Philippa Kaur

Global genetic structure of the pathogen tracks the historical spread of wheatWe assessed the evolutionary trajectory of the pathogen in conjunction with the history of global wheat cultivation (Fig. 1a). For this, we assembled a worldwide collection of Z. tritici isolates from naturally infected fields (Fig. 1b). We selected isolates covering most wheat production areas, both in the center of origin of the crop (i.e., the Fertile Crescent in the Middle East), and in areas where wheat was introduced during the last millennia (i.e., Europe and North Africa), or last centuries (i.e., the Americas and Oceania; Fig. 1c). We called variants in a set of 1109 high-quality short-read resequencing datasets (Supplementary Data 1, 2) covering 42 countries and a broad range of climates. Using a joint genotyping approach, we produced raw variant calls mapped to the telomere-to-telomere assembled reference genome IPO323. To assess genotyping accuracy, we used eight isolates with replicate sequencing data to analyze discrepancies. We adjusted quality thresholds targeting specifically the type of genotyping errors observed in our data set (Fig. S1). The improved filtering yielded 8,406,818 high-confidence short variants (short indels and SNPs). The final variant set included 5,578,488 biallelic SNPs corresponding to 14.1% of the genome.Fig. 1: Global sampling of the wheat pathogen Zymoseptoria tritici retracing the historical spread of its host.a Schematic representation of the introduction of wheat across continents. b Septoria tritici blotch symptoms caused by Z. tritici on wheat leaves. Pictures taken by B. A. McDonald, ETH Zurich. c Map of the sampling scheme for the global collection of 1109 isolates for whole-genome sequencing.Full size imageWe tested whether global diversity patterns of pathogen populations are likely a consequence of the history of wheat cultivation. We first performed unsupervised clustering of genotypes and identified eleven well-supported clusters (Fig. 2a, Figs. S2,3). Over 90% of the genotypes were clearly assigned to a single cluster (Fig. 2a, Supplementary Data 3). Two clusters were identified among genotypes originating from the pathogen center of origin, distinguishing collections from Iran and Middle Eastern regions. Genotypes from Africa and Europe split into two distinct genetic clusters without any apparent secondary structure within clusters. This lack of any fine-scale structure is remarkable given the extensive geographic sampling of European genotypes and suggests extensive gene flow within the continent. Genotypes from Oceania grouped into three distinct clusters marked by collections from Tasmania, the Australian mainland, and New Zealand. Genotypes from North America formed two clusters along a North-South separation. Finally, South American genotypes formed two clusters split along the Andes (Chile versus Argentina and Uruguay). Some uncertainty exists in the assessment of regional population structure by low coverage of major wheat-producing countries such as Russia and Ukraine. Septoria tritici blotch is only sporadically reported in China. In complementary analyses, we found that a phylogenetic network accounting for the high frequency of recombination consistently reflected the global population structure (Fig. S4). A principal component analysis of all genotypes confirmed the nested genetic structure with differentiation at the continent level, subdivisions within some continents and the existence of admixed genotypes (Fig. 2b, Fig. S5).Fig. 2: Global genetic structure based on 1109 genomes.a Map of the genetic clustering based on a thinned genome-wide SNP dataset using sNMF. Each color represents a different genetic cluster, and the sizes of the slices represent the average attribution to the cluster across the isolates from each location. Fractions representing less than 10% of all genotypes of a location were colored in grey to improve clarity. The large pie chart outside of the map represents the proportion of isolates assigned clearly (≥75%) to a single genetic cluster (pure; in teal) and isolates identified as hybrids (admixed) between clusters (in yellow). Names of the clusters include an abbreviation of continents and a more precise geographical location (MEA: Middle East and Africa; NA: North America; SA: South America; OC: Oceania). b Principal component analysis, showing the first and second component (PCs) based on a subset of variants. Colors and shapes indicate the genomic clusters identified with the sNMF method (with hybrids in grey). The marginal distributions represent the distribution for each PC. PCs 1 to 8 are shown in Fig S4. c Population tree based on Treemix, rooted using two genomes from the sister species Z. passerinii and Z. ardabiliae. The colors are the same as in the previous panels and only samples which were fully assigned to a cluster were used. d Diversity estimated with using pi per genetic cluster. The boxplots are ordered according to the tree of panel. c. The lower and upper hinges correspond to the first and third quartiles, the whiskers to the largest value are within 1.5 times the inter-quartile range, and the central horizontal line defines the median. e Linkage disequilibrium (r2) between variants per genetic cluster. Colors are identical among panels.Full size imageWe analyzed the history of population splits and admixture using allele frequency information (Fig. 2c). The analyses largely supported a genetic structure shaped by the introduction of wheat across continents. The historical relationships between clusters show an early divergence of the Middle Eastern and North African clusters matching the early introduction of agriculture in these regions. Populations in Europe and the Americas share a similar time point of divergence consistent with extensive contributions of European genotypes to the Western hemisphere. Oceanian groups have diverged as a single branch from genotypes most closely related to extant European populations. Matching the introduction of wheat to Oceania from the European continent, the Australian and New Zealand pathogen populations share a common origin rooted in European genetic diversity. Populations from Australia show also a striking loss of diversity and higher linkage disequilibrium compared to European diversity consistent with a significant founder effect (Fig. 2d, e). Similarly, populations in South and North America have reduced genetic diversity compared to extant European populations as suggested previously based on Sanger sequencing16. The highest diversity was found in populations from Africa and the Middle East closest to the center of origin. Overall, the global genetic structure of the pathogen reveals multiple founder events associated with the introduction of wheat to new continents.Ongoing gene flow among regions should lead to admixed genotypes. We found that nearly 10% of all analyzed genotypes showed contributions from at least two clusters. The most significant recent gene flow was detected between Middle Eastern/North African clusters and European clusters in North Africa (i.e., Algeria and Tunisia) as well as Southern and Eastern Europe (i.e., France, Italy, Hungary, Ukraine, Portugal, and Spain; Supplementary Data 3). We found a particularly high incidence of recent immigration in a durum wheat population in the south of France. The population consisted only of hybrids or atypical genotypes suggesting either recent migration from North Africa or host specialization on durum wheat varieties. Additionally, we found hybrid genotypes with European ancestry in both North America and in Oceania. The relatively balanced ancestry proportions in these hybrids suggest very recent gene flow dating back to only a few generations. We further investigated past gene flow between clusters by allowing Treemix to infer migration events, thus creating a population network (Fig. S6a–d). Three distinct recent migration events were best explaining the data with specific migration routes from the Middle East/African clusters to North America, from an Australian cluster to South America and between two Oceanian clusters (Fig. S6d). However, the migration events did not affect the overall shape of the inferred population tree (Fig. 2c, Fig. S6b–d). To better understand effects of long-distance gene flow, we investigated the relationship between relatedness among genotypes (i.e., identity-by-state) and geographic distance. At the continent level, we observed a negative relationship between identity-by-state and geographic distance (Fig. S7). The wide distribution of identity-by-state values shows that although closely related isolates tend to be found at closer geographic distance, distantly related isolates can be found at both far and close geographic distances. Long-distance migration events are most likely caused by international trade similar as for other crop pathogens17,18,19. In combination, our findings show an important role of long-distance dispersal impacting the genetic make-up of populations from individual fields to continental scale genetic diversity.Relaxation of genomic defenses against transposable elements concurrent with global spreadTransposable elements (TEs) are drivers of genome evolution. In Z. tritici, TE activity created beneficial mutations for fungicide resistance and virulence on the wheat host20,21. Rapid recent adaptation of the pathogen has benefitted from the activity of TEs with consequences for genome size22. Unchecked transposition of TEs can be deleterious and an array of defenses mechanisms has evolved to counteract their activity both at the genomic and epigenetic level including targeted mutations and silencing23. To analyze the effectiveness of genomic defenses against active TEs, we screened all genomes for evidence of TE insertions. We mapped short-read sequencing data on the reference genome and a species-specific TE sequence library. We classified evidence for TEs in each of the analyzed isolates as reference TEs (i.e. also present in the reference genome) and non-reference TE (i.e. absent). Detected TEs among isolates were binned into loci (width 100 bp) to account for uncertainties about the precise mapping of the insertion point. We found that the frequency spectrum of TE insertions is heavily skewed towards low frequencies with 77% of TE insertions being found in single isolates (~0.1% frequency) and 96% of insertions were found in ten or fewer isolates ( More

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Screening for viral AMGsViral protein annotation using VIBRANT and DRAM-v software combined with manual proofreading identified the viral AMGs in Napahai plateau wetland, including the viral AMGs phoH, phoU and pstS, which were associated with phosphorus metabolism.Phylogenetic analysis of AMGs associated with phosphorus metabolism in Napahai plateau wetlandThere were 24 amino acid sequences of phoH gene in Napahai plateau wetland (Fig. 1A). They were divided into 5 clusters, the largest of which had 10 sequences, while the smallest cluster had only 1 sequence. The remaining 3 clusters contained 6, 5 and 2 sequences, respectively. The phoH gene was genetically diverse in Napahai plateau wetland, which might be related to the different host origins. A total of 74 sequences of phoU gene could be found in seven clusters (Fig. 1B), with the largest cluster containing 27 sequences and the smallest cluster having two sequences. Similar to phoH, phoU was also genetically diverse, but richer than that of phoH. There were 71 pstS sequences forming 9 clusters, with the largest cluster of 23 sequences and the smallest cluster only 1 sequence (Fig. 1C). It could be seen that the genetic diversity of pstS was better than that of phoH and phoU, which might be related to the unique geographical location. Napahai plateau wetland is located in the Three Parallel Rivers of Yunnan protected areas, which forms a complex landscape, and then controls the evolution and characteristics of organisms, thus showing abundant biodiversity. Li et al. obtained 58 phoH gene sequences from Northeastern wetland sediments of China, which were 22%–99% consistent at the amino acid level, and found that the phoH gene could regulate phosphate uptake and metabolism under the low phosphate or phosphate limitation conditions16. However, the exact function remained unclear. The phoH gene clustered into five clusters in Napahai plateau wetland, indicating high genetic diversity. Additionally, water and soil samples were collected from eight separate sampling sites, and there were differences between samples environments, which might also have an impact on the genetic diversity of the three genes.Figure 1Phylogenetic analysis of phosphorus metabolism AMGs in Napahai plateau wetland, different colors represent different branches. (A) Phylogenetic analysis of phoH genes. (B) Phylogenetic analysis of phoU genes. (C) Phylogenetic analysis of pstS genes.Full size imagePhylogenetic analysis and PCoA analysis of AMGs associated with phosphorus metabolism from different habitats and host originsIn order to understand the genetic diversity of viral AMGs (phoH, phoU, pstS) associated with phosphorus metabolism in Napahai plateau wetland, a phylogenetic tree of phosphorus metabolism AMGs from different habitats was constructed, and PCoA analysis was performed (Fig. 2). The results showed that most sequences of phoH, phoU and pstS genes in Napahai plateau wetland clustered individually, especially phoU and pstS genes, and only a few sequences were closely related to those of other habitats. In Fig. 2A, 14 sequences clustered individually and were relatively far from sequences of other habitats, whereas 7 sequences were close to those from freshwater lakes, and other 3 sequences were close to those from rice fields, oceans and other wetlands, respectively. Therefore, the genetic diversity of phoH in Napahai plateau wetland was independent of the habitat. Moreover, some of the phoH sequences were clustered with those of other habitats and distributed in the fourth quadrants (Fig. 2D). From Fig. 2B, apart from 3 sequences which clustered with those from the marine habitats and freshwater lakes, the rest were clustered separately. Whereas in Fig. 2E, apart from only a few sequences, most sequences of phoU were far away from those of different habitats, which was consistent with Fig. 2B. Thus, the genetic diversity of phoU gene in Napahai wetland was also independent of habitat, where the separately clustered sequences may be unique. From Fig. 2C, we can seen that apart from 8 sequences which more closely related to those from the freshwater lake, ocean, rice field, and other wetlands, all the rest were individually clustered. The result was consistent with that of Fig. 2F. Therefore, the genetic diversity of the pstS gene was also habitat-independent.Figure 2Phylogenetic analysis and PCoA of phosphorus metabolism AMGs in different habitats, different colors represent different habitats. (A) Phylogenetic analysis of phoH genes in different habitats. (B) Phylogenetic analysis of phoU genes in different habitats. (C) Phylogenetic analysis of pstS genes in different habitats. (D) PCoA analysis of phoH genes in different habitats. (E) PCoA analysis of phoU genes in different habitats. (F) PCoA analysis of pstS genes in different habitats.Full size imageTo study whether the genetic diversity was related to host origins, three AMGs associated with phosphorus metabolism were selected for phylogenetic and PCoA analyses with AMGs sequences from different host origins (Fig. 3). It showed that some sequences of all three genes were similar to those from different host origins, while the remaining were separately clustered. In Fig. 3A, apart from 14 sequences which clustered with those from fungi, bacteria, non-culturable phages, phages and viruses, all the rest were clustered separately. Whereas, most sequences were clustered with those from different host origins together, and only six sequences were far from other sequences of different host origins based on PCoA analysis (Fig. 3D). Only three sequences were clustered with those of archaea and uncultured archaea, and the rest were clustered together to form independent clusters (Fig. 3B). A small amount of sequences were gathered with bacteria, uncultured bacteria, archaea and uncultured archaea, and the rest were clustered individually (Fig. 3E). As can be seen in Fig. 3C, six sequences were clustered with those of archaea, fungi, bacteria, while the rest were clustered separately. Some sequences were gathered with bacteria, uncultured bacteria, archaea and uncultured archaea, and others were clustered separately (Fig. 3F). PCoA analysis was largely consistent with phylogenetic analysis. So the genetic diversity of phoH, phoU and pstS genes in Napahai plateau wetland was independent of the host origins.Figure 3Phylogenetic analysis and PCoA of phosphorus metabolism AMGs from different host origins, different colors represent different host origins. (A) Phylogenetic analysis of phoH gene from different host origins. (B) Phylogenetic analysis of phoU gene from different host origins. (C) Phylogenetic analysis of pstS gene from different host origins. (D) PCoA analysis of phoH genes from different host origins. (E) PCoA analysis of phoU genes from different host origins. (F) PCoA analysis of pstS genes from different host origins.Full size imageOverall, the genetic diversity of phoH, phoU and pstS genes associated with phosphorus metabolism in Napahai plateau wetland was independent of both the habitats and host origins based on phylogenetic and PCoA analyses. It suggested that three genes showed relatively rich genetic diversity and were not genetically limited by differences in habitats or host origins. Han et al. showed that phoH sequences were widely distributed in soil, freshwater, and seawater environments in different locations around the world, indicating the genetic diversity independent of the environment17, which corroborated the conclusions in our study. Phylogenetic analysis of the 58 viral phoH gene sequences in Northeastern wetland of China revealed that some sequences were clustered with bacterial sequences and others clustered with phages sequences16. In Napahai plateau wetland, some phoH gene sequences were clustered with fungal, bacterial, phage, uncultured phage, and viruses. Hence, the genetic diversity of phoH gene was independent of the host origins in either Northeastern wetland or Napahai plateau wetland. Compared with Northeastern wetland, the phoH genes in Napahai plateau wetland showed more abundant genetic diversity, which may be related to geographical location and climate. Additionally, compared with sequences from different habitats and host sources, partial sequences from Napahai plateau wetland were clustered individually, thus they were unique, which might be related to the unique geography. Napahai plateau wetland is located in the Three Parallel Rivers with low latitude and high altitude, and shows specific characteristics which not found in other habitats, and then the species very different, thus providing the possibility for the emergence of unique genetic sequences. Of course, it would require further verification by subsequent study.As far as the current studies are concerned, most reports on phosphorus AMGs focused on the function. Wang et al. mentioned that the phoH gene regulated phosphate uptake or metabolism under the low phosphorus or phosphate limitation conditions18. Kelly et al. isolated several phages from oligotrophic water bodies with low phosphorus condition, found that they contained the phosphate binding transporter gene pstS by sequencing, which enhanced the host cell with increasing the infection cycle of phages by increasing phosphate utilization19. Gardner et al. studied the PhoR-PhoB two-component regulatory system in E. coli, which regulated the expression of relevant genes according to environmental phosphate concentration and enabled cells to adapt the phosphate starvation20. The phoU existed in many bacteria and was identified as an auxiliary protein of the phosphate-specific transporter system, regulating phosphate metabolism in the host cell acting as phosphate regulators21. Few studies had been conducted on its genetic diversity, therefore, the information on the genetic diversity was relatively scarce.α diversity analysis of phosphorus metabolism AMGs in different habitats and different host originsChao, Shannon and Simpson diversity indices are common mathematical measure of species alpha diversity in the community. Chao focuses on species richness. Shannon index and Simpson index measure species richness and evenness. Simpson reinforces evenness and Shannon reinforces richness22.Sequences from different habitats, such as Napahai plateau wetland, Pacific Ocean, Lake Baikal, Northeast rice fields, glaciers, and wetlands, were selected for α-diversity analysis (Fig. 4). The genetic diversity indices, such as Chao, Shannon and Simpson, calculated based on the OUT dataset, were used to characterize the alpha diversity. Among them, larger Chao values, smaller Simpson values or larger Shannon values indicate higher genetic diversity. Only at the level of Chao values (Fig. 4A,D,G) and Shannon values (Fig. 4B,E,H), the values of phoH, phoU, and pstS in Napahai plateau wetland were greater than those from other habitats, indicating better heritable, which might be related to the unique geographical location and abundant water resources. The geographical location made it unique and less influenced by external factors, and abundant water resources created a rich biodiversity, thus providing a good genetic environment. From the Simpson values (Fig. 4C,F,I), the values of phoU and pstS genes were smaller than those of other habitats, indicating better inherited. For the phoH gene, the Simpson value was closer in magnitude and lower than those in Antarctic Lake and wetlands, indicating better heritable.Figure 4Plots of genetic diversity indices analysis of phosphorus metabolism AMGs in different habitats, different colors represent different genetic diversity indices. (A, D, G) Represent respectively the Chao values of phoH, phoU, and pstS genes in different habitats. (B, E, H) Represent respectively the Shannon values of phoH, phoU, and pstS genes in different habitats. (C, F, I) Represent respectively the Simpson values of phoH, phoU, and pstS genes in different habitats.Full size imageThree AMGs associated with phosphorus metabolism in Napahai plateau wetland were selected for α-diversity analysis with AMGs sequences from different host origins (Fig. 5). In Fig. 5A, the Chao values of phoH gene from bacteria, phages, uncultured phages and uncultured viruses in Napahai plateau wetland were smaller than those of bacteria, phages, uncultured phages and uncultured viruses, indicating the poor genetic diversity. In addition, compared to the genetic diversity of sequences from other host sources, the genetic diversity of phoH gene from bacteria in Napahai plateau wetland was better. As can be seen in Fig. 5D, G, the Chao values of phoU and pstS genes from bacteria in Napahai plateau wetland were greater than those of other host origins, indicating better genetic diversity, while the Chao values of pstS genes from archaea in Napahai plateau wetland were smaller than those of other host origins, indicating poor genetic diversity.Figure 5Plots of genetic diversity indices analysis of phosphorus metabolism AMGs from different host origins, different colors represent different genetic diversity indices. (A, D, G) Represent respectively the Chao values of phoH, phoU, and pstS genes from different host origins. (B, E, H) Represent respectively the Shannon values of phoH, phoU, and pstS genes from different host origins. (C, F, I) Represent respectively the Simpson values of phoH, phoU, and pstS genes from different host origins.Full size imageThe Shannon value of phoH gene from bacteria in Napahai plateau wetland was smaller than that of bacteria and uncultured viruses, indicating poor diversity, but larger than other host sources, indicating better genetic diversity (Fig. 5B). The Shannon values of phoH gene from phages and uncultured phages in Napahai plateau wetland were lower than those of other host origins, indicating poor diversity. The Shannon value of phoH genes from uncultured viruses in Napahai plateau wetland was 0, probably due to sample size too small to calculate the Shannon value. In Fig. 5E, H, the Chao values of phoU and pstS genes from bacteria in Napahai plateaus wetland were greater than those from other host sources, indicating better diversity, while the Shannon value of pstS gene from archaea in the Napahai plateau wetland was 0, probably small sample size.The Simpson values of phoH genes from phage, uncultured phage and uncultured virus in Napahai plateau wetland were smaller than those of other host origins (except uncultured virus), indicating better diversity. The smaller Simpson values of phoH genes related to fungi, phages, uncultured phages, and viruses indicated better diversity, while the larger Simpson values compared to bacteria, phages, and uncultured viruses indicated poor diversity (Fig. 5C). As can be seen in Figs. 5F,I, the Simpson values of phoU genes from bacteria and pstS genes from bacteria and archaea in Napahai plateau wetland were smaller than those of other host origins, indicating better genetic diversity.Currently, most studies on phosphorus AMGs employed phylogenetic analysis16,23. In contrast, relatively few AMGs associated with phosphorus had been reported based on α-diversity analysis, so it was difficult to obtain specific values of α-diversity indices in other studies.Biogeochemical cycling of AMGs associated with phosphorus metabolism in Napahai plateau wetlandViruses are the gene carriers in susceptible hosts, and AMGs introduced by viruses into new hosts can enhance viral replication and/or influence key microbial metabolic pathways of the biogeochemical cycles24. It is well known that phosphorus is an essential nutrient and plays essential roles in cells25. Phosphorus deficiency leads to restricted cell division, down-regulation of photosynthesis, reduced protein and nitrogen content and chlorophyll synthesis26. To study the effect of AMGs associated with phosphorus metabolism, a phosphorus metabolic pathway containing phoH, phoU and pstS genes was constructed based on metagenomic data (Fig. 6). When phosphorus deficiency occurs in the host, it leads to the expression of phoH, phoU and pstS genes. phoH is a phosphate starvation inducible gene, while pstS acts as a phosphate transport gene and phoU belongs to a phosphate regulatory gene that produces dissolved inorganic phosphorus (DIPs), which then undergoes a series of reactions to produce ATP. The generated ATP becomes PolyP under the action of ppK which encoding polyphosphate kinase, or is used in Calvin cycle to provide energy for Ru5P to produce RuBP, or is used for DNA biosynthesis to provide energy. PolyP is regenerate into DIP with ppX which encoding exopolyphosphatase, and also involves in the biosynthesis process of DNA as Pi to provide phosphate for the nucleic acids synthesis. Thus, phosphorus metabolism of AMGs invoved plays a significant role in the life process of the virus and host. In addition, phoE and ugpQ genes also are identified in Napahai plateau wetland, but their roles in the phosphorus cycling are currently unknown and need further study.Figure 6Biogeochemical cycling of AMGs associated with phosphorus metabolism in Napahai plateau wetland. Red line indicates the process of phosphorus metabolism.Full size imageBased on the phylogenetic and PCoA analyses, we found that the phoH, phoU, and pstS genes all showed unique sequences, which might be drive the microorganisms to produce the phosphorus metabolic pathway in Napahai plateau wetland. Of course, in order to prove this pathway, further validation might be done by metabolomics or metabolic flow method. Furthermore, the phosphorus metabolic pathway was poorly reported, so we could not compare with the phosphorus pathway from other environment to find commonalities and differences. More

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Theropod dinosaurs such as Tarbosaurus bataar grew large or small in a range of ways.Credit: Marco Ansaloni/SPL

A sweeping analysis of shin bones has given researchers a glimpse into how some dinosaurs evolved into mega-beasts such as Tyrannosaurus, and others into smaller, bird-like creatures. The work, published this week in Science1, reveals that dinosaurs used more than one evolutionary trick to become larger — or smaller — over time.Prevailing wisdom held that large-bodied animals are bigger than their smaller-bodied relatives because they grow faster during their most rapid period of growth. That trend holds true for modern animals including birds and mammals — elephants and ostriches grow faster than chihuahuas and sparrows, for example.It’s not the case for all animals. Crocodiles and alligators, for instance, become large because they grow for a long time. But palaeontologists had assumed that for theropod dinosaurs — a group that includes the iconic T. rex and which spawned modern birds — large species got big through rapid growth spurts. “It’s kind of become the established idea in dinosaurs,” says palaeontologist Michael D’Emic at Adelphi University in Garden City, New York.But that’s not what D’Emic found when he sawed into the bones of Majungasaurus, a 7-metre-long T. rex relative that lived 66 million years ago on what is now Madagascar. The speed of growth in dinosaurs is recorded in rings laid down each year in their bones. Instead of seeing wide rings corresponding to a rapid adolescent growth spurt, D’Emic found lots of narrow growth rings, suggesting that Majungasaurus had become large over a prolonged period.“I was very surprised,” he says. The next dinosaur he examined, a similar-sized beast called Ceratasaurus, was the opposite — a big dinosaur that grew fast during its growth spurt, says D’Emic.Bone growth ringsOver a decade, D’Emic and his colleagues amassed bone growth-ring measurements from 42 theropod species to see which strategies led to large and small bodies. They found that 31% of theropod species were larger than their ancestors because of faster growth and 28% because of prolonged growth. Meanwhile, 21% became smaller than their ancestors by shortening their growth spurts, and 19% by slowing growth.The study covered theropod species that lived between 230 million years ago and the end of the Cretaceous period 66 million years ago, when a mass-extinction event wiped out the non-avian dinosaurs. It’s “a huge evolutionary timescale”, to include in an analysis, says Vera Weisbecker, an evolutionary biologist at Flinders University in Adelaide, Australia. “That is really impressive,” she says. “It’s just fascinating that there are so many developmental ways to become big or small.”Palaeontologist Kevin Padian at the University of California, Berkeley, says the analysis is the kind of work that needs to be done, animal group by animal group, to understand how body size evolves.Drivers of changeBut Meike Köhler, an evolutionary palaeobiologist at the Catalan Institution for Research and Advanced Studies in Barcelona, Spain, says the findings are not surprising because previous work has shown a range of growth strategies across animal species. Köhler would like to see an analysis that considers what ecological circumstances influenced how animals changed in size over time.Weisbecker says that the growth strategy used might be related to evolutionary pressures. “If you looked at all the ones with explosive early growth, you might be able to test if they happen to be the ones that are more likely to be predated on, for example,” she says.For each species, the growth strategy that led to its individual body size probably related to its unique environment, says Padian. “It’s not a one-size-fits-all, which is a good thing for us to learn,” he says. “We might have thought that, but they’ve documented it.”D’Emic says he and his team are conducting similar analyses on other groups, including mammals — a group that contains many more species to sample — to see whether the diversity is found in other branches of the evolutionary tree. More