genes from cellular life to build their virus particles. Because there are multiple origins for viruses, it’s possible there are multiple ways they’ve originated, says Kuhn, who also served on the ICTV committee and worked on the new taxonomy proposal.Thus, although the viral and cellular trees of life are distinct, the branches touch, and genes pass between the two. Whether viruses count as being ‘alive’ depends on your personal definition of life. Many researchers do not consider them to be living things, but others disagree. “I tend to believe that they are living,” says Hiroyuki Ogata, a bioinformatician working on viruses at Kyoto University in Japan. “They are evolving, they have genetic material composed of DNA and RNA, and they are very important in the evolution of all life.”The current classification is widely recognized as just the first attempt, and some virologists say it’s a bit of a mess. A score of families still lack links to any realm. “The good point is, we are trying to put some order in that mess,” says Martinez-Garcia.World changers With the total mass of viruses on Earth equivalent to that of 75 million blue whales, scientists are certain they make a difference to food webs, ecosystems and even the planet’s atmosphere. The accelerating discovery of new viruses “has revealed a watershed of new ways viruses directly impact ecosystems”, says Matthew Sullivan, an environmental virologist at Ohio State University in Columbus. But scientists are still struggling to quantify how much of an impact they have.“We don’t have a very simple story around here at the moment,” says Ogata. In the ocean, viruses can burst out of their microbial hosts, releasing carbon to be recycled by others that eat the host’s innards and then produce carbon dioxide. But, more recently, scientists have also come to appreciate that popped cells often clump together and sink to the bottom of the ocean, sequestering carbon away from the atmosphere.

Viral genomes collected from thawing permafrost at Stordalen Mire in Sweden have genes that could help break down and release carbon.Credit: Bob Gibbons/Alamy

On land, thawing permafrost is a major source of carbon, says Sullivan, and viruses seem to be instrumental in carbon release from microbes in that environment. In 2018, he and his colleagues described 1,907 viral genomes and fragments collected from thawing permafrost in Sweden, including genes for proteins that might influence how carbon compounds break down and, potentially, become greenhouse gases10.Viruses can also influence other organisms by stirring up their genomes. For example, when viruses transfer antibiotic-resistance genes from one bacterium to another, drug-resistant strains can take over. Over time, this kind of transfer can create major evolutionary shifts in a population, says Camarillo-Guerrero. And not just in bacteria — an estimated 8% of human DNA is of viral origin. For example, our mammalian ancestors acquired a gene essential for placental development from a virus.For many questions about viral lifestyles, scientists will need more than just genomes. They will need to find the virus’s hosts. A virus itself might carry clues: it could be toting a recognizable bit of host genetic material in its own genome, for example.Martinez-Garcia and his colleagues used single-cell genomics to identify the microbes that contained the newly discovered 37-F6 virus. The host, too, is one of the most abundant and diverse organisms in the sea, a bacterium known as Pelagibacter11. In some waters, Pelagibacter makes up half the cells present. If just this one type of virus were to suddenly disappear, says Martinez-Garcia, ocean life would be thrown wildly off balance.To understand a virus’s full impact, scientists need to work out how it changes its host, says Alexandra Worden, an evolutionary ecologist at the GEOMAR Helmholtz Centre for Ocean Research in Kiel, Germany. She’s studying giant viruses that carry genes for light-harvesting proteins called rhodopsins. Theoretically, these genes could be useful to the hosts — for purposes such as energy transfer or signalling — but the sequences can’t confirm that. To find out what’s going on with these rhodopsin genes, Worden plans to culture the host and virus together, and study how the pair function in the combined, ‘virocell’ state. “Cell biology is the only way you can say what that true role is, how does this really affect the carbon cycle,” she says.Back in Florida, Breitbart hasn’t cultured her spider viruses, but she’s learnt some more about them. The pair of viruses belong to a category Breitbart calls mind-boggling for their tiny, circular genomes, encoding just one gene for their protein coat and one for their replication protein. One of the viruses is found only in the spider’s body, never its legs, so she thinks it’s actually infecting some creature the spider eats. The other virus is found throughout the spider’s body, and in its eggs and spiderlings, so she thinks it’s transmitted from parent to offspring12. It doesn’t seem to be doing them any harm, as far as Breitbart can tell.With viruses, “finding them’s actually the easy part”, she says. Picking apart how viruses influence host life cycles and ecology is much trickier. But first, virologists must answer one of the toughest questions of all, Breitbart says: “How do you pick which one to study?”

Nature 595, 22-25 (2021)
doi: https://doi.org/10.1038/d41586-021-01749-7

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Genome skimming provides robust phylogenyPioneering molecular phylogenetic studies in Sepiolida that used short regions of a few mitochondrial and nuclear genes failed to resolve the relationship of major clades9,22,23. To increase the number of phylogenetically informative sites, Sanchez et al.11 sequenced and analyzed the transcriptomes of multiple species of Euprymna Steenstrup, 1887, related bobtail squids including Sepiola parva Sasaki, 1913 and Sepiola birostrata Sasaki, 1918, and several bottletail squids. They found that S. parva grouped with the Euprymna species to the exclusion of S. birostrata, and further morphological analysis led to the formal redefinition of the genus Euprymna and the reassignment of S. parva Sasaki, 1913 to Euprymna parva11. The following year, in an exhaustive study of hectocotylus structure, Bello19 proposed that Euprymna be split back into the original Euprymna Steenstrup, 1998 and a newly defined genus, Eumandya Bello 2020 that contains E. parva Sasaki, 1913 and E. pardalota Reid 2011, two taxa whose arms have two rows of suckers rather than four as in other Euprymna species. Similarly, Bello introduced a new genus, Lusepiola Bello, 2020 that has the effect of renaming Sepiola birostrata Sasaki, 1918 as Lusepiola birostrata. For clarity, we adopt the finer-grained nomenclature of Bello below, but happily note that E. parva and E. pardalota have the same abbreviations in both the notation of Sanchez et al.11 and Bello19.Sanchez et al.11 also emphasized the need for more taxon sampling, careful species assignment, and the inclusion of more informative sites when studying this group of cephalopods. However, the distribution and lifestyle of many lineages of Sepiolida makes the collection of fresh tissue for RNA sequencing very challenging. To overcome this limitation, we sequenced the genomic DNA of several Sepiolida species at shallow coverage up to 3.6× and accessed by this way several mitochondrial and nuclear loci. Most of our samples were carefully identified at the species level based on morphological characters.We recovered the mitochondrial genomes of the species targeted in this study and annotated the 13 protein-coding genes, 22 tRNAs, and two rRNAs (although only the conserved region of the large and small rRNA was obtained for Rondeletiola minor Naef, 1912).Additionally, we also downloaded the complete mitochondrial genomes of S. austrinum and Idiosepius sp., and the transcriptome of E. tasmanica available in the NCBI database. The transcriptome of E. tasmanica was used to extract its complete set of mitochondrial protein-coding genes. We could reconstruct the mitochondrial gene order for all species with complete mtDNA genomes, but we observed no re-arrangement for members of Sepiolidae, and only Sepiadarium austrinum deviated from the arrangement seen in all other Sepiadariidae (Fig. S1).To complement the mitochondrial-based evolutionary history, we also annotated several nuclear loci. As ribosomal gene clusters are present in numerous copies, they were successfully retrieved for almost all the species, except for 28S of the Sepiadariidae sp. specimen, which appeared problematic and was excluded.By mapping reads to the reference genome of E. scolopes, we obtained 3,279,410 loci shared between at least two species and further selected 5215 loci presented in most of our Sepiolidae species, but allowed some missing data in the Euprymna + Eumandya clade. This was done because the phylogenetic relationships of the Euprymna + Eumandya species were previously described in detail in Sanchez et al.11 using transcriptome data. Out of the 5215 loci, 5164 loci had a per-site coverage ranging between two and five. After trimming and removal of regions without informative sites, 577 loci remained. These ultraconserved loci had lengths ranging between 10 and 690 base pairs (bp), with an average of 65 bp. Our alignment matrix had a length of 37,512 bp and consisted of 16,495 distinct site patterns, and variable sites between 1 and 130 bp with an average value of 7 bp. We expected a low value of variable sites because these regions are highly conserved.We considered resolved nodes to be those with the ultrafast bootstrap support and posterior probability larger than 95% and 0.9, respectively. Only the very unresolved nodes were found based on the mito_nc matrix (Fig. 1). However, among the species in these nodes, Adinaefiola ligulata Naef, 1912 was well supported with amino acid sequences from mitochondrial genes (posterior probability of 1 and 94% bootstrap support) and partially by the ultraconserved loci (posterior probability of 1, but only 85% bootstrap support) as sister to the Sepiola clade (Figs. 2 and S2). Moreover, compared to the mito_nc matrix and with identical topology, mito_aa and UCEbob fully resolved the relationship of the Indo-Pacific and Mediterranean Sea Sepiolinae. The tree generated by the nuclear_rRNA produces a topology with most nodes unsupported (Fig. S3), suggesting these markers are too conserved for assessing the relationships among this group.Fig. 1: Phylogeny of Sepiolida based on nucleotide sequences from the mitochondria (mt_nc matrix).The topology of the maximum likelihood tree is shown. Numbers by the nodes indicate bootstrap support and the Bayesian posterior probabilities. Values of bootstrap support and posterior probabilities above 95% and 0.95, respectively, are not shown. (*) indicates that the node was resolved with the mito_aa and UCEbob matrices. (+) indicate that A. ligulata is sister to Sepiola using mito_aa with ultrafast bootstrap support of 94% and a posterior probability of 1. Abbreviations: IP, Indo-Pacific Ocean; MA, Mediterranean Sea, and the Atlantic Ocean.Full size imageFig. 2: Phylogenetic tree of Sepiolida based on conserved nuclear loci (UCEbob matrix).The topology of the maximum likelihood tree is shown. Numbers in by the nodes indicate the bootstrap support and the Bayesian posterior probability. Values of bootstrap support and posterior probabilities above 95% and 0.95, respectively, are not shown. IP, Indo-Pacific Ocean; MA, Mediterranean Sea, and the Atlantic Ocean.Full size imageUsing the UCEbob matrix, the topology and supported relationships of Euprymna + Eumandya species resemble those reported in Sanchez et al.11 using transcriptome sequences, proving our protocol valid when using low coverage sequencing and when a reference genome of the closest related species is available.The position of R. minor showed discordance between mitochondrial and nuclear datasets. Using the mitochondrial matrices, R. minor rendered the Sepietta Naef, 1912 clade paraphyletic, whereas using the UCEbob and rRNA_nc matrices, R. minor appeared sister to the Sepietta clade. These relationships were resolved in both mitochondrial and nuclear-based trees and require further investigation with more DNA markers and a wider population sampling.Molecular systematics of Sepiolida cladesUsing the complete mitochondrial genome, ribosomal nuclear genes, and ultraconserved loci, we recovered the monophyly of the two families of the order Sepiolida—Sepiadariidae and Sepiolidae9,24—and the monophyly of the three described subfamilies of the family Sepiolinae. However, contrary to what is proposed based on morphology in Young24, the Rossinae is not sister to all the remaining Sepiolidae but rather is sister to Heteroteuthinae, although this is unresolved in the UCE phylogeny. With the lack of systematic work on these subfamilies, our robust phylogenetic backbone in Sepiolida using new samples carefully identified by morphology and with museum vouchers, represents a notable advance to clarify the evolution of morphological traits in major clades within the family.Based on morphological characters of the hectocotylus, Bello19 recently split the polyphyletic Sepiola Leach 1817 into Lusepiola, Adinaefiola, and Boletzkyola, reserving Sepiola for the S. atlantica group sensu Naef 1923. These newly defined clades are consistent with our molecular phylogeny here and in Sanchez11, who also noted the polyphyly of Sepiola in the Indo-Pacific lineage.We find that Sepiolinae can be robustly split into two geographically distinct tribes: one that comprises species with known distribution in the Indo-Pacific region (tribe Euprymmini new tribe, defined as Sepiolinae with a closed bursa copulatrix, type genus Euprymna) and the other including all the Mediterranean and Atlantic species (tribe Sepiolini Appellof, 1989, defined here as Sepiolinae with an open bursa copulatrix, type genus Sepiola). Our molecular relationship is consistent with 13 of the 15 apomorphies used in the cladogram shown in Fig. 21 in Bello19. The other two proposed apomorphies in Bello (his apomorphic characters 4 and 6) group two IP lineages, Lusepiola and Inioteuthis, in a clade with species from the Mediterranean and Atlantic. Such relationships contradict our Euprymmini-Sepiolini sister relationship. Moreover, according to our phylogeny, apomorphy 6 of Bello, characterized by the participation of ventral and dorsal pedicels in the formation of the hectocotylus copulatory apparatus, implies that the male ancestor of Sepiolinae had a more developed hectocotylus that was simplified in the Euprymna and Eumandya clades.Among euprymins, we confirmed the monophyly of Euprymna Steenstrup 1887 as found previously by transcriptome analysis11. We also support the monophyly of Eumandya Bello, 2020 (Figs. 1 and 2), grouping the type species E. pardalota with E. parva along with the unnamed “Type 1” Ryukyuan species of Sanchez et al.11, for which only hatchlings were available. The phylogenomic grouping of Ryukyuan “Type 1” with Eumandya suggests that when its adults are found (or hatchlings are raised to maturity), its arms will carry two rows of suckers. We also found an adult of a Ryukyuan “Type 4” (extending the notation of Sanchez et al.11 in the coastal waters of Kume Island, that groups with E. scolopes from Hawaii, suggesting a divergence based on geographic isolation in the North Pacific. We also find that Lusepiola birostrata (formerly Sepiola birostrata) is grouped with Inioteuthis japonica as sister to a clade containing Euprymna, Eumandya, and an unnamed sepioline from Port Kembla, at the northeast of Martin Island in Australian waters.Among the sepiolins, we confirm the monophyly of Sepietta (only for nuclear-genome-based trees, see below). Adinaefiola, another genus erected by Bello19, with Sepiola ligulata Naef 1912 as its type species; was found sister to the Sepiola clade, but only in the tree based on amino acid mitochondrial sequences (mito_aa matrix) with a bootstrap value of 94% and a posterior probability of 1 (Fig. S2).Outside the sepiolines, members of the subfamily Heteroteuthinae are the most elusive and underrepresented in studies of cephalopod systematics due to their oceanic lifestyles. The placement of several heteroteuthin remains controversial. Lindgren et al.9, with six nuclear and four mitochondrial genes downloaded from GenBank found that Sepiolina Naef, 1912 was sister to Heteroteuthis Gray, 1849 + Rossia Owen, 1834+ Stoloteuthis Verril, 1881; rendering the subfamily Heteroteuthinae polyphyletic. In contrast, our work supports the monophyly of Heteroteuthinae by including Stoloteuthis and Heteroteuthis in this subfamily, while Rossia was placed within the Rossinae (Figs. 1, 2, S2). Members of Heteroteuthinae included in this study formed a sister group to a monophyletic Rossinae (Figs. 1, 2, S2). Semirossia, however, rendered the Rossia clade paraphyletic. Further discussion about the position of Semirossia is difficult because of the lack of information about the original source of this specimen in Kawashima et al.25.The light organ and luminescence evolutionBobtail squids are thought to use the bioluminescence of their light organ to camouflage them from predators while foraging and swimming at night through a mechanism called counter-illumination. This has been researched extensively using E. scolopes as a model system26,27,28. Unfortunately, the limited number of sequences available and the misidentification of bobtail squids in the GenBank database11,29,30 have hindered our understanding of the light organ evolution in the whole taxon.Our robust phylogeny and Bayesian reconstruction of ancestral bioluminescence clarify how the light organ and its luminescence have evolved in the family Sepiolidae. Members of Sepiolinae comprise neritic and benthic adults with bilobed light organs, except for two genera: Inioteuthis from the Indo-Pacific region, and the Sepietta species from the Mediterranean Sea and the Atlantic waters. The ancestor of the Sepiolinae very likely possessed a bilobed light organ that harbored luminescent symbiotic bacteria (Fig. 3). This character persisted until the ancestor of the euprymnins and sepiolins. Assuming that R. minor is sister to the Sepietta clade (as shown with the nuclear-based dataset, Fig. 2), it is clear that the bilobed light organ was lost once in Inioteuthis and Sepietta, and simplified to a rounded organ in R. minor. The alternative scenario, where R. minor renders the Sepietta clade paraphyletic (based on mitochondrial matrices, Fig. 1), is less plausible as it implies that the light organ was lost twice in the Sepietta group, once in S. obscura and then in the ancestor of S. neglecta and S. oweniana; or alternatively that it was lost in the ancestor of Sepietta-Rondelentiola followed by a reversion of this character in the lineage of Rondelentiola.Fig. 3: Ancestral character reconstruction (ASR).ASR of (a) the shape of the light organ and (b) the origin of luminescence in the Sepiolida. The posterior probability of each state is shown as a pie chart, mapped tree generated in BEAST (based on mito_nt matrix, see below), with the outgroups removed.Full size imageThe light organ is also present in all members of Heteroteuthinae. These bobtails are pelagic as adults, and their light organ appears as a single visceral organ rather than the bilobed form found in nektobenthic Sepiolinae. In contrast to the bacteriogenic luminescence of the light organ in E. scolopes31, previous studies in H. dispar3 failed to detect symbiotic bacteria and suggested that the luminescence has an autogenic origin. Thus, it seems plausible that the monophyly of Heteroteuthinae found in our study supports the findings in Lindgren et al.9 for convergent evolution of autogenic light organs associated with pelagic lifestyle in many squid, octopus, and Vampyroteuthis Chun, 19039,32.Divergence time of SepiolidaThe absence of fossils for this group limited our calculations of divergence time to the use of secondary calibrations. These calibrations can provide more accurate estimates depending on the type of primary calibrations that are used33. We retrieved secondary calibrations from previous estimations in Tanner et al.15, who used eleven fossil records spanning from coleoids to gastropods in transcriptome-based phylogenetic trees. Specifically, we used the time for the splits of Sepia esculenta and S. officinalis (~91 Mya), Idiosepiidae, and Sepiolida (~132 Mya) and the origin of the Decapodiformes (root age, ~174 Mya) (Fig. 4). These calibrations and our robust phylogenetic trees allow us to investigate the events that shape the divergence of some clades of the order Sepiolida (Figs. 4,  S4).Fig. 4: A chronogram of sepiolids using complete mitochondrial genes.Red dots indicate the nodes with secondary calibrations. K-Pg, refers to the Cretaceous-Paleogene boundary and MSC, to the Messinian salinity crisis.Full size imageSepiolida appeared before the Cretaceous-Paleogene extinction event34, during the middle Mesozoic around 94 Mya (95% HPD = 60.61–130.72). This time frame coincides with the rapid diversification of several oegopsida lineages15,35. Our molecular estimates also indicate that radiation of Sepiolidae and Sepidariidae occurred around the Cretaceous-Paleogene boundaries and is concurrent with the rapid diversification of modern marine percomorph fishes around the globe, after the extinction of Mesozoic fishes36,37.Among the species of Sepiolinae collected in the Mediterranean Sea for this study, only Sepiola robusta Naef, 1912, and Sepiola affinis Naef, 1912 are endemic to the Mediterranean Sea38. The distribution of the other species includes the Mediterranean Sea, North Atlantic Ocean, East Atlantic Ocean, and/or up to the Gulf of Cadiz. The confidence intervals for the split between the Mediterranean-Atlantic and Indo-Pacific lineages, and their diversification, overlap during the early Eocene to the beginning of the Oligocene (Figs. 4 and  S4). This time interval coincides with the end of the Tethys Sea, which separated the Indo-Pacific from the Mediterranean and Atlantic region through the Indian-Mediterranean Seaway39,40. This separation also influenced the divergence of loliginid clades, coinciding with the split between the Eastern Atlantic plus Mediterranean clade (Loligo, Afrololigo, Alloteuthis) and Indo-Pacific clade (Uroteuthis and Loliolus) (~55 Mya based on Fig. 2 in Anderson and Marian41).Our chronogram indicates that the ancestor of Sepiolinae arose prior to the early Eocene around 46 Mya (95% HPD = 25.16–69.49) (Fig. 4), already possessing a bilobed light organ hosting luminescent bacteria (Fig. 3). We estimate that the split between S. affinis and S. intermedia occurred around 2.62 Mya (95% HPD = 0.3–7.4) (Fig. 4) during the end of the Zanclean period, when the Atlantic Ocean refilled the Mediterranean after the Messinian salinity crisis42,43. While S. affinis is a coastal species with a narrow depth limit, S. intermedia inhabits a wider range of deeper waters. It is possible that two populations of their ancestor, each adapted to a different ecological niche and diverged sympatrically in Mediterranean waters, and, after the speciation, S. intermedia extended its distribution outside the Mediterranean to the Gulf of Cadiz44.We also estimate that the split between H. dispar Rüppell, 1844 and H. hawaiiensis (Berry, 1909) occurred around 2.4 Mya (95% HPD = 0.46–5.88), coinciding with the closure of the Isthmus of Panama around 2.8 Mya45. Surveys of these species found H. hawaiiensis in the North Pacific and H. dispar in the North Atlantic Ocean and Mediterranean Sea46. A recent speciation event might be the reason for the lack of morphological differences between the two species46. Thus, these species may be rendered as cryptic species, a phenomenon increasingly reported in oceanic cephalopods47. The sister species of this cryptic species complex, H. dagamensis Robson, 1924, appeared before, around 6 Mya, and is reported with broad distribution in the South Atlantic Ocean off South Africa, the Gulf of Mexico, North Atlantic Ocean between Ireland and Newfoundland in Canada, and the South Pacific Ocean off New Zealand48,49,50.The origin of the Heteroteuthis ancestor of H. dispar, H. hawaiiensis, and H. dagamensis can be placed in the Pacific Ocean. After the formation of the Isthmus of Panama, the northern population of Heteroteuthis might have split into H. hawaiiensis in North Pacific and H. dispar in the Atlantic Ocean (from where it also migrated to the Mediterranean Sea). Meanwhile, the formation of the equatorial currents isolated the southern population of Heteroteuthis and gave rise to H. dagamensis. Then, H. dagamensis extended its distribution from the Southern Pacific to the South Atlantic Ocean, the North Atlantic waters, and the Gulf of Mexico. Analysis of molecular species delimitation, however, suggests that H. dagamensis includes cryptic lineages among Atlantic and New Zealand populations30.While the origin of Heteroteuthis might also be in the Atlantic Ocean, the higher diversity of heteroteuthins in the Pacific (H. hawaiiensis, H. dagamensis, H. ryukyuensis Kubodera, Okutani and Kosuge, 2009, H. nordopacifica Kubodera and Okutani, 2011, and an unknown H. sp. KER (only known from molecular studies49)) than at the Atlantic (H. dispar and H. dagamensis), make its origin at the Atlantic less plausible. Moreover, the Atlantic Heteroteuthis were found nested within Heteroteuthinae species from the Pacific, supporting Pacific Ocean origin (Figs. 1, 4).By sequencing the genomic DNA of sepiolids at low coverage, we recovered complete mitochondrial genomes and nuclear ribosomal genes for most of our collections. Furthermore, mapping reads to the reference genome of E. scolopes allowed us to retrieve additional nuclear-ultraconserved regions. We demonstrate that these nuclear and mitochondrial loci are useful to reconstruct robust phylogenetic trees, especially when the transcriptomes of specimens are difficult to collect, as for sepiolids inhabiting oceanic environments. Finally, our study integrated genomic DNA sequencing with confident morphological identification, which helped to reconstruct the ancestral character of the light organ and its luminescence in sepiolids, and clarify how major lineages have evolved, establishing the existence of distinct Indo-Pacific and Mediterranean-Atlantic subfamilies of Sepiolinae. Our collections and genomically anchored phylogenies will provide a reliable foundation classification of sepiolids for future studies. More