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    Extraction of Rhododendron arboreum Smith flowers from the forest for the livelihood and rural income in Garhwal Himalaya, India

    Study sites and samplingThe study was conducted in Garhwal region (Western Himalaya) from 2016 to 2017 at eight Rhododendron arboreum rich areas in four hill districts (Chamoli, Tehri, Pauri and Rudraprayag). Voucher specimen of Rhododendron arboreum collected and have been deposited in the Herbarium, Botany department, HNB Garhwal University (specimen no. GUH 8510)6. Identification of R. arboreum has been done through A Field Guide book authored by Rai et al.7. Since it is a wild species and flowers have been collected for our research and field study under the permission from competent authority of State Forest Department, Govt. of Uttarakhand. According to IUCN’s Red List Categories and Criteria, globally Rhododendron arboreum comes under Least Concern (LC) category8. These sites are situated between 30°08′47″ to 30°24′06″ N latitude and 78°25′05″ to 79°12′39″ E longitude with altitudes from 1820 m asl in Nandasain and 2270 m asl in Jadipani (Table 1; Fig. 1). All sites were well stocked (mean stand density ≥ 500 tree/ha) with Rhododendron arboreum trees mixed with Quercus leucotrichophora. We referred these resource rich sites as R. arboreum habitats (Table 1). Stratified random sampling method (i.e. stand density and CBH class’s strata) were carried out these eight sites. Total sampled area 0.2 ha in each site; two sample plots (size of each plot is 0.1 ha or 31.62 × 31.62 m) nested within 0.2 ha in each site were laid out for trees enumeration. Sample size (number of R. arboreum tree) for a total population in each site were 166 in Phadkhal; 110 in Khirsu; 104 in Khadpatiya; 166 in Ghimtoli; 80 in Jadipani; 74 in Ranichauri; 74 in Nandasain and 96 in Nauti. Out of the standing trees in sample plots, flower bearing trees were 96 in Phadkhal; 90 in Khirsu; 102 in Khadpatiya; 126 in Ghimtoli; 64 in Jadipani; 58 in Ranichauri; 68 in Nandasain and 82 in Nauti, and without flower or smaller trees were 70 in Phadkhal; 20 in Khirsu; 02 in Khadpatiya; 40 in Ghimtoli; 16 in Jadipani; 16 in Ranichauri; 06 in Nandasain and 14 in Nauti. The individuals of all tree species in each plot were recorded along with their CBH (circumference at breast height, 1.3 m above from the ground). Individuals were categorized as mature trees (≥ 31 cm CBH), saplings (11–30 cm CBH) and seedlings (≤ 10 cm CBH)9. Further all the tree individuals have been grouped into 8 CBH classes: (A) 5–15 cm, (B) 16–25 cm, (C) 26–35 cm, (D) 36–45 cm, (E) 46–55 cm, (F) 56–65 cm, (G) 66–75 cm, (H) 76–85 cm. Recorded data were used for the analysis of density10.Table 1 Physical characteristics of study sites in four districts of Garhwal region.Full size tableFigure 1Locations of Rhododendron arboreum study sites in Garhwal region (ARC GIS software 10.5 version was used for map preparation. The map was created by Mr. Raman Patel, Research scholar, Dept. of Geology, HNB Garhwal University, Srinagar, Uttarakhand, India).Full size imageFlower yield estimationFlower yield (kg/tree) was estimated during full bloom (flowering season/harvest season February–April 2017). In each sample plot, numbers of flower bearing trees varied from 29–63 trees/0.1 ha. At each site of 0.2 ha sample plot, total 40 trees, 05 flower bearing trees in each of the 08 CBH classes were marked for estimation of flower yield. The number of main branches, the number of sub- branches/offshoots per main branches (i.e. average per five randomly selected main branches per tree), and the amount of flower per sub-branches/offshoot (i.e. the average per five offshoots from the low, middle and upper canopy of each tree) were counted form marked individuals. This way flower yield/tree was calculated9,11,12.The flowers from all CBH classes in each site were mixed and weighted in 5 lots of 1 kg each. The number of flowers in each lot was then counted and the mean value (400.0 ± 9.56) was considered as a standard for conversion into kilograms. Based on this conversion flower yield kg/tree was obtained. Flower yield data were pooled and mean yield (kg/tree) for each CBH class (A–H) calculated. For each site, flower yield in kg/0.2 ha was obtained by multiplying flower yield/tree by the density of flower bearing trees/0.2 ha. The total yield kg/ha for each site was calculated as total yield = (yield/ha) × density of flower bear trees/ha9,11,12.Extraction/harvesting and marketing trendsFlower extraction and collection were totally dependent on market availability and accessibility of site; one of the selected sites (Ranichauri) was easily accessible, while Phadkhal, Khirsu and Jadipani were moderately accessible. Khadpatiya, Ghimtoli, Nandasain and Nauti sites were far-flung from market (Table 1). The highest extraction was recorded between second week of February and first week of April. During this period, data was obtained for three consecutive days at each site.Questionnaire based survey was carried out in selective forest fringe villages. Across the sites, total sixteen villages were selected for questionnaire survey, three villages each in Jadipani, Ranichauri, Nandasain and Nauti sites, while one village each in Phadkhal, Khirsu, Khadpatiya and Ghimtoli. In each village 15 families were randomly chosen for semi-structured questionnaire survey.Considering the market availability for trading of the R. arboreum flower products, Nandasain and Nauti sites are located nearest to local market whereas Khadpatiya and Ghimtoli sites are farthest from local market. As far as the access to resources is concerned, four sites represent open and easy access to resource and four sites represent open and moderate access of resource (Table 1). During questionnaire survey, villagers were asked about the number of persons involved in resource collection (hereafter referred as collectors), age of collectors, timing of collection (early morning and late evening) etc. Ten individuals in each group (adults and children) were randomly interviewed on their harvest load to generate data on the average collection per individual, the number of days spent in flower collection, and the total income generated through this activity.Squash/juice making factories are generally located nearby urban centers; local NGOs and small entrepreneurs are engaged in this work. These peoples purchase flower from the collectors or middleman for preparation of value product (squash). Collectors of each families (varied from n = 15 in Nanadasain to n = 31 in Jadipani) and buyers (n = 5 each site) were contacted to obtain information on the benefits accrued. The income values are given in Indian rupees (USD 1 = Rs. 68.00, 2017 exchange rates). Projections of potential (probable/-could generate) income (with flower processed into juice or squash) were made. The involvement of rural inhabitants as flowers collectors and the income that subsequently accrued (within a 10 km radius of fringe area) was also analyzed for sixteen villages across the sites. One adult member from each household was contacted in a village to collect information on involvement of flower collection/extraction.Juice/squash preparation methods and value-added productsThe collected flowers are graded for their size and healthiness and the stamens are separated from petals by laborers in the juice processing unit. Petals are cleaned washed with tap water and grinded into small pieces. The petal mass is retained in the water and then boiled for one hour. The slurry (aqueous solution) obtained in this process is left at room temperature for cooling and when it get cold, filtered through linen cloth. The filtrate solution is the pure juice of the flower. For the preparation of squash from the pure juice, about 2 kg of sugar is boiled in one liter of water. Further one liter of pure juice and a small quantity of citric acid (10 g/2 kg sugar) are added to this solution. The mixture is boiled again for 30 min and then left to cool at room temperature13. The obtained solution known as squash is then filtered through linen cloth and stored into containers and bottles for marketing. For long term storage and good test and aroma small amount of sodium benzoate and vanilla or kawra is also mixed in the squash.Cost–benefit analysis of value- added productsThe cost–benefit analysis of value added products prepared from the R. arboreum flowers was calculated in Rs./day which includes labour charges of workers involved in flower collection and materials/items required for preparation of different value added products viz: sugar, preservatives, essence, plastic containers/bottles, packaging materials etc. Labour charge was calculated on the basis of existing daily wages as per market rates. The monetary output was calculated as per the current market rates of the products (Table 2). The cost- benefit analysis of the squash product prepared from the flowers was calculated as Rs./day which includes: (i) Man days incumbent for the flowers extraction from the forest and for the preparation of squash product, (ii) Essential items such as sugar, preservatives etc. and their monetary equivalents, (iii) The total quantity of squash product and their monetary equivalents.Table 2 Market cost in rupees (Rs.) of essential commodity in the preparation of R. arboreum juice/squash in Garhwal region.Full size tableStatistical analysisData failed to meet the assumption of normality (Shapiro–Wilk test) as well as homogeneity (Levene statistic); therefore, a non-parametric test (i.e. Independent–Samples Kruskal–Wallis test) was applied for one-way ANOVA. However, to find the interaction of site and cbh on flower production (yield), the same data set was subjected to two-way analysis using univariate analysis. To find if (?) flower yield depends on tree diameter or not, data of actual cbh and flower yield per tree were used to determine a correlation (Pearson Correlation Coefficient) between them. In case of correlation found significant then regression equation was developed to predict flower production based on tree diameter. All analysis were performed using IBM-SPSS 16.0 version14.Ethics approval and consent to participateAll necessary approval, free prior informed consent, permit, and certification were secured. This was done to adhere to the ethical standards of human participation in scientific research. This study was approved by Research and Consultancy Cell (Ethics Committee) of HNB Garhwal University, Srinagar Garhwal, Uttarakhand, India. All the methods were performed in accordance with the relevant guidelines and regulations. More

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    Climate impacts and adaptation in US dairy systems 1981–2018

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    Ovicidal activity of spirotetramat and its effect on hatching, development and formation of Frankliniella occidentalis egg

    Toxicity of spirotetramat to F. occidentalis eggsThe indoor toxicity of spirotetramat to 0-h-, 12-h-, and 24-h-old eggs of F. occidentalis using egg dipping method and leaf dipping method was shown in Table 1 after egg hatching was observed for 144 h. The results suggested that the LC50 value gradually decreased as the egg age increased. The two methods have the same trend. And in the leaf dipping method, according to the confidence limits analysis, 0-h-old eggs are significantly more sensitive to spirotretramat than 24-h-old eggs.Table 1 Toxicity of spirotetramat to F. occidentalis eggs.Full size tableEgg external shape observationsExternal morphology of normally developed isolated 0-h-old eggs of F. occidentalis in the control treatment (Fig. 1a) were compared with those treated with spirotetramat (Fig. 1b, c). After spirotetramat treatment, some eggs appeared darker, yellowish-brown, and with embryonic development abnormalities (Fig. 1b); some of the egg embryo cells treated with spirotetramat appeared atrophied, and there were obvious gaps between the outer and the inner egg embryo cells, compared with the control treatment (Fig. 1c). Some eggs ruptured at the top after the egg shell was treated with spirotetramat, and the internal egg embryo cells flowed out, thus failing to form a complete embryo (Fig. 1d); the full egg embryo cells formed a control treatment.Figure 1The effect of spirotetramat on external morphology of isolated 0-h-old eggs of F. occidentalis. (a) 0-h-old isolated eggs of normal developing thrips; (b) yellowish-brown, developmentally deformed eggs after spirotetramat treatment; (c) eggs with shrunken oocytes after spirotetramat treatment; (d) eggs with apical rupture of the eggshell after spirotetramat treatment.Full size imageThe effect of spirotetramat on external morphology of live F. occidentalis 0-h-old eggs (Fig. 2b, c) was compared with normal development in the control treatment (Fig. 2a). Similar to the effect on external morphology of isolated 0-h-old eggs of F. occidentalis, the eggs treated with spirotetramat also showed abnormal embryonic development (Fig. 2b), egg embryo cell atrophy (Fig. 2c) and the phenomenon of rupture of the egg shell and outflow of embryo cells.Figure 2The effect of spirotetramat on external morphology of living 0-h-old eggs of F. occidentalis. (a) External morphology of live 0-h-old eggs of normal developing thrips; (b) developmentally deformed eggs after spirotetramat treatment; (c) eggs with shrunken oocytes after spirotetramat treatment; (d) eggs with apical rupture of the eggshell after spirotetramat treatment.Full size imageCompared with the control (Fig. 3a), the isolated 24-h-old eggs treated with spirotetramat (Fig. 3b) did not show obvious external morphological differences. After spirotetramat treatment, the eggs were still white and plump and with no embryonic deformities, egg cell atrophy or egg shell rupture, and could still develop normally. There were clear red eye spots on the head end, and embryo movement was clearly seen under the super-depth microscope (Fig. 3b). Similarly, the live 24-h-old eggs in the control treatment (Fig. 4a) and those treated with spirotetramat (Fig. 4b) showed no obvious external morphological differences, and the eggs developed normally.Figure 3The effect of spirotetramat on external morphology of isolated 24-h-old eggs of F. occidentalis. (a) Normally developing 24-h-isolated eggs in the control group; (b) 24-h-old eggs after spirotetramat treatment.Full size imageFigure 4The effect of spirotetramat on external morphology living of 24-h-old eggs of F. occidentalis. (a) Normally developing 24-h-live eggs in the control group; (b) live 24-h-old eggs after spirotetramat treatment.Full size imageEffect of egg hatchingThe 0-h-old eggs of F. occidentalis treated with spirotetramat did not hatch normally, and the mortality rate was 100% (Fig. 5). Among them, 77 eggs eventually showed rupture of the egg shell, the internal egg embryo cells flowed out and they did not hatch; 23 eggs showed no changes in external morphology, but did not hatch after continuous observation for 144 h, and showed no developmental phenomena such as embryo movement under a super-depth microscope, which was regarded as egg death. In the control treatment, 96 eggs hatched normally, and only six eggs did not rupture but did not hatch normally and were considered dead.Figure 5Effect of spirotetramat on hatching rate of F. occidentalis 0-h-old eggs.Full size imageThere was no significant difference between the 24-h-old eggs of F. occidentalis treated with spirotetramat and the control treatment. After spirotetramat treatment, 93 eggs hatched normally, and the shells of seven eggs were not ruptured (Fig. 6). Any eggs not hatched after 144 h of continuous observation were considered dead. In the control treatment, 95 eggs hatched normally and five eggs did not rupture but did not hatch normally, and so were considered dead.Figure 6The effect of spirotetramat on hatching rate of F. occidentalis 24-h-old eggs.Full size imageSEM observationsThe F. occidentalis eggs in the control treatment were kidney-shaped, with regular egg morphology, smooth surfaces and no folds or protrusions (Fig. 7a). At 24 h after spirotetramat treatment, part of the egg shells treated with spirotetramat had fallen off the chorion, and the embryonic material was exposed (Fig. 7b). The surface of the egg shell was uneven and severely wrinkled (Fig. 7c). The pores of some eggs treated with spirotetramat were sunken down and shrunken (Fig. 7d). Spirotetramat treatment of 0-h-old eggs affect clearly egg shells, resulting in shrinkage of egg shells, ovarian depression and egg malformations, and destroyed the egg shell structure. Thus, normal embryonic development was affected, and disrupted normal hatching.Figure 7The effect of spirotetramat on the surface of egg shells of F. occidentalis 0-h-old eggs. (a) 0-h-old eggs in the control treatment; (b) eggs shells were shed 24 h after treatment with spirotetramat; (c) the surface of the egg shell was uneven and severely wrinkled; (d) the pores of some eggs were sunken down and shrunken after treatment with spirotetramat.Full size imageThe shells of eggs treated with spirotetramat (Fig. 8b) showed no significant difference compared with controls (Fig. 8a). The eggs of the two groups of F. occidentalis were regular in shape, with smooth surfaces and without folds or protrusions. Thus, development of 24-h-old eggs showed some resistance to spirotetramat. Spirotetramat did not destroy the egg shell surface structure of 24-h-old eggs, indicating a high resistance to spirotetramat.Figure 8The effect of spirotetramat on the egg shell surface of F. occidentalis 24-h-old eggs. (a) 24-h-old eggs in the control treatment; (b) 24-h-old eggs in the spirotetramat treatment.Full size imageTEM observationsThe TEM observations showed that the egg structure of the control treatment was complete, the protoplasm and yolk were clearly observed inside the egg and the yolk was packed in the void of the protoplasm network (Fig. 9a). The egg shell structure was clear, and the outer and inner egg shell were clearly observed, as was the yolk membrane and the dense layer structure (Fig. 9c). Eggs treated with spirotetramat were flocculent, and no clear internal material was observed. The protoplasm and yolk structure were blurred, and flocculation in the protoplasm appeared to agglomerate and form blocks (Fig. 9b). The egg shell structure was unclear, and no clear outer egg shell, inner egg shell, yolk membrane and lamellar structures were observed. The egg shell was also filled with many flocs (Fig. 9d).Figure 9The effect of spirotetramat on the structure of F. occidentalis 0-h-old eggs. (a) and (b) 0-h-old eggs in the control treatment; (c) and (d) 0-h-old eggs in the spirotetramat treatment.Full size imageEffect on embryonic developmentThe initial eggs of the control group were kidney-shaped, white and full of vitellin (Fig. 10a). After 12 h of development, the eggs were larger and of oval shape (Fig. 10b). After 24 h of development, the egg had increased in volume, a partially transparent region appeared in the embryo and the embryo had transparent top follicles (Fig. 10c). After 36 h of development, some yolk granules disappeared and eggs became smooth and translucent (Fig. 10d). After 48 h of development, the insect outline was visible within the egg, a pair of antennae were visible on the head and a red eye point was clearly observed on the head during the blastokinesis phenomenon (Fig. 10e). After 60 h of development, embryo color deepened, the eye point was clearer and the head, femur, tibia and tarsus were clear (Fig. 10f). After 72 h of development, the egg shell began to break at the head, the tail constantly jittered, internal fluid flowed and the larva hatched from the top of the egg (Fig. 10g).Figure 10The embryonic development process of control 0-h-old eggs of F. occidentalis. (a) Control initial eggs; (b) eggs after 12 h of development; (c) eggs after 24 h of development; (d) eggs after 36 h of development; (e) eggs after 48 h of development; (f) eggs after 60 h of development; (g) eggs hatching as larvae after 72 h of development.Full size imageEggs of F. occidentalis were initially white, kidney-shaped and full of vitellin (Fig. 11a). Following treatment with spirotetramat, after 12 h of development, the eggs became large and oval, and the embryo was a pale brown color (Fig. 11b). After 24 h of development, color of the egg deepened to dark brown. There was a gap between the egg and the egg shell, and a small amount of spillage appeared at the end of the egg (Fig. 11c). After 36 h of development, the egg shell ruptured, material flowed out of the egg and embryo development did not proceed (Fig. 11d).Figure 11Effects of spirotetramat on development of 0-h-old eggs of F. occidentalis. (a) Frankliniella occidentalis initial eggs; (b) eggs developing 12 h after spirotetramat treatment; (c) eggs developing 24 h after spirotetramat treatment; (d) eggs developing 36 h after spirotetramat treatment.Full size imageIn the control treatment, the egg volume increased at 24 h, the embryo had a partially transparent area and there was a transparent follicle on the top of the embryo (Fig. 12a). After 12 h of development, some of the yolk particles disappeared and the egg body was smooth and translucent (Fig. 12b). After 24 h of development, the body outline, a pair of antennae and red eye spots were visible, and there was obvious embryo movement (Fig. 12c). At 36 h of development, the head, leg segments, tibia and tarsus were apparent (Fig. 12d). After 48 h of development, the embryo moved violently, internal body fluid flowed and the larva was ready to hatch (Fig. 12e). After 60 h of development, the larva emerged from its shell (Fig. 12f).Figure 1224-h-old eggs embryo development of F. occidentalis in control treatment. (a) Control 24-h-old eggs; (b) eggs after 12 h of development; (c) eggs after 24 h of development; (d) eggs after 36 h of development; (e) eggs after 48 h of development; (f) eggs hatching as larvae after 60 h of development.Full size imageThe 24 h old eggs of F. occidentalis showed enlarged volume, and there were transparent follicles on the top of the embryo (Fig. 13a). After 24 h eggs were treated with spirotetramat, they developed for 12–36 h, and the developmental status was the same as that of the control. The embryos developed normally, and there was no egg body discoloration or egg shell rupture (Fig. 13b–d). After 48 h of development, hairy scales appeared on the surface of the egg shell, and the egg body turned yellowish-brown in color, but the egg shell was not broken and no internal material overflow was seen (Fig. 13e). After 60 h of development, larvae hatched normally (Fig. 13f).Figure 13The effect of spirotetramat on embryonic development of F. occidentalis 24-h-old eggs. (a) Frankliniella occidentalis 24-h-old eggs; (b) eggs developing 12 h after spirotetramat treatment; (c) eggs developing 24 h after spirotetramat treatment; (d) eggs developing 36 h after spirotetramat treatment; (e) eggs developing 48 h after spirotetramat treatment; (f) eggs hatching as larvae after 60 h of development.Full size image More

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    Population structure, biogeography and transmissibility of Mycobacterium tuberculosis

    Detailed population structure of L1–4 and a hierarchical sub-lineage naming systemWe assembled a high-quality data set of whole genomes, antibiotic resistance phenotypes, and geographic sites of isolation for 9584 clinical Mtb samples (“Methods” section and Supplementary Data 1). Of the total, 4939 (52%) were pan-susceptible, i.e., susceptible to at least isoniazid and rifampicin (and all other antibiotics when additional phenotypic data were available), and 4645 (48%) were resistant to one or more antibiotics (Supplementary Fig. 1a). Using the 62 SNS lineage barcode6, 738 isolates were classified as L1 (8%), 2193 as L2 (22%), 1104 as L3 (12%) and 5549 as L4 (58%, Supplementary Fig. 1b). Among the 4939 pan-susceptible isolates, we identified high-quality genome-wide SNSs (83,735 for L1, 56,736 for L2, 76,817 for L3, and 185,622 for L4) that we used in building maximum-likelihood phylogenies for each major lineage (L1–4, “Methods” section). We computed an index of genetic divergence (FST) between groups defined by each bifurcation in each phylogeny. Sub-lineages were defined as monophyletic groups that had high FST ( >0.33) and were also clearly separated from other groups in principal component analysis (PCA, see “Methods” section). We also defined internal groups to sub-lineages (see “Methods” section): an internal group is a monophyletic group genetically divergent (by FST and PCA) from its neighboring groups, but has one or more ancestral branches that show a low degree of divergence or low support (bootstrap values). Internal groups do not represent true sub-lineages in a hierarchical fashion, but defining them allows us to further characterize the Mtb population structure. We provide code to automate all the steps described above. Our approach is scalable and can be used on other organisms (see “Methods” section).To better classify Mtb isolates in the context of the global Mtb population structure, we developed a hierarchical sub-lineage naming scheme (Supplementary Data 2) where each subdivision in the classification corresponds to a split in the phylogenetic tree of each major Mtb lineage. Starting with the global Mtb lineage numbers (e.g., L1), we recursively introduced a subdivision (e.g., from 1.2 to 1.2.1 and 1.2.2) at each bifurcation of the phylogenetic tree whenever both subclades sufficiently diverged. Formally, we defined these splits using bootstrap criteria, and independent validations by FST and PCA (see “Methods” section). Internal groups were denoted with the letter “i” (e.g., 4.1.i1). This proposed system overcomes two major shortcomings of the existing schemas: same-level sub-lineages are never overlapping (unlike the system of Stucki et al.8 sub-lineage 4.10 includes sub-lineages 4.7–4.9), and the names reflect both phylogenetic relationships and genetic similarity (unlike semantic naming such as the “Asia ancestral” lineage in the system of Shitikov et al.7). Further, this naming system can be standardized to automate the process of lineage definition. These advantages come at the price of long sub-lineage names in the case of complex phylogenies (e.g., for L4, sub-lineage 4.10 gets the lineage designation 4.2.1.1.1.1.1.1). For compatibility with naming conventions already in use and to keep names as short as possible, we designed a second, shorthand, naming system that expands the Coll et al. lineage schema by adding new subdivisions and differentiating between sub-lineages and internal groups. For instance, sub-lineage 4.3.1 is designated as 4.3.i1, informing the user that this is an internal group of sub-lineage 4.3. To simplify the use of the hierarchical naming schema and the updated shorthand schema, we provide a table that compares them side by side along with naming systems currently in use (Supplementary Data 2).Using the sub-lineage definition rules and the sub-lineage naming scheme described above, we characterized six previously undescribed sub-lineages of L1 (Fig. 1 and Supplementary Fig. 2); five of which expand the current description of 1.2. We also detected an internal group of 91 isolates (1.1.3.i1) characterized by a long defining branch in the phylogeny (corresponding to 82 SNSs), a high FST (0.48), and geographically restricted to Malawi (85/91, 93% isolates, Fig. 1 and Supplementary Fig. 3). We estimated the date of the emergence of the MRCA of such a group (see “Methods” section) and we found it to be between 1497 and 1754. We found four previously undescribed sub-lineages of L3 (Fig. 2 and Supplementary Fig. 4), revising L3 into four main groups, whereas previously only two partitions of one sub-lineage were characterized (3.1). We found that the latter two partitions are in fact internal groups of the largest sub-lineage (3.1.1) in our revised classification.Fig. 1: Phylogenetic tree reconstruction of lineage 1 (binary tree).Gray circles define splits where the FST (fixation index) calculated using the descendants of the two children nodes is greater than 0.33. The sub-lineages are defined by colored areas (blue: sub-lineages already described in the literature; green: sub-lineages described here; purple: internal sub-lineages). Source data are provided as a Source Data file.Full size imageFig. 2: Phylogenetic tree reconstruction of lineage 3 (binary tree).Gray circles define splits where the FST (fixation index) calculated using the descendants of the two children nodes is greater than 0.33. The sub-lineages are defined by colored areas (green: sub-lineages described here; purple: internal sub-lineages). Source data are provided as a Source Data file.Full size imageL2 is divided into two groups: proto-Beijing and Beijing with the latter in turn partitioned into two groups: ancient- and modern-Beijing7. Each one of these groups is characterized by further subdivisions (three for the ancient-Beijing group and seven for the modern-Beijing group; see Supplementary Fig. 4). We found a new sub-lineage (2.2.1.2, Fig. 3, and Supplementary Fig. 5) within the previously characterized ancient-Beijing group. However, genetic diversity within the modern-Beijing group (2.2.1.1.1) was lower than in the other L2 sub-lineages and the tree topology and FST calculations did not support further hierarchical subdivisions. Although we did find three internal groups of modern-Beijing: two undescribed and one that corresponds to the Central Asia group7. For L4, our results support a complex population structure with 21 sub-lineages and 15 internal groups. In particular, we found 11 previously undescribed sub-lineages and 5 internal groups that expand our understanding of previously characterized sub-lineages (e.g., 4.2.2; 4.2 in the Coll et al. classification) or that were not characterized since these isolates were simply classified as L4 (e.g., 4.11, Fig. 4, and Supplementary Fig. 6) using the other barcodes.Fig. 3: Phylogenetic tree reconstruction of lineage 2 (binary tree).Gray circles define splits where the FST (fixation index) calculated using the descendants of the two children nodes is greater than 0.33. The sub-lineages are defined by colored areas (blue: sub-lineages already described in the literature; green: sub-lineages described here; purple: internal sub-lineages). Source data are provided as a Source Data file.Full size imageFig. 4: Phylogenetic tree reconstruction of lineage 4 (binary tree).Gray circles define splits where the FST (fixation index) calculated using the descendants of the two children nodes is greater than 0.33. The sub-lineages are defined by colored areas (blue: sub-lineages already described in the literature; green: sub-lineages described here; purple: internal sub-lineages). Source data are provided as a Source Data file.Full size imageA new barcode to define L1–4 Mtb sub-lineages and a software package to type Mtb strains from WGS dataWe defined a SNS barcode for distinguishing the obtained sub-lineages (Supplementary Data 3). We characterized new synonymous SNSs found in 100% of isolates from a given sub-lineage, but not in other isolates from the same major lineage, compiling 95 SNSs into an expanded barcode (Supplementary Data 3). We validated the barcode by using it to call sub-lineages in the hold-out set of 4645 resistant isolates and comparing the resulting sub-lineage designations with maximum-likelihood phylogenies inferred from the full SNS data (Supplementary Figs. 7–10). A sub-lineage was validated if it was found in the hold-out data and formed a monophyletic group in the phylogeny. Considering the “recent” sub-lineages, i.e., the most detailed level of classification in our system, we were able to validate eight out of nine L1 sub-lineages including five out of six of the new sub-lineages described here, with the exception of 1.1.1.2. We validated all four new L3 sub-lineages, all five L2 sub-lineages including the one previously undescribed, and 16 of the 21 L4 sub-lineages including two described here. The sub-lineages we could not confirm were not represented by any isolate in the validation phylogenies. We did not observe any paraphyletic sub-lineages in the revised classification system.We developed fast-lineage-caller, a software tool that classifies Mtb genomes using the SNS barcode proposed above. For a given genome, it returns the corresponding sub-lineage as output using our hierarchical naming system in addition to four other existing numerical/semantic naming systems, when applicable (see “Methods” section). The tool also informs the user on how many SNSs support a given lineage call and allows for filtering of low-quality variants. The tool is generalizable and can manage additional barcodes defined by the user to type the core genome of potentially any bacterial species.Geographic distribution of the Mtb sub-lineagesNext, we examined whether certain sub-lineages were geographically restricted, which would support the Mtb-human co-evolution hypothesis, or whether they constituted prevalent circulating sub-lineages in several different countries (i.e., geographically unrestricted)8. We used our SNS barcode to determine the sub-lineages of 17,432 isolates (see “Methods” section) sampled from 74 countries (Supplementary Fig. 11 and Supplementary Data 4, 5). We computed the Simpson diversity index (Sdi) as a measure of geographic diversity that controls for variable sub-lineage frequency (see “Methods” section) for each well-represented sub-lineage or internal group (n  > 20). We hypothesized that geographically unrestricted lineages would have a higher Sdi. We found Sdi to correlate highly (⍴ = 0.68; p-value = 5.7 × 10−7) with the number of continents from which a given sub-lineage was isolated (Supplementary Fig. 12). The Sdi ranged between a minimum of 0.05 and a maximum of 0.72, with a median value of 0.46 (Fig. 5). The known geographically restricted sub-lineages8 had an Sdi between 0.28 and 0.5 (Fig. 5 and Supplementary Table 1), while the known geographically unrestricted sub-lineages8,9 had an Sdi between 0.55 and 0.61 (Fig. 5 and Supplementary Table 2). We found 11 sub-lineages/internal groups with Sdi 0.61 (Supplementary Table 4), i.e., more extreme than previously reported geographically restricted or unrestricted sub-lineages, respectively.Fig. 5: Histogram of the Simpson diversity index calculated for sub-lineages of lineages 1–4.A data set of 17,432 isolates from 74 countries was used to perform this analysis. Yellow triangles designate the Simpson diversity index values of sub-lineages designated as geographically restricted by Stucki et al. Light gray circles designate the Simpson diversity index values of sub-lineages designated as geographically unrestricted by Stucki et al. Source data are provided as a Source Data file.Full size imageWhile the currently known geographically restricted sub-lineages are all in L4, we found evidence of geographic restriction for two sub-lineages/internal groups of L1. The first, the L1 internal group 1.1.3.i1, showed a very low Sdi (0.06) and was only found at high frequency among the circulating L1 isolates in Malawi (Fig. 6). This finding is also in agreement with the L1 phylogeny (Fig. 1) that shows a relatively long (82 SNS) branch defining this group. The second geographically restricted L1 sub-lineage is 1.1.1.1 (Sdi = 0.12) that was only found at high frequency among circulating L1 isolates in South-East Asia (Vietnam and Thailand, Fig. 7). To exclude the possibility that these two groups appeared geographically restricted as a result of oversampling transmission outbreaks, we calculated the distribution of the pairwise SNS distance for each of these two sub-lineages. We measured a median SNS distance of 204 and 401, respectively, refuting this kind of sampling error for these groups (typical pairwise SNS distance in outbreaks 0.67 and results on L4 transmissibility below.Differences in transmissibility between the Mtb global lineagesThe observation that some lineages/sub-lineages are more geographically widespread than others raises the question of whether this results from differences in marginal transmissibility across human populations. On a topological level, we observed L2 and L3 phylogenies to be qualitatively different from those of L1 and L4 (Figs. 1–4): displaying a star-like pattern with shorter internal branches and longer branches near the termini. We confirmed this quantitatively by generating a single phylogenetic tree for all 9584 L1–4 isolates and plotting cumulative branch lengths from root to tip for each main lineage (Supplementary Fig. 20). Star-like topologies have been postulated to associate with rapid or effective viral or bacterial transmission e.g., a “super-spreading” event in outbreak contexts25. To compare transmissibility between the four lineages, we compared the distributions of terminal branch lengths expecting a skew toward shorter terminal branch lengths supporting the idea of higher transmissibility. We found L4 to have the shortest median terminal branch length, followed in order by L2, L3, and L1 (medians: 6.2 × 10−5, 8.2 × 10−5, 10.2 × 10−5, 17.5 × 10−5, respectively; all pairwise two-sided Wilcoxon rank-sum tests significant p-value < 0.001; Fig. 9). Shorter internal node-to-tip distance is a second phylogenetic correlate of transmissibility; the distribution of this measure across the four lineages revealed a similar hierarchy to the terminal branch length distribution (Supplementary Fig. 21). We also computed the cumulative distribution of isolates separated by increasing total pairwise SNS distance (Supplementary Fig. 22). The proportion of L4 isolates separated by More

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    Strong nutrient-plant interactions enhance the stability of ecosystems

    Review of C–R stability theoryTo set the context for how the R–N module will be used to understand the dynamics of nutrient-limited ecosystem models, we first briefly review stability results from modular food web theory. We do this by laying out a set of examples that serve to illustrate that in general, strong C–R interactions promote oscillatory dynamics while carefully placed weak C–R interactions dampen them5. We begin with the Rosenzweig–MacArthur C–R system as our base C–R module (Fig. 1a). It is biologically supported and produces a range of biologically plausible dynamics5, making it an appropriate system for this analysis. It exhibits three different dynamical phases over a gradient of interaction strengths (energetically defined sensu Nilsson et al. 2018) such that increasing the attack rate (({a}_{{CR}})) increases interaction strength15 (Fig. 2). We use the return time after a small perturbation (i.e., eigenvalues) to highlight the natural stability trade-off that occurs as interaction strength is changed, (i.e., the “checkmark” stability pattern)5,6. Equations and parameters can be found in Supplementary Results 1A. We draw your attention to three notable dynamical phases of the C–R module. At low interaction strengths the dominant eigenvalue (({lambda }_{{max }})) is negative and real and the C–R module follows a monotonic return to a stable equilibrium (Fig. 2a). During this phase ({lambda }_{{max }}) decreases from 0 (i.e., where ({a}_{{{CR}}}) allows the consumer to persist) to more negative values and thus stronger interactions tend to increase stability (Fig. 2d, i). At moderate interaction strengths, there is a sudden shift to eigenvalues with a non-zero complex part and population dynamics overshoot the equilibrium (Fig. 2b). Increases in interaction strength then further excite population dynamics and we observe less stable dynamics across this phase (Fig. 2d, ii). Last, the system reaches a Hopf bifurcation where the dominant eigenvalue becomes positive, yielding sustained cycles or oscillations (Fig. 2c, d, iii). As interaction strength increases across this phase, it is difficult to determine stability from the magnitude of a positive dominant eigenvalue; however, destabilization with increased interaction strength is readily observed in that the cycles become increasingly larger oscillations with a high coefficient of variation (CV)5. Note that while the Rosenzweig–MacArthur C–R system is shown here under a single set of parameters, analysis of the Jacobian shows the qualitative results to be general5. Moreover, the qualitative stability pattern remains for a type I and type III functional response5.Fig. 1: C–R and R–N base modules.a Rosenzweig–MacArthur C–R module modelled with Holling type II functional response and logistic resource growth, where (R) is resource biomass and (C) is consumer biomass. Parameters: (r) is the intrinsic growth rate of (R), (K) is the carrying capacity of (R), ({a}_{{mathrm {CR}}}) is the attack rate of (C) on (R), (e) is the assimilation rate of (C), ({R}_{0}) is the half-saturation density of (C), ({m}_{R}) and ({m}_{C}) are the mortality rates of (R) and (C), respectively. b R–N module modelled with a Monod nutrient uptake equation and external nutrient input, where (N) is a limiting-nutrient pool and (R) is the resource biomass. Parameters: ({I}_{N}) is external nutrient input to (N), ({a}_{{RN}}) is nutrient uptake rate by (R), (k) is the half-saturation density of (R), ({l}_{N}) and ({l}_{R}) are nutrient loss rates from (N) and (R), respectively.Full size imageFig. 2: C–R checkmark stability response.d Local stability (real and complex parts of the dominant eigenvalue; ({lambda }_{{max }})) as a function of interaction strength (({a}_{{{mathrm {CR}}}})) for the Rosenzweig–MacArthur C–R module. Time series reflect dynamics associated with region i, ii, and iii, respectively, following a perturbation that removes 50% of consumer biomass: a Stable equilibrium; monotonic dynamics. b Stable equilibrium; overshoot dynamics. c Unstable equilibrium; limit cycle. Boldness of arrows indicates the strength of interaction (({a}_{{CR}})).Full size imageWe now couple C–R modules into higher order food web modules to demonstrate how the addition of weak and/or strong interactions to a system can be used to predict dynamics at steady state (Fig. 3), constituting the “algebra” of C–R modules. Equations and parameters can be found in Supplementary Results 1B–D. We start with the three trophic level food chain (Fig. 3a), consisting of two coupled C–R modules (i.e., C1-R and P–C1). Theory has tended to find two weakly interacting C–R modules to generally produce locally stable equilibria16 (Fig. 3a). Increasing the strength of the C1–R interaction causes it to act like an oscillator (see Fig. 2c, above), and with enough increase this underlying oscillation is reflected in the limit cycles of the entire food chain (Fig. 3b). If the P–C1 interaction is strengthened as well, we end up with two coupled oscillators—the recipe for chaos17,18 (Fig. 3c). As such, coupled strong interactions are not surprisingly the recipe for complex and highly unstable dynamics.Fig. 3: Algebra of C–R modules.Time series showing the general dynamical outcomes for the food chain and diamond module at steady state with varied combinations of C–R interaction strengths. a Weak–weak interaction; point attractor. b Strong–weak interaction; limit cycle. c Strong–strong interaction; chaos. d Strong–strong, weak interaction; limit cycle. e Strong–strong, weak–weak interaction; point attractor.Full size imageFollowing McCann et al.19, we now add a weakly coupled consumer C2 to the food chain system of Fig. 3c. This weak consumer essentially draws energy away from the strong P–C1–R pathway and in doing so partially mutes the coupled oscillators, bringing the dynamics back to a more even limit cycle (Fig. 3d) and under certain conditions can drive equilibrium dynamics19. Last, the predator is weakly coupled to C2, creating a strong and weak pathway. The second weak interaction further draws energy away from the strong pathway, muting the oscillators entirely and bringing the system in this example to a point attractor (Fig. 3e). These examples show that well placed weak interactions (i.e., non-oscillatory phases, Fig. 2a, b) can be used to draw energy away from strong pathways and act as potent stabilizers of potentially oscillatory pathways. Note that weak interactions play a similarly stabilizing role in the omnivory module20 and further, weak interactions have been shown to stabilize large food web networks4,6 suggesting the principles derived from modular theory scale up to whole systems. Taken altogether, the oscillatory nature of strong C–R interactions generally promotes oscillatory dynamics in higher order systems, while the careful placement of weak C–R interactions—which are monotonic in nature—act to dampen oscillations. Although not discussed to our knowledge, we conjecture that if a subsystem exists such that strong interactions lead to monotonic dynamics (i.e., without oscillatory decay), strong interactions in this case would serve as a potent stabilizer. Below, we show the R–N module appears to be such a case.R–N module and stabilityTowards understanding how the R–N subsystem may interact in a higher order system, we first briefly consider the stability of the R–N module alone (akin to what we discussed for the C–R module above). The R–N module consists of a resource that takes up nutrients according to a Monod-like growth term, is open to flows from the external environment as a result of geochemical processes, and nutrients are lost to the external environment according to a linear term11 (Fig. 1b). Performing a local stability analysis about the interior equilibrium reveals the R–N module to be locally stable for all biologically feasible parameterizations, as determined by the signs of the trace and determinant of the Jacobian matrix (see Supplementary Results 2B). We now perform further numerical and analytical analyses to understand how stability is influenced by interaction strength.As the maximum rate of nutrient uptake (({a}_{{RN}})) is increased (i.e., R–N interaction strength), stability is generally increased (Fig. 4d), with the real part of the dominant eigenvalue (({lambda }_{{max }})) tending from 0 (i.e., where ({a}_{{RN}}) allows the resource to persist) towards an asymptote of ({-l}_{R}) (see Supplementary Results 2C). Numerical analysis reveals that the asymptote at ({-l}_{R}) can be approached from above or below depending on the relative leakiness of the R and N compartments (i.e., the rate at which nutrients are lost to the external environment from compartment R (({l}_{R})) and N (({l}_{N}))). For ({l}_{N} , > , {l}_{R}) (Fig. 4d), the R–N module only follows a monotonic return to equilibrium as interaction strength is increased, with increased interaction strength only tending to increased stability (i.e., reduce return time). For ({l}_{N} < {l}_{R}) (Fig. 4d), the R–N module follows a monotonic return to equilibrium for weak (Fig. 4a) and strong (Fig. 4c) interaction strength, but modest overshoot dynamics are observed for intermediate interaction strength (Fig. 4b). Stability tended to increase with interaction strength for weak to intermediate interaction strength (i.e., dominant eigenvalue becomes more negative), then slightly decrease as interaction strength became strong. A special case exists when ({l}_{R}={l}_{N}) (Fig. 4d), where stability increases with interaction strength until ({lambda }_{{max }}) becomes locked in at ({-l}_{R}), indicating stability does not change regardless of any further increase in interaction strength. Overall, the R–N interaction tends to generally stabilize in all cases (dominant eigenvalue goes from zero to a more negative saturating value with monotonic dynamics), although there are some intermediate cases that produce complex eigenvalues that suggest population dynamic overshoot potential (Fig. 4b). Note that we obtain qualitatively similar results when implicitly strengthening the R–N interaction by increasing nutrient loading (see Supplementary Results 2D and Supplementary Fig. 1). Now, given the above framework for coupled C–R modules—where weak C–R interactions with underlying monotonic dynamics dampen the oscillatory potential of strong C–R interactions—the underlying monotonic dynamics of the R–N module suggest that R–N interactions ought to be stabilizing when coupled to strong C–R interactions. Further, the underlying increase in stability (i.e., more rapid return to equilibrium) as R–N interaction strength is increased suggests the stabilizing potential of the R–N module ought to increase as the interaction becomes stronger.Fig. 4: R–N stability response to increasing interaction strength.Time series showing R density following a perturbation that lowered R density to 50% of equilibrium density for a low (({a}_{{RN}}=0.8)), b intermediate (({a}_{{RN}}=1)), and c high maximum rate of nutrient uptake (({a}_{{RN}}=2.8)). d Local stability (dominant eigenvalue; ({lambda }_{{max }})) of the R–N subsystem as ({a}_{{RN}}) is increased for ({l}_{N} , > , {l}_{R}), ({l}_{N}={l}_{R}), and ({l}_{N} < {l}_{R}), where ({l}_{R}) and ({l}_{N}) are the rate at which nutrients are lost to the external environment from compartment R and N, respectively. Solid lines are real parts and dashed lines are complex parts of ({lambda }_{{max }}).Full size imageTo look into this conjecture, we first coupled R–N to multiple configurations of strong and expectantly oscillatory C–R interactions and increased R–N interaction strength (({a}_{{RN}})). Following this, we added nutrient cycling and repeated the experiment to demonstrate that our results can be generalized to nutrient-limited ecosystem models. The full equations and parameter values for each model are listed in Supplementary Results 3A–D and 4A, B. We begin with the C–R–N system, where C–R and R–N are coupled through R (Fig. 5a). The initial increase in ({a}_{{RN}}) implicitly strengthens the C–R interaction and fuels the oscillatory potential of C–R and cycles emerge almost immediately after C is able to persist. As ({a}_{{RN}}) is increased further the cycles disappear and we obverse a steep stabilization phase, followed by a modest period of destabilization. Adding a weakly coupled predator gives a similar outcome, although the system continually stabilizes as ({a}_{{RN}}) is increased (Fig. 5b). If the P–C interaction is strengthened (i.e., both C1–R and P–C1 are strong, the recipe for chaos), R–N is unable to dampen oscillations even with a strong interaction strength, although a strong interaction gives tighter bound cycles than a weak interaction (Fig. 5c). We next add a weakly coupled consumer to the nutrient-limited food chain with strong P–C1 and C1–R interactions (Fig. 5d). As seen previously, this interaction draws energy out of the strong pathway, partially muting oscillatory potential. Thus, the ability for a strong R–N interaction to once again return the system to a stable equilibrium is not surprising. Finally, we add a detrital compartment to show that strong R–N interactions remain potent stabilizers in the context of nutrient cycling (Fig. 6b) when compared to a nutrient-limited food chain without nutrient cycling (Fig. 6a).Fig. 5: Nutrient-limited food chain stability.a–d Non-equilibrium dynamics (log10(C1,max/C1,min)) and equilibrium stability (real part of the dominant eigenvalue; ({lambda }_{{max }})) of the C–R–N, P–C–R–N with a single oscillator, P–C–R–N with coupled oscillators, and P–C1–C2–R–N modules, respectively, as ({a}_{{RN}}) is varied.Full size imageFig. 6: Nutrient-limited ecosystem module stability.a, b Non-equilibrium dynamics (log10(Cmax/Cmin)) and equilibrium stability (real part of the dominant eigenvalue; ({lambda }_{{max }})) of the C–R–N nutrient-limited food chain model and the C–R–N–D nutrient-limited ecosystem model, respectively, as ({a}_{{RN}}) is varied.Full size imageNote that we repeat our analysis of higher order modules by implicitly increasing R–N interaction strength through nutrient loading (see Supplementary Results 3E and 4C and Supplementary Figs. 2 and 3). In all cases, increased nutrient loading led to less stable dynamics, consistent with DeAngelis’ (1992) paradox of enrichment finding where increased nutrient loading lead to destabilizing autotroph–herbivore oscillations. More