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    Functional diversity of Himalayan bat communities declines at high elevation without the loss of phylogenetic diversity

    Study area and sampling locationsWe conducted this study in Kedarnath Wildlife Sanctuary (30° 25′–30° 41′ N, 78° 55′–79° 22′ E), located in Uttarakhand state in the western Himalayas of India. This sanctuary covers a broad elevational gradient from 1400 to 4000 m above sea level (asl) (Fig. 1), with corresponding changes in habitat types: from Himalayan moist temperate forests dominated by Quercus spp. at low elevations, to sub-alpine forests dominated by Rhododendron spp. and alpine meadows at high elevations34. This sanctuary is known to harbour 26 species of bats35.Figure 1Map of India showing the location of the study area, Kedarnath Wildlife Sanctuary, and the sampling locations within the study area. Elevation is in m asl. The map was created using QGIS (v 3.6.3-Noosa) (QGIS Geographical Information System, www.qgis.org). Please note that the geographical boundaries represented in the map may contain areas considered disputed.Full size imageWe sampled at four locations spanning an elevational gradient of 2200 m. Sampling points within each location were spread across the elevations mentioned in parentheses: Mandal (1500–1800 m), Ansuya (2000–2200 m), Chopta (2700–3000 m) and Tungnath (3300–3700 m) (Fig. 1). Sampling was conducted between late-March and mid-May in 2018 and 2019, starting at lower elevations and then moving to higher elevations. This sampling duration coincides with summer in the Himalaya. To comprehensively sample the bat diversity, we employed a combination of automated ultrasonic recorders and capture sampling using mist-netting. Fieldwork was approved by the Internal Committee for Ethics and Animal Welfare, Institute for Zoo and Wildlife Research (approval no. 2018-06-01), and conducted under a permit issued by the Uttarakhand State Forest Department, Government of India (permit no. 2261/5-6).Sampling strategyFor acoustic sampling, we placed full spectrum passive ultrasonic recorders (SongMeter SM4BAT, Wildlife Acoustics, Maynard, MA, USA) in different habitat types (open, forest edge, and forest) at each elevation (hereafter, “passive recordings”). The recorders were programmed to record bat calls for two consecutive nights at each sampling point, from dusk to dawn (9–10 h/night), using a sample rate of 500 kHz/s, an amplitude threshold of 16 dB and a frequency threshold of 5 kHz. The dominant habitats at Ansuya and Tungnath are montane evergreen forests and alpine meadows respectively, therefore only these habitats were sampled at these elevations. The exact number of sampling points per habitat for each elevation is given in Table S1. On separate days after completing acoustic sampling at a site, we set up nylon and monofilament mist nets of 4, 6 or 9 m length, 16 × 16 and 19 × 19 mesh sizes (Ecotone GOC, Sopot, Poland) for four hours following dusk (starting between 18.30 h in early summer and 19.30 h in late summer). The captured bats were handled and measured following the guidelines of the American Society of Mammalogists36. To further refine identification in light of the paucity of taxonomic knowledge in the region, we collected only one specimen each of taxonomically-challenging species in accordance with our field research permit. We measured body mass (accuracy 0.1 g) using a spring balance (Pesola, Schindellegi, Switzerland), and forearm length (accuracy 0.01 mm) with vernier calipers (Swiss Precision Instruments SPI Inc., Melville, NY, USA). Next, we gently stretched the left wing and placed the live animal perpendicular to the background of a graph sheet of 1 × 1 cm grids. We photographed the outstretched wing using a Nikon D3400 DSLR camera at 55 mm zoom from a distance of about 90 cm. Subsequently, we released the bats and recorded their echolocation calls at a distance of 5 to 10 m using a handheld ultrasonic detector (Anabat Walkabout, Titley Scientific, Brendale, QLD, Australia) and saved them as audio files of .wav format. These recordings (henceforth referred to as “reference recordings”) formed the dataset used to develop a call library for identification.Call classifier and analysis of passive recordingsReference recordings from 2018 and 2019 were labelled using Raven Pro 1.5 (Cornell Lab of Ornithology, Ithaca, NY, USA) to generate a dataset of acoustic parameters for identification. We visualized calls using a spectrogram with Hanning window, size 1024 samples with 95% overlap. From each recording, we selected 10 clear pulses and measured the following parameters: average peak frequency, maximum peak frequency, centre frequency, minimum peak frequency, peak frequency at the start and end of the call, bandwidth at 90% peak amplitude, average entropy, and call duration. All frequency variables were measured in Hz and time variables in ms. We used the peak frequency contour to determine start and end frequencies and also used bandwidth at 90% peak amplitude because higher frequencies attenuate quickly with distance from the emitting bat (causing changes to the bandwidth), and these measures are therefore more reliable in field circumstances. Using this labelled call library as a training dataset, we trained a fine K-nearest neighbours classifier using supervised learning within the ‘Classification Learner’ app in MATLAB (Mathworks, Inc., Natick, MA, USA). We further employed fivefold cross-validation to obtain estimates of the accuracy of each classifier in assigning calls to species. Using these pairwise values of relative accuracy (%), we generated confusion matrices for these classifiers where the species identities were represented in the columns and rows as ‘True’ and ‘Predicted’ classes, respectively. Any species with classification accuracy below 85% was clubbed with possible confusion species into a “sonotype”, to improve accuracy of the classifier in the most conservative way possible (Fig. S4). The complete list of sonotypes and their mean echolocation call parameters is presented in Table 1. The classifier identified these sonotypes with  > 80% accuracy, with the exception of Miniopterus and the Plecotus type B call (which, however, we could manually identify because of their call structures and frequencies). For all subsequent analyses on functional diversity and phylogenetic diversity, we used these sonotypes to ensure accurate identification.Table 1 Trait matrix of the sonotypes in our assemblage (FA in mm; fmaxe, pfc.min, and pfc.max in kHz; Duration in ms).Full size tableNext, we analysed the passive recordings manually in Raven Pro. We labelled calls in subsets of 15 min per hour of the passive recordings. For each hour, the 15-min subsets were in the time windows 0–5 min, 20–25 min and 40–45 min, so as to spread out our sampling window across the hour. Following labelling, we obtained sonotype IDs using the classifier, and then verified them manually by visual comparison to the call library to improve discrimination. For every 5-min interval, we made a presence-absence matrix where 1 indicated the presence of a sonotype and 0 indicated its absence. The number of 5-min intervals in which a sonotype was detected (hereby “acoustic detections”) was summed up for each sampling point. We measured the relative abundance of sonotypes as the proportion of its total number of acoustic detections relative to the total number of acoustic detections of all sonotypes in a given elevational location. The use of such a presence-absence framework is akin to ‘Acoustic Activity Index’37 which represents a relatively less biased index of activity that is less affected by differences in vocal behaviour and echolocation frequencies of different species of bats.Assessing detectabilityTo assess the completeness of our species inventory, we estimated the species richness of each sampling point using the first-order Jackknife Estimator (Jack 1)38. Jack 1 is a nonparametric procedure for estimating species richness using presence or absence of a species in a given plot rather than its abundance39. Mean species detectability was calculated as the ratio of the observed to estimated species richness for different sampling point-year combinations40,41. We then assessed whether this mean species detectability depended on the habitat type, year, and location by fitting a linear model with the above-mentioned variables as fixed factor predictors and the mean detectability as a response. We also determined species-level detectability by following the approach of Kéry and Plattner42. If a sonotype was detected by mistnetting or acoustic sampling in sampling event i, we modelled its probability to be detected in sampling event i + 1. For each sonotype, we fitted a generalized linear mixed-effects model (logit link and binomial error distribution) with detection/non-detection as the response variable, and habitat type, location, and year as the fixed factor predictors. Site and species were included as random intercepts. The significance of the fixed effects was assessed with the Likelihood Ratio Test. This test allows one to choose the best of two nested models by assessing the ratio of their likelihoods. The significance of the random effect (species) was assessed by applying a parametric bootstrap (number simulations = 100) to the model with and without the random effect, using the function bootMer of ‘lme4’ package. In short, a parametric bootstrap consists of fitting the model to the data and bootstrapping the obtained residuals. For these and other statistical analyses we used R version 4.0.2 (R Core Team 2020).Taxonomic diversityWe calculated rarefied incidence-based species richness (SR) and Simpson diversity extrapolated to 50 sampling events (the number of sampling events in Mandal) using the ‘iNEXT’ R package43. The calculations were performed on a sonotype-by-sampling point presence-absence matrix with detections from both acoustic sampling and mistnetting pooled together. In the matrix, columns represented sampling units (Night 1, Night 2 and so on) and rows represented sonotype. By using sonotypes instead of species, we likely underestimated the SR, but this underestimation was uniform across elevations and is unlikely to change the pattern of SR with elevation.Functional diversityOur functional trait matrix (Table 1) comprised seven morphological and acoustic traits involved in guild classification, foraging and micro-habitat preferences (abbreviation followed by units): forearm length (FA, mm), aspect ratio (AR), wing loading (WL, N/m2), tip-shape index (I), echolocation peak frequency/frequency of maximum energy (FmaxE, kHz), minimum and maximum frequencies of the peak frequency contour (pfc.min and pfc.max, kHz) and call duration (D, ms). FA was measured in the field using vernier calipers. We used ImageJ (National Institutes of Health, Bethesda, MD, USA)44 to measure total wing area, areas of hand and arm wings and the wingspan from the standardised wing photos that were taken in the field. We calculated AR, WL, and I from these measurements following the equations given in Norberg and Rayner45. AR and WL both represent parameters that are correlated with flight aerodynamics and behaviour. I is influenced by the shape of the wing tip where values of 1 and above indicate broad, triangular tips, while those below 1 indicate acute wing tips. The four acoustic traits represent the shape of the echolocation call and they were measured from the reference recordings using Raven Pro, as described above.We first calculated the means for each of the seven traits across all species within a sonotype (thus obtaining one average trait value for each sonotype) (Table 1) and then used those to compute four multivariate functional diversity (FD) indices: functional richness (FRic), divergence (FDiv), evenness (FEve)46, and dispersion (FDis)47, using the function dbFD() in the ‘FD’ R package47. Our FD measures are unlikely to be underestimated due to the pooling of species into sonotypes because these species were similar in acoustic and morphological traits. FRic is the convex hull volume of the traits of species present in a community, measured in the multidimensional trait space. This measurement is not weighted by abundance, relative abundance or biomass of the species in the community, but, it is standardised such that it ranges from 0 to 1. FDiv reflects the distribution of abundance across taxa (sonotypes in our case) in the functional space. High FDiv means the taxa with extreme trait values are more abundant in a community whereas low FDiv means that those with the trait values close to the centre of the functional space are more abundant48. FEve, on the other hand, measures the evenness in the abundance distribution of taxa in the functional space. FEve is high when all taxa have similar abundances, and it is low when some functional groups are abundant while others are rare48. Lastly, FDis is measured as the mean distance of all taxa to the abundance-weighted trait community centroid. We performed two sets of analyses: one using the number of mistnet captures as a proxy for relative abundance, and another using the number of detections of different sonotypes in 5-min intervals in the passive recordings as a proxy of relative abundance. We did not pool acoustic detections and mistnet captures as they have inherently different detection probabilities and measure different entities (relative number of detections vs. number of captured individuals). Owing to rhinolophid bats at lower elevations being taxonomically and functionally different from the remaining species pool, we performed another set of FD calculations, excluding the four rhinolophid species and using acoustic detections as relative abundance. One species, Tadarida teniotis was commonly detected at all elevations on acoustic recorders, but we were unable to capture it as it foraged high above the ground, and thus were unable to collect morphological trait data. Additionally, in using acoustic detections as a measure of relative abundance, we had to exclude the non-echolocating pteropodid bat Sphaerias blanfordi which was caught only once at Chopta. Therefore, our FD values are likely systematically underestimated across all elevational communities, which does not affect the comparison of community composition across elevations.Phylogenetic diversityUsing the nexus file of a published phylogeny49, we pruned the tree to represent species in the 14 sonotypes. For each of these types, we chose the species most commonly mist-netted as representative of its group. Published DNA sequences are lacking for some of the species in this region, so we chose their closest relatives from the phylogeny instead. Thus, we made the following replacements: (a) Nyctalus leisleri represented the AMN sonotype, (b) Eptesicus serotinus represented the EH sonotype, (c) Murina aurata for Murina sonotype, (d) Myotis longipes for MS sonotype, (e) Pipistrellus javanicus for MP sonotype, and (f) Plecotus turkmenicus for Plecotus sonotype. After pruning the tree, we calculated three indices of phylogenetic diversity using the ‘picante’ R package50: Faith’s phylogenetic diversity (PD), Mean pairwise distance (MPD) and Mean nearest-taxon distance (MNTD). Faith’s PD is a measure of phylogenetic richness which is obtained by summing the branch lengths of the tree connecting the species in the community. MPD and MNTD measure phylogenetic dispersion of communities; whereas MPD measures the average phylogenetic distance among all the taxa in a community, MNTD measures the same for the nearest neighbouring taxa51. We weighted MPD and MNTD by relative abundance of the sonotypes in each community (like FD, the number of detections in five-minute intervals in the passive recordings was used as a proxy of relative abundance).Null model testingAs FD and PD are strongly correlated to species richness52, we used a null model to assess whether the observed was significantly different than expected due to chance alone. We produced the null distribution of each FD and PD index by randomizing the community matrix 999 times using the ‘independent swap’ method53,54, so as to preserve the species richness at each site and the number of sites in which each species can be found. Our randomization was further constrained by elevation, so that the abundances were randomized among the sampling points within each elevation. The null model allows for calculation of an effect size (difference between the observed value and mean of the null distribution). Given the range of FD and PD values, the effect sizes are not comparable across communities with vastly different species richness55. Therefore, standardized effect sizes (SES) of each index were calculated at each site as the difference between the observed value and the mean of the null distribution, divided by the standard deviation of the null distribution. SES  > 1 and SES  More

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    Cooperative herbivory between two important pests of rice

    Plants and insectsRice (Oryza sativa) cultivar Minghui63 was used in this study. Rice plants were grown in a greenhouse at 27 ± 3 °C with 75 ± 10% RH (relative humidity) and a photoperiod of 16:8 h L:D (light:dark). The cultivation of rice plants followed the same procedure as described previously27. Plants were used for experiments when they were at the tillering stage, which occurred about 44–49 days after sowing.C. suppressalis larvae were reared on an artificial diet as described70. Ten percent honey water solution was provided to supply nutrition for the adults. N. lugens were maintained on a BPH-susceptible rice variety Taichung Native 1 (TN1)38. T. japonicum were obtained from Keyun Industry Co., Ltd (Jiyuan, China). Newly emerged adult wasps were maintained in glass tubes (3.5 cm diameter, 20 cm height) and supplied with 10% honey water solution as a food source and were maintained for at least 6 h to ensure free mating, before females were used for the following experiments. All three species were maintained in climatic chambers at 27 ± 1 °C, 75 ± 5% RH, and a photoperiod of 16:8 h L:D.Performance of caterpillars on insect-infested rice plantsMultiple types of rice plants were prepared: (i) uninfested plants, meaning that potted rice plants remained intact without insect infestation; (ii) SSB-infested plants, each potted rice plant was artificially infested with one 3rd instar SSB larva that had been starved for >3 h for 48 h; (iii) BPH-infested plants, each potted rice plant was artificially infested with a mix of fifteen 3rd and 4th instars BPH nymphs for 48 h; (iv) SSB/BPH-infested plants, each potted rice plant was simultaneously infested with one SSB larva and 15 BPH nymphs for 48 h; (v) SSB → BPH-infested plants, each potted rice plant was artificially infested with one SSB larvae alone for the first 24 h, then 15 BPH nymphs were additionally introduced for another 24 h; (vi) BPH → SSB-infested plants, namely each potted rice plant was artificially infested with 15 BPH nymphs for the first 24 h, then one SSB larvae were additionally introduced for another 24 h. Plant treatments were conducted as described in detail in our previous study27. During herbivory treatment, the uninfested plants were placed in a separate room to avoid possible volatile-mediated interference. During the subsequent bioassays, both SSB caterpillar and BPH nymphs remained in or on the rice plants.Two bioassays were conducted to test the performance of C. suppressalis larvae feeding on differently treated rice plants. The first bioassay included the plant treatments i, ii, iii, and vi, and the second bioassay included the plant treatments i, ii, v, and vi. Three 2-day-old larvae of C. suppressalis were gently introduced onto the middle stem of each rice plant using a soft brush. The infested rice plants were then placed in climatic chambers at 27 ± 1 °C, 75 ± 5% relative humidity, and a photoperiod of 16:8 h L:D. The C. suppressalis larvae were retrieved from the rice plants after 7 days feeding, and they were weighed on a precision balance (CPA2250, Sartorius AG, Germany; readability = 0.01 mg). The mean weight of the three caterpillars on each plant was considered as one biological replicate. The experiment was repeated four times using different batches of plants and herbivores, resulting in a total of 30–46 biological replicates for each treatment.Oviposition-preferences of C. suppressalis females choosing among differently infested rice plantsGreenhouse experimentIn the greenhouse, seven choice tests were conducted with C. suppressalis females including (i) SSB-infested plants versus uninfested plants; (ii) BPH-infested plants versus uninfested plants; (iii) SSB/BPH-infested plants versus uninfested plants; (iv) SSB-infested plants versus BPH-infested plants; (v) SSB-infested plants versus SSB/BPH-infested plants; (vi) BPH-infested plants versus SSB/BPH-infested plants; and (vii) the test in which C. suppressalis females were exposed to all four types of rice plants. The experiments were performed as described in detail by Jiao et al.30. In brief, four potted plants were positioned in the four corners of a cage (80 × 80 × 100 cm) made of 80-mesh nylon nets for each test. For paired comparisons, two potted plants belonging to the same treatment were placed in opposite corners of each age, and in the test with four types of rice plants, each type of plant was positioned in one of the four corners of each cage. Five pairs of freshly emerged moths (less than 1 day) were released in each cage, and a clean Petri dish (9 cm diameter) containing a cotton ball soaked with a 10% honey solution was placed in the center of the cage as food source. After 72 h, the number of individual eggs on each plant were determined. The experiment was conducted in a greenhouse at 27 ± 3 °C, 65 ± 10% RH, and a photoperiod of 16:8 h L:D. Each choice test was repeated with 9–11 times (replicates).Field cage experimentThe oviposition preference of SSB females was further assessed in a field near Langfang City (39.58° N, 116.48° E), China. Four choice tests were conducted: (i) SSB-infested plants versus uninfested plants; (ii) BPH-infested plants versus uninfested plants; (iii) SSB/BPH-infested plants versus uninfested plants; and (iv) SSB/BPH-infested plants versus SSB-infested plants. The treated rice plants were prepared as described above and were transplanted into experimental plots (1.5 × 1.5 m). For each pairwise comparison, six plots of rice plants were covered with a screened cage (8 × 5 × 2.5 m) made of 80-mesh nylon net to prevent moths from entering or escaping. Each of the six plots contained nine rice plants of a particular treatment, with three plots per cage representing the same treatment. Plots were separated by a 1 m buffer and they were alternately distributed in a 3 × 2 grid arrangement in each cage (Supplementary Fig. 4). Approximately 50 mating pairs of newly emerged C. suppressalis adults ( More

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    Seasonal diets supersede host species in shaping the distal gut microbiota of Yaks and Tibetan sheep

    Yak and Tibetan sheep thrive under a co-grazing system on the QTP and/or are fed with the same materials; this offers an excellent opportunity to compare the gut microbiota in different host species which share a similar diet. In addition, the grazing systems on the QTP undergo seasonal diets changes in terms of pasture location and forage composition, especially between winter and summer. This presents a good natural “treatment” which helps vary the diets of the yak and Tibetan sheep populations. In the current study, based on a more substantial sample size than the previous study1, we found that diet and environment (represented by seasons winter and summer) superseded host genetics to the family level. That is to say that the gut microbiota of the two animal species showed convergent adaptation to high altitude and harsh environment in QTP, but this convergence had seasonal diets characteristics. These findings may provide a cautionary note for ongoing efforts to link host genetics to gut microbiota composition and function and would provide some food for thought in the breeding of these two livestock groups.The mammalian gut microbiota is acquired from the environment starting at birth, and its assembly and composition is largely shaped by factors such as age, diet, lifestyle, hygiene, and disease state. Researchers subconsciously believe that host species play a greater role than environmental factors when it comes to shaping gut microbiota, especially when there is a large taxonomical difference between the host species. So far, the vast majority of research have focused on the ruminal ecosystem because the rumen is primary site of feed fermentation15,16,17. It is rare to find studies that directly compare the gut microbiota of different species. However, evidence showed that energetically-important microbial products, including VFA (10–13% of total GIT VFA) are produced in the ruminant distal gut3. Hence, it is important to study the composition of distal gut microbiota of ruminants.In this study, at the phylum level, the gut microbiota composition in both groups of livestock was dominated by Bacteroidetes and Firmicutes, which was in agreement with previous reports concerning the yak18. At the same time our result consistently with other study in dairy cows that two dominated phyla Bacteroidetes and Firmicutes found in fecal samples in different seasons were abundant19,20. Firmicutes and Bacteroidetes are responsible for digestion of carbohydrates and proteins, Members of Bacteroidetes having extremely stronger ability to degrade crystalline cellulose. The previous report showed that intestinal microbiome plays an important role in digestion and absorption of the food, and maintaining animals’ health21,22. Intestinal tracts of the ruminants are rich in symbiotic bacteria that helps the body digest plant fibers23,24. Glycans are processed by the distal gut microbiota, generating biologically significant short-chain fatty acids (SCFAs, predominantly acetate, butyrate, and propionate), which serve as the principal energy source for colonocytes25. Fibers may be involved in the regulation of food intake and energy balance via the SCFA-mediated modulation of the secretion of gut hormones26. The higher abundance of Firmicutes and Bacteroidetes in yak may be associated with high-energy consumption at high altitude18.It is worth noting that, at the family level, the dominant genera (Unclassified Ruminococcaceae, Bacteroidaceae, Unclassified BS11, Unclassified Prevotellaceae, Unclassified Christensenellaceae, CF231, Unclassified Mogibacteriaceae and Unclassified Paraprevotellaceae) in the intestines of yak and Tibetan sheep were more greatly influenced by season than genetics (Fig. 5). This has not previously been accurately identified, which may be because there have been few studies into the gut microbial communities of the yak and Tibetan sheep in QTP. So, to improve their husbandry, it is important in the future to study their microbiota profiles using more precise methods such as 16S full-length sequencing or metagenomic sequencing. Ruminococcaceae is a family of autochthonous and benignspecies that primarily inhabit in the caecum and the colon27. It is known that Ruminococcaceae are common in the rumen and hindgut of ruminants, capable of degrading cellulose and starch28. As a member of short chain fatty acid (SCFA) producers, Ruminococcaceae is considered to be the most important fiber and polysaccharides-degrading bacterium in the intestine of herbivores, and produces large amounts of cellulolytic enzymes, including exoglucanases, endoglucanase, glucosidases and hemicellulase29. The microbial community of Yak and Sheep is greatly influenced by alterations in dietary nutrition, Bacteroidaceae have the ability to degrade complex molecules (polysaccharides, proteins) in the intestine18, which can promote the Yak utilizes grasses as its major source of nutrition, due to shortage of grain and other nutrients. Prevotellaceae is responsible for hemicellulose, pectin and high carbohydrate food digestion30. The higher abundance of these microbes may contribute to gaining more energy, and play vital roles in the process of adaption of the hosts to the harsh natural environment15. Bacteroidales BS11 gut group are specialized to active hemicellulose monomeric sugars (e.g., xylose, fucose, mannose and rhamnose) fermentation and short-chain fatty acid (e.g., acetate and butyrate) production that are vital for ruminant energy31. The Bacteroidales BS11 was positively correlated with some metabolites that are involved in amino acid metabolism and biosynthesis, as well as the metabolism of energy sources, such as starch, sucrose, and galactose32.At the genus level, 5-7N15 was most abundant in winter in both animals, on the contrary, the Provotella was predominate. Here, our results indicated that seasonal diets change superseded variations derived from genetic differences between the host species, even though the yak and Tibetan sheep are very different, both taxonomically and in terms of body size. In summer, the forage grass on the Qinghai-Tibet Plateau is dominated by Agropyron cristatum, Elymus nutans, Festuca ovina, Kobresia humilis, Poa pratensis, Stipa aliena, Kobresia pygmaea, Oxytropis biflora, Saussurea hieracioides, Astragalus arnoldii Hemsl. In winter, the main forage was Brachypodium sylvaticum. Carex crebra, Trisetum spicatum and Bupleurum smithii. Stipa has both high palatability and nutritional value, with a high content of crude protein, crude fat, and nitrogen- free extract, and low levels of crude fiber33. The levels of crude protein, crude fat, and nitrogen-free extracts of Brachypodium sylvaticum. Carex crebra, Trisetum spicatum and Bupleurum smithii were lower than that of Stipa, whereas the content of crude fiber was higher than that of Stipa34. Crude protein is the main nutrient of herbage. Crude fat and nitrogen-free extracts provide heat and energy33.Lopes et al. reported that some OTUs known to be functionally relevant for fiber degradation and host development were shared across the entire gastrointestinal tract and present within the feces35. Microbial diversity increases in the distal segments of the gastrointestinal tract. Microbial fermentation appears to be reestablished in the large intestine, with the proportion of acetate, propionate and butyrate being similar to the rumen.Several explanations for this phenomenon are possible. Firstly, both the yak and Tibetan sheep are ruminants. In herbivores, the gut microbiota is dominated by Firmicutes and Bacteroides, the functions of which are related to cellulose digestion36. Therefore, ruminant microbes could possibly be more similar across species than gut microbes from elsewhere.Secondly, the yaks and Tibetan sheep in our study co-grazed from birth to death. As such, the initial gut microbiota source, responsible for populating the remainder of the gut in the months and years after the initial seeding at birth, would necessarily come from the same environment. It has been established that early life events are critical for gut microbiota development and for shaping the adult microbiota. Lifestyle and diet will further influence the composition and function of the gut microbiota. In our study, the investigated animals shared a very similar lifestyle and obtained their diets from the same source. The results revealed that sheep and yaks presented almost identical gut microbiota compositions in the winter, but by the date of collection of the summer samples they were quite different. The reason for this could be that during summer and summer there is pronounced pastoral grass growth, giving the animals more variety and choice in their diets; it is known, after all, that sheep have different diet preferences to yaks37. However, during the winter, the animals have no option but to eat the same food in order to survive until winter.Thirdly, there could be a convergent evolution of gut microbiomes in yaks and Tibetan sheep due to the extremely harsh environment in high-altitude regions1,38. When compared with their low-altitude relatives, cattle (Bos taurus) and ordinary sheep (Ovis aries), metagenomic analyses revealed significant enrichment in rumen microbial genes involving volatile fatty acid-yielding pathways in yaks and Tibetan sheep, whereas methanogenesis pathways were enriched in the cattle metagenome. Analyses of RNA transcriptomes revealed significant upregulation in 36 genes associated with volatile fatty acid transport and absorption in the ruminal epithelium of yaks and Tibetan sheep. This suggests that, aside from host genetics, long-term exposure to harsh environments has allowed the gut microbiome to adapt in order to boost health and survival. In other words, although yaks and Tibetan sheep are very different genetically, their gut microbiota could be similar due to the selection pressures of the high altitude at which they live. Meanwhile, from our data based on functional gene composition (Fig. S3), it is also worth noting that there were no groups clearly distinguished from one another, although the PERMANOVA results indicated both a host and season effect, with the interaction between them being statistically significant. Though factors such as environment and diet (represented by seasons) can trump host genetics, we could not ignore the interplay of these factors as gut microbes are a very complex community.Winter is the harshest period for the survival of yak and Tibetan sheep. To maintain the survival, it’s best to feed the animals with a high protein content. Furthermore, to get more detailed data in different seasons and various dietary habits of yak and sheep, more study should be assessed about intestinal microbiota by collecting feces. More

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    Ecology, evolution and spillover of coronaviruses from bats

    1.Letko, M., Seifert, S. N., Olival, K. J., Plowright, R. K. & Munster, V. J. Bat-borne virus diversity, spillover and emergence. Nat. Rev. Microbiol. 18, 461–471 (2020). This is a review of the overall diversity of bat-borne coronaviruses and research agenda for enhanced characterization of their zoonotic and pandemic potential.CAS 
    PubMed 

    Google Scholar 
    2.Dobson, B. A. P. et al. Ecology and economics for pandemic prevention. Science 369, 379–381 (2020).CAS 
    PubMed 

    Google Scholar 
    3.Plowright, R. K. et al. Land use-induced spillover: a call to action to safeguard environmental, animal, and human health. Lancet Planet. Heal. 5, e237–e245 (2021).
    Google Scholar 
    4.Shivaprakash, K. N., Sen, S., Paul, S., Kiesecker, J. M. & Bawa, K. S. Mammals, wildlife trade, and the next global pandemic. Curr. Biol. 31, 3671–3677.e3 (2021).CAS 
    PubMed 

    Google Scholar 
    5.Huong, N. Q. et al. Coronavirus testing indicates transmission risk increases along wildlife supply chains for human consumption in Viet Nam, 2013–2014. PLoS ONE 15, e0237129 (2020). This study provides evidence that coronavirus detection increases along the supply chain of rodents destined for human consumption.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Xiao, X., Newman, C., Buesching, C. D., Macdonald, D. W. & Zhou, Z.-M. Animal sales from Wuhan wet markets immediately prior to the COVID-19 pandemic. Sci. Rep. 11, 11898 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    7.Chen, L., Liu, B., Yang, J. & Jin, Q. DBatVir: the database of bat-associated viruses. Database 2014, 1–7 (2014).
    Google Scholar 
    8.Tao, Y. et al. Surveillance of bat coronaviruses in Kenya identifies relatives of human coronaviruses NL63 and 229E and their recombination history. J. Virol. 91, 1–16 (2017).CAS 

    Google Scholar 
    9.Cui, J., Li, F. & Shi, Z. L. Origin and evolution of pathogenic coronaviruses. Nat. Rev. Microbiol. 17, 181–192 (2019).CAS 
    PubMed 

    Google Scholar 
    10.Zhou, P. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273 (2020). This is one of the first studies to discover viruses related to SARS-CoV-2 in wild Rhinolophus spp. bats in China.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Forni, D., Cagliani, R., Clerici, M. & Sironi, M. Molecular evolution of human coronavirus genomes. Trends Microbiol. 25, 35–48 (2017).CAS 
    PubMed 

    Google Scholar 
    12.Zhou, P. et al. Fatal swine acute diarrhoea syndrome caused by an HKU2-related coronavirus of bat origin. Nature 556, 255–258 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    13.Wang, N. et al. Serological evidence of bat SARS-related coronavirus infection in humans, China. Virol. Sin. 33, 104–107 (2018). This study provides evidence of potentially undetected spillovers of bat-associated coronaviruses in rural human populations in China.PubMed 
    PubMed Central 

    Google Scholar 
    14.Li, H. et al. Human-animal interactions and bat coronavirus spillover potential among rural residents in southern China. Biosaf. Heal. 1, 84–90 (2019).
    Google Scholar 
    15.Woo, P. C. Y. et al. Discovery of seven novel mammalian and avian coronaviruses in the genus Deltacoronavirus supports bat coronaviruses as the gene source of Alphacoronavirus and Betacoronavirus and avian coronaviruses as the gene source of Gammacoronavirus and Deltacoronavirus. J. Virol. 86, 3995–4008 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Anthony, S. J. et al. Global patterns in coronavirus diversity. Virus Evol. 3, 1–15 (2017). This is a review of ecological patterns of associations between bats and coronaviruses with information up to 2014.
    Google Scholar 
    17.Li, W. et al. Bats are natural reservoirs of SARS-like coronaviruses. Science 310, 676–679 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Lau, S. K. P. et al. Severe acute respiratory syndrome coronavirus-like virus in Chinese horseshoe bats. Proc. Natl Acad. Sci. USA 102, 14040–14045 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    19.Anthony, S. et al. Coronaviruses in bats from Mexico. J. Gen. Virol. 94, 1028–1038 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.Latinne, A. et al. Origin and cross-species transmission of bat coronaviruses in China. Nat. Commun. 11, 4235 (2020). Using 5 years of surveillance data on coronaviruses in bats in China, the authors show that host switching is common in bat coronaviruses, particularly in Rhinolophus spp.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Ithete, N. L. et al. Close relative of human Middle East respiratory syndrome coronavirus in bat, South Africa. Emerg. Infect. Dis. 19, 1697–1699 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    22.Råberg, L., Graham, A. L. & Read, A. F. Decomposing health: tolerance and resistance to parasites in animals. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 364, 37–49 (2009).PubMed 

    Google Scholar 
    23.Schlottau, K. et al. SARS-CoV-2 in fruit bats, ferrets, pigs, and chickens: an experimental transmission study. Lancet Microbe 1, e218–e225 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Munster, V. J. et al. Replication and shedding of MERS-CoV in Jamaican fruit bats (Artibeus jamaicensis). Sci. Rep. 6, 1–10 (2016).
    Google Scholar 
    25.van Doremalen, N. et al. SARS-like coronavirus WIV1-CoV does not replicate in Egyptian fruit bats (Rousettus aegyptiacus). Viruses 10, 727 (2018).PubMed Central 

    Google Scholar 
    26.Plowright, R. K. et al. Transmission or within-host dynamics driving pulses of zoonotic viruses in reservoir–host populations. PLoS Negl. Trop. Dis. 10, 1–21 (2016).
    Google Scholar 
    27.Jeong, J. et al. Persistent infections support maintenance of a coronavirus in a population of Australian bats (Myotis macropus). Epidemiol. Infect. 145, 2053–2061 (2017).CAS 
    PubMed 

    Google Scholar 
    28.Watanabe, S. et al. Bat coronaviruses and experimental infection of bats, the Philippines. Emerg. Infect. Dis. 16, 1217–1223 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    29.Subudhi, S. et al. A persistently infecting coronavirus in hibernating Myotis lucifugus, the North American little brown bat. J. Gen. Virol. 98, 2297–2309 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Widagdo, W. et al. Tissue distribution of the MERS-coronavirus receptor in bats. Sci. Rep. 7, 1–8 (2017).CAS 

    Google Scholar 
    31.Banerjee, A. et al. Selection of viral variants during persistent infection of insectivorous bat cells with Middle East respiratory syndrome coronavirus. Sci. Rep. 10, 7257 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Wang, M.-N. N. et al. Longitudinal surveillance of SARS-like coronaviruses in bats by quantitative real-time PCR. Virol. Sin. 31, 78–80 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    33.Ge, X. Y. et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature 503, 535–538 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Hu, B. et al. Discovery of a rich gene pool of bat SARS-related coronaviruses provides new insights into the origin of SARS coronavirus. PLoS Pathog. 13, 1–27 (2017).CAS 

    Google Scholar 
    35.Smith, C. Australian bat coronaviruses (The University of Queensland, 2015).36.Baldwin, H. J. Epidemiology and ecology of virus and host: bats and coronaviruses in Ghana, West Africa (Macquarie University & Ulm University, 2015).37.Joffrin, L. et al. Bat coronavirus phylogeography in the Western Indian Ocean. Sci. Rep. 10, 1–11 (2020).
    Google Scholar 
    38.Plowright, R. K. et al. Reproduction and nutritional stress are risk factors for Hendra virus infection in little red flying foxes (Pteropus scapulatus). Proc. R. Soc. B Biol. Sci. 275, 861–869 (2008).
    Google Scholar 
    39.Peel, A. J. et al. Synchronous shedding of multiple bat paramyxoviruses coincides with peak periods of Hendra virus spillover. Emerg. Microbes Infect. 8, 1314–1323 (2019). This study provides evidence of co-circulation of multiple viruses in single and multispecies roosts of flying foxes, with higher diversity of viruses in mixed-species roosts.PubMed 
    PubMed Central 

    Google Scholar 
    40.Wacharapluesadee, S. et al. Longitudinal study of age-specific pattern of coronavirus infection in Lyle’s flying fox (Pteropus lylei) in Thailand. Virol. J. 15, 1–10 (2018).
    Google Scholar 
    41.Lau, S. K. P. et al. Ecoepidemiology and complete genome comparison of different strains of severe acute respiratory syndrome-related Rhinolophus bat coronavirus in China reveal bats as a reservoir for acute, self-limiting infection that allows recombination events. J. Virol. 84, 2808–2819 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Willoughby, A., Phelps, K. & Olival, K. A comparative analysis of viral richness and viral sharing in cave-roosting bats. Diversity 9, 35 (2017).
    Google Scholar 
    43.Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E. & Getz, W. M. Superspreading and the effect of individual variation on disease emergence. Nature 438, 355–359 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Lee, K. A. Linking immune defenses and life history at the levels of the individual and the species. Integr. Comp. Biol. 46, 1000–1015 (2006).CAS 
    PubMed 

    Google Scholar 
    45.Robinson, D. P. & Klein, S. L. Pregnancy and pregnancy-associated hormones alter immune responses and disease pathogenesis. Horm. Behav. 62, 263–271 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Pauly, M. et al. Novel alphacoronaviruses and paramyxoviruses cocirculate with type 1 and severe acute respiratory system (SARS)-related betacoronaviruses in synanthropic bats of Luxembourg. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01326-17 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Drexler, J. F. et al. Amplification of emerging viruses in a bat colony. Emerg. Infect. Dis. 17, 449–456 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.Annan, A. et al. Human betacoronavirus 2c EMC/2012-related viruses in bats, Ghana and Europe. Emerg. Infect. Dis. 19, 456–459 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    49.Montecino-Latorre, D. et al. Reproduction of East-African bats may guide risk mitigation for coronavirus spillover. One Heal. Outlook 2, 2 (2020).
    Google Scholar 
    50.Plowright, R. K., Becker, D. J., McCallum, H. & Manlove, K. R. Sampling to elucidate the dynamics of infections in reservoir hosts. Philos. Trans. R. Soc. B Biol. Sci. https://doi.org/10.1098/rstb.2018.0336 (2019).Article 

    Google Scholar 
    51.Vanalli, C. et al. Within-host mechanisms of immune regulation explain the contrasting dynamics of two helminth species in both single and dual infections. PLoS Comput. Biol. 16, 1–19 (2020).
    Google Scholar 
    52.Ge, X. Y. et al. Coexistence of multiple coronaviruses in several bat colonies in an abandoned mineshaft. Virol. Sin. 31, 31–40 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Chu, D. K. W., Peiris, J. S. M., Chen, H., Guan, Y. & Poon, L. L. M. Genomic characterizations of bat coronaviruses (1A, 1B and HKU8) and evidence for co-infections in Miniopterus bats. J. Gen. Virol. 89, 1282–1287 (2008).CAS 
    PubMed 

    Google Scholar 
    54.Drexler, J. F. et al. Genomic characterization of severe acute respiratory syndrome-related coronavirus in European bats and classification of coronaviruses based on partial RNA-dependent RNA polymerase gene sequences. J. Virol. 84, 11336–11349 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    55.Tong, S. et al. Detection of novel SARS-like and other coronaviruses in bats from Kenya. Emerg. Infect. Dis. 15, 482–485 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    56.Wacharapluesadee, S. et al. Diversity of coronavirus in bats from eastern Thailand emerging viruses. Virol. J. 12, 1–7 (2015).
    Google Scholar 
    57.Valitutto, M. T. et al. Detection of novel coronaviruses in bats in Myanmar. PLoS ONE 15, e0230802 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Anthony, S. J. et al. A strategy to estimate unknown viral diversity in mammals. mBio 4, 289 (2013).
    Google Scholar 
    59.Prada, D., Boyd, V., Baker, M. L., O’Dea, M. & Jackson, B. Viral diversity of microbats within the south west botanical province of Western Australia. Viruses 11, 1–21 (2019).
    Google Scholar 
    60.Seltmann, A. et al. Seasonal fluctuations of astrovirus, but not coronavirus shedding in bats inhabiting human-modified tropical forests. Ecohealth 14, 272–284 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    61.Chu, D. K. W., Poon, L. L. M., Guan, Y. & Peiris, J. S. M. Novel astroviruses in insectivorous Bats. J. Virol. 82, 9107–9114 (2008).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Kemenesi, G. et al. Molecular survey of RNA viruses in Hungarian bats: discovering novel astroviruses, coronaviruses, and caliciviruses. Vector Borne Zoonotic Dis. 14, 846–855 (2014).PubMed 

    Google Scholar 
    63.Rizzo, F. et al. Coronavirus and paramyxovirus in bats from northwest Italy. BMC Vet. Res. 13, 1–11 (2017).
    Google Scholar 
    64.Paskey, A. C. et al. The temporal RNA virome patterns of a lesser dawn bat (Eonycteris spelaea) colony revealed by deep sequencing. Virus Evol. 6, 1–14 (2020).
    Google Scholar 
    65.Davy, C. M. et al. White-nose syndrome is associated with increased replication of a naturally persisting coronaviruses in bats. Sci. Rep. 8, 15508 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    66.Woo, P. C. Y., Lau, S. K. P., Huang, Y. & Yuen, K.-Y. Y. Coronavirus diversity, phylogeny and interspecies jumping. Exp. Biol. Med. 234, 1117–1127 (2009).CAS 

    Google Scholar 
    67.Fehr, A. R. & Perlman, S. in Coronaviruses. Methods in Molecular Biology Vol 1282 (eds Maier, H., Bickerton, E. & Britton, P.) 1–23 (Humana Press, 2015).68.Duffy, S., Shackelton, L. A. & Holmes, E. C. Rates of evolutionary change in viruses: patterns and determinants. Nat. Rev. Genet. 9, 267–276 (2008).CAS 
    PubMed 

    Google Scholar 
    69.Jenkins, G. M., Rambaut, A., Pybus, O. G. & Holmes, E. C. Rates of molecular evolution in RNA viruses: a quantitative phylogenetic analysis. J. Mol. Evol. 54, 156–165 (2002).CAS 
    PubMed 

    Google Scholar 
    70.Eckerle, L. D. et al. Infidelity of SARS-CoV Nsp14-exonuclease mutant virus replication is revealed by complete genome sequencing. PLoS Pathog. 6, 1–15 (2010).
    Google Scholar 
    71.Ogando, N. S. et al. The curious case of the nidovirus exoribonuclease: its role in RNA synthesis and replication fidelity. Front. Microbiol. 10, 1–17 (2019).
    Google Scholar 
    72.Nga, P. T. et al. Discovery of the first insect nidovirus, a missing evolutionary link in the emergence of the largest RNA virus genomes. PLoS Pathog. 7, e1002215 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    73.Smith, E., Blanc, H., Vignuzzi, M. & Denison, M. R. Coronaviruses lacking exoribonuclease activity are susceptible to lethal mutagenesis: evidence for proofreading and potential therapeutics. PLoS Pathog. 9, e1003565 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    74.Martin, L. B. et al. Extreme competence: keystone hosts of infections. Trends Ecol. Evol. 34, 303–314 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    75.Graham, R. L. & Baric, R. S. Recombination, reservoirs, and the modular spike: mechanisms of coronavirus cross-species transmission. J. Virol. 84, 3134–3146 (2010).CAS 
    PubMed 

    Google Scholar 
    76.Letko, M. et al. Adaptive evolution of MERS-CoV to species variation in DPP4. Cell Rep. 24, 1730–1737 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    77.Ermonval, M., Baychelier, F. & Tordo, N. What do we know about how hantaviruses interact with their different hosts? Viruses 8, 223 (2016).PubMed Central 

    Google Scholar 
    78.Su, S. et al. Epidemiology, genetic recombination, and pathogenesis of coronaviruses. Trends Microbiol. 24, 490–502 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    79.Lai, M. M. et al. Recombination between nonsegmented RNA genomes of murine coronaviruses. J. Virol. 56, 449–456 (1985).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    80.Tian, P.-F. et al. Evidence of recombinant strains of porcine epidemic diarrhea virus, United States, 2013. Emerg. Infect. Dis. 20, 1731–1734 (2014).
    Google Scholar 
    81.Terada, Y. et al. Emergence of pathogenic coronaviruses in cats by homologous recombination between feline and canine coronaviruses. PLoS ONE 9, e106534 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    82.Decaro, N. et al. Recombinant canine coronaviruses related to transmissible gastroenteritis virus of swine are circulating in dogs. J. Virol. 83, 1532–1537 (2009).CAS 
    PubMed 

    Google Scholar 
    83.Zhang, Y. et al. Genotype shift in human coronavirus OC43 and emergence of a novel genotype by natural recombination. J. Infect. 70, 641–650 (2015).PubMed 

    Google Scholar 
    84.Pyrc, K., Berkhout, B. & van der hoek, L. in Recent Research in Development of Infection & Immunity 3rd edn 25–48 (Transworld Research Network, 2005).85.Woo, P. C. Y. et al. Phylogenetic and recombination analysis of coronavirus HKU1, a novel coronavirus from patients with pneumonia. Arch. Virol. 150, 2299–2311 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    86.Zhang, X. W., Yap, Y. L. & Danchin, A. Testing the hypothesis of a recombinant origin of the SARS-associated coronavirus. Arch. Virol. 150, 1–20 (2005).CAS 
    PubMed 

    Google Scholar 
    87.Stanhope, M. J., Brown, J. R. & Amrine-Madsen, H. Evidence from the evolutionary analysis of nucleotide sequences for a recombinant history of SARS-CoV. Infect. Genet. Evol. 4, 15–19 (2004).CAS 
    PubMed 

    Google Scholar 
    88.Wang, Y. et al. Origin and possible genetic recombination of the middle east respiratory syndrome coronavirus from the first imported case in China: phylogenetics and coalescence analysis. MBio 6, 1–6 (2015).
    Google Scholar 
    89.Huang, C. et al. A bat-derived putative cross-family recombinant coronavirus with a reovirus gene. PLoS Pathog. 12, 1–25 (2016). This study provides evidence of cross-family recombination between coronaviruses and reoviruses.
    Google Scholar 
    90.Drexler, J. F., Corman, V. M. & Drosten, C. Ecology, evolution and classification of bat coronaviruses in the aftermath of SARS. Antivir. Res. 101, 45–56 (2014).CAS 
    PubMed 

    Google Scholar 
    91.Cui, J. et al. Evolutionary relationships between bat coronaviruses and their hosts. Emerg. Infect. Dis. 13, 1526–1532 (2007).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    92.Davies, N. et al. Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England. Sci 372, eabg3055 (2021).CAS 

    Google Scholar 
    93.Reguera, J., Mudgal, G., Santiago, C. & Casasnovas, J. M. A structural view of coronavirus-receptor interactions. Virus Res. 194, 3–15 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    94.Lim, Y., Ng, Y., Tam, J. & Liu, D. Human coronaviruses: a review of virus–host interactions. Diseases 4, 26 (2016).
    Google Scholar 
    95.Masters, P. S. & Perlman, S. Coronaviridae. Fields Virol. 1, 825–858 (2013).
    Google Scholar 
    96.Letko, M., Marzi, A. & Munster, V. Functional assessment of cell entry and receptor usage for SARS-CoV-2 and other lineage B betacoronaviruses. Nat. Microbiol. 5, 562–569 (2020). This study uses a functional viromics platform to rapidly characterize the zoonotic potential of new coronaviruses on the basis of genome sequences.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    97.van Doremalen, N. et al. Host species restriction of middle east respiratory syndrome coronavirus through its receptor, dipeptidyl peptidase 4. J. Virol. 88, 9220–9232 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    98.Conceicao, C. et al. The SARS-CoV-2 spike protein has a broad tropism for mammalian ACE2 proteins. PLoS Biol. 18, e3001016 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    99.Li, W. et al. Receptor and viral determinants of SARS-coronavirus adaptation to human ACE2. EMBO J. 24, 1634–1643 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    100.Damas, J. et al. Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates. Proc. Natl Acad. Sci. USA 117, 22311–22322 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    101.Shi, J. et al. Susceptibility of ferrets, cats, dogs, and other domesticated animals to SARS–coronavirus 2. Science 1020, 1016–1020 (2020).
    Google Scholar 
    102.Oude Munnink, B. B. et al. Transmission of SARS-CoV-2 on mink farms between humans and mink and back to humans. Science 371, 172–177 (2021). The study provides evidence of spillback of SARS-CoV-2 into mink populations, rapid and widespread transmission, and seeding of mink-associated genetic variants back into humans.CAS 
    PubMed 

    Google Scholar 
    103.Hoffmann, M. et al. Differential sensitivity of bat cells to infection by enveloped RNA viruses: coronaviruses, paramyxoviruses, filoviruses, and influenza viruses. PLoS ONE 8, e72942 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    104.Li, W. et al. Animal origins of the severe acute respiratory syndrome coronavirus: insight from ACE2-S-protein interactions. J. Virol. 80, 4211–4219 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    105.Hou, Y. et al. Angiotensin-converting enzyme 2 (ACE2) proteins of different bat species confer variable susceptibility to SARS-CoV entry. Arch. Virol. 155, 1563–1569 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    106.Boni, M. F. et al. Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic. Nat. Microbiol. 5, 1408–1417 (2020). This study provids phylogenetic evidence that suggests that the ancestral lineages from which SARS-CoV-2 may have originated have circulated undetected in bats for decades.CAS 
    PubMed 

    Google Scholar 
    107.Wells, H. L. et al. The evolutionary history of ACE2 usage within the coronavirus subgenus Sarbecovirus. Virus Evol. 7, 1–22 (2021).
    Google Scholar 
    108.Urbanowicz, R. A. et al. Human adaptation of Ebola virus during the West African outbreak. Cell 167, 1079–1087.e5 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    109.Gupta, A. et al. Extrapulmonary manifestations of COVID-19. Nat. Med. 26, 1017–1032 (2020).CAS 

    Google Scholar 
    110.Hamming, I. et al. Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. J. Pathol. 203, 631–637 (2004).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    111.Lam, T. T. Y. et al. Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins. Nature 583, 282–285 (2020).CAS 
    PubMed 

    Google Scholar 
    112.Martina, B. E. E. et al. SARS virus infection of cats and ferrets. Nature 425, 915 (2003).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    113.Munster, V. J. et al. Respiratory disease in rhesus macaques inoculated with SARS-CoV-2. Nature 585, 268–272 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    114.Hou, Y. J. et al. SARS-CoV-2 reverse genetics reveals a variable infection gradient in the respiratory tract. Cell 182, 429–446.e14 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    115.Menachery, V. D. et al. Trypsin treatment unlocks barrier for zoonotic bat coronavirus infection. J. Virol. 94, 1–15 (2019).
    Google Scholar 
    116.Qian, Z., Dominguez, S. R. & Holmes, K. V. Role of the spike glycoprotein of human Middle East respiratory syndrome coronavirus (MERS-CoV) in virus entry and syncytia formation. PLoS ONE 8, 1–12 (2013).
    Google Scholar 
    117.Belouzard, S., Chu, V. C. & Whittaker, G. R. Activation of the SARS coronavirus spike protein via sequential proteolytic cleavage at two distinct sites. Proc. Natl Acad. Sci. USA 106, 5871–5876 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    118.Barlan, A. et al. Receptor variation and susceptibility to middle east respiratory syndrome coronavirus infection. J. Virol. 88, 4953–4961 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    119.Zheng, Y. et al. Lysosomal proteases are a determinant of coronavirus tropism. J. Virol. 92, 1–14 (2018).
    Google Scholar 
    120.Bertram, S. et al. Cleavage and activation of the severe acute respiratory syndrome coronavirus spike protein by human airway trypsin-like protease. J. Virol. 85, 13363–13372 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    121.Matsuyama, S., Ujike, M., Morikawa, S., Tashiro, M. & Taguchi, F. Protease-mediated enhancement of severe acute respiratory syndrome coronavirus infection. Proc. Natl Acad. Sci. USA 102, 12543–12547 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    122.Yang, Y. et al. Receptor usage and cell entry of bat coronavirus HKU4 provide insight into bat-to-human transmission of MERS coronavirus. Proc. Natl Acad. Sci. USA 111, 12516–12521 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    123.Wacharapluesadee, S. et al. Group C betacoronavirus in bat guano fertilizer, Thailand. Emerg. Infect. Dis. 19, 1349–1351 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    124.Luk, H. K. H., Li, X., Fung, J., Lau, S. K. P. & Woo, P. C. Y. Molecular epidemiology, evolution and phylogeny of SARS coronavirus. Infect. Genet. Evol. 71, 21–30 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    125.Wu, K., Peng, G., Wilken, M., Geraghty, R. J. & Li, F. Mechanisms of host receptor adaptation by severe acute respiratory syndrome coronavirus. J. Biol. Chem. 287, 8904–8911 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    126.Daszak, P., Olival, K. J. & Li, H. A strategy to prevent future epidemics similar to the 2019-nCoV outbreak. Biosaf. Heal. 2, 6–8 (2020).
    Google Scholar 
    127.Tidemann, C. & Vardon, M. Pests, pestilence, pollen and pot roasts: the need for community based management of flying foxes in Australia. Aust. Biol. 10, 77–83 (1997).
    Google Scholar 
    128.Mickleburgh, S., Waylen, K. & Racey, P. Bats as bushmeat: a global review. Oryx 43, 217–234 (2009).
    Google Scholar 
    129.Tuttle, M. D. & Moreno, A. Cave-Dwelling Bats of Northern Mexico: Their Value and Conservation Needs (Bat Conservation International, 2005).130.Rulli, M. C., D’Odorico, P., Galli, N. & Hayman, D. T. S. Land-use change and the livestock revolution increase the risk of zoonotic coronavirus transmission from rhinolophid bats. Nat. Food https://doi.org/10.1038/s43016-021-00285-x (2021).Article 

    Google Scholar 
    131.Mckee, C. D., Islam, A., Luby, S. P., Salje, H. & Hudson, P. J. The ecology of Nipah virus in Bangladesh: a nexus of land use change and opportunistic feeding behavior in bats. Viruses 13, 169 (2020).
    Google Scholar 
    132.Kessler, M. K. et al. Changing resource landscapes and spillover of henipaviruses. Ann. N. Y. Acad. Sci. https://doi.org/10.1111/nyas.13910 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    133.Dighe, A., Jombart, T., Van Kerkhove, M. D. & Ferguson, N. A systematic review of MERS-CoV seroprevalence and RNA prevalence in dromedary camels: implications for animal vaccination. Epidemics 29, 100350 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    134.Hegde, S. T. et al. Using healthcare-seeking behaviour to estimate the number of Nipah outbreaks missed by hospital-based surveillance in Bangladesh. Int. J. Epidemiol. 48, 1219–1227 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    135.Glennon, E. E., Jephcott, F. L., Restif, O. & Wood, J. L. N. Estimating undetected Ebola spillovers. PLoS Negl. Trop. Dis. 13, 1–10 (2019).
    Google Scholar 
    136.Matson, M. J., Chertow, D. S. & Munster, V. J. Delayed recognition of Ebola virus disease is associated with longer and larger outbreaks. Emerg. Microbes Infect. 9, 291–301 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    137.Zheng, B. J. et al. SARS-related virus predating SARS outbreak, Hong Kong. Emerg. Infect. Dis. 10, 176–178 (2004). This study provides serological evidence that populations in Hong Kong sampled in 2001 may have been exposed to SARS-CoV or related viruses in bats or other animals before the first SARS outbreaks.PubMed 
    PubMed Central 

    Google Scholar 
    138.Yu, S. et al. Retrospective serological investigation of severe acute respiratory syndrome coronavirus antibodies in recruits from mainland China. Clin. Diagn. Lab. Immunol. 12, 552–554 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    139.Lloyd-Smith, J. O. et al. Epidemic dynamics at the human-animal interface. Science 326, 1362–1367 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    140.Plowright, R. K. et al. Pathways to zoonotic spillover. Nat. Rev. Microbiol. 15, 502–510 (2017). This study formulates a conceptual model for the multiple layers of ecological and cellular barriers that affect the likelihood of pathogen spillover from animals.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    141.Klompus, S. et al. Cross-reactive antibodies against human coronaviruses and the animal coronavirome suggest diagnostics for future zoonotic spillovers. Sci. Immunol. 6, eabe9950 (2021).PubMed 

    Google Scholar 
    142.Field, H. E. Hendra virus ecology and transmission. Curr. Opin. Virol. 16, 120–125 (2016).PubMed 

    Google Scholar 
    143.Chua, K. B. Nipah virus outbreak in Malaysia. J. Clin. Virol. 26, 265–275 (2003).PubMed 

    Google Scholar 
    144.Azhar, E. I. et al. Evidence for camel-to-human transmission of MERS coronavirus. N. Engl. J. Med. 370, 2499–2505 (2014).CAS 
    PubMed 

    Google Scholar 
    145.Memish, Z. A. et al. Respiratory tract samples, viral load, and genome fraction yield in patients with middle east respiratory syndrome. J. Infect. Dis. 210, 1590–1594 (2014).CAS 
    PubMed 

    Google Scholar 
    146.Buchholz, U. et al. Contact investigation of a case of human novel coronavirus infection treated in a German hospital, October-November 2012. Eurosurveillance 18, 1–7 (2013).
    Google Scholar 
    147.Chu, D. K. W. et al. MERS coronaviruses in dromedary camels, Egypt. Emerg. Infect. Dis. 20, 1049–1053 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    148.Zhou, H. et al. Identification of novel bat coronaviruses sheds light on the evolutionary origins of SARS-CoV-2 and related viruses. Cell 184, 4380–4391.e14 (2021). This study reports the discovery of additional coronaviruses related to SARS-CoV-2 in Rhinolophus spp. and use of ecological modelling to highlight areas of southern China and South-East Asia as hotspots of Rhinolophus species diversity.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    149.Larsen, H. D. et al. Preliminary report of an outbreak of SARS-CoV-2 in mink and mink farmers associated with community spread, Denmark, June to November 2020. Eur. Surveill. 26, 2100009 (2021).CAS 

    Google Scholar 
    150.Bertzbach, L. D. et al. SARS-CoV-2 infection of Chinese hamsters (Cricetulus griseus) reproduces COVID-19 pneumonia in a well-established small animal model. Transbound. Emerg. Dis. 68, 1075–1079 (2021).CAS 
    PubMed 

    Google Scholar 
    151.Fagre, A. et al. SARS-CoV-2 infection, neuropathogenesis and transmission among deer mice: Implications for spillback to New World rodents. PLoS Pathog. 17, e1009585 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    152.Imai, M. et al. Syrian hamsters as a small animal model for SARS-CoV-2 infection and countermeasure development. Proc. Natl Acad. Sci. USA 117, 16587–16595 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    153.Sia, S. F. et al. Pathogenesis and transmission of SARS-CoV-2 in golden hamsters. Nature 583, 834–838 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    154.Halfmann, P. J. et al. Transmission of SARS-CoV-2 in domestic cats. N. Engl. J. Med. 383, 592–594 (2020).PubMed 

    Google Scholar 
    155.Griffin, B. D. et al. SARS-CoV-2 infection and transmission in the North American deer mouse. Nat. Commun. 12, 1–10 (2021).
    Google Scholar 
    156.Palmer, M. V. et al. Susceptibility of white-tailed deer (Odocoileus virginianus) to SARS-CoV-2. J. Virol. 95, e00083–21 (2021).CAS 
    PubMed Central 

    Google Scholar 
    157.Plowright, R. K. & Hudson, P. J. From protein to pandemic: the transdisciplinary approach needed to prevent spillover and the next pandemic. Viruses 13, 1298 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    158.Obameso, J. O. et al. The persistent prevalence and evolution of cross-family recombinant coronavirus GCCDC1 among a bat population: a two-year follow-up. Sci. China Life Sci. 60, 1357–1363 (2017). This study provides evidence of coronavirus evolution in a longitudinally sampled population of bats.PubMed 
    PubMed Central 

    Google Scholar 
    159.Lazov, C. et al. Detection and characterization of distinct alphacoronaviruses in five different bat species in Denmark. Viruses 10, 486 (2018).PubMed Central 

    Google Scholar 
    160.Hu, D. et al. Genomic characterization and infectivity of a novel SARS-like coronavirus in Chinese bats. Emerg. Microbes Infect. 7, 1–10 (2018).
    Google Scholar 
    161.Pepin, K. M., Lass, S., Pulliam, J. R. C., Read, A. F. & Lloyd-Smith, J. O. Identifying genetic markers of adaptation for surveillance of viral host jumps. Nat. Rev. Microbiol. 8, 802–813 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    162.Hemida, M. G. et al. Coronavirus infections in horses in Saudi Arabia and Oman. Transbound. Emerg. Dis. 64, 2093–2103 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    163.Zhuang, Q. et al. Surveillance and taxonomic analysis of the coronavirus dominant in pigeons in China. Transbound. Emerg. Dis. 67, 1981–1990 (2020).CAS 

    Google Scholar 
    164.O’Brien, S. J. et al. Genetic basis for species vulnerability in the cheetah. Science 227, 1428–1434 (1985).PubMed 

    Google Scholar 
    165.Herrewegh, A. A. P. M., Smeenk, I., Horzinek, M. C., Rottier, P. J. M. & de Groot, R. J. Feline coronavirus type II strains 79-1683 and 79-1146 originate from a double recombination between feline coronavirus type I and canine coronavirus. J. Virol. 72, 4508–4514 (1998).CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Transpiration rates of red maple (Acer rubrum L.) differ between management contexts in urban forests of Maryland, USA

    1.Askarizadeh, A. et al. From rain tanks to catchments: Use of low-impact development to address hydrologic symptoms of the urban stream syndrome. Environ. Sci. Technol. 49, 11264–11280 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    2.Shuster, W. D., Bonta, J., Thurston, H., Warnemuende, E. & Smith, D. R. Impacts of impervious surface on watershed hydrology: A review. Urban Water J. 2, 263–275 (2005).
    Google Scholar 
    3.Walsh, C. J. et al. The urban stream syndrome: Current knowledge and the search for a cure. J. N. Am. Benthol. Soc. 24, 706–723 (2005).
    Google Scholar 
    4.US EPA. What is Green Infrastructure? US EPA. https://www.epa.gov/green-infrastructure/what-green-infrastructure (2015).5.Hoover, F. A. & Hopton, M. E. Developing a framework for stormwater management: Leveraging ancillary benefits from urban greenspace. Urban Ecosyst. 22, 1139–1148 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    6.Zölch, T., Henze, L., Keilholz, P. & Pauleit, S. Regulating urban surface runoff through nature-based solutions—An assessment at the micro-scale. Environ. Res. 157, 135–144 (2017).PubMed 

    Google Scholar 
    7.Konijnendijk, C. C., Ricard, R. M., Kenney, A. & Randrup, T. B. Defining urban forestry—A comparative perspective of North America and Europe. Urban For. Urban Green. 4, 93–103 (2006).
    Google Scholar 
    8.Berland, A. et al. The role of trees in urban stormwater management. Landsc. Urban Plan. 162, 167–177 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    9.Bartens, J., Day, S. D., Harris, J. R., Dove, J. E. & Wynn, T. M. Can urban tree roots improve infiltration through compacted subsoils for stormwater management?. J. Environ. Qual. 37, 2048–2057 (2008).CAS 
    PubMed 

    Google Scholar 
    10.Geronimo, F. K. F., Maniquiz-Redillas, M. C., Tobio, J. A. S. & Kim, L. H. Treatment of suspended solids and heavy metals from urban stormwater runoff by a tree box filter. Water Sci. Technol. 69, 2460–2467 (2014).CAS 
    PubMed 

    Google Scholar 
    11.Jayasooriya, V. M. & Ng, A. W. M. Tools for modeling of stormwater management and economics of green infrastructure practices: A review. Water Air. Soil Pollut. 225, 2055 (2014).ADS 

    Google Scholar 
    12.Keeley, M. et al. Perspectives on the use of green infrastructure for stormwater management in Cleveland and Milwaukee. Environ. Manag. 51, 1093–1108 (2013).ADS 

    Google Scholar 
    13.Dhakal, K. P. & Chevalier, L. R. Urban stormwater governance: The need for a paradigm shift. Environ. Manag. 57, 1112–1124 (2016).ADS 

    Google Scholar 
    14.Dhakal, K. P. & Chevalier, L. R. Managing urban stormwater for urban sustainability: Barriers and policy solutions for green infrastructure application. J. Environ. Manag. 203, 171–181 (2017).
    Google Scholar 
    15.Tzoulas, K. et al. Promoting ecosystem and human health in urban areas using Green Infrastructure: A literature review. Landsc. Urban Plan. 81, 167–178 (2007).
    Google Scholar 
    16.Kuehler, E., Hathaway, J. & Tirpak, A. Quantifying the benefits of urban forest systems as a component of the green infrastructure stormwater treatment network. Ecohydrology 10, e1813 (2017).
    Google Scholar 
    17.Law, N. L. & Hanson, J. Recommendations of the Expert Panel to Define BMP Effectiveness for Urban Tree Canopy Expansion. Center for Watershed Protection and Chesapeake Stormwater Network. 236. https://owl.cwp.org/mdocs-posts/recommendations-of-the-expert-panel-to-define-bmp-effectiveness-forurban-tree-canopy-expansion/ (Ellicott City, MD, 2016).18.Phillips, T. H., Baker, M. E., Lautar, K., Yesilonis, I. & Pavao-Zuckerman, M. A. The capacity of urban forest patches to infiltrate stormwater is influenced by soil physical properties and soil moisture. J. Environ. Manag. 246, 11–18 (2019).
    Google Scholar 
    19.Zipper, S. C., Schatz, J., Kucharik, C. J. & Loheide, S. P. Urban heat island-induced increases in evapotranspirative demand. Geophys. Res. Lett. 44, 873–881 (2017).ADS 

    Google Scholar 
    20.Riikonen, A., Järvi, L. & Nikinmaa, E. Environmental and crown related factors affecting street tree transpiration in Helsinki, Finland. Urban Ecosyst. 19, 1693–1715 (2016).
    Google Scholar 
    21.Asawa, T., Kiyono, T. & Hoyano, A. Continuous measurement of whole-tree water balance for studying urban tree transpiration. Hydrol. Process. 31, 3056–3068 (2017).ADS 

    Google Scholar 
    22.Hagishima, A., Narita, K. & Tanimoto, J. Field experiment on transpiration from isolated urban plants. Hydrol. Process. 21, 1217–1222 (2007).ADS 

    Google Scholar 
    23.Moriwaki, R. & Kanda, M. Seasonal and diurnal fluxes of radiation, heat, water vapor, and carbon dioxide over a suburban area. J. Appl. Meteorol. 1988–2005(43), 1700–1710 (2004).
    Google Scholar 
    24.Spronken-Smith, R. A., Oke, T. R. & Lowry, W. P. Advection and the surface energy balance across an irrigated urban park. Int. J. Climatol. 20, 1033–1047 (2000).
    Google Scholar 
    25.Giraldo, M. A., Jackson, P. & Van-Horne, W. Suburban Forest Change and Vegetation Water Dynamics in Atlanta, USA. Southeast. Geogr. 55, 193–212 (2015).
    Google Scholar 
    26.Peters, E. B., McFadden, J. P. & Montgomery, R. A. Biological and environmental controls on tree transpiration in a suburban landscape. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2009JG001266 (2010).Article 

    Google Scholar 
    27.Bhaskar, A. S., Hogan, D. M. & Archfield, S. A. Urban base flow with low impact development. Hydrol. Process. 30, 3156–3171 (2016).ADS 

    Google Scholar 
    28.Peters, E. B., Hiller, R. V. & McFadden, J. P. Seasonal contributions of vegetation types to suburban evapotranspiration. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2010JG001463 (2011).Article 

    Google Scholar 
    29.Zhou, W., Wang, J. & Cadenasso, M. L. Effects of the spatial configuration of trees on urban heat mitigation: A comparative study. Remote Sens. Environ. 195, 1–12 (2017).ADS 

    Google Scholar 
    30.McPherson, E. G. Urban forestry: The final frontier?. J. For. 101, 20–25 (2003).
    Google Scholar 
    31.Lefsky, M. A. & McHale, M. R. Volume estimates of trees with complex architecture from terrestrial laser scanning. J. Appl. Remote Sens. 2, 023521 (2008).
    Google Scholar 
    32.Nowak, D.J. Atmospheric carbon dioxide reduction by Chicago’s urban forest. In Chicago’s urban forest ecosystem: Results of the Chicago urban forest climate project.(eds. McPherson, E. G., Nowak, D. J. & Rowntree, R. A.). 83–94 (Gen. Tech. Rep. NE-186. Radnor, PA: U.S. Department of Agriculture, Forest Service, 1994)33.Pataki, D. E., McCarthy, H. R., Litvak, E. & Pincetl, S. Transpiration of urban forests in the Los Angeles metropolitan area. Ecol. Appl. 21, 661–677 (2011).PubMed 

    Google Scholar 
    34.Yılmaz, S., Toy, S., Irmak, M. A. & Yilmaz, H. Determination of climatic differences in three different land uses in the city of Erzurum, Turkey. Build. Environ. 42, 1604–1612 (2007).
    Google Scholar 
    35.Nowak, D. J., Stevens, J. C., Sisinni, S. M. & Luley, C. J. Effects of urban tree management and species selection on atmospheric carbon dioxide. J. Arboric. 28(3), 113–122 (2002).
    Google Scholar 
    36.Nowak, D. J. et al. A ground-based method of assessing urban forest structure and ecosystem services. Aboricult. Urban For. 34(6), 347–358 (2008).
    Google Scholar 
    37.Zipperer, W. C., Sisinni, S. M., Pouyat, R. V. & Foresman, T. W. Urban tree cover: An ecological perspective. Urban Ecosyst. 1, 229–246 (1997).
    Google Scholar 
    38.Oke, T. R. Boundary Layer Climates (Routledge, 1987).
    Google Scholar 
    39.McCarthy, H. R. & Pataki, D. E. Drivers of variability in water use of native and non-native urban trees in the greater Los Angeles area. Urban Ecosyst. 13, 393–414 (2010).
    Google Scholar 
    40.MacFarlane, D. W. & Kane, B. Neighbour effects on tree architecture: functional trade-offs balancing crown competitiveness with wind resistance. Funct. Ecol. 31, 1624–1636 (2017).
    Google Scholar 
    41.Day, S. D., Wiseman, P. E., Dickinson, S. B. & Harris, J. R. Contemporary concepts of root system architecture of urban trees. Arboric. Urban For. 36, 149–159 (2010).
    Google Scholar 
    42.Harrison, J. L., Blagden, M., Green, M. B., Salvucci, G. D. & Templer, P. H. Water sources for red maple trees in a northern hardwood forest under a changing climate. Ecohydrology 13, e2248 (2020).
    Google Scholar 
    43.Marchionni, V. et al. Groundwater buffers drought effects and climate variability in urban reserves. Water Resour. Res. 56, e2019WR026192 (2020).ADS 

    Google Scholar 
    44.Chen, L. et al. Biophysical control of whole tree transpiration under an urban environment in Northern China. J. Hydrol. 402, 388–400 (2011).ADS 

    Google Scholar 
    45.Oogathoo, S., Houle, D., Duchesne, L. & Kneeshaw, D. Vapour pressure deficit and solar radiation are the major drivers of transpiration of balsam fir and black spruce tree species in humid boreal regions, even during a short-term drought. Agric. For. Meteorol. 291, 108063 (2020).ADS 

    Google Scholar 
    46.Rodríguez-Gamir, J., Primo-Millo, E. & Forner-Giner, M. Á. An integrated view of whole-tree hydraulic architecture. Does stomatal or hydraulic conductance determine whole tree transpiration?. PLoS ONE 11, e0155246 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    47.Rogiers, S. Y., Greer, D. H., Hutton, R. J. & Clarke, S. J. Transpiration efficiency of the grapevine cv. Semillon is tied to VPD in warm climates. Ann. Appl. Biol. 158, 106–114 (2011).
    Google Scholar 
    48.Tirpak, R. A., Hathaway, J. M. & Franklin, J. A. Evaluating the influence of design strategies and meteorological factors on tree transpiration in bioretention suspended pavement practices. Ecohydrology 11, e2037 (2018).
    Google Scholar 
    49.Fair, B. A., Metzger, J. D. & Vent, J. Characterization of physical, gaseous, and hydrologic properties of compacted subsoil and its effects on growth and transpiration of two maples grown under greenhouse conditions. Arboric. Urban For. 38, 151–159 (2012).
    Google Scholar 
    50.Kjelgren, R. K. & Clark, J. R. Growth and water relations of Liquidambar styraciflua L. in an urban park and plaza. Trees 7, 195–201 (1993).
    Google Scholar 
    51.Larcher, W. Physiological Plant Ecology: Ecophysiology and Stress Physiology of Functional Groups (Springer, 2003).
    Google Scholar 
    52.Wullschleger, S. D., Wilson, K. B. & Hanson, P. J. Environmental control of whole-plant transpiration, canopy conductance and estimates of the decoupling coefficient for large red maple trees. Agric. For. Meteorol. 104, 157–168 (2000).ADS 

    Google Scholar 
    53.Band, L., Nowak, D., Yang, Y., Endreny, T. & Wang, J. Modeling in the Chesapeake Bay Watershed: effects of trees on stream flow in the Chesapeake Bay. Rep. For. Serv. Agreem. No07­CO‐11242300‐145 (2010).54.Goddard, H. C. Cap and trade for stormwater management. In Economic Incentives for Stormwater Control (ed. Thurston, H.) 211–232 (CRC Press, 2012).
    Google Scholar 
    55.Blanken, P. D. et al. Energy balance and canopy conductance of a boreal aspen forest: Partitioning overstory and understory components. J. Geophys. Res. Atmos. 102, 28915–28927 (1997).ADS 

    Google Scholar 
    56.Wullschleger, S. D., Hanson, P. J. & Todd, D. E. Transpiration from a multi-species deciduous forest as estimated by xylem sap flow techniques. For. Ecol. Manag. 143, 205–213 (2001).
    Google Scholar 
    57.USDA Forest Service. Baltimore Cooperating Experimental Forest – Northern Research Station – USDA Forest Service. https://www.nrs.fs.fed.us/ef/locations/md/baltimore/ (2016).58.NOAA. Find a Station | Data Tools | Climate Data Online (CDO) | National Climatic Data Center (NCDC). https://www.ncdc.noaa.gov/cdo-web/datatools/findstation (2007).59.Campbell, G. S. & Norman, J. An Introduction to Environmental Biophysics (Springer, 2012).
    Google Scholar 
    60.Granier, A. Evaluation of transpiration in a Douglas-fir stand by means of sap flow measurements. Tree Physiol. 3, 309–320 (1987).CAS 
    PubMed 

    Google Scholar 
    61.Lu, P. A direct method for estimating the average sap flux density using a modified Granier measuring system. Funct. Plant Biol. 24, 701–705 (1997).
    Google Scholar 
    62.Granier, A. Une nouvelle méthode pour la mesure du flux de sève brute dans le tronc des arbres. Ann. Sci. For. 42, 193–200 (1985).
    Google Scholar 
    63.Oishi, A. C., Hawthorne, D. A. & Oren, R. Baseliner: An open-source, interactive tool for processing sap flux data from thermal dissipation probes. SoftwareX 5, 139–143 (2016).ADS 

    Google Scholar 
    64.Bates, D. M. & Pinheiro, J. C. Linear and nonlinear mixed-effects models. Conf. Appl. Stat. Agric. https://doi.org/10.4148/2475-7772.1273 (1998).Article 

    Google Scholar 
    65.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).
    Google Scholar 
    66.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. Nlme: Linear and nonlinear mixed effects models (R package version 3.1. 140)[Computer software]. (2019).67.Lenth, R. emmeans: Estimated marginal means, aka least-squares means. R package 1(3), 4 (2019).
    Google Scholar  More

  • in

    Interploidy gene flow involving the sexual-asexual cycle facilitates the diversification of gynogenetic triploid Carassius fish

    1.Muller, H. J. The relation of recombination to mutational advance. Mutat. Res. Mol. Mech. Mutagen. 1, 2–9 (1964).
    Google Scholar 
    2.Maynard Smith, J. The Evolution of Sex (Cambridge University Press, 1978).
    Google Scholar 
    3.Avise, J. C. Clonality (Oxford University Press, 2008).
    Google Scholar 
    4.Hamilton, W. D., Axelrod, R. & Tanese, R. Sexual reproduction as an adaptation to resist parasites (A review). Proc. Natl. Acad. Sci. USA 87, 3566–3573 (1990).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Lynch, M. & Gabriel, W. Mutation load and the survival of small populations. Evolution 44, 1725 (1990).PubMed 

    Google Scholar 
    6.Schurko, A. M., Neiman, M. & Logsdon, J. M. Signs of sex: what we know and how we know it. Trends Ecol. Evol. 24, 208–217 (2009).PubMed 

    Google Scholar 
    7.Verduijn, M. H., Van Dijk, P. J. & Van Damme, J. M. M. The role of tetraploids in the sexual-asexual cycle in dandelions (Taraxacum). Heredity 93, 390–398 (2004).CAS 
    PubMed 

    Google Scholar 
    8.D’Souza, T. G., Storhas, M., Schulenburg, H., Beukeboom, L. W. & Michiels, N. K. Occasional sex in an ‘asexual’ polyploid hermaphrodite. Proc. R. Soc. B Biol. Sci. 271, 1001–1007 (2004).
    Google Scholar 
    9.Schartl, M. et al. Incorporation of subgenomic amounts of DNA as compensation for mutational load in a gynogenetic fish. Nature 373, 68–71 (1995).ADS 

    Google Scholar 
    10.Bogart, J. P., Bi, K., Fu, J., Noble, D. W. A. & Niedzwiecki, J. Unisexual salamanders (genus Ambystoma) present a new reproductive mode for eukaryotes. Genome 50, 119–136 (2007).CAS 
    PubMed 

    Google Scholar 
    11.Hedtke, S. M., Glaubrecht, M. & Hillis, D. M. Rare gene capture in predominantly androgenetic species. Proc. Natl. Acad. Sci. USA 108, 9520–9524 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Warren, W. C. et al. Clonal polymorphism and high heterozygosity in the celibate genome of the Amazon molly. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-018-0473-y (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    13.Flot, J. F. et al. Genomic evidence for ameiotic evolution in the bdelloid rotifer Adineta vaga. Nature 500, 453–457 (2013).ADS 
    CAS 
    PubMed 

    Google Scholar 
    14.Dawley, R. M. & Bogart, J. P. Evolution and Ecology of Unisexual Vertebrates. (Albany, University of the State of New York, State Education Department, New York State Museum, 1989).15.Avise, J. C. Evolutionary perspectives on clonal reproduction in vertebrate animals. Proc. Natl. Acad. Sci. USA 112, 8867–8873 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Stöck, M. et al. Sex chromosomes in meiotic, hemiclonal, clonal and polyploid hybrid vertebrates: Along the ‘extended speciation continuum’. Philos. Trans. R. Soc. B Biol. Sci. 376, 20200103 (2021).17.Fujita, M. K., Singhal, S., Brunes, T. O. & Maldonado, J. A. Evolutionary Dynamics and Consequences of Parthenogenesis in Vertebrates. Annu. Rev. Ecol. Evol. Syst. 51, 191–214 (2020).
    Google Scholar 
    18.Lehtonen, J., Schmidt, D. J., Heubel, K. & Kokko, H. Evolutionary and ecological implications of sexual parasitism. Trends Ecol. Evol. 28, 297–306 (2013).PubMed 

    Google Scholar 
    19.Hosoya, K. Fishes of Japan with pictorial keys to the species, English edn. in (ed. Nakabo, T.) 308–309, 1813–1814 (Tokai University Press, 2013).20.Kobayashi, H., Kawashima, J. & Takeuchi, N. Comparative chromosome studies in the genus Carassius expecially with a finding of polyploidy in the ginbuna (C. auratus langsdorfi). Jpn. J. Ichthyol. 17, 153–160 (1970).
    Google Scholar 
    21.Shimizu, Y., Oshiro, T. & Sakaizumi, M. Electrophoretic studies of diploid, triploid, and tetraploid forms of the Japanese silver crucian carp, Carassius auratus langsdorfii. Jpn. J. Ichthyol. 40, 65–75 (1993).
    Google Scholar 
    22.Eschmeyer, W. N., Fricke, R. & van der Laan, R. Catalog of Fishes: Genera, Species, References. (2017). http://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp.23.Mishina, T. et al. Molecular identification of species and ploidy of Carassius fishes in Lake Biwa, using mtDNA and microsatellite multiplex PCRs. Ichthyol. Res. 61, 169–175 (2014).
    Google Scholar 
    24.Iguchi, K., Yamamoto, G., Matsubara, N. & Nishida, M. Morphological and genetic analysis of fish of a Carassius complex (Cyprinidae) in Lake Kasumigaura with reference to the taxonomic status of two all-female triploid morphs. Biol. J. Linn. Soc. 79, 351–357 (2003).
    Google Scholar 
    25.Ohara, K., Ariyoshi, T., Sumida, E. & Taniguchi, N. Clonal diversity in the Japanese silver crucian carp, Carassius langsdorfii inferred from genetic markers. Zoolog. Sci. 20, 797–804 (2003).PubMed 

    Google Scholar 
    26.Takada, M. et al. Biogeography and evolution of the Carassius auratus-complex in East Asia. BMC Evol. Biol. 10, 7 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    27.Luo, J. et al. Tempo and mode of recurrent polyploidization in the Carassius auratus species complex (Cypriniformes, Cyprinidae). Heredity 112, 415–427 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Murakami, M., Matsuba, C. & Fujitani, H. Characterization of DNA markers isolated from the gynogenetic triploid ginbuna (Carassius auratus langsdorfi) by representational difference analysis. Aquaculture 208, 59–68 (2002).CAS 

    Google Scholar 
    29.Cao, L. et al. Evolutionary dynamics of 18S and 5S rDNA in autotriploid Carassius auratus. Gene 737, 144433 (2020).CAS 
    PubMed 

    Google Scholar 
    30.Yahara, T. Evolution of agamospermous races in Boehmeria and Eupatorium. Plant Species Biol. 5, 183–196 (1990).
    Google Scholar 
    31.Li, C., Ortí, G., Zhang, G. & Lu, G. A practical approach to phylogenomics: the phylogeny of ray-finned fish (Actinopterygii) as a case study. BMC Evol. Biol. 7, 44 (2007).PubMed 
    PubMed Central 

    Google Scholar 
    32.Yue, G. H. & Orban, L. Polymorphic microsatellites from silver crucian carp (Carassius auratus gibelio Bloch) and cross-amplification in common carp (Cyprinus carpio L.). Mol. Ecol. Notes 2, 534–536 (2002).CAS 

    Google Scholar 
    33.Takeshima, H. et al. Rapid and effective isolation of candidate sequences for development of microsatellite markers in 30 fish species by using kit-based target capture and multiplexed parallel sequencing. Conserv. Genet. Resour. 9, 479–490 (2017).
    Google Scholar 
    34.Gao, Y. et al. Quaternary palaeoenvironmental oscillations drove the evolution of the Eurasian Carassius auratus complex (Cypriniformes, Cyprinidae). J. Biogeogr. 39, 2264–2278 (2012).
    Google Scholar 
    35.Konishi, S. & Yoshikawa, S. Immigration times of the two proboscidean species, Stegodon orientalis and Palaeoloxodon naumanni, into the Japanese Islands and the formation of land bridge. Earth Sci. (Chikyu Kagaku) 53, 125–134 (1999).
    Google Scholar 
    36.Kitamura, A., Takano, O., Takata, H. & Omote, H. Late pliocene-early pleistocene paleoceanographic evolution of the Sea of Japan. Palaeogeogr. Palaeoclimatol. Palaeoecol. 172, 81–98 (2001).
    Google Scholar 
    37.Dong, J., Murakami, M., Fujimoto, T., Yamaha, E. & Arai, K. Genetic characterization of the progeny of a pair of the tetraploid silver crucian carp Carassius auratus langsdorfii. Fish. Sci. 79, 935–941 (2013).CAS 

    Google Scholar 
    38.Murakami, M. & Fujitani, H. Polyploid-specific repetitive DNA sequences from triploid ginbuna (Japanese silver crucian carp, Carassius auratus langsdorfi). Genes Genet. Syst. 72, 107–113 (1997).CAS 
    PubMed 

    Google Scholar 
    39.Mada, Y., Miyagawa, M., Hayashi, T., Umino, T. & Arai, K. Production of tetraploids by introduction of sperm nucleus into the eggs of gynogenetic triploid ginbuna Carasius langsdorfii. Aquac. Sci. 49, 103–112 (2001).CAS 

    Google Scholar 
    40.Alves, M. J., Coelho, M. M. & Collares-Pereira, M. J. Evolution in action through hybridisation and polyploidy in an Iberian freshwater fish: A genetic review. Genetica 111, 375–385 (2001).CAS 
    PubMed 

    Google Scholar 
    41.Collares-Pereira, M. J., Matos, I., Morgado-Santos, M. & Coelho, M. M. Natural pathways towards polyploidy in animals: The Squalius alburnoides fish complex as a model system to study genome size and genome reorganization in polyploids. Cytogenet. Genome Res. 140, 97–116 (2013).CAS 
    PubMed 

    Google Scholar 
    42.Lafond, J., Hénault, P., Leung, C. & Angers, B. Unexpected oogenic pathways for the triploid fish chrosomus eos-neogaeus. J. Hered. 110, 370–377 (2019).CAS 
    PubMed 

    Google Scholar 
    43.Gauze, G. F. The Struggle for Existence (The Williams & Wilkins Company, 1934).
    Google Scholar 
    44.Vrijenhoek, R. C. Ecological differentiation among clones: the frozen niche variation model. in Population Biology and Evolution (eds. Wöhrmann, K. & Loeschcke, V.) 217–231 (Springer Berlin Heidelberg, 1984).45.Weeks, A. R. & Hoffmann, A. A. Frequency-dependent selection maintains clonal diversity in an asexual organism. Proc. Natl. Acad. Sci. USA 105, 17872–17877 (2008).46.Vrijenhoek, R. C. Coexistence of clones in a heterogeneous environment. Science 199, 549–552 (1978).ADS 
    CAS 
    PubMed 

    Google Scholar 
    47.Dagan, Y., Liljeroos, K., Jokela, J. & Ben-Ami, F. Clonal diversity driven by parasitism in a freshwater snail. J. Evol. Biol. 26, 2509–2519 (2013).CAS 
    PubMed 

    Google Scholar 
    48.Otto, S. P. & Lenormand, T. Evolution of sex resolving the paradox of sex and recombination. Nat. Rev. Genet. 3, 252–261 (2002).CAS 
    PubMed 

    Google Scholar 
    49.Yamashita, M., Jiang, J., Onozato, H., Nakanishi, T. & Nagahama, Y. A tripolar spindle formed at meiosis I assures the retention of the original ploidy in the gynogenetic triploid. Dev. Growth Differ. 35, 631–636 (1993).
    Google Scholar 
    50.Kobayasi, H. A cytological study on the maturation division in the oogenic process of the Triploid Ginbuna (Carassius auratus langsdorfii). Jpn. J. Ichthyol. 22, 234–240 (1976).
    Google Scholar 
    51.Yamashita, M., Onozato, H., Nakanishi, T. & Nagahama, Y. Breakdown of the sperm nuclear envelope is a prerequisite for male pronucleus formation: Direct evidence from the gynogenetic crucian carp Carassius auratus langsdorfii. Dev. Biol. 137, 155–160 (1990).CAS 
    PubMed 

    Google Scholar 
    52.Kobayasi, H. A cytological study on gynogenesis of the triploid ginbuna (Carassius auratus langsdorfii). Zool. Mag. 80, 316–322 (1971).
    Google Scholar 
    53.Lampert, K. P. & Schartl, M. A little bit is better than nothing: the incomplete parthenogenesis of salamanders, frogs and fish. BMC Biol. 8, 78 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    54.Lu, Y. et al. Fixation of allelic gene expression landscapes and expression bias pattern shape the transcriptome of the clonal Amazon molly. Genome Res. 31, 372–379 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    55.Science Council of Japan. Guidelines for Proper Conduct of Animal Experiments. (2006).56.du Sert, N. P. et al. The arrive guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol. 18, 1–12 (2020).
    Google Scholar 
    57.code by Richard A. Becker, O. S. & version by Ray Brownrigg., A. R. W. R. mapdata: Extra Map Databases. (2018).58.Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. (2013).59.Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Chen, S., Zhou, Y., Chen, Y. & Gu, J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    61.McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Stacklies, W., Redestig, H., Scholz, M., Walther, D. & Selbig, J. pcaMethods – A bioconductor package providing PCA methods for incomplete data. Bioinformatics 23, 1164–1167 (2007).CAS 
    PubMed 

    Google Scholar 
    64.Buerkle, C. A. Maximum-likelihood estimation of a hybrid index based on molecular markers. Mol. Ecol. Notes 5, 684–687 (2005).CAS 

    Google Scholar 
    65.Gompert, Z. & Alex Buerkle, C. Introgress: A software package for mapping components of isolation in hybrids. Mol. Ecol. Resour. 10, 378–384 (2010).CAS 
    PubMed 

    Google Scholar 
    66.Liu, S. et al. Genomic incompatibilities in the diploid and tetraploid offspring of the goldfish × common carp cross. Proc. Natl. Acad. Sci. USA 113, 1327–1332 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Li, C. Y. et al. The transcriptomes of the crucian carp complex (Carassius auratus) provide insights into the distinction between unisexual triploids and sexual diploids. Int. J. Mol. Sci. 15, 9386–9406 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Chen, Z. et al. De novo assembly of the goldfish (Carassius auratus) genome and the evolution of genes after whole-genome duplication. Sci. Adv. 5, 1–13 (2019).
    Google Scholar 
    69.Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).CAS 
    PubMed 

    Google Scholar 
    70.der Auwera, G. A. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra. Genomics in the cloud: Using Docker, GATK, and WDL in Terra (O’Reilly Media, 2020).
    Google Scholar 
    71.Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    72.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 

    Google Scholar 
    73.Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 1–8 (2007).
    Google Scholar 
    74.Liu, H.-T. & Su, T.-T. Pliocene fishes from Yüshe Basin, Shansi. Vertebr. Palasiat. 6, 1–47 (1962).
    Google Scholar 
    75.Rüber, L., Kottelat, M., Tan, H. H., Ng, P. K. L. & Britz, R. Evolution of miniaturization and the phylogenetic position of Paedocypris, comprising the world’s smallest vertebrate. BMC Evol. Biol. 7, 1–10 (2007).
    Google Scholar 
    76.Tominaga, K., Nagata, N., Kitamura, J., Watanabe, K. & Sota, T. Phylogeography of the bitterling Tanakia lanceolata (Teleostei: Cyprinidae) in Japan inferred from mitochondrial cytochrome b gene sequences. Ichthyol. Res. 67, 105–116 (2020).
    Google Scholar 
    77.Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9, 772–772 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    78.Ritchie, A. M., Lo, N. & Ho, S. Y. W. The impact of the tree prior on molecular dating of data sets containing a mixture of inter- and intraspecies sampling. Syst. Biol. 66, 413–425 (2017).PubMed 

    Google Scholar 
    79.Clement, M., Posada, D. & Crandall, K. A. TCS: A computer program to estimate gene genealogies. Mol. Ecol. 9, 1657–1659 (2000).CAS 
    PubMed 

    Google Scholar 
    80.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 

    Google Scholar 
    81.Oksanen, J. et al. vegan: Community Ecology Package. (2017).82.Legendre, P. & Legendre, L. F. J. Numerical Ecology (Elsevier Science, 1998).MATH 

    Google Scholar 
    83.Muggeo, V. M. R. segmented: An R package to fit regression models with broken-line relationships. R NEWS 8(1), 20–25 (2008).
    Google Scholar 
    84.Bruvo, R., Michiels, N. K., D’Souza, T. G. & Schulenburg, H. A simple method for the calculation of microsatellite genotype distances irrespective of ploidy level. Mol. Ecol. 13, 2101–2106 (2004).CAS 
    PubMed 

    Google Scholar 
    85.Clark, L. V. & Jasieniuk, M. polysat: An R package for polyploid microsatellite analysis. Mol. Ecol. Resour. 11, 562–566 (2011).PubMed 

    Google Scholar 
    86.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    87.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    88.Earl, D. A. & von Holdt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).
    Google Scholar 
    89.Rolf, F. J. tpsDig, Digitize Landmarks and Outlines, Version 2.05. (Department of Ecology and Evolution, State University of New York at Stony Brook, 2006).90.Klingenberg, C. P. MorphoJ: An integrated software package for geometric morphometrics. Mol. Ecol. Resour. 11, 353–357 (2011).PubMed 

    Google Scholar  More

  • in

    Spatial and temporal patterns of genetic diversity in Bombus terrestris populations of the Iberian Peninsula and their conservation implications

    1.Sage, R. F. Global change biology: A primer. Glob. Change Biol. 26, 3–30 (2020).ADS 

    Google Scholar 
    2.Sutherland, W. J. et al. A horizon scan of emerging issues for global conservation in 2019. Trends Ecol. Evol. 34, 83–94 (2018).PubMed 

    Google Scholar 
    3.Porto, R. G. et al. Pollination ecosystem services: A comprehensive review of economic values, research funding and policy actions. Food Secur. 12, 1425–1442 (2020).
    Google Scholar 
    4.Potts, S. G. et al. Safeguarding pollinators and their values to human well-being. Nature 540, 1–10 (2016).
    Google Scholar 
    5.Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).PubMed 

    Google Scholar 
    6.Ellis, J. S. et al. Introgression in native populations of Apis mellifera mellifera L: implications for conservation. J. Insect Conserv. 22, 377–390 (2018).
    Google Scholar 
    7.Hart, A. F., Maebe, K., Brown, G., Smagghe, G. & Ings, T. Winter activity unrelated to introgression in British bumblebee Bombus terrestris audax. Apidologie 52, 315–327 (2021).
    Google Scholar 
    8.Ings, T. C., Ward, N. L. & Chittka, L. Can commercially imported bumble bees out-compete their native conspecifics?. J. Appl. Ecol. 43, 940–948 (2006).
    Google Scholar 
    9.Graystock, P., Blane, E. J., McFrederick, Q. S., Goulson, D. & Hughes, W. O. Do managed bees drive parasite spread and emergence in wild bees?. IJP-PAW 5, 64–75 (2016).PubMed 

    Google Scholar 
    10.Chandler, D., Cooper, E. & Prince, G. Are there risks to wild European bumble bees from using commercial stocks of domesticated Bombus terrestris for crop pollination?. J. Apic. Res. 58, 1–17 (2019).
    Google Scholar 
    11.Velthuis, H. H. W. & Doorn, A. A century of advances in bumblebee domestication and the economic and environmental aspects of its commercialization for pollination. Apidologie 37, 421–451 (2006).
    Google Scholar 
    12.Trillo, A. et al. Contrasting occurrence patterns of managed and native bumblebees in natural habitats across a greenhouse landscape gradient. Agric. Ecosyst. Environ. 272, 230–236 (2019).
    Google Scholar 
    13.Lecocq, T., Rasmont, P., Harpke, A. & Schweiger, O. Improving international trade regulation by considering intraspecific variation for invasion risk assessment of commercially traded species: The Bombus terrestris case. Conserv. Lett. 9, 281–289 (2015).
    Google Scholar 
    14.Martinet, B. et al. Global effects of extreme temperatures on wild bumblebees. Conserv. Biol. 35(5), 1507–1518 (2021).PubMed 

    Google Scholar 
    15.Schmid-Hempel, R. et al. The invasion of southern South America by imported bumblebees and associated parasites. J. Anim. Ecol. 83, 823–837 (2014).PubMed 

    Google Scholar 
    16.Aizen, M. A. et al. Coordinated species importation policies are needed to reduce serious invasions globally: The case of alien bumblebees in South America. J. Appl. Ecol. 56, 100–106 (2018).
    Google Scholar 
    17.Tsuchida, K., Yamaguchi, A., Kanbe, Y. & Goka, K. Reproductive interference in an introduced bumblebee: Polyandry may mitigate negative reproductive impact. Insects 10, 59 (2019).PubMed Central 

    Google Scholar 
    18.Rasmont, P., Coppée, A., Michez, D. & De Meulemeester, T. An overview of the Bombus terrestris (L. 1758) subspecies (Hymenoptera: Apidae). Ann. Soc. Entomol. Fr. (N.S.) 44, 243–250 (2008).
    Google Scholar 
    19.Lecocq, T. et al. The alien’s identity: Consequences of taxonomic status for the international bumblebee trade regulations. Biol. Conserv. 195, 169–176 (2016).
    Google Scholar 
    20.Ornosa, C. & Ortiz-Sánchez, F. Hymenoptera: Apoidea I. In Fauna Ibérica Vol. 23 (eds Ramos, M. A. et al.) (Museo Nacional de Ciencias Naturales, CSIC, 2004).
    Google Scholar 
    21.Hewitt, G. M. Mediterranean Peninsulas: The Evolution of Hotspots. In Biodiversity Hotspots (eds Zachos, F. & Habel, J.) 123–147 (Springer-Verlag, 2011).
    Google Scholar 
    22.Ortiz-Sánchez, F. Introducción de Bombus terrestris terrestris (Linnaeus, 1758) en el Sur de España para la polinización de cultivos en invernadero (Hymenoptera, Apidae). Boln. Asoc. Esp. Ent. 16, 247–248 (1992).
    Google Scholar 
    23.Cejas, D., López-López, A., Muñoz, I., Ornosa, C. & De la Rúa, P. Unveiling introgression in bumblebee (Bombus terrestris) populations through mitogenome-based markers. Anim. Genet. 51, 70–77 (2020).CAS 
    PubMed 

    Google Scholar 
    24.Seabra, S. G. et al. Genomic signatures of introgression between commercial and native bumblebees, Bombus terrestris, in western Iberian Peninsula—Implications for conservation and trade regulation. Evol. Appl. 12, 1–13 (2019).
    Google Scholar 
    25.Bartomeus, I., Molina, F. P., Hidalgo-Galiana, A. & Ortego, J. Safeguarding the genetic integrity of native pollinators requires stronger regulations on commercial lines. Ecol. Solut. Evid. 1(1), e12012 (2020).
    Google Scholar 
    26.Coates, D. J., Byrne, M. & Moritz, C. Genetic diversity and conservation units: Dealing with the species-population continuum in the age of genomics. Front. Ecol. Evol. 6, 165 (2018).
    Google Scholar 
    27.Williams, P. H. et al. Genes suggest ancestral colour polymorphisms are shared across morphologically cryptic species in arctic bumblebees. PLoS ONE 10, e0144544 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    28.Gosterit, A. Adverse effects of inbreeding on colony foundation success in bumblebees, Bombus terrestris (Hymenoptera: Apidae). Appl. Entomol. Zool. 51, 521–526 (2016).
    Google Scholar 
    29.Maebe, K., Karise, R., Meeus, I., Mänd, M. & Smagghe, G. Pattern of population structuring between Belgian and Estonian bumblebees. Sci. Rep. 9, 1–8 (2019).CAS 

    Google Scholar 
    30.Allio, R., Donega, S., Galtier, N. & Nabholz, B. Large variation in the ratio of mitochondrial to nuclear mutation rate across animals: Implications for genetic diversity and the use of mitochondrial DNA as a molecular marker. Mol. Biol. Evol. 34, 2762–2772 (2017).CAS 

    Google Scholar 
    31.Patten, M. M., Carioscia, S. A. & Linnen, C. R. Biased introgression of mitochondrial and nuclear genes: A comparison of diploid and haplodiploid systems. Mol. Ecol. 24, 5200–5210 (2015).CAS 
    PubMed 

    Google Scholar 
    32.Gosterit, A. & Baskar, V. C. Impacts of commercialization on the developmental characteristics of native Bombus terrestris (L.) colonies. Insectes Soc. 63, 609–614 (2016).
    Google Scholar 
    33.Moreira, A. S., Horgan, F. G., Murray, T. E. & Kakouli-Duarte, T. Population genetic structure of Bombus terrestris in Europe: Isolation and genetic differentiation of Irish and British populations. Mol. Ecol. 24, 3257–3268 (2015).PubMed 

    Google Scholar 
    34.Zayed, A. Bee genetics and conservation. Apidologie 40, 237–262 (2009).
    Google Scholar 
    35.Schenau, E. & Jha, S. High levels of male diploidy but low levels of genetic structure characterize Bombus vosnesenskii populations across the Western US. Conserv. Genet. 18, 597–605 (2017).
    Google Scholar 
    36.Van Wilgenburg, E., Driessen, G. & Beukeboom, L. W. Single locus complementary sex determination in Hymenoptera: An “unintelligent” design?. Front. Zool. 3, 1–15 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    37.Bogo, G. et al. No evidence for an inbreeding avoidance system in the bumble bee Bombus terrestris. Apidologie 49, 473–483 (2018).
    Google Scholar 
    38.Kent, C. F. et al. Conservation genomics of the declining North American bumblebee Bombus terricola reveals inbreeding and selection on immune genes. Front. Genet. 9, 316 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Cejas, D., Ornosa, C., Muñoz, I. & De la Rúa, P. Searching for molecular markers to differentiate Bombus terrestris (Linnaeus) subspecies in the Iberian Peninsula. Sociobiology 65, 558–565 (2018).
    Google Scholar 
    40.Ministerio de Agricultura, Pesca y Alimentación de España. Encuesta sobre Superficies y Rendimientos de Cultivos (ESYRCE). https://cpage.mpr.gob.es N.I.P.O.: 001-19-051-9 (2021).41.Nei, M. Genetic distance between populations. Am. Nat. 106, 283–292 (1972).
    Google Scholar 
    42.Rannala, B. & Mountain, J. L. Detecting immigration by using multilocus genotypes. PNAS 94, 9197–9201 (1997).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Anderson, E. C. Bayesian inference of species hybrids using multilocus dominant genetic markers. Philos. Trans. R. Soc. B 363(1505), 2841–2850 (2008).
    Google Scholar 
    44.Earl, D. A. & von Holdt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).
    Google Scholar 
    45.Facon, B. et al. Can things get worse when an invasive species hybridizes? The harlequin ladybird Harmonia axyridis in France as a case study. Evol. Appl. 4, 71–88 (2011).PubMed 

    Google Scholar 
    46.Ornosa, C., Torres, F. & De la Rúa, P. Updated list of bumblebees (Hymenoptera: Apidae) from the Spanish Pyrenees with notes on their decline and conservation status. Zootaxa 4237, 41–77 (2017).
    Google Scholar 
    47.Allendorf, F. W., Leary, R. F., Spruell, P. & Wenburg, J. K. The problems with hybrids: Setting conservation guidelines. Trends Ecol. Evol. 16, 613–622 (2001).
    Google Scholar 
    48.Arnold, M. L. & Kunte, K. Adaptive genetic exchange: A tangled history of admixture and evolutionary innovation. Trends Ecol. Evol. 32, 601–611 (2017).PubMed 

    Google Scholar 
    49.Mallet, J. Hybridization as an invasion of the genome. Trends Ecol. Evol. 20, 229–237 (2005).PubMed 

    Google Scholar 
    50.De la Rúa, P. et al. Conserving genetic diversity in the honeybee: Comments on Harpur et al. (2012). Mol. Ecol. 22, 3208–3210 (2013).PubMed 

    Google Scholar 
    51.Estoup, A., Solignac, M., Cornuet, J. M., Goudet, J. & Scholl, A. Genetic differentiation of continental and island populations of Bombus terrestris (Hymenoptera: Apidae) in Europe. Mol. Ecol. 5, 19–31 (1996).CAS 
    PubMed 

    Google Scholar 
    52.Silva, S. E. et al. Population genomics of Bombus terrestris reveals high but unstructured genetic diversity in a potential glacial refugium. Biol. J. Linn. Soc. 129, 259–272 (2020).
    Google Scholar 
    53.Ayabe, T., Hoshiba, H. & Ono, M. Cytological evidence for triploid males and females in the bumblebee, Bombus terrestris. Chromosome Res. 12, 215–223 (2004).CAS 
    PubMed 

    Google Scholar 
    54.Takahashi, J., Ayabe, T., Mitsuhata, M., Shimizu, I. & Ono, M. Diploid male production in a rare and locally distributed bumblebee, Bombus florilegus (Hymenoptera, Apidae). Insectes Soc. 55, 43–50 (2008).
    Google Scholar 
    55.Darvill, B., Ellis, J. S., Lye, G. C. & Goulson, D. Population structure and inbreeding in a rare and declining bumblebee, Bombus muscorum (Hymenoptera: Apidae). Mol. Ecol. 15, 601–611 (2006).CAS 
    PubMed 

    Google Scholar 
    56.Gerloff, C. U. & Schmid-Hempel, P. Inbreeding depression and family variation in a social insect, Bombus terrestris (Hymenoptera: Apidae). Oikos 111, 67–80 (2005).
    Google Scholar 
    57.Kraus, F. B., Wolf, S. & Moritz, R. F. A. Male flight distance and population substructure in the bumblebee Bombus terrestris. J. Anim. Ecol. 78, 247–252 (2009).CAS 
    PubMed 

    Google Scholar 
    58.Ivanova, N., Dewaard, J. & Herbert, D. An inexpensive, automation-friendly protocol for recovering high-quality DNA. Mol. Ecol. Notes 6, 998–1002 (2006).CAS 

    Google Scholar 
    59.Wandeler, P., Hoeck, P. E. & Keller, L. F. Back to the future: museum specimens in population genetics. Trends Ecol. Evol. 22, 634–642 (2007).PubMed 
    PubMed Central 

    Google Scholar 
    60.Rozen, S. & Skaletsky, H. Primer3 on the WWW for general users and for biologist programmers. Bioinform. Methods Protoc. 132, 365–386 (2000).CAS 

    Google Scholar 
    61.Hines, H., Cameron, S. & Williams, P. Molecular phylogeny of the bumble bee subgenus Pyrobombus (Hymenoptera: Apidae: Bombus) with insights into gene utility for lower-level analysis. Invertebr. Syst. 20, 289–303 (2006).CAS 

    Google Scholar 
    62.Estoup, A., Scholl, A., Pouvreau, A. & Solignac, M. Monoandry and polyandry in bumble bees (Hymenoptera; Bombinae) as evidenced by highly variable microsatellites. Mol. Ecol. 4, 89–94 (1995).CAS 
    PubMed 

    Google Scholar 
    63.Cejas, D., Ornosa, C., Muñoz, I. & De la Rúa, P. Preliminary report on cross-species microsatellite amplification for bumblebee biodiversity and conservation studies. Arch. de Zootec. 68, 422–426 (2019).
    Google Scholar 
    64.Wang, J. Computationally efficent sibship and parentage assignment from multilocus marker data. Genetics 191, 183–194 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Piry, S. et al. Geneclass2: A software for genetic assignment and first generation migrant detection. Heredity 95(6), 536–539 (2004).CAS 

    Google Scholar 
    66.Cornuet, J. M., Piry, S., Luikart, G., Estoup, A. & Solignac, M. New methods employing multilocus genotypes to select or exclude populations as origins of individuals. Genetics 153, 1989–2000 (1999).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Peakall, R. & Smouse, P. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 28, 2537–2539 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. & Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
    Google Scholar 
    69.Rousset, F. genepop’007: a complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 

    Google Scholar 
    70.Kalinowski, S. T. HP-RARE 1.0: A computer program for performing rarefaction on measures of allelic richness. Mol. Ecol. Notes 5, 187–189 (2005).CAS 

    Google Scholar 
    71.Goudet, J. FSTAT (version 1.2): A computer program to calculate F-statistics. J. Hered. 86, 485–486 (1995).
    Google Scholar 
    72.R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, http://www.R-project.org (2008).73.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    74.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
    PubMed 

    Google Scholar 
    75.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    76.Rosenberg, N. A. DISTRUCT: A program for the graphical display of population structure. Mol. Ecol. Notes 4, 137–138 (2004).
    Google Scholar 
    77.Jombart, T. & Ahmed, I. adegenet 1.3-1: New tools for the analysis of genome-wide SNP data. Bioinformatics 24, 1403–1405 (2011).
    Google Scholar  More

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    Temperature effects on carbon storage are controlled by soil stabilisation capacities

    Effects of temperature on C storage in soils with contrasting stabilisation capacitiesUsing a space-for-time approach, we defined the effect of temperature on C storage as the proportional reduction in C storage for each 10 oC increase in mean annual temperature. In this context, a value of 1 indicates no change in C storage with temperature, values less than 1 indicate C stocks increase with temperature and values greater than 1 indicate C stocks decline with temperature, with, for example, a value of 2 indicating that C stocks halve for every 10 oC increase in temperature. C storage in the top 50 cm of mineral soil declined strongly with increasing temperature, declining by a factor of ~1.4 per 10 oC (Fig. 1b). Critically, the nature of this relationship was modified by soil clay content; C storage in fine-textured soils with greater stabilisation capacities was affected much less by temperature than C storage in coarse-textured soils (Fig. 2a and Supplementary Fig. 1; factors of up to 1.9 per 10 oC for coarse-textured soils versus factors below 1.2 per 10 oC for finer-textured soils). We also demonstrate that the lower effect of temperature on C storage in fine-textured soils was retained after accounting for potentially confounding variation in precipitation, aridity (actual minus potential evapotranspiration), plant productivity, soil pH and cation exchange capacity (CEXC) (Fig. 2b). While we focus on the top 50 cm, due to the potential for vertical profiles of soil C to be affected by temperature22, very similar results were observed for the top 20 cm (Supplementary Fig. 2). In addition, the negative relationship between clay content and the effect of temperature on C storage was observed independently both above and below 15 oC (Fig. 3b, c).Fig. 2: Texture effects on temperature–soil carbon storage relationships.The effect of texture on the relationships between C storage in the top 50 cm of mineral soil and mean annual temperature in the raw data (a), and after accounting for potential confounding variables (b). The y-axes display the proportional reduction in C storage for each 10 oC increase in mean annual temperature, with higher values indicating greater reductions in soil C with temperature. In panel a, the slopes of the relationships (solid line), together with their 95% confidence intervals (dark grey shaded area), are presented for each of the textural categories, with the slope and 95% confidence interval for the full dataset (dotted line and light grey shaded areas) also presented across the graph for comparison. In panel b, the relationship between soil C storage and temperature after accounting for variation in annual precipitation (light blue), gross primary productivity (GPP; dark green), soil pH (purple), aridity (ET/PET; evapotranspiration minus potential evapotranspiration; navy blue), and cation exchange capacity (CEXC, light green) are shown. The slopes of these relationships (solid lines) together with their 95% confidence intervals (shaded area) are presented for each of the textural categories.Full size imageFig. 3: Comparison between soil profile data and JULES model output.The effect of texture on the relationships between C storage in the top 50 cm of mineral soil in the empirical data (solid lines) and JULES output (dashed lines). The slopes of these relationships (solid lines) together with their 95% confidence intervals (shaded area) are presented for each of the textural categories. Results for the full mean annual temperate range (a), as well as for subsets of the data for sites with mean annual temperatures below 15 oC (b, blue) and above 15 oC (c, red) are shown.Full size imageThe lower effect of temperature on soil C storage in fine-textured soils with greater stabilisation capacities was unexpected given the evidence of the high-temperature sensitivity associated with the decomposition of more protected SOM pools13,14,15. However, the findings from our global analysis are in agreement with a recent Europe-wide synthesis23, which, by compiling data from soil physical fractionation studies, demonstrated that mineral-associated C stocks varied less with temperate than freer particulate pools. Therefore, there is growing evidence that the effect of temperature on soil C storage is higher in soils containing a greater proportion of unprotected C.In the literature, there are apparently contradictory conclusions in terms of how C storage varies across fine-scale climate gradients, in which variation in other factors has been minimised. However, these may potentially be resolved by considering differences in the likely extent of SOM stabilisation. For example, on poorly weathered, relatively coarse-textured, silt loam soils in Alaska, mineral soil C stocks declined strongly with temperature24. In contrast, in Hawaiian forests growing on fine-textured soils with high concentrations of Al and Fe oxides, very little change in soil C storage was observed across a gradient of 5 oC in MAT. This was despite the fact that, in these Hawaiian forests, C storage in unprotected pools on the forest floor was found to decline strongly with temperature5. We suggest that differences in the extent of physicochemical protection in the Alaskan versus Hawaiian soils may explain the contrasting results. Thus, apparently contradictory findings may be resolvable within a single framework in which the relative effect of temperature on C storage in mineral soils declines as the soil’s physicochemical stabilisation capacity, and the proportion of C in protected pools, increase.Overall, our analysis identified C stored in high-latitude soils with limited capacities for stabilising organic matter as likely to be most vulnerable to the impacts of climate change. Such stores, therefore, may require particular attention given the high rates of warming taking place in cooler regions. In contrast, the particularly low effect of temperature on C storage in fine-textured soils in warm climates suggests (Fig. 3c) that the C stocks in many tropical soils may be less vulnerable to climate warming. While a soil warming study in a less weathered tropical soil identified the potential for high rates of C release25, our results are consistent with a recent large-scale analysis that concluded that the temperature sensitivity of soil respiration is generally lowest in tropical environments26. However, because C storage in tropical soils has been shown to be potentially vulnerable to drought27, it should not be concluded that C storage in tropical soils will be unaffected by climate change. Our results do, though, suggest that C stocks in coarse-textured soils at high latitudes are likely to be especially vulnerable to warming (Fig. 3b). Finally, while the dataset contains soil profile information for sites across the full mean annual temperature range investigated (0–30 oC), and there were data on a minimum of 500 profiles in every 5 oC temperature increment, increasing the amount of data available for sites with mean annual temperatures below 5 oC and greater than 20 oC would add further confidence to the findings.Because of their greater stabilisation capacities, fine-textured soils store more soil organic matter18. Therefore, fine and coarse-textured soils could contain similar absolute quantities of highly vulnerable C, and the lower effect of temperature in fine-textured soils could reflect the presence of greater quantities of low-vulnerability organic matter4. Therefore, it is likely to be very important to quantify stocks of unprotected pools, such as free particulate C, in soils with contrasting stabilisation capacities, and to investigate how such stocks vary with climate23. This may make it possible to identify whether there are still important stocks of unprotected organic matter that are vulnerable to climate warming in fine-textured soils with high stabilisation capacities2.Predicting and modelling future rates of C releaseAccurately predicting the response of soil C storage to global warming remains a major challenge. While spatial datasets, such as the ones analysed in this paper, add confidence to the prediction that C will be lost overall and help identify the most vulnerable stocks, they provide limited information on the likely rates or dynamics of C release. In this context, long-term surveys can be extremely valuable. For example, a recent study in Chinese grasslands was able to detect warming-induced soil C losses since the 1960s and, consistent with the global analysis presented here, coarser-textured soils lost far greater amounts of SOM28. Experimental soil warming studies also offer opportunities for further determining the factors controlling soil C storage and predicting rates of C release, although recent syntheses have produced conflicting overall findings29,30. Revisiting the networks of warming studies and considering the findings in the context of soil stabilisation capacities and changes in pools of protected and unprotected SOM may allow for a greater understanding of the observed patterns. For example, C losses from subsoils in response to 5 years of whole profile warming were shown to be dominated by the free particulate C pool31. Therefore, understanding the responses of different pools to warming may offer the potential to generate mechanistic understanding, even where changes in total C storage have been difficult to identify. It should though be recognised that there are major challenges in accurately quantifying relatively short-term changes in soil C stocks, and there are many other variables that differ between soil warming studies, including contrasting changes in plant productivity and rates of C input driven by interactions between C and nutrient cycling32. For these reasons, it may not always be possible to determine the role of soil stabilisation capacities in controlling soil C storage responses to experimental warming30, and observations collected across space and time will likely remain important for contextualising experimental results.Soil texture is included as a factor modifying decomposition rates in the terrestrial C cycle modules of a number of Earth systems models (ESMs), including the United Kingdom ESM (UKESM), whose land surface scheme (the Joint UK Land Environment Simulator (JULES)33) is based around the Rothamsted C model34. Therefore, we investigated whether JULES was already able to represent the patterns that we had observed in the empirical data. In direct contrast to the empirical data, JULES predicted very little variation in soil C with temperature in cooler regions (below 15 oC; Fig. 3b), but predicted a strong effect of temperature on C storage across all textural classes above 15 oC (Fig. 3c). The pattern across the full dataset was confounded by the model simulating only a small number of fine-textured soils at high latitudes (Fig. 3a), and the fact that the relationship between temperature and soil C storage differed so strongly above and below 15 oC. However, crucially, JULES failed to reproduce the greater effect of temperature on C storage in coarse-textured soils and overestimated the effect of temperature on C storage in fine-textured soils in warmer regions. These findings question whether JULES is identifying accurately which soil C stocks are most vulnerable to the effects of climate warming. This is important given the considerable geographical variation in (1) rates of climate warming and (2) the amounts of C stored in mineral soil horizons. In recent years, there have been major efforts made towards developing models that include physicochemical stabilisation mechanisms and yet can potentially be run at the global scale35,36,37. Testing whether such models can better simulate the observed spatial patterns of C storage in soils with contrasting stabilisation capacities would increase confidence in projections of future changes in soil C stocks38.Limitations and future perspectivesAs well as influencing rates of key biological processes, climatic variables also control pedogenesis, rates of mineral weathering and therefore influence the reactivity of soil surfaces26,27,39. Directly determining the binding affinity of mineral surfaces is challenging and would require detailed information on the type of clay minerals present, as well as the abundance of key metal oxides35,36,40, but there is, currently, insufficient data to assess these more detailed variables at the global scale35. However, it has been argued that, at broad spatial scales, soil pH may explain an important proportion of variation in mineral-binding affinities35,41. Furthermore, cation exchange capacity (CEXC) varies with the type of clay minerals present and the binding efficiencies of the mineral surfaces42. In global analyses, texture, pH and CEXC tend to be the three edaphic factors that correlate most strongly with soil C storage18,20. For these reasons, we also accounted for variation in both pH and CEXC, and evaluated whether the relationship between soil texture and the effect of temperature on C storage was retained. We found that it was (Fig. 2b). Thus, we conclude, that within this large dataset, clay content remains a strong predictor of soil stabilisation capacities, both overall, and after accounting for factors that potentially control SOM binding affinities.While we consider that our analysis of how SOM stabilisation capacities determine the effects of temperature on soil C storage is robust, it is also high level. Thus, there is considerable opportunity to further investigate different vulnerabilities of specific pools of SOM, contrasting the roles of mineral protection versus occlusion in aggregates7, determining the importance of SOM binding affinities40, and linking protection mechanisms with the sources of the organic matter (e.g. microbial versus plant derived43). A debate has often revolved around whether climatic versus edaphic factors are more important in controlling patterns of soil C storage. Rather, than focusing on which is more important, for predicting future rates of soil C release, we suggest that a key priority should be on identifying how key edaphic factors determine the vulnerability of contrasting soil C stocks to climate warming. In this context, a recent meta-analysis demonstrated the importance of soil properties in controlling the temperature sensitivity of soil respiration, emphasising how responses to global warming will likely vary substantially between different types of soils in contrasting geoclimatic zones26.Using a large global database, we observed declining C storage with temperature in mineral soils, suggesting that there is the potential for strong positive feedback to climate warming. Critically, however, this overall relationship masked differences between soils with contrasting C stabilisation capacities, as indicated by their textural properties. The data suggest that there are stabilised pools of SOM in fine-textured soils that may be relatively insensitive to the impacts of climate change, but that unprotected pools may be substantially more vulnerable to climate warming than currently predicted. Finally, because at least one major ESM was unable to reflect the observed patterns, we argue that ESMs should be evaluated against their ability to simulate the differences in the effects of temperature on C storage in soils with contrasting textural properties in order to reduce uncertainties in projections of the effect of climate change on future soil C storage. More